def logDropTol( p, droptol = 1e-3 ):
""""""
tmp = pg.RVector( p );
tmp = pg.abs( tmp / droptol )
tmp.setVal( 1.0, pg.find( tmp < 1.0 ) )
#for i, v in enumerate( tmp ):
#tmp[ i ] = abs( tmp[ i ] / droptol );
#if tmp[ i ] < 1.0: tmp[ i ] = 1.0;
tmp = pg.log10( tmp );
tmp *= pg.sign( p );
return tmp;
def createJacobian(self, slowness):
"""Jacobian matrix using a fat-ray approach (Jordi et al. 2016)."""
data = self.data()
self.jacobian().resize(data.size(), self.mesh().cellCount())
# first compute reciprocal travel times for geophone sources
self.computeTravelTimes(slowness, calcOthers=True)
n_data = data.size()
cellSizes = self.mesh().cellSizes()
for i in range(n_data):
iS, iG = int(data("s")[i]), int(data("g")[i])
tsr = self.dataMatrix[iS][iG] # shot-receiver travel time
dt = self.timeMatrix[iS] + self.timeMatrix[iG] - tsr # difference
weight = np.maximum(1 - 2 * self.frequency * dt, 0.0) # 1 on ray
if self.debug:
print(pg.sum(pg.sign(weight)))
wa = weight * cellSizes
self.jacobian()[i] = wa / np.sum(wa) * tsr / slowness
def logDropTol(p, droptol=1e-3):
"""
Example
-------
>>> from pygimli.utils import logDropTol
>>> x = logDropTol((-10,-1,0,1,100))
>>> print(x.array())
[-4. -3. 0. 3. 5.]
"""
tmp = pg.RVector(p)
tmp = pg.abs(tmp / droptol)
tmp.setVal(1.0, pg.find(tmp < 1.0))
tmp = pg.log10(tmp)
tmp *= pg.sign(p)
return tmp
def logDropTol(p, droptol=1e-3):
"""Create logarithmic scaled copy of p.
Examples
--------
>>> from pygimli.utils import logDropTol
>>> x = logDropTol((-10, -1, 0, 1, 100))
>>> print(x.array())
[-4. -3. 0. 3. 5.]
"""
tmp = pg.RVector(p)
tmp = pg.abs(tmp / droptol)
tmp.setVal(1.0, pg.find(tmp < 1.0))
tmp = pg.log10(tmp)
tmp *= pg.sign(p)
return tmp
def createJacobian(self, slowness):
"""Jacobian matrix using a fat-ray approach (Jordi et al. 2016)."""
data = self.data()
self.jacobian().resize(data.size(), self.mesh().cellCount())
# first compute reciprocal travel times for geophone sources
self.computeTravelTimes(slowness, calcOthers=True)
n_data = data.size()
cellSizes = self.mesh().cellSizes()
for i in range(n_data):
iS, iG = int(data("s")[i]), int(data("g")[i])
tsr = self.dataMatrix[iS][iG] # shot-receiver travel time
dt = self.timeMatrix[iS] + self.timeMatrix[iG] - tsr # difference
weight = np.maximum(1 - 2 * self.frequency * dt, 0.0) # 1 on ray
if self.debug:
print(pg.sum(pg.sign(weight)))
wa = weight * cellSizes
if np.sum(wa) > 0: # only if all values are zero
wa /= np.sum(wa)
self.jacobian()[i] = wa * tsr / slowness
def importRes2dInv(filename, verbose=False, return_header=False):
"""Read res2dinv format
Parameters
----------
filename : str
verbose : bool [False]
return_header : bool [False]
Returns
-------
pg.DataContainerERT and (in case of return_header=True)
header dictionary
Format
------
str - title
float - unit spacing [m]
int - Array Number (1-Wenner, 3-Dipole-dipole atm only)
int - Number of Datapoints
float - x-location given in terms of first electrode
use 1 if mid-point location is given
int - 0 for no IP, use 1 if IP present
str - Phase Angle if IP present
str - mrad if IP present
0,90.0 - if IP present
dataBody
"""
def getNonEmptyRow(i, comment='#'):
s = next(i)
while s[0] is comment:
s = next(i)
return s.split('\r\n')[0]
# def getNonEmptyRow(...)
with open(filename, 'r') as fi:
content = fi.readlines()
it = iter(content)
header = {}
header['name'] = getNonEmptyRow(it, comment=';')
header['spacing'] = float(getNonEmptyRow(it, comment=';'))
typrow = getNonEmptyRow(it, comment=';')
typ = int(typrow.rstrip('\n').rstrip('R').rstrip('L'))
if typ == 11:
# independent electrode positions
header['subtype'] = int(getNonEmptyRow(it, comment=';'))
header['dummy'] = getNonEmptyRow(it, comment=';')
isR = int(getNonEmptyRow(it, comment=';'))
nData = int(getNonEmptyRow(it, comment=';'))
xLoc = float(getNonEmptyRow(it, comment=';'))
hasIP = int(getNonEmptyRow(it, comment=';'))
if hasIP:
header['ipQuantity'] = getNonEmptyRow(it, comment=';')
header['ipUnit'] = getNonEmptyRow(it, comment=';')
header['ipData'] = getNonEmptyRow(it, comment=';')
ipline = header['ipData'].rstrip('\n').rstrip('\r').split(' ')
if len(ipline) > 2: # obviously spectral data?
