Changeset 64eecf7 in sasmodels for sasmodels/models/multilayer_vesicle.py

Timestamp:

Sep 5, 2017 11:40:09 AM (7 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

30b60d2

Parents:

a53bf6b (diff), 142a8e2 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Message:

Merge branch 'master' into ticket-510

File:

• sasmodels/models/multilayer_vesicle.py (modified) (2 diffs)

sasmodels/models/multilayer_vesicle.py

@@ -71 +71 @@
   sufficiently fine grained in certain cases. Please report any such occurences
   to the SasView team. Generally, for the best possible experience:
- * Start with the best possible guess
- * Using a priori knowledge, hold as many parameters fixed as possible
- * if N=1, tw (water thickness) must by definition be zero. Both N and tw should
+
+ - Start with the best possible guess
+ - Using a priori knowledge, hold as many parameters fixed as possible
+ - if N=1, tw (water thickness) must by definition be zero. Both N and tw should
    be fixed during fitting.
- * If N>1, use constraints to keep N > 1
- * Because N only really moves in integer steps, it may get "stuck" if the
+ - If N>1, use constraints to keep N > 1
+ - Because N only really moves in integer steps, it may get "stuck" if the
    optimizer step size is too small so care should be taken
    If you experience problems with this please contact the SasView team and let
@@ -149 +150 @@
     return radius + n_shells * (thick_shell + thick_solvent) - thick_solvent
 
-demo = dict(scale=1, background=0,
-            volfraction=0.05,
-            radius=60.0,
-            thick_shell=10.0,
-            thick_solvent=10.0,
-            sld_solvent=6.4,
-            sld=0.4,
-            n_shells=2.0)
+def random():
+    import numpy as np
+    volfraction = 10**np.random.uniform(-3, -0.5)  # scale from 0.1% to 30%
+    radius = 10**np.random.uniform(0, 2.5) # core less than 300 A
+    total_thick = 10**np.random.uniform(2, 4) # up to 10000 A of shells
+    # random number of shells, with shell+solvent thickness > 10 A
+    n_shells = int(10**np.random.uniform(0, np.log10(total_thick)-1)+0.5)
+    # split total shell thickness with preference for shell over solvent;
+    # make sure that shell thickness is at least 1 A
+    one_thick = total_thick/n_shells
+    thick_solvent = 10**np.random.uniform(-2, 0)*(one_thick - 1)
+    thick_shell = one_thick - thick_solvent
+    pars = dict(
+        scale=1,
+        volfraction=volfraction,
+        radius=radius,
+        thick_shell=thick_shell,
+        thick_solvent=thick_solvent,
+        n_shells=n_shells,
+    )
+    return pars
 
 tests = [