"""
Miscellaneous Helpers for NetworkX.

These are not imported into the base networkx namespace but
can be accessed, for example, as

>>> import networkx
>>> networkx.utils.is_string_like('spam')
True
"""
# Authors:      Aric Hagberg (hagberg@lanl.gov),
#               Dan Schult(dschult@colgate.edu),
#               Ben Edwards(bedwards@cs.unm.edu)

#    Copyright (C) 2004-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
from collections import defaultdict
from collections import deque
import warnings
import sys
import uuid
from itertools import tee, chain
import networkx as nx

from itertools import accumulate


# 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_string_like(obj):  # from John Hunter, types-free version
    """Check if obj is string."""
    return isinstance(obj, str)


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 make_list_of_ints(sequence):
    """Return list of ints from sequence of integral numbers.
    
    All elements of the sequence must satisfy int(element) == element
    or a ValueError is raised. Sequence is iterated through once.

    If sequence is a list, the non-int values are replaced with ints.
    So, no new list is created
    """
    msg = 'sequence is not all integers: %s'
    if not isinstance(sequence, list):
        result = []
        for i in sequence:
            try:
                ii = int(i)
            except ValueError:
                raise nx.NetworkXError(msg % i) from None
            if ii != i:
                raise nx.NetworkXError(msg % i)
            result.append(ii)
        return result
    # original sequence is a list... in-place conversion to ints
    for indx, i in enumerate(sequence):
        if isinstance(i, int):
            continue
        try:
            ii = int(i)
        except ValueError:
            raise nx.NetworkXError(msg % i) from None
        if ii != i:
            raise nx.NetworkXError(msg % i)
        sequence[indx] = ii
    return sequence


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


#TODO Remove this?
def make_str(x):
    """Returns the string representation of t."""
    return str(x)


def generate_unique_node():
    """ Generate a unique node label."""
    return str(uuid.uuid1())


def default_opener(filename):
    """Opens `filename` using system's default program.

    Parameters
    ----------
    filename : str
        The path of the file to be opened.

    """
    from subprocess import call

    cmds = {'darwin': ['open'],
            'linux': ['xdg-open'],
            'linux2': ['xdg-open'],
            'win32': ['cmd.exe', '/C', 'start', '']}
    cmd = cmds[sys.platform] + [filename]
    call(cmd)


def dict_to_numpy_array(d, mapping=None):
    """Convert a dictionary of dictionaries to a numpy array
    with optional mapping."""
    try:
        return dict_to_numpy_array2(d, mapping)
    except (AttributeError, TypeError):
        # AttributeError is when no mapping was provided and v.keys() fails.
        # TypeError is when a mapping was provided and d[k1][k2] fails.
        return dict_to_numpy_array1(d, mapping)


def dict_to_numpy_array2(d, mapping=None):
    """Convert a dictionary of dictionaries to a 2d numpy array
    with optional mapping.

    """
    import numpy
    if mapping is None:
        s = set(d.keys())
        for k, v in d.items():
            s.update(v.keys())
        mapping = dict(zip(s, range(len(s))))
    n = len(mapping)
    a = numpy.zeros((n, n))
    for k1, i in mapping.items():
        for k2, j in mapping.items():
            try:
                a[i, j] = d[k1][k2]
            except KeyError:
                pass
    return a


def dict_to_numpy_array1(d, mapping=None):
    """Convert a dictionary of numbers to a 1d numpy array
    with optional mapping.

    """
    import numpy
    if mapping is None:
        s = set(d.keys())
        mapping = dict(zip(s, range(len(s))))
    n = len(mapping)
    a = numpy.zeros(n)
    for k1, i in mapping.items():
        i = mapping[k1]
        a[i] = d[k1]
    return a


def is_iterator(obj):
    """Returns True if and only if the given object is an iterator
    object.

    """
    has_next_attr = hasattr(obj, '__next__') or hasattr(obj, 'next')
    return iter(obj) is obj and has_next_attr


def arbitrary_element(iterable):
    """Returns an arbitrary element of `iterable` without removing it.

    This is most useful for "peeking" at an arbitrary element of a set,
    but can be used for any list, dictionary, etc., as well::

        >>> arbitrary_element({3, 2, 1})
        1
        >>> arbitrary_element('hello')
        'h'

    This function raises a :exc:`ValueError` if `iterable` is an
    iterator (because the current implementation of this function would
    consume an element from the iterator)::

        >>> iterator = iter([1, 2, 3])
        >>> arbitrary_element(iterator)
        Traceback (most recent call last):
            ...
        ValueError: cannot return an arbitrary item from an iterator

    """
    if is_iterator(iterable):
        raise ValueError('cannot return an arbitrary item from an iterator')
    # Another possible implementation is ``for x in iterable: return x``.
    return next(iter(iterable))


# Recipe from the itertools documentation.
def consume(iterator):
    "Consume the iterator entirely."
    # Feed the entire iterator into a zero-length deque.
    deque(iterator, maxlen=0)


# Recipe from the itertools documentation.
def pairwise(iterable, cyclic=False):
    "s -> (s0, s1), (s1, s2), (s2, s3), ..."
    a, b = tee(iterable)
    first = next(b, None)
    if cyclic is True:
        return zip(a, chain(b, (first,)))
    return zip(a, b)


def groups(many_to_one):
    """Converts a many-to-one mapping into a one-to-many mapping.

