class Custom(properties.HasProperties):
"""
"""
times = properties.Array(
'Times at which characteristic decay function is evaluated', dtype=float)
eta = properties.Array(
'Characteristic decay function at evaluation times', dtype=float)
@properties.observer('times')
def _times_observer(self, change):
if self.eta is not None:
if len(change['value']) != len(self.eta):
print('Length of time vector no longer matches length of eta vector')
@properties.observer('eta')
def _eta_observer(self, change):
if self.times is not None:
if len(change['value']) != len(self.times):
print('Length of eta vector no longer matches length of time vector')
def getCharDecay(self):
"""Returns characteristic decay function at specified times"""
assert self.eta is not None, "Characteristic decay must be set"
return self.eta
class Custom(properties.HasProperties):
"""
"""
times = properties.Array(
"Times at which characteristic decay function is evaluated",
dtype=float)
eta = properties.Array("Characteristic decay function at evaluation times",
dtype=float)
@properties.observer("times")
def _times_observer(self, change):
if self.eta is not None:
if len(change["value"]) != len(self.eta):
print(
"Length of time vector no longer matches length of eta vector"
)
@properties.observer("eta")
def _eta_observer(self, change):
if self.times is not None:
if len(change["value"]) != len(self.times):
print(
"Length of eta vector no longer matches length of time vector"
)
def getCharDecay(self):
"""Returns characteristic decay function at specified times"""
if self.eta is None:
raise AssertionError(
"Characteristic decay (Property: eta) must be set.")
return self.eta
class SurveyParametersMixin(properties.HasProperties):
"""
A mixin that has the properties of the survey. It doesn't do anything on
its own
"""
# survey parameters
freqs = properties.Array(
"source frequencies",
required=False,
dtype=float
)
timeSteps = TimeStepArray(
"times-steps at which to solve",
required=False,
dtype=float
)
src_a = properties.Array(
"down-hole z-location for the source",
default=np.r_[0., 0., -975.]
)
src_b = properties.Array(
"B electrode location",
default=np.r_[1e3, 0., 0.]
)
class HalfSineWaveform(BaseWaveform):
"""
A waveform that has a quarter-sine ramp-on and a quarter-cosine ramp-off.
When the end of ramp-on and start of ramp off are on the same spot, it looks
like a half sine wave.
"""
ramp_on = properties.Array("times over which the transmitter ramps on",
shape=(2, ),
dtype=float)
ramp_off = properties.Array("times over which we ramp off the waveform",
shape=(2, ),
dtype=float)
def __init__(self, **kwargs):
super(HalfSineWaveform, self).__init__(**kwargs)
self.hasInitialFields = False
def eval(self, time):
if time < self.ramp_on[0]:
return 0
elif time >= self.ramp_on[0] and time <= self.ramp_on[1]:
return np.sin((np.pi / 2) * ((time - self.ramp_on[0]) /
(self.ramp_on[1] - self.ramp_on[0])))
elif time > self.ramp_on[1] and time < self.ramp_off[0]:
return 1
elif time >= self.ramp_off[0] and time <= self.ramp_off[1]:
return np.cos(
(np.pi / 2) * ((time - self.ramp_off[0]) /
(self.ramp_off[1] - self.ramp_off[0])))
else:
return 0
class TrapezoidWaveform(BaseWaveform):
"""
A waveform that has a linear ramp-on and a linear ramp-off.
