Python collections.abc.Sized() Examples

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [bytes(), str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [bytes(), str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def get_collection_sizes(obj, collections: Optional[Tuple]=None,
                         get_only_non_empty=False):
    """
    Iterates over `collections` of the gives object and gives its byte size
    and number of items in collection
    """
    from pympler import asizeof
    collections = collections or (list, dict, set, deque, abc.Sized)
    if not isinstance(collections, tuple):
        collections = tuple(collections)

    result = []
    for attr_name in dir(obj):
        attr = getattr(obj, attr_name)
        if isinstance(attr, collections) and (
                not get_only_non_empty or len(attr) > 0):
            result.append(
                (attr_name, len(attr), asizeof.asizeof(attr, detail=1)))
    return result 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [bytes(), str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def set_marker(self, marker):
        if (isinstance(marker, np.ndarray) and marker.ndim == 2 and
                marker.shape[1] == 2):
            self._marker_function = self._set_vertices
        elif isinstance(marker, str) and cbook.is_math_text(marker):
            self._marker_function = self._set_mathtext_path
        elif isinstance(marker, Path):
            self._marker_function = self._set_path_marker
        elif (isinstance(marker, Sized) and len(marker) in (2, 3) and
                marker[1] in (0, 1, 2, 3)):
            self._marker_function = self._set_tuple_marker
        elif (not isinstance(marker, (np.ndarray, list)) and
              marker in self.markers):
            self._marker_function = getattr(
                self, '_set_' + self.markers[marker])
        else:
            try:
                Path(marker)
                self._marker_function = self._set_vertices
            except ValueError:
                raise ValueError('Unrecognized marker style {!r}'
                                 .format(marker))

        self._marker = marker
        self._recache() 

def set_marker(self, marker):
        if (isinstance(marker, np.ndarray) and marker.ndim == 2 and
                marker.shape[1] == 2):
            self._marker_function = self._set_vertices
        elif isinstance(marker, str) and cbook.is_math_text(marker):
            self._marker_function = self._set_mathtext_path
        elif isinstance(marker, Path):
            self._marker_function = self._set_path_marker
        elif (isinstance(marker, Sized) and len(marker) in (2, 3) and
                marker[1] in (0, 1, 2, 3)):
            self._marker_function = self._set_tuple_marker
        elif (not isinstance(marker, (np.ndarray, list)) and
              marker in self.markers):
            self._marker_function = getattr(
                self, '_set_' + self.markers[marker])
        else:
            try:
                Path(marker)
                self._marker_function = self._set_vertices
            except ValueError:
                raise ValueError('Unrecognized marker style {!r}'
                                 .format(marker))

        self._marker = marker
        self._recache() 

def set_marker(self, marker):
        if (isinstance(marker, np.ndarray) and marker.ndim == 2 and
                marker.shape[1] == 2):
            self._marker_function = self._set_vertices
        elif isinstance(marker, str) and cbook.is_math_text(marker):
            self._marker_function = self._set_mathtext_path
        elif isinstance(marker, Path):
            self._marker_function = self._set_path_marker
        elif (isinstance(marker, Sized) and len(marker) in (2, 3) and
                marker[1] in (0, 1, 2, 3)):
            self._marker_function = self._set_tuple_marker
        elif (not isinstance(marker, (np.ndarray, list)) and
              marker in self.markers):
            self._marker_function = getattr(
                self, '_set_' + self.markers[marker])
        else:
            try:
                Path(marker)
                self._marker_function = self._set_vertices
            except ValueError:
                raise ValueError('Unrecognized marker style {!r}'
                                 .format(marker))

        self._marker = marker
        self._recache() 

def set_marker(self, marker):
        if (isinstance(marker, np.ndarray) and marker.ndim == 2 and
                marker.shape[1] == 2):
            self._marker_function = self._set_vertices
        elif isinstance(marker, str) and cbook.is_math_text(marker):
            self._marker_function = self._set_mathtext_path
        elif isinstance(marker, Path):
            self._marker_function = self._set_path_marker
        elif (isinstance(marker, Sized) and len(marker) in (2, 3) and
                marker[1] in (0, 1, 2, 3)):
            self._marker_function = self._set_tuple_marker
        elif (not isinstance(marker, (np.ndarray, list)) and
              marker in self.markers):
            self._marker_function = getattr(
                self, '_set_' + self.markers[marker])
        else:
            try:
                Path(marker)
                self._marker_function = self._set_vertices
            except ValueError:
                raise ValueError('Unrecognized marker style {!r}'
                                 .format(marker))

        self._marker = marker
        self._recache() 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       _test_gen(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [bytes(), str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def __call__(self, v):
        if not isinstance(v, abc.Sized):
            raise Invalid(_(u'Input is not a collection'), u'Collection', get_type_name(type(v)))

        length = len(v)

        # Validate
        if self.min is not None and length < self.min:
            raise self.min_error(length)
        if self.max is not None and length > self.max:
            raise self.max_error(length)

