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
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
|
#!/usr/bin/env python3
import argparse
import csv
from functools import partial
import matplotlib.pyplot as plt
import numpy
import re
def readcsv(filename):
"""Read a (BlackBoard) CSV file into a header and data"""
with open(filename, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
data = []
header = next(reader)
for row in reader:
data.append(row)
return header, numpy.array(data)
def parse_float(f):
"""Parse a string into a float and return 0. when that fails"""
try:
return float(f)
except ValueError:
return 0.
def parse_floats(matrix):
"""Apply parse_float on all elements of a matrix"""
m_new = []
for row in matrix:
m_new.append([parse_float(v) for v in row])
return m_new
def remove_zeros(matrix):
"""Remove zeros from a matrix
Typically, BlackBoard grade lists will contain many zeros that we don't
want to distort the boxplots. This function removes zeros on a per-column
basis."""
m_new = []
for col in numpy.transpose(matrix):
m_new.append([v for v in col if v != 0.0])
return numpy.transpose(m_new)
def normalise(matrix):
"""Normalise grades to a 0-100 range
Some grades are given in a 0-10 range, others in a 0-100. This function
normalises *but these two* into a 0-100 range"""
m_new = []
for row in matrix:
m_new.append(row if max(row) > 10.0 else [10 * i for i in row])
return m_new
def strip_header(h):
"""Strip common BlackBoard additions in the CSV header"""
return h.split('instructor.download.column.total')[0]
def remove_empty_lists(headers, data):
"""Remove empty columns and the corresponding headers from a matrix"""
new_headers, new_data = [], []
for h, d in zip(headers, data):
if d != []:
new_headers.append(h)
new_data.append(d)
return new_headers, new_data
def header_regex_callback(header, regex='', invert=False):
"""Check whether a regex occurs in a header
This is an example of a possible callback function."""
match = re.compile(regex).search(header) != None
return match != invert
def participant_wherehas_callback(headers, participant, regex='', invert=False):
"""Check whether a participant has a grade for some assignment"""
regex = re.compile(regex)
mh = [regex.search(h) != None for h in headers[6:]]
for h, d in zip(mh, participant[6:]):
if h and d != '':
return (not invert)
return invert
def check_header_callback(headers, data, header_callback):
"""For each header, check that we want to show it, and remove data if not"""
new_headers, new_data = [], []
for h, d in zip(headers, data):
if header_callback(h):
new_headers.append(h)
new_data.append(d)
return new_headers, new_data
def check_participant_callback(headers, data, participant_callback):
"""For each participant, check that we want to show it, and remove if not"""
new_data = []
for d in data:
if participant_callback(headers, d):
new_data.append(d)
return new_data
def plotgrades(headers, data, skip=0,
participant_callback=lambda x:True, header_callback=lambda x:True):
"""Plot grades corresponding to headers in a boxplot"""
data = check_participant_callback(headers, data, participant_callback)
headers, data = headers[skip:], data[skip:]
data = parse_floats(data)
data = remove_zeros(data)
headers, data = remove_empty_lists(headers, data)
headers, data = check_header_callback(headers, data, header_callback)
data = normalise(data)
headers = [strip_header(h) for h in headers]
if len(data) > 0:
plt.boxplot(data)
ax = plt.gca()
plt.xticks(range(0, len(data) + 1), [''] + headers, rotation=90)
ax.set_ylim([0,100])
plt.show()
def parse_args():
"""Parse command line arguments"""
pars = argparse.ArgumentParser(description='Plot BlackBoard grades')
pars.add_argument('-s', '--skip', metavar='n', type=int, default=0,
help='Skip the first n columns')
pars.add_argument('-w', '--where', metavar='regex', default='',
help='Restrict what grades are shown with a regex')
pars.add_argument('-wh', '--where-has', metavar='regex', default='',
help='Only count participants with a grade for a matching item')
pars.add_argument('-iw', '--invert-where', action='store_true',
help='Invert --where regex')
pars.add_argument('-iwh', '--invert-where-has', action='store_true',
help='Invert --where-has regex')
pars.add_argument('filename', metavar='file',
help='The CSV file as exported by BlackBoard')
return pars.parse_args()
def main():
"""Plot BlackBoard grades from a CSV file in boxplots"""
args = parse_args()
headers, data = readcsv(args.filename)
plotgrades(headers, data, skip=args.skip,
header_callback=partial(header_regex_callback,
regex=args.where, invert=args.invert_where),
participant_callback=partial(participant_wherehas_callback,
regex=args.where_has, invert=args.invert_where_has))
if __name__ == '__main__':
main()
|
