def load_and_make_image(number, res, property):
filename = "square_{:04d}.hdf5".format(number)
data = load(filename)
image = project_gas_pixel_grid(data, res, property)
image_none = project_gas_pixel_grid(data, res, None)
return image / image_none
def create_t_map(number):
this_data = data[number]
# We need to construct sum(T_j W_ij) / sum(W_ij)
norm_grid = project_gas_pixel_grid(this_data, resolution, None)
temp_grid = project_gas_pixel_grid(this_data, resolution, "temperatures")
return temp_grid / norm_grid
def make_plot(filename, array, nx, ny, dx, dy):
"""
Load the data and plop it on the grid using nearest
neighbour searching for finding the 'correct' value of
the density.
"""
data = load_and_extract(filename)
mesh = project_gas_pixel_grid(data, nx)
array.set_array(mesh)
return array,
"""
from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid, scatter
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
data = load("kelvinHelmholtz_0175.hdf5")
resolution = 1024
n_streamline = 512
grid = project_gas_pixel_grid(data, resolution)
sub_mask_velocity = 1024 * 2
x, y, _ = data.gas.coordinates[:].value.T
u, v, _ = data.gas.velocities[:].value.T
h = data.gas.smoothing_lengths.value
u_grid = scatter(x, y, u, h, resolution * 2).T
v_grid = scatter(x, y, v, h, resolution * 2).T
x = np.linspace(0, 1, resolution * 2)
y = np.linspace(0, 1, resolution * 2)
speed = np.sqrt(u_grid * u_grid + v_grid * v_grid)
fig, ax = plt.subplots(figsize=(8, 8), dpi=300)
fig.subplots_adjust(0, 0, 1, 1)
""" Makes an image of snapshot 175.
"""
from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid
import matplotlib.pyplot as plt
data = load("kelvinHelmholtz_0164.hdf5")
grid = project_gas_pixel_grid(data, 7 * 300)
fig, a = plt.subplots(figsize=(7, 7))
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
a.imshow(grid, cmap="Spectral", origin="lower", vmin=2.29, vmax=20.92)
a.set_xticks([])
a.set_yticks([])
fig.savefig("kelvin_helmholtz_highres.pdf")
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid
snap = int(sys.argv[1])
sim = load(f"square_{snap:04d}.hdf5")
resolution = 512
# First create a grid that gets the particle density so we can divide it out later
unweighted_grid = project_gas_pixel_grid(sim, 512, None)
# Set up plotting stuff
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)
def get_data_dump(metadata):
""" Makes an image of snapshot 175.
"""
from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid
import matplotlib.pyplot as plt
data = load("kelvinHelmholtz_0320.hdf5")
empty_grid = project_gas_pixel_grid(data, 7 * 300, None)
grid = project_gas_pixel_grid(data, 7 * 300, "internal_energy") / empty_grid
fig, a = plt.subplots(figsize=(7, 7))
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
a.imshow(grid, cmap="Spectral", origin="lower")
a.set_xticks([])
a.set_yticks([])
fig.savefig("kelvin_helmholtz_highres_energy.png", dpi=300)
if os.path.isfile("{}_{:04d}.hdf5".format(filename, i)):
i += 1
else:
break
if i > 10000:
raise FileNotFoundError(
"Could not find the snapshots in the directory")
frames = tqdm(np.arange(0, i))
# Creation of first frame
fig, ax = plt.subplots(1, 1, figsize=(1, 1), frameon=False)
ax.axis("off") # Remove annoying black frame.
data = load_and_extract("kelvinHelmholtz_0000.hdf5")
mesh = project_gas_pixel_grid(data, dpi)
# Global variable for set_array
plot = ax.imshow(mesh, extent=[0, 1, 0, 1], animated=True, interpolation="none")
anim = FuncAnimation(fig, frame, frames, interval=40, blit=False)
# Remove all whitespace
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)
# Actually make the movie
anim.save("khmovie.mp4", dpi=dpi, bitrate=4096)
Creates a temperature map of the snapshot that is provided
and saves it as temperature_map.png.
"""
import sys
from swiftsimio import load
from swiftsimio.visualisation import project_gas_pixel_grid
from matplotlib.pyplot import imsave
from matplotlib.colors import LogNorm
resolution = 2048
snapshot = sys.argv[1]
try:
output_filename = sys.argv[2]
except IndexError:
output_filename = "temperature_map.png"
data = load(snapshot)
# We need to construct sum(T_j W_ij) / sum(W_ij)
norm_grid = project_gas_pixel_grid(data, resolution, None)
temp_grid = project_gas_pixel_grid(data, resolution, "temperatures")
# Imsave does not take a norm
normalized_grid = LogNorm(vmin=1e2, vmax=1e8)(temp_grid / norm_grid)
imsave(output_filename, normalized_grid, cmap="twilight")
def baked(snap):
grid = project_gas_pixel_grid(data[snap], 1024)
return grid
snap = 40
simulations = {
"anarchy-du-lr": "$64^2$",
"anarchy-du": "$128^2$",
"anarchy-du-hr": "$256^2$",
"anarchy-du-hrhr": "$512^2$",
}
sims = [load(f"{s}/square_{snap:04d}.hdf5") for s in simulations.keys()]
resolution = 512
# First create a grid that gets the particle density so we can divide it out later
density = [
project_gas_pixel_grid(sim, 512, "densities") /
project_gas_pixel_grid(sim, 512, None) for sim in sims
]
# Set up plotting stuff
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)
0 * units,
data.metadata.boxsize[0].to(units),
0 * units,
data.metadata.boxsize[0].to(units),
],
origin="lower",
)
fig.savefig(f"{output_path}/{filename}.png")
plt.close(fig)
return
common_parameters = dict(data=data, resolution=res, parallel=True)
norm = project_gas_pixel_grid(**common_parameters, project=None).T
mass = project_gas_pixel_grid(**common_parameters).T
normed = (lambda project: project_gas_pixel_grid(**common_parameters,
project=project).T / norm)
diff = normed("diffusion_parameters")
visc = normed("viscosity_parameters")
temp = normed("temperatures")
mmf = normed("metal_mass_fractions")
make_image(mass, "inferno", "projected_gas_density")
make_image(diff, cm.ice, "diffusion_parameters", 0.01, 1.0)
make_image(visc, cm.curl, "viscosity_parameters", 0.2, 2.0)
make_image(temp, "twilight", "temperatures", 1e2, 1e8)
make_image(mmf, "cubehelix", "metal_mass_fractions", 0.0012, 1.2)
import cmocean.cm as cm
snap = 40
simulations = {
"minimal": "Density-Energy",
"anarchy-du": "ANARCHY-DU",
"pressure-energy": "Pressure-Energy",
"anarchy-pu": "ANARCHY-PU",
}
sims = [load(f"{s}/square_{snap:04d}.hdf5") for s in simulations.keys()]
resolution = 512
# First create a grid that gets the particle density so we can divide it out later
unweighted_grids = [project_gas_pixel_grid(sim, 512, None) for sim in sims]
# Set up plotting stuff
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)
# Now we can do the plotting.
fig, ax = plt.subplots(4, 3, figsize=(6.974, 6.974 * 4 / 3))
