random()

Help(Random)

Python 3.1.2 (r312:79147, Sep 27 2010, 09:45:41) 
[GCC 4.4.3] on linux2
Type "copyright", "credits" or "license()" for more information.
==== No Subprocess ====
>>> from random import Random
>>> help(Random)
Help on class Random in module random:

class Random(_random.Random)
 |  Random number generator base class used by bound module functions.
 |  
 |  Used to instantiate instances of Random to get generators that don't
 |  share state.
 |  
 |  Class Random can also be subclassed if you want to use a different basic
 |  generator of your own devising: in that case, override the following
 |  methods:  random(), seed(), getstate(), and setstate().
 |  Optionally, implement a getrandbits() method so that randrange()
 |  can cover arbitrarily large ranges.
 |  
 |  Method resolution order:
 |      Random
 |      _random.Random
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __getstate__(self)
 |  
 |  __init__(self, x=None)
 |      Initialize an instance.
 |      
 |      Optional argument x controls seeding, as for Random.seed().
 |  
 |  __reduce__(self)
 |  
 |  __setstate__(self, state)
 |  
 |  betavariate(self, alpha, beta)
 |      Beta distribution.
 |      
 |      Conditions on the parameters are alpha > 0 and beta > 0.
 |      Returned values range between 0 and 1.
 |  
 |  choice(self, seq)
 |      Choose a random element from a non-empty sequence.
 |  
 |  expovariate(self, lambd)
 |      Exponential distribution.
 |      
 |      lambd is 1.0 divided by the desired mean.  It should be
 |      nonzero.  (The parameter would be called "lambda", but that is
 |      a reserved word in Python.)  Returned values range from 0 to
 |      positive infinity if lambd is positive, and from negative
 |      infinity to 0 if lambd is negative.
 |  
 |  gammavariate(self, alpha, beta)
 |      Gamma distribution.  Not the gamma function!
 |      
 |      Conditions on the parameters are alpha > 0 and beta > 0.
 |  
 |  gauss(self, mu, sigma)
 |      Gaussian distribution.
 |      
 |      mu is the mean, and sigma is the standard deviation.  This is
 |      slightly faster than the normalvariate() function.
 |      
 |      Not thread-safe without a lock around calls.
 |  
 |  getstate(self)
 |      Return internal state; can be passed to setstate() later.
 |  
 |  lognormvariate(self, mu, sigma)
 |      Log normal distribution.
 |      
 |      If you take the natural logarithm of this distribution, you'll get a
 |      normal distribution with mean mu and standard deviation sigma.
 |      mu can have any value, and sigma must be greater than zero.
 |  
 |  normalvariate(self, mu, sigma)
 |      Normal distribution.
 |      
 |      mu is the mean, and sigma is the standard deviation.
 |  
 |  paretovariate(self, alpha)
 |      Pareto distribution.  alpha is the shape parameter.
 |  
 |  randint(self, a, b)
 |      Return random integer in range [a, b], including both end points.
 |  
 |  randrange(self, start, stop=None, step=1, int=, default=None, maxwidth=9007199254740992)
 |      Choose a random item from range(start, stop[, step]).
 |      
 |      This fixes the problem with randint() which includes the
 |      endpoint; in Python this is usually not what you want.
 |      Do not supply the 'int', 'default', and 'maxwidth' arguments.
 |  
 |  sample(self, population, k)
 |      Chooses k unique random elements from a population sequence or set.
 |      
 |      Returns a new list containing elements from the population while
 |      leaving the original population unchanged.  The resulting list is
 |      in selection order so that all sub-slices will also be valid random
 |      samples.  This allows raffle winners (the sample) to be partitioned
 |      into grand prize and second place winners (the subslices).
 |      
 |      Members of the population need not be hashable or unique.  If the
 |      population contains repeats, then each occurrence is a possible
 |      selection in the sample.
 |      
 |      To choose a sample in a range of integers, use range as an argument.
 |      This is especially fast and space efficient for sampling from a
 |      large population:   sample(range(10000000), 60)
 |  
 |  seed(self, a=None)
 |      Initialize internal state from hashable object.
 |      
 |      None or no argument seeds from current time or from an operating
 |      system specific randomness source if available.
 |      
 |      If a is not None or an int, hash(a) is used instead.
 |  
 |  setstate(self, state)
 |      Restore internal state from object returned by getstate().
 |  
 |  shuffle(self, x, random=None, int=)
 |      x, random=random.random -> shuffle list x in place; return None.
 |      
 |      Optional arg random is a 0-argument function returning a random
 |      float in [0.0, 1.0); by default, the standard random.random.
 |  
 |  triangular(self, low=0.0, high=1.0, mode=None)
 |      Triangular distribution.
 |      
 |      Continuous distribution bounded by given lower and upper limits,
 |      and having a given mode value in-between.
 |      
 |      http://en.wikipedia.org/wiki/Triangular_distribution
 |  
 |  uniform(self, a, b)
 |      Get a random number in the range [a, b) or [a, b] depending on rounding.
 |  
 |  vonmisesvariate(self, mu, kappa)
 |      Circular data distribution.
 |      
 |      mu is the mean angle, expressed in radians between 0 and 2*pi, and
 |      kappa is the concentration parameter, which must be greater than or
 |      equal to zero.  If kappa is equal to zero, this distribution reduces
 |      to a uniform random angle over the range 0 to 2*pi.
 |  
 |  weibullvariate(self, alpha, beta)
 |      Weibull distribution.
 |      
 |      alpha is the scale parameter and beta is the shape parameter.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  VERSION = 3
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from _random.Random:
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  getrandbits(...)
 |      getrandbits(k) -> x.  Generates a long int with k random bits.
 |  
 |  random(...)
 |      random() -> x in the interval [0, 1).
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from _random.Random:
 |  
 |  __new__ = 
 |      T.__new__(S, ...) -> a new object with type S, a subtype of T

