def timestep(self):
"""
The timestep function computes the advective timestep (CFL)
constraint. The CFL constraint says that information cannot
propagate further than one zone per timestep.
We use the driver.cfl parameter to control what fraction of the
CFL step we actually take.
"""
cfl = self.rp.get_param("driver.cfl")
# get the variables we need
dens = self.cc_data.get_var("density")
xmom = self.cc_data.get_var("x-momentum")
ymom = self.cc_data.get_var("y-momentum")
ener = self.cc_data.get_var("energy")
# we need to compute the pressure
u = xmom/dens
v = ymom/dens
e = (ener - 0.5*dens*(u*u + v*v))/dens
gamma = self.rp.get_param("eos.gamma")
p = eos.pres(gamma, dens, e)
# HACK -- need to update -- this is not general
# compute the sounds speed
cs = np.sqrt(gamma*p/dens)
# the timestep is min(dx/(|u| + cs), dy/(|v| + cs))
xtmp = self.cc_data.grid.dx/(abs(u) + cs)
ytmp = self.cc_data.grid.dy/(abs(v) + cs)
dt = cfl*min(xtmp.min(), ytmp.min())
return dt
def timestep(self):
"""
The timestep function computes the advective timestep (CFL)
constraint. The CFL constraint says that information cannot
propagate further than one zone per timestep.
We use the driver.cfl parameter to control what fraction of the
CFL step we actually take.
"""
cfl = self.rp.get_param("driver.cfl")
# get the variables we need
dens = self.cc_data.get_var("density")
xmom = self.cc_data.get_var("x-momentum")
ymom = self.cc_data.get_var("y-momentum")
ener = self.cc_data.get_var("energy")
# we need to compute the pressure
u = xmom / dens
v = ymom / dens
e = (ener - 0.5 * dens * (u * u + v * v)) / dens
gamma = self.rp.get_param("eos.gamma")
p = eos.pres(gamma, dens, e)
# HACK -- need to update -- this is not general
# compute the sounds speed
cs = np.sqrt(gamma * p / dens)
# the timestep is min(dx/(|u| + cs), dy/(|v| + cs))
xtmp = self.cc_data.grid.dx / (abs(u) + cs)
ytmp = self.cc_data.grid.dy / (abs(v) + cs)
dt = cfl * min(xtmp.min(), ytmp.min())
return dt
def dovis(self):
"""
Do runtime visualization.
"""
plt.clf()
plt.rc("font", size=10)
dens = self.cc_data.get_var("density")
xmom = self.cc_data.get_var("x-momentum")
ymom = self.cc_data.get_var("y-momentum")
ener = self.cc_data.get_var("energy")
# get the velocities
u = xmom / dens
v = ymom / dens
# get the pressure
magvel = u**2 + v**2 # temporarily |U|^2
rhoe = (ener - 0.5 * dens * magvel)
magvel = np.sqrt(magvel)
