PyLops Utilities#

Alongside with its Linear Operators and Solvers, PyLops contains also a number of auxiliary routines performing universal tasks that are used by several operators or simply within one or more Tutorials for the preparation of input data and subsequent visualization of results.

Dot-test#

dottest(Op[, nr, nc, rtol, atol, ...])

Dot test.

Decorators#

add_ndarray_support_to_solver(func)

Decorator which converts a solver-type function that only supports a 1d-array into one that supports one (dimsd-shaped) ndarray.

disable_ndarray_multiplication(func)

Decorator which disables ndarray multiplication.

reshaped([func, forward, swapaxis])

Decorator for the common reshape/flatten pattern used in many operators.

Describe#

describe(Op)

Describe a PyLops operator

Estimators#

trace_hutchinson(Op[, neval, batch_size, ...])

Trace of linear operator using the Hutchinson method.

trace_hutchpp(Op[, neval, sampler, backend])

Trace of linear operator using the Hutch++ method.

trace_nahutchpp(Op[, neval, sampler, c1, ...])

Trace of linear operator using the NA-Hutch++ method.

Metrics#

mae(xref, xcmp)

Mean Absolute Error (MAE)

mse(xref, xcmp)

Mean Square Error (MSE)

snr(xref, xcmp)

Signal to Noise Ratio (SNR)

psnr(xref, xcmp[, xmax, xmin])

Peak Signal to Noise Ratio (PSNR)

Geophysical Reservoir characterization#

avo.zoeppritz_scattering(vp1, vs1, rho1, ...)

Zoeppritz solution.

avo.zoeppritz_element(vp1, vs1, rho1, vp0, ...)

Single element of Zoeppritz solution.

avo.zoeppritz_pp(vp1, vs1, rho1, vp0, vs0, ...)

PP reflection coefficient from the Zoeppritz scattering matrix.

avo.approx_zoeppritz_pp(vp1, vs1, rho1, vp0, ...)

PP reflection coefficient from the approximate Zoeppritz equation.

avo.akirichards(theta, vsvp[, n])

Three terms Aki-Richards approximation.

avo.fatti(theta, vsvp[, n])

Three terms Fatti approximation.

avo.ps(theta, vsvp[, n])

PS reflection coefficient

Scalability test#

scalability_test(Op, x[, workers, forward])

Scalability test.

Sliding and Patching#

sliding1d.sliding1d_design(dimd, nwin, ...)

Design Sliding1D operator

sliding2d.sliding2d_design(dimsd, nwin, ...)

Design Sliding2D operator

sliding3d.sliding3d_design(dimsd, nwin, ...)

Design Sliding3D operator

patch2d.patch2d_design(dimsd, nwin, nover, nop)

Design Patch2D operator

patch3d.patch3d_design(dimsd, nwin, nover, nop)

Design Patch3D operator

Synthetics#

seismicevents.makeaxis(par)

Create axes t, x, and y axes

seismicevents.linear2d(x, t, v, t0, theta, ...)

Linear 2D events

seismicevents.parabolic2d(x, t, t0, px, pxx, ...)

Parabolic 2D events

seismicevents.hyperbolic2d(x, t, t0, vrms, ...)

Hyperbolic 2D events

seismicevents.linear3d(x, y, t, v, t0, ...)

Linear 3D events

seismicevents.parabolic3d(x, y, t, t0, px, ...)

Parabolic 3D events

seismicevents.hyperbolic3d(x, y, t, t0, ...)

Hyperbolic 3D events

marchenko.directwave(wav, trav, nt, dt[, ...])

Analytical direct wave in acoustic media

Signal-processing#

signalprocessing.convmtx(h, n[, offset])

Convolution matrix

signalprocessing.nonstationary_convmtx(H, n)

Convolution matrix from a bank of filters

signalprocessing.dip_estimate(d[, dz, dx, ...])

Local dip estimation

signalprocessing.slope_estimate(d[, dz, dx, ...])

Local slope estimation

Tapers#

tapers.taper2d(nt, nmask, ntap[, tapertype])

2D taper

tapers.taper3d(nt, nmask, ntap[, tapertype])

3D taper

tapers.tapernd(nmask, ntap[, tapertype])

ND taper

Wavelets#

wavelets.gaussian(t[, std])

Gaussian wavelet

wavelets.klauder(t[, f, taper])

Klauder wavelet

wavelets.ormsby(t[, f, taper])

Ormsby wavelet

wavelets.ricker(t[, f0, taper])

Ricker wavelet
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