foo
This commit is contained in:
Felix Delattre
parent
33746b35f9
commit
77e1488830
4 changed files with 343 additions and 87 deletions
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@ -110,6 +110,25 @@ def nse(y_true, y_pred):
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return float(1 - (numerator / denominator))
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def residual_vs_phenocam(fusion_ts, phenocam_ts):
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"""Stats of (fused_GCC − PhenoCam_GCC) on matched dates; None if too few points.
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Mean: positive → fusion systematically above PhenoCam; negative → below; ~0 → unbiased mean.
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Compare BtI vs ItB means at same strategy/σ (``derived.bti_vs_itb_mean_residual``): closer to 0 → less mean bias vs PhenoCam.
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"""
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yf, yp, _dates = match_dates(fusion_ts, phenocam_ts)
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if len(yf) < 2:
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return None
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r = yf - yp
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return {
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"mean": float(np.mean(r)),
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"std": float(np.std(r)),
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"mae": float(np.mean(np.abs(r))),
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"rmse": float(np.sqrt(np.mean(r**2))),
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"n_samples": int(len(r)),
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}
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def calculate_temporal_metrics(fusion_ts, phenocam_ts):
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"""Temporal metrics vs PhenoCam (nse_pc; nse is the same value)."""
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fusion_vals, phenocam_vals, dates = match_dates(fusion_ts, phenocam_ts)
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@ -129,9 +148,54 @@ def calculate_temporal_metrics(fusion_ts, phenocam_ts):
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"n_samples": len(fusion_vals),
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"date_range": {"start": dates[0], "end": dates[-1]} if dates else None,
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}
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rv = residual_vs_phenocam(fusion_ts, phenocam_ts)
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if rv:
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metrics["residual_vs_phenocam"] = rv
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return metrics
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def derived_tier1(temporal: dict) -> dict:
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"""ΔNSE_PC (σ20 − σ30) and paired BtI vs ItB mean residual; needs temporal fusion keys.
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ΔNSE_PC > 0 → NSE_PC higher at σ=20 than σ=30 (tighter EFAST temporal kernel wins).
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ΔNSE_PC < 0 → σ=30 wins (broader smoothing matches PhenoCam better).
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"""
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d_nse = {"bti": {}, "itb": {}}
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for strategy in ("aggressive", "nonaggressive"):
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for mode, suf in (("bti", ""), ("itb", "_itb")):
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k20 = f"{strategy}_sigma20{suf}"
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k30 = f"{strategy}_sigma30{suf}"
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n20 = (temporal.get(k20) or {}).get("nse_pc")
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n30 = (temporal.get(k30) or {}).get("nse_pc")
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if isinstance(n20, (int, float)) and isinstance(n30, (int, float)):
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d_nse[mode][strategy] = float(n20 - n30)
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else:
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d_nse[mode][strategy] = None
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paired = []
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for strategy in ("aggressive", "nonaggressive"):
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for sig in (20, 30):
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kb, ki = f"{strategy}_sigma{sig}", f"{strategy}_sigma{sig}_itb"
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mb = (temporal.get(kb) or {}).get("residual_vs_phenocam", {}).get("mean")
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mi = (temporal.get(ki) or {}).get("residual_vs_phenocam", {}).get("mean")
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paired.append(
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{
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"strategy": strategy,
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"sigma": sig,
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"mean_residual_bti": float(mb)
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if isinstance(mb, (int, float))
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else None,
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"mean_residual_itb": float(mi)
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if isinstance(mi, (int, float))
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else None,
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}
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)
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return {
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"delta_nse_pc_sigma20_minus_sigma30": d_nse,
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"bti_vs_itb_mean_residual": paired,
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}
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def calculate_phenocam_stats(phenocam_ts):
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"""Calculate phenocam summary statistics."""
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values = [v for v in phenocam_ts.values() if v is not None]
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@ -323,6 +387,9 @@ def calculate_all_metrics(season, site_name, site_position):
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if temporal_metrics:
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results["temporal"][scenario_name] = temporal_metrics
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if results["temporal"]:
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results["derived"] = derived_tier1(results["temporal"])
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# Save results
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output_path = Path(f"data/{site_name}/{season}/metrics.json")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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91
run.py
91
run.py
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@ -1,64 +1,73 @@
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from fusion import run_all_efast_scenarios, run_all_efast_itb_scenarios
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from postprocessing import (
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post_process_all_scenarios,
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post_process_all_itb_scenarios,
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post_process_timeseries,
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)
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"""Pipeline entry point.