>>> 

Tarea

Genere un número al azar en Python. Usando el modulo random.

Tarea

Correr el programa del Banco en su computadora.

SimPy-2.3b1

SimPy
=====

SimPy is a process-based discrete-event simulation language based on standard
Python and released under the GNU LGPL.

It provides the modeller with components of a simulation model. These include
processes for active components like customers, messages and vehicles as well
as  resources for passive components that form limited capacity congestion
points (like servers, checkout counters and tunnels). It also provides monitor
variables to aid in gathering statistics. SimPy comes with extensive plotting
capabilities.

The distribution contains in-depth documentation, tutorials, and a large number
of simulation models.

Simulation model developers are encouraged to share their SimPy modeling
techniques with the SimPy community. Please post a message to the SimPy-Users
mailing list: http://lists.sourceforge.net/lists/listinfo/simpy-users

Software developers are also encouraged to interface SimPy with other Python-
accessible packages, such as GUI, database or mapping and to share these new
capabilities with the community under the GNU LGPL.


Installation
------------

SimPy requires Python 2.3 or above. Python 3 is not yet supported, but we are
working on it.

You can install SimPy easily via `PIP `_
(or ``easy_install``)::

    $ pip install SimPy
    $ # or:
    $ easy_install SimPy

You can also download and install SimPy manually::

    $ cd where/you/put/simpy/
    $ python setup.py install


If you want to upgrade an existing installation of SimPy, just add the ``-U``
option to *pip* or *easy_install*, e.g.::

    $ pip install -U SimPy


Getting started
---------------

Run one or more of the programs under *docs/examples/* to see whether Python
finds the SimPy module. If you get an error message like *ImportError: No
module named SimPy*, check if the SimPy packages exists in your site-packages
folder (like /Lib/site-packages).

The tutorial and manuals are in the *docs/html* folder. Many users have
commented that the Bank tutorials are valuable in getting users started on
building their own simple models. Even a few lines of Python and SimPy can
model significant real systems.

For more help, contact the `SimPy-Users mailing list
`_. SimPy users are pretty helpful.

Enjoy simulation programming in SimPy!

Autores de SimPy

SimPy was originally created by Klaus G. Müller and Tony Vignaux in 2002.

In 2008, Ontje Lünsdorf and Stefan Scherfke started to contribute to SimPy and
became active maintainers in 2011.

In 2011, Karen Turner came on board to generally help with all the bits and
pieces that may get forgotten :-)

We’d also like to thank:

- Johannes Koomer for a fix for Store._put.

Tarea

Del programa del Banco, escribir dudas en su blog.