pysource package

Submodules

FD_utils module

FDutils.Rmat(model)

Rotation matrix according to tilt and asymut.

• Parameters: model (Model) – Model structure

FD_utils.laplacian(v, irho)

Laplacian with density div( 1/rho grad) (u)

FD_utils.r2(x)

FD_utils.r3(x)

FDutils.satti(u, v, model)

Tensor factorized SSA TTI wave equation spatial derivatives.

• Parameters:
  • u (TimeFunction) – first TTI field
  • v (TimeFunction) – second TTI field
  • model (Model) – Model structure

FDutils.thomsenmat(model)

Diagonal Matrices with Thomsen parameters for vectorial temporaries computation.

• Parameters: model (Model) – Model structure

checkpoint module

class checkpoint.CheckpointOperator(op, **kwargs)

Devito’s concrete implementation of the ABC pyrevolve.Operator. This class wraps devito.Operator so it conforms to the pyRevolve API. pyRevolve will call apply with arguments tstart and tend. Devito calls these arguments ts and te so the following dict is used to perform the translations between different names.

• Parameters:
  • op (Operator) – devito.Operator object that this object will wrap.
  • args (dict) – If devito.Operator.apply() expects any arguments, they can be provided here to be cached. Any calls to CheckpointOperator.apply() will automatically include these cached arguments in the call to the underlying devito.Operator.apply().

apply(tstart, tend)

If the devito operator requires some extra arguments in the call to apply they can be stored in the args property of this object so pyRevolve calls pyRevolve.Operator.apply() without caring about these extra arguments while this method passes them on correctly to devito.Operator

targnames = {'tend': 'timeM', 'tstart': 'timem'}

class checkpoint.DevitoCheckpoint(objects)

Devito’s concrete implementation of the Checkpoint abstract base class provided by pyRevolve. Holds a list of symbol objects that hold data.

property dtype

data type

get_data(timestep)

returns the data (wavefield) for the time-step timestep

getdatalocation(timestep)

returns the data (wavefield) for the time-step timestep

load()

NotImplementedError

save()

NotImplementedError

property size

The memory consumption of the data contained in a checkpoint.

checkpoint.getsymboldata(symbol, timestep)

Return the symbol corresponding to the data at time-step timestep

geom_utils module

geomutils.geomexpr(model, u, srccoords=None, reccoords=None, wavelet=None, fw=True, nt=None)

Generates the source injection and receiver interpolation. This function is fully abstracted and does not care whether this is a forward or adjoint wave-equation. The source is the source term of the equation The receiver is the measurment term

Therefore, for the adjoint, this function has to be called as: srcrec(model, v, srccoords=rec_coords, …) because the data is the sources

• Parameters:
  • model (Model) – Physical model
  • u (TimeFunction or tuple) – Wavefield to inject into and read from
  • src_coords (Array) – Physical coordinates of the sources
  • rec_coords (Array) – Physical coordinates of the receivers
  • wavelet (Array) – Data for the source
  • fw=True – Whether the direction is forward or backward in time
  • nt (int) – Number of time steps

geomutils.mirrorsource(model, src)

geomutils.srcrec(model, u, srccoords=None, reccoords=None, wavelet=None, nt=None)

interface module

interface.Jadjoint(model, srccoords, wavelet, reccoords, recin, isresidual=False, checkpointing=False, ncheckpoints=None, tsub=1, returnobj=False, freqlist=[], dftsub=None, ic='as', illum=False, ws=None, f0=0.015, bornfwd=False, nlind=False, misfit=None, fw=True)

Jacobian (adjoint fo born modeling operator) operator on a shot record as a source (i.e data residual). Supports three modes: * Checkpinting * Frequency compression (on-the-fly DFT) * Standard zero lag cross correlation over time

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • recin (Array) – Receiver data
  • checkpointing (Bool) – Whether or not to use checkpointing
  • n_checkpoints (Int) – Number of checkpoints for checkpointing
  • maxmem (Float) – Maximum memory to use for checkpointing
  • freq_list (List) – List of frequencies for on-the-fly DFT
  • dft_sub (Int) – Subsampling factor for on-the-fly DFT
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • ws (Array) – Extended source spatial distribution
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation as base propagator
• Returns: Adjoint jacobian on the input data (gradient)
• Return type: Array

interface.Jadjointcheckpointing(model, srccoords, wavelet, reccoords, recin, isresidual=False, ncheckpoints=None, bornfwd=False, returnobj=False, ic='as', ws=None, nlind=False, f0=0.015, misfit=None, illum=False, fw=True)

Jacobian (adjoint fo born modeling operator) operator on a shot record as a source (i.e data residual). Outputs the gradient with Checkpointing.

