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vizualization.py
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vizualization.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
import scipy.interpolate
from matplotlib import animation, cm
import scipy as sp
def animate_trisurf(f, domains, anim_speed=1):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
tri = mtri.Triangulation(x, y)
z = []
for snapshot in f:
z.append(np.concatenate([snapshot[k].flatten() for k in range(len(domains))]))
fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(8, 6))
ax.plot_trisurf(x, y, z[0], triangles=tri.triangles)
height = z[0].max() - z[0].min()
zmin = z[0].min() - 0.5 * height
zmax = z[0].max() + 0.5 * height
ax.set_zlim(zmin, zmax)
def animate(n):
frame = anim_speed * n
ax.cla()
ax.plot_trisurf(x, y, z[frame], triangles=tri.triangles)
ax.set_zlim(zmin, zmax)
return fig
anim = animation.FuncAnimation(fig, animate, frames=len(f) // anim_speed, interval=1, repeat=False)
plt.show()
return anim
def tripcolor_field(f, domains):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
tri = mtri.Triangulation(x, y)
z = np.concatenate([f[k].flatten() for k in range(len(domains))])
fig, ax = plt.subplots(figsize=(8, 6))
pic = ax.tripcolor(tri, z, shading='flat', cmap='seismic')
return pic
def animate_tripcolor(f, domains, anim_speed=1):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
tri = mtri.Triangulation(x, y)
z = []
for snapshot in f:
z.append(np.concatenate([snapshot[k].flatten() for k in range(len(domains))]))
fig, ax = plt.subplots(figsize=(8, 6))
ax.tripcolor(tri, z[0], shading='flat', cmap='seismic')
def animate(n):
frame = anim_speed * n
ax.cla()
ax.tripcolor(tri, z[frame], shading='flat', cmap='seismic')
return fig
anim = animation.FuncAnimation(fig, animate, frames=len(f) // anim_speed, interval=10, repeat=False, blit=True)
plt.show()
return anim
def animate_interpolated_surf(f, domains, anim_speed=1):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
xs = min([domain.xs for domain in domains])
xe = max([domain.xe for domain in domains])
ys = min([domain.ys for domain in domains])
ye = max([domain.ye for domain in domains])
dx = min([domain.dx for domain in domains])
dy = min([domain.dy for domain in domains])
grid_x, grid_y = np.mgrid[xs:xe:dx, ys:ye:dy]
grid_z = []
for snapshot in f:
values = np.concatenate([snapshot[k].flatten() for k in range(len(domains))])
grid_z.append(scipy.interpolate.griddata((x, y), values, (grid_x, grid_y), method='linear'))
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
surf = ax.plot_surface(grid_x, grid_y, grid_z[0])
height = grid_z[0].max() - grid_z[0].min()
zmin = grid_z[0].min() - 0.5 * height
zmax = grid_z[0].max() + 0.5 * height
ax.set_zlim(zmin, zmax)
def animate(n):
frame = anim_speed * n
ax.cla()
ax.plot_surface(grid_x, grid_y, grid_z[frame])
ax.set_zlim(zmin, zmax)
return fig
anim = animation.FuncAnimation(fig, animate, frames=len(f), interval=1, repeat=False, blit=True)
plt.show()
return anim
def surf_interpolated_field(f, domains, surf_nx=100, surf_ny=100):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
z = np.concatenate([f[k].flatten() for k in range(len(domains))])
xs = min([domain.xs for domain in domains])
xe = max([domain.xe for domain in domains])
ys = min([domain.ys for domain in domains])
ye = max([domain.ye for domain in domains])
dx = min([domain.dx for domain in domains])
dy = min([domain.dy for domain in domains])
grid_x, grid_y = np.mgrid[xs:xe:dx, ys:ye:dy]
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
grid_z = scipy.interpolate.griddata((x, y), z, (grid_x, grid_y), method='cubic')
surf = ax.plot_surface(grid_x, grid_y, grid_z)
plt.show()
return surf
def imshow_interpolated_field(f, domains,savefig, title, surf_nx=100, surf_ny=100):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
z = np.concatenate([f[k].flatten() for k in range(len(domains))])
xs = min([domain.xs for domain in domains])
xe = max([domain.xe for domain in domains])
ys = min([domain.ys for domain in domains])
ye = max([domain.ye for domain in domains])
dx = min([domain.dx for domain in domains])
dy = min([domain.dy for domain in domains])
grid_x, grid_y = np.mgrid[xs:xe:dx, ys:ye:dy]
fig, ax = plt.subplots()
grid_z = scipy.interpolate.griddata((x, y), z, (grid_x, grid_y), method='linear')
imshow = ax.imshow(grid_z.T, cmap='seismic')
plt.title(title)
if savefig is True and title is not None:
plt.savefig(f'{title}.png', dpi = 100)
plt.show()
return imshow
def trisurf_field(f, domains):
x = np.concatenate([domain.xx.flatten() for domain in domains])
y = np.concatenate([domain.yy.flatten() for domain in domains])
z = np.concatenate([f[k].flatten() for k in range(len(domains))])
tri = mtri.Triangulation(x, y)
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
trisurf = ax.plot_trisurf(x, y, z, triangles = tri.triangles)#, cmap=plt.cm.Spectral)
plt.show()
return trisurf