chore: cleanup logging package

echopype/__init__.py

@@ -7,10 +7,6 @@
 from .echodata.api import open_converted
 from .echodata.combine import combine_echodata
 from .utils.io import init_ep_dir
-from .utils.log import verbose
-
-# Turn off verbosity for echopype
-verbose(override=False)
 
 init_ep_dir()
 
@@ -25,5 +21,4 @@
     "open_converted",
     "open_raw",
     "utils",
-    "verbose",
 ]

echopype/calibrate/api.py

@@ -1,10 +1,11 @@
+import warnings
+
 import numpy as np
 import xarray as xr
 
 from ..core import SONAR_MODELS
 from ..echodata import EchoData
 from ..echodata.simrad import check_input_args_combination, retrieve_correct_beam_group
-from ..utils.log import _init_logger
 from ..utils.prov import echopype_prov_attrs, source_files_vars
 from .calibrate_azfp import CalibrateAZFP
 from .calibrate_ek import CalibrateEK60, CalibrateEK80
@@ -19,8 +20,6 @@
     "EA640": CalibrateEK80,
 }
 
-logger = _init_logger(__name__)
-
 
 def _compute_cal(
     cal_type,
@@ -44,14 +43,16 @@ def _compute_cal(
         check_input_args_combination(waveform_mode=waveform_mode, encode_mode=encode_mode)
     elif echodata.sonar_model in ("EK60", "AZFP", "AZFP6"):
         if waveform_mode is not None and waveform_mode != "CW":
-            logger.warning(
+            warnings.warn(
                 "This sonar model transmits only narrowband signals (waveform_mode='CW'). "
                 "Calibration will be in CW mode",
+                category=UserWarning,
             )
         if encode_mode is not None and encode_mode != "power":
-            logger.warning(
+            warnings.warn(
                 "This sonar model only record data as power or power/angle samples "
                 "(encode_mode='power'). Calibration will be done on the power samples.",
+                category=UserWarning,
             )
 
     # Check that assume_single_filter_time is correctly passed in.

echopype/calibrate/calibrate_azfp.py

@@ -1,16 +1,15 @@
+import warnings
+
 import numpy as np
 import xarray
 from scipy.interpolate import LinearNDInterpolator
 
 from ..echodata import EchoData
-from ..utils.log import _init_logger
 from .cal_params import get_cal_params_AZFP
 from .calibrate_ek import CalibrateBase
 from .env_params import get_env_params_AZFP
 from .range import compute_range_AZFP
 
-logger = _init_logger(__name__)
-
 # Common Sv_offset values for frequency > 38 kHz
 SV_OFFSET_HF = {
     150: 1.4,
@@ -153,9 +152,10 @@ def compute_Sv_offset(self):
                 try:
                     Sv_offset.append(_calc_azfp_Sv_offset(freq, pulse_len * 1e6))
                 except ValueError:
-                    logger.warning(
+                    warnings.warn(
                         f"The Sv for {freq}Hz and pulse length {pulse_len}us "
-                        "is uncalibrated (Sv_offset=0.0)"
+                        "is uncalibrated (Sv_offset=0.0)",
+                        category=UserWarning,
                     )
                     Sv_offset.append(0.0)
 

echopype/calibrate/calibrate_base.py

@@ -1,11 +1,9 @@
 import abc
+import warnings
 
 from ..echodata import EchoData
-from ..utils.log import _init_logger
 from .ecs import ECSParser
 
-logger = _init_logger(__name__)
-
 
 class CalibrateBase(abc.ABC):
     """Class to handle calibration for all sonar models."""
@@ -19,9 +17,10 @@ def __init__(self, echodata: EchoData, env_params=None, cal_params=None, ecs_fil
         # Set ECS to overwrite user-provided dict
         if self.ecs_file is not None:
             if env_params is not None or cal_params is not None:
-                logger.warning(
+                warnings.warn(
                     "The ECS file takes precedence when it is provided. "
-                    "Parameter values provided in 'env_params' and 'cal_params' will not be used!"
+                    "Parameter values provided in 'env_params' and 'cal_params' will not be used!",
+                    category=UserWarning,
                 )
 
