mpdsp 0.4.1.post2

Python integration layer for the mixed-precision DSP library

mp-dsp-python

Python integration layer for the mixed-precision-dsp C++ library, providing nanobind bindings, matplotlib visualizations, and Jupyter notebooks for the full DSP domain.

Why

The mixed-precision-dsp library is a C++20 header-only DSP library covering signals, windows, quantization, IIR/FIR filtering, spectral analysis, signal conditioning, estimation (Kalman/LMS/RLS), image processing, and numerical analysis — all parameterized on arithmetic type for mixed-precision research.

DSP researchers work in Python. Jupyter notebooks, matplotlib, SciPy, and NumPy are the standard tools for prototyping, analysis, and publication-quality visualization. This repository bridges the gap: C++ does the mixed-precision math across the full DSP domain; Python orchestrates experiments and presents results.

Without this layer, every mixed-precision experiment requires writing a C++ application, exporting CSV, and hand-crafting plotting scripts. With mp-dsp-python, the entire sw::dsp library is accessible from a single import mpdsp statement.

import mpdsp
import numpy as np
import matplotlib.pyplot as plt

# Signal generation
signal = mpdsp.sine(length=2000, frequency=440, sample_rate=44100)
noise = mpdsp.gaussian_noise(length=2000, stddev=0.1)
noisy = signal + noise

# Windowing
window = mpdsp.hamming(2000)
windowed = noisy * window

# Spectral analysis
freqs, psd = mpdsp.psd(windowed, sample_rate=44100)
plt.semilogy(freqs, psd)

# IIR filtering with mixed precision
filt = mpdsp.butterworth_lowpass(order=4, sample_rate=44100, cutoff=1000)
ref    = filt.process(signal, dtype="reference")      # double/double/double
posit  = filt.process(signal, dtype="posit_full")      # double/posit<32,2>/posit<16,1>
print(f"SQNR: {mpdsp.sqnr_db(ref, posit):.1f} dB")

# Image processing
img = mpdsp.checkerboard(256, 256, block_size=8)
edges = mpdsp.canny(img, low_threshold=0.1, high_threshold=0.3, sigma=1.0)
mpdsp.write_pgm("edges.pgm", edges)

# Estimation
kf = mpdsp.KalmanFilter(state_dim=2, meas_dim=1)
# ... configure and run

# Analysis
margin = filt.stability_margin()
poles = filt.poles()
sensitivity = filt.worst_case_sensitivity()

What

Full DSP Domain Coverage

mp-dsp-python exposes every module of the C++ library to Python:

Bindings marked ✓ are available today; ⏳ are planned for 0.5.0 (see issue tracker for the per-module roadmap).

