Practical, portable, performant digital signal processing.
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
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
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, 1024); /* 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 -lpthread -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 -lm and -lpthread. 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.
1 maintainer
Release files for doppler-dsp 0.51.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| doppler_dsp-0.51.1.tar.gz | 23.5 MB | Details |
Built distributions (wheels)
Total release size: 163.5 MB
Release files / doppler_dsp-0.51.1.tar.gz
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| Size | 8.0 MB |
| Tags | CPython 3.9 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 |
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| Download URL | doppler_dsp-0.51.1-cp39-cp39-macosx_11_0_arm64.whl |
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| Size | 7.4 MB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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