Understanding pixy output

Output file contents

pixy writes one output file per summary statistic that you request. Each file is a tab-separated table, named [output_prefix]_[stat].txt (e.g. pixy_pi.txt, pixy_watterson_theta.txt). The columns in each file are documented below.

Within-population nucleotide diversity (pi)

File: [prefix]_pi.txt

pop: The ID of the population from the populations file.
chromosome: The chromosome or contig.
window_pos_1: First position of the genomic window.
window_pos_2: Last position of the genomic window.
avg_pi: Average per-site nucleotide diversity for the window. pixy computes the weighted average nucleotide diversity per site over all sites in the window, where weights are determined by the number of genotyped samples at each site.
no_sites: Total number of sites in the window with at least one valid genotype. This is included for the user and is not directly used in any calculation.
count_diffs: Raw number of pairwise differences between all genotypes in the window. This is the numerator of avg_pi.
count_comparisons: Raw number of non-missing pairwise comparisons between all genotypes in the window. This is the denominator of avg_pi.
count_missing: Raw number of missing pairwise comparisons in the window.

Between-population nucleotide divergence (dxy)

File: [prefix]_dxy.txt

pop1, pop2: The IDs of the two populations being compared.
chromosome, window_pos_1, window_pos_2: The chromosome and window coordinates (as for pi).
avg_dxy: Average per-site nucleotide divergence for the window.
no_sites: Number of sites in the window with at least one valid genotype in both populations.
count_diffs, count_comparisons, count_missing: Raw numerator, denominator and missing-comparison counts, defined analogously to the pi file but across the two populations.

F_ST (fst)

File: [prefix]_fst.txt

pop1, pop2: The IDs of the two populations being compared.
chromosome, window_pos_1, window_pos_2: Window coordinates.
avg_wc_fst or avg_hudson_fst: The window-averaged F_ST, per SNP (not per site). Which column name you see depends on --fst_type: wc (Weir & Cockerham 1984, the default) produces avg_wc_fst; hudson (Hudson 1992 / Bhatia et al. 2013) produces avg_hudson_fst.
no_snps: Total number of variable sites (SNPs) in the window.

Watterson's θ (watterson_theta)

File: [prefix]_watterson_theta.txt

Watterson's θ is an estimator of the population mutation rate computed from the number of segregating sites. The unbiased estimator implemented in pixy corrects for missing data and arbitrary ploidy. See Bailey, Stevison & Samuk (2025) for the derivation.

pop: The ID of the population.
chromosome, window_pos_1, window_pos_2: Window coordinates.
avg_watterson_theta: Per-site Watterson's θ for the window.
no_sites: Total number of sites in the window with at least one valid genotype in the focal population.
raw_watterson_theta: Sum of per-site θ contributions over the window, used as the numerator of avg_watterson_theta.
no_var_sites: Number of segregating (variant) sites in the window that contributed to the estimate.
weighted_no_sites: Sum of the per-site weights across the window. Used as the denominator of avg_watterson_theta.

Tajima's D (tajima_d)

File: [prefix]_tajima_d.txt

Tajima's D contrasts two estimators of θ — π (based on pairwise differences) and Watterson's θ (based on segregating sites) — to detect departures from neutrality. pixy reports the unbiased estimator described in Bailey, Stevison & Samuk (2025), which handles missing data correctly.

pop: The ID of the population.
chromosome, window_pos_1, window_pos_2: Window coordinates.
tajima_d: Tajima's D for the window.
no_sites: Total number of sites in the window with at least one valid genotype.
raw_pi, raw_watterson_theta: The unbiased per-site π and Watterson's θ values used to compute D (the numerator is raw_pi - raw_watterson_theta).
tajima_d_stdev: Standard deviation of the D statistic over the window (the denominator).

Working with pixy output data

For plotting workflows (long-format conversion, multi-statistic panels, genome-wide plots) see Plotting pixy output.

Post-hoc aggregating

If you want to combine information across windows after the fact (e.g. by averaging), do not simply average the per-window summary statistics. Instead, sum the raw counts and recompute the ratio. For pi and dxy:

(window 1 count_diffs + window 2 count_diffs) /
(window 1 count_comparisons + window 2 count_comparisons)

The same principle applies to Watterson's θ — sum the raw_watterson_theta and weighted_no_sites columns across windows and divide. For Tajima's D, recompute from the raw π and θ contributions; do not average tajima_d values directly across windows.