class BaseIPSimulation2D(BaseDCSimulation2D):
eta, etaMap, etaDeriv = props.Invertible("Electrical Chargeability (V/V)")
_data_type = properties.StringChoice(
"IP data type",
default="volt",
choices=["volt", "apparent_chargeability"],
)
data_type = deprecate_property(
_data_type,
"data_type",
new_name="receiver.data_type",
removal_version="0.17.0",
future_warn=True,
)
fieldsPair = Fields2D
_Jmatrix = None
_f = None # the DC fields
_sign = 1.0
_pred = None
_scale = None
gtgdiag = None
def fields(self, m):
if self.verbose:
print(">> Compute DC fields")
if self._f is None:
# re-uses the DC simulation's fields method
self._f = super().fields(None)
if self._scale is None:
scale = Data(self.survey, np.full(self.survey.nD, self._sign))
f = self.fields_to_space(self._f)
# loop through receievers to check if they need to set the _dc_voltage
for src in self.survey.source_list:
for rx in src.receiver_list:
if (rx.data_type == "apparent_chargeability"
or self._data_type == "apparent_chargeability"):
scale[src,
rx] = self._sign / rx.eval(src, self.mesh, f)
self._scale = scale.dobs
self._pred = self.forward(m, f=self._f)
return self._f
def dpred(self, m=None, f=None):
"""
Predicted data.
.. math::
d_\\text{pred} = Pf(m)
"""
# return self.Jvec(m, m, f=f)
if f is None:
f = self.fields(m)
return self._pred
def getJtJdiag(self, m, W=None):
if self.gtgdiag is None:
J = self.getJ(m)
if W is None:
W = self._scale**2
else:
W = (self._scale * W.diagonal())**2
self.gtgdiag = np.einsum("i,ij,ij->j", W, J, J)
return self.gtgdiag
def Jvec(self, m, v, f=None):
return self._scale * super().Jvec(m, v, f)
def forward(self, m, f=None):
return self.Jvec(m, m, f=f)
def Jtvec(self, m, v, f=None):
return super().Jtvec(m, v * self._scale, f)
@property
def deleteTheseOnModelUpdate(self):
toDelete = []
return toDelete
@property
def MeSigmaDerivMat(self):
"""
Derivative of MeSigma with respect to the model
"""
if getattr(self, "_MeSigmaDerivMat", None) is None:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
self._MeSigmaDerivMat = (self.mesh.getEdgeInnerProductDeriv(
np.ones(self.mesh.nC))(np.ones(self.mesh.nE)) *
dsigma_dlogsigma)
return self._MeSigmaDerivMat
# TODO: This should take a vector
def MeSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MeSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MeSigmaDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MeSigmaDerivMat * v)
else:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
if adjoint:
return dsigma_dlogsigma.T * (
self.mesh.getEdgeInnerProductDeriv(self.sigma)(u).T * v)
else:
return self.mesh.getEdgeInnerProductDeriv(
self.sigma)(u) * (dsigma_dlogsigma * v)
@property
def MccRhoiDerivMat(self):
"""
Derivative of MccRho with respect to the model
"""
if getattr(self, "_MccRhoiDerivMat", None) is None:
rho = self.rho
vol = self.mesh.vol
drho_dlogrho = sdiag(rho) * self.etaDeriv
self._MccRhoiDerivMat = sdiag(vol * (-1.0 / rho**2)) * drho_dlogrho
return self._MccRhoiDerivMat
def MccRhoiDeriv(self, u, v, adjoint=False):
"""
Derivative of :code:`MccRhoi` with respect to the model.
"""
if len(self.rho.shape) > 1:
if self.rho.shape[1] > self.mesh.dim:
raise NotImplementedError(
"Full anisotropy is not implemented for MccRhoiDeriv.")
if self.storeInnerProduct:
if adjoint:
return self.MccRhoiDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MccRhoiDerivMat * v)
else:
vol = self.mesh.vol
rho = self.rho
drho_dlogrho = sdiag(rho) * self.etaDeriv
if adjoint:
return drho_dlogrho.T * (sdiag(u * vol * (-1.0 / rho**2)) * v)
else:
return sdiag(u * vol * (-1.0 / rho**2)) * (drho_dlogrho * v)
@property
def MnSigmaDerivMat(self):
"""
Derivative of MnSigma with respect to the model
"""
if getattr(self, "_MnSigmaDerivMat", None) is None:
sigma = self.sigma
vol = self.mesh.vol
dsigma_dlogsigma = sdiag(sigma) * self.etaDeriv
self._MnSigmaDerivMat = self.mesh.aveN2CC.T * sdiag(
vol) * dsigma_dlogsigma
return self._MnSigmaDerivMat
def MnSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MnSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MnSigmaDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MnSigmaDerivMat * v)
else:
sigma = self.sigma
vol = self.mesh.vol
dsigma_dlogsigma = sdiag(sigma) * self.etaDeriv
if adjoint:
return dsigma_dlogsigma.T * (vol * (self.mesh.aveN2CC *
(u * v)))
else:
dsig_dm_v = dsigma_dlogsigma * v
return u * (self.mesh.aveN2CC.T * (vol * dsig_dm_v))
class BaseRx(SimPEG.Survey.BaseTimeRx):
locs = None
rxType = None
data_type = properties.StringChoice(
"Type of DC-IP survey",
required=True,
default="volt",
choices=["volt", "apparent_resistivity", "apparent_chargeability"])
data_type = 'volt'
knownRxTypes = {
'phi': ['phi', None],
'ex': ['e', 'x'],
'ey': ['e', 'y'],
'ez': ['e', 'z'],
'jx': ['j', 'x'],
'jy': ['j', 'y'],
'jz': ['j', 'z'],
}
def __init__(self, locs, times, rxType, **kwargs):
SimPEG.Survey.BaseTimeRx.__init__(self, locs, times, rxType, **kwargs)
@property
def dc_voltage(self):
return self._dc_voltage
@property
def projField(self):
"""Field Type projection (e.g. e b ...)"""
return self.knownRxTypes[self.rxType][0]
def projGLoc(self, f):
"""Grid Location projection (e.g. Ex Fy ...)"""
comp = self.knownRxTypes[self.rxType][1]
if comp is not None:
return f._GLoc(self.rxType) + comp
return f._GLoc(self.rxType)
def getTimeP(self, timesall):
"""
Returns the time projection matrix.
.. note::
This is not stored in memory, but is created on demand.
"""
time_inds = np.in1d(timesall, self.times)
return time_inds
def eval(self, src, mesh, f):
P = self.getP(mesh, self.projGLoc(f))
return P * f[src, self.projField]
def evalDeriv(self, src, mesh, f, v, adjoint=False):
P = self.getP(mesh, self.projGLoc(f))
if not adjoint:
return P * v
elif adjoint:
return P.T * v
class BaseRx(survey.BaseRx):
"""
Frequency domain receiver base class
:param numpy.ndarray locations: receiver locations (ie. :code:`np.r_[x,y,z]`)
:param string orientation: receiver orientation 'x', 'y' or 'z'
:param string component: real or imaginary component 'real' or 'imag'
"""
orientation = properties.StringChoice(
"orientation of the receiver. Must currently be 'x', 'y', 'z'",
["x", "y", "z"])
component = properties.StringChoice(
"component of the field (real or imag)",
{
"real": ["re", "in-phase", "in phase"],
"imag": ["imaginary", "im", "out-of-phase", "out of phase"],
},
)
projComp = deprecate_property(
orientation,
"projComp",
new_name="orientation",
removal_version="0.16.0",
future_warn=True,
)
def __init__(self, locations, orientation=None, component=None, **kwargs):
proj = kwargs.pop("projComp", None)
if proj is not None:
self.projComp = proj
else:
self.orientation = orientation
self.component = component
super(BaseRx, self).__init__(locations, **kwargs)
def projGLoc(self, f):
"""Grid Location projection (e.g. Ex Fy ...)"""
return f._GLoc(self.projField) + self.orientation
def eval(self, src, mesh, f):
"""
Project fields to receivers to get data.
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: FDEM source
:param discretize.base.BaseMesh mesh: mesh used
:param Fields f: fields object
:rtype: numpy.ndarray
:return: fields projected to recievers
"""
P = self.getP(mesh, self.projGLoc(f))
f_part_complex = f[src, self.projField]
f_part = getattr(f_part_complex,
self.component) # real or imag component
return P * f_part
def evalDeriv(self, src, mesh, f, du_dm_v=None, v=None, adjoint=False):
"""
Derivative of projected fields with respect to the inversion model times a vector.
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: FDEM source
:param discretize.base.BaseMesh mesh: mesh used
:param Fields f: fields object
:param numpy.ndarray v: vector to multiply
:rtype: numpy.ndarray
:return: fields projected to recievers
"""
df_dmFun = getattr(f, "_{0}Deriv".format(self.projField), None)
assert v is not None, "v must be provided to compute the deriv or adjoint"
P = self.getP(mesh, self.projGLoc(f))
if not adjoint:
assert (
du_dm_v is not None
), "du_dm_v must be provided to evaluate the receiver deriv"
df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
Pv_complex = P * df_dm_v
Pv = getattr(Pv_complex, self.component)
return Pv
elif adjoint:
PTv_real = P.T * v
if self.component == "imag":
PTv = 1j * PTv_real
elif self.component == "real":
PTv = PTv_real.astype(complex)
else:
raise NotImplementedError("must be real or imag")
df_duT, df_dmT = df_dmFun(src, None, PTv, adjoint=True)
if self.component == "imag": # conjugate
df_duT *= -1
df_dmT *= -1
return df_duT, df_dmT
class Simulation3DIntegral(BasePFSimulation):
"""
magnetic simulation in integral form.
"""
chi, chiMap, chiDeriv = props.Invertible(
"Magnetic Susceptibility (SI)", default=1.0
)
modelType = properties.StringChoice(
"Type of magnetization model",
choices=["susceptibility", "vector"],
default="susceptibility",
)
is_amplitude_data = properties.Boolean(
"Whether the supplied data is amplitude data", default=False
)
def __init__(self, mesh, **kwargs):
super().__init__(mesh, **kwargs)
self._G = None
self._M = None
self._gtg_diagonal = None
self.modelMap = self.chiMap
setKwargs(self, **kwargs)
@property
def M(self):
"""
M: ndarray
Magnetization matrix
"""
if getattr(self, "_M", None) is None:
if self.modelType == "vector":
self._M = sp.identity(self.nC) * self.survey.source_field.parameters[0]
else:
mag = mat_utils.dip_azimuth2cartesian(
np.ones(self.nC) * self.survey.source_field.parameters[1],
np.ones(self.nC) * self.survey.source_field.parameters[2],
)
self._M = sp.vstack(
(
sdiag(mag[:, 0] * self.survey.source_field.parameters[0]),
sdiag(mag[:, 1] * self.survey.source_field.parameters[0]),
sdiag(mag[:, 2] * self.survey.source_field.parameters[0]),
)
)
return self._M
@M.setter
def M(self, M):
"""
Create magnetization matrix from unit vector orientation
:parameter
M: array (3*nC,) or (nC, 3)
"""
if self.modelType == "vector":
self._M = sdiag(mkvc(M) * self.survey.source_field.parameters[0])
else:
M = M.reshape((-1, 3))
self._M = sp.vstack(
(
sdiag(M[:, 0] * self.survey.source_field.parameters[0]),
sdiag(M[:, 1] * self.survey.source_field.parameters[0]),
sdiag(M[:, 2] * self.survey.source_field.parameters[0]),
)
)
def fields(self, model):
model = self.chiMap * model
if self.store_sensitivities == "forward_only":
self.model = model
fields = mkvc(self.linear_operator())
else:
fields = np.asarray(self.G @ model.astype(np.float32))
if self.is_amplitude_data:
fields = self.compute_amplitude(fields)
return fields
@property
def G(self):
if getattr(self, "_G", None) is None:
self._G = self.linear_operator()
return self._G
@property
def nD(self):
"""
Number of data
"""
self._nD = self.survey.receiver_locations.shape[0]
return self._nD
@property
def tmi_projection(self):
if getattr(self, "_tmi_projection", None) is None:
# Convert from north to cartesian
self._tmi_projection = mat_utils.dip_azimuth2cartesian(
self.survey.source_field.parameters[1],
self.survey.source_field.parameters[2],
)
return self._tmi_projection
def getJtJdiag(self, m, W=None):
"""
Return the diagonal of JtJ
"""
self.model = m
if W is None:
W = np.ones(self.nD)
else:
W = W.diagonal() ** 2
if getattr(self, "_gtg_diagonal", None) is None:
diag = np.zeros(self.G.shape[1])
if not self.is_amplitude_data:
for i in range(len(W)):
diag += W[i] * (self.G[i] * self.G[i])
else:
fieldDeriv = self.fieldDeriv
Gx = self.G[::3]
Gy = self.G[1::3]
Gz = self.G[2::3]
for i in range(len(W)):
row = (
fieldDeriv[0, i] * Gx[i]
+ fieldDeriv[1, i] * Gy[i]
+ fieldDeriv[2, i] * Gz[i]
)
diag += W[i] * (row * row)
self._gtg_diagonal = diag
else:
diag = self._gtg_diagonal
return mkvc((sdiag(np.sqrt(diag)) @ self.chiDeriv).power(2).sum(axis=0))
def Jvec(self, m, v, f=None):
if self.chi is None:
self.model = np.zeros(self.G.shape[1])
dmu_dm_v = self.chiDeriv @ v
Jvec = self.G @ dmu_dm_v.astype(np.float32)
if self.is_amplitude_data:
Jvec = Jvec.reshape((-1, 3)).T
fieldDeriv_Jvec = self.fieldDeriv * Jvec
return fieldDeriv_Jvec[0] + fieldDeriv_Jvec[1] + fieldDeriv_Jvec[2]
else:
return Jvec
def Jtvec(self, m, v, f=None):
if self.chi is None:
self.model = np.zeros(self.G.shape[1])
if self.is_amplitude_data:
v = (self.fieldDeriv * v).T.reshape(-1)
Jtvec = self.G.T @ v.astype(np.float32)
return np.asarray(self.chiDeriv.T @ Jtvec)
@property
def fieldDeriv(self):
if self.chi is None:
self.model = np.zeros(self.G.shape[1])
if getattr(self, "_fieldDeriv", None) is None:
fields = np.asarray(self.G.dot((self.chiMap @ self.chi).astype(np.float32)))
b_xyz = self.normalized_fields(fields)
self._fieldDeriv = b_xyz
return self._fieldDeriv
@classmethod
def normalized_fields(cls, fields):
"""
Return the normalized B fields
"""
# Get field amplitude
amp = cls.compute_amplitude(fields.astype(np.float64))
return fields.reshape((3, -1), order="F") / amp[None, :]
@classmethod
def compute_amplitude(cls, b_xyz):
"""
Compute amplitude of the magnetic field
"""
amplitude = np.linalg.norm(b_xyz.reshape((3, -1), order="F"), axis=0)
return amplitude
def evaluate_integral(self, receiver_location, components):
"""
Load in the active nodes of a tensor mesh and computes the magnetic
forward relation between a cuboid and a given observation
location outside the Earth [obsx, obsy, obsz]
INPUT:
receiver_location: [obsx, obsy, obsz] nC x 3 Array
components: list[str]
List of magnetic components chosen from:
'bx', 'by', 'bz', 'bxx', 'bxy', 'bxz', 'byy', 'byz', 'bzz'
OUTPUT:
Tx = [Txx Txy Txz]
Ty = [Tyx Tyy Tyz]
Tz = [Tzx Tzy Tzz]
"""
# TODO: This should probably be converted to C
eps = 1e-8 # add a small value to the locations to avoid /0
rows = {component: np.zeros(3 * self.Xn.shape[0]) for component in components}
# number of cells in mesh
nC = self.Xn.shape[0]
# comp. pos. differences for tne, bsw nodes
dz2 = self.Zn[:, 1] - receiver_location[2] + eps
dz1 = self.Zn[:, 0] - receiver_location[2] + eps
dy2 = self.Yn[:, 1] - receiver_location[1] + eps
dy1 = self.Yn[:, 0] - receiver_location[1] + eps
dx2 = self.Xn[:, 1] - receiver_location[0] + eps
dx1 = self.Xn[:, 0] - receiver_location[0] + eps
# comp. squared diff
dx2dx2 = dx2 ** 2.0
dx1dx1 = dx1 ** 2.0
dy2dy2 = dy2 ** 2.0
dy1dy1 = dy1 ** 2.0
dz2dz2 = dz2 ** 2.0
dz1dz1 = dz1 ** 2.0
# 2D radius component squared of corner nodes
R1 = dy2dy2 + dx2dx2
R2 = dy2dy2 + dx1dx1
R3 = dy1dy1 + dx2dx2
R4 = dy1dy1 + dx1dx1
# radius to each cell node
r1 = np.sqrt(dz2dz2 + R2) + eps
r2 = np.sqrt(dz2dz2 + R1) + eps
r3 = np.sqrt(dz1dz1 + R1) + eps
r4 = np.sqrt(dz1dz1 + R2) + eps
r5 = np.sqrt(dz2dz2 + R3) + eps
r6 = np.sqrt(dz2dz2 + R4) + eps
r7 = np.sqrt(dz1dz1 + R4) + eps
r8 = np.sqrt(dz1dz1 + R3) + eps
# compactify argument calculations
arg1_ = dx1 + dy2 + r1
arg1 = dy2 + dz2 + r1
arg2 = dx1 + dz2 + r1
arg3 = dx1 + r1
arg4 = dy2 + r1
arg5 = dz2 + r1
arg6_ = dx2 + dy2 + r2
arg6 = dy2 + dz2 + r2
arg7 = dx2 + dz2 + r2
arg8 = dx2 + r2
arg9 = dy2 + r2
arg10 = dz2 + r2
arg11_ = dx2 + dy2 + r3
arg11 = dy2 + dz1 + r3
arg12 = dx2 + dz1 + r3
arg13 = dx2 + r3
arg14 = dy2 + r3
arg15 = dz1 + r3
arg16_ = dx1 + dy2 + r4
arg16 = dy2 + dz1 + r4
arg17 = dx1 + dz1 + r4
arg18 = dx1 + r4
arg19 = dy2 + r4
arg20 = dz1 + r4
arg21_ = dx2 + dy1 + r5
arg21 = dy1 + dz2 + r5
arg22 = dx2 + dz2 + r5
arg23 = dx2 + r5
arg24 = dy1 + r5
arg25 = dz2 + r5
arg26_ = dx1 + dy1 + r6
arg26 = dy1 + dz2 + r6
arg27 = dx1 + dz2 + r6
arg28 = dx1 + r6
arg29 = dy1 + r6
arg30 = dz2 + r6
arg31_ = dx1 + dy1 + r7
arg31 = dy1 + dz1 + r7
arg32 = dx1 + dz1 + r7
arg33 = dx1 + r7
arg34 = dy1 + r7
arg35 = dz1 + r7
arg36_ = dx2 + dy1 + r8
arg36 = dy1 + dz1 + r8
arg37 = dx2 + dz1 + r8
arg38 = dx2 + r8
arg39 = dy1 + r8
arg40 = dz1 + r8
if ("bxx" in components) or ("bzz" in components):
rows["bxx"] = np.zeros((1, 3 * nC))
rows["bxx"][0, 0:nC] = 2 * (
((dx1 ** 2 - r1 * arg1) / (r1 * arg1 ** 2 + dx1 ** 2 * r1 + eps))
- ((dx2 ** 2 - r2 * arg6) / (r2 * arg6 ** 2 + dx2 ** 2 * r2 + eps))
+ ((dx2 ** 2 - r3 * arg11) / (r3 * arg11 ** 2 + dx2 ** 2 * r3 + eps))
- ((dx1 ** 2 - r4 * arg16) / (r4 * arg16 ** 2 + dx1 ** 2 * r4 + eps))
+ ((dx2 ** 2 - r5 * arg21) / (r5 * arg21 ** 2 + dx2 ** 2 * r5 + eps))
- ((dx1 ** 2 - r6 * arg26) / (r6 * arg26 ** 2 + dx1 ** 2 * r6 + eps))
+ ((dx1 ** 2 - r7 * arg31) / (r7 * arg31 ** 2 + dx1 ** 2 * r7 + eps))
- ((dx2 ** 2 - r8 * arg36) / (r8 * arg36 ** 2 + dx2 ** 2 * r8 + eps))
)
rows["bxx"][0, nC : 2 * nC] = (
dx2 / (r5 * arg25 + eps)
- dx2 / (r2 * arg10 + eps)
+ dx2 / (r3 * arg15 + eps)
- dx2 / (r8 * arg40 + eps)
+ dx1 / (r1 * arg5 + eps)
- dx1 / (r6 * arg30 + eps)
+ dx1 / (r7 * arg35 + eps)
- dx1 / (r4 * arg20 + eps)
)
rows["bxx"][0, 2 * nC :] = (
dx1 / (r1 * arg4 + eps)
- dx2 / (r2 * arg9 + eps)
+ dx2 / (r3 * arg14 + eps)
- dx1 / (r4 * arg19 + eps)
+ dx2 / (r5 * arg24 + eps)
- dx1 / (r6 * arg29 + eps)
+ dx1 / (r7 * arg34 + eps)
- dx2 / (r8 * arg39 + eps)
)
rows["bxx"] /= 4 * np.pi
rows["bxx"] *= self.M
if ("byy" in components) or ("bzz" in components):
rows["byy"] = np.zeros((1, 3 * nC))
