import modeler.api
stream = modeler.script.stream()
node = stream.create("extension_importer", "extension_importer")
node.setPropertyValue("syntax_type", "Python")

python_script = """
import spss.pyspark
from pyspark.sql.types import *

cxt = spss.pyspark.runtime.getContext()

_schema = StructType([StructField('id', LongType(), nullable=False), \
StructField('age', LongType(), nullable=True), \
StructField('Sex', StringType(), nullable=True), \
StructField('BP', StringType(), nullable=True), \
StructField('Cholesterol', StringType(), nullable=True), \
StructField('K', DoubleType(), nullable=True), \
StructField('Na', DoubleType(), nullable=True), \
StructField('Drug', StringType(), nullable=True)])

if cxt.isComputeDataModelOnly(): 
	cxt.setSparkOutputSchema(_schema)
else:
	df = cxt.getSparkInputData()
	if df is None:
		drugList=[(1,23,'F','HIGH','HIGH',0.792535,0.031258,'drugY'), \
(2,47,'M','LOW','HIGH',0.739309,0.056468,'drugC'),\
              (3,47,'M','LOW','HIGH',0.697269,0.068944,'drugC'),\
              (4,28,'F','NORMAL','HIGH',0.563682,0.072289,'drugX'),\
              (5,61,'F','LOW','HIGH',0.559294,0.030998,'drugY'),\
              (6,22,'F','NORMAL','HIGH',0.676901,0.078647,'drugX'),\
              (7,49,'F','NORMAL','HIGH',0.789637,0.048518,'drugY'),\
              (8,41,'M','LOW','HIGH',0.766635,0.069461,'drugC'),\
              (9,60,'M','NORMAL','HIGH',0.777205,0.05123,'drugY'),\
              (10,43,'M','LOW','NORMAL',0.526102,0.027164,'drugY')]
		sqlcxt = cxt.getSparkSQLContext()
		rdd = cxt.getSparkContext().parallelize(drugList)
		print 'pyspark read data count = '+str(rdd.count())
		df = sqlcxt.createDataFrame(rdd, _schema)

	cxt.setSparkOutputData(df)
"""

node.setPropertyValue("python_syntax", python_script)

node.setPropertyValue("syntax_type", "R")

R_script = """# 'JSON Import' Node v1.0 for IBM SPSS Modeler    
# 'RJSONIO' package created by Duncan Temple Lang - http://cran.r-project.org/web/packages/RJSONIO
# 'plyr' package created by Hadley Wickham http://cran.r-project.org/web/packages/plyr
# Node developer: Danil Savine - IBM Extreme Blue 2014  
# Description: This node allows you to import into SPSS a table data from  a JSON.
# Install function for packages       
packages <- function(x){
  x <- as.character(match.call()[[2]])
  if (!require(x,character.only=TRUE)){
    install.packages(pkgs=x,repos="http://cran.r-project.org")
    require(x,character.only=TRUE)
  }
}
#  packages
packages(RJSONIO)
packages(plyr)
### This function is used to generate automatically the dataModel
getMetaData <- function (data) {
  if (dim(data)[1]<=0) {
    
    print("Warning : modelerData has no line, all fieldStorage fields set to strings")
    getStorage <- function(x){return("string")}
    
  } else {
    
    getStorage <- function(x) {
      res <- NULL
      #if x is a factor, typeof will return an integer so we treat the case on the side
      if(is.factor(x)) {
        res <- "string"
      } else {
        res <- switch(typeof(unlist(x)),
                      integer = "integer",
                      double = "real",
                      character = "string",
                      "string")
      }
      return (res)
    }
  }
  
  col = vector("list", dim(data)[2])
  for (i in 1:dim(data)[2]) {
    col[[i]] <- c(fieldName=names(data[i]),
                  fieldLabel="",
                  fieldStorage=getStorage(data[i]),
                  fieldMeasure="",
                  fieldFormat="",
                  fieldRole="")
  }
  mdm<-do.call(cbind,col)
  mdm<-data.frame(mdm)
  return(mdm)
}
# From JSON to a list
txt <- readLines('C:/test.json')
formatedtxt <- paste(txt, collapse = '')
json.list <- fromJSON(formatedtxt)
  # Apply path to json.list
  if(strsplit(x='true', split='
  ' ,fixed=TRUE)[[1]][1]) {
    path.list <- unlist(strsplit(x='id_array', split=','))
    i = 1
    while(i<length(path.list)+1){
      if(is.null(getElement(json.list, path.list[i]))){
         json.list <- json.list[[1]]
      }else{
      json.list <- getElement(json.list, path.list[i])
      i <- i+1
      }
    }
  }
# From list to dataframe via unlisted json
i <-1
filled <- data.frame()
while(i < length(json.list)+ 1){
  unlisted.json <- unlist(json.list[[i]])
  to.fill <- data.frame(t(as.data.frame(unlisted.json, row.names = names(unlisted.json))), stringsAsFactors=FALSE)
  filled <- rbind.fill(filled,to.fill)
  i <-  1 + i
}
# Export to SPSS Modeler Data
modelerData <- filled
print(modelerData)
modelerDataModel <- getMetaData(modelerData)
print(modelerDataModel)

"""

node.setPropertyValue("r_syntax", R_script)