sverchok VS gnuradio

I made this lil animation with Sverchok Nodes and the help of this nice tutorial. The song is Nachtexpress by Extrarausch.

A more generic equivalent seems to be PyFlow: https://wonderworks-software.github.io/PyFlow/.

GNU Radio is targeted to a specific domain and the collection of existing nodes reflects that, similar tools exist for other domains, e.g. for CAD there is Sverchok which works with Blender: https://nortikin.github.io/sverchok/.

Sverchok is pretty amazing considering it all done via python api. https://github.com/nortikin/sverchok

Parametric modelling is sure fun. Haven't tried OpenSCAD yet, but done some in Blender with Sverchok addon (screenshot from manual) - pretty powerful stuff. The parametric modelling is getting even into vanilla Blender now with geometry nodes.

""" in in_points v in values s in xi v out out_values s """ import numpy as np import sys from sverchok.utils.logging import exception, info from sverchok.data_structure import zip_long_repeat try: import scipy from scipy.interpolate import interpn except ImportError as e: info("SciPy module is not available. Please refer to https://github.com/nortikin/sverchok/wiki/Non-standard-Python-modules-installation for how to install it.") raise e out_values = [] for everypoint in xi: new_values = interpn(points, values, xi, method='linear', bounds_error=True, fill_value=0) out_values.append(new_values)

Also check out the addon Sverchok, I think at the moment it is still much more powerful than the new geometry nodes, basically a full-blown Grasshopper (from Rhino3D) alternative. Would be nice to see it added into the main program at some point.

https://github.com/nortikin/sverchok

You can try Sverchok(“Grasshopper” in russian), this tool designed specifically for this purposes: https://github.com/nortikin/sverchok It’s kinda weird, notoriously hard to learn, but will do the trick.

In gr-dtv transmitter examples for Gnuradio, I see some times people use a resampler block before the RF hardware sink. Say our sampling rate is ~9.14Msps which satisfies the Nyquist criterion because our samples are complex numbers.

2.1. Thanks for that tip, I forgot that I was able to check the source code of the WBFM Receive block. As you have said, there are mostly the same. There are some differences between how values are picked. The WBFM Receive block would be a synonym of Quadrature demod => Fir Filter (decimation => Low pass filter) => FM Deemphasis. 2.3. My question there is why 10 and not 20 or 100. I understand that the idea is to reduce the sample rate asap, but what I don't understand is why those values were picked and how can I understand what would be the "correct" or "best" value. 2.4. I'm not fully understanding what you said. If I check the WB FM recieve source code the values that are supplied as the cutoff freq and transition width of the Low pass filter differ from the one of the example. The webfm would apply a sample rate / decimation / 2 - sample rate / decimation / 32 as a cutoff freq and a sample rate / decimation / 32 as a transition transition width. Calculating those values would end up in different that the ones supplied in this second example. Again, is there a rule of thumb to pick these values?.

Gaussian filter is used only on the tx side, so specifying bt in the receiver makes no sense. Take a look at gfsk mod/demod blocks implementation: https://github.com/gnuradio/gnuradio/blob/master/gr-digital/python/digital/gfsk.py

GNU Radio

Here's the filter coefficients used for the GNU-Radio interpolator block to get you started. This is a 7th order interpolator (i.e., 8 FIR taps) with very good performance. Each "row" of the array sets the delay in steps of sample_time / 128.

From their README: “open-source software development toolkit that provides signal processing blocks to implement software radios.” https://github.com/gnuradio/gnuradio