{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/11"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"},{"label":"Hardware Drivers","url":"https://skillfed.io/packages/category/system-hardware-hardware-drivers"}],"enrichment":{"capability":"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.","skillfed_tags":["signal-processing","dsp","sdr"],"use_cases":["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."],"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.\n\nThe 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\u20133.14 on macOS ARM64 and Linux x86_64/aarch64, reducing installation friction for common platforms.","worth_installing":"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."},"id":"doppler-dsp","links":{"html":"https://skillfed.io/packages/doppler-dsp","md":"https://skillfed.io/packages/doppler-dsp.md","pypi":"https://pypi.org/project/doppler-dsp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"doppler-dsp","python_support":"supports_current","summary":"Practical, portable, performant digital signal processing."},"popularity":{"monthly_downloads":73729,"position":14940,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.42.0"}