"""
ramp_on = properties.Array("""times over which the transmitter ramps on
[time starting to ramp on, time fully on]
""",
shape=(2, ),
dtype=float)
ramp_off = properties.Array("""times over which we ramp off the waveform
[time starting to ramp off, time off]
""",
shape=(2, ),
dtype=float)
def __init__(self, **kwargs):
super(TrapezoidWaveform, self).__init__(**kwargs)
self.hasInitialFields = False
def eval(self, time):
if time < self.ramp_on[0]:
return 0
elif time >= self.ramp_on[0] and time <= self.ramp_on[1]:
return ((1. / (self.ramp_on[1] - self.ramp_on[0])) *
(time - self.ramp_on[0]))
elif time > self.ramp_on[1] and time < self.ramp_off[0]:
return 1
elif time >= self.ramp_off[0] and time <= self.ramp_off[1]:
return (1 - (1. / (self.ramp_off[1] - self.ramp_off[0])) *
(time - self.ramp_off[0]))
else:
return 0
class QuarterSineRampOnWaveform(BaseWaveform):
"""
A waveform that has a quarter-sine ramp-on and a linear ramp-off
"""
ramp_on = properties.Array("times over which the transmitter ramps on",
shape=(2, ),
dtype=float)
ramp_off = properties.Array("times over which we ramp off the waveform",
shape=(2, ),
dtype=float)
def __init__(self, **kwargs):
super(QuarterSineRampOnWaveform, self).__init__(**kwargs)
self.hasInitialFields = False
def eval(self, time):
if time < self.ramp_on[0]:
return 0
elif time >= self.ramp_on[0] and time <= self.ramp_on[1]:
return np.sin(np.pi / 2 * (1.0 /
(self.ramp_on[1] - self.ramp_on[0])) *
(time - self.ramp_on[0]))
elif time > self.ramp_on[1] and time < self.ramp_off[0]:
return 1
elif time >= self.ramp_off[0] and time <= self.ramp_off[1]:
return 1 - (1.0 / (self.ramp_off[1] - self.ramp_off[0])) * (
time - self.ramp_off[0])
else:
return 0
class SurveyParametersMixin(properties.HasProperties):
"""
A mixin that has the properties of the survey. It doesn't do anything on
its own
"""
# survey parameters
freqs = properties.Array(
"source frequencies",
required=False,
dtype=float
)
timeSteps = TimeStepArray(
"times-steps at which to solve",
required=False,
dtype=float
)
src_a = properties.Array(
"down-hole z-location for the source"
# default=np.r_[0., 0., 0.]
)
src_b = properties.Array(
"B electrode location"
# default=np.r_[CASING_L, 0., 0.]
)
@property
def info_survey(self):
info = "\n ---- Survey ---- "
# # src locations
# info += "\n\n src_a: {:s}".format(str(self.src_a))
# info += "\n src_b: {:s}".format(str(self.src_b))
# info += "\n"
# frequencies or times
if self.freqs is not None:
info += (
"\n {:1.0f} frequencies. "
"min: {:1.1e} Hz, max: {:1.1e} Hz".format(
len(self.freqs), self.freqs.min(), self.freqs.max()
)
)
if self.timeSteps is not None:
info += (
"\n {:1.0f} time steps. min time step: {:1.1e} s, "
"max time step: {:1.1e} s. Total time: {:1.1e} s".format(
len(self.timeSteps), self.timeSteps.min(),
self.timeSteps.max(), self.timeSteps.sum()
)
)
return info
class TargetMixin(BaseCasing):
target_radius = properties.Array(
"radial extent of the target (m) [min, max]",
shape=(2,),
default=np.r_[0., 25.]
)
target_z = properties.Array(
"vertical extent of the target (m) [min, max]",
shape=(2,),
default=np.r_[-925., -900.]
)
target_theta = properties.Array(
"azimuthal extent of the target (m) [min, max]",
shape=(2,),
default=np.r_[0., 2*np.pi]
)
sigma_target = properties.Float(
"conductivity of the target (S/m)",
min=0.,
default=SIGMA_BACK
)
def indx_target(self, mesh):
return (
(mesh.gridCC[:, 0] >= self.target_radius[0]) &
(mesh.gridCC[:, 0] <= self.target_radius[1])
)
def indy_target(self, mesh):
return (
(mesh.gridCC[:, 1] >= self.target_theta[0]) &
(mesh.gridCC[:, 1] <= self.target_theta[1])
)
def indz_target(self, mesh):
return (
(mesh.gridCC[:, 2] >= self.target_z[0]) &
(mesh.gridCC[:, 2] <= self.target_z[1])
)
def ind_target(self, mesh):
return (
self.indx_target(mesh) & self.indy_target(mesh) &
self.indz_target(mesh)
)
def add_sigma_target(self, mesh, sigma):
ind_target = self.ind_target(mesh)
sigma[ind_target] = self.sigma_target
return sigma
class VolumeGridGeometry(ProjectElementGeometry):
"""Contains spatial information of a 3D grid volume."""
