Source code for Orange.data.variable

import re
import warnings
from collections import Iterable

from datetime import datetime, timedelta, timezone
from numbers import Number, Real, Integral
from math import isnan, floor
from pickle import PickleError

import numpy as np
import scipy.sparse as sp

from Orange.data import _variable
from Orange.util import Registry, hex_to_color, Reprable,\
    OrangeDeprecationWarning

__all__ = ["Unknown", "MISSING_VALUES", "make_variable", "is_discrete_values",
           "Value", "Variable", "ContinuousVariable", "DiscreteVariable",
           "StringVariable", "TimeVariable"]


# For storing unknowns
Unknown = ValueUnknown = float("nan")
# For checking for unknowns
MISSING_VALUES = {np.nan, "?", "nan", ".", "", "NA", "~", None}

DISCRETE_MAX_VALUES = 3  # == 2 + nan
MAX_NUM_OF_DECIMALS = 5
# the variable with more than 100 different values should not be StringVariable
DISCRETE_MAX_ALLOWED_VALUES = 100


def make_variable(cls, compute_value, *args):
    if compute_value is not None:
        return cls(*args, compute_value=compute_value)
    else:
        # For compatibility with old pickles
        return cls(*args)


def is_discrete_values(values):
    """
    Return set of uniques if `values` is an iterable of discrete values
    else False if non-discrete, or None if indeterminate.

    Note
    ----
    Assumes consistent type of items of `values`.
    """
    if not len(values):
        return None
    # If the first few values are, or can be converted to, floats,
    # the type is numeric
    try:
        isinstance(next(iter(values)), Number) or \
        [float(v) for _, v in zip(range(min(3, len(values))), values)]
    except ValueError:
        is_numeric = False
        max_values = int(round(len(values)**.7))
    else:
        is_numeric = True
        max_values = DISCRETE_MAX_VALUES

    # If more than max values => not discrete
    unique = set()
    for i in values:
        unique.add(i)
        if (len(unique) > max_values or
                len(unique) > DISCRETE_MAX_ALLOWED_VALUES):
            return False

    # Strip NaN from unique
    unique = {i for i in unique
              if (not i in MISSING_VALUES and
                  not (isinstance(i, Number) and np.isnan(i)))}

    # All NaNs => indeterminate
    if not unique:
        return None

    # Strings with |values| < max_unique
    if not is_numeric:
        return unique

    # Handle numbers
    try:
        unique_float = set(map(float, unique))
    except ValueError:
        # Converting all the values to floats resulted in an error.
        # Since the values have enough unique values, they are probably
        # string values and discrete.
        return unique

    # If only values are {0, 1} or {1, 2} (or a subset of those sets) => discrete
    return (not (unique_float - {0, 1}) or
            not (unique_float - {1, 2})) and unique


[docs]class Value(float): """ The class representing a value. The class is not used to store values but only to return them in contexts in which we want the value to be accompanied with the descriptor, for instance to print the symbolic value of discrete variables. The class is derived from `float`, with an additional attribute `variable` which holds the descriptor of type :obj:`Orange.data.Variable`. If the value continuous or discrete, it is stored as a float. Other types of values, like strings, are stored in the attribute `value`. The class overloads the methods for printing out the value: `variable.repr_val` and `variable.str_val` are used to get a suitable representation of the value. Equivalence operator is overloaded as follows: - unknown values are equal; if one value is unknown and the other is not, they are different; - if the value is compared with the string, the value is converted to a string using `variable.str_val` and the two strings are compared - if the value is stored in attribute `value`, it is compared with the given other value - otherwise, the inherited comparison operator for `float` is called. Finally, value defines a hash, so values can be put in sets and appear as keys in dictionaries. .. attribute:: variable (:obj:`Orange.data.Variable`) Descriptor; used for printing out and for comparing with strings .. attribute:: value Value; the value can be of arbitrary type and is used only for variables that are neither discrete nor continuous. If `value` is `None`, the derived `float` value is used. """ __slots__ = "variable", "_value" def __new__(cls, variable, value=Unknown): """ Construct a new instance of Value with the given descriptor and value. If the argument `value` can be converted to float, it is stored as `float` and the attribute `value` is set to `None`. Otherwise, the inherited float is set to `Unknown` and the value is held by the attribute `value`. :param variable: descriptor :type variable: Orange.data.Variable :param value: value """ if variable.is_primitive(): self = super().__new__(cls, value) self.variable = variable self._value = None else: isunknown = value == variable.Unknown self = super().__new__( cls, np.nan if isunknown else np.finfo(float).min) self.variable = variable self._value = value return self def __init__(self, _, __=Unknown): pass def __repr__(self): return "Value('%s', %s)" % (self.variable.name, self.variable.repr_val(self)) def __str__(self): return self.variable.str_val(self) def __eq__(self, other): if isinstance(self, Real) and isnan(self): return (isinstance(other, Real) and isnan(other) or other in self.variable.unknown_str) if isinstance(other, str): return self.variable.str_val(self) == other if isinstance(other, Value): return self.value == other.value return super().__eq__(other) def __ne__(self, other): return not self.__eq__(other) def __lt__(self, other): if self.variable.is_primitive(): if isinstance(other, str): return super().__lt__(self.variable.to_val(other)) else: return super().__lt__(other) else: if isinstance(other, str): return self.value < other else: return self.value < other.value def __le__(self, other): return self.__lt__(other) or self.__eq__(other) def __gt__(self, other): return not self.__le__(other) def __ge__(self, other): return not self.__lt__(other) def __contains__(self, other): if (self._value is not None and isinstance(self._value, str) and isinstance(other, str)): return other in self._value raise TypeError("invalid operation on Value()") def __hash__(self): if self.variable.is_discrete: # It is not possible to hash the id and the domain value to the # same number as required by __eq__. # hash(1) == hash(Value(DiscreteVariable("var", ["red", "green", "blue"]), 1)) == hash("green") # User should hash directly ids or domain values instead. raise TypeError("unhashable type - cannot hash values of discrete variables!") if self._value is None: return super().__hash__() else: return hash(self._value) @property def value(self): if self.variable.is_discrete: return Unknown if isnan(self) else self.variable.values[int(self)] if self.variable.is_string: return self._value return float(self) def __getnewargs__(self): return self.variable, float(self) def __getstate__(self): return dict(value=getattr(self, '_value', None)) def __setstate__(self, state): self._value = state.get('value', None)
class VariableMeta(Registry): pass class _predicatedescriptor(property): """ A property that behaves as a class method if accessed via a class >>> class A: ... foo = False ... @_predicatedescriptor ... def is_foo(self): ... return self.foo ... >>> a = A() >>> a.is_foo False >>> A.is_foo(a) False """ def __get__(self, instance, objtype=None): if instance is None: return self.fget else: return super().__get__(instance, objtype)
[docs]class Variable(Reprable, metaclass=VariableMeta): """ The base class for variable descriptors contains the variable's name and some basic properties. .. attribute:: name The name of the variable. .. attribute:: unknown_str A set of values that represent unknowns in conversion from textual formats. Default is `{"?", ".", "", "NA", "~", None}`. .. attribute:: compute_value A function for computing the variable's value when converting from another domain which does not contain this variable. The function will be called with a data set (`Orange.data.Table`) and has to return an array of computed values for all its instances. The base class defines a static method `compute_value`, which returns `Unknown`. Non-primitive variables must redefine it to return `None`. .. attribute:: sparse A flag about sparsity of the variable. When set, the variable suggests it should be stored in a sparse matrix. .. attribute:: source_variable An optional descriptor of the source variable - if any - from which this variable is derived and computed via :obj:`compute_value`. .. attribute:: attributes A dictionary with user-defined attributes of the variable """ Unknown = ValueUnknown def __init__(self, name="", compute_value=None, *, sparse=False): """ Construct a variable descriptor. """ if not name: warnings.warn("Variable must have a name", OrangeDeprecationWarning, stacklevel=3) self._name = name self._compute_value = compute_value self.unknown_str = MISSING_VALUES self.source_variable = None self.sparse = sparse self.attributes = {} self._colors = None @property def name(self): return self._name @property def colors(self): # unreachable; pragma: no cover return self._colors @colors.setter def colors(self, value): self._colors = value def make_proxy(self): """ Copy the variable and set the master to `self.master` or to `self`. :return: copy of self :rtype: Variable """ var = self.__class__(self.name) var.__dict__.update(self.__dict__) var.attributes = dict(self.attributes) return var def __eq__(self, other): return type(self) is type(other) \ and self.name == other.name \ and self._compute_value == other._compute_value def __hash__(self): return hash((self.name, type(self), self._compute_value)) @classmethod def make(cls, name, *args, **kwargs): """ Return an existing continuous variable with the given name, or construct and return a new one. """ return cls(name, *args, **kwargs) @classmethod def _clear_cache(cls): warnings.warn( "_clear_cache is no longer needed and thus deprecated") @staticmethod def _clear_all_caches(): warnings.warn( "_clear_all_caches is no longer needed and thus deprecated")
[docs] @classmethod def is_primitive(cls, var=None): """ `True` if the variable's values are stored as floats. Non-primitive variables can appear in the data only as meta attributes. """ to_check = cls if var is None else type(var) return issubclass(to_check, (DiscreteVariable, ContinuousVariable))
@_predicatedescriptor def is_discrete(self): return isinstance(self, DiscreteVariable) @_predicatedescriptor def is_continuous(self): return isinstance(self, ContinuousVariable) @_predicatedescriptor def is_string(self): return isinstance(self, StringVariable) @_predicatedescriptor def is_time(self): return isinstance(self, TimeVariable) def repr_val(self, val): """ Return a textual representation of variable's value `val`. Argument `val` must be a float (for primitive variables) or an arbitrary Python object (for non-primitives). Derived classes must overload the function. """ raise RuntimeError("variable descriptors must overload repr_val()") str_val = repr_val
[docs] def to_val(self, s): """ Convert the given argument to a value of the variable. The argument can be a string, a number or `None`. For primitive variables, the base class provides a method that returns :obj:`~Orange.data.Unknown` if `s` is found in :obj:`~Orange.data.Variable.unknown_str`, and raises an exception otherwise. For non-primitive variables it returns the argument itself. Derived classes of primitive variables must overload the function. :param s: value, represented as a number, string or `None` :type s: str, float or None :rtype: float or object """ if not self.is_primitive(): return s if s in self.unknown_str: return Unknown raise RuntimeError( "primitive variable descriptors must overload to_val()")
[docs] def val_from_str_add(self, s): """ Convert the given string to a value of the variable. The method is similar to :obj:`to_val` except that it only accepts strings and that it adds new values to the variable's domain where applicable. The base class method calls `to_val`. :param s: symbolic representation of the value :type s: str :rtype: float or object """ return self.to_val(s)
def __str__(self): return self.name @property def compute_value(self): return self._compute_value def __reduce__(self): if not self.name: raise PickleError("Variables without names cannot be pickled") # Use make to unpickle variables. return make_variable, (self.__class__, self._compute_value, self.name), self.__dict__ def copy(self, compute_value=None, *, name=None, **kwargs): var = type(self)(name=name or self.name, compute_value=compute_value or self.compute_value, sparse=self.sparse, **kwargs) var.attributes = dict(self.attributes) return var
del _predicatedescriptor
[docs]class ContinuousVariable(Variable): """ Descriptor for continuous variables. .. attribute:: number_of_decimals The number of decimals when the value is printed out (default: 3). .. attribute:: adjust_decimals A flag regulating whether the `number_of_decimals` is being adjusted by :obj:`to_val`. The value of `number_of_decimals` is set to 3 and `adjust_decimals` is set to 2. When :obj:`val_from_str_add` is called for the first time with a string as an argument, `number_of_decimals` is set to the number of decimals in the string and `adjust_decimals` is set to 1. In the subsequent calls of `to_val`, the nubmer of decimals is increased if the string argument has a larger number of decimals. If the `number_of_decimals` is set manually, `adjust_decimals` is set to 0 to prevent changes by `to_val`. """ TYPE_HEADERS = ('continuous', 'c', 'numeric', 'n') def __init__(self, name="", number_of_decimals=None, compute_value=None, *, sparse=False): """ Construct a new continuous variable. The number of decimals is set to three, but adjusted at the first call of :obj:`to_val`. """ super().__init__(name, compute_value, sparse=sparse) if number_of_decimals is None: self._number_of_decimals = 3 self.adjust_decimals = 2 self._format_str = "%g" else: self.number_of_decimals = number_of_decimals @property def number_of_decimals(self): return self._number_of_decimals @property def format_str(self): return self._format_str @format_str.setter def format_str(self, value): self._format_str = value @Variable.colors.getter def colors(self): if self._colors is not None: return self._colors try: col1, col2, black = self.attributes["colors"] return (hex_to_color(col1), hex_to_color(col2), black) except (KeyError, ValueError): # User-provided colors were not available or invalid return ((0, 0, 255), (255, 255, 0), False) # noinspection PyAttributeOutsideInit @number_of_decimals.setter def number_of_decimals(self, x): self._number_of_decimals = x self.adjust_decimals = 0 if self._number_of_decimals <= MAX_NUM_OF_DECIMALS: self._format_str = "%.{}f".format(self.number_of_decimals) else: self._format_str = "%g"
[docs] def to_val(self, s): """ Convert a value, given as an instance of an arbitrary type, to a float. """ if s in self.unknown_str: return Unknown return float(s)
[docs] def val_from_str_add(self, s): """ Convert a value from a string and adjust the number of decimals if `adjust_decimals` is non-zero. """ return _variable.val_from_str_add_cont(self, s)
def repr_val(self, val): """ Return the value as a string with the prescribed number of decimals. """ if isnan(val): return "?" return self._format_str % val str_val = repr_val def copy(self, compute_value=None, *, name=None, **kwargs): var = super().copy(compute_value=compute_value, name=name, number_of_decimals=self.number_of_decimals, **kwargs) var.adjust_decimals = self.adjust_decimals var.format_str = self._format_str return var
[docs]class DiscreteVariable(Variable): """ Descriptor for symbolic, discrete variables. Values of discrete variables are stored as floats; the numbers corresponds to indices in the list of values. .. attribute:: values A list of variable's values. .. attribute:: ordered Some algorithms (and, in particular, visualizations) may sometime reorder the values of the variable, e.g. alphabetically. This flag hints that the given order of values is "natural" (e.g. "small", "middle", "large") and should not be changed. """ TYPE_HEADERS = ('discrete', 'd', 'categorical') presorted_values = [] def __init__(self, name="", values=(), ordered=False, compute_value=None, *, sparse=False): """ Construct a discrete variable descriptor with the given values. """ self.values = list(values) if not all(isinstance(value, str) for value in self.values): raise TypeError("values of DiscreteVariables must be strings") super().__init__(name, compute_value, sparse=sparse) self.ordered = ordered def get_mapping_from(self, other): return np.array( [self.values.index(value) if value in self.values else np.nan for value in other.values], dtype=float) def get_mapper_from(self, other): mapping = self.get_mapping_from(other) if not mapping.size: # Nans in data are temporarily replaced with 0, mapped and changed # back to nans. This would fail is mapping[0] is out of range. mapping = np.array([np.nan]) def mapper(value, col_idx=None): # In-place mapping if col_idx is not None: if sp.issparse(value) and mapping[0] != 0: raise ValueError( "In-place mapping of sparse matrices must map 0 to 0") # CSR requires mapping of non-contiguous area if sp.isspmatrix_csr(value): col = value.indices == col_idx nans = np.isnan(value.data) * col value.data[nans] = 0 value.data[col] = mapping[value.data[col].astype(int)] value.data[nans] = np.nan return # Dense and CSC map a contiguous area if isinstance(value, np.ndarray) and value.ndim == 2: col = value[:, col_idx] elif sp.isspmatrix_csc(value): col = value.data[value.indptr[col_idx] :value.indptr[col_idx + 1]] else: raise ValueError( "In-place column mapping requires a 2d array or" "a csc or csr matrix.") nans = np.isnan(col) col[nans] = 0 col[:] = mapping[col.astype(int)] col[nans] = np.nan return # Mapping into a copy if isinstance(value, (int, float)): return mapping[int(value)] if value == value else value if isinstance(value, str): return mapping[other.values.index(value)] if isinstance(value, np.ndarray): if not (value.ndim == 1 or value.ndim != 2 and min(value.shape) != 1): raise ValueError( f"Column mapping can't map {value.ndim}-d objects") if value.dtype == object: value = value.astype(float) # this happens with metas try: nans = np.isnan(value) except TypeError: # suppose it's already an integer type return mapping[value] value = value.astype(int) value[nans] = 0 value = mapping[value] value[nans] = np.nan return value if sp.issparse(value): if min(value.shape) != 1: raise ValueError("Column mapping can't map " f"{value.ndim}-dimensional objects") if mapping[0] != 0 and not np.isnan(mapping[0]): return mapper(np.array(value.todense()).flatten()) value = value.copy() value.data = mapper(value.data) return value if isinstance(value, Iterable): return type(value)(mapping[int(val)] if val == val else val for val in value) raise ValueError( f"invalid type for value(s): {type(value).__name__}") return mapper @Variable.colors.getter def colors(self): if self._colors is not None: colors = np.array(self._colors) elif not self.values: colors = np.zeros((0, 3)) # to match additional colors in vstacks else: from Orange.widgets.utils.colorpalette import ColorPaletteGenerator default = tuple(ColorPaletteGenerator.palette(self)) colors = self.attributes.get('colors', ()) colors = tuple(hex_to_color(color) for color in colors) \ + default[len(colors):] colors = np.array(colors) colors.flags.writeable = False return colors
