geocat.comp.climatologies.climatology#

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

Compute climatologies for a specified time frequency.

Parameters

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

Climatology frequency alias. Accepted alias:
- ‘day’: for daily climatologies
- ‘month’: for monthly climatologies
- ‘year’: for annual climatologies
- ‘season’: for seasonal climatologies
time_coord_name (str, optional) – 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 climatology 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.climatology(ts, 'year')
<xarray.DataArray (year: 2, lat: 1, lon: 1)>
array([[[ 5.5]],
    [[17.5]]])
Coordinates:
* year     (year) int64 2000 2001
Dimensions without coordinates: lat, lon
>>> geocat.comp.climatology(ts, 'season')
<xarray.DataArray (season: 4, lat: 1, lon: 1)>
array([[[10.]],
    [[12.]],
    [[ 9.]],
    [[15.]]])
Coordinates:
* season   (season) object 'DJF' 'JJA' 'MAM' 'SON'
Dimensions without coordinates: lat, lon