library(RCurl)
library(curl)
library(jsonlite)
library(gsubfn)
library(gplots)
library(RColorBrewer)

#clean any junk
rm(list = ls())

#need hash to work for lists
people_company_dictionary<- new.env(hash=TRUE)

#functions
companies <- function (names)
{
  companys = list()
  for (name in names) {
    for (n in name) {
    
      company <- member_company(n)
      if (company != '') {
        companys[[length(companys) + 1]] <- member_company(n)
      } else {
        #cant find them in ldap, use the email string as a guess
        #This means that in many cases people no linger in LDAP get counted as Lianro folks as
        #the original member company can no longer be determined.
        warning(paste(n,"not found in LDAP, guessing from email string"))
        companys[[length(companys) + 1]] <- strapplyc(n, "@(.*)", simplify = TRUE)
      }
    }
  }
  company <- unique(companys)

  return (company)
}

member_company <- function (name)
{
  company <- people_company_dictionary[[name]]
  if (is.null(company)) {
    #not in hash, need to look it up
    ldap_command <-
      paste ('python',
             '/home/mike/bin/linaro-ldap-lookup.py',
             'mike.holmes',
             name)
    
    ldap_raw <- as.character(system(ldap_command, intern = TRUE))
    
    if (length(ldap_raw) == 0) {
      #that name could not be found
      company <- ""
    } else {
      #Pass back just the company
      ldap <- fromJSON(ldap_raw, simplifyDataFrame = TRUE)
      company <- ldap$company
    }
    
    #fix any names that look like directories
    company <- gsub("/", "_", company)
    #save in dictionary
    people_company_dictionary[[name]] <- company
  } 
  
  return (company)
}

display_barplot <- function(report, m, suffix) {
  report_name <- paste(report["name"], suffix, ".png",sep = "")
  png(file=report_name, title = report["name"])
  
  #display raw data
  cat("Generating", report_name, "\n")
  print(m)
  
  # following code limits the lowest and highest color to 5%, and 95% of your range, respectively
  quantile.range <- quantile(m, probs = seq(0, 1, 0.01))
  palette.breaks <-
    seq(quantile.range["5%"], quantile.range["95%"], 0.1)
  
  # use http://colorbrewer2.org/ to find optimal divergent color palette (or set own)
  color.palette  <-
    colorRampPalette(c("#FC8D59", "#FFFFBF", "#91CF60"))(length(palette.breaks) - 1)

  watcher_barplot <-
    barplot2(m,      ylab = "interest",
             xlab = "project",col = color.palette)
  dev.off()
}

watcher_barplot <- function(report) {
  baseurl <- "https://projects.linaro.org/rest/api/2/"
  fields <- "&fields=watches,key,project"
  
  type <- "search?"
  jql <- paste ("jql=project+in+(",report["team"],")+AND+issuetype+in+(Initiative,Epic)&maxResults=2000", sep = "")
  
  url <- paste(baseurl, type, jql, fields, sep = "")
  cat (url,"\n")
  webpage <- curl (url, handle = h)
  
  #get data it into R
  jira_raw <- fromJSON(webpage, simplifyDataFrame = TRUE)
  # extract the data node
  keys <- jira_raw$issues$key

  #x axis the number of projects
  projects <-  unique(jira_raw$issues$fields$project$key)
  num_projects <- length(projects)
  
  #projects
  #from the website now get watchers names with another query for all keys found previously
  fields <- "/watchers"
  type <- "issue/"
 
  names = list()
  for (key in keys) {
    url <- paste(baseurl, type, key, fields, sep = "")
    webpage <- curl (url, handle = h)
    jira_raw_names <- fromJSON(webpage, simplifyDataFrame = TRUE)

    names[[length(names) + 1]] <-
      as.list(jira_raw_names$watchers$name)
  }
  
  #create empty matrix
  comp <- companies(names)
  num_comp <- length(comp)
  m <- matrix(NA, nrow = num_comp, ncol = num_projects)
  colnames(m) <- projects
  rownames(m) <- comp
  m[] <- 0L
  
  #from the website now get watchers names with another query for all keys found previously
  fields <- "/watchers"
  type <- "issue/"

  for (key in keys) {
    #y
    mike <- match (key, keys)
    team <- jira_raw$issues$fields$project$key[mike]
    y <- match (team, projects)
    