             # Parse ECS file to a dict
@@ -118,11 +117,12 @@ def _check_echodata_backscatter_size(self):
 
         # Raise Warning if above 2.0
         if total_gb > 2.0:
-            logger.warning(
+            warnings.warn(
                 "The Echodata backscatter variables are large and can cause memory issues. "
                 "Consider modifying the workflow that uses compute_Sv as below: "
                 "Prior to `compute_Sv` run `echodata.chunk(CHUNK_DICTIONARY) "
                 "and after `compute_Sv` run `ds_Sv.to_zarr(ZARR_STORE, compute=True)`. "
                 "This will ensure that the computation is lazily evaluated, "
-                "with the results stored directly in a Zarr store on disk, rather then in memory."
+                "with the results stored directly in a Zarr store on disk, rather then in memory.",
+                category=ResourceWarning,
             )

echopype/calibrate/calibrate_ek.py

@@ -1,11 +1,11 @@
+import warnings
 from typing import Dict
 
 import numpy as np
 import xarray as xr
 
 from ..echodata import EchoData
 from ..echodata.simrad import retrieve_correct_beam_group
-from ..utils.log import _init_logger
 from .cal_params import _get_interp_da, get_cal_params_EK
 from .calibrate_base import CalibrateBase
 from .ecs import conform_channel_order, ecs_ds2dict, ecs_ev2ep
@@ -19,8 +19,6 @@
 from .env_params import get_env_params_EK
 from .range import compute_range_EK, range_mod_TVG_EK
 
-logger = _init_logger(__name__)
-
 
 def _slice_beam_vend(beam, vend, slice_dict):
     beam = beam.sel(
@@ -126,10 +124,10 @@
                     ping_time=beam["ping_time"],
                 )
             except Exception as e:
-                logger.warning(
+                warnings.warn(
                     "Could not compute tau_effective from transmit signal in power encoding mode; "
-                    "falling back to transmit_duration_nominal. Error: %s",
-                    repr(e),
+                    f"falling back to transmit_duration_nominal. Error: {e!r}",
+                    category=RuntimeWarning,
                 )
                 tau_effective = beam["transmit_duration_nominal"].isel(ping_time=0)
 
@@ -593,11 +591,11 @@
                     ping_time=self.beam["ping_time"],
                 )
             except Exception as e:
-                logger.warning(
+                warnings.warn(
                     "Could not compute tau_effective "
                     "from transmit signal in complex encoding mode; "
-                    "falling back to transmit_duration_nominal. Error: %s",
-                    repr(e),
+                    f"falling back to transmit_duration_nominal. Error: {e!r}",
+                    category=RuntimeWarning,
                 )
                 tau_effective = self.beam["transmit_duration_nominal"].isel(ping_time=0)
             # Use pulse_duration in place of tau_effective for GPT channels

echopype/calibrate/ecs.py

@@ -6,11 +6,6 @@
 import numpy as np
 import xarray as xr
 
-from ..utils.log import _init_logger
-
-logger = _init_logger(__name__)
-
-
 # String matcher for parser
 SEPARATOR = re.compile(r"#=+#\n")
 STATUS_CRUDE = re.compile(r"#\s*(?P<status>(.+))\s*#\n")  # noqa

echopype/clean/api.py

@@ -2,14 +2,14 @@
 Functions for reducing variabilities in backscatter data.
 """
 
+import warnings
 from functools import partial
 
 import numpy as np
 import xarray as xr
 
 from ..commongrid.utils import _parse_x_bin
 from ..utils.compute import _lin2log, _log2lin
-from ..utils.log import _init_logger
 from ..utils.prov import add_processing_level, echopype_prov_attrs, insert_input_processing_level
 from .transient_noise.transient_fielding import transient_noise_fielding
 from .transient_noise.transient_matecho import transient_noise_matecho
@@ -24,8 +24,6 @@
     pool_Sv,
 )
 
-logger = _init_logger(__name__)
-
 
 def mask_transient_noise(
     ds_Sv: xr.Dataset,
@@ -137,10 +135,11 @@ def mask_transient_noise(
     elif func == "nanmedian":
         # Warn when `func=nanmedian` since the sorting overhead makes it incredibly slow compared to
         # other non-sorting aggregations like `nanmean`.
-        logger.warning(
+        warnings.warn(
             "`func=nanmedian` is an incredibly slow operation due to the overhead sorting. "
             "We plan to add the Fielding Transient Noise Filter in the future"
-            "described here: https://github.com/OSOceanAcoustics/echopype/issues/1352"
+            "described here: https://github.com/OSOceanAcoustics/echopype/issues/1352",
+            category=ResourceWarning,
         )
         func = np.nanmedian
 

echopype/commongrid/api.py

@@ -2,7 +2,6 @@
 Functions for enhancing the spatial and temporal coherence of data.
 """
 
-import logging
 import warnings
 from typing import Literal
 
@@ -29,8 +28,6 @@
     ping_time_bin_parsing_and_conversion,
 )
 
-logger = logging.getLogger(__name__)
-
 
 @add_processing_level("L3*")
 def compute_MVBS(

echopype/commongrid/utils.py

@@ -1,5 +1,5 @@
-import logging
 import re
+import warnings
 from typing import Literal, Optional, Tuple, Union
 
 import numpy as np
@@ -11,8 +11,6 @@
 from ..consolidate.api import POSITION_VARIABLES
 from ..utils.compute import _lin2log, _log2lin
 
