--- a/formatter.py	Thu May 25 09:06:00 2023 +0000
+++ b/formatter.py	Tue Jun 27 14:25:59 2023 +0000
@@ -3,68 +3,7 @@
 from pandas import DataFrame
 
 
-def create_long_table(data: DataFrame, value_id: str) -> DataFrame:
-    """Convert the table from compact into long format.
-    See DataFrame.melt(...).
-
-    Args:
-        data (DataFrame): The data table to convert.
-        value_id (str): The name to assign to the added column through conversion to long format.
-
-    Returns:
-        DataFrame: Table in long format.
-    """
-    return data.transpose().melt(ignore_index=False, var_name='compound', value_name=value_id)
-
-
-def join_df(x: DataFrame, y: DataFrame, on=[], how="inner") -> DataFrame:
-    """Shortcut functions to join to dataframes on columns and index
-
-    Args:
-        x (DataFrame): Table X
-        y (DataFrame): Table Y
-        on (list, optional): Columns on which to join. Defaults to [].
-        how (str, optional): Join method, see DataFrame.join(...). Defaults to "inner".
-
-    Returns:
-        DataFrame: Joined dataframe.
-    """
-    df_x = x.set_index([x.index] + on)
-    df_y = y.set_index([y.index] + on)
-    combined = df_x.join(df_y, how=how)
-    return combined
-
-
-def get_top_k_matches(data: DataFrame, k: int) -> DataFrame:
-    """Function to get top k matches from dataframe with scores.
-
-    Args:
-        data (DataFrame): A table with score column.
-        k (int): Number of top scores to retrieve.
-
-    Returns:
-        DataFrame: Table containing only the top k best matches for each compound.
-    """
-    return data.groupby(level=0, group_keys=False).apply(DataFrame.nlargest, n=k, columns=['score'])
-
-
-def filter_thresholds(data: DataFrame, t_score: float, t_matches: float) -> DataFrame:
-    """Filter a dataframe with scores and matches to only contain values above specified thresholds.
-
-    Args:
-        data (DataFrame): Table to filter.
-        t_score (float): Score threshold.
-        t_matches (float): Matches threshold.
-
-    Returns:
-        DataFrame: Filtered dataframe.
-    """
-    filtered = data[data['score'] > t_score]
-    filtered = filtered[filtered['matches'] > t_matches]
-    return filtered
-
-
-def scores_to_dataframes(scores):
+def scores_to_dataframe(scores):
     """Unpack scores from matchms.scores into two dataframes of scores and matches.
 
     Args:
@@ -74,13 +13,12 @@
         DataFrame: Scores
         DataFrame: Matches
     """
-    query_names = [spectra.metadata['compound_name'] for spectra in scores.queries]
-    reference_names = [spectra.metadata['compound_name'] for spectra in scores.references]
+    dataframe = DataFrame(columns=['query', 'reference', *scores.scores.score_names])
 
-    dataframe_scores = DataFrame(data=[entry["score"] for entry in scores.scores], index=reference_names, columns=query_names)
-    dataframe_matches = DataFrame(data=[entry["matches"] for entry in scores.scores], index=reference_names, columns=query_names)
+    for i, (row, col) in enumerate(zip(scores.scores.row, scores.scores.col)):
+        dataframe.loc[i] = [scores.queries[col].metadata['compound_name'], scores.references[row].metadata['compound_name'], *scores.scores.data[i]]
 
-    return dataframe_scores, dataframe_matches
+    return dataframe
 
 
 def load_data(scores_filename: str) -> DataFrame:
@@ -93,47 +31,19 @@
         DataFrame: Joined dataframe on compounds containing scores and matches in long format.
     """
     scores = scores_from_json(scores_filename)
-    scores, matches = scores_to_dataframes(scores)
+    scores = scores_to_dataframe(scores)
 
-    scores_long = create_long_table(scores, 'score')
-    matches_long = create_long_table(matches, 'matches')
-
-    combined = join_df(matches_long, scores_long, on=['compound'], how='inner')
-    return combined
+    return scores
 
 
-@click.group()
+@click.group(invoke_without_command=True)
 @click.option('--sf', 'scores_filename', type=click.Path(exists=True), required=True)
 @click.option('--o', 'output_filename', type=click.Path(writable=True), required=True)
-@click.pass_context
-def cli(ctx, scores_filename, output_filename):
-    ctx.ensure_object(dict)
-    ctx.obj['data'] = load_data(scores_filename)
+def cli(scores_filename, output_filename):
+    result = load_data(scores_filename)
+    result.to_csv(output_filename, sep="\t", index=False)
     pass
 
 
-@cli.command()
-@click.option('--st', 'scores_threshold', type=float, required=True)
-@click.option('--mt', 'matches_threshold', type=float, required=True)
-@click.pass_context
-def get_thresholded_data(ctx, scores_threshold, matches_threshold):
-    result = filter_thresholds(ctx.obj['data'], scores_threshold, matches_threshold)
-    return result
-
-
-@cli.command()
-@click.option('--k', 'k', type=int, required=True)
-@click.pass_context
-def get_top_k_data(ctx, k):
-    result = get_top_k_matches(ctx.obj['data'], k)
-    return result
-
-
-@cli.result_callback()
-def write_output(result: DataFrame, scores_filename, output_filename):
-    result = result.reset_index().rename(columns={'level_0': 'query', 'compound': 'reference'})
-    result.to_csv(output_filename, sep="\t", index=False)
-
-
 if __name__ == '__main__':
-    cli(obj={})
+    cli()