# Counting programming language mentions in astronomy papers¶

"Late 2016 Edition" Author: Juan Nunez-Iglesias.

Adapted from code written by Thomas P. Robitaille and updated by Chris Beaumont.

A couple of years ago I came across this tweet by Chris Beaumont, showing Python overtaking Matlab and rapidly gaining ground on IDL in astronomy.

I've referred to that plot a couple of times in the past, but now that I wanted to use it in a talk, I thought it was time to update it. Hence, this notebook.

First, let's import everything we need. You can install it all using either conda or pip.

In [1]:
%matplotlib inline
import os

import brewer2mpl
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
from prettyplotlib.utils import remove_chartjunk
from datetime import datetime, date


Let's set some nice Matplotlib defaults. Note that there is a deprecation warning when setting the default color cycle, but I can't be bothered tracking down the fix. (It is not the simple replacement suggested by the deprecation message.)

In [2]:
mpl.rcParams['axes.color_cycle'] = brewer2mpl.get_map('Paired', 'qualitative', 12).mpl_colors[1::2] + [(0.94, 0.01, 0.50)]
mpl.rcParams['figure.figsize'] = (9,6)
mpl.rcParams['font.size'] = 14

/Users/nuneziglesiasj/anaconda/envs/ana3/lib/python3.5/site-packages/matplotlib/__init__.py:878: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))


Next, we import the ads Python library, which simplifies queries to the Astrophysics Data System. (The original notebooks used requests to create direct JSON queries, but the API appears to have changed in the meantime. I hope that using the ads library, someone else will take care of keeping the API queries up to date.)

To run this notebook, you need to get a free API key to allow queries to the ADS system. Create an account at ADS, log in, and then look for "Generate a new key" under your user profile.

Then, copy that key into a file called .ads/dev_key in your home directory. (You can also pass it as a string using the token=<my_key> keyword argument to SearchQuery, below)

In [3]:
import ads as ads


The ADS system has a daily limit to the number of queries you can perform with a given key (I think 5,000, as of this writing). So that you're not wasting queries while you're developing, you can use the ads.sandbox package that will return mock results to your queries. Uncomment the following cell to use the sandbox instead of the real API.

In [4]:
# Uncomment the following line to use the sandbox


Next, we write a function that will count up how many results an individual query and year return, as well as a function to combine related queries (such as 'MATLAB' and 'Matlab').

In [5]:
def yearly_counts(query='', years=(2000, 2017),
acknowledgements=False):
if acknowledgements:
query = 'ack:' + query
modifiers = ' '.join(['year:%i'])
full_query = ' '.join([query, modifiers])
filter_query = ['database:astronomy',
'property:refereed']
results = []
for year in range(*years):
fq=filter_query)
papers.execute()
count = int(papers.response.numFound)
total_papers.execute()
total_count = int(total_papers.response.numFound)
now = datetime.now().timetuple()
if year == now.tm_year:
days_in_year = date(year, 12, 31).timetuple().tm_yday
count *= days_in_year / now.tm_yday
total_count *= days_in_year / now.tm_yday
results.append([year, count, total_count])
return np.array(results)

def combine_results(res):
combined = res[0]
for r in res[1:]:
combined[:, 1:] += r[:, 1:]
return combined


Finally, create a dictionary mapping languages to queries. I've left some of the original queries commented out, but you can uncomment them if you care about those languages in astronomy.

As a side note, a simple measure of how annoying a language's name is is given by the number of queries necessary to find its mentions.

In [6]:
from collections import OrderedDict

languages = OrderedDict([
('IDL', ['IDL']),
('Matlab', ['MATLAB', 'Matlab']),
('Python', ['Python']),
#    ('Fortran', ['Fortran', 'FORTRAN']),
#    ('Java', ['Java']),
#    ('C', ['C programming language', 'C language',
#          'C code', 'C library', 'C module']),
#    ('R', ['R programming language', 'R language',
#          'R code', 'R library', 'R module']),
])


The next cell runs the queries. Don't waste those API hits!

In [7]:
results = {name: combine_results([yearly_counts(query)
for query in queries])
for name, queries in languages.items()}


Finally, define a function to plot the results:

In [8]:
def trendlines(queries, norm=False):
fig, ax = plt.subplots()
for lang in languages:
counts = queries[lang]
x = counts[:, 0]
y = np.copy(counts[:, 1])
if norm:
y /= counts[:, 2]
ax.plot(x, y * 100, label=lang, lw=4, alpha=0.8)
ax.set_xlim(np.min(x), np.max(x))
ax.set_xlabel('Year')
ax.set_ylabel('Percent of Refereed\nPublications Mentioning')
ax.legend(loc='upper left', frameon=False)
remove_chartjunk(ax, ['top', 'right'])

In [9]:
trendlines(results, norm=True)
plt.savefig('python-vs-matlab-vs-IDL-in-astro.pdf')


There you have it: some time in early 2015, Python overtook IDL as the most mentioned (and probably the most used) programming language in astronomy!