e = rhoe / dens
# HACK -- this needs to be generalized
# access gamma from the cc_data object so we can use dovis
# outside of a running simulation.
gamma = self.cc_data.get_aux("gamma")
p = eos.pres(gamma, dens, e)
myg = self.cc_data.grid
# figure out the geometry
L_x = self.cc_data.grid.xmax - self.cc_data.grid.xmin
L_y = self.cc_data.grid.ymax - self.cc_data.grid.ymin
orientation = "vertical"
shrink = 1.0
sparseX = 0
allYlabel = 1
if (L_x > 2 * L_y):
# we want 4 rows:
# rho
# |U|
# p
# e
fig, axes = plt.subplots(nrows=4, ncols=1, num=1)
orientation = "horizontal"
if (L_x > 4 * L_y):
shrink = 0.75
onLeft = list(range(self.vars.nvar))
elif (L_y > 2 * L_x):
# we want 4 columns: rho |U| p e
fig, axes = plt.subplots(nrows=1, ncols=4, num=1)
if (L_y >= 3 * L_x):
shrink = 0.5
sparseX = 1
allYlabel = 0
onLeft = [0]
else:
# 2x2 grid of plots with
#
# rho |u|
# p e
fig, axes = plt.subplots(nrows=2, ncols=2, num=1)
plt.subplots_adjust(hspace=0.25)
onLeft = [0, 2]
fields = [dens, magvel, p, e]
field_names = [r"$\rho$", r"U", "p", "e"]
for n in range(4):
ax = axes.flat[n]
v = fields[n]
img = ax.imshow(np.transpose(v[myg.ilo:myg.ihi + 1,
myg.jlo:myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
ax.set_xlabel("x")
if n == 0:
ax.set_ylabel("y")
elif allYlabel:
ax.set_ylabel("y")
ax.set_title(field_names[n])
if not n in onLeft:
ax.yaxis.offsetText.set_visible(False)
if n > 0: ax.get_yaxis().set_visible(False)
if sparseX:
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
plt.colorbar(img, ax=ax, orientation=orientation, shrink=shrink)
plt.figtext(0.05, 0.0125, "t = %10.5f" % self.cc_data.t)
plt.draw()
def dovis(self):
"""
Do runtime visualization.
"""
plt.clf()
plt.rc("font", size=10)
dens = self.cc_data.get_var("density")
xmom = self.cc_data.get_var("x-momentum")
ymom = self.cc_data.get_var("y-momentum")
ener = self.cc_data.get_var("energy")
# get the velocities
u = xmom/dens
v = ymom/dens
# get the pressure
magvel = u**2 + v**2 # temporarily |U|^2
rhoe = (ener - 0.5*dens*magvel)
magvel = np.sqrt(magvel)
e = rhoe/dens
# HACK -- this needs to be generalized
# access gamma from the cc_data object so we can use dovis
# outside of a running simulation.
gamma = self.cc_data.get_aux("gamma")
p = eos.pres(gamma, dens, e)
myg = self.cc_data.grid
# figure out the geometry
L_x = self.cc_data.grid.xmax - self.cc_data.grid.xmin
L_y = self.cc_data.grid.ymax - self.cc_data.grid.ymin
orientation = "vertical"
shrink = 1.0
sparseX = 0
allYlabel = 1
if (L_x > 2*L_y):
# we want 4 rows:
# rho
# |U|
# p
# e
fig, axes = plt.subplots(nrows=4, ncols=1, num=1)
orientation = "horizontal"
if (L_x > 4*L_y):
shrink = 0.75
onLeft = list(range(self.vars.nvar))
elif (L_y > 2*L_x):
# we want 4 columns: rho |U| p e
fig, axes = plt.subplots(nrows=1, ncols=4, num=1)
if (L_y >= 3*L_x):
shrink = 0.5
sparseX = 1
allYlabel = 0
onLeft = [0]
else:
# 2x2 grid of plots with
#
# rho |u|
# p e
fig, axes = plt.subplots(nrows=2, ncols=2, num=1)
plt.subplots_adjust(hspace=0.25)
onLeft = [0,2]
fields = [dens, magvel, p, e]
field_names = [r"$\rho$", r"U", "p", "e"]
for n in range(4):
ax = axes.flat[n]
v = fields[n]
img = ax.imshow(np.transpose(v[myg.ilo:myg.ihi+1,
myg.jlo:myg.jhi+1]),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
ax.set_xlabel("x")
if n == 0:
ax.set_ylabel("y")
elif allYlabel:
ax.set_ylabel("y")
ax.set_title(field_names[n])
if not n in onLeft:
ax.yaxis.offsetText.set_visible(False)
if n > 0: ax.get_yaxis().set_visible(False)
if sparseX:
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
plt.colorbar(img, ax=ax, orientation=orientation, shrink=shrink)
plt.figtext(0.05,0.0125, "t = %10.5f" % self.cc_data.t)
plt.draw()