import cmtutils import pylab as plt %matplotlib inline plt.rcParams['figure.figsize'] = (10., 8.) plt.rcParams['font.size'] = 16
Load in the reviewer data from 24th July alongside the subject areas.
reviewers = cmtutils.reviewers(filename='2014-07-24_reviewer_export.xls', subject_file='2014-07-24_reviewerSubjectAreas.xls')
Loaded Users. Loaded Reviewer Subjects.
A little helper function for histogramming papers.
def histogram_papers(papers, title): papers = papers.fillna(0) a= plt.hist(papers, bins=papers.max()-papers.min(), range=(papers.min(), papers.max()), align='mid') plt.title(title) plt.grid(False) print papers.max()
Now let's start plotting. First we histogram the reviewing body showing the number of reviewers who've had the corresponding numbers of papers (since 2007).
histogram_papers(reviewers.users[reviewers.users.IsMetaReviewer=='No'].PapersSince2007, 'Reviewers: Number of NIPS papers since 2007')
Same idea, but more recent expertise, how many papers have reviewers had since 2012?
histogram_papers(reviewers.users[reviewers.users.IsMetaReviewer=='No'].PapersSince2012, 'Reviewers: Number of NIPS papers since 2012')
Now the same for Area Chairs, how many papers have they had since 2007?
histogram_papers(reviewers.users[reviewers.users.IsMetaReviewer=='Yes'].PapersSince2007, 'Area Chairs: Number of NIPS papers since 2007')
And finally the recent experience of Area Chairs. How many papers have they had since 2012?
histogram_papers(reviewers.users[reviewers.users.IsMetaReviewer=='Yes'].PapersSince2012, 'Area Chairs: Number of NIPS papers since 2012')