Hi Troels, Are all these R1-fitting R1rho models now tested in the test suite?
Cheers, Edward On 5 August 2014 20:47, <[email protected]> wrote: > Author: tlinnet > Date: Tue Aug 5 20:47:24 2014 > New Revision: 24969 > > URL: http://svn.gna.org/viewcvs/relax?rev=24969&view=rev > Log: > Split target function of model TAP03, into a calc and two func_TAP03* > variants. > > One target function will use measured R1 values, while one target function > will use the fitted R1 values. > > They will use the same calculation function. > > 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=24969&r1=24968&r2=24969&view=diff > ============================================================================== > --- branches/R1_fitting/target_functions/relax_disp.py (original) > +++ branches/R1_fitting/target_functions/relax_disp.py Tue Aug 5 20:47:24 > 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_LIST_R1RHO_W_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, M ODEL_PARAM_R20B, MODEL_TAP03, MODEL_TP02, MODEL_TP02_FIT_R1, 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_LIST_R1RHO_W_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, M ODEL_PARAM_R20B, MODEL_TAP03, MODEL_TAP03_FIT_R1, MODEL_TP02, MODEL_TP02_FIT_R1, MODEL_TSMFK01 > > > class Dispersion: > @@ -526,6 +526,8 @@ > self.func = self.func_TP02_fit_r1 > if model == MODEL_TAP03: > self.func = self.func_TAP03 > + if model == MODEL_TAP03_FIT_R1: > + self.func = self.func_TAP03_fit_r1 > if model == MODEL_MP05: > self.func = self.func_MP05 > if model == MODEL_NS_R1RHO_2SITE: > @@ -900,6 +902,44 @@ > return chi2_rankN(self.values, self.back_calc, self.errors) > > > + def calc_TAP03(self, R1=None, r1rho_prime=None, dw=None, pA=None, > kex=None): > + """Calculation function for the Trott, Abergel and Palmer (2003) > R1rho off-resonance 2-site model. > + > + @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 > + @keyword dw: The chemical shift differences in ppm for > each spin. > + @type dw: list of float > + @keyword pA: The population of state A. > + @type pA: float > + @keyword kex: The rate of exchange. > + @type kex: 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 ) > + > + # Convert dw from ppm to rad/s. Use the out argument, to pass > directly to structure. > + multiply( multiply.outer( dw.reshape(1, self.NS), self.nm_no_nd_ones > ), self.frqs, out=self.dw_struct ) > + > + # Back calculate the R1rho values. > + r1rho_TAP03(r1rho_prime=self.r1rho_prime_struct, > omega=self.chemical_shifts, offset=self.offset, pA=pA, dw=self.dw_struct, > kex=kex, R1=R1, spin_lock_fields=self.spin_lock_omega1, > spin_lock_fields2=self.spin_lock_omega1_squared, back_calc=self.back_calc) > + > + # 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_TP02(self, R1=None, r1rho_prime=None, dw=None, pA=None, > kex=None): > """Calculation function for the Trott and Palmer (2002) R1rho > off-resonance 2-site model. > > @@ -1879,30 +1919,40 @@ > params = dot(params, self.scaling_matrix) > > # Unpack the parameter values. > - R20 = params[:self.end_index[0]] > + r1rho_prime = params[:self.end_index[0]] > dw = params[self.end_index[0]:self.end_index[1]] > pA = params[self.end_index[1]] > kex = params[self.end_index[1]+1] > > - # Convert dw from ppm to rad/s. Use the out argument, to pass > directly to structure. > - multiply( multiply.outer( dw.reshape(1, self.NS), self.nm_no_nd_ones > ), self.frqs, out=self.dw_struct ) > - > - # Reshape R20 to per experiment, spin and frequency. > - self.r20_struct[:] = multiply.outer( R20.reshape(self.NE, self.NS, > self.NM), self.no_nd_ones ) > - > - # Back calculate the R1rho values. > - r1rho_TAP03(r1rho_prime=self.r20_struct, omega=self.chemical_shifts, > offset=self.offset, pA=pA, dw=self.dw_struct, kex=kex, R1=self.r1, > spin_lock_fields=self.spin_lock_omega1, > spin_lock_fields2=self.spin_lock_omega1_squared, back_calc=self.back_calc) > - > - # 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) > + # Calculate and return the chi-squared value. > + return self.calc_TAP03(R1=self.r1, r1rho_prime=r1rho_prime, dw=dw, > pA=pA, kex=kex) > + > + > + def func_TAP03_fit_r1(self, params): > + """Target function for the Trott, Abergel and Palmer (2003) R1rho > off-resonance 2-site model, 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]] > + dw = params[self.end_index[1]:self.end_index[2]] > + pA = params[self.end_index[2]] > + kex = params[self.end_index[2]+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_TAP03(R1=self.r1_struct, r1rho_prime=r1rho_prime, > dw=dw, pA=pA, kex=kex) > > > def func_TP02(self, params): > > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-commits mailing list > [email protected] > > 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 [email protected] 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
