util.py

import os, sys, warnings, operator
import numbers
import inspect
import itertools
import string, fnmatch
import unicodedata
import datetime as dt
from collections import defaultdict, Counter
from functools import partial
from contextlib import contextmanager

import numpy as np
import param

import json

try:
    from cyordereddict import OrderedDict
except:
    from collections import OrderedDict

datetime_types = (np.datetime64, dt.datetime)

try:
    import pandas as pd # noqa (optional import)
    datetime_types = datetime_types + (pd.tslib.Timestamp,)
except ImportError:
    pd = None

try:
    import dask.dataframe as dd
except ImportError:
    dd = None




class HashableJSON(json.JSONEncoder):
    """
    Extends JSONEncoder to generate a hashable string for as many types
    of object as possible including nested objects and objects that are
    not normally hashable. The purpose of this class is to generate
    unique strings that once hashed are suitable for use in memoization
    and other cases where deep equality must be tested without storing
    the entire object.

    By default JSONEncoder supports booleans, numbers, strings, lists,
    tuples and dictionaries. In order to support other types such as
    sets, datetime objects and mutable objects such as pandas Dataframes
    or numpy arrays, HashableJSON has to convert these types to
    datastructures that can normally be represented as JSON.

    Support for other object types may need to be introduced in
    future. By default, unrecognized object types are represented by
    their id.

    One limitation of this approach is that dictionaries with composite
    keys (e.g tuples) are not supported due to the JSON spec.
    """
    string_hashable = (dt.datetime,)
    repr_hashable = ()

    def default(self, obj):
        if isinstance(obj, set):
            return hash(frozenset(obj))
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        if pd and isinstance(obj, (pd.Series, pd.DataFrame)):
            return repr(sorted(list(obj.to_dict().items())))
        elif isinstance(obj, self.string_hashable):
            return str(obj)
        elif isinstance(obj, self.repr_hashable):
            return repr(obj)
        try:
            return hash(obj)
        except:
            return id(obj)



def deephash(obj):
    """
    Given an object, return a hash using HashableJSON. This hash is not
    architecture, Python version or platform independent.
    """
    try:
        return hash(json.dumps(obj, cls=HashableJSON, sort_keys=True))
    except:
        return None


# Python3 compatibility
import types
if sys.version_info.major == 3:
    basestring = str
    unicode = str
    generator_types = (zip, range, types.GeneratorType)
else:
    basestring = basestring
    unicode = unicode
    from itertools import izip
    generator_types = (izip, xrange, types.GeneratorType)



def argspec(callable_obj):
    """
    Returns an ArgSpec object for functions, staticmethods, instance
    methods, classmethods and partials.

    Note that the args list for instance and class methods are those as
    seen by the user. In other words, the first argument which is
    conventionally called 'self' or 'cls' is omitted in these cases.
    """
    if (isinstance(callable_obj, type)
        and issubclass(callable_obj, param.ParameterizedFunction)):
        # Parameterized function.__call__ considered function in py3 but not py2
        spec = inspect.getargspec(callable_obj.__call__)
        args=spec.args[1:]
    elif inspect.isfunction(callable_obj):    # functions and staticmethods
        return inspect.getargspec(callable_obj)
    elif isinstance(callable_obj, partial): # partials
        arglen = len(callable_obj.args)
        spec =  inspect.getargspec(callable_obj.func)
        args = [arg for arg in spec.args[arglen:] if arg not in callable_obj.keywords]
    elif inspect.ismethod(callable_obj):    # instance and class methods
        spec = inspect.getargspec(callable_obj)
        args = spec.args[1:]
    else:                                   # callable objects
        return argspec(callable_obj.__call__)

    return inspect.ArgSpec(args     = args,
                           varargs  = spec.varargs,
                           keywords = spec.keywords,
                           defaults = spec.defaults)



def validate_dynamic_argspec(argspec, kdims, streams):
    """
    Utility used by DynamicMap to ensure the supplied callback has an
    appropriate signature.

    If validation succeeds, returns a list of strings to be zipped with
    the positional arguments i.e kdim values. The zipped values can then
    be merged with the stream values to pass everything to the Callable
    as keywords.

    If the callbacks use *args, None is returned to indicate that kdim
    values must be passed to the Callable by position. In this
    situation, Callable passes *args and **kwargs directly to the
    callback.

    If the callback doesn't use **kwargs, the accepted keywords are
    validated against the stream parameter names.
    """

    kdims = [kdim.name for kdim in kdims]
    stream_params = stream_parameters(streams)
    defaults = argspec.defaults if argspec.defaults else []
    all_posargs = argspec.args[:-len(defaults)] if defaults else argspec.args
    # Filter out any posargs for streams
    posargs = [arg for arg in all_posargs if arg not in stream_params]
    kwargs = argspec.args[-len(defaults):]

    if argspec.keywords is None:
        unassigned_streams = set(stream_params) - set(argspec.args)
        if unassigned_streams:
            raise KeyError('Callable missing keywords to accept %s stream parameters'
                           % ', '.join(unassigned_streams))

    if kdims == []:                  # Can be no posargs, stream kwargs already validated
        return []
    if set(kdims) == set(posargs):   # Posargs match exactly, can all be passed as kwargs
        return kdims
    elif len(posargs) == len(kdims): # Posargs match kdims length, supplying names
        if argspec.args[:len(kdims)] != posargs:
            raise KeyError('Unmatched positional kdim arguments only '
                           'allowed at the start of the signature')

        return posargs
    elif argspec.varargs:            # Posargs missing, passed to Callable directly
        return None
    elif set(posargs) - set(kdims):
        raise KeyError('Callable accepts more positional arguments {posargs} '
                       'than there are key dimensions {kdims}'.format(posargs=posargs,
                                                                      kdims=kdims))
    elif set(kdims).issubset(set(kwargs)): # Key dims can be supplied by keyword
        return kdims
    elif set(kdims).issubset(set(posargs+kwargs)):
        return kdims
    else:
        raise KeyError('Callback signature over {names} does not accommodate '
                       'required kdims {kdims}'.format(names=list(set(posargs+kwargs)),
                                                       kdims=kdims))



def process_ellipses(obj, key, vdim_selection=False):
    """
    Helper function to pad a __getitem__ key with the right number of
    empty slices (i.e :) when the key contains an Ellipsis (...).

    If the vdim_selection flag is true, check if the end of the key
    contains strings or Dimension objects in obj. If so, extra padding
    will not be applied for the value dimensions (i.e the resulting key
    will be exactly one longer than the number of kdims). Note: this
    flag should not be used for composite types.
    """
    if isinstance(key, np.ndarray) and key.dtype.kind == 'b':
        return key
    wrapped_key = wrap_tuple(key)
    if wrapped_key.count(Ellipsis)== 0:
        return key
    if wrapped_key.count(Ellipsis)!=1:
        raise Exception("Only one ellipsis allowed at a time.")
    dim_count = len(obj.dimensions())
    index = wrapped_key.index(Ellipsis)
    head = wrapped_key[:index]
    tail = wrapped_key[index+1:]

    padlen = dim_count - (len(head) + len(tail))
    if vdim_selection:
        # If the end of the key (i.e the tail) is in vdims, pad to len(kdims)+1
        if wrapped_key[-1] in obj.vdims:
            padlen = (len(obj.kdims) +1 ) - len(head+tail)
    return head + ((slice(None),) * padlen) + tail


def bytes_to_unicode(value):
    """
    Safely casts bytestring to unicode
    """
    if isinstance(value, bytes):
        return unicode(value.decode('utf-8'))
    return value


def capitalize_unicode_name(s):
    """
    Turns a string such as 'capital delta' into the shortened,
    capitalized version, in this case simply 'Delta'. Used as a
    transform in sanitize_identifier.
    """
    index = s.find('capital')
    if index == -1: return s
    tail = s[index:].replace('capital', '').strip()
    tail = tail[0].upper() + tail[1:]
    return s[:index] + tail


class Aliases(object):
    """
    Helper class useful for defining a set of alias tuples on a single object.

    For instance, when defining a group or label with an alias, instead
    of setting tuples in the constructor, you could use
    ``aliases.water`` if you first define:

    >>> aliases = Aliases(water='H_2O', glucose='C_6H_{12}O_6')
    >>> aliases.water
    ('water', 'H_2O')

    This may be used to conveniently define aliases for groups, labels
    or dimension names.
    """
    def __init__(self, **kwargs):
        for k,v in kwargs.items():
            setattr(self, k, (k,v))



class sanitize_identifier_fn(param.ParameterizedFunction):
    """
    Sanitizes group/label values for use in AttrTree attribute
    access. Depending on the version parameter, either sanitization
    appropriate for Python 2 (no unicode gn identifiers allowed) or
    Python 3 (some unicode allowed) is used.

    Note that if you are using Python 3, you can switch to version 2
    for compatibility but you cannot enable relaxed sanitization if
    you are using Python 2.

    Special characters are sanitized using their (lowercase) unicode
    name using the unicodedata module. For instance:

    >>> unicodedata.name(u'$').lower()
    'dollar sign'

    As these names are often very long, this parameterized function
    allows filtered, substitions and transforms to help shorten these
    names appropriately.
    """

    version = param.ObjectSelector(sys.version_info.major, objects=[2,3], doc="""
        The sanitization version. If set to 2, more aggresive
        sanitization appropriate for Python 2 is applied. Otherwise,
        if set to 3, more relaxed, Python 3 sanitization is used.""")

    capitalize = param.Boolean(default=True, doc="""
       Whether the first letter should be converted to
       uppercase. Note, this will only be applied to ASCII characters
       in order to make sure paths aren't confused with method
       names.""")

    eliminations = param.List(['extended', 'accent', 'small', 'letter', 'sign', 'digit',
                               'latin', 'greek', 'arabic-indic', 'with', 'dollar'], doc="""
       Lowercase strings to be eliminated from the unicode names in
       order to shorten the sanitized name ( lowercase). Redundant
       strings should be removed but too much elimination could cause
       two unique strings to map to the same sanitized output.""")

    substitutions = param.Dict(default={'circumflex':'power',
                                        'asterisk':'times',
                                        'solidus':'over'}, doc="""
       Lowercase substitutions of substrings in unicode names. For
       instance the ^ character has the name 'circumflex accent' even
       though it is more typically used for exponentiation. Note that
       substitutions occur after filtering and that there should be no
       ordering dependence between substitutions.""")

    transforms = param.List(default=[capitalize_unicode_name], doc="""
       List of string transformation functions to apply after
       filtering and substitution in order to further compress the
       unicode name. For instance, the default capitalize_unicode_name
       function will turn the string "capital delta" into "Delta".""")

    disallowed = param.List(default=['trait_names', '_ipython_display_',
                                     '_getAttributeNames'], doc="""
       An explicit list of name that should not be allowed as
       attribute names on Tree objects.

