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added itb bti comparison.

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This commit is contained in:
Felix Delattre 2026-06-17 11:55:42 +02:00
parent a8852bc997
commit f188dd38ab
3 changed files with 444 additions and 14 deletions
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252
6-statistics-fusion-order.py Normal file
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@ -0,0 +1,252 @@
"""Step 6: Paired ItB-vs-BtI significance test across the full sample.
Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
- ``metrics/{year}/{site}/metrics.json`` — per-site validation metrics (Step 5)
Outputs (``data/statistics_fusion_order/``):
- ``{year}.json`` — paired Wilcoxon + t-test summary for NSE, RMSE, nRMSE, r
CLI:
- ``--evaluation-year`` (default 2025)
- ``--alpha`` (default 0.05) — significance threshold for ``better_order``
This step aggregates across all sites with Step 5 output; it does not accept
``--site`` (a single-site filter would not support a sample-level test).
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import numpy as np
from scipy.stats import ttest_rel, wilcoxon
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
DATA_DIR = Path("data")
DEFAULT_YEAR = 2025
DEFAULT_ALPHA = 0.05
METRICS = ["nse", "rmse", "nrmse", "r"]
LOWER_IS_BETTER = {"rmse", "nrmse"}
MIN_PAIRS_WARNING = 6
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _r4(v: float | None) -> float | None:
return round(v, 4) if v is not None else None
def _load_site_metrics(year: int) -> list[dict[str, Any]]:
"""Return parsed ``metrics.json`` payloads for every site under ``{year}``."""
metrics_dir = DATA_DIR / "metrics" / str(year)
if not metrics_dir.is_dir():
return []
payloads: list[dict[str, Any]] = []
for site_dir in sorted(metrics_dir.iterdir()):
if not site_dir.is_dir():
continue
path = site_dir / "metrics.json"
if not path.is_file():
continue
payloads.append(json.loads(path.read_text()))
return payloads
def collect_pairs(
site_metrics: list[dict[str, Any]], metric: str
) -> tuple[list[float], list[float], int]:
"""Return paired BtI / ItB values for ``metric`` and count of dropped sites."""
bti_vals: list[float] = []
itb_vals: list[float] = []
n_dropped = 0
for payload in site_metrics:
bti = payload.get("bti")
itb = payload.get("itb")
if not isinstance(bti, dict) or not isinstance(itb, dict):
n_dropped += 1
continue
bti_v = bti.get(metric)
itb_v = itb.get(metric)
if bti_v is None or itb_v is None:
n_dropped += 1
continue
bti_vals.append(float(bti_v))
itb_vals.append(float(itb_v))
return bti_vals, itb_vals, n_dropped
def _better_order(
bti_vals: list[float],
itb_vals: list[float],
metric: str,
p_value: float | None,
alpha: float,
) -> str:
"""Name the better fusion order when Wilcoxon p < alpha, else no difference."""
if p_value is None or p_value >= alpha:
return "no significant difference"
mean_diff = float(np.mean(itb_vals) - np.mean(bti_vals))
if metric in LOWER_IS_BETTER:
return "itb" if mean_diff < 0 else "bti"
return "itb" if mean_diff > 0 else "bti"
def paired_test(
bti_vals: list[float],
itb_vals: list[float],
metric: str,
alpha: float,
) -> dict[str, Any]:
"""Run paired Wilcoxon (primary) and t-test; return summary dict."""
