remove monotonicity checkv1.0.2

2 files changed, 43 insertions, 43 deletions

diff --git a/pyproject.toml b/pyproject.toml
index 888af00..e0b5d54 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "scopekit"
-version = "1.0.1"
+version = "1.0.2"
 description = "General-purpose oscilloscope plotting components."
 authors = [{ name = "Sam Scholten", email = "s.scholten@uq.edu.au" }]
 requires-python = ">=3.8"
diff --git a/src/scopekit/data_manager.py b/src/scopekit/data_manager.py
index 2f13ce3..8d337ad 100644
--- a/src/scopekit/data_manager.py
+++ b/src/scopekit/data_manager.py
@@ -206,20 +206,20 @@ class TimeSeriesDataManager:
             warnings.warn(f"Initialising trace {trace_idx} with empty arrays.", UserWarning)
             return
 
-        # Check time array is monotonic
-        if len(t) > 1:
-            # Use a small epsilon for floating-point comparison
-            tolerance = 1e-9
-            if not np.all(np.diff(t) > tolerance):
-                problematic_diffs = np.diff(t)[np.diff(t) <= tolerance]
-                warnings.warn(
-                    f"Time array for trace {trace_idx} is not strictly monotonic increasing within tolerance {tolerance}. "
-                    f"Problematic diffs (first 10): {problematic_diffs[:10]}. "
-                    f"This may affect analysis results.", UserWarning
-                )
+        # # Check time array is monotonic
+        # if len(t) > 1:
+        #     # Use a small epsilon for floating-point comparison
+        #     tolerance = 1e-9
+        #     if not np.all(np.diff(t) > tolerance):
+        #         problematic_diffs = np.diff(t)[np.diff(t) <= tolerance]
+        #         warnings.warn(
+        #             f"Time array for trace {trace_idx} is not strictly monotonic increasing within tolerance {tolerance}. "
+        #             f"Problematic diffs (first 10): {problematic_diffs[:10]}. "
+        #             f"This may affect analysis results.", UserWarning
+        #         )
 
             # Check for non-uniform sampling
-            self._check_uniform_sampling(t, trace_idx)
+            # self._check_uniform_sampling(t, trace_idx)
 
     @property
     def overlay_lines(self) -> List[Dict[str, Any]]:
@@ -394,33 +394,33 @@ class TimeSeriesDataManager:
 
         return t_masked, x_masked
 
-    def _check_uniform_sampling(self, t: np.ndarray, trace_idx: int = 0) -> None:
-        """
-        Check if time array is uniformly sampled and issue warnings if not.
-
-        Parameters
-        ----------
-        t : np.ndarray
-            Time array to check.
-        trace_idx : int, default=0
-            Index of the trace being checked (for warning messages).
-        """
-        if len(t) < 3:
-            return  # Not enough points to check uniformity
-
-        # Calculate time differences
-        dt = np.diff(t)
-
-        # Calculate statistics
-        dt_mean = np.mean(dt)
-        dt_std = np.std(dt)
-        dt_cv = dt_std / dt_mean if dt_mean > 1e-15 else 0  # Coefficient of variation
-
-        # Check for significant non-uniformity
-        # CV > 0.1 (10%) indicates potentially problematic non-uniformity
-        if dt_cv > 0.1:
-            warnings.warn(
-                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
-            )
+#     def _check_uniform_sampling(self, t: np.ndarray, trace_idx: int = 0) -> None:
+#         """
+#         Check if time array is uniformly sampled and issue warnings if not.
+# 
+#         Parameters
+#         ----------
+#         t : np.ndarray
+#             Time array to check.
+#         trace_idx : int, default=0
+#             Index of the trace being checked (for warning messages).
+#         """
+#         if len(t) < 3:
+#             return  # Not enough points to check uniformity
+# 
+#         # Calculate time differences
+#         dt = np.diff(t)
+# 
+#         # Calculate statistics
+#         dt_mean = np.mean(dt)
+#         dt_std = np.std(dt)
+#         dt_cv = dt_std / dt_mean if dt_mean > 1e-15 else 0  # Coefficient of variation
+# 
+#         # Check for significant non-uniformity
+#         # CV > 0.1 (10%) indicates potentially problematic non-uniformity
+#         if dt_cv > 0.1:
+#             warnings.warn(
+#                 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
+#             )