"""

Create some graphs and files with statistical results about authors contributions

@type idiomas: list of strings

@param idiomas: list of strings indicating the language versions to process

"""

for idioma in idiomas:

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

#acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_pages")

#dbaccess.query_SQL(acceso[1], "page_id, page_namespace", "page", where="page_namespace=0", create="pag_namespace")

tcnoann=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_NoAnnons_author_"+idioma)

tcauthor=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_author_"+idioma)

#tc_ann=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_Annons_author_text_"+idioma)

dbaccess.close_Connection(acceso[0])

data=__tup_to_list(tcnoann)

listay_tcnoann=data.pop()

listax=data.pop()

data=__tup_to_list(tcauthor)

listay_tcauthor=data.pop()

listax=data.pop()

#data=__tup_to_list(tc_ann)

#listay_tc_ann=data.pop()

#listax=data.pop()

r.png("graphics/"+idioma+"/gini_TContrib_NoAnn_"+idioma+".png")

__lorenz_Curve(listay_tcnoann)

r.png("graphics/"+idioma+"/gini_TContrib_"+idioma+".png")

__lorenz_Curve(listay_tcauthor)

#r.png("graphics/"+idioma+"/gini_TContrib_Ann_"+idioma+".png")

#__lorenz_Curve(listay_tc_ann)

listaidiomas=["dewiki", "jawiki", "frwiki", "plwiki", "nlwiki", "itwiki", "ptwiki", "eswiki", "svwiki"]

## lista=["eswiki", "svwiki"]

r.png("graphics/AAA/gini_comparative_top10.png")

flag=0

for idioma in listaidiomas:

print "Starting comparative Gini analysis for language..."+idioma+"\n"

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

tcnoann=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_NoAnnons_author_"+idioma)

dbaccess.close_Connection(acceso[0])

data=__tup_to_list(tcnoann)

listay_tcnoann=data.pop()

listax=data.pop()

if flag==0:

_lorenz_Comp_Curves(listay_tcnoann,flag)

flag=1

else:

_lorenz_Comp_Curves(listay_tcnoann,flag)

r.dev_off()

"""

Create histograms depicting article size distribution for a certain language version

@type idiomas: list of strings

@param idiomas: list of strings indicating the language versions to process

"""

filenames=["boxplot_log.png", "histogram_log.png", "histogram_log_low.png", "histogram_log_high.png", "ecdf_log_low.png", "ecdf_log_high.png", "data/page_len_log.data", "/data/histograms.info", "ecdf_total.png"]

for idioma in idiomas:

print "Creando histogramas para el idioma ... "+idioma

#Print to another file the names of graphics files, following the order in the GNU R script histogram.R

f=open("./data/hist_files_names.data",'w')

for line in filenames:

f.write("./graphics/"+idioma+"/"+line+"\n")

f.close()

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

#Considering only database pages corresponding to articles, with NAMESPACE=MAIN=0

#dbaccess.dropTab_SQL(acceso[1], "aux")

#dbaccess.query_SQL(acceso[1],"page_id, page_len","page", where="page_namespace=0", order="page_len", create="aux")

result=dbaccess.query_SQL(acceso[1], "page_id, page_len", "aux")

dbaccess.close_Connection(acceso[0])

data=__tup_to_list(result)

page_len=data.pop()

for i in range(len(page_len)):

if page_len[i]!=0:

page_len[i]=math.log10(page_len[i])

#Print to another file a list with article sizes to plot histograms

f=open("./graphics/"+idioma+"/data/page_len_log.data", 'w')

for value in page_len:

f.writelines(str(value)+"\n")

f.close()

#CALL THE GNU R SCRIPT Histogram.R

succ=os.system("R --vanilla < ./histogram.R > debug_R")

if succ==0:

"""

Create some graphs summarizing the evolution in time of critical quantitative

parameters for each language version to explore

@type idiomas: list of strings

@param idiomas: list of strings indicating the language versions to process

"""

## ¡¡WARNING!! Please be careful when selecting values from tables storing evolution in time of number of articles, size etc.