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • recin (Array) – Receiver data
  • checkpointing (Bool) – Whether or not to use checkpointing
  • n_checkpoints (Int) – Number of checkpoints for checkpointing
  • maxmem (Float) – Maximum memory to use for checkpointing
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • ws (Array) – Extended source spatial distribution
  • is_residual (Bool) – Whether to treat the input as the residual or as the observed data
  • born_fwd (Bool) – Whether to use the forward or linearized forward modeling operator
  • nlind (Bool) – Whether to remove the non linear data from the input data. This option is only available in combination with born_fwd
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation as base propagator
• Returns: Adjoint jacobian on the input data (gradient)
• Return type: Array

interface.Jadjointfreq(model, srccoords, wavelet, reccoords, recin, freqlist=[], isresidual=False, returnobj=False, nlind=False, dftsub=None, ic='as', ws=None, born_fwd=False, f0=0.015, misfit=None, illum=False, fw=True)

Jacobian (adjoint fo born modeling operator) operator on a shot record as a source (i.e data residual). Outputs the gradient with Frequency compression (on-the-fly DFT).

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • recin (Array) – Receiver data
  • freq_list (List) – List of frequencies for on-the-fly DFT
  • dft_sub (Int) – Subsampling factor for on-the-fly DFT
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • ws (Array) – Extended source spatial distribution
  • is_residual (Bool) – Whether to treat the input as the residual or as the observed data
  • born_fwd (Bool) – Whether to use the forward or linearized forward modeling operator
  • nlind (Bool) – Whether to remove the non linear data from the input data. This option is only available in combination with born_fwd
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation as base propagator
• Returns: Adjoint jacobian on the input data (gradient)
• Return type: Array

interface.Jadjointstandard(model, srccoords, wavelet, reccoords, recin, isresidual=False, returnobj=False, bornfwd=False, illum=False, ic='as', ws=None, tsub=1, nlind=False, f0=0.015, misfit=None, fw=True)

Adjoint Jacobian (adjoint fo born modeling operator) operator on a shot record as a source (i.e data residual). Outputs the gradient with standard zero lag cross correlation over time.

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • recin (Array) – Receiver data
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • ws (Array) – Extended source spatial distribution
  • is_residual (Bool) – Whether to treat the input as the residual or as the observed data
  • born_fwd (Bool) – Whether to use the forward or linearized forward modeling operator
  • nlind (Bool) – Whether to remove the non linear data from the input data. This option is only available in combination with born_fwd
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation as base propagator
• Returns: Adjoint jacobian on the input data (gradient)
• Return type: Array

interface.adjointw(model, reccoords, data, wavelet, f0=0.015, illum=False, fw=True)

Adjoint/backward modeling of a shot record (receivers as source) for an extended source setup Pw*F^T*Pr^T*d_obs.

• Parameters:
  • model (Model) – Physical model
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • data (Array) – Shot gather
  • wavelet (Array) – Time signature of the forward source for stacking along time
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: spatial distribution
• Return type: Array

interface.bornrec(model, srccoords, wavelet, rec_coords, ic='as', f0=0.015, illum=False, fw=True)

Linearized (Born) modeling of a point source for a model perturbation (square slowness) dm.

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Shot record
• Return type: Array

interface.bornrecw(model, weight, wavelet, rec_coords, ic='as', f0=0.015, illum=False, fw=True)

Linearized (Born) modeling of an extended source for a model perturbation (square slowness) dm with an extended source

• Parameters:
  • model (Model) – Physical model
  • weight (Array) – Spatial distriubtion of the extended source
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • ic (String) – Imaging conditions (“as”, “isic” or “fwi”), defaults to “as”
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Shot record
• Return type: Array

interface.forwardnorec(model, src_coords, wavelet, f0=0.015, illum=False, fw=True)

Forward modeling of a point source without receiver.

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Wavefield
• Return type: Array

interface.forwardrec(model, srccoords, wavelet, rec_coords, f0=0.015, illum=False, fw=True)

Modeling of a point source with receivers Pr*F*Ps^T*q.

