# python_utils package

## Submodules

## python_utils.decorators module

This module provides various utility decorators for Python functions and methods.

The decorators include:

1. set_attributes: Sets attributes on functions and classes.

2. listify: Converts any generator to a list or other collection.

3. sample: Limits calls to a function based on a sample rate.

4. wraps_classmethod: Wraps classmethods with type info from a regular method.

Each decorator is designed to enhance the functionality of Python functions and methods in a simple and reusable manner.

python_utils.decorators.listify(collection: ~typing.Callable[[~typing.Iterable[~python_utils.decorators._T]], ~typing.Collection[~python_utils.decorators._T]] = <class 'list'>, allow_empty: bool = True) → Callable[[Callable[[...], Iterable[_T] | None]], Callable[[...], Collection[_T]]]

  Convert any generator to a list or other type of collection.

      >>> @listify()
      ... def generator():
      ...     yield 1
      ...     yield 2
      ...     yield 3

      >>> generator()
      [1, 2, 3]

      >>> @listify()
      ... def empty_generator():
      ...     pass

      >>> empty_generator()
      []

      >>> @listify(allow_empty=False)
      ... def empty_generator_not_allowed():
      ...     pass

      >>> empty_generator_not_allowed()
      Traceback (most recent call last):
      ...
      TypeError: ... `allow_empty` is `False`

      >>> @listify(collection=set)
      ... def set_generator():
      ...     yield 1
      ...     yield 1
      ...     yield 2

      >>> set_generator()
      {1, 2}

      >>> @listify(collection=dict)
      ... def dict_generator():
      ...     yield 'a', 1
      ...     yield 'b', 2

      >>> dict_generator()
      {'a': 1, 'b': 2}

python_utils.decorators.sample(sample_rate: float) → Callable[[Callable[[_P], _T]], Callable[[_P], _T | None]]

  Limit calls to a function based on given sample rate. Number of calls to the function will be roughly equal to sample_rate percentage.

  Usage:

      >>> @sample(0.5)
      ... def demo_function(*args, **kwargs):
      ...     return 1

  Calls to demo_function will be limited to 50% approximatly.

python_utils.decorators.set_attributes(**kwargs: Any) → Callable[[...], Any]

  Decorator to set attributes on functions and classes.

  A common usage for this pattern is the Django Admin where functions can get an optional short_description. To illustrate:

  Example from the Django admin using this decorator: https://docs.djangoproject.com/en/3.0/ref/contrib/admin/#django.contrib.admin.ModelAdmin.list_display

  Our simplified version:

      >>> @set_attributes(short_description='Name')
      ... def upper_case_name(self, obj):
      ...     return ('%s %s' % (obj.first_name, obj.last_name)).upper()

  The standard Django version:

      >>> def upper_case_name(obj):
      ...     return ('%s %s' % (obj.first_name, obj.last_name)).upper()

      >>> upper_case_name.short_description = 'Name'

python_utils.decorators.wraps_classmethod(wrapped: Callable[[Concatenate[_S, _P]], _T]) → Callable[[Callable[[Concatenate[Any, _P]], _T]], Callable[[Concatenate[_S, _P]], _T]]

  Like functools.wraps, but for wrapping classmethods with the type info from a regular method.

## python_utils.converters module

This module provides utility functions for type conversion.

Functions:

  - to_int: Convert a string to an integer with optional regular expression

  matching. - to_float: Convert a string to a float with optional regular expression matching. - to_unicode: Convert objects to Unicode strings. - to_str: Convert objects to byte strings. - scale_1024: Scale a number down to a suitable size based on powers of 1024. - remap: Remap a value from one range to another.

python_utils.converters.remap(value: Decimal, old_min: Decimal | float, old_max: Decimal | float, new_min: Decimal | float, new_max: Decimal | float) → Decimal
python_utils.converters.remap(value: Decimal | float, old_min: Decimal, old_max: Decimal | float, new_min: Decimal | float, new_max: Decimal | float) → Decimal
python_utils.converters.remap(value: Decimal | float, old_min: Decimal | float, old_max: Decimal, new_min: Decimal | float, new_max: Decimal | float) → Decimal
python_utils.converters.remap(value: Decimal | float, old_min: Decimal | float, old_max: Decimal | float, new_min: Decimal, new_max: Decimal | float) → Decimal
python_utils.converters.remap(value: Decimal | float, old_min: Decimal | float, old_max: Decimal | float, new_min: Decimal | float, new_max: Decimal) → Decimal
python_utils.converters.remap(value: float, old_min: float, old_max: float, new_min: float, new_max: float) → float

  remap a value from one range into another.

      >>> remap(500, 0, 1000, 0, 100)
      50
      >>> remap(250.0, 0.0, 1000.0, 0.0, 100.0)
      25.0
      >>> remap(-75, -100, 0, -1000, 0)
      -750
      >>> remap(33, 0, 100, -500, 500)
      -170
      >>> remap(decimal.Decimal('250.0'), 0.0, 1000.0, 0.0, 100.0)
      Decimal('25.0')

  This is a great use case example. Take an AVR that has dB values the minimum being -80dB and the maximum being 10dB and you want to convert volume percent to the equilivint in that dB range

      >>> remap(46.0, 0.0, 100.0, -80.0, 10.0)
      -38.6

  I added using decimal.Decimal so floating point math errors can be avoided. Here is an example of a floating point math error >>> 0.1 + 0.1 + 0.1 0.30000000000000004

  If floating point remaps need to be done my suggstion is to pass at least one parameter as a decimal.Decimal. This will ensure that the output from this function is accurate. I left passing floats for backwards compatability and there is no conversion done from float to decimal.Decimal unless one of the passed parameters has a type of decimal.Decimal. This will ensure that any existing code that uses this funtion will work exactly how it has in the past.

  Some edge cases to test >>> remap(1, 0, 0, 1, 2) Traceback (most recent call last): … ValueError: Input range (0-0) is empty

      >>> remap(1, 1, 2, 0, 0)
      Traceback (most recent call last):
      ...
      ValueError: Output range (0-0) is empty

  Args:

    value (int, float, decimal.Decimal): Value to be converted. old_min (int, float, decimal.Decimal): Minimum of the range for the

    > value that has been passed.

    old_max (int, float, decimal.Decimal): Maximum of the range for the

      value that has been passed.

    new_min (int, float, decimal.Decimal): The minimum of the new range. new_max (int, float, decimal.Decimal): The maximum of the new range.

  Returns: int, float, decimal.Decimal: Value that has been re-ranged. If

    any of the parameters passed is a decimal.Decimal, all of the parameters will be converted to decimal.Decimal. The same thing also happens if one of the parameters is a float. Otherwise, all parameters will get converted into an int. Technically, you can pass a str of an integer and it will get converted. The returned value type will be decimal.Decimal if any of the passed parameters are decimal.Decimal, the return type will be float if any of the passed parameters are a float, otherwise the returned type will be int.

python_utils.converters.scale_1024(x: int | float, n_prefixes: int) → Tuple[int | float, int | float]

  Scale a number down to a suitable size, based on powers of 1024.