## You must always use a GROUP BY(pageCount, limitDate) clause, due to

## periods of inactivity that could generate duplicate entries in the graphics

filenames=["page_dates.data", "page_Count_evol.data", "page_Len_Sum_log.data", "contribs_evol.data", "nspaces.data", "nspace_distrib.data", "diffArticles.data", "authors.data", "diff_authors_x_article.data", "authors_authors_per_pagelen.data", "pagelen_authors_per_pagelen.data"]

filenames_out=["Tot_num_articles_absx_absy.png", "Tot_num_articles_absx_logy.png", "Tot_num_articles_logx_logy.png", "Tot_pagelensum_absx_absy.png", "Tot_pagelensum_absx_logy.png", "Tot_pagelensum_logx_logy.png", "Tot_contribs_absx_absy.png", "Tot_contribs_absx_logy.png", "Tot_contribs_logx_logy.png", "Diffs_articles_per_author.png", "Diffs_authors_per_article.png", "Diff_authors_against_page_len.png"]

for idioma in idiomas:

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

#acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_pages")

result=dbaccess.query_SQL(acceso[1], "pageCount, limitDate", "stats_Evolution_Content_months_"+idioma, group="(limitDate)")

result2=dbaccess.query_SQL(acceso[1], "pageLenSum, limitDate", "stats_Evolution_Content_months_"+idioma, group="(limitDate)")

result3=dbaccess.query_SQL(acceso[1], "contribs, limitDate", "stats_Evolution_Content_months_"+idioma, group="(limitDate)")

resultnspace=dbaccess.query_SQL(acceso[1], "pages_nspace, namespace", "stats_nspace_"+idioma)

diffArticlesNoann=dbaccess.query_SQL(acceso[1], "author, theCount", "stats_Article_NoAnnons_author_"+idioma)

diffInitNoann=dbaccess.query_SQL(acceso[1], "author, theCount", "stats_Article_Init_NoAnnons_author_"+idioma)

totRevperArticle=dbaccess.query_SQL(acceso[1], "page_id, theCount", "stats_Contrib_NoAnnons_page_id_"+idioma)

diffAuthorperArticle=dbaccess.query_SQL(acceso[1], "page_id, theCount", "stats_Article_NoAnnons_page_id_"+idioma)

dautxplen=dbaccess.query_SQL(acceso[1], "page_len, authors", "stats_pagelen_difauthors_"+idioma)

dbaccess.close_Connection(acceso[0])

data=__tup_to_list(result, 1)

dates_x=data.pop()

page_Count=data.pop()

## if idioma=="frwiki":

data2=__tup_to_list(result2, 2)

dates_x=data2.pop()

dates_x.pop(0)

dates_x.pop(0)

page_Len_Sum=data2.pop()

page_Len_Sum.pop(0)

page_Len_Sum.pop(0)

## else:

## data2=__tup_to_list(result2, 1)

## dates_x=data2.pop()

## page_Len_Sum=data2.pop()

data3=__tup_to_list(result3, 1)

dates_x=data3.pop()

contribs=data3.pop()

datanspace=__tup_to_list(resultnspace)

namespaces=datanspace.pop()

pages_nspace=datanspace.pop()

dataDiffArticlesNoann=__tup_to_list(diffArticlesNoann)

diffArticles=dataDiffArticlesNoann.pop()

authors=dataDiffArticlesNoann.pop()

dataDiffInitNoann=__tup_to_list(diffInitNoann)

diffInitArticles=dataDiffInitNoann.pop()

authors=dataDiffInitNoann.pop()

datatotRevperArticle=__tup_to_list(totRevperArticle)

totalRev=datatotRevperArticle.pop()

article=datatotRevperArticle.pop()

datadiffAuthorperArticle=__tup_to_list(diffAuthorperArticle)

diffAuthors=datadiffAuthorperArticle.pop()

article=datadiffAuthorperArticle.pop()

datadautxplen=__tup_to_list(dautxplen)

autxplen=datadautxplen.pop()

lenautxplen=datadautxplen.pop()

