Actividad 2

Comandos básicos

# esto es un comentario
# correr instrucciones ctrl + enter "Run"
2+2 #toda la línea = situado en línea

## [1] 4

#color azul en consula = todo bien
5-3

## [1] 2

3-9

## [1] -6

asignación de nombres a variables, tablas, etc.

### asignación de nombres a variables, tablas, etc.  
# <- operador de asginación <- 
xyy <- 5
yyz <- 12
xyy+yyz

## [1] 17

www<- xyy+yyz

### encontrar ayuda de funciones
#?rnorm
secu <-seq(0.5, 7.5,0.5)
# [] posiciones
# () Arg de función, u operaciones
apropos("sequ")

## [1] "sequence"         "sequence.default"

apropos("norm")

##  [1] "dlnorm"        "dnorm"         "norm"          "normalizePath"
##  [5] "plnorm"        "pnorm"         "qlnorm"        "qnorm"        
##  [9] "qqnorm"        "rlnorm"        "rnorm"

apropos ("log")

##  [1] "as.data.frame.logical" "as.logical"            "as.logical.factor"    
##  [4] "dlogis"                "is.logical"            "log"                  
##  [7] "log10"                 "log1p"                 "log2"                 
## [10] "logb"                  "Logic"                 "logical"              
## [13] "logLik"                "loglin"                "plogis"               
## [16] "qlogis"                "rlogis"                "SSlogis"              
## [19] "winDialog"             "winDialogString"

operaciones vectoriales y matriciales

indagar posiciones y extraer elementos de vectores/raices

vect1<-15
vect2<-vect1<-15
vect3<-c(vect1,vect2,20,24) #c es de concatenar = unir/juntar en un elemento
vect4<-seq(from=10,to=40,by=10) #longitud 4
vect5<-rep(5,6) #rep = repetir
vect6<-rep(vect4,4)
vect7<-rep(vect4,each=4)
length(vect7)

## [1] 16

dim_vec<-length(vect4)

#operaciones vectoriales y matriciales
#indagar posiciones y extraer elementos de vectores/raices


#setencias = azul
#errores o Warnings = rojo
#resultados = negro

2da clase 23/08/21

operaciones “punto” por vector

## [1]  75  75 100 120

## [1] 225 225 300 360

##      [,1] [,2] [,3] [,4]
## [1,]    5    7    3    9
## [2,]    7   12    6    7

## [1] 2 4

## [1] 4 2

##      [,1] [,2] [,3]
## [1,]   33   39    6
## [2,]   15   24   12
## [3,]   21   36   63
## [4,]   63   18   24

Álgebra de matrices

# t(practi_mat3)
# inv_practi_mat4<-solve(practi_mat4) #MAtriz cuadrada n x n, inversa
# practi_mat4<-practi_mat3[-4,]

#%*% #cuando hablamos de matricies, usar %*% hay que tener cuidado que los elementos sean compatibles los elementos internos de la matriz deben de ser igual como 2x3 3x4
  
#Matriz por su inversa = identidad
# practi_mat3%*%matri2
#sistemas de ecuaciones
#5x-3y+2z=1
#-2x+2y-z=5
#4x+2y-4z=-3
#coeficientes<-matrix(c(5,-3,2,-2,2,-1,4,2,-4),byrow=TRUE,ncol=3)
#respuestas<-c(1,5,-3)
#?solve
#solucion<-solve(coeficientes,respuestas) #asignamos el nombre solucion
#solve(coeficientes)
#ainv<-solve(coeficientes) #inversa de dicha matriz
#length(solucion)

###
#intentemos x=b*a^(-1)
#  1x3   3x3
#respuestas%*%ainv
#ainv%*%respuestas #Esta es la buena


#solve (coeficientes + practi_mat4)
# (coeficientes + practi_mat4)%*%solve(coeficientes + practi_mat4)

3ra clase 30/08/2021

MARCOS DE DATOS

vari2<-c("M","H","M","H","H","M","M","M","M","H","H","H","M")
vari3<-c("1","0","1","0","0","1","1","1","1","0","0","0","1")
matriz7<-cbind(vect6,vect7)
dim(matriz7)

## [1] 16  2

cbind(vari2,vari3)

##       vari2 vari3
##  [1,] "M"   "1"  
##  [2,] "H"   "0"  
##  [3,] "M"   "1"  
##  [4,] "H"   "0"  
##  [5,] "H"   "0"  
##  [6,] "M"   "1"  
##  [7,] "M"   "1"  
##  [8,] "M"   "1"  
##  [9,] "M"   "1"  
## [10,] "H"   "0"  
## [11,] "H"   "0"  
## [12,] "H"   "0"  
## [13,] "M"   "1"

matriz8<-cbind(vari2,vari3)
dim(matriz8)

## [1] 13  2

matriz9<-cbind(vect6[1:13],vari2) #todo a no numérico

matriz9<-cbind(vect6[1:13],vari2) #Todo a no numérico

marcos de datos #conviene en la misma tabla, distintos tipos de elementos

data.frame(vector=vect6[1:13], vector2=vari2)

##    vector vector2
## 1      10       M
## 2      20       H
## 3      30       M
## 4      40       H
## 5      10       H
## 6      20       M
## 7      30       M
## 8      40       M
## 9      10       M
## 10     20       H
## 11     30       H
## 12     40       H
## 13     10       M

data.frame(vector=vect6[1:13], vector2=vari3)

##    vector vector2
## 1      10       1
## 2      20       0
## 3      30       1
## 4      40       0
## 5      10       0
## 6      20       1
## 7      30       1
## 8      40       1
## 9      10       1
## 10     20       0
## 11     30       0
## 12     40       0
## 13     10       1

data.frame(vector=as.numeric(vect6[1:13]), vector2=as.factor(vari3))

##    vector vector2
## 1      10       1
## 2      20       0
## 3      30       1
## 4      40       0
## 5      10       0
## 6      20       1
## 7      30       1
## 8      40       1
## 9      10       1
## 10     20       0
## 11     30       0
## 12     40       0
## 13     10       1

matriz11 <-data.frame(vector=as.numeric(vect6[1:13]), vector2=as.factor(vari3))

matriz12<-data.frame(Vector=as.numeric(vect6[1:13]),Vector2=as.factor(vari3),
                     Vector3=as.numeric(vect7[1:13]),
                     Vector4=as.factor(vari2))

#preguntas informativas
is.data.frame(matriz12)

## [1] TRUE

is.data.frame(matriz9)

## [1] FALSE

is.numeric(matriz12$Vector)

## [1] TRUE

is.numeric(matriz12$Vector2)

## [1] FALSE

is.factor(matriz12$Vector)

## [1] FALSE

is.factor(matriz12$Vector2) #preguntar...

## [1] TRUE

#vector=numerico o no-numérico...


View(matriz12) #ver todos los elementos de la matriz

Base de datos

#install.packages("readxl") #comprar el foco e instalar el foco
#library(readxl) # Encender

#install.packages("kernlab")#comprar el foco e instalar el foco
#library(kernlab) #Encender

#library(readxl)
#datos10<-read_excel("C:/Users/ARRA/OneDrive/Desktop/CIDE/programacion R/datos_ags_estado_2020.xlsx", sheet="Hoja1", range="A1:HV481", na = c("*","N/D"))


#mean(as.numeric(datos10$POBTOT))
#mean(datos10$POBTOT)

# cual es la población total de los 11 municipios?
# cuál es el total de hombres de los 11 municipios?
# cuál es el total de mujeres de los 11 municipios? proporción

# 1) Total (sum)
#aggregate(as.numeric(POBTOT)~NOM_MUN,data=datos10,mean)
#aggregate(as.numeric(POBTOT)~NOM_MUN,data=datos10,sum)
#aggregate(as.numeric(POBFEM)~NOM_MUN,data=datos10,sum)
#aggregate(as.numeric(POBMAS)~NOM_MUN,data=datos10,sum)

#tabla1<-aggregate(cbind(as.numeric(POBTOT),as.numeric(POBFEM),as.numeric(POBMAS) )~NOM_MUN,
#          data=datos10,sum)

#tabla1<-aggregate(cbind(as.numeric(POBTOT),as.numeric(POBFEM),as.numeric(POBMAS) )~NOM_MUN,
#                  data=datos10,sum)

#prop_fem<-tabla1$V2/tabla1$V1
#tabla2<-data.frame(tabla1,Prop_fem=prop_fem)


# Aprox. personas por vivienda en cada municipio

# cuántas viviendas / cuantas personas (X)? (vivindas)

#PROM_OCUP

#VIVTOT
#POBTOT

#tabla15<-aggregate(cbind(as.numeric(VIVTOT),as.numeric(POBTOT))~NOM_MUN,data=datos10,FUN=sum)
#tabla15$Pro_viv<-tabla15$V2/tabla15$V1


#datos10
#NOM_NUM== "San José de Gracia"
#MUN== "011"


#san_jose<-which(datos10$NOM_MUN=="San José de Gracia")

#datos_san_jose<-datos10[san_jose,]
#datos_san_jose$POBTOT
#sum(as.numeric(datos_san_jose$POBTOT))

#tabla18<-aggregate(cbind(as.numeric(POBTOT),as.numeric(POBFEM))~NOM_MUN+AGEB,
#                   data=datos10,FUN=sum)
#tabla18$Prop_muj<-tabla18$V2/tabla18$V1


#?sort

#as.numeric(datos10$POBTOT)

#AGEB 1424 AGS,

#mean(as.numeric((datos10$P_0A2_F))

#     mean(as.numeric(datos10$P_0A2_F), na.rm = TRUE)
  
#sum(is.na(datos10$P_0A2_F))        
#is.na(as.numeric(datos10$P_0A2_F))

#mean(as.numeric(datos10$P_0A2_F),na.rm=TRUE)
#sum(is.na(as.numeric(datos10$P_0A2_F)))


#summary(as.numeric(datos10$P_0A2_F))v
#summary(as.numeric(datos10$P_0A2_F))[7]



#En la Lógica de R: 0 = FALSO, 1 = VERDADERO


#¿promedio por municipio?
#aggregate(as.numeric(P_0A2_F)~NOM_MUN,FUN=mean,data=datos10,na.rm=TRUE)


#Coeficiente de variación...
#FUN = función realizado por nosotros... ¿y cómo la realizamos?

# FUNCIONES PERSONALIZADAS # FUNCIONES DEL USUARIO #
#¿ENTRADA DE LA FUNCIÓN? ¿CUÁL ES LA SALIDA?

# PROGRAMAN COMO SE REQUIERA

# FUNCIONES PERSONALIZADAS
# 1) rango (max-min)
# 2) coeficiente de variabilidad sigma/abs (media)
# 3) media cortada: Descartar los mayor y menor

#rango<-function(x){max(x)-min(x)}

#setosa
#rango(iris3[,1,1])
#apply(iris3[,,1],2, mean) #Setosa
#apply(iris3[,,1],2, rango)
#apply(iris3[,,1],1, rango)




#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#          data=iris,mean)

#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#          data=iris,rango)

# * Ayuda de funciones muy genéricas
#?"function"
#?"*"
#?mean

# 2) coeficiente de variabilidad = sigma/abs(media)
#3coef_var<-function(x){sd(x)/abs(mean(x))}

#apply(iris3[,,1],2,mean)  #Setosa
#apply(iris3[,,1],2,coef_var)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#          data=iris,coef_var)

#3) Media cortada: Descartar los mayor y menos

#iris3[,1,1]
#max(iris3[,1,1])
#min(iris3[,1,1])

#descarte<-c(which(iris3[,3,1]==max(iris3[,3,1])),
#            which(iris3[,3,1]==min(iris3[,3,1])))

#mean(iris3[-descarte,1,1])

#desc_media<-function(x){
#  descarte<-c(which(x==max(x)),
#              which(x==min(x)))
  
#  mean(x[-descarte]) }


#desc_media(iris3[,3,1])
#desc_media(iris3[,4,1])
#mean(iris3[,3,1]

   
     
#     apply(iris3[,,1],2,desc_media)
#     aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#               data=iris,desc_media)
     
 
### QUITARLE LOS DOS ÚLTIMOS Y LOS DOS PRIMEEROS ""
 
# * sort

#     longi<-length(iris3[,3,1])
#     posis_quit<-c(1,2,longi-1,longi)
#     ordenado<-sort(iris3[,3,1],decreasing=TRUE)
#     mean(ordenado[-posis_quit])
     
#     corte_dos<-function(w){
#       longi<-length(w)
#       posis_quit<-c(1,2,longi-1,longi)
#       ordenado<-sort(w,decreasing=TRUE)
#       mean(w[-posis_quit]) }
     
#     corte_dos(iris[,4])
     
#     corte_dos(iris[,2])
     
#     apply(iris3[,,1],2,corte_dos)
#     aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#               data=iris,corte_dos)

Funciones personalizadas más complejas día 21-09-2021

#Igreso de una tabla (descomponerla, o sacar la correlaciones)
iris

##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

eigen(cor(iris[,-5])) #Eigen = matrices cuadradas

## eigen() decomposition
## $values
## [1] 2.91849782 0.91403047 0.14675688 0.02071484
## 
## $vectors
##            [,1]        [,2]       [,3]       [,4]
## [1,]  0.5210659 -0.37741762  0.7195664  0.2612863
## [2,] -0.2693474 -0.92329566 -0.2443818 -0.1235096
## [3,]  0.5804131 -0.02449161 -0.1421264 -0.8014492
## [4,]  0.5648565 -0.06694199 -0.6342727  0.5235971

descomposi <-function(w){
return(eigen(cor(w))$vector)  
}

descomposi(iris3[,,1])

##           [,1]       [,2]          [,3]        [,4]
## [1,] 0.6044164  0.3349908 -0.0673598261  0.71966982
## [2,] 0.5756194  0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350  0.7328356536 -0.01475204

descomposi(iris3[,1:3,2])

##            [,1]       [,2]        [,3]
## [1,] -0.5954707  0.4261450  0.68103976
## [2,] -0.5289341 -0.8460217  0.06690233
## [3,] -0.6046845  0.3203868 -0.72918374

descomposi(iris3[1:10,,2])

##            [,1]       [,2]       [,3]        [,4]
## [1,] -0.5023311  0.5888137  0.1850325  0.60557812
## [2,] -0.4971351 -0.0577743 -0.8607149 -0.09321322
## [3,] -0.5247149  0.1978622  0.3700007 -0.74069177
## [4,] -0.4745466 -0.7815440  0.2967024  0.27561229

descomposi2<-function(w){
  lista1<-eigen(cor(w))
  return(list(lista1$vectors,lista1$values))
}
descomposi2(iris3[,,1])

## [[1]]
##           [,1]       [,2]          [,3]        [,4]
## [1,] 0.6044164  0.3349908 -0.0673598261  0.71966982
## [2,] 0.5756194  0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350  0.7328356536 -0.01475204
## 
## [[2]]
## [1] 2.0585402 1.0221782 0.6678202 0.2514613

descomposi2(iris3[,,1])[[1]][,1]

## [1] 0.6044164 0.5756194 0.3754348 0.4029788

descomposi2(iris3[,,1])[[2]][3]

## [1] 0.6678202

descomposi3<-function(w){
  lista1<-eigen(cor(w))
  return(list(Vector=lista1$vectors,Valores=lista1$values))
}
descomposi3(iris3[,,1])$Vector

##           [,1]       [,2]          [,3]        [,4]
## [1,] 0.6044164  0.3349908 -0.0673598261  0.71966982
## [2,] 0.5756194  0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350  0.7328356536 -0.01475204

descomposi3(iris3[,,1])$Valores

## [1] 2.0585402 1.0221782 0.6678202 0.2514613

#Ingreso de dos o más argumentos (resolver sistema de ecuaciones)
coeficiente<-matrix(c(3,2,1,5,3,4,1,1,-1),ncol=3,byrow=TRUE)
respuesta<-c(1,2,1)

solve(coeficiente,respuesta)

