Source code for holoviews.core.util

import builtins
import datetime as dt
import hashlib
import inspect
import itertools
import json
import numbers
import operator
import pickle
import string
import sys
import time
import types
import unicodedata
import warnings
from collections import defaultdict, namedtuple
from contextlib import contextmanager
from functools import partial
from threading import Event, Thread
from types import FunctionType

import numpy as np
import pandas as pd
import param
from packaging.version import Version

# Python 2 builtins
basestring = str
long = int
unicode = str
cmp = lambda a, b: (a>b)-(a<b)

get_keywords = operator.attrgetter('varkw')
generator_types = (zip, range, types.GeneratorType)
numpy_version = Version(np.__version__)
param_version = Version(param.__version__)

datetime_types = (np.datetime64, dt.datetime,, dt.time)
timedelta_types = (np.timedelta64, dt.timedelta,)
arraylike_types = (np.ndarray,)
masked_types = ()

anonymous_dimension_label = '_'

disallow_refs = {'allow_refs': False} if param_version > Version('2.0.0rc1') else {}

# Argspec was removed in Python 3.11
ArgSpec = namedtuple('ArgSpec', 'args varargs keywords defaults')

_NP_SIZE_LARGE = 1_000_000
_NP_SAMPLE_SIZE = 1_000_000
_PANDAS_ROWS_LARGE = 1_000_000

pandas_version = Version(pd.__version__)
    if pandas_version >= Version('1.3.0'):
        from pandas.core.dtypes.dtypes import DatetimeTZDtype as DatetimeTZDtypeType
        from pandas.core.dtypes.generic import (
            ABCIndex as ABCIndexClass,
    elif pandas_version >= Version('0.24.0'):
        from pandas.core.dtypes.dtypes import DatetimeTZDtype as DatetimeTZDtypeType
        from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
    elif pandas_version > Version('0.20.0'):
        from pandas.core.dtypes.dtypes import DatetimeTZDtypeType
        from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
        from pandas.types.dtypes import DatetimeTZDtypeType
        from pandas.types.dtypes.generic import ABCIndexClass, ABCSeries
    pandas_datetime_types = (pd.Timestamp, DatetimeTZDtypeType, pd.Period)
    pandas_timedelta_types = (pd.Timedelta,)
    datetime_types = datetime_types + pandas_datetime_types
    timedelta_types = timedelta_types + pandas_timedelta_types
    arraylike_types = arraylike_types + (ABCSeries, ABCIndexClass)
    if pandas_version > Version('0.23.0'):
        from pandas.core.dtypes.generic import ABCExtensionArray
        arraylike_types = arraylike_types + (ABCExtensionArray,)
    if pandas_version > Version('1.0'):
        from pandas.core.arrays.masked import BaseMaskedArray
        masked_types = (BaseMaskedArray,)
except Exception as e:
    param.main.param.warning('pandas could not register all extension types '
                                'imports failed with the following error: %s' % e)

    import cftime
    cftime_types = (cftime.datetime,)
    datetime_types += cftime_types
except ImportError:
    cftime_types = ()
_STANDARD_CALENDARS = {'standard', 'gregorian', 'proleptic_gregorian'}

# To avoid pandas warning about using DataFrameGroupBy.function
# introduced in Pandas 2.1.
# MRE: pd.DataFrame([0, 1]).groupby(0).aggregate(np.mean)
# Copied from here:
    builtins.sum: "sum",
    builtins.max: "max",
    builtins.min: "min",
    np.all: "all",
    np.any: "any",
    np.sum: "sum",
    np.nansum: "sum",
    np.mean: "mean",
    np.nanmean: "mean", "prod",
    np.nanprod: "prod",
    np.std: "std",
    np.nanstd: "std",
    np.var: "var",
    np.nanvar: "var",
    np.median: "median",
    np.nanmedian: "median",
    np.max: "max",
    np.nanmax: "max",
    np.min: "min",
    np.nanmin: "min",
    np.cumprod: "cumprod",
    np.nancumprod: "cumprod",
    np.cumsum: "cumsum",
    np.nancumsum: "cumsum",

[docs]class VersionError(Exception): "Raised when there is a library version mismatch." def __init__(self, msg, version=None, min_version=None, **kwargs): self.version = version self.min_version = min_version super().__init__(msg, **kwargs)
[docs]class Config(param.ParameterizedFunction): """ Set of boolean configuration values to change HoloViews' global behavior. Typically used to control warnings relating to deprecations or set global parameter such as style 'themes'. """ future_deprecations = param.Boolean(default=False, doc=""" Whether to warn about future deprecations""") image_rtol = param.Number(default=10e-4, doc=""" The tolerance used to enforce regular sampling for regular, gridded data where regular sampling is expected. Expressed as the maximal allowable sampling difference between sample locations.""") no_padding = param.Boolean(default=False, doc=""" Disable default padding (introduced in 1.13.0).""") warn_options_call = param.Boolean(default=True, doc=""" Whether to warn when the deprecated __call__ options syntax is used (the opts method should now be used instead). It is recommended that users switch this on to update any uses of __call__ as it will be deprecated in future.""") default_cmap = param.String(default='kbc_r', doc=""" Global default colormap. Prior to HoloViews 1.14.0, the default value was 'fire' which can be set for backwards compatibility.""") default_gridded_cmap = param.String(default='kbc_r', doc=""" Global default colormap for gridded elements (i.e. Image, Raster and QuadMesh). Can be set to 'fire' to match raster defaults prior to HoloViews 1.14.0 while allowing the default_cmap to be the value of 'kbc_r' used in HoloViews >= 1.14.0""") default_heatmap_cmap = param.String(default='kbc_r', doc=""" Global default colormap for HeatMap elements. Prior to HoloViews 1.14.0, the default value was the 'RdYlBu_r' colormap.""") def __call__(self, **params): self.param.update(**params) return self
config = Config() def _int_to_bytes(i): num_bytes = (i.bit_length() + 8) // 8 return i.to_bytes(num_bytes, "little", signed=True)
[docs]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 = ()