• Parameters:
  • model (Model) – Physical model
  • src_coords (Array) – Coordiantes of the source(s)
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Shot record
• Return type: Array

interface.forwardrecw(model, weight, wavelet, rec_coords, f0=0.015, illum=False, fw=True)

Forward modeling of an extended source with receivers Pr*F*Pw^T*w

• Parameters:
  • model (Model) – Physical model
  • weights (Array) – Spatial distribution of the extended source.
  • wavelet (Array) – Source signature
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Shot record
• Return type: Array

interface.forwardwfsrc(model, u, rec_coords, f0=0.015, illum=False, fw=True)

Forward modeling of a full wavefield source Pr*F*u.

• Parameters:
  • model (Model) – Physical model
  • u (TimeFunction or Array) – Time-space dependent wavefield
  • rec_coords (Array) – Coordiantes of the receiver(s)
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Shot record
• Return type: Array

interface.forwardwfsrc_norec(model, u, f0=0.015, illum=False, fw=True)

Forward modeling of a full wavefield source without receiver F*u.

• Parameters:
  • model (Model) – Physical model
  • u (TimeFunction or Array) – Time-space dependent wavefield
  • f0 (float) – peak frequency
  • illum (bool) – Whether to compute illumination during propagation
  • fw (bool) – Whether it is forward or adjoint propagation
• Returns: Wavefield
• Return type: Array

interface.wrifunc(model, srccoords, wavelet, reccoords, recin, yin, ic='as', ws=None, tsub=1, grad='m', gradcorr=False, alphaop=False, wfun=None, eps=0, freqlist=[], wfilt=None, f0=0.015)

Time domain wavefield reconstruction inversion wrapper

kernels module

kernels.SLS2ndorder(model, p, fw=True, q=None, f0=0.015)

Viscoacoustic 2nd SLS wave equation. https://library.seg.org/doi/10.1190/geo2013-0030.1

Bulk modulus moved to rhs. The adjoint equation is directly derived as the discrete adjoint of the forward PDE which leads to a slightly different formulation than in the paper.

• Parameters:
  • model (Model) – Physical model
  • u1 (TimeFunction) – Pressure field
  • u2 (TimeFunction) – Attenuation Memory variable
  • fw (Bool) – Whether forward or backward in time propagation
  • q (TimeFunction or Expr) – Full time-space source as a tuple (one value for each component)
  • f0 (Peak frequency)

kernels.acoustic_kernel(model, u, fw=True, q=None)

Acoustic wave equation time stepper

• Parameters:
  • model (Model) – Physical model
  • u (TimeFunction or tuple) – wavefield (tuple if TTI)
  • fw (Bool) – Whether forward or backward in time propagation
  • q (TimeFunction or Expr) – Full time-space source

kernels.elastic_kernel(model, v, tau, fw=True, q=None)

Elastic wave equation time stepper

• Parameters:
  • model (Model) – Physical model
  • v (VectorTimeFunction) – Particle Velocity
  • tau (TensorTimeFunction) – Stress tensor
  • fw (Bool) – Whether forward or backward in time propagation
  • q (TimeFunction or Expr) – Full time-space source as a tuple (one value for each component)

kernels.tti_kernel(model, u1, u2, fw=True, q=None)

TTI wave equation time stepper

• Parameters:
  • model (Model) – Physical model
  • u1 (TimeFunction) – First component (pseudo-P) of the wavefield
  • u2 (TimeFunction) – Second component (pseudo-S) of the wavefield
  • fw (Bool) – Whether forward or backward in time propagation
  • q (TimeFunction or Expr) – Full time-space source as a tuple (one value for each component)

kernels.vsmaskderivs(eq, tau, vs)

kernels.wave_kernel(model, u, fw=True, q=None, f0=0.015)

Pde kernel corresponding the the model for the input wavefield

• Parameters:
  • model (Model) – Physical model
  • u (TimeFunction or tuple) – wavefield (tuple if TTI or Viscoacoustic)
  • fw (Bool) – Whether forward or backward in time propagation
  • q (TimeFunction or Expr) – Full time-space source
  • f0 (Peak frequency)

models module

class models.Model(origin, spacing, shape, space_order=8, nbl=40, dtype=<class 'numpy.float32'>, m=None, epsilon=None, delta=None, theta=None, phi=None, rho=None, b=None, qp=None, lam=None, mu=None, dm=None, fs=False, **kwargs)

The physical model used in seismic inversion : shape_pml = np.array(shape) + 2 * self.nbl processes.

