# Plot a slice of a 3D `TensorMesh` solution to a Laplace's equaiton.
#
# First build the mesh:
#
from matplotlib import pyplot as plt
import discretize
from scipy.sparse.linalg import spsolve
hx = [(5, 2, -1.3), (2, 4), (5, 2, 1.3)]
hy = [(2, 2, -1.3), (2, 6), (2, 2, 1.3)]
hz = [(2, 2, -1.3), (2, 6), (2, 2, 1.3)]
M = discretize.TensorMesh([hx, hy, hz])
#
# then build the necessary parts of the PDE:
#
q = np.zeros(M.vnC)
q[[4, 4], [4, 4], [2, 6]]=[-1, 1]
q = discretize.utils.mkvc(q)
A = M.face_divergence * M.cell_gradient
b = spsolve(A, q)
#
# and finaly, plot the vector values of the result, which are defined on faces
#
M.plot_slice(M.cell_gradient*b, 'F', view='vec', grid=True, pcolor_opts={'alpha':0.8})
plt.show()
#
# We can use the `slice_loc kwarg to tell `plot_slice` where to slice the mesh.
# Let's create a mesh with a random model and plot slice of it. The `slice_loc`
# kwarg automatically determines the indices for slicing the mesh along a plane with
# the given normal.
#
M = discretize.TensorMesh([32, 32, 32])
v = discretize.utils.random_model(M.vnC, random_seed=789).reshape(-1, order='F')
x_slice, y_slice, z_slice = 0.75, 0.25, 0.9
plt.figure(figsize=(7.5, 3))
ax = plt.subplot(131)
M.plot_slice(v, normal='X', slice_loc=x_slice, ax=ax)
ax = plt.subplot(132)
M.plot_slice(v, normal='Y', slice_loc=y_slice, ax=ax)
ax = plt.subplot(133)
M.plot_slice(v, normal='Z', slice_loc=z_slice, ax=ax)
plt.tight_layout()
plt.show()
#
# This also works for `TreeMesh`. We create a mesh here that is refined within three
# boxes, along with a base level of refinement.
#
TM = discretize.TreeMesh([32, 32, 32])
TM.refine(3, finalize=False)
BSW = [[0.25, 0.25, 0.25], [0.15, 0.15, 0.15], [0.1, 0.1, 0.1]]
TNE = [[0.75, 0.75, 0.75], [0.85, 0.85, 0.85], [0.9, 0.9, 0.9]]
levels = [6, 5, 4]
TM.refine_box(BSW, TNE, levels)
v_TM = discretize.utils.volume_average(M, TM, v)
plt.figure(figsize=(7.5, 3))
ax = plt.subplot(131)
TM.plot_slice(v_TM, normal='X', slice_loc=x_slice, ax=ax)
ax = plt.subplot(132)
TM.plot_slice(v_TM, normal='Y', slice_loc=y_slice, ax=ax)
ax = plt.subplot(133)
TM.plot_slice(v_TM, normal='Z', slice_loc=z_slice, ax=ax)
plt.tight_layout()
plt.show()