1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
|
#!/usr/bin/python3
import sys
import pandas as pd
import numpy as np
import os
# infile = 'wa.csv'
infile = sys.argv[1]
f = open(infile)
l= f.readlines()
h= l[0]
hsub=l[1] # TheExpertTA keep headers in 2 lines (mixed with max possible points)
maxPossible=l[1]
f.close()
# clean up of headers
h=h.strip()
h = h.replace('"', '')
hsub = hsub.strip()
hsub = hsub.replace('"', '')
hsub = hsub.split(',')
headers = h.split(',')
# we should fail hard if this column names are not present
headers[hsub.index('Last')]='LastName'
headers[hsub.index('First')]='FirstName'
headers[hsub.index('Email')]='UserName'
headers[hsub.index('Student No')]='SID'
# headers[0]='FullName'
# headers[1]='UserName'
# headers[2]='SID'
# headers[3]='TotalPcnt'
# headers[4]='TotalScore'
d = pd.read_csv(infile, skiprows=[0], header=None, names=headers)
# First row contains max points information, so let's reassign it
d.loc[0, 'UserName']='_Max_Points_'
d.loc[0, 'LastName']='MaxScore'
d.loc[0, 'FirstName']='MaxScore'
d.loc[0, 'SID']=pd.NA
specialUsers=[]
specialUsers.append('_Max_Points_')
# lets add row which will be in charge of the column type
d=pd.concat([d, pd.DataFrame({'UserName': ['_Col_Category_']}, columns=d.columns)], ignore_index=True)
specialUsers.append('_Col_Category_')
# cleanup
# c = d.columns
# c=c.drop(['FullName', 'UserName', 'SID'])
# index = d[c] == 'ND'
# d[index] = np.nan
# index = d[c] == 'NS'
# d[index] = np.nan
# TheExperTA last column contains 'Averages' per student which we do not need
d.drop(columns=['Averages'], inplace=True)
# TheExperTA last row contains Averages per assignment which we do not need
row = d[(d['LastName']=='Averages') & (d['FirstName'].isna()) & (d['UserName'].isna())]
d.drop(row.index, inplace=True)
# hand tuned fixes
d['UserName'].replace('@email.wm.edu$', '@wm.edu', regex=True, inplace=True)
# d['UserName'].replace('phanng@hotmail.com@tj.va$', 'kphan@wm.edu', regex=True, inplace=True)
# Now let's convert percentage which TheExperTA reports to points as GradeTable expects
for c in d.columns:
if c in ['LastName', 'FirstName', 'UserName', 'SID']:
continue
maxP = d.loc[(d['UserName'] == '_Max_Points_')][c].values[0]
d.loc[(d['UserName'] == '_Max_Points_')][c]
index = ~d['UserName'].isin( specialUsers )
d.loc[index,c] *= maxP/100 # convert percentage to points
# now we are trying to guess column category
if 'hw' in c.lower():
d.loc[(d['UserName'] == '_Col_Category_'), c] = 'HomeWork'
d.to_csv('TheExperTA.csv', index=False)
# now import to sqlite3
os.popen('rm -f TheExperTA.db')
p = os.popen('printf ".mode csv\n.import \"TheExperTA.csv\" export_table\n.q" | sqlite3 TheExperTA.db')
p.close()
|
