---
id: doppler-dsp
version: "0.42.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# doppler-dsp — Practical, portable, performant digital signal processing.

License: permissive · Maintenance: active · Downloads: 73.7K/mo

## What it is and what it does

Doppler is a C99 digital signal processing library wrapped for Python, implementing core DSP algorithms (FFT, FIR, NCO, polyphase resampling, DDC, AGC) with support for file, buffer, and NATS-based streaming. The Python layer is a thin type-conversion and lifetime-bridging wrapper around a single C implementation, so bugs are fixed once and performance reflects real hardware. It includes a scenario-driven waveform generator (wfmgen) with byte-identical behavior across CLI, Python, and C interfaces.

The package targets signal processing, software-defined radio (SDR), and scientific computing workflows where both performance and language flexibility matter. It depends only on numpy at runtime and provides pre-built wheels for Python 3.9–3.14 on macOS ARM64 and Linux x86_64/aarch64, reducing installation friction for common platforms.

Use it for:

- Compute FFTs and apply FIR filters to complex signal streams in real-time DSP pipelines.
- Generate test waveforms (QPSK, etc.) with controlled SNR for signal processing validation.
- Build SDR applications combining NCO tuning, resampling, and DDC in a single library.
- Stream DSP results to NATS brokers for distributed signal monitoring and analysis.
- Prototype DSP algorithms in Python with the same performance and behavior as the C implementation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a C99 digital signal processing library with Python bindings, offering FFT, FIR filters, NCO, polyphase resampling, DDC, AGC, and waveform generation with file, buffer, and NATS-based streaming.

Yes, if you need performant DSP primitives in Python with a proven C core. The active maintenance, MIT license, no known vulnerabilities, and single lightweight runtime dependency make it low-risk. Medium install friction is offset by pre-built wheels for modern Python versions. Best suited for signal processing, SDR, and scientific computing; less relevant for general-purpose applications.

## Install

pip install doppler-dsp
uv add doppler-dsp
poetry add doppler-dsp

## Installing doppler-dsp

Before you install: Medium friction due to compiled wheels for multiple Python versions and architectures. Active maintenance with a release 7 days ago and recent commits. Single runtime dependency on numpy.

License in practice: MIT license is permissive; you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install doppler-dsp

from doppler.spectral import FFT
import numpy as np

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

Requires Python 3.9 or later; pre-built wheels available for Python 3.9–3.14 on macOS ARM64 and Linux x86_64/aarch64.

Verify before relying:

- Whether the NATS streaming component (libdoppler_stream) is included in the PyPI wheel or requires separate installation.
- Performance characteristics on specific hardware platforms beyond the Ryzen 7 AI 350 example.
- Whether byte-identical CLI/Python/C parity is maintained across all algorithms or only specific ones.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 73.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

digital signal processing library, FFT and FIR filtering, DSP waveform generation, signal processing with Python, real-time DSP streaming, NCO and resampling, SDR signal processing, signal-processing, dsp, sdr

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