Geeky post again – no math this time, but computer code.

I’m sure people have done this before, but I thought it would be a nice opportunity to practice my Python skills to write a small script for the following problem.. Usually when I read a scientific article I watch out for the following elements:

I also like to place the research in one of the four quadrants in this post. I find it helpful to make an overview of these questions in a table:

But here’s the problem: I usually make my notes in a bibtex file (as a good geek should), which looks like this:

@ARTICLE{Kompas2006PacEconBull,
  author = {Kompas, T. and Che, T.N.},
  title = {Economic profit and optimal effort in the Western and Central Pacific tuna fisheries},
  journal = {Pacific Economic Bulletin},
  year = {2006},
  volume = {21},
  pages = {46-62},
  number = {3},
  data = {SPC data},
  innovation = {Estimates optimal effort levels and allocation across species},
  quadrant = {3},
  keywords = {tuna; bioeconomic model; optimisation; Pacific},
  method = {Multifleet, multispecies, multiregion bioeconomic model},
  results = {Effort reduction needed; optimal stocks larger than BMSY},
  relevance = {Policy debate on MEY}
}

@ARTICLE{Kompas2005JProdAnalysis,
  author = {Kompas, Tom and Che, Tuong Nhu},
  title = {Efficiency gains and cost reductions from individual transferable quotas: A stochastic cost frontier for the Australian South East fishery},
  journal = {Journal of Productivity Analysis},
  year = {2005},
  volume = {23},
  pages = {285-307},
  number = {3},
  quadrant = {3},
  data = {AFMA and ABARE survey data},
  innovation = {Estimates efficiency gains quota trade for Southeast Trawl Fishery, AU},
  keywords = {individual transferable quotas; stochastic cost frontier; fishery efficiency; ITQs},
  method = {Stochastic frontier analysis},
  relevance = {Policy debate on ITQs.},
  results = {ITQs gave efficiency gains}
}

I don’t want to copy it all by hand, so I wrote this little script in Python to convert all entries in the bibtex file to a csv file:

import csv
from bibtexparser.bparser import BibTexParser
from dicttoxml import dicttoxml
from operator import itemgetter

def readFirstAuthor(inpList,num):
    author1 = ""
    x = inpList[num]['author']
    for j in x:
        if j != ',':
            author1+=j
        else:
            break
    return author1

def selectDict(inpList,name):
    outObj = []
    for i in range(len(inpList)):
        if name in inpList[i]['author'] and \

            inpList[i]['type']=='article':
            outObj.append(inpList[i])
    return(outObj)

def selectFieldsDict(inpList,fieldNames):
    outObj = []
    for i in range(len(inpList)):
        temp = {}
        for n in fieldNames:
            if n == 'author':
                author1 = readFirstAuthor(inpList,i)
                temp['author'] = author1
            else:
                if n in inpList[i]:
                    temp[n] = inpList[i][n]
                else:
                    temp[n] = 'blank'
        outObj.append(temp)
    return(outObj)

fieldnames = ['author','year','journal','quadrant',\

    'innovation','method','data','results','relevance']

with open('BibTexFile.bib', 'r') as bibfile:
    bp = BibTexParser(bibfile)
    
record_list = bp.get_entry_list()
record_dict = bp.get_entry_dict()

dictSelection = selectDict(record_list,'Kompas')

fieldSelection = selectFieldsDict(dictSelection,fieldnames)


test = sorted(fieldSelection, key=itemgetter('year'))


test_file = open('output.csv','wb')
csvwriter = csv.DictWriter(test_file, delimiter=',',\

    fieldnames=fieldnames)
csvwriter.writerow(dict((fn,fn) for fn in fieldnames))
for row in test:
     csvwriter.writerow(row)
test_file.close()

If you are a Python developer: any comments on this are welcome. I’m sure it’s not perfect.