Remove unused arguments

disdrodb/api/create_directories.py

@@ -216,7 +216,6 @@ def create_l0_directory_structure(
     station_name,
     force,
     product,
-    verbose=False,
 ):
     """Create directory structure for the first L0 DISDRODB product.
 

disdrodb/issue/checks.py

@@ -82,7 +82,7 @@ def _check_timestep_datetime_accuracy(timesteps, unit="s"):
     return timesteps
 
 
-def _check_timestep_string_second_accuracy(timesteps, n=19):
+def _check_timestep_string_second_accuracy(timesteps):
     """Check the timesteps string are provided with second accuracy.
 
     Note: it assumes the YYYY-mm-dd HH:MM:SS format

disdrodb/l0/check_standards.py

@@ -56,7 +56,7 @@ def _check_valid_range(df, dict_data_range, verbose=False):
 
     if len(list_wrong_columns) > 0:
         msg = f"Columns {list_wrong_columns} has values outside the expected data range."
-        log_error(logger=logger, msg=msg, verbose=False)
+        log_error(logger=logger, msg=msg, verbose=verbose)
         raise ValueError(msg)
 
 
@@ -81,7 +81,7 @@ def _check_valid_values(df, dict_valid_values, verbose=False):
 
     if len(list_wrong_columns) > 0:
         msg = "\n".join(list_msg)
-        log_error(logger=logger, msg=msg, verbose=False)
+        log_error(logger=logger, msg=msg, verbose=verbose)
         raise ValueError(f"Columns {list_wrong_columns} have invalid values.")
 
 
@@ -109,7 +109,7 @@ def _check_raw_fields_available(df: pd.DataFrame, sensor_name: str, verbose: boo
     # Check that raw_drop_number is present
     if "raw_drop_number" not in df.columns:
         msg = "The 'raw_drop_number' column is not present in the dataframe."
-        log_error(logger=logger, msg=msg, verbose=False)
+        log_error(logger=logger, msg=msg, verbose=verbose)
         raise ValueError(msg)
 
     # Report additional raw arrays that are missing
@@ -218,4 +218,5 @@ def check_l0a_standards(df: pd.DataFrame, sensor_name: str, verbose: bool = True
 
 def check_l0b_standards(x: str) -> None:
     # - Check for realistic values after having removed the flags !!!!
+    x = "noqa"  # noqa F841
     pass

disdrodb/l0/l0_processing.py

@@ -438,7 +438,6 @@ def run_l0a(
         product="L0A",
         station_name=station_name,
         force=force,
-        verbose=verbose,
     )
 
     # -------------------------------------------------------------------------.
@@ -726,7 +725,6 @@ def run_l0b_from_nc(
         product="L0B",
         station_name=station_name,
         force=force,
-        verbose=verbose,
     )
 
     # -------------------------------------------------------------------------.

disdrodb/l0/l0a_processing.py

@@ -138,7 +138,7 @@ def _check_df_sanitizer_fun(df_sanitizer_fun):
         raise ValueError("The `df_sanitizer_fun` must have only `df` as input argument!")
 
 
-def _check_not_empty_dataframe(df, verbose=False):
+def _check_not_empty_dataframe(df):
     if len(df.index) == 0:
         msg = " - The file is empty and has been skipped."
         log_error(logger=logger, msg=msg, verbose=False)
@@ -288,7 +288,7 @@ def remove_issue_timesteps(df, issue_dict, verbose=False):
     return df
 
 
-def cast_column_dtypes(df: pd.DataFrame, sensor_name: str, verbose: bool = False) -> pd.DataFrame:
+def cast_column_dtypes(df: pd.DataFrame, sensor_name: str) -> pd.DataFrame:
     """Convert ``'object'`` dataframe columns into DISDRODB L0A dtype standards.
 
     Parameters
@@ -297,8 +297,6 @@ def cast_column_dtypes(df: pd.DataFrame, sensor_name: str, verbose: bool = False
         Input dataframe.
     sensor_name : str
         Name of the sensor.
-    verbose : bool
-        Whether to verbose the processing. The default is ``False``.
 
