Changeset b297ba9 in sasmodels for sasmodels/models/spinodal.py

Timestamp:

Mar 20, 2019 5:03:50 PM (5 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

4e28511

Parents:

0d362b7

Message:

lint

File:

• sasmodels/models/spinodal.py (modified) (5 diffs)

sasmodels/models/spinodal.py

@@ -3 +3 @@
 ----------
 
-This model calculates the SAS signal of a phase separating system 
-undergoing spinodal decomposition. The scattering intensity $I(q)$ is calculated 
-as 
+This model calculates the SAS signal of a phase separating system
+undergoing spinodal decomposition. The scattering intensity $I(q)$ is calculated
+as
 
 .. math::
     I(q) = I_{max}\frac{(1+\gamma/2)x^2}{\gamma/2+x^{2+\gamma}}+B
 
-where $x=q/q_0$, $q_0$ is the peak position, $I_{max}$ is the intensity 
-at $q_0$ (parameterised as the $scale$ parameter), and $B$ is a flat 
-background. The spinodal wavelength, $\Lambda$, is given by $2\pi/q_0$. 
+where $x=q/q_0$, $q_0$ is the peak position, $I_{max}$ is the intensity
+at $q_0$ (parameterised as the $scale$ parameter), and $B$ is a flat
+background. The spinodal wavelength, $\Lambda$, is given by $2\pi/q_0$.
 
-The definition of $I_{max}$ in the literature varies. Hashimoto *et al* (1991) 
-define it as 
+The definition of $I_{max}$ in the literature varies. Hashimoto *et al* (1991)
+define it as
 
 .. math::
     I_{max} = \Lambda^3\Delta\rho^2
-    
-whereas Meier & Strobl (1987) give 
+
+whereas Meier & Strobl (1987) give
 
 .. math::
     I_{max} = V_z\Delta\rho^2
-    
+
 where $V_z$ is the volume per monomer unit.
 
-The exponent $\gamma$ is equal to $d+1$ for off-critical concentration 
-mixtures (smooth interfaces) and $2d$ for critical concentration mixtures 
-(entangled interfaces), where $d$ is the dimensionality (ie, 1, 2, 3) of the 
-system. Thus 2 <= $\gamma$ <= 6. A transition from $\gamma=d+1$ to $\gamma=2d$ 
-is expected near the percolation threshold. 
+The exponent $\gamma$ is equal to $d+1$ for off-critical concentration
+mixtures (smooth interfaces) and $2d$ for critical concentration mixtures
+(entangled interfaces), where $d$ is the dimensionality (ie, 1, 2, 3) of the
+system. Thus 2 <= $\gamma$ <= 6. A transition from $\gamma=d+1$ to $\gamma=2d$
+is expected near the percolation threshold.
 
-As this function tends to zero as $q$ tends to zero, in practice it may be 
-necessary to combine it with another function describing the low-angle 
+As this function tends to zero as $q$ tends to zero, in practice it may be
+necessary to combine it with another function describing the low-angle
 scattering, or to simply omit the low-angle scattering from the fit.
 
@@ -41 +41 @@
 
 H. Furukawa. Dynamics-scaling theory for phase-separating unmixing mixtures:
-Growth rates of droplets and scaling properties of autocorrelation functions. 
+Growth rates of droplets and scaling properties of autocorrelation functions.
 Physica A 123, 497 (1984).
 
-H. Meier & G. Strobl. Small-Angle X-ray Scattering Study of Spinodal 
-Decomposition in Polystyrene/Poly(styrene-co-bromostyrene) Blends. 
+H. Meier & G. Strobl. Small-Angle X-ray Scattering Study of Spinodal
+Decomposition in Polystyrene/Poly(styrene-co-bromostyrene) Blends.
 Macromolecules 20, 649-654 (1987).
 
-T. Hashimoto, M. Takenaka & H. Jinnai. Scattering Studies of Self-Assembling 
-Processes of Polymer Blends in Spinodal Decomposition. 
+T. Hashimoto, M. Takenaka & H. Jinnai. Scattering Studies of Self-Assembling
+Processes of Polymer Blends in Spinodal Decomposition.
 J. Appl. Cryst. 24, 457-466 (1991).
 
@@ -68 +68 @@
 
       List of default parameters:
-      
+
       Imax = correlation peak intensity at q_0
       background = incoherent background
@@ -74 +74 @@
       q_0 = correlation peak position [1/A]
       x = q/q_0"""
-      
+
 category = "shape-independent"
 
@@ -100 +100 @@
 
 def random():
+    """Return a random parameter set for the model."""
     pars = dict(
         scale=10**np.random.uniform(1, 3),