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Changed: Structure Overhal

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DerGrumpf
2025-02-14 13:34:31 +01:00
parent c320b27664
commit 033a1fa94f
27 changed files with 1059 additions and 528 deletions
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assets/Student_list.csv
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@@ -1,38 +1,39 @@
First Name,Last Name,Sex,Group,Grader,Tutorial 1,Tutorial 2,Extended Applications,Numpy & MatPlotLib,SciPy,Monte Carlo,Pandas & Seaborn,Folium,Statistical Test Methods,Data Analysis
Abdalaziz,Abunjaila,Male,DiKum,30 Percent,30.5,15,18,28,17,17,17,22,0,18
Marleen,Adolphi,Female,MeWi6,30 Percent,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,MeWi1,30 Percent,28.5,15,18,32,20,20,21,24,20,23
Skofiare,Berisha,Female,DiKum,30 Percent,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,MeWi2,30 Percent,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,MeWi3,30 Percent,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,MeWi4,30 Percent,31,15,15,34,20,20,21,27,19,21
Lena,Fricke,Female,MeWi4,30 Percent,0,0,0,0,0,0,0,0,0,0
Nele,Grundke,Female,MeWi6,30 Percent,23.5,13,16,28,20,17,21,18,22,11
Anna,Grünewald,Female,MeWi3,30 Percent,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,NoGroup,30 Percent,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,MeWi1,30 Percent,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,MeWi1,30 Percent,30,15,18,33,20,20,21.5,26,20,22
Julia,Limbach,Female,MeWi6,30 Percent,27.5,12,18,29,11,19,17.5,26,24,28
Viktoria,Litza,Female,MeWi5,30 Percent,21.5,15,18,27,13,20,22,21,21,30
Leonie,Manthey,Female,MeWi1,30 Percent,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,MeWi2,30 Percent,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,MeWi5,30 Percent,22.5,15,17,34,13,10,20,21,19,6
Donika,Nuhiu,Female,MeWi5,30 Percent,31,13.5,18,35,14,10,17,18,19,8
Julia,Renner,Female,MeWi4,30 Percent,27.5,10,14,32,20,17,11,20,24,14
Fabian,Rothberger,Male,MeWi3,30 Percent,30.5,15,18,34,17,17,19,22,18,30
Natascha,Rott,Female,MeWi1,30 Percent,29.5,12,18,32,19,20,21,26,23,26
Isabel,Rudolf,Female,MeWi4,30 Percent,27.5,9,17,34,16,19,19,21,16,14
Melina,Sablotny,Female,MeWi6,30 Percent,31,15,18,33,20,20,21,19,11,28
Alea,Schleier,Female,DiKum,30 Percent,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,MeWi3,30 Percent,29.5,15,17,34,19,20,19,22,18,27
Marie,Seeger,Female,DiKum,30 Percent,27.5,15,18,32,14,9,17,22,9,25
Lucy,Thiele,Female,MeWi6,30 Percent,27.5,15,18,27,20,17,19,18,22,25
Lara,Troschke,Female,MeWi2,30 Percent,28.5,14,17,28,13,19,21,25,12,24
Inga-Brit,Turschner,Female,MeWi2,30 Percent,25.5,14,18,34,20,16,19,22,17,30
Alea,Unger,Female,MeWi5,30 Percent,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,MeWi5,30 Percent,28.5,14,18,34,17,20,19,24,12,22
Katharina,Walz,Female,MeWi4,30 Percent,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,NoGroup,30 Percent,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,DiKum,30 Percent,30,15,18,30,14,17,19,14,16,24
