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histograms.R
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library(ggplot2)
library(corrplot)
#
save_img = function(name,fact=1,square=FALSE){
if(square){
width = 100* fact
height = 100 * fact
}else{width = 145* fact
height = 90 * fact
}
file_name = paste('~/dissertacao/latex/img/',name,'.png', sep='')
ggsave(file_name, width = width, height = height,units = 'mm')
}
erro.medio = function(x,y){
z = x-y
return(mean(z))
}
erro.absoluto = function(x,y){
z = abs(x-y)
return(mean(z))
}
erro.ae95 = function(x,y,p=.95){
z = abs(x-y)
return(quantile(z,p))
}
rmse = function(x,y){
z = (mean((x-y)**2))**(1/2)
return(z)
}
#
# CONCRETE + EPS ----
samp = read.csv('/media/marcelo/OS/dissertacao/experiment/')
df = rbind(
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster0/means_cluster0.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster1/means_cluster1.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster2/means_cluster2.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster3/means_cluster3.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster4/means_cluster4.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster5/means_cluster5.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster6/means_cluster6.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster7/means_cluster7.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster8/means_cluster8.csv'),
read.csv('/media/marcelo/OS/dissertacao/experiment/cluster9/means_cluster9.csv')
)
df_eps = subset(df, substr(df$file,1,1) == 'e')
df_ref = subset(df, substr(df$file,1,1) == 'r')
# df_ref2 = subset(df_ref, df_ref$ehf < .95)
# df_eps = subset(df_eps, df_ref$ehf < .95)
# df_ref = df_ref2
df_dif = data.frame(
'case' = substr(df_eps$file,5,8),
'zone' = df_eps$zone,
'temp' = df_ref$temp - df_eps$temp,
'ach' = df_ref$ach - df_eps$ach,
'ehf' = df_ref$ehf - df_eps$ehf
)
ach_mean = mean(df_dif$ach)
ehf_mean = mean(df_dif$ehf)
ggplot(df_dif,aes(df_dif$ehf)) +
geom_histogram(binwidth = .001)+
ggtitle('Diferenças no EHF') +
xlab('EHF Referência - EHF EPS + Concreto') +
ylab('Número de casos') +
annotate("text", x = -.02, y = 1000, label = paste("Média =",round(ehf_mean,5))) +
annotate("text", x = -.02, y = 1000*.95, label = paste("AE95 =",round(quantile(abs(df_dif$ehf),.95),4)))
ggplot(df_dif,aes(df_dif$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH') +
xlab('ACH analítico - ACH tpu') +
ylab('Número de casos') +
annotate("text", x = -35, y = 700, label = paste("Média =",round(ach_mean,3),'ach'))
ggplot(df_ref,aes(df_ref$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('EHF Referência') +
xlab('EHF') +
ylab('Número de casos') +
annotate("text", x = 0.8, y = 300, label = paste("Média =",round(mean(df_ref$ehf),5))) +
annotate("text", x = 0.8, y = 300*.93, label = paste("AE95 =",round(quantile(abs(df_ref$ehf),.95),4)))
ggplot(df_ref,aes(df_ref$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('ACH Referência') +
xlab('ACH') +
ylab('Número de casos') +
annotate("text", x = 100, y = 60, label = paste("Média =",round(mean(df_ref$ach),2),'ach'))
# CONCRETE + EPS 2 ----
# samp = read.csv('/media/marcelo/OS/dissertacao/sample_diss.csv')
samp = read.csv('/media/marcelo/OS/dissertacao/sample_eq_wall.csv')
par1_wall_u = 17.5
par1_wall_ct = 220
par2a_wall_u = 3.962
par2a_wall_ct = 138.272
par2b_wall_u = 3.962
par2b_wall_ct = 69.136
par3_wall_u = .773
par3_wall_ct = 29.7875
samp$area = 20+80*samp$area
samp$zone_height = 2.4+.8*samp$zone_height
samp$azimuth = 180*samp$azimuth
samp$ratio = .5 + 1.5*samp$ratio
samp$wwr = .1+.5*samp$wwr
samp$people = .05+.45*samp$people
samp$absorptance = .3 + .6*samp$absorptance
samp = samp[rep(seq_len(nrow(samp)), each=6),]
# df = rbind(
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster0/means_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster1/means_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster2/means_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster3/means_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster4/means_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster5/means_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster6/means_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster7/means_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster8/means_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/wall/cluster9/means_cluster9.csv')
# )
#
# df = rbind(
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster0/means_parede_eq_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster1/means_parede_eq_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster2/means_parede_eq_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster3/means_parede_eq_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster4/means_parede_eq_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster5/means_parede_eq_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster6/means_parede_eq_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster7/means_parede_eq_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster8/means_parede_eq_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/parede_eq/cluster9/means_parede_eq_cluster9.csv')
# )
df = read.csv('/media/marcelo/OS/dissertacao/parede_eq/means_parede_eq.csv')
df_eps_par1 = subset(df, grepl(pattern = 'eq_par1',df$file)) # epsconc_par1
df_ref_par1 = subset(df, grepl(pattern = 'ref_par1',df$file)) # refwall_par1
df_eps_par2a = subset(df, grepl(pattern = 'eq_par2a',df$file)) # epsconc_par2a
df_ref_par2a = subset(df, grepl(pattern = 'ref_par2b',df$file)) # refwall_par2a
df_eps_par2b = subset(df, grepl(pattern = 'eq_par2b',df$file)) # epsconc_par2b
df_ref_par2b = subset(df, grepl(pattern = 'ref_par2b',df$file)) # refwall_par2b
df_eps_par3 = subset(df, grepl(pattern = 'eq_par3',df$file)) # epsconc_par3
df_ref_par3 = subset(df, grepl(pattern = 'ref_par3',df$file)) # refwall_par3
# bind to sample
df_eps_par1 = cbind(df_eps_par1, samp)
df_ref_par1 = cbind(df_ref_par1, samp)
df_eps_par2a = cbind(df_eps_par2a, samp)
df_ref_par2a = cbind(df_ref_par2a, samp)
df_eps_par2b = cbind(df_eps_par2b, samp)
df_ref_par2b = cbind(df_ref_par2b, samp)
df_eps_par3 = cbind(df_eps_par3, samp)
df_ref_par3 = cbind(df_ref_par3, samp)
df_eps_par1$wall_u = par1_wall_u
df_eps_par1$wall_ct = par1_wall_ct
df_ref_par1$wall_u = par1_wall_u
df_ref_par1$wall_ct = par1_wall_ct
df_eps_par2a$wall_u = par2a_wall_u
df_eps_par2a$wall_ct = par2a_wall_ct
df_ref_par2a$wall_u = par2a_wall_u
df_ref_par2a$wall_ct = par2a_wall_ct
df_eps_par2b$wall_u = par2b_wall_u
df_eps_par2b$wall_ct = par2b_wall_ct
df_ref_par2b$wall_u = par2b_wall_u
df_ref_par2b$wall_ct = par2b_wall_ct
df_eps_par3$wall_u = par3_wall_u
df_eps_par3$wall_ct = par3_wall_ct
df_ref_par3$wall_u = par3_wall_u
df_ref_par3$wall_ct = par3_wall_ct
# df_ref2 = subset(df_ref, df_ref$ehf < .95)
# df_eps = subset(df_eps, df_ref$ehf < .95)
# df_ref = df_ref2
library(corrplot)
cor_matrix_1 = cor(df_ref_par2b[,c("temp","temp_max","ach","ehf","area",
"ratio","zone_height", "azimuth","absorptance",
"wwr","people")])
corrplot.mixed(cor_matrix_1, lower = "number", upper = "ellipse",
tl.pos = "lt", number.cex = 0.6, bg = "black",
tl.col = "black", tl.srt = 90, tl.cex = 0.8)
df_dif_par1 = data.frame(
'case' = substr(df_eps_par1$file,14,17),
'zone' = df_eps_par1$zone,
'temp' = df_ref_par1$temp - df_eps_par1$temp,
'ach' = df_ref_par1$ach - df_eps_par1$ach,
'ehf' = df_ref_par1$ehf - df_eps_par1$ehf
)
df_dif_par2a = data.frame(
'case' = substr(df_eps_par2a$file,14,17),
'zone' = df_eps_par2a$zone,
'temp' = df_ref_par2a$temp - df_eps_par2a$temp,
'ach' = df_ref_par2a$ach - df_eps_par2a$ach,
'ehf' = df_ref_par2a$ehf - df_eps_par2a$ehf
)
df_dif_par2b = data.frame(
'case' = substr(df_eps_par2b$file,14,17),
'zone' = df_eps_par2b$zone,
'temp' = df_ref_par2b$temp - df_eps_par2b$temp,
'ach' = df_ref_par2b$ach - df_eps_par2b$ach,
'ehf' = df_ref_par2b$ehf - df_eps_par2b$ehf
)
df_dif_par3 = data.frame(
'case' = substr(df_eps_par3$file,14,17),
'zone' = df_eps_par3$zone,
'temp' = df_ref_par3$temp - df_eps_par3$temp,
'ach' = df_ref_par3$ach - df_eps_par3$ach,
'ehf' = df_ref_par3$ehf - df_eps_par3$ehf
)
tempmax = 40
df_dif_par1 = subset(df_dif_par1, df_ref_par1$temp_max < tempmax)
df_dif_par2a = subset(df_dif_par2a, df_ref_par2a$temp_max < tempmax)
df_dif_par2b = subset(df_dif_par2b, df_ref_par2b$temp_max < tempmax)
df_dif_par3 = subset(df_dif_par3, df_ref_par3$temp_max < tempmax)
ach_mean_par1 = mean(df_dif_par1$ach)
ehf_mean_par1 = mean(df_dif_par1$ehf)
temp_mean_par1 = mean(df_dif_par1$temp)
ach_mean_par2a = mean(df_dif_par2a$ach)
ehf_mean_par2a = mean(df_dif_par2a$ehf)
temp_mean_par2a = mean(df_dif_par2a$temp)
ach_mean_par2b = mean(df_dif_par2b$ach)
ehf_mean_par2b = mean(df_dif_par2b$ehf)
temp_mean_par2b = mean(df_dif_par2b$temp)
ach_mean_par3 = mean(df_dif_par3$ach)
ehf_mean_par3 = mean(df_dif_par3$ehf)
temp_mean_par3 = mean(df_dif_par3$temp)
df_dif = rbind(df_dif_par1, df_dif_par2b,df_dif_par3)
ach_mean = mean(df_dif$ach)
ehf_mean = mean(df_dif$ehf)
# EHFSCATTERPLOT
ggplot(df_ref_par1,aes(df_ref_par1$ehf, df_eps_par1$ehf)) +
geom_point(alpha=.1) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1))
ggplot(df_ref_par2a,aes(df_ref_par2a$ehf, df_eps_par2a$ehf)) +
geom_point(alpha=.25) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
# ggtitle('Comparação EHF Alvenaria inteira') +
xlab('Parede Referência - EHF (-)') +
ylab('Parede Equivalente - valor total da CT\nEHF (-)') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(erro.medio(df_ref_par2a$ehf, df_eps_par2a$ehf),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 = ",round(erro.ae95(df_ref_par2a$ehf, df_eps_par2a$ehf),4),0,sep=''))
# save_img('paredeeq_EHF_par2a_scatter')
erro.absoluto(df_ref_par2a$ehf, df_eps_par2a$ehf)
mean(df_ref_par2a$ehf-df_eps_par2a$ehf)
mean(abs(df_ref_par2a$ehf-df_eps_par2a$ehf))
quantile((abs(df_ref_par2a$ehf-df_eps_par2a$ehf)),.95)
ggplot(df_ref_par2b,aes(df_ref_par2b$ehf, df_eps_par2b$ehf)) +
geom_point(alpha=.25) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
# ggtitle('Comparação EHF Alvenaria Metade') +
xlab('Parede Referência - EHF (-)') +
ylab('Parede Equivalente - metade do valor da CT\nEHF (-)') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(erro.medio(df_ref_par2b$ehf, df_eps_par2b$ehf),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 =",round(erro.ae95(df_ref_par2b$ehf, df_eps_par2b$ehf),4)))
# save_img('paredeeq_EHF_par2b_scatter')
erro.absoluto(df_ref_par2b$ehf, df_eps_par2b$ehf)
erro.absoluto(df_ref_par3$ehf, df_eps_par3$ehf)
mean(df_ref_par2b$ehf-df_eps_par2b$ehf)
mean(abs(df_ref_par2b$ehf-df_eps_par2b$ehf))
quantile((abs(df_ref_par2b$ehf-df_eps_par2b$ehf)),.95)
ggplot(df_ref_par3,aes(df_ref_par3$ehf, df_eps_par3$ehf)) +
geom_point(alpha=.25) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
# ggtitle('Comparação EHF Gesso + Isolamento') +
xlab('Parede Referência - EHF (-)') +
ylab('Parede Equivalente - EHF (-)') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(erro.medio(df_ref_par3$ehf, df_eps_par3$ehf),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 =",round(erro.ae95(df_ref_par3$ehf, df_eps_par3$ehf),4)))
# save_img('paredeeq_EHF_par3_scatter')
erro.absoluto(df_ref_par3$ehf, df_eps_par3$ehf)
mean(df_ref_par3$ehf-df_eps_par3$ehf)
mean(abs(df_ref_par3$ehf-df_eps_par3$ehf))
df_ref = rbind(df_ref_par2b,df_ref_par3)
df_eps = rbind(df_eps_par2b,df_eps_par3)
ggplot(df_ref,aes(df_ref$ehf, df_eps$ehf)) +
geom_point(alpha=.2) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
ggtitle('Comparação EHF') +
xlab('Parede Referência') +
ylab('Parede Equivalente') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(mean(abs(df_ref$ehf)-abs(df_eps$ehf)),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 =",round(quantile((abs(df_ref$ehf)-abs(df_eps$ehf)),.95),4)))
save_img('paredeeq_EHF_scatter')
mean(df_ref$ehf-df_eps$ehf)
mean(abs(df_ref$ehf-df_eps$ehf))