     Returns
     -------
@@ -328,7 +326,7 @@ def cast_column_dtypes(df: pd.DataFrame, sensor_name: str, verbose: bool = False
     return df
 
 
-def coerce_corrupted_values_to_nan(df: pd.DataFrame, sensor_name: str, verbose: bool = False) -> pd.DataFrame:
+def coerce_corrupted_values_to_nan(df: pd.DataFrame, sensor_name: str) -> pd.DataFrame:
     """Coerce corrupted values in dataframe numeric columns to ``np.nan``.
 
     Parameters
@@ -337,8 +335,6 @@ def coerce_corrupted_values_to_nan(df: pd.DataFrame, sensor_name: str, verbose:
         Input dataframe.
     sensor_name : str
         Name of the sensor.
-    verbose : bool
-        Whether to verbose the processing. The default is ``False``.
 
     Returns
     -------
@@ -361,7 +357,7 @@ def coerce_corrupted_values_to_nan(df: pd.DataFrame, sensor_name: str, verbose:
     return df
 
 
-def strip_string_spaces(df: pd.DataFrame, sensor_name: str, verbose: bool = False) -> pd.DataFrame:
+def strip_string_spaces(df: pd.DataFrame, sensor_name: str) -> pd.DataFrame:
     """Strip leading/trailing spaces from dataframe string columns.
 
     Parameters
@@ -370,8 +366,6 @@ def strip_string_spaces(df: pd.DataFrame, sensor_name: str, verbose: bool = Fals
         Input dataframe.
     sensor_name : str
         Name of the sensor.
-    verbose : bool
-        Whether to verbose the processing. The default is ``False``.
 
     Returns
     -------
@@ -392,7 +386,7 @@ def strip_string_spaces(df: pd.DataFrame, sensor_name: str, verbose: bool = Fals
             try:
                 df[column] = df[column].str.strip()
             except AttributeError:
-                msg = f"AttributeError: The column {column} is not a string/object dtype."
+                msg = f"The column {column} is not a string/object dtype."
                 log_error(logger=logger, msg=msg, verbose=False)
                 raise AttributeError(msg)
     return df
@@ -618,7 +612,7 @@ def process_raw_file(
     )
 
     # - Check if file empty
-    _check_not_empty_dataframe(df=df, verbose=verbose)
+    _check_not_empty_dataframe(df=df)
 
     # - Check dataframe column number matches columns_names
     _check_matching_column_number(df, column_names, verbose=False)
@@ -637,10 +631,10 @@ def process_raw_file(
     df = remove_issue_timesteps(df, issue_dict=issue_dict, verbose=verbose)
 
     # - Coerce numeric columns corrupted values to np.nan
-    df = coerce_corrupted_values_to_nan(df, sensor_name=sensor_name, verbose=verbose)
+    df = coerce_corrupted_values_to_nan(df, sensor_name=sensor_name)
 
     # - Strip trailing/leading space from string columns
-    df = strip_string_spaces(df, sensor_name=sensor_name, verbose=verbose)
+    df = strip_string_spaces(df, sensor_name=sensor_name)
 
     # - Strip first and last delimiter from the raw arrays
     df = strip_delimiter_from_raw_arrays(df)
@@ -649,7 +643,7 @@ def process_raw_file(
     df = remove_corrupted_rows(df)
 
     # - Cast dataframe to dtypes
-    df = cast_column_dtypes(df, sensor_name=sensor_name, verbose=verbose)
+    df = cast_column_dtypes(df, sensor_name=sensor_name)
 
     # - Replace nan flags values with np.nans
     df = replace_nan_flags(df, sensor_name=sensor_name, verbose=verbose)
@@ -729,7 +723,7 @@ def write_l0a(
             row_group_size=row_group_size,
         )
         msg = f"The Pandas Dataframe has been written as an Apache Parquet file to {filepath}."
-        log_info(logger=logger, msg=msg, verbose=False)
+        log_info(logger=logger, msg=msg, verbose=verbose)
     except Exception as e:
         msg = f" - The Pandas DataFrame cannot be written as an Apache Parquet file. The error is: \n {e}."
         log_error(logger=logger, msg=msg, verbose=False)

disdrodb/l0/l0b_processing.py

@@ -339,7 +339,7 @@ def _set_attrs_dict(ds, attrs_dict):
 
 def _set_coordinate_attributes(ds):
     # Get attributes dictionary
-    attrs_dict = get_coords_attrs_dict(ds)
+    attrs_dict = get_coords_attrs_dict()
     # Set attributes
     ds = _set_attrs_dict(ds, attrs_dict)
     return ds

disdrodb/l0/standards.py

@@ -263,7 +263,7 @@ def get_l0b_cf_attrs_dict(sensor_name: str) -> dict:
 #### Coordinates attributes
 
