Hi Edward.
I partly implemented this.
But I think some of the functionality still missing in the run(self)
part of the analysis.
I think this part will give problems.
--------
# Select the model.
self.interpreter.relax_disp.select_model(model)
# Copy the R2eff values from the R2eff model data pipe.
if model != MODEL_R2EFF and MODEL_R2EFF in self.models:
self.interpreter.value.copy(pipe_from=self.name_pipe(MODEL_R2EFF),
pipe_to=model_pipe, param='r2eff')
# Calculate the R2eff values for the fixed relaxation time
period data types.
if model == MODEL_R2EFF and not has_exponential_exp_type():
self.interpreter.minimise.calculate()
---------
Just reading in R2eff values by relax_disp.r2eff_read, will not give
enough information?
best
Troels
2014-08-18 18:46 GMT+02:00 Edward d'Auvergne <[email protected]>:
> I would suggest a slightly different solution. If the data pipe sent
> into the auto-analysis already contains R2eff values and errors, then
> skip that model optimisation. The flag can be used anyway to force
> the R2eff parameter optimisation.
>
> On another note, this flag is not necessarily related to the pre_run
> and can be used without a pre-run. For example, a user like Nikolai
> could have files of R2eff values that are read in via the
> relax_disp.r2eff_read. So the flag would be better called
> optimise_r2eff. You currently use the flag when no pre-run data is
> used, so that part needs no changing.
>
> Cheers,
>
> Edward
>
>
>
> On 12 August 2014 15:23, <[email protected]> wrote:
>> Author: tlinnet
>> Date: Tue Aug 12 15:23:57 2014
>> New Revision: 25000
>>
>> URL: http://svn.gna.org/viewcvs/relax?rev=25000&view=rev
>> Log:
>> Added keyword to relax_disp auto analysis, if R2eff values should be
>> optimised.
>>
>> Here optimisation means minimisation and Monte Carlo simulations of the
>> error.
>>
>> sr #3135(https://gna.org/support/?3135): Optimisation of the R1 relaxation
>> rate for the off-resonance R1rho relaxation dispersion models.
>>
>> Modified:
>> branches/R1_fitting/auto_analyses/relax_disp.py
>>
>> Modified: branches/R1_fitting/auto_analyses/relax_disp.py
>> URL:
>> http://svn.gna.org/viewcvs/relax/branches/R1_fitting/auto_analyses/relax_disp.py?rev=25000&r1=24999&r2=25000&view=diff
>> ==============================================================================
>> --- branches/R1_fitting/auto_analyses/relax_disp.py (original)
>> +++ branches/R1_fitting/auto_analyses/relax_disp.py Tue Aug 12 15:23:57
>> 2014
>> @@ -51,38 +51,39 @@
>> opt_func_tol = 1e-25
>> opt_max_iterations = int(1e7)
>>
>> - def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None,
>> models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, exp_mc_sim_num=None,
>> modsel='AIC', pre_run_dir=None, insignificance=0.0, numeric_only=False,
>> mc_sim_all_models=False, eliminate=True, set_grid_r20=False):
>> + def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None,
>> models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, exp_mc_sim_num=None,
>> modsel='AIC', pre_run_dir=None, optimise_pre_run_r2eff=True,
>> insignificance=0.0, numeric_only=False, mc_sim_all_models=False,
>> eliminate=True, set_grid_r20=False):
>> """Perform a full relaxation dispersion analysis for the given list
>> of models.
>>
>> - @keyword pipe_name: The name of the data pipe containing
>> all of the data for the analysis.
>> - @type pipe_name: str
>> - @keyword pipe_bundle: The data pipe bundle to associate all
>> spawned data pipes with.
>> - @type pipe_bundle: str
>> - @keyword results_dir: The directory where results files are
>> saved.
>> - @type results_dir: str
>> - @keyword models: The list of relaxation dispersion
>> models to optimise.
>> - @type models: list of str
>> - @keyword grid_inc: Number of grid search increments. If
>> set to None, then the grid search will be turned off and the default
>> parameter values will be used instead.
>> - @type grid_inc: int or None
>> - @keyword mc_sim_num: The number of Monte Carlo simulations
>> to be used for error analysis at the end of the analysis.
>> - @type mc_sim_num: int
>> - @keyword exp_mc_sim_num: The number of Monte Carlo simulations
>> for the error analysis in the 'R2eff' model when exponential curves are
>> fitted. This defaults to the value of the mc_sim_num argument when not
>> given. For the 2-point fixed-time calculation for the 'R2eff' model, this
>> argument is ignored.
>> - @type exp_mc_sim_num: int or None
>> - @type
>> - @keyword modsel: The model selection technique to use in
>> the analysis to determine which model is the best for each spin cluster.
>> This can currently be one of 'AIC', 'AICc', and 'BIC'.
>> - @type modsel: str
>> - @keyword pre_run_dir: The optional directory containing the
>> dispersion auto-analysis results from a previous run. The optimised
>> parameters from these previous results will be used as the starting point
>> for optimisation rather than performing a grid search. This is essential
>> for when large spin clusters are specified, as a grid search becomes
>> prohibitively expensive with clusters of three or more spins. At some point
>> a RelaxError will occur because the grid search is impossibly large. For
>> the cluster specific parameters, i.e. the populations of the states and the
>> exchange parameters, an average value will be used as the starting point.
>> For all other parameters, the R20 values for each spin and magnetic field,
>> as well as the parameters related to the chemical shift difference dw, the
>> optimised values of the previous run will be directly copied.
>> - @type pre_run_dir: None or str
>> - @keyword insignificance: The R2eff/R1rho value in rad/s by which
>> to judge insignificance. If the maximum difference between two points on
>> all dispersion curves for a spin is less than this value, that spin will be
>> deselected. This does not affect the 'No Rex' model. Set this value to 0.0
>> to use all data. The value will be passed on to the
>> relax_disp.insignificance user function.
>> - @type insignificance: float
>> - @keyword numeric_only: The class of models to use in the model
>> selection. The default of False allows all dispersion models to be used in
>> the analysis (no exchange, the analytic models and the numeric models). The
>> value of True will activate a pure numeric solution - the analytic models
>> will be optimised, as they are very useful for replacing the grid search for
>> the numeric models, but the final model selection will not include them.
>> - @type numeric_only: bool
>> - @keyword mc_sim_all_models: A flag which if True will cause Monte
>> Carlo simulations to be performed for each individual model. Otherwise
>> Monte Carlo simulations will be reserved for the final model.
>> - @type mc_sim_all_models: bool
>> - @keyword eliminate: A flag which if True will enable the
>> elimination of failed models and failed Monte Carlo simulations through the
>> eliminate user function.
>> - @type eliminate: bool
>> - @keyword set_grid_r20: A flag which if True will set the grid
>> R20 values from the minimum R2eff values through the r20_from_min_r2eff user
>> function. This will speed up the grid search with a factor
>> GRID_INC^(Nr_spec_freq). For a CPMG experiment with two fields and standard
>> GRID_INC=21, the speed-up is a factor 441.
>> - @type set_grid_r20: bool
>> + @keyword pipe_name: The name of the data pipe
>> containing all of the data for the analysis.
>> + @type pipe_name: str
>> + @keyword pipe_bundle: The data pipe bundle to
>> associate all spawned data pipes with.
>> + @type pipe_bundle: str
>> + @keyword results_dir: The directory where results
>> files are saved.
>> + @type results_dir: str
>> + @keyword models: The list of relaxation
>> dispersion models to optimise.
>> + @type models: list of str
>> + @keyword grid_inc: Number of grid search
>> increments. If set to None, then the grid search will be turned off and the
>> default parameter values will be used instead.
>> + @type grid_inc: int or None
>> + @keyword mc_sim_num: The number of Monte Carlo
>> simulations to be used for error analysis at the end of the analysis.
>> + @type mc_sim_num: int
>> + @keyword exp_mc_sim_num: The number of Monte Carlo
>> simulations for the error analysis in the 'R2eff' model when exponential
>> curves are fitted. This defaults to the value of the mc_sim_num argument
>> when not given. For the 2-point fixed-time calculation for the 'R2eff'
>> model, this argument is ignored.
>> + @type exp_mc_sim_num: int or None
>> + @keyword modsel: The model selection technique
>> to use in the analysis to determine which model is the best for each spin
>> cluster. This can currently be one of 'AIC', 'AICc', and 'BIC'.
>> + @type modsel: str
>> + @keyword pre_run_dir: The optional directory
>> containing the dispersion auto-analysis results from a previous run. The
>> optimised parameters from these previous results will be used as the
>> starting point for optimisation rather than performing a grid search. This
>> is essential for when large spin clusters are specified, as a grid search
>> becomes prohibitively expensive with clusters of three or more spins. At
>> some point a RelaxError will occur because the grid search is impossibly
>> large. For the cluster specific parameters, i.e. the populations of the
>> states and the exchange parameters, an average value will be used as the
>> starting point. For all other parameters, the R20 values for each spin and
>> magnetic field, as well as the parameters related to the chemical shift
>> difference dw, the optimised values of the previous run will be directly
>> copied.
>> + @type pre_run_dir: None or str
>> + @keyword optimise_pre_run_r2eff: Flag to specify if the read
>> previous R2eff results should be optimised. For R1rho models where the
>> error of R2eff values are determined by Monte-Carlo simulations, it can be
>> valuable to make an initial R2eff run with a high number of Monte-Carlo
>> simulations. Any subsequent model analysis can then be based on these R2eff
>> values, without optimising the R2eff values.
>> + @type optimise_pre_run_r2eff: bool
>> + @keyword insignificance: The R2eff/R1rho value in rad/s
>> by which to judge insignificance. If the maximum difference between two
>> points on all dispersion curves for a spin is less than this value, that
>> spin will be deselected. This does not affect the 'No Rex' model. Set this
>> value to 0.0 to use all data. The value will be passed on to the
>> relax_disp.insignificance user function.
>> + @type insignificance: float
>> + @keyword numeric_only: The class of models to use in
>> the model selection. The default of False allows all dispersion models to
>> be used in the analysis (no exchange, the analytic models and the numeric
>> models). The value of True will activate a pure numeric solution - the
>> analytic models will be optimised, as they are very useful for replacing the
>> grid search for the numeric models, but the final model selection will not
>> include them.
>> + @type numeric_only: bool
>> + @keyword mc_sim_all_models: A flag which if True will cause
>> Monte Carlo simulations to be performed for each individual model.
>> Otherwise Monte Carlo simulations will be reserved for the final model.
>> + @type mc_sim_all_models: bool
>> + @keyword eliminate: A flag which if True will
>> enable the elimination of failed models and failed Monte Carlo simulations
>> through the eliminate user function.
>> + @type eliminate: bool
>> + @keyword set_grid_r20: A flag which if True will set
>> the grid R20 values from the minimum R2eff values through the
>> r20_from_min_r2eff user function. This will speed up the grid search with a
>> factor GRID_INC^(Nr_spec_freq). For a CPMG experiment with two fields and
>> standard GRID_INC=21, the speed-up is a factor 441.
>> + @type set_grid_r20: bool
>> """
>>
>> # Printout.
>> @@ -104,6 +105,7 @@
>> self.exp_mc_sim_num = exp_mc_sim_num
>> self.modsel = modsel
>> self.pre_run_dir = pre_run_dir
>> + self.optimise_pre_run_r2eff = optimise_pre_run_r2eff
>> self.insignificance = insignificance
>> self.set_grid_r20 = set_grid_r20
>> self.numeric_only = numeric_only
>> @@ -481,14 +483,21 @@
>> self.interpreter.value.set(param=param, index=None)
>>
>> # Minimise.
>> - self.interpreter.minimise.execute('simplex',
>> func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations,
>> constraints=True)
>> + if model == MODEL_R2EFF:
>> + if self.optimise_pre_run_r2eff:
>> + self.interpreter.minimise.execute('simplex',
>> func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations,
>> constraints=True)
>> + else:
>> + pass
>> + else:
>> + self.interpreter.minimise.execute('simplex',
>> func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations,
>> constraints=True)
>> +
>>
>> # Model elimination.
>> if self.eliminate:
>> self.interpreter.eliminate()
>>
>> # Monte Carlo simulations.
>> - if self.mc_sim_all_models or len(self.models) < 2 or model ==
>> MODEL_R2EFF:
>> + if self.mc_sim_all_models or len(self.models) < 2 or (model ==
>> MODEL_R2EFF and self.optimise_pre_run_r2eff):
>> if model == MODEL_R2EFF and self.exp_mc_sim_num != None:
>>
>> self.interpreter.monte_carlo.setup(number=self.exp_mc_sim_num)
>> else:
>>
>>
>> _______________________________________________
>> relax (http://www.nmr-relax.com)
>>
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>>
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>
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