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. The 2026-08-02 bindings-gap roadmap (epic #100) closed 18 sub-issues across 5 phases, bringing coverage from ~65% to ~93% of the v0.6.0 surface — see docs/gap_analysis_2026-08-02.md for the module-by-module state. For the complete enumeration of every public name with signatures and one-line descriptions, see docs/api_reference.md.

Module	C++ Headers	Python API	Description
signals	`generators.hpp`, `sampling.hpp`	`mpdsp.sine()`, `mpdsp.chirp()`, `mpdsp.impulse()`, `mpdsp.step()`, `mpdsp.ramp()`, `mpdsp.multitone()`, `mpdsp.white_noise()`, `mpdsp.gaussian_noise()`, `mpdsp.pink_noise()`, `mpdsp.upsample()`, `mpdsp.downsample()`, ...	Full signal generator suite returning NumPy arrays. Rate-conversion helpers (`upsample`/`downsample`) are zero-insert / naive decimation — for anti-aliasing pair them with FIR / halfband / polyphase.
windows	`hamming.hpp`, `hanning.hpp`, `blackman.hpp`, `kaiser.hpp`, `tukey.hpp`, `gaussian.hpp`, `dolph_chebyshev.hpp`, `bartlett_hann.hpp`, `flat_top.hpp`, `rectangular.hpp`	`mpdsp.hamming()`, `mpdsp.hanning()`, `mpdsp.blackman()`, `mpdsp.kaiser()`, `mpdsp.tukey()`, `mpdsp.gaussian()`, `mpdsp.dolph_chebyshev()`, `mpdsp.bartlett_hann()`, `mpdsp.flat_top()`, `mpdsp.rectangular()`	All 10 window functions bound.
quantization	`adc.hpp`, `dac.hpp`, `dither.hpp`, `noise_shaping.hpp`, `sqnr.hpp`	`mpdsp.adc()`, `mpdsp.dac()`, `mpdsp.sqnr_db()`, `mpdsp.measure_sqnr_db()`, `mpdsp.RPDFDither()`, `mpdsp.TPDFDither()`, `mpdsp.FirstOrderNoiseShaper()`, ...	ADC/DAC modeling with type dispatch. RPDF/TPDF dithering and first-order error-feedback noise shaping for quantization improvement. 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()`, `IIRFilter.from_coefficients(list)`, ...	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. `IIRFilter.from_coefficients()` imports filters designed elsewhere (scipy, MATLAB, hand-cascaded).
filter/fir	`fir_filter.hpp`, `fir_design.hpp`, `remez.hpp`, `overlap.hpp`, `filtfilt.hpp`	`mpdsp.fir_lowpass()`, `mpdsp.fir_bandpass()`, `mpdsp.fir_filter()`, `mpdsp.remez()`, `mpdsp.remez_lowpass()`, `mpdsp.remez_bandpass()`, `mpdsp.filtfilt()`, `mpdsp.OverlapAddConvolver()`, `mpdsp.OverlapSaveConvolver()`, ...	FIR window-method design, Parks-McClellan (Remez) equiripple design, zero-phase forward-backward filtering (`filtfilt`, scipy analogue), block-FFT convolvers for long signals.
spectral	`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.welch()`, `mpdsp.spectrogram()`, `mpdsp.ztransform()`, `mpdsp.freqz()`, `mpdsp.group_delay()`, `mpdsp.laplace_freqs()`	FFT (Cooley-Tukey), power spectral density (single-shot `psd` and averaged `welch`), STFT/spectrogram, Z-transform and Laplace evaluation. All primitives accept `dtype=` for mixed-precision arithmetic.