  Returns the scaled number and the power of 1024 used.

  Use to format numbers of bytes to KiB, MiB, etc.

      >>> scale_1024(310, 3)
      (310.0, 0)
      >>> scale_1024(2048, 3)
      (2.0, 1)
      >>> scale_1024(0, 2)
      (0.0, 0)
      >>> scale_1024(0.5, 2)
      (0.5, 0)
      >>> scale_1024(1, 2)
      (1.0, 0)

python_utils.converters.to_float(input_: str, default: int = 0, exception: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = (<class 'ValueError'>, <class 'TypeError'>), regexp: ~typing.Pattern[str] | str | ~typing.Literal[True] | None = None) → int | float

  Convert the given input_ to an integer or return default.

  When trying to convert the exceptions given in the exception parameter are automatically catched and the default will be returned.

  The regexp parameter allows for a regular expression to find the digits in a string. When True it will automatically match any digit in the string. When a (regexp) object (has a search method) is given, that will be used. When a string is given, re.compile will be run over it first

  The last group of the regexp will be used as value

      >>> '%.2f' % to_float('abc')
      '0.00'
      >>> '%.2f' % to_float('1')
      '1.00'
      >>> '%.2f' % to_float('abc123.456', regexp=True)
      '123.46'
      >>> '%.2f' % to_float('abc123', regexp=True)
      '123.00'
      >>> '%.2f' % to_float('abc0.456', regexp=True)
      '0.46'
      >>> '%.2f' % to_float('abc123.456', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('123.456abc', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('abc123.46abc', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('abc123abc456', regexp=re.compile(r'(\d+(\.\d+|))'))
      '123.00'
      >>> '%.2f' % to_float('abc', regexp=r'(\d+)')
      '0.00'
      >>> '%.2f' % to_float('abc123', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('123abc', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('abc123abc', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('abc123abc456', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('1234', default=1)
      '1234.00'
      >>> '%.2f' % to_float('abc', default=1)
      '1.00'
      >>> '%.2f' % to_float('abc', regexp=123)
      Traceback (most recent call last):
      ...
      TypeError: unknown argument for regexp parameter

python_utils.converters.to_int(input_: str | None = None, default: int = 0, exception: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = (<class 'ValueError'>, <class 'TypeError'>), regexp: ~typing.Pattern[str] | str | ~typing.Literal[True] | None = None) → int

  Convert the given input to an integer or return default.

  When trying to convert the exceptions given in the exception parameter are automatically catched and the default will be returned.

  The regexp parameter allows for a regular expression to find the digits in a string. When True it will automatically match any digit in the string. When a (regexp) object (has a search method) is given, that will be used. WHen a string is given, re.compile will be run over it first

  The last group of the regexp will be used as value

      >>> to_int('abc')
      0
      >>> to_int('1')
      1
      >>> to_int('')
      0
      >>> to_int()
      0
      >>> to_int('abc123')
      0
      >>> to_int('123abc')
      0
      >>> to_int('abc123', regexp=True)
      123
      >>> to_int('123abc', regexp=True)
      123
      >>> to_int('abc123abc', regexp=True)
      123
      >>> to_int('abc123abc456', regexp=True)
      123
      >>> to_int('abc123', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('123abc', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123abc', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123abc456', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123', regexp=r'(\d+)')
      123
      >>> to_int('123abc', regexp=r'(\d+)')
      123
      >>> to_int('abc', regexp=r'(\d+)')
      0
      >>> to_int('abc123abc', regexp=r'(\d+)')
      123
      >>> to_int('abc123abc456', regexp=r'(\d+)')
      123
      >>> to_int('1234', default=1)
      1234
      >>> to_int('abc', default=1)
      1
      >>> to_int('abc', regexp=123)
      Traceback (most recent call last):
      ...
      TypeError: unknown argument for regexp parameter: 123

python_utils.converters.to_str(input_: str | bytes, encoding: str = 'utf-8', errors: str = 'replace') → bytes

  Convert objects to string, encodes to the given encoding.

  Return type:

    str

      >>> to_str('a')
      b'a'
      >>> to_str('a')
      b'a'
      >>> to_str(b'a')
      b'a'
      >>> class Foo(object):
      ...     __str__ = lambda s: 'a'
      >>> to_str(Foo())
      'a'
      >>> to_str(Foo)
      "<class 'python_utils.converters.Foo'>"

python_utils.converters.to_unicode(input_: str | bytes, encoding: str = 'utf-8', errors: str = 'replace') → str

  Convert objects to unicode, if needed decodes string with the given encoding and errors settings.

  Return type:

    str

      >>> to_unicode(b'a')
      'a'
      >>> to_unicode('a')
      'a'
      >>> to_unicode('a')
      'a'
      >>> class Foo(object):
      ...     __str__ = lambda s: 'a'
      >>> to_unicode(Foo())
      'a'
      >>> to_unicode(Foo)
      "<class 'python_utils.converters.Foo'>"

## python_utils.formatters module

This module provides utility functions for formatting strings and dates.

Functions:
  camel_to_underscore(name: str) -> str:

    Convert camel case style naming to underscore/snake case style naming.

  apply_recursive(function: Callable[[str], str], data: OptionalScope = None,

    > **kwargs: Any) -> OptionalScope:

    Apply a function to all keys in a scope recursively.

  timesince(dt: Union[datetime.datetime, datetime.timedelta],

    > default: str = ‘just now’) -> str:

    Returns string representing ‘time since’ e.g. 3 days ago, 5 hours ago.

python_utils.formatters.apply_recursive(function: Callable[[str], str], data: Dict[str, Any] | None = None, **kwargs: Any) → Dict[str, Any] | None

  Apply a function to all keys in a scope recursively.

      >>> apply_recursive(camel_to_underscore, {'SpamEggsAndBacon': 'spam'})
      {'spam_eggs_and_bacon': 'spam'}
      >>> apply_recursive(
      ...     camel_to_underscore,
      ...     {
      ...         'SpamEggsAndBacon': {
      ...             'SpamEggsAndBacon': 'spam',
      ...         }
      ...     },
      ... )
      {'spam_eggs_and_bacon': {'spam_eggs_and_bacon': 'spam'}}

      >>> a = {'a_b_c': 123, 'def': {'DeF': 456}}
      >>> b = apply_recursive(camel_to_underscore, a)
      >>> b
      {'a_b_c': 123, 'def': {'de_f': 456}}

      >>> apply_recursive(camel_to_underscore, None)

python_utils.formatters.camel_to_underscore(name: str) → str

  Convert camel case style naming to underscore/snake case style naming.

