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#!/usr/bin/env python3
import argparse
import csv
from functools import partial
import itertools
import matplotlib.pyplot as plt
import numpy
from operator import itemgetter
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='\t', 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 matrix:
m_new.append([v for v in col if v != 0.0])
return 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('[Total Pts:')[0]
def remove_empty_lists(headers, data):
"""Remove empty columns and the corresponding headers from a matrix"""
new_headers, new_data = [], []
for h, col in zip(headers, numpy.transpose(data)):
if not all([x == 0.0 for x in col]):
new_headers.append(h)
new_data.append(col)
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 make_groups(headers, data, column):
i = 0
for h in headers:
if column in h:
break
i += 1
data = list(numpy.transpose(data))
data.sort(key=itemgetter(i))
data = [list(x) for _, x in itertools.groupby(data, itemgetter(i))]
new_headers = []
new_data = []
j = 0
for h in headers:
for group in data:
if not all([x[j] == 0.0 for x in group]):
new_headers.append(h + ' (group: ' + str(group[0][i]) + ')')
new_data.append([x[j] for x in group])
j += 1
return new_headers, new_data
def plotgrades(headers, data, skip=0,
participant_callback=lambda x:True, header_callback=lambda x:True,
group_participants_column=None):
"""Plot grades corresponding to headers in a boxplot"""
data = check_participant_callback(headers, data, participant_callback)
headers, data = map(strip_header, headers[skip:]), data[skip:]
data = parse_floats(data)
headers, data = remove_empty_lists(headers, data)
if group_participants_column is not None:
headers, data = make_groups(headers, data, group_participants_column)
data = remove_zeros(data)
headers, data = check_header_callback(headers, data, header_callback)
data = normalise(data)
if len(data) > 0:
plt.boxplot(data)
ax = plt.gca()
plt.xticks(range(1, 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('-g', '--group-by', metavar='column', default=None,
help='Group participants by this column')
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),
group_participants_column=args.group_by)
if __name__ == '__main__':
main()
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