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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,103 +1,91 @@ | ||
| import ../impulse/signal | ||
| import arraymancer | ||
| import std/unittest | ||
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| proc test_kaiser(): bool = | ||
| proc test_kaiser() = | ||
| ## Test the `kaiser` window function | ||
| let kw1 = kaiser(4) | ||
| let kw2 = kaiser(4, 5.0) | ||
| let kw3 = kaiser(4, 8.0) | ||
| let expected_kw1 = [0.03671089, 0.7753221 , 0.7753221 , 0.03671089].toTensor | ||
| let expected_kw3 = [0.00233883, 0.65247867, 0.65247867, 0.00233883].toTensor | ||
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| result = kw1.mean_absolute_error(expected_kw1) < 1e-8 and | ||
| kw1.mean_absolute_error(kw2) < 1e-8 and | ||
| kw3.mean_absolute_error(expected_kw3) < 1e-8 | ||
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| proc test_firls(): bool = | ||
| test "Kaiser window": | ||
| let kw1 = kaiser(4) | ||
| let kw2 = kaiser(4, 5.0) | ||
| let kw3 = kaiser(4, 8.0) | ||
| let expected_kw1 = [0.03671089, 0.7753221 , 0.7753221 , 0.03671089].toTensor | ||
| let expected_kw3 = [0.00233883, 0.65247867, 0.65247867, 0.00233883].toTensor | ||
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| check: kw1.mean_absolute_error(expected_kw1) < 1e-8 | ||
| check: kw1.mean_absolute_error(kw2) < 1e-8 | ||
| check: kw3.mean_absolute_error(expected_kw3) < 1e-8 | ||
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| proc test_firls() = | ||
| ## Test the `firls` function | ||
| block: | ||
| let expected = [0.1264964, 0.278552488, 0.34506779765, 0.278552488, 0.1264964].toTensor | ||
| if expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 0.3], [0.4, 1.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor)) > 1e-8: | ||
| return false | ||
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||
| # Same filter as above, but using rank-1, even length tensors as inputs | ||
| if expected.mean_absolute_error( | ||
| firls(4, | ||
| [0.0, 0.3, 0.4, 1.0].toTensor, | ||
| [1.0, 1.0, 0.0, 0.0].toTensor)) > 1e-8: | ||
| return false | ||
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||
| # Same filter as above, but using unnormalized frequencies and a sampling | ||
| # frequency of 10.0 Hz (note how all the frequencies are 5 times greater | ||
| # because the default fs is 2.0): | ||
| if expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 1.5], [2.0, 5.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor, | ||
| fs = 10.0)) > 1e-8: | ||
| return false | ||
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| block: | ||
| # Same kind of filter, but give more weight to the pass-through constraint | ||
| let expected = [0.110353444, 0.28447325, 0.36086805, 0.28447325, 0.110353444].toTensor | ||
| if expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 0.3], [0.4, 1.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor, | ||
| weights = [1.0, 0.5].toTensor)) > 1e-8: | ||
| return false | ||
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| block: | ||
| # A more complex, order 5, Type II, low-pass filter | ||
| let expected = [-0.05603328, 0.146107441, 0.43071645, 0.43071645, 0.146107441, -0.05603328].toTensor | ||
| if expected.mean_absolute_error( | ||
| firls(5, | ||
| [[0.0, 0.3], [0.3, 0.6], [0.6, 1.0]].toTensor, | ||
| [[1.0, 1.0], [1.0, 0.2], [0.0, 0.0]].toTensor)) > 1e-8: | ||
| return false | ||
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| block: | ||
| # Example of an order 6 Type IV high-pass FIR filter: | ||
| let expected = [-0.13944975, 0.2851858, -0.25859575, 0.0, 0.25859575, -0.2851858, 0.13944975].toTensor | ||
| if expected.mean_absolute_error(firls(6, | ||
| [[0.0, 0.4], [0.6, 1.0]].toTensor, [[0.0, 0.0], [0.9, 1.0]].toTensor, | ||
| symmetric = false)) > 1e-8: | ||
| return false | ||
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| return true | ||
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| proc test_upfirdn(): bool = | ||
| # FIR filter | ||
| if upfirdn([1, 1, 1].toTensor, [1, 1, 1].toTensor) != [1, 2, 3, 2, 1].toTensor: | ||
| return false | ||
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| # Upsampling with zero insertion | ||
| if upfirdn([1, 2, 3].toTensor, [1].toTensor, 3) != [1, 0, 0, 2, 0, 0, 3].toTensor: | ||
| return false | ||
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| # Upsampling with sample-and-hold | ||