        # Ok
        return v 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not np.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], str)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not cbook.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], str)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Reversible, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def is_iterable_and_sized(obj):
    return isinstance(obj, Iterable) and isinstance(obj, Sized) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Reversible, Sized, Container, Callable:
            class C:
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def _loop(self, output, element, context):
		var = element.attrib['var']
		expr = element.attrib['expr']
		items = expr(context)
		if not isinstance(items, abc.Iterable):
			raise TypeError('Can not iterate over: %s' % items)
		elif not isinstance(items, abc.Sized):
			# cast to list to ensure we have a len()
			items = list(items)

		# set "loop"
		outer = context.get('loop')
		if isinstance(outer, TemplateLoopState):
			loop = TemplateLoopState(len(items), outer)
		else:
			loop = TemplateLoopState(len(items), None)
		context['loop'] = loop

		# do the iterations
		myiter = MovingWindowIter(items)
		for i, items in enumerate(myiter):
			loop._update(i, myiter)
			self._set(context, var, items[1]) # set var
			self.__call__(output, element, context) # recurs

		# restore "loop"
		context['loop'] = outer 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def _is_list(obj):
    return (
        isinstance(obj, Sized) and isinstance(obj, Iterable) and
        not isinstance(obj, (str, Set, Mapping))
    ) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not np.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], str)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not cbook.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], str)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not cbook.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], str)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def track(
        self,
        sequence: Union[Iterable[ProgressType], Sequence[ProgressType]],
        total: int = None,
        task_id: Optional[TaskID] = None,
        description="Working...",
    ) -> Iterable[ProgressType]:
        """[summary]
        
        Args:
            sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress.
            total: (int, optional): Total number of steps. Default is len(sequence).
            task_id: (TaskID): Task to track. Default is new task.
            description: (str, optional): Description of task, if new task is created.
        
        Returns:
            Iterable[ProgressType]: An iterable of values taken from the provided sequence.
        """
        if total is None:
            if isinstance(sequence, Sized):
                task_total = len(sequence)
            else:
                raise ValueError(
                    f"unable to get size of {sequence!r}, please specify 'total'"
                )
        else:
            task_total = total

        if task_id is None:
            task_id = self.add_task(description, total=task_total)
        else:
            self.update(task_id, total=task_total)
        with self:
            for completed, value in enumerate(sequence, 1):
                yield value
                self.update(task_id, completed=completed) 

def test_parameter_grid():

    # Test basic properties of ParameterGrid.
    params1 = {"foo": [1, 2, 3]}
    grid1 = ParameterGrid(params1)
    assert isinstance(grid1, Iterable)
    assert isinstance(grid1, Sized)
    assert_equal(len(grid1), 3)
    assert_grid_iter_equals_getitem(grid1)

    params2 = {"foo": [4, 2],
               "bar": ["ham", "spam", "eggs"]}
    grid2 = ParameterGrid(params2)
    assert_equal(len(grid2), 6)

    # loop to assert we can iterate over the grid multiple times
    for i in range(2):
        # tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
        points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
        assert_equal(points,
                     set(("bar", x, "foo", y)
                         for x, y in product(params2["bar"], params2["foo"])))
    assert_grid_iter_equals_getitem(grid2)

    # Special case: empty grid (useful to get default estimator settings)
    empty = ParameterGrid({})
    assert_equal(len(empty), 1)
    assert_equal(list(empty), [{}])
    assert_grid_iter_equals_getitem(empty)
    assert_raises(IndexError, lambda: empty[1])

    has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
    assert_equal(len(has_empty), 4)
    assert_equal(list(has_empty), [{'C': 1}, {'C': 10}, {}, {'C': .5}])
    assert_grid_iter_equals_getitem(has_empty) 

def compute_row_reduction(func, value, **kwargs):
    final_sizes = {len(x) for x in value if isinstance(x, Sized)}
    if not final_sizes:
        return func(value)
    (final_size,) = final_sizes
    raw = func(list(map(promote_to_sequence(final_size), value)), **kwargs)
    return pd.Series(raw).squeeze() 

def track(
    sequence: Union[Sequence[ProgressType], Iterable[ProgressType]],
    description="Working...",
    total: int = None,
    auto_refresh=True,
    console: Optional[Console] = None,
    transient: bool = False,
    get_time: Callable[[], float] = None,
) -> Iterable[ProgressType]:
    """Track progress of processing a sequence.
    
    Args:
        sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over.
        description (str, optional): Description of task show next to progress bar. Defaults to "Working".
        total: (int, optional): Total number of steps. Default is len(sequence).
        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
        transient: (bool, optional): Clear the progress on exit. Defaults to False.
        console (Console, optional): Console to write to. Default creates internal Console instance.
    
    Returns:
        Iterable[ProgressType]: An iterable of the values in the sequence.
    
    """
    progress = Progress(
        auto_refresh=auto_refresh,
        console=console,
        transient=transient,
        get_time=get_time,
    )

    task_total = total
    if task_total is None:
        if isinstance(sequence, Sized):
            task_total = len(sequence)
        else:
            raise ValueError(
                f"unable to get size of {sequence!r}, please specify 'total'"
            )

    task_id = progress.add_task(description, total=task_total)
    with progress:
        for completed, value in enumerate(sequence, 1):
            yield value
            progress.update(task_id, completed=completed)
            if not auto_refresh:
                progress.refresh()