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What's new in OpenDPD V2.1

OpenDPD V2.1 introduces a comprehensive visualization and plotting system that lets you observe model training dynamics in real time and generate publication-quality figures.

Real-Time Training Visualization

  • Per-epoch plot generation during both PA modeling (train_pa) and DPD learning (train_dpd). Enable with the --plot flag and control frequency with --plot_every N.
  • Plot types include: PSD (Power Spectral Density), AM/AM, AM/PM, constellation diagrams, waveform overlays, and prediction error plots — generated for both validation and test sets at each epoch.
  • Fixed-axis re-rendering: After training completes, all epoch plots are automatically re-rendered with globally consistent axis limits for fair visual comparison across epochs.

Animated GIF Generation

  • Automatic GIF animations are created from per-epoch plots at the end of training, producing smooth animations that show how the model learns over time.
  • Configurable duration via --gif_duration (default: 10 seconds). For 100 epochs at 10 seconds, this yields 10 fps for smooth playback.
  • GIFs are generated for every plot type (PSD, AM/AM, AM/PM, constellation, waveform, error) and overview panels.

DPD Training Animation

Interactive Training Dashboard

  • An HTML dashboard (dashboard.html) is generated after training, providing an interactive view of all epoch plots with a slider to scrub through epochs.

Training Curve Plots

  • Loss, ACLR, EVM, and NMSE curves are plotted across all epochs at the end of training, saved under training_curves/.

Comparison Plots (New plot Step)

  • A new --step plot command generates side-by-side comparison figures of PA output without DPD vs. with DPD, including PSD, AM/AM, AM/PM, constellation, waveform, and a metrics summary table.
  • Available via CLI (python main.py --step plot) and the Python API (opendpd.plot_dpd()).

OFDM Constellation Demodulation

  • A new Demodulator module (datasets/demodulator.py) supports OFDM demodulation for constellation diagram generation, with per-dataset configurations for APA and DPA signal types.

Python API Enhancements

  • opendpd.train_pa() and opendpd.train_dpd() now accept plot=True and plot_every=N parameters.
  • New opendpd.plot_dpd() function for generating comparison plots from Python.

Example Usage

# Train DPD with per-epoch plotting and 10-second GIF animations
python main.py --dataset_name DPA_200MHz --step train_dpd --plot --plot_every 1 --gif_duration 10.0 --accelerator cuda

# Generate comparison plots (without DPD vs. with DPD)
python main.py --dataset_name DPA_200MHz --step plot --accelerator cuda

import opendpd

# Train with visualization
opendpd.train_dpd(dataset_name='DPA_200MHz', n_epochs=100, plot=True, plot_every=1)

# Generate comparison plots
opendpd.plot_dpd(dataset_name='DPA_200MHz')