dchi2(self,
params,
diff_type,
diff_params,
model,
relax_data,
errors,
print_flag=0)
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Function to create the chi-squared gradient.
Function arguments
~~~~~~~~~~~~~~~~~~
1: params - a list containing the parameter values specific for the given model.
The order of parameters must be as follows:
m1 - {S2}
m2 - {S2, te}
m3 - {S2, Rex}
m4 - {S2, te, Rex}
m5 - {S2f, S2s, ts}
2: diff_type - string. The diffusion tensor, ie 'iso', 'axial', 'aniso'
3: diff_params - array. An array with the diffusion parameters
4: model - string. The model
5: relax_data - array. An array containing the experimental relaxation values.
6: errors - array. An array containing the experimental errors.
The chi-sqared gradient
~~~~~~~~~~~~~~~~~~~~~~~
Data structure: self.data.dchi2
Dimension: 1D, (parameters)
Type: Numeric array, Float64
Dependencies: self.data.ri, self.data.dri
Required by: None
Formula
~~~~~~~
_n_
dChi2 \ / Ri - Ri() dRi() \
------- = -2 > | ---------- . ------- |
dthetaj /__ \ sigma_i**2 dthetaj /
i=1
where:
Ri are the values of the measured relaxation data set.
Ri() are the values of the back calculated relaxation data set.
sigma_i are the values of the error set.
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