spectrum	`realtime_spectrum.hpp`, `detectors.hpp`, `rbw_filter.hpp`, `vbw_filter.hpp`, `swept_lo.hpp`, `front_end_corrector.hpp`, `trace_averaging.hpp`, `waterfall_buffer.hpp`, `markers.hpp`	`mpdsp.RealtimeSpectrum()`, `mpdsp.detect_peak()` + `_sample`/`_average`/`_rms`/`_negative_peak`/`detect(mode)`, `mpdsp.RBWFilter()`, `mpdsp.VBWFilter()`, `mpdsp.SweptLO()`, `mpdsp.FrontEndCorrector()`, `mpdsp.CalibrationProfile()`, `mpdsp.TraceAverager()`, `mpdsp.WaterfallBuffer()`, `mpdsp.Marker`/`DeltaMarker`, `mpdsp.find_peaks()`, `mpdsp.harmonic_markers()`	Full spectrum-analyzer stack: streaming FFT engine + 5 detector reducers, resolution / video bandwidth filters, swept local oscillator, front-end equalization, cross-sweep trace averaging (5 modes), 2D waterfall memory, marker + peak-finder primitives.
acquisition	`nco.hpp`, `cic.hpp`, `halfband.hpp`, `polyphase_decimator.hpp`, `ddc.hpp`, `decimation_chain.hpp`	`mpdsp.NCO()`, `mpdsp.CICDecimator()`, `mpdsp.CICInterpolator()`, `mpdsp.HalfBandFilter()`, `mpdsp.PolyphaseDecimator()`, `mpdsp.PolyphaseInterpolator()`, `mpdsp.DDC()`, `mpdsp.DecimationChain()`, `mpdsp.design_halfband()`, `mpdsp.design_cic_compensator()`, `mpdsp.polyphase_decompose()`, `nco.measure_sfdr_db()`, `cic.check_bit_growth()`	High-rate acquisition pipeline (numerically-controlled oscillator, CIC decimator/interpolator, halfband/polyphase filters). `DDC` composes NCO mixing with matched I/Q polyphase decimation to bring an IF band to complex baseband; `DecimationChain` cascades heterogeneous decimation stages (ADC → CIC → halfband → FIR) with per-stage rate bookkeeping, and `design_cic_compensator` inverts CIC passband droop. NCO and CIC also carry precision-analysis methods (`measure_sfdr_db`, `check_bit_growth`).
multirate	`channelizer.hpp`, `fractional_delay.hpp`	`mpdsp.Channelizer()`, `mpdsp.FractionalDelay()`, `mpdsp.channelizer_prototype_bank()`	Bellanger polyphase channelizer — splits a wideband input into M uniformly-spaced complex baseband channels for roughly one prototype-filter evaluation per input sample, rather than one per channel. Polyphase fractional-sample delay with 1/L resolution, measured accurate to better than 0.01 samples at unity gain.
conditioning	`envelope.hpp`, `compressor.hpp`, `agc.hpp`, `src.hpp`	`mpdsp.PeakEnvelope()`, `mpdsp.RMSEnvelope()`, `mpdsp.Compressor()`, `mpdsp.AGC()`, `mpdsp.RationalResampler()`	Envelope followers (peak, RMS). Dynamic range compressor with soft knee. Automatic gain control. Polyphase L/M rate conversion (scipy `resample_poly` analogue).
estimation	`kalman.hpp`, `ekf.hpp`, `ukf.hpp`, `lms.hpp`, `rls.hpp`	`mpdsp.KalmanFilter()`, `mpdsp.ExtendedKalmanFilter()`, `mpdsp.UnscentedKalmanFilter()`, `mpdsp.LMSFilter()`, `mpdsp.NLMSFilter()`, `mpdsp.RLSFilter()`	Linear Kalman + nonlinear EKF (Python callbacks for f, F, h, H) + UKF (Python callbacks for f, h — no Jacobians). 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).
instrument	`measurements.hpp`, `peak_detect.hpp`, `ring_buffer.hpp`	`mpdsp.peak_to_peak()`, `mpdsp.instrument_mean()`, `mpdsp.instrument_rms()`, `mpdsp.rise_time()`, `mpdsp.fall_time()`, `mpdsp.period()`, `mpdsp.frequency()`, `mpdsp.PeakDetectDecimator()`, `mpdsp.TriggerRingBuffer()`	Oscilloscope-style stateless measurements (7 primitives), scope min/max-preserving decimator, pre/post-trigger capture with 4-state lifecycle. `mean`/`rms` prefixed with `instrument_` to avoid shadowing `numpy.mean`/`numpy.rms`.
