diff options
Diffstat (limited to 'bindings')
| -rw-r--r-- | bindings/Makefile | 2 | ||||
| -rw-r--r-- | bindings/test_galearn_pdm.py | 124 |
2 files changed, 105 insertions, 21 deletions
diff --git a/bindings/Makefile b/bindings/Makefile index bdab0f0..5a80cac 100644 --- a/bindings/Makefile +++ b/bindings/Makefile @@ -15,7 +15,7 @@ ${SHARED_LIB}: ../cocotb_try/cic3_pdm.v ./sim_cic3_pdm.cc verilator --cc --lib-create sim --build -j 0 -Wall $^ check: ${PY_MODULE} - PYTHONPATH=../tools python test_galearn_pdm.py + PYTHONPATH=../tools python -m pytest -v test_galearn_pdm.py -s ${PY_MODULE}: ${SHARED_LIB} python setup.py build_ext --inplace diff --git a/bindings/test_galearn_pdm.py b/bindings/test_galearn_pdm.py index 8a82de6..68ed1c3 100644 --- a/bindings/test_galearn_pdm.py +++ b/bindings/test_galearn_pdm.py @@ -1,39 +1,123 @@ +""" +Test the PDM filter +""" + import time +import os +import sys import numpy +import pandas +import pytest from pcm2pdm import convert from testsignal import generate_test_tone from test_pdm import plot_reconstruct import galearn_pdm -sr = 16000 -decimation = 64 +SAMPLERATE_DEFAULT = 16000 +DECIMATION = 64 + +# a place to put diagnostic outputs, like plots etc +here = os.path.dirname(__file__) +out_dir = os.path.join(here, 'out') + +enable_plotting = bool(int(os.environ.get('TEST_ENABLE_PLOTS', '1'))) + +def ensure_dir_for_file(path): + directory = os.path.dirname(path) + if not os.path.exists(directory): + os.makedirs(directory) + -def test_one(): +def find_forward_shift(reference, signal, max_shift=None): + np = numpy + from scipy.signal import correlate + + correlation = correlate(signal, reference, mode='full') + lags = np.arange(-len(reference) + 1, len(signal)) + + # Keep only non-negative lags + positive_mask = lags >= 0 + if max_shift: + positive_mask &= (lags <= max_shift) + + if not np.any(positive_mask): + return None + + correlation = correlation[positive_mask] + lags = lags[positive_mask] + + df = pandas.DataFrame({ + 'lag': lags, + 'correlation': correlation, + }) + #print(df) - sig = generate_test_tone(duration_sec=0.004, - freqs=[1000.0], noise_level=0.0, sample_rate=sr, amplitude=0.9, + shift = lags[np.argmax(correlation)] + return shift + + +# TODO: test closer to Nyquist (sr/2), like 6 Khz for 16 khz samplerate +# Need to take gain reduction/shift into account, since CIC filter has a falloff at high frequencies +@pytest.mark.parametrize('frequency', [50, 250, 2000]) +def test_sine_simple(frequency): + function = sys._getframe().f_code.co_name # looks up function name + test_name = f'{function},frequency={frequency}' + sr = SAMPLERATE_DEFAULT + test_duration = 10 * (1/frequency) # have a reasonable set of samples + + # Generate test data + pcm_input = generate_test_tone(duration_sec=test_duration, + freqs=[frequency], noise_level=0.0, sample_rate=sr, amplitude=0.9, ) + pdm_input = convert(pcm_input) + out = numpy.zeros(shape=len(pdm_input)//DECIMATION, dtype=numpy.int16) + + # Process using filter + n_samples = galearn_pdm.process(pdm_input, out) + out = out / 1024 # XXX: where does this magical come from? + + # Compensate for delay through filter + delay = find_forward_shift(pcm_input, out) + out_shifted = out[delay:-1] + input_trimmed = pcm_input[:len(out_shifted)] + #out_shifted = out_shifted[5:-5] + #input_trimmed = input_trimmed[5:-5] + error = out_shifted - input_trimmed - pdm_data = convert(sig) - inp = pdm_data - out = numpy.zeros(shape=len(inp)//decimation, dtype=numpy.int16) + # Plot diagnostics, if enabled + if enable_plotting: + plot_path = os.path.join(out_dir, test_name, 'reconstructed.png') + ensure_dir_for_file(plot_path) + fig = plot_reconstruct(pcm_input, pdm_input, out, sr=sr, + aspect=6.0, pcm_marker='o') + fig.savefig(plot_path) + print('Wrote', plot_path) - start = time.time() - n_samples = galearn_pdm.process(inp, out) - out = out / 1024 - duration = time.time() - start - #print('d', duration) + plot_path = os.path.join(out_dir, test_name, 'shifted.png') + ensure_dir_for_file(plot_path) + fig = plot_reconstruct(input_trimmed, pdm_input, error, sr=sr, + aspect=6.0, pcm_marker='o') + fig.savefig(plot_path) + print('Wrote', plot_path) - fig = plot_reconstruct(sig, pdm_data, out, sr=sr, aspect=6.0) - plot_path = 'pdm_verilated_1khz.png' - fig.savefig(plot_path) + # Do checks + n_stages = 3 + expect_delay = n_stages + 1 # XXX: might not be fully correct + assert delay == expect_delay + delay = expect_delay - #assert out[0] == 2 - #assert out[-1] == 2 + # Check that waveform is quite similar + # NOTE: at high frequencies there is an expected reduction in gain/amplitude + # if one would compensate for that, could probably tighten these limits + mse = numpy.mean(error**2) + mae = numpy.mean(numpy.abs(error)) + assert mse < 0.10 + assert mae < 0.06 + # Check there is no DC + average = numpy.mean(out) + assert abs(average) < 0.06 -if __name__ == '__main__': - test_one() |