# LUPA!!!
df_maxdif = df_ref
df_maxdif$dif = df_ref$ehf-df_eps$ehf
df_maxdif$file[df_maxdif$dif == max(df_maxdif$dif)]
df_maxdif$zone[df_maxdif$dif == max(df_maxdif$dif)]
df_ref$ehf[df_ref$file == 'whole_ref_par2b_014']
df_eps$ehf[df_eps$file == 'whole_eq_par2b_014']
df_maxdif = subset(df_ref, df_ref$file == 'whole_ref_par2b_014')
df_maxdif = subset(df_maxdif, abs(df_maxdif$dif) > quantile((abs(df_ref$ehf)-abs(df_eps$ehf)),.95))
library(Hmisc)
hist.data.frame(df_maxdif)
lhs_values = c(
0.3743797,
0.5575577,
0.1659301,
0.4676304,
0.4468507,
0.7144547,
0.4426933,
0.5107219,
0.1674508,
0.427916,
0.4971307,
0.04757653,
0.7653883,
0.7422462,
0.3100196,
0.1650274)
lhs_names = colnames(df_maxdif)[8:23]
df_eqwallcomp = data.frame('parameter'=lhs_names,'value'=lhs_values)
ggplot(df_eqwallcomp,aes(df_eqwallcomp$parameter,df_eqwallcomp$value)) +
geom_col() + ylim(c(0,1))
# HISTOGRAMAS
ggplot(df_dif,aes(df_dif$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF') +
xlab('EHF Referência - EHF Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = .05, y = 1000, label = paste("Média =",round(ehf_mean,5))) +
annotate("text", x = .05, y = 1000*.9, label = paste("AE95 =",round(quantile(abs(df_dif$ehf),.95),4)))
save_img('paredeeq_EHF')
ggplot(df_dif,aes(df_dif$temp)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças na Temperatura Operativa') +
xlab('Temp. Referência - Temp. Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = -1, y = 1300, label = paste("Média =",round(temp_mean,3),'°C'))+
annotate("text", x = -1, y = 1300*.9, label = paste("AE95 =",round(quantile(abs(df_dif$temp),.95),4),'°C'))
save_img('paredeeq_temp')
ggplot(df_dif,aes(df_dif$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH') +
xlab('ACH Referência - ACH Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = 1, y = 1700, label = paste("Média =",round(ach_mean,3),'ach'))
save_img('paredeeq_ACH')
# Par 1
ggplot(df_dif_par2a,aes(df_dif_par1$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Parede Concreto') +
xlab('EHF Referência - EHF Parede equivalente') +
ylab('Número de casos') #+
annotate("text", x = .01, y = 550, label = paste("Média =",round(ehf_mean_par1,5))) +
annotate("text", x = .01, y = 550*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par1$ehf),.95),4)))
save_img('paredeeq_EHF_par1')
ggplot(df_dif_par1,aes(df_dif_par1$temp)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças na Temperatura Operativa Parede Concreto') +
xlab('Temp. Referência - Temp. Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = -1, y = 300, label = paste("Média =",round(temp_mean_par1,3),'°C'))+
annotate("text", x = -1, y = 300*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par1$temp),.95),4),'°C'))
save_img('paredeeq_temp_par1')
ggplot(df_dif_par1,aes(df_dif_par1$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH Parede Concreto') +
xlab('ACH Referência - ACH Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = 1, y = 500, label = paste("Média =",round(ach_mean_par1,3),'ach'))
save_img('paredeeq_ACH_par1')
# Par 2a
ggplot(df_dif_par2a,aes(df_dif_par2a$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Parede Alvenaria CT Total') +
xlab('EHF Referência - EHF Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = .05, y = 150, label = paste("Média =",round(ehf_mean_par2a,5))) +
annotate("text", x = .05, y = 150*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par2a$ehf),.95),4)))
save_img('paredeeq_EHF_par2a')
ggplot(df_dif_par2a,aes(df_dif_par2a$temp)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças na Temperatura Operativa Parede Alvenaria CT Total') +
xlab('Temp. Referência - Temp. Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = -1, y = 300, label = paste("Média =",round(temp_mean_par2a,3),'°C'))+
annotate("text", x = -1, y = 300*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par2a$temp),.95),4),'°C'))
save_img('paredeeq_temp_par2a')
ggplot(df_dif_par2a,aes(df_dif_par2a$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH Parede Alvenaria CT Total') +
xlab('ACH Referência - ACH Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = 1, y = 500, label = paste("Média =",round(ach_mean_par2a,3),'ach'))
save_img('paredeeq_ACH_par2a')
# Par 2b
ggplot(df_dif_par2b,aes(df_dif_par2b$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Parede Alvenaria CT Metade') +