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Active snippet below only **regenerates metrics.json** (temporal, baseline,
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`derived`, `residual_vs_phenocam`). Requires existing post-processed GCC
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timeseries under `data/{site}/{season}/processed_*`.
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Un-comment imports and steps below for acquisition → fusion → post-process.
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"""
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# from fusion import run_all_efast_scenarios, run_all_efast_itb_scenarios
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# from postprocessing import (
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# post_process_all_scenarios,
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# post_process_all_itb_scenarios,
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# post_process_timeseries,
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# )
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# from acquisition_s2 import download_s2
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# from acquisition_s3 import download_s3
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# from acquisition_phenocam import download_phenocam
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from preselection import create_timeseries
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from preparation import (
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prepare_s2,
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prepare_s3,
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prepare_s2_gcc_for_itb,
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prepare_s3_gcc_for_itb,
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)
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from metrics_indices import create_prepared_fusion_timeseries
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# from preselection import create_timeseries
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# from preparation import (
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# prepare_s2,
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# prepare_s3,
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# prepare_s2_gcc_for_itb,
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# prepare_s3_gcc_for_itb,
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# )
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# from metrics_indices import create_prepared_fusion_timeseries
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from metrics_stats import calculate_all_metrics
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from phenology_timesat import write_phenocam_phenology_for_site
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# from phenology_timesat import write_phenocam_phenology_for_site
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def run_pipeline(season, site_position, site_name):
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"""Run pipeline."""
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"""Run pipeline (metrics-only by default; see module docstring)."""
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try:
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# print(f"Downloading S2, S3, and PhenoCam: {site_name}, {season}")
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# download_s2(season, site_position, site_name)
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# download_s3(season, site_position, site_name)
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# download_phenocam(season, site_position, site_name)
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print(f"PhenoCam phenology (50 % amplitude): {site_name}, {season}")
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write_phenocam_phenology_for_site(site_name, season)
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# print(f"PhenoCam phenology (50 % amplitude): {site_name}, {season}")
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# write_phenocam_phenology_for_site(site_name, season)
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print(f"Creating preselection timeseries: {site_name}, {season}")
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create_timeseries(season, site_position, site_name)
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# print(f"Creating preselection timeseries: {site_name}, {season}")
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# create_timeseries(season, site_position, site_name)
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print(f"Preparing S2 and S3 for fusion: {site_name}, {season}")
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for strategy in ["aggressive", "nonaggressive"]:
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prepare_s2(season, site_position, site_name, cleaning_strategy=strategy)
