lilio.calendar

Lilio’s main Calendar module.

Module Contents

class lilio.calendar.Interval(role: Literal[target, precursor], length: str | dict, gap: str | dict = '0d')

Basic construction element of calendar for defining precursors and targets.

Construct the basic element of the calendar.

The Interval is characterised by its type (either target or precursor), its length and the gap between it and the previous interval of its type (or the anchor date, if the interval is the first target/first precursor).

Parameters:

• role – The type of interval. Either “target” or “precursor”.

• length – The length of the interval. This can either be a pandas-like frequency string (e.g. “10d”, “2W”, or “3M”), or a pandas.DateOffset compatible dictionary such as {days=10}, {weeks=2}, or {months=1, weeks=2}.

• gap – The gap between the previous interval and this interval. Valid inputs are the same as the length keyword argument. Defaults to “0d”.

Example

>>> from lilio import Interval
>>> iv = Interval("target", length="7d")
>>> iv
Interval(role='target', length='7d', gap='0d')

You can modify the interval’s properties in-place:

>>> iv.gap = "1W"
>>> iv
Interval(role='target', length='7d', gap='1W')

property is_target

Return whether this Intervals is a target interval.

property role

Return the type of interval.

property length

Return the length of the interval, as a pandas.DateOffset.

property length_dateoffset

Return the length property as a dateoffset.

property gap

Returns the gap of the interval, as a pandas.DateOffset.

property gap_dateoffset

Get the gap property as a dateoffset.

class lilio.calendar.Calendar(anchor: str, allow_overlap: bool = False, mapping: None | _MappingYears | _MappingData = None, intervals: None | list[Interval] = None)

Build a calendar from scratch with basic construction elements.

Instantiate a basic container for building calendar using basic blocks.

This is a highly flexible calendar which allows the user to build their own calendar with the basic building blocks of target and precursor periods.

Users have the freedom to create calendar with customized intervals, gap between intervals, and even overlapped intervals. They need to manage the calendar themselves.

Some shorthand calendars, such as a daily_calendar, weekly_calendar and monthly_calendar are available in lilio.calendar_shorthands. These can be used to easily construct basic calendars with only a few parameters, but do not have the flexibility that this calendar builder module provides.

Parameters:

• anchor –

  String denoting the anchor date. The following inputs are valid: - “MM-DD” for a month and day. E.g. “12-31”. - “MM” for only a month, e.g. “4” for March. - English names and abbreviations of months. E.g. “December” or

  ”jan”.

  • ”Www” for a week number, e.g. “W05” for the fifth week of the

    year.

  • ”Www-D” for a week number plus day of week. E.g. “W01-4” for the

    first thursday of the year.

• allow_overlap – If overlapping intervals between years is allowed or not. Default behaviour is False, which means that anchor years will be skipped to avoid data being shared between anchor years.

• mapping – Calendar mapping. Input in the form: (“years”, 2000, 2020) or (“data”, pd.Timestamp(“2000-01-01”), pd.Timestamp(“2020-01-01”)). The calendar mapping is usually set with the map_years or map_to_data methods.

• intervals – A list of Interval objects that should be appended to the calendar when it is initialized.

Example

Instantiate a custom calendar and appending target/precursor periods.

>>> import lilio
>>> calendar = lilio.Calendar(anchor="12-31")
>>> calendar  
Calendar(
    anchor='12-31',
    allow_overlap=False,
    mapping=None,
    intervals=None
)

property n_targets: int

Return the number of targets.

property n_precursors: int

Return the number of precursors.

property anchor

Return the anchor.

property allow_overlap

if overlapping intervals are allowed or not.

Type:

Returns the allow_overlap

property mapping: None | Literal[years, data]

Return the mapping of the calendar. Either None, “years”, or “data”.

property flat: pandas.DataFrame

Returns the flattened intervals.

add_intervals(role: Literal[target, precursor], length: str, gap: str = '0d', n: int = 1) → None

Add one or more intervals to the calendar.

The interval can be a target or a precursor, and can be defined by its length, a possible gap between this interval and the preceding interval.

Parameters:

• role – Either a ‘target’ or ‘precursor’ interval(s).

• length – The length of the interval(s), in a format of ‘5d’ for five days, ‘2W’ for two weeks, or ‘1M’ for one month.

• gap – The gap between this interval and the preceding target/precursor interval. Same format as the length argument.

• n – The number of intervals which should be added to the calendar. Defaults to 1.

map_years(start: int, end: int)

Add a start and end year mapping to the calendar.

If the start and end years are the same, the intervals for only that single year are returned by calendar.get_intervals().

Parameters:

• start – The first year for which the calendar will be realized

• end – The last year for which the calendar will be realized

Returns:

The calendar mapped to the input start and end year.

map_to_data(input_data: pandas.Series | pandas.DataFrame | xarray.Dataset | xarray.DataArray)

Map the calendar to input data period.

Stores the first and last intervals of the input data to the calendar, so that the intervals can cover the data to the greatest extent.

Parameters:

input_data – Input data for datetime mapping. Its index must be either pandas.DatetimeIndex, or an xarray time coordinate with datetime data.

Returns:

The calendar mapped to the input data period.

get_intervals() → pandas.DataFrame

Retrieve updated intervals from the Calendar object.

show() → pandas.DataFrame

Display the intervals the Calendar will generate for the current setup.

Returns:

Dataframe containing the calendar intervals.

Return type:

pd.Dataframe

visualize(n_years: int = 3, interactive: bool = False, relative_dates: bool = False, show_length: bool = False, add_legend: bool = True, ax=None, **bokeh_kwargs) → None

Plot a visualization of the current calendar setup, to aid in user setup.

Note: The interactive visualization requires the bokeh package to be installed in the active Python environment.

Parameters:

• n_years – Sets the maximum number of anchor years that should be shown. By default only the most recent 3 are visualized, to ensure that they fit within the plot.

• interactive – If False, matplotlib will be used for the visualization. If True, bokeh will be used.

• relative_dates – Toggles if the intervals should be displayed relative to the anchor date, or as absolute dates.

• show_length – Toggles if the frequency of the intervals should be displayed. Defaults to False (Matplotlib plotter only).

• add_legend – Toggles if a legend should be added to the plot (Matplotlib only)

• ax – Matplotlib axis object to plot the visualization into.

• **bokeh_kwargs – Keyword arguments to pass to Bokeh’s plotting.figure. See https://docs.bokeh.org/en/latest/docs/reference/plotting/figure.html for a list of possible keyword arguments.