updated project structure

first_try/compareS.py

@@ -0,0 +1,141 @@
+import csv
+from Levenshtein import distance
+import pandas as pd
+import uuid
+
+
+# TODO Filter für Spalten, ggfs. Klasse benötigt
+
+# TODO Filter für Dublikate hier wird dann die Klasse benötigt
+
+
+def read_csv(file_path):
+    data = []
+    with open(file_path, newline='', encoding='utf-8') as csvfile:
+        reader = csv.reader(csvfile, delimiter=';')
+        for row in reader:
+            data.append((row[0].strip(), row[1].strip()))
+    return data
+
+
+def similar_sets(pair, data):
+    similar_pairs = []
+    for item in data:
+        if distance(pair[0], item[0]) <= 1 and distance(pair[1], item[1]) <= 1:
+            similar_pairs.append(item)
+    return similar_pairs
+
+
+def compare_csv(file1, file2):
+    data1 = read_csv(file1)
+    data2 = read_csv(file2)
+
+    common_pairs = set(data1) & set(data2)
+    unique_pairs1 = set(data1) - common_pairs
+    unique_pairs2 = set(data2) - common_pairs
+
+    return common_pairs, unique_pairs1, unique_pairs2, data1, data2
+
+
+def find_similar_pairs(pair, other_data):
+    similar_pairs = []
+    for item in other_data:
+        if distance(pair[0], item[0]) <= 2 and distance(pair[1], item[1]) <= 2:
+            similar_pairs.append(item)
+    return similar_pairs
+
+
+def create_uuid():
+    return str(uuid.uuid4())
+
+
+def add_hl_tag(row):
+    klasse = str(row['klasse']).lstrip('0')
+    return 'HL0707104-' + klasse
+
+
+def create_import_list(path, path_new, old_pairs, new_pairs, common_pairs):
+    system_data = pd.read_csv(path, sep=';', encoding='utf-8')
+    system_data = system_data[~system_data[['name', 'vorname']].apply(tuple, axis=1).isin(old_pairs)]
+    system_data = system_data.drop('username', axis=1, errors='ignore')
+    system_data = system_data.drop('klasse', axis=1, errors='ignore')
+    # print(len(system_data))
+    # print(system_data)
+    new_data = pd.read_csv(path_new, sep=';', encoding='utf-8')
+
+    # Bei Schüler: alte Klassen gelöscht, mit neuen Klassen aus new-data auffüllen
+
+    matches = new_data[~new_data[['name', 'vorname']].apply(tuple, axis=1).isin(new_pairs)]
+    matches.loc[:, 'klasse'] = matches.apply(add_hl_tag, axis=1)
+    # print(len(matches))
+    # print(matches)
+    system_data = pd.merge(system_data, matches, how='outer', left_on=['name', 'vorname'], right_on=['name', 'vorname'])
+    system_data = system_data[['name', 'vorname', 'klasse', 'schuelerid']]
+    # print(system_data)
+    print(len(system_data))
+
+    new_data = new_data[~new_data[['name', 'vorname']].apply(tuple, axis=1).isin(common_pairs)]
+    # new_data = new_data.drop('Unnamed: 2', axis=1, errors='ignore')
+    new_uuids = []
+    for row in range(len(new_data)):
+        new_uuids.append(create_uuid())
+    # Klasse?? Unterschied zwischen Lehrer und Schüler
+    # new_data['klasse'] = None
+    new_data.loc[:, 'klasse'] = new_data.apply(add_hl_tag, axis=1)
+    new_data.insert(loc=2, column='schuelerid', value=new_uuids)
+    mailUserQuota = 1024
+    oxUserQuota = 5120
+    oxContext = 25
+
+    # print(new_data)
+    print(len(new_data))
+
+    # vor dem merge daten ergänzen
+    # import_df = pd.merge(system_data, new_data, how='outer', left_on=['name', 'vorname', 'mailUserQuota', 'oxUserQuota', 'oxContext'], right_on=['name', 'vorname', 'mailUserQuota', 'oxUserQuota', 'oxContext'])
+    # import_df = pd.merge(system_data, new_data, how='outer',
+    #                      left_on=['name', 'vorname', 'klasse', 'schuelerid', 'mailUserQuota', 'oxUserQuota',
+    #                               'oxContext'],
+    #                      right_on=['name', 'vorname', 'klasse', 'schuelerid', 'mailUserQuota', 'oxUserQuota',
+    #                                'oxContext'])
+    import_df = pd.merge(system_data, new_data, how='outer',
+                         left_on=['name', 'vorname', 'klasse', 'schuelerid'],
+                         right_on=['name', 'vorname', 'klasse', 'schuelerid'])
+    import_df['mailUserQuota'] = mailUserQuota
+    import_df['oxUserQuota'] = oxUserQuota
+    import_df['oxContext'] = oxContext
+
+    # pd.set_option('display.max_rows', None)
+    # pd.set_option('display.max_columns', None)
+    # print(import_df)
+    # pd.reset_option('display.max_rows')
+    # pd.reset_option('display.max_columns')
+    out_path = '../Data/GPS/outputSchueler.csv'
+    import_df.to_csv(out_path, sep=';', index=False)
+    print(len(import_df))
+    print('Schüler Import Liste erzeugt')
+    print('Testuser manuell nachtragen!!')
+
+
+def main():
+    file1_path = '../Data/GPS/gpsSnew.csv'
+    file2_path = '../Data/GPS/gpsSold2.cvs'
+    file3_path = '../Data/GPS/gpsSold.csv'
+
+    common_pairs, new_pairs, old_pairs, data1, data2 = compare_csv(file1_path, file2_path)
+
+    print(f"Anzahl der übereinstimmenden Paare: {len(common_pairs)}")
+    print(f"Anzahl der neuen Einträge: {len(new_pairs)}")
+    print(f"Anzahl der veralteten Einträge: {len(old_pairs)}")
+
+    # Paare nur aus nicht zugeordneten Paaren aus neuer Liste erstellen
+    # for pair in data1:
+    #     similar_pairs_list2 = find_similar_pairs(pair, set(data2) - {pair})
+    #
+    #     if similar_pairs_list2:
+    #         print(f"Ähnliche Paare in neuer Liste {pair} aktuell im System: {similar_pairs_list2}")
+
+    create_import_list(file3_path, file1_path, old_pairs, new_pairs, common_pairs)
+
+
+if __name__ == "__main__":
+    main()