def main( argv ):
from optparse import OptionParser
parser = OptionParser( "Curvefit - fits data in datafile with different curves\n usage: %prog [options] Datafile" )
parser.add_option("-v", "--verbose", dest = "verbose", action = "store_true"
, help = "be verbose", default = False )
parser.add_option("-n", "--np", dest = "np", type = "int", default = 1
, help = "Number of polynomials" )
(options, args) = parser.parse_args()
if len( args ) == 0:
parser.print_help()
print("Please add a datafile.")
sys.exit( 2 )
else:
datafile = args[ 0 ];
xy = g.RMatrix()
g.loadMatrixCol( xy, datafile );
if options.verbose:
print("data:", xy)
# two coefficients and x-vector (first data column)
f = FunctionModelling( options.np + 1, xy[ 0 ] )
# initialize inversion with data and forward operator and set options
inv = g.RInversion( xy[ 1 ], f, True, True );
# constant absolute error of 0.01 (not necessary, only for chi^2)
inv.setAbsoluteError( 0.01 );
# the problem is well-posed and does not need regularization
inv.setLambda( 0 );
# actual inversion run yielding coefficient model
coeff = inv.run();
# get actual response and write to file.
g.save( inv.response(), "resp.out" );
# print result to screen and save coefficient vector to file
s = "y = " + str( round( coeff[ 0 ] * 1000 ) /1000 )
for i in range( 1, options.np+1 ):
s = s + " + " + str( round( coeff[ i ] *1000 ) / 1000 ) + " x^" + str( i )
print(s)
g.save( coeff, "out.vec" );
P.plot( xy[0], xy[1], 'rx', xy[0], inv.response(), 'b-' )
P.title(s)
P.xlabel("x");
P.ylabel("y");
P.legend(("measured", "fitted"),loc="upper left");
P.show()
def _createParameterContraintsLines(mesh, cMat, cWeight=None):
"""TODO Documentme."""
C = pg.RMatrix()
if isinstance(cMat, pg.SparseMapMatrix):
cMat.save('tmpC.matrix')
pg.loadMatrixCol(C, 'tmpC.matrix')
else:
C = cMat
cellList = dict()
for c in mesh.cells():
pID = c.marker()
if pID not in cellList:
cellList[pID] = []
cellList[pID].append(c)
paraCenter = dict()
for pID, vals in list(cellList.items()):
p = pg.RVector3(0.0, 0.0, 0.0)
for c in vals:
p += c.center()
p /= float(len(vals))
paraCenter[pID] = p
nConstraints = C[0].size()
start = []
end = []
# swatch = pg.Stopwatch(True) # not used
for i in range(0, int(nConstraints / 2)):
# print i
# if i == 3: break;
idL = int(C[1][i * 2])
idR = int(C[1][i * 2 + 1])
p1 = paraCenter[idL]
p2 = paraCenter[idR]
if cWeight is not None:
pa = pg.RVector3(p1 + (p2 - p1) / 2.0 * (1.0 - cWeight[i]))
pb = pg.RVector3(p2 + (p1 - p2) / 2.0 * (1.0 - cWeight[i]))
else:
pa = p1
pb = p2
# print(i, idL, idR, p1, p2)
start.append(pa)
end.append(pb)
# updateAxes_(ax) # not existing
return start, end
def _createParameterContraintsLines(mesh, cMat, cWeight=None):
"""TODO Documentme."""
C = pg.RMatrix()
if isinstance(cMat, pg.SparseMapMatrix):
cMat.save('tmpC.matrix')
pg.loadMatrixCol(C, 'tmpC.matrix')
else:
C = cMat
cellList = dict()
for c in mesh.cells():
pID = c.marker()
if pID not in cellList:
cellList[pID] = []
cellList[pID].append(c)
paraCenter = dict()
for pID, vals in list(cellList.items()):
p = pg.RVector3(0.0, 0.0, 0.0)
for c in vals:
p += c.center()
p /= float(len(vals))
paraCenter[pID] = p
nConstraints = C[0].size()
start = []
end = []
# swatch = pg.Stopwatch(True) # not used
for i in range(0, int(nConstraints / 2)):
# print i
# if i == 3: break;
idL = int(C[1][i * 2])
idR = int(C[1][i * 2 + 1])
p1 = paraCenter[idL]
p2 = paraCenter[idR]
if cWeight is not None:
pa = pg.RVector3(p1 + (p2 - p1) / 2.0 * (1.0 - cWeight[i]))
pb = pg.RVector3(p2 + (p1 - p2) / 2.0 * (1.0 - cWeight[i]))
else:
pa = p1
pb = p2
# print(i, idL, idR, p1, p2)
start.append(pa)
end.append(pb)
# updateAxes_(ax) # not existing
return start, end
def loadSIPallData(filename, outnumpy=False):
"""load SIP data with the columns ab/2,mn/2,rhoa and PHI with the
corresponding frequencies in the first row."""
if outnumpy:
A = N.loadtxt(filename)
fr = A[0, 3:]
ab2 = A[1:, 0]
mn2 = A[1:, 1]
rhoa = A[1:, 2]
PHI = A[1:, 3:]
else:
A = pg.RMatrix()
pg.loadMatrixCol(A, 'sch/dc.ves')
ndata = A.cols()
ab2 = A[0](1, ndata)
mn2 = A[1](1, ndata)
rhoa = A[2](1, ndata)
PHI = pg.RMatrix()
fr = []
for i in range(3, A.rows()):
fr.append(A[i][0])
PHI.push_back(A[i](1, ndata))
return ab2, mn2, rhoa, PHI, fr
def loadmrsproject(mydir):
"""
load mrs project from given directory (zkernel.ve)
(datacube.dat, KR/KI.bmat, zkernel.vec)
"""
if mydir is None:
mydir = '.'
if mydir[-1] != '/':
mydir = mydir + '/'
# load files from directory
zvec = pg.Vector(mydir + 'zkernel.vec')
KR = pg.Matrix(mydir + 'KR.bmat')
KI = pg.Matrix(mydir + 'KI.bmat')
A = pg.Matrix()
pg.loadMatrixCol(A, mydir + 'datacube.dat')
t = np.array(A[0])
# data
datvec = pg.Vector()
for i in range(1, len(A)):
datvec = pg.cat(datvec, A[i])
# print len(A), len(t)
return KR, KI, zvec, t, datvec
def loadmrsproject(mydir):
"""
load mrs project from given directory (zkernel.ve)
(datacube.dat, KR/KI.bmat, zkernel.vec)
"""
if mydir is None:
mydir = '.'
if mydir[-1] != '/':
mydir = mydir + '/'
# load files from directory
zvec = pg.RVector(mydir + 'zkernel.vec')
KR = pg.RMatrix(mydir + 'KR.bmat')
KI = pg.RMatrix(mydir + 'KI.bmat')
A = pg.RMatrix()
pg.loadMatrixCol(A, mydir + 'datacube.dat')
t = N.array(A[0])
# data
datvec = pg.RVector()
for i in range(1, len(A)):
datvec = pg.cat(datvec, A[i])
# print len(A), len(t)
return KR, KI, zvec, t, datvec
def loadSIPallData(filename, outnumpy=False):
"""load SIP data with the columns ab/2,mn/2,rhoa and PHI with the
corresponding frequencies in the first row."""
if outnumpy:
A = N.loadtxt(filename)
fr = A[0, 3:]
ab2 = A[1:, 0]
mn2 = A[1:, 1]
rhoa = A[1:, 2]
PHI = A[1:, 3:]
else:
A = pg.RMatrix()
pg.loadMatrixCol(A, 'sch/dc.ves')
ndata = A.cols()
ab2 = A[0](1, ndata)
mn2 = A[1](1, ndata)
rhoa = A[2](1, ndata)
PHI = pg.RMatrix()
fr = []
for i in range(3, A.rows()):
fr.append(A[i][0])
PHI.push_back(A[i](1, ndata))
return ab2, mn2, rhoa, PHI, fr
def _createParameterContraintsLines(mesh, cMat, cWeight=None):
"""TODO Documentme."""
