ADCToolbox#
ADC characterization for Python
ADCToolbox
Spectrum analysis, oversampling workflows, SAR modeling, digital calibration, and debug dashboards for converter development.
Complete Documentation#
Browse the Sphinx docs
- Installation
- Quick Start Guide
- API Reference
- Fundamental Utilities (fundamentals)
- Spectrum Analysis (spectrum)
- Analog Output Analysis (aout)
- Digital Output Analysis (dout)
- ADC Behavioral Models (models)
- Signal Generation (siggen)
- Time-Interleaved ADC Analysis (timeinterleave)
- Oversampling Analysis (oversampling)
- Toolsets and Dashboards (toolset)
- Module Overview
- Algorithm Documentation
- Examples
- Python vs MATLAB Parity
- Changelog
Common Starting Points#
Set up the package, upgrade an existing install, and verify your local Python environment.
Run the first spectrum analysis and generate analog and digital debug dashboards.
Use ready-to-run scripts for spectrum, signal generation, calibration, debug, conversions, time interleaving, and oversampling.
Browse public functions by module with signatures and docstrings.
Analysis Areas#
FFT-based SNDR, SNR, SFDR, THD, ENOB, NSD, windowing, averaging, and polar spectrum analysis.
Vectorized SAR conversion, digital reconstruction, ideal weights, and mismatch-aware behavioral modeling.
Foreground bit-weight calibration from sine-wave decisions, plus lightweight and example-driven workflows.
Error decomposition, phase-plane views, residual statistics, static nonlinearity fitting, and INL from sine tests.
Bit activity, overflow checks, ENOB sweeps, residual scatter plots, and weight radix analysis.
Delta-sigma and oversampling analysis utilities, including NTF visualization, in-band filtering, noise shaping, and OSR sweeps.
Outputs#
Spectrum metrics
Analog dashboard
Digital dashboard