n_pairs = len(bti_vals)
bti_arr = np.array(bti_vals, dtype=float)
itb_arr = np.array(itb_vals, dtype=float)
diffs = itb_arr - bti_arr
result: dict[str, Any] = {
"n_pairs": n_pairs,
"bti_mean": _r4(float(bti_arr.mean())) if n_pairs else None,
"bti_median": _r4(float(np.median(bti_arr))) if n_pairs else None,
"itb_mean": _r4(float(itb_arr.mean())) if n_pairs else None,
"itb_median": _r4(float(np.median(itb_arr))) if n_pairs else None,
"mean_diff": _r4(float(diffs.mean())) if n_pairs else None,
"median_diff": _r4(float(np.median(diffs))) if n_pairs else None,
"wilcoxon": {"statistic": None, "p_value": None},
"ttest": {"statistic": None, "p_value": None},
"better_order": "insufficient data",
}
if n_pairs < 2:
return result
wilcoxon_stat: float | None = None
wilcoxon_p: float | None = None
if np.any(diffs != 0):
try:
w_stat, w_p = wilcoxon(itb_arr, bti_arr)
wilcoxon_stat = float(w_stat)
wilcoxon_p = float(w_p)
except ValueError:
pass
t_stat, t_p = ttest_rel(itb_arr, bti_arr)
result["wilcoxon"] = {
"statistic": _r4(wilcoxon_stat),
"p_value": _r4(wilcoxon_p),
}
result["ttest"] = {
"statistic": _r4(float(t_stat)),
"p_value": _r4(float(t_p)),
}
result["better_order"] = _better_order(
bti_vals, itb_vals, metric, wilcoxon_p, alpha
)
return result
def _print_summary(
year: int, alpha: float, n_sites_total: int, metrics_out: dict
) -> None:
print(f"\nPaired ItB vs BtI test — {year} (alpha={alpha}, sites={n_sites_total})")
print(
f"{'metric':<8} {'n':>4} {'BtI mean':>10} {'ItB mean':>10} "
f"{'diff':>10} {'W p':>8} {'t p':>8} better"
)
print("-" * 78)
for metric in METRICS:
m = metrics_out[metric]
bti_mean = m["bti_mean"]
itb_mean = m["itb_mean"]
mean_diff = m["mean_diff"]
w_p = m["wilcoxon"]["p_value"]
t_p = m["ttest"]["p_value"]
better = m["better_order"]
def _fmt(v: float | None) -> str:
return f"{v:10.4f}" if v is not None else f"{'—':>10}"
print(
f"{metric:<8} {m['n_pairs']:>4} {_fmt(bti_mean)} {_fmt(itb_mean)} "
f"{_fmt(mean_diff)} "
f"{w_p if w_p is not None else '—':>8} "
f"{t_p if t_p is not None else '—':>8} {better}"
)
if 0 < m["n_pairs"] < MIN_PAIRS_WARNING:
print(
f" warning: only {m['n_pairs']} pair(s) for {metric}; "
"interpret p-values cautiously"
)
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
parser.add_argument(
"--alpha",
type=float,
default=DEFAULT_ALPHA,
help="Significance threshold for better_order (default 0.05)",
)
args = parser.parse_args()
year = args.evaluation_year
alpha = args.alpha
site_metrics = _load_site_metrics(year)
n_sites_total = len(site_metrics)
if n_sites_total == 0:
raise SystemExit(
f"No Step 5 metrics found under {DATA_DIR / 'metrics' / str(year)}"
)
metrics_out: dict[str, Any] = {}
for metric in METRICS:
bti_vals, itb_vals, n_dropped = collect_pairs(site_metrics, metric)
summary = paired_test(bti_vals, itb_vals, metric, alpha)
summary["n_dropped"] = n_dropped
metrics_out[metric] = summary
payload = {
"year": year,
"alpha": alpha,
"n_sites_total": n_sites_total,
"metrics": metrics_out,
}
out_dir = DATA_DIR / "statistics_fusion_order"
out_dir.mkdir(parents=True, exist_ok=True)
out_path = out_dir / f"{year}.json"
out_path.write_text(json.dumps(payload, separators=(",", ":")))
_print_summary(year, alpha, n_sites_total, metrics_out)
print(f"\nWritten → {out_path}")
if __name__ == "__main__":
main()

7
README.md
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@ -1,6 +1,6 @@
# EFAST fusion with phenocam validation.
End-to-end pipeline from selecting sites from the global [PhenoCam Network](https://phenocam.nau.edu/) to run [EFAST](https://github.com/DHI-GRAS/efast) spatio-temporal fusion with Sentinel-2 / Sentinel-3 and validate GCCs accross sensors. The five numbered steps cover site selection, Sentinel data acquisition, different fusion orders, and accuracy metrics, all feeding a static web QA viewer.