## [1] -4  6  1

res_ecua<-function(matriz,respu){
  solve(matriz,respu) }

res_ecua2<-function(x,y){
  solucion<-solve(x,y)
  return(paste0("La solución al sistema es: x=",solucion[1]))
}
res_ecua2(y=respuesta,x=coeficiente)

## [1] "La solución al sistema es: x=-3.99999999999999"

coef2<-matrix(c(5,-3,-1,1,4,-6,2,3,4),ncol=3,byrow=TRUE)
resp2<-c(1,-1,9)

res_ecua2(y=resp2,x=coef2)

## [1] "La solución al sistema es: x=1"

res_ecua3<-function(x,y){
  solucion<-solve(x,y)
  return(paste0("La solución al sistema es: x=",solucion[1]))
  return(paste0("La solución al sistema es: y=",solucion[2]))
  return(paste0("La solución al sistema es: z=",solucion[3]))
}
res_ecua3(y=resp2,x=coef2)

## [1] "La solución al sistema es: x=1"

res_ecua3<-function(x,y){
  solucion<-solve(x,y)
  return(paste0("La solución al sistema es: x=",solucion[1],
         "La solución al sistema es: y=",solucion[2],
         "La solución al sistema es: z=",solucion[3]) )
}
res_ecua3(y=resp2,x=coef2)

## [1] "La solución al sistema es: x=1La solución al sistema es: y=1La solución al sistema es: z=1"

###

res_ecua3<-function(x,y){
  solucion<-solve(x,y)
  return(paste0("La solución al sistema es: x=",solucion[1],". ",
                "La solución al sistema es: y=",solucion[2],". ",
                "La solución al sistema es: z=",solucion[3]) )
}
res_ecua3(y=resp2,x=coef2)

## [1] "La solución al sistema es: x=1. La solución al sistema es: y=1. La solución al sistema es: z=1"

res_ecua3(y=respuesta,x=coeficiente)

## [1] "La solución al sistema es: x=-3.99999999999999. La solución al sistema es: y=5.99999999999999. La solución al sistema es: z=0.999999999999997"

#res_ecua3<-function(x,y){
 # solucion<-solve(x,y)
#  return(paste0("La solución al sistema es: x=",solucion[1],", ",
#                "y= ",solucion[2],", ",
#                "z= ",solucion[3]) )

Algunás funciones útiles (Segunda sesión)

covid_oaxaca <-read.csv("C:\\Users\\ARRA\\OneDrive\\Desktop\\CIDE\\programacion R\\clase con excel\\clase con excel\\bases covid oax\\covid_oaxaca.csv")
fallecidos_oax<-read.csv("C:\\Users\\ARRA\\OneDrive\\Desktop\\CIDE\\programacion R\\clase con excel\\clase con excel\\bases covid oax\\fallecidos.csv")


funcionas<-as.Date(covid_oaxaca$FECHA_INGRESO,format="%d/%m/%Y")

as.Date #Transform dates

## function (x, ...) 
## UseMethod("as.Date")
## <bytecode: 0x00000000170e7050>
## <environment: namespace:base>

strptime #Handling time and dates at the same time

## function (x, format, tz = "") 
## {
##     y <- .Internal(strptime(as.character(x), format, tz))
##     names(y$year) <- names(x)
##     y
## }
## <bytecode: 0x00000000170e4710>
## <environment: namespace:base>

funcionas2<-as.Date(covid_oaxaca$FECHA_INGRESO,format="%d/%m/%Y")

funcionas2<-as.Date(covid_oaxaca$FECHA_INGRESO,formart="%Y-%m-%d")
funcionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%Y-%m-%d")
#hist(as.numeric(funcionas2-funcionas3)))

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

install.packages("lubridate")

## Warning: package 'lubridate' is in use and will not be installed

funcionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%d/%m/%Y")

table(month(funcionas2))

## 
##    1    2    3    4    5    6    7    8    9   10 
##  162  149  384  912 3358 6812 7155 4721 5233 4481

hist(as.numeric(funcionas2-funcionas3))

funcionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%Y-%m-%d")

library(lubridate)
#day(funcionas2)
table(month(funcionas2))

## 
##    1    2    3    4    5    6    7    8    9   10 
##  162  149  384  912 3358 6812 7155 4721 5233 4481

#hist(as.numeric(funcionas2-funcionas3))

Datos de bosque harvard

manejo de substrings y fecha-hora

#s.Date #Transform dates
strptime #Handling time and dates at the same time

## function (x, format, tz = "") 
## {
##     y <- .Internal(strptime(as.character(x), format, tz))
##     names(y$year) <- names(x)
##     y
## }
## <bytecode: 0x00000000170e4710>
## <environment: namespace:base>

library(lubridate)

bosque <-read.csv("C:\\Users\\ARRA\\OneDrive\\Desktop\\CIDE\\programacion R\\clase con excel\\datos bosque harvard\\datos bosque harvard.csv")

#View(bosque)

bosque$datetime[1]

## [1] "2005-01-01T00:15"

substr(bosque$datetime[1],1,10)

## [1] "2005-01-01"

substr(bosque$datetime[1],12,16)

## [1] "00:15"

pruebaxxs<-"Clase de R en la METPOL"
substr(pruebaxxs,9,22)

## [1] " R en la METPO"

strsplit(pruebaxxs," ") #separa texto de acuerdo a una condición

## [[1]]
## [1] "Clase"  "de"     "R"      "en"     "la"     "METPOL"

#strsplit(bosque$datetime[1],"T"
         
#         bosque$datetime[1]
#         fechax<-substr(bosque$datetime[1],1,10)
#         horax<-substr(bosque$datetime[1],12,16)
#         hora_fechx<-paste0(fechax,"",horax)
         
         
         
#         hora_fechaxx<-strptime(hora_fechx, format="%Y-%m-%d %H:%M")
         
#         
#         hour(hora_fechaxx)
#         minute(hora_fechaxx)
#         year(hora_fechaxx)
#         month(hora_fechaxx)
         
  
         
 
#hagan una función (function)... que transforme 
# 2005-01-01T00:15 a uno de tipo strptime
 
#         trans_fecha<-function(dia_fecha){        
         
  ###con funcion "s"apply
#           trans_fecha(bosque$datetime)
#           lapply(bosque$datetime,FUN = trans_fecha)
#           sapply(bosque$datetime,FUN = trans_fecha)
           
#           trans_fecha(bosque$datetime)
 #          unlist(lapply(bosque$datetime,FUN = trans_fecha)) #A elementos, no de tablas
#           sapply(bosque$datetime,FUN = trans_fecha) #A elementos, no de tablas
           
 ### funcion profe
#           trans_fecha<-function(dia_fecha){
#             fechax<-substr(dia_fecha,1,10)
#             horax<-substr(dia_fecha,12,16)
#             hora_fechx<-paste0(fechax," ",horax)
#             return(hora_fechx)
#           }
 #          trans_fecha(bosque$datetime[2])
           
           #apply por filas? Por columnas? Pero aquí queremos
           #por cada elemento de un vector!!
           
 #          trans_fecha2<-function(trans_fecha){
#             vecto_lar<-unlist(strsplit(as.character(trans_fecha),"T"))
##             return(paste0(vecto_lar[1]," ",vecto_lar[2])) }
   
           
           
#           datetime2<-trans_fecha(bosque$datetime)
#           bosque$datetime22<-datetime2
#           bosque$datetime22
           
#           bosque$mes_registro<-month(bosque$datetime22)
#           bosque$year_registro<-year(bosque$datetime22)
           
#           aggregate(airt~mes_registro,FUN = mean,data=bosque)
#           aggregate(airt~year_registro,FUN = mean,data=bosque)
           
           
           
#           month(bosque$datetime22[10000])
#           is.numeric(bosque$airt)
           
                      
#temperatura  promedio en dicho bosque en los meses          
#Enero = 2°C
#Febrero = 4°C
           
#¿cuándo es menor la temperatura promedio, entre 3am y 5:59am, o 10pm y 11:59pm?
 
###solución profe
#           grupo1<-which(hour(bosque$datetime22)>=3 & hour(bosque$datetime22)<6)
#           grupo2<-which(hour(bosque$datetime22)>=22 & hour(bosque$datetime22)<=23)
           
#           mean(bosque[grupo1,3],na.rm = TRUE)
#           mean(bosque[grupo2,3],na.rm = TRUE)
           #La temperatura entre 3 y 5:59 es más baja
           
           #Otra manera
#           grupo11<-c(which(hour(bosque$datetime22)==3),
#                      which(hour(bosque$datetime22)==4),
#                      which(hour(bosque$datetime22)==5) )
           
#           grupo22<-c(which(hour(bosque$datetime22)==22),
#                      which(hour(bosque$datetime22)==23) )
           
#           mean(bosque[grupo11,3],na.rm=TRUE)
#           mean(bosque[grupo22,3],na.rm=TRUE)
           
           
           
           
                     
           
           
           
     ##### intento      
#           bosque$hour_registro<-hour(bosque$datetime22)
#           HoursAverage<-aggregate(airt~hour_registro, FUN=mean, data=bosque)
#           Morning<-mean(HoursAverage[4:6,2])
#           Night<-mean(HoursAverage[22:23,2])
#           Morning>Night
#          Night>Morning                     

Gráfico y tablas relacionadas (05/10/21)

#datos_covid20<-read.csv("C:/Users/ARRA/Downloads/COVID2020_5estados.csv")
#View(datos_covid20)
library(readxl)      




#catalo_enti<-read_excel("C:/Users/ARRA/Downloads/201128 Catalogos.xlsx", ,sheet = "Catálogo de ENTIDADES")    

#catalo_sexo<-read_excel("C:/Users/ARRA/Downloads/201128 Catalogos.xlsx",sheet = "Catálogo SEXO")

#View(catalo_enti) 
#tail(catalo_enti)         

#datos_covid20$ENTIDAD_UM         
#catalo_enti$CLAVE_ENTIDAD


#Función para emparejar tablas

#empate_estadoUM<-merge(datos_covid20,catalo_enti,by.x="ENTIDAD_UM",by.y="CLAVE_ENTIDAD", sort = FALSE)
#catalo_enti$CLAVE_ENTIDAD<-as.numeric(catalo_enti$CLAVE_ENTIDAD)
#dim(empate_estadoUM)
#dim(datos_covid20) #Tablas, su dimensión
#?merge
#head(empate_estadoUM)

#empate_estadoUM2<-merge(x=empate_estadoUM,y=catalo_sexo,by.x="SEXO",by.y="CLAVE",sort=FALSE)
#dim(empate_estadoUM2)


### ¿proporción de mujeres con covid en cada estado?

#table(empate_estadoUM2$DESCRIPCIÓN,empate_estadoUM2$ENTIDAD_FEDERATIVA)

#table(empate_estadoUM2$DESCRIPCIÓN,empate_estadoUM2$SEXO)


#¿Proporción de mujeres con covid en cada estado?
#TAREA "proporcion de covid en cada uno de los 4 estados analizados" # PARTICIPACIÓN




#datos_covid20 <- read.csv("C:/Users/ARRA/Downloads/COVID2020_5estados (1).csv")
#View(cinco_esta)

#catalo_enti<-read_excel("C:/Users/ARRA/Downloads/201128 Catalogos.xlsx", ,sheet = "Catálogo de ENTIDADES") 


#catalo_muni<-read_excel("D:\\M\\02_manejo\\BasesCovi\\201128 Catalogos.xlsx", sheet = "cat muni", col_types = c("numeric",  "text", "numeric")) 

#catalo_muni<-read_excel("C:/Users/ARRA/Downloads/201128 Catalogos.xlsx",sheet = "Catálogo MUNICIPIOS", col_types = c("numeric",  "text", "numeric"))
#head(catalo_muni)
#View(catalo_muni)

#merge(datos_covid20,catalo_enti,by.x = "ENTIDAD_UM",by.y="CLAVE_ENTIDAD",all.x=TRUE)

#tabla_munic<-merge(datos_covid20,catalo_muni,by.x=c("ENTIDAD_RES","MUNICIPIO_RES"),
#                   by.y=c("CLAVE_ENTIDAD","CLAVE_MUNICIPIO"))

#dim(tabla_munic)

Clase martes 19/10/2021 (segunda parte de la actividad 2)

Casos acumulados de estos cuatro estados (en su conjunto)

ts_ingreso<-aggregate(unos~FECHA_INGRESO,data=datos_covid20,sum,echo=FALSE)

ts_ingreso$FECHA_INGRESO<- as.Date(ts_ingreso$FECHA_INGRESO,echo=FALSE)

plot(ts_ingreso)

#ejercicio guidado: boxplot de los cuatro estados, #respecto a los casos detectados por día ##### BOXPLOT

boxplot(ts_ingreso$unos)

sexo_fecha<-aggregate(unos~FECHA_INGRESO+SEXO,data=datos_covid20,sum,echo=FALSE)

boxplot(unos~SEXO,data=sexo_fecha, horizontal = T)

Gráfico de barras por mes

respecto al mes ¿cómo se ven?

CLASE MARTES 26.10.21

Vectorizar nuestro nombre

nombresss<-"ARRAXID UGALDE HERNANDEZ"

LETTERS

##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T" "U" "V" "W" "X" "Y" "Z"

nombresss<-gsub(" ","",nombresss) 
strsplit(nombresss, NULL)

## [[1]]
##  [1] "A" "R" "R" "A" "X" "I" "D" "U" "G" "A" "L" "D" "E" "H" "E" "R" "N" "A" "N"
## [20] "D" "E" "Z"

match(unlist(strsplit("ARRAXIDUGALDEHERNANDEZ", split="")), LETTERS)

##  [1]  1 18 18  1 24  9  4 21  7  1 12  4  5  8  5 18 14  1 14  4  5 26

######

#?"for"
# ?"&&

vecto_nom<-c("ARRAXID", "UGALDE", "HERNANDEZ")

for (i in 1:3) {print(vecto_nom[i])
  
}

## [1] "ARRAXID"
## [1] "UGALDE"
## [1] "HERNANDEZ"

palabra<-"METPOL"

strsplit(palabra,"")

## [[1]]
## [1] "M" "E" "T" "P" "O" "L"

palabra2<-unlist(strsplit(palabra,""))

vector_palabra<-rep(NA,6)

for(k in 1:6){
  vector_palabra[k]<-which(LETTERS==palabra2[k])
}




which(LETTERS==palabra2[1])

## [1] 13

which(LETTERS==palabra2[2])

## [1] 5

which(LETTERS==palabra2[3])

## [1] 20

which(LETTERS==palabra2[4])

## [1] 16

which(LETTERS==palabra2[5])

## [1] 15

which(LETTERS==palabra2[6])

## [1] 12

CLASE MARTES 26.10.21. (segunda sesión)

# 1) Suma acumulada del 1 al 100 (loops)
# 2) Multiplicación de todos los números
# 3) rnorm(100) ¿Mayor o menor que 0? Etiquetarlos
# 4) Raiz cuadrada y le resta 1 a 50 números
# 5) Aplicar la función de Coeficiente de variabilidad a 
# 1000 conjuntos de distribución normal estándar de longitud 50 números



#1) ejercicio 1, Sumatoria

#cumsum(numeross)  #n(n-1)/2  100(101)/2

sumatoria<-rep(0,100) #A sumar
numeross<-seq(1,100) #Serie de datos

sumatoria[1]<-numeross[1] #Valor igual para la posi 1

sumatoria[2]<-sumatoria[1]+numeross[2] #Para el caso 2
sumatoria[3]<-sumatoria[2]+numeross[3] #Para el caso 3
sumatoria[4]<-sumatoria[3]+numeross[4] #Para el caso 5
# sumatoria[j]<-sumatoria[j-1]+numeross[j] #para el caso j