## Introduce in data list results form queries in the proper order

## corresponding with the name files we pass to the GNU R script summary_evol.R

for i in range(len(page_Len_Sum)):

if page_Len_Sum[i]!=0:

page_Len_Sum[i]=math.log10(page_Len_Sum[i])

dataList=[dates_x, page_Count, page_Len_Sum, contribs, namespaces, pages_nspace, diffArticles, authors, diffAuthors, autxplen, lenautxplen]

for filename, data in zip (filenames, dataList):

if(filename.find('date')!=-1):

__makeDatesFile(idioma, filename, data)

else:

__makeDataFile(idioma, filename, data)

######################################

#Pass data filenames to the GNU R script with a file

f=open("./data/summary_files_names.data",'w')

for line in filenames:

f.write("./graphics/"+idioma+"/data/"+line+"\n")

f.close()

#Idem with graphic output filenames

f=open("./data/summary_files_out.data",'w')

for line in filenames_out:

f.write("./graphics/"+idioma+"/"+line+"\n")

f.close()

#CALL THE GNU R SCRIPT summary_evol.R

succ=os.system("R --vanilla < ./summary_evol.R > debug_R")

if succ==0:

print "Funcion summary_evol ejecutada con exito para el lenguage... "+idioma

## print "paso 1"

## r.png("graphics/"+idioma+"/gini_Diff_Articles_NoAnn_"+idioma+".png")

## __lorenz_Curve(diffArticles)

## print "paso 2"

## r.png("graphics/"+idioma+"/gini_Diff_Init_Articles_NoAnn_"+idioma+".png")

## __lorenz_Curve(diffInitArticles)

###COMPUTER INTENSIVE!!###

#r.png("graphics/"+idioma+"/gini_Total_Revisions_per_Article_NoAnn_"+idioma+".png")

"""

Create some graphs following the research presented by Jakob Voss in his paper

Mesuring Wikipedia (ISSI 2005)

@type idiomas: list of strings

@param idiomas: list of strings indicating the language versions to process

"""

## Generates some graphics reproducing those in Measuring Wikipedia article

filenames=["total_edits.data", "noannons_edits.data", "annon_edits.data", "authors_per_article_desc.data", "articles_per_logged_author_desc.data", "articles_per_anonymous_author_desc.data"]

filenames_out=["total_edits_per_author.png", "total_edits_per_noannon_author.png", "total_edits_per_annon_author.png", "diff_authors_per_article_descending.png", "diff_articles_per_logged_author_descending.png", "diff_articles_per_anonymous_author_descending.png"]

for idioma in idiomas:

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

## acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_pages")

#Combined evolution graphics

#ALL THESE GRAPHICS ARE ALREADY GENERATED BY ERIK ZATCHE'S OFFICIAL PERL SCRIPTS

#Database size

#Total number of words

#Total number of internal links

#Number of articles (including redirects)

#Number of active wikipedians (more than 5 contributions in a given month)

#Number of very active wikipedians (more than 100 contributions in a given month)

#Namespace size

#OK, it is generated in summary_evol() method

#Evolution in time of article size (histogram)

#IDEA: Download page.sql files for a language for each semester period

#Number of distinct authors per article (descending sorted graphic)

#Already generated in summary_evol, ONLY NEED TO SORT AND ADJUST IN GNU R

diffAuthorperArticle=dbaccess.query_SQL(acceso[1], "page_id, theCount", "stats_Article_NoAnnons_page_id_"+idioma)

#Number of distinct articles per author (descending sorted graphic)