tensor_u = properties.Array('Tensor cell widths, u-direction',
shape=('*', ),
dtype=float)
tensor_v = properties.Array('Tensor cell widths, v-direction',
shape=('*', ),
dtype=float)
tensor_w = properties.Array('Tensor cell widths, w-direction',
shape=('*', ),
dtype=float)
axis_u = properties.Vector3('Vector orientation of u-direction',
default='X',
length=1)
axis_v = properties.Vector3('Vector orientation of v-direction',
default='Y',
length=1)
axis_w = properties.Vector3('Vector orientation of w-direction',
default='Z',
length=1)
_valid_locations = ('vertices', 'cells')
def location_length(self, location):
"""Return correct data length based on location"""
if location == 'cells':
return self.num_cells
return self.num_nodes
@property
def num_nodes(self):
"""Number of nodes (vertices)"""
return ((len(self.tensor_u) + 1) * (len(self.tensor_v) + 1) *
(len(self.tensor_w) + 1))
@property
def num_cells(self):
"""Number of cells"""
return len(self.tensor_u) * len(self.tensor_v) * len(self.tensor_w)
@properties.validator
def _validate_mesh(self):
"""Check if mesh content is built correctly"""
if not (np.abs(self.axis_u.dot(self.axis_v) < 1e-6) and #pylint: disable=no-member
np.abs(self.axis_v.dot(self.axis_w) < 1e-6) and #pylint: disable=no-member
np.abs(self.axis_w.dot(self.axis_u) < 1e-6)): #pylint: disable=no-member
raise ValueError('axis_u, axis_v, and axis_w must be orthogonal')
return True
class ManyProperties(properties.HasProperties):
mystr = properties.String(
'my string',
serializer=reverse,
deserializer=reverse,
)
myarr = properties.Array(
'my array',
serializer=to_string,
deserializer=from_string,
)
myinst = properties.Instance(
'my HP1',
instance_class=HP1,
serializer=serialize_a_only,
deserializer=deserialize_from_a,
)
mylist = properties.List(
'my list of HP1',
prop=HP1,
serializer=sum_of_a,
deserializer=from_sum,
)
myunion = properties.Union(
'string or HP1',
props=(HP1, properties.String('')),
serializer=just_the_classname,
deserializer=reverse,
)
class SingleLayer(Halfspace):
"""
A model consisting of air, subsurface and a single subsurface layer
"""
sigma_layer = properties.Float(
"conductivity of the layer (S/m)",
default=1e-2
)
layer_z = properties.Array(
"z-limits of the layer",
shape=(2,),
default=np.r_[-1000., -900.]
)
def ind_layer(self, mesh):
return (
(mesh.gridCC[:, 2] < self.layer_z[1]) &
(mesh.gridCC[:, 2] > self.layer_z[0])
)
def sigma(self, mesh):
sigma = super(self, sigma)(mesh)
sigma[self.ind_layer(mesh)] = self.sigma_layer
return sigma
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",
error=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",
error=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",
error=True,
)
class Point(BaseRxVRM):
"""Point receiver"""
times = properties.Array('Observation times', dtype=float)
fieldType = properties.StringChoice('Field type',
choices=["h", "b", "dhdt", "dbdt"])
fieldComp = properties.StringChoice('Component of response',
choices=["x", "y", "z"])
# def __init__(self, locs, times, fieldType, fieldComp, **kwargs):
def __init__(self, locs, **kwargs):
if locs.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__(locs, **kwargs)
@property
def nTimes(self):
"""Number of measurements times."""
