utils¶

class pymethods.utils.Descriptor(name: str)¶: Base descriptor class

class pymethods.utils.LockedDescriptor(name: str)¶

class pymethods.utils.NoInputFunctionAlias(name: str, store=False)¶

pymethods.utils.cummulative_decompose(iterable: Iterable) → List¶

decompose an itearble cummutavely, for example, cummulative_decompose(‘string’) = [‘s’, ‘st’, ‘str’,…]

Args:: iterable (Iterable): an iterable object
Returns:: List: list of the cummulatively decomposed iterable

class pymethods.utils.IsSubsetString(main_string: str, case_sensitive=False)¶

case_sensitive¶

property decomposed_string¶

main_string¶

class pymethods.utils.IsAny(*comaprison_objects: Iterable[Union[type, object]])¶

abstract check(obj: object, comparison: type) → bool¶

abstract method that compares obj to comparison,

Args:: obj (object): object to be checked comparison (type): type to compare with
Raises:: NotImplementedError:
Returns:: bool: True if passes checks else False

comaprison_objects¶

class pymethods.utils.Isinstance(*comaprison_objects: Iterable[Union[type, object]])¶

check(obj: object, comparison: type) → bool¶

Isinstance

test if obj is an instance of the comparison type

Args:: obj (object): object to be checked comparison (type): type to compare with
Returns:: bool: True if instance

comaprison_objects¶

class pymethods.utils.Issubclass(*comaprison_objects: Iterable[Union[type, object]])¶

check(obj: object, comparison: type) → bool¶

Issubclass

test if obj is a subclass of the comparison type

Args:: obj (object): object to be checked comparison (type): type to compare with
Returns:: bool: True if subclass

comaprison_objects¶

pymethods.utils.is_none(obj: object) → bool¶

check if the obj is None

Args:: obj (object):
Returns:: bool: True if None

class pymethods.utils.Isin(*comaprison_objects: Iterable[Union[type, object]])¶

check(obj: object, comparison: Iterable) → bool¶

Isinany

check if the object is in any of the self.comaprison_objects

Args:: IsAny ([type]): [description] obj (object): [description] comparison (Iterable): [description]

comaprison_objects¶

pymethods.utils.hex_memory(obj: object)¶

class pymethods.utils.SequentialFunction(*args)¶

class pymethods.utils.AliasDict(alias_names: Iterable[Hashable], obj: object)¶

register_key(key: Hashable) → None¶

Registers a new key in the dictionary. Speed up lookup of strings by interning its values https://en.wikipedia.org/wiki/String_interning this is done by storing only a single copy of each distinc string value

Args:

key (Hashable): hashable object to: register as new key

class pymethods.utils.LenDimShape(axis: Union[int, str])¶

set_dim_extractor() → None¶

pymethods.utils.len_dim_shape_axis(obj: object, axis=0) → int¶

Find the len of the dimension of array or object with shape property containing a __len__ method. Ferformed along specified axis

Args:: obj (object): [description] axis (int, optional): dimension to find the length of. Defaults to 0.
Returns:: int: [description]

pymethods.utils.len_dim_shape_longest(obj: object) → int¶

len_dim_shape_longest [summary]

Find the len of the longest dimension of array or object with shape property that contains a __len__ method

Args:

array (object): object containing a shape: property with a len method

Returns:

int: len of shape

pymethods.utils.len_dim_shape_shortest(obj: object) → int¶

Find the len of the shortest dimension of array or object with shape property that contains a __len__ method

Args:

array (object): object containing a shape: property with a len method

Returns:

int: len of shape

pymethods.utils.is_1d(vectors: numpy.array) → bool¶

check if the object is 1 dimensional

Args:: vectors (np.array): array to check
Returns:: bool: True if the data is 1d False if not

pymethods.utils.len_shape(obj: object) → int¶

len the length of the shape of an object

Args:

obj (object): object with shape: property

Returns:

int: the length of the shape

pymethods.utils.make_column(vectors: numpy.ndarray) → numpy.ndarray¶

convert a vector into column vector. This assumes that the vector has lenshape 1 otherwise it is passed directly

Args:: vectors (np.ndarray):
Returns:: np.ndarray:

pymethods.utils.make_row(vectors: numpy.ndarray) → numpy.ndarray¶

convert a vector into row vector. This assumes that the vector has lenshape 1 otherwise it is passed directly

Args:: vectors (np.ndarray):
Returns:: np.ndarray:

pymethods.utils.chunk_iterable(indexable: Iterable, num_chunk: int, callbacks=None) → list¶

For some iterable generate num_chunk, approximately even number of chunks

Args:: iterable (Iterable): an iterable to be chunked num_chunk (int): total number of chunks to generate callbacks (Iterable[Callable]): an iterable containing callable methods

which take two arguments (indexed, indexable). These callbacks operate over these indexable values and outputs the values of the current chunk
Returns:: list: a list of lists of chunks from the iterabel

pymethods.utils.enumerate_chunk(iterable: Iterable, num: int) → list¶

pymethods.utils.sort_pair_list_and_extract_items(mp_list: list) → list¶

sort_and_extract_mp_list

A list when enumerated and zipped has the form:: [(key, item_1),…, (key, item_n)]

This method sorts the input list by the key and extracts a list of the items.

Args:: mp_list (list): enumerated zipped list
Returns:: list: the sorted list of items

pymethods.utils.memory_size(array: numpy.ndarray, out='bytes')¶

pymethods.utils.time_func(func, *args, **kwargs)¶

pymethods.utils.gap_space(start, lim, gap)¶

pymethods.utils.make_odd(input)¶