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Kroměříž 2012

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<header>
  <aside>May 28, 2013</aside>
  <a href="../" rel="author">Michal Škop</a>
</header>

<h1>Kroměříž 2012</h1>

<form>
        <label for="slider">Slider:</label>
        <input name="slider" id="slider" min="2012.25" max="2012.75" value="2012.25" step=".01" type="range" />
</form>
<p>
    <a href="#" id="play" data-role="button" data-inline="true" data-icon="refresh"><span id="playText">Play ></span></a>
</p>

 <p id="chart"><svg><defs id="gradients"></defs></svg></p>
 <!-- <p id="slide">XXX</p> -->



<p>Method: weighted PCA; Time intervals: 6 months (projection of 6-month blocks into all data)</p>
<p>Data thanks to <a href="//blog.aktualne.centrum.cz/blogy/vojtech-navratil.php">V. Navrátil</a></p>
<p>Using <a href="https://github.com/michalskop/mpv_motion">Models of parliamentary voting in motion</a></p>


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<script>

// Various accessors that specify the four dimensions of data to visualize.
function x(d) { return d.d1; }
function y(d) { return d.d2; }
function z(d) { return d.d3; }
function radius(d) { return 1; }
function color(d) { return d.color; }
//function color(d) { return d.group; }
function key(d) { return d.name; }
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svg.append("text")
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// Add the year label; the value is set on transition.
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    .attr("text-anchor", "end")
    .attr("y", height - 24)
    .attr("x", width)
    .text("2012/1");

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d3.json("cz_kromeriz_2012_1h.json", function(nations) {

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		//.attr("style", function (d) { return "display:" + display(d)+";";});
		.attr("display", function (d) { return display(d);});
        //alert(dot.data);
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  // Defines a sort order so that the smallest dots are drawn on top.
  function order(a, b) {
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  // Tweens the entire chart by first tweening the year, and then the data.
  // For the interpolated data, the dots and label are redrawn.
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    var year = d3.interpolateNumber(2012.25,2012.75);
    return function(t) { displayYear(year(t)); };
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  // Updates the display to show the specified year.
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  // Interpolates the dataset for the given (fractional) year.
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        //category: d.group
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cz_kromeriz_2012_1h.json