#Idem as in the previous case

diffArticlesNoann=dbaccess.query_SQL(acceso[1], "author, theCount", "stats_Article_NoAnnons_author_"+idioma)

diffArticlesAnn=dbaccess.query_SQL(acceso[1], "author_text, theCount", "stats_Article_Annons_author_text_"+idioma)

data=__tup_to_list(diffAuthorperArticle)

lisdiffauthorartic=data.pop()

data=__tup_to_list(diffArticlesNoann)

lisdiffarticleaut=data.pop()

data=__tup_to_list(diffArticlesAnn,2)

lisdiffarticleannon=data.pop()

## Ordenamos los resultados para que se puedan ajustar a una Power Law

lisdiffauthorartic.sort(reverse=True)

lisdiffarticleaut.sort(reverse=True)

lisdiffarticleannon.sort(reverse=True)

#Number of edtis per author

#Retrieve results from database

#We have already created GINI graphics for this parameter

#ALSO AVAILABLE DATABASE TABLES WITH EVOLUTION IN TIME OF THIS PARAMETER

tcnoann=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_NoAnnons_author_"+idioma)

tcauthor=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_author_"+idioma)

tc_ann=dbaccess.query_SQL(acceso[1]," * ","stats_Contrib_Annons_author_text_"+idioma)

data=__tup_to_list(tcnoann)

listcnoann=data.pop()

data=__tup_to_list(tcauthor)

listcauthors=data.pop()

#BTW, we are also obtaining but not using the IP adresses of annon users

data=__tup_to_list(tc_ann,2)

listcann=data.pop()

## Arranging results in a decreasing way to adjust them to a power law

listcnoann.sort(reverse=True)

listcauthors.sort(reverse=True)

listcann.sort(reverse=True)

#Ingoing and outgoing number of links per article

#STILL TO BE DEVELOPED

#NEED TO FIRST IDENTIFY LINKS FOR A GIVEN ARTICLE IN THE DATABASE

#LINKS TABLES MAY HELP, but in these dump versions they are all empty!!!

#BROKEN LINKS also need to be considered

dbaccess.close_Connection(acceso[0])

dataList=[listcauthors, listcnoann, listcann, lisdiffauthorartic, lisdiffarticleaut, lisdiffarticleannon]

for filename, data in zip (filenames, dataList):

if(filename.find('date')!=-1):

__makeDatesFile(idioma, filename, data)

else:

__makeDataFile(idioma, filename, data)

#Pass data filenames to the GNU R script with a file

f=open("./data/measuring_files_names.data",'w')

for line in filenames:

f.write("./graphics/"+idioma+"/data/"+line+"\n")

f.close()

#Idem with graphic output filenames

f=open("./data/measuring_files_out.data",'w')

for line in filenames_out:

f.write("./graphics/"+idioma+"/"+line+"\n")

f.close()

#CALL GNU R SCRIPT measuring_Wiki.R

succ=os.system("R --vanilla < ./measuring_Wiki.R > debug_R")

if succ==0:

for idioma in idiomas:

list_admins=test_admins.process_admins(idioma)

num_admins=list_admins.pop()

where_clause1=list_admins.pop()

acceso = dbaccess.get_Connection("localhost", 3306, "root", "phoenix", idioma+"_stub")

admins_ids=dbaccess.raw_query_SQL(acceso[1], "SELECT DISTINCT(author) FROM stats_"+idioma+" WHERE "+where_clause1+" LIMIT "+str(num_admins))

## MONTAR WHERE CLAUSE CON ADMINS IDS

list_admins_ids=[]

for item in list_admins_ids:

list_admins_ids.append(int(item[0]))

where_clause2=test_admins.process_users_ids(list_admins_ids,idioma)

edits_admin_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_NoAnnons_months_author_"+idioma+" ", where=where_clause2, group="year, month ", order="year, month")

dates_admins=[]

admins_contribs=[]

for element in edits_admin_month:

dates_admins.append(list(element[0:2]))

admins_contribs.append(int(element[2]))