header['ipNumGates'] = int(ipline[0])
header['ipDelay'] = float(ipline[1])
header['onTime'] = float(ipline[-2])
header['offTime'] = float(ipline[-1])
header['ipDT'] = np.array(ipline[2:-2], dtype=float)
header['ipGateT'] = np.cumsum(np.hstack((header['ipDelay'],
header['ipDT'])))
data = pg.DataContainerERT()
data.resize(nData)
if typ == 9 or typ == 10:
raise Exception("Don't know how to read:" + str(typ))
if typ == 11 or typ == 12 or typ == 13: # mixed array
res = pg.Vector(nData, 0.0)
ip = pg.Vector(nData, 0.0)
specIP = []
for i in range(nData):
vals = getNonEmptyRow(it, comment=';').replace(',', ' ').split()
# row starts with 4
if int(vals[0]) == 4:
eaID = data.createSensor(pg.Pos(float(vals[1]),
float(vals[2])))
ebID = data.createSensor(pg.Pos(float(vals[3]),
float(vals[4])))
emID = data.createSensor(pg.Pos(float(vals[5]),
float(vals[6])))
enID = data.createSensor(pg.Pos(float(vals[7]),
float(vals[8])))
elif int(vals[0]) == 3:
eaID = data.createSensor(pg.Pos(float(vals[1]),
float(vals[2])))
ebID = -1
emID = data.createSensor(pg.Pos(float(vals[3]),
float(vals[4])))
enID = data.createSensor(pg.Pos(float(vals[5]),
float(vals[6])))
elif int(vals[0]) == 2:
eaID = data.createSensor(pg.Pos(float(vals[1]),
float(vals[2])))
ebID = -1
emID = data.createSensor(pg.Pos(float(vals[3]),
float(vals[4])))
enID = -1
else:
raise Exception('dont know how to handle row', vals[0])
res[i] = float(vals[int(vals[0])*2+1])
if hasIP:
# ip[i] = float(vals[int(vals[0])*2+2])
ipCol = int(vals[0])*2+2
ip[i] = float(vals[ipCol])
if 'ipNumGates' in header:
specIP.append(vals[ipCol:])
data.createFourPointData(i, eaID, ebID, emID, enID)
if isR:
data.set('r', res)
else:
data.set('rhoa', res)
if hasIP:
data.set('ip', ip)
if 'ipNumGates' in header:
A = np.array(specIP, dtype=float)
A[A > 1000] = -999
A[A < -1000] = -999
for i in range(header['ipNumGates']):
data.set('ip'+str(i+1), A[:, i])
data.sortSensorsX()
data.sortSensorsIndex()
if return_header:
return data, header
else:
return data
# amount of values per collumn per typ
nntyp = [0, 3, 3, 4, 3, 3, 4, 4, 3, 0, 0, 8, 10]
nn = nntyp[typ] + hasIP
# dataBody = pg.Matrix(nn, nData)
dataBody = np.zeros((nn, nData))
for i in range(nData):
vals = getNonEmptyRow(it, comment=';').replace(',', ' ').split()
dataBody[:, i] = np.array(vals, dtype=float)
# for j in range(nn):
# dataBody[j][i] = float(vals[j])
XX = dataBody[0]
EL = dataBody[1]
SP = pg.Vector(nData, 1.0)
if nn - hasIP == 4:
SP = dataBody[2]
AA = None
BB = None
NN = None
MM = None
if typ == 1: # Wenner
AA = XX - xLoc * EL * 1.5
MM = AA + EL
NN = MM + EL
BB = NN + EL
elif typ == 2: # Pole-Pole
AA = XX - xLoc * EL * 0.5
MM = AA + EL
elif typ == 3: # Dipole-Dipole
AA = XX - xLoc * EL * (SP / 2. + 1.)
BB = AA + EL
MM = BB + SP * EL
NN = MM + EL
pass
elif typ == 3: # Dipole-Dipole
AA = XX - xLoc * EL * (SP / 2. + 1.)
BB = AA + EL
MM = BB + SP * EL
NN = MM + EL
elif typ == 4: # WENNER-BETA
AA = XX - xLoc * EL * 1.5
BB = AA + EL
MM = BB + EL
NN = MM + EL
elif typ == 5: # WENNER-GAMMA
AA = XX - xLoc * EL * 1.5
MM = AA + EL
BB = MM + EL
NN = BB + EL
elif typ == 6: # POLE-DIPOLE
AA = XX - xLoc * SP * EL - (SP - 1.) * (SP < 0.) * EL
MM = AA + SP * EL
NN = MM + pg.sign(SP) * EL
elif typ == 7: # SCHLUMBERGER
AA = XX - xLoc * EL * (SP + 0.5)
MM = AA + SP * EL
NN = MM + EL
BB = NN + SP * EL
else:
raise Exception('Datatype ' + str(typ) + ' not yet suppoted')
for i in range(len(AA)):
if AA is not None:
eaID = data.createSensor(pg.Pos(AA[i], 0.0))
else:
eaID = -1
if BB is not None:
ebID = data.createSensor(pg.Pos(BB[i], 0.0))
else:
ebID = -1
if MM is not None:
emID = data.createSensor(pg.Pos(MM[i], 0.0))
else:
emID = -1
if NN is not None:
enID = data.createSensor(pg.Pos(NN[i], 0.0))
else:
enID = -1
data.createFourPointData(i, eaID, ebID, emID, enID)
data.set('rhoa', dataBody[nn - hasIP - 1])
if hasIP:
data.set('ip', dataBody[nn - 1])
data.sortSensorsX()
if return_header:
return data, header
else:
return data