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prepare_s3(season, site_position, site_name, cleaning_strategy=strategy)
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# print(f"Preparing S2 and S3 for fusion: {site_name}, {season}")
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# for strategy in ["aggressive", "nonaggressive"]:
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# prepare_s2(season, site_position, site_name, cleaning_strategy=strategy)
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# prepare_s3(season, site_position, site_name, cleaning_strategy=strategy)
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print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
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run_all_efast_scenarios(season, site_position, site_name)
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# print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
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# run_all_efast_scenarios(season, site_position, site_name)
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print(f"Index-then-Blend (ItB): {site_name}, {season}")
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for strategy in ["aggressive", "nonaggressive"]:
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prepare_s2_gcc_for_itb(
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season, site_position, site_name, cleaning_strategy=strategy
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)
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prepare_s3_gcc_for_itb(
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season, site_position, site_name, cleaning_strategy=strategy
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)
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run_all_efast_itb_scenarios(season, site_position, site_name)
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post_process_all_itb_scenarios(season, site_position, site_name)
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# print(f"Index-then-Blend (ItB): {site_name}, {season}")
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# for strategy in ["aggressive", "nonaggressive"]:
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# prepare_s2_gcc_for_itb(
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# season, site_position, site_name, cleaning_strategy=strategy
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# )
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# prepare_s3_gcc_for_itb(
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# season, site_position, site_name, cleaning_strategy=strategy
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# )
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# run_all_efast_itb_scenarios(season, site_position, site_name)
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# post_process_all_itb_scenarios(season, site_position, site_name)
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print(f"Creating prepared/fusion timeseries: {site_name}, {season}")
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create_prepared_fusion_timeseries(season, site_position, site_name)
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# print(f"Creating prepared/fusion timeseries: {site_name}, {season}")
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# create_prepared_fusion_timeseries(season, site_position, site_name)
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print(f"Post-processing (crop): {site_name}, {season}")
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post_process_all_scenarios(season, site_position, site_name)
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post_process_timeseries(season, site_position, site_name)
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# print(f"Post-processing (crop): {site_name}, {season}")
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# post_process_all_scenarios(season, site_position, site_name)
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# post_process_timeseries(season, site_position, site_name)
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print(f"Calculating metrics: {site_name}, {season}")
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calculate_all_metrics(season, site_name, site_position)
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@ -136,7 +136,7 @@
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<div class="combined-plot">
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<div class="combined-plot-label">Metrics vs PhenoCam (fusion scenarios)</div>
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<p style="margin:4px 0 8px; font-size:11px; color:#555; max-width:720px;">
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R² (variance explained), nRMSE (RMSE normalised by PhenoCam σ), NSE_PC (Nash–Sutcliffe vs PhenoCam). Other metrics remain in <code>metrics.json</code>.
