def snapped_coordinates(self, value): value = validators.iterable(value, allow_empty=True, minimum_length=2, maximum_length=2) self.snapped_longitude = value[0] self.snapped_latitude = value[1]
def align_max_deviation(self, val: Tuple[float, float]): val = validators.iterable( val, minimum_length=2, maximum_length=2, allow_empty=True ) self._align_max_deviation = val if val is None else tuple([ validators.numeric(i, minimum=0) for i in val ])
def ground_temperatures(self, value): if checkers.is_numeric(value): ground_temperatures = [value] * 12 elif checkers.is_iterable(value): ground_temperature = validators.iterable(value, minimum_length=12, maximum_length=12) ground_temperatures = [temp for temp in ground_temperature] else: raise ValueError( "Input error for value 'ground_temperature'. Value must " "be numeric or an iterable of length 12.") self._ground_temperatures = ground_temperatures
def is_iterable(obj, forbid_literals = (str, bytes), minimum_length = None, maximum_length = None, **kwargs): """Indicate whether ``obj`` is iterable. :param forbid_literals: A collection of literals that will be considered invalid even if they are (actually) iterable. Defaults to a :class:`tuple <python:tuple>` containing :class:`str <python:str>` and :class:`bytes <python:bytes>`. :type forbid_literals: iterable :param minimum_length: If supplied, indicates the minimum number of members needed to be valid. :type minimum_length: :class:`int <python:int>` :param maximum_length: If supplied, indicates the minimum number of members needed to be valid. :type maximum_length: :class:`int <python:int>` :returns: ``True`` if ``obj`` is a valid iterable, ``False`` if not. :rtype: :class:`bool <python:bool>` :raises SyntaxError: if ``kwargs`` contains duplicate keyword parameters or duplicates keyword parameters passed to the underlying validator """ if obj is None: return False if obj in forbid_literals: return False try: obj = validators.iterable(obj, allow_empty = True, forbid_literals = forbid_literals, minimum_length = minimum_length, maximum_length = maximum_length, **kwargs) assert isinstance(obj, Iterable) except SyntaxError as error: raise error except Exception: return False return True
def missing_range_metadata(self, value): value = validators.iterable(value, allow_empty = True) if not value: self._missing_range_metadata = None else: ranges = [validators.dict(x, allow_empty = False) for x in value] validated_ranges = [] for range in ranges: if 'high' not in range or 'low' not in range: raise ValueError('missing_range_metadata requires a "high" and "low"' ' boundary to be defined.') validated_range = { 'high': validators.numeric(range.get('high'), allow_empty = False), 'low': validators.numeric(range.get('low'), allow_empty = False) } validated_ranges.append(validated_range) self._missing_range_metadata = validated_ranges
def iterable__to_dict(iterable, format, max_nesting=0, current_nesting=0, is_dumping=False, config_set=None): """Given an iterable, traverse it and execute ``_to_dict()`` if present. :param iterable: An iterable to traverse. :type iterable: iterable :param format: The format to which the :class:`dict <python:dict>` will ultimately be serialized. Accepts: ``'csv'``, ``'json'``, ``'yaml'``, and ``'dict'``. :type format: :class:`str <python:str>` :param max_nesting: The maximum number of levels that the resulting :class:`dict <python:dict>` object can be nested. If set to ``0``, will not nest other serializable objects. Defaults to ``0``. :type max_nesting: :class:`int <python:int>` :param current_nesting: The current nesting level at which the :class:`dict <python:dict>` representation will reside. Defaults to ``0``. :type current_nesting: :class:`int <python:int>` :param is_dumping: If ``True``, returns all attributes. Defaults to ``False``. :type is_dumping: :class:`bool <python:bool>` :param config_set: If not :obj:`None <python:None>`, the named configuration set to use when processing the input. Defaults to :obj:`None <python:None>`. :type config_set: :class:`str <python:str>` / :obj:`None <python:None>` :returns: Collection of values, possibly converted to :class:`dict <python:dict>` objects. :rtype: :class:`list <python:list>` of objects :raises InvalidFormatError: if ``format`` is not an acceptable value :raises ValueError: if ``iterable`` is not an iterable """ next_nesting = current_nesting + 1 if format not in ['csv', 'json', 'yaml', 'dict']: raise InvalidFormatError("format '%s' not supported" % format) iterable = validators.iterable(iterable, allow_empty=True, forbid_literals=(str, bytes, dict)) if iterable is None: return [] if current_nesting > max_nesting: raise MaximumNestingExceededError( 'current nesting level (%s) exceeds maximum %s' % (current_nesting, max_nesting)) items = [] for item in iterable: try: new_item = item._to_dict(format, max_nesting=max_nesting, current_nesting=next_nesting, is_dumping=is_dumping, config_set=config_set) except AttributeError: try: new_item = iterable__to_dict(item, format, max_nesting=max_nesting, current_nesting=next_nesting, is_dumping=is_dumping, config_set=config_set) except NotAnIterableError: new_item = item items.append(new_item) return items
