DivComponent

sans.models.DivComponent

Provide base functionality for all model components @author: Mathieu Doucet / UTK @contact: mathieu.doucet@nist.gov

class sans.models.DivComponent.DivComponent

Bases: sans.models.BaseComponent.BaseComponent

Basic model component for Division Provides basic arithmetics

__add__()

__div__()

__init__()

@param base: component to div @param other: component to div by

__module__ = 'sans.models.DivComponent'

__mul__()

__str__()

Returns:string representatio

__sub__()

_clone()

Internal utility function to copy the internal data members to a fresh copy.

calculate_ER()

clone()

Returns a new object identical to the current object

evalDistribution()

Evaluate a distribution of q-values.

• For 1D, a numpy array is expected as input:

  evalDistribution(q)

where q is a numpy array.

• For 2D, a list of numpy arrays are expected: [qx_prime,qy_prime], where 1D arrays,

qx_prime = [ qx[0], qx[1], qx[2], ....] and qy_prime = [ qy[0], qy[1], qy[2], ....]

Then get q = numpy.sqrt(qx_prime^2+qy_prime^2)

that is a qr in 1D array; q = [q[0], q[1], q[2], ....]

Note :

Due to 2D speed issue, no anisotropic scattering is supported for python models, thus C-models should have

their own evalDistribution methods.

The method is then called the following way:

evalDistribution(q) where q is a numpy array.

Parameters:qdist – ndarray of scalar q-values or list [qx,qy] where qx,qy are 1D ndarrays

getDispParamList()

Return a list of all available parameters for the model

getParam()

Set the value of a model parameter @param name: name of the parameter @return: value of the parameter

getParamList()

Return a list of all available parameters for the model

getParamListWithToken()

getParamWithToken()

is_fittable()

Check if a given parameter is fittable or not

Parameters:par_name – the parameter name to check

run()

Evaluate each part of the component and sum the results @param x: input parameter @return: value of the model at x

runXY()

Evaluate each part of the component and sum the results @param x: input parameter @return: value of the model at x

setParam()

Set the value of a model parameter @param name: name of parameter to set @param value: value to give the paramter

setParamWithToken()