Changeset 8a20be5 in sasmodels for lamellarcode.py

Timestamp:

Jul 10, 2014 3:05:08 PM (10 years ago)

Author:

HMP1 <helen.park@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, release_v0.94, release_v0.95, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

8faffcd

Parents:

5378e40

Message:

Added a fit2 (fits two different models at different angles)
(preliminary) Added CoreshellCyl? and CapCyl? Kernels
(preliminary) Updated kernels to include functions

File:

• lamellarcode.py (modified) (4 diffs)

lamellarcode.py

@@ -3 +3 @@
 
 import numpy as np
-import math
 import pyopencl as cl
 from weights import GaussianDispersion
+
+def set_precision(src, qx, qy, dtype):
+    qx = np.ascontiguousarray(qx, dtype=dtype)
+    qy = np.ascontiguousarray(qy, dtype=dtype)
+    if dtype == 'double':
+        header = """\
+#pragma OPENCL EXTENSION cl_khr_fp64: enable
+#define real double
+"""
+        return header+src,qx,qy
+    else:
+        return src,qx,qy
 
 
@@ -12 +23 @@
         'scale':1, 'bi_thick':1, 'sld_bi':1e-6, 'sld_sol':0, 'background':0,
     }
-    PD_PARS = ['bi_thick']
 
-    def __init__(self, qx, qy):
+    def __init__(self, qx, qy, dtype='float'):
 
-        self.qx = np.asarray(qx, np.float32)
-        self.qy = np.asarray(qy, np.float32)
         #create context, queue, and build program
         self.ctx = cl.create_some_context()
         self.queue = cl.CommandQueue(self.ctx)
-        self.prg = cl.Program(self.ctx, open('Kernel-Lamellar.cpp').read()).build()
+        src,qx,qy = set_precision(open('Kernel-Lamellar.cpp').read(), qx, qy, dtype=dtype)
+        self.prg = cl.Program(self.ctx, src).build()
+        self.qx, self.qy = qx, qy
 
         #buffers
@@ -38 +48 @@
         sub = pars['sld_bi'] - pars['sld_sol']
 
+        real = np.float32 if self.qx.dtype == np.dtype('float32') else np.float64
         for i in xrange(len(bi_thick.weight)):
-            self.prg.LamellarKernel(self.queue, self.qx.shape, None, self.qx_b, self.qy_b, self.res_b, np.float32(bi_thick.value[i]),
-                                    np.float32(pars['scale']), np.float32(sub), np.float32(pars['background']), np.uint32(self.qx.size))
+            self.prg.LamellarKernel(self.queue, self.qx.shape, None, self.qx_b, self.qy_b, self.res_b, real(bi_thick.value[i]),
+                                    real(pars['scale']), real(sub), real(pars['background']), np.uint32(self.qx.size))
             cl.enqueue_copy(self.queue, self.res, self.res_b)
 
@@ -47 +58 @@
 
         return sum/norm + pars['background']
-
-    def lamellar_fit(self, pars, b_n=10, b_w=.1, sigma=3):
-
-        bi_thick = GaussianDispersion(b_n, b_w, sigma)
-        bi_thick.value, bi_thick.weight = bi_thick.get_weights(pars.bi_thick, 0, 1000, True)
-
-        sum, norm = 0.0, 0.0
-
-        for i in xrange(len(bi_thick.weight)):
-            self.prg.LamellarKernel(self.queue, self.qx.shape, None, self.qx_b, self.qy_b, self.res_b, np.float32(bi_thick.value[i]),
-                                    np.float32(pars.scale), np.float32(pars.sld_bi), np.float32(pars.sld_sol),
-                                    np.float32(pars.background), np.uint32(self.qx.size))
-            cl.enqueue_copy(self.queue, self.res, self.res_b)
-
-            sum += bi_thick.weight[i]*self.res
-            norm += bi_thick.weight[i]
-
-        return sum/norm + pars.background
-
 
 def demo():