Preserve observed progression steps in exercise forecasts

forecasting.py

@@ -134,11 +134,19 @@ class TransitionModel:
         probabilities = np.exp(-distance) * 0.5 ** (self.age / 8)
         increase = self.dw > 0.01
         decrease = self.dw < -0.01
+        # Preserve the observed mix of increments; the mode alone can turn
+        # alternating small/large plate jumps into permanently tiny steps.
+        ordinary_increase = self.dw <= self.source_w * 0.15
+        probabilities[:, increase & ~ordinary_increase] = 0
         probabilities[:, increase] *= (reps >= self.profile['ceiling'])[:, None]
+        # A rep-building transition is not evidence of stalling at the ceiling.
+        # Exclude it there instead of clipping its gain into an artificial repeat.
+        rep_gain = (abs(self.dw) < 0.01) & (self.dr > 0)
+        probabilities[:, rep_gain] *= (reps < self.profile['ceiling'])[:, None]
         # Calendar horizons beyond ~12 sessions are scenarios. Damp the rate of
         # further load advances rather than extrapolating beginner gains forever.
         probabilities[:, increase] *= 0.98 ** max(0, step - 12)
-        delta_w = np.where(increase, self.profile['increment'], self.dw)
+        delta_w = self.dw
         delta_w = np.maximum(delta_w, -weights[:, None] * 0.15)
         out_w = np.maximum(weights[:, None] + delta_w, 0.5)
         out_r = np.clip(reps[:, None] + self.dr, 1, 20)