#Ciclo
#for(j in 2:100){
#  sumatoria[j]<-sumatoria[j-1]+numeross[j]}


# ejercicio 2), multiplicamos de 1 al 10

valores_mult<-seq(1,10)

vector_multacu<-rep(NA,10)

vector_multacu[1]<-valores_mult[1]



vector_multacu[2]<-vector_multacu[1] * valores_mult[2]

vector_multacu[3]<-vector_multacu[2] * valores_mult[3]

vector_multacu[4]<-vector_multacu[3] * valores_mult[4]

vector_multacu[5]<-vector_multacu[4] * valores_mult[5]

for(k in 2:10) { 
  vector_multacu[k]<-vector_multacu[k-1]*valores_mult[k]
}

#de forma simplificada es igual:
prod(1:10)

## [1] 3628800

###ejercicio 3), generar 100 con rnorm()

nume_aleato<-rnorm(100) #Normal (0,1)
etiqueta_alea<-rep(NA,100)

etiqueta_alea[which(nume_aleato>0)]<-"POSITIVO"
etiqueta_alea[which(nume_aleato<0)]<-"NEGATIVO"
table(etiqueta_alea)

## etiqueta_alea
## NEGATIVO POSITIVO 
##       45       55

#IFs
if(nume_aleato[1]>0){etiqueta_alea[1]<-"POSITIVO"}else{etiqueta_alea[1]<-"NEGATIVO"}
if(nume_aleato[2]>0){etiqueta_alea[2]<-"POSITIVO"}else{etiqueta_alea[2]<-"NEGATIVO"}
if(nume_aleato[7]>0){etiqueta_alea[7]<-"POSITIVO"}else{etiqueta_alea[7]<-"NEGATIVO"}

for(k in 1:100){
  if(nume_aleato[k]>0){etiqueta_alea[k]<-"POSITIVO"}else{etiqueta_alea[k]<-"NEGATIVO"} }

# data.frame(numero_aleato,etiqueta_alea)

#sqrt(valor positivo)

raiz_cua<-rep(NA,100)
for(k in 1:100){
  if(nume_aleato[k]>0){raiz_cua[k]<-sqrt(nume_aleato[k])}else{raiz_cua[k]<-"NÚMERO IMAGINARIO"} }

as.numeric(raiz_cua)

## Warning: NAs introducidos por coerción

##   [1]        NA        NA 1.1801927 0.3202701 0.5991681 0.3368185 1.4160750
##   [8]        NA 0.2823678 1.0212620 1.4716180 0.6248762 0.9317060        NA
##  [15]        NA        NA 1.2844096        NA 0.6909568        NA 1.2573327
##  [22] 0.7776520        NA        NA        NA 0.5197137 0.3373663 0.9936688
##  [29]        NA        NA 0.4529703 0.8877385        NA        NA 0.7936496
##  [36]        NA 0.6356532 0.9055845        NA        NA 0.5279333 0.9334779
##  [43] 1.2568332 0.9219049        NA 1.1857493 1.1883466 0.9829784        NA
##  [50]        NA        NA 0.1559158 1.1078980 0.5381440        NA 0.4832251
##  [57] 1.1180969        NA        NA        NA        NA 0.5727129        NA
##  [64] 0.8422235 0.1792535 0.9217471        NA 0.8746578        NA        NA
##  [71] 0.5352341        NA        NA 0.8324409        NA 0.5357532 0.9217253
##  [78] 1.2603324        NA        NA 0.4155878        NA 1.1035908 0.6521834
##  [85]        NA        NA 0.1364923        NA        NA 0.9591499        NA
##  [92] 1.4595507 0.8470323 0.5192289        NA 0.5593708        NA 0.5472621
##  [99]        NA 0.9976581

Clase Martes 09/11/21

# 5) Aplicar la función de Coeficiente de variabilidad a 
# 1000 conjuntos de distribución normal estándar de longitud 50 números

# 6) ¿Qué es más rápido en lo anterior, un apply o un for-loop?

# if ...
# 7) Ejemplo elaborado de codificación de variables (Hombre, mujer) (meh!)

# 8) vector de longitud 1000 con distribución normal estándar, sacarle
# la raiz cuadrada a cada uno (solo a los positivos)

# 9) Generar una matriz de 1000 x 50 y aplicar sqrt a cada valor,
# almacenarlo en otra matriz (hacer un doble loop-for)

# 10) La media de cada fila y cada columna... generar un vector con
# cada una (almacenar uno por uno... generar un vector "vacío")

# 11) Generar un vector donde pruebe si el CV es menor o mayor a cierto
# valor y generarle una etiqueta





# 1) Generar matriz con dim. 1000 x 50 con valores "Nulos" (NA)
# 2) Rellenar dicha matriz con 50 muestras de tamaño 100 de Norm(0,1)
# 3) A cada valor de esa matriz, obtener la raiz cuadrada si es un valor >0, y si no, "Etiqueta"
# 4) Almacenar los resultados anteriores en una nueva matriz (1000 x 50)

# 5) De cada fila, y de cada columna, obtener su CV (CV = una función personalizada)
# ¿Paralelizar (apply's) o repetición(for-loop)


#####
# 1) Matriz a rellenar
mat_fic<-matrix(rep(NA,50*1000),ncol=50)
dim(mat_fic)

## [1] 1000   50

for(j in 1:50){
  set.seed(1235+j)
  mat_fic[,j]<-rnorm(1000,0,1) }

mean(mat_fic[,50])

## [1] 0.01789922

#Para un valor
if(mat_fic[4,1]>0){sqrt(mat_fic[4,1])}else{NA}

## [1] 0.978885

#Para una columna

vect_fic<- rep(NA, 1000)

vect_fic[1]<- if(mat_fic[1,1]>0){sqrt(mat_fic[1,1])}else{NA}
vect_fic[2]<- if(mat_fic[2,1]>0){sqrt(mat_fic[2,1])}else{NA}
vect_fic[3]<- if(mat_fic[3,1]>0){sqrt(mat_fic[3,1])}else{NA}
vect_fic[4]<- if(mat_fic[4,1]>0){sqrt(mat_fic[4,1])}else{NA}
vect_fic[5]<- if(mat_fic[5,1]>0){sqrt(mat_fic[5,1])}else{NA}

vect_fic[1:5]

## [1]       NA       NA       NA 0.978885       NA

for(k in 1:1000) {
  vect_fic[k]<-if(mat_fic[k,1]>0){sqrt(mat_fic[k,1])}else{NA} }


#####

##### varias columnda
#Loops anidados
# j para columnas
#k para renglones
matriz_ficti2<-matrix(rep(NA,50*1000), ncol=50)

for(j in 1:50){
  for(k in 1:1000) {
    matriz_ficti2[k,j]<-if(mat_fic[k,j]>0){sqrt(mat_fic[k,j])}else{NA} }
}
matriz_ficti2[1:5,50]

## [1]        NA 0.8389151 0.5244680        NA 1.3705986

## 1) ¿que es el coeficiente de variabilidad? ¿cómo se calcula?
### 2) obtener el cv de todas las columnas, y todas las filas
# de la tabla mat_fic


#para columnas
sd(mat_fic[1,])/abs(mean(mat_fic[1,]))

## [1] 8.303273

vecc<-rep(NA,50)
for (c in 1:50) {
  vecc[c]<-sd(mat_fic[,c])/abs(mean(mat_fic[,c]))
  
}

#para filas
vecr<-rep(NA,1000)
for(r in 1:1000){
  vecr[r]<-sd(mat_fic[r,])/abs(mean(mat_fic[r,]))
}


### profe

#Para las columnas
start_time <- Sys.time()
#for(i in 1:50){
#  vec_col_cv[i]<-coef_var(mat_fic[,i])}
end_time <- Sys.time()
end_time - start_time

## Time difference of 0.00100112 secs

start_time <- Sys.time()
#vec_col_cv<-apply(mat_fic,2,coef_var)
end_time <- Sys.time()
end_time - start_time

## Time difference of 0.0004999638 secs

#Para los renglones
#¿Cuánto tiempo me toma este ciclo?
start_time <- Sys.time()
#for(j in 1:1000){
#  vec_reng_cv[j]<-coef_var(mat_fic[j,])}
end_time <- Sys.time()
end_time - start_time

## Time difference of 0.001000881 secs

start_time <- Sys.time()
#vec_reng_cv<-apply(mat_fic,1,coef_var)
end_time <- Sys.time()
end_time - start_time

## Time difference of 0.0004999638 secs

######

clase martes 16.11.21 (datos espaciales)

library(rgdal)

## Loading required package: sp

## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## 
## rgdal: version: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/ARRA/OneDrive/Documentos/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/ARRA/OneDrive/Documentos/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/ARRA/OneDrive/Documentos/R/win-library/4.1/rgdal/proj

mapa_jalisco<-readOGR("C:\\Users\\ARRA\\OneDrive\\Desktop\\archivos clase de hector\\extraccion", layer = "exp_jal")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\ARRA\OneDrive\Desktop\archivos clase de hector\extraccion", layer: "exp_jal"
## with 125 features
## It has 4 fields

plot(mapa_jalisco)

plot(mapa_jalisco[74,])

####################

mapa_jalisco@data[74,]

##    CVEGEO CVE_ENT CVE_MUN             NOMGEO
## 73  14064      14     064 Ojuelos de Jalisco

#############
#Descargar datos del censo Población y Vivienda 2020
library(readxl)
datos_inegi_jal<- read_excel("C:/Users/ARRA/OneDrive/Desktop/archivos clase de hector/extraccion/ITER_14XLSX20.xlsx")
datos_inegi_jal2 <- read.csv("C:/Users/ARRA/OneDrive/Desktop/archivos clase de hector/extraccion/ITER_14CSV20.csv")