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R² (variance explained), nRMSE (RMSE normalised by PhenoCam σ), NSE_PC (Nash–Sutcliffe vs PhenoCam). <b>ΔNSE_PC</b> = NSE_PC(σ20)−NSE_PC(σ30): positive → σ20 better; negative → σ30 better. <b>Mean residual</b> (fused−PhenoCam): positive → fusion high vs PhenoCam on average; negative → low; compare BtI vs ItB in the same row (closer to 0 = less mean bias). Tables at the top when <code>metrics.json</code> has <code>derived</code> (regenerate with <code>metrics_stats.py</code> / <code>run.py</code>).
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</p>
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<div id="metricsTable" style="overflow-x: auto; margin-top: 10px;"></div>
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</div>
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@ -606,12 +606,57 @@
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return;
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}
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const fmtD = (v) => {
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const n = Number(v);
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return Number.isFinite(n) ? n.toFixed(3) : "—";
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};
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const d = metricsData.derived;
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let html = "";
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if (d && d.delta_nse_pc_sigma20_minus_sigma30) {
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const dn = d.delta_nse_pc_sigma20_minus_sigma30;
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let anyDelta = false;
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for (const mode of ["bti", "itb"]) {
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for (const strat of ["aggressive", "nonaggressive"]) {
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if (Number.isFinite(Number(dn[mode]?.[strat]))) anyDelta = true;
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}
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}
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html +=
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"<p style='margin:0 0 2px; font-size:11px; font-weight:600;'>ΔNSE_PC (σ20 − σ30)</p>";
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html +=
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"<p style='margin:0 0 6px; font-size:10px; color:#555; line-height:1.35;'>NSE_PC(σ20) − NSE_PC(σ30): <b>+</b> → σ20 better, <b>−</b> → σ30 better.</p>";
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html +=
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"<table style='width:100%; border-collapse:collapse; font-size:11px; margin-bottom:10px;'><thead><tr style='background:#f5f5f5;'><th style='padding:6px; text-align:left;'>Mode</th><th style='padding:6px; text-align:left;'>Strategy</th><th style='padding:6px; text-align:right;'>ΔNSE_PC</th></tr></thead><tbody>";
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for (const mode of ["bti", "itb"]) {
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for (const strat of ["aggressive", "nonaggressive"]) {
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const v = dn[mode]?.[strat];
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html += `<tr style='border-bottom:1px solid #eee;'><td style='padding:6px;'>${mode.toUpperCase()}</td><td style='padding:6px;'>${strat}</td><td style='padding:6px; text-align:right; font-family:monospace;'>${fmtD(v)}</td></tr>`;
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}
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}
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html += "</tbody></table>";
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if (!anyDelta) {
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html +=
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"<p style='margin:-4px 0 10px; font-size:10px; color:#666;'>ΔNSE_PC needs both σ20 and σ30 rows in temporal (BtI and ItB) with nse_pc.</p>";
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}
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}
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if (d && d.bti_vs_itb_mean_residual && d.bti_vs_itb_mean_residual.length) {
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html +=
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"<p style='margin:0 0 2px; font-size:11px; font-weight:600;'>Mean residual (fused − PhenoCam): BtI vs ItB</p>";
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html +=
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"<p style='margin:0 0 6px; font-size:10px; color:#555; line-height:1.35;'>Each cell: mean(fused−PhenoCam) on matched dates. <b>+</b> overestimates, <b>−</b> underestimates; <b>~0</b> little mean bias (see R²/MAE for overall fit). Same row: column closer to 0 → less systematic offset vs PhenoCam (RQ1.1).</p>";
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html +=
