Digests Of The Astronomy Arxiv

Reading time ~3 minutes

I took another look at ADS-python (a python tool for ADS) developed by Andy Casey. I modified his example script to email myself a digest of all of the papers published by my institute in the past month. I set it up as an automated cron job (10 0 1 * * python script.py) to be run on the 1st of each month so I don’t have to run the script anymore to get the digest. You will need FluentMail for this to work. You will also need to allow Gmail to receive login requests from less familiar apps. If you want to use a different email client see here. I’ve setup a GitHub repository for these codes and future codes relating to work done with ADS.

# Core libraries
from time import localtime
import sys,os
import subprocess as sub

# Module Libraries
import ads
from fluentmail import FluentMail

mail = FluentMail('smtp.gmail.com', 465, 'SSL')
email_address = 'brendan.f.griffen@gmail.com'
email_password = 'password'
output_directory = "./"
download_papers = False
my_affiliation = '"Kavli Institute For Astrophysics"'

if __name__ == "__main__":

    # Let's do it for *last* month
    current_time = localtime()

    # Last month
    year = current_time.tm_year - 1 if current_time.tm_mon == 1 else current_time.tm_year
    month = current_time.tm_mon - 1 if current_time.tm_mon > 1 else 12
    # Get all the articles
    articles1 = ads.search(
        filter="database:astronomy AND property:refereed",
        dates="{year}/{month}".format(year=year, month=month))

    articles = list(articles1)
    print("There were {0} articles found for {year}/{month} with MKI co-authors.".format(len(articles),year=year, month=month))

    # Sort articles
    sorted_articles = sorted(articles,
        key=lambda article: [(my_affiliation.strip('"').lower() in affiliation.lower()) for affiliation in article.aff].index(True))

    # Option to download these papers
    if download_papers:
        output_folder = output_directory + "{year}_{month}".format(year=year,month=month)
        cmd_make_this_months_folder = "mkdir -p " + output_folder

        filename = output_folder + "/" + article.bibcode + ".pdf"
        if not os.path.isfile(filename):
            ads.retrieve_article(article, output_filename=output_folder + "/" + article.bibcode + ".pdf")

    # Construct email content
    email_content = "There were {num} articles published by astronomy researchers from the {my_affiliation} last month ({year}/{month})"\

    for article in sorted_articles:
        email_content += article.author[0].split(",")[0] + " et al.\n" +\
                         article.title[0] + "\n" + article.abstract + "\n" +\
                         article.url + "\n\n"

    # Email yourself the digest
    mail.credentials(email_address, email_password)\
    .subject('Monthly Institute Arxiv Digest')\

Here is an example of what one of these emails might look like:


I usually read new papers through my RSS reader Feedly, which has a feature which tells you roughly how many people have also bookmarked that paper (see the left side). I think this is a rough proxy for ‘interest’ and I often take more notice of papers with a high number of bookmarks.

Most popular

I wanted a ‘top 10 most popular’ list from the ADS listings from ‘yesterday’ - whenever that may be. It is convenient to know what is making headlines in the community.

# Core libraries
from time import localtime
import numpy as np

# Module Libraries
import ads
from fluentmail import FluentMail

mail = FluentMail('smtp.gmail.com', 465, 'SSL')
email_address = 'brendan.f.griffen@gmail.com'
email_password = 'password'
email_content = ""

if __name__ == "__main__":
    current_time = localtime()
    year = current_time.tm_year
    month = current_time.tm_mon
    day = current_time.tm_mday-1 # yesterday
    # Get papers
    papers = list(ads.query(dates="{year}/{month}/{day}"\
            .format(year=year, month=month, day=day),filter="database:astronomy", rows=200))

    # Get number of reads
    num_reads_list = []
    for paper in papers:
        num_reads = paper.metrics['all_reads']['Total_number_of_reads']
    idx_num_reads = np.argsort(np.array(num_reads_list))[::-1]
    # Construct email content
    email_content = "Top viewed papers for {day}/{month}/{year}\n\n"\
                    .format(year=year, month=month, day=day)

    for idx,i in enumerate(idx_num_reads[:10]):
        email_content += str(idx+1) + ". " + \
        papers[i].author[0].split(",")[0] + " et al. > # reads: " + \
        str(papers[i].metrics['all_reads']['Total_number_of_reads']) + "\n" + \
        papers[i].title[0] + "\n" + \
        papers[i].abstract + "\n" + \
        papers[i].url + "\n\n"
    print email_content
    # Email content
    mail.credentials(email_address, email_password)\
    .subject("Yesterday's most popular papers!")\

Here is an example of what one of these emails might look like:

Most popular

This is really only the beginning of the sort of thing you can do with nice Python-esk access to the ADS database.

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