       By default, prevents IPython from creating an entry called
       Trait_names due to an inconvenient getattr check (during
       tab-completion).""")

    disable_leading_underscore = param.Boolean(default=False, doc="""
       Whether leading underscores should be allowed to be sanitized
       with the leading prefix.""")

    aliases = param.Dict(default={}, doc="""
       A dictionary of aliases mapping long strings to their short,
       sanitized equivalents""")

    prefix = 'A_'

    _lookup_table = param.Dict(default={}, doc="""
       Cache of previously computed sanitizations""")


    @param.parameterized.bothmethod
    def add_aliases(self_or_cls, **kwargs):
        """
        Conveniently add new aliases as keyword arguments. For instance
        you can add a new alias with add_aliases(short='Longer string')
        """
        self_or_cls.aliases.update({v:k for k,v in kwargs.items()})

    @param.parameterized.bothmethod
    def remove_aliases(self_or_cls, aliases):
        """
        Remove a list of aliases.
        """
        for k,v in self_or_cls.aliases.items():
            if v in aliases:
                self_or_cls.aliases.pop(k)

    @param.parameterized.bothmethod
    def allowable(self_or_cls, name, disable_leading_underscore=None):
       disabled_reprs = ['javascript', 'jpeg', 'json', 'latex',
                         'latex', 'pdf', 'png', 'svg', 'markdown']
       disabled_ = (self_or_cls.disable_leading_underscore
                    if disable_leading_underscore is None
                    else disable_leading_underscore)
       if disabled_ and name.startswith('_'):
          return False
       isrepr = any(('_repr_%s_' % el) == name for el in disabled_reprs)
       return (name not in self_or_cls.disallowed) and not isrepr

    @param.parameterized.bothmethod
    def prefixed(self, identifier, version):
        """
        Whether or not the identifier will be prefixed.
        Strings that require the prefix are generally not recommended.
        """
        invalid_starting = ['Mn', 'Mc', 'Nd', 'Pc']
        if identifier.startswith('_'):  return True
        return((identifier[0] in string.digits) if version==2
               else (unicodedata.category(identifier[0]) in invalid_starting))

    @param.parameterized.bothmethod
    def remove_diacritics(self_or_cls, identifier):
        """
        Remove diacritics and accents from the input leaving other
        unicode characters alone."""
        chars = ''
        for c in identifier:
            replacement = unicodedata.normalize('NFKD', c).encode('ASCII', 'ignore')
            if replacement != '':
                chars += bytes_to_unicode(replacement)
            else:
                chars += c
        return chars

    @param.parameterized.bothmethod
    def shortened_character_name(self_or_cls, c, eliminations=[], substitutions={}, transforms=[]):
        """
        Given a unicode character c, return the shortened unicode name
        (as a list of tokens) by applying the eliminations,
        substitutions and transforms.
        """
        name = unicodedata.name(c).lower()
        # Filtering
        for elim in eliminations:
            name = name.replace(elim, '')
        # Substitition
        for i,o in substitutions.items():
            name = name.replace(i, o)
        for transform in transforms:
            name = transform(name)
        return ' '.join(name.strip().split()).replace(' ','_').replace('-','_')


    def __call__(self, name, escape=True, version=None):
        if name in [None, '']:
           return name
        elif name in self.aliases:
            return self.aliases[name]
        elif name in self._lookup_table:
           return self._lookup_table[name]
        name = bytes_to_unicode(name)
        version = self.version if version is None else version
        if not self.allowable(name):
            raise AttributeError("String %r is in the disallowed list of attribute names: %r" % self.disallowed)

        if version == 2:
            name = self.remove_diacritics(name)
        if self.capitalize and name and name[0] in string.ascii_lowercase:
            name = name[0].upper()+name[1:]

        sanitized = (self.sanitize_py2(name) if version==2 else self.sanitize_py3(name))
        if self.prefixed(name, version):
           sanitized = self.prefix + sanitized
        self._lookup_table[name] = sanitized
        return sanitized


    def _process_underscores(self, tokens):
        "Strip underscores to make sure the number is correct after join"
        groups = [[str(''.join(el))] if b else list(el)
                  for (b,el) in itertools.groupby(tokens, lambda k: k=='_')]
        flattened = [el for group in groups for el in group]
        processed = []
        for token in flattened:
            if token == '_':  continue
            if token.startswith('_'):
                token = str(token[1:])
            if token.endswith('_'):
                token = str(token[:-1])
            processed.append(token)
        return processed

    def sanitize_py2(self, name):
        # This fix works but masks an issue in self.sanitize (py2)
        prefix = '_' if name.startswith('_') else ''
        valid_chars = string.ascii_letters+string.digits+'_'
        return prefix + str('_'.join(self.sanitize(name, lambda c: c in valid_chars)))


    def sanitize_py3(self, name):
        if not name.isidentifier():
            return '_'.join(self.sanitize(name, lambda c: ('_'+c).isidentifier()))
        else:
            return name

