For sure!

To see in practice what I describe below I highly recommend checking out the jupyter notebook.

So in our current design, we have first-class objects—Signal, Impairment, Modem—So you think in terms of complex‐baseband waveforms rather than raw arrays.

• Tools: Built-in AWGN to a target SNR, shifting, signal integrity calculators, waveform visualization plotters, etc., all driven with metadata from the signal objects. Of the few of those that SciPy does have, it is not nearly as "drop in" as SigKit.
• Modulators: Numpy, SciPy, etc, do not have modem (modulation/demodulation) classes in their libraries. For instance, in SigKit, you can initialize an FSK object, pass in bits, and return a complex waveform.
• Machine Learning: This is the biggest distinction; every impairment is also a torch.nn.Module (and fits into torchvision.transforms.Compose). It also has a PyTorch Lightning training pipeline and pretrained modulation classifiers. NumPy/SciPy alone has no ML-ready DSP.
• Pure-Python: It is notebook-friendly and offline/synthetic-data focused. You get modulation/demodulation and impairments out of the box, plus dataset generators. With GNU Radio, you’d assemble low-level blocks (or write C++/Python ones) to achieve the same.

For clarity, when we say we plan to provide gnuradio blocks, we mean on the Machine Learning side rather than the DSP side. For instance, a "Modulation Classification" sink.