rows["byy"][0, 0:nC] = (
dy2 / (r3 * arg15 + eps)
- dy2 / (r2 * arg10 + eps)
+ dy1 / (r5 * arg25 + eps)
- dy1 / (r8 * arg40 + eps)
+ dy2 / (r1 * arg5 + eps)
- dy2 / (r4 * arg20 + eps)
+ dy1 / (r7 * arg35 + eps)
- dy1 / (r6 * arg30 + eps)
)
rows["byy"][0, nC : 2 * nC] = 2 * (
((dy2 ** 2 - r1 * arg2) / (r1 * arg2 ** 2 + dy2 ** 2 * r1 + eps))
- ((dy2 ** 2 - r2 * arg7) / (r2 * arg7 ** 2 + dy2 ** 2 * r2 + eps))
+ ((dy2 ** 2 - r3 * arg12) / (r3 * arg12 ** 2 + dy2 ** 2 * r3 + eps))
- ((dy2 ** 2 - r4 * arg17) / (r4 * arg17 ** 2 + dy2 ** 2 * r4 + eps))
+ ((dy1 ** 2 - r5 * arg22) / (r5 * arg22 ** 2 + dy1 ** 2 * r5 + eps))
- ((dy1 ** 2 - r6 * arg27) / (r6 * arg27 ** 2 + dy1 ** 2 * r6 + eps))
+ ((dy1 ** 2 - r7 * arg32) / (r7 * arg32 ** 2 + dy1 ** 2 * r7 + eps))
- ((dy1 ** 2 - r8 * arg37) / (r8 * arg37 ** 2 + dy1 ** 2 * r8 + eps))
)
rows["byy"][0, 2 * nC :] = (
dy2 / (r1 * arg3 + eps)
- dy2 / (r2 * arg8 + eps)
+ dy2 / (r3 * arg13 + eps)
- dy2 / (r4 * arg18 + eps)
+ dy1 / (r5 * arg23 + eps)
- dy1 / (r6 * arg28 + eps)
+ dy1 / (r7 * arg33 + eps)
- dy1 / (r8 * arg38 + eps)
)
rows["byy"] /= 4 * np.pi
rows["byy"] *= self.M
if "bzz" in components:
rows["bzz"] = -rows["bxx"] - rows["byy"]
if "bxy" in components:
rows["bxy"] = np.zeros((1, 3 * nC))
rows["bxy"][0, 0:nC] = 2 * (
((dx1 * arg4) / (r1 * arg1 ** 2 + (dx1 ** 2) * r1 + eps))
- ((dx2 * arg9) / (r2 * arg6 ** 2 + (dx2 ** 2) * r2 + eps))
+ ((dx2 * arg14) / (r3 * arg11 ** 2 + (dx2 ** 2) * r3 + eps))
- ((dx1 * arg19) / (r4 * arg16 ** 2 + (dx1 ** 2) * r4 + eps))
+ ((dx2 * arg24) / (r5 * arg21 ** 2 + (dx2 ** 2) * r5 + eps))
- ((dx1 * arg29) / (r6 * arg26 ** 2 + (dx1 ** 2) * r6 + eps))
+ ((dx1 * arg34) / (r7 * arg31 ** 2 + (dx1 ** 2) * r7 + eps))
- ((dx2 * arg39) / (r8 * arg36 ** 2 + (dx2 ** 2) * r8 + eps))
)
rows["bxy"][0, nC : 2 * nC] = (
dy2 / (r1 * arg5 + eps)
- dy2 / (r2 * arg10 + eps)
+ dy2 / (r3 * arg15 + eps)
- dy2 / (r4 * arg20 + eps)
+ dy1 / (r5 * arg25 + eps)
- dy1 / (r6 * arg30 + eps)
+ dy1 / (r7 * arg35 + eps)
- dy1 / (r8 * arg40 + eps)
)
rows["bxy"][0, 2 * nC :] = (
1 / r1 - 1 / r2 + 1 / r3 - 1 / r4 + 1 / r5 - 1 / r6 + 1 / r7 - 1 / r8
)
rows["bxy"] /= 4 * np.pi
rows["bxy"] *= self.M
if "bxz" in components:
rows["bxz"] = np.zeros((1, 3 * nC))
rows["bxz"][0, 0:nC] = 2 * (
((dx1 * arg5) / (r1 * (arg1 ** 2) + (dx1 ** 2) * r1 + eps))
- ((dx2 * arg10) / (r2 * (arg6 ** 2) + (dx2 ** 2) * r2 + eps))
+ ((dx2 * arg15) / (r3 * (arg11 ** 2) + (dx2 ** 2) * r3 + eps))
- ((dx1 * arg20) / (r4 * (arg16 ** 2) + (dx1 ** 2) * r4 + eps))
+ ((dx2 * arg25) / (r5 * (arg21 ** 2) + (dx2 ** 2) * r5 + eps))
- ((dx1 * arg30) / (r6 * (arg26 ** 2) + (dx1 ** 2) * r6 + eps))
+ ((dx1 * arg35) / (r7 * (arg31 ** 2) + (dx1 ** 2) * r7 + eps))
- ((dx2 * arg40) / (r8 * (arg36 ** 2) + (dx2 ** 2) * r8 + eps))
)
rows["bxz"][0, nC : 2 * nC] = (
1 / r1 - 1 / r2 + 1 / r3 - 1 / r4 + 1 / r5 - 1 / r6 + 1 / r7 - 1 / r8
)
rows["bxz"][0, 2 * nC :] = (
dz2 / (r1 * arg4 + eps)
- dz2 / (r2 * arg9 + eps)
+ dz1 / (r3 * arg14 + eps)
- dz1 / (r4 * arg19 + eps)
+ dz2 / (r5 * arg24 + eps)
- dz2 / (r6 * arg29 + eps)
+ dz1 / (r7 * arg34 + eps)
- dz1 / (r8 * arg39 + eps)
)
rows["bxz"] /= 4 * np.pi
rows["bxz"] *= self.M
if "byz" in components:
rows["byz"] = np.zeros((1, 3 * nC))
rows["byz"][0, 0:nC] = (
1 / r3 - 1 / r2 + 1 / r5 - 1 / r8 + 1 / r1 - 1 / r4 + 1 / r7 - 1 / r6
)
rows["byz"][0, nC : 2 * nC] = 2 * (
(((dy2 * arg5) / (r1 * (arg2 ** 2) + (dy2 ** 2) * r1 + eps)))
- (((dy2 * arg10) / (r2 * (arg7 ** 2) + (dy2 ** 2) * r2 + eps)))
+ (((dy2 * arg15) / (r3 * (arg12 ** 2) + (dy2 ** 2) * r3 + eps)))
- (((dy2 * arg20) / (r4 * (arg17 ** 2) + (dy2 ** 2) * r4 + eps)))
+ (((dy1 * arg25) / (r5 * (arg22 ** 2) + (dy1 ** 2) * r5 + eps)))
- (((dy1 * arg30) / (r6 * (arg27 ** 2) + (dy1 ** 2) * r6 + eps)))
+ (((dy1 * arg35) / (r7 * (arg32 ** 2) + (dy1 ** 2) * r7 + eps)))
- (((dy1 * arg40) / (r8 * (arg37 ** 2) + (dy1 ** 2) * r8 + eps)))
)
rows["byz"][0, 2 * nC :] = (
dz2 / (r1 * arg3 + eps)
- dz2 / (r2 * arg8 + eps)
+ dz1 / (r3 * arg13 + eps)
- dz1 / (r4 * arg18 + eps)
+ dz2 / (r5 * arg23 + eps)
- dz2 / (r6 * arg28 + eps)
+ dz1 / (r7 * arg33 + eps)
- dz1 / (r8 * arg38 + eps)
)
rows["byz"] /= 4 * np.pi
rows["byz"] *= self.M
if ("bx" in components) or ("tmi" in components):
rows["bx"] = np.zeros((1, 3 * nC))
rows["bx"][0, 0:nC] = (
(-2 * np.arctan2(dx1, arg1 + eps))
- (-2 * np.arctan2(dx2, arg6 + eps))
+ (-2 * np.arctan2(dx2, arg11 + eps))
- (-2 * np.arctan2(dx1, arg16 + eps))
+ (-2 * np.arctan2(dx2, arg21 + eps))
- (-2 * np.arctan2(dx1, arg26 + eps))
+ (-2 * np.arctan2(dx1, arg31 + eps))
- (-2 * np.arctan2(dx2, arg36 + eps))
)
rows["bx"][0, nC : 2 * nC] = (
np.log(arg5)
- np.log(arg10)
+ np.log(arg15)
- np.log(arg20)
+ np.log(arg25)
- np.log(arg30)
+ np.log(arg35)
- np.log(arg40)
)
rows["bx"][0, 2 * nC :] = (
(np.log(arg4) - np.log(arg9))
+ (np.log(arg14) - np.log(arg19))
+ (np.log(arg24) - np.log(arg29))
+ (np.log(arg34) - np.log(arg39))
)
rows["bx"] /= -4 * np.pi
rows["bx"] *= self.M
if ("by" in components) or ("tmi" in components):
rows["by"] = np.zeros((1, 3 * nC))
rows["by"][0, 0:nC] = (
np.log(arg5)
- np.log(arg10)
+ np.log(arg15)
- np.log(arg20)
+ np.log(arg25)
- np.log(arg30)
+ np.log(arg35)
- np.log(arg40)
)
rows["by"][0, nC : 2 * nC] = (
(-2 * np.arctan2(dy2, arg2 + eps))
- (-2 * np.arctan2(dy2, arg7 + eps))
+ (-2 * np.arctan2(dy2, arg12 + eps))
- (-2 * np.arctan2(dy2, arg17 + eps))
+ (-2 * np.arctan2(dy1, arg22 + eps))
- (-2 * np.arctan2(dy1, arg27 + eps))
+ (-2 * np.arctan2(dy1, arg32 + eps))
- (-2 * np.arctan2(dy1, arg37 + eps))
)
rows["by"][0, 2 * nC :] = (
(np.log(arg3) - np.log(arg8))
+ (np.log(arg13) - np.log(arg18))
+ (np.log(arg23) - np.log(arg28))
+ (np.log(arg33) - np.log(arg38))
)
rows["by"] /= -4 * np.pi
rows["by"] *= self.M
if ("bz" in components) or ("tmi" in components):
rows["bz"] = np.zeros((1, 3 * nC))
rows["bz"][0, 0:nC] = (
np.log(arg4)
- np.log(arg9)
+ np.log(arg14)
- np.log(arg19)
+ np.log(arg24)
- np.log(arg29)
+ np.log(arg34)
- np.log(arg39)
)
rows["bz"][0, nC : 2 * nC] = (
(np.log(arg3) - np.log(arg8))
+ (np.log(arg13) - np.log(arg18))
+ (np.log(arg23) - np.log(arg28))
+ (np.log(arg33) - np.log(arg38))
)
rows["bz"][0, 2 * nC :] = (
(-2 * np.arctan2(dz2, arg1_ + eps))
- (-2 * np.arctan2(dz2, arg6_ + eps))
+ (-2 * np.arctan2(dz1, arg11_ + eps))
- (-2 * np.arctan2(dz1, arg16_ + eps))
+ (-2 * np.arctan2(dz2, arg21_ + eps))
- (-2 * np.arctan2(dz2, arg26_ + eps))
+ (-2 * np.arctan2(dz1, arg31_ + eps))
- (-2 * np.arctan2(dz1, arg36_ + eps))
)
rows["bz"] /= -4 * np.pi
rows["bz"] *= self.M
if "tmi" in components:
rows["tmi"] = np.dot(
self.tmi_projection, np.r_[rows["bx"], rows["by"], rows["bz"]]
)
return np.vstack([rows[component] for component in components])
@property
def deleteTheseOnModelUpdate(self):
deletes = super().deleteTheseOnModelUpdate
if self.is_amplitude_data:
deletes += ["_gtg_diagonal"]
return deletes
@property
def coordinate_system(self):
raise AttributeError(
"The coordinate_system property has been removed. "
"Instead make use of `SimPEG.maps.SphericalSystem`."
)
class IO(properties.HasProperties):
"""
"""
# Survey
survey_layout = properties.StringChoice(
"Survey geometry of DC surveys",
default="SURFACE",
choices=["SURFACE", "BOREHOLE", "GENERAL"])
survey_type = properties.StringChoice(
"DC-IP Survey type",
default="dipole-dipole",
choices=["dipole-dipole", "pole-dipole", "dipole-pole", "pole-pole"])
dimension = properties.Integer("Dimension of electrode locations",
default=2,
required=True)
a_locations = properties.Array(
"locations of the positive (+) current electrodes",
required=True,
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
b_locations = properties.Array(
"locations of the negative (-) current electrodes",
required=True,
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
m_locations = properties.Array(
"locations of the positive (+) potential electrodes",
required=True,
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
n_locations = properties.Array(
"locations of the negative (-) potential electrodes",
required=True,
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
electrode_locations = properties.Array(
"unique locations of a, b, m, n electrodes",
required=True,
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
# Data
data_dc_type = properties.StringChoice("Type of DC-IP survey",
required=True,
default="volt",
choices=[
"volt",
"apparent_resistivity",
"apparent_conductivity",
])
data_dc = properties.Array(
"Measured DC data",
shape=('*', ),
dtype=float # data are floats
)
data_ip_type = properties.StringChoice("Type of DC-IP survey",
required=True,
default="volt",
choices=[
"volt",
"apparent_chargeability",
])
data_ip = properties.Array(
"Measured IP data",
shape=('*', ),
dtype=float # data are floats
)
data_sip_type = properties.StringChoice("Type of DC-IP survey",
required=True,
default="volt",
choices=[
"volt",
"apparent_chargeability",
])
data_sip = properties.Array(
"Measured Spectral IP data",
shape=('*', '*'),
dtype=float # data are floats
)
times_ip = properties.Array(
"Time channels of measured Spectral IP voltages (s)",
required=True,
shape=('*', ),
dtype=float # data are floats
)
G = properties.Array(
"Geometric factor of DC-IP survey",
shape=('*', ),
dtype=float # data are floats
)
grids = properties.Array(
"Spatial grids for plotting pseudo-section",
shape=('*', '*'),
dtype=float # data are floats
)
space_type = properties.StringChoice(
"Assumption to compute apparent resistivity",
default="half-space",
choices=["half-space", "whole-space"])
line_inds = properties.Array(
"Line indices",
required=True,
shape=('*', ),
dtype=int # data are floats
)
sort_inds = properties.Array(
"Sorting indices from ABMN",
required=True,
shape=('*', ),
dtype=int # data are floats
)
# Related to Physics and Discretization
mesh = properties.Instance("Mesh for discretization",
BaseMesh,
required=True)
dx = properties.Float(
"Length of corecell in x-direction",
required=True,
)
dy = properties.Float("Length of corecell in y-direction", required=True)
dy = properties.Float("Length of corecell in z-direction", required=True)
npad_x = properties.Integer("The number of padding cells x-direction",
required=True,
default=5)
npad_y = properties.Integer("The number of padding cells y-direction",
required=True,
default=5)
npad_z = properties.Integer("The number of padding cells z-direction",
required=True,
default=5)
pad_rate_x = properties.Float(
"Expansion rate of padding cells in x-direction",
required=True,
default=1.3)
pad_rate_y = properties.Float(
"Expansion rate of padding cells in y-direction",
required=True,
default=1.3)
pad_rate_z = properties.Float(
"Expansion rate of padding cells in z-direction",
required=True,
default=1.3)
ncell_per_dipole = properties.Integer(
"The number of cells between dipole electrodes",
required=True,
default=4)
# For synthetic surveys
x0 = None
lineLength = None
a = None
n_spacing = None
n_data = None
def __init__(self, **kwargs):
super(IO, self).__init__(**kwargs)
warnings.warn(
"code under construction - API might change in the future")
# Properties
@property
def voltages(self):
"""
Votages (V)
"""
if self.data_dc_type.lower() == "volt":
return self.data_dc
elif self.data_dc_type.lower() == "apparent_resistivity":
return self.data_dc * self.G
elif self.data_dc_type.lower() == "apparent_conductivity":
return self.apparent_conductivity / (self.data_dc * self.G)
else:
raise NotImplementedError()
@property
def apparent_resistivity(self):
"""
Apparent Resistivity (Ohm-m)
"""
if self.data_dc_type.lower() == "apparent_resistivity":
return self.data_dc
elif self.data_dc_type.lower() == "volt":
return self.data_dc / self.G
elif self.data_dc_type.lower() == "apparent_conductivity":
return 1. / self.data_dc
else:
print(self.data_dc_type.lower())
raise NotImplementedError()
@property
def apparent_conductivity(self):
"""
Apparent Conductivity (S/m)
"""
if self.data_dc_type.lower() == "apparent_conductivity":
return self.data_dc
elif self.data_dc_type.lower() == "apparent_resistivity":
return 1. / self.data_dc
elif self.data_dc_type.lower() == "volt":
return 1. / self.data_dc * self.G
# For IP
@property
def voltages_ip(self):
"""
IP votages (V)
"""
if self.data_ip_type.lower() == "volt":
return self.data_ip
elif self.data_ip_type.lower() == "apparent_chargeability":
if self.voltages is None:
raise Exception(
"DC voltages must be set to compute IP voltages")
return self.data_ip * self.voltages
else:
raise NotImplementedError()
# For SIP
@property
def voltages_sip(self):
"""
IP votages (V)
"""
if self.data_sip_type.lower() == "volt":
return self.data_sip
elif self.data_sip_type.lower() == "apparent_chargeability":
if self.voltages is None:
raise Exception(
"DC voltages must be set to compute IP voltages")
return Utils.sdiag(self.voltages) * self.data_sip
else:
raise NotImplementedError()
@property
def apparent_chargeability(self):
"""
Apparent Conductivity (S/m)
"""
if self.data_ip_type.lower() == "apparent_chargeability":
return self.data_ip
elif self.data_ip_type.lower() == "volt":
if self.voltages is None:
raise Exception(
"DC voltages must be set to compute Apparent Chargeability"
)
return self.data_ip / self.voltages
else:
raise NotImplementedError()
# For SIP
@property
def apparent_chargeability_sip(self):
"""
Apparent Conductivity (S/m)
"""
if self.data_sip_type.lower() == "apparent_chargeability":
return self.data_sip
elif self.data_sip_type.lower() == "volt":
if self.voltages is None:
raise Exception(
"DC voltages must be set to compute Apparent Chargeability"
)
return Utils.sdiag(1. / self.voltages) * self.data_sip
else:
raise NotImplementedError()
def geometric_factor(self, survey):
"""
Compute geometric factor, G, using locational informaition
in survey object
"""
geometric_factor = SimPEG.EM.Static.Utils.StaticUtils.geometric_factor
G = geometric_factor(survey,
survey_type=self.survey_type,
space_type=self.space_type)
return G
def from_ambn_locations_to_survey(self,
a_locations,
b_locations,
m_locations,
n_locations,
survey_type=None,
data_dc=None,
data_ip=None,
data_sip=None,
data_dc_type="volt",
data_ip_type="volt",
data_sip_type="volt",
fname=None,
dimension=2,
line_inds=None,
times_ip=None):
"""
read A, B, M, N electrode location and data (V or apparent_resistivity)
"""
self.a_locations = a_locations.copy()
self.b_locations = b_locations.copy()
self.m_locations = m_locations.copy()
self.n_locations = n_locations.copy()
self.survey_type = survey_type
self.dimension = dimension
self.data_dc_type = data_dc_type
self.data_ip_type = data_ip_type
self.data_sip_type = data_sip_type
if times_ip is not None:
self.times_ip = times_ip
uniqSrc = Utils.uniqueRows(np.c_[self.a_locations, self.b_locations])
uniqElec = SimPEG.Utils.uniqueRows(
np.vstack((self.a_locations, self.b_locations, self.m_locations,
self.n_locations)))
self.electrode_locations = uniqElec[0]
nSrc = uniqSrc[0].shape[0]
ndata = self.a_locations.shape[0]
if self.survey_layout == "SURFACE":
# 2D locations
srcLists = []
sort_inds = []
for iSrc in range(nSrc):
inds = uniqSrc[2] == iSrc
sort_inds.append(np.arange(ndata)[inds])
locsM = self.m_locations[inds, :]
locsN = self.n_locations[inds, :]
if dimension == 2:
if survey_type in ['dipole-dipole', 'pole-dipole']:
rx = Rx.Dipole_ky(locsM, locsN)
elif survey_type in ['dipole-pole', 'pole-pole']:
rx = Rx.Pole_ky(locsM)
elif dimension == 3:
if survey_type in ['dipole-dipole', 'pole-dipole']:
rx = Rx.Dipole(locsM, locsN)
elif survey_type in ['dipole-pole', 'pole-pole']:
rx = Rx.Pole(locsM)
else:
raise NotImplementedError()
if dimension == 2:
locA = uniqSrc[0][iSrc, :2]
locB = uniqSrc[0][iSrc, 2:]
elif dimension == 3:
locA = uniqSrc[0][iSrc, :3]
locB = uniqSrc[0][iSrc, 3:]
if survey_type in ['dipole-dipole', 'dipole-pole']:
src = Src.Dipole([rx], locA, locB)
elif survey_type in ['pole-dipole', 'pole-pole']:
src = Src.Pole([rx], locA)
srcLists.append(src)
self.sort_inds = np.hstack(sort_inds)
if dimension == 2:
survey = Survey_ky(srcLists)
elif dimension == 3:
survey = Survey(srcLists)
else:
raise NotImplementedError()
self.a_locations = self.a_locations[self.sort_inds, :]
self.b_locations = self.b_locations[self.sort_inds, :]
self.m_locations = self.m_locations[self.sort_inds, :]
self.n_locations = self.n_locations[self.sort_inds, :]
self.G = self.geometric_factor(survey)
if data_dc is not None:
self.data_dc = data_dc[self.sort_inds]
if data_ip is not None:
self.data_ip = data_ip[self.sort_inds]
if data_sip is not None:
self.data_sip = data_sip[self.sort_inds, :]
if line_inds is not None:
self.line_inds = line_inds[self.sort_inds]
# Here we ignore ... z-locations
self.n_data = survey.nD
midABx = (self.a_locations[:, 0] + self.b_locations[:, 0]) * 0.5
midMNx = (self.m_locations[:, 0] + self.n_locations[:, 0]) * 0.5
if dimension == 2:
z = abs(midABx - midMNx) * 1. / 3.
