Hi,
Yes, this is deliberate. The 3-site models are already almost
impossible to optimise as they have so many minima, and this is an
active research area in the dispersion field. Introducing R20A !=
R20B != R20C increases the number of minima exponentially, making the
problem totally impossible to solve. So that is why we have R20A =
R20B = R20C in the model. It might be possible to create 'full'
versions of these models where these rates are different, but I think
the field needs to develop further before this is of any use.
But the infrastructure in lib.dispersion does support R20A != R20B !=
R20C. The reason for this is that the lib.dispersion modules can be
directly used to investigate the optimisation space problems. I would
like to have this as a powerful tool for investigating this
multi-site, multi-rate problem in the future.
Regards,
Edward
On 24 June 2014 11:14, Troels Emtekær Linnet <tlinnet@xxxxxxxxxxxxx> wrote:
Hi Ed.
As far as I can see, R20B, and R20C is not used in ns_mmq_2site and 3site?
def func_ns_mmq_2site(self, params):
"""Target function for the combined SQ, ZQ, DQ and MQ CPMG numeric
solution.
@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.
R20 = params[:self.end_index[0]]
dw = params[self.end_index[0]:self.end_index[1]]
dwH = params[self.end_index[1]:self.end_index[2]]
pA = params[self.end_index[2]]
kex = params[self.end_index[2]+1]
But they are parsed to the lib function.
# Back calculate the R2eff values for each experiment type.
self.r2eff_ns_mmq[ei](M0=self.M0, R20A=r20, R20B=r20, pA=pA,
dw=aliased_dw, dwH=aliased_dwH, kex=kex,
inv_tcpmg=self.inv_relax_times[ei], tcp=self.tau_cpmg[ei],
back_calc=self.back_calc[ei], num_points=self.num_disp_points[ei],
power=self.power[ei])
Is this by purpose?
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Re: R20B and R20C in ns_mmq_2site and 3site