BEPolyelectrolyte

sans.models.BEPolyelectrolyte

Provide F(x) = K*1/(4*pi*Lb*(alpha)^(2))*(q^(2)+k2)/(1+(r02)^(2))*(q^(2)+k2) *(q^(2)-(12*h*C/b^(2))) BEPolyelectrolyte as a BaseComponent model

class sans.models.BEPolyelectrolyte.BEPolyelectrolyte

Bases: sans.models.BaseComponent.BaseComponent

Class that evaluates a BEPolyelectrolyte.

F(x) = K*1/(4*pi*Lb*(alpha)^(2))*(q^(2)+k2)/(1+(r02)^(2))*(q^(2)+k2) *(q^(2)-(12*h*C/b^(2)))

The model has Eight parameters:

K = Constrast factor of the polymer Lb = Bjerrum length H = virial parameter B = monomer length Cs = Concentration of monovalent salt alpha = ionazation degree C = polymer molar concentration bkd = background

_BEPoly()

Evaluate F(x) = K*1/(4*pi*Lb*(alpha)^(2))*(q^(2)+k2)/(1+(r02)^(2))

*(q^(2)+k2)*(q^(2)-(12*h*C/b^(2)))

has 3 internal parameters :

The inverse Debye Length: K2 = 4*pi*Lb*(2*Cs+alpha*C) r02 =1/alpha/Ca^(0.5)*(B/(48*pi*Lb)^(0.5)) Ca = C*6.022136e-4

__add__()

__div__()

__init__()

Initialization

__module__ = 'sans.models.BEPolyelectrolyte'

__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

Parameters:name – name 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 the model @param x: input q-value (float or [float, float] as [r, theta]) @return: (debye value)

runXY()

Evaluate the model @param x: input q-value (float or [float, float] as [qx, qy]) @return: debye value

setParam()

Set the value of a model parameter

Parameters:

• name – name of the parameter
• value – value of the parameter

setParamWithToken()