def plot(self,input=None,**kwargs):
import matplotlib.pylab as plt
if input is None: input=self.true_perm
if 'ax' in kwargs:
ax=kwargs.pop('ax')
if self.gdim==2:
if len(input)==self.dim: input=self._pts2sim(input)
im=ax.tripcolor(self.pts[:,0],self.pts[:,1],self.tri,np.real(input),shading='flat')
elif self.gdim==3:
if len(input)==self.fwd.n_tri: input=self._sim2pts(input)
plt.axes(ax)
im=mplot.tetplot(self.pts,self.tri,vertex_color=np.real(input),alpha=1.0)
return im
else:
if self.gdim==2:
if len(input)==self.dim: input=self._pts2sim(input)
plt.tripcolor(self.pts[:,0],self.pts[:,1],self.tri,np.real(input),shading='flat')
elif self.gdim==3:
if len(input)==self.fwd.n_tri: input=self._sim2pts(input)
mplot.tetplot(self.pts,self.tri,vertex_color=np.real(input),alpha=1.0)
plt.show()
ms, el_pos = mesh.create(h0=0.15, bbox=bbox)
no2xy = ms['node']
el2no = ms['element']
# report the status of the 2D mesh
quality.stats(no2xy, el2no)
""" 1. FEM forward simulations """
# setup EIT scan conditions
el_dist, step = 7, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(ms, el_pos)
# in python, index start from 0
ex_line = ex_mat[1].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'alpha': 100.0}]
ms_test = mesh.set_alpha(ms, anomaly=anomaly, background=1.0)
tri_perm = ms_test['alpha']
node_perm = pdeprtni(no2xy, el2no, np.real(tri_perm))
# solving once using fem
f, _ = fwd.solve_once(ex_line, tri_perm)
f = np.real(f)
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(no2xy, el2no, vertex_color=f, alpha=1.0)
pts = mesh_obj['node']
tri = mesh_obj['element']
# report the status of the 2D mesh
quality.stats(pts, tri)
""" 1. FEM forward simulations """
# setup EIT scan conditions
el_dist, step = 7, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(mesh_obj, el_pos)
# in python, index start from 0
ex_line = ex_mat[1].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'perm': 100.0}]
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly, background=1.0)
tri_perm = mesh_new['perm']
node_perm = sim2pts(pts, tri, np.real(tri_perm))
# solving once using fem
f, _ = fwd.solve(ex_line, perm=tri_perm)
f = np.real(f)
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(pts, tri, vertex_color=f, alpha=1.0)
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(ms, el_pos)
# in python, index start from 0
ex_line = ex_mat[2].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'alpha': 100.0}]
ms_test = mesh.set_alpha(ms, anomaly=anomaly, background=1.0)
tri_perm = ms_test['alpha']
node_perm = pdeprtni(no2xy, el2no, np.real(tri_perm))
# solving once using fem
# f, _ = fwd.solve_once(ex_line, tri_perm)
# f = np.real(f)
# calculate simulated data
f0 = fwd.solve(ex_mat, step=step, perm=ms['alpha'])
f1 = fwd.solve(ex_mat, step=step, perm=ms_test['alpha'])
""" 3. JAC solver """
# number of stimulation lines/patterns
eit = jac.JAC(ms, el_pos, ex_mat=ex_mat, step=step, perm=1., parser='std')
eit.setup(p=0.50, lamb=1e-4, method='kotre')
ds = eit.solve(f1.v, f0.v)
node_ds = pdeprtni(no2xy, el2no, np.real(ds))
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(no2xy, el2no, vertex_color=node_ds, alpha=1.0)
# coding: utf-8
# pylint: disable=invalid-name
""" demo for distmesh 3D """
# Copyright (c) Benyuan Liu. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
from __future__ import division, absolute_import, print_function
import numpy as np
import pyeit.mesh as mesh
import pyeit.mesh.plot as mplot
# tetrahedron meshing in a 3D bbox
bbox = [[-1.2, -1.2, -1.2], [1.2, 1.2, 1.2]]
ms, el_pos = mesh.create(h0=0.15, bbox=bbox)
# print mesh quality
p = ms['node']
t = ms['element']
print('points =', p.shape)
print('simplices =', t.shape)
# plot
mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
# create random color
f = np.random.randn(p.shape[0])
# mplot.tetplot(p, t, f, alpha=0.25)
# in python, index start from 0
ex_line = ex_mat[2].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'perm': 100.0}]
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly, background=1.0)
tri_perm = mesh_new['perm']
node_perm = sim2pts(pts, tri, np.real(tri_perm))
# solving once using fem
# f, _ = fwd.solve(ex_line, tri_perm)
# f = np.real(f)
# calculate simulated data
f0 = fwd.solve_eit(ex_mat, step=step, perm=mesh_obj['perm'])
f1 = fwd.solve_eit(ex_mat, step=step, perm=mesh_new['perm'])
""" 3. JAC solver """
# number of stimulation lines/patterns
eit = jac.JAC(mesh_obj,
el_pos,
ex_mat=ex_mat,
step=step,
perm=1.,
parser='std')
eit.setup(p=0.50, lamb=1e-3, method='kotre')
ds = eit.solve(f1.v, f0.v, normalize=False)
node_ds = sim2pts(pts, tri, np.real(ds))
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(pts, tri, vertex_color=node_ds, alpha=1.0)
# coding: utf-8
# pylint: disable=invalid-name
# author: benyuan liu
""" demo for distmesh """
from __future__ import division, absolute_import, print_function
import numpy as np
import pyeit.mesh as mesh
import pyeit.mesh.plot as mplot
# build tetrahedron
# 3D tetrahedron must have a bbox
bbox = [[-1, -1, -1], [1, 1, 1]]
# save calling convention as distmesh 2D
ms, elPos = mesh.create(h0=0.2, bbox=bbox)
p = ms['node']
t = ms['element']
# print mesh quality
print('points =', p.shape)
print('simplices =', t.shape)
# create random color
f = np.random.randn(p.shape[0])
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(p, t, f, alpha=0.25)
# test
# pylint: disable=no-member
""" demo for distmesh """
from __future__ import absolute_import
import numpy as np
import pyeit.mesh as mesh
import pyeit.mesh.plot as mplot
# build tetrahedron
# 3D tetrahedron must have a bbox
bbox = [[-1, -1, -1], [1, 1, 1]]
# save calling convention as distmesh 2D
ms, elPos = mesh.create(h0=0.2, bbox=bbox)
p = ms['node']
t = ms['element']
# print mesh quality
print('points =', p.shape)
print('simplices =', t.shape)
# create random color
f = np.random.randn(p.shape[0])
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(p, t, f, alpha=0.25)
no2xy = ms['node']
el2no = ms['element']
# report the status of the 2D mesh
quality.fstats(no2xy, el2no)
""" 1. FEM forward simulations """
# setup EIT scan conditions
elDist, step = 7, 1
exMtx = eit_scan_lines(16, elDist)
# calculate simulated data
fwd = forward(ms, elPos)
# in python, index start from 0
exLine = exMtx[1].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'alpha': 100.0}]
ms_test = mesh.set_alpha(ms, anom=anomaly, background=1.0)
tri_perm = ms_test['alpha']
node_perm = pdeprtni(no2xy, el2no, np.real(tri_perm))
# solving once using fem
f, _ = fwd.solve_once(exLine, tri_perm)
f = np.real(f)
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(no2xy, el2no, vertex_color=f, alpha=1.0)
# calculate simulated data
fwd = forward(ms, elPos)
# in python, index start from 0
exLine = exMtx[2].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'alpha': 100.0}]
ms_test = mesh.set_alpha(ms, anom=anomaly, background=1.0)
tri_perm = ms_test['alpha']
node_perm = pdeprtni(no2xy, el2no, np.real(tri_perm))
# solving once using fem
# f, _ = fwd.solve_once(exLine, tri_perm)
# f = np.real(f)
# calculate simulated data
f0 = fwd.solve(exMtx, step=step, perm=ms['alpha'])
f1 = fwd.solve(exMtx, step=step, perm=ms_test['alpha'])
""" 3. JAC solver """
# number of excitation lines & excitation patterns
eit = jac.JAC(ms, elPos, exMtx=exMtx, step=step,
perm=1., parser='std',
p=0.50, lamb=1e-4, method='kotre')
ds = eit.solve(f1.v, f0.v)
node_ds = pdeprtni(no2xy, el2no, np.real(ds))
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(no2xy, el2no, vertex_color=node_ds, alpha=1.0)