[docs] def default(self, obj): if isinstance(obj, set): return hash(frozenset(obj)) elif isinstance(obj, np.ndarray): h ="md5") for s in obj.shape: h.update(_int_to_bytes(s)) if obj.size >= _NP_SIZE_LARGE: state = np.random.RandomState(0) obj = state.choice(obj.flat, size=_NP_SAMPLE_SIZE) h.update(obj.tobytes()) return h.hexdigest() if isinstance(obj, (pd.Series, pd.DataFrame)): if len(obj) > _PANDAS_ROWS_LARGE: obj = obj.sample(n=_PANDAS_SAMPLE_SIZE, random_state=0) try: pd_values = list(pd.util.hash_pandas_object(obj, index=True).values) except TypeError: # Use pickle if pandas cannot hash the object for example if # it contains unhashable objects. pd_values = [pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)] if isinstance(obj, pd.Series): columns = [] elif isinstance(obj.columns, pd.MultiIndex): columns = [name for cols in obj.columns for name in cols] else: columns = list(obj.columns) all_vals = pd_values + columns + list(obj.index.names) h = hashlib.md5() for val in all_vals: if not isinstance(val, bytes): val = str(val).encode("utf-8") h.update(val) return h.hexdigest() elif isinstance(obj, self.string_hashable): return str(obj) elif isinstance(obj, self.repr_hashable): return repr(obj) try: return hash(obj) except Exception: return id(obj)
[docs]def merge_option_dicts(old_opts, new_opts): """ Update the old_opts option dictionary with the options defined in new_opts. Instead of a shallow update as would be performed by calling old_opts.update(new_opts), this updates the dictionaries of all option types separately. Given two dictionaries old_opts = {'a': {'x': 'old', 'y': 'old'}} and new_opts = {'a': {'y': 'new', 'z': 'new'}, 'b': {'k': 'new'}} this returns a dictionary {'a': {'x': 'old', 'y': 'new', 'z': 'new'}, 'b': {'k': 'new'}} """ merged = dict(old_opts) for option_type, options in new_opts.items(): if option_type not in merged: merged[option_type] = {} merged[option_type].update(options) return merged
[docs]def merge_options_to_dict(options): """ Given a collection of Option objects or partial option dictionaries, merge everything to a single dictionary. """ merged_options = {} for obj in options: if isinstance(obj,dict): new_opts = obj else: new_opts = {obj.key: obj.kwargs} merged_options = merge_option_dicts(merged_options, new_opts) return merged_options
[docs]def deprecated_opts_signature(args, kwargs): """ Utility to help with the deprecation of the old .opts method signature Returns whether opts.apply_groups should be used (as a bool) and the corresponding options. """ from .options import Options groups = set(Options._option_groups) opts = {kw for kw in kwargs if kw != 'clone'} apply_groups = False options = None new_kwargs = {} if len(args) > 0 and isinstance(args[0], dict): apply_groups = True if (not set(args[0]).issubset(groups) and all(isinstance(v, dict) and (not set(v).issubset(groups) or not v) for v in args[0].values())): apply_groups = False elif set(args[0].keys()) <= groups: new_kwargs = args[0] else: options = args[0] elif opts and opts.issubset(set(groups)): apply_groups = True elif kwargs.get('options', None) is not None: apply_groups = True elif not args and not kwargs: apply_groups = True return apply_groups, options, new_kwargs
[docs]class periodic(Thread): """ Run a callback count times with a given period without blocking. If count is None, will run till timeout (which may be forever if None). """ def __init__(self, period, count, callback, timeout=None, block=False): if isinstance(count, int): if count < 0: raise ValueError('Count value must be positive') elif count is not None: raise ValueError('Count value must be a positive integer or None') if block is False and count is None and timeout is None: raise ValueError('When using a non-blocking thread, please specify ' 'either a count or a timeout') super().__init__() self.period = period self.callback = callback self.count = count self.counter = 0 self.block = block self.timeout = timeout self._completed = Event() self._start_time = None @property def completed(self): return self._completed.is_set()
[docs] def start(self): self._start_time = time.time() if self.block is False: super().start() else:
def stop(self): self.timeout = None self._completed.set() def __repr__(self): return f'periodic({self.period}, {self.count}, {callable_name(self.callback)})' def __str__(self): return repr(self)
[docs] def run(self): while not self.completed: if self.block: time.sleep(self.period) else: self._completed.wait(self.period) self.counter += 1 try: self.callback(self.counter) except Exception: self.stop() if self.timeout is not None: dt = (time.time() - self._start_time) if dt > self.timeout: self.stop() if self.counter == self.count: self.stop()
[docs]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 Exception: return None
[docs]def tree_attribute(identifier): """ Predicate that returns True for custom attributes added to AttrTrees that are not methods, properties or internal attributes. These custom attributes start with a capitalized character when applicable (not applicable to underscore or certain unicode characters) """ if identifier == '': return True if identifier[0].upper().isupper() is False and identifier[0] != '_': return True else: return identifier[0].isupper()
[docs]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.getfullargspec(callable_obj.__call__) args = spec.args[1:] elif inspect.isfunction(callable_obj): # functions and staticmethods spec = inspect.getfullargspec(callable_obj) args = spec.args elif isinstance(callable_obj, partial): # partials arglen = len(callable_obj.args) spec = inspect.getfullargspec(callable_obj.func) args = [arg for arg in spec.args[arglen:] if arg not in callable_obj.keywords] if inspect.ismethod(callable_obj.func): args = args[1:] elif inspect.ismethod(callable_obj): # instance and class methods spec = inspect.getfullargspec(callable_obj) args = spec.args[1:] elif isinstance(callable_obj, type) and issubclass(callable_obj, param.Parameterized): return argspec(callable_obj.__init__) elif callable(callable_obj): # callable objects return argspec(callable_obj.__call__) else: raise ValueError("Cannot determine argspec for non-callable type.") keywords = get_keywords(spec) return ArgSpec(args=args, varargs=spec.varargs, keywords=keywords, defaults=spec.defaults)
[docs]def validate_dynamic_argspec(callback, 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. """ argspec = callback.argspec name = kdims = [ 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: unassigned = ','.join(unassigned_streams) raise KeyError(f'Callable {name!r} missing keywords to ' f'accept stream parameters: {unassigned}') if len(posargs) > len(kdims) + len(stream_params): raise KeyError(f'Callable {name!r} accepts more positional arguments than ' 'there are kdims and stream parameters') 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 ' f'the start of the signature of {name!r}') return posargs elif argspec.varargs: # Posargs missing, passed to Callable directly return None elif set(posargs) - set(kdims): raise KeyError(f'Callable {name!r} accepts more positional arguments {posargs} ' f'than there are key dimensions {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 elif argspec.keywords: return kdims else: names = list(set(posargs+kwargs)) raise KeyError(f'Callback {name!r} signature over {names} does not accommodate ' f'required kdims {kdims}')
[docs]def callable_name(callable_obj): """ Attempt to return a meaningful name identifying a callable or generator """ try: if (isinstance(callable_obj, type) and issubclass(callable_obj, param.ParameterizedFunction)): return callable_obj.__name__ elif (isinstance(callable_obj, param.Parameterized) and 'operation' in callable_obj.param): return callable_obj.operation.__name__ elif isinstance(callable_obj, partial): return str(callable_obj) elif inspect.isfunction(callable_obj): # functions and staticmethods return callable_obj.__name__ elif inspect.ismethod(callable_obj): # instance and class methods return callable_obj.__func__.__qualname__.replace('.__call__', '') elif isinstance(callable_obj, types.GeneratorType): return callable_obj.__name__ else: return type(callable_obj).__name__ except Exception: return str(callable_obj)
[docs]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 getattr(getattr(key, 'dtype', None), 'kind', None) == 'b': return key wrapped_key = wrap_tuple(key) ellipse_count = sum(1 for k in wrapped_key if k is Ellipsis) if ellipse_count == 0: return key elif ellipse_count != 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
[docs]def bytes_to_unicode(value): """ Safely casts bytestring to unicode """ if isinstance(value, bytes): return value.decode('utf-8') return value
[docs]def get_method_owner(method): """ Gets the instance that owns the supplied method """ if isinstance(method, partial): method = method.func return method.__self__
[docs]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
[docs]class sanitize_identifier_fn(param.ParameterizedFunction): """ Sanitizes group/label values for use in AttrTree attribute access. Special characters are sanitized using their (lowercase) unicode name using the unicodedata module. For instance: >>>'$').lower() 'dollar sign' As these names are often very long, this parameterized function allows filtered, substitutions and transforms to help shorten these names appropriately. """ 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(default=['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""")
[docs] @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()})
[docs] @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(f'_repr_{el}_' == name for el in disabled_reprs) return (name not in self_or_cls.disallowed) and not isrepr
[docs] @param.parameterized.bothmethod def prefixed(self, identifier): """ 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 unicodedata.category(identifier[0]) in invalid_starting
[docs] @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
[docs] @param.parameterized.bothmethod def shortened_character_name(self_or_cls, c, eliminations=None, substitutions=None, transforms=None): """ Given a unicode character c, return the shortened unicode name (as a list of tokens) by applying the eliminations, substitutions and transforms. """ if transforms is None: transforms = [] if substitutions is None: substitutions = {} if eliminations is None: eliminations = [] name = # Filtering for elim in eliminations: name = name.replace(elim, '') # Substitution 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): 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) if not self.allowable(name): raise AttributeError(f"String {name!r} is in the disallowed list of attribute names: {self.disallowed!r}") if self.capitalize and name and name[0] in string.ascii_lowercase: name = name[0].upper()+name[1:] sanitized = self.sanitize_py3(name) if self.prefixed(name): 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_py3(self, name): if not name.isidentifier(): return '_'.join(self.sanitize(name, lambda c: ('_'+c).isidentifier())) else: return name
[docs] 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(r"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)
[docs]def isscalar(val): """ Value is scalar or None """ return val is None or np.isscalar(val) or isinstance(val, datetime_types)
def isnumeric(val): if isinstance(val, (str, bool, np.bool_)): return False try: float(val) return True except Exception: return False
[docs]def isequal(value1, value2): """Compare two values, returning a boolean. Will apply the comparison to all elements of an array/dataframe. """ try: check = (value1 is value2) or (value1 == value2) if not isinstance(check, bool) and hasattr(check, "all"): check = check.all() return bool(check) except Exception: return False