xlab('EHF Referência - EHF Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = .05, y = 150, label = paste("Média =",round(ehf_mean_par2b,5))) +
annotate("text", x = .05, y = 150*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par2b$ehf),.95),4)))
save_img('paredeeq_EHF_par2b')
ggplot(df_dif_par2b,aes(df_dif_par2b$temp)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças na Temperatura Operativa Parede Alvenaria CT Metade') +
xlab('Temp. Referência - Temp. Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = -1, y = 300, label = paste("Média =",round(temp_mean_par2b,3),'°C'))+
annotate("text", x = -1, y = 300*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par2b$temp),.95),4),'°C'))
save_img('paredeeq_temp_par2b')
ggplot(df_dif_par2b,aes(df_dif_par2b$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH Parede Alvenaria CT Metade') +
xlab('ACH Referência - ACH Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = 1, y = 500, label = paste("Média =",round(ach_mean_par2b,3),'ach'))
save_img('paredeeq_ACH_par2b')
# Par 3
ggplot(df_dif_par3,aes(df_dif_par3$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Parede Gesso + Isolamento') +
xlab('EHF Referência - EHF Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = .05, y = 250, label = paste("Média =",round(ehf_mean_par3,5))) +
annotate("text", x = .05, y = 250*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par3$ehf),.95),4)))
save_img('paredeeq_EHF_par3')
ggplot(df_dif_par3,aes(df_dif_par3$temp)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças na Temperatura Operativa Parede Gesso + Isolamento') +
xlab('Temp. Referência - Temp. Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = .5, y = 400, label = paste("Média =",round(temp_mean_par3,3),'°C'))+
annotate("text", x = .5, y = 400*.9, label = paste("AE95 =",round(quantile(abs(df_dif_par3$temp),.95),4),'°C'))
save_img('paredeeq_temp_par3')
ggplot(df_dif_par3,aes(df_dif_par3$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH Parede Gesso + Isolamento') +
xlab('ACH Referência - ACH Parede equivalente') +
ylab('Número de casos') +
annotate("text", x = 1, y = 500, label = paste("Média =",round(ach_mean_par3,3),'ach'))
save_img('paredeeq_ACH_par3')
df_ref = subset(df, substr(df$file,1,1) == 'r' & df$temp_max < tempmax) # refwall_par1
ggplot(df_ref,aes(df_ref$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('EHF Referência') +
xlab('EHF') +
ylab('Número de casos') +
annotate("text", x = 0.8, y = 30, label = paste("Média =",round(mean(df_ref$ehf),5))) +
annotate("text", x = 0.8, y = 30*.93, label = paste("AE95 =",round(quantile(abs(df_ref$ehf),.95),4)))
ggplot(df_ref,aes(df_ref$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('ACH Referência') +
xlab('ACH') +
ylab('Número de casos') +
annotate("text", x = 100, y = 60, label = paste("Média =",round(mean(df_ref$ach),2),'ach'))
# OUTDOORS x ADIABATIC ----
# df = rbind(
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster0/means_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster1/means_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster2/means_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster3/means_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster4/means_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster5/means_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster6/means_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster7/means_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster8/means_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/noafn/cluster9/means_cluster9.csv')
# )
df = read.csv('/media/marcelo/OS/dissertacao/single_zone/means_single_zone.csv')
df_out = subset(df, substr(df$file,1,1) == 'o')
df_adi = subset(df, substr(df$file,1,1) == 'a')
df_ref = subset(df, substr(df$file,1,1) == 'w')
df_out <- df_out[order(df_out$file),]
df_adi <- df_adi[order(df_adi$file),]
df_dif_out = data.frame(
'case' = substr(df_out$file,10,12),
'zone' = df_out$zone,
'temp' = df_ref$temp - df_out$temp,
'ehf' = df_ref$ehf - df_out$ehf
)
df_dif_adi = data.frame(
'case' = substr(df_adi$file,11,13),
'zone' = df_adi$zone,
'temp' = df_ref$temp - df_adi$temp,
'ehf' = df_ref$ehf - df_adi$ehf
)
temp_mean_out = mean(df_dif_out$temp)
temp_mean_adi = mean(df_dif_adi$temp)
ehf_mean_out = mean(df_dif_out$ehf)
ehf_mean_adi = mean(df_dif_adi$ehf)
dif_out_adi = df_dif_out$ehf - df_dif_adi$ehf
max(dif_out_adi)
min(dif_out_adi) # erros sempre maiores no outdoors!