| if upfirdn([1, 2, 3].toTensor, [1, 1, 1].toTensor, 3) != [1, 1, 1, 2, 2, 2, 3, 3, 3].toTensor: | ||
| return false | ||
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| # Linear interpolation | ||
| if upfirdn([1.0, 1.0, 1.0].toTensor, [0.5, 1.0, 0.5].toTensor, 2) != [0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5].toTensor: | ||
| return false | ||
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| # Decimation by 3 | ||
| if upfirdn(arange(10), [1].toTensor, 1, 3) != [0, 3, 6, 9].toTensor: | ||
| return false | ||
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| # Linear interp, rate 2/3 | ||
| if upfirdn(arange(10.0), [0.5, 1.0, 0.5].toTensor, 2, 3) != [0.0, 1.0, 2.5, 4, 5.5, 7.0, 8.5].toTensor: | ||
| return false | ||
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| return true | ||
| test "firls": | ||
| block: | ||
| let expected = [0.1264964, 0.278552488, 0.34506779765, 0.278552488, 0.1264964].toTensor | ||
| check: expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 0.3], [0.4, 1.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor)) < 1e-8 | ||
|
|
||
| # Same filter as above, but using rank-1, even length tensors as inputs | ||
| check: expected.mean_absolute_error( | ||
| firls(4, | ||
| [0.0, 0.3, 0.4, 1.0].toTensor, | ||
| [1.0, 1.0, 0.0, 0.0].toTensor)) < 1e-8 | ||
|
|
||
| # Same filter as above, but using unnormalized frequencies and a sampling | ||
| # frequency of 10.0 Hz (note how all the frequencies are 5 times greater | ||
| # because the default fs is 2.0): | ||
| check: expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 1.5], [2.0, 5.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor, | ||
| fs = 10.0)) < 1e-8 | ||
|
|
||
| block: | ||
| # Same kind of filter, but give more weight to the pass-through constraint | ||
| let expected = [0.110353444, 0.28447325, 0.36086805, 0.28447325, 0.110353444].toTensor | ||
| check: expected.mean_absolute_error( | ||
| firls(4, | ||
| [[0.0, 0.3], [0.4, 1.0]].toTensor, | ||
| [[1.0, 1.0], [0.0, 0.0]].toTensor, | ||
| weights = [1.0, 0.5].toTensor)) < 1e-8 | ||
|
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||
| block: | ||
| # A more complex, order 5, Type II, low-pass filter | ||
| let expected = [-0.05603328, 0.146107441, 0.43071645, 0.43071645, 0.146107441, -0.05603328].toTensor | ||
| check: expected.mean_absolute_error( | ||
| firls(5, | ||
| [[0.0, 0.3], [0.3, 0.6], [0.6, 1.0]].toTensor, | ||
| [[1.0, 1.0], [1.0, 0.2], [0.0, 0.0]].toTensor)) < 1e-8 | ||
|
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||
| block: | ||
| # Example of an order 6 Type IV high-pass FIR filter: | ||
| let expected = [-0.13944975, 0.2851858, -0.25859575, 0.0, 0.25859575, -0.2851858, 0.13944975].toTensor | ||
| check: expected.mean_absolute_error(firls(6, | ||
| [[0.0, 0.4], [0.6, 1.0]].toTensor, [[0.0, 0.0], [0.9, 1.0]].toTensor, | ||
| symmetric = false)) < 1e-8 | ||
|
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| proc test_upfirdn() = | ||
| test "upfirdn": | ||
| # FIR filter | ||
| check: upfirdn([1, 1, 1].toTensor, [1, 1, 1].toTensor) == [1, 2, 3, 2, 1].toTensor | ||
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| # Upsampling with zero insertion | ||
| check: upfirdn([1, 2, 3].toTensor, [1].toTensor, 3) == [1, 0, 0, 2, 0, 0, 3].toTensor | ||
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| # Upsampling with sample-and-hold | ||
| check: upfirdn([1, 2, 3].toTensor, [1, 1, 1].toTensor, 3) == [1, 1, 1, 2, 2, 2, 3, 3, 3].toTensor | ||
|
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| # Linear interpolation | ||
| check: upfirdn([1.0, 1.0, 1.0].toTensor, [0.5, 1.0, 0.5].toTensor, 2) == [0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5].toTensor | ||
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| # Decimation by 3 | ||
| check: upfirdn(arange(10), [1].toTensor, 1, 3) == [0, 3, 6, 9].toTensor | ||
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| # Linear interp, rate 2/3 | ||
| check: upfirdn(arange(10.0), [0.5, 1.0, 0.5].toTensor, 2, 3) == [0.0, 1.0, 2.5, 4, 5.5, 7.0, 8.5].toTensor | ||
|
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| # Run the tests | ||
| doAssert test_kaiser() | ||
| doAssert test_firls() | ||
| doAssert test_upfirdn() | ||
| suite "Signal": | ||
| test_kaiser() | ||
| test_firls() | ||
| test_upfirdn() |