[docs]def asarray(arraylike, strict=True): """ Converts arraylike objects to NumPy ndarray types. Errors if object is not arraylike and strict option is enabled. """ if isinstance(arraylike, np.ndarray): return arraylike elif isinstance(arraylike, list): return np.asarray(arraylike, dtype=object) elif not isinstance(arraylike, np.ndarray) and isinstance(arraylike, arraylike_types): return arraylike.values elif hasattr(arraylike, '__array__'): return np.asarray(arraylike) elif strict: raise ValueError(f'Could not convert {type(arraylike)} type to array') return arraylike
nat_as_integer = np.datetime64('NAT').view('i8')
[docs]def isnat(val): """ Checks if the value is a NaT. Should only be called on datetimelike objects. """ if (isinstance(val, (np.datetime64, np.timedelta64)) or (isinstance(val, np.ndarray) and val.dtype.kind == 'M')): if numpy_version >= Version('1.13'): return np.isnat(val) else: return val.view('i8') == nat_as_integer elif val is pd.NaT: return True elif isinstance(val, pandas_datetime_types+pandas_timedelta_types): return pd.isna(val) else: return False
[docs]def isfinite(val): """ Helper function to determine if scalar or array value is finite extending np.isfinite with support for None, string, datetime types. """ is_dask = is_dask_array(val) if not np.isscalar(val) and not is_dask: if isinstance(val, return ~val.mask & isfinite( elif isinstance(val, masked_types): return ~val.isna() & isfinite(val._data) val = asarray(val, strict=False) if val is None: return False elif is_dask: import dask.array as da return da.isfinite(val) elif isinstance(val, np.ndarray): if val.dtype.kind == 'M': return ~isnat(val) elif val.dtype.kind == 'O': return np.array([isfinite(v) for v in val], dtype=bool) elif val.dtype.kind in 'US': return ~pd.isna(val) finite = np.isfinite(val) if pandas_version >= Version('1.0.0'): finite &= ~pd.isna(val) return finite elif isinstance(val, datetime_types+timedelta_types): return not isnat(val) elif isinstance(val, (str, bytes)): return True finite = np.isfinite(val) if pandas_version >= Version('1.0.0'): if finite is pd.NA: return False return finite & ~pd.isna(np.asarray(val)) return finite
[docs]def isdatetime(value): """ Whether the array or scalar is recognized datetime type. """ if isinstance(value, np.ndarray): return (value.dtype.kind == "M" or (value.dtype.kind == "O" and len(value) and isinstance(value[0], datetime_types))) else: return isinstance(value, datetime_types)
[docs]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 Exception: limits = (np.nan, np.nan) return limits
[docs]def find_range(values, soft_range=None): """ 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. """ if soft_range is None: soft_range = [] 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() with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered') return np.nanmin(values), np.nanmax(values) except Exception: try: values = sorted(values) return (values[0], values[-1]) except Exception: return (None, None)
[docs]def max_range(ranges, combined=True): """ Computes the maximal lower and upper bounds from a list bounds. Args: ranges (list of tuples): A list of range tuples combined (boolean, optional): Whether to combine bounds Whether range should be computed on lower and upper bound independently or both at once Returns: The maximum range as a single tuple """ try: with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered') values = [tuple(np.nan if v is None else v for v in r) for r in ranges] if any(isinstance(v, datetime_types) and not isinstance(v, cftime_types+(dt.time,)) for r in values for v in r): converted = [] for l, h in values: if isinstance(l, pd.Period) and isinstance(h, pd.Period): l = l.to_timestamp().to_datetime64() h = h.to_timestamp().to_datetime64() elif isinstance(l, datetime_types) and isinstance(h, datetime_types): l, h = (pd.Timestamp(l).to_datetime64(), pd.Timestamp(h).to_datetime64()) converted.append((l, h)) values = converted arr = np.array(values) if not len(arr): return np.nan, np.nan elif arr.dtype.kind in 'OSU': arr = list(python2sort([ v for r in values for v in r if not is_nan(v) and v is not None])) return arr[0], arr[-1] elif arr.dtype.kind in 'M': drange = ((arr.min(), arr.max()) if combined else (arr[:, 0].min(), arr[:, 1].max())) return drange if combined: return (np.nanmin(arr), np.nanmax(arr)) else: return (np.nanmin(arr[:, 0]), np.nanmax(arr[:, 1])) except Exception: return (np.nan, np.nan)
[docs]def range_pad(lower, upper, padding=None, log=False): """ Pads the range by a fraction of the interval """ if padding is not None and not isinstance(padding, tuple): padding = (padding, padding) if is_number(lower) and is_number(upper) and padding is not None: if not isinstance(lower, datetime_types) and log and lower > 0 and upper > 0: log_min = np.log(lower) / np.log(10) log_max = np.log(upper) / np.log(10) lspan = (log_max-log_min)*(1+padding[0]*2) uspan = (log_max-log_min)*(1+padding[1]*2) center = (log_min+log_max) / 2.0 start, end = np.power(10, center-lspan/2.), np.power(10, center+uspan/2.) else: if isinstance(lower, datetime_types) and not isinstance(lower, cftime_types): # Ensure timedelta can be safely divided lower, upper = np.datetime64(lower), np.datetime64(upper) span = (upper-lower).astype('>m8[ns]') else: span = (upper-lower) lpad = span*(padding[0]) upad = span*(padding[1]) start, end = lower-lpad, upper+upad else: start, end = lower, upper return start, end
[docs]def dimension_range(lower, upper, hard_range, soft_range, padding=None, log=False): """ Computes the range along a dimension by combining the data range with the Dimension soft_range and range. """ plower, pupper = range_pad(lower, upper, padding, log) if isfinite(soft_range[0]) and soft_range[0] <= lower: lower = soft_range[0] else: lower = max_range([(plower, None), (soft_range[0], None)])[0] if isfinite(soft_range[1]) and soft_range[1] >= upper: upper = soft_range[1] else: upper = max_range([(None, pupper), (None, soft_range[1])])[1] dmin, dmax = hard_range lower = lower if dmin is None or not isfinite(dmin) else dmin upper = upper if dmax is None or not isfinite(dmax) else dmax return lower, upper
