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.

New here? Start with Start Here — a one-page map from "what are you trying to do" to the right doc.

Navigate — Quick Start · Architecture · Gallery · Examples · Guides · Waveform Generator · Design · Contributing

API Reference — Full Python + C API index

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. The full generated table lives in Benchmarks; run make bench to measure on your hardware.

Quick start

See Quick Start for the full walkthrough.

Python

Install

[!NOTE] Isolate your install from system python with a virtual environment!

python3 -m venv .venv
. .venv/bin/activate

pip install doppler-dsp

Compute FFT

from doppler.spectral import FFT
import numpy as np

x = np.random.randn(1024).astype(np.complex64)
X = FFT(1024).execute_cf32(x)
print(f"FFT: {len(x)} samples in -> {X.shape[0]} complex64 bins out")

Create a 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
print(f"generated {len(iq)} QPSK samples")

C

[!TIP] Don't have jbx yet?

. <(curl -sSL https://just-buildit.github.io/get-jb.sh)

Install

Get libdoppler.a/libdoppler.so plus headers, ready to link, in one command:

jbx get-doppler

Compute FFT

/* example.c */

#include <complex.h>
#include <stdio.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);
  printf("FFT: 1024 samples in -> 1024 complex bins out\n");
  return 0;
}

Compile and run

cc example.c -I "$HOME/.local/doppler/include" "$HOME/.local/doppler/lib/libdoppler.a" -lm -o example
./example

Other install methods

Prefer a custom prefix or no jbx? Grab a pre-built release tarball by hand — no toolchain, no building doppler itself — and extract it to $PREFIX; you get the same libdoppler.a/libdoppler.so plus headers. See C Library for find_package/pkg-config integration and building from source.

Build

Building from source gets you the C library, examples, and Rust FFI bindings — see Build from source if you just want the C library without cloning the repo.

git clone https://github.com/doppler-dsp/doppler
cd doppler
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 NATS stream component (libdoppler_stream) vendors nats.c (Apache-2.0) — it too is pure C99, so no C++ toolchain is needed anywhere in the build.

Project links

Data verified by PyPI on Jul 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.47.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for hunterdsp from gravatar.com hunterdsp

Credits

Author: doppler contributors

GitHub Statistics

Data verified by PyPI on Jul 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Provides Extra

cli specan specan-web

Tags

dsp sdr fft fir nco signal-processing nats iq

Classifiers

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0.47.0

19 files

0.46.0

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0.45.0

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0.44.0

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0.43.2

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0.39.0

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This release

0.37.2 This release

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0.37.1

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0.3.0

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0.2.7

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0.2.6

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