if self.times is not None:
return len(self.times)
@property
def nLocs(self):
"""Number of locations."""
return self.locs.shape[0]
@property
def nD(self):
"""Number of data in the receiver."""
if self.times is not None:
return self.locs.shape[0] * len(self.times)
class ManyProperties(properties.HasProperties):
mystr = properties.String(
'my string',
required=False,
)
myarr = properties.Array(
'my array',
required=False,
)
myinst = properties.Instance(
'my HP1',
instance_class=HP1,
required=False,
)
mylist = properties.List(
'my list of HP1',
prop=HP1,
required=False,
default=properties.utils.undefined
)
myunion = properties.Union(
'string or HP1',
props=(HP1, properties.String('')),
required=False,
)
class Point(BaseRxVRM):
"""Point receiver"""
times = properties.Array('Observation times', dtype=float)
fieldType = properties.StringChoice('Field type',
choices=["h", "b", "dhdt", "dbdt"])
fieldComp = properties.StringChoice('Component of response',
choices=["x", "y", "z"])
# def __init__(self, locs, times, fieldType, fieldComp, **kwargs):
def __init__(self, locs, **kwargs):
assert locs.shape[1] == 3, 'locs must in 3-D (x,y,z).'
super(Point, self).__init__(locs, **kwargs)
@property
def nTimes(self):
"""Number of measurements times."""
if self.times is not None:
return len(self.times)
@property
def nLocs(self):
"""Number of locations."""
return self.locs.shape[0]
@property
def nD(self):
"""Number of data in the receiver."""
if self.times is not None:
return self.locs.shape[0] * len(self.times)
class LineCurrent(BaseFDEMSrc):
"""
Line current source. Given the wire path provided by the (n,3) loc
array the cells intersected by the wire path are identified and integrated
src terms are computed
:param list rxList: receiver list
:param float freq: src frequency
:param (n,3) array locations: points defining src path
"""
location = properties.Array("location of the source", shape=("*", 3))
current = properties.Float("current in the line", default=1.0)
def Mejs(self, simulation):
if getattr(self, "_Mejs", None) is None:
mesh = simulation.mesh
locs = self.location
self._Mejs = self.current * segmented_line_current_source_term(
mesh, locs)
return self._Mejs
def getRHSdc(self, simulation):
Grad = simulation.mesh.nodalGrad
return Grad.T * self.Mejs(simulation)
def s_m(self, simulation):
return Zero()
def s_e(self, simulation):
if simulation._formulation != "EB":
raise NotImplementedError(
"LineCurrents are only implemented for EB formulations")
return self.Mejs(simulation)
class SurveyVRM(BaseSurvey):
""""""
source_list = properties.List(
"A list of sources for the survey",
properties.Instance("A SimPEG source", BaseSrcVRM),
default=[],
)
t_active = properties.Array(
"Boolean array where True denotes active data in the inversion",
dtype=bool)
def __init__(self, source_list=None, **kwargs):
t_active = kwargs.pop("t_active", None)
super(SurveyVRM, self).__init__(source_list=source_list, **kwargs)
self._nD_all = self.vnD.sum()
self._nD = self._nD_all
if t_active is None:
self.t_active = np.ones(self._nD_all, dtype=bool)
else:
self.t_active = t_active
@properties.validator("t_active")
def _t_active_validator(self, change):
if self._nD_all != len(change["value"]):
raise ValueError(
"Length of t_active boolean array must equal number of data. Number of data is %i"
% self._nD_all)
@property
def nD(self):
if self._nD is None:
self._nD = np.sum(self.t_active)
return self._nD
def set_active_interval(self, tmin, tmax):
"""Set active times using an interval"""
srcList = self.source_list
nSrc = len(srcList)
tActBool = np.array([])
for pp in range(0, nSrc):
rxList = srcList[pp].receiver_list
nRx = len(rxList)
for qq in range(0, nRx):
times = rxList[qq].times
nLoc = np.shape(rxList[qq].locations)[0]
tActBool = np.r_[tActBool, np.kron(np.ones(nLoc), times)]
self.t_active = (tActBool >= tmin) & (tActBool <= tmax)
self._nD = np.sum(self.t_active)
class GlobalAEMSurvey(Survey.BaseSurvey, properties.HasProperties):
# This assumes a multiple sounding locations
rx_locations = properties.Array("Receiver locations ",
dtype=float,
shape=('*', 3))
src_locations = properties.Array("Source locations ",
dtype=float,
shape=('*', 3))
topo = properties.Array("Topography", dtype=float, shape=('*', 3))
half_switch = properties.Bool("Switch for half-space", default=False)
_pred = None
@Utils.requires('prob')
def dpred(self, m, f=None):
"""
Return predicted data.