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"<table style='width:100%; border-collapse:collapse; font-size:11px; margin-bottom:10px;'><thead><tr style='background:#f5f5f5;'><th style='padding:6px;'>Strategy</th><th style='padding:6px;'>σ</th><th style='padding:6px; text-align:right;'>BtI</th><th style='padding:6px; text-align:right;'>ItB</th></tr></thead><tbody>";
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for (const row of d.bti_vs_itb_mean_residual) {
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html += `<tr style='border-bottom:1px solid #eee;'><td style='padding:6px;'>${row.strategy}</td><td style='padding:6px;'>${row.sigma}</td><td style='padding:6px; text-align:right; font-family:monospace;'>${fmtD(row.mean_residual_bti)}</td><td style='padding:6px; text-align:right; font-family:monospace;'>${fmtD(row.mean_residual_itb)}</td></tr>`;
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}
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html += "</tbody></table>";
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}
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const scenarios = ["aggressive_sigma20", "aggressive_sigma30", "nonaggressive_sigma20", "nonaggressive_sigma30"];
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const scenarioNames = ["Aggressive σ20", "Aggressive σ30", "Non-aggressive σ20", "Non-aggressive σ30"];
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const metrics = ["r_squared", "nrmse", "nse_pc"];
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const metricLabels = { r_squared: "R²", nrmse: "nRMSE", nse_pc: "NSE_PC" };
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let html = "<table style='width:100%; border-collapse:collapse; font-size:11px;'>";
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html += "<table style='width:100%; border-collapse:collapse; font-size:11px;'>";
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html += "<thead><tr style='background:#f5f5f5; border-bottom:2px solid #ccc;'>";
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html += "<th style='padding:8px; text-align:left;'>Scenario</th>";
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metrics.forEach(m => html += `<th style='padding:8px; text-align:right;'>${metricLabels[m]}</th>`);
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@ -14,18 +14,36 @@
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.selectors select { padding: 5px 10px; font-size: 14px; margin-right: 15px; }
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h1 { font-size: 22px; }
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h2 { font-size: 16px; margin-top: 24px; color: #333; }
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h2:first-of-type { margin-top: 8px; }
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h3 { font-size: 14px; margin: 14px 0 6px 0; color: #444; font-weight: 600; }
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table { border-collapse: collapse; width: 100%; font-size: 13px; margin-bottom: 12px; }
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th, td { border: 1px solid #ccc; padding: 6px 8px; text-align: left; }
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th { background: #f5f5f5; }
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td.num { text-align: right; font-variant-numeric: tabular-nums; }
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.fusion-block table { margin-bottom: 4px; }
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.fusion-block table + table { margin-top: 12px; }
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.section-note { font-size: 12px; color: #555; margin: -6px 0 8px 0; max-width: 720px; line-height: 1.45; }
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.section-note code { background: #f1f1f1; padding: 1px 4px; border-radius: 3px; font-size: 11px; }
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.intro { font-size: 13px; color: #333; background: #fafafa; border: 1px solid #e5e5e5;
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padding: 10px 12px; border-radius: 4px; margin-bottom: 18px; line-height: 1.5; }
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.intro ul { margin: 6px 0 0 18px; padding: 0; }
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.intro li { margin-bottom: 2px; }
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.intro-short { margin-bottom: 0; }
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details.definitions { margin-top: 28px; font-size: 13px; border: 1px solid #e5e5e5; border-radius: 4px; padding: 8px 12px; background: #fafafa; }
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details.definitions summary { cursor: pointer; font-weight: 600; color: #333; }
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details.definitions ul { margin: 8px 0 0 18px; padding: 0; }
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details.definitions li { margin-bottom: 4px; }
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.scenario-key { font-size: 11px; color: #666; font-weight: normal; }
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.empty { color: #666; font-style: italic; }