End-to-end pipeline from selecting sites from the global [PhenoCam Network](https://phenocam.nau.edu/) to run [EFAST](https://github.com/DHI-GRAS/efast) spatio-temporal fusion with Sentinel-2 / Sentinel-3 and validate GCCs accross sensors. The numbered steps cover site selection, Sentinel data acquisition, different fusion orders, accuracy metrics, and sample-level statistics, all feeding a static web QA viewer.
---
@ -13,6 +13,7 @@ End-to-end pipeline from selecting sites from the global [PhenoCam Network](http
| 3 | `3-sentinel-data.py` | Acquire S2 (Earth Search COG) and S3 OLCI SYN L2 (CDSE OpenEO); prepare REFL, DIST_CLOUD, and composite GeoTIFFs |
| 4 | `4-fusion.py` | Run EFAST BtI (fuse reflectance → GCC) and ItB (fuse GCC directly) for each screened site |
| 5 | `5-metrics.py` | Extract PhenoCam-matched timeseries, compute NSE/RMSE/r baselines and fusion metrics, emit per-site JSON and webapp manifest |
| 6 | `6-statistics-fusion-order.py` | Paired ItB-vs-BtI significance test (Wilcoxon + t-test) across all sites |
---
@ -41,9 +42,10 @@ uv run python 2-phenocam-screening.py --evaluation-year 2025
uv run python 3-sentinel-data.py --evaluation-year 2025
uv run python 4-fusion.py --evaluation-year 2025
uv run python 5-metrics.py --evaluation-year 2025
uv run python 6-statistics-fusion-order.py --evaluation-year 2025
```
All steps accept `--evaluation-year` (default `2025`) and `--site` (optional, for single-site runs). Steps 3–5 are resumable — existing output files are skipped.
Steps 1–5 accept `--evaluation-year` (default `2025`) and `--site` (optional, for single-site runs). Step 6 is a full-sample aggregate and only accepts `--evaluation-year` and `--alpha` (default `0.05`). Steps 3–5 are resumable — existing output files are skipped.
```bash
# single site
@ -70,6 +72,7 @@ Step 3 S3 download uses CDSE OpenEO (`SENTINEL3_SYN_L2_SYN`). Set `CDSE_USER` an
| `fusion/{year}/{site}/bti/`, `.../itb/` | 4 | BtI fused reflectance + GCC; ItB fused GCC |
| `metrics/{year}/{site}/` | 5 | Per-site timeseries, metrics, covariates JSON |
| `metrics/manifest.json` | 5 | Webapp manifest (years + site metadata) |
| `statistics_fusion_order/{year}.json` | 6 | Paired ItB-vs-BtI test summary (NSE, RMSE, nRMSE, r) |
---

199
index.html
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@ -147,14 +147,42 @@ body { margin: 0; font: 13px/1.4 system-ui, sans-serif; background: #f5f5f5; col
display: flex; align-items: center; gap: 12px; padding: 10px 14px;
background: #1a1a2e; color: #eee; flex-shrink: 0;
}
#worldHeader h2 { margin: 0; font-size: 14px; font-weight: 600; color: #7eb8f7; flex: 1; }
#worldHeader .world-meta { font-size: 12px; color: #aaa; }
#overlayTabs { display: flex; gap: 4px; flex: 1; }
.otab {
padding: 4px 12px; border-radius: 4px; font-size: 13px; cursor: pointer;
border: 1px solid #555; background: transparent; color: #ccc;
}
.otab.active { background: #2a3f5f; color: #dceeff; border-color: #4a6fa5; }
.otab:hover:not(.active) { background: rgba(255, 255, 255, 0.07); }
#worldHeader .world-meta { font-size: 12px; color: #aaa; flex-shrink: 0; }
#worldClose {
font-size: 13px; padding: 4px 12px; border-radius: 4px;
border: 1px solid #666; background: transparent; color: #ddd; cursor: pointer;
flex-shrink: 0;
}
#worldClose:hover { background: rgba(255, 255, 255, 0.08); }
#worldMap { flex: 1; min-height: 0; }
#statsPanel { flex: 1; min-height: 0; overflow-y: auto; padding: 20px 24px; display: none; background: #f5f5f5; }
