We assess matplotlib questions in two of our courses, but we have
a very complex question type that is almost certainly too daunting for a relative newcomer (though you're welcome to try your luck with it if you're feeling brave).
Our question type displays in the results table the plots the students draw side-by-side with our expected plots. But the displayed images are just for show - the actual grading is done by comparing a textual description of selected properties of the displayed image with the expected description.
The question contains as one of its support files a module __plottools.py (attached) that defines a function print_plot_info() to output selected properties of the most-recently drawn graph. We insert a call to this function after each test (using the Extra field in the author form). The simplest calls are just
print_plot_info('lines')or
print_plot_info('bars')
thought there are lots of other parameters. The function generates output such as (for a bar chart):
Plot title: 'Temperatures during the week'
X-axis label: 'Day of week'
Y-axis label: 'Temperature'
(x, y) grid lines enabled: (False, False)
X-axis ticks at 0, 1, 2, 3, 4
X-axis tick labels:
Monday, Tuesday, Wednesday, Thursday, Friday
Bars:
Bar0: x = 0, height = 15.0
Bar1: x = 1, height = 18.0
Bar2: x = 2, height = 19.0
Bar3: x = 3, height = 21.0
Bar4: x = 4, height = 16.0
However, even this function is probably a bit daunting, and it needs scipy installed on the Jobe server (as well as numpy and matplotlib, of course) in order to interpolate y values at selected x values when displaying line plots. But perhaps it gives you some ideas to work with?