    def sanitize(self, name, valid_fn):
        "Accumulate blocks of hex and separate blocks by underscores"
        invalid = {'\a':'a','\b':'b', '\v':'v','\f':'f','\r':'r'}
        for cc in filter(lambda el: el in name, invalid.keys()):
            raise Exception("Please use a raw string or escape control code '\%s'"
                            % invalid[cc])
        sanitized, chars = [], ''
        for split in name.split():
            for c in split:
                if valid_fn(c): chars += str(c) if c=='_' else c
                else:
                    short = self.shortened_character_name(c, self.eliminations,
                                                         self.substitutions,
                                                         self.transforms)
                    sanitized.extend([chars] if chars else [])
                    if short != '':
                       sanitized.append(short)
                    chars = ''
            if chars:
                sanitized.extend([chars])
                chars=''
        return self._process_underscores(sanitized + ([chars] if chars else []))

sanitize_identifier = sanitize_identifier_fn.instance()


group_sanitizer = sanitize_identifier_fn.instance()
label_sanitizer = sanitize_identifier_fn.instance()
dimension_sanitizer = sanitize_identifier_fn.instance(capitalize=False)


def isnumeric(val):
    if isinstance(val, (basestring, bool, np.bool_)):
        return False
    try:
        float(val)
        return True
    except:
        return False


def find_minmax(lims, olims):
    """
    Takes (a1, a2) and (b1, b2) as input and returns
    (np.nanmin(a1, b1), np.nanmax(a2, b2)). Used to calculate
    min and max values of a number of items.
    """
    try:
        limzip = zip(list(lims), list(olims), [np.nanmin, np.nanmax])
        limits = tuple([float(fn([l, ol])) for l, ol, fn in limzip])
    except:
        limits = (np.NaN, np.NaN)
    return limits


def find_range(values, soft_range=[]):
    """
    Safely finds either the numerical min and max of
    a set of values, falling back to the first and
    the last value in the sorted list of values.
    """
    try:
        values = np.array(values)
        values = np.squeeze(values) if len(values.shape) > 1 else values
        if len(soft_range):
            values = np.concatenate([values, soft_range])
        if values.dtype.kind == 'M':
            return values.min(), values.max()
        return np.nanmin(values), np.nanmax(values)
    except:
        try:
            values = sorted(values)
            return (values[0], values[-1])
        except:
            return (None, None)


def max_range(ranges):
    """
    Computes the maximal lower and upper bounds from a list bounds.
    """
    try:
        with warnings.catch_warnings():
            warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
            values = [r for r in ranges for v in r if v is not None]
            if pd and all(isinstance(v, pd.tslib.Timestamp) for r in values for v in r):
                values = [(v1.to_datetime64(), v2.to_datetime64()) for v1, v2 in values]
            arr = np.array(values)
            if arr.dtype.kind in 'OSU':
                arr = np.sort([v for v in arr.flat if not is_nan(v)])
                return arr[0], arr[-1]
            if arr.dtype.kind in 'M':
                return arr[:, 0].min(), arr[:, 1].max()
            return (np.nanmin(arr[:, 0]), np.nanmax(arr[:, 1]))
    except:
        return (np.NaN, np.NaN)


def max_extents(extents, zrange=False):
    """
    Computes the maximal extent in 2D and 3D space from
    list of 4-tuples or 6-tuples. If zrange is enabled
    all extents are converted to 6-tuples to comput
    x-, y- and z-limits.
    """
    if zrange:
        num = 6
        inds = [(0, 3), (1, 4), (2, 5)]
        extents = [e if len(e) == 6 else (e[0], e[1], None,
                                          e[2], e[3], None)
                   for e in extents]
    else:
        num = 4
        inds = [(0, 2), (1, 3)]
    arr = list(zip(*extents)) if extents else []
    extents = [np.NaN] * num
    if len(arr) == 0:
        return extents
    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
        for lidx, uidx in inds:
            lower = [v for v in arr[lidx] if v is not None and not is_nan(v)]
            upper = [v for v in arr[uidx] if v is not None and not is_nan(v)]
            if lower and isinstance(lower[0], datetime_types):
                extents[lidx] = np.min(lower)
            elif any(isinstance(l, basestring) for l in lower):
                extents[lidx] = np.sort(lower)[0]
            elif lower:
                extents[lidx] = np.nanmin(lower)
            if upper and isinstance(upper[0], datetime_types):
                extents[uidx] = np.max(upper)
            elif any(isinstance(u, basestring) for u in upper):
                extents[uidx] = np.sort(upper)[-1]
            elif upper:
                extents[uidx] = np.nanmax(upper)
    return tuple(extents)


def int_to_alpha(n, upper=True):
    "Generates alphanumeric labels of form A-Z, AA-ZZ etc."
    casenum = 65 if upper else 97
    label = ''
    count= 0
    if n == 0: return str(chr(n + casenum))
    while n >= 0:
        mod, div = n % 26, n
        for _ in range(count):
            div //= 26
        div %= 26
        if count == 0:
            val = mod
        else:
            val = div
        label += str(chr(val + casenum))
        count += 1
        n -= 26**count
    return label[::-1]


def int_to_roman(input):
   if type(input) != type(1):
      raise TypeError("expected integer, got %s" % type(input))
   if not 0 < input < 4000:
      raise ValueError("Argument must be between 1 and 3999")
   ints = (1000, 900,  500, 400, 100,  90, 50,  40, 10,  9,   5,  4,   1)
   nums = ('M',  'CM', 'D', 'CD','C', 'XC','L','XL','X','IX','V','IV','I')
   result = ""
   for i in range(len(ints)):
      count = int(input / ints[i])
      result += nums[i] * count
      input -= ints[i] * count
   return result


