Use square size data for encoder tests
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@ -33,7 +33,7 @@ class DAEncoder:
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def _chunkify_data(self, data: bytes) -> ChunksMatrix:
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size: int = self.params.column_count * self.params.bytes_per_field_element
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return ChunksMatrix(
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Row([bytes(chunk) for chunk in batched(b, self.params.bytes_per_field_element)])
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Row(Chunk(bytes(chunk)) for chunk in batched(b, self.params.bytes_per_field_element))
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for b in batched(data, size)
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)
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@ -15,3 +15,6 @@ class TestCommon(TestCase):
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matrix = ChunksMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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expected = ChunksMatrix([[1, 4, 7], [2, 5, 8], [3, 6, 9]])
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self.assertEqual(matrix.transposed(), expected)
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matrix = ChunksMatrix([[1, 2, 3], [4, 5, 6]])
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expected = ChunksMatrix([[1, 4], [2, 5], [3, 6]])
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self.assertEqual(matrix.transposed(), expected)
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@ -14,7 +14,7 @@ class TestEncoder(TestCase):
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def setUp(self):
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self.params: DAEncoderParams = DAEncoderParams(column_count=16, bytes_per_field_element=32)
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self.encoder: DAEncoder = DAEncoder(self.params)
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self.elements = 100
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self.elements = 32
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self.data = bytearray(
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chain.from_iterable(
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randrange(BLS_MODULUS).to_bytes(length=self.params.bytes_per_field_element, byteorder='big')
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@ -76,9 +76,9 @@ class TestEncoder(TestCase):
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def test_compute_column_kzg_commitments(self):
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chunks_matrix = self.encoder._chunkify_data(self.data)
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polynomials, commitments = zip(*self.encoder._compute_row_kzg_commitments(chunks_matrix.transposed()))
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self.assertEqual(len(commitments), len(chunks_matrix))
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self.assertEqual(len(polynomials), len(chunks_matrix))
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polynomials, commitments = zip(*self.encoder._compute_column_kzg_commitments(chunks_matrix))
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self.assertEqual(len(commitments), len(chunks_matrix[0]))
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self.assertEqual(len(polynomials), len(chunks_matrix[0]))
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def test_generate_aggregated_column_commitments(self):
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pass
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