# parallelepiped model # Note: model title and parameter table are inserted automatically r""" The form factor is normalized by the particle volume. For information about polarised and magnetic scattering, see the :ref:`magnetism` documentation. Definition ---------- This model calculates the scattering from a rectangular parallelepiped (:numref:`parallelepiped-image`). If you need to apply polydispersity, see also :ref:`rectangular-prism`. .. _parallelepiped-image: .. figure:: img/parallelepiped_geometry.jpg Parallelepiped with the corresponding definition of sides. The three dimensions of the parallelepiped (strictly here a cuboid) may be given in *any* size order. To avoid multiple fit solutions, especially with Monte-Carlo fit methods, it may be advisable to restrict their ranges. There may be a number of closely similar "best fits", so some trial and error, or fixing of some dimensions at expected values, may help. The 1D scattering intensity $I(q)$ is calculated as: .. Comment by Miguel Gonzalez: I am modifying the original text because I find the notation a little bit confusing. I think that in most textbooks/papers, the notation P(Q) is used for the form factor (adim, P(Q=0)=1), although F(q) seems also to be used. But here (as for many other models), P(q) is used to represent the scattering intensity (in cm-1 normally). It would be good to agree on a common notation. .. math:: I(q) = \frac{\text{scale}}{V} (\Delta\rho \cdot V)^2 \left< P(q, \alpha) \right> + \text{background} where the volume $V = A B C$, the contrast is defined as $\Delta\rho = \rho_\text{p} - \rho_\text{solvent}$, $P(q, \alpha)$ is the form factor corresponding to a parallelepiped oriented at an angle $\alpha$ (angle between the long axis C and $\vec q$), and the averaging $\left<\ldots\right>$ is applied over all orientations. Assuming $a = A/B < 1$, $b = B /B = 1$, and $c = C/B > 1$, the form factor is given by (Mittelbach and Porod, 1961) .. math:: P(q, \alpha) = \int_0^1 \phi_Q\left(\mu \sqrt{1-\sigma^2},a\right) \left[S(\mu c \sigma/2)\right]^2 d\sigma with .. math:: \phi_Q(\mu,a) &= \int_0^1 \left\{S\left[\frac{\mu}{2}\cos\left(\frac{\pi}{2}u\right)\right] S\left[\frac{\mu a}{2}\sin\left(\frac{\pi}{2}u\right)\right] \right\}^2 du \\ S(x) &= \frac{\sin x}{x} \\ \mu &= qB The scattering intensity per unit volume is returned in units of |cm^-1|. NB: The 2nd virial coefficient of the parallelepiped is calculated based on the averaged effective radius, after appropriately sorting the three dimensions, to give an oblate or prolate particle, $(=\sqrt{AB/\pi})$ and length $(= C)$ values, and used as the effective radius for $S(q)$ when $P(q) \cdot S(q)$ is applied. For 2d data the orientation of the particle is required, described using angles $\theta$, $\phi$ and $\Psi$ as in the diagrams below, for further details of the calculation and angular dispersions see :ref:`orientation` . .. Comment by Miguel Gonzalez: The following text has been commented because I think there are two mistakes. Psi is the rotational angle around C (but I cannot understand what it means against the q plane) and psi=0 corresponds to a||x and b||y. The angle $\Psi$ is the rotational angle around the $C$ axis against the $q$ plane. For example, $\Psi = 0$ when the $B$ axis is parallel to the $x$-axis of the detector. The angle $\Psi$ is the rotational angle around the $C$ axis. For $\theta = 0$ and $\phi = 0$, $\Psi = 0$ corresponds to the $B$ axis oriented parallel to the y-axis of the detector with $A$ along the x-axis. For other $\theta$, $\phi$ values, the parallelepiped has to be first rotated $\theta$ degrees in the $z-x$ plane and then $\phi$ degrees around the $z$ axis, before doing a final rotation of $\Psi$ degrees around the resulting $C$ axis of the particle to obtain the final orientation of the parallelepiped. .. _parallelepiped-orientation: .. figure:: img/parallelepiped_angle_definition.png Definition of the angles for oriented parallelepiped, shown with $A (abc[2] - abc[1]) length = np.where(selector, abc[0], abc[2]) # surface average radius (rough approximation) radius = sqrt(np.where(~selector, abc[0]*abc[1], abc[1]*abc[2]) / pi) ddd = 0.75 * radius * (2*radius*length + (length + radius)*(length + pi*radius)) return 0.5 * (ddd) ** (1. / 3.) # VR defaults to 1.0 def random(): length = 10**np.random.uniform(1, 4.7, size=3) pars = dict( length_a=length[0], length_b=length[1], length_c=length[2], ) return pars # parameters for demo demo = dict(scale=1, background=0, sld=6.3, sld_solvent=1.0, length_a=35, length_b=75, length_c=400, theta=45, phi=30, psi=15, length_a_pd=0.1, length_a_pd_n=10, length_b_pd=0.1, length_b_pd_n=1, length_c_pd=0.1, length_c_pd_n=1, theta_pd=10, theta_pd_n=1, phi_pd=10, phi_pd_n=1, psi_pd=10, psi_pd_n=10) # rkh 7/4/17 add random unit test for 2d, note make all params different, # 2d values not tested against other codes or models qx, qy = 0.2 * cos(pi/6.), 0.2 * sin(pi/6.) tests = [[{}, 0.2, 0.17758004974], [{}, [0.2], [0.17758004974]], [{'theta':10.0, 'phi':20.0}, (qx, qy), 0.0089517140475], [{'theta':10.0, 'phi':20.0}, [(qx, qy)], [0.0089517140475]], ] del qx, qy # not necessary to delete, but cleaner