def unique_iterator(seq):
    """
    Returns an iterator containing all non-duplicate elements
    in the input sequence.
    """
    seen = set()
    for item in seq:
        if item not in seen:
            seen.add(item)
            yield item


def unique_array(arr):
    """
    Returns an array of unique values in the input order
    """
    if not len(arr):
        return arr
    elif pd:
        return pd.unique(arr)
    else:
        _, uniq_inds = np.unique(arr, return_index=True)
        return arr[np.sort(uniq_inds)]


def match_spec(element, specification):
    """
    Matches the group.label specification of the supplied
    element against the supplied specification dictionary
    returning the value of the best match.
    """
    match_tuple = ()
    match = specification.get((), {})
    for spec in [type(element).__name__,
                 group_sanitizer(element.group, escape=False),
                 label_sanitizer(element.label, escape=False)]:
        match_tuple += (spec,)
        if match_tuple in specification:
            match = specification[match_tuple]
    return match


def python2sort(x,key=None):
    if len(x) == 0: return x
    it = iter(x)
    groups = [[next(it)]]
    for item in it:
        for group in groups:
            try:
                item_precedence = item if key is None else key(item)
                group_precedence = group[0] if key is None else key(group[0])
                item_precedence < group_precedence  # exception if not comparable
                group.append(item)
                break
            except TypeError:
                continue
        else:  # did not break, make new group
            groups.append([item])
    return itertools.chain.from_iterable(sorted(group, key=key) for group in groups)


def merge_dimensions(dimensions_list):
    """
    Merges lists of fully or partially overlapping dimensions by
    merging their values.

    >>> from holoviews import Dimension
    >>> dim_list = [[Dimension('A', values=[1, 2, 3]), Dimension('B')],
    ...             [Dimension('A', values=[2, 3, 4])]]
    >>> dimensions = merge_dimensions(dim_list)
    >>> dimensions
    [Dimension('A'), Dimension('B')]
    >>> dimensions[0].values
    [1, 2, 3, 4]
    """
    dvalues = defaultdict(list)
    dimensions = []
    for dims in dimensions_list:
        for d in dims:
            dvalues[d.name].append(d.values)
            if d not in dimensions:
                dimensions.append(d)
    dvalues = {k: list(unique_iterator(itertools.chain(*vals)))
               for k, vals in dvalues.items()}
    return [d(values=dvalues.get(d.name, [])) for d in dimensions]


def dimension_sort(odict, kdims, vdims, categorical, key_index, cached_values):
    """
    Sorts data by key using usual Python tuple sorting semantics
    or sorts in categorical order for any categorical Dimensions.
    """
    sortkws = {}
    ndims = len(kdims)
    dimensions = kdims+vdims
    indexes = [(dimensions[i], int(i not in range(ndims)),
                    i if i in range(ndims) else i-ndims)
                for i in key_index]
    cached_values = {d: [None]+vals for d, vals in cached_values.items()}

    if len(set(key_index)) != len(key_index):
        raise ValueError("Cannot sort on duplicated dimensions")
    elif categorical:
       sortkws['key'] = lambda x: tuple(cached_values[dim.name].index(x[t][d])
                                        if dim.values else x[t][d]
                                        for i, (dim, t, d) in enumerate(indexes))
    elif key_index != list(range(len(kdims+vdims))):
        sortkws['key'] = lambda x: tuple(x[t][d] for _, t, d in indexes)
    if sys.version_info.major == 3:
        return python2sort(odict.items(), **sortkws)
    else:
        return sorted(odict.items(), **sortkws)


# Copied from param should make param version public
def is_number(obj):
    if isinstance(obj, numbers.Number): return True
    # The extra check is for classes that behave like numbers, such as those
    # found in numpy, gmpy, etc.
    elif (hasattr(obj, '__int__') and hasattr(obj, '__add__')): return True
    # This is for older versions of gmpy
    elif hasattr(obj, 'qdiv'): return True
    else: return False


class ProgressIndicator(param.Parameterized):
    """
    Baseclass for any ProgressIndicator that indicates progress
    as a completion percentage.
    """

    percent_range = param.NumericTuple(default=(0.0, 100.0), doc="""
        The total percentage spanned by the progress bar when called
        with a value between 0% and 100%. This allows an overall
        completion in percent to be broken down into smaller sub-tasks
        that individually complete to 100 percent.""")

    label = param.String(default='Progress', allow_None=True, doc="""
        The label of the current progress bar.""")

    def __call__(self, completion):
        raise NotImplementedError


def sort_topologically(graph):
    """
    Stackless topological sorting.

