grid_process.time_utils#

Module to wrap some time utility functions

Attributes#

`logger`
`dataset_accessor`

Classes#

TimeUtils

Xarray accessors class for time utility methods

Functions#

`to_datetime_tzaware`(→ pandas.DatetimeIndex)	Converts an array of numpy.datetime64 objects to a timezone-aware
`set_daily`(→ xarray.Dataset)	Sets timestamps of daily aggregations at the start of the day (i.e. 00:00).

Module Contents#

logger#

class TimeUtils(ds: xarray.Dataset)[source]#: Xarray accessors class for time utility methods

dataset_accessor#

to_datetime_tzaware(timestamps: numpy.ndarray) → pandas.DatetimeIndex[source]#

Converts an array of numpy.datetime64 objects to a timezone-aware pandas.DatetimeIndex representing time in UTC.

Parameters:: timestamps (array of naive numpy.datetime64) – assumed to be in UTC time
Returns:: out – UTC timezone aware
Return type:: pandas.DatetimeIndex

set_daily(ds: xarray.Dataset, lt_duplicates: bool = True, lt_hr: int = 0) → xarray.Dataset[source]#

Sets timestamps of daily aggregations at the start of the day (i.e. 00:00). Daily parameters are stored in the DWH following this convention. If lt_duplicates=True, selects correct validtimes between summer and winter local time duplicates first, otherwise assume daily aggregation are for UTC periods.

Parameters:

ds (xarray.Dataset) – with a valid time dimension (numpy.datetime64). When lt_duplicates=True, duplicated timestamps corresponding to summer (UTC+2) and winter (UTC+1) localtime periods are expected as for a dataset returned by interval_stat() with arg lt_duplicates set to True
lt_duplicates (bool) – whether the dataset contains duplicated timestamps corresponding to summer (UTC+2) and winter (UTC+1) localtime periods. Default is True
lt_hr (int) – hour of the day representing the end of the daily period in localtime. Only used when lt_duplicates=True. Default is 0, meaning the end of the period is 00:00 J+1 LT

Returns:

ds_out – dataset where daily timestamps are set to 00:00 of the respective day. Optionally, selection of correct timestamps between summer/winter duplicates based on day of year is done

Return type:

xarray.Dataset