    `many_to_one` must be a dictionary whose keys and values are all
    :term:`hashable`.

    The return value is a dictionary mapping values from `many_to_one`
    to sets of keys from `many_to_one` that have that value.

    For example::

        >>> from networkx.utils import groups
        >>> many_to_one = {'a': 1, 'b': 1, 'c': 2, 'd': 3, 'e': 3}
        >>> groups(many_to_one)  # doctest: +SKIP
        {1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}}

    """
    one_to_many = defaultdict(set)
    for v, k in many_to_one.items():
        one_to_many[k].add(v)
    return dict(one_to_many)


def to_tuple(x):
    """Converts lists to tuples.

    For example::

        >>> from networkx.utils import to_tuple
        >>> a_list = [1, 2, [1, 4]]
        >>> to_tuple(a_list)
        (1, 2, (1, 4))

    """
    if not isinstance(x, (tuple, list)):
        return x
    return tuple(map(to_tuple, x))


def create_random_state(random_state=None):
    """Returns a numpy.random.RandomState instance depending on input.

    Parameters
    ----------
    random_state : int or RandomState instance or None  optional (default=None)
        If int, return a numpy.random.RandomState instance set with seed=int.
        if numpy.random.RandomState instance, return it.
        if None or numpy.random, return the global random number generator used
        by numpy.random.
    """
    import numpy as np

    if random_state is None or random_state is np.random:
        return np.random.mtrand._rand
    if isinstance(random_state, np.random.RandomState):
        return random_state
    if isinstance(random_state, int):
        return np.random.RandomState(random_state)
    msg = '%r cannot be used to generate a numpy.random.RandomState instance'
    raise ValueError(msg % random_state)


class PythonRandomInterface(object):
    try:
        def __init__(self, rng=None):
            import numpy
            if rng is None:
                self._rng = numpy.random.mtrand._rand
            self._rng = rng
    except ImportError:
        msg = 'numpy not found, only random.random available.'
        warnings.warn(msg, ImportWarning)

    def random(self):
        return self._rng.random_sample()

    def uniform(self, a, b):
        return a + (b - a) * self._rng.random_sample()

    def randrange(self, a, b=None):
        return self._rng.randint(a, b)

    def choice(self, seq):
        return seq[self._rng.randint(0, len(seq))]

    def gauss(self, mu, sigma):
        return self._rng.normal(mu, sigma)

    def shuffle(self, seq):
        return self._rng.shuffle(seq)

#    Some methods don't match API for numpy RandomState.
#    Commented out versions are not used by NetworkX

    def sample(self, seq, k):
        return self._rng.choice(list(seq), size=(k,), replace=False)

    def randint(self, a, b):
        return self._rng.randint(a, b + 1)

#    exponential as expovariate with 1/argument,
    def expovariate(self, scale):
        return self._rng.exponential(1/scale)

#    pareto as paretovariate with 1/argument,
    def paretovariate(self, shape):
        return self._rng.pareto(shape)

#    weibull as weibullvariate multiplied by beta,
#    def weibullvariate(self, alpha, beta):
#        return self._rng.weibull(alpha) * beta
#
#    def triangular(self, low, high, mode):
#        return self._rng.triangular(low, mode, high)
#
#    def choices(self, seq, weights=None, cum_weights=None, k=1):
#        return self._rng.choice(seq


def create_py_random_state(random_state=None):
    """Returns a random.Random instance depending on input.

    Parameters
    ----------
    random_state : int or random number generator or None (default=None)
        If int, return a random.Random instance set with seed=int.
        if random.Random instance, return it.
        if None or the `random` package, return the global random number
        generator used by `random`.
        if np.random package, return the global numpy random number
        generator wrapped in a PythonRandomInterface class.
        if np.random.RandomState instance, return it wrapped in
        PythonRandomInterface
        if a PythonRandomInterface instance, return it
    """
    import random
    try:
        import numpy as np
        if random_state is np.random:
            return PythonRandomInterface(np.random.mtrand._rand)
        if isinstance(random_state, np.random.RandomState):
            return PythonRandomInterface(random_state)
        if isinstance(random_state, PythonRandomInterface):
            return random_state
        has_numpy = True
    except ImportError:
        has_numpy = False

    if random_state is None or random_state is random:
        return random._inst
    if isinstance(random_state, random.Random):
        return random_state
    if isinstance(random_state, int):
        return random.Random(random_state)
    msg = '%r cannot be used to generate a random.Random instance'
    raise ValueError(msg % random_state)


# fixture for pytest
def setup_module(module):
    import pytest
    numpy = pytest.importorskip('numpy')
