Initial docs added to recover_shifted_data() and recover_original_data()
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@ -520,6 +520,12 @@ def recover_shifted_data(cell_ids: Sequence[CellID],
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Sequence[BLSFieldElement],
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Sequence[BLSFieldElement],
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BLSFieldElement]:
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"""
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Given Z(x), return polynomial Q_1(x)=(E*Z)(k*x) and Q_2(x)=Z(k*x) and k^{-1}
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"""
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shift_factor = BLSFieldElement(PRIMITIVE_ROOT_OF_UNITY)
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shift_inv = div(BLSFieldElement(1), shift_factor)
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extended_evaluation_rbo = [0] * (FIELD_ELEMENTS_PER_BLOB * 2)
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for cell_id, cell in zip(cell_ids, cells):
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start = cell_id * FIELD_ELEMENTS_PER_CELL
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@ -527,15 +533,15 @@ def recover_shifted_data(cell_ids: Sequence[CellID],
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extended_evaluation_rbo[start:end] = cell
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extended_evaluation = bit_reversal_permutation(extended_evaluation_rbo)
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# Compute (E*Z)(x)
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extended_evaluation_times_zero = [BLSFieldElement(int(a) * int(b) % BLS_MODULUS)
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for a, b in zip(zero_poly_eval, extended_evaluation)]
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extended_evaluations_fft = fft_field(extended_evaluation_times_zero, roots_of_unity_extended, inv=True)
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shift_factor = BLSFieldElement(PRIMITIVE_ROOT_OF_UNITY)
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shift_inv = div(BLSFieldElement(1), shift_factor)
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# Compute (E*Z)(k*x)
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shifted_extended_evaluation = shift_polynomialcoeff(extended_evaluations_fft, shift_factor)
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# Compute Z(k*x)
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shifted_zero_poly = shift_polynomialcoeff(zero_poly_coeff, shift_factor)
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eval_shifted_extended_evaluation = fft_field(shifted_extended_evaluation, roots_of_unity_extended)
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@ -551,6 +557,10 @@ def recover_original_data(eval_shifted_extended_evaluation: Sequence[BLSFieldEle
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eval_shifted_zero_poly: Sequence[BLSFieldElement],
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shift_inv: BLSFieldElement,
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roots_of_unity_extended: Sequence[BLSFieldElement]) -> Sequence[BLSFieldElement]:
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"""
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Given Q_1, Q_2 and k^{-1}, compute P(x)
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"""
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# Compute Q_3 = Q_1(x)/Q_2(x) = P(k*x)
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eval_shifted_reconstructed_poly = [
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div(a, b)
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for a, b in zip(eval_shifted_extended_evaluation, eval_shifted_zero_poly)
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@ -558,6 +568,7 @@ def recover_original_data(eval_shifted_extended_evaluation: Sequence[BLSFieldEle
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shifted_reconstructed_poly = fft_field(eval_shifted_reconstructed_poly, roots_of_unity_extended, inv=True)
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# Unshift P(k*x) by k^{-1} to get P(x)
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reconstructed_poly = shift_polynomialcoeff(shifted_reconstructed_poly, shift_inv)
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reconstructed_data = bit_reversal_permutation(fft_field(reconstructed_poly, roots_of_unity_extended))
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@ -571,10 +582,11 @@ def recover_original_data(eval_shifted_extended_evaluation: Sequence[BLSFieldEle
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def recover_polynomial(cell_ids: Sequence[CellID],
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cells_bytes: Sequence[Vector[Bytes32, FIELD_ELEMENTS_PER_CELL]]) -> Polynomial:
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"""
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Recovers a polynomial from 2 * FIELD_ELEMENTS_PER_CELL evaluations, half of which can be missing.
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Recover original polynomial from 2 * FIELD_ELEMENTS_PER_CELL evaluations, half of which can be missing. This
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algorithm uses FFTs to recover cells faster than using Lagrange implementation, as can be seen here:
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https://ethresear.ch/t/reed-solomon-erasure-code-recovery-in-n-log-2-n-time-with-ffts/3039
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This algorithm uses FFTs to recover cells faster than using Lagrange implementation. However,
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a faster version thanks to Qi Zhou can be found here:
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A faster version thanks to Qi Zhou can be found here:
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https://github.com/ethereum/research/blob/51b530a53bd4147d123ab3e390a9d08605c2cdb8/polynomial_reconstruction/polynomial_reconstruction_danksharding.py
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Public method.
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