Predicted data, (`_pred`) are computed when
self.prob.fields is called.
"""
if f is None:
f = self.prob.fields(m)
return self._pred
@property
def n_sounding(self):
"""
# of Receiver locations
"""
return self.rx_locations.shape[0]
def read_xyz_data(self, fname):
"""
Read csv file format
This is a place holder at this point
"""
pass
@property
def nD(self):
# Need to generalize this for the dual moment data
if getattr(self, '_nD', None) is None:
self._nD = self.nD_vec.sum()
return self._nD
class SurfaceGridGeometry(ProjectElementGeometry):
"""Contains spatial information of a 2D grid"""
tensor_u = properties.Array('Grid cell widths, u-direction',
shape=('*', ),
dtype=float)
tensor_v = properties.Array('Grid cell widths, v-direction',
shape=('*', ),
dtype=float)
axis_u = properties.Vector3('Vector orientation of u-direction',
default='X',
length=1)
axis_v = properties.Vector3('Vector orientation of v-direction',
default='Y',
length=1)
offset_w = properties.Instance('Node offset', ScalarArray, required=False)
_valid_locations = ('vertices', 'faces')
def location_length(self, location):
"""Return correct data length based on location"""
if location == 'faces':
return self.num_cells
return self.num_nodes
@property
def num_nodes(self):
"""Number of nodes (vertices)"""
return (len(self.tensor_u) + 1) * (len(self.tensor_v) + 1)
@property
def num_cells(self):
"""Number of cells (faces)"""
return len(self.tensor_u) * len(self.tensor_v)
@properties.validator
def _validate_mesh(self):
"""Check if mesh content is built correctly"""
if not np.abs(self.axis_u.dot(self.axis_v)) < 1e-6: #pylint: disable=no-member
raise ValueError('axis_u and axis_v must be orthogonal')
if self.offset_w is properties.undefined or self.offset_w is None:
return True
if len(self.offset_w.array) != self.num_nodes:
raise ValueError(
'Length of offset_w, {zlen}, must equal number of nodes, '
'{nnode}'.format(zlen=len(self.offset_w),
nnode=self.num_nodes))
return True
class BaseSparse(BaseRegularization):
"""
Base class for building up the components of the Sparse Regularization
"""
def __init__(self, mesh, **kwargs):
self._stashedR = None
super(BaseSparse, self).__init__(mesh=mesh, **kwargs)
model = properties.Array("current model", dtype=float)
epsilon = properties.Float("Threshold value for the model norm",
default=1e-3,
required=True)
norm = properties.Array("norm used", dtype=float)
space = properties.String("By default inherit the objctive",
default='linear')
gradientType = properties.String("type of gradient", default='components')
scale = properties.Array(
"General nob for scaling",
dtype=float,
)
# Give the option to scale or not
scaledIRLS = properties.Bool("Scale the gradients of the IRLS norms",
default=True)
@properties.validator('scale')
def _validate_scale(self, change):
if change['value'] is not None:
# todo: residual size? we need to know the expected end shape
if self._nC_residual != '*':
assert len(change['value']) == self._nC_residual, (
'scale must be length {} not {}'.format(
self._nC_residual, len(change['value'])))
@property
def stashedR(self):
return self._stashedR
@stashedR.setter
def stashedR(self, value):
self._stashedR = value
class GlobalEM1DSurvey(Survey.BaseSurvey, properties.HasProperties):
# This assumes a multiple sounding locations
rx_locations = properties.Array("Receiver locations ",
dtype=float,
shape=('*', 3))
src_locations = properties.Array("Source locations ",
dtype=float,
shape=('*', 3))
topo = properties.Array("Topography", dtype=float, shape=('*', 3))
_pred = None
@Utils.requires('prob')
def dpred(self, m, f=None):
"""
Return predicted data.