• Parameters:
  • origin (tuple of floats) – Origin of the model in m as a tuple in (x,y,z) order.
  • spacing (tuple of floats) – Grid size in m as a Tuple in (x,y,z) order.
  • shape (tuple of int) – Number of grid points size in (x,y,z) order.
  • space_order (int) – Order of the spatial stencil discretisation.
  • m (array_like or float) – Squared slownes in s^2/km^2
  • nbl (int , optional) – The number of absorbin layers for boundary damping.
  • dtype (np.float32 or np.float64) – Defaults to 32.
  • epsilon (array_like or float , optional) – Thomsen epsilon parameter (0<epsilon<1).
  • delta (array_like or float) – Thomsen delta parameter (0<delta<1), delta<epsilon.
  • theta (array_like or float) – Tilt angle in radian.
  • phi (array_like or float) – Asymuth angle in radian.
  • dt (Float) – User provided computational time-step
  • abox (Float) – Whether to use the exapanding box, defaults to true

abox(src, rec, fw=True)

property critical_dt

Critical computational time step value from the CFL condition.

property dim

Spatial dimension of the problem and model domain.

property domain_size

Physical size of the domain as determined by shape and spacing

property dt

User provided dt

property dtype

Data type for all assocaited data objects.

property fs_dim

property is_elastic

Whether the model is TTI or isotopic

property is_tti

Whether the model is TTI or isotopic

property is_viscoacoustic

Whether the model is TTI or isotopic

property padsizes

property physical

property physical_parameters

List of physical parameteres

physical_params(**kwargs)

Return all set physical parameters and update to input values if provided

property space_dimensions

Spatial dimensions of the grid

property space_order

Spatial discretization order

property spacing

Grid spacing for all fields in the physical model.

property spacing_map

Map between spacing symbols and their values for each SpaceDimension.

property vp

Symbolic representation of the velocity vp = sqrt(1 / m)

property zero_thomsen

propagators module

propagators.adjoint(*args, **kwargs)

propagators.born(model, srccoords, rcvcoords, wavelet, save=False, qwf=None, returnop=False, ic='as', freqlist=None, dftsub=None, ws=None, tsub=1, nlind=False, f0=0.015, illum=False, fw=True)

Low level propagator, to be used through interface.py Compute linearized wavefield U = J(m)* δ m and related quantities.

propagators.forward(model, srccoords, rcvcoords, wavelet, save=False, qwf=None, returnop=False, freqlist=None, dftsub=None, normwf=False, wfun=None, ws=None, wr=None, tsub=1, f0=0.015, illum=False, fw=True, **kwargs)

Low level propagator, to be used through interface.py Compute forward wavefield u = A(m)^{-1}*f and related quantities (u(xrcv))

propagators.forwardgrad(model, srccoords, rcv_coords, wavelet, v, q=None, ws=None, ic='as', w=None, freq=None, f0=0.015, **kwargs)

Low level propagator, to be used through interface.py Compute forward wavefield u = A(m)^{-1}*f and related quantities (u(xrcv))

propagators.gradient(model, residual, rcvcoords, u, returnop=False, fw=True, w=None, freq=None, dft_sub=None, ic='as', f0=0.015, save=True, illum=False)

Low level propagator, to be used through interface.py Compute the action of the adjoint Jacobian onto a residual J’* δ d.

sensitivity module

sensitivity.Loss(dsyn, dobs, dt, is_residual=False, misfit=None)

L2 loss and residual between the synthetic data dsyn and observed data dobs

• Parameters:
  • dsyn (SparseTimeFunction or tuple) – Synthetic data or tuple (background, linearized) data
  • dobs (SparseTimeFunction) – Observed data
  • dt (float) – Time sampling rate
  • is_residual (bool) – Whether input dobs is already the data residual
  • misfit (function) – User provided function of the form: misifit(dsyn, dob) -> obj, adjoint_source

sensitivity.basic_src(model, u, **kwargs)

Basic source for linearized modeling

• Parameters:
  • model (Model) – Model containing the perturbation dm
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)

sensitivity.crosscorrfreq(u, v, model, freq=None, dftsub=None, **kwargs)

Standard cross-correlation imaging condition with on-th-fly-dft

• Parameters:
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • v (TimeFunction or Tuple) – Adjoint wavefield (tuple of fields for TTI)
  • model (Model) – Model structure
  • freq (Array) – Array of frequencies for on-the-fly DFT
  • factor (int) – Subsampling factor for DFT

sensitivity.crosscorr_time(u, v, model, **kwargs)

Cross correlation of forward and adjoint wavefield

• Parameters:
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • v (TimeFunction or Tuple) – Adjoint wavefield (tuple of fields for TTI)
  • model (Model) – Model structure

sensitivity.func_name(freq=None, ic='as')

