Dec 282013
 

Now that we have the CS Department’s GitLab server set up, and CS-140 Lab 1 is rewritten and tested using the new server, I’ve started to think about how to automate my interactions with the server. I had already  written some Bash scripts to interact with the Bitbucket server to get student code, convert it to PDF files, and put it back on the server after grading. Those scripts should still work fine with GitLab, since it’s just git on a different server.

One thing that I had not been able to automate previously is the step of issuing a pull request for students to merge my grading branch into their repository. This was not too much of an issue when there were only 6 students in the summer class (so only 3 repositories per lab assignment), but it was going to take more time with ~48 students in the spring class. While reading RSS feeds, I came across a post mentioning the GitLab API. This could be the solution to my problems! And there’s a Python module for the API! I had already been writing Python scripts to make my grading easier, and had been starting to rewrite my Bash scripts in Python.

I started playing with the GitLab API in Python, and had managed to create a merge request (GitLab’s term for pull request.) I had also noticed that you could create GitLab accounts through the API. This seemed like something I should pursue – creating ~48 accounts per semester seemed like something that should be automated.

Since I intended to post my code on Github, one of the first issues I had to address is how to avoid publishing my private token for GitLab. I could have put in a dummy token before pushing my code, but I would have to remember to do that before every time I committed my code. The solution to this issue was solved through the use of the .gitignore file. If I put my token into a file, then I could add a line to my .gitignore file so that it would not be committed.

# Private GitLab Token - not to be stored in repository #
########################################################
gitlabtoken.txt

Then I could just read the token out of the file, and use that string.

# Get my private GitLab token
# stored in a file so that I can .gitignore the file
token = open('gitlabtoken.txt').readline().strip()

After importing the pyapi-gitlab module, I could use that token, along with the server’s URL to create a GitLab object. Notice, that I had to turn ssl verification off, since we only have a self-signed certificate.

# Create a GitLab object
# For our server, verify_ssl has to be False, since we have a self-signed certificate
git = gitlab.Gitlab(GITLAB_URL, token, verify_ssl=False)

Creating a user account is pretty simple using the API:

# Create the account  
success = git.createuser(name, username, password, email)

The returned success value is a boolean — either it worked, or it failed (but you can’t tell why…).

One thing that’s a bit odd about the createuser call, is that you have set a password for the user, but the notification email to the doesn’t include the password. (If you create a user account from the web interface, it generates a random password, includes it in the notification email to the user, and requires the user to change their password when first logging in.) And, the password you set doesn’t seem to work either!

So, I’m just telling the students that they should use the “Forgot Password” link to have a password reset email sent to them, and then proceed from there. (If this is ever fixed, I’ll have to generated a random password.)

Getting the class list as a CSV file from the Blackboard Grade Center is pretty easy, and the first three rows contain the student’s last name, first name, and username. I can use those three strings to generate the name, username, and email needed for the createuser API call.

The only challenge with processing the CSV file is that Blackboard puts some strange character at the beginning of the file, so the file has to be opened with utf-8 encoding. (And the header line needs to be thrown away.)

The last thing I wanted to add is a way to have optional verbose output, so that I could see if the user creation was working. (I decided that it should always notify the user if the account creation failed.)  To do this I had to learn two new things about Python: how to parse arguments1, and how to send output to stderr.

I used the argparse module:

import argparse
# Set up to parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('filename', help='Blackboard CSV filename with user information')
parser.add_argument('-v', '--verbose', help='increase output verbosity', action='store_true')
args = parser.parse_args()

and used the verbose argument to determine what to print:

if not success:
    sys.stderr.write('Failed to create acccount for: '+name+ ', '+username+', '+email+'\n') 
elif args.verbose:
    sys.stderr.write('Created account for: '+name+', '+username+', '+email+'\n')

Full code is on Github here.

  1. I already knew how to do simple argument parsing, but I wanted to learn how to deal with optional arguments.
Dec 132013
 

Downloading student assignment files from Blackboard as a single zip file saves a lot of time — you don’t have to individually open each “attempt”, download the file (renaming it in the process, so you don’t keep overwriting the previous file, since they are all named “Homework1.pdf” 😉 ), and then move on to the next one. Instead you get one convenient .zip file that contains all of the assignment files.

Unfortunately, Blackboard does some other things that make your life a bit more difficult. Once you unzip the file, you will find:

  1. The student files are renamed from filename.ext to assignmentname_username_attempt_datetime_filename.ext
  2. A text file is created for each student named assignmentname_username_attempt_datetime.txt even if the student has not entered any text data or comments.

Checking all of the text files to see if they really contain a comment and deleting those that don’t, and renaming all of the assignment files to username.ext so that I can start grading them 1 This process takes 15 minutes or more per assignment, which certainly lowers my enthusiasm for grading.

Today, I decided that I should write some code to automate this task. The time it would take to write the script would be recouped in only a few assignments. I decided to write the script in Python because I could easily see how to do the string manipulations. My shell scripting string manipulations are not as good. I would have to learn how to do the file system manipulations in Python, but I figured that would be relatively simple.