 
-def get_coords_attrs_dict(ds):
+def get_coords_attrs_dict():
     """Return dictionary with DISDRODB coordinates attributes."""
     attrs_dict = {}
     # Define diameter attributes

disdrodb/metadata/search.py

@@ -142,7 +142,7 @@ def _get_list_all_metadata(base_dir, data_sources=None, campaign_names=None, sta
     return list(set(metadata_filepaths))
 
 
-def _get_list_metadata_with_data(base_dir, data_sources=None, campaign_names=None, station_names=None):
+def _get_list_metadata_with_data(base_dir, data_sources=None, campaign_names=None):
     """
     Get the list of metadata filepaths that have corresponding data in the DISDRODB raw archive.
 
@@ -159,9 +159,6 @@ def _get_list_metadata_with_data(base_dir, data_sources=None, campaign_names=Non
         Name of the campaign(s) of interest.
         The name(s) must be UPPER CASE.
         The default is ``None``.
-    station_names : str or list of str
-        Station names of interest.
-        The default is ``None``.
 
     Returns
     -------

disdrodb/tests/test_l0/test_l0a_processing.py

@@ -205,13 +205,13 @@ def test_strip_string_spaces(create_test_config_files):
 def test_coerce_corrupted_values_to_nan(create_test_config_files):
     # Test with a valid dataframe
     df = pd.DataFrame({"key_4": ["1"]})
-    df_out = coerce_corrupted_values_to_nan(df, sensor_name=TEST_SENSOR_NAME, verbose=False)
+    df_out = coerce_corrupted_values_to_nan(df, sensor_name=TEST_SENSOR_NAME)
 
     assert df.equals(df_out)
 
     # Test with a wrong dataframe
     df = pd.DataFrame({"key_4": ["text"]})
-    df_out = coerce_corrupted_values_to_nan(df, sensor_name=TEST_SENSOR_NAME, verbose=False)
+    df_out = coerce_corrupted_values_to_nan(df, sensor_name=TEST_SENSOR_NAME)
     assert pd.isnull(df_out["key_4"][0])
 
 
@@ -343,7 +343,7 @@ def test_cast_column_dtypes():
     })
     # Call the function
     sensor_name = "OTT_Parsivel"
-    df_out = cast_column_dtypes(df, sensor_name, verbose=False)
+    df_out = cast_column_dtypes(df, sensor_name)
     # Check that the output dataframe has the correct column types
     assert str(df_out["time"].dtype) == "datetime64[s]"
     assert str(df_out["station_number"].dtype) == "object"
@@ -352,7 +352,7 @@ def test_cast_column_dtypes():
     # Assert raise error if can not cast
     df["altitude"] = "text"
     with pytest.raises(ValueError):
-        cast_column_dtypes(df, sensor_name, verbose=False)
+        cast_column_dtypes(df, sensor_name)
 
 
 def test_remove_rows_with_missing_time():

disdrodb/utils/netcdf.py

@@ -401,7 +401,7 @@ def _concatenate_datasets(list_ds, dim="time", verbose=False):
         msg = "Start concatenating with xr.concat."
         log_info(logger=logger, msg=msg, verbose=verbose)
 
-        ds = xr.concat(list_ds, dim="time", coords="minimal", compat="override")
+        ds = xr.concat(list_ds, dim=dim, coords="minimal", compat="override")
 
         msg = "Concatenation with xr.concat has been successful."
         log_info(logger=logger, msg=msg, verbose=verbose)

pyproject.toml

@@ -155,7 +155,7 @@ select = [
     # flake8-return
     "RET",
     # flake8-unused-arguments
-    # "ARG",
+    "ARG",
     # flake8-raise
     "RSE",
     # flake8-pytest-style

tutorials/reader_preparation.ipynb

@@ -276,7 +276,6 @@
     "    processed_dir=processed_dir,\n",
     "    station_name=station_name,\n",
     "    force=force,\n",
-    "    verbose=False,\n",
     "    product=\"L0A\",\n",
     ")"
    ]