.stat-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(340px, 1fr)); gap: 14px; margin-top: 4px; }
.stat-card { background: #fff; border: 1px solid #e0e0e0; border-radius: 6px; padding: 14px 16px; }
.stat-card h3 { margin: 0 0 10px; font-size: 14px; color: #1a1a2e; display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
.stat-badge {
font-size: 11px; padding: 2px 7px; border-radius: 10px; font-weight: 600;
background: #e8f5e9; color: #1a6e2e;
}
.stat-badge.itb { background: #fff3e0; color: #c75c00; }
.stat-badge.bti { background: #e3f2fd; color: #0d47a1; }
.stat-badge.none { background: #f5f5f5; color: #777; font-weight: 400; }
.stat-badge.insuf { background: #fce4ec; color: #b71c1c; font-weight: 400; }
.stat-row-table { width: 100%; border-collapse: collapse; font-size: 12px; }
.stat-row-table td { padding: 3px 0; vertical-align: top; }
.stat-row-table .slabel { color: #888; width: 46%; }
.stat-row-table .sval { color: #222; font-variant-numeric: tabular-nums; }
.stat-pval { display: inline-block; font-family: monospace; }
.stat-pval.sig { color: #1a6e2e; font-weight: 600; }
.stat-divider { border: none; border-top: 1px solid #f0f0f0; margin: 8px 0; }
.stat-summary { font-size: 12px; color: #666; margin-bottom: 14px; }
.stat-nodata { color: #999; padding: 40px; text-align: center; font-size: 13px; }
.world-popup { font-size: 12px; line-height: 1.35; }
.world-popup b { display: block; margin-bottom: 2px; }
.world-popup .veg { color: #2e7d32; font-size: 11px; }
@ -173,11 +201,15 @@ body { margin: 0; font: 13px/1.4 system-ui, sans-serif; background: #f5f5f5; col
<div id="worldOverlay" aria-hidden="true">
<div id="worldPanel">
<div id="worldHeader">
<h2>Worldwide sites</h2>
<div id="overlayTabs">
<button type="button" class="otab active" data-tab="map">Worldwide sites</button>
<button type="button" class="otab" data-tab="stats">Statistics</button>
</div>
<span class="world-meta" id="worldMeta"></span>
<button type="button" id="worldClose">Close</button>
</div>
<div id="worldMap"></div>
<div id="statsPanel"></div>
</div>
</div>
@ -270,6 +302,9 @@ let fusionMode = "bti"; // bti | itb
let miniMapInst = null, miniMarker = null;
let worldMapInst = null, worldCluster = null;
let worldOverlayOpen = false;
let overlayTab = "map";
let statsData = null;
let statsYear = null;
const maps3 = {}; // { s2, fusion, s3 } Leaflet instances
const overlays3 = {}; // current ImageOverlay per map
const markers3 = {}; // site dot markers per map
@ -298,6 +333,22 @@ const SERIES_LABELS = {
s2: "S2 raw", s3: "S3 raw",
};
const METRIC_META = {
nse: { label: "NSE", full: "Nash–Sutcliffe Efficiency", better: "higher" },
rmse: { label: "RMSE", full: "Root Mean Square Error", better: "lower" },
nrmse: { label: "nRMSE", full: "Normalised RMSE", better: "lower" },
r: { label: "r", full: "Pearson correlation", better: "higher" },
};
const STAT_METRICS = ["nse", "rmse", "nrmse", "r"];
const BADGE_CLASS = {
itb: "itb",
bti: "bti",
"no significant difference": "none",
"insufficient data": "insuf",
};
const INSPECTOR_SERIES = [
{ key: "phenocam", label: "PhenoCam", cols: [{ h: "gcc_90", k: "gcc_90" }] },
{ key: "bands_s2", label: "S2 reflectance", cols: ["B02","B03","B04"].map(b => ({ h: b, k: b })) },
@ -503,6 +554,118 @@ function renderSitePanel(meta, cov) {
`<table class="site-meta-table"><tbody>${rows.join("")}</tbody></table>${species}`;
}
function fmtStat(v, decimals = 4) {
return v != null ? v.toFixed(decimals) : "—";
}
function fmtPval(p, alpha) {
if (p == null) return "—";
const cls = p < alpha ? "stat-pval sig" : "stat-pval";
