Source code for cate.ops.timeseries

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"""
Description
===========

Simple time-series extraction operation.

Functions
=========
"""

import xarray as xr

from cate.core.op import op_input, op, op_return
from cate.ops.select import select_var
from cate.core.types import VarNamesLike, PointLike
from cate.util.monitor import Monitor


[docs]@op(tags=['timeseries', 'temporal', 'filter', 'point'], version='1.0') @op_input('point', data_type=PointLike) @op_input('method', value_set=['nearest', 'ffill', 'bfill']) @op_input('var', value_set_source='ds', data_type=VarNamesLike) @op_return(add_history=True) def tseries_point(ds: xr.Dataset, point: PointLike.TYPE, var: VarNamesLike.TYPE = None, method: str = 'nearest') -> xr.Dataset: """ Extract time-series from *ds* at given *lon*, *lat* position using interpolation *method* for each *var* given in a comma separated list of variables. The operation returns a new timeseries dataset, that contains the point timeseries for all required variables with original variable meta-information preserved. If a variable has more than three dimensions, the resulting timeseries variable will preserve all other dimensions except for lon/lat. :param ds: The dataset from which to perform timeseries extraction. :param point: Point to extract, e.g. (lon,lat) :param var: Variable(s) for which to perform the timeseries selection if none is given, all variables in the dataset will be used. :param method: Interpolation method to use. :return: A timeseries dataset """ point = PointLike.convert(point) lon = point.x lat = point.y if not var: var = '*' retset = select_var(ds, var=var) indexers = {'lat': lat, 'lon': lon} retset = retset.sel(method=method, **indexers) # The dataset is no longer a spatial dataset -> drop associated global # attributes drop = ['geospatial_bounds_crs', 'geospatial_bounds_vertical_crs', 'geospatial_vertical_min', 'geospatial_vertical_max', 'geospatial_vertical_positive', 'geospatial_vertical_units', 'geospatial_vertical_resolution', 'geospatial_lon_min', 'geospatial_lat_min', 'geospatial_lon_max', 'geospatial_lat_max'] for key in drop: retset.attrs.pop(key, None) return retset
[docs]@op(tags=['timeseries', 'temporal'], version='1.0') @op_input('ds') @op_input('var', value_set_source='ds', data_type=VarNamesLike) @op_return(add_history=True) def tseries_mean(ds: xr.Dataset, var: VarNamesLike.TYPE, std_suffix: str = '_std', calculate_std: bool = True, monitor: Monitor = Monitor.NONE) -> xr.Dataset: """ Extract spatial mean timeseries of the provided variables, return the dataset that in addition to all the information in the given dataset contains also timeseries data for the provided variables, following naming convention 'var_name1_ts_mean' If a data variable with more dimensions than time/lat/lon is provided, the data will be reduced by taking the mean of all data values at a single time position resulting in one dimensional timeseries data variable. :param ds: The dataset from which to perform timeseries extraction. :param var: Variables for which to perform timeseries extraction :param calculate_std: Whether to calculate std in addition to mean :param std_suffix: Std suffix to use for resulting datasets, if std is calculated. :param monitor: a progress monitor. :return: Dataset with timeseries variables """ if not var: var = '*' retset = select_var(ds, var) names = retset.data_vars.keys() with monitor.starting("Calculate mean", total_work=len(names)): for name in names: dims = list(ds[name].dims) dims.remove('time') with monitor.child(1).observing("Calculate mean"): retset[name] = retset[name].mean(dim=dims, keep_attrs=True) retset[name].attrs['Cate_Description'] = 'Mean aggregated over {} at each point in time.'.format(dims) std_name = name + std_suffix retset[std_name] = ds[name].std(dim=dims) retset[std_name].attrs['Cate_Description'] = 'Accompanying std values for variable \'{}\''.format(name) return retset