🚧 Use a temporary sqlite db

src/mods4pandas/mods4pandas.py

@@ -3,7 +3,9 @@ import csv
 import logging
 import os
 import re
+import sqlite3
 import warnings
+import sys
 from lxml import etree as ET
 from itertools import groupby
 from operator import attrgetter
@@ -394,7 +396,45 @@ def process(mets_files: List[str], output_file: str, output_page_info: str):
         else:
             mets_files_real.append(m)
 
+    current_columns = []
+
+    def valid_column_key(k):
+        if re.match("^[a-zA-Z0-9 _-]+$", k):
+            return True
+        else:
+            return False
+
+    def insert_into_db(con, d: Dict):
+        # Create table if necessary
+        if not current_columns:
+            for k in d.keys():
+                assert valid_column_key(k), f"\"{k}\" is not a valid column name"
+                current_columns.append(k)
+            con.execute(f"CREATE TABLE mods_info({",".join(f"\"{c}\"" for c in current_columns)})")
+
+        # Add columns if necessary
+        for k in d.keys():
+            if not k in current_columns:
+                assert valid_column_key(k), f"\"{k}\" is not a valid column name"
+                current_columns.append(k)
+                con.execute(f"ALTER TABLE mods_info ADD COLUMN \"{k}\"")
+
+        # Insert
+        # Unfortunately, Python3's sqlite3 does not like named placeholders with spaces, so we
+        # have use qmark style here.
+        columns = d.keys()
+        con.execute(
+            "INSERT INTO mods_info"
+            f"( {",".join(f"\"{c}\"" for c in columns)} )"
+            "VALUES"
+            f"( {",".join("?" for c in columns)} )",
+            [str(d[c]) for c in columns]
+        )
+
+
     # Process METS files
+    output_file_sqlite3 = output_file + ".sqlite3"
+    con = sqlite3.connect(output_file_sqlite3)
     with open(output_file + '.warnings.csv', 'w') as csvfile:
         csvwriter = csv.writer(csvfile)
         mods_info = []
@@ -423,9 +463,11 @@ def process(mets_files: List[str], output_file: str, output_page_info: str):
                     if output_page_info:
                         page_info_doc: list[dict] = pages_to_dict(mets, raise_errors=True)
 
-                    mods_info.append(d)
-                    if output_page_info:
-                        page_info.extend(page_info_doc)
+                    insert_into_db(con, d)
+                    con.commit()
+                    #TODO
+                    #if output_page_info:
+                    #    page_info.extend(page_info_doc)
 
                     if caught_warnings:
                         # PyCharm thinks caught_warnings is not Iterable:
@@ -433,8 +475,7 @@ def process(mets_files: List[str], output_file: str, output_page_info: str):
                         for caught_warning in caught_warnings:
                             csvwriter.writerow([mets_file, caught_warning.message])
             except Exception as e:
-                logger.error('Exception in {}: {}'.format(mets_file, e))
-                #import traceback; traceback.print_exc()
+                logger.exception('Exception in {}'.format(mets_file))
 
     # Convert the mods_info List[Dict] to a pandas DataFrame
     mods_info_df = dicts_to_df(mods_info, index_column="recordInfo_recordIdentifier")