Replace test_custom_lorentz3.yaml

file: 

    name: 29983_merged.txt

    qmin: 0.01

    qmax: 0.30

output_files: test_custom_lorentz3

sa_parameters: 

    temperatures: 100

    temperature_rate: 0.90

    parameter_rate: 0.95

    iterations: 1000

    models_to_generate: 3

model:

    name: lorentzian3

    category: shape-independent

    scale:

        fixed: [1.00]

    background: 

        linear: [0.000, 0.10]

    scale_1: 

        linear: [10.0, 100.0]

    length_1: 

        linear: [500.0, 1500.0]

    exponent_1: 

        linear: [2.5, 3.5] 

    scale_2: 

        linear: [1.0, 10.0]

    length_2: 

        linear: [50.0, 150.0]

    exponent_2: 

        linear: [1.5, 2.5] 

    scale_3: 

        linear: [0.1, 1.0]

    length_3: 

        linear: [5.0, 15.0]

    exponent_3: 

        linear: [0.5, 1.5] 

import numpy as np

from numpy import inf, power

name = "lorentzian3"

title = "This model calculates an empirical functional form for SAS data \

characterized by three Lorentzian-type functions."

description = """I(q) = scale_1/(1.0 + pow((q*length_1),exponent_1))

             + scale_2/(1.0 + pow((q*length_2),exponent_2) )

             + scale_3/(1.0 + pow((q*length_3),exponent_3) )+ background

             scale_1    = Lorentzian term scaling #1

             length_1   = Lorentzian screening length #1 [A]

             exponent_1 = Lorentzian exponent #1

             scale_2    = Lorentzian term scaling #2

             length_2   = Lorentzian screening length #2 [A]

             exponent_2 = Lorentzian exponent #2

             scale_3    = Lorentzian term scaling #3

             length_3   = Lorentzian screening length #3 [A]

             exponent_3 = Lorentzian exponent #3

             background = Incoherent background

        """

category = "shape-independent"

# pylint: disable=bad-whitespace, line-too-long

#            ["name", "units", default, [lower, upper], "type", "description"],

parameters = [["lorentz_scale_1",  "",     100.0, [-inf, inf], "", "First power law scale factor"],

              ["lorentz_length_1", "Ang", 1000.0, [-inf, inf], "", "First Lorentzian screening length"],

              ["lorentz_exp_1",    "",      3.0, [-inf, inf], "", "First exponent of power law"],

              ["lorentz_scale_2",  "",      10.0, [-inf, inf], "", "Second scale factor for broad Lorentzian peak"],

              ["lorentz_length_2", "Ang",  100.0, [-inf, inf], "", "Second Lorentzian screening length"],

              ["lorentz_exp_2",    "",      2.0, [-inf, inf], "", "Second exponent of power law"],

              ["lorentz_scale_3",  "",      1.0, [-inf, inf], "", "Third scale factor for broad Lorentzian peak"],

              ["lorentz_length_3", "Ang",  10.0, [-inf, inf], "", "Third Lorentzian screening length"],

              ["lorentz_exp_3",    "",      1.0, [-inf, inf], "", "Third exponent of power law"],

             ]

# pylint: enable=bad-whitespace, line-too-long

def Iq(q,

       lorentz_scale_1=100.0,

       lorentz_length_1=1000.0,

       lorentz_exp_1=3.0,

       lorentz_scale_2=10.0,

       lorentz_length_2=100.0,

       lorentz_exp_2=2.0)

       lorentz_scale_2=1.0,

       lorentz_length_2=10.0,

       lorentz_exp_2=1.0):

    """

    :param q:                   Input q-value (float or [float, float])

    :param lorentz_scale_1:     Second scale factor for broad Lorentzian peak

    :param lorentz_length_1:    First Lorentzian screening length

    :param lorentz_exp_1:       Exponent of the first Lorentz function

    :param lorentz_scale_2:     Second scale factor for broad Lorentzian peak

    :param lorentz_length_2:    Second Lorentzian screening length

    :param lorentz_exp_2:       Exponent of the second Lorentz function

    :param lorentz_scale_3:     Third scale factor for broad Lorentzian peak

    :param lorentz_length_3:    Third Lorentzian screening length

    :param lorentz_exp_3:       Exponent of the Third Lorentz function

    :return:                    Calculated intensity

    """

# pylint: disable=bad-whitespace

    intensity  = lorentz_scale_1/(1.0 +

                                  power(q*lorentz_length_1, lorentz_exp_1))

    intensity += lorentz_scale_2/(1.0 +

                                  power(q*lorentz_length_2, lorentz_exp_2))

    intensity += lorentz_scale_3/(1.0 +

                                  power(q*lorentz_length_3, lorentz_exp_3))

# pylint: enable=bad-whitespace

    return intensity

Iq.vectorized = True  # Iq accepts an array of q values

def random():

    """Return a random parameter set for the model."""

    scale = 10**np.random.uniform(0, 4, 3)

    length = 10**np.random.uniform(1, 4, 3)

    expon = np.random.uniform(1, 6, 3)

    pars = dict(

        #background=0,

        scale=1, # scale provided in model

        lorentz_scale_1=scale[0],

        lorentz_length_1=length[0],

        lorentz_exp_1=expon[0],

        lorentz_scale_2=scale[1],

        lorentz_length_2=length[1],

        lorentz_exp_2=expon[1],

        lorentz_scale_3=scale[2],

        lorentz_length_3=length[2],

        lorentz_exp_3=expon[2],

    )

    return pars