x = (midABx + midMNx) * 0.5
self.grids = np.c_[x, z]
elif dimension == 3:
midABy = (self.a_locations[:, 1] +
self.b_locations[:, 1]) * 0.5
midMNy = (self.m_locations[:, 1] +
self.n_locations[:, 1]) * 0.5
z = np.sqrt((midABx - midMNx)**2 +
(midABy - midMNy)**2) * 1. / 3.
x = (midABx + midMNx) * 0.5
y = (midABy + midMNy) * 0.5
self.grids = np.c_[x, y, z]
else:
raise Exception()
else:
raise NotImplementedError()
return survey
def set_mesh(self,
topo=None,
dx=None,
dy=None,
dz=None,
n_spacing=None,
corezlength=None,
npad_x=7,
npad_y=7,
npad_z=7,
pad_rate_x=1.3,
pad_rate_y=1.3,
pad_rate_z=1.3,
ncell_per_dipole=4,
mesh_type='TensorMesh',
dimension=2,
method='nearest'):
"""
Set up a mesh for a given DC survey
"""
if mesh_type == 'TreeMesh':
raise NotImplementedError()
# 2D or 3D mesh
if dimension in [2, 3]:
if dimension == 2:
z_ind = 1
else:
z_ind = 2
a = abs(np.diff(np.sort(self.electrode_locations[:, 0]))).min()
lineLength = abs(self.electrode_locations[:, 0].max() -
self.electrode_locations[:, 0].min())
dx_ideal = a / ncell_per_dipole
if dx is None:
dx = dx_ideal
warnings.warn(
"dx is set to {} m (samllest electrode spacing ({}) / {})".
format(dx, a, ncell_per_dipole))
if dz is None:
dz = dx * 0.5
warnings.warn("dz ({} m) is set to dx ({} m) / {}".format(
dz, dx, 2))
x0 = self.electrode_locations[:, 0].min()
if topo is None:
locs = np.sort(self.electrode_locations, axis=0)
else:
locs = np.vstack((topo, self.electrode_locations))
if dx > dx_ideal:
# warnings.warn(
# "Input dx ({}) is greater than expected \n We recommend using {:0.1e} m cells, that is, {} cells per {0.1e} m dipole length".format(dx, dx_ideal, ncell_per_dipole, a)
# )
pass
self.dx = dx
self.dz = dz
self.npad_x = npad_x
self.npad_z = npad_z
self.pad_rate_x = pad_rate_x
self.pad_rate_z = pad_rate_z
self.ncell_per_dipole = ncell_per_dipole
zmax = locs[:, z_ind].max()
zmin = locs[:, z_ind].min()
# 3 cells each for buffer
corexlength = lineLength + dx * 6
if corezlength is None:
corezlength = self.grids[:, z_ind].max()
ncx = np.round(corexlength / dx)
ncz = np.round(corezlength / dz)
hx = [(dx, npad_x, -pad_rate_x), (dx, ncx),
(dx, npad_x, pad_rate_x)]
hz = [(dz, npad_z, -pad_rate_z), (dz, ncz)]
x0_mesh = -(
(dx * pad_rate_x**(np.arange(npad_x) + 1)).sum() + dx * 3 - x0)
z0_mesh = -((dz * pad_rate_z**
(np.arange(npad_z) + 1)).sum() + dz * ncz) + zmax
# For 2D mesh
if dimension == 2:
h = [hx, hz]
x0_for_mesh = [x0_mesh, z0_mesh]
self.xyzlim = np.vstack(
(np.r_[x0, x0 + lineLength], np.r_[zmax - corezlength,
zmax]))
fill_value = "extrapolate"
# For 3D mesh
else:
if dy is None:
raise Exception("You must input dy (m)")
self.dy = dy
self.npad_y = npad_y
self.pad_rate_y = pad_rate_y
ylocs = np.unique(self.electrode_locations[:, 1])
ymin, ymax = ylocs.min(), ylocs.max()
# 3 cells each for buffer in y-direction
coreylength = ymax - ymin + dy * 6
ncy = np.round(coreylength / dy)
hy = [(dy, npad_y, -pad_rate_y), (dy, ncy),
(dy, npad_y, pad_rate_y)]
y0 = ylocs.min() - dy / 2.
y0_mesh = -((dy * pad_rate_y**(np.arange(npad_y) + 1)).sum() +
dy * 3 - y0)
h = [hx, hy, hz]
x0_for_mesh = [x0_mesh, y0_mesh, z0_mesh]
self.xyzlim = np.vstack(
(np.r_[x0, x0 + lineLength], np.r_[ymin - dy * 3,
ymax + dy * 3],
np.r_[zmax - corezlength, zmax]))
fill_value = np.nan
mesh = Mesh.TensorMesh(h, x0=x0_for_mesh)
actind = Utils.surface2ind_topo(mesh,
locs,
method=method,
fill_value=fill_value)
else:
raise NotImplementedError()
return mesh, actind
def plotPseudoSection(
self,
data_type="apparent_resistivity",
data=None,
dataloc=True,
aspect_ratio=2,
scale="log",
cmap="viridis",
ncontour=10,
ax=None,
figname=None,
clim=None,
label=None,
iline=0,
):
"""
Plot 2D pseudo-section for DC-IP data
"""
matplotlib.rcParams['font.size'] = 12
if ax is None:
fig = plt.figure(figsize=(10, 5))
ax = plt.subplot(111)
if self.dimension == 2:
inds = np.ones(self.n_data, dtype=bool)
grids = self.grids.copy()
elif self.dimension == 3:
inds = self.line_inds == iline
grids = self.grids[inds, :][:, [0, 2]]
else:
raise NotImplementedError()
if data_type == "apparent_resistivity":
if data is None:
val = self.apparent_resistivity[inds]
else:
val = data.copy()[inds]
label = "Apparent Res. ($\Omega$m)"
elif data_type == "volt":
if data is None:
val = self.voltages[inds]
else:
val = data.copy()[inds]
label = "Voltage (V)"
elif data_type == "apparent_conductivity":
if data is None:
val = self.apparent_conductivity[inds]
else:
val = data.copy()[inds]
label = "Apparent Cond. (S/m)"
elif data_type == "apparent_chargeability":
if data is not None:
val = data.copy()[inds]
else:
val = self.apparent_chargeability.copy()[inds] * 1e3
label = "Apparent Charg. (mV/V)"
elif data_type == "volt_ip":
if data is not None:
val = data.copy()[inds]
else:
val = self.voltages_ip.copy()[inds] * 1e3
label = "Secondary voltage. (mV)"
else:
print(data_type)
raise NotImplementedError()
if scale == "log":
fmt = "10$^{%.1f}$"
elif scale == "linear":
fmt = "%.1e"
else:
raise NotImplementedError()
out = Utils.plot2Ddata(grids,
val,
contourOpts={'cmap': cmap},
ax=ax,
dataloc=dataloc,
scale=scale,
ncontour=ncontour,
clim=clim)
ax.invert_yaxis()
ax.set_xlabel("x (m)")
ax.set_yticklabels([])
ax.set_ylabel("n-spacing")
cb = plt.colorbar(out[0], fraction=0.01, format=fmt, ax=ax)
cb.set_label(label)
cb.set_ticks(out[0].levels)
ax.set_aspect(aspect_ratio)
plt.tight_layout()
if figname is not None:
fig.savefig(figname, dpi=200)
class Simulation2DNodal(BaseDCSimulation2D):
"""
2.5D nodal DC problem
"""
_solutionType = "phiSolution"
_formulation = "EB" # CC potentials means J is on faces
fieldsPair = Fields2DNodal
fieldsPair_fwd = Fields3DNodal
_gradT = None
bc_type = properties.StringChoice(
"Type of boundary condition to use for simulation. Note that Robin and Mixed "
"are equivalent.",
choices=["Neumann", "Robin", "Mixed"],
default="Robin",
)
def __init__(self, mesh, **kwargs):
BaseDCSimulation2D.__init__(self, mesh, **kwargs)
self.solver_opts["is_symmetric"] = True
self.solver_opts["is_positive_definite"] = True
def getA(self, ky):
"""
Make the A matrix for the cell centered DC resistivity problem
A = D MfRhoI G
"""
# To handle Mixed boundary condition
self.setBC(ky=ky)
MeSigma = self.MeSigma
MnSigma = self.MnSigma
Grad = self.mesh.nodalGrad
if self._gradT is None:
self._gradT = Grad.T.tocsr() # cache the .tocsr()
GradT = self._gradT
A = GradT * MeSigma * Grad + ky**2 * MnSigma
if self.bc_type != "Neumann":
try:
A = A + sdiag(self._AvgBC[ky] @ self.sigma)
except ValueError as err:
if len(self.sigma) != len(self.mesh):
raise NotImplementedError(
"Anisotropic conductivity is not supported for Robin boundary "
"conditions, please use 'Neumann'.")
else:
raise err
return A
def getADeriv(self, ky, u, v, adjoint=False):
Grad = self.mesh.nodalGrad
if adjoint:
out = self.MeSigmaDeriv(
Grad * u.flatten(), Grad * v, adjoint=adjoint
) + ky**2 * self.MnSigmaDeriv(u.flatten(), v, adjoint=adjoint)
else:
out = Grad.T * self.MeSigmaDeriv(
Grad * u.flatten(), v, adjoint=adjoint
) + ky**2 * self.MnSigmaDeriv(u.flatten(), v, adjoint=adjoint)
if self.bc_type != "Neumann" and self.sigmaMap is not None:
if getattr(self, "_MBC_sigma", None) is None:
self._MBC_sigma = {}
if ky not in self._MBC_sigma:
self._MBC_sigma[ky] = self._AvgBC[ky] @ self.sigmaDeriv
if not isinstance(u, Zero):
u = u.flatten()
if v.ndim > 1:
u = u[:, None]
if not adjoint:
out += u * (self._MBC_sigma[ky] @ v)
else:
out += self._MBC_sigma[ky].T @ (u * v)
return out
def getRHS(self, ky):
"""
RHS for the DC problem
q
"""
RHS = self.getSourceTerm(ky)
return RHS
def getRHSDeriv(self, ky, src, v, adjoint=False):
"""
Derivative of the right hand side with respect to the model
"""
# TODO: add qDeriv for RHS depending on m
# qDeriv = src.evalDeriv(self, ky, adjoint=adjoint)
# return qDeriv
return Zero()
def setBC(self, ky=None):
if self.bc_type == "Dirichlet":
# do nothing
raise ValueError(
"Dirichlet conditions are not supported in the Nodal formulation"
)
elif self.bc_type == "Neumann":
if self.verbose:
print(
"Homogeneous Neumann is the natural BC for this Nodal discretization."
)
return
else:
if getattr(self, "_AvgBC", None) is None:
self._AvgBC = {}
if ky in self._AvgBC:
return
mesh = self.mesh
# calculate alpha, beta, gamma at the boundary faces
boundary_faces = mesh.boundary_faces
boundary_normals = mesh.boundary_face_outward_normals
n_bf = len(boundary_faces)
alpha = np.zeros(n_bf)
# assume a source point at the middle of the top of the mesh
middle = np.median(mesh.nodes, axis=0)
top_v = np.max(mesh.nodes[:, -1])
source_point = np.r_[middle[:-1], top_v]
r_vec = boundary_faces - source_point
r = np.linalg.norm(r_vec, axis=-1)
r_hat = r_vec / r[:, None]
r_dot_n = np.einsum("ij,ij->i", r_hat, boundary_normals)
# determine faces that are on the sides and bottom of the mesh...
if mesh._meshType.lower() == "tree":
not_top = boundary_faces[:, -1] != top_v
else:
# mesh faces are ordered, faces_x, faces_y, faces_z so...
is_b = make_boundary_bool(mesh.shape_faces_y)
is_t = np.zeros(mesh.shape_faces_y, dtype=bool, order="F")
is_t[:, -1] = True
is_t = is_t.reshape(-1, order="F")[is_b]
not_top = np.zeros(boundary_faces.shape[0], dtype=bool)
not_top[-len(is_t):] = ~is_t
# use the exponentiall scaled modified bessel function of second kind,
# (the division will cancel out the scaling)
# This is more stable for large values of ky * r
# actual ratio is k1/k0...
alpha[not_top] = (ky * k1e(ky * r) / k0e(ky * r) *
r_dot_n)[not_top]
P_bf = self.mesh.project_face_to_boundary_face
AvgN2Fb = P_bf @ self.mesh.average_node_to_face
AvgCC2Fb = P_bf @ self.mesh.average_cell_to_face
AvgCC2Fb = sdiag(alpha * (P_bf @ self.mesh.face_areas)) @ AvgCC2Fb
self._AvgBC[ky] = AvgN2Fb.T @ AvgCC2Fb
class Point1DImpedance(BaseRx):
"""
Natural source 1D impedance receiver class
:param string component: real or imaginary component 'real' or 'imag'
"""
component = properties.StringChoice(
"component of the field (real or imag)",
{
"real": ["re", "in-phase", "in phase"],
"imag": ["imaginary", "im", "out-of-phase", "out of phase"],
},
)
orientation = "yx"
def __init__(self, locs, component="real"):
self.component = component
BaseRx.__init__(self, locs)
@property
def mesh(self):
return self._mesh
@mesh.setter
def mesh(self, value):
if value is getattr(self, "_mesh", None):
pass
else:
self._mesh = value
# Utility for convienece
def _sDiag(self, t):
return sdiag(mkvc(t, 2))
@property
def src(self):
return self._src
@src.setter
def src(self, value):
self._src = value
@property
def f(self):
return self._f
@f.setter
def f(self, value):
self._f = value
@property
def Pex(self):
if getattr(self, "_Pex", None) is None:
self._Pex = self._mesh.getInterpolationMat(self.locations[:, -1],
"Fx")
return self._Pex
@property
def Pbx(self):
if getattr(self, "_Pbx", None) is None:
self._Pbx = self._mesh.getInterpolationMat(self.locations[:, -1],
"Ex")
return self._Pbx
@property
def _ex(self):
return self.Pex * mkvc(self.f[self.src, "e_1d"], 2)
@property
def _hx(self):
return self.Pbx * mkvc(self.f[self.src, "b_1d"], 2) / mu_0
def _ex_u(self, v):
return self.Pex * self.f._eDeriv_u(self.src, v)
def _hx_u(self, v):
return self.Pbx * self.f._bDeriv_u(self.src, v) / mu_0
def _aex_u(self, v):
return self.f._eDeriv_u(self.src, self.Pex.T * v, adjoint=True)
def _ahx_u(self, v):
return self.f._bDeriv_u(self.src, self.Pbx.T * v, adjoint=True) / mu_0
@property
def _Hd(self):
return self._sDiag(1.0 / self._hx)
def eval(self, src, mesh, f, return_complex=False):
"""
Project the fields to natural source data.
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: NSEM source
:param discretize.TensorMesh mesh: Mesh defining the topology of the problem
:param SimPEG.electromagnetics.frequency_domain.fields.FieldsFDEM f: NSEM fields object of the source
:param bool (optional) return_complex: Flag for return the complex evaluation
:rtype: numpy.ndarray
:return: Evaluated data for the receiver
"""
# NOTE: Maybe set this as a property
self.src = src
self.mesh = mesh
self.f = f
rx_eval_complex = -self._Hd * self._ex
# Return the full impedance
if return_complex:
return rx_eval_complex
return getattr(rx_eval_complex, self.component)
def evalDeriv(self, src, mesh, f, v, adjoint=False):
"""method evalDeriv
The derivative of the projection wrt u
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: NSEM source
:param discretize.TensorMesh mesh: Mesh defining the topology of the problem
:param SimPEG.electromagnetics.frequency_domain.fields.FieldsFDEM f: NSEM fields object of the source
:param numpy.ndarray v: vector of size (nU,) (adjoint=False) and size (nD,) (adjoint=True)
:rtype: numpy.ndarray
:return: Calculated derivative (nD,) (adjoint=False) and (nP,2) (adjoint=True) for both polarizations
"""
self.src = src
self.mesh = mesh
self.f = f
if adjoint:
Z1d = self.eval(src, mesh, f, True)
def aZ_N_uV(x):
return -self._aex_u(x)
def aZ_D_uV(x):
return self._ahx_u(x)
rx_deriv = aZ_N_uV(self._Hd.T * v) - aZ_D_uV(
self._sDiag(Z1d).T * self._Hd.T * v)
if self.component == "imag":
rx_deriv_component = 1j * rx_deriv
elif self.component == "real":
rx_deriv_component = rx_deriv.astype(complex)
else:
Z1d = self.eval(src, mesh, f, True)
Z_N_uV = -self._ex_u(v)
Z_D_uV = self._hx_u(v)
# Evaluate
rx_deriv = self._Hd * (Z_N_uV - self._sDiag(Z1d) * Z_D_uV)
rx_deriv_component = np.array(getattr(rx_deriv, self.component))
return rx_deriv_component
class BaseEM1DSurvey(Survey.BaseSurvey, properties.HasProperties):
"""
Base EM1D Survey
"""
frequency = properties.Array("Frequency (Hz)", dtype=float)
rx_location = properties.Array("Receiver location (x, y, z)", dtype=float)
src_location = properties.Array("Source location (x, y, z)", dtype=float)
src_path = properties.Array(
"Source path (xi, yi, zi), i=0,...N",
dtype=float
)
src_type = properties.StringChoice(
"Source type",
default="VMD",
choices=[
"VMD", "CircularLoop", "piecewise_segment"
]
)
offset = properties.Array("Src-Rx offsets", dtype=float)
rx_type = properties.StringChoice(
"Source location",
default="Hz",
choices=["Hz", "ppm", "Bz", "dBzdt"]
)
field_type = properties.StringChoice(
"Field type",
default="secondary",
choices=["total", "secondary"]
)
depth = properties.Array("Depth of the layers", dtype=float)
topo = properties.Array("Topography (x, y, z)", dtype=float)
I = properties.Float("Src loop current", default=1.)
a = properties.Float("Src loop radius", default=1.)
half_switch = properties.Bool("Switch for half-space", default=False)
def __init__(self, **kwargs):
Survey.BaseSurvey.__init__(self, **kwargs)
@property
def h(self):
"""
Source height
"""
return self.src_location[2]-self.topo[2]
@property
def z(self):
"""
Receiver height
"""
return self.rx_location[2]-self.topo[2]
@property
def dz(self):
"""
Source height - Rx height
"""
return self.z - self.h
@property
def n_layer(self):
"""
Srource height
"""
if self.half_switch is False:
return self.depth.size
elif self.half_switch is True:
return int(1)
@property
def n_frequency(self):
"""
# of frequency
"""
return int(self.frequency.size)
@property
def src_paths_on_x(self):
"""
# of frequency
"""
if getattr(self, '_src_paths_on_x', None) is None:
offset = np.unique(self.offset)
if offset.size != 1:
raise Exception(
"For the sourth paths, only single offset works!"