# ACH Cpeq SCATTERPLOT
ggplot(df_ref,aes(df_ref$ehf, df_adi$ehf)) +
geom_point(alpha=.1) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1))
ggplot(df_ref,aes(df_ref$ehf, df_adi$ehf)) +
geom_point(alpha=.15) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
# ggtitle('Comparação EHF Adiabático') +
xlab('Simulação detalhada - EHF (-)') +
ylab('Parede adiabática - EHF (-)') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(erro.medio(df_ref$ehf, df_adi$ehf),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 =",round(erro.ae95(df_ref$ehf, df_adi$ehf),4)))
save_img('szadi_EHF_scatter')
mean(df_ref$ehf-df_adi$ehf)
mean(abs(df_ref$ehf-df_adi$ehf))
quantile(abs(df_ref$ehf-df_adi$ehf),.95)
ggplot(df_ref,aes(df_ref$ehf, df_out$ehf)) +
geom_point(alpha=.15) +
geom_abline() +
xlim(c(0,1)) + ylim(c(0,1)) +
# ggtitle('Comparação EHF Outdoors') +
xlab('Simulação detalhada - EHF (-)') +
ylab('Parede '~italic(outdoors)~' - EHF (-)') +
annotate("text", x = .2, y = .9, label = paste("Média =",round(erro.medio(df_ref$ehf, df_out$ehf),4))) +
annotate("text", x = .2, y = .8, label = paste("AE95 =",round(erro.ae95(df_ref$ehf, df_out$ehf),4)))
save_img('szout_EHF_scatter')
mean(df_ref$ehf-df_out$ehf)
mean(abs(df_ref$ehf-df_out$ehf))
quantile(abs(df_ref$ehf-df_out$ehf),.95)
ggplot(df_dif_out,aes(df_dif_out$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Outdoors') +
xlab('EHF Referência - EHF Singlezone') +
ylab('Número de casos') +
annotate("text", x = 0.2, y = 600, label = paste("Média =",round(ehf_mean_out,5))) +
annotate("text", x = 0.2, y = 600*.9, label = paste("AE95 =",round(quantile(abs(df_dif_out$ehf),.95),4)))
save_img('szout_EHF')
ggplot(df_dif_adi,aes(df_dif_adi$ehf)) +
geom_histogram(binwidth = .01)+
ggtitle('Diferenças no EHF Adiabático') +
xlab('EHF Whole - EHF Singlezone') +
ylab('Número de casos') +
annotate("text", x = -.1, y = 1000, label = paste("Média =",round(ehf_mean_adi,5))) +
annotate("text", x = -.1, y = 1000*.9, label = paste("AE95 =",round(quantile(abs(df_dif_adi$ehf),.95),4)))
save_img('szadi_EHF')
ggplot(df_dif_out,aes(df_dif_out$temp)) +
geom_histogram(binwidth = .05)+
ggtitle('Diferenças de Temp. Operativa Outdoors') +
xlab('Temp Referência - Temp Singlezone (°C)') +
ylab('Número de casos') +
annotate("text", x = -.25, y = 500, label = paste("Média =",round(temp_mean_out,5),'°C')) +
annotate("text", x = -.25, y = 500*.9, label = paste("AE95 =",round(quantile(abs(df_dif_out$temp),.95),4),'°C'))
save_img('szout_temp')
ggplot(df_dif_adi,aes(df_dif_adi$temp)) +
geom_histogram(binwidth = .05)+
ggtitle('Diferenças de Temp. Operativa Adiabático') +
xlab('Temp Referência - Temp Singlezone (°C)') +
ylab('Número de casos') +
annotate("text", x = -1.1, y = 500, label = paste("Média =",round(temp_mean_adi,5),'°C')) +
annotate("text", x = -1.1, y = 500*.9, label = paste("AE95 =",round(quantile(abs(df_dif_adi$temp),.95),4),'°C'))
save_img('szadi_temp')
ggplot(df_ref,aes(df_ref$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('EHF Referência') +
xlab('EHF') +
ylab('Número de casos') +
annotate("text", x = 0.6, y = 120, label = paste("Média =",round(mean(df_ref$ehf),5))) +
annotate("text", x = 0.6, y = 120*.93, label = paste("AE95 =",round(quantile(abs(df_ref$ehf),.95),4)))
# Cp Eq ----
df = rbind(
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster0/means_cluster0.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster1/means_cluster1.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster2/means_cluster2.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster3/means_cluster3.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster4/means_cluster4.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster5/means_cluster5.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster6/means_cluster6.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster7/means_cluster7.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster8/means_cluster8.csv'),
read.csv('/media/marcelo/OS/dissertacao/validatecp/cluster9/means_cluster9.csv')
)
# df_noeq = rbind(
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster0/sz_means_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster1/sz_means_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster2/sz_means_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster3/sz_means_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster4/sz_means_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster5/sz_means_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster6/sz_means_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster7/sz_means_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster8/sz_means_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster9/sz_means_cluster9.csv')
# )
samp = read.csv('/media/marcelo/OS/dissertacao/sample_cpeq.csv')
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq/means_cp_eq.csv')
# for(i in 0:3){
df_cpeq = subset(df, grepl(pattern = 'cp',df$file)) # substr(df$file,1,2) == 'cp')