datos_inegi_jal$LOC

##     [1] "0000" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0009"
##    [11] "0010" "0011" "0012" "0013" "0014" "0016" "0018" "0019" "0020" "0021"
##    [21] "0022" "0023" "0024" "0026" "0027" "0028" "0029" "0030" "0031" "0032"
##    [31] "0033" "0034" "0035" "0036" "0037" "0038" "0040" "0041" "0042" "0043"
##    [41] "0044" "0046" "0047" "0048" "0049" "0052" "0053" "0054" "0055" "0056"
##    [51] "0057" "0059" "0060" "0062" "0063" "0066" "0067" "0068" "0069" "0070"
##    [61] "0071" "0072" "0073" "0074" "0075" "0078" "0079" "0081" "0084" "0086"
##    [71] "0087" "0088" "0094" "0102" "0104" "0106" "0107" "0108" "0109" "0111"
##    [81] "0112" "0113" "0115" "0116" "0118" "0119" "0122" "0123" "0128" "0129"
##    [91] "0131" "0132" "0133" "0135" "0137" "0139" "0141" "0142" "9998" "9999"
##   [101] "0000" "0001" "0002" "0004" "0005" "0007" "0008" "0009" "0010" "0013"
##   [111] "0019" "0021" "0024" "0025" "0027" "0030" "0034" "0035" "0036" "0037"
##   [121] "0039" "0040" "0041" "0043" "0048" "0053" "0055" "0058" "0061" "0062"
##   [131] "0063" "0064" "0065" "9998" "9999" "0000" "0001" "0004" "0007" "0019"
##   [141] "0021" "0028" "0030" "0031" "0032" "0038" "0051" "0056" "0057" "0058"
##   [151] "0059" "0068" "0074" "0080" "0082" "0083" "0086" "0094" "0096" "0097"
##   [161] "0099" "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0007" "0008"
##   [171] "0010" "0014" "0017" "0020" "0022" "0023" "0025" "0026" "0030" "0032"
##   [181] "0039" "0053" "0061" "0064" "0066" "9998" "9999" "0000" "0001" "0002"
##   [191] "0003" "0004" "0005" "0007" "0008" "0009" "0010" "0013" "0015" "0020"
##   [201] "0022" "0026" "0029" "0031" "0032" "0035" "0036" "0039" "0040" "0043"
##   [211] "0045" "0056" "0065" "0070" "0071" "0074" "0075" "9998" "9999" "0000"
##   [221] "0001" "0002" "0003" "0004" "0005" "0007" "0010" "0011" "0012" "0013"
##   [231] "0014" "0015" "0017" "0018" "0019" "0020" "0022" "0023" "0024" "0025"
##   [241] "0026" "0027" "0028" "0030" "0031" "0032" "0033" "0034" "0037" "0038"
##   [251] "0039" "0040" "0041" "0042" "0043" "0044" "0045" "0046" "0047" "0048"
##   [261] "0049" "0050" "0051" "0052" "0053" "0054" "0057" "0058" "0059" "0061"
##   [271] "0062" "0070" "0073" "0075" "0076" "0077" "0079" "0080" "0082" "0083"
##   [281] "0084" "0085" "0087" "0088" "0089" "0091" "0092" "0095" "0097" "0098"
##   [291] "0100" "0102" "0107" "0109" "0111" "0112" "0116" "0121" "0122" "0123"
##   [301] "0130" "0133" "0137" "0140" "0142" "0147" "0148" "0149" "0153" "0156"
##   [311] "0157" "0158" "0160" "0162" "0165" "0167" "0171" "0174" "0182" "0185"
##   [321] "0187" "0189" "0190" "0192" "0193" "0194" "9998" "9999" "0000" "0001"
##   [331] "0003" "0004" "0008" "0009" "0010" "0012" "0013" "0014" "0015" "0022"
##   [341] "0076" "0086" "9998" "0000" "0001" "0002" "0003" "0004" "0005" "0006"
##   [351] "0007" "0009" "0011" "0014" "0016" "0021" "0022" "0024" "0026" "0028"
##   [361] "0032" "0036" "0045" "0046" "0048" "0050" "0051" "0054" "0056" "0057"
##   [371] "0058" "0061" "0063" "0064" "0065" "0066" "0068" "0072" "0073" "0076"
##   [381] "0078" "0083" "0089" "0090" "0093" "0095" "0096" "0097" "0101" "0102"
##   [391] "0103" "0106" "0108" "0110" "0113" "0115" "0116" "0117" "0118" "0120"
##   [401] "0127" "0130" "0131" "0133" "0137" "0139" "0141" "0142" "0143" "0145"
##   [411] "0146" "0147" "0150" "0151" "0153" "0154" "0156" "0158" "0160" "0161"
##   [421] "0162" "0167" "0171" "0172" "0175" "0177" "0178" "0181" "0184" "0187"
##   [431] "0188" "0189" "0191" "0193" "0194" "0196" "0198" "0201" "0202" "0203"
##   [441] "0207" "0210" "0212" "0213" "0214" "0216" "0217" "0222" "0225" "0228"
##   [451] "0230" "0231" "0234" "0238" "0239" "0241" "0243" "0244" "0245" "0247"
##   [461] "0248" "0249" "0250" "0257" "0258" "0261" "0262" "0263" "0266" "0267"
##   [471] "0269" "0271" "0272" "0274" "0275" "0277" "0279" "0280" "0281" "0283"
##   [481] "0287" "0288" "0289" "0294" "0295" "0301" "0303" "0307" "0311" "0317"
##   [491] "0318" "0320" "0321" "0322" "0327" "0328" "0331" "0337" "0339" "0342"
##   [501] "0343" "0345" "0346" "0348" "0351" "0352" "0353" "0358" "0360" "0361"
##   [511] "0371" "0372" "0375" "0376" "0378" "0379" "0381" "0382" "0390" "0393"
##   [521] "0394" "0395" "0396" "0403" "0405" "0406" "0408" "0409" "0411" "0412"
##   [531] "0415" "0417" "0418" "0422" "0423" "0424" "0425" "0494" "0497" "0500"
##   [541] "0501" "0503" "0509" "0511" "0512" "0513" "0516" "0519" "0520" "0521"
##   [551] "0523" "0525" "0532" "0533" "0536" "0537" "0538" "0540" "0541" "0543"
##   [561] "0548" "0552" "0553" "0557" "0560" "0567" "0570" "0571" "0574" "0575"
##   [571] "0576" "0579" "0582" "0585" "0586" "0587" "0589" "0590" "0592" "0593"
##   [581] "0596" "0602" "0606" "0607" "0610" "0611" "0612" "0614" "0617" "0621"
##   [591] "0624" "0626" "0627" "0631" "0632" "0633" "0635" "0637" "0638" "0639"
##   [601] "0641" "0643" "0645" "0646" "0649" "0650" "0657" "0659" "0661" "0662"
##   [611] "0664" "0665" "0666" "0667" "0668" "0669" "0672" "0673" "0675" "0676"
##   [621] "0677" "0681" "0682" "0683" "0684" "0686" "0687" "0688" "0689" "0690"
##   [631] "0691" "0694" "0695" "0696" "0697" "0698" "0701" "9998" "9999" "0000"
##   [641] "0001" "0003" "0004" "0005" "0006" "0007" "0009" "0012" "0014" "0020"
##   [651] "0021" "0024" "0030" "0032" "0034" "0041" "0043" "0044" "0048" "0054"
##   [661] "0055" "0057" "0060" "0065" "0066" "0067" "0068" "0069" "0070" "0071"
##   [671] "0072" "0073" "0074" "0075" "0076" "0077" "0078" "0079" "0080" "0081"
##   [681] "9998" "9999" "0000" "0001" "0003" "0016" "0021" "0023" "0024" "0025"
##   [691] "0026" "0027" "0028" "0029" "0032" "0033" "0046" "0051" "0052" "0055"
##   [701] "0058" "0060" "0061" "0107" "9998" "0000" "0001" "0003" "0008" "0009"
##   [711] "0011" "0015" "0016" "0017" "0021" "0022" "0024" "0026" "0029" "0030"
##   [721] "0033" "0041" "0047" "0054" "0068" "9998" "9999" "0000" "0001" "0002"
##   [731] "0003" "0004" "0005" "0010" "0012" "0013" "0016" "0021" "0032" "0038"
##   [741] "0039" "0040" "0041" "0044" "0045" "0046" "0049" "0050" "0053" "0056"
##   [751] "0058" "0059" "0060" "0061" "0063" "0064" "0066" "0067" "0073" "0074"
##   [761] "0076" "0090" "0095" "0103" "0104" "0107" "0109" "0111" "0114" "0116"
##   [771] "0119" "0130" "0139" "0141" "0142" "0148" "0149" "0152" "0155" "0156"
##   [781] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007"
##   [791] "0008" "0010" "0011" "0012" "0013" "0014" "0016" "0017" "0018" "0019"
##   [801] "0023" "0024" "0025" "0026" "0028" "0029" "0030" "0032" "0035" "0036"
##   [811] "0037" "0038" "0039" "0041" "0043" "0044" "0045" "0046" "0047" "0048"
##   [821] "0050" "0051" "0052" "0054" "0055" "0057" "0058" "0060" "0061" "0065"
##   [831] "0067" "0068" "0069" "0070" "0074" "0075" "0076" "0077" "0078" "0080"
##   [841] "0082" "0083" "0084" "0085" "0087" "0089" "0090" "0091" "0092" "0093"
##   [851] "0094" "0096" "0098" "0099" "0101" "0105" "0106" "0109" "0114" "0121"
##   [861] "0123" "0125" "0127" "0128" "0130" "0131" "0134" "0136" "0137" "0141"
##   [871] "0142" "0145" "0146" "0147" "0148" "0151" "0154" "0155" "0160" "0165"
##   [881] "0166" "0167" "0169" "0173" "0175" "0177" "0178" "0180" "0185" "0188"
##   [891] "0191" "0195" "0203" "0204" "0205" "0207" "0211" "0212" "0213" "0215"
##   [901] "0216" "0218" "0219" "0220" "0221" "0224" "0225" "0227" "0230" "0232"
##   [911] "0235" "0240" "0241" "0242" "0243" "0245" "0246" "0248" "9998" "9999"
##   [921] "0000" "0001" "0003" "0004" "0005" "0006" "0008" "0009" "0010" "0013"
##   [931] "0015" "0016" "0019" "0020" "0021" "0023" "0024" "0025" "0026" "0028"
##   [941] "0029" "0031" "0045" "0047" "0056" "0057" "0062" "0063" "0068" "0071"
##   [951] "0073" "0079" "9998" "9999" "0000" "0001" "0002" "0003" "0005" "0007"
##   [961] "0009" "0011" "0012" "0013" "0014" "0015" "0017" "0019" "0020" "0022"
##   [971] "0023" "0024" "0025" "0026" "0027" "0028" "0031" "0033" "0036" "0038"
##   [981] "0041" "0042" "0046" "0047" "0048" "0051" "0053" "0055" "0058" "0060"
##   [991] "0063" "0064" "0068" "0069" "0071" "0075" "0076" "0077" "0079" "0081"
##  [1001] "0082" "0084" "0085" "0086" "0088" "0097" "0102" "0104" "0105" "0107"
##  [1011] "0108" "0112" "0116" "0119" "0120" "0125" "0130" "0131" "0132" "0133"
##  [1021] "0134" "0136" "0138" "0139" "0140" "0142" "0144" "0146" "0152" "0154"
##  [1031] "0156" "0158" "0160" "0161" "0163" "0165" "0175" "0176" "0178" "0186"
##  [1041] "0190" "0194" "0195" "0203" "0204" "0207" "0208" "0212" "0213" "0216"
##  [1051] "0225" "0227" "0237" "0239" "0242" "0244" "0247" "0248" "0251" "0252"
##  [1061] "0253" "0255" "0257" "0258" "0259" "0264" "0265" "0266" "0267" "0270"
##  [1071] "0273" "0275" "0276" "0283" "0287" "0296" "0298" "0301" "0302" "0304"
##  [1081] "0309" "0314" "0333" "0337" "0347" "0348" "0358" "0360" "0369" "0372"
##  [1091] "0375" "0378" "0379" "0382" "0383" "9998" "9999" "0000" "0001" "0002"
##  [1101] "0003" "0004" "0005" "0008" "0010" "0011" "0012" "0014" "0015" "0017"
##  [1111] "0018" "0020" "0024" "0027" "0029" "0030" "0033" "0035" "0037" "0038"
##  [1121] "0039" "0043" "0046" "0047" "0048" "0049" "0050" "0051" "0052" "0053"
##  [1131] "0054" "0055" "0069" "0070" "0071" "0072" "0073" "0075" "0077" "0079"
##  [1141] "0080" "0082" "0083" "0085" "0086" "0087" "0089" "0093" "0095" "0100"
##  [1151] "0101" "0103" "0104" "0106" "0108" "0112" "0114" "0115" "0116" "0121"
##  [1161] "0122" "0124" "0128" "0133" "0141" "0142" "0145" "0152" "0156" "0160"
##  [1171] "0161" "0162" "0164" "0170" "9998" "9999" "0000" "0001" "0005" "0006"
##  [1181] "0012" "0013" "0017" "0018" "0020" "0026" "0037" "0041" "0042" "0054"
##  [1191] "0056" "0058" "0064" "0066" "0076" "0078" "0083" "0084" "0085" "0086"
##  [1201] "0087" "0088" "0093" "0096" "0097" "0100" "0102" "0103" "0104" "0105"
##  [1211] "0110" "0111" "0112" "0116" "0119" "0120" "0121" "0123" "0124" "0125"
##  [1221] "0130" "0133" "0134" "0135" "0139" "0141" "0145" "0150" "0181" "0182"
##  [1231] "0188" "0200" "0203" "0205" "0206" "0211" "0214" "0215" "0227" "0230"
##  [1241] "0233" "0242" "0245" "0247" "0250" "0251" "0253" "9998" "9999" "0000"
##  [1251] "0001" "0003" "0005" "0007" "0008" "0009" "0010" "0011" "0012" "0013"
##  [1261] "0015" "0017" "0018" "0019" "0022" "0023" "0024" "0026" "0027" "0028"
##  [1271] "0029" "0031" "0032" "0033" "0034" "0036" "0037" "0038" "0039" "0040"
##  [1281] "0041" "0042" "0055" "0060" "0061" "0062" "0071" "0074" "0075" "0086"
##  [1291] "0089" "0095" "0109" "0112" "0123" "0129" "0135" "0139" "0144" "0149"
##  [1301] "0154" "0174" "0178" "0180" "0191" "0195" "0204" "0210" "0211" "0214"
##  [1311] "0220" "0230" "0231" "0232" "0240" "0243" "0244" "0245" "0246" "0251"
##  [1321] "0257" "0259" "0262" "0264" "0265" "0266" "9998" "9999" "0000" "0001"
##  [1331] "0002" "0007" "0010" "0013" "0017" "0018" "0020" "0023" "0026" "0027"
##  [1341] "0032" "0035" "0036" "0040" "0044" "0048" "0050" "0052" "0061" "0063"
##  [1351] "0067" "0070" "0072" "0073" "0075" "0076" "0081" "0085" "0092" "0094"
##  [1361] "0096" "0098" "0100" "0101" "0102" "0103" "0105" "0109" "0110" "0111"
##  [1371] "0114" "0119" "0131" "0133" "0136" "0137" "0138" "0152" "0153" "0158"
##  [1381] "0162" "0165" "0167" "0174" "0177" "0187" "0188" "0193" "0197" "0200"
##  [1391] "0201" "0217" "0221" "0238" "0239" "0243" "0244" "0249" "0261" "0262"
##  [1401] "0263" "0266" "0275" "0276" "0277" "0285" "0286" "0287" "0292" "0293"
##  [1411] "0296" "0307" "0315" "0334" "0335" "0336" "0340" "0345" "0350" "0351"
##  [1421] "0352" "0358" "0359" "0362" "0366" "0367" "0368" "0375" "0377" "0381"
##  [1431] "0384" "0387" "0389" "0390" "0391" "0394" "0396" "0397" "0400" "0405"
##  [1441] "0407" "0411" "0412" "0413" "0415" "0417" "0420" "0421" "0423" "0425"
##  [1451] "0427" "0428" "0429" "0430" "0431" "0432" "0436" "0438" "0440" "0441"
##  [1461] "0443" "0444" "9998" "9999" "0000" "0001" "0007" "0010" "0013" "0020"
##  [1471] "0021" "0022" "0032" "0033" "0034" "0035" "0037" "0038" "0041" "0043"
##  [1481] "0047" "0053" "0055" "0057" "0060" "0062" "0063" "0064" "0065" "0066"
##  [1491] "0069" "0070" "0073" "0074" "0075" "0076" "0078" "0079" "0082" "0086"
##  [1501] "0087" "0088" "0090" "0091" "0092" "0094" "0095" "0096" "0097" "0099"
##  [1511] "0100" "0101" "0102" "0106" "0107" "0109" "0110" "0111" "0114" "0116"
##  [1521] "0117" "0121" "0123" "0125" "0127" "0128" "0129" "0130" "0131" "0134"
##  [1531] "0135" "0136" "0138" "0148" "0152" "0153" "0155" "0161" "0164" "0177"
##  [1541] "0191" "0193" "0194" "0196" "0203" "0207" "0208" "0211" "0212" "0213"
##  [1551] "0216" "0217" "0219" "0220" "0225" "0226" "0228" "0234" "0237" "0238"
##  [1561] "0240" "0241" "0242" "0245" "0248" "0249" "0251" "0258" "0260" "0263"
##  [1571] "0264" "0267" "0271" "0273" "0279" "0282" "0284" "0285" "0286" "0287"
##  [1581] "0289" "0293" "0295" "0296" "0297" "0298" "0299" "0300" "0302" "0303"
##  [1591] "0309" "0311" "0312" "0313" "9998" "9999" "0000" "0001" "0002" "0003"
##  [1601] "0008" "0011" "0015" "0018" "0020" "0021" "0024" "0029" "0033" "0034"
##  [1611] "0035" "0042" "0043" "0044" "0046" "0049" "0050" "0051" "0053" "0057"
##  [1621] "0060" "0061" "0062" "0064" "0068" "0075" "0078" "0084" "0089" "0092"
##  [1631] "0094" "0103" "0104" "0109" "0131" "0135" "0136" "0140" "0162" "0164"
##  [1641] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0007" "0009"
##  [1651] "0012" "0016" "0017" "0024" "0030" "0033" "0034" "0036" "0047" "0049"
##  [1661] "0051" "0058" "0059" "0062" "0068" "0069" "0071" "0077" "0082" "0087"
##  [1671] "0094" "0100" "0102" "0103" "0104" "0107" "0108" "0109" "0110" "0112"
##  [1681] "0113" "0115" "0118" "0119" "0120" "0123" "0124" "0133" "0134" "0136"
##  [1691] "0138" "0139" "0141" "0143" "0144" "0145" "0146" "0147" "0150" "0151"
##  [1701] "0154" "0157" "0159" "0162" "0163" "9998" "9999" "0000" "0001" "0003"
##  [1711] "0005" "0008" "0011" "0014" "0021" "0024" "0028" "0037" "0038" "0042"
##  [1721] "0069" "0071" "0074" "0076" "0093" "0101" "0102" "0125" "0137" "0139"
##  [1731] "0149" "0152" "0167" "0171" "0174" "0194" "0206" "0207" "0211" "0222"
##  [1741] "0232" "0234" "0235" "0236" "0237" "0238" "9998" "9999" "0000" "0001"
##  [1751] "0002" "0003" "0004" "0005" "0007" "0008" "0010" "0011" "0013" "0015"
##  [1761] "0016" "0017" "0018" "0019" "0020" "0021" "0022" "0023" "0024" "0032"
##  [1771] "0037" "0038" "0040" "0041" "0043" "0045" "0047" "0052" "0053" "0055"
##  [1781] "0056" "0058" "0060" "0062" "0066" "0067" "0068" "0069" "9998" "9999"
##  [1791] "0000" "0001" "0005" "0006" "0007" "0008" "0012" "0013" "0014" "0016"
##  [1801] "0018" "0020" "0023" "0026" "0028" "0031" "0032" "0035" "0037" "0042"
##  [1811] "0043" "0045" "0051" "0058" "0061" "0062" "0063" "0065" "0066" "0067"
##  [1821] "0068" "0070" "0074" "0075" "0076" "0079" "0080" "0085" "0089" "0090"
##  [1831] "0099" "0103" "0107" "0112" "0119" "0120" "0128" "0130" "0133" "0135"
##  [1841] "0136" "0140" "0143" "0144" "0145" "0147" "0149" "0153" "0154" "0169"
##  [1851] "0178" "0179" "0190" "0193" "0195" "0196" "0200" "0204" "0207" "9998"
##  [1861] "9999" "0000" "0001" "0005" "0009" "0014" "0018" "0021" "0024" "0025"
##  [1871] "0030" "0031" "0032" "0223" "0228" "0233" "0235" "0238" "0239" "0241"
##  [1881] "0242" "0243" "0244" "9998" "0000" "0001" "0005" "0008" "0010" "0012"
##  [1891] "0014" "0021" "0025" "0029" "0030" "0031" "0032" "0033" "0034" "0035"
##  [1901] "0038" "0040" "0041" "0042" "0043" "0044" "0049" "0051" "0054" "0055"
##  [1911] "0056" "0059" "0060" "0061" "0063" "0067" "0068" "0071" "0076" "0078"
##  [1921] "0080" "0081" "0084" "0087" "0092" "0093" "0095" "0096" "0099" "0101"
##  [1931] "0103" "0104" "0106" "0110" "0111" "0114" "0115" "0116" "0121" "0128"
##  [1941] "0129" "0132" "0134" "0138" "0140" "0148" "0150" "0152" "0153" "0162"
##  [1951] "0163" "0164" "0165" "0167" "0168" "0174" "0176" "0181" "0188" "0191"
##  [1961] "0192" "0195" "0196" "0199" "0204" "0205" "0208" "0209" "0210" "0212"
##  [1971] "0213" "0215" "0216" "0218" "0220" "0221" "0227" "0228" "0231" "0232"
##  [1981] "0237" "0238" "0239" "0240" "0246" "0247" "0248" "0252" "0254" "0255"
##  [1991] "0256" "0259" "0262" "0264" "0273" "0279" "0282" "0283" "0284" "0286"
##  [2001] "0287" "0288" "0294" "0295" "0296" "0297" "9998" "9999" "0000" "0001"
##  [2011] "0004" "0005" "0007" "0008" "0009" "0010" "0016" "0017" "0019" "0021"
##  [2021] "0022" "0023" "0026" "0028" "0029" "0031" "0052" "0060" "0061" "0062"
##  [2031] "0064" "0065" "0068" "0069" "0073" "9998" "9999" "0000" "0001" "0002"
##  [2041] "0003" "0004" "0005" "0006" "0008" "0010" "0012" "0013" "0015" "0017"
##  [2051] "0018" "0019" "0020" "0022" "0024" "0025" "0027" "0028" "0029" "0030"
##  [2061] "0031" "0035" "0036" "0040" "0042" "0043" "0044" "0045" "0047" "0048"
##  [2071] "0049" "0050" "0051" "0054" "0057" "0058" "0059" "0060" "0062" "0063"
##  [2081] "0066" "0069" "0071" "0072" "0074" "0076" "0077" "0079" "0080" "0081"
##  [2091] "0083" "0084" "0085" "0086" "0088" "0089" "0091" "0092" "0093" "0094"
##  [2101] "0095" "0097" "0099" "0101" "0102" "0103" "0104" "0105" "0106" "0107"
##  [2111] "0108" "0109" "0110" "0111" "0112" "0113" "0115" "0116" "0117" "0118"
##  [2121] "0120" "0122" "0123" "0124" "0126" "0128" "0134" "0135" "0138" "0144"
##  [2131] "0145" "0146" "0152" "0154" "0155" "0156" "0167" "0168" "0169" "0170"
##  [2141] "0173" "0174" "0176" "0178" "0180" "0183" "0188" "0189" "0191" "0192"
##  [2151] "0194" "0195" "0196" "0197" "0198" "0200" "0203" "0205" "0206" "0212"
##  [2161] "0216" "0217" "0219" "0222" "0227" "0228" "0231" "0232" "0234" "0236"
##  [2171] "0237" "0239" "0242" "0243" "0245" "0247" "0248" "0249" "0251" "0253"
##  [2181] "0259" "0260" "0261" "0262" "9998" "9999" "0000" "0001" "0002" "0003"
##  [2191] "0004" "0005" "0006" "0007" "0018" "0024" "0025" "0026" "0027" "0031"
##  [2201] "0038" "0039" "0041" "0042" "0044" "0048" "0050" "0051" "0052" "0053"
##  [2211] "0054" "0055" "0056" "0057" "0058" "0060" "0064" "0065" "0066" "0067"
##  [2221] "0072" "0074" "0077" "0078" "0081" "0082" "0086" "0087" "0092" "0093"
##  [2231] "0094" "0096" "0099" "0100" "0101" "0102" "0104" "9998" "9999" "0000"
##  [2241] "0001" "0003" "0007" "0012" "0013" "0014" "0016" "0018" "0020" "0021"
##  [2251] "0022" "0024" "0029" "0030" "0035" "0037" "0039" "0040" "0045" "0050"
##  [2261] "0051" "0055" "0058" "0060" "0064" "0066" "0069" "0070" "0071" "0073"
##  [2271] "0074" "0088" "0089" "0090" "0091" "0093" "0094" "0096" "0098" "0109"
##  [2281] "0111" "0118" "0119" "0120" "0124" "0126" "0137" "0140" "0141" "0152"
##  [2291] "0160" "0161" "0176" "0179" "0189" "0190" "0206" "0211" "0214" "0215"
##  [2301] "0223" "0234" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005"
##  [2311] "0006" "0008" "0009" "0010" "0012" "0014" "0016" "0020" "0024" "0025"
##  [2321] "0027" "0034" "0040" "0045" "0047" "0053" "0054" "0055" "9998" "9999"
##  [2331] "0000" "0001" "0002" "0006" "0007" "0008" "0010" "0011" "0013" "0014"
##  [2341] "0019" "0022" "0023" "0024" "0026" "0027" "0029" "0030" "0033" "0035"
##  [2351] "0036" "0037" "0038" "0039" "0044" "0046" "0048" "0049" "0051" "0053"
##  [2361] "0054" "0056" "0058" "0059" "0060" "0061" "0062" "0064" "0065" "0067"
##  [2371] "0068" "0069" "0070" "0071" "0072" "0073" "0074" "0076" "0077" "0078"
##  [2381] "0079" "0080" "0081" "0085" "0091" "0092" "0094" "0095" "0097" "0101"
##  [2391] "0103" "0108" "0110" "0119" "0122" "0132" "0133" "0137" "0139" "0141"
##  [2401] "0142" "0144" "0146" "0151" "0152" "0153" "0159" "0160" "0163" "0167"
##  [2411] "0168" "0169" "0171" "0172" "0174" "0176" "0177" "9998" "9999" "0000"
##  [2421] "0001" "0003" "0008" "0013" "0015" "0017" "0020" "0021" "0023" "0034"
##  [2431] "0042" "0050" "0057" "0065" "0069" "0073" "0074" "0077" "9998" "0000"
##  [2441] "0001" "0002" "0003" "0017" "0021" "0025" "0032" "0033" "0034" "0035"
##  [2451] "0036" "0039" "0040" "0044" "0048" "0050" "0051" "0054" "0061" "0062"
##  [2461] "0066" "0069" "0073" "0075" "0079" "0081" "0082" "0085" "0086" "0087"
##  [2471] "0090" "0100" "0104" "0108" "0110" "0117" "0123" "0125" "0127" "0128"
##  [2481] "0132" "0134" "0136" "0138" "0139" "0140" "0148" "0150" "0156" "0158"
##  [2491] "0161" "0162" "0169" "0172" "0176" "0178" "0181" "0184" "0190" "0194"
##  [2501] "0202" "0203" "0207" "0210" "0213" "0216" "0217" "0218" "0222" "0225"
##  [2511] "0226" "0228" "0230" "0235" "0236" "0238" "0241" "0243" "0244" "0245"
##  [2521] "0246" "0247" "0249" "0251" "0252" "0259" "0260" "0261" "0264" "0268"
##  [2531] "0269" "0270" "0273" "0277" "0278" "0283" "0284" "0286" "0292" "0293"
##  [2541] "0295" "0300" "0301" "0302" "0303" "0305" "0313" "0314" "0320" "0329"
##  [2551] "0331" "0332" "0350" "0351" "0355" "0357" "0359" "0363" "0375" "0379"
##  [2561] "0381" "0382" "0383" "0385" "0386" "0395" "0398" "0399" "0406" "0407"
##  [2571] "0409" "0410" "0412" "0413" "0414" "0415" "0416" "0417" "0419" "0421"
##  [2581] "0422" "0423" "0425" "0427" "0428" "0429" "0430" "0431" "0432" "0433"
##  [2591] "0434" "0435" "0438" "0440" "0442" "0443" "0446" "0449" "0452" "0453"
##  [2601] "0454" "0460" "0463" "0466" "0468" "0471" "0472" "0473" "0474" "0475"
##  [2611] "0476" "0478" "0481" "0483" "0487" "0491" "0492" "0495" "0497" "0498"
##  [2621] "0500" "0501" "0502" "0504" "0507" "0508" "0509" "0510" "0516" "0521"
##  [2631] "0523" "0524" "0525" "0526" "0527" "0529" "0534" "0536" "0542" "0546"
##  [2641] "0549" "0551" "0552" "0563" "0564" "0566" "0568" "0569" "0571" "0573"
##  [2651] "0575" "0576" "0577" "0590" "0591" "0594" "0598" "0599" "0602" "0603"
##  [2661] "0604" "0605" "0606" "0608" "0609" "0610" "0611" "0612" "0613" "0617"
##  [2671] "0619" "0620" "0622" "0624" "0625" "0627" "0631" "0632" "0633" "0634"
##  [2681] "0635" "0636" "0637" "0638" "0639" "0640" "0641" "0642" "0643" "0646"
##  [2691] "0647" "0650" "0653" "0655" "0656" "0657" "0659" "0661" "0663" "0666"
##  [2701] "0671" "0672" "0673" "0677" "0678" "0679" "0682" "0683" "0684" "0685"
##  [2711] "0687" "0691" "0692" "0693" "0694" "0696" "0699" "0705" "0706" "0707"
##  [2721] "0710" "0711" "0713" "0714" "0716" "0717" "0719" "0720" "0721" "0722"
##  [2731] "0723" "0724" "0727" "0728" "0731" "0732" "0734" "0736" "0737" "0738"
##  [2741] "0739" "0740" "0741" "0743" "0744" "0750" "0755" "0757" "0763" "0764"
##  [2751] "0765" "0766" "0769" "0774" "0776" "0777" "0778" "0779" "0781" "0782"
##  [2761] "0786" "0787" "0789" "0790" "0791" "0792" "0793" "0794" "0795" "0796"
##  [2771] "0797" "0800" "0802" "9998" "9999" "0000" "0001" "0003" "0012" "0014"
##  [2781] "0019" "0020" "0025" "0027" "0030" "0031" "0032" "0036" "0037" "0039"
##  [2791] "0046" "0047" "0052" "0055" "0057" "0058" "0059" "0060" "0066" "0076"
##  [2801] "0083" "0086" "0090" "0102" "0127" "0132" "0133" "9998" "9999" "0000"
##  [2811] "0001" "0002" "0007" "0009" "0011" "0012" "0018" "0020" "0024" "0025"
##  [2821] "0026" "0027" "0031" "0043" "0048" "0054" "0057" "0060" "0076" "0079"
##  [2831] "0080" "0081" "0082" "0087" "0088" "0089" "0090" "0093" "0098" "0115"
##  [2841] "0118" "0119" "0126" "0133" "0135" "0138" "0161" "0162" "0164" "0167"
##  [2851] "0168" "9998" "9999" "0000" "0001" "0015" "0016" "0022" "0027" "0028"
##  [2861] "0029" "0030" "0032" "0033" "0034" "0042" "0044" "0045" "0049" "0053"
##  [2871] "0061" "0065" "0070" "0072" "0074" "0076" "0077" "0078" "0084" "0090"
##  [2881] "0095" "0097" "0098" "0099" "0100" "0113" "0115" "0116" "0117" "0119"
##  [2891] "0121" "0126" "0130" "0131" "0132" "0155" "0166" "0170" "0171" "0175"
##  [2901] "0183" "0234" "0260" "0261" "0263" "0264" "9998" "9999" "0000" "0001"
##  [2911] "0002" "9998" "0000" "0001" "0008" "0010" "0011" "0013" "0015" "0016"
##  [2921] "0018" "0019" "0021" "0023" "0025" "0030" "0031" "0035" "0038" "0039"
##  [2931] "0043" "0044" "0046" "0047" "0048" "0049" "0057" "0060" "0064" "0065"
##  [2941] "0067" "0070" "0071" "0072" "0074" "0077" "0078" "0082" "0085" "0086"
##  [2951] "0095" "0100" "0121" "0122" "0123" "0130" "0147" "0149" "0156" "0163"
##  [2961] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007"
##  [2971] "0009" "0010" "0012" "0013" "0014" "0015" "0017" "0018" "0019" "0020"
##  [2981] "0021" "0022" "0023" "0026" "0044" "0045" "0046" "0052" "0055" "0062"
##  [2991] "0063" "0068" "0074" "0077" "0078" "0084" "0093" "0095" "0117" "9998"
##  [3001] "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008" "0009" "0010"
##  [3011] "0012" "0013" "0014" "0015" "0016" "0018" "0019" "0020" "0021" "0022"
##  [3021] "0023" "0024" "0026" "0027" "0028" "0029" "0030" "0031" "0033" "0034"
##  [3031] "0035" "0036" "0037" "0038" "0039" "0041" "0042" "0043" "0044" "0046"
##  [3041] "0047" "0048" "0049" "0061" "0063" "0067" "0082" "0105" "0110" "0117"
