Integrating Nuand SDR with Python for Real-Time Signal Analysis

[ethanhawkins]

Hi everyone,
I’m working with a Nuand SDR device and want to perform real-time signal analysis using Python. I’ve seen some examples using MATLAB and GNU Radio, but I’m wondering if there’s a more lightweight way to stream IQ data directly into Python, process it, and visualize it live. Are there any recommended libraries or approaches for achieving low-latency real-time analysis with Nuand SDRs?

[abigaazer]

Hi everyone,
I’m working with a Nuand SDR device and want to perform real-time signal analysis using Python. I’ve seen some examples using MATLAB and GNU Radio, but I’m wondering if there’s a more lightweight way to stream IQ Geometry Dash data directly into Python, process it, and visualize it live. Are there any recommended libraries or approaches for achieving low-latency real-time analysis with Nuand SDRs?

If you find that Python's native overhead is causing dropped samples at high sample rates (e.g., $>20$ Msps), use the bladeRF-cli FIFO approach. This offloads the heavy lifting of data streaming to the optimized C-based CLI.
- Create a Named Pipe (Linux/macOS): mkfifo /tmp/iq_stream
- Stream from CLI: Use rx config file=/tmp/iq_stream n=0 and rx start in the bladeRF-cli.
- Read in Python: Use numpy.fromfile or a standard file stream to read from /tmp/iq_stream.
Using python-bladerf, your core loop would look like this:

Code: Select all

import bladerf
import numpy as np

# Initialize device
sdr = bladerf.BladeRF()
rx_ch = sdr.Channel(bladerf.CHANNEL_RX(0))

# Configure (Frequency, Sample Rate, Bandwidth)
rx_ch.frequency = 2.4e9
rx_ch.sample_rate = 10e6
rx_ch.bandwidth = 5e6

# Setup Sync Stream
sdr.sync_config(layout=bladerf.ChannelLayout.RX_X1,
                fmt=bladerf.Format.SC16_Q11,
                num_buffers=16, buffer_size=8192, 
                num_transfers=8, timeout_ms=1000)

# Real-time processing loop
bytes_per_sample = 4
buf = bytearray(8192 * bytes_per_sample)

while True:
    sdr.sync_rx(buf, 8192) # Stream data into buffer
    iq = np.frombuffer(buf, dtype=np.int16).astype(np.float32).view(np.complex64)
    # Perform your DSP here (e.g., np.fft.fft(iq))