v0.0¶

v0.0.14¶

Released May 7, 2021

Breaking changes¶

Rename GFArray.Eye() to GFArray.Identity().
Rename chinese_remainder_theorem() to crt().

Changes¶

Lots of performance improvements.
Additional linear algebra support.
Various bug fixes.

Contributors¶

Baalateja Kataru (@BK-Modding)
Matt Hostetter (@mhostetter)

v0.0.15¶

Released May 12, 2021

Breaking changes¶

Rename poly_exp_mod() to poly_pow() to mimic the native pow() function.
Rename fermat_primality_test() to is_prime_fermat().
Rename miller_rabin_primality_test() to is_prime_miller_rabin().

Changes¶

Massive linear algebra speed-ups. (See #88)
Massive polynomial speed-ups. (See #88)
Various Galois field performance enhancements. (See #92)
Support np.convolve() for two Galois field arrays.
Allow polynomial arithmetic with Galois field scalars (of the same field). (See #99), e.g.

>>> GF = galois.GF(3)

>>> p = galois.Poly([1,2,0], field=GF)
Poly(x^2 + 2x, GF(3))

>>> p * GF(2)
Poly(2x^2 + x, GF(3))

Allow creation of 0-degree polynomials from integers. (See #99), e.g.

>>> p = galois.Poly(1)
Poly(1, GF(2))

Add the four Oakley fields from RFC 2409.
Speed-up unit tests.
Restructure API reference.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.16¶

Released May 19, 2021

Changes¶

Add Field() alias of GF() class factory.
Add finite groups modulo n with Group() class factory.
Add is_group(), is_field(), is_prime_field(), is_extension_field().
Add polynomial constructor Poly.String().
Add polynomial factorization in poly_factors().
Add np.vdot() support.
Fix PyPI packaging issue from v0.0.15.
Fix bug in creation of 0-degree polynomials.
Fix bug in poly_gcd() not returning monic GCD polynomials.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.17¶

Released June 15, 2021

Breaking changes¶

Rename FieldMeta to FieldClass.
Remove target keyword from FieldClass.compile() until there is better support for GPUs.
Consolidate verify_irreducible and verify_primitive keyword arguments into verify for the galois.GF() class factory function.
Remove group arrays until there is more complete support.

Changes¶

Speed-up Galois field class creation time.
Speed-up JIT compilation time by caching functions.
Speed-up Poly.roots() by JIT compiling it.
Add BCH codes with galois.BCH.
Add ability to generate irreducible polynomials with irreducible_poly() and irreducible_polys().
Add ability to generate primitive polynomials with primitive_poly() and primitive_polys().
Add computation of the minimal polynomial of an element of an extension field with minimal_poly().
Add display of arithmetic tables with FieldClass.arithmetic_table().
Add display of field element representation table with FieldClass.repr_table().
Add Berlekamp-Massey algorithm in berlekamp_massey().
Enable ipython tab-completion of Galois field classes.
Cleanup API reference page.
Add introduction to Galois fields tutorials.
Fix bug in is_primitive() where some reducible polynomials were marked irreducible.
Fix bug in integer<–>polynomial conversions for large binary polynomials.
Fix bug in “power” display mode of 0.
Other minor bug fixes.

Contributors¶

Dominik Wernberger (@Werni2A)
Matt Hostetter (@mhostetter)

v0.0.18¶

Released July 6, 2021

Breaking changes¶

Make API more consistent with software like Matlab and Wolfram:
- Rename galois.prime_factors() to galois.factors().
- Rename galois.gcd() to galois.egcd() and add galois.gcd() for conventional GCD.
- Rename galois.poly_gcd() to galois.poly_egcd() and add galois.poly_gcd() for conventional GCD.
- Rename galois.euler_totient() to galois.euler_phi().
- Rename galois.carmichael() to galois.carmichael_lambda().
- Rename galois.is_prime_fermat() to galois.fermat_primality_test().
- Rename galois.is_prime_miller_rabin() to galois.miller_rabin_primality_test().
Rename polynomial search method keyword argument values from ["smallest", "largest", "random"] to ["min", "max", "random"].

Changes¶

Clean up galois API and dir() so only public classes and functions are displayed.
Speed-up galois.is_primitive() test and search for primitive polynomials in galois.primitive_poly().
Speed-up galois.is_smooth().
Add Reed-Solomon codes in galois.ReedSolomon.
Add shortened BCH and Reed-Solomon codes.
Add error detection for BCH and Reed-Solomon with the detect() method.
Add general cyclic linear block code functions.
Add Matlab default primitive polynomial with galois.matlab_primitive_poly().
Add number theoretic functions:
- Add galois.legendre_symbol(), galois.jacobi_symbol(), galois.kronecker_symbol().
- Add galois.divisors(), galois.divisor_sigma().
- Add galois.is_composite(), galois.is_prime_power(), galois.is_perfect_power(), galois.is_square_free(), galois.is_powersmooth().
- Add galois.are_coprime().
Clean up integer factorization algorithms and add some to public API:
- Add galois.perfect_power(), galois.trial_division(), galois.pollard_p1(), galois.pollard_rho().
Clean up API reference structure and hierarchy.
Fix minor bugs in BCH codes.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.19¶

Released August 9, 2021

Breaking changes¶

Remove unnecessary is_field() function. Use isinstance(x, galois.FieldClass) or isinstance(x, galois.FieldArray) instead.
Remove log_naive() function. Might be re-added later through np.log() on a multiplicative group array.
Rename mode kwarg in galois.GF() to compile.
Revert np.copy() override that always returns a subclass. Now, by default it does not return a subclass. To return a Galois field array, use x.copy() or np.copy(x, subok=True) instead.

Changes¶

Improve documentation.
Improve unit test coverage.
Add benchmarking tests.
Add initial LFSR implementation.
Add display kwarg to galois.GF() class factory to set the display mode at class-creation time.
Add Poly.reverse() method.
Allow polynomial strings as input to galois.GF(). For example, galois.GF(2**4, irreducible_poly="x^4 + x + 1").
Enable np.divmod() and np.remainder() on Galois field arrays. The remainder is always zero, though.
Fix bug in bch_valid_codes() where repetition codes weren’t included.
Various minor bug fixes.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.20¶

Released August 24, 2021

Breaking changes¶

Move poly_gcd() functionality into gcd().
Move poly_egcd() functionality into egcd().
Move poly_factors() functionality into factors().

Changes¶

Fix polynomial factorization algorithms. Previously only parital factorization was implemented.
Support generating and testing irreducible and primitive polynomials over extension fields.
Support polynomial input to is_square_free().
Minor documentation improvements.
Pin Numba dependency to <0.54

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.21¶

Released September 2, 2021

Changes¶

Fix docstrings and code completion for Python classes that weren’t rendering correctly in an IDE.
Various documentation improvements.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.22¶

Released December 3, 2021

Breaking changes¶

Random integer generation is handled using new style random generators. Now each .Random() call will generate a new seed rather than using the NumPy “global” seed used with np.random.randint().

Add a seed=None keyword argument to FieldArray.Random() and Poly.Random(). A reproducible script can be constructed like this:

rng = np.random.default_rng(123456789)
x = GF.Random(10, seed=rng)
y = GF.Random(10, seed=rng)
poly = galois.Poly.Random(5, seed=rng, field=GF)

Changes¶

Official support for Python 3.9.
Major performance improvements to “large” finite fields (those with dtype=np.object_).
Minor performance improvements to all finite fields.
Add the Number Theoretic Transform (NTT) in ntt() and intt().
Add the trace of finite field elements in FieldArray.field_trace().
Add the norm of finite field elements in FieldArray.field_norm().
Support len() on Poly objects, which returns the length of the coefficient array (polynomial order + 1).
Support x.dot(y) syntax for the expression np.dot(x, y).
Minimum NumPy version bumped to 1.18.4 for new style random usage.
Various bug fixes.

Contributors¶

Iyán Méndez Veiga (@iyanmv)
Matt Hostetter (@mhostetter)

v0.0.23¶

Released January 14, 2022

Changes¶

Add support for Python 3.10.
Add support for NumPy 1.21.
Add support for Numba 0.55.
Add type hints to library API.
Add FieldArray.characteristic_poly() method to return the characteristic polynomial of a square matrix.
Add Poly.coefficients() method to return the coefficient array with fixed size and order.
Fix bug in Poly.Degrees() when duplicate degrees were present.
Fix bug in Reed-Solomon decode when c != 1.
Various other bug fixes.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.24¶

Released February 12, 2022

Breaking changes¶

Move galois.minimal_poly() functionality into FieldArray.minimal_poly().
Refactor FieldArray.lup_decompose() into FieldArray.plu_decompose().
Raise ValueError instead of returning None for prev_prime(2).
Return (n, 1) from perfect_power(n) if n is not a perfect power rather than returning None.

Changes¶

Compute a finite field element’s square root (if it exists) with np.sqrt().
Test if finite field elements have a square root with FieldArray.is_quadratic_residue().
List which finite field elements are/aren’t quadratic residues (have a square root) with FieldClass.quadratic_residues and FieldClass.quadratic_non_residues.
Compute standard vector spaces with FieldArray.row_space(), FieldArray.column_space(), FieldArray.left_null_space(), and FieldArray.null_space().
Compute a finite field element’s additive and multiplicative orders with FieldArray.additive_order() and FieldArray.multiplicative_order().
Evaluate polynomials at square matrix inputs using f(X, elementwise=False).
Compute the characteristic polynomial of a single element or square matrix with FieldArray.characteristic_poly().
Compute the minimal polynomial of a single element with FieldArray.minimal_poly().
Compute a Lagrange interpolating polynomial with lagrange_poly(x, y).
Support non-square matrix inputs to FieldArray.lu_decompose() and FieldArray.plu_decompose().
Support polynomial scalar multiplication. Now p * 3 is valid syntax and represents p + p + p.
Allow polynomial comparison with integers and field scalars. Now galois.Poly([0]) == 0 and galois.Poly([0]) == GF(0) return True rather than raising TypeError.
Support testing 0-degree polynomials for irreducibility and primitivity.
Extend crt() to work over non co-prime moduli.
Extend prev_prime() and next_prime() to work over arbitrarily large inputs.
Allow negative integer inputs to primes(), is_prime(), is_composite(), is_prime_power(), is_perfect_power(), is_square_free(), is_smooth(), and is_powersmooth().
Fix various type hinting errors.
Various other bug fixes.

Contributors¶

Iyán Méndez Veiga (@iyanmv)
Matt Hostetter (@mhostetter)

v0.0.25¶

Released March 21, 2022

Breaking changes¶

Separated LFSR into FLFSR/GLFSR and fixed/redefined terms (feedback poly, characteristic poly, state). (#285)

Removed galois.pow() and replaced it with the built-in pow(). (#300)

>>> f = galois.Poly([6, 3, 0, 1], field=galois.GF(7))
>>> g = galois.Poly([5, 0, 3], field=galois.GF(7))
>>> pow(f, 123456789, g)
Poly(6x + 2, GF(7))

Removed FieldClass.properties and replaced with FieldClass.__str__. (#289)

>>> GF = galois.GF(3**5)
>>> print(GF)
Galois Field:
  name: GF(3^5)
  characteristic: 3
  degree: 5
  order: 243
  irreducible_poly: x^5 + 2x + 1
  is_primitive_poly: True
  primitive_element: x

Differentiated repr() and str() for Galois field arrays, like NumPy. repr() displays the finite field’s order, but str() does not.

>>> GF = galois.GF(31, display="power")
>>> x = GF([1, 23, 0, 15])
>>> x
GF([   1, α^27,    0, α^21], order=31)
>>> print(x)
[   1, α^27,    0, α^21]

Renamed Poly.String() to Poly.Str(). Removed Poly.string and replaced it with Poly.__str__. (#300)

>>> f = galois.Poly.Str("x^3 + x + 1"); f
Poly(x^3 + x + 1, GF(2))
>>> str(f)
'x^3 + x + 1'

Renamed Poly.Integer() to Poly.Int(). Removed Poly.integer and replaced it with Poly.__int__. (#300)
```
>>> f = galois.Poly.Int(11); f
Poly(x^3 + x + 1, GF(2))
>>> int(f)
11
```

Changes¶

Fixed bug in Fibonacci/Galois LFSRs where feedback polynomial wasn’t interpreted correctly for fields with characteristic greater than 2. (#299)

Utilized memoization for expensive search routines (irreducible_poly() and primitive_poly()) to speed-up subsequent calls. (#295)

In [2]: %time galois.primitive_poly(7, 4)
CPU times: user 675 ms, sys: 6.24 ms, total: 682 ms
Wall time: 741 ms
Out[2]: Poly(x^4 + x^2 + 3x + 5, GF(7))

In [3]: %time galois.primitive_poly(7, 4)
CPU times: user 30 µs, sys: 0 ns, total: 30 µs
Wall time: 31.7 µs
Out[3]: Poly(x^4 + x^2 + 3x + 5, GF(7))

Added support for bin(), oct(), and hex() on Poly objects. (#300)

>>> f = galois.Poly.Int(11); f
Poly(x^3 + x + 1, GF(2))
>>> bin(f)
'0b1011'
>>> oct(f)
'0o13'
>>> hex(f)
'0xb'

Made Galois field arrays display with fixed-width elements, like NumPy. (#270)
Achieved speed-up of repr() and str() on Galois field arrays of at least 25x. Achieved a much greater speed-up for large arrays, since now elements converted to ... are no longer needlessly converted to their string representation. (#270)
Overhauled documentation and website. Type hints are now displayed in the API reference. (#263)
Various bug fixes.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.26¶

Released March 30, 2022

Breaking changes¶

Removed the Poly.copy() method as it was unnecessary. Polynomial objects are immutable. Use g = f wherever g = f.copy() was previously used. (#320)
Disabled true division f / g on polynomials since true division was not actually being performed. Use floor division f // g moving forward. (#312)
Refactored irreducible_polys() to return an iterator rather than list. Use list(irreducible_polys(...)) to obtain the previous output. (#325)
Refactored primitive_polys() to return an iterator rather than list. Use list(primitive_polys(...)) to obtain the previous output. (#325)
Refactored primitive_root() and primitive_roots(). (#325)
- Added method keyword argument and removed reverse from primitive_root(). Use primitive_root(..., method="max") where primitive_root(..., reverse=True) was previously used.
- Refactored primitive_roots() to now return an iterator rather than list. Use list(primitive_roots(...)) to obtain the previous output.
Refactored primitive_element() and primitive_elements(). (#325)
- Added method keyword argument to primitive_element().
- Removed start, stop, and reverse arguments from both functions.
Search functions now raise RuntimeError instead of returning None when failing to find an answer. This applies to primitive_root(), pollard_p1(), and pollard_rho(). (#312)

Changes¶

The galois.Poly class no longer returns subclasses BinaryPoly, DensePoly, and SparsePoly. Instead, only Poly classes are returned. The classes otherwise operate the same. (#320)

Made Galois field array creation much more efficient by avoiding redundant element verification. (#317)

Scalar creation is 625% faster.

In [2]: GF = galois.GF(3**5)

# v0.0.25
In [3]: %timeit GF(10)
21.2 µs ± 181 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

# v0.0.26
In [3]: %timeit GF(10)
2.88 µs ± 8.03 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Nested iterable array creation is 150% faster.

# v0.0.25
In [4]: %timeit GF([[10, 20], [30, 40]])
53.6 µs ± 436 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

# v0.0.26
In [4]: %timeit GF([[10, 20], [30, 40]])
20.9 µs ± 11.2 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Nested iterable (with strings) array creation is 25% faster.

# v0.0.25
In [5]: %timeit GF([[10, "2x^2 + 2"], ["x^3 + x", 40]])
67.9 µs ± 910 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

# v0.0.26
In [5]: %timeit GF([[10, "2x^2 + 2"], ["x^3 + x", 40]])
54.7 µs ± 16.3 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Made array arithmetic 35% faster by avoiding unnecessary element verification of outputs. (#309)

In [2]: GF = galois.GF(3**5)

In [3]: x = GF.Random((10_000), seed=1)

In [4]: y = GF.Random((10_000), seed=2)

# v0.0.25
In [6]: %timeit x * y
39.4 µs ± 237 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

# v0.0.26
In [6]: %timeit x * y
28.8 µs ± 172 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Made polynomial arithmetic over binary fields 10,900% faster by making polynomial creation from integers much more efficient. (#320)

In [5]: f
Out[5]: Poly(x^10 + x^9 + x^6 + x^5 + x^3 + x, GF(2))

In [6]: g
Out[6]: Poly(x^10 + x^7 + x^4 + 1, GF(2))

# v0.0.25
In [7]: %timeit f * g
283 µs ± 6.31 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

# v0.0.26
In [7]: %timeit f * g
2.57 µs ± 54.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

JIT-compiled polynomial modular exponentiation. (#306)

Binary fields are 14,225% faster.

In [5]: f
Out[5]: Poly(x^10 + x^9 + x^6 + x^5 + x^3 + x, GF(2))

In [6]: g
Out[6]: Poly(x^5 + x^2, GF(2))

# v0.0.25
In [7]: %timeit pow(f, 123456789, g)
19.2 ms ± 206 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

# v0.0.26
In [7]: %timeit pow(f, 123456789, g)
134 µs ± 2.21 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Other fields are 325% faster.

In [6]: f
Out[6]: Poly(242x^10 + 216x^9 + 32x^8 + 114x^7 + 230x^6 + 179x^5 + 5x^4 + 124x^3 + 96x^2 + 159x + 77, GF(3^5))

In [7]: g
Out[7]: Poly(183x^5 + 83x^4 + 101x^3 + 203x^2 + 68x + 2, GF(3^5))

# v0.0.25
In [8]: %timeit pow(f, 123456789, g)
17.6 ms ± 61.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# v0.0.26
In [8]: %timeit pow(f, 123456789, g)
4.19 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Made irreducible and primitive polynomial search routines faster. (#306, #309, #317, #320)

Binary fields are 1,950% faster.

# v0.0.25
In [2]: %time f = galois.primitive_poly(2, 14)
CPU times: user 296 ms, sys: 70.9 ms, total: 367 ms
Wall time: 313 ms

# v0.0.26
In [2]: %time f = galois.primitive_poly(2, 14)
CPU times: user 14.7 ms, sys: 5.53 ms, total: 20.2 ms
Wall time: 15.3 ms

Other fields are 400% faster.

# v0.0.25
In [5]: %time f = galois.primitive_poly(7, 10)
CPU times: user 2.22 s, sys: 0 ns, total: 2.22 s
Wall time: 2.21 s

# v0.0.26
In [4]: %time f = galois.primitive_poly(7, 10)
CPU times: user 442 ms, sys: 0 ns, total: 442 ms
Wall time: 439 ms

Made FieldArray.Vector() 100% faster and FieldArray.vector() 25% faster by making better use of divmod() when converting between integer and vector representations. (#322)

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.27¶

Released April 22, 2022

Breaking changes¶

Sunsetted support for Python 3.6. This was necessary to support forward references with from __future__ import annotations (available in Python 3.7+). That import is required to support the type aliases in the new galois.typing subpackage. (#339)
Removed the FieldClass metaclass from the public API. It was previously included due to an inability of Sphinx to document class properties. In this release, we monkey patched Sphinx to document all classmethods, class properties, and instance methods in FieldArray itself. (#343)
- Use issubclass(GF, galois.FieldArray) anywhere isinstance(GF, galois.FieldClass) was previously used.
- Annotate with Type[galois.FieldArray] anywhere galois.FieldClass was previously used.

Changes¶

Added the galois.typing subpackage, similar to np.typing. It contains type hints for common coercible data types used throughout the library, including ElementLike, ArrayLike, and PolyLike. With these type hints, the annotations are simpler and more clear. (#339)
Modified functions to accept coercible data types wherever possible. For example, functions now accept PolyLike objects instead of strictly Poly instances. (#339)
Added Array which is an abstract base class of FieldArray (and RingArray in a future release). (#336)

Added support for the DFT over any finite field using np.fft.fft() and np.fft.ifft(). (#335)

>>> x
GF([127, 191,  69,  35, 221, 242, 193, 108,  72, 102,  80, 163,  13,  74,
    218, 159, 207,  12, 159, 129,  92,  71], order=3^5)
>>> X = np.fft.fft(x); X
GF([ 16,  17,  20, 137,  58, 166, 178,  52,  19, 109, 115,  93,  99, 214,
    187, 235, 195,  96, 232,  45, 241,  24], order=3^5)
>>> np.fft.ifft(X)
GF([127, 191,  69,  35, 221, 242, 193, 108,  72, 102,  80, 163,  13,  74,
    218, 159, 207,  12, 159, 129,  92,  71], order=3^5)

Implemented the Cooley-Tukey radix-2 \(O(N\ \textrm{log}(N))\) algorithm for the NTT and JIT compiled it. (#333)

In [2]: x = list(range(1, 1024 + 1))

# v0.0.26
In [4]: %timeit X = galois.ntt(x)
5.2 ms ± 121 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# v0.0.27
In [4]: %timeit X = galois.ntt(x)
695 µs ± 4.56 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

Added the FieldArray.primitive_root_of_unity() classmethod. (#333)

>>> GF = galois.GF(3**5)
>>> GF.primitive_root_of_unity(22)
GF(39, order=3^5)

Added the FieldArray.primitive_roots_of_unity() classmethod. (#333)

>>> GF = galois.GF(3**5)
>>> GF.primitive_roots_of_unity(22)
GF([ 14,  39,  44,  59, 109, 114, 136, 200, 206, 226], order=3^5)

Made 0-th degree coefficients more differentiated when using the polynomial element representation. (#328)

# v0.0.26
>>> print(f)
(α^2 + α + 1)x^4 + (α^3)x + α^3 + 2α^2 + 2α + 2
# v0.0.27
>>> print(f)
(α^2 + α + 1)x^4 + (α^3)x + (α^3 + 2α^2 + 2α + 2)

Restructured code base for clarity. (#336)
Fixed display of overloaded functions in API reference. (#337)
Fixed broken “References” sections in API reference. (#281)
Fixed other small bugs.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.28¶

Released May 11, 2022

Changes¶

Modified JIT-compiled functions to use explicit calculation or lookup tables. Previously, JIT functions only used explicit calculation routines. Now all ufuncs and functions are JIT-compiled once on first invocation, but use the current ufunc_mode to determine the arithmetic used. This provides a significant performance boost for fields which use lookup tables by default. The greatest performance improvement can be seen in \(\mathrm{GF}(p^m)\) fields. (#354)

Polynomial multiplication is 210% faster.

In [2]: GF = galois.GF(7**5)

In [3]: f = galois.Poly.Random(10, seed=1, field=GF)

In [4]: g = galois.Poly.Random(5, seed=2, field=GF)

# v0.0.27
In [6]: %timeit f * g
168 µs ± 722 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

# v0.0.28
In [6]: %timeit f * g
54 µs ± 574 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Polynomial modular exponentiation is 5,310% faster.

# v0.0.27
In [8]: %timeit pow(f, 123456789, g)
5.9 ms ± 9.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# v0.0.28
In [8]: %timeit pow(f, 123456789, g)
109 µs ± 527 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Matrix multiplication is 6,690% faster.

In [9]: A = GF.Random((100, 100), seed=1)

In [10]: B = GF.Random((100, 100), seed=2)

# v0.0.27
In [12]: %timeit A @ B
1.1 s ± 4.76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# v0.0.28
In [12]: %timeit A @ B
16.2 ms ± 50.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Simplified FieldArray subclasses’ repr() and str(). Since these classes may be displayed in error logs, a concise representation is necessary. (#350)
```
>>> GF = galois.GF(3**5)
>>> GF
<class 'galois.GF(3^5)'>
```

Added back FieldArray.properties for a detailed description of the finite field’s relevant properties. (#350)

>>> GF = galois.GF(3**5)
>>> print(GF.properties)
Galois Field:
  name: GF(3^5)
  characteristic: 3
  degree: 5
  order: 243
  irreducible_poly: x^5 + 2x + 1
  is_primitive_poly: True
  primitive_element: x

Increased code coverage.
Various documentation fixes.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.29¶

Released May 18, 2022

Breaking changes¶

Moved galois.square_free_factorization() function into Poly.square_free_factors() method. (#362)
Moved galois.distinct_degree_factorization() function into Poly.distinct_degree_factors() method. (#362)
Moved galois.equal_degree_factorization() function into Poly.equal_degree_factors() method. (#362)
Moved galois.is_irreducible() function into Poly.is_irreducible() method. This is a method, not property, to indicate it is a computationally expensive operation. (#362)
Moved galois.is_primitive() function into Poly.is_primitive() method. This is a method, not property, to indicate it is a computationally expensive operation. (#362)
Moved galois.is_monic() function into Poly.is_monic property. (#362)

Changes¶

Added galois.set_printoptions() function to modify package-wide printing options. This is the equivalent of np.set_printoptions(). (#363)

In [1]: GF = galois.GF(3**5, display="poly")

In [2]: a = GF([109, 83]); a
Out[2]: GF([α^4 + α^3 + 1,       α^4 + 2], order=3^5)

In [3]: f = galois.Poly([3, 0, 5, 2], field=galois.GF(7)); f
Out[3]: Poly(3x^3 + 5x + 2, GF(7))

In [4]: galois.set_printoptions(coeffs="asc")

In [5]: a
Out[5]: GF([1 + α^3 + α^4,       2 + α^4], order=3^5)

In [6]: f
Out[6]: Poly(2 + 5x + 3x^3, GF(7))

Added galois.get_printoptions() function to return the current package-wide printing options. This is the equivalent of np.get_printoptions(). (#363)
Added galois.printoptions() context manager to modify printing options inside of a with statement. This is the equivalent of np.printoptions(). (#363)
Added a separate Poly.factors() method, in addition to the polymorphic galois.factors(). (#362)
Added a separate Poly.is_square_free() method, in addition to the polymorphic galois.is_square_free(). This is a method, not property, to indicate it is a computationally expensive operation. (#362)
Fixed a bug (believed to be introduced in v0.0.26) where Poly.degree occasionally returned np.int64 instead of int. This could cause overflow in certain large integer operations (e.g., computing \(q^m\) when determining if a degree-\(m\) polynomial over \(\mathrm{GF}(q)\) is irreducible). When the integer overflowed, this created erroneous results. (#360, #361)
Increased code coverage.

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.30¶

Released July 12, 2022

Changes¶

Added support for NumPy 1.22 with Numba 0.55.2. This allows users to upgrade NumPy and avoid recently discovered vulnerabilities CVE-2021-34141, CVE-2021-41496, and CVE-2021-41495. (#366)

Made FieldArray.repr_table() more compact. (#367)

In [2]: GF = galois.GF(3**3)

In [3]: print(GF.repr_table())
 Power     Polynomial      Vector    Integer
------- --------------- ----------- ---------
   0           0         [0, 0, 0]      0
  x^0          1         [0, 0, 1]      1
  x^1          x         [0, 1, 0]      3
  x^2         x^2        [1, 0, 0]      9
  x^3        x + 2       [0, 1, 2]      5
  x^4       x^2 + 2x     [1, 2, 0]      15
  x^5     2x^2 + x + 2   [2, 1, 2]      23
  x^6     x^2 + x + 1    [1, 1, 1]      13
  x^7     x^2 + 2x + 2   [1, 2, 2]      17
  x^8       2x^2 + 2     [2, 0, 2]      20
  x^9        x + 1       [0, 1, 1]      4
  x^10      x^2 + x      [1, 1, 0]      12
  x^11    x^2 + x + 2    [1, 1, 2]      14
  x^12      x^2 + 2      [1, 0, 2]      11
  x^13         2         [0, 0, 2]      2
  x^14         2x        [0, 2, 0]      6
  x^15        2x^2       [2, 0, 0]      18
  x^16       2x + 1      [0, 2, 1]      7
  x^17      2x^2 + x     [2, 1, 0]      21
  x^18    x^2 + 2x + 1   [1, 2, 1]      16
  x^19   2x^2 + 2x + 2   [2, 2, 2]      26
  x^20    2x^2 + x + 1   [2, 1, 1]      22
  x^21      x^2 + 1      [1, 0, 1]      10
  x^22       2x + 2      [0, 2, 2]      8
  x^23     2x^2 + 2x     [2, 2, 0]      24
  x^24   2x^2 + 2x + 1   [2, 2, 1]      25
  x^25      2x^2 + 1     [2, 0, 1]      19

Made FieldArray.arithmetic_table() more compact. (#367)

In [2]: GF = galois.GF(13)

In [3]: print(GF.arithmetic_table("*"))
x * y |  0   1   2   3   4   5   6   7   8   9  10  11  12
------|----------------------------------------------------
|  0   0   0   0   0   0   0   0   0   0   0   0   0
|  0   1   2   3   4   5   6   7   8   9  10  11  12
|  0   2   4   6   8  10  12   1   3   5   7   9  11
|  0   3   6   9  12   2   5   8  11   1   4   7  10
|  0   4   8  12   3   7  11   2   6  10   1   5   9
|  0   5  10   2   7  12   4   9   1   6  11   3   8
|  0   6  12   5  11   4  10   3   9   2   8   1   7
|  0   7   1   8   2   9   3  10   4  11   5  12   6
|  0   8   3  11   6   1   9   4  12   7   2  10   5
|  0   9   5   1  10   6   2  11   7   3  12   8   4
|  0  10   7   4   1  11   8   5   2  12   9   6   3
|  0  11   9   7   5   3   1  12  10   8   6   4   2
|  0  12  11  10   9   8   7   6   5   4   3   2   1

Contributors¶

Iyán Méndez Veiga (@iyanmv)
Matt Hostetter (@mhostetter)

v0.0.31¶

Released July 24, 2022

Breaking changes¶

Renamed FieldArray.Elements() classmethod to FieldArray.elements class property. This naming convention is more consistent with primitive_elements, units, quadratic_residues, and quadratic_non_residues. (#373)
```
>>> GF = galois.GF(3**2, display="poly")
>>> GF.elements
GF([     0,      1,      2,      α,  α + 1,  α + 2,     2α, 2α + 1,
    2α + 2], order=3^2)
```
Renamed BCH.systematic to BCH.is_systematic. (#376)
Renamed ReedSolomon.systematic to ReedSolomon.is_systematic. (#376)

Changes¶

Added support for polynomial composition in Poly.__call__(). (#377)

>>> GF = galois.GF(3**5)
>>> f = galois.Poly([37, 123, 0, 201], field=GF); f
Poly(37x^3 + 123x^2 + 201, GF(3^5))
>>> g = galois.Poly([55, 0, 1], field=GF); g
Poly(55x^2 + 1, GF(3^5))
>>> f(g)
Poly(77x^6 + 5x^4 + 104x^2 + 1, GF(3^5))

Added FieldArray.units class property. (#373)