##  [3051] "0130" "0132" "0133" "0134" "0140" "0148" "0151" "0163" "0172" "0173"
##  [3061] "0175" "0176" "0183" "0199" "0200" "9998" "9999" "0000" "0001" "0004"
##  [3071] "0006" "0007" "0009" "0010" "0012" "0015" "0016" "0022" "0024" "0025"
##  [3081] "0026" "0030" "0031" "0033" "0036" "0039" "0040" "0041" "0042" "0048"
##  [3091] "0052" "0057" "0060" "0062" "0063" "0066" "0067" "0068" "0074" "0081"
##  [3101] "0085" "0086" "0091" "0092" "0094" "0095" "0097" "0099" "0101" "0102"
##  [3111] "0105" "0110" "0111" "0114" "0115" "0117" "0123" "0124" "0125" "0126"
##  [3121] "0132" "0135" "0136" "0137" "0139" "0140" "0141" "0146" "0147" "0148"
##  [3131] "0150" "0152" "0154" "0159" "0161" "0165" "0166" "0173" "0176" "0182"
##  [3141] "0183" "0186" "0190" "0191" "0202" "0212" "0213" "0216" "0220" "0226"
##  [3151] "0227" "0232" "0236" "0242" "0247" "0249" "0252" "0253" "0257" "0263"
##  [3161] "0276" "0281" "0284" "0290" "0291" "0306" "0308" "0325" "0329" "0330"
##  [3171] "0331" "0338" "0345" "0350" "0358" "0363" "9998" "9999" "0000" "0001"
##  [3181] "0002" "0003" "0004" "0005" "0006" "0007" "0009" "0010" "0011" "0012"
##  [3191] "0014" "0018" "0019" "0020" "0027" "0030" "0033" "0034" "0035" "0041"
##  [3201] "0042" "0043" "0048" "0049" "0050" "0051" "0054" "0057" "0059" "0060"
##  [3211] "0062" "0064" "0067" "0069" "0070" "0071" "0073" "0074" "0075" "0077"
##  [3221] "0078" "0079" "0080" "0082" "0083" "0084" "0085" "0089" "0091" "0094"
##  [3231] "0096" "0097" "0099" "0101" "0103" "0105" "0106" "0109" "0110" "0113"
##  [3241] "0114" "0116" "0120" "0127" "0128" "0129" "0130" "0131" "0134" "0135"
##  [3251] "0137" "0139" "0141" "0142" "0145" "0147" "0152" "0153" "0154" "0155"
##  [3261] "0156" "0157" "0161" "0162" "0163" "0164" "0165" "0166" "0168" "0169"
##  [3271] "0170" "0171" "0172" "0173" "0174" "0175" "0176" "0177" "0178" "0179"
##  [3281] "0180" "0181" "9998" "9999" "0000" "0001" "0005" "0007" "0008" "0009"
##  [3291] "0010" "0011" "0013" "0016" "0021" "0022" "0024" "0025" "0026" "0027"
##  [3301] "0028" "0030" "0032" "0033" "0034" "0035" "0040" "0043" "0045" "0046"
##  [3311] "0047" "0048" "0050" "0051" "0054" "0056" "0057" "0060" "0063" "0065"
##  [3321] "0071" "0072" "0076" "0077" "0078" "0079" "0080" "0081" "0083" "0084"
##  [3331] "0088" "0089" "0093" "0095" "0098" "0099" "0100" "0101" "0104" "0106"
##  [3341] "0107" "0108" "0109" "0110" "0114" "0115" "0116" "0117" "0119" "0120"
##  [3351] "0121" "0122" "0123" "0124" "0126" "0127" "0130" "0131" "0132" "0135"
##  [3361] "0136" "0137" "0139" "0140" "0141" "0142" "0144" "0147" "0148" "0151"
##  [3371] "0157" "0158" "0159" "0164" "0165" "0169" "0174" "0179" "0181" "0186"
##  [3381] "0199" "0203" "0205" "0206" "0207" "0208" "0210" "0211" "0212" "0214"
##  [3391] "0217" "0218" "0221" "0226" "0227" "0231" "0232" "0233" "0234" "0236"
##  [3401] "0239" "0240" "0241" "0242" "0250" "0253" "0255" "0258" "0264" "0265"
##  [3411] "0266" "0268" "0272" "0273" "0274" "0278" "0279" "0280" "0281" "0289"
##  [3421] "0292" "0295" "0297" "0298" "0302" "0306" "0308" "0310" "0317" "0319"
##  [3431] "0320" "0327" "0330" "0333" "0337" "0341" "0344" "0345" "0346" "0347"
##  [3441] "0351" "0352" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0008"
##  [3451] "0012" "0013" "0014" "0017" "0018" "0020" "0021" "0024" "0025" "0026"
##  [3461] "0027" "0028" "0029" "0031" "0032" "0034" "0035" "0037" "0038" "0041"
##  [3471] "0042" "0045" "0046" "0047" "0051" "0052" "0055" "0056" "0057" "0059"
##  [3481] "0061" "0062" "0064" "0065" "0066" "0067" "0068" "0069" "0070" "0071"
##  [3491] "0072" "0074" "0076" "0078" "0080" "0081" "0087" "0088" "0091" "0092"
##  [3501] "0093" "0095" "0098" "0100" "0101" "0102" "0103" "0104" "0108" "0110"
##  [3511] "0111" "0112" "0113" "0114" "0115" "0116" "0117" "0118" "0120" "0121"
##  [3521] "0122" "0123" "0130" "0133" "0136" "0139" "0142" "0143" "0144" "0147"
##  [3531] "0149" "0151" "0152" "0153" "0154" "0163" "0177" "0178" "0181" "0182"
##  [3541] "0183" "0189" "0192" "0193" "0194" "0195" "0198" "0199" "0203" "0206"
##  [3551] "0207" "0210" "0213" "0217" "0223" "0224" "0228" "0230" "0232" "0235"
##  [3561] "0238" "0239" "0241" "0242" "0243" "0244" "0245" "0246" "0249" "0251"
##  [3571] "0252" "0253" "0254" "0255" "0256" "0257" "0258" "0259" "0260" "0261"
##  [3581] "0262" "0265" "0266" "0268" "0271" "0273" "0274" "0275" "0277" "0283"
##  [3591] "0284" "0285" "0286" "0291" "0292" "0293" "0295" "0296" "0297" "0298"
##  [3601] "0299" "0300" "0301" "0302" "0303" "0304" "0307" "0309" "9998" "9999"
##  [3611] "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0028" "0029"
##  [3621] "0033" "0038" "0039" "0044" "0045" "0056" "0057" "0058" "0061" "0063"
##  [3631] "9998" "9999" "0000" "0001" "0003" "0004" "0006" "0007" "0009" "0011"
##  [3641] "0015" "0017" "0018" "0019" "0021" "0022" "0023" "0024" "0025" "0026"
##  [3651] "0027" "0029" "0030" "0032" "0033" "0034" "0037" "0038" "0039" "0040"
##  [3661] "0042" "0044" "0046" "0048" "0049" "0050" "0052" "0053" "0055" "0056"
##  [3671] "0058" "0060" "0061" "0063" "0065" "0066" "0067" "0069" "0070" "0071"
##  [3681] "0074" "0075" "0076" "0077" "0079" "0080" "0082" "0083" "0084" "0086"
##  [3691] "0087" "0089" "0090" "0093" "0096" "0097" "0099" "0101" "0102" "0103"
##  [3701] "0104" "0105" "0106" "0109" "0110" "0111" "0113" "0114" "0116" "0118"
##  [3711] "0120" "0121" "0122" "0124" "0126" "0128" "0129" "0132" "0133" "0134"
##  [3721] "0136" "0137" "0138" "0139" "0140" "0142" "0143" "0145" "0146" "0149"
##  [3731] "0150" "0151" "0152" "0159" "0164" "0169" "0175" "0176" "0180" "0181"
##  [3741] "0183" "0187" "0192" "0202" "0203" "0204" "0205" "0206" "0207" "0214"
##  [3751] "0215" "0217" "0218" "0219" "0220" "0221" "0222" "0223" "0224" "0225"
##  [3761] "0226" "0227" "0228" "0229" "0232" "0237" "0238" "0239" "0243" "0245"
##  [3771] "0249" "0250" "0251" "0254" "0256" "0259" "0260" "0263" "0264" "0265"
##  [3781] "0266" "0267" "0271" "0272" "0273" "0274" "0275" "0276" "0278" "0286"
##  [3791] "0288" "0293" "0294" "0295" "0296" "0297" "9998" "9999" "0000" "0001"
##  [3801] "0005" "0006" "0008" "0009" "0011" "0014" "0015" "0019" "0021" "0023"
##  [3811] "0026" "0028" "0030" "0031" "0033" "0037" "0038" "0039" "0040" "0042"
##  [3821] "0049" "0053" "0055" "0058" "0062" "0064" "0069" "0073" "0078" "0079"
##  [3831] "0086" "0087" "0088" "0089" "0090" "0091" "0096" "0097" "0098" "0099"
##  [3841] "0100" "0108" "0109" "0112" "0113" "0116" "0117" "0118" "0126" "0128"
##  [3851] "0130" "0131" "0134" "0136" "0137" "0139" "0140" "0148" "0153" "0154"
##  [3861] "0157" "0159" "0163" "0165" "0170" "0173" "0174" "0177" "0178" "0179"
##  [3871] "0185" "0187" "0188" "0189" "0191" "0195" "0197" "0199" "0200" "0210"
##  [3881] "0219" "0220" "0221" "0222" "0226" "0227" "0230" "0235" "0245" "0248"
##  [3891] "0250" "0253" "0256" "0258" "0260" "0261" "0266" "0267" "0268" "0269"
##  [3901] "0270" "0274" "0276" "0278" "0283" "0286" "0289" "0291" "0294" "0304"
##  [3911] "0306" "0307" "0308" "0310" "0312" "0313" "0314" "0317" "0318" "0321"
##  [3921] "0345" "0347" "0355" "0358" "0362" "0363" "0368" "0427" "0435" "0437"
##  [3931] "0442" "0448" "0452" "0458" "0461" "0464" "0466" "0467" "0474" "0475"
##  [3941] "0476" "0479" "0480" "0481" "0483" "0485" "0496" "0497" "0503" "0505"
##  [3951] "0510" "0511" "0512" "0513" "9998" "9999" "0000" "0001" "0002" "0003"
##  [3961] "0004" "0005" "0007" "0008" "0010" "0011" "0012" "0013" "0014" "0015"
##  [3971] "0016" "0017" "0019" "0020" "0021" "0022" "0024" "0025" "0027" "0029"
##  [3981] "0031" "0039" "0045" "0046" "0047" "0048" "0050" "0051" "0053" "0054"
##  [3991] "0057" "0066" "0070" "0071" "0076" "0077" "0078" "0079" "0080" "0089"
##  [4001] "0090" "0091" "0098" "0101" "0103" "0105" "0108" "0109" "9998" "9999"
##  [4011] "0000" "0001" "0002" "0004" "0007" "0008" "0009" "0010" "0011" "0013"
##  [4021] "0014" "0015" "0016" "0017" "0019" "0021" "0024" "0027" "0028" "0029"
##  [4031] "0030" "0031" "0035" "0036" "0039" "0043" "0044" "0048" "9998" "9999"
##  [4041] "0000" "0001" "0002" "0005" "0007" "0008" "0009" "0012" "0014" "0018"
##  [4051] "0023" "0025" "0028" "0032" "0033" "0035" "0043" "0051" "0056" "0057"
##  [4061] "0059" "0064" "0066" "9998" "9999" "0000" "0001" "0002" "0006" "0007"
##  [4071] "0010" "0019" "0020" "0021" "0022" "0024" "0025" "0026" "0027" "0028"
##  [4081] "0030" "0031" "0032" "0034" "0035" "0036" "0037" "0038" "0042" "0044"
##  [4091] "0046" "0048" "0050" "0051" "0053" "0055" "0057" "0059" "0063" "0064"
##  [4101] "0065" "0066" "0067" "0069" "0070" "0071" "0072" "0075" "0076" "0077"
##  [4111] "0079" "0081" "0082" "0084" "0085" "0086" "0090" "0091" "0093" "0094"
##  [4121] "0095" "0096" "0098" "0099" "0101" "0102" "0103" "0104" "0107" "0110"
##  [4131] "0113" "0115" "0117" "0118" "0119" "0120" "0121" "0122" "0124" "0125"
##  [4141] "0127" "0129" "0131" "0132" "0133" "0135" "0138" "0142" "0144" "0145"
##  [4151] "0153" "0154" "0156" "0158" "0159" "0161" "0165" "0166" "0167" "0168"
##  [4161] "0171" "0176" "0177" "0178" "0179" "0183" "0184" "0185" "0188" "0189"
##  [4171] "0190" "0193" "0194" "0195" "0196" "0198" "0199" "0201" "0211" "0213"
##  [4181] "0215" "0216" "0219" "0222" "0224" "0225" "0228" "0230" "0233" "0234"
##  [4191] "0237" "0240" "0241" "0242" "0245" "0246" "0248" "0249" "0250" "0255"
##  [4201] "0256" "0258" "0260" "0261" "0265" "0268" "0270" "0273" "0275" "0277"
##  [4211] "0278" "0279" "0281" "0284" "0287" "0291" "0292" "0293" "0299" "0301"
##  [4221] "0307" "0308" "0309" "0310" "0311" "0313" "0317" "0322" "0323" "0324"
##  [4231] "0326" "0328" "0332" "0333" "0334" "0336" "0337" "0338" "0342" "0344"
##  [4241] "0345" "0347" "0348" "0351" "0360" "0361" "0363" "0365" "0367" "0369"
##  [4251] "0375" "0378" "0386" "0387" "0388" "0390" "0391" "0392" "0393" "0400"
##  [4261] "0401" "0404" "0408" "0410" "0415" "0419" "0431" "0433" "0434" "0446"
##  [4271] "0458" "0461" "0464" "0467" "0468" "0473" "0474" "0475" "0476" "0478"
##  [4281] "0479" "0483" "0484" "0488" "0489" "0490" "0492" "0494" "0495" "0496"
##  [4291] "0498" "0502" "0503" "0506" "0515" "0517" "0518" "0520" "0521" "0525"
##  [4301] "0527" "0530" "0531" "0532" "0537" "0540" "0541" "0545" "0551" "0557"
##  [4311] "0559" "0560" "0565" "0566" "0570" "0573" "0579" "0580" "0581" "0582"
##  [4321] "0583" "0587" "0589" "0590" "0591" "0592" "0594" "0600" "0604" "0605"
##  [4331] "0606" "0613" "0617" "0619" "0622" "0623" "0624" "0625" "0626" "0627"
##  [4341] "0628" "0632" "0633" "0634" "0637" "0638" "0641" "0643" "0649" "0650"
##  [4351] "0651" "0656" "0657" "0658" "0659" "0663" "0665" "0667" "0668" "0671"
##  [4361] "0672" "0675" "0677" "0680" "0683" "0684" "0686" "0687" "0688" "0689"
##  [4371] "0690" "0691" "0692" "0694" "0695" "0696" "0697" "0699" "0701" "0702"
##  [4381] "0705" "0706" "0707" "0709" "0710" "0711" "0712" "0713" "0715" "0716"
##  [4391] "0718" "0721" "0722" "0723" "0724" "0726" "0729" "0730" "0732" "0734"
##  [4401] "0735" "0736" "0737" "0738" "0739" "0740" "0742" "0744" "0745" "0749"
##  [4411] "0750" "0751" "0752" "0754" "0755" "0757" "0759" "0763" "0765" "0766"
##  [4421] "0767" "0768" "0771" "0772" "0773" "0776" "0777" "0779" "0783" "0784"
##  [4431] "0785" "0793" "0794" "0795" "0799" "0800" "0801" "0802" "0805" "0806"
##  [4441] "0807" "0808" "0811" "0812" "0813" "0814" "0815" "0816" "0817" "0818"
##  [4451] "0819" "0820" "0821" "0823" "0824" "0825" "0826" "0828" "0829" "0830"
##  [4461] "0831" "0832" "0833" "0834" "0837" "0838" "0840" "0841" "0842" "0843"
##  [4471] "0845" "0846" "0847" "0849" "0852" "0857" "0859" "0861" "0862" "0863"
##  [4481] "0865" "0866" "0867" "0869" "0870" "0871" "0873" "0874" "0878" "0880"
##  [4491] "0883" "0884" "0885" "0887" "0888" "0891" "0893" "0895" "0896" "0898"
##  [4501] "0900" "0901" "0903" "0904" "0907" "0908" "0909" "0910" "0912" "0914"
##  [4511] "0916" "0917" "0918" "0919" "0920" "0921" "0922" "0923" "0926" "0927"
##  [4521] "0928" "0929" "0931" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
##  [4531] "0005" "0006" "0007" "0008" "0010" "0043" "0049" "9998" "0000" "0001"
##  [4541] "0002" "0003" "0004" "0006" "0007" "0008" "0009" "0010" "0015" "0021"
##  [4551] "0037" "0039" "0043" "0048" "0049" "0052" "0062" "0063" "9998" "0000"
##  [4561] "0001" "0002" "0003" "0005" "0006" "0017" "0021" "0024" "0027" "0032"
##  [4571] "0039" "0041" "0043" "0044" "0045" "0047" "0049" "0052" "0054" "0055"
##  [4581] "0059" "0066" "0069" "0070" "0071" "0078" "0082" "0087" "0089" "0092"
##  [4591] "0095" "0106" "0108" "0109" "0110" "0112" "0120" "0121" "0125" "0133"
##  [4601] "0135" "0136" "0137" "0139" "0141" "0142" "0144" "0145" "0146" "0147"
##  [4611] "0163" "0171" "0175" "0177" "0192" "0196" "0197" "0199" "0204" "0208"
##  [4621] "0214" "0215" "0219" "0227" "0232" "0240" "0252" "0255" "0257" "9998"
##  [4631] "9999" "0000" "0001" "0002" "0003" "0012" "0013" "0014" "0016" "0018"
##  [4641] "0024" "0038" "0040" "9998" "9999" "0000" "0001" "0002" "0006" "0008"
##  [4651] "0014" "0017" "0018" "0020" "0021" "0023" "0030" "0032" "0035" "0038"
##  [4661] "0043" "0044" "0047" "0048" "0049" "0052" "0055" "0056" "0059" "0060"
##  [4671] "0064" "0066" "0067" "0068" "0069" "0072" "0075" "0076" "0078" "0079"
##  [4681] "0083" "0085" "0086" "0087" "0089" "0094" "0097" "0100" "0101" "0106"
##  [4691] "0112" "0113" "0114" "0116" "0117" "0119" "0120" "0122" "0125" "0126"
##  [4701] "0127" "0131" "0133" "0134" "0136" "0137" "0138" "0140" "0142" "0146"
##  [4711] "0152" "0154" "0156" "0160" "0161" "0162" "0163" "0164" "0165" "0166"
##  [4721] "0168" "0169" "0174" "0178" "0182" "0187" "0190" "0192" "0193" "0194"
##  [4731] "0195" "0196" "0197" "0198" "0201" "0202" "0205" "0208" "0210" "0211"
##  [4741] "0214" "0215" "0216" "0217" "0218" "0221" "0224" "0225" "0226" "0231"
##  [4751] "0234" "0241" "0242" "0249" "0251" "0252" "0253" "0257" "0258" "0260"
##  [4761] "0263" "0264" "0267" "0268" "0272" "0279" "0290" "0291" "0294" "0295"
##  [4771] "0299" "0300" "0302" "0303" "0304" "0305" "0306" "0308" "0309" "0315"
##  [4781] "0316" "0319" "0323" "0324" "0328" "0329" "0330" "0333" "0334" "0335"
##  [4791] "0340" "0341" "0342" "0362" "0363" "0366" "0367" "0368" "0379" "0380"
##  [4801] "0383" "9998" "9999" "0000" "0001" "0006" "0007" "0008" "0009" "0012"
##  [4811] "0013" "0015" "0016" "0017" "0022" "0023" "0025" "0026" "0028" "0029"
##  [4821] "0030" "0032" "0035" "0038" "0039" "0041" "0042" "0043" "0044" "0045"
##  [4831] "0047" "0048" "0053" "0055" "0056" "0058" "0065" "0068" "0069" "0071"
##  [4841] "0072" "0074" "0076" "0077" "0084" "0089" "0090" "0095" "0097" "0099"
##  [4851] "0100" "0104" "0105" "0107" "0108" "0109" "0110" "0113" "0114" "0115"
##  [4861] "0116" "0118" "9998" "9999" "0000" "0001" "0002" "0008" "0009" "0011"
##  [4871] "0012" "0013" "0017" "0019" "0020" "0021" "0026" "0027" "0029" "0033"
##  [4881] "0034" "0039" "0041" "0050" "0053" "0054" "0056" "0057" "0058" "0060"
##  [4891] "0063" "0065" "0071" "0074" "0078" "0079" "0095" "0097" "0100" "0103"
##  [4901] "0105" "0108" "0115" "0118" "0133" "0135" "0136" "0137" "0146" "0156"
##  [4911] "0157" "0167" "0169" "0170" "9998" "9999" "0000" "0001" "0003" "0005"
##  [4921] "0006" "0008" "0011" "0013" "0015" "0024" "0025" "0027" "0031" "0033"
##  [4931] "0036" "0037" "0039" "0040" "0042" "0045" "0048" "0051" "0053" "0056"
##  [4941] "0058" "0065" "0066" "0067" "0077" "0080" "0082" "0087" "0088" "0091"
##  [4951] "0095" "0098" "0100" "0104" "0105" "0109" "0110" "0117" "0120" "0129"
##  [4961] "0130" "0131" "0132" "0139" "0140" "0142" "0143" "0146" "0149" "0150"
##  [4971] "0151" "0152" "0158" "0163" "0164" "0165" "0166" "0168" "0177" "0178"
##  [4981] "0186" "0187" "0190" "0191" "0193" "0195" "0197" "0198" "0201" "0202"
##  [4991] "0204" "0205" "0207" "0208" "0213" "0221" "0227" "0228" "0229" "0230"
##  [5001] "0235" "0236" "0238" "0240" "0241" "0245" "0248" "0251" "0259" "0268"
##  [5011] "0293" "0294" "0298" "0302" "0309" "0325" "0326" "0328" "0330" "0331"
##  [5021] "0335" "0336" "0337" "0350" "0351" "0358" "0363" "0365" "0367" "0373"
##  [5031] "0374" "0376" "0377" "0380" "0384" "0385" "0388" "0393" "0394" "0402"
##  [5041] "0404" "0406" "0412" "0413" "0416" "0419" "0420" "0421" "0423" "0425"
##  [5051] "0426" "0427" "0428" "0430" "0432" "0434" "0435" "0441" "0442" "0444"
##  [5061] "0446" "0448" "0451" "0454" "0455" "0457" "0458" "0462" "0465" "0467"
##  [5071] "0468" "0470" "0472" "0474" "0475" "0477" "0478" "0480" "0482" "0483"
##  [5081] "0484" "0486" "0488" "0489" "0490" "0491" "0492" "0493" "0494" "0495"
##  [5091] "0499" "0500" "0503" "0506" "0508" "0510" "0514" "0515" "0517" "0520"
##  [5101] "0521" "0522" "0524" "0525" "0528" "0532" "0533" "0535" "0538" "0539"
##  [5111] "0540" "0541" "0542" "0544" "0545" "0547" "0548" "0552" "0555" "0556"
##  [5121] "0557" "0560" "0563" "0564" "0570" "0571" "0573" "0575" "0589" "0590"
##  [5131] "0591" "0592" "0593" "0595" "0596" "0598" "0600" "0601" "0603" "0605"
##  [5141] "0606" "0608" "0609" "0610" "0611" "0615" "0617" "0618" "0619" "0621"
##  [5151] "0622" "0626" "0627" "0629" "0632" "0634" "0637" "0646" "0648" "0649"
##  [5161] "0651" "0661" "0662" "0663" "0666" "0668" "0679" "0684" "0703" "0704"
##  [5171] "0705" "0731" "0737" "0745" "0749" "0760" "0763" "0765" "0768" "0773"
##  [5181] "0775" "0777" "0778" "0780" "0784" "0787" "0793" "0799" "0802" "0807"
##  [5191] "0810" "0815" "0818" "0821" "0822" "0824" "0831" "0835" "0839" "0844"
##  [5201] "0848" "0850" "0851" "0854" "0855" "0857" "0858" "0859" "0861" "0862"
##  [5211] "0863" "0865" "0867" "0870" "0873" "0874" "0877" "0879" "0880" "0883"
##  [5221] "0885" "0887" "0891" "0892" "0895" "0902" "0906" "0908" "0913" "0917"
##  [5231] "0920" "0922" "0923" "0926" "0927" "0929" "0931" "0932" "0933" "0935"
##  [5241] "0937" "0940" "0942" "0945" "0948" "0952" "0953" "0954" "0958" "0959"
##  [5251] "0960" "0961" "0963" "0966" "0967" "0968" "0971" "0974" "0976" "0981"
##  [5261] "0983" "0984" "0985" "0990" "0992" "0994" "0999" "1000" "1004" "1006"
##  [5271] "1008" "1014" "1019" "1024" "1025" "1026" "1028" "1029" "1031" "1032"
##  [5281] "1034" "1035" "1036" "1037" "1039" "1040" "1041" "1042" "1044" "1045"
##  [5291] "1046" "1052" "1053" "1055" "1058" "1059" "1062" "1063" "1064" "1067"
##  [5301] "1068" "1069" "1070" "1073" "1074" "1078" "1079" "1080" "1082" "1084"
##  [5311] "1087" "1095" "1096" "1099" "1100" "1101" "1102" "1103" "1104" "1106"
##  [5321] "1107" "1108" "1110" "1111" "1112" "1115" "1116" "1124" "1127" "1128"
##  [5331] "1130" "1145" "1146" "1147" "1148" "1151" "1153" "1154" "1155" "1157"
##  [5341] "1158" "1159" "1160" "1161" "1162" "1163" "1164" "1165" "1166" "1167"
##  [5351] "1168" "1170" "1171" "1172" "1173" "1174" "1176" "1178" "1179" "1181"
##  [5361] "1182" "1183" "1185" "1186" "1188" "1189" "1193" "1194" "1196" "1197"
##  [5371] "1199" "1200" "1203" "1206" "1208" "1209" "1213" "1214" "1216" "1217"
##  [5381] "1218" "1220" "1221" "1222" "1224" "1225" "1227" "1229" "1235" "1236"
##  [5391] "1238" "1239" "1242" "1243" "1244" "1246" "1248" "1249" "1251" "1252"
##  [5401] "1253" "1254" "1256" "1260" "1261" "1262" "1263" "1264" "1265" "1266"
##  [5411] "1267" "1268" "1269" "1270" "1271" "1273" "1274" "1275" "9998" "9999"
##  [5421] "0000" "0001" "0003" "0004" "0007" "0008" "0010" "0011" "0012" "0015"
##  [5431] "0016" "0019" "0021" "0022" "0023" "0024" "0025" "0027" "0030" "0033"
##  [5441] "0034" "0036" "0043" "0054" "0066" "0073" "0075" "0079" "0084" "0092"
##  [5451] "0095" "0096" "0099" "0103" "0107" "0108" "0109" "9998" "9999" "0000"
##  [5461] "0001" "0009" "0010" "0011" "0012" "0014" "0016" "0017" "0018" "0019"
##  [5471] "0021" "0022" "0023" "0024" "0026" "0028" "0029" "0031" "0032" "0040"
##  [5481] "0041" "0043" "0045" "0047" "0060" "0062" "0063" "0066" "0069" "0072"
##  [5491] "0075" "0077" "0080" "0081" "0082" "0090" "0092" "0098" "0100" "0102"
##  [5501] "0106" "0109" "0112" "0113" "0117" "0118" "0121" "0122" "0123" "0125"
##  [5511] "0126" "9998" "9999" "0000" "0001" "0003" "0007" "0009" "0015" "0017"
##  [5521] "0019" "0020" "0021" "0024" "0025" "0026" "0028" "0029" "0032" "0033"
##  [5531] "0035" "0038" "0039" "0045" "0046" "0048" "0049" "0053" "0057" "0058"
##  [5541] "0063" "0065" "0067" "0071" "0072" "0090" "0092" "0096" "0097" "0105"
##  [5551] "0108" "0114" "0117" "0120" "0123" "0127" "0128" "0130" "0140" "0142"
##  [5561] "0144" "0149" "0151" "0155" "0156" "0163" "0164" "0168" "0171" "0183"
##  [5571] "0184" "9998" "9999" "0000" "0001" "0003" "0005" "0009" "0011" "0018"
##  [5581] "0019" "0020" "0021" "0024" "0025" "0031" "0032" "0034" "0035" "0037"
##  [5591] "0038" "0040" "0042" "0044" "0045" "0049" "0050" "0051" "0052" "0063"
##  [5601] "0065" "0066" "0067" "0069" "0070" "0072" "0073" "0074" "0075" "0081"
##  [5611] "0083" "0084" "0086" "0087" "0093" "0096" "0098" "0100" "0104" "0108"
##  [5621] "0109" "0121" "0127" "0132" "0134" "0136" "0137" "0141" "0144" "0147"
##  [5631] "0148" "0149" "0151" "0153" "0154" "0156" "0161" "0162" "0164" "0175"
##  [5641] "0187" "0195" "0196" "0198" "0200" "0202" "0207" "0210" "0230" "0238"
##  [5651] "0254" "0255" "0256" "0269" "0335" "0336" "0346" "0349" "0354" "0366"
##  [5661] "0373" "0379" "0394" "0396" "0398" "0401" "0406" "0407" "0408" "0416"
##  [5671] "0419" "0420" "0427" "0430" "0432" "0449" "0451" "0461" "9998" "9999"
##  [5681] "0000" "0001" "0002" "0004" "0006" "0007" "0010" "0011" "0013" "0015"
##  [5691] "0016" "0017" "0019" "0020" "0021" "0022" "0025" "0026" "0027" "0028"
##  [5701] "0030" "0031" "0032" "0033" "0036" "0037" "0040" "0041" "0042" "0043"
##  [5711] "0044" "0045" "0048" "0051" "0052" "0054" "0056" "0057" "0058" "0059"
##  [5721] "0060" "0062" "0067" "0075" "0076" "0079" "0080" "0081" "0089" "0092"
##  [5731] "0096" "0102" "0106" "0109" "0114" "0122" "0123" "0124" "0133" "0136"
##  [5741] "0142" "0145" "0147" "0152" "0153" "9998" "9999" "0000" "0001" "0004"
##  [5751] "0007" "0008" "0009" "0011" "0014" "0015" "0022" "0023" "0024" "0027"
##  [5761] "0028" "0030" "0031" "0038" "0043" "0044" "0045" "0048" "0049" "0050"
##  [5771] "0052" "0057" "0058" "0059" "0061" "0066" "0068" "0073" "0077" "0078"
##  [5781] "0085" "0086" "0103" "0104" "0109" "0112" "0115" "0117" "0120" "0121"
##  [5791] "0126" "0127" "0128" "0130" "0131" "0133" "0134" "0141" "0142" "0148"
##  [5801] "0156" "0157" "0158" "0162" "0164" "0166" "0167" "0168" "0169" "0173"
##  [5811] "0178" "0179" "0180" "0182" "0183" "0190" "0191" "0194" "0195" "0198"
##  [5821] "0202" "0204" "0205" "0208" "0222" "0227" "0231" "0232" "0235" "0239"
##  [5831] "0240" "0242" "0244" "0245" "0246" "0247" "0248" "0249" "0251" "0252"
##  [5841] "0255" "0257" "0258" "0260" "0261" "0262" "0267" "0268" "9998" "9999"
##  [5851] "0000" "0001" "0002" "0005" "0008" "0009" "0010" "0021" "0023" "0025"
##  [5861] "0027" "0030" "0032" "0039" "0040" "0042" "0043" "0044" "0049" "0052"
##  [5871] "0053" "0055" "0057" "0063" "0064" "0065" "0066" "0069" "0076" "0082"
##  [5881] "0084" "0087" "0091" "0100" "0101" "0105" "0107" "0115" "0116" "0119"
##  [5891] "0123" "0130" "0131" "0132" "0136" "0139" "0141" "0145" "0146" "0147"
##  [5901] "0148" "0153" "0157" "0160" "0163" "0164" "0165" "0166" "0167" "0168"
##  [5911] "0169" "0170" "0172" "0173" "0174" "0175" "0178" "0180" "0182" "0185"
##  [5921] "0186" "0190" "0191" "0192" "0193" "0204" "0206" "0208" "0215" "0217"
##  [5931] "0219" "0228" "0233" "0240" "0242" "0244" "0245" "0254" "0255" "0256"
##  [5941] "0266" "0269" "0270" "0274" "0429" "0437" "0447" "0449" "0451" "0471"
##  [5951] "0479" "0483" "0488" "0490" "0494" "0496" "0500" "0506" "0508" "0512"
##  [5961] "0513" "0518" "0521" "0526" "0527" "0530" "0534" "0540" "0542" "0544"
##  [5971] "0545" "0549" "0551" "0558" "0571" "0573" "0574" "0580" "0593" "0597"
##  [5981] "0598" "0602" "0603" "0607" "0608" "0619" "9998" "9999" "0000" "0001"
##  [5991] "0002" "0006" "0010" "0013" "0018" "0020" "0023" "0024" "0028" "0030"
##  [6001] "0031" "0033" "0040" "0041" "0043" "0044" "0045" "0047" "0049" "0054"
##  [6011] "0057" "0059" "0061" "0063" "0064" "0068" "0073" "0077" "0079" "0080"
##  [6021] "0082" "0083" "0086" "0087" "0088" "0090" "0091" "0092" "0095" "0097"
##  [6031] "0098" "0099" "0101" "0106" "0107" "0108" "0111" "0117" "0118" "0119"
##  [6041] "0123" "0129" "0132" "0136" "0137" "0138" "0139" "0140" "0142" "0143"
##  [6051] "0145" "0148" "0149" "0151" "0153" "0154" "0155" "0156" "0161" "0163"
##  [6061] "0164" "0165" "0166" "0167" "0169" "0170" "0173" "0175" "0189" "0193"
##  [6071] "0201" "0202" "0203" "0204" "0212" "0216" "0217" "0220" "0221" "0222"
##  [6081] "0224" "0228" "0232" "0237" "0239" "0240" "0243" "0245" "0257" "0263"
##  [6091] "0267" "0268" "0270" "0271" "0278" "0279" "9998" "9999" "0000" "0001"