[docs]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 compute 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, str) 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, str) for u in upper): extents[uidx] = np.sort(upper)[-1] elif upper: extents[uidx] = np.nanmax(upper) return tuple(extents)
[docs]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 not isinstance(input, int): raise TypeError(f"expected integer, got {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
[docs]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
[docs]def lzip(*args): """ zip function that returns a list. """ return list(zip(*args))
[docs]def unique_zip(*args): """ Returns a unique list of zipped values. """ return list(unique_iterator(zip(*args)))
[docs]def unique_array(arr): """ Returns an array of unique values in the input order. Args: arr (np.ndarray or list): The array to compute unique values on Returns: A new array of unique values """ if not len(arr): return np.asarray(arr) if isinstance(arr, np.ndarray) and arr.dtype.kind not in 'MO': # Avoid expensive unpacking if not potentially datetime return pd.unique(arr) values = [] for v in arr: if (isinstance(v, datetime_types) and not isinstance(v, cftime_types)): v = pd.Timestamp(v).to_datetime64() elif isinstance(getattr(v, "dtype", None), pd.CategoricalDtype): v = v.dtype.categories values.append(v) return pd.unique(np.asarray(values).ravel())
[docs]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(, 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 # noqa: B015, TypeError 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)
[docs]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[].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.clone(values=dvalues.get(, [])) for d in dimensions]
[docs]def dimension_sort(odict, kdims, vdims, key_index): """ 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 = { [None]+list(d.values) for d in dimensions} if len(set(key_index)) != len(key_index): raise ValueError("Cannot sort on duplicated dimensions") else: sortkws['key'] = lambda x: tuple(cached_values[].index(x[t][d]) if dim.values else x[t][d] for i, (dim, t, d) in enumerate(indexes)) return python2sort(odict.items(), **sortkws)
# Copied from param should make param version public def is_number(obj): if isinstance(obj, numbers.Number): return True elif isinstance(obj, np.str_): return False elif np.__version__[0] < "2" and isinstance(obj, np.unicode_): return False # 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
[docs]def is_float(obj): """ Checks if the argument is a floating-point scalar. """ return isinstance(obj, (float, np.floating))
[docs]def is_int(obj, int_like=False): """ Checks for int types including the native Python type and NumPy-like objects Args: obj: Object to check for integer type int_like (boolean): Check for float types with integer value Returns: Boolean indicating whether the supplied value is of integer type. """ real_int = isinstance(obj, int) or getattr(getattr(obj, 'dtype', None), 'kind', 'o') in 'ui' if real_int or (int_like and hasattr(obj, 'is_integer') and obj.is_integer()): return True return False
[docs]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
[docs]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())))
[docs]def is_cyclic(graph): """ Return True if the directed graph g has a cycle. The directed graph should be represented as a dictionary 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)
[docs]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)
[docs]def get_overlay_spec(o, k, v): """ Gets the + key spec from an Element in an Overlay. """ k = wrap_tuple(k) return ((type(v).__name__,, v.label) + k if len(o.kdims) else (type(v).__name__,) + k)
[docs]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] if len(okeys) == 1 and okeys[0] not 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)]
[docs]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
[docs]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 is None and depth is 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)
[docs]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(**{dimensions[ndims-depth]: k}), dimensions, v, depth-1) else: for s in selects: items.append((s,**{dimensions[-1]: s[-1]}))) return items
[docs]def get_spec(obj): """ Gets the spec from any labeled data object. """ return (obj.__class__.__name__,, obj.label)
[docs]def is_dataframe(data): """ Checks whether the supplied data is of DataFrame type. """ dd = None if 'dask.dataframe' in sys.modules and 'pandas' in sys.modules: import dask.dataframe as dd return((isinstance(data, pd.DataFrame)) or (dd is not None and isinstance(data, dd.DataFrame)))
[docs]def is_series(data): """ Checks whether the supplied data is of Series type. """ dd = None if 'dask.dataframe' in sys.modules: import dask.dataframe as dd return (isinstance(data, pd.Series) or (dd is not None and isinstance(data, dd.Series)))
def is_dask_array(data): da = None if 'dask.array' in sys.modules: import dask.array as da return (da is not None and isinstance(data, da.Array)) def is_cupy_array(data): if 'cupy' in sys.modules: import cupy return isinstance(data, cupy.ndarray) return False def is_ibis_expr(data): if 'ibis' in sys.modules: import ibis return isinstance(data, ibis.expr.types.ColumnExpr) return False def get_param_values(data): params = dict(kdims=data.kdims, vdims=data.vdims, label=data.label) if ( != data.param.objects(False)['group'].default and not isinstance(type(data).group, property)): params['group'] = return params
[docs]def is_param_method(obj, has_deps=False): """Whether the object is a method on a parameterized object. Args: obj: Object to check has_deps (boolean, optional): Check for dependencies Whether to also check whether the method has been annotated with param.depends Returns: A boolean value indicating whether the object is a method on a Parameterized object and if enabled whether it has any dependencies """ parameterized = (inspect.ismethod(obj) and isinstance(get_method_owner(obj), param.Parameterized)) if parameterized and has_deps: return getattr(obj, "_dinfo", {}).get('dependencies') return parameterized
[docs]def resolve_dependent_value(value): """Resolves parameter dependencies on the supplied value Resolves parameter values, Parameterized instance methods, parameterized functions with dependencies on the supplied value, including such parameters embedded in a list, tuple, dictionary, or slice. Args: value: A value which will be resolved Returns: A new value where any parameter dependencies have been resolved. """ range_widget = False if isinstance(value, list): value = [resolve_dependent_value(v) for v in value] elif isinstance(value, tuple): value = tuple(resolve_dependent_value(v) for v in value) elif isinstance(value, dict): value = { resolve_dependent_value(k): resolve_dependent_value(v) for k, v in value.items() } elif isinstance(value, slice): value = slice( resolve_dependent_value(value.start), resolve_dependent_value(value.stop), resolve_dependent_value(value.step), ) if 'panel' in sys.modules: from panel.depends import param_value_if_widget from panel.widgets import RangeSlider range_widget = isinstance(value, RangeSlider) if param_version > Version('2.0.0rc1'): value = param.parameterized.resolve_value(value) else: value = param_value_if_widget(value) if is_param_method(value, has_deps=True): value = value() elif isinstance(value, param.Parameter) and isinstance(value.owner, param.Parameterized): value = getattr(value.owner, elif isinstance(value, FunctionType) and hasattr(value, '_dinfo'): deps = value._dinfo args = (getattr(p.owner, for p in deps.get('dependencies', [])) kwargs = {k: getattr(p.owner, for k, p in deps.get('kw', {}).items()} value = value(*args, **kwargs) if isinstance(value, tuple) and range_widget: value = slice(*value) return value
[docs]def resolve_dependent_kwargs(kwargs): """Resolves parameter dependencies in the supplied dictionary Resolves parameter values, Parameterized instance methods and parameterized functions with dependencies in the supplied dictionary. Args: kwargs (dict): A dictionary of keyword arguments Returns: A new dictionary where any parameter dependencies have been resolved. """ return {k: resolve_dependent_value(v) for k, v in kwargs.items()}
[docs]@contextmanager def disable_constant(parameterized): """ Temporarily set parameters on Parameterized object to constant=False. """ params = parameterized.param.objects('existing').values() constants = [p.constant for p in params] for p in params: p.constant = False try: yield finally: for (p, const) in zip(params, constants): p.constant = const
[docs]def get_ndmapping_label(ndmapping, attr): """ Function to get the first non-auxiliary object label attribute from an NdMapping. """ label = None els = iter( while label is None: try: el = next(els) except StopIteration: return None if not getattr(el, '_auxiliary_component', True): label = getattr(el, attr) if attr == 'group': tp = type(el).__name__ if tp == label: return None return label
[docs]def wrap_tuple(unwrapped): """ Wraps any non-tuple types in a tuple """ return (unwrapped if isinstance(unwrapped, tuple) else (unwrapped,))
[docs]def stream_name_mapping(stream, exclude_params=None, 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. """ if exclude_params is None: exclude_params = ['name'] from ..streams import Params if isinstance(stream, Params): mapping = {} for p in stream.parameters: if isinstance(p, str): mapping[p] = stream._rename.get(p, p) else: mapping[] = stream._rename.get((p.owner,, else: filtered = [k for k in stream.param 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
[docs]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
[docs]def stream_parameters(streams, no_duplicates=True, exclude=None): """ 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. """ if exclude is None: exclude = ['name', '_memoize_key'] from ..streams import Params param_groups = {} for s in streams: if not s.contents and isinstance(s.hashkey, dict): param_groups[s] = list(s.hashkey) else: param_groups[s] = list(s.contents) if no_duplicates: seen, clashes = {}, [] clash_streams = [] for s in streams: if isinstance(s, Params): continue for c in param_groups[s]: if c in seen: clashes.append(c) if seen[c] not in clash_streams: clash_streams.append(seen[c]) clash_streams.append(s) else: seen[c] = s clashes = sorted(clashes) if clashes: clashing = ', '.join([repr(c) for c in clash_streams[:-1]]) raise Exception(f'The supplied stream objects {clashing} and {clash_streams[-1]} ' f'clash on the following parameters: {clashes!r}') return [name for group in param_groups.values() for name in group if name not in exclude]
[docs]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]
[docs]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]
[docs]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)
[docs]def drop_streams(streams, kdims, keys): """ Drop any dimensioned 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]) get = operator.itemgetter(*inds) # itemgetter used for performance keys = (get(k) for k in keys) return dims, ([wrap_tuple(k) for k in keys] if len(inds) == 1 else list(keys))
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 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: yield (wrap_tuple(key), obj)
[docs]def capitalize(string): """ Capitalizes the first letter of a string. """ if string: return string[0].upper() + string[1:] else: return string
[docs]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.label) if item.label else (,) return tuple(capitalize(fn(p)) for (p, fn) in zip(path, sanitizers))
[docs]def make_path_unique(path, counts, new): """ Given a path, a list of existing paths and counts for each of the existing paths. """ added = False while any(path == c[:i] for c in counts for i in range(1, len(c)+1)): count = counts[path] counts[path] += 1 if (not new and len(path) > 1) or added: path = path[:-1] else: added = True path = path + (int_to_roman(count),) if len(path) == 1: path = path + (int_to_roman(counts.get(path, 1)),) if path not in counts: counts[path] = 1 return path
[docs]class ndmapping_groupby(param.ParameterizedFunction): """ Apply a groupby operation to an NdMapping, using pandas to improve performance (if available). """ sort = param.Boolean(default=False, doc='Whether to apply a sorted groupby') def __call__(self, ndmapping, dimensions, container_type, group_type, sort=False, **kwargs): return self.groupby_pandas(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 = [ for d in ndmapping.kdims] inds = [ndmapping.get_dimension_index(dim) for dim in idims] getter = operator.itemgetter(*inds) if inds else lambda x: () multi_index = pd.MultiIndex.from_tuples(ndmapping.keys(), names=all_dims) df = pd.DataFrame(list(map(wrap_tuple, ndmapping.values())), index=multi_index) # TODO: Look at sort here kwargs = dict(dict(get_param_values(ndmapping), kdims=idims), sort=sort, **kwargs) with warnings.catch_warnings(): # Pandas 2.1 raises this warning, can be ignored as the future behavior is what # we already do with wrap_tuple. MRE: list(pd.DataFrame([0]).groupby(level=[0])) warnings.filterwarnings( 'ignore', category=FutureWarning, message="Creating a Groupby object with a length-1" ) groups = ((wrap_tuple(k), group_type(dict(unpack_group(group, getter)), **kwargs)) for k, group in df.groupby(level=[ for d in dimensions], sort=sort)) 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, sort=sort) @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 = [ 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)
[docs]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)
[docs]def cross_index(values, index): """ Allows efficiently indexing into a cartesian product without expanding it. The values should be defined as a list of iterables making up the cartesian product and a linear index, returning the cross product of the values at the supplied index. """ lengths = [len(v) for v in values] length = if index >= length: raise IndexError('Index %d out of bounds for cross-product of size %d' % (index, length)) indexes = [] for i in range(1, len(values))[::-1]: p =[-i:]) indexes.append(index//p) index -= indexes[-1] * p indexes.append(index) return tuple(v[i] for v, i in zip(values, indexes))
[docs]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'f{i}'] = array return recarray.argsort()
[docs]def dimensioned_streams(dmap): """ Given a DynamicMap return all streams that have any dimensioned parameters, i.e. parameters also listed in the key dimensions. """ dimensioned = [] for stream in dmap.streams: stream_params = stream_parameters([stream]) if {str(k) for k in dmap.kdims} & set(stream_params): dimensioned.append(stream) return dimensioned
[docs]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. """ irregular = [ for d in dataset.kdims if d is not dim and dataset.interface.irregular(dataset, d)] if irregular: array = dataset.interface.coords(dataset, dim, True) example = dataset.interface.values(dataset, irregular[0], True, False) return array * np.ones_like(example) else: arrays = [dataset.interface.coords(dataset,, True) for d in dataset.kdims] idx = dataset.get_dimension_index(dim) return cartesian_product(arrays, flat=False)[idx].T
[docs]def dt64_to_dt(dt64): """ Safely converts NumPy datetime64 to a datetime object. """ ts = (dt64 - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's') return dt.datetime(1970,1,1,0,0,0) + dt.timedelta(seconds=ts)
[docs]def is_nan(x): """ Checks whether value is NaN on arbitrary types """ try: return np.isnan(x) except Exception: return False
[docs]def bound_range(vals, density, time_unit='us'): """ 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. """ if not len(vals): return(np.nan, np.nan, density, False) low, high = vals.min(), vals.max() invert = False if len(vals) > 1 and vals[0] > vals[1]: invert = True if not density: with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'invalid value encountered in (double_scalars|scalar divide)') full_precision_density = compute_density(low, high, len(vals)-1) with np.errstate(over='ignore'): density = round(full_precision_density, sys.float_info.dig) if density in (0, np.inf): density = full_precision_density if density == 0: raise ValueError('Could not determine Image density, ensure it has a non-zero range.') halfd = 0.5/density if isinstance(low, datetime_types): halfd = np.timedelta64(int(round(halfd)), time_unit) return low-halfd, high+halfd, density, invert
[docs]def validate_regular_sampling(values, rtol=10e-6): """ Validates regular sampling of a 1D array ensuring that the difference in sampling steps is at most rtol times the smallest sampling step. Returns a boolean indicating whether the sampling is regular. """ diffs = np.diff(values) return (len(diffs) < 1) or abs(diffs.min()-diffs.max()) < abs(diffs.min()*rtol)
[docs]def compute_density(start, end, length, time_unit='us'): """ Computes a grid density given the edges and number of samples. Handles datetime grids correctly by computing timedeltas and computing a density for the given time_unit. """ if isinstance(start, int): start = float(start) if isinstance(end, int): end = float(end) diff = end-start if isinstance(diff, timedelta_types): if isinstance(diff, np.timedelta64): diff = np.timedelta64(diff, time_unit).tolist() tscale = 1./np.timedelta64(1, time_unit).tolist().total_seconds() return (length/(diff.total_seconds()*tscale)) else: return length/diff
[docs]def date_range(start, end, length, time_unit='us'): """ Computes a date range given a start date, end date and the number of samples. """ step = (1./compute_density(start, end, length, time_unit)) if isinstance(start, pd.Timestamp): start = start.to_datetime64() step = np.timedelta64(int(round(step)), time_unit) return start+step/2.+np.arange(length)*step