[{"name":"Arno\u0161t \u0160krabal","d1":[[2012.25,2.3395],[2012.75,2.047]],"d2":[[2012.25,-1.5937],[2012.75,0.2378]],"color":[[2012.25,"#008000"],[2012.75,"#008000"]]},{"name":"Blanka \u0160im\u016fnkov\u00e1","d1":[[2012.25,-1.3891],[2012.75,-0.7452]],"d2":[[2012.25,0.9902],[2012.75,-0.5172]],"color":[[2012.25,"#023484"],[2012.75,"#023484"]]},{"name":"Daniela Hebnarov\u00e1","d1":[[2012.25,-1.3082],[2012.75,-1]],"d2":[[2012.25,0.7204],[2012.75,0.2608]],"color":[[2012.25,"#023484"],[2012.75,"#023484"]]},{"name":"Esma Opravilov\u00e1","d1":[[2012.25,-0.8974],[2012.75,1.81]],"d2":[[2012.25,-0.0216],[2012.75,1.5568]],"color":[[2012.25,"#E01C07"],[2012.75,"#E01C07"]]},{"name":"Jan \u017d\u00e1rsk\u00fd","d1":[[2012.25,-0.9831],[2012.75,-0.4314]],"d2":[[2012.25,1.3063],[2012.75,0.7685]],"color":[[2012.25,"#BBBBBB"],[2012.75,"#BBBBBB"]]},{"name":"Jarmila \u010c\u00edhalov\u00e1","d1":[[2012.25,-1.3891],[2012.75,-1.055]],"d2":[[2012.25,0.9902],[2012.75,0.0362]],"color":[[2012.25,"#BBBBBB"],[2012.75,"#BBBBBB"]]},{"name":"Jaroslav Adam\u00edk","d1":[[2012.25,-1.3149],[2012.75,-1.1437]],"d2":[[2012.25,1.0376],[2012.75,-0.5975]],"color":[[2012.25,"#E01C07"],[2012.75,"#E01C07"]]},{"name":"Jaroslav Nov\u00e1k","d1":[[2012.25,-0.5861],[2012.75,0.0998]],"d2":[[2012.25,1.1129],[2012.75,-0.297]],"color":[[2012.25,"#673B6C"],[2012.75,"#673B6C"]]},{"name":"Jitka Dvo\u0159\u00e1kov\u00e1","d1":[[2012.25,1.3624],[2012.75,0.9329]],"d2":[[2012.25,-1.6426],[2012.75,-0.2109]],"color":[[2012.25,"#023484"],[2012.75,"#023484"]]},{"name":"Karel Chv\u00e1tal","d1":[[2012.25,0.7252],[2012.75,-0.7691]],"d2":[[2012.25,-1.3653],[2012.75,-1.3806]],"color":[[2012.25,"#023484"],[2012.75,"#023484"]]},{"name":"Karel Hol\u00edk","d1":[[2012.25,-1.312],[2012.75,-1.212]],"d2":[[2012.25,0.7188],[2012.75,-0.3821]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Karel Sm\u00ed\u0161ek","d1":[[2012.25,1.4612],[2012.75,1.7838]],"d2":[[2012.25,-0.1761],[2012.75,1.9188]],"color":[[2012.25,"#FFFF00"],[2012.75,"#FFFF00"]]},{"name":"Lea P\u00edskovsk\u00e1","d1":[[2012.25,2.2795],[2012.75,1.9099]],"d2":[[2012.25,-0.8859],[2012.75,1.8937]],"color":[[2012.25,"#4444FF"],[2012.75,"#4444FF"]]},{"name":"Lenka Mergenthalov\u00e1","d1":[[2012.25,-1.515],[2012.75,-0.9166]],"d2":[[2012.25,1.0081],[2012.75,-0.4005]],"color":[[2012.25,"#673B6C"],[2012.75,"#673B6C"]]},{"name":"Marek \u0160indler","d1":[[2012.25,-1.3082],[2012.75,-1.0081]],"d2":[[2012.25,0.7204],[2012.75,0.2697]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Miloslava Noskov\u00e1","d1":[[2012.25,0.8102],[2012.75,-0.9345]],"d2":[[2012.25,-1.2402],[2012.75,-1.2369]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Milo\u0161 Mal\u00fd","d1":[[2012.25,-1.3005],[2012.75,-1.0735]],"d2":[[2012.25,0.749],[2012.75,-0.3096]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Olga Sehnalov\u00e1","d1":[[2012.25,1.4554],[2012.75,1.8262]],"d2":[[2012.25,-1.4809],[2012.75,0.2193]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Ond\u0159ej Debef","d1":[[2012.25,1.4768],[2012.75,1.5239]],"d2":[[2012.25,-0.667],[2012.75,0.7991]],"color":[[2012.25,"#FFFF00"],[2012.75,"#FFFF00"]]},{"name":"Pavel Moty\u010dka","d1":[[2012.25,2.0955],[2012.75,2.3033]],"d2":[[2012.25,-1.0729],[2012.75,1.0272]],"color":[[2012.25,"#FFFF00"],[2012.75,"#FFFF00"]]},{"name":"Richard Kreml","d1":[[2012.25,2.2736],[2012.75,1.7469]],"d2":[[2012.25,-1.5219],[2012.75,-0.7606]],"color":[[2012.25,"#008000"],[2012.75,"#008000"]]},{"name":"Vladim\u00edr Ken\u0161a","d1":[[2012.25,-1.0722],[2012.75,-1.1796]],"d2":[[2012.25,0.6921],[2012.75,-0.4713]],"color":[[2012.25,"#E01C07"],[2012.75,"#E01C07"]]},{"name":"Vladim\u00edr K\u0159eme\u010dek","d1":[[2012.25,-1.1457],[2012.75,-0.978]],"d2":[[2012.25,0.6688],[2012.75,-0.6676]],"color":[[2012.25,"#E01C07"],[2012.75,"#E01C07"]]},{"name":"V\u011bra Knapkov\u00e1","d1":[[2012.25,-1.3891],[2012.75,-0.7601]],"d2":[[2012.25,0.9902],[2012.75,-0.5348]],"color":[[2012.25,"#023484"],[2012.75,"#023484"]]},{"name":"V\u011bra Nov\u00e1kov\u00e1","d1":[[2012.25,1.734],[2012.75,0.3097]],"d2":[[2012.25,-1.6756],[2012.75,-1.3395]],"color":[[2012.25,"#F18811"],[2012.75,"#F18811"]]},{"name":"Zde\u0148ka Dokoupilov\u00e1","d1":[[2012.25,2.249],[2012.75,2.3364]],"d2":[[2012.25,-0.7497],[2012.75,0.9451]],"color":[[2012.25,"#008000"],[2012.75,"#008000"]]},{"name":"\u0160\u00e1rka Ka\u0161p\u00e1rkov\u00e1","d1":[[2012.25,-1.515],[2012.75,-0.5048]],"d2":[[2012.25,1.0081],[2012.75,0.4888]],"color":[[2012.25,"#BBBBBB"],[2012.75,"#BBBBBB"]]}]