Get key for imaging condition/linearized source function

sensitivity.fwi_freq(*ar, **kw)

sensitivity.fwi_src(*ar, **kw)

sensitivity.fwi_time(*ar, **kw)

sensitivity.gradexpr(gradm, u, v, model, w=None, freq=None, dftsub=None, ic='as')

Gradient update stencil

• Parameters:
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • v (TimeFunction or Tuple) – Adjoint wavefield (tuple of fields for TTI)
  • model (Model) – Model structure
  • w (Float or Expr (**optional )) – Weight for the gradient expression (default=1)
  • freq (Array) – Array of frequencies for on-the-fly DFT
  • factor (int) – Subsampling factor for DFT
  • isic (Bool) – Whether or not to use inverse scattering imaging condition (not supported yet)

sensitivity.inner_grad(u, v)

Inner product of the gradient of two fields

• Parameters:
  • u (TimeFunction) – First field
  • v (TimeFunction) – Second field

sensitivity.isic_freq(u, v, model, **kwargs)

Inverse scattering imaging condition

• Parameters:
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • v (TimeFunction or Tuple) – Adjoint wavefield (tuple of fields for TTI)
  • model (Model) – Model structure

sensitivity.isic_src(model, u, **kwargs)

ISIC source for linearized modeling

• Parameters:
  • model (Model) – Model containing the perturbation dm
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)

sensitivity.isic_time(u, v, model, **kwargs)

Inverse scattering imaging condition

• Parameters:
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • v (TimeFunction or Tuple) – Adjoint wavefield (tuple of fields for TTI)
  • model (Model) – Model structure

sensitivity.lin_src(model, u, ic='as')

Source for linearized modeling

• Parameters:
  • model (Model) – Model containing the perturbation dm
  • u (TimeFunction or Tuple) – Forward wavefield (tuple of fields for TTI or dft)
  • ic (String) – Imaging condition of which we compute the linearized source

sources module

class sources.PointSource(*args)

Symbolic data object for a set of sparse point sources

• Parameters:
  • name (String) – Name of the symbol representing this source
  • grid (Grid) – Grid object defining the computational domain.
  • coordinates (Array) – Point coordinates for this source
  • data ( (**Optional ) Data) – values to initialise point data
  • ntime (Int (**Optional )) – Number of timesteps for which to allocate data
  • npoint (Int (**Optional ))
  • source (Number of sparse points represented by this)
  • dimension (Dimension (**Optional )) – object for representing the number of points in this source
  • Note
  • fully (either the dimensions ntime and npoint or the)
  • provided. (initialised data array need to be)

defaultassumptions *= {'commutative': True, 'complex': True, 'extendedreal': True, 'finite': True, 'hermitian': True, 'imaginary': False, 'infinite': False, 'real': True}*

is_commutative : bool | None = True

is_complex = True

isextendedreal : bool | None = True

is_finite = True

is_hermitian = True

is_imaginary = False

is_infinite = False

is_real : bool | None = True

sources.Receiver

alias of PointSource

class sources.RickerSource(*args)

Symbolic object that encapsulate a set of sources with a pre-defined Ricker wavelet: http://subsurfwiki.org/wiki/Ricker_wavelet name: Name for the resulting symbol grid: Grid object defining the computational domain. f0: Peak frequency for Ricker wavelet in kHz time: Discretized values of time in ms

defaultassumptions *= {'commutative': True, 'complex': True, 'extendedreal': True, 'finite': True, 'hermitian': True, 'imaginary': False, 'infinite': False, 'real': True}*

is_commutative : bool | None = True

is_complex = True

isextendedreal : bool | None = True

is_finite = True

is_hermitian = True

is_imaginary = False

is_infinite = False

is_real : bool | None = True

wavelet(timev)

class sources.TimeAxis(start=None, step=None, num=None, stop=None)

Data object to store the TimeAxis. Exactly three of the four key arguments must be prescribed. Because of remainder values it is not possible to create a TimeAxis that exactly adhears to the inputs therefore start, stop, step and num values should be taken from the TimeAxis object rather than relying upon the input values. The four possible cases are: * start is None: start = step*(1 - num) + stop * step is None: step = (stop - start)/(num - 1) * num is None: num = ceil((stop - start + step)/step) and because of remainder stop = step*(num - 1) + start * stop is None: stop = step*(num - 1) + start

• Parameters:
  • start (float , optional) – Start of time axis.
  • step (float , optional) – Time interval.
  • num (int , optional) – Number of values (Note: this is the number of intervals + 1). Stop value is reset to correct for remainder.
  • stop (float , optional) – End time.

property time_values

wave_utils module

Module contents