Reduce non-uniform sampling warning verbosityv1.0.1

- Change threshold from 1% to 10% CV coefficient of variation
- Remove detailed worst-point reporting
- Combine warnings into a single concise message
- Only warn on severe irregularities that might affect analysis

1 files changed, 6 insertions, 31 deletions

diff --git a/src/scopekit/data_manager.py b/src/scopekit/data_manager.py
index 78a8ea3..2f13ce3 100644
--- a/src/scopekit/data_manager.py
+++ b/src/scopekit/data_manager.py
@@ -414,38 +414,13 @@ class TimeSeriesDataManager:
         # Calculate statistics
         dt_mean = np.mean(dt)
         dt_std = np.std(dt)
-        dt_cv = dt_std / dt_mean if dt_mean > 0 else 0  # Coefficient of variation
+        dt_cv = dt_std / dt_mean if dt_mean > 1e-15 else 0  # Coefficient of variation
 
         # Check for significant non-uniformity
-        # CV > 0.01 (1%) indicates potentially problematic non-uniformity
-        if dt_cv > 0.01:
+        # CV > 0.1 (10%) indicates potentially problematic non-uniformity
+        if dt_cv > 0.1:
             warnings.warn(
-                f"Non-uniform sampling detected in trace {trace_idx}: "
-                f"mean dt={dt_mean:.3e}s, std={dt_std:.3e}s, CV={dt_cv:.2%}", UserWarning
+                f"Severe non-uniform sampling detected in trace {trace_idx}: "
+                f"mean dt={dt_mean:.3e}s, std={dt_std:.3e}s, CV={dt_cv:.2%}. "
+                f"May affect analysis results.", UserWarning
             )
-
-            # More detailed warning for severe non-uniformity
-            if dt_cv > 0.05:  # 5% variation
-                # Find the most extreme deviations
-                dt_median = np.median(dt)
-                rel_deviations = np.abs(dt - dt_median) / dt_median
-                worst_indices = np.argsort(rel_deviations)[-5:]  # 5 worst points
-
-                worst_deviations = []
-                for idx in reversed(worst_indices):
-                    if (
-                        rel_deviations[idx] > 0.1
-                    ):  # Only report significant deviations (>10%)
-                        worst_deviations.append(
-                            f"at t={t[idx]:.3e}s: dt={dt[idx]:.3e}s ({rel_deviations[idx]:.1%} deviation)"
-                        )
-
-                if worst_deviations:
-                    warnings.warn(
-                        f"Severe sampling irregularities detected in trace {trace_idx}. "
-                        f"Worst points: {'; '.join(worst_deviations)}", UserWarning
-                    )
-                    warnings.warn(
-                        "Non-uniform sampling may affect analysis results, especially for "
-                        "frequency-domain analysis or event detection.", UserWarning
-                    )