C = pg.RMatrix()
if isinstance(cMat, pg.SparseMapMatrix):
cMat.save('tmpC.matrix')
pg.loadMatrixCol(C, 'tmpC.matrix')
else:
C = cMat
paraMarker = mesh.cellMarkers()
cellList = dict()
for cID in range(len(paraMarker)):
if cID not in cellList:
cellList[cID] = []
cellList[cID].append(mesh.cell(cID))
paraCenter = dict()
for cID, vals in list(cellList.items()):
p = pg.RVector3(0.0, 0.0, 0.0)
for c in vals:
p += c.center()
p /= float(len(vals))
paraCenter[cID] = p
nConstraints = C[0].size()
start = []
end = []
# swatch = pg.Stopwatch(True) # not used
for i in range(0, int(nConstraints / 2)):
# print i
# if i == 1000: break;
idL = int(C[1][i * 2])
idR = int(C[1][i * 2 + 1])
# leftCells = []
# rightCells = []
# for c, index in enumerate(paraMarker):
# if idL == index:
# leftCells.append(mesh.cell(c))
# if idR == index:
# rightCells.append(mesh.cell(c))
# p1 = pg.RVector3(0.0,0.0);
# for c in leftCells:
# p1 += c.center()
# p1 /= float(len(leftCells))
# p2 = pg.RVector3(0.0,0.0);
# for c in rightCells:
# p2 += c.center()
# print cWeight[i]
# p2 /= float(len(rightCells))
p1 = paraCenter[idL]
p2 = paraCenter[idR]
if cWeight is not None:
pa = pg.RVector3(p1 + (p2 - p1) / 2.0 * (1.0 - cWeight[i]))
pb = pg.RVector3(p2 + (p1 - p2) / 2.0 * (1.0 - cWeight[i]))
else:
pa = p1
pb = p2
start.append(pa)
end.append(pb)
# updateAxes_(ax) # not existing
return start, end
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pygimli as g
import pylab as P
from pygimli.utils.base import draw1dmodel
nlay = 30
lam = 20.
errPerc = 3.
isBlocky = False
abmnr=g.RMatrix()
g.loadMatrixCol(abmnr,"sond1-100.ves")
ab2 = abmnr[0]
mn2 = abmnr[1]
rhoa = abmnr[2]
maxDep = max( ab2 ) / 2.
print("Maximum depth estimated to ", maxDep)
thk = g.RVector( nlay - 1, maxDep / ( nlay - 1 ) )
thk *= ( maxDep / sum( thk ) / 3 )
transRho = g.RTransLog()
transRhoa = g.RTransLog()
f=g.DC1dRhoModelling( thk, ab2, mn2 )
inv = g.RInversion( rhoa, f, True )
model = g.RVector( nlay, P.median( rhoa ) )
inv.setModel( model )
#!/usr/bin/env python # -*- coding: utf-8 -*- import pygimli as g import pylab as P from pygimli.utils.base import draw1dmodel nlay = 4 lam = 200. errPerc = 3. abmnr = g.RMatrix() g.loadMatrixCol(abmnr, "sond1-100.ves") ab2 = abmnr[0] mn2 = abmnr[1] rhoa = abmnr[2] transRho = g.RTransLogLU(1., 1000.) transThk = g.RTransLog() transRhoa = g.RTransLog() f = g.DC1dModelling(nlay, ab2, mn2) f.region(0).setTransModel(transThk) f.region(1).setTransModel(transRho) paraDepth = max(ab2) / 3 f.region(0).setStartValue(max(ab2) / 3. / nlay / 2.) f.region(1).setStartValue(P.median(rhoa)) model = f.createStartVector() model[nlay] *= 1.5
def createParameterContraintsLines(mesh, cMat, cWeight=None):
"""
What is this?
"""
C = pg.RMatrix()
if isinstance(cMat, pg.DSparseMapMatrix):
cMat.save('tmpC.matrix')
pg.loadMatrixCol(C, 'tmpC.matrix')
else:
C = cMat
paraMarker = mesh.cellMarker()
cellList = dict()
for cID in range(len(paraMarker)):
if cID not in cellList:
cellList[cID] = []
cellList[cID].append(mesh.cell(cID))
paraCenter = dict()
for cID, vals in list(cellList.items()):
p = pg.RVector3(0.0, 0.0, 0.0)
for c in vals:
p += c.center()
p /= float(len(vals))
paraCenter[cID] = p
nConstraints = C[0].size()
start = []
end = []
# swatch = pg.Stopwatch(True) # not used
for i in range(0, nConstraints / 2):
# print i
# if i == 1000: break;
idL = int(C[1][i * 2])
idR = int(C[1][i * 2 + 1])
# leftCells = []
# rightCells = []
# for c, index in enumerate(paraMarker):
# if idL == index:
# leftCells.append(mesh.cell(c))
# if idR == index:
# rightCells.append(mesh.cell(c))
# p1 = pg.RVector3(0.0,0.0);
# for c in leftCells:
# p1 += c.center()
# p1 /= float(len(leftCells))
# p2 = pg.RVector3(0.0,0.0);
# for c in rightCells:
# p2 += c.center()
# print cWeight[i]
# p2 /= float(len(rightCells))
p1 = paraCenter[idL]
p2 = paraCenter[idR]
if cWeight is not None:
pa = pg.RVector3(p1 + (p2 - p1) / 2.0 * (1.0 - cWeight[i]))
pb = pg.RVector3(p2 + (p1 - p2) / 2.0 * (1.0 - cWeight[i]))
else:
pa = p1
pb = p2
start.append(pa)
end.append(pb)
# updateAxes_(axes) # not existing
return start, end
def _createParameterContraintsLines(mesh, cMat, cWeights=None):
"""Create line segments representing constrains.