cz_kromeriz_2012_1h.r

.libPaths("/home/michal/R/x86_64-pc-linux-gnu-library/3.0")
#install.packages("reshape2")
library("reshape2")
#install.packages("sqldf")
library("sqldf")

path = "/home/michal/project/mpv_motion/www/analyses/"
dataname = "cz_kromeriz_2012"
analysisname = "cz_kromeriz_2012_1h"

#lower limit to eliminate from calculations
lo_limit = .1
#lower limit to eliminate from projections
lo_limitT = lo_limit

#raw data in 3 columns (values in [-1,1])
Graw = read.table(paste(path,dataname,"/",dataname,".csv",sep=""), header=FALSE, sep=",")
#data divisions x persons
G = acast(Graw,V2~V1,value.var='V3')
#scaled divisions x persons (mean=0 and sd=1 for each division)
H=t(scale(t(G),scale=TRUE))

#weights
#weights 1, based on number of persons in division
w1 = apply(abs(G)==1,1,sum,na.rm=TRUE)/max(apply(abs(G)==1,1,sum,na.rm=TRUE))
w1[is.na(w1)] = 0
#weights 2, "100:100" vs. "195:5"
w2 = 1 - abs(apply(G==1,1,sum,na.rm=TRUE) - apply(G==-1,1,sum,na.rm=TRUE))/apply(!is.na(G),1,sum) 
w2[is.na(w2)] = 0

	#analytics
	#plot(w1)
	#plot(w2)
	#plot(w1*w2)

#weighted scaled divisions x persons
Hw = H * w1 * w2

#index of missing data
#index of missing data divisions x persons
HI = H
HI[!is.na(H)]=1
HI[is.na(H)] = 0

#weighted scaled with NA->0 division x persons
Hw0 = Hw
Hw0[is.na(Hw)]=0

#eliminate persons with too few votes (weighted)

#weights for non missing data division x persons
HIw = HI*w1*w2
#sum of weights of divisions for each persons
tmp = apply(HIw,2,sum)
pw = tmp/max(tmp)
#index of persons in calculation
pI = pw > lo_limit
#weighted scaled with NA->0 and cutted persons with too few votes division x persons
Hw0c = Hw0[,pI]
#index of missing data cutted persons with too few votes divisions x persons
HIc = HI[,pI]

#"covariance" matrix adjusted according to missing data
Hcov=t(Hw0c)%*%Hw0c * 1/(t(HIc)%*%HIc) * (dim(Hw0c)[1])
#Hcov=t(Hw0)%*%Hw0 * 1/(t(HI)%*%HI-1) * (dim(H)[1]-1)

#substitution of missing data in "covariance" matrix
Hcov0 = Hcov
Hcov0[is.na(Hcov)] = 0			#********* 

#eigendecomposition
He=eigen(Hcov0)
# V (rotation values of persons)
V = He$vectors
#projected divisions into dimensions
Hy=Hw0c%*%V

	#analytics
	#plot(Hy[,1],Hy[,2])
	#plot(sqrt(He$values[1:10]))

#sigma matrix 
sigma = diag(sqrt(He$values))
sigma[is.na(sigma)] = 0

#projection of persons into dimensions
Hproj = t(sigma%*%t(V))
	#analytics
	#plot(Hproj[,1],Hproj[,2])
	
	#second projection
    #Hproj2 = t(t(U) %*% Hw0c)
    	#without missing values should be equal
    #plot(Hproj[,1],Hproj[,1])
    #plot(Hproj[,2],Hproj[,2])