analysis	`stability.hpp`, `sensitivity.hpp`, `condition.hpp`, `acquisition_precision.hpp`	`filt.stability_margin()`, `filt.condition_number()`, `filt.worst_case_sensitivity()`, `filt.pole_displacement(dtype)`, `mpdsp.coefficient_sensitivity()`, `mpdsp.biquad_condition_number()`, `mpdsp.enob_from_snr_db()`, `mpdsp.snr_db()`, `mpdsp.CICBitGrowthReport`, `mpdsp.AcquisitionPrecisionRow`, `mpdsp.write_acquisition_csv()`	Coefficient-level (free function) and cascade-level (filter method) stability / sensitivity / conditioning analysis. Acquisition-pipeline precision metrics (ENOB, SNR) plus a CSV writer schema-compatible with the C++ precision-sweep outputs.
math	`polynomial.hpp`, `quadratic.hpp`, `elliptic_integrals.hpp`, `root_finder.hpp`	`mpdsp.evaluate_polynomial()`, `mpdsp.multiply_polynomials()`, `mpdsp.solve_quadratic()` (+ `_1`, `_2`), `mpdsp.elliptic_K()`, `mpdsp.RootFinder()`	Numerical utilities for advanced filter design: Horner polynomial evaluation, polynomial multiplication (convolution), quadratic solver returning complex roots, complete elliptic integral (Cauer filter design), Laguerre polynomial root finder up to degree 32.
transfer_function	`pole_zero.hpp`, `bode.hpp`	`mpdsp.butterworth_prototype()`, `mpdsp.chebyshev1_prototype()`, `mpdsp.chebyshev2_prototype()`, `mpdsp.bessel_prototype()`, `mpdsp.elliptic_prototype()`, `mpdsp.lp_to_hp()`, `mpdsp.lp_to_bp()`, `mpdsp.lp_to_bs()`, `mpdsp.apply_bilinear()`, `mpdsp.PoleZeroPlot`, `mpdsp.sweep_bode()`, `mpdsp.BodeResult`	Analog (s-plane) prototypes for all five classical families, with LP→HP/BP/BS frequency transforms and the bilinear map to the z-plane — the pre-warp view a designed filter's digital response hides. `sweep_bode` measures a filter's response empirically by driving it with a settled sine per frequency, so unlike `frequency_response()` it registers sample-path quantization at the chosen `dtype`.
types	`projection.hpp`, `transfer_function.hpp`, `biquad_coefficients.hpp`, `pole_zero_pair.hpp`, `complex_pair.hpp`	`mpdsp.TransferFunction()`, `mpdsp.ContinuousTransferFunction()`, `mpdsp.project_onto()`, `mpdsp.projection_error()`, `mpdsp.BiquadCoefficients()`, `mpdsp.PoleZeroPair()`, `mpdsp.ComplexPair()`, `mpdsp.to_transfer_function(filt)`	Rational transfer function H(z) = B(z)/A(z) with complex-plane evaluation, frequency response, stability check, and cascade via `*`. Structured biquad-level types with read-write fields for constructing filters from raw coefficients. Type-projection round-trip for quantifying quantization loss outside the filter path.
io	`wav.hpp`, `csv.hpp`, `pgm.hpp`, `ppm.hpp`, `bmp.hpp`	`mpdsp.read_wav()`, `mpdsp.write_wav()`, `mpdsp.read_pgm()`, `mpdsp.write_pgm()`, `mpdsp.read_ppm()`, `mpdsp.write_ppm()`, `mpdsp.read_bmp()`, `mpdsp.write_bmp()`, CSV via `mpdsp.load_sweep()`	WAV audio (8/16/24/32-bit integer PCM read+write, 32-bit float PCM read). PGM/PPM/BMP image I/O. CSV signal I/O. All converting to/from NumPy arrays.

