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This tutorial is not complete.<br>
Using the {{caution}} template.
{{caution|This tutorial is incomplete.}}
= Intro =
This tutorial is based on the analysis of R1rho data, analysed in a master thesis.
# 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 = 102000
# The result directory.
from auto_analyses.relax_disp import Relax_disp
from data_store import Relax_data_store; ds = Relax_data_store()
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05
#########################################
# The models to analyse.
if not hasattr(ds, 'models'):
#ds.models = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_MP05, MODEL_TP02_FIT_R1MODEL_DPL94, MODEL_TAP03_FIT_R1MODEL_TP02, MODEL_MP05_FIT_R1MODEL_TAP03] ds.models = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94]
# The number of increments per parameter, to split up the search interval in grid search.
# Load the previous results into the base pipe.
results.read(file='results', dir=ds.pre_run_dir)
# If R1 is not measured, then do R1 fitting.
r1_fit=True
# Run the analysis.
Relax_disp(pipe_name=ds.pipe_name, pipe_bundle=ds.pipe_bundle, results_dir=ds.results_dir, models=ds.models, grid_inc=ds.grid_inc, mc_sim_num=ds.mc_sim_num, modsel=ds.modsel, r1_fit=r1_fit)
</source>
file: '''4_inspect_results.py'''.
<source lang="Python">
# Python module imports.
from os import getcwd, sep
import re
# relax module imports.
from pipe_control.mol_res_spin import generate_spin_string, return_spin, spin_loop
from specific_analyses.relax_disp.data import generate_r20_key, loop_exp_frq
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO, MODEL_DPL94, MODEL_TP02, MODEL_TAP03, MODEL_MP05
#########################################
#### Setup
results_dir = getcwd() + sep + 'results_models'
# Load the previous state
state.load(state='final_state.bz2', dir=results_dir)
# Display all pipes
pipe.display()
# Define models which have been analysed.
#MODELS = [MODEL_NOREX_R1RHO MODEL_MP05, MODEL_DPL94, MODEL_TP02, MODEL_TAP03, MODEL_MP05]
MODELS = [MODEL_NOREX_R1RHO, MODEL_DPL94]
# Print results for each model.
print("\n################")
print("Printing results")
print("################\n")
# Store all the pipe names.
pipes = []
for model in MODELS:
# Skip R2eff model.
if model == MODEL_R2EFF:
continue
# Switch to pipe.
pipe_name = '%s - relax_disp' % (model)
pipes.append(pipe_name)
pipe.switch(pipe_name=pipe_name)
print("\nModel: %s" % (model))
# Loop over the spins.
for cur_spin, mol_name, resi, resn, spin_id in spin_loop(full_info=True, return_id=True, skip_desel=True):
# Generate spin string.
spin_string = generate_spin_string(spin=cur_spin, mol_name=mol_name, res_num=resi, res_name=resn)
# Loop over the parameters.
print("\nOptimised parameters for spin: %s" % (spin_string))
for param in cur_spin.params + ['chi2']:
# Get the value.
if param in ['r1_fit', 'r2']:
for exp_type, frq, ei, mi in loop_exp_frq(return_indices=True):
# Generate the R20 key.
r20_key = generate_r20_key(exp_type=exp_type, frq=frq)
# Get the value.
value = getattr(cur_spin, param)[r20_key]
# Print value.
print("%-10s %-6s %-6s %3.8f" % ("Parameter:", param, "Value:", value))
# For all other parameters.
else:
# Get the value.
value = getattr(cur_spin, param)
# Print value.
print("%-10s %-6s %-6s %3.8f" % ("Parameter:", param, "Value:", value))
# Print the final pipe.
pipe.switch(pipe_name='%s - relax_disp' % ('final'))
print("\nFinal pipe")
# Loop over the spins.
for cur_spin, mol_name, resi, resn, spin_id in spin_loop(full_info=True, return_id=True, skip_desel=True):
# Generate spin string.
spin_string = generate_spin_string(spin=cur_spin, mol_name=mol_name, res_num=resi, res_name=resn)
# Loop over the parameters.
print("\nOptimised model for spin: %s" % (spin_string))
param = 'model'
# Get the value.
value = getattr(cur_spin, param)
print("%-10s %-6s %-6s %6s" % ("Parameter:", param, "Value:", value))
# Print the model selection
print("Printing the model selection")
model_selection(method='AIC', modsel_pipe='test', pipes=pipes)
pipe.display()
</source>
=== 5_clustered_analyses.py ===
This a script file to do a clustered analysis.
file: '''5_clustered_analyses.py'''.
<source lang="Python">
# Python module imports.
from os import getcwd, sep
import re
# relax module imports.
from auto_analyses.relax_disp import Relax_disp
from data_store import Relax_data_store; ds = Relax_data_store()
from pipe_control.mol_res_spin import spin_loop
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO, MODEL_DPL94, MODEL_TP02, MODEL_TAP03, MODEL_MP05
#########################################
#### 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.
if not hasattr(ds, 'models'):
#ds.models = [MODEL_NOREX_R1RHO, MODEL_DPL94, MODEL_TP02, MODEL_TAP03, MODEL_MP05]
ds.models = [MODEL_DPL94]
# 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.
#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()
# Now cluster spins.
#relax_disp.cluster('model_cluster', ":1-100")
for cur_spin, mol_name, resi, resn, spin_id in spin_loop(full_info=True, return_id=True, skip_desel=True):
# Here one could write some advanced selecting rules.
relax_disp.cluster('model_cluster', spin_id)
# See the clustering in the current data pipe "cdp".
for key, value in cdp.clustering.iteritems():
print key, value
# 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)
# Print parameter kex after 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)
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)
</source>
= See also =
[[Category:Tutorials]]
