Source code for climpred.stats

"""Statistical functions to diagnose potential predictability due to variability."""

from typing import Any, List, Union

import numpy as np
import xarray as xr
from xskillscore import pearson_r

    from xrft import power_spectrum
except ImportError:
    power_spectrum = None

[docs] def rm_poly( ds: Union[xr.Dataset, xr.DataArray], dim: str = "time", deg: int = 2, **kwargs: Any ) -> Union[xr.Dataset, xr.DataArray]: """Remove degree polynomial of degree ``deg`` along dimension ``dim``.""" coefficients = ds.polyfit(dim, deg=deg, **kwargs) coord = ds[dim] fits = [] if isinstance(ds, xr.Dataset): for v in coefficients: name = v.replace("_polyfit_coefficients", "") fit = xr.polyval(coord, coefficients[v]).rename(name) fits.append(fit) fits = xr.merge(fits) elif isinstance(ds, xr.DataArray): name = v = list(coefficients.data_vars)[0] fits = xr.polyval(coord, coefficients[v]).rename(name) with xr.set_options(keep_attrs=True): ds_rm_poly = ds - fits return ds_rm_poly
[docs] def rm_trend( ds: Union[xr.Dataset, xr.DataArray], dim: str = "time", **kwargs: Any ) -> Union[xr.Dataset, xr.DataArray]: """ Remove degree polynomial along dimension ``dim`` Using :py:class:`~climpred.stats.rm_poly` with ``deg = 1``.""" return rm_poly(ds, dim=dim, deg=1, **kwargs)
[docs] def decorrelation_time( da: Union[xr.Dataset, xr.DataArray], iterations: int = 20, dim: str = "time" ) -> Union[xr.Dataset, xr.DataArray]: r"""Calculate the decorrelaton time of a time series. .. math:: \tau_{d} = 1 + 2 * \sum_{k=1}^{r}(\alpha_{k})^{k} Args: da: input. iterations: Number of iterations to run the above formula. dim: Time dimension for xarray object. Returns: Decorrelation time of time series. Reference: * Storch, H. v, and Francis W. Zwiers. Statistical Analysis in Climate Research. Cambridge; New York: Cambridge University Press, 1999., p.373 """ def _lag_corr(x, y, dim, lead): """Help function to shift the two time series and correlate.""" N = x[dim].size normal = x.isel({dim: slice(0, N - lead)}) shifted = y.isel({dim: slice(0 + lead, N)}) # Align dimensions for xarray operation shifted[dim] = normal[dim] return pearson_r(normal, shifted, dim) one = xr.ones_like(da.isel({dim: 0})) one = one.where(da.isel({dim: 0}).notnull()) return one + 2 * xr.concat( [_lag_corr(da, da, dim=dim, lead=i) ** i for i in range(1, iterations)], "it" ).sum("it")
[docs] def dpp( ds: Union[xr.Dataset, xr.DataArray], dim: str = "time", m: int = 10, chunk: bool = True, ) -> Union[xr.Dataset, xr.DataArray]: r"""Calculate the Diagnostic Potential Predictability (DPP). .. math:: DPP_{\mathrm{unbiased}}(m) = \frac{\sigma^{2}_{m} - \frac{1}{m}\cdot\sigma^{2}}{\sigma^{2}} Note: Resplandy et al. 2015 and Seferian et al. 2018 calculate unbiased DPP in a slightly different way: chunk=False. Args: ds: control simulation with time dimension as years. dim: dimension to apply DPP on. Default: ``"time"``. m: separation time scale in years between predictable low-freq component and high-freq noise. chunk: Whether chunking is applied. Default: True. If False, then uses Resplandy 2015 / Seferian 2018 method. Returns: ds without time dimension. References: * Boer, G. J. “Long Time-Scale Potential Predictability in an Ensemble of Coupled Climate