## PASAR A UN ARCHIVO PARA PLOT (FIG 2)

## RECUPERAMOS CONTRIBUCIONES TOTALES POR MESES

total_edits_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, group="year, month ")

dates_contribs=[]

total_contribs=[]

for element in total_edits_month:

dates_contribs.append(list(element[0:2]))

total_contribs.append(int(element[2]))

## DIVIDIR LA PRIMERA LISTA POR LA SEGUNDA

perc_contribs_admins=[]

for admin_contrib, total_contrib in zip(admins_contribs, total_contribs):

perc_contribs_admins.append((float(admin_contrib)/total_contrib))

## PASAR A UN ARCHIVO PARA PLOT (FIG 1)

## FIG 4 TOTAL EDITS MADE BY USERS WITH DIFFERENT EDIT LEVELS

## CREATE CLUSTER OF USERS IDENTIFIED BY CONTRIBUTIONS LEVEL

## 5 LEVELS: <100, 100-1K, 1K-5K, 5K-10K, >10K

users_level1=[]

users_level2=[]

users_level3=[]

users_level4=[]

users_level5=[]

level1=dbaccess.query_SQL(acceso[1], select="DISTINCT(author)", tables="stats_Contrib_author_"+idioma, where="theCount<=100")

for userid in level1:

users_level1.append(int(userid[0]))

level2=dbaccess.query_SQL(acceso[1], select="DISTINCT(author)", tables="stats_Contrib_author_"+idioma, where="theCount>100 AND theCount<=1000")

for userid in level2:

users_level2.append(int(userid[0]))

level3=dbaccess.query_SQL(acceso[1], select="DISTINCT(author)", tables="stats_Contrib_author_"+idioma, where="theCount>1000 AND theCount<=5000")

for userid in level3:

users_level3.append(int(userid[0]))

level4=dbaccess.query_SQL(acceso[1], select="DISTINCT(author)", tables="stats_Contrib_author_"+idioma, where="theCount>5000 AND theCount<=10000")

for userid in level4:

users_level4.append(int(userid[0]))

level5=dbaccess.query_SQL(acceso[1], select="DISTINCT(author)", tables="stats_Contrib_author_"+idioma, where="theCount>10000")

for userid in level5:

users_level5.append(int(userid[0]))

where_clause_level1=test_admins.process_users_ids(users_level1,idioma)

where_clause_level2=test_admins.process_users_ids(users_level2,idioma)

where_clause_level3=test_admins.process_users_ids(users_level3,idioma)

where_clause_level4=test_admins.process_users_ids(users_level4,idioma)

where_clause_level5=test_admins.process_users_ids(users_level5,idioma)

contribs_level1_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level1, group="year, month")

contribs_level2_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level2, group="year, month")

contribs_level3_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level3, group="year, month")

contribs_level4_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level4, group="year, month")

contribs_level5_month=dbaccess.query_SQL(acceso[1], select="year, month, SUM(theCount)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level5, group="year, month")

list_level1=__process_contribs(contribs_level1_month, total_contribs)

perc_contribs_level1=list_level1.pop()

contribs_level1=list_level1.pop()

dates_level1=list_level1.pop()

list_level2=__process_contribs(contribs_level2_month, total_contribs)

perc_contribs_level2=list_level2.pop()

contribs_level2=list_level2.pop()

dates_level2=list_level2.pop()

list_level3=__process_contribs(contribs_level3_month, total_contribs)

perc_contribs_level3=list_level3.pop()

contribs_level3=list_level3.pop()

dates_level3=list_level1.pop()

list_level4=__process_contribs(contribs_level4_month, total_contribs)

perc_contribs_level4=list_level4.pop()

contribs_level4=list_level4.pop()

dates_level4=list_level4.pop()

list_level5=__process_contribs(contribs_level5_month, total_contribs)

perc_contribs_level5=list_level5.pop()

contribs_level5=list_level5.pop()

dates_level5=list_level5.pop()

