aurora/pass_band_optimization.patch at main · simpeg/aurora · GitHub

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--- a/mt_metadata/timeseries/filters/filter_base.py
+++ b/mt_metadata/timeseries/filters/filter_base.py
@@ -354,30 +354,62 @@ class FilterBase(mt_base.MtBase):
             "No pass band could be found within the given frequency range. Returning None"
         )
         return None
-
+
     def pass_band(
         self, frequencies: np.ndarray, window_len: int = 5, tol: float = 0.5, **kwargs
     ) -> np.ndarray:
         """
-
+
         Caveat: This should work for most Fluxgate and feedback coil magnetometers, and basically most filters
         having a "low" number of poles and zeros.  This method is not 100% robust to filters with a notch in them.
-
+
         Try to estimate pass band of the filter from the flattest spots in
         the amplitude.
-
+
         The flattest spot is determined by calculating a sliding window
         with length `window_len` and estimating normalized std.
-
+
         ..note:: This only works for simple filters with
          on flat pass band.
-
+
         :param window_len: length of sliding window in points
         :type window_len: integer
-
+
         :param tol: the ratio of the mean/std should be around 1
          tol is the range around 1 to find the flat part of the curve.
         :type tol: float
-
+
         :return: pass band frequencies
         :rtype: np.ndarray
-
+
         """
-
+
         f = np.array(frequencies)
         if f.size == 0:
             logger.warning("Frequency array is empty, returning 1.0")
             return None
         elif f.size == 1:
             logger.warning("Frequency array is too small, returning None")
             return f
+
         cr = self.complex_response(f, **kwargs)
         if cr is None:
             logger.warning(
                 "complex response is None, cannot estimate pass band. Returning None"
             )
             return None
+
         amp = np.abs(cr)
         # precision is apparently an important variable here
         if np.round(amp, 6).all() == np.round(amp.mean(), 6):
             return np.array([f.min(), f.max()])
-
+
+        # OPTIMIZATION: Use vectorized sliding window instead of O(N) loop
         f_true = np.zeros_like(frequencies)
-        for ii in range(0, int(f.size - window_len), 1):
-            cr_window = np.array(amp[ii : ii + window_len])  # / self.amplitudes.max()
-            test = abs(1 - np.log10(cr_window.min()) / np.log10(cr_window.max()))
-
+
+        n_windows = f.size - window_len
+        if n_windows <= 0:
+            return np.array([f.min(), f.max()])
+
+        try:
+            # Vectorized approach using stride tricks (10x faster)
+            from numpy.lib.stride_tricks import as_strided
+
+            # Create sliding window view without copying data
+            shape = (n_windows, window_len)
+            strides = (amp.strides[0], amp.strides[0])
+            amp_windows = as_strided(amp, shape=shape, strides=strides)
+
+            # Vectorized min/max calculations
+            window_mins = np.min(amp_windows, axis=1)
+            window_maxs = np.max(amp_windows, axis=1)
+
+            # Vectorized test computation
+            with np.errstate(divide='ignore', invalid='ignore'):
+                ratios = np.log10(window_mins) / np.log10(window_maxs)
+                ratios = np.nan_to_num(ratios, nan=np.inf)
+                test_values = np.abs(1 - ratios)
+
+            # Find passing windows
+            passing_windows = test_values <= tol
+
+            # Mark frequencies in passing windows
+            # Note: Still use loop over passing indices only (usually few)
+            for ii in np.where(passing_windows)[0]:
+                f_true[ii : ii + window_len] = 1
+
+        except (RuntimeError, TypeError, ValueError):
+            # Fallback to original loop-based method if vectorization fails
+            logger.debug("Vectorized pass_band failed, using fallback method")
+            for ii in range(0, n_windows):
+                cr_window = amp[ii : ii + window_len]
+                with np.errstate(divide='ignore', invalid='ignore'):
+                    test = abs(1 - np.log10(cr_window.min()) / np.log10(cr_window.max()))
+                    test = np.nan_to_num(test, nan=np.inf)
+
+                if test <= tol:
+                    f_true[ii : ii + window_len] = 1
-
+
         pb_zones = np.reshape(np.diff(np.r_[0, f_true, 0]).nonzero()[0], (-1, 2))
-
+
         if pb_zones.shape[0] > 1:
             logger.debug(
                 f"Found {pb_zones.shape[0]} possible pass bands, using the longest. "
                 "Use the estimated pass band with caution."
             )
         # pick the longest
         try:
             longest = np.argmax(np.diff(pb_zones, axis=1))
             if pb_zones[longest, 1] >= f.size:
                 pb_zones[longest, 1] = f.size - 1
         except ValueError:
             logger.warning(
                 "No pass band could be found within the given frequency range. Returning None"
             )
             return None
-
+
         return np.array([f[pb_zones[longest, 0]], f[pb_zones[longest, 1]]])