    graph = {
        3: [1],
        5: [3],
        4: [2],
        6: [4],
    }

    sort_topologically(graph)
    [[1, 2], [3, 4], [5, 6]]
    """
    levels_by_name = {}
    names_by_level = defaultdict(list)

    def add_level_to_name(name, level):
        levels_by_name[name] = level
        names_by_level[level].append(name)


    def walk_depth_first(name):
        stack = [name]
        while(stack):
            name = stack.pop()
            if name in levels_by_name:
                continue

            if name not in graph or not graph[name]:
                level = 0
                add_level_to_name(name, level)
                continue

            children = graph[name]

            children_not_calculated = [child for child in children if child not in levels_by_name]
            if children_not_calculated:
                stack.append(name)
                stack.extend(children_not_calculated)
                continue

            level = 1 + max(levels_by_name[lname] for lname in children)
            add_level_to_name(name, level)

    for name in graph:
        walk_depth_first(name)

    return list(itertools.takewhile(lambda x: x is not None,
                                    (names_by_level.get(i, None)
                                     for i in itertools.count())))


def is_cyclic(graph):
    """
    Return True if the directed graph g has a cycle. The directed graph
    should be represented as adictionary mapping of edges for each node.
    """
    path = set()

    def visit(vertex):
        path.add(vertex)
        for neighbour in graph.get(vertex, ()):
            if neighbour in path or visit(neighbour):
                return True
        path.remove(vertex)
        return False

    return any(visit(v) for v in graph)


def one_to_one(graph, nodes):
    """
    Return True if graph contains only one to one mappings. The
    directed graph should be represented as a dictionary mapping of
    edges for each node. Nodes should be passed a simple list.
    """
    edges = itertools.chain.from_iterable(graph.values())
    return len(graph) == len(nodes) and len(set(edges)) == len(nodes)


def get_overlay_spec(o, k, v):
    """
    Gets the type.group.label + key spec from an Element in an Overlay.
    """
    k = wrap_tuple(k)
    return ((type(v).__name__, v.group, v.label) + k if len(o.kdims) else
            (type(v).__name__,) + k)


def layer_sort(hmap):
   """
   Find a global ordering for layers in a HoloMap of CompositeOverlay
   types.
   """
   orderings = {}
   for o in hmap:
      okeys = [get_overlay_spec(o, k, v) for k, v in o.data.items()]
      if len(okeys) == 1 and not okeys[0] in orderings:
         orderings[okeys[0]] = []
      else:
         orderings.update({k: [] if k == v else [v] for k, v in zip(okeys[1:], okeys)})
   return [i for g in sort_topologically(orderings) for i in sorted(g)]


def layer_groups(ordering, length=2):
   """
   Splits a global ordering of Layers into groups based on a slice of
   the spec.  The grouping behavior can be modified by changing the
   length of spec the entries are grouped by.
   """
   group_orderings = defaultdict(list)
   for el in ordering:
      group_orderings[el[:length]].append(el)
   return group_orderings


def group_select(selects, length=None, depth=None):
    """
    Given a list of key tuples to select, groups them into sensible
    chunks to avoid duplicating indexing operations.
    """
    if length == None and depth == None:
        length = depth = len(selects[0])
    getter = operator.itemgetter(depth-length)
    if length > 1:
        selects = sorted(selects, key=getter)
        grouped_selects = defaultdict(dict)
        for k, v in itertools.groupby(selects, getter):
            grouped_selects[k] = group_select(list(v), length-1, depth)
        return grouped_selects
    else:
        return list(selects)


def iterative_select(obj, dimensions, selects, depth=None):
    """
    Takes the output of group_select selecting subgroups iteratively,
    avoiding duplicating select operations.
    """
    ndims = len(dimensions)
    depth = depth if depth is not None else ndims
    items = []
    if isinstance(selects, dict):
        for k, v in selects.items():
            items += iterative_select(obj.select(**{dimensions[ndims-depth]: k}),
                                      dimensions, v, depth-1)
    else:
        for s in selects:
            items.append((s, obj.select(**{dimensions[-1]: s[-1]})))
    return items


def get_spec(obj):
   """
   Gets the spec from any labeled data object.
   """
   return (obj.__class__.__name__,
           obj.group, obj.label)


def find_file(folder, filename):
    """
    Find a file given folder and filename. If the filename can be
    resolved directly returns otherwise walks the supplied folder.
    """
    matches = []
    if os.path.isabs(filename) and os.path.isfile(filename):
        return filename
    for root, _, filenames in os.walk(folder):
        for fn in fnmatch.filter(filenames, filename):
            matches.append(os.path.join(root, fn))
    if not matches:
        raise IOError('File %s could not be found' % filename)
    return matches[-1]


def is_dataframe(data):
    """
    Checks whether the supplied data is DatFrame type.
    """
    return((pd is not None and isinstance(data, pd.DataFrame)) or
          (dd is not None and isinstance(data, dd.DataFrame)))


def get_param_values(data):
    params = dict(kdims=data.kdims, vdims=data.vdims,
                  label=data.label)
    if (data.group != data.params()['group'].default and not
        isinstance(type(data).group, property)):
        params['group'] = data.group
    return params


@contextmanager
def disable_constant(parameterized):
    """
    Temporarily set parameters on Parameterized object to
    constant=False.
    """
    params = parameterized.params().values()
    constants = [p.constant for p in params]
    for param in params:
        param.constant = False
    try:
        yield
    except:
        raise
    finally:
        for (param, const) in zip(params, constants):
            param.constant = const


def get_ndmapping_label(ndmapping, attr):
    """
    Function to get the first non-auxiliary object
    label attribute from an NdMapping.
    """
    label = None
    els = itervalues(ndmapping.data)
    while label is None:
        try:
            el = next(els)
        except StopIteration:
            return None
        if not el._auxiliary_component:
            label = getattr(el, attr)
    if attr == 'group':
        tp = type(el).__name__
        if tp == label:
            return None
    return label


def wrap_tuple(unwrapped):
    """ Wraps any non-tuple types in a tuple """
    return (unwrapped if isinstance(unwrapped, tuple) else (unwrapped,))


def stream_name_mapping(stream, exclude_params=['name'], reverse=False):
    """
    Return a complete dictionary mapping between stream parameter names
    to their applicable renames, excluding parameters listed in
    exclude_params.

