geocat.comp.climatologies.anomaly#

geocat.comp.climatologies.anomaly(dset, freq, time_coord_name=None)#

Compute anomalies for a specified time frequency.

Parameters

dset (xarray.Dataset, xarray.DataArray) – The data on which to operate
freq (str) –

Anomaly frequency alias. Accepted alias:
- ‘day’: for daily anomalies
- ‘month’: for monthly anomalies
- ‘year’: for annual anomalies
- ‘season’: for seasonal anomalies
time_coord_name (str) – Name for time coordinate to use. Defaults to None and infers the name from the data.

Returns

computed_dset (xarray.Dataset, xarray.DataArray) – The computed anomaly data

Examples

>>> import xarray as xr
>>> import pandas as pd
>>> import numpy as np
>>> import geocat.comp
>>> dates = pd.date_range(start="2000/01/01", freq="M", periods=24)
>>> ts = xr.DataArray(np.arange(24).reshape(24, 1, 1), dims=["time", "lat", "lon"], coords={"time": dates})
>>> ts
<xarray.DataArray (time: 24, lat: 1, lon: 1)>
array([[[ 0]],
    [[ 1]],
    [[ 2]],

    [[21]],
    [[22]],
    [[23]]])
Coordinates:
* time     (time) datetime64[ns] 2000-01-31 2000-02-29 ... 2001-12-31
Dimensions without coordinates: lat, lon
>>> geocat.comp.anomaly(ts, 'season')
<xarray.DataArray (time: 24, lat: 1, lon: 1)>
array([[[-10.]],
    [[ -9.]],
    [[ -7.]],

    [[  6.]],
    [[  7.]],
    [[ 13.]]])
Coordinates:
* time     (time) datetime64[ns] 2000-01-31 2000-02-29 ... 2001-12-31
    season   (time) <U3 'DJF' 'DJF' 'MAM' 'MAM' ... 'SON' 'SON' 'SON' 'DJF'
Dimensions without coordinates: lat, lon