earthkit.transforms.temporal package

Module contents

Temporal transformations for earthkit data objects.

Typically this is done with an xarray representation of data. Some pandas methods are used for indexing and selecting data.

max(*_args, **kwargs)	Calculate the max of an xarray.dataarray or xarray.dataset along the time/date dimension.
mean(*_args, **kwargs)	Calculate the mean of an xarray.dataarray or xarray.dataset along the time/date dimension.
median(*_args, **kwargs)	Calculate the median of an xarray.dataarray or xarray.dataset along the time/date dimension.
min(*_args, **kwargs)	Calculate the minimum of an xarray.dataarray or xarray.dataset along the time/date dimension.
reduce(dataarray, *_args[, time_dim])	Reduce an xarray.dataarray/dataset along the time/date dimension using a specified how method.
rolling_reduce(dataarray[, window_length, ...])	Return reduced data using a moving window over the time dimension.
std(*_args, **kwargs)	Calculate the standard deviation of an xarray.dataarray/dataset along the time/date dimension.
sum(*_args, **kwargs)	Calculate the sum of an xarray.dataarray/dataset along the time/date dimension.
daily_max(*_args, **kwargs)	Calculate the daily maximum.
daily_mean(*_args, **kwargs)	Return the daily mean of the datacube.
daily_median(*_args, **kwargs)	Return the daily median of the datacube.
daily_min(*_args, **kwargs)	Calculate the daily minimum.
daily_reduce(dataarray[, how, time_dim])	Group data by day and reduce using the given how method.
daily_std(*_args, **kwargs)	Calculate the daily standard deviation.
daily_sum(*_args, **kwargs)	Calculate the daily sum (accumulation).
monthly_max(*_args, **kwargs)	Calculate the monthly max.
monthly_mean(*_args, **kwargs)	Calculate the monthly mean.
monthly_median(*_args, **kwargs)	Calculate the monthly median.
monthly_min(*_args, **kwargs)	Calculate the monthly min.
monthly_reduce(dataarray[, how, time_dim])	Group data by month and reduce using the given how method.
monthly_std(*_args, **kwargs)	Calculate the monthly standard deviation.
monthly_sum(*_args, **kwargs)	Calculate the monthly sum/accumulation along the time dimension.
standardise_time(dataarray[, target_format, ...])	Convert time coordinates to a standard format using the Gregorian calendar.
accumulation_to_rate(dataarray, *_args, ...)	Convert a variable accumulated from the beginning of the forecast to a rate.
deaccumulate(dataarray, *_args, **_kwargs)	Alias for accumulation_to_rate function with rate_units set to 'step_length'.