    #x
    names = list()
    url <- paste(baseurl, type, key, fields, sep = "")
    webpage <- curl (url, handle = h)
    jira_raw_names <- fromJSON(webpage, simplifyDataFrame = TRUE)
    
    names[[length(names) + 1]] <- as.list(jira_raw_names$watchers$name)
    interested = companies(names)
    for (c in interested)
    {
      x <- match (c, comp)
      m[x, y] <- m[x, y] + 1
    }
  }
  
  print ( member_company ('mike.holmes@linaro.org'))
  #remove companies
#  rows.to.delete <- c('linaro.org', 'broadcom.com', 'character')
 # for (d in rows.to.delete) {
  #  m <- as.matrix(m[!grepl(d, rownames(m)),])
#  }
  for (c in comp) {
    
    v <- as.vector(m[c,])
    names(v) <- projects
    report["name"] <- c
    
    display_barplot(report, v, "_wt")
  }
  setwd(home)
}


sponsor_barplot <- function (report) {
  baseurl <- "https://projects.linaro.org/rest/api/2/"
  fields <- "&fields=customfield_10101,key,project"
  type <- "search?"
  
  jql <-
    paste(
      "jql=project+in+(",
      report["team"],
      ")+AND+issuetype+in+(Initiative,Epic)&maxResults=1000",
      sep = ""
    )
  url <- paste(baseurl, type, jql, fields, sep = "")
  cat (url,"\n")
  
  webpage <- curl (url, handle = h)
  
  #get data it into R
  jira_raw <- fromJSON(webpage, simplifyDataFrame = TRUE)
  #list of every issue
  keys <- jira_raw$issues$key
  
  #x axis the number of projects
  projects <-  unique(jira_raw$issues$fields$project$key)
  num_projects <- length(projects)
  
  #y axis the companies or sponsors
  sponsor <- jira_raw$issues$fields$customfield_10101
  
  names = list()
  for (key in keys) {
    s <- sponsor[[match (key, keys)]][[2]]
    for (n in s) {
      names[[length(names) + 1]] <- n
    }
  }
  
  #create empty matrix
  comp <- unique(names)
  num_comp <- length(comp)
  m <- matrix(NA, nrow = num_comp, ncol = num_projects)
  colnames(m) <- projects
  rownames(m) <- comp
  m[] <- 0L
  
  # count companies per project by looking at every key and update matrix
  
  for (key in keys) {
    #y
    mike <- match (key, keys)
    team <- jira_raw$issues$fields$project$key[mike]
    y <- match (team, projects)
    
    #x
    s <- sponsor[[match (key, keys)]][[2]]
    for (c in s)
    {
      x <- match (c, comp)
      m[x, y] <- m[x, y] + 1
    }
  }
  
  #remove companies we don't like ;)
#  rows.to.delete <- c('Linaro')
#  for (d in rows.to.delete) {
#    m <- as.matrix(m[!grepl(d, rownames(m)),])
 # }

 for (c in comp) {

    v <- as.vector(m[c,])
    names(v) <- projects
    report["name"] <- c

    display_barplot(report, v, "_sp")
  }
}

#from the website get the initiatives list for the four teams
id <- Sys.getenv(c("JIRA_USERNAME", "JIRA_PASSWORD","JIRA_OUTPUT"))
if (id[1] == '') {
  print ("export JIRA_USERNAME='john.doe@linaro.org'")
  q()
}
if (id[2] == '') {
  print ("export JIRA_PASSWORD='my-super-secret-password'")
  q()
}

if (id[3] == '') {
  home <- paste(getwd(),"/company", sep="")
} else {
  home <- paste(id[3],"/company", sep="")
}

print(paste("Outputdir = ", home))
dir.create(home)
setwd(home)

user_password <- paste(id[1], id[2], sep=":")

h <- new_handle(failonerror = TRUE)
handle_setopt(h, verbose = 0)
handle_setopt(h, userpwd = user_password)
handle_setopt(h, httpauth = 1)

report_name <- c("ti")
team_list <-
  c(
    "SWG,KWG,PMWG,VIRT,LSK,LHG,LEG,LNG,LITE,LMG,CTT,BB,LAVA,LAB,QA,SYS,TCWG"
  )
reports <-
  data.frame(name = report_name ,
             team = team_list, 
             stringsAsFactors = FALSE)

apply(reports, 1, sponsor_barplot)
apply(reports, 1, watcher_barplot)

warning()

setwd(home)