Module	C++ Headers	Python API	Description
signals ✓	generators.hpp, signal.hpp, sampling.hpp	mpdsp.sine(), mpdsp.chirp(), mpdsp.impulse(), mpdsp.white_noise(), mpdsp.gaussian_noise(), mpdsp.pink_noise(), ...	Signal generators returning NumPy arrays.
windows ✓	hamming.hpp, hanning.hpp, blackman.hpp, kaiser.hpp, ...	mpdsp.hamming(), mpdsp.kaiser(), ...	Window functions returning NumPy arrays.
quantization ✓ partial	adc.hpp, dac.hpp, dither.hpp, noise_shaping.hpp, sqnr.hpp	mpdsp.adc(), mpdsp.sqnr_db(), mpdsp.measure_sqnr_db(), mpdsp.max_absolute_error(), ... (⏳ dac, rpdf_dither, noise-shaping)	ADC modeling with type dispatch. SQNR measurement — the core metric for mixed-precision evaluation.
filter/iir ✓	butterworth.hpp, chebyshev1.hpp, chebyshev2.hpp, elliptic.hpp, bessel.hpp, legendre.hpp, rbj.hpp	mpdsp.butterworth_lowpass(), mpdsp.chebyshev1_highpass(), mpdsp.elliptic_bandpass(), mpdsp.rbj_lowshelf(), ...	All 7 IIR families with LP/HP/BP/BS (and RBJ shelf/allpass) variants. Design in double, process with type dispatch. Filter objects expose poles(), frequency_response(), stability_margin(), condition_number(), pole_displacement(), worst_case_sensitivity() as methods.
filter/fir ✓	fir_filter.hpp, fir_design.hpp	mpdsp.fir_lowpass(), mpdsp.fir_bandpass(), mpdsp.fir_filter(), ...	FIR filter design (window method). Direct convolution.
spectral ✓ partial	fft.hpp, dft.hpp, psd.hpp, spectrogram.hpp, ztransform.hpp, laplace.hpp	mpdsp.fft(), mpdsp.ifft(), mpdsp.fft_magnitude_db(), mpdsp.psd(), mpdsp.periodogram(), mpdsp.spectrogram() (⏳ ztransform, laplace)	FFT (Cooley-Tukey), power spectral density, STFT/spectrogram.
conditioning ✓	envelope.hpp, compressor.hpp, agc.hpp	mpdsp.PeakEnvelope(), mpdsp.RMSEnvelope(), mpdsp.Compressor(), mpdsp.AGC()	Envelope followers (peak, RMS). Dynamic range compressor with soft knee. Automatic gain control.
estimation ✓	kalman.hpp, lms.hpp, rls.hpp	mpdsp.KalmanFilter(), mpdsp.LMSFilter(), mpdsp.NLMSFilter(), mpdsp.RLSFilter()	Linear Kalman filter with predict/update. LMS/NLMS adaptive filters. RLS with forgetting factor. State matrices as NumPy 2D arrays.
image ✓	image.hpp, convolve2d.hpp, separable.hpp, morphology.hpp, edge.hpp, generators.hpp	mpdsp.convolve2d(), mpdsp.gaussian_blur(), mpdsp.sobel_x(), mpdsp.canny(), mpdsp.dilate(), mpdsp.checkerboard(), ...	2D convolution, separable filters, Gaussian/box blur. Morphological operations (erode, dilate, open, close, gradient, tophat). Sobel, Prewitt, Canny edge detection. Image generators (checkerboard, zone plate, gradients, noise, blobs).
io ✓ partial	wav.hpp, csv.hpp, pgm.hpp, ppm.hpp, bmp.hpp	mpdsp.read_pgm(), mpdsp.write_pgm(), mpdsp.read_ppm(), mpdsp.write_ppm(), mpdsp.read_bmp(), mpdsp.write_bmp() (⏳ read_wav/write_wav; CSV via mpdsp.load_sweep())	PGM/PPM/BMP image I/O. CSV signal I/O. Converting to/from NumPy arrays.
analysis ✓ via filter methods	stability.hpp, sensitivity.hpp, condition.hpp	filt.stability_margin(), filt.condition_number(), filt.worst_case_sensitivity(), filt.pole_displacement(dtype)	Stability/sensitivity/conditioning analysis exposed as methods on filter objects rather than free functions. pole_displacement takes a dtype string and reports how far poles drift under that arithmetic.
types ⏳	projection.hpp, transfer_function.hpp	(⏳ project_onto, transfer_function — planned for 0.5.0)	Type projection/embedding operators. Transfer function evaluation and cascade.

Mixed-Precision Type Dispatch

Every processing function that operates on data accepts a dtype parameter selecting the arithmetic configuration. Python never sees C++ template types — it passes a string key and gets back float64 NumPy arrays.

# Same API, different arithmetic — IIR/FIR filters
result_f32  = filt.process(signal, dtype="gpu_baseline")    # float state+sample
result_p16  = filt.process(signal, dtype="posit_full")      # posit<32,2> / posit<16,1>
result_half = filt.process(signal, dtype="half")            # cfloat<16,5> throughout

# Image processing — convolve2d, separable_filter, gaussian_blur,
# box_blur, sobel_x/y, prewitt_x/y, gradient_magnitude, canny, rgb_to_gray
edges_ref = mpdsp.canny(img, 0.1, 0.3, dtype="reference")
edges_p8  = mpdsp.canny(img, 0.1, 0.3, dtype="tiny_posit")

# Quantization — adc, measure_sqnr_db
quantized = mpdsp.adc(signal, dtype="half")

# Conditioning — PeakEnvelope, RMSEnvelope, Compressor, AGC
comp = mpdsp.Compressor(sample_rate=44100, threshold_db=-12.0, ratio=4.0,
                        attack_ms=5.0, release_ms=50.0, dtype="posit_full")

# Estimation — KalmanFilter, LMSFilter, NLMSFilter, RLSFilter
kf = mpdsp.KalmanFilter(2, 1, dtype="cf24")

Spectral primitives (fft, psd, spectrogram, ...) are pure double-precision in 0.4.x — mixed-precision dispatch on transforms is planned for 0.5.0. Signal generators and window functions are intentionally reference-precision (they are not part of a mixed-precision datapath).