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.err { color: #a00; }
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details.how-read {
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font-size: 12px; color: #333; line-height: 1.5; max-width: 820px; margin: 0 0 18px 0;
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padding: 8px 12px 10px; border: 1px solid #ccd; border-radius: 4px; background: #f8fafc;
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}
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details.how-read summary {
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cursor: pointer; font-weight: 600; font-size: 13px; color: #111; margin-bottom: 0;
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}
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details.how-read ol { margin: 10px 0 0; padding-left: 1.35rem; }
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details.how-read li { margin-bottom: 7px; }
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details.how-read li:last-child { margin-bottom: 0; }
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</style>
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</head>
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<body>
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@ -56,6 +74,14 @@
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nrmse: "nRMSE",
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nse_pc: "NSE_PC",
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};
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const FUSION_BTI_ROWS = [
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["aggressive_sigma20", "Aggressive", 20],
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["aggressive_sigma30", "Aggressive", 30],
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["nonaggressive_sigma20", "Non-aggressive", 20],
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["nonaggressive_sigma30", "Non-aggressive", 30],
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];
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function mv(m, c) {
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return c === "nse_pc" ? (m.nse_pc ?? m.nse) : m[c];
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}
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|
|
@ -81,21 +107,64 @@
|
|||
return Number.isInteger(v) ? String(v) : v.toFixed(4);
|
||||
}
|
||||
|
||||
function tableSection(title, obj) {
|
||||
const heading = title ? `<h2>${title}</h2>` : "";
|
||||
if (!obj || typeof obj !== "object" || !Object.keys(obj).length) {
|
||||
return `${heading}<p class="empty">No data</p>`;
|
||||
}
|
||||
const keys = Object.keys(obj).sort();
|
||||
let head = `<tr><th>Scenario</th>${DISPLAY_METRIC_COLS.map((c) => `<th>${DISPLAY_METRIC_LABELS[c]}</th>`).join("")}</tr>`;
|
||||
const rows = keys.map((k) => {
|
||||
const m = obj[k] || {};
|
||||
return `<tr><td>${k}</td>${DISPLAY_METRIC_COLS.map((c) => `<td class="num">${fmtMetric(c, mv(m, c))}</td>`).join("")}</tr>`;
|
||||
}).join("");
|
||||
return `${heading}<table>${head}${rows}</table>`;
|
||||
function fusionMeanResidual(m) {
|
||||
const x = m?.residual_vs_phenocam?.mean;
|
||||
const n = Number(x);
|
||||
return Number.isFinite(n) ? n : null;
|
||||
}
|
||||
|
||||
function baselineSection(b) {
|
||||
function fusionSubTableRows(temporal, keysWithLabels, includeMeanResid) {
|
||||
const parts = [];
|
||||
for (const [key, stratLabel, sig] of keysWithLabels) {
|
||||
const m = temporal[key];
|
||||
if (!m) continue;
|
||||
const mr = fusionMeanResidual(m);
|
||||
const meanCell = includeMeanResid
|
||||
? `<td class="num">${mr !== null ? mr.toFixed(3) : "—"}</td>`
|
||||
: "";
|
||||
parts.push(
|
||||
`<tr><td>${stratLabel}, σ=${sig} <span class="scenario-key">(${key})</span></td>${DISPLAY_METRIC_COLS.map((c) => `<td class="num">${fmtMetric(c, mv(m, c))}</td>`).join("")}${meanCell}</tr>`
|
||||
);
|
||||
}
|
||||
return parts;
|
||||
}
|
||||
|
||||
function fusionTables(temporal) {
|
||||
if (!temporal || typeof temporal !== "object") {
|
||||
return `<p class="empty">No fusion temporal data</p>`;
|
||||
}
|
||||
const itbRows = FUSION_BTI_ROWS.map(([k, s, sig]) => [`${k}_itb`, s, sig]);
|
||||
const allKeys = [...FUSION_BTI_ROWS.map((r) => r[0]), ...itbRows.map((r) => r[0])];
|
||||
let showMean = false;
|
||||
for (const k of allKeys) {
|
||||
if (fusionMeanResidual(temporal[k]) !== null) {
|
||||
showMean = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
const btiBody = fusionSubTableRows(temporal, FUSION_BTI_ROWS, showMean);
|
||||
const itbBody = fusionSubTableRows(temporal, itbRows, showMean);
|
||||
if (!btiBody.length && !itbBody.length) {
|
||||
return `<p class="empty">No fusion scenarios in temporal</p>`;
|
||||
}
|
||||
const meanTh = showMean ? `<th class="num">Mean resid.</th>` : "";
|
||||
const head = `<tr><th>Setting</th>${DISPLAY_METRIC_COLS.map((c) => `<th class="num">${DISPLAY_METRIC_LABELS[c]}</th>`).join("")}${meanTh}</tr>`;
|
||||
|
||||
let h = `<div class="fusion-block">`;
|
||||
if (btiBody.length) {
|
||||
h += `<h3>Bands-then-Index (BtI)</h3>`;
|
||||
h += `<table>${head}${btiBody.join("")}</table>`;
|
||||
}
|
||||
if (itbBody.length) {
|
||||
h += `<h3>Index-then-Bands (ItB)</h3>`;
|
||||
h += `<table>${head}${itbBody.join("")}</table>`;
|
||||
}
|
||||
h += `</div>`;
|
||||
return h;
|
||||
}
|
||||
|
||||
/** Returns only <table>…</table> or empty string (no heading). */
|
||||
function baselineTable(b) {
|
||||
if (!b || typeof b !== "object") return "";
|
||||
const rows = [];
|
||||
const pushRow = (label, m) => {
|
||||
|
|
@ -111,8 +180,74 @@
|
|||
pushRow(`S2 Whittaker λ=400 (${strat})`, b.s2_whittaker_lambda400?.[strat]);
|
||||
}
|
||||
if (!rows.length) return "";
|
||||
const head = `<tr><th>Baseline</th>${DISPLAY_METRIC_COLS.map((c) => `<th>${DISPLAY_METRIC_LABELS[c]}</th>`).join("")}</tr>`;
|
||||
return `<h2>Baselines (temporal vs PhenoCam)</h2><table>${head}${rows.join("")}</table>`;
|
||||
const head = `<tr><th>Baseline</th>${DISPLAY_METRIC_COLS.map((c) => `<th class="num">${DISPLAY_METRIC_LABELS[c]}</th>`).join("")}</tr>`;
|
||||
return `<table>${head}${rows.join("")}</table>`;
|
||||
}
|
||||
|
||||
function fmtFixed3(v) {
|
||||
const n = Number(v);
|
||||
return Number.isFinite(n) ? n.toFixed(3) : "—";
|
||||
}
|
||||
|
||||
function derivedSection(d) {
|
||||
if (!d) return "";
|
||||
const dn = d.delta_nse_pc_sigma20_minus_sigma30;
|
||||
const paired = d.bti_vs_itb_mean_residual || [];
|
||||
if (!dn && !paired.length) return "";
|
||||
|
||||
let h = `<h2>Summaries</h2>`;
|
||||
h += `<p class="section-note">Same numbers as Fusion, condensed. First table: which σ fits PhenoCam better (NSE_PC only). Second: mean bias BtI vs ItB.</p>`;
|
||||
if (dn) {
|
||||
h += `<p class="section-note"><b>ΔNSE_PC</b> = NSE_PC(σ20) − NSE_PC(σ30). <b>+</b> → σ20 better. <b>−</b> → σ30 better.</p>`;
|
||||
h += `<table><tr><th>Mode</th><th>Strategy</th><th class="num">ΔNSE_PC</th></tr>`;
|
||||
let anyDelta = false;
|
||||
for (const mode of ["bti", "itb"]) {
|
||||
for (const strat of ["aggressive", "nonaggressive"]) {
|
||||
const v = dn[mode]?.[strat];
|
||||
if (Number.isFinite(Number(v))) anyDelta = true;
|
||||
h += `<tr><td>${mode.toUpperCase()}</td><td>${strat}</td><td class="num">${fmtFixed3(v)}</td></tr>`;
|
||||
}
|
||||
}
|
||||
h += `</table>`;
|
||||
if (!anyDelta) {
|
||||
h += `<p class="section-note">ΔNSE_PC needs both σ20 and σ30 fusion rows in <code>temporal</code> (BtI and ItB). Re-run <code>metrics_stats</code>.</p>`;
|
||||
}
|
||||
}
|
||||
if (paired.length) {
|
||||
h += `<p class="section-note">Mean(fused − PhenoCam) per row. <b>+</b> / <b>−</b> = average over / under PhenoCam. Closer to <b>0</b> in a column = less bias for that workflow.</p>`;
|
||||
h += `<table><tr><th>Strategy</th><th>σ</th><th class="num">Mean residual BtI</th><th class="num">Mean residual ItB</th></tr>`;
|
||||
for (const row of paired) {
|
||||
h += `<tr><td>${row.strategy}</td><td>${row.sigma}</td><td class="num">${fmtFixed3(row.mean_residual_bti)}</td><td class="num">${fmtFixed3(row.mean_residual_itb)}</td></tr>`;
|
||||
}
|
||||
h += `</table>`;
|
||||
}
|
||||
return h;
|
||||
}
|
||||
|
||||
function howToReadBlock() {
|
||||
return `<details class="how-read">
|
||||
<summary>How to read</summary>
|
||||
<ol>
|
||||
<li>All scores are satellite or fusion <b>GCC</b> vs <b>PhenoCam GCC</b> at the site 3×3 window, <b>same calendar days</b> only. Extra stats: <code>metrics.json</code>.</li>
|
||||
<li><b>R²</b>, <b>NSE_PC</b>: higher = better. <b>nRMSE</b>: lower = better.</li>
|
||||
<li><b>Fusion:</b> same row number in BtI and in ItB = same screening + same σ — compare left/right. Down one block = change screening or σ.</li>
|
||||
<li><b>Mean resid.</b> (if present): mean(fused − PhenoCam). Sign = average bias; use R² / nRMSE / NSE_PC for overall fit.</li>
|
||||
<li><b>Summaries:</b> ΔNSE_PC = NSE at σ20 minus NSE at σ30 (+ means σ20 wins). Paired table: closer to 0 = less mean bias.</li>
|
||||
</ol>
|
||||
</details>`;
|
||||
}
|
||||
|
||||
function definitionsDetails() {
|
||||
return `<details class="definitions">
|
||||
<summary>Definitions</summary>
|
||||
<ul>
|
||||
<li><b>BtI</b>: fuse reflectance bands, then GCC.</li>
|
||||
<li><b>ItB</b>: GCC on S2 and S3, then fuse GCC.</li>
|
||||
<li><b>Scenario</b>: screening (<code>aggressive</code> / <code>nonaggressive</code>) × σ (20 / 30 days).</li>
|
||||
<li><a href="phenology.html">Phenology</a> — PhenoCam SOS/EOS (TIMESAT).</li>
|
||||
<li><code>metrics.json</code> — also Pearson <i>r</i>, RMSE, MAE, <code>n_samples</code>.</li>
|
||||
</ul>
|
||||
</details>`;
|
||||
}
|
||||
|
||||
function render(data) {
|
||||
|
|
@ -122,35 +257,35 @@
|
|||
return;
|
||||
}
|
||||
let html = "";
|
||||
html += `
|
||||
<div class="intro">
|
||||
All metrics compare a greenness index (GCC) from satellite products against PhenoCam
|
||||
ground-truth GCC at the site's 3×3 pixel window.