def from_iterable(value): return validators.iterable(value, allow_empty=True)
def backoff(to_execute, args = None, kwargs = None, strategy = None, retry_execute = None, retry_args = None, retry_kwargs = None, max_tries = None, max_delay = None, catch_exceptions = None, on_failure = None, on_success = None): """Retry a function call multiple times with a delay per the strategy given. :param to_execute: The function call that is to be attempted. :type to_execute: callable :param args: The positional arguments to pass to the function on the first attempt. If ``retry_args`` is :class:`None <python:None>`, will re-use these arguments on retry attempts as well. :type args: iterable / :class:`None <python:None>`. :param kwargs: The keyword arguments to pass to the function on the first attempt. If ``retry_kwargs`` is :class:`None <python:None>`, will re-use these keyword arguments on retry attempts as well. :type kwargs: :class:`dict <python:dict>` / :class:`None <python:None>` :param strategy: The :class:`BackoffStrategy` to use when determining the delay between retry attempts. If :class:`None <python:None>`, defaults to :class:`Exponential`. :type strategy: :class:`BackoffStrategy` :param retry_execute: The function to call on retry attempts. If :class:`None <python:None>`, will retry ``to_execute``. Defaults to :class:`None <python:None>`. :type retry_execute: callable / :class:`None <python:None>` :param retry_args: The positional arguments to pass to the function on retry attempts. If :class:`None <python:None>`, will re-use ``args``. Defaults to :class:`None <python:None>`. :type retry_args: iterable / :class:`None <python:None>` :param retry_kwargs: The keyword arguments to pass to the function on retry attempts. If :class:`None <python:None>`, will re-use ``kwargs``. Defaults to :class:`None <python:None>`. :type subsequent_kwargs: :class:`dict <python:dict>` / :class:`None <python:None>` :param max_tries: The maximum number of times to attempt the call. If :class:`None <python:None>`, will apply an environment variable ``BACKOFF_DEFAULT_TRIES``. If that environment variable is not set, will apply a default of ``3``. :type max_tries: int / :class:`None <python:None>` :param max_delay: The maximum number of seconds to wait befor giving up once and for all. If :class:`None <python:None>`, will apply an environment variable ``BACKOFF_DEFAULT_DELAY`` if that environment variable is set. If it is not set, will not apply a max delay at all. :type max_delay: :class:`None <python:None>` / int :param catch_exceptions: The ``type(exception)`` to catch and retry. If :class:`None <python:None>`, will catch all exceptions. Defaults to :class:`None <python:None>`. .. caution:: The iterable must contain one or more types of exception *instances*, and not class objects. For example: .. code-block:: python # GOOD: catch_exceptions = (type(ValueError()), type(TypeError())) # BAD: catch_exceptions = (type(ValueError), type(ValueError)) # BAD: catch_exceptions = (ValueError, TypeError) # BAD: catch_exceptions = (ValueError(), TypeError()) :type catch_exceptions: iterable of form ``[type(exception()), ...]`` :param on_failure: The :class:`exception <python:Exception>` or function to call when all retry attempts have failed. If :class:`None <python:None>`, will raise the last-caught :class:`exception <python:Exception>`. If an :class:`exception <python:Exception>`, will raise the exception with the same message as the last-caught exception. If a function, will call the function and pass the last-raised exception, its message, and stacktrace to the function. Defaults to :class:`None <python:None>`. :type on_failure: :class:`Exception <python:Exception>` / function / :class:`None <python:None>` :param on_success: The function to call when the operation was successful. The function receives the result of the ``to_execute`` or ``retry_execute`` function that was successful, and is called before that result is returned to whatever code called the backoff function. If :class:`None <python:None>`, will just return the result of ``to_execute`` or ``retry_execute`` without calling a handler. Defaults to :class:`None <python:None>`. :type on_success: callable / :class:`None <python:None>` :returns: The result of the attempted function. Example: .. code-block:: python