Changes

echo "# DIRN I deltadof2 dpwr2slock ncyc trim ss sfrqapod_rmsd" > $OUT

set apodrmsd=`showApod test.ft2 | grep "REMARK Automated Noise Std Dev in Processed Data:" | awk '{print $9}'`echo "$DIRN $I $deltadof2 $dpwr2slock $ncyc $trim $ss $sfrq$apodrmsd" >> $OUT

== making a spin file from SPARKY list Preparation ==relax does not yet has the possibility to read spins from a sparky file. [https://gna.org/support/?3044 See support request]. So we create one. <source lang="bash">set ATOMS=`tail -n+4 peaks_list.tab | awk '{print $7}'`set SCRIPT=relax_2_spins.py foreach I (`seq 1 ${#ATOMS}`)set ATOM=${ATOMS[$I]}; set SPIN=`echo $ATOM | sed -e "s/N-HN//g"`; set RESN=`echo $SPIN | sed -e "s/[0-9]*//g"`; set RESI=`echo $SPIN | sed -e "s/[A-Za-z]//g"`echo $ATOM $SPIN $RESN $RESIecho "spin.create(spin_name='N', spin_num=$I, res_name='$RESN', res_num=$RESI, mol_name=None)" >> $SCRIPTend cat $SCRIPT</source> == Prepare directory for relax run ===

== relax script for setting experiment settings to spectra = See unique parameters ===<source lang="bash">tail -n +2 exp_parameters.txt | awk '{print $3}' | sort -k1,1n | uniqtail -n +2 exp_parameters.txt | awk '{print $4}' | sort -k1,1n | uniqtail -n +2 exp_parameters.txt | awk '{print $5}' | sort -k1,1n | uniqtail -n +2 exp_parameters.txt | awk '{print $6}' | sort -k1,1n | uniqtail -n +2 exp_parameters.txt | awk '{print $7}' | sort -k1,1n | uniqtail -n +2 exp_parameters.txt | awk '{print $8}' | sort -k1,1n | uniq</source> == Scripts ===== 1_setup_r1rho.py ===Add the following python relax This a script file to be able to call the setup. file: '''1_setup_r1rho.py'''.<source lang="Python"># Python module imports.from os import getcwd, sep # relax directorymodule imports.from data_store import Relax_data_store; ds = Relax_data_store() ############################################# Setup# The pipe names.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 pathif not hasattr(ds, 'data_path'): ds.data_path = getcwd()

This can be modified as wanted############################################ Start setup# Create the data pipe.<br>This is to save "time" on the tedious work on setting the experimental conditions for each spectrapipe.create(pipe_name=ds.pipe_name, bundle=ds.pipe_bundle, pipe_type=ds.pipe_type)

'''relax_3_spectra_settings.py'''<source lang="python"># Loop over Read the spectra settingsspins.ncycfilespectrum.read_spins(file=open('ncyc1_0_46_0_max_standard.txtser',dir=ds.data_path+sep+'rpeak_lists')

# Make empty ncyclistName the isotope for field strength scaling.ncyclist spin.isotope(isotope= []'15N')

i = 0for line in ncycfile: ncyc = line# Load the experiments settings file.split()[0] time_T2 expfile = floatopen(lineds.split()[1]) vcpmg = linedata_path+sep+'exp_parameters_sort.split(txt', 'r')[2] set_sfrq expfileslines = float(lineexpfile.splitreadlines()[3]) rmsd_err = float(lineexpfile.splitclose()[4])

# Test if spectrum is a reference# Read the chemical shift data. if floatchemical_shift.read(vcpmgfile='1_0_46_0_max_standard.ser', dir=ds.data_path+sep+'peak_lists')  ## The lock power to field, has been found in an calibration experiment.spin_lock_field_strengths_Hz == {'35': 431.0, '39': 651.2, '41': 800.5, '43': 984.0, '46': 1341.11, '48': 1648.5} ## Apply spectra settings.for i in range(len(expfileslines)): line = expfileslines[i] if line[0] == "#": vcpmg = Nonecontinue