  If there are existing underscores they will be collapsed with the to-be-added underscores. Multiple consecutive capital letters will not be split except for the last one.

      >>> camel_to_underscore('SpamEggsAndBacon')
      'spam_eggs_and_bacon'
      >>> camel_to_underscore('Spam_and_bacon')
      'spam_and_bacon'
      >>> camel_to_underscore('Spam_And_Bacon')
      'spam_and_bacon'
      >>> camel_to_underscore('__SpamAndBacon__')
      '__spam_and_bacon__'
      >>> camel_to_underscore('__SpamANDBacon__')
      '__spam_and_bacon__'

python_utils.formatters.timesince(dt: datetime | timedelta, default: str = 'just now') → str

  Returns string representing ‘time since’ e.g. 3 days ago, 5 hours ago etc.

      >>> now = datetime.datetime.now()
      >>> timesince(now)
      'just now'
      >>> timesince(now - datetime.timedelta(seconds=1))
      '1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=2))
      '2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=60))
      '1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=61))
      '1 minute and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=62))
      '1 minute and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=120))
      '2 minutes ago'
      >>> timesince(now - datetime.timedelta(seconds=121))
      '2 minutes and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=122))
      '2 minutes and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3599))
      '59 minutes and 59 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3600))
      '1 hour ago'
      >>> timesince(now - datetime.timedelta(seconds=3601))
      '1 hour and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=3602))
      '1 hour and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3660))
      '1 hour and 1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=3661))
      '1 hour and 1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=3720))
      '1 hour and 2 minutes ago'
      >>> timesince(now - datetime.timedelta(seconds=3721))
      '1 hour and 2 minutes ago'
      >>> timesince(datetime.timedelta(seconds=3721))
      '1 hour and 2 minutes ago'

## python_utils.import_ module

This module provides utilities for importing modules and handling exceptions.

Classes:
  DummyError(Exception):

    A custom exception class used as a default for exception handling.

Functions:
  import_global(name, modules=None, exceptions=DummyError, locals_=None,

    globals_=None, level=-1): Imports the requested items into the global scope, with support for relative imports and custom exception handling.

exception python_utils.import_.DummyError

  Bases: `Exception`

  A custom exception class used as a default for exception handling.

python_utils.import_.DummyException

  alias of `DummyError`

python_utils.import_.import_global(name: str, modules: ~typing.List[str] | None = None, exceptions: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = <class 'python_utils.import_.DummyError'>, locals_: ~typing.Dict[str, ~typing.Any] | None = None, globals_: ~typing.Dict[str, ~typing.Any] | None = None, level: int = -1) → Any

  Import the requested items into the global scope.

  WARNING! this method _will_ overwrite your global scope If you have a variable named path and you call import_global(‘sys’) it will be overwritten with sys.path

  Args:

    name (str): the name of the module to import, e.g. sys modules (str): the modules to import, use None for everything exceptions (Exception): the exception to catch, e.g. ImportError locals_: the locals() method (in case you need a different scope) globals_: the globals() method (in case you need a different scope) level (int): the level to import from, this can be used for relative imports

## python_utils.logger module

This module provides a base class LoggerBase and a derived class Logged for adding logging capabilities to classes. The LoggerBase class expects a logger attribute to be a logging.Logger or compatible instance and provides methods for logging at various levels. The Logged class automatically adds a named logger to the class.

Classes:
  LoggerBase:

    A base class that adds logging utilities to a class.

  Logged:

    A derived class that automatically adds a named logger to a class.

Example:

      >>> class MyClass(Logged):
      ...     def __init__(self):
      ...         Logged.__init__(self)

      >>> my_class = MyClass()
      >>> my_class.debug('debug')
      >>> my_class.info('info')
      >>> my_class.warning('warning')
      >>> my_class.error('error')
      >>> my_class.exception('exception')
      >>> my_class.log(0, 'log')

class python_utils.logger.Logged(*args: Any, **kwargs: Any)

  Bases: `LoggerBase`

  Class which automatically adds a named logger to your class when interiting.

  Adds easy access to debug, info, warning, error, exception and log methods

      >>> class MyClass(Logged):
      ...     def __init__(self):
      ...         Logged.__init__(self)

      >>> my_class = MyClass()
      >>> my_class.debug('debug')
      >>> my_class.info('info')
      >>> my_class.warning('warning')
      >>> my_class.error('error')
      >>> my_class.exception('exception')
      >>> my_class.log(0, 'log')

      >>> my_class._Logged__get_name('spam')
      'spam'

  logger: Logger

## python_utils.terminal module

This module provides functions to get the terminal size across different platforms.

Functions:

  get_terminal_size: Get the current size of the terminal. _get_terminal_size_windows: Get terminal size on Windows. _get_terminal_size_tput: Get terminal size using tput. _get_terminal_size_linux: Get terminal size on Linux.

Usage example:

      >>> width, height = get_terminal_size()

python_utils.terminal.get_terminal_size() → tuple[int, int]

  Get the current size of your terminal.

  Multiple returns are not always a good idea, but in this case it greatly simplifies the code so I believe it’s justified. It’s not the prettiest function but that’s never really possible with cross-platform code.

  Returns:

    width, height: Two integers containing width and height

## python_utils.time module

This module provides utility functions for handling time-related operations.

Functions: - timedelta_to_seconds: Convert a timedelta to seconds with microseconds as

> fraction.

- delta_to_seconds: Convert a timedelta or numeric interval to seconds.

- delta_to_seconds_or_none: Convert a timedelta to seconds or return None.

- format_time: Format a timestamp (timedelta, datetime, or seconds) to a string.

- timeout_generator: Generate items from an iterable until a timeout is reached.

- aio_timeout_generator: Asynchronously generate items from an iterable until a timeout is reached.

- aio_generator_timeout_detector: Detect if an async generator has not yielded an element for a set amount of time.

- aio_generator_timeout_detector_decorator: Decorator for aio_generator_timeout_detector.

async python_utils.time.aio_generator_timeout_detector(generator: ~typing.AsyncGenerator[~python_utils.time._T, None], timeout: ~datetime.timedelta | int | float | None = None, total_timeout: ~datetime.timedelta | int | float | None = None, on_timeout: ~typing.Callable[[~typing.AsyncGenerator[~python_utils.time._T, None], ~datetime.timedelta | int | float | None, ~datetime.timedelta | int | float | None, BaseException], ~typing.Any] | None = <function reraise>, **on_timeout_kwargs: ~typing.Mapping[str, ~typing.Any]) → AsyncGenerator[_T, None]

  This function is used to detect if an asyncio generator has not yielded an element for a set amount of time.