    If reverse is True, the mapping is from the renamed strings to the
    original stream parameter names.
    """
    filtered = [k for k in stream.params().keys() if k not in exclude_params]
    mapping = {k:stream._rename.get(k,k) for k in filtered}
    if reverse:
        return {v:k for k,v in mapping.items()}
    else:
        return mapping

def rename_stream_kwargs(stream, kwargs, reverse=False):
    """
    Given a stream and a kwargs dictionary of parameter values, map to
    the corresponding dictionary where the keys are substituted with the
    appropriately renamed string.

    If reverse, the output will be a dictionary using the original
    parameter names given a dictionary using the renamed equivalents.
    """
    mapped_kwargs = {}
    mapping = stream_name_mapping(stream, reverse=reverse)
    for k,v in kwargs.items():
        if k not in mapping:
            msg = 'Could not map key {key} {direction} renamed equivalent'
            direction = 'from' if reverse else 'to'
            raise KeyError(msg.format(key=repr(k), direction=direction))
        mapped_kwargs[mapping[k]] = v
    return mapped_kwargs


def stream_parameters(streams, no_duplicates=True, exclude=['name']):
    """
    Given a list of streams, return a flat list of parameter name,
    excluding those listed in the exclude list.

    If no_duplicates is enabled, a KeyError will be raised if there are
    parameter name clashes across the streams.
    """
    param_groups = [s.contents.keys() for s in streams]
    names = [name for group in param_groups for name in group]

    if no_duplicates:
        clashes = set([n for n in names if names.count(n) > 1])
        if clashes:
            raise KeyError('Parameter name clashes for keys: %r' % clashes)
    return [name for name in names if name not in exclude]


def dimensionless_contents(streams, kdims, no_duplicates=True):
    """
    Return a list of stream parameters that have not been associated
    with any of the key dimensions.
    """
    names = stream_parameters(streams, no_duplicates)
    return [name for name in names if name not in kdims]


def unbound_dimensions(streams, kdims, no_duplicates=True):
    """
    Return a list of dimensions that have not been associated with
    any streams.
    """
    params = stream_parameters(streams, no_duplicates)
    return [d for d in kdims if d not in params]


def wrap_tuple_streams(unwrapped, kdims, streams):
    """
    Fills in tuple keys with dimensioned stream values as appropriate.
    """
    param_groups = [(s.contents.keys(), s) for s in streams]
    pairs = [(name,s)  for (group, s) in param_groups for name in group]
    substituted = []
    for pos,el in enumerate(wrap_tuple(unwrapped)):
        if el is None and pos < len(kdims):
            matches = [(name,s) for (name,s) in pairs if name==kdims[pos].name]
            if len(matches) == 1:
                (name, stream) = matches[0]
                el = stream.contents[name]
        substituted.append(el)
    return tuple(substituted)


def drop_streams(streams, kdims, keys):
    """
    Drop any dimensionsed streams from the keys and kdims.
    """
    stream_params = stream_parameters(streams)
    inds, dims = zip(*[(ind, kdim) for ind, kdim in enumerate(kdims)
                       if kdim not in stream_params])
    return dims, [tuple(key[ind] for ind in inds) for key in keys]


def itervalues(obj):
    "Get value iterator from dictionary for Python 2 and 3"
    return iter(obj.values()) if sys.version_info.major == 3 else obj.itervalues()


def iterkeys(obj):
    "Get key iterator from dictionary for Python 2 and 3"
    return iter(obj.keys()) if sys.version_info.major == 3 else obj.iterkeys()


def get_unique_keys(ndmapping, dimensions):
    inds = [ndmapping.get_dimension_index(dim) for dim in dimensions]
    getter = operator.itemgetter(*inds)
    return unique_iterator(getter(key) if len(inds) > 1 else (key[inds[0]],)
                           for key in ndmapping.data.keys())


def unpack_group(group, getter):
    for k, v in group.iterrows():
        obj = v.values[0]
        key = getter(k)
        if hasattr(obj, 'kdims'):
            yield (key, obj)
        else:
            obj = tuple(v)
            yield (wrap_tuple(key), obj)


def capitalize(string):
    """
    Capitalizes the first letter of a string.
    """
    return string[0].upper() + string[1:]


def get_path(item):
    """
    Gets a path from an Labelled object or from a tuple of an existing
    path and a labelled object. The path strings are sanitized and
    capitalized.
    """
    sanitizers = [group_sanitizer, label_sanitizer]
    if isinstance(item, tuple):
        path, item = item
        if item.label:
            if len(path) > 1 and item.label == path[1]:
                path = path[:2]
            else:
                path = path[:1] + (item.label,)
        else:
            path = path[:1]
    else:
        path = (item.group, item.label) if item.label else (item.group,)
    return tuple(capitalize(fn(p)) for (p, fn) in zip(path, sanitizers))


def make_path_unique(path, counts):
    """
    Given a path, a list of existing paths and counts for each of the
    existing paths.
    """
    while path in counts:
        count = counts[path]
        counts[path] += 1
        path = path + (int_to_roman(count),)
    if len(path) == 1:
        path = path + (int_to_roman(counts.get(path, 1)),)
    return path


class ndmapping_groupby(param.ParameterizedFunction):
    """
    Apply a groupby operation to an NdMapping, using pandas to improve
    performance (if available).
    """

    def __call__(self, ndmapping, dimensions, container_type,
                 group_type, sort=False, **kwargs):
        try:
            import pandas # noqa (optional import)
            groupby = self.groupby_pandas
        except:
            groupby = self.groupby_python
        return groupby(ndmapping, dimensions, container_type,
                       group_type, sort=sort, **kwargs)