[docs] def to_val(self, s): """ Convert the given argument to a value of the variable (`float`). If the argument is numeric, its value is returned without checking whether it is integer and within bounds. `Unknown` is returned if the argument is one of the representations for unknown values. Otherwise, the argument must be a string and the method returns its index in :obj:`values`. :param s: values, represented as a number, string or `None` :rtype: float """ if s is None: return ValueUnknown if isinstance(s, Integral): return s if isinstance(s, Real): return s if isnan(s) else floor(s + 0.25) if s in self.unknown_str: return ValueUnknown if not isinstance(s, str): raise TypeError('Cannot convert {} to value of "{}"'.format( type(s).__name__, self.name)) return self.values.index(s)
def add_value(self, s): """ Add a value `s` to the list of values. """ if not isinstance(s, str): raise TypeError("values of DiscreteVariables must be strings") self.values.append(s) self._colors = None
[docs] def val_from_str_add(self, s): """ Similar to :obj:`to_val`, except that it accepts only strings and that it adds the value to the list if it does not exist yet. :param s: symbolic representation of the value :type s: str :rtype: float """ s = str(s) if s is not None else s try: return ValueUnknown if s in self.unknown_str \ else self.values.index(s) except ValueError: self.add_value(s) return len(self.values) - 1
def repr_val(self, val): """ Return a textual representation of the value (`self.values[int(val)]`) or "?" if the value is unknown. :param val: value :type val: float (should be whole number) :rtype: str """ if isnan(val): return "?" return '{}'.format(self.values[int(val)]) str_val = repr_val def __reduce__(self): if not self.name: raise PickleError("Variables without names cannot be pickled") __dict__ = dict(self.__dict__) __dict__.pop("values") return make_variable, (self.__class__, self._compute_value, self.name, self.values, self.ordered), \ __dict__ def copy(self, compute_value=None, *, name=None, **_): return super().copy(compute_value=compute_value, name=name, values=self.values, ordered=self.ordered)
[docs]class StringVariable(Variable): """ Descriptor for string variables. String variables can only appear as meta attributes. """ Unknown = "" TYPE_HEADERS = ('string', 's', 'text')
[docs] def to_val(self, s): """ Return the value as a string. If it is already a string, the same object is returned. """ if s is None: return "" if isinstance(s, str): return s return str(s)
val_from_str_add = to_val
[docs] @staticmethod def str_val(val): """Return a string representation of the value.""" if val is "": return "?" if isinstance(val, Value): if val.value is "": return "?" val = val.value return str(val)
def repr_val(self, val): """Return a string representation of the value.""" return '"{}"'.format(self.str_val(val))
[docs]class TimeVariable(ContinuousVariable): """ TimeVariable is a continuous variable with Unix epoch (1970-01-01 00:00:00+0000) as the origin (0.0). Later dates are positive real numbers (equivalent to Unix timestamp, with microseconds in the fraction part), and the dates before it map to the negative real numbers. Unfortunately due to limitation of Python datetime, only dates with year >= 1 (A.D.) are supported. If time is specified without a date, Unix epoch is assumed. If time is specified wihout an UTC offset, localtime is assumed. """ _all_vars = {} TYPE_HEADERS = ('time', 't') UNIX_EPOCH = datetime(1970, 1, 1) _ISO_FORMATS = [ # have_date, have_time, format_str # in order of decreased probability (1, 1, '%Y-%m-%d %H:%M:%S%z'), (1, 1, '%Y-%m-%d %H:%M:%S'), (1, 1, '%Y-%m-%d %H:%M'), (1, 1, '%Y-%m-%dT%H:%M:%S%z'), (1, 1, '%Y-%m-%dT%H:%M:%S'), (1, 0, '%Y-%m-%d'), (1, 1, '%Y-%m-%d %H:%M:%S.%f'), (1, 1, '%Y-%m-%dT%H:%M:%S.%f'), (1, 1, '%Y-%m-%d %H:%M:%S.%f%z'), (1, 1, '%Y-%m-%dT%H:%M:%S.%f%z'), (1, 1, '%Y%m%dT%H%M%S%z'), (1, 1, '%Y%m%d%H%M%S%z'), (0, 1, '%H:%M:%S.%f'), (0, 1, '%H:%M:%S'), (0, 1, '%H:%M'), # These parse as continuous features (plain numbers) (1, 1, '%Y%m%dT%H%M%S'), (1, 1, '%Y%m%d%H%M%S'), (1, 0, '%Y%m%d'), (1, 0, '%Y%j'), (1, 0, '%Y'), (0, 1, '%H%M%S.%f'), # BUG: In Python as in C, %j doesn't necessitate 0-padding, # so these two lines must be in this order (1, 0, '%Y-%m'), (1, 0, '%Y-%j'), ] # The regex that matches all above formats REGEX = (r'^(' r'\d{1,4}-\d{2}-\d{2}([ T]\d{2}:\d{2}(:\d{2}(\.\d+)?([+-]\d{4})?)?)?|' r'\d{1,4}\d{2}\d{2}(T?\d{2}\d{2}\d{2}([+-]\d{4})?)?|' r'\d{2}:\d{2}(:\d{2}(\.\d+)?)?|' r'\d{2}\d{2}\d{2}\.