Pre-Instantiated Configurations

Config	CoeffScalar	StateScalar	SampleScalar	Target
reference	double	double	double	Ground truth
gpu_baseline	double	float	float	GPU / embedded CPU
ml_hw	double	float	cfloat<16,5> (IEEE half)	ML accelerator
posit_full	double	posit<32,2>	posit<16,1>	Posit arithmetic research
tiny_posit	double	posit<8,2>	posit<8,2>	Ultra-low-power edge
cf24	double	cfloat<24,5>	cfloat<24,5>	Custom 24-bit float research
half	double	cfloat<16,5>	cfloat<16,5>	IEEE half throughout

Query the live set at runtime with mpdsp.available_dtypes(). Fixed-point and integer configurations (sensor ADCs, FPGA datapaths) are planned for 0.5.0 alongside the remaining upstream bindings.

Coefficients are always designed in double — design-time precision is non-negotiable for IIR filters (see the educational guide). For algorithms that don't have a design/runtime split (FFT, convolution, Kalman), all three scalars use the target configuration.

Visualization Toolkit

Beyond bindings, mp-dsp-python provides matplotlib helpers and Jupyter notebooks tailored to mixed-precision DSP research:

Visualization	Description
Magnitude/phase response	Filter frequency response overlaid across arithmetic types
Impulse response	Time-domain comparison of filter outputs
SQNR heatmap	Filter family × arithmetic type, colored by SQNR (dB)
SQNR bar chart	Grouped bars per filter family
Pole-zero diagram	Unit circle with reference vs. displaced poles
Spectrogram	Time-frequency display from STFT
PSD comparison	Power spectral density across arithmetic types
Image pipeline	Side-by-side: original → noisy → filtered → edges
Sensor noise analysis	SQNR vs. bit-width for image processing
Precision-cost frontier	SQNR vs. bits-per-sample Pareto plot
Kalman tracking	State estimation convergence across types

How

Repository Structure

mp-dsp-python/
├── CMakeLists.txt                  # nanobind + sw::dsp + Universal
├── src/
│   ├── bindings.cpp                # nanobind module definition
│   ├── types.hpp                   # ArithConfig enum + dispatch table
│   ├── signal_bindings.cpp         # signals + windows → NumPy
│   ├── filter_bindings.cpp         # IIR/FIR design + process
│   ├── spectral_bindings.cpp       # FFT, PSD, spectrogram
│   ├── conditioning_bindings.cpp   # envelope, compressor, AGC
│   ├── estimation_bindings.cpp     # Kalman, LMS, RLS
│   ├── image_bindings.cpp          # 2D convolution, morphology, edge
│   ├── quantization_bindings.cpp   # ADC/DAC, dither, SQNR
│   ├── analysis_bindings.cpp       # stability, sensitivity, condition
│   └── io_bindings.cpp             # WAV, PGM, PPM, BMP, CSV
├── python/
│   └── mpdsp/
│       ├── __init__.py             # Public API surface
│       ├── filters.py              # Pythonic filter wrapper classes
│       ├── spectral.py             # Spectral analysis helpers
│       ├── estimation.py           # Kalman/adaptive filter wrappers
│       ├── image.py                # Image processing helpers
│       ├── plotting.py             # matplotlib convenience functions
│       └── io.py                   # File I/O + CSV import
├── notebooks/
│   ├── 01_signals_and_spectra.ipynb    # Signal generation, FFT, PSD
│   ├── 02_iir_precision.ipynb          # Mixed-precision IIR comparison
│   ├── 03_fir_and_windows.ipynb        # FIR design, window functions
│   ├── 04_quantization.ipynb           # ADC/DAC, dithering, SQNR
│   ├── 05_conditioning.ipynb           # Envelope, compression, AGC
│   ├── 06_estimation.ipynb             # Kalman tracking, LMS adaptive
│   ├── 07_image_processing.ipynb       # 2D filtering, edge detection
│   ├── 08_sensor_noise.ipynb           # Sensor noise precision analysis
│   └── 09_numerical_analysis.ipynb     # Stability, sensitivity, condition
├── scripts/
│   ├── plot_precision.py           # Magnitude/phase from CSV
│   ├── plot_heatmap.py             # SQNR heatmap from CSV
│   ├── plot_pole_zero.py           # Pole-zero on unit circle
│   └── plot_dashboard.py           # Streamlit interactive dashboard
├── tests/
│   ├── test_signals.py
│   ├── test_filters.py
│   ├── test_spectral.py
│   ├── test_image.py
│   └── test_estimation.py
└── README.md

Build

# Prerequisites: Python 3.9+, CMake 3.22+, C++20 compiler
pip install nanobind numpy matplotlib

# Build the C++ extension module
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

# Install in development mode
pip install -e .

The build system finds mixed-precision-dsp, Universal, and MTL5 via CMake find_package or FetchContent.

