try:
plt.style.use("mnras_durham")
except:
rcParams = {
"font.serif": ["STIX", "Times New Roman", "Times"],
"font.family": ["serif"],
"mathtext.fontset": "stix",
"font.size": 8,
}
plt.rcParams.update(rcParams)
# See analyticSolution for params.
# Read in the "solution" data and calculate those that don't exist.
ref = analytic(time=sim.metadata.time.value)
r_shock = ref.pop("r_shock")
def get_data_dump(metadata):
"""
Gets a big data dump from the SWIFT metadata
"""
try:
viscosity = metadata.viscosity_info
except:
viscosity = "No info"
try:
diffusion = metadata.diffusion_info
}
square_binned = {
k: stats.binned_statistic(data["x"],
v**2,
statistic="mean",
bins=x_bin_edge)[0]
for k, v in data.items()
}
sigma = {
k: np.sqrt(v2 - v**2)
for k, v2, v in zip(binned.keys(), square_binned.values(), binned.values())
}
# Read in the "solution" data and calculate those that don't exist.
ref, x_values_conv = analytic(time=time,
return_x_positions_for_convergence=True)
# We only want to plot this for the region that we actually have data for, hence the masking.
mask = np.logical_and(ref["x"] < np.max(data["x"]),
ref["x"] > np.min(data["x"]))
ref = {k: v[mask] for k, v in ref.items()}
# Now we can do the plotting.
fig, ax = plt.subplots(2, 3, figsize=(6.974, 6.974 * (2.0 / 3.0)))
ax = ax.flatten()
# These are stored in priority order
plot = dict(
v="Velocity ($v_x$)",
u="Internal Energy ($u$)",
rho=r"Density ($\rho$)",
data = [
dict(
x=sim.gas.coordinates.value[:, 0],
v=sim.gas.velocities.value[:, 0],
u=try_read(sim, "internal_energy"),
S=try_read(sim, "entropy"),
P=try_read(sim, "pressure"),
rho=try_read(sim, "density"),
y=sim.gas.coordinates.value[:, 1],
z=sim.gas.coordinates.value[:, 2],
) for sim in sims
]
ref = analytic(sims[0].metadata.time.value)
# We only want to plot this for the region that we actually have data for, hence the masking.
masks = [
np.logical_and(ref["x"] < np.max(d["x"]), ref["x"] > np.min(d["x"]))
for d in data
]
refs = [{k: v[mask] for k, v in ref.items()} for mask in masks]
# Set up plotting stuff
try:
plt.style.use("mnras_durham")
except:
rcParams = {
"font.serif": ["STIX", "Times New Roman", "Times"],
"font.family": ["serif"],
}
square_binned = {
k: stats.binned_statistic(data["x"],
v**2,
statistic="mean",
bins=x_bin_edge)[0]
for k, v in data.items()
}
sigma = {
k: np.sqrt(v2 - v**2)
for k, v2, v in zip(binned.keys(), square_binned.values(), binned.values())
}
# Read in the "solution" data and calculate those that don't exist.
ref = analytic(time=time)
# We only want to plot this for the region that we actually have data for, hence the masking.
mask = np.logical_and(ref["x"] < np.max(data["x"]),
ref["x"] > np.min(data["x"]))
ref = {k: v[mask] for k, v in ref.items()}
# Now we can do the plotting.
fig, ax = plt.subplots(2, 3, figsize=(6.974, 6.974 * (2.0 / 3.0)))
ax = ax.flatten()
# These are stored in priority order
plot = dict(
v_phi="Azimuthal Velocity ($v_phi$)",
u="Internal Energy ($u$)",
rho=r"Density ($\rho$)",
try:
plt.style.use("spheric_durham")
except:
rcParams = {
"font.serif": ["STIX", "Times New Roman", "Times"],
"font.family": ["serif"],
"mathtext.fontset": "stix",
"font.size": 8,
}
plt.rcParams.update(rcParams)
# See analyticSolution for params.
# Read in the "solution" data and calculate those that don't exist.
ref = analytic()
def get_data_dump(metadata):
"""
Gets a big data dump from the SWIFT metadata
"""
try:
viscosity = metadata.viscosity_info
except:
viscosity = "No info"
try:
diffusion = metadata.diffusion_info
except:
