gmpy2 provides access to an experimental integer type called xmpz. The xmpz type is a mutable integer type. In-place operations (+=, //=, etc.) modify the orignal object and do not create a new object. Instances of xmpz cannot be used as dictionary keys.
>>> import gmpy2 >>> from gmpy2 import xmpz >>> a = xmpz(123) >>> b = a >>> a += 1 >>> a xmpz(124) >>> b xmpz(124)
The ability to change an xmpz object in-place allows for efficient and rapid bit manipulation.
Individual bits can be set or cleared:
>>> a=1 >>> a xmpz(1148)
Slice notation is supported. The bits referenced by a slice can be either ‘read from’ or ‘written to’. To clear a slice of bits, use a source value of 0. In 2s-complement format, 0 is represented by an arbitrary number of 0-bits. To set a slice of bits, use a source value of ~0. The tilde operator inverts, or complements the bits in an integer. (~0 is -1 so you can also use -1.) In 2s-complement format, -1 is represented by an arbitrary number of 1-bits.
If a value for stop is specified in a slice assignment and the actual bit-length of the xmpz is less than stop, then the destination xmpz is logically padded with 0-bits to length stop.
>>> a=xmpz(0) >>> a[8:16] = ~0 >>> bin(a) '0b1111111100000000' >>> a[4:12] = ~a[4:12] >>> bin(a) '0b1111000011110000'
Bits can be reversed:
>>> bin(a) '0b10001111100' >>> a[::] = a[::-1] >>> bin(a) '0b111110001'
The iter_bits() method returns a generator that returns True or False for each bit position. The methods iter_clear(), and iter_set() return generators that return the bit positions that are 1 or 0. The methods support arguments start and stop that define the beginning and ending bit positions that are used. To mimic the behavior of slices. the bit positions checked include start but the last position checked is stop - 1.
>>> a=xmpz(117) >>> bin(a) '0b1110101' >>> list(a.iter_bits()) [True, False, True, False, True, True, True] >>> list(a.iter_clear()) [1, 3] >>> list(a.iter_set()) [0, 2, 4, 5, 6] >>> list(a.iter_bits(stop=12)) [True, False, True, False, True, True, True, False, False, False, False, False]
The following program uses the Sieve of Eratosthenes to generate a list of prime numbers.
from __future__ import print_function import time import gmpy2 def sieve(limit=1000000): '''Returns a generator that yields the prime numbers up to limit.''' # Increment by 1 to account for the fact that slices do not include # the last index value but we do want to include the last value for # calculating a list of primes. sieve_limit = gmpy2.isqrt(limit) + 1 limit += 1 # Mark bit positions 0 and 1 as not prime. bitmap = gmpy2.xmpz(3) # Process 2 separately. This allows us to use p+p for the step size # when sieving the remaining primes. bitmap[4 : limit : 2] = -1 # Sieve the remaining primes. for p in bitmap.iter_clear(3, sieve_limit): bitmap[p*p : limit : p+p] = -1 return bitmap.iter_clear(2, limit) if __name__ == "__main__": start = time.time() result = list(sieve()) print(time.time() - start) print(len(result))
The following functions are based on mpz_lucas.c and mpz_prp.c by David Cleaver.
A good reference for probable prime testing is http://www.pseudoprime.com/pseudo.html
is_euler_prp(n,a) will return True if n is an Euler (also known as Solovay-Strassen) probable prime to the base a.
is_extra_strong_lucas_prp(n,p) will return True if n is an extra strong Lucas probable prime with parameters (p,1).
is_fermat_prp(n,a) will return True if n is a Fermat probable prime to the base a.
is_fibonacci_prp(n,p,q) will return True if n is an Fibonacci probable prime with parameters (p,q).
is_lucas_prp(n,p,q) will return True if n is a Lucas probable prime with parameters (p,q).
is_strong_lucas_prp(n,p,q) will return True if n is a strong Lucas probable prime with parameters (p,q).
is_strong_prp(n,a) will return True if n is an strong (also known as Miller-Rabin) probable prime to the base a.