>>> GF = galois.GF(3**2, display="poly")
>>> GF.units
GF([     1,      2,      α,  α + 1,  α + 2,     2α, 2α + 1, 2α + 2],
   order=3^2)

Documentation¶

Reworked API reference using Sphinx Immaterial’s python-apigen. (#370)
Shortened website URLs to use directories. https://galois.readthedocs.io/en/v0.0.30/getting-started.html is converted to https://galois.readthedocs.io/en/v0.0.31/getting-started/. (#370)

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.32¶

Released July 28, 2022

Breaking changes¶

Changed “quadratic residue” language in Galois fields to “square”. This seems to be more canonical. Quadratic residue connotes quadratic residue modulo \(p\), which is a square in \(\mathrm{GF}(p)\). However, a quadratic residue modulo \(p^m\) is not a square in \(\mathrm{GF}(p^m)\). Hopefully the “square” language is more clear. (#392)
- Renamed FieldArray.is_quadratic_residue to FieldArray.is_square.
- Renamed FieldArray.quadratic_residues to FieldArray.squares.
- Renamed FieldArray.quadratic_non_residues to FieldArray.non_squares.

Changes¶

Added support for Numba 0.56.x. (#389)

Added general logarithm base any primitive element in FieldArray.log(). (#385)

>>> GF = galois.GF(3**5)
>>> x = GF.Random(10, low=1); x
GF([215, 176,  52,  20, 236,  48, 217, 131,  13,  57], order=3^5)
>>> beta = GF.primitive_elements[-1]; beta
GF(242, order=3^5)
>>> i = x.log(beta); i
array([171, 240, 109,  65, 162,  57,  34, 166,  72,  56])
>>> np.array_equal(beta ** i, x)
True

Added Pollard-\(\rho\) discrete logarithm for certain \(\mathrm{GF}(2^m)\) fields. The algorithm is only applicable to fields whose multiplicative group has prime order. It has complexity \(O(\sqrt{n})\) compared to \(O(n)\) for the brute-force algorithm. In this example, Pollard-\(\rho\) is 1650% faster than brute force. (#385)

In [3]: GF = galois.GF(2**19, compile="jit-calculate")

In [4]: galois.is_prime(GF.order - 1)
Out[4]: True

In [5]: x = GF.Random(100, low=1, seed=1)

# v0.0.31
In [6]: %timeit np.log(x)
80.3 ms ± 55.8 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

# v0.0.32
In [6]: %timeit np.log(x)
4.59 ms ± 90.5 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

Added Pohlig-Hellman discrete logarithm to replace the brute-force search. It has complexity \(O(\sum_{i=1}^{r} e_i(\textrm{lg}\ n + \sqrt{p_i}))\) compared to \(O(n)\) for the brute-force algorithm. It is especially efficient for fields whose multiplicative group has smooth order. In this example with \(p^m - 1\) smooth, Pohlig-Hellman is ~3,000,000% faster than brute force. (#387)

In [3]: GF = galois.GF(491954233)

# The multiplicative group's order is smooth
In [4]: galois.factors(GF.order - 1)
Out[4]: ([2, 3, 7, 11, 19, 14011], [3, 1, 1, 1, 1, 1])

In [5]: x = GF.Random(1, low=1, seed=1)

# v0.0.31
In [6]: %timeit np.log(x)
1.82 s ± 2.95 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# v0.0.32
In [6]: %timeit np.log(x)
61.3 µs ± 14.6 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

Added Itoh-Tsujii inversion algorithm for extension fields, which is 35% faster than inversion with Fermat’s Little Theorem. (#383)

In [3]: GF = galois.GF(109987**4)

In [4]: x = GF.Random(100, low=1, seed=1)

# v0.0.31
In [5]: %timeit np.reciprocal(x)
646 ms ± 834 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

# v0.0.32
In [5]: %timeit np.reciprocal(x)
479 ms ± 26.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Fixed a bug where FieldArray subclasses and instances could not be pickled. (#393)

Contributors¶

Matt Hostetter (@mhostetter)

v0.0.33¶

Released August 26, 2022

Breaking changes¶

Modified FEC encode(), detect(), and decode() methods to always return FieldArray instances, not np.ndarray. (#397)
- Invoke .view(np.ndarray) on the output to convert it back to a NumPy array, if needed.

Changes¶

Added support for ArrayLike inputs to FEC encode(), detect(), and decode() methods. (#397)
Modified library packaging to use pyproject.toml and a src/ source code folder. (#404)

Contributors¶

Matt Hostetter (@mhostetter)

Last update: Jul 03, 2024