)
xy_rot, xy_obs_rot, angle = rotate_to_x_axis(
np.flipud(xy), np.r_[offset, 0.]
)
return self._src_paths
@Utils.requires('prob')
def dpred(self, m, f=None):
"""
Computes predicted data.
Here we do not store predicted data
because projection (`d = P(f)`) is cheap.
"""
if f is None:
f = self.prob.fields(m)
return Utils.mkvc(self.projectFields(f))
class EM1DSurveyTD(BaseEM1DSurvey):
"""docstring for EM1DSurveyTD"""
time = properties.Array(
"Time channels (s) at current off-time", dtype=float
)
wave_type = properties.StringChoice(
"Source location",
default="stepoff",
choices=["stepoff", "general"]
)
moment_type = properties.StringChoice(
"Source moment type",
default="single",
choices=["single", "dual"]
)
n_pulse = properties.Integer(
"The number of pulses",
)
base_frequency = properties.Float(
"Base frequency (Hz)"
)
time_input_currents = properties.Array(
"Time for input currents", dtype=float
)
input_currents = properties.Array(
"Input currents", dtype=float
)
use_lowpass_filter = properties.Bool(
"Switch for low pass filter", default=False
)
high_cut_frequency = properties.Float(
"High cut frequency for low pass filter (Hz)",
default=210*1e3
)
# Predicted data
_pred = None
# ------------- For dual moment ------------- #
time_dual_moment = properties.Array(
"Off-time channels (s) for the dual moment", dtype=float
)
time_input_currents_dual_moment = properties.Array(
"Time for input currents (dual moment)", dtype=float
)
input_currents_dual_moment = properties.Array(
"Input currents (dual moment)", dtype=float
)
base_frequency_dual_moment = properties.Float(
"Base frequency for the dual moment (Hz)"
)
def __init__(self, **kwargs):
BaseEM1DSurvey.__init__(self, **kwargs)
if self.time is None:
raise Exception("time is required!")
# Use Sin filter for frequency to time transform
self.fftfilt = filters.key_81_CosSin_2009()
self.set_frequency()
if self.src_type == "VMD":
if self.offset is None:
raise Exception("offset is required!")
if self.offset.size == 1:
self.offset = self.offset * np.ones(self.n_frequency)
@property
def time_int(self):
"""
Time channels (s) for interpolation"
"""
if getattr(self, '_time_int', None) is None:
if self.moment_type == "single":
time = self.time
pulse_period = self.pulse_period
period = self.period
# Dual moment
else:
time = np.unique(np.r_[self.time, self.time_dual_moment])
pulse_period = np.maximum(
self.pulse_period, self.pulse_period_dual_moment
)
period = np.maximum(self.period, self.period_dual_moment)
tmin = time[time>0.].min()
if self.n_pulse == 1:
tmax = time.max() + pulse_period
elif self.n_pulse == 2:
tmax = time.max() + pulse_period + period/2.
else:
raise NotImplementedError("n_pulse must be either 1 or 2")
n_time = int((np.log10(tmax)-np.log10(tmin))*10+1)
self._time_int = np.logspace(
np.log10(tmin), np.log10(tmax), n_time
)
# print (tmin, tmax)
return self._time_int
@property
def n_time(self):
return int(self.time.size)
@property
def period(self):
return 1./self.base_frequency
@property
def pulse_period(self):
Tp = (
self.time_input_currents.max() -
self.time_input_currents.min()
)
return Tp
# ------------- For dual moment ------------- #
@property
def n_time_dual_moment(self):
return int(self.time_dual_moment.size)
@property
def period_dual_moment(self):
return 1./self.base_frequency_dual_moment
@property
def pulse_period_dual_moment(self):
Tp = (
self.time_input_currents_dual_moment.max() -
self.time_input_currents_dual_moment.min()
)
return Tp
@property
def nD(self):
"""
# of data
"""
if self.moment_type == "single":
return self.n_time
else:
return self.n_time + self.n_time_dual_moment
@property
def lowpass_filter(self):
"""
Low pass filter values
"""
if getattr(self, '_lowpass_filter', None) is None:
# self._lowpass_filter = butterworth_type_filter(
# self.frequency, self.high_cut_frequency
# )
self._lowpass_filter = (1+1j*(self.frequency/self.high_cut_frequency))**-1
self._lowpass_filter *= (1+1j*(self.frequency/3e5))**-0.99
# For actual butterworth filter
# filter_frequency, values = butter_lowpass_filter(
# self.high_cut_frequency
# )
# lowpass_func = interp1d(
# filter_frequency, values, fill_value='extrapolate'
# )
# self._lowpass_filter = lowpass_func(self.frequency)
return self._lowpass_filter
def set_frequency(self, pts_per_dec=-1):
"""
Compute Frequency reqired for frequency to time transform
"""
if self.wave_type == "general":
_, frequency, ft, ftarg = check_time(
self.time_int, -1, 'dlf',
{'pts_per_dec': pts_per_dec, 'dlf': self.fftfilt}, 0
)
elif self.wave_type == "stepoff":
_, frequency, ft, ftarg = check_time(
self.time, -1, 'dlf',
{'pts_per_dec': pts_per_dec, 'dlf': self.fftfilt}, 0,
)
else:
raise Exception("wave_type must be either general or stepoff")
self.frequency = frequency
self.ftarg = ftarg
def projectFields(self, u):
"""
Transform frequency domain responses to time domain responses
"""
# Compute frequency domain reponses right at filter coefficient values
# Src waveform: Step-off
if self.use_lowpass_filter:
factor = self.lowpass_filter.copy()
else:
factor = np.ones_like(self.frequency, dtype=complex)
if self.rx_type == 'Bz':
factor *= 1./(2j*np.pi*self.frequency)
if self.wave_type == 'stepoff':
# Compute EM responses
if u.size == self.n_frequency:
resp, _ = fourier_dlf(
u.flatten()*factor, self.time,
self.frequency, self.ftarg
)
# Compute EM sensitivities
else:
resp = np.zeros(
(self.n_time, self.n_layer), dtype=np.float64, order='F')
# )
# TODO: remove for loop
for i in range(self.n_layer):
resp_i, _ = fourier_dlf(
u[:, i]*factor, self.time,
self.frequency, self.ftarg
)
resp[:, i] = resp_i
# Evaluate piecewise linear input current waveforms
# Using Fittermann's approach (19XX) with Gaussian Quadrature
elif self.wave_type == 'general':
# Compute EM responses
if u.size == self.n_frequency:
resp_int, _ = fourier_dlf(
u.flatten()*factor, self.time_int,
self.frequency, self.ftarg
)
# step_func = interp1d(
# self.time_int, resp_int
# )
step_func = iuSpline(
np.log10(self.time_int), resp_int
)
resp = piecewise_pulse_fast(
step_func, self.time,
self.time_input_currents, self.input_currents,
self.period, n_pulse=self.n_pulse
)
# Compute response for the dual moment
if self.moment_type == "dual":
resp_dual_moment = piecewise_pulse_fast(
step_func, self.time_dual_moment,
self.time_input_currents_dual_moment,
self.input_currents_dual_moment,
self.period_dual_moment,
n_pulse=self.n_pulse
)
# concatenate dual moment response
# so, ordering is the first moment data
# then the second moment data.
resp = np.r_[resp, resp_dual_moment]
# Compute EM sensitivities
else:
if self.moment_type == "single":
resp = np.zeros(
(self.n_time, self.n_layer),
dtype=np.float64, order='F'
)
else:
# For dual moment
resp = np.zeros(
(self.n_time+self.n_time_dual_moment, self.n_layer),
dtype=np.float64, order='F')
# TODO: remove for loop (?)
for i in range(self.n_layer):
resp_int_i, _ = fourier_dlf(
u[:, i]*factor, self.time_int,
self.frequency, self.ftarg
)
# step_func = interp1d(
# self.time_int, resp_int_i
# )
step_func = iuSpline(
np.log10(self.time_int), resp_int_i
)
resp_i = piecewise_pulse_fast(
step_func, self.time,
self.time_input_currents, self.input_currents,
self.period, n_pulse=self.n_pulse
)
if self.moment_type == "single":
resp[:, i] = resp_i
else:
resp_dual_moment_i = piecewise_pulse_fast(
step_func,
self.time_dual_moment,
self.time_input_currents_dual_moment,
self.input_currents_dual_moment,
self.period_dual_moment,
n_pulse=self.n_pulse
)
resp[:, i] = np.r_[resp_i, resp_dual_moment_i]
return resp * (-2.0/np.pi) * mu_0
@Utils.requires('prob')
def dpred(self, m, f=None):
"""
Computes predicted data.
Predicted data (`_pred`) are computed and stored
when self.prob.fields(m) is called.
"""
if f is None:
f = self.prob.fields(m)
return self._pred
class BaseRx(BaseSimPEGRx):
"""
Base DC receiver
"""
orientation = properties.StringChoice(
"orientation of the receiver. Must currently be 'x', 'y', 'z'", ["x", "y", "z"]
)
projField = properties.StringChoice(
"field to be projected in the calculation of the data",
choices=["phi", "e", "j"],
default="phi",
)
_geometric_factor = {}
def __init__(self, locations=None, **kwargs):
super(BaseRx, self).__init__(**kwargs)
if locations is not None:
self.locations = locations
# @property
# def projField(self):
# """Field Type projection (e.g. e b ...)"""
# return self.knownRxTypes[self.rxType][0]
data_type = properties.StringChoice(
"Type of DC-IP survey",
required=True,
default="volt",
choices=["volt", "apparent_resistivity", "apparent_chargeability"],
)
# data_type = 'volt'
# knownRxTypes = {
# 'phi': ['phi', None],
# 'ex': ['e', 'x'],
# 'ey': ['e', 'y'],
# 'ez': ['e', 'z'],
# 'jx': ['j', 'x'],
# 'jy': ['j', 'y'],
# 'jz': ['j', 'z'],
# }
@property
def geometric_factor(self):
return self._geometric_factor
def projGLoc(self, f):
"""Grid Location projection (e.g. Ex Fy ...)"""
# field = self.knownRxTypes[self.rxType][0]
# orientation = self.knownRxTypes[self.rxType][1]
if self.orientation is not None:
return f._GLoc(self.projField) + self.orientation
return f._GLoc(self.projField)
def eval(self, src, mesh, f):
P = self.getP(mesh, self.projGLoc(f))
proj_f = self.projField
if proj_f == "phi":
proj_f = "phiSolution"
v = P * f[src, proj_f]
if self.data_type == "apparent_resistivity":
try:
if mesh.dim == 2:
return v / self.geometric_factor[src][:, None]
return v / self.geometric_factor[src]
except KeyError:
raise KeyError(
"Receiver geometric factor has not been set, please execute "
"survey.set_geometric_factor()"
)
return v
def evalDeriv(self, src, mesh, f, v=None, adjoint=False):
P = self.getP(mesh, self.projGLoc(f))
factor = None
if self.data_type == "apparent_resistivity":
factor = 1.0 / self.geometric_factor[src]
if v is None:
if factor is not None:
P = sdiag(factor) @ P
if adjoint:
return P.T
return P
if not adjoint:
v = P @ v
if factor is not None:
v = factor * v
return v
elif adjoint:
if factor is not None:
v = factor * v
return P.T @ v
class EM1DSurveyFD(BaseEM1DSurvey):
"""
Freqency-domain EM1D survey
"""
# Nfreq = None
switch_real_imag = properties.StringChoice(
"Switch for real and imaginary part of the data",
default="all",
choices=["all", "real", "imag"]
)
def __init__(self, **kwargs):
BaseEM1DSurvey.__init__(self, **kwargs)
if self.src_type == "VMD":
if self.offset is None:
raise Exception("offset is required!")
if self.offset.size == 1:
self.offset = self.offset * np.ones(self.n_frequency)
@property
def nD(self):
"""
# of data
"""
if self.switch_real_imag == "all":
return int(self.frequency.size * 2)
elif (
self.switch_real_imag == "imag" or self.switch_real_imag == "real"
):
return int(self.n_frequency)
@property
def hz_primary(self):
# Assumes HCP only at the moment
if self.src_type == 'VMD':
return -1./(4*np.pi*self.offset**3)
elif self.src_type == 'CircularLoop':
return self.I/(2*self.a) * np.ones_like(self.frequency)
else:
raise NotImplementedError()
def projectFields(self, u):
"""
Decompose frequency domain EM responses as real and imaginary
components
"""
ureal = (u.real).copy()
uimag = (u.imag).copy()
if self.rx_type == 'Hz':
factor = 1.
elif self.rx_type == 'ppm':
factor = 1./self.hz_primary * 1e6
if self.switch_real_imag == 'all':
ureal = (u.real).copy()
uimag = (u.imag).copy()
if ureal.ndim == 1 or 0:
resp = np.r_[ureal*factor, uimag*factor]
elif ureal.ndim == 2:
if np.isscalar(factor):
resp = np.vstack(
(factor*ureal, factor*uimag)
)
else:
resp = np.vstack(
(Utils.sdiag(factor)*ureal, Utils.sdiag(factor)*uimag)
)
else:
raise NotImplementedError()
elif self.switch_real_imag == 'real':
resp = (u.real).copy()
elif self.switch_real_imag == 'imag':
resp = (u.imag).copy()
else:
raise NotImplementedError()
return resp
class Conversation(BaseConversation):
"""This represents a full conversation result."""
type = properties.StringChoice(
'The type of conversation.',
choices=['email', 'chat', 'phone', 'spam'],
required=True,
)
folder_id = properties.Integer(
'ID of the Mailbox Folder to which this conversation resides.',
required=True,
)
is_draft = properties.Bool(
'Is this a draft conversation? This property duplicates ``draft``, '
'but both are received in API responses at the same time so neither '
'can be considered "deprecated".', )
draft = properties.Bool(
'Is this a draft conversation? This property duplicates '
'``is_draft``, but both are received in API responses at the same '
'time so neither can be considered "deprecated".', )
owner = properties.Instance(
'The Help Scout user who is currently assigned to this conversation.',
instance_class=Person,
required=True,
)
mailbox = properties.Instance(
'The mailbox to which this conversation belongs.',
instance_class=MailboxRef,
required=True,
)
customer = properties.Instance(
'The customer who this conversation is associated with.',
instance_class=Person,
required=True,
)
created_by = properties.Instance(
'The ``Person`` who created this conversation. The ``type`` property '
'will specify whether it was created by a ``user`` or a ``customer``.',
instance_class=Person,
)
created_by_type = properties.String(
'The type of user that created this conversation.', )
created_at = properties.DateTime(
'UTC time when this conversation was created.', )
closed_at = properties.DateTime(
'UTC time when this conversation was closed. Null if not closed.', )
closed_by = properties.Instance(
'The Help Scout user who closed this conversation.',
instance_class=Person,
)
source = properties.Instance(
'Specifies the method in which this conversation was created.',
instance_class=Source,
)
threads = properties.List(
'Threads associated with the conversation.',
prop=Thread,
)
cc = properties.List(
'Emails that are CCd.',
prop=properties.String('Email Address', ),
)
bcc = properties.List(
'Emails that are BCCd.',
prop=properties.String('Email Address', ),
)
tags = properties.List(
'Tags for the conversation',
prop=properties.String('Tag Name'),
)
spam = properties.Bool('If this conversation is marked as SPAM.', )
locked = properties.Bool('If this conversation is locked from editing.')