[docs]def parse_datetime(date): """ Parses dates specified as string or integer or pandas Timestamp """ return pd.to_datetime(date).to_datetime64()
[docs]def parse_datetime_selection(sel): """ Parses string selection specs as datetimes. """ if isinstance(sel, str) or isdatetime(sel): sel = parse_datetime(sel) if isinstance(sel, slice): if isinstance(sel.start, str) or isdatetime(sel.start): sel = slice(parse_datetime(sel.start), sel.stop) if isinstance(sel.stop, str) or isdatetime(sel.stop): sel = slice(sel.start, parse_datetime(sel.stop)) if isinstance(sel, (set, list)): sel = [parse_datetime(v) if isinstance(v, str) else v for v in sel] return sel
[docs]def dt_to_int(value, time_unit='us'): """ Converts a datetime type to an integer with the supplied time unit. """ if isinstance(value, pd.Period): value = value.to_timestamp() if isinstance(value, pd.Timestamp): try: value = value.to_datetime64() except Exception: value = np.datetime64(value.to_pydatetime()) if isinstance(value, cftime_types): return cftime_to_timestamp(value, time_unit) # date class is a parent for datetime class if isinstance(value, and not isinstance(value, dt.datetime): value = dt.datetime(*value.timetuple()[:6]) # Handle datetime64 separately if isinstance(value, np.datetime64): try: value = np.datetime64(value, 'ns') tscale = (np.timedelta64(1, time_unit)/np.timedelta64(1, 'ns')) return int(value.tolist() / tscale) except Exception: # If it can't handle ns precision fall back to datetime value = value.tolist() if time_unit == 'ns': tscale = 1e9 else: tscale = 1./np.timedelta64(1, time_unit).tolist().total_seconds() if value.tzinfo is None: _epoch = dt.datetime(1970, 1, 1) else: _epoch = dt.datetime(1970, 1, 1, tzinfo=dt.timezone.utc) return int((value - _epoch).total_seconds() * tscale)
[docs]def cftime_to_timestamp(date, time_unit='us'): """Converts cftime to timestamp since epoch in milliseconds Non-standard calendars (e.g. Julian or no leap calendars) are converted to standard Gregorian calendar. This can cause extra space to be added for dates that don't exist in the original calendar. In order to handle these dates correctly a custom bokeh model with support for other calendars would have to be defined. Args: date: cftime datetime object (or array) Returns: time_unit since 1970-01-01 00:00:00 """ import cftime if time_unit == 'us': tscale = 1 else: tscale = (np.timedelta64(1, 'us')/np.timedelta64(1, time_unit)) return cftime.date2num(date,'microseconds since 1970-01-01 00:00:00', calendar='standard')*tscale
[docs]def search_indices(values, source): """ Given a set of values returns the indices of each of those values in the source array. """ try: orig_indices = source.argsort() except TypeError: # Can fail for something like this: # np.array(['circle15', np.nan], dtype=object).argsort() source = source.astype(str) values = values.astype(str) orig_indices = source.argsort() return orig_indices[np.searchsorted(source[orig_indices], values)]
[docs]def compute_edges(edges): """ Computes edges as midpoints of the bin centers. The first and last boundaries are equidistant from the first and last midpoints respectively. """ edges = np.asarray(edges) if edges.dtype.kind == 'i': edges = edges.astype('f') midpoints = (edges[:-1] + edges[1:])/2.0 boundaries = (2*edges[0] - midpoints[0], 2*edges[-1] - midpoints[-1]) return np.concatenate([boundaries[:1], midpoints, boundaries[-1:]])
[docs]def mimebundle_to_html(bundle): """ Converts a MIME bundle into HTML. """ if isinstance(bundle, tuple): data, metadata = bundle else: data = bundle html = data.get('text/html', '') if 'application/javascript' in data: js = data['application/javascript'] html += f'\n<script type="application/javascript">{js}</script>' return html
[docs]def numpy_scalar_to_python(scalar): """ Converts a NumPy scalar to a regular python type. """ scalar_type = type(scalar) if issubclass(scalar_type, np.float64): return float(scalar) elif issubclass(scalar_type, np.int_): return int(scalar) return scalar
[docs]def closest_match(match, specs, depth=0): """ Recursively iterates over type, group, label and overlay key, finding the closest matching spec. """ if len(match) == 0: return None new_specs = [] match_lengths = [] for i, spec in specs: if spec[0] == match[0]: new_specs.append((i, spec[1:])) else: if all(isinstance(s[0], str) for s in [spec, match]): match_length = max(i for i in range(len(match[0])) if match[0].startswith(spec[0][:i])) elif is_number(match[0]) and is_number(spec[0]): m = bool(match[0]) if isinstance(match[0], np.bool_) else match[0] s = bool(spec[0]) if isinstance(spec[0], np.bool_) else spec[0] match_length = -abs(m-s) else: match_length = 0 match_lengths.append((i, match_length, spec[0])) if len(new_specs) == 1: return new_specs[0][0] elif new_specs: depth = depth+1 return closest_match(match[1:], new_specs, depth) elif depth == 0 or not match_lengths: return None else: return sorted(match_lengths, key=lambda x: -x[1])[0][0]
[docs]def cast_array_to_int64(array): """ Convert a numpy array to `int64`. Suppress the following warning emitted by Numpy, which as of 12/2021 has been extensively discussed ( and whose fate (possible revert) has not yet been settled: FutureWarning: casting datetime64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead. """ with warnings.catch_warnings(): warnings.filterwarnings( action='ignore', message='casting datetime64', category=FutureWarning, ) return array.astype('int64')
[docs]def flatten(line): """ Flatten an arbitrarily nested sequence. Inspired by: pd.core.common.flatten Parameters ---------- line : sequence The sequence to flatten Notes ----- This only flattens list, tuple, and dict sequences. Returns ------- flattened : generator """ for element in line: if any(isinstance(element, tp) for tp in (list, tuple, dict)): yield from flatten(element) else: yield element