#sigma^-1 matrix
sigma_1 = diag(sqrt(1/He$values))
sigma_1[is.na(sigma_1)] = 0

# U (rotation values of divisions)
U = Hw0c%*%V%*%sigma_1

#U%*%sigma%*%t(V) != Hw0c ;because of adjusting of "covariance" matrix


# NEW MP (or partial)
#New persons / partial (like a projection of divisions from a smaller time interval into all divisions)


analdb = dbConnect(SQLite(), dbname=paste(path,dataname,"/",analysisname,"/",analysisname,".sqlite3",sep=""))
datadb = dbConnect(SQLite(), dbname=paste(path,dataname,"/",dataname,".sqlite3",sep=""))

rotation = strsplit(dbGetQuery(analdb,"SELECT orientation FROM analysis_info")[1,1],",")[[1]]
rot_mp_rank = which(dimnames(Hcov)[[1]] == rotation[1])
for (j in 2:length(rotation)) {
  if (Hproj[rot_mp_rank,j-1] * as.double(rotation[j]) < 0) {
    U[,j-1] = -1*U[,j-1]
  }
}

aU = abs(U)

mp_names = dbGetQuery(datadb,"SELECT name FROM mp ORDER BY CAST(code as INTEGER)")
attr(G,'dimnames')[2][[1]] = mp_names$name
#attr(Hw0c,'dimnames')[2][[1]] = mp_names$name[pI]

interval_names = dbGetQuery(analdb,"SELECT distinct(interval_name) FROM analysis_division_in_interval ORDER BY interval_name")

    


for (i in 1:dim(interval_names)[1]) {

    TIf = dbGetQuery(analdb,paste("SELECT division_code, CASE interval_name='",interval_names[i,],"' WHEN 1 THEN 'TRUE' ELSE 'FALSE' END as in_interval FROM analysis_division_in_interval ORDER BY division_code",sep=""))
    
    TIf$in_interval = as.logical(TIf$in_interval)
    
    max_division_code = TIf$division_code[max(which(as.logical(TIf$in_interval)))]
    min_division_code = TIf$division_code[min(which(as.logical(TIf$in_interval)))]
    
    TI = TIf$in_interval
    
    GTc = G[,pI]
    GTc[!TI,] = NA
    HTc = (GTc - attr(H,"scaled:center"))/attr(H,"scaled:scale")
    
    HTIc = HTc
	HTIc[!is.na(HTIc)] = 1
	HTIc[is.na(HTIc)] = 0
    
    HTw0c = HTc * w1 * w2
    HTw0c[is.na(HTw0c)] = 0
    

	#weights for non missing data division x persons
	HTIcw = HTIc*w1*w2
	#sum of weights of divisions for each persons
	tmp = apply(HTIcw,2,sum)
	pTw = tmp/max(tmp)
	#index of persons in calculation
	pTI = pTw > lo_limitT
	
	#weighted scaled with NA->0 and cutted persons with too few votes division x persons
	HTw0cc = HTw0c[,pTI]
	#index of missing data cutted persons with too few votes divisions x persons
	HTIcc = HTIc[,pTI]
    
    
	dweights = t(t(aU)%*%HTIcc / apply(aU,2,sum))  	#person x division
    dweights[is.na(dweights)] = 0
    
    HTw0ccproj = t(HTw0cc)%*%U / dweights


    parties = as.matrix(dbGetQuery(datadb,paste('SELECT COALESCE(NULLIF(tmax.code,""),tmin.code) as code, COALESCE(NULLIF(tmax.color,""),tmin.color) as color FROM (SELECT  mvf.mp_code, g.code, g.color FROM mp_vote_full as mvf  LEFT JOIN "group" as g  ON mvf.group_code=g.code  WHERE division_code="',max_division_code,'") as tmax LEFT JOIN  (SELECT  mvf.mp_code, g.code, g.color FROM mp_vote_full as mvf  LEFT JOIN "group" as g  ON mvf.group_code=g.code  WHERE division_code="',min_division_code,'") as tmin ON tmax.mp_code=tmin.mp_code  ORDER BY CAST(tmax.mp_code as INTEGER)',sep="")))
    partiescc = (parties[pI,])[pTI,]
    
    output = format(HTw0ccproj[,1:2],scientific=FALSE,digits=3)
    write.csv(format(cbind(output,partiescc),digits=3),file=paste(path,dataname,"/",analysisname,"/",interval_names[i,],"_result.csv",sep=""))
     #*************************
}