The first step is getting a list of all the files in the directory (leaving out all of the subdirectories)2:

onlyfiles = [ f for f in os.listdir(dir) if os.path.isfile(os.path.join(os.curdir,f)) ]

The next step is filtering that list to get just the .txt files:

txtfiles = [ f for f in onlyfiles if '.txt' in f ]

Then you can search the contents of the textfiles. You’ll notice that there are two characteristic phrases that indicate no text data and no comments. You can just delete the files that contain both of those:

for f in txtfiles:
    file = open(f)
    contents = file.read()
    file.close()
    if 'There are no student comments for this assignment' in contents and \
       'There is no student submission text data for this assignment.' in contents:
        os.remove(f)
        print('Deleted', f)

After refreshing the list of files to be just the remaining files, you can go about renaming the files. They all have _attempt_ embedded in their filename. Then you want to strip off everything up-to-and-including the first underscore, and from the second underscore up to the file extension. Then rename the file.

for f in onlyfiles:
    if '_attempt_' in f:
        first = f.find('_') # location of first underscore
        second = f.find('_',first+1) # location of second underscore
        extension = f[f.rfind('.'):] # get file extension
        newf = f[first+1:second] + extension
        os.rename(f, newf)
        print('Renamed', f, 'to', newf)

There are probably other features I can add, but this works well enough for now. Back to grading…

Full code is on GitHub here.

  1. I may still have to convert some of them to PDFs, if the students have not followed instructions, since I grade them by marking up the PDFs on my iPad. But that’s something I’ll tackle later. For my programming classes, I do that with my grading scripts which are still a work-in-progress.
  2. http://stackoverflow.com/a/3207973
Dec 022013
 

In my CS-135 Programming for Non-CS Majors class, one of the primary objectives for the students is to learn to work with collections of data in files. I’m always happy when this requires manipulations that can’t be performed with other tools that the students are comfortable with — thus motivating the need to learn to code.

This afternoon in class, students were working in groups on their final projects. Two groups came up against some problems in getting their data into a format that could be easily processed in Python. Both cases involved data that was only available in the form of PDF files.

The old standby of selecting text and pasting it into Excel did not provide nice columns of information. Our second attempt was to export the data as text.

Case 1

In the first case, we got text data that looked like:

Biology 306 N/A 306
Biotechnology 80 26 106
Business Administration 748 N/A 748
Chemistry 141 N/A 141
Communication 245 N/A 245
Communication Sciences & Disorders 218 N/A 218
Community Health 158 N/A 158
Computer Science 116 N/A 116
Criminal Justice 445 N/A 445
Early Childhood Education 80 19 99
Early Childhood Education, Non-Licensure 26 N/A 26

This looked promising – we’ve dealt with one-record-per-line-space-delimited data files in class before. You just need to read a line at a time, and use Python’s string split method to turn it into a list… But — wait! — the first item  is a variable number of words separated by spaces. That will make for some messy lists — they’ll all be of different lengths:

['Communication', '245', 'N/A', '245']
['Communication', 'Sciences', '&', 'Disorders', '218', 'N/A', '218']
['Community', 'Health', '158', 'N/A', '158']

Here’s the solution: Python lists can be indexed from the end using negative indices. So, we can definitely get at the last three values (numbers of majors — undergraduate, graduate, and total). Assuming a list in a variable department, they are at positions department[-3], department[-2], and department[-1] respectively.

But, what about the department name, which may be in multiple list items? Well, we can get it as a sub-list, using list slicing: department[:-3] yields:

['Communication']
['Communication', 'Sciences', '&', 'Disorders']
['Community', 'Health']

All that’s left is to concatenate them together into a single string:

name = ''
for item in department[:-3]:
    name = name + item + ' '

Full code is here: https://gist.github.com/kwurst/7761789

Case 2

In the second case, we got text data that looked like:

Boston    00350000    4368    65.9    15.2    0.8    2.1    15.9    0.1
Boston Collegiate Charter (District)    04490000    34    67.6    32.4    0.0    0.0    0.0    0.0
Boston Day and Evening Academy Charter (District)    04240000    162    13.0    55.6    0.0    6.8    24.7    0.0
Boston Green Academy
Horace Mann Charter School
(District)    04110000    72    70.8    26.4    0.0    1.4    1.4    0.0
Boston Preparatory Charter Public (District)    04160000    27    74.1    11.1    0.0    3.7    11.1    0.0
Bourne    00360000    145    90.3    4.8    0.0    2.1    2.8    0.0
Braintree    00400000    369    95.1    3.3    0.3    0.3    1.1    0.0

Which could be fixed the same way, except for the fact that some of the district names ended up broken across multiple lines. (I’m not sure why this happened, and it turned out that exporting the data in a different way fixed the problem. But I’d already found a solution, so I’m going to document it here…)

Working from the assumption that the district org code always starts with a zero (I know — not a good assumption, but it works in this case…), the solution involves checking for lines with no zero in them and concatenating them together. Then you can treat the lines as in Case 1.

for line in f:
    while line.find('0') == -1:
        line = line + f.readline()

Full code is here: https://gist.github.com/kwurst/7761789