pylops
Linear operators for large-scale inverse problems with matrix-free representations. Use when Claude needs to: (1) Define linear operators for forward/adjoint operations, (2) Solve inverse problems (deconvolution, imaging, tomography), (3) Apply signal processing transforms (FFT, convolution, derivatives), (4) Compose operators for complex workflows, (5) Perform regularized inversion with smoothness or sparsity constraints, (6) Process seismic or image data at scale.
npx skills add majiayu000/claude-skill-registry --skill pylops --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyLops - Linear Operators Library ## Quick Reference ```python import numpy as np import pylops # Create operator and apply forward/adjoint A = pylops.FirstDerivative(n=100, dtype='float64') y = A @ x # Forward: y = A @ x x_adj = A.H @ y # Adjoint: x = A.H @ y x_est = A / y # Solve inverse problem ``` ## Key Classes | Class | Purpose | |-------|---------| | `LinearOperator` | Base class for all operators | | `VStack/HStack` | Vertical/horizontal operator stacking | | `BlockDiag` | Block diagonal operator composition | ## Essential Operations ### Basic Operators ```python # Diagonal operator D = pylops.Diagonal(np.array([1., 2., 3.])) y = D @ x; x_adj = D.H @ y # Derivatives D1 = pylops.FirstDerivative(n, dtype='float64') D2 = pylops.SecondDerivative(n, dtype='float64') G = pylops.Gradient(dims=(64, 64), dtype='float64') ``` ### Convolution ```python wavelet = np.sin(np.linspace(0, 2*np.pi, 21)) * np.hanning(21) C = pylops.signalprocessing.Convolve1D(n, h=wavelet, offset=10) y = C @ x # Convolve x_adj = C.H @ y # Correlation (adjoint) ``` ### Compose and Stack Operators ```python # Chain: y = C @ B @ A @ x composed = pylops.Smoothing1D(5, n) @ pylops.FirstDerivative(n) @ pylops.Id
- Quick Reference
- Key Classes
- Essential Operations
- Basic Operators
- Convolution
- Compose and Stack Operators
- Solve Inverse Problems
- Iterative Solvers
- Sparsity-Promoting Inversion
- Verify Adjoint (Dot Test)
- Common Patterns
- Seismic Deconvolution
- Image Denoising with TV
- When to Use vs Alternatives
What does the pylops skill do?
Linear operators for large-scale inverse problems with matrix-free representations. Use when Claude needs to: (1) Define linear operators for forward/adjoint operations, (2) Solve inverse problems (deconvolution, imaging, tomography), (3) Apply signal processing transforms (FFT, convolution, derivatives), (4) Compose operators for complex workflows, (5) Perform regularized inversion with smoothness or sparsity constraints, (6) Process seismic or image data at scale.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pylops --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.