easyclimate.core.stat

Basic statistical analysis of weather and climate variables

Functions

calc_corr_spatial(data_input, x[, time_dim, ...])	Calculate Pearson correlation coefficients and corresponding p-values between spatial data and a time series using scipy.stats.pearsonr.
calc_lead_lag_correlation_coefficients(pcs, ...)	Compute lead-lag correlation coefficients for specified pairs of indexes.
calc_leadlag_corr_spatial(data_input, x, ...)	Calculate Pearson correlation coefficients and corresponding p-values between spatial data and a time series with specified lead or lag shifts, using scipy.stats.pearsonr or xarray methods.
calc_levenetestSpatialPattern_spatial(...[, ...])	Perform Levene test for equal variances of two independent sptial samples along with other axis (i.e. 'time') of scores.
calc_multiple_linear_regression_spatial(...)	Apply multiple linear regression to dataset across spatial dimensions.
calc_non_centered_corr(data_input1, data_input2)	Compute the non-centered (uncentered) correlation coefficient between two xarray DataArrays.
calc_pattern_corr(data_input1, data_input2)	Compute the pattern correlation (non-centered) between two xarray DataArrays over their common spatial dimensions.
calc_timeseries_correlations(da[, dim])	Calculate the correlation matrix between multiple DataArray time series.
calc_ttestSpatialPattern_spatial(...[, dim, ...])	Calculate the T-test for the means of two independent sptial samples along with other axis (i.e. 'time') of scores.
calc_windmask_ttestSpatialPattern_spatial(...)	Generate a significance mask for T-tests on the means of two independent spatial zonal (u) and meridional (v) wind samples, aggregated over the specified dimension (default 'time').
remove_sst_trend(ssta[, spatial_dims])	Remove the global mean SST anomaly trend from the SST anomaly field for EOF/PC analysis and so on.