user_modified_at = properties.DateTime(
'Last time that this conversation was edited by a user.', )
class Survey(BaseEMSurvey, properties.HasProperties):
"""
Base DC survey
"""
rxPair = Rx.BaseRx
srcPair = Src.BaseSrc
# Survey
survey_geometry = properties.StringChoice(
"Survey geometry of DC surveys",
default="surface",
choices=["surface", "borehole", "general"])
survey_type = properties.StringChoice(
"DC-IP Survey type",
default="dipole-dipole",
choices=["dipole-dipole", "pole-dipole", "dipole-pole", "pole-pole"])
a_locations = properties.Array(
"locations of the positive (+) current electrodes",
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
b_locations = properties.Array(
"locations of the negative (-) current electrodes",
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
m_locations = properties.Array(
"locations of the positive (+) potential electrodes",
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
n_locations = properties.Array(
"locations of the negative (-) potential electrodes",
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
electrode_locations = properties.Array(
"unique locations of a, b, m, n electrodes",
shape=('*', '*'), # ('*', 3) for 3D or ('*', 2) for 2D
dtype=float # data are floats
)
electrodes_info = None
topo_function = None
def __init__(self, srcList, **kwargs):
BaseEMSurvey.__init__(self, srcList, **kwargs)
def set_geometric_factor(self,
data_type="volt",
survey_type='dipole-dipole',
space_type='half-space'):
geometric_factor = SimPEG.EM.Static.Utils.geometric_factor(
self, survey_type=survey_type, space_type=space_type)
geometric_factor = SimPEG.Survey.Data(self, geometric_factor)
for src in self.srcList:
for rx in src.rxList:
rx._geometric_factor = geometric_factor[src, rx]
rx.data_type = data_type
return geometric_factor
def getABMN_locations(self):
a_locations = []
b_locations = []
m_locations = []
n_locations = []
for src in self.srcList:
for rx in src.rxList:
nRx = rx.nD
# Pole Source
if isinstance(src, Src.Pole):
a_locations.append(
src.loc.reshape([1, -1]).repeat(nRx, axis=0))
b_locations.append(
src.loc.reshape([1, -1]).repeat(nRx, axis=0))
# Dipole Source
elif isinstance(src, Src.Dipole):
a_locations.append(src.loc[0].reshape([1,
-1]).repeat(nRx,
axis=0))
b_locations.append(src.loc[1].reshape([1,
-1]).repeat(nRx,
axis=0))
# Pole RX
if isinstance(rx, Rx.Pole) or isinstance(rx, Rx.Pole_ky):
m_locations.append(rx.locs)
n_locations.append(rx.locs)
# Dipole RX
elif isinstance(rx, Rx.Dipole) or isinstance(rx, Rx.Dipole_ky):
m_locations.append(rx.locs[0])
n_locations.append(rx.locs[1])
self.a_locations = np.vstack(a_locations)
self.b_locations = np.vstack(b_locations)
self.m_locations = np.vstack(m_locations)
self.n_locations = np.vstack(n_locations)
def drapeTopo(self,
mesh,
actind,
option='top',
topography=None,
force=False):
if self.a_locations is None:
self.getABMN_locations()
# 2D
if mesh.dim == 2:
if self.survey_geometry == "surface":
if self.electrodes_info is None or force:
self.electrodes_info = SimPEG.Utils.uniqueRows(
np.hstack((
self.a_locations[:, 0],
self.b_locations[:, 0],
self.m_locations[:, 0],
self.n_locations[:, 0],
)).reshape([-1, 1]))
self.electrode_locations = SimPEG.EM.Static.Utils.drapeTopotoLoc(
mesh,
self.electrodes_info[0].flatten(),
actind=actind,
option=option)
temp = (
self.electrode_locations[self.electrodes_info[2],
1]).reshape(
(self.a_locations.shape[0],
4),
order="F")
self.a_locations = np.c_[self.a_locations[:, 0], temp[:, 0]]
self.b_locations = np.c_[self.b_locations[:, 0], temp[:, 1]]
self.m_locations = np.c_[self.m_locations[:, 0], temp[:, 2]]
self.n_locations = np.c_[self.n_locations[:, 0], temp[:, 3]]
# Make interpolation function
self.topo_function = interp1d(self.electrode_locations[:, 0],
self.electrode_locations[:, 1])
# Loop over all Src and Rx locs and Drape topo
for src in self.srcList:
# Pole Src
if isinstance(src, Src.Pole):
locA = src.loc.flatten()
z_SrcA = self.topo_function(locA[0])
src.loc = np.array([locA[0], z_SrcA])
for rx in src.rxList:
# Pole Rx
if isinstance(rx, Rx.Pole) or isinstance(
rx, Rx.Pole_ky):
locM = rx.locs.copy()
z_RxM = self.topo_function(locM[:, 0])
rx.locs = np.c_[locM[:, 0], z_RxM]
# Dipole Rx
elif isinstance(rx, Rx.Dipole) or isinstance(
rx, Rx.Dipole_ky):
locM = rx.locs[0].copy()
locN = rx.locs[1].copy()
z_RxM = self.topo_function(locM[:, 0])
z_RxN = self.topo_function(locN[:, 0])
rx.locs[0] = np.c_[locM[:, 0], z_RxM]
rx.locs[1] = np.c_[locN[:, 0], z_RxN]
else:
raise Exception()
# Dipole Src
elif isinstance(src, Src.Dipole):
locA = src.loc[0].flatten()
locB = src.loc[1].flatten()
z_SrcA = self.topo_function(locA[0])
z_SrcB = self.topo_function(locB[0])
src.loc[0] = np.array([locA[0], z_SrcA])
src.loc[1] = np.array([locB[0], z_SrcB])
for rx in src.rxList:
# Pole Rx
if isinstance(rx, Rx.Pole) or isinstance(
rx, Rx.Pole_ky):
locM = rx.locs.copy()
z_RxM = self.topo_function(locM[:, 0])
rx.locs = np.c_[locM[:, 0], z_RxM]
# Dipole Rx
elif isinstance(rx, Rx.Dipole) or isinstance(
rx, Rx.Dipole_ky):
locM = rx.locs[0].copy()
locN = rx.locs[1].copy()
z_RxM = self.topo_function(locM[:, 0])
z_RxN = self.topo_function(locN[:, 0])
rx.locs[0] = np.c_[locM[:, 0], z_RxM]
rx.locs[1] = np.c_[locN[:, 0], z_RxN]
else:
raise Exception()
elif self.survey_geometry == "borehole":
raise Exception(
"Not implemented yet for borehole survey_geometry")
else:
raise Exception(
"Input valid survey survey_geometry: surface or borehole")
if mesh.dim == 3:
if self.survey_geometry == "surface":
if self.electrodes_info is None or force:
self.electrodes_info = SimPEG.Utils.uniqueRows(
np.vstack((
self.a_locations[:, :2],
self.b_locations[:, :2],
self.m_locations[:, :2],
self.n_locations[:, :2],
)))
self.electrode_locations = SimPEG.EM.Static.Utils.drapeTopotoLoc(
mesh,
self.electrodes_info[0],
actind=actind,
topo=topography)
temp = (
self.electrode_locations[self.electrodes_info[2],
1]).reshape(
(self.a_locations.shape[0],
4),
order="F")
self.a_locations = np.c_[self.a_locations[:, :2], temp[:, 0]]
self.b_locations = np.c_[self.b_locations[:, :2], temp[:, 1]]
self.m_locations = np.c_[self.m_locations[:, :2], temp[:, 2]]
self.n_locations = np.c_[self.n_locations[:, :2], temp[:, 3]]
# Make interpolation function
self.topo_function = NearestNDInterpolator(
self.electrode_locations[:, :2],
self.electrode_locations[:, 2])
# Loop over all Src and Rx locs and Drape topo
for src in self.srcList:
# Pole Src
if isinstance(src, Src.Pole):
locA = src.loc.reshape([1, -1])
z_SrcA = self.topo_function(locA[0, :2])
src.loc = np.r_[locA[0, :2].flatten(), z_SrcA]
for rx in src.rxList:
# Pole Rx
if isinstance(rx, Rx.Pole):
locM = rx.locs.copy()
z_RxM = self.topo_function(locM[:, :2])
rx.locs = np.c_[locM[:, 0], z_RxM]
# Dipole Rx
elif isinstance(rx, Rx.Dipole):
locM = rx.locs[0].copy()
locN = rx.locs[1].copy()
z_RxM = self.topo_function(locM[:, :2])
z_RxN = self.topo_function(locN[:, :2])
rx.locs[0] = np.c_[locM[:, :2], z_RxM]
rx.locs[1] = np.c_[locN[:, :2], z_RxN]
else:
raise Exception()
# Dipole Src
elif isinstance(src, Src.Dipole):
locA = src.loc[0].reshape([1, -1])
locB = src.loc[1].reshape([1, -1])
z_SrcA = self.topo_function(locA[0, :2])
z_SrcB = self.topo_function(locB[0, :2])
src.loc[0] = np.r_[locA[0, :2].flatten(), z_SrcA]
src.loc[1] = np.r_[locB[0, :2].flatten(), z_SrcB]
for rx in src.rxList:
# Pole Rx
if isinstance(rx, Rx.Pole):
locM = rx.locs.copy()
z_RxM = self.topo_function(locM[:, :2])
rx.locs = np.c_[locM[:, :2], z_RxM]
# Dipole Rx
elif isinstance(rx, Rx.Dipole):
locM = rx.locs[0].copy()
locN = rx.locs[1].copy()
z_RxM = self.topo_function(locM[:, :2])
z_RxN = self.topo_function(locN[:, :2])
rx.locs[0] = np.c_[locM[:, :2], z_RxM]
rx.locs[1] = np.c_[locN[:, :2], z_RxN]
else:
raise Exception()
elif self.survey_geometry == "borehole":
raise Exception(
"Not implemented yet for borehole survey_geometry")
else:
raise Exception(
"Input valid survey survey_geometry: surface or borehole")
class BasePFSimulation(LinearSimulation):
actInd = properties.Array("Array of active cells (ground)",
dtype=(bool, int),
default=None)
n_cpu = properties.Integer(
"Number of processors used for the forward simulation",
default=int(multiprocessing.cpu_count()),
)
store_sensitivities = properties.StringChoice(
"Compute and store G",
choices=["disk", "ram", "forward_only"],
default="ram")
def __init__(self, mesh, **kwargs):
LinearSimulation.__init__(self, mesh, **kwargs)
# Find non-zero cells
if getattr(self, "actInd", None) is not None:
if self.actInd.dtype == "bool":
indices = np.where(self.actInd)[0]
else:
indices = self.actInd
else:
indices = np.asarray(range(self.mesh.nC))
self.nC = len(indices)
# Create active cell projector
projection = csr((np.ones(self.nC), (indices, range(self.nC))),
shape=(self.mesh.nC, self.nC))
# Create vectors of nodal location for the lower and upper corners
bsw = self.mesh.gridCC - self.mesh.h_gridded / 2.0
tne = self.mesh.gridCC + self.mesh.h_gridded / 2.0
xn1, xn2 = bsw[:, 0], tne[:, 0]
yn1, yn2 = bsw[:, 1], tne[:, 1]
self.Yn = projection.T * np.c_[mkvc(yn1), mkvc(yn2)]
self.Xn = projection.T * np.c_[mkvc(xn1), mkvc(xn2)]
# Allows for 2D mesh where Zn is defined by user
if self.mesh.dim > 2:
zn1, zn2 = bsw[:, 2], tne[:, 2]
self.Zn = projection.T * np.c_[mkvc(zn1), mkvc(zn2)]
def linear_operator(self):
self.nC = self.modelMap.shape[0]
components = np.array(list(self.survey.components.keys()))
active_components = np.hstack([
np.c_[values] for values in self.survey.components.values()
]).tolist()
nD = self.survey.nD
if self.store_sensitivities == "disk":
sens_name = self.sensitivity_path + "sensitivity.npy"
if os.path.exists(sens_name):
# do not pull array completely into ram, just need to check the size
kernel = np.load(sens_name, mmap_mode="r")
if kernel.shape == (nD, self.nC):
print(
f"Found sensitivity file at {sens_name} with expected shape"
)
kernel = np.asarray(kernel)
return kernel
# Single threaded
if self.store_sensitivities != "forward_only":
kernel = np.vstack([
self.evaluate_integral(receiver, components[component])
for receiver, component in zip(
self.survey.receiver_locations.tolist(), active_components)
])
else:
kernel = np.hstack([
self.evaluate_integral(receiver,
components[component]).dot(self.model)
for receiver, component in zip(
self.survey.receiver_locations.tolist(), active_components)
])
if self.store_sensitivities == "disk":
print(f"writing sensitivity to {sens_name}")
os.makedirs(self.sensitivity_path, exist_ok=True)
np.save(sens_name, kernel)
return kernel
def evaluate_integral(self):
"""
evaluate_integral
Compute the forward linear relationship between the model and the physics at a point.
:param self:
:return:
"""
raise RuntimeError(
f"Integral calculations must implemented by the subclass {self}.")
@property
def forwardOnly(self):
"""The forwardOnly property has been deprecated. Please set the store_sensitivites
property instead. This will be removed in version 0.15.0 of SimPEG
"""
warnings.warn(
"The forwardOnly property has been deprecated. Please set the store_sensitivites "
"property instead. This will be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
return self.store_sensitivities == "forward_only"
@forwardOnly.setter
def forwardOnly(self, other):
warnings.warn(
"Do not set parallelized. If interested, try out "
"loading dask for parallelism by doing ``import SimPEG.dask``. This will "
"be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
if self.other:
self.store_sensitivities = "forward_only"
@property
def parallelized(self):
"""The parallelized property has been removed. If interested, try out
loading dask for parallelism by doing ``import SimPEG.dask``. This will
be removed in version 0.15.0 of SimPEG
"""
warnings.warn(
"parallelized has been deprecated. If interested, try out "
"loading dask for parallelism by doing ``import SimPEG.dask``. "
"This will be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
return False
@parallelized.setter
def parallelized(self, other):
warnings.warn(
"Do not set parallelized. If interested, try out "
"loading dask for parallelism by doing ``import SimPEG.dask``. This will"
"be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
@property
def n_cpu(self):
"""The parallelized property has been removed. If interested, try out
loading dask for parallelism by doing ``import SimPEG.dask``. This will
be removed in version 0.15.0 of SimPEG
"""
warnings.warn(
"n_cpu has been deprecated. If interested, try out "
"loading dask for parallelism by doing ``import SimPEG.dask``. "
"This will be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
return 1
@parallelized.setter
def n_cpu(self, other):
warnings.warn(
"Do not set n_cpu. If interested, try out "
"loading dask for parallelism by doing ``import SimPEG.dask``. This will"
"be removed in version 0.15.0 of SimPEG",
DeprecationWarning,
)
class MagneticIntegral(Problem.LinearProblem):
chi, chiMap, chiDeriv = Props.Invertible(
"Magnetic Susceptibility (SI)",
default=1.
)
forwardOnly = False # If false, matrix is store to memory (watch your RAM)
actInd = None #: Active cell indices provided
M = None #: Magnetization matrix provided, otherwise all induced
rx_type = 'tmi' #: Receiver type either "tmi" | "xyz"
magType = 'H0'
equiSourceLayer = False
silent = False # Don't display progress on screen
W = None
gtgdiag = None
memory_saving_mode = False
n_cpu = None
parallelized = False
coordinate_system = properties.StringChoice(
"Type of coordinate system we are regularizing in",
choices=['cartesian', 'spherical'],
default='cartesian'
)
modelType = properties.StringChoice(
"Type of magnetization model",
choices=['susceptibility', 'vector', 'amplitude'],
default='susceptibility'
)
def __init__(self, mesh, **kwargs):
assert mesh.dim == 3, 'Integral formulation only available for 3D mesh'
Problem.BaseProblem.__init__(self, mesh, **kwargs)
def fields(self, m):
if self.coordinate_system == 'cartesian':
m = self.chiMap*(m)
else:
m = self.chiMap*(matutils.spherical2cartesian(m.reshape((int(len(m)/3), 3), order='F')))
if self.forwardOnly:
# Compute the linear operation without forming the full dense F
fields = self.Intrgl_Fwr_Op(m=m)
else:
if getattr(self, '_Mxyz', None) is not None:
fields = np.dot(self.G, (self.Mxyz*m).astype(np.float32))
else:
fields = np.dot(self.G, m.astype(np.float32))
if self.modelType == 'amplitude':
fields = self.calcAmpData(fields.astype(np.float64))
return fields.astype(np.float64)
def calcAmpData(self, Bxyz):
"""
Compute amplitude of the field
"""
amplitude = np.sum(
Bxyz.reshape((3, self.nD), order='F')**2., axis=0
)**0.5
return amplitude
@property
def G(self):
if not self.ispaired:
raise Exception('Need to pair!')
if getattr(self, '_G', None) is None:
if self.modelType == 'vector':
self.magType = 'full'
self._G = self.Intrgl_Fwr_Op(magType=self.magType,
rx_type=self.rx_type)
return self._G
@property
def nD(self):
"""
Number of data
"""
self._nD = self.survey.srcField.rxList[0].locs.shape[0]
return self._nD
@property
def ProjTMI(self):
if not self.ispaired:
raise Exception('Need to pair!')
if getattr(self, '_ProjTMI', None) is None:
# Convert Bdecination from north to cartesian
self._ProjTMI = Utils.matutils.dip_azimuth2cartesian(
self.survey.srcField.param[1],
self.survey.srcField.param[2]
)
return self._ProjTMI
def getJtJdiag(self, m, W=None):
"""
Return the diagonal of JtJ
"""
dmudm = self.chiMap.deriv(m)
self._dSdm = None
self._dfdm = None
self.model = m
if (self.gtgdiag is None) and (self.modelType != 'amplitude'):
if W is None:
w = np.ones(self.G.shape[1])
else:
w = W.diagonal()
self.gtgdiag = np.zeros(dmudm.shape[1])
for ii in range(self.G.shape[0]):
self.gtgdiag += (w[ii]*self.G[ii, :]*dmudm)**2.
if self.coordinate_system == 'cartesian':
if self.modelType == 'amplitude':
return np.sum((W * self.dfdm * self.G * dmudm)**2., axis=0)
else:
return self.gtgdiag
else: # spherical
if self.modelType == 'amplitude':
return np.sum(((W * self.dfdm) * self.G * (self.dSdm * dmudm))**2., axis=0)
else:
Japprox = sdiag(mkvc(self.gtgdiag)**0.5*dmudm.T) * (self.dSdm * dmudm)
return mkvc(np.sum(Japprox.power(2), axis=0))
def getJ(self, m, f=None):
"""
Sensitivity matrix
"""
if self.coordinate_system == 'cartesian':
dmudm = self.chiMap.deriv(m)
else: # spherical
dmudm = self.dSdm * self.chiMap.deriv(m)
if self.modelType == 'amplitude':
return self.dfdm * (self.G * dmudm)
else:
return self.G * dmudm
def Jvec(self, m, v, f=None):
if self.coordinate_system == 'cartesian':
dmudm = self.chiMap.deriv(m)
else:
dmudm = self.dSdm * self.chiMap.deriv(m)
if getattr(self, '_Mxyz', None) is not None:
vec = np.dot(self.G, (self.Mxyz*(dmudm*v)).astype(np.float32))
else:
vec = np.dot(self.G, (dmudm*v).astype(np.float32))
if self.modelType == 'amplitude':
return self.dfdm*vec.astype(np.float64)
else:
return vec.astype(np.float64)
def Jtvec(self, m, v, f=None):
if self.coordinate_system == 'spherical':
dmudm = self.dSdm * self.chiMap.deriv(m)
else:
dmudm = self.chiMap.deriv(m)
if self.modelType == 'amplitude':
if getattr(self, '_Mxyz', None) is not None:
vec = self.Mxyz.T*np.dot(self.G.T, (self.dfdm.T*v).astype(np.float32)).astype(np.float64)
else:
vec = np.dot(self.G.T, (self.dfdm.T*v).astype(np.float32))
else:
vec = np.dot(self.G.T, v.astype(np.float32))
return dmudm.T * vec.astype(np.float64)
@property
def dSdm(self):
if getattr(self, '_dSdm', None) is None:
if self.model is None:
raise Exception('Requires a chi')
nC = int(len(self.model)/3)
m_xyz = self.chiMap * matutils.spherical2cartesian(self.model.reshape((nC, 3), order='F'))
nC = int(m_xyz.shape[0]/3.)
m_atp = matutils.cartesian2spherical(m_xyz.reshape((nC, 3), order='F'))
a = m_atp[:nC]
t = m_atp[nC:2*nC]
p = m_atp[2*nC:]
Sx = sp.hstack([sp.diags(np.cos(t)*np.cos(p), 0),
sp.diags(-a*np.sin(t)*np.cos(p), 0),
sp.diags(-a*np.cos(t)*np.sin(p), 0)])
Sy = sp.hstack([sp.diags(np.cos(t)*np.sin(p), 0),
sp.diags(-a*np.sin(t)*np.sin(p), 0),
sp.diags(a*np.cos(t)*np.cos(p), 0)])
Sz = sp.hstack([sp.diags(np.sin(t), 0),
sp.diags(a*np.cos(t), 0),
sp.csr_matrix((nC, nC))])
self._dSdm = sp.vstack([Sx, Sy, Sz])
return self._dSdm
@property
def modelMap(self):
"""
Call for general mapping of the problem
"""
return self.chiMap
@property
def dfdm(self):
if self.model is None:
self.model = np.zeros(self.G.shape[1])
if getattr(self, '_dfdm', None) is None:
Bxyz = self.Bxyz_a(self.chiMap * self.model)
# Bx = sp.spdiags(Bxyz[:, 0], 0, self.nD, self.nD)
# By = sp.spdiags(Bxyz[:, 1], 0, self.nD, self.nD)
# Bz = sp.spdiags(Bxyz[:, 2], 0, self.nD, self.nD)
ii = np.kron(np.asarray(range(self.survey.nD), dtype='int'), np.ones(3))
jj = np.asarray(range(3*self.survey.nD), dtype='int')
# (data, (row, col)), shape=(3, 3))
# P = s
self._dfdm = sp.csr_matrix(( mkvc(Bxyz), (ii,jj)), shape=(self.survey.nD, 3*self.survey.nD))
return self._dfdm
def Bxyz_a(self, m):
"""
Return the normalized B fields
"""
# Get field data
if self.coordinate_system == 'spherical':
m = matutils.atp2xyz(m)
if getattr(self, '_Mxyz', None) is not None:
Bxyz = np.dot(self.G, (self.Mxyz*m).astype(np.float32))
else:
Bxyz = np.dot(self.G, m.astype(np.float32))
amp = self.calcAmpData(Bxyz.astype(np.float64))
Bamp = sp.spdiags(1./amp, 0, self.nD, self.nD)
return (Bxyz.reshape((3, self.nD), order='F')*Bamp)
def Intrgl_Fwr_Op(self, m=None, magType='H0', rx_type='tmi'):
"""
Magnetic forward operator in integral form
magType = 'H0' | 'x' | 'y' | 'z'
rx_type = 'tmi' | 'x' | 'y' | 'z'
Return
_G = Linear forward operator | (forwardOnly)=data
"""
if m is not None:
self.model = self.chiMap*m
# Find non-zero cells
if getattr(self, 'actInd', None) is not None:
if self.actInd.dtype == 'bool':
inds = np.where(self.actInd)[0]
else:
inds = self.actInd
else:
inds = np.asarray(range(self.mesh.nC))
nC = len(inds)
# Create active cell projector
P = sp.csr_matrix((np.ones(nC), (inds, range(nC))),
shape=(self.mesh.nC, nC))
# Create vectors of nodal location
# (lower and upper coners for each cell)
if isinstance(self.mesh, Mesh.TreeMesh):
# Get upper and lower corners of each cell
bsw = (self.mesh.gridCC - self.mesh.h_gridded/2.)
tne = (self.mesh.gridCC + self.mesh.h_gridded/2.)
xn1, xn2 = bsw[:, 0], tne[:, 0]
yn1, yn2 = bsw[:, 1], tne[:, 1]
zn1, zn2 = bsw[:, 2], tne[:, 2]
else:
xn = self.mesh.vectorNx
yn = self.mesh.vectorNy
zn = self.mesh.vectorNz
yn2, xn2, zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:])
yn1, xn1, zn1 = np.meshgrid(yn[:-1], xn[:-1], zn[:-1])
# If equivalent source, use semi-infite prism
if self.equiSourceLayer:
zn1 -= 1000.