Quick Start: CSV Plotting (No Build Required)

The plotting scripts work immediately with CSV output from the C++ precision sweep, without building any nanobind module:

# In the mixed-precision-dsp repo:
cd build && ./applications/mp_comparison/iir_precision_sweep /tmp/csv_output

# In this repo:
python scripts/plot_precision.py /tmp/csv_output
python scripts/plot_heatmap.py /tmp/csv_output
python scripts/plot_pole_zero.py /tmp/csv_output

Quick Start: Full Python API

import mpdsp
import numpy as np
import matplotlib.pyplot as plt

# --- Signal Processing ---
# Generate and analyze signals
signal = mpdsp.sine(2000, frequency=440, sample_rate=44100)
window = mpdsp.blackman(2000)
freqs, psd = mpdsp.psd(signal * window, sample_rate=44100)

# --- Filtering ---
# Design and compare IIR filters across arithmetic types
filt = mpdsp.butterworth_lowpass(order=4, sample_rate=44100, cutoff=1000)
results = {}
for dtype in ["reference", "gpu_baseline", "posit_full", "half"]:
    results[dtype] = filt.process(signal, dtype=dtype)
    if dtype != "reference":
        sqnr = mpdsp.sqnr_db(results["reference"], results[dtype])
        print(f"  {dtype:20s}  SQNR = {sqnr:.1f} dB")

# --- Spectral Analysis ---
# FFT is pure double-precision in 0.4.x (mixed-precision dispatch on
# transforms is planned for 0.5.0). The returned tuple is (real, imag).
real, imag = mpdsp.fft(signal)

# --- Image Processing ---
# Full image pipeline
img = mpdsp.checkerboard(256, 256, block_size=16)
noisy = mpdsp.add_noise(img, stddev=0.1)
denoised = mpdsp.gaussian_blur(noisy, sigma=1.5)
edges = mpdsp.canny(denoised, low_threshold=0.1, high_threshold=0.3)

# Compare edge detection across arithmetic types
edges_ref = mpdsp.canny(denoised, 0.1, 0.3, dtype="reference")
edges_p8  = mpdsp.canny(denoised, 0.1, 0.3, dtype="tiny_posit")
agreement = np.mean(edges_ref == edges_p8)
print(f"  Edge agreement (posit<8,2>): {agreement:.1%}")

# --- Estimation ---
# Kalman filter tracking
kf = mpdsp.KalmanFilter(state_dim=4, meas_dim=2)
# configure F, H, Q, R matrices as NumPy arrays
# kf.predict(); kf.update(measurement)

# --- Analysis ---
# Numerical quality tools
print(f"  Stability margin: {filt.stability_margin():.4f}")
print(f"  Condition number: {filt.condition_number():.2e}")
print(f"  Worst sensitivity: {filt.worst_case_sensitivity():.4f}")

Relationship to mixed-precision-dsp

This repository is the Python integration layer for the full stillwater-sc/mixed-precision-dsp C++ library. The C++ library implements 12 DSP modules with mixed-precision arithmetic; this repo makes all of them accessible to Python researchers.

Design Documents

• Python integration architecture — dispatch mechanism, pre-instantiated configs
• Projection/embedding generalization — type conversion across domains
• Mixed-precision IIR guide — numerical sensitivity primer
• OpenCV API comparison — image processing design rationale

Dependencies

Library	Purpose	Repository
mixed-precision-dsp	C++ DSP algorithms (all 12 modules)	stillwater-sc/mixed-precision-dsp
Universal	Number type arithmetic (posit, cfloat, fixpnt, ...)	stillwater-sc/universal
MTL5	Dense/sparse linear algebra	stillwater-sc/mtl5
nanobind	C++ ↔ Python bindings	wjakob/nanobind
NumPy	Array interop (all data passes through NumPy)	—
matplotlib	2D visualization	—
Streamlit	Interactive dashboard (Phase 7)	—

License

MIT License. Copyright (c) 2024-2026 Stillwater Supercomputing, Inc.