Predicted data, (`_pred`) are computed when
self.prob.fields is called.
"""
if f is None:
f = self.prob.fields(m)
return self._pred
@property
def n_sounding(self):
"""
# of Receiver locations
"""
return self.rx_locations.shape[0]
@property
def n_layer(self):
"""
# of Receiver locations
"""
return self.prob.n_layer
def read_xyz_data(self, fname):
"""
Read csv file format
This is a place holder at this point
"""
pass
class Int3Array(ScalarArray):
"""Shared n x 3 array of integers"""
array = properties.Array(
'Shared n x 3 Int Array',
dtype=int,
shape=('*', 3),
serializer=array_serializer,
deserializer=array_deserializer(('*', 3))
)
class HasNoCoerceArray(properties.HasProperties):
arr = properties.Array('', coerce=False)
vec2 = properties.Vector2('', coerce=False)
@properties.Array('', coerce=False)
def dynamic_arr(self):
if getattr(self, '_dynamic_arr', None) is None:
self._dynamic_arr = np.ones(5)
return self._dynamic_arr
class MaterialComponent(properties.HasProperties):
"""Defines the extent of a material component in the grid"""
i = properties.Array(
'Range of element indices in the X-direction',
shape=(2, ),
dtype=int,
)
j = properties.Array(
'Range of element indices in the Y-direction',
shape=(2, ),
dtype=int,
)
k = properties.Array(
'Range of element indices in the Z-direction',
shape=(2, ),
dtype=int,
)
class GridInfo(properties.HasProperties):
ncol = properties.Integer('number of columns', min=2)
nrow = properties.Integer('number of rows', min=2)
xll = properties.Float('x-value of lower-left corner')
yll = properties.Float('y-value of lower-left corner')
xsize = properties.Float('x-axis spacing')
ysize = properties.Float('y-axis spacing')
zmin = properties.Float('minimum data value', required=False)
zmax = properties.Float('maximum data value', required=False)
data = properties.Array('grid of data values', shape=('*', '*'))
@properties.validator
def _validate_datasize(self):
if self.data.shape != (self.ncol, self.nrow):
raise ValueError('Data shape must be ncol x nrow')
def to_steno3d(self, project=None, as_topo=True):
"""Create a project from GridInfo
Optional input:
project: Preexisting Steno3D project to add .grd file components
to. If not provided, a new project will be created.
verbose: Print messages and warnings during file parsing.
(default: True)
as_topo: If True, add data from grid file as topography.
Otherwise only add the data as color on a flat surface.
(default: True)
Output:
Steno3D Project with GridInfo components
"""
self.validate()
if project is None:
project = steno3d.Project(description='From surfer grid file')
elif not isinstance(project, steno3d.Project):
raise steno3d.parsers.ParseError('project must be Steno3D Project')
surf = steno3d.Surface(
project=project,
mesh=steno3d.Mesh2DGrid(O=[self.xll, self.yll, 0],
h1=np.ones(self.ncol - 1) * self.xsize,
h2=np.ones(self.nrow - 1) * self.ysize),
data=[
dict(location='N',
data=steno3d.DataArray(array=self.data.flatten()))
])
if as_topo:
surf.mesh.Z = self.data.flatten()
return project
class ArrayOpts(properties.HasProperties):
myarray = properties.Array('my array')
myarray222 = properties.Array('my 3x3x3 array', shape=(2, 2, 2))
myarrayfloat = properties.Array('my float array', dtype=float)
myarrayint = properties.Array('my int array', dtype=int)
myarraybool = properties.Array('my bool array', dtype=bool)
myarraycomplex = properties.Array('my complex array',
dtype=complex)
class KnownSystems(properties.HasProperties):
"""Simple Class for KnownSystems."""
