Remove unused code, add Discussions URL and other minor changes

CONTRIBUTING.md

@@ -38,7 +38,7 @@ to <[email protected]>.
 
 > If you want to ask a question, we assume that you have read the available [Documentation](https://xl2times.readthedocs.io/).
 
-Before you ask a question, it is best to search for existing [Issues](https://github.com/etsap-TIMES/xl2times/issues) that might help you. In case you have found a suitable issue and still need clarification, you can write your question in this issue. It is also advisable to search the internet for answers first.
+Before you ask a question, it is best to check [Discussions](https://github.com/etsap-TIMES/xl2times/discussions) and to search for existing [Issues](https://github.com/etsap-TIMES/xl2times/issues) that might help you. In case you have found a suitable issue and still need clarification, you can write your question in this issue. It is also advisable to search the internet for answers first.
 
 If you then still feel the need to ask a question and need clarification, we recommend the following:
 

README.md

@@ -143,4 +143,4 @@ python -m twine upload dist/*
 
 ## Contributing
 
-This project welcomes contributions and suggestions. See [CODE_OF_CONDUCT.md](https://github.com/etsap-TIMES/xl2times/blob/main/CODE_OF_CONDUCT.md) and [CONTRIBUTING.md](https://github.com/etsap-TIMES/xl2times/blob/main/CONTRIBUTING.md) for more details.
+This project welcomes contributions and suggestions. See [Code of Conduct](https://github.com/etsap-TIMES/xl2times/blob/main/CODE_OF_CONDUCT.md) and [Contributing](https://github.com/etsap-TIMES/xl2times/blob/main/CONTRIBUTING.md) for more details.

xl2times/transforms.py

@@ -428,7 +428,7 @@ def merge_tables(
                 if not all(index):
                     for _, row in df[~index].iterrows():
                         region, sets, process = row[["region", "sets", "process"]]
-                        print(
+                        logger.warning(
                             f"WARNING: Unknown process set {sets} specified for process {process}"
                             f" in region {region}. The record will be dropped."
                         )
@@ -1384,7 +1384,8 @@ def _process_comm_groups_vectorised(
 
     Returns
     -------
-        Processed DataFrame with a new column "DefaultVedaPCG" set to True for the default pcg in eachregion/process/io combination.
+        Processed DataFrame with a new column "DefaultVedaPCG" set to True for the default pcg in
+        each region/process/io combination.
     """
 
     def _set_default_veda_pcg(group):
@@ -2292,11 +2293,13 @@ def _match_wildcards(
     result_col
         Name of the column to store the matched results in.
     explode
-        Whether to explode the  results_col ('process'/'commodities') column into a long-format table. (Default value = False)
+        Whether to explode the  results_col ('process'/'commodities') column into a long-format table.
+        (Default value = False)
 
     Returns
     -------
-        The table with the wildcard columns removed and the results of the wildcard matches added as a column named `results_col`
+        The table with the wildcard columns removed and the results of the wildcard matches added as a
+        column named `results_col`
     """
     wild_cols = list(col_map.keys())
 
@@ -2331,7 +2334,7 @@ def _match_wildcards(
         .drop(columns=wild_cols)
     )
 
-    # TODO TFM_UPD has existing (but empty) 'process' and 'commodity' columns here.  Is it ok to drop existing columns here?
+    # TODO TFM_UPD has existing (but empty) 'process' and 'commodity' columns. Is it ok to drop existing columns here?
     if f"{result_col}_old" in df.columns:
         if not df[f"{result_col}_old"].isna().all():
             logger.warning(
@@ -2549,8 +2552,8 @@ def explode_process_commodity_cols(
     """Explodes the process and commodity columns in the tables that contain them as
     lists after process_wildcards.
 
-    We store wildcard matches for these columns as lists and explode them late here for performance reasons - to avoid row-wise processing that
-    would otherwise need to iterate over very long tables.
+    We store wildcard matches for these columns as lists and explode them late here for performance
+    reasons - to avoid row-wise processing that would otherwise need to iterate over very long tables.
     """
     for tag in tables:
         df = tables[tag]

xl2times/utils.py

@@ -7,9 +7,7 @@
 import gzip
 import os
 import pickle
-import re
 import sys
-from collections.abc import Iterable
 from dataclasses import replace
 from math import floor, log10
 from pathlib import Path, PurePath
@@ -173,53 +171,6 @@ def merge_columns(
     return numpy.concatenate(columns)
 
 
-def apply_wildcards(
-    df: DataFrame, candidates: Iterable[str], wildcard_col: str, output_col: str
-):
-    """Apply wildcards values to a list of candidates. Wildcards are values containing
-    '*'. For example, a value containing '*SOLID*' would include all the values in
-    'candidates' containing 'SOLID' in the middle.
-
-    TODO unused. Remove?
-
-    Parameters
-    ----------
-    df
-        Dataframe containing all values.
-    candidates
-        List of candidate strings to apply the wildcard to.
-    wildcard_col
-        Name of column containing the wildcards.
-    output_col
-        Name of the column to dump the wildcard matches to.
-
-    Returns
-    -------
-    type
-        A dataframe containing all the wildcard matches on its 'output_col' column.
-    """
-    wildcard_map = {}
-    all_wildcards = df[wildcard_col].unique()
-    for wildcard_string in all_wildcards:
-        if wildcard_string is None:
-            wildcard_map[wildcard_string] = None
-        else:
-            wildcard_list = wildcard_string.split(",")
-            current_list = []
-            for wildcard in wildcard_list:
-                if wildcard.startswith("-"):
-                    w = wildcard[1:]
-                    regexp = re.compile(w.replace("*", ".*"))
-                    current_list = [s for s in current_list if not regexp.match(s)]
-                else:
-                    regexp = re.compile(wildcard.replace("*", ".*"))
-                    additions = [s for s in candidates if regexp.match(s)]
-                    current_list = sorted(set(current_list + additions))
-            wildcard_map[wildcard_string] = current_list
-
-    df[output_col] = df[wildcard_col].map(wildcard_map)
-
-
 def missing_value_inherit(df: DataFrame, colname: str) -> None:
     """For each None value in the specifed column of the dataframe, replace it with the
     last non-None value. If no previous non-None value is found leave it as it is. This
@@ -381,7 +332,7 @@ def get_logger(log_name: str = default_log_name, log_dir: str = ".") -> loguru.L
     Parameters
     ----------
     log_name
-        Name of the log. Corresponding log file will be called {log_name}.log in the . (Default value = default_log_name)
+        Name of the log. Corresponding log file will be called {log_name}.log. (Default value = default_log_name)
     log_dir
         Directory to write the log file to. Default is the current working directory.
 
@@ -452,7 +403,8 @@ def compare_df_dict(
     df_after
         the second dictionary of DataFrames to compare
     sort_cols
-        whether to sort the columns before comparing.  Set True if the column order is unimportant. (Default value = True)
+        whether to sort the columns before comparing.  Set True if the column order
+        is unimportant. (Default value = True)
     context_rows
         number of rows to show around the first difference (Default value = 2)
     """