self.Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)]
self.Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)]
self.Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)]
# survey = self.survey
self.rxLoc = self.survey.srcField.rxList[0].locs
if magType == 'H0':
if getattr(self, 'M', None) is None:
self.M = matutils.dip_azimuth2cartesian(np.ones(nC) * self.survey.srcField.param[1],
np.ones(nC) * self.survey.srcField.param[2])
Mx = sdiag(self.M[:, 0] * self.survey.srcField.param[0])
My = sdiag(self.M[:, 1] * self.survey.srcField.param[0])
Mz = sdiag(self.M[:, 2] * self.survey.srcField.param[0])
self.Mxyz = sp.vstack((Mx, My, Mz))
elif magType == 'full':
self.Mxyz = sp.identity(3*nC) * self.survey.srcField.param[0]
else:
raise Exception('magType must be: "H0" or "full"')
# Loop through all observations and create forward operator (nD-by-nC)
print("Begin forward: M=" + magType + ", Rx type= " + self.rx_type)
# Switch to determine if the process has to be run in parallel
job = Forward(
rxLoc=self.rxLoc, Xn=self.Xn, Yn=self.Yn, Zn=self.Zn,
n_cpu=self.n_cpu, forwardOnly=self.forwardOnly,
model=self.model, rx_type=self.rx_type, Mxyz=self.Mxyz,
P=self.ProjTMI, parallelized=self.parallelized
)
G = job.calculate()
return G
class DataArray(BaseData):
"""Data array with unique values at every point in the mesh
.. note:
DataArray custom colormap is currently unsupported on
steno3d.com
"""
_resource_class = 'array'
array = properties.Array(
doc='Data, unique values at every point in the mesh',
shape=('*', ),
dtype=(float, int),
serializer=array_serializer,
deserializer=array_download(('*', ), (float, int)),
)
order = properties.StringChoice(
doc='Data array order, for data on grid meshes',
choices={
'c': ('C-STYLE', 'NUMPY', 'ROW-MAJOR', 'ROW'),
'f': ('FORTRAN', 'MATLAB', 'COLUMN-MAJOR', 'COLUMN', 'COL')
},
default='c',
)
colormap = properties.List(
doc='Colormap applied to data range or categories',
prop=properties.Color(''),
min_length=1,
max_length=256,
required=False,
)
def __init__(self, array=None, **kwargs):
super(DataArray, self).__init__(**kwargs)
if array is not None:
self.array = array
def _nbytes(self, arr=None):
if arr is None or (isinstance(arr, string_types) and arr == 'array'):
arr = self.array
if isinstance(arr, np.ndarray):
return arr.astype('f4').nbytes
raise ValueError('DataArray cannot calculate the number of '
'bytes of {}'.format(arr))
@properties.observer('array')
def _reject_large_files(self, change):
self._validate_file_size(change['name'], change['value'])
@properties.validator
def _validate_array(self):
self._validate_file_size('array', self.array)
return True
def _get_dirty_data(self, force=False):
datadict = super(DataArray, self)._get_dirty_data(force)
dirty = self._dirty_props
if 'order' in dirty or force:
datadict['order'] = self.order
if self.colormap and ('colormap' in dirty or force):
datadict['colormap'] = dumps(self.colormap)
return datadict
def _get_dirty_files(self, force=False):
files = super(DataArray, self)._get_dirty_files(force)
dirty = self._dirty_props
if 'array' in dirty or force:
files['array'] = self._props['array'].serialize(self.array)
return files
@classmethod
def _build_from_json(cls, json, **kwargs):
data = DataArray(title=kwargs['title'],
description=kwargs['description'],
order=json['order'],
array=cls._props['array'].deserialize(
json['array'],
input_dtype=json.get('arrayType', None),
))
if json.get('colormap'):
data.colormap = json['colormap']
return data
@classmethod
def _build_from_omf(cls, omf_data):
data = dict(location='N' if omf_data.location == 'vertices' else 'CC',
data=DataArray(title=omf_data.name,
description=omf_data.description,
array=omf_data.array.array))
if omf_data.colormap:
dlims = np.linspace(np.nanmin(omf_data.array.array),
np.nanmax(omf_data.array.array), 257)
dlims = (dlims[:-1] + dlims[1:]) / 2
omf_dlims = np.linspace(
omf_data.colormap.limits[0],
omf_data.colormap.limits[1],
len(omf_data.colormap.gradient.array) + 1,
)
omf_dlims = (omf_dlims[:-1] + omf_dlims[1:]) / 2
colormap = []
for dval in dlims:
colormap += [
omf_data.colormap.gradient.array[np.argmin(
np.abs(omf_dlims - dval))]
]
data['data'].colormap = colormap
return data
def _to_omf(self):
if self.order != 'c':
raise ValueError('OMF data must be "c" order')
import omf
data = omf.ScalarData(
name=self.title or '',
description=self.description or '',
array=omf.ScalarArray(self.array),
)
if self.colormap:
data.colormap = omf.ScalarColormap(
gradient=omf.ColorArray(self.colormap, ),
limits=[np.min(self.array),
np.max(self.array)],
)
return data
class Simulation2DCellCentered(BaseDCSimulation2D):
"""
2.5D cell centered DC problem
"""
_solutionType = "phiSolution"
_formulation = "HJ" # CC potentials means J is on faces
fieldsPair = Fields2DCellCentered
fieldsPair_fwd = Fields3DCellCentered
bc_type = properties.StringChoice(
"Type of boundary condition to use for simulation. Note that Robin and Mixed "
"are equivalent.",
choices=["Dirichlet", "Neumann", "Robin", "Mixed"],
default="Robin",
)
def __init__(self, mesh, **kwargs):
BaseDCSimulation2D.__init__(self, mesh, **kwargs)
V = sdiag(self.mesh.cell_volumes)
self.Div = V @ self.mesh.face_divergence
self.Grad = self.Div.T
def getA(self, ky):
"""
Make the A matrix for the cell centered DC resistivity problem
A = D MfRhoI G
"""
# To handle Mixed boundary condition
self.setBC(ky=ky)
D = self.Div
G = self.Grad
if self.bc_type != "Dirichlet":
G = G - self._MBC[ky]
MfRhoI = self.MfRhoI
# Get resistivity rho
A = D * MfRhoI * G + ky**2 * self.MccRhoi
if self.bc_type == "Neumann":
A[0, 0] = A[0, 0] + 1.0
return A
def getADeriv(self, ky, u, v, adjoint=False):
D = self.Div
G = self.Grad
if self.bc_type != "Dirichlet":
G = G - self._MBC[ky]
if adjoint:
return self.MfRhoIDeriv(
G * u.flatten(), D.T * v, adjoint=adjoint
) + ky**2 * self.MccRhoiDeriv(u.flatten(), v, adjoint=adjoint)
else:
return D * self.MfRhoIDeriv(G * u.flatten(), v, adjoint=adjoint
) + ky**2 * self.MccRhoiDeriv(
u.flatten(), v, adjoint=adjoint)
def getRHS(self, ky):
"""
RHS for the DC problem
q
"""
RHS = self.getSourceTerm(ky)
return RHS
def getRHSDeriv(self, ky, src, v, adjoint=False):
"""
Derivative of the right hand side with respect to the model
"""
# TODO: add qDeriv for RHS depending on m
# qDeriv = src.evalDeriv(self, ky, adjoint=adjoint)
# return qDeriv
return Zero()
def setBC(self, ky=None):
if self.bc_type == "Dirichlet":
return
if getattr(self, "_MBC", None) is None:
self._MBC = {}
if ky in self._MBC:
# I have already created the BC matrix for this wavenumber
return
if self.bc_type == "Neumann":
alpha, beta, gamma = 0, 1, 0
else:
mesh = self.mesh
boundary_faces = mesh.boundary_faces
boundary_normals = mesh.boundary_face_outward_normals
n_bf = len(boundary_faces)
# Top gets 0 Neumann
alpha = np.zeros(n_bf)
beta = np.ones(n_bf)
gamma = 0
# assume a source point at the middle of the top of the mesh
middle = np.median(mesh.nodes, axis=0)
top_v = np.max(mesh.nodes[:, -1])
source_point = np.r_[middle[:-1], top_v]
r_vec = boundary_faces - source_point
r = np.linalg.norm(r_vec, axis=-1)
r_hat = r_vec / r[:, None]
r_dot_n = np.einsum("ij,ij->i", r_hat, boundary_normals)
# determine faces that are on the sides and bottom of the mesh...
if mesh._meshType.lower() == "tree":
not_top = boundary_faces[:, -1] != top_v
else:
# mesh faces are ordered, faces_x, faces_y, faces_z so...
is_b = make_boundary_bool(mesh.shape_faces_y)
is_t = np.zeros(mesh.shape_faces_y, dtype=bool, order="F")
is_t[:, -1] = True
is_t = is_t.reshape(-1, order="F")[is_b]
not_top = np.zeros(boundary_faces.shape[0], dtype=bool)
not_top[-len(is_t):] = ~is_t
# use the exponentialy scaled modified bessel function of second kind,
# (the division will cancel out the scaling)
# This is more stable for large values of ky * r
# actual ratio is k1/k0...
alpha[not_top] = (ky * k1e(ky * r) / k0e(ky * r) *
r_dot_n)[not_top]
B, bc = self.mesh.cell_gradient_weak_form_robin(alpha, beta, gamma)
# bc should always be 0 because gamma was always 0 above
self._MBC[ky] = B
class BaseTimeRx(BaseRx):
"""SimPEG Receiver Object for time-domain simulations"""
times = properties.Array(
"times where the recievers measure data", shape=("*",), required=True
)
projTLoc = properties.StringChoice(
"location on the time mesh where the data are projected from",
choices=["N", "CC"],
default="N",
)
def __init__(self, locations=None, times=None, **kwargs):
super(BaseTimeRx, self).__init__(locations=locations, **kwargs)
if times is not None:
self.times = times
@property
def nD(self):
"""Number of data in the receiver."""
return self.locations.shape[0] * len(self.times)
def getSpatialP(self, mesh):
"""
Returns the spatial projection matrix.
.. note::
This is not stored in memory, but is created on demand.
"""
return mesh.getInterpolationMat(self.locations, self.projGLoc)
def getTimeP(self, timeMesh):
"""
Returns the time projection matrix.
.. note::
This is not stored in memory, but is created on demand.
"""
return timeMesh.getInterpolationMat(self.times, self.projTLoc)
def getP(self, mesh, timeMesh):
"""
Returns the projection matrices as a
list for all components collected by
the receivers.
.. note::
Projection matrices are stored as a dictionary (mesh, timeMesh)
if storeProjections is True
"""
if (mesh, timeMesh) in self._Ps:
return self._Ps[(mesh, timeMesh)]
Ps = self.getSpatialP(mesh)
Pt = self.getTimeP(timeMesh)
P = sp.kron(Pt, Ps)
if self.storeProjections:
self._Ps[(mesh, timeMesh)] = P
return P
class BaseRxNSEM_Point(BaseRx):
"""
Natural source receiver base class.
Assumes that the data locations are xyz coordinates.
:param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
:param string orientation: receiver orientation 'x', 'y' or 'z'
:param string component: real or imaginary component 'real' or 'imag'
"""
component = properties.StringChoice(
"component of the field (real or imag)",
{
"real": ["re", "in-phase", "in phase"],
"imag": ["imaginary", "im", "out-of-phase", "out of phase"],
},
)
def __init__(self, locs, orientation=None, component=None):
self.orientation = orientation
self.component = component
BaseRx.__init__(self, locs)
# Set a mesh property - TODO: remove the following properties
@property
def mesh(self):
return self._mesh
@mesh.setter
def mesh(self, value):
if value is getattr(self, "_mesh", None):
pass
else:
self._mesh = value
@property
def src(self):
return self._src
@src.setter
def src(self, value):
self._src = value
@property
def f(self):
return self._f
@f.setter
def f(self, value):
self._f = value
def _locs_e(self):
if self.locations.ndim == 3:
loc = self.locations[:, :, 0]
else:
loc = self.locations
return loc
def _locs_b(self):
if self.locations.ndim == 3:
loc = self.locations[:, :, 1]
else:
loc = self.locations
return loc
# Location projection
@property
def Pex(self):
if getattr(self, "_Pex", None) is None:
self._Pex = self._mesh.getInterpolationMat(self._locs_e(), "Ex")
return self._Pex
@property
def Pey(self):
if getattr(self, "_Pey", None) is None:
self._Pey = self._mesh.getInterpolationMat(self._locs_e(), "Ey")
return self._Pey
@property
def Pbx(self):
if getattr(self, "_Pbx", None) is None:
self._Pbx = self._mesh.getInterpolationMat(self._locs_b(), "Fx")
return self._Pbx
@property
def Pby(self):
if getattr(self, "_Pby", None) is None:
self._Pby = self._mesh.getInterpolationMat(self._locs_b(), "Fy")
return self._Pby
@property
def Pbz(self):
if getattr(self, "_Pbz", None) is None:
self._Pbz = self._mesh.getInterpolationMat(self._locs_e(), "Fz")
return self._Pbz
# Utility for convienece
def _sDiag(self, t):
return sdiag(mkvc(t, 2))
# Get the components of the fields
# px: x-polaration and py: y-polaration.
@property
def _ex_px(self):
return self.Pex * self.f[self.src, "e_px"]
@property
def _ey_px(self):
return self.Pey * self.f[self.src, "e_px"]
@property
def _ex_py(self):
return self.Pex * self.f[self.src, "e_py"]
@property
def _ey_py(self):
return self.Pey * self.f[self.src, "e_py"]
@property
def _hx_px(self):
return self.Pbx * self.f[self.src, "b_px"] / mu_0
@property
def _hy_px(self):
return self.Pby * self.f[self.src, "b_px"] / mu_0
@property
def _hz_px(self):
return self.Pbz * self.f[self.src, "b_px"] / mu_0
@property
def _hx_py(self):
return self.Pbx * self.f[self.src, "b_py"] / mu_0
@property
def _hy_py(self):
return self.Pby * self.f[self.src, "b_py"] / mu_0
@property
def _hz_py(self):
return self.Pbz * self.f[self.src, "b_py"] / mu_0
# Get the derivatives
def _ex_px_u(self, vec):
return self.Pex * self.f._e_pxDeriv_u(self.src, vec)
def _ey_px_u(self, vec):
return self.Pey * self.f._e_pxDeriv_u(self.src, vec)
def _ex_py_u(self, vec):
return self.Pex * self.f._e_pyDeriv_u(self.src, vec)
def _ey_py_u(self, vec):
return self.Pey * self.f._e_pyDeriv_u(self.src, vec)
def _hx_px_u(self, vec):
return self.Pbx * self.f._b_pxDeriv_u(self.src, vec) / mu_0
def _hy_px_u(self, vec):
return self.Pby * self.f._b_pxDeriv_u(self.src, vec) / mu_0
def _hz_px_u(self, vec):
return self.Pbz * self.f._b_pxDeriv_u(self.src, vec) / mu_0
def _hx_py_u(self, vec):
return self.Pbx * self.f._b_pyDeriv_u(self.src, vec) / mu_0
def _hy_py_u(self, vec):
return self.Pby * self.f._b_pyDeriv_u(self.src, vec) / mu_0
def _hz_py_u(self, vec):
return self.Pbz * self.f._b_pyDeriv_u(self.src, vec) / mu_0
# Define the components of the derivative
@property
def _Hd(self):
return self._sDiag(1.0 / (self._sDiag(self._hx_px) * self._hy_py -
self._sDiag(self._hx_py) * self._hy_px))
def _Hd_uV(self, v):
return (self._sDiag(self._hy_py) * self._hx_px_u(v) +
self._sDiag(self._hx_px) * self._hy_py_u(v) -
self._sDiag(self._hx_py) * self._hy_px_u(v) -
self._sDiag(self._hy_px) * self._hx_py_u(v))
# Adjoint
@property
def _aex_px(self):
return mkvc(mkvc(self.f[self.src, "e_px"], 2).T * self.Pex.T)
@property
def _aey_px(self):
return mkvc(mkvc(self.f[self.src, "e_px"], 2).T * self.Pey.T)
@property
def _aex_py(self):
return mkvc(mkvc(self.f[self.src, "e_py"], 2).T * self.Pex.T)
@property
def _aey_py(self):
return mkvc(mkvc(self.f[self.src, "e_py"], 2).T * self.Pey.T)
@property
def _ahx_px(self):
return mkvc(mkvc(self.f[self.src, "b_px"], 2).T / mu_0 * self.Pbx.T)
@property
def _ahy_px(self):
return mkvc(mkvc(self.f[self.src, "b_px"], 2).T / mu_0 * self.Pby.T)
@property
def _ahz_px(self):
return mkvc(mkvc(self.f[self.src, "b_px"], 2).T / mu_0 * self.Pbz.T)
@property
def _ahx_py(self):
return mkvc(mkvc(self.f[self.src, "b_py"], 2).T / mu_0 * self.Pbx.T)
@property
def _ahy_py(self):
return mkvc(mkvc(self.f[self.src, "b_py"], 2).T / mu_0 * self.Pby.T)
@property
def _ahz_py(self):
return mkvc(mkvc(self.f[self.src, "b_py"], 2).T / mu_0 * self.Pbz.T)
# NOTE: need to add a .T at the end for the output to be (nU,)
def _aex_px_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return self.f._e_pxDeriv_u(
self.src,
self.Pex.T * mkvc(vec, ),
adjoint=True,
)
def _aey_px_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return self.f._e_pxDeriv_u(
self.src,
self.Pey.T * mkvc(vec, ),
adjoint=True,
)
def _aex_py_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return self.f._e_pyDeriv_u(
self.src,
self.Pex.T * mkvc(vec, ),
adjoint=True,
)
def _aey_py_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return self.f._e_pyDeriv_u(
self.src,
self.Pey.T * mkvc(vec, ),
adjoint=True,
)
def _ahx_px_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pxDeriv_u(
self.src,
self.Pbx.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
def _ahy_px_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pxDeriv_u(
self.src,
self.Pby.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
def _ahz_px_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pxDeriv_u(
self.src,
self.Pbz.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
def _ahx_py_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pyDeriv_u(
self.src,
self.Pbx.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
def _ahy_py_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pyDeriv_u(
self.src,
self.Pby.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
def _ahz_py_u(self, vec):
""""""
# vec is (nD,) and returns a (nU,)
return (self.f._b_pyDeriv_u(
self.src,
self.Pbz.T * mkvc(vec, ),
adjoint=True,
) / mu_0)
# Define the components of the derivative
@property
def _aHd(self):
return self._sDiag(1.0 / (self._sDiag(self._ahx_px) * self._ahy_py -
self._sDiag(self._ahx_py) * self._ahy_px))
def _aHd_uV(self, x):
return (self._ahx_px_u(self._sDiag(self._ahy_py) * x) +
self._ahx_px_u(self._sDiag(self._ahy_py) * x) -
self._ahy_px_u(self._sDiag(self._ahx_py) * x) -
self._ahx_py_u(self._sDiag(self._ahy_px) * x))
def eval(self, src, mesh, f, return_complex=False):
"""
Function to evaluate datum for this receiver
"""
raise NotImplementedError(
"SimPEG.EM.NSEM receiver has to have an eval method")
def evalDeriv(self, src, mesh, f, v, adjoint=False):
"""
Function to evaluate datum for this receiver
"""
raise NotImplementedError(
"SimPEG.EM.NSEM receiver has to have an evalDeriv method")
class BaseRx(properties.HasProperties):
"""SimPEG Receiver Object"""
# TODO: write a validator that checks against mesh dimension in the
# BaseSimulation
# TODO: location
locations = RxLocationArray(
"Locations of the receivers (nRx x nDim)", shape=("*", "*"), required=True
)
# TODO: project_grid?
projGLoc = properties.StringChoice(
"Projection grid location, default is CC",
choices=["CC", "Fx", "Fy", "Fz", "Ex", "Ey", "Ez", "N"],
default="CC",
)
# TODO: store_projections
storeProjections = properties.Bool(
"Store calls to getP (organized by mesh)", default=True
)
_uid = properties.Uuid("unique ID for the receiver")
_Ps = properties.Dictionary("dictonary for storing projections",)
def __init__(self, locations=None, **kwargs):
super(BaseRx, self).__init__(**kwargs)
if locations is not None:
self.locations = locations
rxType = kwargs.pop("rxType", None)
if rxType is not None:
warnings.warn(
"BaseRx no longer has an rxType. Each rxType should instead "
"be a different receiver class."
)
if getattr(self, "_Ps", None) is None:
self._Ps = {}
locs = deprecate_property(
locations, "locs", new_name="locations", removal_version="0.16.0", error=True,
)
@property
def nD(self):
"""Number of data in the receiver."""
return self.locations.shape[0]
def getP(self, mesh, projGLoc=None):
"""
Returns the projection matrices as a
list for all components collected by
the receivers.
.. note::
Projection matrices are stored as a dictionary listed by meshes.
"""
if projGLoc is None:
projGLoc = self.projGLoc
if (mesh, projGLoc) in self._Ps:
return self._Ps[(mesh, projGLoc)]
P = mesh.getInterpolationMat(self.locations, projGLoc)
if self.storeProjections:
self._Ps[(mesh, projGLoc)] = P
return P
def eval(self, **kwargs):
raise NotImplementedError(
"the eval method for {} has not been implemented".format(self)
)
def evalDeriv(self, **kwargs):
raise NotImplementedError(
"the evalDeriv method for {} has not been implemented".format(self)
)
class Point3DTipper(BaseRxNSEM_Point):
"""
Natural source 3D tipper receiver base class
:param numpy.ndarray locs: receiver locations (ie. :code:`np.r_[x,y,z]`)
:param string orientation: receiver orientation 'x', 'y' or 'z'
:param string component: real or imaginary component 'real' or 'imag'
"""
orientation = properties.StringChoice(
"orientation of the receiver. Must currently be 'zx', 'zy'",
["zx", "zy"])
def __init__(self, locs, orientation="zx", component="real"):
super().__init__(locs, orientation=orientation, component=component)
def eval(self, src, mesh, f, return_complex=False):
"""
Project the fields to natural source data.
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: The source of the fields to project
:param discretize.TensorMesh mesh: Mesh defining the topology of the problem
:param SimPEG.electromagnetics.frequency_domain.fields.FieldsFDEM f: Natural source fields object to project
:rtype: numpy.ndarray
:return: Evaluated component of the impedance data
"""
# NOTE: Maybe set this as a property
self.src = src
self.mesh = mesh
self.f = f
if "zx" in self.orientation:
Tij = -self._hy_px * self._hz_py + self._hy_py * self._hz_px
if "zy" in self.orientation:
Tij = self._hx_px * self._hz_py - self._hx_py * self._hz_px
rx_eval_complex = self._Hd * Tij
# Return the full impedance
if return_complex:
return rx_eval_complex
return getattr(rx_eval_complex, self.component)
def evalDeriv(self, src, mesh, f, v, adjoint=False):
"""
The derivative of the projection wrt u
:param SimPEG.electromagnetics.frequency_domain.sources.BaseFDEMSrc src: NSEM source
:param discretize.TensorMesh mesh: Mesh defining the topology of the problem
:param SimPEG.electromagnetics.frequency_domain.fields.FieldsFDEM f: NSEM fields object of the source
:param numpy.ndarray v: Random vector of size
:rtype: numpy.ndarray
:return: Calculated derivative (nD,) (adjoint=False) and (nP,2) (adjoint=True)
for both polarizations
"""
self.src = src
self.mesh = mesh
self.f = f
if adjoint:
if "zx" in self.orientation:
Tij = self._sDiag(self._aHd *
(-self._sDiag(self._ahz_py) * self._ahy_px +
self._sDiag(self._ahz_px) * self._ahy_py))
def TijN_uV(x):
return (-self._ahz_py_u(self._sDiag(self._ahy_px) * x) -
self._ahy_px_u(self._sDiag(self._ahz_py) * x) +
self._ahy_py_u(self._sDiag(self._ahz_px) * x) +
self._ahz_px_u(self._sDiag(self._ahy_py) * x))
elif "zy" in self.orientation:
Tij = self._sDiag(self._aHd *
(self._sDiag(self._ahz_py) * self._ahx_px -
self._sDiag(self._ahz_px) * self._ahx_py))
def TijN_uV(x):
return (self._ahx_px_u(self._sDiag(self._ahz_py) * x) +
self._ahz_py_u(self._sDiag(self._ahx_px) * x) -
self._ahx_py_u(self._sDiag(self._ahz_px) * x) -
self._ahz_px_u(self._sDiag(self._ahx_py) * x))
# Calculate the complex derivative
rx_deriv_real = TijN_uV(self._aHd * v) - self._aHd_uV(
Tij.T * self._aHd * v)
# NOTE: Need to reshape the output to go from 2*nU array to a (nU,2) matrix for each polarization
# rx_deriv_real = np.hstack((mkvc(rx_deriv_real[:len(rx_deriv_real)/2],2),mkvc(rx_deriv_real[len(rx_deriv_real)/2::],2)))
rx_deriv_real = rx_deriv_real.reshape((2, self.mesh.nE)).T
# Extract the data
if self.component == "imag":
rx_deriv_component = 1j * rx_deriv_real
elif self.component == "real":
rx_deriv_component = rx_deriv_real.astype(complex)
else:
if "zx" in self.orientation:
TijN_uV = (-self._sDiag(self._hy_px) * self._hz_py_u(v) -
self._sDiag(self._hz_py) * self._hy_px_u(v) +
self._sDiag(self._hy_py) * self._hz_px_u(v) +
self._sDiag(self._hz_px) * self._hy_py_u(v))
elif "zy" in self.orientation:
TijN_uV = (self._sDiag(self._hz_py) * self._hx_px_u(v) +
self._sDiag(self._hx_px) * self._hz_py_u(v) -
self._sDiag(self._hx_py) * self._hz_px_u(v) -
self._sDiag(self._hz_px) * self._hx_py_u(v))
Tij = self.eval(src, mesh, f, True)
# Calculate the complex derivative
rx_deriv_complex = self._Hd * (TijN_uV -
self._sDiag(Tij) * self._Hd_uV(v))
rx_deriv_component = np.array(
getattr(rx_deriv_complex, self.component))
return rx_deriv_component
class BaseIPSimulation(BaseDCSimulation):
eta, etaMap, etaDeriv = props.Invertible("Electrical Chargeability")
_data_type = properties.StringChoice(
"IP data type", default="volt", choices=["volt", "apparent_chargeability"],
)
data_type = deprecate_property(
_data_type,
"data_type",
new_name="receiver.data_type",
removal_version="0.17.0",
future_warn=True,
)
Ainv = None
_f = None
_Jmatrix = None
gtgdiag = None
_sign = None
_pred = None
_scale = None
def fields(self, m=None):
if m is not None:
self.model = m
# sensitivity matrix is fixed
# self._Jmatrix = None
if self._f is None:
if self.verbose is True:
print(">> Solve DC problem")
self._f = super().fields(m=None)
if self._scale is None:
scale = Data(self.survey, np.full(self.survey.nD, self._sign))
# loop through receievers to check if they need to set the _dc_voltage
for src in self.survey.source_list:
for rx in src.receiver_list:
if (
rx.data_type == "apparent_chargeability"
or self._data_type == "apparent_chargeability"
):
scale[src, rx] = self._sign / rx.eval(src, self.mesh, self._f)
self._scale = scale.dobs
if self.verbose is True:
print(">> Compute predicted data")
self._pred = self.forward(m, f=self._f)
# if not self.storeJ:
# self.Ainv.clean()
return self._f
def dpred(self, m=None, f=None):
"""
Predicted data.
.. math::
d_\\text{pred} = Pf(m)
"""
if f is None:
f = self.fields(m)
return self._pred
def getJtJdiag(self, m, W=None):
"""
Return the diagonal of JtJ
"""
if self.gtgdiag is None:
J = self.getJ(m)
if W is None:
W = self._scale ** 2
else:
W = (self._scale * W.diagonal()) ** 2
self.gtgdiag = np.einsum("i,ij,ij->j", W, J, J)
return self.gtgdiag
# @profile
def Jvec(self, m, v, f=None):
return self._scale * super().Jvec(m, v, f)
def forward(self, m, f=None):
return np.asarray(self.Jvec(m, m, f=f))
def Jtvec(self, m, v, f=None):
return super().Jtvec(m, v * self._scale, f)
def delete_these_for_sensitivity(self):
del self._Jmatrix, self._MfRhoI, self._MeSigma
@property
def deleteTheseOnModelUpdate(self):
toDelete = []
return toDelete
@property
def MfRhoDerivMat(self):
"""
Derivative of MfRho with respect to the model
"""
if getattr(self, "_MfRhoDerivMat", None) is None:
drho_dlogrho = sdiag(self.rho) * self.etaDeriv
self._MfRhoDerivMat = (
self.mesh.getFaceInnerProductDeriv(np.ones(self.mesh.nC))(
np.ones(self.mesh.nF)
)
* drho_dlogrho
)
return self._MfRhoDerivMat
def MfRhoIDeriv(self, u, v, adjoint=False):
"""
Derivative of :code:`MfRhoI` with respect to the model.
"""
dMfRhoI_dI = -self.MfRhoI ** 2
if self.storeInnerProduct:
if adjoint:
return self.MfRhoDerivMat.T * (sdiag(u) * (dMfRhoI_dI.T * v))
else:
return dMfRhoI_dI * (sdiag(u) * (self.MfRhoDerivMat * v))
else:
dMf_drho = self.mesh.getFaceInnerProductDeriv(self.rho)(u)
drho_dlogrho = sdiag(self.rho) * self.etaDeriv
if adjoint:
return drho_dlogrho.T * (dMf_drho.T * (dMfRhoI_dI.T * v))
else:
return dMfRhoI_dI * (dMf_drho * (drho_dlogrho * v))
@property
def MeSigmaDerivMat(self):
"""
Derivative of MeSigma with respect to the model
"""
if getattr(self, "_MeSigmaDerivMat", None) is None:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
self._MeSigmaDerivMat = (
self.mesh.getEdgeInnerProductDeriv(np.ones(self.mesh.nC))(
np.ones(self.mesh.nE)
)
* dsigma_dlogsigma
)
return self._MeSigmaDerivMat
# TODO: This should take a vector
def MeSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MeSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MeSigmaDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MeSigmaDerivMat * v)
else:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
if adjoint:
return dsigma_dlogsigma.T * (
self.mesh.getEdgeInnerProductDeriv(self.sigma)(u).T * v
)
else:
return self.mesh.getEdgeInnerProductDeriv(self.sigma)(u) * (
dsigma_dlogsigma * v
)
class BaseIPSimulation2D(BaseDCSimulation2D):
sigma = props.PhysicalProperty("Electrical conductivity (S/m)")
rho = props.PhysicalProperty("Electrical resistivity (Ohm m)")
props.Reciprocal(sigma, rho)
eta, etaMap, etaDeriv = props.Invertible("Electrical Chargeability (V/V)")
data_type = properties.StringChoice(
"IP data type", default="volt", choices=["volt", "apparent_chargeability"],
)
fieldsPair = Fields2D
_Jmatrix = None
_f = None
sign = None
_pred = None
def fields(self, m):
if self.verbose:
print(">> Compute DC fields")
if self._f is None:
# re-uses the DC simulation's fields method
self._f = super().fields(None)
if self.verbose is True:
print(">> Data type is apparaent chargeability")
# call dpred function in 2D DC simulation
# set data type as volt ... for DC simulation
data_types = []
for src in self.survey.source_list:
for rx in src.receiver_list:
data_types.append(rx.data_type)
rx.data_type = "volt"
dc_voltage = super().dpred(m=[], f=self._f)
dc_data = Data(self.survey, dc_voltage)
icount = 0
for src in self.survey.source_list:
for rx in src.receiver_list:
rx.data_type = data_types[icount]
rx._dc_voltage = dc_data[src, rx]
rx._Ps = {}
icount += 1
self._pred = self.forward(m, f=self._f)
return self._f
def dpred(self, m=None, f=None):
"""
Predicted data.
.. math::
d_\\text{pred} = Pf(m)
"""
# return self.Jvec(m, m, f=f)
if f is None:
f = self.fields(m)
return self._pred
def Jvec(self, m, v, f=None):
self.model = m
J = self.getJ(m, f=f)
Jv = J.dot(v)
return self.sign * Jv
def forward(self, m, f=None):
return self.Jvec(m, m, f=f)
def Jtvec(self, m, v, f=None):
self.model = m
J = self.getJ(m, f=f)
Jtv = J.T.dot(v)
return self.sign * Jtv
@property
def deleteTheseOnModelUpdate(self):
toDelete = []
return toDelete
@property
def MeSigmaDerivMat(self):
"""
Derivative of MeSigma with respect to the model
"""
if getattr(self, "_MeSigmaDerivMat", None) is None:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
self._MeSigmaDerivMat = (
self.mesh.getEdgeInnerProductDeriv(np.ones(self.mesh.nC))(
np.ones(self.mesh.nE)
)
* dsigma_dlogsigma
)
return self._MeSigmaDerivMat
# TODO: This should take a vector
def MeSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MeSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MeSigmaDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MeSigmaDerivMat * v)
else:
dsigma_dlogsigma = sdiag(self.sigma) * self.etaDeriv
if adjoint:
return dsigma_dlogsigma.T * (
self.mesh.getEdgeInnerProductDeriv(self.sigma)(u).T * v
)
else:
return self.mesh.getEdgeInnerProductDeriv(self.sigma)(u) * (
dsigma_dlogsigma * v
)
@property
def MccRhoiDerivMat(self):
"""
Derivative of MccRho with respect to the model
"""
if getattr(self, "_MccRhoiDerivMat", None) is None:
rho = self.rho
vol = self.mesh.vol
drho_dlogrho = sdiag(rho) * self.etaDeriv
self._MccRhoiDerivMat = sdiag(vol * (-1.0 / rho ** 2)) * drho_dlogrho
return self._MccRhoiDerivMat
def MccRhoiDeriv(self, u, v, adjoint=False):
"""
Derivative of :code:`MccRhoi` with respect to the model.
"""
if len(self.rho.shape) > 1:
if self.rho.shape[1] > self.mesh.dim:
raise NotImplementedError(
"Full anisotropy is not implemented for MccRhoiDeriv."
)
if self.storeInnerProduct:
if adjoint:
return self.MccRhoiDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MccRhoiDerivMat * v)
else:
vol = self.mesh.vol
rho = self.rho
drho_dlogrho = sdiag(rho) * self.etaDeriv
if adjoint:
return drho_dlogrho.T * (sdia(u * vol * (-1.0 / rho ** 2)) * v)
else:
return sdiag(u * vol * (-1.0 / rho ** 2)) * (drho_dlogrho * v)
@property
def MnSigmaDerivMat(self):
"""
Derivative of MnSigma with respect to the model
"""
if getattr(self, "_MnSigmaDerivMat", None) is None:
sigma = self.sigma
vol = self.mesh.vol
dsigma_dlogsigma = sdiag(sigma) * self.etaDeriv
self._MnSigmaDerivMat = self.mesh.aveN2CC.T * sdiag(vol) * dsigma_dlogsigma
return self._MnSigmaDerivMat
def MnSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MnSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MnSigmaDerivMat.T * (sdiag(u) * v)
else:
return sdiag(u) * (self.MnSigmaDerivMat * v)
else:
sigma = self.sigma
vol = self.mesh.vol
dsigma_dlogsigma = sdiag(sigma) * self.etaDeriv
if adjoint:
return dsigma_dlogsigma.T * (vol * (self.mesh.aveN2CC * (u * v)))
else:
dsig_dm_v = dsigma_dlogsigma * v
return u * (self.mesh.aveN2CC.T * (vol * dsig_dm_v))
class Array(_BaseFile):
"""File resource for numeric array binary storage
In addition to file metadata properties, Array instances have
an :code:`array` attribute. This attribute can be set
with a numeric list or a numpy array, and doing so will
dynamically fill in the other properties.
"""
SUB_TYPE = 'array'
dtype = properties.StringChoice(
'Data type of array',
choices=ARRAY_DTYPES,
)
shape = properties.List(
'Dimensions of the array',
properties.Integer('', min=0),
min_length=1,
)
content_type = properties.StringChoice(
'Content type of the file',
choices=['application/octet-stream'],
default='application/octet-stream',
)
@property
def array(self):
"""Reference to underlying numpy array
This attribute does not need to be set; however, doing so will
set the associated metadata.
"""
return getattr(self, '_array', None)
@array.setter
def array(self, value):
if not isinstance(value, (tuple, list, np.ndarray)):
raise ValueError('Array must be numpy array, list, or tuple')
self._array = np.array(value)
kind = self._array.dtype.kind
if kind not in NUMPY_DTYPES:
raise ValueError('Invalid kind of array: {}'.format(kind))
sizes = NUMPY_DTYPES[kind]
itemsize = self._array.dtype.itemsize
for size in sizes:
if itemsize <= size:
itemsize = size
break
else:
itemsize = sizes[-1]
dtype = '<{}{}'.format(kind, itemsize)
if kind == 'f':
mask = ~np.isnan(self._array)
close = np.allclose(self._array[mask],
self._array.astype(dtype)[mask])
else:
close = (self._array == self._array.astype(dtype)).all()
if not close:
raise ValueError(
'Converting array type {} to supported type {} failed'.format(
self._array.dtype, dtype))
self._array = self._array.astype(dtype)
self.dtype = dtype
self.shape = list(self._array.shape)
self.content_length = self._array.nbytes
def __init__(self, array=None, **kwargs):
"""Array initialization includes the optional argument array
By including this argument, assigning a numeric array to
a :class:`properties.Instance` property will coerce the numeric
array into an Array resource.
"""
super(Array, self).__init__(**kwargs)
if array is not None:
self.array = array
@properties.validator('content_length')
def _validate_content_length(self, change):
"""Ensure content_length matches specified shape and dtype
content_length may be smaller than the size expected from
shape/dtype, if the data is compressed. However, it cannot be
larger.
"""
if not self.dtype or not self.shape:
return
length = np.dtype(ARRAY_DTYPES[self.dtype][0]).itemsize
for dim in self.shape:
length *= dim
if change['value'] > length:
raise properties.ValidationError(
message='content_length is too large for given shape/dtype',
reason='invalid',
prop='content_length',
instance=self,
)
def is_1d(self):
"""Helper method to indicate if array is 1D"""
if self.shape is None:
return False
return all([dim == 1 for dim in self.shape[1:]])
class Survey(BaseSurvey):
"""
Base DC survey
"""
source_list = properties.List(
"A list of sources for the survey",
properties.Instance("A DC source", Src.BaseSrc),
default=[],
)
# Survey
survey_geometry = properties.StringChoice(
"Survey geometry of DC surveys",
default="surface",
choices=["surface", "borehole", "general"],
)
survey_type = properties.StringChoice(
"DC-IP Survey type",
default="dipole-dipole",
choices=["dipole-dipole", "pole-dipole", "dipole-pole", "pole-pole"],
)
def __init__(self, source_list, **kwargs):
super(Survey, self).__init__(source_list, **kwargs)
def __repr__(self):
return (f"{self.__class__.__name__}({self.survey_type}; "
f"#sources: {self.nSrc}; #data: {self.nD})")
@property
def locations_a(self):
"""
Location of the positive (+) current electrodes for each datum
"""
if getattr(self, "_locations_a", None) is None:
self._set_abmn_locations()
return self._locations_a
@property
def locations_b(self):
"""
Location of the negative (-) current electrodes for each datum
"""
if getattr(self, "_locations_b", None) is None:
self._set_abmn_locations()
return self._locations_b
@property
def locations_m(self):
"""
Location of the positive (+) potential electrodes for each datum
"""
if getattr(self, "_locations_m", None) is None:
self._set_abmn_locations()
return self._locations_m
@property
def locations_n(self):
"""
Location of the negative (-) potential electrodes for each datum
"""
if getattr(self, "_locations_n", None) is None:
self._set_abmn_locations()
return self._locations_n
a_locations = deprecate_property(
locations_a,
"a_locations",
new_name="locations_a",
removal_version="0.16.0",
future_warn=True,
)
b_locations = deprecate_property(
locations_b,
"b_locations",
new_name="locations_b",
removal_version="0.16.0",
future_warn=True,
)
m_locations = deprecate_property(
locations_m,
"m_locations",
new_name="locations_m",
removal_version="0.16.0",
future_warn=True,
)
n_locations = deprecate_property(
locations_n,
"n_locations",
new_name="locations_n",
removal_version="0.16.0",
future_warn=True,
)
@property
def unique_electrode_locations(self):
"""
Unique locations of the A, B, M, N electrodes
"""
loc_a = self.locations_a
loc_b = self.locations_b
loc_m = self.locations_m
loc_n = self.locations_n
return np.unique(np.vstack((loc_a, loc_b, loc_m, loc_n)), axis=0)
electrode_locations = deprecate_property(
unique_electrode_locations,
"electrode_locations",
new_name="unique_electrode_locations",
removal_version="0.16.0",
)
@property
def source_locations(self):
"""
Returns, in order, the source locations for all sources in the survey.
Input:
:param self: SimPEG.electromagnetics.static.resistivity.Survey
Output:
:return source_locations: List of np.ndarray containing the A and B
electrode locations.