## FIG 5 PLOT 4b

## FIG 6 AVERAGE NUMBER OF EDITS PER USER PER MONTH FOR EACH LEVEL

## RETRIEVE NUM USERS FOR EACH MONTH IN EACH LEVEL WHO HAVE MADE AT LEAST ONE CONTRIB

num_users_1_month=dbaccess.query_SQL(acceso[1], select="COUNT(DISTINCT author)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level1, group="year, month")

num_users_2_month=dbaccess.query_SQL(acceso[1], select="COUNT(DISTINCT author)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level2, group="year, month")

num_users_3_month=dbaccess.query_SQL(acceso[1], select="COUNT(DISTINCT author)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level3, group="year, month")

num_users_4_month=dbaccess.query_SQL(acceso[1], select="COUNT(DISTINCT author)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level4, group="year, month")

num_users_5_month=dbaccess.query_SQL(acceso[1], select="COUNT(DISTINCT author)", tables="stats_Contrib_months_author_"+idioma, where=where_clause_level5, group="year, month")

list_users_1_month=[]

for element in num_users_1_month:

list_users_1_month.append(int(element[0]))

list_users_2_month=[]

for element in num_users_2_month:

list_users_2_month.append(int(element[0]))

list_users_3_month=[]

for element in num_users_3_month:

list_users_3_month.append(int(element[0]))

list_users_4_month=[]

for element in num_users_4_month:

list_users_4_month.append(int(element[0]))

list_users_5_month=[]

for element in num_users_5_month:

list_users_5_month.append(int(element[0]))

## DIVIDE TOT NUM CONTRIBS PER LEVEL PER MONTH BY THE NUM USERS FOR EACH MONTH IN EACH LEVEL

avg_contribs_user_1_month=[]

for contribmonth, usermonth in zip(contribs_level1, list_users_1_month):

avg_contribs_user_1_month.append(float(contribmonth)/usermonth)

avg_contribs_user_2_month=[]

for contribmonth, usermonth in zip(contribs_level2, list_users_2_month):

avg_contribs_user_2_month.append(float(contribmonth)/usermonth)

avg_contribs_user_3_month=[]

for contribmonth, usermonth in zip(contribs_level3, list_users_3_month):

avg_contribs_user_3_month.append(float(contribmonth)/usermonth)

avg_contribs_user_4_month=[]

for contribmonth, usermonth in zip(contribs_level4, list_users_4_month):

avg_contribs_user_4_month.append(float(contribmonth)/usermonth)

avg_contribs_user_5_month=[]

for contribmonth, usermonth in zip(contribs_level5, list_users_5_month):

avg_contribs_user_5_month.append(float(contribmonth)/usermonth)

## FIG 7 POPULATION GROWTH FOR EACH USER GROUP

## SIMPLY RETRIEVE list_users_X_month

## FIG 8 % OF TOTAL POPULATION OF EACH USER GROUP

perc_users_1_months=[]

perc_users_2_months=[]

perc_users_3_months=[]

perc_users_4_months=[]

perc_users_5_months=[]

for e1, e2, e3, e4, e5 in zip(list_users_1_month,list_users_2_month,list_users_3_month,list_users_4_month,list_users_5_month):

total_users_month=e1+e2+e3+e4+e5

perc_users_1_months.append((float(e1)/total_users_month))

perc_users_2_months.append((float(e2)/total_users_month))

perc_users_3_months.append((float(e3)/total_users_month))

perc_users_4_months.append((float(e4)/total_users_month))

perc_users_5_months.append((float(e5)/total_users_month))

