def minmax(cfdfile, coolfile):
### Get data
cfd = TorchCFD(cfdfile)
cool = TorchCool(coolfile)
nh = cfd.get_var('nh')
tem = cfd.get_var('tem')
crate = cool.get_var('cool')
L = np.absolute(cool.get_var('lcool')/(nh*nh))
E = 1.5*nh*1.3806488e-16*tem
with np.errstate(divide='ignore', over='ignore'):
dt = E / np.absolute(crate) / 3.15569e7
dt[crate == 0] = 1e10
return [nh.min(), tem.min(), E.min(), L.min(), dt.min(), nh.max(), tem.max(), E.min(), L.max(), dt.max()]
plt.rc('xtick',**{'labelsize':6})
plt.rc('ytick',**{'labelsize':6})
fformat = 'jpg'
log = True
### Parse arguements
parser = argparse.ArgumentParser(description='Plots 2D image of CFD cooling data.')
parser.add_argument('cfdfile', metavar='cfdfile', type=str, help='CFD file to produce image.')
parser.add_argument('coolfile', metavar='coolfile', type=str, help='Cooling file to produce image.')
args = parser.parse_args()
outputfile = os.path.splitext(args.cfdfile)[0] + '.' + fformat
### Data set up.
cfd = TorchCFD(args.cfdfile, axial=True)
cool = TorchCool(args.coolfile, axial=True)
nh = cfd.get_var('nh')
tem = cfd.get_var('tem')
crate = cool.get_var('cool')
L = np.absolute(cool.get_var('lcool')/(nh*nh))
E = 1.5*nh*1.3806488e-16*tem
with np.errstate(divide='ignore', over='ignore'):
dt = E / np.absolute(crate) / 3.15569e7
dt[crate == 0] = 1e10
dt[~np.isfinite(dt)] = 1e10
L[dt > 20] = L[dt < 20].min()
vs = [nh, tem, L, dt]
for i in range(3):
help='Input directory with CFD output data.')
args = parser.parse_args()
input_dir = args.input_dir
if not input_dir.endswith('/'):
input_dir += '/'
input_regex = input_dir + '*.txt'
time = []
radius = []
for inputfile in glob.iglob(input_regex):
### Data set up.
torchData = TorchCFD(inputfile, axial=True)
den = torchData.get_var('den')
deni = torchData.interpolate(den, 'nearest')
### Find bubble radius.
index = 0
max_den = 0
for i in range(11, torchData.nx):
if deni[i,0] > max_den:
index = i
max_den = deni[i,0]
time.append(t)
radius.append(torchData.xi[0, index])
zipped = zip(time, radius)
zipped.sort()