Abdalaziz,Abunjaila,Male,DiKum,30%,30.5,15,18,28,17,17,17,22,0,18
Marleen,Adolphi,Female,MeWi6,30%,29.5,15,18,32,19,20,17,24,23,0
Sarina,Apel,Female,MeWi1,30%,28.5,15,18,32,20,20,21,24,20,23
Skofiare,Berisha,Female,DiKum,30%,29.5,13,18,34,20,17,20,26,16,0
Aurela,Brahimi,Female,MeWi2,30%,17.5,15,15.5,26,16,17,19,16,0,0
Cam Thu,Do,Female,MeWi3,30%,31,15,18,34,19,20,21.5,22,12,0
Nova,Eib,Female,MeWi4,30%,31,15,15,34,20,20,21,27,19,21
Lena,Fricke,Female,MeWi4,30%,0,0,0,0,0,0,0,0,0,0
Nele,Grundke,Female,MeWi6,30%,23.5,13,16,28,20,17,21,18,22,11
Anna,Grünewald,Female,MeWi3,30%,12,14,16,29,16,15,19,9,0,0
Yannik,Haupt,Male,NoGroup,30%,18,6,14,21,13,2,9,0,0,0
Janna,Heiny,Female,MeWi1,30%,30,15,18,33,18,20,22,25,24,30
Milena,Krieger,Female,MeWi1,30%,30,15,18,33,20,20,21.5,26,20,22
Julia,Limbach,Female,MeWi6,30%,27.5,12,18,29,11,19,17.5,26,24,28
Viktoria,Litza,Female,MeWi5,30%,21.5,15,18,27,13,20,22,21,21,30
Leonie,Manthey,Female,MeWi1,30%,28.5,14,18,29,20,10,18,23,16,28
Izabel,Mike,Female,MeWi2,30%,29.5,15,15,35,11,15,19,21,21,27
Lea,Noglik,Female,MeWi5,30%,22.5,15,17,34,13,10,20,21,19,6
Donika,Nuhiu,Female,MeWi5,30%,31,13.5,18,35,14,10,17,18,19,8
Julia,Renner,Female,MeWi4,30%,27.5,10,14,32,20,17,11,20,24,14
Fabian,Rothberger,Male,MeWi3,30%,30.5,15,18,34,17,17,19,22,18,30
Natascha,Rott,Female,MeWi1,30%,29.5,12,18,32,19,20,21,26,23,26
Isabel,Rudolf,Female,MeWi4,30%,27.5,9,17,34,16,19,19,21,16,14
Melina,Sablotny,Female,MeWi6,30%,31,15,18,33,20,20,21,19,11,28
Alea,Schleier,Female,DiKum,30%,27,14,18,34,16,18,21.5,22,15,22
Flemming,Schur,Male,MeWi3,30%,29.5,15,17,34,19,20,19,22,18,27
Marie,Seeger,Female,DiKum,30%,27.5,15,18,32,14,9,17,22,9,25
Lucy,Thiele,Female,MeWi6,30%,27.5,15,18,27,20,17,19,18,22,25
Lara,Troschke,Female,MeWi2,30%,28.5,14,17,28,13,19,21,25,12,24
Inga-Brit,Turschner,Female,MeWi2,30%,25.5,14,18,34,20,16,19,22,17,30
Alea,Unger,Female,MeWi5,30%,30,12,18,31,20,20,21,22,15,21.5
Marie,Wallbaum,Female,MeWi5,30%,28.5,14,18,34,17,20,19,24,12,22
Katharina,Walz,Female,MeWi4,30%,31,15,18,31,19,19,17,24,17,14.5
Xiaowei,Wang,Male,NoGroup,30%,30.5,14,18,26,19,17,0,0,0,0
Lilly-Lu,Warnken,Female,DiKum,30%,30,15,18,30,14,17,19,14,16,24
,,,,,,,,,,,,,,
,,,,,,,,,,,,,,
1 First Name Last Name Sex Group Grader Tutorial 1 Tutorial 2 Extended Applications Numpy & MatPlotLib SciPy Monte Carlo Pandas & Seaborn Folium Statistical Test Methods Data Analysis
2 Abdalaziz Abunjaila Male DiKum 30 Percent 30% 30.5 15 18 28 17 17 17 22 0 18
3 Marleen Adolphi Female MeWi6 30 Percent 30% 29.5 15 18 32 19 20 17 24 23 0
4 Sarina Apel Female MeWi1 30 Percent 30% 28.5 15 18 32 20 20 21 24 20 23
5 Skofiare Berisha Female DiKum 30 Percent 30% 29.5 13 18 34 20 17 20 26 16 0
6 Aurela Brahimi Female MeWi2 30 Percent 30% 17.5 15 15.5 26 16 17 19 16 0 0
7 Cam Thu Do Female MeWi3 30 Percent 30% 31 15 18 34 19 20 21.5 22 12 0
8 Nova Eib Female MeWi4 30 Percent 30% 31 15 15 34 20 20 21 27 19 21
9 Lena Fricke Female MeWi4 30 Percent 30% 0 0 0 0 0 0 0 0 0 0
