239 lines
8.5 KiB
Python
239 lines
8.5 KiB
Python
import datetime
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from dataclasses import dataclass
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from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
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@dataclass
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class FincalOptions:
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date_format: str = '%Y-%m-%d'
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closest: str = 'before' # after
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@dataclass(frozen=True)
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class Frequency:
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name: str
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freq_type: str
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value: int
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days: int
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symbol: str
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class AllFrequencies:
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D = Frequency('daily', 'days', 1, 1, 'D')
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W = Frequency('weekly', 'days', 7, 7, 'W')
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M = Frequency('monthly', 'months', 1, 30, 'M')
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Q = Frequency('quarterly', 'months', 3, 91, 'Q')
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H = Frequency('half-yearly', 'months', 6, 182, 'H')
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Y = Frequency('annual', 'years', 1, 365, 'Y')
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def _preprocess_timeseries(
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data: Union[
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Sequence[Iterable[Union[str, datetime.datetime, float]]],
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Sequence[Mapping[str, Union[float, datetime.datetime]]],
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Sequence[Mapping[Union[str, datetime.datetime], float]],
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Mapping[Union[str, datetime.datetime], float]
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],
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date_format: str
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) -> List[Tuple[datetime.datetime, float]]:
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"""Converts any type of list to the correct type"""
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if isinstance(data, Sequence):
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if isinstance(data[0], Mapping):
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if len(data[0].keys()) == 2:
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current_data = [tuple(i.values()) for i in data]
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elif len(data[0].keys()) == 1:
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current_data = [tuple(*i.items()) for i in data]
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else:
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raise TypeError("Could not parse the data")
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current_data = _preprocess_timeseries(current_data, date_format)
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elif isinstance(data[0], Sequence):
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if isinstance(data[0][0], str):
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current_data = []
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for i in data:
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row = datetime.datetime.strptime(i[0], date_format), i[1]
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current_data.append(row)
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elif isinstance(data[0][0], datetime.datetime):
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current_data = [(i, j) for i, j in data]
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else:
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raise TypeError("Could not parse the data")
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else:
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raise TypeError("Could not parse the data")
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elif isinstance(data, Mapping):
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current_data = [(k, v) for k, v in data.items()]
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current_data = _preprocess_timeseries(current_data, date_format)
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else:
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raise TypeError("Could not parse the data")
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current_data.sort()
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return current_data
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def _preprocess_match_options(as_on_match: str, prior_match: str, closest: str) -> datetime.timedelta:
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"""Checks the arguments and returns appropriate timedelta objects"""
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deltas = {'exact': 0, 'previous': -1, 'next': 1}
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if closest not in deltas.keys():
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raise ValueError(f"Invalid closest argument: {closest}")
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as_on_match = closest if as_on_match == 'closest' else as_on_match
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prior_match = closest if prior_match == 'closest' else prior_match
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if as_on_match in deltas.keys():
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as_on_delta = datetime.timedelta(days=deltas[as_on_match])
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else:
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raise ValueError(f"Invalid as_on_match argument: {as_on_match}")
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if prior_match in deltas.keys():
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prior_delta = datetime.timedelta(days=deltas[prior_match])
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else:
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raise ValueError(f"Invalid prior_match argument: {prior_match}")
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return as_on_delta, prior_delta
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class IndexSlicer:
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def __init__(self, parent_obj):
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self.parent = parent_obj
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def __getitem__(self, n):
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all_keys = list(self.parent.time_series)
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if isinstance(n, int):
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keys = [all_keys[n]]
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else:
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keys = all_keys[n]
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item = [(key, self.parent.time_series[key]) for key in keys]
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if len(item) == 1:
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return item[0]
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return item
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class TimeSeriesCore:
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"""Defines the core building blocks of a TimeSeries object"""
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def __init__(
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self,
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data: List[Iterable],
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frequency: Literal['D', 'W', 'M', 'Q', 'H', 'Y'],
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date_format: str = "%Y-%m-%d"
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):
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"""Instantiate a TimeSeries object
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Parameters
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----------
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data : List[tuple]
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Time Series data in the form of list of tuples.
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The first element of each tuple should be a date and second element should be a value.
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date_format : str, optional, default "%Y-%m-%d"
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Specify the format of the date
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Required only if the first argument of tuples is a string. Otherwise ignored.
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frequency : str, optional, default "infer"
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The frequency of the time series. Default is infer.
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The class will try to infer the frequency automatically and adjust to the closest member.
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Note that inferring frequencies can fail if the data is too irregular.
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Valid values are {D, W, M, Q, H, Y}
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"""
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data = _preprocess_timeseries(data, date_format=date_format)
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self.time_series = dict(data)
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self.dates = list(self.time_series)
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if len(self.time_series) != len(data):
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print("Warning: The input data contains duplicate dates which have been ignored.")
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self.start_date = self.dates[0]
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self.end_date = self.dates[-1]
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self.frequency = getattr(AllFrequencies, frequency)
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def _get_printable_slice(self, n: int):
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"""Returns a slice of the dataframe from beginning and end"""
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printable = {}
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iter_f = iter(self.time_series)
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first_n = [next(iter_f) for i in range(n//2)]
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iter_b = reversed(self.time_series)
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last_n = [next(iter_b) for i in range(n//2)]
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last_n.sort()
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printable['start'] = [str((i, self.time_series[i])) for i in first_n]
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printable['end'] = [str((i, self.time_series[i])) for i in last_n]
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return printable
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def __repr__(self):
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if len(self.time_series) > 6:
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printable = self._get_printable_slice(6)
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printable_str = "{}([{}\n\t ...\n\t {}], frequency={})".format(
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self.__class__.__name__,
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',\n\t '.join(printable['start']),
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',\n\t '.join(printable['end']),
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repr(self.frequency.symbol)
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)
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else:
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printable_str = "{}([{}], frequency={})".format(
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self.__class__.__name__,
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',\n\t'.join([str(i) for i in self.time_series.items()]),
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repr(self.frequency.symbol)
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)
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return printable_str
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def __str__(self):
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if len(self.time_series) > 6:
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printable = self._get_printable_slice(6)
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printable_str = "[{}\n ...\n {}]".format(
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',\n '.join(printable['start']),
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',\n '.join(printable['end']),
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)
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else:
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printable_str = "[{}]".format(',\n '.join([str(i) for i in self.time_series.items()]))
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return printable_str
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def __getitem__(self, key):
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if isinstance(key, int):
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raise KeyError(f"{key}. For index based slicing, use .iloc[{key}]")
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elif isinstance(key, datetime.datetime):
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item = (key, self.time_series[key])
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if isinstance(key, str):
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try:
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dt_key = datetime.datetime.strptime(key, FincalOptions.date_format)
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item = (dt_key, self.time_series[dt_key])
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except ValueError:
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raise KeyError(f"{repr(key)}. If you passed a date as a string, "
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"try setting the date format using Fincal.Options.date_format")
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except KeyError:
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raise KeyError(f"{repr(key)}. This date is not available.")
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elif isinstance(key, Sequence):
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item = [(k, self.time_series[k]) for k in key]
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else:
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raise TypeError(f"Invalid type {repr(type(key).__name__)} for slicing.")
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return item
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def __len__(self):
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return len(self.time_series)
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def head(self, n: int = 6):
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"""Returns the first n items of the TimeSeries object"""
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keys = list(self.time_series.keys())
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keys = keys[:n]
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result = [(key, self.time_series[key]) for key in keys]
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return result
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def tail(self, n: int = 6):
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"""Returns the last n items of the TimeSeries object"""
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keys = list(self.time_series.keys())
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keys = keys[-n:]
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result = [(key, self.time_series[key]) for key in keys]
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return result
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@property
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def iloc(self):
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"""Returns an item or a set of items based on index"""
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return IndexSlicer(self)
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