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mesmer.stats.lowess

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mesmer.stats.lowess#

mesmer.stats.lowess(data, dim, *, combine_dim=None, n_steps=None, frac=None, use_coords=True, it=0)#

LOWESS (Locally Weighted Scatterplot Smoothing) for xarray objects

Parameters:

  • data (xr.DataArray | xr.Dataset) – Data to smooth (y-values).

  • dim (str) – Dimension along which to smooth (x-dimension)

  • combine_dim (str, default: None) – Dimension along which to pool the data. This will stack the data and estimate the smoothing on the stacked data.

  • n_steps (int) – The number of data points used to estimate each y-value, must be between 0 and the length of dim. If given used to calculate frac. Exactly one of n_steps and frac must be given.

  • frac (float) – The fraction of the data used when estimating each y-value. Between 0 and 1. Exactly one of n_steps and frac must be given.

  • use_coords (boolean, default: True) – If True uses data[dim] as x-values else uses np.arange(data[dim].size) (useful if dim are time coordinates).

  • it (int, default: 0) – The number of residual-based re-weightings to perform.

Returns:

out (xr.DataArray | xr.Dataset) – LOWESS smoothed array

See also

statsmodels.nonparametric.smoothers_lowess.lowess

Notes

For it=0, the following three options are equivalent:

mesmer.stats.lowess(data.mean("cells"), "time", frac=0.3)
mesmer.stats.lowess(data, "time", combine_dim="cells", frac=0.3)
mesmer.stats.lowess(data, "time", frac=0.3).mean("cells")
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