C_max = ad["chi"].max()
C_min = ad["chi"].min()
time_data.append(i.current_time)
#==============================================
# Surface_1 rendering Rho_max/2
#==============================================
Debug = time.time()
#Isosurface value
ISO = Rho_max / 2.
surface = i.surface(ad, "rho", ISO)
colors = yt.apply_colormap(surface["chi"], cmap_name="hot")
fig = plt.figure(figsize=(12, 10))
ax = fig.gca(projection='3d')
p3dc = Poly3DCollection(surface.triangles, linewidth=0.0)
p3dc.set_facecolors(colors[0, :, :] / 255.)
ax.add_collection(p3dc)
max_extent = (surface.vertices.max(axis=1) -
surface.vertices.min(axis=1)).max()
centers = (surface.vertices.max(axis=1) + surface.vertices.min(axis=1)) / 2
bounds = np.zeros([3, 2])
bounds[:, 0] = centers[:] - max_extent / 2
bounds[:, 1] = centers[:] + max_extent / 2
ax.auto_scale_xyz(bounds[0, :], bounds[1, :], bounds[2, :])
m = cm.ScalarMappable(cmap="hot")
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import matplotlib.pyplot as plt
# Load the dataset
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# Create a sphere object centered on the highest density point in the simulation
# with radius 1 Mpc
sphere = ds.sphere("max", (1.0, "Mpc"))
# Identify the isodensity surface in this sphere with density = 1e-24 g/cm^3
surface = ds.surface(sphere, "density", 1e-24)
# Color this isodensity surface according to the log of the temperature field
colors = yt.apply_colormap(np.log10(surface["temperature"]), cmap_name="hot")
# Create a 3D matplotlib figure for visualizing the surface
fig = plt.figure()
ax = fig.gca(projection='3d')
p3dc = Poly3DCollection(surface.triangles, linewidth=0.0)
# Set the surface colors in the right scaling [0,1]
p3dc.set_facecolors(colors[0, :, :] / 255.)
ax.add_collection(p3dc)
# Let's keep the axis ratio fixed in all directions by taking the maximum
# extent in one dimension and make it the bounds in all dimensions
max_extent = (surface.vertices.max(axis=1) -
surface.vertices.min(axis=1)).max()
centers = (surface.vertices.max(axis=1) + surface.vertices.min(axis=1)) / 2
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import matplotlib.pyplot as plt
# Load the dataset
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# Create a sphere object centered on the highest density point in the simulation
# with radius 1 Mpc
sphere = ds.sphere("max", (1.0, "Mpc"))
# Identify the isodensity surface in this sphere with density = 1e-24 g/cm^3
surface = ds.surface(sphere, "density", 1e-24)
# Color this isodensity surface according to the log of the temperature field
colors = yt.apply_colormap(np.log10(surface["temperature"]), cmap_name="hot")
# Create a 3D matplotlib figure for visualizing the surface
fig = plt.figure()
ax = fig.gca(projection='3d')
p3dc = Poly3DCollection(surface.triangles, linewidth=0.0)
# Set the surface colors in the right scaling [0,1]
p3dc.set_facecolors(colors[0,:,:]/255.)
ax.add_collection(p3dc)
# Let's keep the axis ratio fixed in all directions by taking the maximum
# extent in one dimension and make it the bounds in all dimensions
max_extent = (surface.vertices.max(axis=1) - surface.vertices.min(axis=1)).max()
centers = (surface.vertices.max(axis=1) + surface.vertices.min(axis=1)) / 2
bounds = np.zeros([3,2])