"""
C = None
if isinstance(cMat, pg.matrix.SparseMapMatrix):
tmp = pg.optImport('tempfile')
_, tmpFile = tmp.mkstemp(suffix='.matrix')
C = pg.Matrix()
cMat.save(tmpFile)
pg.loadMatrixCol(C, tmpFile)
try:
import os
os.remove(tmpFile)
except Exception as e:
pg.error(e)
print("can't remove:", tmpFile)
else:
C = cMat
cellList = dict()
for c in mesh.cells():
pID = c.marker()
if pID not in cellList:
cellList[pID] = []
cellList[pID].append(c)
paraCenter = dict()
for pID, vals in list(cellList.items()):
p = pg.RVector3(0.0, 0.0, 0.0)
for c in vals:
p += c.center()
p /= float(len(vals))
paraCenter[pID] = p
nConstraints = cMat.rows()
start = []
end = []
# swatch = pg.core.Stopwatch(True) # not used
i = -1
while i < C[0].size():
cID = C[0][i]
a = C[1][i]
b = None
if i < C[0].size() - 1:
if C[0][i + 1] == cID:
b = C[1][i + 1]
i += 1
if b is not None:
if cWeights[cID] > 0:
p1 = paraCenter[a]
p2 = paraCenter[b]
if cWeights is not None:
pa = pg.RVector3(p1 + (p2 - p1) / 2.0 *
(1.0 - cWeights[cID]))
pb = pg.RVector3(p2 + (p1 - p2) / 2.0 *
(1.0 - cWeights[cID]))
else:
pa = p1
pb = p2
start.append(pa)
end.append(pb)
else:
start.append(paraCenter[a])
end.append(paraCenter[a])
i += 1
return start, end
def main(argv):
from optparse import OptionParser
parser = OptionParser(
"Curvefit - fits data in datafile with different curves\n usage: %prog [options] Datafile"
)
parser.add_option("-v",
"--verbose",
dest="verbose",
action="store_true",
help="be verbose",
default=False)
parser.add_option("-n",
"--np",
dest="np",
type="int",
default=1,
help="Number of polynomials")
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
print("Please add a datafile.")
sys.exit(2)
else:
datafile = args[0]
xy = g.RMatrix()
g.loadMatrixCol(xy, datafile)
if options.verbose:
print("data:", xy)
# two coefficients and x-vector (first data column)
f = FunctionModelling(options.np + 1, xy[0])
# initialize inversion with data and forward operator and set options
inv = g.RInversion(xy[1], f, True, True)
# constant absolute error of 0.01 (not necessary, only for chi^2)
inv.setAbsoluteError(0.01)
# the problem is well-posed and does not need regularization
inv.setLambda(0)
# actual inversion run yielding coefficient model
coeff = inv.run()
# get actual response and write to file.
g.save(inv.response(), "resp.out")
# print result to screen and save coefficient vector to file
s = "y = " + str(round(coeff[0] * 1000) / 1000)
for i in range(1, options.np + 1):
s = s + " + " + str(round(coeff[i] * 1000) / 1000) + " x^" + str(i)
print(s)
g.save(coeff, "out.vec")
P.plot(xy[0], xy[1], 'rx', xy[0], inv.response(), 'b-')
P.title(s)
P.xlabel("x")
P.ylabel("y")
P.legend(("measured", "fitted"), loc="upper left")
P.show()