10 Nele Grundke Female MeWi6 30 Percent 30% 23.5 13 16 28 20 17 21 18 22 11
11 Anna Grünewald Female MeWi3 30 Percent 30% 12 14 16 29 16 15 19 9 0 0
12 Yannik Haupt Male NoGroup 30 Percent 30% 18 6 14 21 13 2 9 0 0 0
13 Janna Heiny Female MeWi1 30 Percent 30% 30 15 18 33 18 20 22 25 24 30
14 Milena Krieger Female MeWi1 30 Percent 30% 30 15 18 33 20 20 21.5 26 20 22
15 Julia Limbach Female MeWi6 30 Percent 30% 27.5 12 18 29 11 19 17.5 26 24 28
16 Viktoria Litza Female MeWi5 30 Percent 30% 21.5 15 18 27 13 20 22 21 21 30
17 Leonie Manthey Female MeWi1 30 Percent 30% 28.5 14 18 29 20 10 18 23 16 28
18 Izabel Mike Female MeWi2 30 Percent 30% 29.5 15 15 35 11 15 19 21 21 27
19 Lea Noglik Female MeWi5 30 Percent 30% 22.5 15 17 34 13 10 20 21 19 6
20 Donika Nuhiu Female MeWi5 30 Percent 30% 31 13.5 18 35 14 10 17 18 19 8
21 Julia Renner Female MeWi4 30 Percent 30% 27.5 10 14 32 20 17 11 20 24 14
22 Fabian Rothberger Male MeWi3 30 Percent 30% 30.5 15 18 34 17 17 19 22 18 30
23 Natascha Rott Female MeWi1 30 Percent 30% 29.5 12 18 32 19 20 21 26 23 26
24 Isabel Rudolf Female MeWi4 30 Percent 30% 27.5 9 17 34 16 19 19 21 16 14
25 Melina Sablotny Female MeWi6 30 Percent 30% 31 15 18 33 20 20 21 19 11 28
26 Alea Schleier Female DiKum 30 Percent 30% 27 14 18 34 16 18 21.5 22 15 22
27 Flemming Schur Male MeWi3 30 Percent 30% 29.5 15 17 34 19 20 19 22 18 27
28 Marie Seeger Female DiKum 30 Percent 30% 27.5 15 18 32 14 9 17 22 9 25
29 Lucy Thiele Female MeWi6 30 Percent 30% 27.5 15 18 27 20 17 19 18 22 25
30 Lara Troschke Female MeWi2 30 Percent 30% 28.5 14 17 28 13 19 21 25 12 24
31 Inga-Brit Turschner Female MeWi2 30 Percent 30% 25.5 14 18 34 20 16 19 22 17 30
32 Alea Unger Female MeWi5 30 Percent 30% 30 12 18 31 20 20 21 22 15 21.5
33 Marie Wallbaum Female MeWi5 30 Percent 30% 28.5 14 18 34 17 20 19 24 12 22
34 Katharina Walz Female MeWi4 30 Percent 30% 31 15 18 31 19 19 17 24 17 14.5
35 Xiaowei Wang Male NoGroup 30 Percent 30% 30.5 14 18 26 19 17 0 0 0 0
36 Lilly-Lu Warnken Female DiKum 30 Percent 30% 30 15 18 30 14 17 19 14 16 24
37
38
39

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assets/WiSe_24_25.db
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10
assets/convert.py
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@@ -1,8 +1,9 @@
import pandas as pd
import pprint
import sys
sys.path.append('..')
sys.path.append('../grapher/dbmodel')
from model import *
from utils import *
df = pd.read_csv("Student_list.csv")
df = df.dropna()
@@ -31,10 +32,8 @@ groups = {
}
print(df)
init_db('WiSe_24_25.db')
db.init("WiSe_24_25.db")
db.connect()
db.create_tables([Class, Student, Lecture, Submission, Group])
# Create Class
clas = Class.create(name='WiSe 24/25')
@@ -55,7 +54,8 @@ for index, row in df.iterrows():
sex=row["Sex"],
class_id=clas.id,
group_id=Group.select().where(Group.name == row["Group"]),
grader=row["Grader"]
grader=row["Grader"],
residence_id=-1
)
for title, points in list(row.to_dict().items())[5:]: