def createAnimation(out, stream=False):
mesh = pg.load(out + '.bms')
meshC = pg.load(out + 'C.bms')
densMatrix = pg.load(out + 'density.bmat')
vels = np.load(out + 'velo.bmat.npy')
cMin = 9e99
cMax = -9e99
for d in densMatrix:
cMin = min(cMin, pg.min(d))
cMax = max(cMax, pg.max(d))
if stream:
pg.viewer.mpl.saveAnimation(mesh,
densMatrix,
out + '-stream',
plc=None,
label='',
cMin=cMin,
cMax=cMax,
logScale=False,
cmap=None,
vData=vels,
coarseMesh=meshC,
color='white')
else:
pg.viewer.mpl.saveAnimation(mesh,
densMatrix,
out,
plc=None,
label='',
cMin=cMin,
cMax=cMax,
logScale=False,
cmap=None)
def createAnimation(out, stream=False):
mesh = pg.load(out + '.bms')
meshC = pg.load(out + 'C.bms')
densMatrix = pg.load(out + 'density.bmat')
vels = np.load(out+'velo.bmat.npy')
cMin= 9e99
cMax= -9e99
for d in densMatrix:
cMin = min(cMin, pg.min(d))
cMax = max(cMax, pg.max(d))
if stream:
pg.mplviewer.saveAnimation(mesh, densMatrix, out + '-stream',
plc=None, label='',
cMin=cMin, cMax=cMax, logScale=False, cmap=None,
vData=vels, coarseMesh=meshC, color='white'
)
else:
pg.mplviewer.saveAnimation(mesh, densMatrix, out,
plc=None, label='',
cMin=cMin, cMax=cMax, logScale=False, cmap=None)
def createJacobian(self, model):
"""Create Jacobian matrix."""
if self.subPotentials is None:
self.response(model)
J = self.jacobian()
J.resize(self.data.size(), self.regionManager().parameterCount())
cells = self.mesh().findCellByMarker(0, -1)
Si = pg.ElementMatrix()
St = pg.ElementMatrix()
u = self.subPotentials
pg.tic()
if self.verbose():
print("Calculate sensitivity matrix for model: ", min(model),
max(model))
Jt = pg.RMatrix(self.data.size(),
self.regionManager().parameterCount())
for kIdx, w in enumerate(self.w):
k = self.k[kIdx]
w = self.w[kIdx]
Jt *= 0.
A = pg.ElementMatrixMap()
for i, c in enumerate(cells):
modelIdx = c.marker()
# 2.5D
Si.u2(c)
Si *= k * k
Si += St.ux2uy2uz2(c)
# 3D
# Si.ux2uy2uz2(c); w = w* 2
A.add(modelIdx, Si)
for dataIdx in range(self.data.size()):
a = int(self.data('a')[dataIdx])
b = int(self.data('b')[dataIdx])
m = int(self.data('m')[dataIdx])
n = int(self.data('n')[dataIdx])
Jt[dataIdx] = A.mult(u[kIdx][a] - u[kIdx][b],
u[kIdx][m] - u[kIdx][n])
J += w * Jt
m2 = model * model
k = self.data('k')
for i in range(J.rows()):
J[i] /= (m2 / k[i])
if self.verbose():
sumsens = np.zeros(J.rows())
for i in range(J.rows()):
sumsens[i] = pg.sum(J[i])
print("sens sum: median = ", pg.median(sumsens), " min = ",
pg.min(sumsens), " max = ", pg.max(sumsens))
import numpy as np
import pybert as pb
import pygimli as pg
pg.verbose = print # temporary
import pygimli.meshtools as mt
from settings import depth_5000, depth_5198, erte, rste
ertData = pb.load("ert.data")
print("Number of electrodes:", ertData.sensorCount())
print(ertData)
rstData = pg.DataContainer("rst.data", "s g")
print("Number of shot/receivers:", rstData.sensorCount())
maxrst = pg.max(pg.x(rstData.sensors()))
idx = []
for i, sensor in enumerate(ertData.sensors()):
if sensor[0] >= 50.0:
idx.append(i)
ertData.removeSensorIdx(idx)
ertData.removeInvalid()
ertData.removeUnusedSensors()
ertData.set("err", pg.RVector(ertData.size(), erte))
ertData.save("ert_filtered.data")
rstData.set("err", pg.RVector(rstData.size(), rste))
#
# # Remove two data points with high v_a at zero-offset
def createJacobian(self, model):
"""Create Jacobian matrix."""
if self.subPotentials is None:
self.response(model)
J = self.jacobian()
J.resize(self.data.size(), self.regionManager().parameterCount())
cells = self.mesh().findCellByMarker(0, -1)
Si = pg.ElementMatrix()
St = pg.ElementMatrix()
u = self.subPotentials
pg.tic()
if self.verbose():
print("Calculate sensitivity matrix for model: ",
min(model), max(model))
Jt = pg.RMatrix(self.data.size(),
self.regionManager().parameterCount())
for kIdx, w in enumerate(self.w):
k = self.k[kIdx]
w = self.w[kIdx]
Jt *= 0.
A = pg.ElementMatrixMap()
for i, c in enumerate(cells):
modelIdx = c.marker()
# 2.5D
Si.u2(c)
Si *= k * k
Si += St.ux2uy2uz2(c)
# 3D
# Si.ux2uy2uz2(c); w = w* 2
A.add(modelIdx, Si)
for dataIdx in range(self.data.size()):
a = int(self.data('a')[dataIdx])
b = int(self.data('b')[dataIdx])
m = int(self.data('m')[dataIdx])
n = int(self.data('n')[dataIdx])
Jt[dataIdx] = A.mult(u[kIdx][a] - u[kIdx][b],
u[kIdx][m] - u[kIdx][n])
J += w * Jt
m2 = model*model
k = self.data('k')
for i in range(J.rows()):
J[i] /= (m2 / k[i])
if self.verbose():
sumsens = np.zeros(J.rows())
for i in range(J.rows()):
sumsens[i] = pg.sum(J[i])
print("sens sum: median = ", pg.median(sumsens),
" min = ", pg.min(sumsens),
" max = ", pg.max(sumsens))