From 61cd3b6bc53c151863bd99fb2a03b29157a876ca Mon Sep 17 00:00:00 2001
From: Adrian Kummerlaender
Date: Thu, 13 Jun 2019 21:19:49 +0200
Subject: Tidy up symbolic kernel generation

---
 symbolic/D2Q9.py | 45 +++++++++++++++++++++++++++++----------------
 1 file changed, 29 insertions(+), 16 deletions(-)

(limited to 'symbolic')

diff --git a/symbolic/D2Q9.py b/symbolic/D2Q9.py
index a3c2503..8d42245 100644
--- a/symbolic/D2Q9.py
+++ b/symbolic/D2Q9.py
@@ -9,29 +9,42 @@ w = [ Rational(*x) for x in [(1,36), (1,9), (1,36), (1,9), (4,9), (1,9), (1,36),
 
 c_s = sqrt(Rational(1,3))
 
-rho, tau = symbols('rho tau')
-
 f_next = symarray('f_next', q)
 f_curr = symarray('f_curr', q)
 
-u = Matrix(symarray('u', d))
+def moments(f = f_curr, optimize = True):
+    rho = symbols('rho')
+    u   = Matrix(symarray('u', d))
+
+    exprs = [ Assignment(rho, sum(f)) ]
+
+    for i, u_i in enumerate(u):
+        exprs.append(Assignment(u_i, sum([ (c_j*f[j])[i] for j, c_j in enumerate(c) ]) / sum(f)))
 
-moments = [ Assignment(rho, sum(f_curr)) ]
+    if optimize:
+        return cse(exprs, optimizations='basic', symbols=numbered_symbols(prefix='m'))
+    else:
+        return ([], exprs)
 
-for i, u_i in enumerate(u):
-    moments.append(Assignment(u_i, sum([ (c_j*f_curr[j])[i] for j, c_j in enumerate(c) ]) / sum(f_curr)))
+def equilibrium():
+    rho = symbols('rho')
+    u   = Matrix(symarray('u', d))
 
-moments_opt = cse(moments, optimizations='basic', symbols=numbered_symbols(prefix='m'))
+    f_eq = []
 
-f_eq = []
+    for i, c_i in enumerate(c):
+        f_eq_i = w[i] * rho * (  1
+                               + c_i.dot(u)    /    c_s**2
+                               + c_i.dot(u)**2 / (2*c_s**4)
+                               - u.dot(u)      / (2*c_s**2) )
+        f_eq.append(f_eq_i)
 
-for i, c_i in enumerate(c):
-    f_eq_i = w[i] * rho * (  1
-                           + c_i.dot(u)    /    c_s**2
-                           + c_i.dot(u)**2 / (2*c_s**4)
-                           - u.dot(u)      / (2*c_s**2) )
-    f_eq.append(f_eq_i)
+    return f_eq
 
-collide = [ Assignment(f_next[i], f_curr[i] + 1/tau * ( f_eq_i - f_curr[i] )) for i, f_eq_i in enumerate(f_eq) ]
+def bgk(tau, f_eq = equilibrium(), optimize = True):
+    exprs = [ Assignment(f_next[i], f_curr[i] + 1/tau * ( f_eq_i - f_curr[i] )) for i, f_eq_i in enumerate(f_eq) ]
 
-collide_opt = cse(collide, optimizations='basic')
+    if optimize:
+        return cse(exprs, optimizations='basic')
+    else:
+        return ([], exprs)
-- 
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