###############################

## FINAL DUTIES, TRANSFER DATA AND EXECUTE R SCRIPT

filenames=["dates_admin_contrib.data","contribs_admins_months.data", "perc_contribs_months.data","dates_level1_contrib.data", "contribs_level1_months.data", "perc_contribs_level1_months.data", "dates_level2_contrib.data", "contribs_level2_months.data", "perc_contribs_level2_months.data","dates_level3_contrib.data", "contribs_level3_months.data", "perc_contribs_level3_months.data","dates_level4_contrib.data", "contribs_level4_months.data", "perc_contribs_level4_months.data","dates_level5_contrib.data" ,"contribs_level5_months.data", "perc_contribs_level5_months.data", "avg_contribs_user_1_month.data", "avg_contribs_user_2_month.data", "avg_contribs_user_3_month.data", "avg_contribs_user_4_month.data", "avg_contribs_user_5_month.data", "users_1_month.data", "users_2_month.data", "users_3_month.data", "users_4_month.data", "users_5_month.data", "perc_users_1_months.data","perc_users_2_months.data", "perc_users_3_months.data", "perc_users_4_months.data", "perc_users_5_months.data"]

filenames_out=["Figure1.png", "Figure_2.png", "Figure4.png", "Figure5.png", "Figure6.png", "Figure7.png", "Figure8.png"]

dataList=[dates_contribs, admins_contribs, perc_contribs_admins, dates_level1, contribs_level1, perc_contribs_level1,dates_level2, contribs_level2, perc_contribs_level2,dates_level3, contribs_level3, perc_contribs_level3, dates_level4, contribs_level4, perc_contribs_level4,dates_level5, contribs_level5, perc_contribs_level5, avg_contribs_user_1_month, avg_contribs_user_2_month, avg_contribs_user_3_month, avg_contribs_user_4_month, avg_contribs_user_5_month, list_users_1_month, list_users_2_month, list_users_3_month, list_users_4_month, list_users_5_month, perc_users_1_months, perc_users_2_months, perc_users_3_months, perc_users_4_months, perc_users_5_months]

for filename, data in zip (filenames, dataList):

if(filename.find('date')!=-1):

f=open("./graphics/"+idioma+"/data/"+filename, 'w')

for adate in data:

f.writelines(str(adate)+"\n")

f.close()

else:

__makeDataFile(idioma, filename, data)

#Pass data filenames to the GNU R script with a file

f=open("./data/community_contrib_files_names.data",'w')

for line in filenames:

f.write("./graphics/"+idioma+"/data/"+line+"\n")

f.close()

#Idem with graphic output filenames

f=open("./data/community_contrib_files_out.data",'w')

for line in filenames_out:

f.write("./graphics/"+idioma+"/"+line+"\n")

f.close()

#CALL GNU R SCRIPT measuring_Wiki.R

succ=os.system("R --vanilla < ./community_contrib.R > debug_R")

if succ==0:

dates_contribs=[]

list_contribs_level=[]

for element in contribs_level_month:

dates_contribs.append(list(element[0:2]))

list_contribs_level.append(int(element[2]))

## REMEMBER TO PLOT the list contribs_level1 in FIG 5

perc_contribs_level=[]

for contrib_level, total_contrib in zip(list_contribs_level, total_contribs):

perc_contribs_level.append((float(contrib_level)/total_contrib))

"""

Uses RPY module to depict a Lorenz curve useful for GINI graphs

@type values: list of ints

@param values: list of integers summarizing total contributions for each registered author

"""

x_values=[]

for i in range(0, len(values)+1):

x_values.append(100.0*(float(i)/len(values)))

values.insert(0, 0)

y_values_lorenz=[]

for j in range(len(values)):

y_values_lorenz.append(sum(values[0:j+1]))

for k in range(len(y_values_lorenz)):

y_values_lorenz[k]=100.0*(float(y_values_lorenz[k])/y_values_lorenz[len(y_values_lorenz)-1])

g_coeff=__gini_Coef(values)

r.plot(x_values, y_values_lorenz, xlab="(%)Authors",ylab="(%)Cumulative contribution", main="Cumulative distribution function", type="l", col=2)

r.legend(10, 80, legend="Gini Coefficient = %f" % g_coeff)

r.legend(10, 100, legend=r.c("Line of perfect equality", "Lorenz curve"), col=r.c(1,2), pch=r.c(1,2))

r.lines(x_values, x_values)

