Hi, If all the 'No Rex' models are merged into a single model, then the methods here would remain identical. However the logic of the "if model == MODEL_NOREX:" block would be modified to select the correct target function based on the data that the user input. Or even simpler, the logic could be placed higher up in a function in the specific_analyses.relax_disp.data module, and then a new argument added to the target function class for choosing between the target functions. Note that a 4th target function will probably be added in the future when off-resonance CPMG data is handled, to allow R1 to be optimised with this data.
Cheers, Edward On 4 August 2014 19:22, <tlin...@nmr-relax.com> wrote: > Author: tlinnet > Date: Mon Aug 4 19:22:10 2014 > New Revision: 24945 > > URL: http://svn.gna.org/viewcvs/relax?rev=24945&view=rev > Log: > Implemented target and calculation function for MODEL_NOREX_R1RHO, > MODEL_NOREX_R1RHO_FIT_R1. > > bug #22440(https://gna.org/bugs/?22440): The "NOREX" model is not covering > R1rho models. > 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/target_functions/relax_disp.py > > Modified: branches/R1_fitting/target_functions/relax_disp.py > URL: > http://svn.gna.org/viewcvs/relax/branches/R1_fitting/target_functions/relax_disp.py?rev=24945&r1=24944&r2=24945&view=diff > ============================================================================== > --- branches/R1_fitting/target_functions/relax_disp.py (original) > +++ branches/R1_fitting/target_functions/relax_disp.py Mon Aug 4 19:22:10 > 2014 > @@ -55,7 +55,7 @@ > from lib.errors import RelaxError > from lib.float import isNaN > from target_functions.chi2 import chi2_rankN > -from specific_analyses.relax_disp.variables import EXP_TYPE_CPMG_DQ, > EXP_TYPE_CPMG_MQ, EXP_TYPE_CPMG_PROTON_MQ, EXP_TYPE_CPMG_PROTON_SQ, > EXP_TYPE_CPMG_SQ, EXP_TYPE_CPMG_ZQ, EXP_TYPE_LIST_CPMG, EXP_TYPE_R1RHO, > MODEL_B14, MODEL_B14_FULL, MODEL_CR72, MODEL_CR72_FULL, MODEL_DPL94, > MODEL_DPL94_FIT_R1, MODEL_IT99, MODEL_LIST_CPMG, MODEL_LIST_FULL, > MODEL_LIST_MMQ, MODEL_LIST_MQ_CPMG, MODEL_LIST_NUMERIC, MODEL_LIST_R1RHO, > MODEL_LIST_R1RHO_FULL, MODEL_LIST_R1RHO_FIT_R1, MODEL_LM63, MODEL_LM63_3SITE, > MODEL_M61, MODEL_M61B, MODEL_MP05, MODEL_MMQ_CR72, MODEL_NOREX, > MODEL_NS_CPMG_2SITE_3D, MODEL_NS_CPMG_2SITE_3D_FULL, > MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_NS_CPMG_2SITE_STAR, > MODEL_NS_CPMG_2SITE_STAR_FULL, MODEL_NS_MMQ_2SITE, MODEL_NS_MMQ_3SITE, > MODEL_NS_MMQ_3SITE_LINEAR, MODEL_NS_R1RHO_2SITE, MODEL_NS_R1RHO_3SITE, > MODEL_NS_R1RHO_3SITE_LINEAR, MODEL_PARAM_DW_MIX_DOUBLE, > MODEL_PARAM_DW_MIX_QUADRUPLE, MODEL_PARAM_INV_RELAX_TIMES, MODEL_PARAM_R20B, > MODEL_TAP03, MODEL_TP02, MODEL_TSMFK01 > +from specific_analyses.relax_disp.variables import EXP_TYPE_CPMG_DQ, > EXP_TYPE_CPMG_MQ, EXP_TYPE_CPMG_PROTON_MQ, EXP_TYPE_CPMG_PROTON_SQ, > EXP_TYPE_CPMG_SQ, EXP_TYPE_CPMG_ZQ, EXP_TYPE_LIST_CPMG, EXP_TYPE_R1RHO, > MODEL_B14, MODEL_B14_FULL, MODEL_CR72, MODEL_CR72_FULL, MODEL_DPL94, > MODEL_DPL94_FIT_R1, MODEL_IT99, MODEL_LIST_CPMG, MODEL_LIST_FULL, > MODEL_LIST_MMQ, MODEL_LIST_MQ_CPMG, MODEL_LIST_NUMERIC, MODEL_LIST_R1RHO, > MODEL_LIST_R1RHO_FULL, MODEL_LIST_R1RHO_FIT_R1, MODEL_LM63, MODEL_LM63_3SITE, > MODEL_M61, MODEL_M61B, MODEL_MP05, MODEL_MMQ_CR72, MODEL_NOREX, > MODEL_NOREX_R1RHO, MODEL_NOREX_R1RHO_FIT_R1, MODEL_NS_CPMG_2SITE_3D, > MODEL_NS_CPMG_2SITE_3D_FULL, MODEL_NS_CPMG_2SITE_EXPANDED, > MODEL_NS_CPMG_2SITE_STAR, MODEL_NS_CPMG_2SITE_STAR_FULL, MODEL_NS_MMQ_2SITE, > MODEL_NS_MMQ_3SITE, MODEL_NS_MMQ_3SITE_LINEAR, MODEL_NS_R1RHO_2SITE, > MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR, MODEL_PARAM_DW_MIX_DOUBLE, > MODEL_PARAM_DW_MIX_QUADRUPLE, MODEL_PARAM_INV_RELAX_TIMES, MODEL_PARAM_R20B, > MODEL_ TAP03, MODEL_TP02, MODEL_TSMFK01 > > > class Dispersion: > @@ -482,6 +482,10 @@ > # Set up the model. > if model == MODEL_NOREX: > self.func = self.func_NOREX > + if model == MODEL_NOREX_R1RHO: > + self.func = self.func_NOREX_R1RHO > + if model == MODEL_NOREX_R1RHO_FIT_R1: > + self.func = self.func_NOREX_R1RHO_FIT_R1 > if model == MODEL_LM63: > self.func = self.func_LM63 > if model == MODEL_LM63_3SITE: > @@ -653,6 +657,35 @@ > return chi2_rankN(self.values, self.back_calc, self.errors) > > > + def calc_NOREX_R1RHO(self, R1=None, r1rho_prime=None): > + """Calculation function for no exchange, for R1rho off resonance > models. > + > + @keyword R1: The R1 value. > + @type R1: list of float > + @keyword r1rho_prime: The R1rho value for all states in the > absence of exchange. > + @type r1rho_prime: list of float > + @return: The chi-squared value. > + @rtype: float > + """ > + > + # Reshape r1rho_prime to per experiment, spin and frequency. > + self.r1rho_prime_struct[:] = multiply.outer( > r1rho_prime.reshape(self.NE, self.NS, self.NM), self.no_nd_ones ) > + > + # Make back calculation. > + self.back_calc[:] = R1 * cos(self.tilt_angles)**2 + > self.r1rho_prime_struct * sin(self.tilt_angles)**2 > + > + # Clean the data for all values, which is left over at the end of > arrays. > + self.back_calc = self.back_calc*self.disp_struct > + > + # For all missing data points, set the back-calculated value to the > measured values so that it has no effect on the chi-squared value. > + if self.has_missing: > + # Replace with values. > + self.back_calc[self.mask_replace_blank.mask] = > self.values[self.mask_replace_blank.mask] > + > + # Return the total chi-squared value. > + return chi2_rankN(self.values, self.back_calc, self.errors) > + > + > def calc_ns_cpmg_2site_3D_chi2(self, R20A=None, R20B=None, dw=None, > pA=None, kex=None): > """Calculate the chi-squared value of the 'NS CPMG 2-site' models. > > @@ -1395,6 +1428,50 @@ > return chi2_rankN(self.values, self.back_calc, self.errors) > > > + def func_NOREX_R1RHO(self, params): > + """Target function for no exchange, for R1rho off resonance models. > + > + @param params: The vector of parameter values. > + @type params: numpy rank-1 float array > + @return: The chi-squared value. > + @rtype: float > + """ > + > + # Scaling. > + if self.scaling_flag: > + params = dot(params, self.scaling_matrix) > + > + # Unpack the parameter values. > + r1rho_prime = params > + > + # Calculate and return the chi-squared value. > + return self.calc_NOREX_R1RHO(R1=self.r1, r1rho_prime=r1rho_prime) > + > + > + def func_NOREX_R1RHO_FIT_R1(self, params): > + """Target function for no exchange, for R1rho off resonance models, > where R1 is fitted. > + > + @param params: The vector of parameter values. > + @type params: numpy rank-1 float array > + @return: The chi-squared value. > + @rtype: float > + """ > + > + # Scaling. > + if self.scaling_flag: > + params = dot(params, self.scaling_matrix) > + > + # Unpack the parameter values. > + r1 = params[:self.end_index[0]] > + r1rho_prime = params[self.end_index[0]:self.end_index[1]] > + > + # Reshape R1 to per experiment, spin and frequency. > + self.r1_struct[:] = multiply.outer( r1.reshape(self.NE, self.NS, > self.NM), self.no_nd_ones ) > + > + # Calculate and return the chi-squared value. > + return self.calc_NOREX_R1RHO(R1=self.r1_struct, > r1rho_prime=r1rho_prime) > + > + > def func_ns_cpmg_2site_3D(self, params): > """Target function for the reduced numerical solution for the 2-site > Bloch-McConnell equations. > > > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-commits mailing list > relax-comm...@gna.org > > To unsubscribe from this list, get a password > reminder, or change your subscription options, > visit the list information page at > https://mail.gna.org/listinfo/relax-commits _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list relax-devel@gna.org To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