vcpmg # DIRN I deltadof2 dpwr2slock ncyc trim ss sfrq DIRN = roundline.split()[0] I = int(line.split()[1]) deltadof2 = line.split()[2] dpwr2slock = line.split()[3] ncyc = int(line.split()[4]) trim = float(line.split()[5]) ss = int(line.split()[6]) set_sfrq = float(line.split()[7]) apod_rmsd = float(vcpmgline.split()[8]) spin_lock_field_strength = spin_lock_field_strengths_Hz[dpwr2slock]  # Calculate spin_lock time time_sl = 2*ncyc*trim  # Define file name for peak list. FNAME = "%s_%s_%s_%s_max_standard.ser"%(I, deltadof2, dpwr2slock, ncyc) sp_id = "%s_%s_%s_%s"%(I, deltadof2, dpwr2slock, ncyc)  # Load the peak intensities. spectrum.read_intensities(file=FNAME, dir=ds.data_path+sep+'peak_lists', spectrum_id=sp_id, int_method='height')  # Set the peak intensity errors, as defined as the baseplane RMSD. spectrum.baseplane_rmsd(error=apod_rmsd, spectrum_id=sp_id)  # Set the relaxation dispersion experiment type. relax_disp.exp_type(spectrum_id=sp_id, exp_type='R1rho')  # Set The spin-lock field strength, nu1, in Hz relax_disp.spin_lock_field(spectrum_id=sp_id, field=spin_lock_field_strength)  # Calculating the spin-lock offset in ppm, from offsets values provided in Hz. frq_N15_Hz = yOBS * 1E6 offset_ppm_N15 = float(deltadof2) / frq_N15_Hz * 1E6 omega_rf_ppm = centerPPM_N15 + offset_ppm_N15  # Set The spin-lock offset, omega_rf, in ppm. relax_disp.spin_lock_offset(spectrum_id=sp_id, offset=omega_rf_ppm)  # Set the relaxation times (in s). relax_disp.relax_time(spectrum_id=sp_id, time=time_sl)  # Set the spectrometer frequency. spectrometer.frequency(id=sp_id, frq=set_sfrq, units='MHz')  # Read the R1 data# We do not read the R1 data, but rather with R1.# relax_data.read(ri_id='R1', ri_type='R1', frq=cdp.spectrometer_frq_list[0], file='R1_fitted_values.txt', dir=data_path, mol_name_col=1, res_num_col=2, res_name_col=3, spin_num_col=4, spin_name_col=5, data_col=6, error_col=7)</source> === 2_pre_run_r2eff.py ===This a script file to run the R2eff values only, with a high number of Monte Carlo simulations. file: '''2_pre_run_r2eff.py'''.<source lang="Python"># Python module imports.from os import getcwd, sepimport re # relax module imports.from auto_analyses.relax_disp import Relax_dispfrom data_store import Relax_data_store; ds = Relax_data_store()from specific_analyses.relax_disp.variables import MODEL_R2EFF  ############################################# Setup# The data pathif 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' ## 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  ############################################ Run script with setup.script(file='1_setup_r1rho.py', dir=ds.data_path) # To speed up the analysis, only select a few spins.deselect.all() # Load the experiments settings file.residues = open(ds.data_path+sep+'global_fit_residues.txt', 'r')residueslines = residues.readlines()residues.close() # Split the line string into number and text.r = re.compile("([a-zA-Z]+)([0-9]+)([a-zA-Z]+)(-)([a-zA-Z]+)") for i, line in enumerate(residueslines): if line[0] == "#": continue else: re_split = r.match(line) #print re_split.groups() resn = re_split.group(1) resi = int(re_split.group(2)) isotope = re_split.group(3)  select.spin(spin_id=':%i@%s'%(resi, isotope), change_all=False) # 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, exp_mc_sim_num=ds.exp_mc_sim_num)</source> === 3_analyse_models.py ===This a script file to analyse the models. file: '''3_analyse_models.py'''.<source lang="Python"># Python module imports.from os import getcwd, sepimport re # relax module imports.from auto_analyses.relax_disp import Relax_dispfrom data_store import Relax_data_store; ds = Relax_data_store()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')): # Set pipe name, bundle and type. ds.pipe_name = 'base pipe' ds.pipe_bundle = 'relax_disp' ds.pipe_type = 'relax_disp' # The data pathif not hasattr(ds, 'data_path'): ds.data_path = getcwd() # The models to analyse.if not hasattr(ds, 'models'): #ds.models = [MODEL_NOREX_R1RHO, MODEL_MP05, MODEL_DPL94, MODEL_TP02, MODEL_TAP03] ds.models = [MODEL_NOREX_R1RHO, MODEL_DPL94] # 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' # The previous result directory with R2eff values.if not hasattr(ds, 'pre_run_dir'): ds.pre_run_dir = getcwd() + sep + 'results_R2eff' + sep + 'R2eff' # The result directory.if not hasattr(ds, 'results_dir'): ds.results_dir = getcwd() + sep + 'results_models' ## 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.pipe.create(pipe_name=ds.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) # 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> === 4_inspect_results.py ===This a script file to inspect results in relax. file: '''4_inspect_results.py'''.<source lang="Python"># Python module imports.from os import getcwd, sepimport re # relax module imports.from pipe_control.mol_res_spin import generate_spin_string, return_spin, spin_loopfrom specific_analyses.relax_disp.data import generate_r20_key, loop_exp_frqfrom specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO, MODEL_DPL94, MODEL_TP02, MODEL_TAP03, MODEL_MP05 ############################################# Setupresults_dir = getcwd() + sep + 'results_models' # Load the previous statestate.load(state='final_state.bz2', dir=results_dir) # Display all pipespipe.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 selectionprint("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, sepimport re # relax module imports.from auto_analyses.relax_disp import Relax_dispfrom data_store import Relax_data_store; ds = Relax_data_store()from pipe_control.mol_res_spin import spin_loopfrom 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 pathif 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

