Add support for importing moodle result csv files.

backend/helpers.py

@@ -0,0 +1,21 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+reserved_columns = ['Nachname', 'Vorname', 'Punkte', 'Note']
+
+def update_columns(new_columns, table):
+    if len(table) > 0:
+        # Drop table columns that are not in the list of columns
+        for column in table.columns:
+            if column not in new_columns and column not in reserved_columns+list(st.session_state.session.students_df.columns):
+                table.drop(column, axis=1, inplace=True)
+        # Add columns that are in the list of columns but not in the table
+        for column in new_columns:
+            if column not in table.columns:
+                table[column] = pd.Series(np.nan, index=table.index, dtype='float64')
+        # Reorder columns
+        before_reorder = table.columns
+        original_columns = [col for col in before_reorder if col not in new_columns]
+        table = table[original_columns + new_columns]
+    return table

backend/moodle_io.py

@@ -0,0 +1,47 @@
+import pandas as pd
+import numpy as np
+from io import StringIO
+
+
+pandas_json_format = 'table'
+
+def read_csv(table_str: StringIO) -> pd.DataFrame:
+
+    try:
+        df = pd.read_csv(table_str, engine='python', skipfooter=2)
+    except:
+        return pd.DataFrame()
+
+    df = df.set_index(['Nachname', 'Vorname'])
+
+
+    # Rename columns to replace commas.
+    # Replace with _ as AggGrid does not seem to support columns with decimals in name
+    df = df.rename(columns=lambda x: x.replace(',','_'))    
+    exercises = [col for col in list(df.columns) if 'F' in col]
+    df.update(df[exercises].map(lambda v: v.replace(',','.'))) # German floats -> international floats
+    df.update(df[exercises].map(lambda v: v.replace('-','0.0'))) # '-' -> 0
+    df = df.astype({k: 'float64' for k in exercises})
+
+    return df
+
+
+def parse_header(df: pd.DataFrame) -> tuple[int, list[str]]:
+    max_points = [col for col in list(df.columns) if 'Bewertung' in col]
+    if len(max_points) != 1:
+        raise ValueError("Could not find column 'Bewertung' in table!")
+    max_points = float(max_points[0].split('/', 1)[1].replace('_','.'))
+
+    exercises = [col for col in list(df.columns) if 'F' in col]
+    if len(exercises) < 1:
+        raise ValueError("Table does not seem to contain exercise points!")
+    return max_points, exercises
+    
+
+def merge_points(points_df_hisio: pd.DataFrame, points_df_moodle: pd.DataFrame) -> tuple[pd.DataFrame, int]:
+    points_df_hisio = points_df_hisio.reset_index().set_index(['Nachname', 'Vorname'])
+    common_keys = points_df_hisio.index.intersection(points_df_moodle.index)
+    update_count = len(common_keys)
+    points_df_hisio.update(points_df_moodle)
+    points_df_hisio = points_df_hisio.reset_index().set_index('Matrikelnummer')
+    return points_df_hisio, update_count