API reference#

This page provides an auto-generated summary of mesmers’ API.

Statistical functions#

`LinearRegression`()	Ordinary least squares Linear Regression for xr.DataArray objects.
`fit`(predictors, target, dim[, weights, ...])	Fit a linear model
`predict`(predictors[, exclude])	Predict using the linear model.
`residuals`(predictors, target)	Calculate the residuals of the fitted linear model
`to_netcdf`(filename, **kwargs)	save params to a netCDF file
`from_netcdf`(filename, **kwargs)	read params from a netCDF file

`_select_ar_order_scen_ens`(*objs, dim, ...[, ic])	Select the order of an autoregressive process and potentially calculate the median over ensemble members and scenarios
`_fit_auto_regression_scen_ens`(*objs, dim, ...)	fit an auto regression and potentially calculate the mean over ensemble members and scenarios
`select_ar_order`(data, dim, maxlag[, ic])	Select the order of an autoregressive process
`fit_auto_regression`(data, dim, lags)	fit an auto regression
`draw_auto_regression_uncorrelated`(ar_params, ...)	draw time series of an auto regression process
`draw_auto_regression_correlated`(ar_params, ...)	draw time series of an auto regression process with spatially-correlated innovations

`adjust_covariance_ar1`(covariance, ar_coefs)	adjust localized empirical covariance matrix for autoregressive process of order one
`find_localized_empirical_covariance`(data, ...)	determine localized empirical covariance by cross validation

lowess(data, dim, *[, combine_dim, n_steps, ...])

LOWESS (Locally Weighted Scatterplot Smoothing) for xarray objects

`gaspari_cohn_correlation_matrices`(geodist, ...)	Gaspari-Cohn correlation matrices for a range of localisation radii
`gaspari_cohn`(r)	smooth, exponentially decaying Gaspari-Cohn correlation function

`wrap_to_180`(obj[, lon_name])	wrap array with longitude to [-180..180)
`wrap_to_360`(obj[, lon_name])	wrap array with longitude to [0..360)
`stack_lat_lon`(data, *[, x_dim, y_dim, ...])	Stack a regular lat-lon grid to a 1D (unstructured) grid
`unstack_lat_lon_and_align`(data, coords_orig, *)	unstack an 1D grid to a regular lat-lon grid and align with original coords
`unstack_lat_lon`(data, *[, x_dim, y_dim, ...])	unstack an 1D grid to a regular lat-lon grid but do not align
`align_to_coords`(data, coords_orig)	align an unstacked lat-lon grid with its original coords

`mask_ocean_fraction`(data, threshold, *[, ...])	mask out ocean using fractional overlap
`mask_ocean`(data, *[, x_coords, y_coords])	mask out ocean
`mask_antarctica`(data, *[, y_coords])	mask out ocean
`mask_3D_frac_approx`(regions, lon, lat, **kwargs)	3D mask of the fractional overlap of a set of regions for the given lat/ lon grid

`global_mean`(data[, weights, x_dim, y_dim])	calculate global weighted mean
`lat_weights`(lat_coords)	area weights based on the cosine of the latitude
`weighted_mean`(data, weights[, dims])	weighted mean - convenience function which ignores data_vars missing dims

geodist_exact(lon, lat[, equal_dim_suffixes])

exact great circle distance based on WSG 84

`fit_volcanic_influence`(tas_residuals, ...[, ...])	estimate volcanic influence on temperature residuals using aerosol optical depth observations as proxy
`superimpose_volcanic_influence`(...[, dim, ...])	superimpose volcanic influence on smooth temperature anomalies using aerosol optical depth observations as proxy