df_noeq = subset(df, grepl(pattern = 'no',df$file)) # substr(df$file,1,2) == 'no')
df_ref = subset(df, grepl(pattern = 'whole',df$file)) # substr(df$file,1,1) == 'w')
df_noeq <- df_noeq[order(df_noeq$file),]
df_cpeq <- df_cpeq[order(df_cpeq$file),]
df_ref <- df_ref[order(df_ref$file),]
# ehfmin= .6+(i*.1)
# ehfmax = .7+(i*.1)
# df_ref2 = subset(df_ref, df_ref$ehf > ehfmin & df_ref$ehf < ehfmax)
# df_cpeq = subset(df_cpeq, df_ref$ehf > ehfmin & df_ref$ehf < ehfmax)
# df_noeq = subset(df_noeq, df_ref$ehf > ehfmin & df_ref$ehf < ehfmax)
# tempmax = 40
# df_ref2 = subset(df_ref, df_ref$temp_max < tempmax) #.95)
# df_cpeq = subset(df_cpeq, df_ref$temp_max < tempmax) #.95)
# df_noeq = subset(df_noeq, df_ref$temp_max < tempmax) #.95)
df_ref = df_ref2
df_dif_cpeq = data.frame(
'case' = substr(df_cpeq$file,6,9),
'zone' = df_cpeq$zone,
'ach' = df_ref$ach - df_cpeq$ach,
'temp' = df_ref$temp - df_cpeq$temp,
'ehf' = df_ref$ehf - df_cpeq$ehf
)
df_dif_noeq = data.frame(
'case' = substr(df_noeq$file,6,9),
'zone' = df_noeq$zone,
'ach' = df_ref$ach - df_noeq$ach,
'temp' = df_ref$temp - df_noeq$temp,
'ehf' = df_ref$ehf - df_noeq$ehf
)
ehf_mean_cpeq = mean(df_dif_cpeq$ehf)
max(df_dif_cpeq$ehf)
min(df_dif_cpeq$ehf)
temp_mean_cpeq = mean(df_dif_cpeq$temp)
max(df_dif_cpeq$temp)
ach_mean_cpeq = mean(df_dif_cpeq$ach)
max(df_dif_cpeq$ach)
min(df_dif_cpeq$ach)
ehf_mean_noeq = mean(df_dif_noeq$ehf)
max(df_dif_noeq$ehf)
temp_mean_noeq = mean(df_dif_noeq$temp)
max(df_dif_noeq$temp)
ach_mean_noeq = mean(df_dif_noeq$ach)
max(df_dif_noeq$ach)
min(df_dif_noeq$ach)
print(ehf_mean_cpeq)
print(ehf_mean_noeq)
# }
ggplot(df_dif_cpeq,aes(df_dif_cpeq$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('Diferenças no EHF Cp equivalente') +
xlab('EHF Referência - EHF Cp_eq') +
ylab('Número de casos') #+
# xlim(c(-.1,.1)) +
# xlim(c(-.3,.22)) +
ylim(c(0,8)) +
annotate("text", x = -0.1, y = 7, label = paste("Média =",round(ehf_mean_cpeq,5))) +
annotate("text", x = -0.1, y = 7*.93, label = paste("AE95 =",round(quantile(abs(df_dif_cpeq$ehf),.95),4)))
# annotate("text", x = -0.08, y = 3000, label = paste("Média =",round(ehf_mean_noeq,5))) +
# annotate("text", x = -0.08, y = 3000*.93, label = paste("AE95 =",round(quantile(abs(df_dif_noeq$ehf),.95),4)))
ggplot(df_dif_cpeq,aes(df_dif_cpeq$temp)) +
geom_histogram(binwidth = .5)+
ggtitle('Diferenças no TEMP Cp equivalente') +
xlab('TEMP Referência - TEMP Cp_eq') +
ylab('Número de casos') #+
xlim(c(-.1,.1)) +
annotate("text", x = -0.08, y = 3000, label = paste("Média =",round(ehf_mean_cpeq,5))) +
annotate("text", x = -0.08, y = 3000*.93, label = paste("AE95 =",round(quantile(abs(df_dif_cpeq$ehf),.95),4)))
ggplot(df_dif_cpeq,aes(df_dif_cpeq$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH') +
xlab('ACH Referência - ACH Cp_eq') +
ylab('Número de casos')# +
# ylim(c(0,1100)) +
annotate("text", x = 20, y = 2000, label = paste("Média =",round(ach_mean_cpeq,3),'ach'))
ggplot(df_dif_noeq,aes(df_dif_noeq$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('Diferenças no EHF no equivalente') +
xlab('EHF Referência - EHF no_eq') +
ylab('Número de casos') #+
# xlim(c(-.1,.1)) +
xlim(c(-.3,.2)) +
ylim(c(0,8)) +
annotate("text", x = -0.1, y = 7, label = paste("Média =",round(ehf_mean_noeq,5))) +
annotate("text", x = -0.1, y = 7*.93, label = paste("AE95 =",round(quantile(abs(df_dif_noeq$ehf),.95),4)))
# annotate("text", x = -0.08, y = 3000, label = paste("Média =",round(ehf_mean_noeq,5))) +
# annotate("text", x = -0.08, y = 3000*.93, label = paste("AE95 =",round(quantile(abs(df_dif_noeq$ehf),.95),4)))
ggplot(df_dif_noeq,aes(df_dif_noeq$temp)) +
geom_histogram(binwidth = .5)+
ggtitle('Diferenças no TEMP no equivalente') +
xlab('EHF Referência - EHF no_eq') +
ylab('Número de casos') #+
xlim(c(-.1,.1)) +
annotate("text", x = -0.08, y = 3000, label = paste("Média =",round(ehf_mean_noeq,5))) +
annotate("text", x = -0.08, y = 3000*.93, label = paste("AE95 =",round(quantile(abs(df_dif_noeq$ehf),.95),4)))
ggplot(df_dif_noeq,aes(df_dif_noeq$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('Diferenças no ACH') +
xlab('ACH Referência - ACH no_eq') +
ylab('Número de casos') #+
# ylim(c(0,1100)) +
annotate("text", x = 20, y = 2000, label = paste("Média =",round(ach_mean_noeq,3),'ach'))
ggplot(df_ref,aes(df_ref$ehf)) +
geom_histogram(binwidth = .005)+
ggtitle('EHF Referência') +
xlab('EHF') +
ylab('Número de casos')# +
annotate("text", x = 0.8, y = 3500, label = paste("Média =",round(mean(df_ref$ehf),5))) +
annotate("text", x = 0.8, y = 3500*.93, label = paste("AE95 =",round(quantile(abs(df_ref$ehf),.95),4)))
quantile(abs(df_ref$ehf),.05)
ggplot(df_ref,aes(df_ref$ach)) +
geom_histogram(binwidth = .5) +
ggtitle('ACH Referência') +
xlab('ACH') +
ylab('Número de casos') #+
annotate("text", x = 20, y = 750, label = paste("Média =",round(mean(df_ref$ach),2),'ach'))
df_single_0413_3 = read.csv('/media/marcelo/OS/dissertacao/cpeq/cluster4/single_0413_3out.csv')
max(df_single_0413_3$OFFICE.Zone.Operative.Temperature..C..Hourly.)