from backoff_utils import backoff def some_function(arg1, arg2, kwarg1 = None): # Function does something pass result = backoff(some_function, args = ['value1', 'value2'], kwargs = { 'kwarg1': 'value3' }, max_tries = 3, max_delay = 30, strategy = strategies.Exponential) """ # pylint: disable=too-many-branches,too-many-statements if to_execute is None: raise ValueError('to_execute cannot be None') elif not checkers.is_callable(to_execute): raise TypeError('to_execute must be callable') if strategy is None: strategy = strategies.Exponential if not hasattr(strategy, 'IS_INSTANTIATED'): raise TypeError('strategy must be a BackoffStrategy or descendent') if not strategy.IS_INSTANTIATED: test_strategy = strategy(attempt = 0) else: test_strategy = strategy if not checkers.is_type(test_strategy, 'BackoffStrategy'): raise TypeError('strategy must be a BackoffStrategy or descendent') if args: args = validators.iterable(args) if kwargs: kwargs = validators.dict(kwargs) if retry_execute is None: retry_execute = to_execute elif not checkers.is_callable(retry_execute): raise TypeError('retry_execute must be None or a callable') if not retry_args: retry_args = args else: retry_args = validators.iterable(retry_args) if not retry_kwargs: retry_kwargs = kwargs else: retry_kwargs = validators.dict(retry_kwargs) if max_tries is None: max_tries = DEFAULT_MAX_TRIES max_tries = validators.integer(max_tries) if max_delay is None: max_delay = DEFAULT_MAX_DELAY if catch_exceptions is None: catch_exceptions = [type(Exception())] else: if not checkers.is_iterable(catch_exceptions): catch_exceptions = [catch_exceptions] catch_exceptions = validators.iterable(catch_exceptions) if on_failure is not None and not checkers.is_callable(on_failure): raise TypeError('on_failure must be None or a callable') if on_success is not None and not checkers.is_callable(on_success): raise TypeError('on_success must be None or a callable') cached_error = None return_value = None returned = False failover_counter = 0 start_time = datetime.utcnow() while failover_counter <= (max_tries): elapsed_time = (datetime.utcnow() - start_time).total_seconds() if max_delay is not None and elapsed_time >= max_delay: if cached_error is None: raise BackoffTimeoutError('backoff timed out after:' ' {}s'.format(elapsed_time)) else: _handle_failure(on_failure, cached_error) if failover_counter == 0: try: if args is not None and kwargs is not None: return_value = to_execute(*args, **kwargs) elif args is not None: return_value = to_execute(*args) elif kwargs is not None: return_value = to_execute(**kwargs) else: return_value = to_execute() returned = True break except Exception as error: # pylint: disable=broad-except if type(error) in catch_exceptions: cached_error = error strategy.delay(failover_counter) failover_counter += 1 continue else: _handle_failure(on_failure = on_failure, error = error) return else: try: if retry_args is not None and retry_kwargs is not None: return_value = retry_execute(*retry_args, **retry_kwargs) elif retry_args is not None: return_value = retry_execute(*retry_args) elif retry_kwargs is not None: return_value = retry_execute(**retry_kwargs) else: return_value = retry_execute() returned = True break except Exception as error: # pylint: disable=broad-except if type(error) in catch_exceptions: strategy.delay(failover_counter) cached_error = error failover_counter += 1 continue else: _handle_failure(on_failure = on_failure, error = error) return if not returned: _handle_failure(on_failure = on_failure, error = cached_error) return elif returned and on_success is not None: on_success(return_value) return return_value
def _read_spss(data: Union[bytes, BytesIO, 'os.PathLike[Any]'], limit: Optional[int] = None, offset: int = 0, exclude_variables: Optional[List[str]] = None, include_variables: Optional[List[str]] = None, metadata_only: bool = False, apply_labels: bool = False, labels_as_categories: bool = True, missing_as_NaN: bool = False, convert_datetimes: bool = True, dates_as_datetime64: bool = False, **kwargs): """Internal function that reads an SPSS (.sav or .zsav) file and returns a :class:`tuple <python:tuple>` with a Pandas :class:`DataFrame <pandas:pandas.DataFrame>` object and a metadata :class:`dict <python:dict>`. :param data: The SPSS data to load. Accepts either a series of bytes or a filename. :type data: Path-like filename, :class:`bytes <python:bytes>` or :class:`BytesIO <python:io.bytesIO>` :param limit: The number of records to read from the data. If :obj:`None <python:None>` will return all records. Defaults to :obj:`None <python:None>`. :type limit: :class:`int <python:int>` or :obj:`None <python:None>` :param offset: The record at which to start reading the data. Defaults to 0 (first