feat: wavelets

backend/services/wavelet_engine.py

@@ -4,12 +4,15 @@ c:\\DataS\\InstrumentSimulator\\backend\\app\\wavelet.py (project "Macro Causal
 
 No project-specific dependencies — pure numpy/ssqueezepy, safe to call from any service.
 """
+import logging
 import warnings
 
 import numpy as np
 from ssqueezepy import cwt, icwt
 from ssqueezepy.utils.cwt_utils import center_frequency
 
+logger = logging.getLogger(__name__)
+
 MIN_SCALES_PER_BAND = 4
 
 # Fixed period (days) lower-bounds for bands 0..5, independent of the analysis
@@ -72,6 +75,7 @@ def band_decompose(
         mask = (log_scales >= lo) & (log_scales <= hi if i == num_levels - 1 else log_scales < hi)
         scales_band = scales[mask]
 
+        reconstruction_failed = False
         if len(scales_band) >= MIN_SCALES_PER_BAND:
             try:
                 recon = icwt(Wx[mask, :], wavelet=wavelet, scales=scales_band, x_len=n)
@@ -79,14 +83,26 @@ def band_decompose(
                 # ssqueezepy's internal scale-type inference (log vs linear spacing
                 # detection) can raise on some narrow slices depending on how many
                 # scales land in this bucket for this specific series length.
-                # Treat as a negligible band instead of failing the whole request.
+                # Treat as a negligible band instead of failing the whole request —
+                # but log it (previously silently swallowed) and flag it, since a flat
+                # zero band otherwise looks identical to "genuinely no energy here".
+                logger.warning(
+                    "band_decompose: icwt failed for band %d (n=%d scales, wavelet=%s) — "
+                    "falling back to a zero band", i, len(scales_band), wavelet, exc_info=True,
+                )
                 recon = np.zeros(n)
+                reconstruction_failed = True
             scale_lo, scale_hi = float(scales_band.min()), float(scales_band.max())
         else:
             # Too few scales in this bucket for a stable reconstruction; report a
             # flat zero band (its content ends up folded into the residual) so the
             # band list always stays num_levels long.
+            logger.warning(
+                "band_decompose: only %d scale(s) in band %d (< %d minimum) — falling back to a zero band",
+                len(scales_band), i, MIN_SCALES_PER_BAND,
+            )
             recon = np.zeros(n)
+            reconstruction_failed = True
             scale_lo, scale_hi = float(np.exp(lo)), float(np.exp(hi))
 
         freq_at_scale_hi = center_frequency(wavelet, scale=scale_hi, N=n)
@@ -105,6 +121,7 @@ def band_decompose(
                 "period_low_days": period_low_days,
                 "period_high_days": period_high_days,
                 "series": [round(float(v), 6) for v in recon],
+                "reconstruction_failed": reconstruction_failed,
             }
         )
 
@@ -148,6 +165,7 @@ def windowed_band_decompose(
     all_residual: list[float] = []
     band_series: list[list[float]] = [[] for _ in range(num_levels)]
     band_meta: list[dict | None] = [None] * num_levels
+    band_failed: list[bool] = [False] * num_levels
 
     chunk_count = 0
     start = 0
@@ -164,6 +182,10 @@ def windowed_band_decompose(
         for band in chunk["bands"]:
             band_series[band["index"]].extend(band["series"])
             band_meta[band["index"]] = band
+            # OR across chunks: if any slice couldn't reconstruct this band, flag the
+            # whole concatenated series as unreliable rather than losing that signal
+            # when a later chunk happens to succeed and overwrites band_meta.
+            band_failed[band["index"]] = band_failed[band["index"]] or band.get("reconstruction_failed", False)
         start = end
 
     bands = []
@@ -176,6 +198,7 @@ def windowed_band_decompose(
                 "period_low_days": meta["period_low_days"],
                 "period_high_days": meta["period_high_days"],
                 "series": band_series[i],
+                "reconstruction_failed": band_failed[i],
             }
         )
 
@@ -216,6 +239,7 @@ def rolling_causal_bands(
     out_original: list[float] = []
     band_series: list[list[float]] = [[] for _ in range(num_levels)]
     band_labels = [f"bande {i + 1}" for i in range(num_levels)]
+    band_failed = [False] * num_levels
     last_tip: list[float] | None = None
     recomputations = 0
 
@@ -229,6 +253,8 @@ def rolling_causal_bands(
             result = band_decompose(window_values, window_dates, num_levels, wavelet)
             last_tip = [band["series"][-1] for band in result["bands"]]
             band_labels = [band["label"] for band in result["bands"]]
+            for i, band in enumerate(result["bands"]):
+                band_failed[i] = band_failed[i] or band.get("reconstruction_failed", False)
             recomputations += 1
         out_dates.append(dates[t])
         out_original.append(values[t])
@@ -242,6 +268,7 @@ def rolling_causal_bands(
             "period_low_days": None,
             "period_high_days": None,
             "series": band_series[i],
+            "reconstruction_failed": band_failed[i],
         }
         for i in range(num_levels)
     ]
@@ -324,6 +351,7 @@ def band_decompose_ssq(
         hi_period = period_edges[i + 1] if i + 1 < len(period_edges) else float(periods.max())
         mask = (periods >= lo_period) & (periods <= hi_period if i == num_levels - 1 else periods < hi_period)
 
+        reconstruction_failed = False
         if mask.any():
             try:
                 Tx_band = np.zeros_like(Tx)
@@ -333,12 +361,20 @@ def band_decompose_ssq(
                     recon = np.real(issq_cwt(Tx_band, wavelet=wavelet))
             except Exception:
                 # Defensive, mirroring band_decompose: never let one band's edge
-                # case fail the whole request.
+                # case fail the whole request — but log it and flag it (see
+                # band_decompose's identical fix for why this used to be silent).
+                logger.warning(
+                    "band_decompose_ssq: issq_cwt failed for band %d (wavelet=%s) — "
+                    "falling back to a zero band", i, wavelet, exc_info=True,
+                )
                 recon = np.zeros(n)
+                reconstruction_failed = True
             energy = np.sum(np.abs(Tx[mask, :]) ** 2, axis=0)
         else:
+            logger.warning("band_decompose_ssq: no frequencies fall in band %d — falling back to a zero band", i)
             recon = np.zeros(n)
             energy = np.zeros(n)
+            reconstruction_failed = True
 
         period_low_days = round(float(lo_period), 1)
         period_high_days = round(float(hi_period), 1)
@@ -350,6 +386,7 @@ def band_decompose_ssq(
                 "period_high_days": period_high_days,
                 "series": [round(float(v), 6) for v in recon],
                 "energy": [round(float(v), 8) for v in energy],
+                "reconstruction_failed": reconstruction_failed,
             }
         )
 
@@ -397,6 +434,7 @@ def rolling_causal_bands_ssq(
     energy_series: list[list[float]] = [[] for _ in range(num_levels)]
     ridge_series: list[float | None] = []
     band_labels = [f"bande {i + 1}" for i in range(num_levels)]
+    band_failed = [False] * num_levels
     last_tip: list[float] | None = None
     last_energy_tip: list[float] | None = None
     last_ridge_tip: float | None = None
@@ -414,6 +452,8 @@ def rolling_causal_bands_ssq(
             last_energy_tip = [band["energy"][-1] for band in result["bands"]]
             last_ridge_tip = result["ridge_period_days"][-1]
             band_labels = [band["label"] for band in result["bands"]]
+            for i, band in enumerate(result["bands"]):
+                band_failed[i] = band_failed[i] or band.get("reconstruction_failed", False)
             recomputations += 1
         out_dates.append(dates[t])
         out_original.append(values[t])
@@ -430,6 +470,7 @@ def rolling_causal_bands_ssq(
             "period_high_days": None,
             "series": band_series[i],
             "energy": energy_series[i],
+            "reconstruction_failed": band_failed[i],
         }
         for i in range(num_levels)
     ]

frontend/src/pages/InstrumentDashboard.tsx

@@ -1719,7 +1719,7 @@ export default function InstrumentDashboard({ instrumentIdProp, isVisible }: { i
   const WAVELET_SLOPE_LAG = 5
 
   const waveletEnergy = useMemo(() => {
-    const bands: { label: string; series: number[] }[] = waveletData?.bands ?? []
+    const bands: { label: string; series: number[]; reconstruction_failed?: boolean }[] = waveletData?.bands ?? []
     const n = bands[0]?.series?.length ?? 0
     if (!bands.length || n === 0) return null
 
@@ -2184,6 +2184,9 @@ export default function InstrumentDashboard({ instrumentIdProp, isVisible }: { i
                           })}
                         />
                         <span style={{ color: WAVELET_BAND_COLORS[i % WAVELET_BAND_COLORS.length] }}>{b.label}</span>
+                        {b.reconstruction_failed && (
+                          <span className="text-amber-400" title="Reconstruction indisponible pour cette bande (pas assez d'échelles, ou échec interne de la librairie ondelette) — la série est à zéro, ce n'est pas 'pas d'énergie'.">⚠</span>
+                        )}
                       </label>
                     ))}
                   </div>
@@ -2272,6 +2275,9 @@ export default function InstrumentDashboard({ instrumentIdProp, isVisible }: { i
                                   <div className="flex items-center gap-2">
                                     <span style={{ color }}>{b.label}</span>
                                     {isSlow && <span className="text-slate-600">(lente)</span>}
+                                    {b.reconstruction_failed && (
+                                      <span className="text-amber-400" title="Reconstruction indisponible pour cette bande — série à zéro, les chiffres ci-contre ne reflètent pas une vraie absence d'énergie.">⚠</span>
+                                    )}
                                     <Sparkline values={sparkValues} color={color} />
                                   </div>
                                 </td>