Should the param_name not be 'r1_fit' here?  Just as 'R2' and 'r2' are
not 'R2_fit' and 'r2_fit', the 'r1_fit' would be better as 'r1'.  If
R1 is a model parameter, then is it by definition fit.

Cheers,

Edward


On 4 August 2014 16:27,  <[email protected]> wrote:
> Author: tlinnet
> Date: Mon Aug  4 16:27:44 2014
> New Revision: 24929
>
> URL: http://svn.gna.org/viewcvs/relax?rev=24929&view=rev
> Log:
> Added linear linear_constraints for paramter "r1_fit".
>
> 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/specific_analyses/relax_disp/parameters.py
>
> Modified: branches/R1_fitting/specific_analyses/relax_disp/parameters.py
> URL: 
> http://svn.gna.org/viewcvs/relax/branches/R1_fitting/specific_analyses/relax_disp/parameters.py?rev=24929&r1=24928&r2=24929&view=diff
> ==============================================================================
> --- branches/R1_fitting/specific_analyses/relax_disp/parameters.py      
> (original)
> +++ branches/R1_fitting/specific_analyses/relax_disp/parameters.py      Mon 
> Aug  4 16:27:44 2014
> @@ -430,6 +430,7 @@
>
>      The different constraints used within different models are::
>
> +        0 <= R1_fit <= 200
>          0 <= R2 <= 200
>          0 <= R2A <= 200
>          0 <= R2B <= 200
> @@ -455,6 +456,10 @@
>
>      In the notation A.x >= b, where A is a matrix of coefficients, x is an 
> array of parameter values, and b is a vector of scalars, these inequality 
> constraints are::
>
> +        | 1  0  0 |     |  R1_fit  |      |    0    |
> +        |         |     |          |      |         |
> +        |-1  0  0 |     |  R1_fit  |      |  -200   |
> +        |         |     |          |      |         |
>          | 1  0  0 |     |    R2    |      |    0    |
>          |         |     |          |      |         |
>          |-1  0  0 |     |    R2    |      |  -200   |
> @@ -535,6 +540,16 @@
>              A[j][param_index] = 1.0
>              b.append(0.0)
>              j += 1
> +
> +        # The fitted longitudinal relaxation rates (0 <= r1_fit <= 200).
> +        elif param_name in ['R1_fit']:
> +            A.append(zero_array * 0.0)
> +            A.append(zero_array * 0.0)
> +            A[j][param_index] = 1.0
> +            A[j+1][param_index] = -1.0
> +            b.append(0.0)
> +            b.append(-200.0 / scaling_matrix[param_index, param_index])
> +            j += 2
>
>          # The transversal relaxation rates (0 <= r2 <= 200).
>          elif param_name in ['r2', 'r2a', 'r2b']:
>
>
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