-logger = logging.getLogger(__name__)
-
 
 def compute_raw_MVBS(
     ds_Sv: xr.Dataset,
@@ -585,7 +583,7 @@ def _groupby_x_along_channels(
 
     # Set correct range_var just in case
     if x_var == "distance_nmi" and range_var != "depth":
-        logger.warning("x_var is 'distance_nmi', setting range_var to 'depth'")
+        warnings.warn("x_var is 'distance_nmi', setting range_var to 'depth'", category=UserWarning)
         range_var = "depth"
 
     # average should be done in linear domain
@@ -603,8 +601,9 @@ def _groupby_x_along_channels(
     )
     for array_name, array in named_arrays.items():
         if np.isnan(array).any():
-            logging.warning(
-                f"The ```{array_name}``` coordinate array contain NaNs. {aggregation_msg}"
+            warnings.warn(
+                f"The ```{array_name}``` coordinate array contain NaNs. {aggregation_msg}",
+                category=UserWarning,
             )
 
     # Use the first dimension as the grouping dimension for generality

echopype/consolidate/api.py

@@ -1,6 +1,7 @@
 import datetime
 import pathlib
 import sys
+import warnings
 from numbers import Number
 from pathlib import Path
 from typing import Optional, Union
@@ -14,7 +15,6 @@
 from ..echodata.simrad import retrieve_correct_beam_group
 from ..utils.align import align_to_ping_time
 from ..utils.io import get_file_format, open_source
-from ..utils.log import _init_logger
 from ..utils.prov import add_processing_level
 from .ek_depth_utils import (
     ek_use_beam_angles,
@@ -24,8 +24,6 @@
 from .loc_utils import check_and_drop_loc_time_dim_duplicates, check_loc_vars_validity, sel_nmea
 from .split_beam_angle import get_angle_complex_samples, get_angle_power_samples
 
-logger = _init_logger(__name__)
-
 POSITION_VARIABLES = ["latitude", "longitude"]
 
 
@@ -137,13 +135,15 @@ def add_depth(
 
     # Log warnings when group variables are not used
     if depth_offset is not None and use_platform_vertical_offsets:
-        logger.warning(
+        warnings.warn(
             "When `depth_offset` is specified, platform vertical offset "
-            "variables will not be used."
+            "variables will not be used.",
+            category=UserWarning,
         )
     if tilt is not None and (use_beam_angles or use_platform_angles):
-        logger.warning(
-            "When `tilt` is specified, beam/platform angle variables will " "not be used."
+        warnings.warn(
+            "When `tilt` is specified, beam/platform angle variables will " "not be used.",
+            category=UserWarning,
         )
 
     if echodata:

echopype/consolidate/ek_depth_utils.py

@@ -1,29 +1,29 @@
+import warnings
+
 import numpy as np
 import xarray as xr
 from scipy.spatial.transform import Rotation as R
 
 from ..utils.align import align_to_ping_time
-from ..utils.log import _init_logger
-
-logger = _init_logger(__name__)
 
 
 def _check_and_log_nans(
     echodata_group: xr.Dataset, group_name: str, variable_names: list[str]
 ) -> None:
     """
-    Checks for NaNs in Echodata group variables and raises logger warning.
+    Checks for NaNs in Echodata group variables and raises UserWarning.
     """
     # Iterate through group variable names
     for variable_name in variable_names:
         # Extract group and check if it contains any NaNs
         group_var = echodata_group[variable_name]
         # Log warning if the group variable contains any NaNs
         if np.any(np.isnan(group_var.values)):
-            logger.warning(
+            warnings.warn(
                 f"The Echodata `{group_name}` group `{variable_name}` variable array contains "
                 "NaNs. This will result in NaNs in the final `depth` array. Consider filling the "
-                "NaNs and calling `.add_depth(...)` again."
+                "NaNs and calling `.add_depth(...)` again.",
+                category=UserWarning,
             )
 
 
@@ -98,14 +98,18 @@ def ek_use_beam_angles(beam_ds: xr.Dataset) -> xr.DataArray:
     # Warn if any nonzero vector is not normalized
     tolerance = 1e-8
     if ((norm > tolerance) & (np.abs(norm - 1) > tolerance)).any():
-        logger.warning(
+        warnings.warn(
             "Beam direction vector was not normalized; applying normalization. "
-            "By definition, it should have been normalized."
+            "By definition, it should have been normalized.",
+            category=UserWarning,
         )
 
     # Warn if any channel has a (nearly) zero vector
     if (norm < tolerance).any():
-        logger.warning("Some beam direction vectors are zero. Outputting NaN for those channels.")
+        warnings.warn(
+            "Some beam direction vectors are zero. Outputting NaN for those channels.",
+            category=UserWarning,
+        )
 
     # For channels with near-zero norm, we return NaN. Otherwise, we return the normalized
     # z component.