Getting started

One install gets you the whole stack. Pick the entry point that matches how you want to work — Python, or plain language.

Install

pip install sweepx

That pulls the engine (sweep-solver) and the natural-language layer (sweep-agent). Import as sweep, not sweepx — same pattern as scikit-learn → sklearn:

import sweep
print(sweep.__version__)

Python 3.10+. The pure-Python torch / JAX backends need nothing else; the native-CUDA backend (impl='c') is JIT-compiled against your own PyTorch on first use and needs a CUDA GPU with nvcc >= 12.4:

import sweep
print(sweep.is_torch_binding_available())   # torch + CUDA GPU + nvcc present?
sweep.precompile()                          # optional: build it now (~3-5 min, then cached)

Your first forward model

import numpy as np, torch
from sweep.equations import Acoustic
from sweep.propagator.torch import PropTorch
from sweep.signal import ricker

dev    = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
solver = PropTorch(Acoustic(device=dev), shape=(96, 128), dh=10.0, dt=2e-3, dev=dev)

wavelet   = ricker(np.arange(600) * 2e-3 - 0.12, f=10.0).astype(np.float32)
sources   = np.array([[64, 2]], dtype=np.int64)
receivers = np.array([[[ix, 4] for ix in range(0, 128, 2)]], dtype=np.int64)
vp        = torch.full((96, 128), 2000.0, device=dev)

gather = solver(wavelet, sources, receivers, models=[vp])   # (1, nt, nrec, 1)

Your first gradient

Every propagator call is an autograd node, so inversion is ordinary PyTorch:

vp   = vp.clone().requires_grad_(True)
pred = solver(wavelet, sources, receivers, models=[vp])
loss = 0.5 * (pred - obs).pow(2).sum()
loss.backward()          # vp.grad is a plain torch Tensor — feed any optimizer

…or say it in plain language

sweep-agent turns a sentence into a validated run, driven by a local LLM (Ollama on a Mac, vLLM on a GPU node):

sweep-agent chat
>>> load the Marmousi benchmark model and run a forward — show the shot gather

Where to go next

• Solver

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  Equations, propagators, backends, boundary-saving — and 26 runnable notebooks under Examples.

• Agent

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  Tools, install tiers, and how to point it at any OpenAI-compatible LLM.

• API reference

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  Every equation, propagator, and operator, documented from source.