avoid all logging in DSP functions

DSP methods have been tested and documented, and avoiding use of loggers within them can help streamline parallelized action implementations

scos_actions/signal_processing/apd.py

@@ -1,13 +1,8 @@
-import logging
 from typing import Tuple
 
 import numexpr as ne
 import numpy as np
 
-from scos_actions.signal_processing.unit_conversion import convert_linear_to_dB
-
-logger = logging.getLogger(__name__)
-
 
 def get_apd(
     time_data: np.ndarray,

scos_actions/signal_processing/fft.py

@@ -1,13 +1,10 @@
-import logging
 import os
 
 import numexpr as ne
 import numpy as np
 from scipy.fft import fft as sp_fft
 from scipy.signal import get_window
 
-logger = logging.getLogger(__name__)
-
 
 def get_fft(
     time_data: np.ndarray,

scos_actions/signal_processing/filtering.py

@@ -1,15 +1,8 @@
-import logging
-from multiprocessing.sharedctypes import Value
 from typing import Tuple, Union
 
-import numexpr as ne
 import numpy as np
 from scipy.signal import ellip, ellipord, firwin, kaiserord, sos2zpk, sosfreqz
 
-from scos_actions.signal_processing.unit_conversion import convert_linear_to_dB
-
-logger = logging.getLogger(__name__)
-
 
 def generate_elliptic_iir_low_pass_filter(
     gpass_dB: float,
@@ -46,7 +39,6 @@ def generate_elliptic_iir_low_pass_filter(
         pb_edge_Hz, sb_edge_Hz, gpass_dB, gstop_dB, False, sample_rate_Hz
     )
     sos = ellip(ord, gpass_dB, gstop_dB, wn, "lowpass", False, "sos", sample_rate_Hz)
-    logger.debug(f"Generated low-pass IIR filter with order {ord}.")
     return sos
 
 
@@ -80,9 +72,6 @@ def generate_fir_low_pass_filter(
         True,
         fs=sample_rate_Hz,
     )
-    logger.debug(
-        f"Generated Type {'I' if ord % 2 == 0 else 'II'} low-pass FIR filter with order {ord} and length {ord + 1}."
-    )
     return taps
 
 
@@ -150,10 +139,6 @@ def get_iir_enbw(
         raise ValueError(
             "Supplied frequency values must fall within +/- Nyquist frequency at baseband."
         )
-    logger.debug(
-        f"Calculating filter ENBW using a frequency response of {len(worN)} points "
-        + f"from {min(worN)} Hz to {max(worN)} Hz."
-    )
     w, h = get_iir_frequency_response(sos, worN, sample_rate_Hz)
     dw = np.mean(np.diff(w))
     h = np.abs(h) ** 2.0

scos_actions/signal_processing/power_analysis.py

@@ -1,11 +1,8 @@
-import logging
 from enum import Enum, EnumMeta
 
 import numexpr as ne
 import numpy as np
 
-logger = logging.getLogger(__name__)
-
 
 def calculate_power_watts(val_volts, impedance_ohms: float = 50.0):
     """