|
||||
<ul>
|
||||
<li><b>BtI</b> (<i>Bands-then-Index</i>): fuse S2/S3 reflectance, then compute GCC from the fused bands.</li>
|
||||
<li><b>ItB</b> (<i>Index-then-Bands</i>): compute GCC from S2 and S3 first, then fuse the GCC rasters.</li>
|
||||
<li>Scenarios combine a cloud-screening <b>strategy</b> (<code>aggressive</code> / <code>nonaggressive</code>)
|
||||
and an EFAST fusion <b>σ</b> (<code>sigma20</code> / <code>sigma30</code>).</li>
|
||||
<li>Tables list <b>R²</b> (fit vs PhenoCam), <b>nRMSE</b> (RMSE normalised by PhenoCam variability),
|
||||
and <b>NSE_PC</b> (Nash–Sutcliffe using PhenoCam as reference). Pearson <i>r</i>, RMSE, MAE, and sample counts stay in <code>metrics.json</code>.</li>
|
||||
</ul>
|
||||
html += `<div class="intro intro-short">
|
||||
GCC at the 3×3 site window vs PhenoCam. Sections: PhenoCam → baselines → fusion (BtI, then ItB) → summaries.
|
||||
<code>data/${siteName}/${season}/metrics.json</code>
|
||||
</div>`;
|
||||
html += howToReadBlock();
|
||||
|
||||
if (data.phenocam_stats) {
|
||||
html += `<h2>PhenoCam</h2>`;
|
||||
html += `<p class="section-note">Summary statistics of the PhenoCam GCC timeseries used as ground truth for this site and season.</p>`;
|
||||
html += `<h2>PhenoCam (ground truth)</h2>`;
|
||||
html += `<p class="section-note">Camera ROI GCC (not compared to itself). Dates / SOS–EOS: <a href="phenology.html">Phenology</a>.</p>`;
|
||||
html += `<table><tr><th>mean</th><th>std</th><th>min</th><th>max</th><th>n</th></tr><tr>`;
|
||||
const p = data.phenocam_stats;
|
||||
html += `<td class="num">${fmt(p.mean)}</td><td class="num">${fmt(p.std)}</td><td class="num">${fmt(p.min)}</td><td class="num">${fmt(p.max)}</td><td class="num">${fmt(p.n_samples)}</td></tr></table>`;
|
||||
}
|
||||
const baselineHtml = baselineSection(data.baseline);
|
||||
if (baselineHtml) {
|
||||
html += `<h2>Baselines (temporal vs PhenoCam)</h2>`;
|
||||
html += `<p class="section-note">Reference GCC series <i>before</i> any fusion: raw S2 (all dates and cloud-screened per strategy), S3 composite per strategy, and a Whittaker-smoothed S2 series (λ=400). Useful to see what fusion has to beat.</p>`;
|
||||
html += baselineHtml.replace(/^<h2>[^<]*<\/h2>/, "");
|
||||
|
||||
const baselineTbl = baselineTable(data.baseline);
|
||||
if (baselineTbl) {
|
||||
html += `<h2>Baselines (vs PhenoCam)</h2>`;
|
||||
html += `<p class="section-note">Same columns as fusion (vs PhenoCam). Higher R² / NSE_PC, lower nRMSE = better. S3 = coarse-only; Whittaker = smoothed S2-only.</p>`;
|
||||
html += baselineTbl;
|
||||
}
|
||||
html += `<h2>Temporal (vs PhenoCam)</h2>`;
|
||||
html += `<p class="section-note">Per-scenario agreement between the fusion GCC <b>timeseries</b> at the site 3×3 window and the PhenoCam GCC timeseries, across all matched dates. Scenarios ending in <code>_itb</code> are Index-then-Bands; the others are Bands-then-Index.</p>`;
|
||||
html += tableSection("", data.temporal);
|
||||
|
||||
html += `<h2>Fusion (vs PhenoCam)</h2>`;
|
||||
html += `<p class="section-note">BtI block vs ItB block: same row = same screening + σ. Within a block: four EFAST combinations.</p>`;
|
||||
html += fusionTables(data.temporal || {});
|
||||
|
||||
html += derivedSection(data.derived);
|
||||
|
||||
html += definitionsDetails();
|
||||
|
||||
el.innerHTML = html || `<p class="empty">Empty metrics file</p>`;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue