crp/OpenMC
1
0
Fork 0
mirror of https://github.com/openmc-dev/openmc.git synced 2026-07-28 06:05:58 -04:00
Code Issues Projects Releases Packages Wiki Activity Actions 5

organized rms post processing script into functions

Browse source
This commit is contained in:
Bryan Herman 2012-03-06 12:46:28 -05:00
parent 4ea142cc27
commit 9952e216b4
1 changed files with 15 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

25
src/utils/eigenfunction_rms.py
View file

@ -17,6 +17,7 @@ class EigenFunction:
'''Initializes the eigenfunction'''
self.function = 0.
self.data = data
self.meanfunction = 0.
#
def read_hdf5(self,h5_file,cycle):
#
@ -27,7 +28,7 @@ class EigenFunction:
dataset = f[group]
self.function = np.empty(dataset.shape,dataset.dtype)
dataset.read_direct(self.function)
self.function = self.function * ((16)**2-5*2**2)*177
self.function = self.function
self.iamref = 'F'
#
def set_reference(self):
@ -40,8 +41,8 @@ class EigenFunction:
#
'''Computes RMS value'''
Np = self.function.size
Np = ((16)**2-5*2**2)*177
tmp = (self.function - EigenFunction.reference)**2
Np = 41772
tmp = (self.meanfunction - EigenFunction.reference)**2
tmp2 = tmp.sum()
self.rms = np.sqrt((1.0/float(Np))*tmp2)
#
@ -58,8 +59,11 @@ def read_runs(runpath,hdfile,cycle_start,cycle_end,run_start,run_end,data):
while j <= run_end:
tmp.read_hdf5(runpath+str(j)+'/'+hdfile,i) # read hdf5 file
runs[j-run_start] = tmp.function # put function into runs
if i == cycle_start:
print runs[j-run_start,0,150,150,0]
j += 1
meantmp.function = np.average(runs, axis=0) # compute the mean
meantmp.meanfunction = np.average(runs, axis=0) # compute the mean
meantmp.function = runs
runlist.append(meantmp)
print 'Read in from path: '+runpath+' Cycle: '+str(i)
i += 1
@ -71,13 +75,14 @@ def create_reference(runpath,hdfile,cycle,run_start,run_end,data):
print 'Calculating Reference solution...'
tmp = EigenFunction(data)
tmp.read_hdf5(runpath+str(run_start)+'/'+hdfile,cycle) # load first eigenfunction
print 'Read in: '+runpath+str(1)+hdfile+' '+str(tmp.function[0,150,150,0])
indices = tmp.function.shape # extent of all dimensions
ref = np.zeros((run_end,indices[0],indices[1],indices[2],indices[3])) # initialize ref array
ref[0] = tmp.function # set the first run in ref
i = run_start + 1
while i <= run_end: # begin loop around all runs
tmp.read_hdf5(runpath+str(i)+'/'+hdfile,cycle)
print 'Read in: '+runpath+str(i)+hdfile
print 'Read in: '+runpath+str(i)+hdfile+' '+str(tmp.function[0,150,150,0])
ref[i - run_start] = tmp.function
i += 1
meanref = np.average(ref, axis=0) # compute average of all runs
@ -101,7 +106,7 @@ def plot_rms(rms):
ax = plt.subplot(111)
size = rms.shape[0]
x = np.linspace(1,size,size)*1e6
y = (rms[size-1]/x[size-1]**(-0.5))*x**(-0.5)
y = (rms[0]/x[0]**(-0.5))*x**(-0.5)
plt.loglog(x,rms*100,'b+')
plt.loglog(x,y*100,'g--')
ax.xaxis.grid(True,'minor')
@ -162,11 +167,11 @@ if __name__ == "__main__":
else:
# calculate reference solution
runpath = '/media/Backup/opr_runs/1mil/run'
runpath = '/media/Backup/opr_runs/64mil/run'
hdfile = 'output.h5'
cycle = 840
cycle = 210
run_start = 1
run_end = 25
run_end = 4
data = 'openmc_src'
meanref = create_reference(runpath,hdfile,cycle,run_start,run_end,data)
@ -176,7 +181,7 @@ if __name__ == "__main__":
cycle_start = 201
cycle_end = 840
run_start = 1
run_end = 1
run_end = 10
data = 'openmc_src'
onemil = read_runs(runpath,hdfile,cycle_start,cycle_end,run_start,run_end,data)