    @param.parameterized.bothmethod
    def groupby_pandas(self_or_cls, ndmapping, dimensions, container_type,
                       group_type, sort=False, **kwargs):
        if 'kdims' in kwargs:
            idims = [ndmapping.get_dimension(d) for d in kwargs['kdims']]
        else:
            idims = [dim for dim in ndmapping.kdims if dim not in dimensions]

        all_dims = [d.name for d in ndmapping.kdims]
        inds = [ndmapping.get_dimension_index(dim) for dim in idims]
        getter = operator.itemgetter(*inds) if inds else lambda x: tuple()

        multi_index = pd.MultiIndex.from_tuples(ndmapping.keys(), names=all_dims)
        df = pd.DataFrame(list(map(wrap_tuple, ndmapping.values())), index=multi_index)

        kwargs = dict(dict(get_param_values(ndmapping), kdims=idims), **kwargs)
        groups = ((wrap_tuple(k), group_type(OrderedDict(unpack_group(group, getter)), **kwargs))
                   for k, group in df.groupby(level=[d.name for d in dimensions]))

        if sort:
            selects = list(get_unique_keys(ndmapping, dimensions))
            groups = sorted(groups, key=lambda x: selects.index(x[0]))
        return container_type(groups, kdims=dimensions)

    @param.parameterized.bothmethod
    def groupby_python(self_or_cls, ndmapping, dimensions, container_type,
                       group_type, sort=False, **kwargs):
        idims = [dim for dim in ndmapping.kdims if dim not in dimensions]
        dim_names = [dim.name for dim in dimensions]
        selects = get_unique_keys(ndmapping, dimensions)
        selects = group_select(list(selects))
        groups = [(k, group_type((v.reindex(idims) if hasattr(v, 'kdims')
                                  else [((), (v,))]), **kwargs))
                  for k, v in iterative_select(ndmapping, dim_names, selects)]
        return container_type(groups, kdims=dimensions)


def cartesian_product(arrays, flat=True, copy=False):
    """
    Efficient cartesian product of a list of 1D arrays returning the
    expanded array views for each dimensions. By default arrays are
    flattened, which may be controlled with the flat flag. The array
    views can be turned into regular arrays with the copy flag.
    """
    arrays = np.broadcast_arrays(*np.ix_(*arrays))
    if flat:
        return tuple(arr.flatten() if copy else arr.flat for arr in arrays)
    return tuple(arr.copy() if copy else arr for arr in arrays)


def arglexsort(arrays):
    """
    Returns the indices of the lexicographical sorting
    order of the supplied arrays.
    """
    dtypes = ','.join(array.dtype.str for array in arrays)
    recarray = np.empty(len(arrays[0]), dtype=dtypes)
    for i, array in enumerate(arrays):
        recarray['f%s' % i] = array
    return recarray.argsort()


def get_dynamic_item(map_obj, dimensions, key):
    """
    Looks up an item in a DynamicMap given a list of dimensions
    and a corresponding key. The dimensions must be a subset
    of the map_obj key dimensions.
    """
    dmaps = map_obj.traverse(lambda x: x, ['DynamicMap'])
    dmap = dmaps[0] if dmaps else map_obj
    if key == () and (not dimensions or not dmap.kdims):
        map_obj.traverse(lambda x: x[()], ['DynamicMap'])
        return key, map_obj.map(lambda x: x.last, ['DynamicMap'])
    elif isinstance(key, tuple):
        dims = {d.name: k for d, k in zip(dimensions, key)
                if d in map_obj.kdims}
        key = tuple(dims.get(d.name) for d in map_obj.kdims)
        el = map_obj.select(['DynamicMap', 'HoloMap'], **dims)
    else:
        el = None
    return key, el


def expand_grid_coords(dataset, dim):
    """
    Expand the coordinates along a dimension of the gridded
    dataset into an ND-array matching the dimensionality of
    the dataset.
    """
    arrays = [dataset.interface.coords(dataset, d.name, True)
              for d in dataset.kdims]
    idx = dataset.get_dimension_index(dim)
    return cartesian_product(arrays, flat=False)[idx]


def dt64_to_dt(dt64):
    """
    Safely converts NumPy datetime64 to a datetime object.
    """
    ts = (dt64 - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's')
    return dt.datetime.utcfromtimestamp(ts)


def is_nan(x):
    """
    Checks whether value is NaN on arbitrary types
    """
    try:
        return np.isnan(x)
    except:
        return False


def bound_range(vals, density):
    """
    Computes a bounding range and density from a number of samples
    assumed to be evenly spaced. Density is rounded to machine precision
    using significant digits reported by sys.float_info.dig.
    """
    low, high = vals.min(), vals.max()
    invert = False
    if vals[0] > vals[1]:
        invert = True
    if not density:
        density = round(1./((high-low)/(len(vals)-1)), sys.float_info.dig)
    halfd = 0.5/density
    return low-halfd, high+halfd, density, invert