remove log statements in methods run by Ray workers

scos_actions/signal_processing/apd.py

@@ -57,27 +57,26 @@ def get_apd(
     ne.evaluate("20*log10(all_amps)", out=all_amps)
 
     if bin_size_dB is None or bin_size_dB == 0:
-        # No downsampling
-        if min_bin is not None or max_bin is not None:
-            logger.warning(
-                f"APD bin edge specified but no downsampling is being performed"
-            )
+        # No downsampling. min_bin and max_bin ignored.
         a = np.sort(all_amps)
         p = 1 - ((np.arange(len(a)) + 1) / len(a))
         # Replace peak amplitude 0 count with NaN
         p[-1] = np.nan
     else:
         # Dynamically get bin edges if necessary
         if min_bin is None:
-            logger.debug("Setting APD minimum bin edge to minimum recorded amplitude")
             min_bin = np.nanmin(all_amps)
         if max_bin is None:
-            logger.debug("Setting APD maximum bin edge to maximum recorded amplitude")
             max_bin = np.nanmax(all_amps)
         if min_bin >= max_bin:
-            logger.error(
+            raise ValueError(
                 f"Minimum APD bin {min_bin} is not less than maximum {max_bin}"
             )
+        # Check that downsampling range is evenly spanned by bins
+        if not ((max_bin - min_bin) / bin_size_dB).is_integer():
+            raise ValueError(
+                "APD downsampling range is not evenly spanned by configured bin size."
+            )
         # Scale bin edges to the correct units if necessary
         if impedance_ohms is not None:
             min_bin, max_bin = (
@@ -88,11 +87,7 @@ def get_apd(
         assert np.isclose(
             a[1] - a[0], bin_size_dB
         )  # Checks against undesired arange behavior
-        if not ((max_bin - min_bin) / bin_size_dB).is_integer():
-            logger.debug(
-                "APD downsampling range is not evenly spanned by configured bin size.\n"
-                + f"Check that the following amplitude bin edges are correct: {a}"
-            )
+
         # Get counts of amplitudes exceeding each bin value
         p = sample_ccdf(all_amps, a)
         # Replace any 0 probabilities with NaN
@@ -102,8 +97,6 @@ def get_apd(
     if impedance_ohms is not None:
         ne.evaluate("a-(10*log10(impedance_ohms))", out=a)
 
-    logger.debug(f"APD result length: {len(a)} samples.")
-
     return p, a
 
 

scos_actions/signal_processing/fft.py

@@ -63,7 +63,6 @@ def get_fft(
     :return: The transformed input, scaled based on the specified
         normalization mode.
     """
-    logger.debug("Computing FFTs")
     # Make sure num_ffts and fft_size are integers
     if isinstance(fft_size, int) and isinstance(num_ffts, int):
         pass
@@ -79,25 +78,16 @@ def get_fft(
 
     # Get num_ffts for default case: as many as possible
     if num_ffts <= 0:
-        logger.info("Number of FFTs not specified. Using as many as possible.")
         num_ffts = int(len(time_data) // fft_size)
-        logger.info(
-            f"Number of FFTs set to {num_ffts} based on specified FFT size {fft_size}"
-        )
 
     # Determine if truncation will occur and raise a warning if so
     if len(time_data) != fft_size * num_ffts:
-        thrown_away_samples = len(time_data) - (fft_size * num_ffts)
-        msg = "Time domain data length is not divisible by num_ffts.\nTime"
-        msg += "domain data will be truncated; Throwing away last "
-        msg += f"{thrown_away_samples} sample(s)."
-        logger.warning(msg)
+        msg = "Time domain data length is not divisible by num_ffts * fft_size.\n"
+        msg += "Try zero-padding the input or adjusting FFT parameters."
+        raise ValueError(msg)
 
     # Resize time data for FFTs
     time_data = np.reshape(time_data[: num_ffts * fft_size], (num_ffts, fft_size))
-    logger.debug(
-        f"Num. FFTs: {num_ffts}, FFT Size: {fft_size}, Data shape: {time_data.shape}"
-    )
 
     # Apply the FFT window if provided
     if fft_window is not None:

scos_actions/signal_processing/power_analysis.py

@@ -146,7 +146,6 @@ def apply_power_detector(
         detector_functions = [np.min, np.max, np.mean, np.median]
 
     # Get functions based on specified detector
-    logger.debug(f"Applying power detectors: {detectors}")
     applied_detectors = []
     if "min" in detectors:
         applied_detectors.append(detector_functions[0])