def testNodes(self):
# Test if node values are returned when the interpolating grid
# is exactly along the nodes
for i in range(lcgrid):
cgrid[i] = i + 1
flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout, nthreads, 0)
self.assertEqual(flag, 0)
for i in range(lcgrid):
self.assertAlmostEqual(dataout.GetVal(0, 0, i), datain.GetVal(0, 0, 0, i))
def testNodes(self):
# Test if node values are returned when the interpolating grid
# is exactly along the nodes
for i in range(lcgrid):
cgrid[i] = i + 1
flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout,
nthreads, 0)
self.assertEqual(flag, 0)
for i in range(lcgrid):
self.assertAlmostEqual(dataout.GetVal(0, 0, i),
datain.GetVal(0, 0, 0, i))
def testExtrap2(self):
# Test if extrapolation caught and data is correctly extrapolated (extrap = off)
for i in range(lcgrid - 2):
cgrid[i+1] = i + 2
cgrid[0] = 0
cgrid[lcgrid-1] = 11
flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout, nthreads, 0)
self.assertEqual(flag, 1)
for i in range(lcgrid - 2):
self.assertAlmostEqual(dataout.GetVal(0, 0, i+1), datain.GetVal(0, 0, 0, i+1))
self.assertEqual(dataout.GetVal(0, 0, 0), 0)
self.assertEqual(dataout.GetVal(0, 0, lcgrid-1), 0)
def testExtrap2(self):
# Test if extrapolation caught and data is correctly extrapolated (extrap = off)
for i in range(lcgrid - 2):
cgrid[i + 1] = i + 2
cgrid[0] = 0
cgrid[lcgrid - 1] = 11
flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout,
nthreads, 0)
self.assertEqual(flag, 1)
for i in range(lcgrid - 2):
self.assertAlmostEqual(dataout.GetVal(0, 0, i + 1),
datain.GetVal(0, 0, 0, i + 1))
self.assertEqual(dataout.GetVal(0, 0, 0), 0)
self.assertEqual(dataout.GetVal(0, 0, lcgrid - 1), 0)
# Fill cgrid with numbers
for i in range(0, lcgrid):
cgrid[i] = 2.5 + i / 2.0
# Print the input w~ and c~
dummy = "wc"
for i in range(0, 2):
print "%c~ is:" % dummy[i]
for j in range(0, dim4):
print "%6.3f" % datain.GetVal(i, 0, 0, j),
print ""
# Print cgrid
print "cgrid is:"
for i in range(0, lcgrid):
print "%6.3f" % cgrid[i],
print ""
# Interpolate w~ along the cgrid
flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout)
if flag == 1:
print "Interpolation failed: interpolation value out of bounds"
exit()
# Print w~ values interpolated along cgrid
print "w~ inteprolated along cgrid is:"
for i in range(0, lcgrid):
print "%6.3f " % dataout.GetVal(0, 0, i),
print ""
for i in range(0, lcgrid): cgrid[i] = 2.5 + i/2.0 # Print the input w~ and c~ dummy = "wc" for i in range(0, 2): print "%c~ is:" % dummy[i] for j in range(0, dim4): print "%6.3f" % datain.GetVal(i, 0, 0, j), print "" # Print cgrid print "cgrid is:" for i in range(0, lcgrid): print "%6.3f" % cgrid[i], print "" # Interpolate w~ along the cgrid flag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout) if flag == 1: print "Interpolation failed: interpolation value out of bounds" exit() # Print w~ values interpolated along cgrid print "w~ inteprolated along cgrid is:" for i in range(0, lcgrid): print "%6.3f " % dataout.GetVal(0, 0, i), print ""
for k in range(ZvarPoints):
if i == 0:
datain.SetVal(i,j,k,l,convolutedST[l].GetVal(k,j))
if i == 1:
datain.SetVal(i,j,k,l,convolutedC[l].GetVal(k,j))
# Run fit to grid and print results
if options["gridextrapolate"][0] == 'yes':
extrap = 1
print("WARNING: extrapolating to fit to cgrid (may take minutes)")
elif options["gridextrapolate"][0] == 'no':
extrap = 0
print("No extrapolation used to fit to grid")
else:
raise IOError ("must say <yes> or <no> for extrapolate in fittogrid:")
f2gflag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout, int(options["nothreads"][0]), extrap)
FinalData = np.zeros((lcgrid, dim2, dim3))
f = open("".join(["output/",options["OutputFile"][0]]),'w')
f.write('C \tZmean \tZvar \tSourceTerm \n')
for j in range(lcgrid):
for i in range(dim2):
for k in range(dim3):
FinalData[j,i,k] = dataout.GetVal(i,k,j)
f.write('%8.5g\t %8.5g\t %8.5g\t %g\n' % (cgrid[j], Zmean[i], Zvar[k], FinalData[j,i,k]))
f.close()
print "\nFinal data written to file: output/%s" % options["OutputFile"][0]
# Create contour plots of the chemical source term
noplots = 10
if ZvarPoints > (noplots + 1):
plots = range(noplots)
for k in range(ZvarPoints):
if i == 0:
datain.SetVal(i, j, k, l, convolutedST[l].GetVal(k, j))
if i == 1:
datain.SetVal(i, j, k, l, convolutedC[l].GetVal(k, j))
# Run fit to grid and print results
if options["gridextrapolate"][0] == 'yes':
extrap = 1
print("WARNING: extrapolating to fit to cgrid (may take minutes)")
elif options["gridextrapolate"][0] == 'no':
extrap = 0
print("No extrapolation used to fit to grid")
else:
raise IOError("must say <yes> or <no> for extrapolate in fittogrid:")
f2gflag = fittogrid.fittogrid_func(datain, cgrid, interp, dataout,
int(options["nothreads"][0]), extrap)
FinalData = np.zeros((lcgrid, dim2, dim3))
f = open("".join(["output/", options["OutputFile"][0]]), 'w')
f.write('C \tZmean \tZvar \tSourceTerm \n')
for j in range(lcgrid):
for i in range(dim2):
for k in range(dim3):
FinalData[j, i, k] = dataout.GetVal(i, k, j)
f.write('%8.5g\t %8.5g\t %8.5g\t %g\n' %
(cgrid[j], Zmean[i], Zvar[k], FinalData[j, i, k]))
f.close()
print "\nFinal data written to file: output/%s" % options["OutputFile"][0]
# Create contour plots of the chemical source term
noplots = 10
if ZvarPoints > (noplots + 1):