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, ifft, psd, welch, periodogram, spectrogram) all accept dtype=; inputs and outputs stay double/ complex128 at the Python layer while the internal arithmetic runs at the selected precision. Signal generators are intentionally reference- precision (they aren't part of a mixed-precision datapath). Window functions accept dtype= for cases where the window itself is part of a precision study.

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>	Mixed-precision posit pipeline
`cf24`	double	cfloat<24,5>	cfloat<24,5>	Custom 24-bit float research
`half`	double	cfloat<16,5>	cfloat<16,5>	IEEE half throughout
`sensor_8bit`	double	double	integer<8>	Standard 8-bit sensor ADC
`sensor_6bit`	double	double	integer<6>	Noise-limited sensor
`fpga_fixed`	double	fixpnt<32,24>	fixpnt<16,12>	FPGA fixed-point datapath

Posit taxonomy grid — posit<N, es> single-type configs for N ∈ {8, 16, 32}, es ∈ {0, 1, 2}. All three scalars (coefficient, state, sample) use the same posit type, so these cells cleanly compare ES-vs-precision tradeoff at fixed bit width:

Config	Posit type	Notes
`posit_8_0` / `posit_8_1` / `posit_8_2`	`posit<8, 0/1/2>`	`posit_8_2` is canonical for 8-bit; `tiny_posit` is a legacy alias
`posit_16_0` / `posit_16_1` / `posit_16_2`	`posit<16, 0/1/2>`	`posit_16_1` is the standard 16-bit posit (also used as posit_full's sample)
`posit_32_0` / `posit_32_1` / `posit_32_2`	`posit<32, 0/1/2>`	`posit_32_2` is the standard 32-bit posit (also used as posit_full's state)

Query the live set at runtime with mpdsp.available_dtypes() (18 entries). Sample-scalar bit width per config is available via mpdsp.bits_of(dtype) — useful for labeling the x-axis of precision-vs-cost plots. For posit grid cells the ES dimension doesn't affect bit width, so every posit_N_* reports N; plotting a full sweep gives 3 points stacked vertically at each width showing ES's effect on SQNR.

Coefficients are designed in double by default — design-time precision is what keeps an IIR cascade well-conditioned (see the educational guide). The classical IIR families (Butterworth, Chebyshev, Bessel, Legendre, Elliptic) design in double unconditionally; dtype= on those filters selects the processing path only.

The designers that do expose a coeff_dtype= knob — the seven rbj_* biquads and the FIR/Remez designers — offer it to measure what design-time precision costs, not to recommend spending it. The result is still stored in double, so the knob isolates the arithmetic used to compute the coefficients from the arithmetic used to hold them. Its dual is IIRFilter.pole_displacement(dtype), which quantizes an already-designed cascade: coeff_dtype asks what computing in T costs, pole_displacement asks what storing in T costs.

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

Interactive Filter Designer

A Streamlit dashboard at scripts/plot_dashboard.py exposes every IIR family (Butterworth, Chebyshev I/II, Bessel, Legendre, Elliptic, RBJ biquads) with live magnitude/phase plots, pole-zero diagrams, impulse and step response, and a side-by-side mixed-precision comparison across all 7 arithmetic configurations — modeled on Vinnie Falco's classic DSPFilters demo, with the mixed-precision angle that is the whole point of this library.

pip install mpdsp[dashboard]
streamlit run scripts/plot_dashboard.py

Full walkthrough (install paths for local / SSH-tunnel / LAN, tab-by-tab tour, mixed-precision interpretation guide, export conventions) in docs/dashboard.md.

How

Repository Structure

mp-dsp-python/
├── CMakeLists.txt                  # nanobind + sw::dsp + Universal + MTL5
├── src/
│   ├── bindings.cpp                # nanobind module definition
│   ├── types.hpp                   # ArithConfig enum + dispatch table
│   ├── types_bindings.cpp          # TransferFunction, structured biquad types
│   ├── _binding_helpers.hpp        # Shared marshalling + dispatch helpers
│   ├── BINDING_PATTERNS.md         # Contributor notes on binding conventions
│   ├── signal_bindings.cpp         # signals + windows + WAV I/O
│   ├── filter_bindings.cpp         # IIR/FIR design, filtfilt, remez, overlap
│   ├── spectral_bindings.cpp      # FFT, PSD, welch, spectrogram
│   ├── spectrum_bindings.cpp      # analyzer stack (RealtimeSpectrum, RBW/VBW, ...)
│   ├── conditioning_bindings.cpp  # envelope, compressor, AGC, RationalResampler
│   ├── estimation_bindings.cpp    # Kalman + EKF + UKF + LMS/NLMS/RLS
│   ├── acquisition_bindings.cpp   # NCO, CIC, halfband, polyphase
│   ├── instrument_bindings.cpp    # scope measurements, PeakDetectDecimator, TriggerRingBuffer
│   ├── image_bindings.cpp         # 2D convolution, morphology, edge
│   ├── quantization_bindings.cpp  # ADC/DAC, dither, SQNR
│   ├── analysis_bindings.cpp      # stability, sensitivity, condition, acquisition-precision
│   └── math_bindings.cpp          # polynomial, quadratic, elliptic_K, RootFinder
├── python/
│   └── mpdsp/
│       ├── __init__.py             # Public API surface
│       ├── filters.py              # Pythonic filter wrapper classes
│       ├── estimation.py           # Kalman/adaptive filter wrappers
│       ├── image.py                # Image processing helpers
│       ├── analysis.py             # Analysis helpers
│       ├── plotting.py             # matplotlib convenience functions
│       └── io.py                   # File I/O + CSV import
├── notebooks/
│   ├── 02_iir_precision.ipynb          # Mixed-precision IIR comparison
│   ├── 03_fir_and_windows.ipynb        # FIR design, window functions
│   ├── 04_interactive_precision.ipynb  # Interactive precision sweep
│   ├── 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/                          # 16 test files, ~1250 tests
│   ├── test_signals.py             # generators + windows (bundled)
│   ├── test_filters.py             # IIR + FIR + Remez + Overlap + filtfilt
│   ├── test_spectral.py            # FFT, PSD, welch, spectrogram
│   ├── test_spectrum.py            # analyzer stack (RealtimeSpectrum, RBW/VBW, ...)
│   ├── test_conditioning.py        # envelope, AGC, RationalResampler
│   ├── test_estimation.py          # Kalman + EKF + UKF + adaptive
│   ├── test_acquisition.py         # NCO, CIC, halfband, polyphase
│   ├── test_instrument.py          # scope measurements + capture primitives
│   ├── test_analysis.py            # stability, sensitivity, acquisition-precision
│   ├── test_math.py                # polynomial, quadratic, RootFinder, elliptic_K
│   ├── test_types.py               # TransferFunction + structured biquad types
│   ├── test_image.py               # image processing
│   ├── test_quantization.py        # ADC/DAC, dither, SQNR
│   ├── test_io.py                  # WAV/PGM/PPM/BMP round-trips
│   ├── test_scripts.py             # CSV-plotting script smoke tests
│   └── test_version.py             # version lockstep check
├── docs/
│   ├── api_reference.md
│   ├── dashboard.md
│   ├── publishing.md
│   ├── gap_analysis_2026-08-01.md  # Pre-roadmap coverage snapshot
│   └── gap_analysis_2026-08-02.md  # Post-roadmap coverage snapshot
└── 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 resolves mixed-precision-dsp, Universal, and MTL5 in this order:

Sibling clone — if a checkout exists at ../mixed-precision-dsp, ../universal, or ../mtl5, it is used directly. This is the recommended workflow when iterating across the C++ stack and the Python bindings together.
find_package — MTL5 only; checks for an installed package config.
FetchContent — pulled from GitHub at the pin tags below. Used by cibuildwheel and any other environment without local siblings.

Minimum versions enforced at configure time on the sibling-clone path (stale checkouts abort with a clear error and the git checkout command needed to fix them):

Peer	Floor (sibling-path)	FetchContent pin
`mixed-precision-dsp`	≥ 0.6.0	`v0.6.0`
`universal`	≥ 4.6.11	`v4.6.11`
`mtl5`	≥ 5.7.0	`v5.7.0`

Note: the MTL5 floor was bumped from 5.2.1 → 5.7.0 in 2026-08-02 as prep for UnscentedKalmanFilter, which uses mtl::ldlt_factor (landed in MTL5 v5.3.0). Jumping straight to the latest 5.x release keeps the project on current upstream.

Only the DSP pin is constrained to lag the floor during a development cycle: it moves in lockstep with project(VERSION) (see tests/test_version.py::test_lockstep_prefix) and only advances at release time. The universal and mtl5 pins are free to track the floor — keeping them current avoids CI building in a configuration strictly weaker than what sibling-path devs require.

Override at configure time with -DMPDSP_REQUIRED_DSP_VERSION=... (lower the floor for experimentation) or -DMPDSP_DSP_PIN=main (build against an unreleased upstream).

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 / ifft / psd / periodogram / spectrogram all accept dtype=.
# Returned tuple is (real, imag).
real, imag = mpdsp.fft(signal, dtype="posit_full")

# --- 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 17 DSP modules with mixed-precision arithmetic; this repo makes essentially all of them accessible to Python researchers (~93% of the v0.6.0 surface after the 2026-08-02 bindings-gap roadmap; see docs/gap_analysis_2026-08-02.md for the current coverage state and residual gaps).

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 (optional)	—

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Release history Release notifications | RSS feed

This release

0.9.0

Aug 12, 2026

0.5.0.post3

Apr 20, 2026

0.5.0.post2

Apr 20, 2026

0.5.0.post1

Apr 20, 2026

0.5.0

Apr 20, 2026

0.4.1.post2

Apr 18, 2026

0.4.1.post1

Apr 18, 2026

mpdsp 0.9.0

mp-dsp-python

Why

What

Full DSP Domain Coverage

Mixed-Precision Type Dispatch

Pre-Instantiated Configurations

Visualization Toolkit

Interactive Filter Designer

How

Repository Structure

Build

Quick Start: CSV Plotting (No Build Required)

Quick Start: Full Python API

Relationship to mixed-precision-dsp

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1 maintainer

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