return `<span class="${cls}">${p.toFixed(4)}</span>`;
}
function betterOrderLabel(order) {
if (order === "itb") return "ItB better";
if (order === "bti") return "BtI better";
if (order === "no significant difference") return "No significant difference";
if (order === "insufficient data") return "Insufficient data";
return order;
}
function updateWorldMeta() {
const sites = manifest?.sites?.[currentYear] || {};
const n = Object.values(sites).filter(m => m.has_fusion).length;
if (overlayTab === "stats") {
const nPairs = statsData?.metrics?.nse?.n_pairs;
qs("#worldMeta").textContent = nPairs != null
? `${nPairs} paired site${nPairs === 1 ? "" : "s"} · α=${statsData.alpha ?? 0.05} · ${currentYear}`
: `${n} fusion site${n === 1 ? "" : "s"} · ${currentYear}`;
} else {
qs("#worldMeta").textContent =
`${n} fusion site${n === 1 ? "" : "s"} · ${currentYear}`;
}
}
function renderStatsPanel(data) {
const panel = qs("#statsPanel");
const alpha = data.alpha ?? 0.05;
const cards = STAT_METRICS.map(key => {
const meta = METRIC_META[key];
const m = data.metrics?.[key] || {};
const badgeCls = BADGE_CLASS[m.better_order] || "none";
const badge = `<span class="stat-badge ${badgeCls}">${betterOrderLabel(m.better_order)}</span>`;
const row = (label, val) =>
`<tr><td class="slabel">${label}</td><td class="sval">${val}</td></tr>`;
return `<div class="stat-card">
<h3>${meta.label} <span style="font-weight:400;color:#888;font-size:12px">${meta.full}</span> ${badge}</h3>
<div style="font-size:11px;color:#999;margin-bottom:8px">${meta.better} is better</div>
<table class="stat-row-table">
${row("BtI mean", fmtStat(m.bti_mean))}
${row("BtI median", fmtStat(m.bti_median))}
${row("ItB mean", fmtStat(m.itb_mean))}
${row("ItB median", fmtStat(m.itb_median))}
${row("Diff (ItB − BtI) mean", fmtStat(m.mean_diff))}
${row("Diff (ItB − BtI) median", fmtStat(m.median_diff))}
</table>
<hr class="stat-divider">
<table class="stat-row-table">
${row("Wilcoxon W", m.wilcoxon?.statistic ?? "—")}
${row("Wilcoxon p", fmtPval(m.wilcoxon?.p_value, alpha))}
${row("Paired t", m.ttest?.statistic ?? "—")}
${row("Paired t p", fmtPval(m.ttest?.p_value, alpha))}
</table>
<hr class="stat-divider">
<table class="stat-row-table">
${row("Paired sites", m.n_pairs ?? "—")}
${row("Dropped sites", m.n_dropped ?? "—")}
</table>
</div>`;
}).join("");
panel.innerHTML =
`<div class="stat-summary">Paired ItB vs BtI test across ${data.n_sites_total ?? "—"} site(s) with Step 5 metrics · significance α=${alpha}</div>` +
`<div class="stat-grid">${cards}</div>`;
updateWorldMeta();
}
async function loadStatsPanel() {
const panel = qs("#statsPanel");
panel.innerHTML = '<div class="stat-nodata">Loading…</div>';
try {
const data = await fetch(`data/statistics_fusion_order/${currentYear}.json`)
.then(r => { if (!r.ok) throw new Error(); return r.json(); });
statsData = data;
statsYear = currentYear;
renderStatsPanel(data);
} catch {
statsData = null;
statsYear = null;
panel.innerHTML =
'<div class="stat-nodata">No statistics file found — run 6-statistics-fusion-order.py first.</div>';
updateWorldMeta();
}
}
function switchOverlayTab(tab, updateHash = true) {
overlayTab = tab;
document.querySelectorAll(".otab").forEach(btn =>
btn.classList.toggle("active", btn.dataset.tab === tab));
qs("#worldMap").style.display = tab === "map" ? "block" : "none";
qs("#statsPanel").style.display = tab === "stats" ? "block" : "none";
if (tab === "stats") {
if (statsYear !== currentYear || !statsData) loadStatsPanel();
else updateWorldMeta();
if (updateHash) setHash("statistics");
return;
}
buildWorldMap();
requestAnimationFrame(() => worldMapInst?.invalidateSize());
if (updateHash) setHash("worldwide");
}
// ── init ──
async function init() {
try {
@ -519,11 +682,18 @@ async function init() {
yearSel.addEventListener("change", () => {
currentYear = +yearSel.value;
statsData = null;
statsYear = null;
buildSiteList();
if (worldOverlayOpen) buildWorldMap();
if (worldOverlayOpen) {
if (overlayTab === "stats") loadStatsPanel();
else buildWorldMap();
}
});
qs("#worldMapBtn").addEventListener("click", () => openWorldOverlay());
document.querySelectorAll(".otab").forEach(btn =>
btn.addEventListener("click", () => switchOverlayTab(btn.dataset.tab)));
qs("#worldClose").addEventListener("click", () => closeWorldOverlay());
qs("#worldOverlay").addEventListener("click", e => {
if (e.target === qs("#worldOverlay")) closeWorldOverlay();
@ -551,11 +721,12 @@ async function init() {
onHashChange();
}
// ── hash routing (#worldwide, #2025/sitename) ──
// ── hash routing (#worldwide, #statistics, #2025/sitename) ──
function parseHash() {
const raw = location.hash.replace(/^#/, "").trim();
if (!raw) return { view: null, year: null, site: null };
if (raw === "worldwide") return { view: "worldwide", year: null, site: null };
if (raw === "statistics") return { view: "statistics", year: null, site: null };
const parts = raw.split("/");
if (parts.length === 2 && /^\d{4}$/.test(parts[0]))
return { view: "site", year: +parts[0], site: decodeURIComponent(parts[1]) };
@ -565,6 +736,7 @@ function parseHash() {
function setHash(view, year, site) {
let hash = "";
if (view === "worldwide") hash = "worldwide";
else if (view === "statistics") hash = "statistics";
else if (view === "site" && year && site)
hash = `${year}/${encodeURIComponent(site)}`;
const next = hash ? `#${hash}` : "";
@ -574,7 +746,11 @@ function setHash(view, year, site) {
function onHashChange() {
const { view, year, site } = parseHash();
if (view === "worldwide") {
openWorldOverlay(false);
openWorldOverlay(false, "map");
return;
}
if (view === "statistics") {
openWorldOverlay(false, "stats");
return;
}
if (view === "site" && year && site && manifest?.sites?.[year]?.[site]?.has_fusion) {
@ -592,15 +768,13 @@ function onHashChange() {
}
// ── worldwide map overlay ──
function openWorldOverlay(updateHash = true) {
function openWorldOverlay(updateHash = true, tab = "map") {
if (!manifest) return;
worldOverlayOpen = true;
const overlay = qs("#worldOverlay");
overlay.classList.add("open");
overlay.setAttribute("aria-hidden", "false");
if (updateHash) setHash("worldwide");
buildWorldMap();
requestAnimationFrame(() => worldMapInst?.invalidateSize());
switchOverlayTab(tab, updateHash);
}
function closeWorldOverlay(updateHash = true) {
@ -608,7 +782,8 @@ function closeWorldOverlay(updateHash = true) {
const overlay = qs("#worldOverlay");
overlay.classList.remove("open");
overlay.setAttribute("aria-hidden", "true");
if (updateHash && parseHash().view === "worldwide") {
const view = parseHash().view;
if (updateHash && (view === "worldwide" || view === "statistics")) {
if (currentSite) setHash("site", currentYear, currentSite);
else history.replaceState(null, "", location.pathname + location.search);
}
@ -712,7 +887,7 @@ function buildSiteList() {
list.appendChild(li);
}
const h = parseHash();
if (h.view === "worldwide") return;
if (h.view === "worldwide" || h.view === "statistics") return;
if (h.view === "site" && h.year === currentYear && sites[h.site]?.has_fusion) {
selectSite(h.site);
return;