grid_process.timeseries

Module for operations on timeseries data

Attributes

logger
dataset_accessor
SunInterpOptions
ReindexOptions
InterpMethods
FillnaMethods
XR_INTERP_METHODS

Classes

Timeseries

Xarray accessors class for operations on timeseries

Functions

interptogranularity(→ xarray.Dataset)	Interpolate the dataset to the specified time granularity. Where
fill_na(→ xarray.Dataset)	Fill NaNs in the temporal dimension. Wrapper for different NaN
setindex_validtime(→ xarray.Dataset)	Assign valid time dimension and coordinates (i.e. numpy.datetime64). The dataset
smoothing(→ xarray.Dataset)	Smooth timeseries by applying a moving weighted average.

Module Contents

logger

class Timeseries(ds: xarray.Dataset)

Xarray accessors class for operations on timeseries

dataset_accessor

SunInterpOptions

ReindexOptions

InterpMethods

FillnaMethods

XR_INTERP_METHODS

interptogranularity(ds: xarray.Dataset, granularity: str = '1h', method: InterpMethods = 'linear', **kwargs: Any) → xarray.Dataset

Interpolate the dataset to the specified time granularity. Where the dataset granularity is smaller than the specified one, the dataset granularity remains (no aggregation is performed).

Parameters:

• ds (xarray.Dataset) – Must have a temporal dimension expressed as numpy.datetime64 (valid time) or numpy.timedelta64 (lead time). Typical temporal dimensions names are supported, and can be extended via environment variables:

  • Valid time: export GRID_PROCESS__data_model__validtime (e.g., ‘[“time”, “valid_time”, “dummy_time”]’)

  • Lead time: export GRID_PROCESS__data_model__leadtime (e.g., ‘[“lead_time”, “step”, “dummy_leadtime”]’)

  • Forecast reference time: export GRID_PROCESS__data_model__reftime (e.g., ‘[“forecast_reference_time”, “ref_time”, “reftime”, “dummy_reftime”]’)

  • When the temporal coordinates variables have a CF compliant standard_name attribute, arbitrary names can be used directly.

• granularity (str, optional) – (maximum) granularity of the resulting time series. Must be one of the pandas.date_range freq options. Default is ‘1h’

• method (str, optional) – interpolation method for filling the time series. One of {‘nearest’, ‘bfill’, ‘ffill’, ‘bcum’, ‘fillval’, ‘sundur’, ‘sunrad’, ‘linear’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘polynomial’, ‘barycentric’, ‘krogh’, ‘pchip’, ‘spline’, ‘akima’}. Default is ‘linear’

• **kwargs (any) – keyword arguments passed to the respective interpolation sub-function defined by the method

Returns:

ds_out – contains the full time series resulting from the time interpolation at the specified granularity

Return type:

xarray.Dataset

See also

fill_na(), xarray.Dataset.reindex, xarray.Dataset.interp

Note

Consider using ‘slinear’ instead of ‘linear’ for similar results with possibly faster computations and lower memory footprint.

fill_na(ds: xarray.Dataset, method: FillnaMethods = 'linear', **kwargs: Any) → xarray.Dataset

Fill NaNs in the temporal dimension. Wrapper for different NaN interpolation routines.

Parameters:

• ds (xarray.Dataset) – Must have a temporal dimension expressed as numpy.datetime64 (valid time) or numpy.timedelta64 (lead time). Typical temporal dimensions names are supported, and can be extended via environment variables:

  • Valid time: export GRID_PROCESS__data_model__validtime (e.g., ‘[“time”, “valid_time”, “dummy_time”]’)

  • Lead time: export GRID_PROCESS__data_model__leadtime (e.g., ‘[“lead_time”, “step”, “dummy_leadtime”]’)

  • When the temporal coordinates variables have a CF compliant standard_name attribute, arbitrary names can be used directly.

• method (str) – interpolation method for filling the time series. One of {‘bfill’, ‘ffill’, ‘fillval’, ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘polynomial’, ‘barycentric’, ‘krogh’, ‘pchip’, ‘spline’, ‘akima’}. Default is ‘linear’

• **kwargs (any) – arguments passed to the respective NaN interpolation sub-function defined by the method

Returns:

ds_out – with NaNs filled. No extrapolation is performed, so NaNs on the side of the first and last real values remain NaNs

Return type:

xarray.Dataset

See also

interptogranularity(), xarray.Dataset.interpolate_na, xarray.Dataset.bfill, xarray.Dataset.ffill, xarray.Dataset.fillna

setindex_validtime(ds: xarray.Dataset, last_ref: bool = True) → xarray.Dataset

Assign valid time dimension and coordinates (i.e. numpy.datetime64). The dataset can have zero, one or several forecast reference time and one or several lead time. In case several ref time and lead time are present, the function combines the different model runs by selecting from the most recent one where timestamps overlap.

Parameters:

• ds (xarray.Dataset) – Must have a forecast reference time dimension and/or a lead time one Typical temporal dimensions names are supported, and can be extended via environment variables:

  • Lead time: export GRID_PROCESS__data_model__leadtime (e.g., ‘[“lead_time”, “step”, “dummy_leadtime”]’)

  • Forecast reference time: export GRID_PROCESS__data_model__reftime (e.g., ‘[“forecast_reference_time”, “ref_time”, “reftime”, “dummy_reftime”]’)

  • When the temporal coordinates variables have a CF compliant standard_name attribute, arbitrary names can be used directly.

• last_ref (bool) – whether to add the last forecast reference time. Used only if dimensions forecast reference time and lead time both exists and have length greater than 1. Default is True.

Returns:

ds_out – with a valid time dimension labelled as numpy.datetime64.

Return type:

xarray.Dataset

Note

This function can be used to combine many runs of the same model into a continuous timeseries

smoothing(ds: xarray.Dataset, w: collections.abc.Sequence[float]) → xarray.Dataset

Smooth timeseries by applying a moving weighted average.

Parameters:

• ds (xarray.Dataset) – Must have a valid time or lead time dimension

• w (sequence of floats) – weights of the moving window

Returns:

ds – contains the smoothed timeseries

Return type:

xarray.Dataset