  The on_timeout argument is called with the generator, timeout, total_timeout, exception and the extra **kwargs to this function as arguments. If on_timeout is not specified, the exception is reraised. If on_timeout is None, the exception is silently ignored and the generator will finish as normal.

python_utils.time.aio_generator_timeout_detector_decorator(timeout: ~datetime.timedelta | int | float | None = None, total_timeout: ~datetime.timedelta | int | float | None = None, on_timeout: ~typing.Callable[[~typing.AsyncGenerator[~typing.Any, None], ~datetime.timedelta | int | float | None, ~datetime.timedelta | int | float | None, BaseException], ~typing.Any] | None = <function reraise>, **on_timeout_kwargs: ~typing.Mapping[str, ~typing.Any]) → Callable[[Callable[[_P], AsyncGenerator[_T, None]]], Callable[[_P], AsyncGenerator[_T, None]]]

  A decorator wrapper for aio_generator_timeout_detector.

async python_utils.time.aio_timeout_generator(timeout: ~datetime.timedelta | int | float, interval: ~datetime.timedelta | int | float = datetime.timedelta(seconds=1), iterable: ~typing.AsyncIterable[~python_utils.time._T] | ~typing.Callable[[...], ~typing.AsyncIterable[~python_utils.time._T]] = <function acount>, interval_multiplier: float = 1.0, maximum_interval: ~datetime.timedelta | int | float | None = None) → AsyncGenerator[_T, None]

  Async generator that walks through the given async iterable (a counter by default) until the float_timeout is reached with a configurable float_interval between items.

  The interval_exponent automatically increases the float_timeout with each run. Note that if the float_interval is less than 1, 1/interval_exponent will be used so the float_interval is always growing. To double the float_interval with each run, specify 2.

  Doctests and asyncio are not friends, so no examples. But this function is effectively the same as the timeout_generator but it uses async for instead.

python_utils.time.delta_to_seconds(interval: timedelta | int | float) → int | float

  Convert a timedelta to seconds.

      >>> delta_to_seconds(datetime.timedelta(seconds=1))
      1
      >>> delta_to_seconds(datetime.timedelta(seconds=1, microseconds=1))
      1.000001
      >>> delta_to_seconds(1)
      1
      >>> delta_to_seconds('whatever')
      Traceback (most recent call last):
          ...
      TypeError: Unknown type ...

python_utils.time.delta_to_seconds_or_none(interval: timedelta | int | float | None) → int | float | None

  Convert a timedelta to seconds or return None.

python_utils.time.format_time(timestamp: timedelta | date | datetime | str | int | float | None, precision: timedelta = datetime.timedelta(seconds=1)) → str

  Formats timedelta/datetime/seconds.

      >>> format_time('1')
      '0:00:01'
      >>> format_time(1.234)
      '0:00:01'
      >>> format_time(1)
      '0:00:01'
      >>> format_time(datetime.datetime(2000, 1, 2, 3, 4, 5, 6))
      '2000-01-02 03:04:05'
      >>> format_time(datetime.date(2000, 1, 2))
      '2000-01-02'
      >>> format_time(datetime.timedelta(seconds=3661))
      '1:01:01'
      >>> format_time(None)
      '--:--:--'
      >>> format_time(format_time)
      Traceback (most recent call last):
          ...
      TypeError: Unknown type ...

python_utils.time.timedelta_to_seconds(delta: timedelta) → int | float

  Convert a timedelta to seconds with the microseconds as fraction.

  Note that this method has become largely obsolete with the timedelta.total_seconds() method introduced in Python 2.7.

      >>> from datetime import timedelta
      >>> '%d' % timedelta_to_seconds(timedelta(days=1))
      '86400'
      >>> '%d' % timedelta_to_seconds(timedelta(seconds=1))
      '1'
      >>> '%.6f' % timedelta_to_seconds(timedelta(seconds=1, microseconds=1))
      '1.000001'
      >>> '%.6f' % timedelta_to_seconds(timedelta(microseconds=1))
      '0.000001'

python_utils.time.timeout_generator(timeout: ~datetime.timedelta | int | float, interval: ~datetime.timedelta | int | float = datetime.timedelta(seconds=1), iterable: ~typing.Iterable[~python_utils.time._T] | ~typing.Callable[[], ~typing.Iterable[~python_utils.time._T]] = <class 'itertools.count'>, interval_multiplier: float = 1.0, maximum_interval: ~datetime.timedelta | int | float | None = None) → Iterable[_T]

  Generator that walks through the given iterable (a counter by default) until the float_timeout is reached with a configurable float_interval between items.

  This can be used to limit the time spent on a slow operation. This can be useful for testing slow APIs so you get a small sample of the data in a reasonable amount of time.

      >>> for i in timeout_generator(0.1, 0.06):
      ...     # Put your slow code here
      ...     print(i)
      0
      1
      2
      >>> timeout = datetime.timedelta(seconds=0.1)
      >>> interval = datetime.timedelta(seconds=0.06)
      >>> for i in timeout_generator(timeout, interval, itertools.count()):
      ...     print(i)
      0
      1
      2
      >>> for i in timeout_generator(1, interval=0.1, iterable='ab'):
      ...     print(i)
      a
      b

      >>> timeout = datetime.timedelta(seconds=0.1)
      >>> interval = datetime.timedelta(seconds=0.06)
      >>> for i in timeout_generator(timeout, interval, interval_multiplier=2):
      ...     print(i)
      0
      1
      2

## Module contents

This module initializes the python_utils package by importing various submodules and functions.

Submodules:

  aio converters decorators formatters generators import_ logger terminal time types

Functions:

  acount remap scale_1024 to_float to_int to_str to_unicode listify set_attributes raise_exception reraise camel_to_underscore timesince abatcher batcher import_global get_terminal_size aio_generator_timeout_detector aio_generator_timeout_detector_decorator aio_timeout_generator delta_to_seconds delta_to_seconds_or_none format_time timedelta_to_seconds timeout_generator

Classes:

  CastedDict LazyCastedDict UniqueList Logged LoggerBase

class python_utils.CastedDict(key_cast: Callable[[...], KT] | None = None, value_cast: Callable[[...], VT] | None = None, *args: Mapping[KT, VT] | Iterable[Tuple[KT, VT]] | Iterable[Mapping[KT, VT]] | _typeshed.SupportsKeysAndGetItem[KT, VT], **kwargs: VT)

  Bases: `CastedDictBase`[`KT`, `VT`]

  Custom dictionary that casts keys and values to the specified typing.

