# Note: model title and parameter table are inserted automatically r""" This model provides the scattering intensity, $I(q)$, for a lyotropic lamellar phase where a random distribution in solution are assumed. The SLD of the head region is taken to be different from the SLD of the tail region. Definition ---------- The scattering intensity $I(q)$ is .. math:: I(q) = 2\pi\frac{\text{scale}}{2(\delta_H + \delta_T)} P(q) \frac{1}{q^2} The form factor $P(q)$ is .. math:: P(q) = \frac{4}{q^2} \left\lbrace \Delta \rho_H \left[\sin[q(\delta_H + \delta_T)\ - \sin(q\delta_T)\right] + \Delta\rho_T\sin(q\delta_T) \right\rbrace^2 where $\delta_T$ is *length_tail*, $\delta_H$ is *length_head*, $\Delta\rho_H$ is the head contrast (*sld_head* $-$ *sld_solvent*), and $\Delta\rho_T$ is tail contrast (*sld* $-$ *sld_solvent*). The total thickness of the lamellar sheet is $\delta_H + \delta_T + \delta_T + \delta_H$. Note that in a non aqueous solvent the chemical "head" group may be the "Tail region" and vice-versa. The 2D scattering intensity is calculated in the same way as 1D, where the $q$ vector is defined as .. math:: q = \sqrt{q_x^2 + q_y^2} References ---------- .. [#] F Nallet, R Laversanne, and D Roux, *J. Phys. II France*, 3, (1993) 487-502 .. [#] J Berghausen, J Zipfel, P Lindner, W Richtering, *J. Phys. Chem. B*, 105, (2001) 11081-11088 Source ------ `lamellar_hg.py `_ Authorship and Verification ---------------------------- * **Author:** * **Last Modified by:** * **Last Reviewed by:** S King and P Butler **Date** April 17, 2014 * **Source added by :** Steve King **Date:** March 25, 2019 """ import numpy as np from numpy import inf name = "lamellar_hg" title = "Random lamellar phase with Head and Tail Groups" description = """\ [Random lamellar phase with Head and Tail Groups] I(q)= 2*pi*P(q)/(2(H+T)*q^(2)), where P(q)= see manual layer thickness =(H+T+T+H) = 2(Head+Tail) sld = Tail scattering length density sld_head = Head scattering length density sld_solvent = solvent scattering length density background = incoherent background scale = scale factor """ category = "shape:lamellae" # pylint: disable=bad-whitespace, line-too-long # ["name", "units", default, [lower, upper], "type","description"], parameters = [["length_tail", "Ang", 15, [0, inf], "volume", "Tail thickness ( total = H+T+T+H)"], ["length_head", "Ang", 10, [0, inf], "volume", "Head thickness"], ["sld", "1e-6/Ang^2", 0.4, [-inf,inf], "sld", "Tail scattering length density"], ["sld_head", "1e-6/Ang^2", 3.0, [-inf,inf], "sld", "Head scattering length density"], ["sld_solvent", "1e-6/Ang^2", 6, [-inf,inf], "sld", "Solvent scattering length density"]] # pylint: enable=bad-whitespace, line-too-long # No volume normalization despite having a volume parameter # This should perhaps be volume normalized? form_volume = """ return 1.0; """ Iq = """ const double qsq = q*q; const double drh = sld_head - sld_solvent; const double drt = sld - sld_solvent; //correction 13FEB06 by L.Porcar const double qT = q*length_tail; double Pq, inten; Pq = drh*(sin(q*(length_head+length_tail))-sin(qT)) + drt*sin(qT); Pq *= Pq; Pq *= 4.0/(qsq); inten = 2.0e-4*M_PI*Pq/qsq; // normalize by the bilayer thickness inten /= 2.0*(length_head+length_tail); return inten; """ def random(): """Return a random parameter set for the model.""" thickness = 10**np.random.uniform(1, 4) length_head = thickness * np.random.uniform(0, 1) length_tail = thickness - length_head pars = dict( length_head=length_head, length_tail=length_tail, ) return pars demo = dict(scale=1, background=0, length_tail=15, length_head=10, sld=0.4, sld_head=3.0, sld_solvent=6.0, length_tail_pd=0.2, length_tail_pd_n=40, length_head_pd=0.01, length_head_pd_n=40) # tests = [ [{'scale': 1.0, 'background': 0.0, 'length_tail': 15.0, 'length_head': 10.0, 'sld': 0.4, 'sld_head': 3.0, 'sld_solvent': 6.0}, [0.001], [653143.9209]], ] # ADDED by: RKH ON: 18Mar2016 converted from sasview previously, now renaming everything & sorting the docs