lilio.utils
Commonly used utility functions for Lilio.
Module Contents
- lilio.utils.check_timeseries(data: pandas.Series | pandas.DataFrame | xarray.DataArray | xarray.Dataset) None[source]
Check if input data contains valid time data.
- Checks if:
Input data is pd.Dataframe/pd.Series/xr.Dataset/xr.DataArray.
Input data has a time index (pd), or a dim named time containing datetime values
- lilio.utils.is_dask_array(data: xarray.DataArray | xarray.Dataset) bool[source]
Check if the xarray dataset/array has any dask arrays.
- lilio.utils.check_time_dim_xarray(data) None[source]
Check if an xarray data has a time dimensions with time data.
- lilio.utils.check_time_dim_pandas(data) None[source]
Check if pandas data has an index with time data.
- lilio.utils.check_empty_intervals(indices_list: list[numpy.ndarray]) None[source]
Check for empty intervals in the resampling data.
- Parameters:
indices_list – A list, where each item is an array with indices corresponding to the to-be-resampled data’s time axis.
- Raises:
UserWarning – If the data is insufficient.
- Returns:
None
- lilio.utils.infer_input_data_freq(data: pandas.Series | pandas.DataFrame | xarray.DataArray | xarray.Dataset) pandas.Timedelta[source]
Infer the frequency of the input data, for comparison with the calendar freq.
- Parameters:
data – Pandas or xarray data to infer the frequency of.
- Returns:
a pd.Timedelta
- lilio.utils.replace_month_length(length: str) str[source]
Replace month lengths with an equivalent length in days.
- lilio.utils.get_smallest_calendar_freq(calendar: lilio.Calendar) pandas.Timedelta[source]
Return the smallest length of the calendar’s intervals as a Timedelta.
- lilio.utils.check_input_frequency(calendar: lilio.Calendar, data: pandas.Series | pandas.DataFrame | xarray.DataArray | xarray.Dataset)[source]
Compare the frequency of (input) data to the frequency of the calendar.
Note: Pandas and xarray have the builtin function infer_freq, but this function is not robust enough for our purpose, so we have to manually infer the frequency if the builtin one fails.
- lilio.utils.convert_interval_to_bounds(data: xarray.Dataset) xarray.Dataset[source]
Convert pandas intervals to bounds in a xarray Dataset.
pd.Interval objects cannot be written to netCDF. To allow writing the calendar-resampled data to netCDF these intervals have to be converted to bounds. This function adds a ‘bounds’ dimension, with ‘left’ and ‘right’ coordinates, and converts the ‘interval’ coordinates to this system.
- Parameters:
data – Input data with intervals as pd.Interval objects.
- Returns:
Input data with the intervals converted to bounds.
- lilio.utils.check_reserved_names(input_data: pandas.Series | pandas.DataFrame | xarray.DataArray | xarray.Dataset) None[source]
Check if reserved names are already in the input data. E.g. “anchor_year”.
- lilio.utils.get_month_names() dict[source]
Generate a dictionary with English lowercase month names and abbreviations.
- Returns:
- Dictionary containing the English names of the months, including their
abbreviations, linked to the number of each month. E.g. {‘december’: 12, ‘jan’: 1}