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! """Fix incompatible imports and module references that must be fixed after fix_imports.""" from . import fix_imports MAPPING = { 'whichdb': 'dbm', 'anydbm': 'dbm', } class FixImports2(fix_imports.FixImports): run_order = 7 mapping = MAPPING 2Ô :mod:`random` --- Generate pseudo-random numbers ================================================ .. module:: random :synopsis: Generate pseudo-random numbers with various common distributions. **Source code:** :source:`Lib/random.py` -------------- This module implements pseudo-random number generators for various distributions. For integers, uniform selection from a range. For sequences, uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement. On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available. Almost all module functions depend on the basic function :func:`random`, which generates a random float uniformly in the semi-open range [0.0, 1.0). Python uses the Mersenne Twister as the core generator. It produces 53-bit precision floats and has a period of 2\*\*19937-1. The underlying implementation in C is both fast and threadsafe. The Mersenne Twister is one of the most extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes. The functions supplied by this module are actually bound methods of a hidden instance of the :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state. This is especially useful for multi-threaded programs, creating a different instance of :class:`Random` for each thread, and using the :meth:`jumpahead` method to make it likely that the generated sequences seen by each thread don't overlap. Class :class:`Random` can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the :meth:`random`, :meth:`seed`, :meth:`getstate`, :meth:`setstate` and :meth:`jumpahead` methods. Optionally, a new generator can supply a :meth:`getrandbits` method --- this allows :meth:`randrange` to produce selections over an arbitrarily large range. .. versionadded:: 2.4 the :meth:`getrandbits` method. As an example of subclassing, the :mod:`random` module provides the :class:`WichmannHill` class that implements an alternative generator in pure Python. The class provides a backward compatible way to reproduce results from earlier versions of Python, which used the Wichmann-Hill algorithm as the core generator. Note that this Wichmann-Hill generator can no longer be recommended: its period is too short by contemporary standards, and the sequence generated is known to fail some stringent randomness tests. See the references below for a recent variant that repairs these flaws. .. versionchanged:: 2.3 MersenneTwister replaced Wichmann-Hill as the default generator. The :mod:`random` module also provides the :class:`SystemRandom` class which uses the system function :func:`os.urandom` to generate random numbers from sources provided by the operating system. Bookkeeping functions: .. function:: seed([x]) Initialize the basic random number generator. Optional argument *x* can be any :term:`hashable` object. If *x* is omitted or ``None``, current system time is used; current system time is also used to initialize the generator when the m