mailRe: r24969 - /branches/R1_fitting/target_functions/relax_disp.py


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Posted by Edward d'Auvergne on August 18, 2014 - 15:10:
Hi,

If you think this is sufficient, then that's ok.  Otherwise system
tests such as Relax_disp.test_tp02_data_to_tp02 could be duplicated
and the model changed.

Regards,

Edward


On 18 August 2014 12:39, Troels Emtekær Linnet <tlinnet@xxxxxxxxx> wrote:
Hi Edward.

Currently only one system test is covering them.

On 18 Aug 2014 12:17, "Edward d'Auvergne" <edward@xxxxxxxxxxxxx> wrote:

Hi Troels,

Are all these R1-fitting R1rho models now tested in the test suite?

Cheers,

Edward



On 5 August 2014 20:47,  <tlinnet@xxxxxxxxxxxxx> 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, 
MODEL_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, 
MODEL_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):


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