"""
src_a = []
src_b = []
for src in self.source_list:
src_a.append(src.location_a)
src_b.append(src.location_b)
return [np.vstack(src_a), np.vstack(src_b)]
def set_geometric_factor(
self,
space_type="half-space",
data_type=None,
survey_type=None,
):
if data_type is not None:
warnings.warn(
"The data_type kwarg is deprecated, please set the data_type on the "
"receiver object itself. This behavoir will be removed in SimPEG "
"0.16.0",
DeprecationWarning,
)
if survey_type is not None:
warnings.warn(
"The survey_type parameter is no longer needed, and it will be removed "
"in SimPEG 0.16.0.",
FutureWarning,
)
geometric_factor = static_utils.geometric_factor(self,
space_type=space_type)
geometric_factor = data.Data(self, geometric_factor)
for source in self.source_list:
for rx in source.receiver_list:
if data_type is not None:
rx.data_type = data_type
if rx.data_type == "apparent_resistivity":
rx._geometric_factor[source] = geometric_factor[source, rx]
return geometric_factor
def _set_abmn_locations(self):
locations_a = []
locations_b = []
locations_m = []
locations_n = []
for source in self.source_list:
for rx in source.receiver_list:
nRx = rx.nD
# Pole Source
if isinstance(source, Src.Pole):
locations_a.append(
source.location.reshape([1, -1]).repeat(nRx, axis=0))
locations_b.append(
source.location.reshape([1, -1]).repeat(nRx, axis=0))
# Dipole Source
elif isinstance(source, Src.Dipole):
locations_a.append(source.location[0].reshape(
[1, -1]).repeat(nRx, axis=0))
locations_b.append(source.location[1].reshape(
[1, -1]).repeat(nRx, axis=0))
# Pole RX
if isinstance(rx, Rx.Pole):
locations_m.append(rx.locations)
locations_n.append(rx.locations)
# Dipole RX
elif isinstance(rx, Rx.Dipole):
locations_m.append(rx.locations[0])
locations_n.append(rx.locations[1])
self._locations_a = np.vstack(locations_a)
self._locations_b = np.vstack(locations_b)
self._locations_m = np.vstack(locations_m)
self._locations_n = np.vstack(locations_n)
def getABMN_locations(self):
warnings.warn(
"The getABMN_locations method has been deprecated. Please instead "
"ask for the property of interest: survey.locations_a, "
"survey.locations_b, survey.locations_m, or survey.locations_n. "
"This will be removed in version 0.16.0 of SimPEG",
FutureWarning,
)
def drape_electrodes_on_topography(self,
mesh,
actind,
option="top",
topography=None,
force=False):
"""Shift electrode locations to be on [top] of the active cells."""
if self.survey_geometry == "surface":
loc_a = self.locations_a
loc_b = self.locations_b
loc_m = self.locations_m
loc_n = self.locations_n
unique_electrodes, inv = np.unique(np.vstack(
(loc_a, loc_b, loc_m, loc_n)),
return_inverse=True,
axis=0)
inv_a, inv = inv[:len(loc_a)], inv[len(loc_a):]
inv_b, inv = inv[:len(loc_b)], inv[len(loc_b):]
inv_m, inv_n = inv[:len(loc_m)], inv[len(loc_m):]
electrodes_shifted = drapeTopotoLoc(mesh,
unique_electrodes,
actind=actind,
option=option)
a_shifted = electrodes_shifted[inv_a]
b_shifted = electrodes_shifted[inv_b]
m_shifted = electrodes_shifted[inv_m]
n_shifted = electrodes_shifted[inv_n]
# These should all be in the same order as the survey datas
ind = 0
for src in self.source_list:
a_loc, b_loc = a_shifted[ind], b_shifted[ind]
if isinstance(src, Src.Pole):
src.location = a_loc
else:
src.location = [a_loc, b_loc]
for rx in src.receiver_list:
end = ind + rx.nD
m_locs, n_locs = m_shifted[ind:end], n_shifted[ind:end]
if isinstance(rx, Rx.Pole):
rx.locations = m_locs
else:
rx.locations = [m_locs, n_locs]
ind = end
self._locations_a = a_shifted
self._locations_b = b_shifted
self._locations_m = m_shifted
self._locations_n = n_shifted
elif self.survey_geometry == "borehole":
raise Exception("Not implemented yet for borehole survey_geometry")
else:
raise Exception(
"Input valid survey survey_geometry: surface or borehole")
def drapeTopo(self, *args, **kwargs):
warnings.warn(
"The drapeTopo method has been deprecated. Please instead "
"use the drape_electrodes_on_topography method. "
"This will be removed in version 0.16.0 of SimPEG",
FutureWarning,
)
self.drape_electrodes_on_topography(*args, **kwargs)
class DataBasic(_BaseData):
"""Basic numeric attribute data
This data type is defined by an array that directly maps
to an element geometry. For no-data values, use NaN.
Currently, this only accepts 1D arrays and, when applied to a grid,
the array must be stored in row-major order.
"""
BASE_TYPE = 'data'
SUB_TYPE = 'basic'
array = Pointer(
'Array of numeric values at locations on an element geometry, '
'specified by the location property',
Array,
)
location = properties.StringChoice(
'Location of the data on geometry',
choices={
'nodes': ['N', 'node', 'vertices', 'corners'],
'cells': ['CC', 'cell', 'segments', 'faces', 'blocks'],
})
mappings = properties.List(
'Mappings associated with the data',
prop=properties.Union(
'',
props=[
Pointer('', MappingContinuous),
Pointer('', MappingDiscrete),
],
),
max_length=100,
default=list,
)
@properties.validator('array')
def _validate_array_1d(self, change):
"""Ensure the array is 1D"""
if (isinstance(change['value'], string_types)
or change['value'] is properties.undefined):
return
if not change['value'].is_1d():
raise properties.ValidationError(
message='{} must use 1D array'.format(
self.__class__.__name__, ),
reason='invalid',
prop='array',
instance=self,
)
def to_omf(self, cell_location):
self.validate()
if self.location == 'nodes':
location = 'vertices'
else:
location = cell_location
omf_data = omf.ScalarData(
name=self.name or '',
description=self.description or '',
location=location,
array=self.array.array,
)
return omf_data
class SimulationNDCellCentered(BaseTimeSimulation):
"""Richards Simulation"""
hydraulic_conductivity = properties.Instance(
"hydraulic conductivity function", BaseHydraulicConductivity)
water_retention = properties.Instance("water retention curve",
BaseWaterRetention)
# TODO: This can also be a function(time, u_ii)
boundary_conditions = properties.Array("boundary conditions")
initial_conditions = properties.Array("initial conditions")
debug = properties.Bool("Show all messages", default=False)
method = properties.StringChoice(
"Formulation used, See notes in Celia et al., 1990",
default="mixed",
choices=["mixed", "head"],
)
do_newton = properties.Bool("Do a Newton iteration vs. a Picard iteration",
default=False)
root_finder_max_iter = properties.Integer(
"Maximum iterations for root_finder iteration", default=30)
root_finder_tol = properties.Float("tolerance of the root_finder",
default=1e-4)
@properties.observer("model")
def _on_model_change(self, change):
"""Update the nested model functions when the
model of the problem changes.
Specifically :code:`hydraulic_conductivity` and
:code:`water_retention` models are updated iff they have mappings.
"""
if (not self.hydraulic_conductivity.needs_model
and not self.water_retention.needs_model):
warnings.warn("There is no model to set.")
return
model = change["value"]
if self.hydraulic_conductivity.needs_model:
self.hydraulic_conductivity.model = model
if self.water_retention.needs_model:
self.water_retention.model = model
def getBoundaryConditions(self, ii, u_ii):
if isinstance(self.boundary_conditions, np.ndarray):
return self.boundary_conditions
time = self.time_mesh.vectorCCx[ii]
return self.boundary_conditions(time, u_ii)
@properties.observer(
["do_newton", "root_finder_max_iter", "root_finder_tol"])
def _on_root_finder_update(self, change):
"""Setting do_newton etc. will clear the root_finder,
which will be reinitialized when called
"""
if hasattr(self, "_root_finder"):
del self._root_finder
@property
def root_finder(self):
"""Root-finding Algorithm"""
if getattr(self, "_root_finder", None) is None:
self._root_finder = optimization.NewtonRoot(
doLS=self.do_newton,
maxIter=self.root_finder_max_iter,
tol=self.root_finder_tol,
Solver=self.solver,
)
return self._root_finder
@utils.timeIt
def fields(self, m=None):
if self.water_retention.needs_model or self.hydraulic_conductivity.needs_model:
assert m is not None
else:
assert m is None
tic = time.time()
u = list(range(self.nT + 1))
u[0] = self.initial_conditions
for ii, dt in enumerate(self.time_steps):
bc = self.getBoundaryConditions(ii, u[ii])
u[ii + 1] = self.root_finder.root(
lambda hn1m, return_g=True: self.getResidual(
m, u[ii], hn1m, dt, bc, return_g=return_g),
u[ii],
)
if self.debug:
print("Solving Fields ({0:4d}/{1:d} - {2:3.1f}% Done) {3:d} "
"Iterations, {4:4.2f} seconds".format(
ii + 1,
self.nT,
100.0 * (ii + 1) / self.nT,
self.root_finder.iter,
time.time() - tic,
))
return u
def dpred(self, m, f=None):
"""Create the projected data from a model.
The field, f, (if provided) will be used for the predicted data
instead of recalculating the fields (which may be expensive!).
.. math::
d_\\text{pred} = P(f(m), m)
Where P is a projection of the fields onto the data space.
"""
if f is None:
f = self.fields(m)
Ds = list(range(len(self.survey.receiver_list)))
for ii, rx in enumerate(self.survey.receiver_list):
Ds[ii] = rx(f, self)
return np.concatenate(Ds)
@property
def Dz(self):
if self.mesh.dim == 1:
return self.mesh.faceDivx
if self.mesh.dim == 2:
mats = (utils.spzeros(self.mesh.nC,
self.mesh.vnF[0]), self.mesh.faceDivy)
elif self.mesh.dim == 3:
mats = (
utils.spzeros(self.mesh.nC,
self.mesh.vnF[0] + self.mesh.vnF[1]),
self.mesh.faceDivz,
)
return sp.hstack(mats, format="csr")
@utils.timeIt
def diagsJacobian(self, m, hn, hn1, dt, bc):
"""Diagonals and rhs of the jacobian system
The matrix that we are computing has the form::
.- -. .- -. .- -.
| Adiag | | h1 | | b1 |
| Asub Adiag | | h2 | | b2 |
| Asub Adiag | | h3 | = | b3 |
| ... ... | | .. | | .. |
| Asub Adiag | | hn | | bn |
'- -' '- -' '- -'
"""
if m is not None:
self.model = m
DIV = self.mesh.faceDiv
GRAD = self.mesh.cellGrad
BC = self.mesh.cellGradBC
AV = self.mesh.aveF2CC.T
Dz = self.Dz
dT = self.water_retention.derivU(hn)
dT1 = self.water_retention.derivU(hn1)
dTm = self.water_retention.derivM(hn)
dTm1 = self.water_retention.derivM(hn1)
K1 = self.hydraulic_conductivity(hn1)
dK1 = self.hydraulic_conductivity.derivU(hn1)
dKm1 = self.hydraulic_conductivity.derivM(hn1)
# Compute part of the derivative of:
#
# DIV*diag(GRAD*hn1+BC*bc)*(AV*(1.0/K))^-1
DdiagGh1 = DIV * utils.sdiag(GRAD * hn1 + BC * bc)
diagAVk2_AVdiagK2 = (utils.sdiag(
(AV * (1.0 / K1))**(-2)) * AV * utils.sdiag(K1**(-2)))
Asub = (-1.0 / dt) * dT
Adiag = ((1.0 / dt) * dT1 - DdiagGh1 * diagAVk2_AVdiagK2 * dK1 -
DIV * utils.sdiag(1.0 / (AV * (1.0 / K1))) * GRAD -
Dz * diagAVk2_AVdiagK2 * dK1)
B = (DdiagGh1 * diagAVk2_AVdiagK2 * dKm1 +
Dz * diagAVk2_AVdiagK2 * dKm1 + (1.0 / dt) * (dTm - dTm1))
return Asub, Adiag, B
@utils.timeIt
def getResidual(self, m, hn, h, dt, bc, return_g=True):
"""Used by the root finder when going between timesteps
Where h is the proposed value for the next time iterate (h_{n+1})
"""
if m is not None:
self.model = m
DIV = self.mesh.faceDiv
GRAD = self.mesh.cellGrad
BC = self.mesh.cellGradBC
AV = self.mesh.aveF2CC.T
Dz = self.Dz
T = self.water_retention(h)
dT = self.water_retention.derivU(h)
Tn = self.water_retention(hn)
K = self.hydraulic_conductivity(h)
dK = self.hydraulic_conductivity.derivU(h)
aveK = 1.0 / (AV * (1.0 / K))
RHS = DIV * utils.sdiag(aveK) * (GRAD * h + BC * bc) + Dz * aveK
if self.method == "mixed":
r = (T - Tn) / dt - RHS
elif self.method == "head":
r = dT * (h - hn) / dt - RHS
if not return_g:
return r
J = dT / dt - DIV * utils.sdiag(aveK) * GRAD
if self.do_newton:
DDharmAve = utils.sdiag(aveK**2) * AV * utils.sdiag(K**(-2)) * dK
J = J - DIV * utils.sdiag(GRAD * h +
BC * bc) * DDharmAve - Dz * DDharmAve
return r, J
@utils.timeIt
def Jfull(self, m=None, f=None):
if f is None:
f = self.fields(m)
nn = len(f) - 1
Asubs, Adiags, Bs = list(range(nn)), list(range(nn)), list(range(nn))
for ii in range(nn):
dt = self.time_steps[ii]
bc = self.getBoundaryConditions(ii, f[ii])
Asubs[ii], Adiags[ii], Bs[ii] = self.diagsJacobian(
m, f[ii], f[ii + 1], dt, bc)
Ad = sp.block_diag(Adiags)
zRight = utils.spzeros((len(Asubs) - 1) * Asubs[0].shape[0],
Adiags[0].shape[1])
zTop = utils.spzeros(Adiags[0].shape[0],
len(Adiags) * Adiags[0].shape[1])
As = sp.vstack((zTop, sp.hstack((sp.block_diag(Asubs[1:]), zRight))))
A = As + Ad
B = np.array(sp.vstack(Bs).todense())
Ainv = self.solver(A, **self.solver_opts)
AinvB = Ainv * B
z = np.zeros((self.mesh.nC, B.shape[1]))
du_dm = np.vstack((z, AinvB))
J = self.survey.deriv(self, f, du_dm_v=du_dm) # not multiplied by v
return J
@utils.timeIt
def Jvec(self, m, v, f=None):
if f is None:
f = self.fields(m)
JvC = list(range(len(f) -
1)) # Cell to hold each row of the long vector
# This is done via forward substitution.
bc = self.getBoundaryConditions(0, f[0])
temp, Adiag, B = self.diagsJacobian(m, f[0], f[1], self.time_steps[0],
bc)
Adiaginv = self.solver(Adiag, **self.solver_opts)
JvC[0] = Adiaginv * (B * v)
for ii in range(1, len(f) - 1):
bc = self.getBoundaryConditions(ii, f[ii])
Asub, Adiag, B = self.diagsJacobian(m, f[ii], f[ii + 1],
self.time_steps[ii], bc)
Adiaginv = self.solver(Adiag, **self.solver_opts)
JvC[ii] = Adiaginv * (B * v - Asub * JvC[ii - 1])
du_dm_v = np.concatenate([np.zeros(self.mesh.nC)] + JvC)
Jv = self.survey.deriv(self, f, du_dm_v=du_dm_v, v=v)
return Jv
@utils.timeIt
def Jtvec(self, m, v, f=None):
if f is None:
f = self.field(m)
PTv, PTdv = self.survey.derivAdjoint(self, f, v=v)
# This is done via backward substitution.
minus = 0
BJtv = 0
for ii in range(len(f) - 1, 0, -1):
bc = self.getBoundaryConditions(ii - 1, f[ii - 1])
Asub, Adiag, B = self.diagsJacobian(m, f[ii - 1], f[ii],
self.time_steps[ii - 1], bc)
# select the correct part of v
vpart = list(
range((ii) * Adiag.shape[0], (ii + 1) * Adiag.shape[0]))
AdiaginvT = self.solver(Adiag.T, **self.solver_opts)
JTvC = AdiaginvT * (PTv[vpart] - minus)
minus = Asub.T * JTvC # this is now the super diagonal.
BJtv = BJtv + B.T * JTvC
return BJtv + PTdv
class Point(BaseRx):
"""Point receiver"""
times = properties.Array("Observation times", dtype=float)
fieldType = properties.StringChoice(
"Field type", choices=["h", "b", "dhdt", "dbdt"]
)
orientation = properties.StringChoice(
"Component of response", choices=["x", "y", "z"]
)
def __init__(self, locations=None, **kwargs):
if locations.shape[1] != 3:
raise ValueError(
"Rx locations (xi,yi,zi) must be np.array(N,3) where N is the number of stations"
)
super(Point, self).__init__(locations, **kwargs)
@property
def n_times(self):
"""Number of measurements times."""
if self.times is not None:
return len(self.times)
nTimes = deprecate_property(
n_times,
"nTimes",
new_name="n_times",
removal_version="0.16.0",
future_warn=True,
)
@property
def n_locations(self):
"""Number of locations."""
return self.locations.shape[0]
nLocs = deprecate_property(
n_locations,
"nLocs",
new_name="n_locations",
removal_version="0.16.0",
future_warn=True,
)
@property
def nD(self):
"""Number of data in the receiver."""
if self.times is not None:
return self.locations.shape[0] * len(self.times)
fieldComp = deprecate_property(
orientation,
"fieldComp",
new_name="orientation",
removal_version="0.16.0",
future_warn=True,
)
class BaseTDEMSrc(BaseEMSrc):
# rxPair = Rx
waveformPair = BaseWaveform #: type of waveform to pair with
waveform = None #: source waveform
srcType = properties.StringChoice(
"is the source a galvanic of inductive source",
choices=["inductive", "galvanic"],
)
def __init__(self, rxList, **kwargs):
super(BaseTDEMSrc, self).__init__(rxList, **kwargs)
@property
def waveform(self):
"A waveform instance is not None"
return getattr(self, '_waveform', None)
@waveform.setter
def waveform(self, val):
if self.waveform is None:
val._assertMatchesPair(self.waveformPair)
self._waveform = val
else:
self._waveform = self.StepOffWaveform(val)
def __init__(self, rxList, waveform=StepOffWaveform(), **kwargs):
self.waveform = waveform
BaseEMSrc.__init__(self, rxList, **kwargs)
def bInitial(self, prob):
return Zero()
def bInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def eInitial(self, prob):
return Zero()
def eInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def hInitial(self, prob):
return Zero()
def hInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def jInitial(self, prob):
return Zero()
def jInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def eval(self, prob, time):
s_m = self.s_m(prob, time)
s_e = self.s_e(prob, time)
return s_m, s_e
def evalDeriv(self, prob, time, v=None, adjoint=False):
if v is not None:
return (self.s_mDeriv(prob, time, v, adjoint),
self.s_eDeriv(prob, time, v, adjoint))
else:
return (lambda v: self.s_mDeriv(prob, time, v, adjoint),
lambda v: self.s_eDeriv(prob, time, v, adjoint))
def s_m(self, prob, time):
return Zero()
def s_e(self, prob, time):
return Zero()
def s_mDeriv(self, prob, time, v=None, adjoint=False):
return Zero()
def s_eDeriv(self, prob, time, v=None, adjoint=False):
return Zero()
class BaseTDEMSrc(BaseEMSrc):
# rxPair = Rx
waveform = properties.Instance(
"A source waveform", BaseWaveform, default=StepOffWaveform()
)
srcType = properties.StringChoice(
"is the source a galvanic of inductive source",
choices=["inductive", "galvanic"],
)
def __init__(self, receiver_list=None, **kwargs):
if receiver_list is not None:
kwargs["receiver_list"] = receiver_list
super(BaseTDEMSrc, self).__init__(**kwargs)
def bInitial(self, prob):
return Zero()
def bInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def eInitial(self, prob):
return Zero()
def eInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def hInitial(self, prob):
return Zero()
def hInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def jInitial(self, prob):
return Zero()
def jInitialDeriv(self, prob, v=None, adjoint=False, f=None):
return Zero()
def eval(self, prob, time):
s_m = self.s_m(prob, time)
s_e = self.s_e(prob, time)
return s_m, s_e
def evalDeriv(self, prob, time, v=None, adjoint=False):
if v is not None:
return (
self.s_mDeriv(prob, time, v, adjoint),
self.s_eDeriv(prob, time, v, adjoint),
)
else:
return (
lambda v: self.s_mDeriv(prob, time, v, adjoint),
lambda v: self.s_eDeriv(prob, time, v, adjoint),
)
def s_m(self, prob, time):
return Zero()
def s_e(self, prob, time):
return Zero()
def s_mDeriv(self, prob, time, v=None, adjoint=False):
return Zero()
def s_eDeriv(self, prob, time, v=None, adjoint=False):
return Zero()