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require_relative '../lazar/lib/lazar'
include OpenTox
#$mongo.database.drop
#$gridfs = $mongo.database.fs # recreate GridFS indexes
# compare duplicates within datasets
old = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","LOAEL_mmol_corrected_smiles.csv")
new = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","swissRat_chron_LOAEL.csv")
combined = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","LOAEL-rat-combined.csv")
compound_vector = []
value_vector = []
dataset_vector = []
vars = []
[old, new].each do |dataset|
vars << []
#vars[dataset.name] = []
p dataset.name
p dataset.compounds.size
p dataset.duplicates(dataset.features.first).size
dataset.duplicates.each do |cid,values|
R.assign "values", values
var = R.eval("var(-log(values))").to_f
vars.last << var
#smi = Compound.find(cid).smiles
smi = cid.to_s
values.each do |val|
compound_vector << smi
value_vector << - Math.log(val)
dataset_vector << dataset.name
end
#vars << { :var => var, :values => values, :smiles => smi }
end
#vars.sort!{|a,b| a[:var] <=> b[:var]}
#vars.each do |dup|
#dup[:values].each do |v|
#compound_vector << dup[:smiles]
#value_vector << v
#end
#end
end
#p vars
# TODO statistical test for variances
R.assign "vars1", vars[0]
R.assign "vars2", vars[1]
print "p-value: #{R.eval("t.test(vars1,vars2)$p.value").to_f}"
R.assign "smi", compound_vector
R.assign "values", value_vector
R.assign "dataset", dataset_vector
R.eval "df <- data.frame(factor(smi),values,factor(dataset))"
R.eval "img <- ggplot(df, aes(smi,values,ymin = min(values), ymax=max(values),color=dataset))"
R.eval "img <- img + scale_x_discrete(breaks=NULL) + geom_point() + coord_flip()"# + xlab('-log(LOAEL)'), ylab('Compound')"
R.eval "ggsave(file='/home/ch/opentox/lazar-nestec-data/loael_variance.svg', plot=img)"
#R.eval "print(img)"
#R.eval "write.csv(df,'/home/ch/opentox/lazar-nestec-data/loael.csv')"
#`inkview /home/ch/opentox/lazar-nestec-data/loael_variance.svg`
#R.eval "ggsave(file='test.svg', plot=img)"
=begin
# compare datasets
# compare combined datasets
file = File.join(File.dirname(__FILE__),"regression","LOAEL-rat-combined.csv")
d = Dataset.from_csv_file file
replicates = []
compounds = d.compound_ids.uniq
sds = []
compounds.each do |cid|
compound_idxs = d.compound_ids.each_index.select{|i| d.compound_ids[i] == cid}
if compound_idxs.size > 1
vals = compound_idxs.collect{|i| d.data_entries[i].first }
R.assign "values", vals
#sd = R.eval("sd(-log(values))").to_f
sd = R.eval("var(values)").to_f
sds << { :sd => sd, :values => vals, :smiles => Compound.find(cid).smiles }
#replicates << compound_idxs.collect{|i| d.data_entries[i].first }
#replicates[Compound.find(cid).smiles] = compound_idxs.collect{|i| d.data_entries[i].first }
end
end
p sds.sort{|a,b| a[:sd] <=> b[:sd]}
#R.assign "replicates", replicates
#R.assign "compounds", compounds.collect{|id| Compound.find id }
#R.eval "df = data.frame(compounds,replicates)"
#library(ggplot2)
#qplot(compounds, replicates, data=df, geom="boxplot")
#p replicates.to_json
#p replicates.size
# http://www.unc.edu/courses/2008spring/psyc/270/001/variance.html
=end
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