Models.” Climate Dynamics 23, no. 1 (August 1, 2004): 29–44. * Resplandy, L., R. Séférian, and L. Bopp. “Natural Variability of CO2 and O2 Fluxes: What Can We Learn from Centuries-Long Climate Models Simulations?” Journal of Geophysical Research: Oceans 120, no. 1 (January 2015): 384–404. * Séférian, Roland, Sarah Berthet, and Matthieu Chevallier. “Assessing the Decadal Predictability of Land and Ocean Carbon Uptake.” Geophysical Research Letters, March 15, 2018. """ def _chunking( ds: Union[xr.Dataset, xr.DataArray], dim: str = "time", number_chunks: Union[bool, int] = False, chunk_length: Union[bool, int] = False, ) -> Union[xr.Dataset, xr.DataArray]: """ Separate data into chunks and reshapes chunks in a c dimension. Specify either the number chunks or the length of chunks. Needed for dpp. Args: ds: control simulation with time dimension as years. dim: dimension to apply chunking to. Default: time chunk_length: see dpp(m) number_chunks: number of chunks in the return data. Returns: chunked ds but with additional dimension c. """ if number_chunks and not chunk_length: chunk_length = np.floor(ds[dim].size / number_chunks) cmin = int(ds[dim].min()) elif not number_chunks and chunk_length: cmin = int(ds[dim].min()) number_chunks = int(np.floor(ds[dim].size / chunk_length)) else: raise KeyError("set number_chunks or chunk_length to True") c = ds.sel({dim: slice(cmin, cmin + chunk_length - 1)}) c = c.expand_dims("c") c["c"] = [0] for i in range(1, number_chunks): c2 = ds.sel( {dim: slice(cmin + chunk_length * i, cmin + (i + 1) * chunk_length - 1)} ) c2 = c2.expand_dims("c") c2["c"] = [i] c2[dim] = c[dim] c = xr.concat([c, c2], "c") return c if not chunk: # Resplandy 2015, Seferian 2018 s2v = ds.rolling({dim: m}).mean().var(dim) s2 = ds.var(dim) if chunk: # Boer 2004 ppvf # first chunk chunked_means = _chunking(ds, dim=dim, chunk_length=m).mean(dim) # sub means in chunks chunked_deviations = _chunking(ds, dim=dim, chunk_length=m) - chunked_means s2v = chunked_means.var("c") s2e = chunked_deviations.var([dim, "c"]) s2 = s2v + s2e dpp = (s2v - s2 / (m)) / s2 return dpp
[docs] def varweighted_mean_period( da: Union[xr.Dataset, xr.DataArray], dim: Union[str, List[str]] = "time", **kwargs: Any, ) -> Union[xr.Dataset, xr.DataArray]: r"""Calculate the variance weighted mean period of time series. .. math:: P_{x} = \frac{\sum_k V(f_k,x)}{\sum_k f_k \cdot V(f_k,x)} Args: da: input data including dim. dim: Name of time dimension. for **kwargs see xrft.power_spectrum Reference: * Branstator, Grant, and Haiyan Teng. “Two Limits of Initial-Value Decadal Predictability in a CGCM." Journal of Climate 23, no. 23 (August 27, 2010): 6292-6311. See also: """ if power_spectrum is None: raise ImportError( "xrft is not installed; see" "" ) if isinstance(da, xr.Dataset): return, dim=dim, **kwargs) da = da.fillna(0.0) # dim should be list if isinstance(dim, str): dim = [dim] assert isinstance(dim, list) ps = power_spectrum(da, dim=dim, **kwargs) # take pos for d in dim: ps = ps.where(ps[f"freq_{d}"] > 0) # weighted average vwmp = ps for d in dim: vwmp = vwmp.sum(f"freq_{d}") / ((vwmp * vwmp[f"freq_{d}"]).sum(f"freq_{d}")) for d in dim: if f"freq_{d}_spacing" in vwmp.coords: del vwmp.coords[f"freq_{d}_spacing"] return vwmp