\d+|' r'\d{1,4}(-?\d{2,3})?' r')$') class InvalidDateTimeFormatError(ValueError): def __init__(self, date_string): super().__init__( "Invalid datetime format '{}'. " "Only ISO 8601 supported.".format(date_string)) _matches_iso_format = re.compile(REGEX).match # UTC offset and associated timezone. If parsed datetime values provide an # offset, it is used for display. If not all values have the same offset, # +0000 (=UTC) timezone is used and utc_offset is set to False. utc_offset = None timezone = timezone.utc def __init__(self, *args, have_date=0, have_time=0, **kwargs): super().__init__(*args, **kwargs) self.have_date = have_date self.have_time = have_time def copy(self, compute_value=None, *, name=None, **_): return super().copy(compute_value=compute_value, name=name, have_date=self.have_date, have_time=self.have_time) @staticmethod def _tzre_sub(s, _subtz=re.compile(r'([+-])(\d\d):(\d\d)$').sub): # Replace +ZZ:ZZ with ISO-compatible +ZZZZ, or strip +0000 return s[:-6] if s.endswith(('+00:00', '-00:00')) else _subtz(r'\1\2\3', s) def repr_val(self, val): if isnan(val): return '?' if not self.have_date and not self.have_time: # The time is relative, unitless. The value is absolute. return str(val.value) if isinstance(val, Value) else str(val) # If you know how to simplify this, be my guest seconds = int(val) microseconds = int(round((val - seconds) * 1e6)) if val < 0: if microseconds: seconds, microseconds = seconds - 1, int(1e6) + microseconds date = datetime.fromtimestamp(0, tz=self.timezone) + timedelta(seconds=seconds) else: date = datetime.fromtimestamp(seconds, tz=self.timezone) date = str(date.replace(microsecond=microseconds)) if self.have_date and not self.have_time: date = date.split()[0] elif not self.have_date and self.have_time: date = date.split()[1] date = self._tzre_sub(date) return date str_val = repr_val
[docs] def parse(self, datestr): """ Return `datestr`, a datetime provided in one of ISO 8601 formats, parsed as a real number. Value 0 marks the Unix epoch, positive values are the dates after it, negative before. If date is unspecified, epoch date is assumed. If time is unspecified, 00:00:00.0 is assumed. If timezone is unspecified, local time is assumed. """ if datestr in MISSING_VALUES: return Unknown datestr = datestr.strip().rstrip('Z') if not self._matches_iso_format(datestr): try: # If it is a number, assume it is a unix timestamp value = float(datestr) self.have_date = self.have_time = 1 return value except ValueError: raise self.InvalidDateTimeFormatError(datestr) for i, (have_date, have_time, fmt) in enumerate(self._ISO_FORMATS): try: dt = datetime.strptime(datestr, fmt) except ValueError: continue else: # Pop this most-recently-used format to front if 0 < i < len(self._ISO_FORMATS) - 2: self._ISO_FORMATS[i], self._ISO_FORMATS[0] = \ self._ISO_FORMATS[0], self._ISO_FORMATS[i] self.have_date |= have_date self.have_time |= have_time if not have_date: dt = dt.replace(self.UNIX_EPOCH.year, self.UNIX_EPOCH.month, self.UNIX_EPOCH.day) break else: raise self.InvalidDateTimeFormatError(datestr) # Remember UTC offset. If not all parsed values share the same offset, # remember none of it. offset = dt.utcoffset() if self.utc_offset is not False: if offset and self.utc_offset is None: self.utc_offset = offset self.timezone = timezone(offset) elif self.utc_offset != offset: self.utc_offset = False self.timezone = timezone.utc # Convert time to UTC timezone. In dates without timezone, # localtime is assumed. See also: # https://docs.python.org/3.4/library/datetime.html#datetime.datetime.timestamp if dt.tzinfo: dt -= dt.utcoffset() dt = dt.replace(tzinfo=timezone.utc) # Unix epoch is the origin, older dates are negative try: return dt.timestamp() except OverflowError: return -(self.UNIX_EPOCH - dt).total_seconds()
def parse_exact_iso(self, datestr): """ This function is a meta function to `parse` function. It checks whether the date is of the iso format - it does not accept float-like date. """ if not self._matches_iso_format(datestr): raise self.InvalidDateTimeFormatError(datestr) return self.parse(datestr) def to_val(self, s): """ Convert a value, given as an instance of an arbitrary type, to a float. """ if isinstance(s, str): return self.parse(s) else: return super().to_val(s)