epraya.Genio#

Genio(Hamer, Expe, Vara, exper, eps=1e-10, maximal=100, datype='data', mode='p', seed=451, Mr=70)#

Fitting function for the experimental data using the genetic algorithm. Creates a population of 35*N individuals, where N is the number of parameters that will change, that have combinations of the possible values of the parameters. This individuals are evaluated, select and cross to produce a new population.

The algorithm uses a normal distribution for the mutation probability and a the numpy random int generator for the cross probability.

Parameters:

  • Hamer (Class) – Container for the hamiltonian parameters.

  • Expe (Class) – Container for the experimental conditions.

  • Vara (Class) – Container for the range and parameters to vary.

  • exper (np.array) – Experimental spectrum data to fit.

  • eps (float) – Tolerance value for the error. Default is 1e-10

  • maximal (int) – Max. number of generations to evalue.

  • datype (str) – Type of data to do the fitting, can be ‘data’, the data as it’s or ‘integral’, its first integral.

  • mode (str) – Defines the sample type, ‘p’ for powder and ‘c’ for monocristal.

  • seed (int) – Seed for the uniform distribution

  • Mr (int) – Number of divisions for the Delaunay grid.

Returns:

  • Ham (Class) – Container for the fitted hamiltonian parameters.

  • fitresult (Class) – Container for the results of the Fitting function.