#r.lines(r.lowess(log(lista)))

x_values=[]

for i in range(0, len(values)+1):

x_values.append(100.0*(float(i)/len(values)))

values.insert(0, 0)

y_values_lorenz=[]

for j in range(len(values)):

y_values_lorenz.append(sum(values[0:j+1]))

for k in range(len(y_values_lorenz)):

y_values_lorenz[k]=100.0*(float(y_values_lorenz[k])/y_values_lorenz[len(y_values_lorenz)-1])

g_coeff=__gini_Coef(values)

if flag==0:

r.plot(x_values, y_values_lorenz, xlab="(%)Authors",ylab="(%)Cumulative contribution", main="Cumulative distribution function", type="l", col=2)

## r.legend(10, 80, legend="Gini Coefficient = %f" % g_coeff)

## r.legend(10, 100, legend=r.c("Line of perfect equality", "Lorenz curve"), col=r.c(1,2), pch=r.c(1,2))

r.lines(x_values, x_values)

"""

Plots a GINI graph for author contributions

@type values: list of ints

@param values: list of integers summarizing total contributions for each registered author

@type insert: boolean flag

@param insert: warns the method about inserting a (0,0) tuple at the beginning of the graphic to depict an accurate curve

"""

if insert:

values.insert(0,0)

sum_numerator=0

sum_denominator=0

for i in range(1, len(values)):

sum_numerator+=(len(values)-i)*values[i]

sum_denominator+=values[i]

g_coeff= (1.0/(len(values)-1))*(len(values)-2*(sum_numerator/sum_denominator))

"""

A method to convert a tuple of bidimensional tuples return by a database query to a list

of bidimensional lists we can use in other methods

@type result: tuple of bidimensional tuples

@param result: the tuple received as a result of a database query

@type flag: int flag

@param insert: indicates 0 (int, int) tuples; 1 (int, string) tuples; 2 (string, string) tuples

"""

## NEED TO BE IMPROVED, UNNECESARY AUX VARIABLES, SEE PRUEBA.PY

aux=list(result)

for i in range(len(aux)):

aux[i]=list(aux[i])

listax=[]

listay=[]

for i in range(len(aux)):

if flag==0:

listax.append(int(aux[i][0]))

listay.append(int(aux[i][1]))

elif flag==1:

listax.append(int(aux[i][0]))

listay.append(aux[i][1])

elif flag==2:

listax.append(aux[i][0])

listay.append(aux[i][1])

"""

Create data files to transfer results to GNU R

@type idioma: string

@param idioma: indicates the language version we are processing

@type filename: string

@param filename: name of the file to which we want to transfer data

@type data: list

@param data: list of data we write in the file

"""

f=open("./graphics/"+idioma+"/data/"+filename, 'w')

for value in data:

f.writelines(str(value)+"\n")

"""

Create files to transfer dates results to GNU R

@type idioma: string

@param idioma: indicates the language version we are processing

@type filename: string

@param filename: name of the file to which we want to transfer data

@type dates: list of string

@param dates: list of dates we write in the file

"""

f=open("./graphics/"+idioma+"/data/"+filename, 'w')

for adate in dates:

f.writelines(str(adate).split()[0]+"\n")

"""

Generates appropiate directory hierarchy to store graphics, data files and results files

@type idiomas: list of strings

@param idioma: language versions we want to process

"""

directorios=os.listdir("./")

if ("graphics" not in directorios):

os.makedirs("./graphics")

else:

dir_lang=os.listdir("./graphics/")

for idioma in idiomas:

if idioma not in dir_lang:

os.makedirs("./graphics/"+idioma+"/data")

if("data" not in directorios):

"""

Katapult function for the rest of the graphic methods in this module

@type idiomas: list of strings

@param idiomas: indicates the language version we are processing

"""

## idiomas=["eswiki"]

create_dirs(idiomas)

contributions(idiomas)

histogram(idiomas)

summary_evol(idiomas)

measuring(idiomas)