  Note that you can specify the types for mypy and type hinting with: CastedDict[int, int](int, int)

      >>> d: CastedDict[int, int] = CastedDict(int, int)
      >>> d[1] = 2
      >>> d['3'] = '4'
      >>> d.update({'5': '6'})
      >>> d.update([('7', '8')])
      >>> d
      {1: 2, 3: 4, 5: 6, 7: 8}
      >>> list(d.keys())
      [1, 3, 5, 7]
      >>> list(d)
      [1, 3, 5, 7]
      >>> list(d.values())
      [2, 4, 6, 8]
      >>> list(d.items())
      [(1, 2), (3, 4), (5, 6), (7, 8)]
      >>> d[3]
      4

  # Casts are optional and can be disabled by passing None as the cast >>> d = CastedDict() >>> d[1] = 2 >>> d[‘3’] = ‘4’ >>> d.update({‘5’: ‘6’}) >>> d.update([(‘7’, ‘8’)]) >>> d {1: 2, ‘3’: ‘4’, ‘5’: ‘6’, ‘7’: ‘8’}

class python_utils.LazyCastedDict(key_cast: Callable[[...], KT] | None = None, value_cast: Callable[[...], VT] | None = None, *args: Mapping[KT, VT] | Iterable[Tuple[KT, VT]] | Iterable[Mapping[KT, VT]] | _typeshed.SupportsKeysAndGetItem[KT, VT], **kwargs: VT)

  Bases: `CastedDictBase`[`KT`, `VT`]

  Custom dictionary that casts keys and lazily casts values to the specified typing. Note that the values are cast only when they are accessed and are not cached between executions.

  Note that you can specify the types for mypy and type hinting with: LazyCastedDict[int, int](int, int)

      >>> d: LazyCastedDict[int, int] = LazyCastedDict(int, int)
      >>> d[1] = 2
      >>> d['3'] = '4'
      >>> d.update({'5': '6'})
      >>> d.update([('7', '8')])
      >>> d
      {1: 2, 3: '4', 5: '6', 7: '8'}
      >>> list(d.keys())
      [1, 3, 5, 7]
      >>> list(d)
      [1, 3, 5, 7]
      >>> list(d.values())
      [2, 4, 6, 8]
      >>> list(d.items())
      [(1, 2), (3, 4), (5, 6), (7, 8)]
      >>> d[3]
      4

  # Casts are optional and can be disabled by passing None as the cast >>> d = LazyCastedDict() >>> d[1] = 2 >>> d[‘3’] = ‘4’ >>> d.update({‘5’: ‘6’}) >>> d.update([(‘7’, ‘8’)]) >>> d {1: 2, ‘3’: ‘4’, ‘5’: ‘6’, ‘7’: ‘8’} >>> list(d.keys()) [1, ‘3’, ‘5’, ‘7’] >>> list(d.values()) [2, ‘4’, ‘6’, ‘8’]

      >>> list(d.items())
      [(1, 2), ('3', '4'), ('5', '6'), ('7', '8')]
      >>> d['3']
      '4'

  items() → Generator[Tuple[KT, VT], None, None]

    Returns a generator of the dictionary’s items, casting the values if a value cast callable is provided.

    Yields:
      types.Generator[types.Tuple[KT, VT], None, None]: A generator of

        the dictionary’s items.

  values() → Generator[VT, None, None]

    Returns a generator of the dictionary’s values, casting the values if a value cast callable is provided.

    Yields:
      types.Generator[VT, None, None]: A generator of the dictionary’s

        values.

class python_utils.Logged(*args: Any, **kwargs: Any)

  Bases: `LoggerBase`

  Class which automatically adds a named logger to your class when interiting.

  Adds easy access to debug, info, warning, error, exception and log methods

      >>> class MyClass(Logged):
      ...     def __init__(self):
      ...         Logged.__init__(self)

      >>> my_class = MyClass()
      >>> my_class.debug('debug')
      >>> my_class.info('info')
      >>> my_class.warning('warning')
      >>> my_class.error('error')
      >>> my_class.exception('exception')
      >>> my_class.log(0, 'log')

      >>> my_class._Logged__get_name('spam')
      'spam'

  logger: Logger

class python_utils.LoggerBase

  Bases: `ABC`

  Class which automatically adds logging utilities to your class when interiting. Expects logger to be a logging.Logger or compatible instance.

  Adds easy access to debug, info, warning, error, exception and log methods

      >>> class MyClass(LoggerBase):
      ...     logger = logging.getLogger(__name__)
      ...
      ...     def __init__(self):
      ...         Logged.__init__(self)

      >>> my_class = MyClass()
      >>> my_class.debug('debug')
      >>> my_class.info('info')
      >>> my_class.warning('warning')
      >>> my_class.error('error')
      >>> my_class.exception('exception')
      >>> my_class.log(0, 'log')

  classmethod critical(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  classmethod debug(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  classmethod error(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  classmethod exception(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  classmethod info(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  classmethod log(level: int, msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None
  logger: Any
  classmethod warning(msg: object, *args: object, exc_info: bool | Tuple[Type[BaseException], BaseException, TracebackType | None] | Tuple[None, None, None] | BaseException | None = None, stack_info: bool = False, stacklevel: int = 1, extra: Mapping[str, object] | None = None) → None

class python_utils.UniqueList(*args: HT, on_duplicate: Literal['ignore', 'raise'] = 'ignore')

  Bases: `List`[`HT`]

  A list that only allows unique values. Duplicate values are ignored by default, but can be configured to raise an exception instead.

      >>> l = UniqueList(1, 2, 3)
      >>> l.append(4)
      >>> l.append(4)
      >>> l.insert(0, 4)
      >>> l.insert(0, 5)
      >>> l[1] = 10
      >>> l
      [5, 10, 2, 3, 4]

      >>> l = UniqueList(1, 2, 3, on_duplicate='raise')
      >>> l.append(4)
      >>> l.append(4)
      Traceback (most recent call last):
      ...
      ValueError: Duplicate value: 4
      >>> l.insert(0, 4)
      Traceback (most recent call last):
      ...
      ValueError: Duplicate value: 4
      >>> 4 in l
      True
      >>> l[0]
      1
      >>> l[1] = 4
      Traceback (most recent call last):
      ...
      ValueError: Duplicate value: 4

  append(value: HT) → None

    Appends a value to the list, ensuring uniqueness.

    Args:

      value (HT): The value to append.

    Raises:
      ValueError: If the value is a duplicate and on_duplicate is set

        to ‘raise’.

  insert(index: SupportsIndex, value: HT) → None

    Inserts a value at the specified index, ensuring uniqueness.

    Args:

      index (types.SupportsIndex): The index to insert the value at. value (HT): The value to insert.

    Raises:
      ValueError: If the value is a duplicate and on_duplicate is set

        to ‘raise’.

async python_utils.abatcher(generator: AsyncGenerator[_T, None] | AsyncIterator[_T], batch_size: int | None = None, interval: timedelta | int | float | None = None) → AsyncGenerator[List[_T], None]

  Asyncio generator wrapper that returns items with a given batch size or interval (whichever is reached first).

  Args:

    generator: The async generator or iterator to batch. batch_size (types.Optional[int], optional): The number of items per

    > batch. Defaults to None.

    interval (types.Optional[types.delta_type], optional): The time

      interval to wait before yielding a batch. Defaults to None.

