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Practical, portable, performant digital signal processing.

Project description

doppler — signal, shifted

Practical, portable, performant digital signal processing.

CI Docs PyPI Python License: MIT

C99 Rust uv Ruff

doppler is a C99 DSP library — NCO, FIR, FFT, polyphase resampling, DDC, AGC and more — with file, buffer, and NATS-based streaming, and a scenario-driven waveform generator (wfmgen) with byte-identical CLI/Python/C parity. Python and Rust wrap the same C core — no second implementation, no divergence, full SIMD throughput from any language.

NavigateQuick Start · Architecture · Examples · Guides · Waveform Generator

API ReferenceC API · Python: FFT · Python: Spectral · Python: Waveform · Python: Resample · Python: Filter · Python: Measurement · Python: DDC · Python: Accumulator


Why it's built this way

Every algorithm lives in C exactly once. The Python layer is type conversion and lifetime bridging — a few hundred lines of glue, not a reimplementation. Bugs get fixed once, benchmarks reflect real hardware, and a C transmitter talks to a Python subscriber without surprises.

Performance

On a Ryzen 7 AI 350 (-O2): NCO raw accumulator ~15 GSa/s, LO CF32 ~1.8 GSa/s, FIR CF32 ~900 MSa/s, FFT CF32 (N=4096) ~180 MSa/s, polyphase resampler (2× decim) ~70 MSa/s. Run make bench to measure on your hardware.

Quick start

Pythonpip install doppler-dsp (bundles all native deps, no system libraries needed); see Quick Start for the full walkthrough.

from doppler.spectral import FFT
import numpy as np

x = np.random.randn(1024).astype(np.complex64)
X = FFT(1024).execute_cf32(x)

Waveform

from doppler.wfm import Synth

synth = Synth(type="qpsk", fs=1e6, snr=12.0, snr_mode="esno", sps=8, seed=1)
iq = synth.steps(4096)   # complex64 ndarray

C — the C library isn't part of the pip install wheel. Grab a pre-built release tarball — no toolchain, no building doppler itself — and extract it to $PREFIX (jbx get-doppler does exactly this in one command); you get libdoppler.a/libdoppler.so plus headers, ready to link. (Building from source instead? See that section — it uses build/ tree paths, not $PREFIX.) See C Library for find_package/pkg-config integration.

#include <complex.h>
#include <fft/fft_core.h>

int main(void)
{
  float complex in[1024]  = { 0 };   /* fill with your samples */
  float complex out[1024];

  fft_state_t *fft = fft_create(1024, -1, 1);  /* n, sign, nthreads */
  fft_execute_cf32(fft, in, 1024, out);        /* in,out: float complex[1024] */
  fft_destroy(fft);
  return 0;
}
# after extracting the tarball to $PREFIX (install/c.md) --
# find_package/pkg-config integration there too
cc example.c -I "$PREFIX/include" "$PREFIX/lib/libdoppler.a" -lm -o example

Build

[!TIP] Don't have jbx yet? make install-deps bootstraps it for you. (Or by hand: . <(curl -sSL https://just-buildit.github.io/get-jb.sh).)

make install-deps       # bootstrap jbx (if needed) + install system deps
make                    # C library
make pyext              # + Python bindings
make test               # CTest suite
make bench              # benchmarks

Docs

Full docs: doppler-dsp.github.io/doppler

Licensing

MIT. The core C library is pure C99 and links only -lm. Its FFT uses the vendored pocketfft (BSD-3-Clause) for double precision and arbitrary sizes, and the vendored PFFFT (Pommier/FFTPACK, BSD) for the native single-precision SIMD path. The optional stream component (libdoppler_stream) vendors nats.c (Apache-2.0).

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The following attestation bundles were made for doppler_dsp-0.33.3-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

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The following attestation bundles were made for doppler_dsp-0.33.3-cp39-cp39-macosx_14_0_arm64.whl:

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