def visualize(files):
output = []
for fn in parallel_objects(files, njobs=-1):
pf = load(fn)
for field in FIELDS:
slc = SlicePlot(pf, 'z', field)
output.append(slc.save(fn.replace('.h5', '_%s.png' % field))[0])
return output
def visualize(files):
output = []
for fn in parallel_objects(files, njobs=-1):
pf = load(fn)
for field in FIELDS:
slc = SlicePlot(pf, 'z', field)
output.append(slc.save(fn.replace('.h5', '_%s.png' % field))[0])
return output
def visualize(files):
output = []
for fn in parallel_objects(files, njobs=-1):
pf = load(fn)
for field in FIELDS:
slc = SlicePlot(pf, 'z', field)
if field == 'curz':
slc.set_cmap(field, 'bwr')
maxabs = abs(slc._frb[field]).max()
slc.set_log(field, False)
slc.set_zlim(field, -maxabs, maxabs)
output.append(slc.save(fn.replace('.h5', '_%s.png' % field))[0])
return output
xc = map(np.float64,args.x)
zc = map(np.float64,args.z)
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', ct.p05)
h = 0.146484375
phi = 0.5
c1 = np.array([xc[0], phi, zc[0]])
c2 = np.array([xc[1], phi, zc[1]])
patch1 = [c1[0] - h, c1[0] + h, c1[2]-h, c1[2]+h]
patch2 = [c2[0] - h, c2[0] + h, c2[2]-h, c2[2]+h]
vmin = 0.0
vmax = 0.1 * 0.2
first_pass = True
for fn in parallel_objects(args.files, njobs=-1):
pf = load(fn)
field = "dend"
le = pf.domain_left_edge * pf['au']
re = pf.domain_right_edge * pf['au']
s = pf.h.slice(1, phi, fields=["dend"])
# = pf.h.proj(1, 'dend')
#fac = pf['au'] / (2.0 * pf['dend'] * np.pi)
fac = 1./pf['dend']
if first_pass:
c1 /= pf.units['au']
c2 /= pf.units['au']
ext = [ le[0], re[0], le[2], re[2] ]
fig = plt.figure(0, figsize=(14,10))
fig.clf()