  Yields:

    types.AsyncGenerator[types.List[_T], None]: A generator that yields batches of items.

async python_utils.acount(start: _N = 0, step: _N = 1, delay: float = 0, stop: _N | None = None) → AsyncIterator[_N]

  Asyncio version of itertools.count().

async python_utils.aio_generator_timeout_detector(generator: ~typing.AsyncGenerator[~python_utils.time._T, None], timeout: ~datetime.timedelta | int | float | None = None, total_timeout: ~datetime.timedelta | int | float | None = None, on_timeout: ~typing.Callable[[~typing.AsyncGenerator[~python_utils.time._T, None], ~datetime.timedelta | int | float | None, ~datetime.timedelta | int | float | None, BaseException], ~typing.Any] | None = <function reraise>, **on_timeout_kwargs: ~typing.Mapping[str, ~typing.Any]) → AsyncGenerator[_T, None]

  This function is used to detect if an asyncio generator has not yielded an element for a set amount of time.

  The on_timeout argument is called with the generator, timeout, total_timeout, exception and the extra **kwargs to this function as arguments. If on_timeout is not specified, the exception is reraised. If on_timeout is None, the exception is silently ignored and the generator will finish as normal.

python_utils.aio_generator_timeout_detector_decorator(timeout: ~datetime.timedelta | int | float | None = None, total_timeout: ~datetime.timedelta | int | float | None = None, on_timeout: ~typing.Callable[[~typing.AsyncGenerator[~typing.Any, None], ~datetime.timedelta | int | float | None, ~datetime.timedelta | int | float | None, BaseException], ~typing.Any] | None = <function reraise>, **on_timeout_kwargs: ~typing.Mapping[str, ~typing.Any]) → Callable[[Callable[[_P], AsyncGenerator[_T, None]]], Callable[[_P], AsyncGenerator[_T, None]]]

  A decorator wrapper for aio_generator_timeout_detector.

async python_utils.aio_timeout_generator(timeout: ~datetime.timedelta | int | float, interval: ~datetime.timedelta | int | float = datetime.timedelta(seconds=1), iterable: ~typing.AsyncIterable[~python_utils.time._T] | ~typing.Callable[[...], ~typing.AsyncIterable[~python_utils.time._T]] = <function acount>, interval_multiplier: float = 1.0, maximum_interval: ~datetime.timedelta | int | float | None = None) → AsyncGenerator[_T, None]

  Async generator that walks through the given async iterable (a counter by default) until the float_timeout is reached with a configurable float_interval between items.

  The interval_exponent automatically increases the float_timeout with each run. Note that if the float_interval is less than 1, 1/interval_exponent will be used so the float_interval is always growing. To double the float_interval with each run, specify 2.

  Doctests and asyncio are not friends, so no examples. But this function is effectively the same as the timeout_generator but it uses async for instead.

python_utils.batcher(iterable: Iterable[_T], batch_size: int = 10) → Generator[List[_T], None, None]

  Generator wrapper that returns items with a given batch size.

  Args:

    iterable (types.Iterable[_T]): The iterable to batch. batch_size (int, optional): The number of items per batch. Defaults

    > to 10.

  Yields:

    types.Generator[types.List[_T], None, None]: A generator that yields batches of items.

python_utils.camel_to_underscore(name: str) → str

  Convert camel case style naming to underscore/snake case style naming.

  If there are existing underscores they will be collapsed with the to-be-added underscores. Multiple consecutive capital letters will not be split except for the last one.

      >>> camel_to_underscore('SpamEggsAndBacon')
      'spam_eggs_and_bacon'
      >>> camel_to_underscore('Spam_and_bacon')
      'spam_and_bacon'
      >>> camel_to_underscore('Spam_And_Bacon')
      'spam_and_bacon'
      >>> camel_to_underscore('__SpamAndBacon__')
      '__spam_and_bacon__'
      >>> camel_to_underscore('__SpamANDBacon__')
      '__spam_and_bacon__'

python_utils.delta_to_seconds(interval: timedelta | int | float) → int | float

  Convert a timedelta to seconds.

      >>> delta_to_seconds(datetime.timedelta(seconds=1))
      1
      >>> delta_to_seconds(datetime.timedelta(seconds=1, microseconds=1))
      1.000001
      >>> delta_to_seconds(1)
      1
      >>> delta_to_seconds('whatever')
      Traceback (most recent call last):
          ...
      TypeError: Unknown type ...

python_utils.delta_to_seconds_or_none(interval: timedelta | int | float | None) → int | float | None

  Convert a timedelta to seconds or return None.

python_utils.format_time(timestamp: timedelta | date | datetime | str | int | float | None, precision: timedelta = datetime.timedelta(seconds=1)) → str

  Formats timedelta/datetime/seconds.

      >>> format_time('1')
      '0:00:01'
      >>> format_time(1.234)
      '0:00:01'
      >>> format_time(1)
      '0:00:01'
      >>> format_time(datetime.datetime(2000, 1, 2, 3, 4, 5, 6))
      '2000-01-02 03:04:05'
      >>> format_time(datetime.date(2000, 1, 2))
      '2000-01-02'
      >>> format_time(datetime.timedelta(seconds=3661))
      '1:01:01'
      >>> format_time(None)
      '--:--:--'
      >>> format_time(format_time)
      Traceback (most recent call last):
          ...
      TypeError: Unknown type ...

python_utils.get_terminal_size() → tuple[int, int]

  Get the current size of your terminal.

  Multiple returns are not always a good idea, but in this case it greatly simplifies the code so I believe it’s justified. It’s not the prettiest function but that’s never really possible with cross-platform code.

  Returns:

    width, height: Two integers containing width and height

python_utils.import_global(name: str, modules: ~typing.List[str] | None = None, exceptions: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = <class 'python_utils.import_.DummyError'>, locals_: ~typing.Dict[str, ~typing.Any] | None = None, globals_: ~typing.Dict[str, ~typing.Any] | None = None, level: int = -1) → Any

  Import the requested items into the global scope.

  WARNING! this method _will_ overwrite your global scope If you have a variable named path and you call import_global(‘sys’) it will be overwritten with sys.path

  Args:

    name (str): the name of the module to import, e.g. sys modules (str): the modules to import, use None for everything exceptions (Exception): the exception to catch, e.g. ImportError locals_: the locals() method (in case you need a different scope) globals_: the globals() method (in case you need a different scope) level (int): the level to import from, this can be used for relative imports

python_utils.listify(collection: ~typing.Callable[[~typing.Iterable[~python_utils.decorators._T]], ~typing.Collection[~python_utils.decorators._T]] = <class 'list'>, allow_empty: bool = True) → Callable[[Callable[[...], Iterable[_T] | None]], Callable[[...], Collection[_T]]]

  Convert any generator to a list or other type of collection.

