Dating a floating series

A floating series is undated wood — a beam from a historic building, a remnant or sub-fossil log — whose rings are measured but not yet tied to calendar years. xdate_floater estimates its calendar placement by sliding it across a dated reference collection and scoring the fit at every offset.

The idea

You need two things:

• a dated reference collection (a DataFrame from readers, indexed by calendar year), and
• the floating series itself (a pandas.Series of ring widths; its index does not need to be calendar years).

xdate_floater builds a master from the reference, transforms the floater the same way, and tests every alignment with at least min_overlap overlapping rings, reporting the correlation and crossdating statistics at each.

Example

import dplpy as dpl

reference = dpl.readers("reference_site.rwl", header=True)
placed = dpl.xdate_floater(reference, floating_series, series_name="beam1")

Best match for 'beam1': 1727 to 1983  (t = 17.83, r = 0.747, eff_df = 254,
p_bonf = 8.52e-44, 1/p = >1 million, IF = >1000, overlap n = 254)

The return value is a dictionary:

• placed["best"] — the top-scoring placement: min_year, max_year, r, t, eff_df, p_bonf, one_over_p, isolation_factor, and n.
• placed["floater_cor_stats"] — every tested offset, highest-t first, so you can inspect the runners-up.
• with return_rwl=True, also placed["placed"] (the floater at its best-fit years) and placed["combined"] (floater merged with the reference).

Reading the statistics

Statistic	What it tells you
r	The sliding correlation between floater and master at that offset.
t	The Baillie & Pilcher (1973) t statistic, r·√(df−2)/√(1−r²). Higher is stronger; values ≥ ~3.5 are the classic acceptance threshold.
eff_df	Effective degrees of freedom, reduced from the raw overlap for autocorrelation (Wigley et al. 1987).
p_bonf	One-tailed p-value of t, Bonferroni-corrected for the many offsets tested.
isolation_factor (IF)	How many times larger the runner-up's p-value is than the best offset's. A large IF means the date stands out cleanly from the alternatives.

The best placement is chosen by the highest t, which accounts for the degrees of freedom at each overlap. A confident date has a high t, a tiny p_bonf, and a large isolation factor — as in the example above.

Useful arguments

• min_overlap (default 50) — the minimum number of overlapping rings an alignment must have to be scored.
• transform (default "pw") and prewhiten — how the series are detrended and prewhitened before correlation.
• corr (default "spearman") — the correlation method.
• make_plot=True — plot the sliding t against end year, marking the best fit.

Relationship to dplR

xdate_floater is based on dplR's xdate.floater and extends it with the t statistic and additional crossdating statistics of Wilson (2026). It differs from dplR in two documented ways: the best placement is selected by highest t (dplR uses the highest correlation r), and dplPy does not apply dplR's future-year trim. See the xdate_floater reference and Coming from dplR.