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RadioDataset samples are commonly split I/Q with shape (2, T) (row 0 = I, row 1 = Q). `sample_spectrogram_plot` assumed 1-D complex, so `len(sample)` returned 2 (the I/Q axis) — the compatibility gate saw `2 < 32` and every dataset showed "doesn't have sufficient signal data for spectrogram visualization", and the fallback path raised "need at least 32 samples, got 2". Normalize each sample to a 1-D complex signal before measuring/plotting: - add `_to_complex_1d` (complex any-shape -> flat; (2, ...) I/Q rows and (..., 2) I/Q cols -> I + jQ; real 1-D -> real signal; None on empty). - compatibility gate and `sample_spectrogram_plot` normalize first, then use the true length; plot returns the styled "Not Available" figure for unusable/too-short (<32) samples. - `_compute_spectrogram` measures length via reshape(-1) (belt-and-suspenders). Tests: parametrized `_to_complex_1d` and `sample_spectrogram_plot` over complex 1-D, (2, T) rows, (T, 2) cols, real 1-D, and (2, 4, 256) multi-channel I/Q (all render), plus (2,)/empty (still "Not Available"). 16 tests pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""Tests for spectrogram visualization across RadioDataset sample layouts.
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Regression: split-I/Q samples with shape ``(2, T)`` previously reported a length
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of 2 (the I/Q axis) instead of ``T``, so every such dataset was rejected with
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"doesn't have sufficient signal data for spectrogram visualization".
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"""
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import numpy as np
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import pandas as pd
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import pytest
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from ria_toolkit_oss.viz.radio_dataset import _to_complex_1d, sample_spectrogram_plot
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class _FakeDataset:
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"""Minimal RadioDataset stand-in: a one-row metadata frame + a fixed sample."""
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def __init__(self, sample):
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self._sample = np.asarray(sample)
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self.metadata = pd.DataFrame({"modulation": ["bpsk"]})
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def __getitem__(self, idx):
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return self._sample
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def _has_spectrogram(fig):
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"""True when fig is a real spectrogram (a Heatmap trace), not an error card."""
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return any(getattr(tr, "type", None) == "heatmap" for tr in fig.data)
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# --- _to_complex_1d ---------------------------------------------------------
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@pytest.mark.parametrize(
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"sample, expected_len",
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[
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(np.exp(1j * np.linspace(0, 1, 1024)), 1024), # complex 1-D
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(np.ones((2, 1024)), 1024), # split I/Q rows (2, T)
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(np.ones((1024, 2)), 1024), # split I/Q cols (T, 2)
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(np.ones(1024), 1024), # real 1-D
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(np.ones((2, 4, 256)), 1024), # multi-channel I/Q rows
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],
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)
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def test_to_complex_1d_normalizes(sample, expected_len):
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sig = _to_complex_1d(sample)
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assert sig is not None
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assert sig.ndim == 1
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assert sig.size == expected_len
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assert np.iscomplexobj(sig)
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def test_to_complex_1d_combines_iq_rows():
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arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) # I row, Q row
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assert np.allclose(_to_complex_1d(arr), np.array([1 + 4j, 2 + 5j, 3 + 6j]))
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def test_to_complex_1d_combines_iq_cols():
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arr = np.array([[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]]) # (T, 2)
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assert np.allclose(_to_complex_1d(arr), np.array([1 + 4j, 2 + 5j, 3 + 6j]))
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@pytest.mark.parametrize("sample", [None, np.array([])])
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def test_to_complex_1d_returns_none_for_empty(sample):
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assert _to_complex_1d(sample) is None
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# --- sample_spectrogram_plot ------------------------------------------------
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@pytest.mark.parametrize(
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"sample",
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[
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np.exp(1j * np.linspace(0, 10, 1024)), # complex 1-D
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np.random.randn(2, 1024), # split I/Q rows <-- the reported bug
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np.random.randn(1024, 2), # split I/Q cols
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np.random.randn(1024), # real 1-D
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np.random.randn(2, 4, 256), # multi-channel I/Q
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],
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)
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def test_sample_spectrogram_renders(sample):
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fig = sample_spectrogram_plot(_FakeDataset(sample), sample_idx=0)
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assert _has_spectrogram(fig), "expected a real spectrogram, got an error/unavailable figure"
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@pytest.mark.parametrize("sample", [np.random.randn(2), np.array([])])
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def test_sample_spectrogram_too_short_returns_error(sample):
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fig = sample_spectrogram_plot(_FakeDataset(sample), sample_idx=0)
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assert not _has_spectrogram(fig), "expected the 'Not Available' figure for too-short/empty samples"
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