mean(df_single_0413_3$OFFICE.Zone.Operative.Temperature..C..Hourly.[df_single_0413_3$SCH_OCUPACAO.Schedule.Value....Hourly. > 0])
#
# CRACK ----
#
# df = rbind(
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster0/means_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster1/means_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster2/means_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster3/means_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster4/means_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster5/means_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster6/means_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster7/means_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster8/means_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster9/means_cluster9.csv')
# )
#
# df_2 = rbind(
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster0/means_cluster0_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster1/means_cluster1_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster2/means_cluster2_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster3/means_cluster3_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster4/means_cluster4_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster5/means_cluster5_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster6/means_cluster6_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster7/means_cluster7_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster8/means_cluster8_2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster9/means_cluster9_2.csv')
# )
#
# df_3 = rbind(
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster0/means_cluster0_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster1/means_cluster1_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster2/means_cluster2_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster3/means_cluster3_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster4/means_cluster4_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster5/means_cluster5_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster6/means_cluster6_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster7/means_cluster7_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster8/means_cluster8_3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster9/means_cluster9_3.csv')
# )
#
# df_015 = rbind(
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster0/015means_cluster0.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster1/015means_cluster1.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster2/015means_cluster2.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster3/015means_cluster3.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster4/015means_cluster4.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster5/015means_cluster5.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster6/015means_cluster6.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster7/015means_cluster7.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster8/015means_cluster8.csv'),
# read.csv('/media/marcelo/OS/dissertacao/crack/cluster9/015means_cluster9.csv')
# )
samp = read.csv('/media/marcelo/OS/dissertacao/sample_cpeq.csv')
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq/means_cp_eq.csv')
df_noeq = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra_noeq/means_cp_eq_extra_noeq.csv') # 10, 30, 50, 70, 90, 99
samp = read.csv('/media/marcelo/OS/dissertacao/sample_cpeq.csv')
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq/means_cp_eq.csv')
df_ref = subset(df, grepl(pattern = 'whole',df$file)) # substr(df$file,1,1) == 'w') # & df$ehf < .9)
df_ref = cbind(df_ref, samp)
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra/means_cp_eq_extra.csv')
df_cp_040 = subset(df, grepl(pattern = '_cpeq_40_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_045 = subset(df, grepl(pattern = '_cpeq_45_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_040 = subset(df, grepl(pattern = '_noeq_40_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_045 = subset(df, grepl(pattern = '_noeq_45_',df$file)) # substr(df$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra2/means_cp_eq_extra2.csv')
df_cp_060 = subset(df, grepl(pattern = '_cpeq_60_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_055 = subset(df, grepl(pattern = '_cpeq_55_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_060 = subset(df, grepl(pattern = '_noeq_60_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_055 = subset(df, grepl(pattern = '_noeq_55_',df$file)) # substr(df$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra3/means_cp_eq_extra3.csv')
df_cp_070 = subset(df, grepl(pattern = '_cpeq_70_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_080 = subset(df, grepl(pattern = '_cpeq_80_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_070 = subset(df, grepl(pattern = '_noeq_70_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_080 = subset(df, grepl(pattern = '_noeq_80_',df$file)) # substr(df$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra4/means_cp_eq_extra4.csv')
df_cp_090 = subset(df, grepl(pattern = '_cpeq_90_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_095 = subset(df, grepl(pattern = '_cpeq_95_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_099 = subset(df, grepl(pattern = '_cpeq_99_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_ref = subset(df, grepl(pattern = 'whole',df$file)) # substr(df$file,1,1) == 'w') # & df$ehf < .9)
df_ref = cbind(df_ref, samp)
# df_001 = subset(df, substr(df$file,1,3) == '001') # & df_ref$ehf < .9)
df_cp_010 = subset(df, grepl(pattern = '_cpeq_10_',df$file)) # substr(df$file,1,10) == '010crack_c') # & df_ref$ehf < .9) #
df_cp_015 = subset(df, grepl(pattern = '_cpeq_15_',df$file)) # substr(df_015$file,1,10) == '015crack_c') # & df_ref$ehf < .9) #
df_cp_020 = subset(df, grepl(pattern = '_cpeq_20_',df$file)) # substr(df$file,1,10) == '020crack_c') # & df_ref$ehf < .9)
df_cp_030 = subset(df, grepl(pattern = '_cpeq_30_',df$file)) # substr(df$file,1,10) == '030crack_c') # & df_ref$ehf < .9)
df_cp_040 = subset(df, grepl(pattern = '_cpeq_40_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_050 = subset(df, grepl(pattern = '_cpeq_50_',df$file)) # substr(df_noeq$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df_no_010 = subset(df_noeq, grepl(pattern = '_noeq_10_',df_noeq$file)) # substr(df_noeq$file,1,10) == '010crack_c') # & df_ref$ehf < .9) #