record). :type offset: :class:`int <python:int>` :param exclude_variables: A list of the variables that should be ignored when reading data. Defaults to :obj:`None <python:None>`. :type exclude_variables: iterable of :class:`str <python:str>` or :obj:`None <python:None>` :param include_variables: A list of the variables that should be explicitly included when reading data. Defaults to :obj:`None <python:None>`. :type include_variables: iterable of :class:`str <python:str>` or :obj:`None <python:None>` :param metadata_only: If ``True``, will return no data records in the resulting :class:`DataFrame <pandas:pandas.DataFrame>` but will return a complete metadata :class:`dict <python:dict>`. Defaults to ``False``. :type metadata_only: :class:`bool <python:bool>` :param apply_labels: If ``True``, converts the numerically-coded values in the raw data to their human-readable labels. Defaults to ``False``. :type apply_labels: :class:`bool <python:bool>` :param labels_as_categories: If ``True``, will convert labeled or formatted values to Pandas :term:`categories <pandas:category>`. Defaults to ``True``. .. caution:: This parameter will only have an effect if the ``apply_labels`` parameter is ``True``. :type labels_as_categories: :class:`bool <python:bool>` :param missing_as_NaN: If ``True``, will return any missing values as :class:`NaN <pandas:NaN>`. Otherwise will return missing values as per the configuration of missing value representation stored in the underlying SPSS data. Defaults to ``False``, which applies the missing value representation configured in the SPSS data itself. :type missing_as_NaN: :class:`bool <python:bool>` :param convert_datetimes: if ``True``, will convert the native integer representation of datetime values in the SPSS data to Pythonic :class:`datetime <python:datetime.datetime>`, or :class:`date <python:datetime.date>`, etc. representations (or Pandas :class:`datetime64 <pandas:datetime64>`, depending on the ``dates_as_datetime64`` parameter). If ``False``, will leave the original integer representation. Defaults to ``True``. :type convert_datetimes: :class:`bool <python:bool>` :param dates_as_datetime64: If ``True``, will return any date values as Pandas :class:`datetime64 <pandas.datetime64>` types. Defaults to ``False``. .. caution:: This parameter is only applied if ``convert_datetimes`` is set to ``True``. :type dates_as_datetime64: :class:`bool <python:bool>` :returns: A :class:`DataFrame <pandas:DataFrame>` representation of the SPSS data (or :obj:`None <python:None>`) and a :class:`Metadata` representation of the dataset's metadata / data map. :rtype: :class:`pandas.DataFrame <pandas:DataFrame>`/:obj:`None <python:None>` and :class:`Metadata` """ if not any([ checkers.is_file(data), checkers.is_bytesIO(data), checkers.is_type(data, bytes) ]): raise errors.InvalidDataFormatError( 'data must be a filename, BytesIO, or bytes ' f'object. Was: {data.__class__.__name__}') limit = validators.integer(limit, allow_empty=True, minimum=0) offset = validators.integer(offset, minimum=0) exclude_variables = validators.iterable(exclude_variables, allow_empty=True) if exclude_variables: exclude_variables = [validators.string(x) for x in exclude_variables] include_variables = validators.iterable(include_variables, allow_empty=True) if include_variables: include_variables = [validators.string(x) for x in include_variables] if not checkers.is_file(data): with tempfile.NamedTemporaryFile(delete=False) as temp_file: temp_file.write(data) temp_file_name = temp_file.name df, meta = pyreadstat.read_sav( temp_file_name, metadataonly=metadata_only, dates_as_pandas_datetime=dates_as_datetime64, apply_value_formats=apply_labels, formats_as_category=labels_as_categories, usecols=include_variables, user_missing=not missing_as_NaN, disable_datetime_conversion=not convert_datetimes, row_limit=limit or 0, row_offset=offset, **kwargs) os.remove(temp_file_name) else: df, meta = pyreadstat.read_sav( data, metadataonly=metadata_only, dates_as_pandas_datetime=dates_as_datetime64, apply_value_formats=apply_labels, formats_as_category=labels_as_categories, usecols=include_variables, user_missing=not missing_as_NaN, disable_datetime_conversion=not convert_datetimes, row_limit=limit or 0, row_offset=offset, **kwargs) metadata = Metadata.from_pyreadstat(meta) if exclude_variables: df = df.drop(exclude_variables, axis=1) if metadata.column_metadata: for variable in exclude_variables: metadata.column_metadata.pop(variable, None) return df, metadata
def video_hunt_keys(self, val: list): self._video_hunt_keys = validators.iterable(val, allow_empty=True)
def video_hunt_embeddings(self, val: List[List[float]]): self._video_hunt_embeddings = validators.iterable( val, allow_empty=True )