BaseFrequency = properties.Float("WaveFormCurrent", )
WaveFormCurrent = properties.Array("WaveFormCurrent",
dtype=float,
shape=('*', ))
WaveFormTime = properties.Array("WaveFormTime", dtype=float, shape=('*', ))
t_peak = properties.Float("Peak waveform time", )
t0 = properties.Float("Zero waveform time", )
tref = properties.Float("Reference time for current off", )
ModellingLoopRadius = properties.Float("ModellingLoopRadius", )
WindowTimeStart = properties.Array("WindowTimeStart",
dtype=float,
shape=('*', ))
WindowTimeEnd = properties.Array("WindowTimeEnd",
dtype=float,
shape=('*', ))
def __init__(self, name):
self.name = name
def save(self, fname=None):
data = self.serialize()
if fname is None:
fname = self.name
with open(fname + '.json', 'w') as outfile:
json.dump(data, outfile, ensure_ascii=False)
class SquareLoop(BaseRxVRM):
"""Square loop receiver
Measurements with this type of receiver are the field, integrated over the
area of the loop, then multiplied by the number of coils, then normalized
by the dipole moment. As a result, the units for fields predicted with this
type of receiver are the same as 'h', 'b', 'dhdt' and 'dbdt', respectively.
"""
times = properties.Array('Observation times', dtype=float)
width = properties.Float('Square loop width', min=1e-6)
nTurns = properties.Integer('Number of loop turns', min=1, default=1)
quadOrder = properties.Integer(
'Order for numerical quadrature integration over loop',
min=1,
max=7,
default=3)
fieldType = properties.StringChoice('Field type',
choices=["h", "b", "dhdt", "dbdt"])
fieldComp = properties.StringChoice('Component of response',
choices=["x", "y", "z"])
def __init__(self, locs, **kwargs):
if locs.shape[1] != 3:
raise ValueError(
'Rx locations (xi,yi,zi) must be np.array(N,3) where N is the number of stations'
)
super(SquareLoop, self).__init__(locs, **kwargs)
@property
def nTimes(self):
"""Number of measurements times."""
if self.times is not None:
return len(self.times)
@property
def nLocs(self):
"""Number of locations."""
return self.locs.shape[0]
@property
def nD(self):
"""Number of data in the receiver."""
if self.times is not None:
return self.locs.shape[0] * len(self.times)
class Models(properties.HasProperties):
"""A container for static models"""
_models = properties.Dictionary(
'The data dictionary.',
key_prop=properties.String('The model names'),
value_prop=properties.Array('The data values',
dtype=(float, int, bool),
shape=None))
@property
def shape(self):
return list(self._models.values())[0].shape
@properties.validator
def _validate_models(self):
shp = self.shape
for k, d in self._models.items():
if d.shape != shp:
raise RuntimeError(
'Validation Failed: dimesnion mismatch between models.')
return True
def __getitem__(self, key):
"""Get a model by its string name and time index"""
return self._models[key]
def __setitem__(self, key, value):
if self._models is None:
self._models = {}
self._models[key] = value
def keys(self):
return self._models.keys()
def values(self):
return self._models.values()
def items(self):
return self._models.items()
def save(self, filename):
with open(filename, 'w') as f:
json.dump(self.serialize(), f)
return filename
class ScalarArray(UidModel):
"""Class with unique ID and data array"""
array = properties.Array(
'Shared Scalar Array',
serializer=array_serializer,
deserializer=array_deserializer(('*',))
)
def __init__(self, array=None, **kwargs):
super(ScalarArray, self).__init__(**kwargs)
if array is not None:
self.array = array
def __len__(self):
return self.array.__len__()
def __getitem__(self, i):
return self.array.__getitem__(i)
