I have a 3D numpy array of temperature values on a grid. From this I can compute the gradients using dTdx, dTdy, dYdz = np.gradient(T)
. Now I'm only interested in the values of the gradients on the isosurface where the temperature is 900. What I want to do is something like (pseudo-codish):
import nympy as np
def regular(x,y,z,q=100,k=175,a=7.1e-5):
R = np.sqrt(x**2+y**2+z**2)
return 100 / (2*np.pi*k) * (1/R) * np.exp(-0.5/a*(R+x))
x = np.arange(-1.5,0.5+res/2,res)*1e-3
y = np.arange(-1.0,1.0+res/2,res)*1e-3
z = np.arange(0.0,0.5+res/2,res)*1e-3
Y,X,Z = np.meshgrid(y,x,z)
T = regular(X,Y,Z)
dTdx, dTdy, dYdz = np.gradient(T)
(xind,yind,zind) = <package>.get_contour_indices(X,Y,Z,T,value=900)
x_gradients_at_isosurface = dTdx[xind,yind,zind]
...
I've tried:
import numpy as np
from skimage import measure
contour_data = measure.find_contours(T[:,:,0],900)
contour_data = np.int_(np.round(contour_data[0]))
xs,ys = contour_data[:,0],contour_data[:,1]
gradients_of_interest = np.array([G[x,y,0] for x,y in zip( xs,ys )])
which works fine, but only works for 2D data. I'm looking for the 3D equivalent. I've found the following:
import plotly.graph_objects as go
surf = go.Isosurface(x=X.flatten(),y=Y.flatten(),z=Z.flatten(),value=T.flatten(),isomin=900,isomax=900)
fig = go.Figure(data=surf)
plt.show()
But I'm not interested in plotting it. I want to know the indices where the temperature is T=900 so I can use it on the gradients. Any ideas?
You need
skimage.measure.marching_cubes
.