df_no_015 = subset(df_noeq, grepl(pattern = '_noeq_15_',df_noeq$file)) # substr(df_015$file,1,10) == '015crack_c') # & df_ref$ehf < .9) #
df_no_020 = subset(df_noeq, grepl(pattern = '_noeq_20_',df_noeq$file)) # substr(df_noeq$file,1,10) == '020crack_c') # & df_ref$ehf < .9)
df_no_030 = subset(df_noeq, grepl(pattern = '_noeq_30_',df_noeq$file)) # substr(df_noeq$file,1,10) == '030crack_c') # & df_ref$ehf < .9)
df_no_040 = subset(df_noeq, grepl(pattern = '_noeq_40_',df_noeq$file)) # substr(df_noeq$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_050 = subset(df_noeq, grepl(pattern = '_noeq_50_',df_noeq$file)) # substr(df_noeq$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df_no_070 = subset(df_noeq, grepl(pattern = '_noeq_70_',df_noeq$file)) # substr(df_noeq$file,1,10) == '070crack_c') # & df_ref$ehf < .9)
df_no_090 = subset(df_noeq, grepl(pattern = '_noeq_90_',df_noeq$file)) # substr(df_noeq$file,1,10) == '090crack_c') # & df_ref$ehf < .9)
df_no_099 = subset(df_noeq, grepl(pattern = '_noeq_99_',df_noeq$file)) # substr(df_noeq$file,1,10) == '099crack_c') # & df_ref$ehf < .9)
df = read.csv('/media/marcelo/OS/dissertacao/cpeq/means_cpeq.csv')
df_extra = read.csv('/media/marcelo/OS/dissertacao/cp_eq_extra/means_cp_eq_extra.csv')
df_cp_000 = subset(df_extra, grepl(pattern = '_cpeq_00_',df_extra$file)) # substr(df$file,1,10) == '000crack_c') # & df_ref$ehf < .9) #
df_cp_001 = subset(df_extra, grepl(pattern = '_cpeq_01_',df_extra$file)) # substr(df$file,1,10) == '001crack_c') # & df_ref$ehf < .9) #
df_cp_010 = subset(df, grepl(pattern = '_cpeq_10_',df$file)) # substr(df$file,1,10) == '010crack_c') # & df_ref$ehf < .9) #
# df_cp_015 = subset(df, grepl(pattern = '_cpeq_15_',df$file)) # substr(df_015$file,1,10) == '015crack_c') # & df_ref$ehf < .9) #
df_cp_020 = subset(df, grepl(pattern = '_cpeq_20_',df$file)) # substr(df$file,1,10) == '020crack_c') # & df_ref$ehf < .9)
df_cp_030 = subset(df, grepl(pattern = '_cpeq_30_',df$file)) # substr(df$file,1,10) == '030crack_c') # & df_ref$ehf < .9)
df_cp_040 = subset(df, grepl(pattern = '_cpeq_40_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_cp_050 = subset(df, grepl(pattern = '_cpeq_50_',df$file)) # substr(df_noeq$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df_cp_060 = subset(df, grepl(pattern = '_cpeq_60_',df$file)) # substr(df_noeq$file,1,10) == '060crack_c') # & df_ref$ehf < .9)
df_cp_070 = subset(df, grepl(pattern = '_cpeq_70_',df$file)) # substr(df_noeq$file,1,10) == '070crack_c') # & df_ref$ehf < .9)
df_cp_080 = subset(df, grepl(pattern = '_cpeq_80_',df$file)) # substr(df_noeq$file,1,10) == '080crack_c') # & df_ref$ehf < .9)
df_cp_090 = subset(df, grepl(pattern = '_cpeq_90_',df$file)) # substr(df_noeq$file,1,10) == '090crack_c') # & df_ref$ehf < .9)
df_cp_099 = subset(df, grepl(pattern = '_cpeq_99_',df$file)) # substr(df_noeq$file,1,10) == '099crack_c') # & df_ref$ehf < .9)
df_no_000 = subset(df_extra, grepl(pattern = '_noeq_00_',df_extra$file)) # substr(df$file,1,10) == '000crack_c') # & df_ref$ehf < .9) #
df_no_001 = subset(df_extra, grepl(pattern = '_noeq_01_',df_extra$file)) # substr(df$file,1,10) == '001crack_c') # & df_ref$ehf < .9) #
df_no_010 = subset(df, grepl(pattern = '_noeq_10_',df$file)) # substr(df$file,1,10) == '010crack_c') # & df_ref$ehf < .9) #
# df_no_015 = subset(df, grepl(pattern = '_noeq_15_',df$file)) # substr(df_015$file,1,10) == '015crack_c') # & df_ref$ehf < .9) #
df_no_020 = subset(df, grepl(pattern = '_noeq_20_',df$file)) # substr(df$file,1,10) == '020crack_c') # & df_ref$ehf < .9)
df_no_030 = subset(df, grepl(pattern = '_noeq_30_',df$file)) # substr(df$file,1,10) == '030crack_c') # & df_ref$ehf < .9)
df_no_040 = subset(df, grepl(pattern = '_noeq_40_',df$file)) # substr(df$file,1,10) == '040crack_c') # & df_ref$ehf < .9)
df_no_050 = subset(df, grepl(pattern = '_noeq_50_',df$file)) # substr(df$file,1,10) == '050crack_c') # & df_ref$ehf < .9)
df_no_060 = subset(df, grepl(pattern = '_noeq_60_',df$file)) # substr(df$file,1,10) == '060crack_c') # & df_ref$ehf < .9)
df_no_070 = subset(df, grepl(pattern = '_noeq_70_',df$file)) # substr(df$file,1,10) == '070crack_c') # & df_ref$ehf < .9)
df_no_080 = subset(df, grepl(pattern = '_noeq_80_',df$file)) # substr(df$file,1,10) == '080crack_c') # & df_ref$ehf < .9)
df_no_090 = subset(df, grepl(pattern = '_noeq_90_',df$file)) # substr(df$file,1,10) == '090crack_c') # & df_ref$ehf < .9)
df_no_099 = subset(df, grepl(pattern = '_noeq_99_',df$file)) # substr(df$file,1,10) == '099crack_c') # & df_ref$ehf < .9)
df_ref = subset(df, grepl(pattern = 'whole',df$file)) # substr(df$file,1,1) == 'w') # & df$ehf < .9)
samp = read.csv('/media/marcelo/OS/dissertacao/sample_cpeq.csv')
df_ref = cbind(df_ref, samp)
df_half = df_cp_010[1:(nrow(df_cp_010)/2),]
nrow(unique(df_half))
df_cp_010 = df_cp_010[1:(nrow(df_cp_010)/2),]
df_half = df_no_010[1:(nrow(df_no_010)/2),]
nrow(unique(df_half))
df_no_010 = df_no_010[1:(nrow(df_no_010)/2),]
df_cpeq = rbind(df_cp_000,df_cp_001,df_cp_010,df_cp_020) # ,df_cp_030,df_cp_040,df_cp_050,df_cp_060,
# df_cp_070,df_cp_080,df_cp_090,df_cp_099) # df_cp_045,df_cp_055,df_cp_095,
df_noeq = rbind(df_no_000,df_no_001,df_no_010,df_no_020) # ,df_no_030,df_no_040,df_no_050,df_no_060,
# df_no_070,df_no_080,df_no_090,df_no_099) # df_no_045,df_no_055,
Cqs = c('00','01','10','20') # ,'30','40','50','60','70','80','90','99')
cq_compare = data.frame('Method'=rep(NA,2*length(Cqs)),'Cq'=rep(NA,2*length(Cqs)),
'RMSE'=rep(NA,2*length(Cqs)),'mean'=rep(NA,2*length(Cqs)),'abs_mean'=rep(NA,2*length(Cqs)),'AE95'=rep(NA,2*length(Cqs)),
'RMSE_ehf'=rep(NA,2*length(Cqs)),'mean_ehf'=rep(NA,2*length(Cqs)),'abs_mean_ehf'=rep(NA,2*length(Cqs)),'AE95_ehf'=rep(NA,2*length(Cqs)))
line=1
for(Cq in Cqs){
sub_cpeq = subset(df_cpeq,grepl(pattern = paste('_cpeq_',Cq,sep=''),df_cpeq$file))
sub_noeq = subset(df_noeq,grepl(pattern = paste('_noeq_',Cq,sep=''),df_noeq$file))
cq_compare$Method[line] = 'Cp equivalente'
cq_compare$Cq[line] = Cq
cq_compare$RMSE[line] = (sum((sub_cpeq$ach-df_ref$ach)**2)/nrow(df_ref))**(1/2)
cq_compare$mean[line] = mean(sub_cpeq$ach-df_ref$ach)
cq_compare$abs_mean[line] = mean(abs(sub_cpeq$ach-df_ref$ach))
cq_compare$AE95[line] = quantile(abs(sub_cpeq$ach-df_ref$ach),.95)
cq_compare$RMSE_ehf[line] = (sum((sub_cpeq$ehf-df_ref$ehf)**2)/nrow(df_ref))**(1/2)