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…ove flattening output
Add decoders algorithms for contrast and deconvolution based encoders
Codecov Report✅ All modified and coverable lines are covered by tests. @@ Coverage Diff @@
## main #47 +/- ##
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+ Coverage 97.75% 99.77% +2.02%
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Lines 401 448 +47
Branches 112 109 -3
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+ Hits 392 447 +55
+ Misses 4 0 -4
+ Partials 5 1 -4
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| signal = np.array([1.0, 2.5, 5.0, 3.0, 1.5, 0.5, 0.2]) | ||
| spikes, fir_bank, shift, norm = modified_hough_spiker(signal, window_length=3, cutoff=0.1, threshold=0.5) | ||
| # norm should be > 1 since signal exceeds 1 after shift | ||
| self.assertTrue(norm[0] > 1) |
| def test_round_trip_large_amplitude_applies_norm(self): | ||
| signal = np.array([1.0, 2.5, 5.0, 3.0, 1.5, 0.5, 0.2]) | ||
| spikes, fir_bank, shift, norm = hough_spiker(signal, window_length=3, cutoff=0.1) | ||
| self.assertTrue(norm[0] > 1) |
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