      >>> @listify()
      ... def generator():
      ...     yield 1
      ...     yield 2
      ...     yield 3

      >>> generator()
      [1, 2, 3]

      >>> @listify()
      ... def empty_generator():
      ...     pass

      >>> empty_generator()
      []

      >>> @listify(allow_empty=False)
      ... def empty_generator_not_allowed():
      ...     pass

      >>> empty_generator_not_allowed()
      Traceback (most recent call last):
      ...
      TypeError: ... `allow_empty` is `False`

      >>> @listify(collection=set)
      ... def set_generator():
      ...     yield 1
      ...     yield 1
      ...     yield 2

      >>> set_generator()
      {1, 2}

      >>> @listify(collection=dict)
      ... def dict_generator():
      ...     yield 'a', 1
      ...     yield 'b', 2

      >>> dict_generator()
      {'a': 1, 'b': 2}

python_utils.raise_exception(exception_class: Type[Exception], *args: Any, **kwargs: Any) → Callable[[...], None]

  Returns a function that raises an exception of the given type with the given arguments.

      >>> raise_exception(ValueError, 'spam')('eggs')
      Traceback (most recent call last):
          ...
      ValueError: spam

python_utils.remap(value: _TN, old_min: _TN, old_max: _TN, new_min: _TN, new_max: _TN) → _TN

  remap a value from one range into another.

      >>> remap(500, 0, 1000, 0, 100)
      50
      >>> remap(250.0, 0.0, 1000.0, 0.0, 100.0)
      25.0
      >>> remap(-75, -100, 0, -1000, 0)
      -750
      >>> remap(33, 0, 100, -500, 500)
      -170
      >>> remap(decimal.Decimal('250.0'), 0.0, 1000.0, 0.0, 100.0)
      Decimal('25.0')

  This is a great use case example. Take an AVR that has dB values the minimum being -80dB and the maximum being 10dB and you want to convert volume percent to the equilivint in that dB range

      >>> remap(46.0, 0.0, 100.0, -80.0, 10.0)
      -38.6

  I added using decimal.Decimal so floating point math errors can be avoided. Here is an example of a floating point math error >>> 0.1 + 0.1 + 0.1 0.30000000000000004

  If floating point remaps need to be done my suggstion is to pass at least one parameter as a decimal.Decimal. This will ensure that the output from this function is accurate. I left passing floats for backwards compatability and there is no conversion done from float to decimal.Decimal unless one of the passed parameters has a type of decimal.Decimal. This will ensure that any existing code that uses this funtion will work exactly how it has in the past.

  Some edge cases to test >>> remap(1, 0, 0, 1, 2) Traceback (most recent call last): … ValueError: Input range (0-0) is empty

      >>> remap(1, 1, 2, 0, 0)
      Traceback (most recent call last):
      ...
      ValueError: Output range (0-0) is empty

  Args:

    value (int, float, decimal.Decimal): Value to be converted. old_min (int, float, decimal.Decimal): Minimum of the range for the

    > value that has been passed.

    old_max (int, float, decimal.Decimal): Maximum of the range for the

      value that has been passed.

    new_min (int, float, decimal.Decimal): The minimum of the new range. new_max (int, float, decimal.Decimal): The maximum of the new range.

  Returns: int, float, decimal.Decimal: Value that has been re-ranged. If

    any of the parameters passed is a decimal.Decimal, all of the parameters will be converted to decimal.Decimal. The same thing also happens if one of the parameters is a float. Otherwise, all parameters will get converted into an int. Technically, you can pass a str of an integer and it will get converted. The returned value type will be decimal.Decimal if any of the passed parameters are decimal.Decimal, the return type will be float if any of the passed parameters are a float, otherwise the returned type will be int.

python_utils.reraise(*args: Any, **kwargs: Any) → Any

  Reraises the current exception.

  This function seems useless, but it can be useful when you need to pass a callable to another function that raises an exception.

python_utils.scale_1024(x: int | float, n_prefixes: int) → Tuple[int | float, int | float]

  Scale a number down to a suitable size, based on powers of 1024.

  Returns the scaled number and the power of 1024 used.

  Use to format numbers of bytes to KiB, MiB, etc.

      >>> scale_1024(310, 3)
      (310.0, 0)
      >>> scale_1024(2048, 3)
      (2.0, 1)
      >>> scale_1024(0, 2)
      (0.0, 0)
      >>> scale_1024(0.5, 2)
      (0.5, 0)
      >>> scale_1024(1, 2)
      (1.0, 0)

python_utils.set_attributes(**kwargs: Any) → Callable[[...], Any]

  Decorator to set attributes on functions and classes.

  A common usage for this pattern is the Django Admin where functions can get an optional short_description. To illustrate:

  Example from the Django admin using this decorator: https://docs.djangoproject.com/en/3.0/ref/contrib/admin/#django.contrib.admin.ModelAdmin.list_display

  Our simplified version:

      >>> @set_attributes(short_description='Name')
      ... def upper_case_name(self, obj):
      ...     return ('%s %s' % (obj.first_name, obj.last_name)).upper()

  The standard Django version:

      >>> def upper_case_name(obj):
      ...     return ('%s %s' % (obj.first_name, obj.last_name)).upper()

      >>> upper_case_name.short_description = 'Name'

python_utils.timedelta_to_seconds(delta: timedelta) → int | float

  Convert a timedelta to seconds with the microseconds as fraction.

  Note that this method has become largely obsolete with the timedelta.total_seconds() method introduced in Python 2.7.

      >>> from datetime import timedelta
      >>> '%d' % timedelta_to_seconds(timedelta(days=1))
      '86400'
      >>> '%d' % timedelta_to_seconds(timedelta(seconds=1))
      '1'
      >>> '%.6f' % timedelta_to_seconds(timedelta(seconds=1, microseconds=1))
      '1.000001'
      >>> '%.6f' % timedelta_to_seconds(timedelta(microseconds=1))
      '0.000001'

python_utils.timeout_generator(timeout: ~datetime.timedelta | int | float, interval: ~datetime.timedelta | int | float = datetime.timedelta(seconds=1), iterable: ~typing.Iterable[~python_utils.time._T] | ~typing.Callable[[], ~typing.Iterable[~python_utils.time._T]] = <class 'itertools.count'>, interval_multiplier: float = 1.0, maximum_interval: ~datetime.timedelta | int | float | None = None) → Iterable[_T]

  Generator that walks through the given iterable (a counter by default) until the float_timeout is reached with a configurable float_interval between items.

  This can be used to limit the time spent on a slow operation. This can be useful for testing slow APIs so you get a small sample of the data in a reasonable amount of time.

