ntpstats — NetworkTime analysis toolkit

Validate, evaluate and study network time synchronisation. ntpstats reads the logs written by today's NTP implementations (ntpd 4.2.8, NTPsec, chrony), measures servers directly with a privacy-preserving SNTP client, and computes the clock-offset, network-delay and frequency-stability statistics used in timing research and in telecom standards (ADEV, MDEV, TDEV, Hadamard, MTIE, floor packet percentage) with confidence intervals and noise identification. A built-in simulator with ground truth turns it into a test bench for synchronisation algorithms.

📖 Documentation: thiagodefreitas.github.io/NetworkTime · Wiki · pip install ntpstats

It started as a Google Summer of Code 2012 project for the NTP Project (kept unchanged in legacy/); version 2 is a complete rewrite. See docs/STATE_OF_THE_ART.md for what changed in NTP since 2012 and what was wrong with the original code, docs/INTEROP.md for live results against public NTP/NTS servers, CHANGELOG.md for releases and ROADMAP.md for where it is going, and docs/LANDSCAPE.md for how it relates to Stable32, TimeLab, allantools, linuxptp, PTP Track Hound and other tools.

Contributions welcome, from industry and research alike: sample logs and instrument exports are as valuable as code. Look for issues labelled good first issue, industry or research, and see CONTRIBUTING.md. Using it in a paper? Please cite it (CITATION.cff).

Highlights

• Every common source, auto-detected:

  • ntpd/NTPsec loopstats, peerstats (per peer, sys.peer picked automatically) and rawstats (offset/delay recomputed from the four on-wire timestamps);
  • chrony tracking.log, measurements.log, statistics.log and refclocks.log (GNSS/PPS reference clocks);
  • PTP: linuxptp ptp4l, phc2sys, ts2phc output from stdout, syslog or journald (monotonic stamps are mapped to UTC when the journal prefix is present);
  • packet captures (pcap/pcapng, including nanosecond and hardware timestamps): NTP exchanges are matched and measured from the capture host's clock;
  • generic CSV and the 2012 estimators.log.

  Every sign convention is normalised to reference − local, following each implementation's documentation (table below).

• Stability analysis done right: non-overlapping and overlapping ADEV, MDEV, TDEV, overlapping Hadamard, total, modified total and time total deviation (TOTDEV, MTOT, TTOT), Theo1, TheoBR, TheoH, MTIE (O(N log N)) and TIErms. The results reproduce the NIST SP 1065 test suites to 7 digits, and each statistic comes with

  • χ² confidence intervals from the exact equivalent degrees of freedom of the discrete power-law model (Monte Carlo verified),
  • per-τ power-law noise identification (lag-1 autocorrelation method),
  • correct τ₀ from the data and gap handling that never invents data (irregular logs are put on a grid; terms that would span a gap are dropped, not interpolated),
  • dynamic (sliding-window) views to expose non-stationarity,
  • limit-mask checks against user-supplied CSV masks (tau,tdev, tau,mtie, … e.g. network TDEV/MTIE limits), with margins and PASS/FAIL,
  • compare a source against a reference (PPS, GNSS or a better server) to get the error's bias, RMS, TDEV and MTIE.

• Works with the tools you have:

  • Stable32 data files in and out (ntpstats convert);
  • an allantools-compatible API (from ntpstats.compat import allantools);
  • Prometheus/OpenMetrics from monitor/watch, including an error bound and rolling TDEV, plus a Grafana dashboard, alert rules and a docker-compose stack in contrib/.

• Network metrics: delay floor and queueing distribution, Mills' offset-vs-delay wedge, asymmetry indicator, floor packet percentage (ITU-T G.8260-style), NTP clock-filter (minimum delay) selection.

• Estimators: two-state Kalman filter with irregular sampling, noise parameters fitted from the data's ADEV, innovation gating, delay-aware measurement weighting and an RTS smoother.

• Research bench: simulated clocks and networks with ground truth, and pluggable estimators scored by ntpstats bench:

  • simulator: power-law oscillator noise (white/flicker PM, white/flicker/random-walk FM), frequency offset, drift, temperature wander, asymmetric queueing paths, route changes, congestion and outages, and multi-server scenarios with falsetickers;
  • reference algorithms: Kalman/RTS, NTP clock filter, chrony-style regression, RADclock-style feed-forward, and RFC 5905 select/cluster/combine;
  • bring your own estimator via a small API or a package entry point; scenarios can be presets or TOML files;
  • reproducible reports as tables, CSV/JSON or self-contained HTML.

• Reports: ntpstats report writes a single offline HTML file with charts, CI tables, network analysis, parameters and input hashes (also a Report button in the UI).

• Measurement clients (they never set the clock):

  • NTPv4 SNTP with a random transmit timestamp (data minimisation), origin check, Kiss-o'-Death and 2036 era handling;
  • NTS (RFC 8915): NTS-KE over TLS 1.3 with certificate and host-name checks, AES-SIV authenticated exchanges and cookie renewal (optional extra ntpstats[nts]);
  • experimental NTPv5 (draft-ietf-ntp-ntpv5-09): v5 header with cookies, timescale and era, plus the NTPv4→v5 upgrade probe;
  • a polite monitor (RATE back-off, jitter), and watch, which samples the local chrony (chronyc -c tracking) or ntpd/NTPsec (ntpq -c rv) without log files.

• Lightweight UI: ntpstats ui starts a local web app. It runs on the Python standard library HTTP server with one static page and uPlot (≈50 kB, bundled, MIT), so there is no Qt, Electron, Node or CDN, and it works offline. Light and dark themes, drag-and-drop, overlay comparison, zoom-to-analyse, PNG/CSV export and a live monitor.

• Small footprint: the only runtime dependency is numpy. matplotlib is optional (static/publication figures).

Install

pip install ntpstats                # core + UI (numpy only)
pip install "ntpstats[plot]"        # + matplotlib figures
pip install "ntpstats[nts]"         # + NTS client (pyOpenSSL, cryptography)
pip install git+https://github.com/thiagodefreitas/NetworkTime.git   # latest master
# from a checkout, for development:
pip install -e ".[test,plot,nts,docs]" && pytest

Python ≥ 3.9.

Quick start

# Web UI (opens your browser at http://127.0.0.1:8123)
ntpstats ui /var/log/chrony/measurements.log /var/log/ntpstats/peerstats

# Summary statistics
ntpstats info /var/log/ntpstats/loopstats.20260928

# Stability table with 95 % confidence intervals and noise identification
ntpstats stability /var/log/chrony/tracking.log -k oadev,mdev,tdev,mtie --ci 0.95

# Check TDEV against a limit mask (CSV "tau,tdev"); exit code 3 on failure
ntpstats stability ptp.log -k tdev,mtie --mask my-tdev-mask.csv

# Validate a client against a reference (e.g. chrony vs a PPS refclock or GNSS host)
ntpstats compare /var/log/chrony/tracking.log /var/log/chrony/refclocks.log --ref-peer PPS0

# Sliding-window stability matrix (time x tau) as CSV
ntpstats dynamic peerstats --peer 192.0.2.10 -k mdev

# Network behaviour of one peer
ntpstats network /var/log/ntpstats/peerstats --peer 192.0.2.10

# Filters (Kalman / RTS smoother / min-delay) -> CSV
ntpstats filter peerstats --peer 192.0.2.10 -m rts -o smoothed.csv

# Publication figure (needs matplotlib)
ntpstats plot peerstats --all-peers -k oadev,tdev --detrend linear -o report.pdf

# Measure a server yourself (never sets the clock)
ntpstats query time.cloudflare.com -c 4
ntpstats monitor pool.ntp.org ptbtime1.ptb.de -i 64 -o mylog.csv

# Test bench: simulate NTP exchanges and score the built-in estimators
ntpstats simulate --preset internet --benchmark

Scenario 'internet': 1350 exchanges, poll 64 s, seed 1
   estimator                               rms       bias  p95 |err|  max |err|
   raw measurements                    1.65 ms    -659 µs    3.61 ms    14.1 ms
   Kalman                               617 µs    -569 µs     945 µs    1.84 ms
   Kalman + delay weighting             183 µs    -139 µs     355 µs    1.31 ms
   RTS smoother + delay weighting       210 µs    -202 µs     293 µs     315 µs
   min-delay filter (8)                 112 µs     761 ns     248 µs     752 µs

More measurement commands:

ntpstats query time.cloudflare.com --nts            # NTS-authenticated (pip install 'ntpstats[nts]')
ntpstats query ntpd-rs.example.net --ntpv5          # experimental NTPv5 draft-09
ntpstats query pool.ntp.org --probe-v5              # does the server offer NTPv5?
ntpstats watch chrony -i 16 -o chrony-live.csv      # local daemon, no log files needed
ntpstats info capture.pcapng                        # NTP exchanges from a packet capture
ntpstats stability /var/log/ptp4l.log -k tdev,mtie  # PTP servo offsets

Benchmarking synchronisation algorithms

ntpstats bench --list                                        # estimators and preset scenarios
ntpstats bench internet falseticker examples/scenarios/*.toml --seeds 1-10 \
        --html bench.html --csv bench.csv                    # full comparison
python examples/04_custom_estimator.py                       # plug in your own algorithm

Example reports: benchmark, peerstats analysis (download and open in a browser).

Some findings the bench makes visible:

• On a congested WAN, the delay-aware estimators (regression, feed-forward, min-delay, delay-weighted Kalman) cut the error 10–30× compared with the raw measurements.
• An asymmetric route change (route-change preset) biases every estimator by half the one-way step, and no amount of filtering can see it.
• RFC 5905 selection rejects falsetickers only when their error exceeds the root distance (≈ delay/2). Smaller ones are indistinguishable from path asymmetry by design.

Common options: --format (override detection), --peer, --all-peers, --start/--end (POSIX seconds or ISO 8601 UTC), --outliers K (drop > K·MAD after detrending), --json/--csv output.

Sign conventions

Enabling the logs

• chrony (/etc/chrony/chrony.conf or /etc/chrony.conf): log tracking measurements statistics refclocks and logdir /var/log/chrony.
• linuxptp: run ptp4l/phc2sys with -m (stdout) or collect journalctl -u ptp4l -o short-iso-precise, which gives UTC time stamps.
• captures: tcpdump -i eth0 -j adapter_unsynced --time-stamp-precision=nano -w ntp.pcap udp port 123.
• ntpd / NTPsec (ntp.conf): statsdir /var/log/ntpstats/, statistics loopstats peerstats rawstats, filegen peerstats file peerstats type day enable (same for the others).

Docker

docker build -t ntpstats .
docker run --rm -p 8123:8123 -v "$PWD:/data" ntpstats ui /data/peerstats --host 0.0.0.0 --no-browser
docker run --rm ntpstats query --nts time.cloudflare.com

Performance

On a modest shared CPU: parsing takes about 1 s per million loopstats lines (2.4 s for peerstats) using numpy's C tokenizer, and a full stability analysis (OADEV/MDEV/TDEV/HDEV with exact-EDF confidence intervals) of 1 million samples takes about 1.2 s. The UI only receives decimated data (min/max per bucket), so it stays responsive with large files.

Python API

from ntpstats import load_one
from ntpstats.stability import series_stability
from ntpstats.analysis import summary, compare
from ntpstats.simulate import Scenario, simulate_ntp
from ntpstats.filters import kalman_series

s = load_one("/var/log/chrony/measurements.log", peer="192.0.2.10")
print(summary(s)["range_90"])
for r in series_stability(s, kinds=("oadev", "tdev"), ci=0.95):
    print(r.kind, r.taus, r.dev, r.lo, r.hi, r.alpha)

meas, truth = simulate_ntp(Scenario(duration=86400, seed=1))
print(compare(kalman_series(meas, smooth=True), truth))

More in examples/: 01_quickstart.py, 02_benchmark_filters.py (a template for evaluating your own algorithm), 03_report_figure.py, and make_example_logs.py, which regenerates the sample logs in examples/data/ in each native format.

Validation

pytest runs about 170 tests (plus ruff lint and coverage in CI), and none of them need network access:

• every estimator is checked against a literal implementation of the NIST SP 1065 sums, against the analytic log-log slopes of the five power-law noise types, and against a frozen table of values from an independent implementation (no third-party stability library is needed);
• the confidence-interval coverage is checked by Monte Carlo;
• noise identification is checked for α = +2 … −2;
• parsers are checked on the line layouts from the ntpd and chrony documentation, and by cross-checking that the same simulated peer reads identically from peerstats, rawstats and chrony logs;
• the SNTP, NTPv5 and NTS clients are checked against local test servers (offset, delay, KoD, spoofed replies, timeouts, 2036 rollover, TAI timescale, forged NTS responses, certificate mismatch);
• the pcap/pcapng readers are checked on synthesised captures (VLAN tags, nanosecond resolution, NTPv4 and v5 matching), and the linuxptp parser on the exact pr_info formats from the linuxptp source;
• the web API is checked, including its CSRF, Host-header and path-traversal protections.

CI runs on Python 3.9, 3.11 and 3.13.

Security notes

The UI binds to 127.0.0.1, rejects foreign Host headers (DNS rebinding), requires a custom header on all state-changing requests (CSRF) and sends a strict Content-Security-Policy. It has no authentication, so do not expose it with --host 0.0.0.0 on untrusted networks. The SNTP client is for measurement only: it does not authenticate time (for NTS use chrony or NTPsec and analyse their logs).

Project layout

src/ntpstats/     parsers, pcap, stability, edf, masks, analysis, network, filters, simulate,
                  estimators, bench, report, sntp (v4/v5), nts, sources (chronyc/ntpq),
                  monitor, cli, plotting
src/ntpstats/web  stdlib HTTP server + static UI (uPlot vendored)
tests/            pytest suite (+ frozen reference data)
examples/         scripts and sample logs
docs/             state of the art, live interop results, example reports, screenshots
legacy/           the original 2012 GSoC code, untouched

License and citation

MIT License. © 2012–2026 Thiago de Freitas (@thiagodefreitas). Free for commercial and non-commercial use; please keep the copyright notice and credit the author (see NOTICE). If you use it in research, please cite it via CITATION.cff. The bundled uPlot is MIT-licensed © Leon Sorokin. The code under legacy/ keeps its original 2012 license.