# Add ncyc to list######################################## ncyclist# Create the data pipe.appendini_pipe_name = '%s - %s' % (intds.models[0], ds.pipe_bundle)pipe.create(ncyc)pipe_name=ini_pipe_name, bundle=ds.pipe_bundle, pipe_type=ds.pipe_type)

# Set Load the current spectrum idprevious results into the base pipe. current_id results.read(file='results', dir= "Z_A%s"%(ids.pre_run_dir)

# Set Create a new pipe, where the current experiment typeclustering analysis will happen. relax_disp# We will copy the pipe to get all information.exp_typepipe.copy(spectrum_idpipe_from=current_idini_pipe_name, exp_typepipe_to='cpmg fixed'ds.pipe_name, bundle_to=ds.pipe_bundle_cluster)pipe.switch(ds.pipe_name)

# Set the peak intensity errors, as defined as the baseplane RMSDpipe. spectrum.baseplane_rmsddisplay(error=rmsd_err, spectrum_id=current_id)

# Set the NMR field strength of the spectrumNow cluster spins. spectrometer#relax_disp.frequencycluster(id'model_cluster', ":1-100")for cur_spin, mol_name, resi, resn, spin_id in spin_loop(full_info=current_idTrue, frqreturn_id=set_sfrqTrue, unitsskip_desel=True): # Here one could write some advanced selecting rules. relax_disp.cluster('MHzmodel_cluster', spin_id)

# Relaxation dispersion CPMG constant time delay T (See the clustering in s)the current data pipe "cdp". relax_dispfor key, value in cdp.clustering.relax_timeiteritems(spectrum_id=current_id): print key, time=time_T2)value

# Set the relaxation dispersion CPMG frequenciesPrint parameter kex before copying. relax_disp.cpmg_frqfor cur_spin, mol_name, resi, resn, spin_id in spin_loop(spectrum_idfull_info=current_idTrue, cpmg_frqreturn_id=vcpmgTrue, skip_desel=True): print(cur_spin.kex)

i +## 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= 1ini_pipe_name, pipe_to=ds.pipe_name)

# Specify the duplicated spectraPrint parameter kex after copying.#spectrum.replicatedfor cur_spin, mol_name, resi, resn, spin_id in spin_loop(spectrum_idsfull_info=['Z_A1'True, 'Z_A15']return_id=True, skip_desel=True): print(cur_spin.kex)

# The automatic waydublicates = map(lambda val: (val, [i for i in xrange(len(ncyclist)) if ncyclist[i] == val]), ncyclist)for dub in dublicates: ncyc, list_index_occur = dub if len(list_index_occur) > 1: id_list = [] for list_index in list_index_occur: id_list.append('Z_A%s'%list_index) # We don't setup replications, since we have RMSD values from background noise print id_list #spectrumpipe.replicateddisplay(spectrum_ids=id_list)

# Delete replicate spectrumRun the analysis.spectrumRelax_disp(pipe_name=ds.delete('Z_A15'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)