      >>> for i in timeout_generator(0.1, 0.06):
      ...     # Put your slow code here
      ...     print(i)
      0
      1
      2
      >>> timeout = datetime.timedelta(seconds=0.1)
      >>> interval = datetime.timedelta(seconds=0.06)
      >>> for i in timeout_generator(timeout, interval, itertools.count()):
      ...     print(i)
      0
      1
      2
      >>> for i in timeout_generator(1, interval=0.1, iterable='ab'):
      ...     print(i)
      a
      b

      >>> timeout = datetime.timedelta(seconds=0.1)
      >>> interval = datetime.timedelta(seconds=0.06)
      >>> for i in timeout_generator(timeout, interval, interval_multiplier=2):
      ...     print(i)
      0
      1
      2

python_utils.timesince(dt: datetime | timedelta, default: str = 'just now') → str

  Returns string representing ‘time since’ e.g. 3 days ago, 5 hours ago etc.

      >>> now = datetime.datetime.now()
      >>> timesince(now)
      'just now'
      >>> timesince(now - datetime.timedelta(seconds=1))
      '1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=2))
      '2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=60))
      '1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=61))
      '1 minute and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=62))
      '1 minute and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=120))
      '2 minutes ago'
      >>> timesince(now - datetime.timedelta(seconds=121))
      '2 minutes and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=122))
      '2 minutes and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3599))
      '59 minutes and 59 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3600))
      '1 hour ago'
      >>> timesince(now - datetime.timedelta(seconds=3601))
      '1 hour and 1 second ago'
      >>> timesince(now - datetime.timedelta(seconds=3602))
      '1 hour and 2 seconds ago'
      >>> timesince(now - datetime.timedelta(seconds=3660))
      '1 hour and 1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=3661))
      '1 hour and 1 minute ago'
      >>> timesince(now - datetime.timedelta(seconds=3720))
      '1 hour and 2 minutes ago'
      >>> timesince(now - datetime.timedelta(seconds=3721))
      '1 hour and 2 minutes ago'
      >>> timesince(datetime.timedelta(seconds=3721))
      '1 hour and 2 minutes ago'

python_utils.to_float(input_: str, default: int = 0, exception: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = (<class 'ValueError'>, <class 'TypeError'>), regexp: ~typing.Pattern[str] | str | ~typing.Literal[True] | None = None) → int | float

  Convert the given input_ to an integer or return default.

  When trying to convert the exceptions given in the exception parameter are automatically catched and the default will be returned.

  The regexp parameter allows for a regular expression to find the digits in a string. When True it will automatically match any digit in the string. When a (regexp) object (has a search method) is given, that will be used. When a string is given, re.compile will be run over it first

  The last group of the regexp will be used as value

      >>> '%.2f' % to_float('abc')
      '0.00'
      >>> '%.2f' % to_float('1')
      '1.00'
      >>> '%.2f' % to_float('abc123.456', regexp=True)
      '123.46'
      >>> '%.2f' % to_float('abc123', regexp=True)
      '123.00'
      >>> '%.2f' % to_float('abc0.456', regexp=True)
      '0.46'
      >>> '%.2f' % to_float('abc123.456', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('123.456abc', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('abc123.46abc', regexp=re.compile(r'(\d+\.\d+)'))
      '123.46'
      >>> '%.2f' % to_float('abc123abc456', regexp=re.compile(r'(\d+(\.\d+|))'))
      '123.00'
      >>> '%.2f' % to_float('abc', regexp=r'(\d+)')
      '0.00'
      >>> '%.2f' % to_float('abc123', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('123abc', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('abc123abc', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('abc123abc456', regexp=r'(\d+)')
      '123.00'
      >>> '%.2f' % to_float('1234', default=1)
      '1234.00'
      >>> '%.2f' % to_float('abc', default=1)
      '1.00'
      >>> '%.2f' % to_float('abc', regexp=123)
      Traceback (most recent call last):
      ...
      TypeError: unknown argument for regexp parameter

python_utils.to_int(input_: str | None = None, default: int = 0, exception: ~typing.Tuple[~typing.Type[Exception], ...] | ~typing.Type[Exception] = (<class 'ValueError'>, <class 'TypeError'>), regexp: ~typing.Pattern[str] | str | ~typing.Literal[True] | None = None) → int

  Convert the given input to an integer or return default.

  When trying to convert the exceptions given in the exception parameter are automatically catched and the default will be returned.

  The regexp parameter allows for a regular expression to find the digits in a string. When True it will automatically match any digit in the string. When a (regexp) object (has a search method) is given, that will be used. WHen a string is given, re.compile will be run over it first

  The last group of the regexp will be used as value

      >>> to_int('abc')
      0
      >>> to_int('1')
      1
      >>> to_int('')
      0
      >>> to_int()
      0
      >>> to_int('abc123')
      0
      >>> to_int('123abc')
      0
      >>> to_int('abc123', regexp=True)
      123
      >>> to_int('123abc', regexp=True)
      123
      >>> to_int('abc123abc', regexp=True)
      123
      >>> to_int('abc123abc456', regexp=True)
      123
      >>> to_int('abc123', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('123abc', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123abc', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123abc456', regexp=re.compile(r'(\d+)'))
      123
      >>> to_int('abc123', regexp=r'(\d+)')
      123
      >>> to_int('123abc', regexp=r'(\d+)')
      123
      >>> to_int('abc', regexp=r'(\d+)')
      0
      >>> to_int('abc123abc', regexp=r'(\d+)')
      123
      >>> to_int('abc123abc456', regexp=r'(\d+)')
      123
      >>> to_int('1234', default=1)
      1234
      >>> to_int('abc', default=1)
      1
      >>> to_int('abc', regexp=123)
      Traceback (most recent call last):
      ...
      TypeError: unknown argument for regexp parameter: 123

python_utils.to_str(input_: str | bytes, encoding: str = 'utf-8', errors: str = 'replace') → bytes

  Convert objects to string, encodes to the given encoding.

  Return type:

    str

      >>> to_str('a')
      b'a'
      >>> to_str('a')
      b'a'
      >>> to_str(b'a')
      b'a'
      >>> class Foo(object):
      ...     __str__ = lambda s: 'a'
      >>> to_str(Foo())
      'a'
      >>> to_str(Foo)
      "<class 'python_utils.converters.Foo'>"

python_utils.to_unicode(input_: str | bytes, encoding: str = 'utf-8', errors: str = 'replace') → str

  Convert objects to unicode, if needed decodes string with the given encoding and errors settings.

  Return type:

    str

      >>> to_unicode(b'a')
      'a'
      >>> to_unicode('a')
      'a'
      >>> to_unicode('a')
      'a'
      >>> class Foo(object):
      ...     __str__ = lambda s: 'a'
      >>> to_unicode(Foo())
      'a'
      >>> to_unicode(Foo)
      "<class 'python_utils.converters.Foo'>"