__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult(dschult@colgate.edu)"""
#    Copyright (C) 2004-2008 by 
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
import random

### some cookbook stuff

# used in deciding whether something is a bunch of nodes, edges, etc.
# see G.add_nodes and others in Graph Class in networkx/base.py
def is_singleton(obj):
    """Is string_like or not iterable."""
    return hasattr(obj,"capitalize") or not hasattr(obj,"__iter__")

def is_string_like(obj): # from John Hunter, types-free version
    """Check if obj is string."""
#    if hasattr(obj, 'shape'): return False # this is a workaround
                                       # for a bug in numeric<23.1
    try:
        obj + ''
    except (TypeError, ValueError):
        return False
    return True

 
def iterable(obj):
    """ Return True if obj is iterable with a well-defined len()"""
    if hasattr(obj,"__iter__"): return True
    try:
        len(obj)
    except:
        return False
    return True

def flatten(obj, result=None):
    """ Return flattened version of (possibly nested) iterable object. """
    if not iterable(obj) or is_string_like(obj):
        return obj
    if result is None:
        result = []
    for item in obj:
        if not iterable(item) or is_string_like(item):
            result.append(item)
        else:
            flatten(item, result)
    return obj.__class__(result)

def is_list_of_ints( intlist ):
    """ Return True if list is a list of ints. """
    if not isinstance(intlist,list): return False
    for i in intlist:
        if not isinstance(i,int): return False
    return True

def _get_fh(path, mode='r'):
    """ Return a file handle for given path.

    Path can be a string or a file handle.

    Attempt to uncompress/compress files ending in '.gz' and '.bz2'.

    """
    if is_string_like(path):
        if path.endswith('.gz'):
            import gzip
            fh = gzip.open(path,mode=mode)
        elif path.endswith('.bz2'):
            import bz2
            fh = bz2.BZ2File(path,mode=mode)
        else:
            fh = file(path,mode=mode)
    elif hasattr(path, 'read'):
        fh = path
    else:
        raise ValueError('path must be a string or file handle')
    return fh


##def iterable(obj):
##  """ Return True if obj is iterable with a well-defined len()"""
##    try:
##      len(obj)
##    except:
##      return False
##    else:
##      return True


# some helpers for choosing random sequences from distributions
# uses scipy: www.scipy.org

def scipy_pareto_sequence(n,exponent=1.0):
    """
    Return sample sequence of length n from a Pareto distribution.

    """
    try: 
        import scipy.stats as stats
    except ImportError:
        print "Import error: not able to import scipy"
        return
    random._inst = random.Random()
    stats.seed(random.randint(1,2**30),random.randint(1,2**30))
    return stats.pareto(exponent,size=n)


def scipy_powerlaw_sequence(n,exponent=2.0):
    """
    Return sample sequence of length n from a power law distribution.

    """
    try: 
        import scipy.stats as stats
    except ImportError:
        print "Import error: not able to import scipy"
        return
    random._inst = random.Random()
    stats.seed(random.randint(1,2**30),random.randint(1,2**30))
    return stats.pareto(exponent-1,size=n)


def scipy_poisson_sequence(n,mu=1.0):
    """
    Return sample sequence of length n from a Poisson distribution.

    """
    try: 
        import scipy.stats as stats
    except ImportError:
        print "Import error: not able to import scipy"
        return
    random._inst = random.Random()
    stats.seed(random.randint(1,2**30),random.randint(1,2**30))
    return stats.poisson(mu,size=n)

def scipy_uniform_sequence(n):
    """
    Return sample sequence of length n from a uniform distribution.

    """
    try: 
        import scipy.stats as stats
    except ImportError:
        print "Import error: not able to import scipy"
        return
    random._inst = random.Random()
    stats.seed(random.randint(1,2**30),random.randint(1,2**30))
    return stats.uniform(size=n)

def scipy_discrete_sequence(n,distribution=False):
    """
    Return sample sequence of length n from a given discrete distribution

    distribution=histogram of values, will be normalized

    """
    try: 
        import scipy.stats as stats
    except ImportError:
        print "Import error: not able to import scipy"
        return
    import bisect
    if not distribution:
        return "no distribution specified"
    p=distribution
    random._inst = random.Random()

    # make CDF out of distribution to use for sample
    cdf=[]
    cdf.append(0.0)
    psum=float(sum(p))
    for i in range(0,len(p)):
        cdf.append(cdf[i]+p[i]/psum)

    # get a uniform random number
    stats.seed(random.randint(1,2**30),random.randint(1,2**30))
    inputseq=stats.uniform(size=n)

    # choose from CDF
    seq=[bisect.bisect_left(cdf,s)-1 for s in inputseq]
    return seq


# The same helpers for choosing random sequences from distributions
# uses Python's random module
# http://www.python.org/doc/current/lib/module-random.html

def pareto_sequence(n,exponent=1.0):
    """
    Return sample sequence of length n from a Pareto distribution.
    """
    return [random.paretovariate(exponent) for i in xrange(n)]


def powerlaw_sequence(n,exponent=2.0):
    """
    Return sample sequence of length n from a power law distribution.
    """
    return [random.paretovariate(exponent-1) for i in xrange(n)]


def uniform_sequence(n):
    """
    Return sample sequence of length n from a uniform distribution.
    """
    return [ random.uniform(0,n) for i in xrange(n)]


def cumulative_distribution(distribution):
    """Return normalized cumulative distribution from discrete distribution."""

    cdf=[]
    cdf.append(0.0)
    psum=float(sum(distribution))
    for i in range(0,len(distribution)):
        cdf.append(cdf[i]+distribution[i]/psum)
    return cdf        


def discrete_sequence(n, distribution=None, cdistribution=None):
    """
    Return sample sequence of length n from a given discrete distribution
    or discrete cumulative distribution. 

    One of the following must be specified.  

    distribution = histogram of values, will be normalized
    
    cdistribution = normalized discrete cumulative distribution

    """
    import bisect

    if cdistribution is not None:
        cdf=cdistribution
    elif distribution is not None:
        cdf=cumulative_distribution(distribution)
    else:
        raise InputError, \
                  "discrete_sequence: distribution or cdistribution missing"
        

    # get a uniform random number
    inputseq=[random.random() for i in xrange(n)]

    # choose from CDF
    seq=[bisect.bisect_left(cdf,s)-1 for s in inputseq]
    return seq

class UnionFind:
    def __init__(self):
        """Create a new empty union-find structure."""
        self.weights = {}
        self.parents = {}

    def __getitem__(self, object):
        """Find and return the name of the set containing the object."""

        # check for previously unknown object
        if object not in self.parents:
            self.parents[object] = object
            self.weights[object] = 1
            return object

        # find path of objects leading to the root
        path = [object]
        root = self.parents[object]
        while root != path[-1]:
            path.append(root)
            root = self.parents[root]

        # compress the path and return
        for ancestor in path:
            self.parents[ancestor] = root
        return root
        
    def __iter__(self):
        """Iterate through all items ever found or unioned by this structure."""
        return iter(self.parents)

    def union(self, *objects):
        """Find the sets containing the objects and merge them all."""
        roots = [self[x] for x in objects]
        heaviest = max([(self.weights[r],r) for r in roots])[1]
        for r in roots:
            if r != heaviest:
                self.weights[heaviest] += self.weights[r]
                self.parents[r] = heaviest