##  [6101] "0002" "0004" "0009" "0011" "0012" "0013" "0014" "0020" "0021" "0026"
##  [6111] "0027" "0034" "0035" "0043" "0044" "0051" "0061" "0064" "0083" "0086"
##  [6121] "0089" "0096" "0105" "0111" "0114" "0115" "0116" "0117" "0118" "0120"
##  [6131] "0121" "0124" "0125" "0126" "0127" "0128" "0129" "0130" "0131" "0132"
##  [6141] "0133" "0134" "9998" "9999" "0000" "0001" "0003" "0004" "0010" "0012"
##  [6151] "0013" "0017" "0018" "0023" "0024" "0028" "0029" "0030" "0035" "0036"
##  [6161] "0042" "0044" "0045" "0049" "0050" "0054" "0056" "0060" "0061" "0066"
##  [6171] "0067" "0069" "0071" "0073" "0074" "0075" "0076" "0079" "0081" "0083"
##  [6181] "0084" "0087" "0089" "0090" "0091" "0095" "0096" "0098" "0099" "0109"
##  [6191] "0112" "0115" "0117" "0118" "0121" "0162" "0167" "0168" "0170" "0178"
##  [6201] "0179" "0180" "0181" "0201" "0209" "0211" "0212" "0216" "0218" "0219"
##  [6211] "0226" "0231" "0235" "0239" "0251" "0263" "0267" "0269" "0272" "9998"
##  [6221] "9999" "0000" "0001" "0002" "0005" "0006" "0011" "0012" "0014" "0015"
##  [6231] "0016" "0018" "0021" "0022" "0024" "0025" "0032" "0033" "0036" "0043"
##  [6241] "0046" "0049" "0053" "0054" "0056" "0057" "0063" "0064" "0069" "0074"
##  [6251] "0075" "0076" "0078" "0079" "0081" "0082" "0083" "0086" "0090" "0091"
##  [6261] "0092" "0094" "0098" "0100" "0102" "0103" "0105" "0107" "0111" "0120"
##  [6271] "0126" "0130" "0131" "0147" "0148" "0151" "0153" "0154" "0155" "0156"
##  [6281] "0161" "0162" "0166" "0167" "0168" "0197" "0209" "0213" "0219" "0220"
##  [6291] "0223" "0224" "0225" "0226" "0227" "0228" "0229" "0230" "0231" "0232"
##  [6301] "0234" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0007"
##  [6311] "0008" "0010" "0011" "0012" "0013" "0019" "0020" "0025" "0026" "0027"
##  [6321] "0028" "0031" "0038" "0040" "0041" "0045" "0046" "0047" "0048" "0049"
##  [6331] "0051" "0052" "0053" "0055" "0056" "0057" "0058" "0059" "0064" "0065"
##  [6341] "0066" "0067" "0070" "0071" "0072" "0075" "0078" "0081" "0082" "0085"
##  [6351] "0087" "0092" "0097" "0101" "0103" "0107" "0108" "0111" "0112" "0113"
##  [6361] "0115" "0116" "0117" "0118" "0120" "0121" "0122" "0124" "0127" "0128"
##  [6371] "0133" "0134" "0135" "0136" "0138" "0140" "0142" "0148" "0149" "0150"
##  [6381] "0152" "0154" "0157" "0158" "0160" "0161" "0162" "0165" "0166" "0167"
##  [6391] "0170" "0173" "0174" "0178" "0179" "0180" "0181" "0182" "0183" "0185"
##  [6401] "0186" "0187" "0188" "0189" "0190" "0194" "0195" "0196" "0197" "0198"
##  [6411] "0200" "0204" "0206" "0208" "0209" "0210" "0212" "0214" "0215" "0216"
##  [6421] "0218" "0219" "0220" "0221" "0223" "0224" "0225" "0227" "0228" "0229"
##  [6431] "0232" "0233" "0234" "0236" "0237" "0239" "0241" "0242" "0243" "0244"
##  [6441] "0246" "0247" "0251" "0252" "0253" "0254" "0256" "0258" "0260" "0264"
##  [6451] "0268" "0269" "0270" "0271" "0273" "0275" "0276" "0277" "0281" "0282"
##  [6461] "0291" "0292" "0294" "0295" "0296" "0297" "0300" "0302" "0303" "0304"
##  [6471] "0305" "0311" "0313" "0314" "0316" "0317" "0319" "0321" "0323" "0329"
##  [6481] "0332" "0333" "0335" "0336" "0337" "0338" "0340" "0343" "0345" "0346"
##  [6491] "0347" "0349" "0350" "0351" "0352" "0354" "0356" "0357" "0358" "0360"
##  [6501] "0362" "0363" "0364" "0365" "0366" "0367" "0368" "0369" "0371" "0372"
##  [6511] "0373" "0374" "0375" "0376" "0378" "0379" "0380" "0381" "0382" "0384"
##  [6521] "0385" "0387" "0389" "0390" "0391" "0392" "0393" "0394" "0395" "0396"
##  [6531] "0398" "0399" "0401" "0402" "0403" "0404" "0405" "0408" "0409" "0414"
##  [6541] "0417" "0418" "0419" "0420" "0421" "0423" "0424" "0425" "0426" "0427"
##  [6551] "0428" "0429" "0430" "0431" "0432" "0433" "0434" "0435" "0437" "0438"
##  [6561] "0439" "0440" "0441" "0445" "9998" "9999" "0000" "0001" "0002" "0003"
##  [6571] "0006" "0009" "0011" "0013" "0014" "0015" "0017" "0019" "0020" "0021"
##  [6581] "0022" "0025" "0030" "0031" "0032" "0034" "0036" "0038" "0039" "0041"
##  [6591] "0042" "0043" "0044" "0045" "0046" "0048" "0052" "0053" "0054" "0056"
##  [6601] "0057" "0061" "0062" "0065" "0072" "0073" "0075" "0078" "0090" "0103"
##  [6611] "0109" "0114" "0117" "0122" "0123" "0127" "9998" "9999" "0000" "0001"
##  [6621] "0003" "0012" "0019" "0020" "0022" "0039" "0044" "0047" "0048" "0049"
##  [6631] "0051" "0052" "0060" "0068" "0071" "0076" "0082" "0086" "0088" "0090"
##  [6641] "0093" "0112" "0195" "0196" "0197" "0198" "0199" "9998" "9999" "0000"
##  [6651] "0001" "0002" "0003" "0007" "0015" "0027" "0028" "0032" "0033" "0034"
##  [6661] "0036" "0037" "0040" "0041" "0047" "0057" "0064" "0067" "0073" "0074"
##  [6671] "0083" "0085" "0086" "0092" "0093" "0096" "0097" "0098" "0099" "0103"
##  [6681] "0105" "0110" "0112" "0128" "0131" "0133" "0139" "0140" "0141" "0153"
##  [6691] "0158" "0174" "0185" "0188" "0190" "0191" "0202" "0207" "0210" "0214"
##  [6701] "0220" "0227" "0229" "0236" "0237" "0241" "0244" "0250" "0253" "0271"
##  [6711] "0274" "0281" "0283" "0285" "0286" "0289" "9998" "9999" "0000" "0001"
##  [6721] "0002" "0003" "0004" "0005" "0006" "0007" "0009" "0010" "0011" "0012"
##  [6731] "0013" "0014" "0015" "0016" "0017" "0018" "0019" "0020" "0022" "0023"
##  [6741] "0024" "0027" "0032" "0037" "0040" "0051" "0053" "0054" "0063" "0064"
##  [6751] "0065" "0066" "0074" "0075" "0083" "0084" "0086" "9998" "9999" "0000"
##  [6761] "0001" "0003" "0005" "0006" "0007" "0009" "0012" "0013" "0014" "0020"
##  [6771] "0022" "0023" "0026" "0027" "0034" "0037" "0039" "0041" "0045" "0046"
##  [6781] "0047" "0049" "0051" "0054" "0055" "0056" "0057" "0058" "0059" "0060"
##  [6791] "0061" "0062" "0063" "0064" "0065" "0066" "0068" "0070" "0074" "0080"
##  [6801] "0081" "0082" "0086" "0087" "0088" "0093" "0095" "0096" "0100" "0103"
##  [6811] "0104" "0105" "0108" "0112" "0115" "0117" "0120" "0121" "0122" "0123"
##  [6821] "0125" "0126" "0129" "0131" "0133" "0134" "0135" "0137" "0138" "0139"
##  [6831] "0141" "0144" "0146" "0148" "0153" "0155" "0157" "0158" "0164" "0165"
##  [6841] "0167" "0170" "0173" "0175" "0176" "0178" "0180" "0181" "0182" "0183"
##  [6851] "0184" "0185" "0186" "0187" "0189" "0190" "0191" "0195" "0196" "0198"
##  [6861] "0202" "0203" "0204" "0216" "0217" "0218" "0220" "0224" "0225" "0226"
##  [6871] "0227" "0228" "0233" "0240" "0241" "0245" "0250" "0253" "0254" "0258"
##  [6881] "0261" "0267" "0270" "0274" "0307" "0308" "0309" "0310" "0313" "0317"
##  [6891] "0320" "0324" "0327" "0328" "0330" "0333" "0336" "0338" "0339" "0346"
##  [6901] "0350" "0352" "0354" "0355" "0357" "0358" "0359" "0360" "0361" "0363"
##  [6911] "0364" "0368" "0370" "0371" "0378" "9998" "9999" "0000" "0001" "0008"
##  [6921] "0011" "0013" "0014" "0026" "0029" "0030" "0032" "0065" "0073" "0087"
##  [6931] "9998" "0000" "0001" "0005" "0014" "0015" "0016" "0019" "0020" "0021"
##  [6941] "0028" "0031" "0032" "0033" "0034" "0035" "0037" "0040" "0041" "0045"
##  [6951] "0061" "0062" "0065" "0067" "0068" "0069" "0070" "0071" "0079" "0082"
##  [6961] "0084" "0085" "0086" "0087" "0092" "0095" "0101" "0103" "0104" "0108"
##  [6971] "0111" "0112" "0114" "0115" "0120" "0122" "0124" "0127" "0129" "0137"
##  [6981] "0141" "0146" "0148" "0150" "0156" "0157" "0161" "0167" "0170" "0171"
##  [6991] "0172" "0180" "0181" "0185" "0191" "0198" "0200" "0215" "0227" "0229"
##  [7001] "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008" "0009"
##  [7011] "0011" "0012" "0014" "0016" "0022" "0023" "0026" "0027" "0028" "0030"
##  [7021] "0034" "0038" "0046" "0054" "0056" "9999" "0000" "0001" "0003" "0004"
##  [7031] "0010" "0020" "0025" "0026" "0031" "0041" "0046" "0049" "0055" "0062"
##  [7041] "0065" "0067" "0073" "0078" "0080" "0086" "0102" "0114" "0116" "0117"
##  [7051] "0134" "0136" "0146" "0151" "0159" "9998" "9999" "0000" "0001" "0002"
##  [7061] "0004" "0005" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0014"
##  [7071] "0015" "0016" "0017" "0018" "0024" "0025" "0026" "0033" "0034" "0035"
##  [7081] "0039" "0043" "0049" "0054" "0075" "0082" "0085" "0092" "0104" "0107"
##  [7091] "0110" "0112" "0117" "0118" "0119" "0123" "0125" "0128" "0130" "0132"
##  [7101] "0134" "0138" "0141" "0142" "0144" "0153" "0154" "0156" "0159" "0163"
##  [7111] "0166" "0168" "0176" "0177" "0179" "0180" "0181" "0182" "0188" "0191"
##  [7121] "0192" "0198" "0205" "0207" "0209" "0210" "0212" "0213" "0215" "0217"
##  [7131] "0219" "0220" "0221" "0222" "0228" "0229" "0236" "0240" "0248" "0249"
##  [7141] "0252" "0253" "0255" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
##  [7151] "0006" "0007" "0008" "0010" "0015" "0024" "0026" "0028" "0029" "0030"
##  [7161] "0033" "0038" "0041" "0044" "0045" "0050" "0053" "0054" "0063" "0066"
##  [7171] "0070" "0072" "0077" "0079" "0081" "0082" "0083" "0084" "0086" "0090"
##  [7181] "0098" "0100" "0103" "0106" "0111" "0113" "0115" "0119" "0120" "0129"
##  [7191] "0131" "0134" "0136" "0144" "0146" "0151" "0155" "0156" "0160" "0163"
##  [7201] "0167" "0169" "0172" "0174" "0175" "0181" "0183" "0188" "0191" "0204"
##  [7211] "0206" "0209" "0215" "0217" "0218" "0223" "0224" "0227" "0228" "0233"
##  [7221] "0234" "0235" "0237" "0241" "0247" "0249" "0251" "0252" "0257" "0264"
##  [7231] "0266" "0268" "0269" "0270" "0271" "0272" "0276" "0277" "0278" "0279"
##  [7241] "0280" "0284" "0285" "0287" "0288" "0289" "0292" "0299" "0300" "0305"
##  [7251] "0311" "0406" "0410" "0411" "0417" "0429" "0430" "0442" "0452" "0453"
##  [7261] "0454" "0455" "0460" "0464" "0476" "0488" "0502" "0504" "0505" "0513"
##  [7271] "0517" "0519" "0523" "0524" "0527" "0541" "0549" "0551" "0553" "0554"
##  [7281] "0555" "0556" "0561" "0566" "0568" "0575" "0589" "0592" "0594" "0596"
##  [7291] "0760" "0766" "0767" "9998" "9999" "0000" "0001" "0002" "0005" "0006"
##  [7301] "0010" "0011" "0013" "0020" "0024" "0025" "0031" "0032" "0033" "0034"
##  [7311] "0035" "0037" "0038" "0040" "0042" "0050" "0051" "0052" "0053" "0058"
##  [7321] "0060" "0061" "0063" "0066" "0071" "0072" "0076" "0077" "0080" "0083"
##  [7331] "0084" "0087" "0088" "0089" "0090" "0094" "0095" "0096" "0098" "0103"
##  [7341] "0104" "0105" "0106" "0111" "0112" "0116" "0118" "0120" "0123" "0124"
##  [7351] "0126" "0128" "0129" "0131" "0133" "0135" "0137" "0140" "0141" "0142"
##  [7361] "0146" "0148" "0152" "0153" "0154" "0155" "0156" "0157" "0161" "0163"
##  [7371] "0164" "0170" "0172" "0173" "0174" "0179" "0183" "0184" "0188" "0189"
##  [7381] "0190" "0195" "0205" "0222" "0229" "0230" "0231" "0232" "0235" "0239"
##  [7391] "0242" "0248" "0251" "0252" "0259" "0260" "0261" "0264" "0266" "0272"
##  [7401] "0275" "0276" "0288" "0302" "0304" "0310" "0315" "0319" "0322" "0323"
##  [7411] "0324" "0332" "0335" "0339" "0345" "0346" "0352" "0357" "0361" "0385"
##  [7421] "0391" "0398" "0400" "0412" "0418" "0422" "0423" "0432" "0433" "0435"
##  [7431] "0442" "0443" "0444" "0447" "0449" "0453" "0455" "0458" "0460" "0461"
##  [7441] "0462" "0463" "0464" "0466" "0467" "0468" "0469" "0470" "0473" "0477"
##  [7451] "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0006" "0007" "0009"
##  [7461] "0010" "0012" "0015" "0016" "0017" "0019" "0020" "0024" "0025" "0026"
##  [7471] "0030" "0031" "0032" "0033" "0034" "0035" "0036" "0038" "0040" "0042"
##  [7481] "0043" "0044" "0047" "0050" "0051" "0052" "0059" "0062" "0075" "0087"
##  [7491] "0089" "0092" "0093" "0094" "0097" "0098" "0102" "0105" "0111" "0113"
##  [7501] "0115" "0119" "0125" "0127" "0130" "0132" "0135" "0137" "0140" "0143"
##  [7511] "0147" "0148" "0150" "0151" "0152" "0154" "0156" "0157" "0161" "0162"
##  [7521] "0164" "0167" "0169" "0172" "0180" "0185" "0194" "0195" "0196" "0197"
##  [7531] "0198" "0201" "0202" "0206" "0207" "0209" "0210" "0211" "0215" "0223"
##  [7541] "0229" "0230" "0232" "0233" "0234" "0239" "0240" "9998" "9999" "0000"
##  [7551] "0001" "0008" "0009" "0015" "0017" "0027" "0031" "0032" "0043" "0047"
##  [7561] "0050" "0070" "0072" "0084" "0087" "0089" "0095" "0096" "0100" "0102"
##  [7571] "0103" "0107" "0108" "0111" "0116" "0132" "0144" "0145" "0148" "0149"
##  [7581] "0155" "0156" "0160" "0161" "0169" "0172" "0177" "0178" "0180" "0182"
##  [7591] "0185" "0190" "0198" "0207" "0209" "0210" "0212" "0213" "0220" "0227"
##  [7601] "0233" "0238" "0239" "0242" "0244" "0246" "0250" "0251" "0253" "0256"
##  [7611] "0261" "0262" "0272" "0273" "0274" "0280" "0287" "0289" "0290" "0291"
##  [7621] "0296" "0298" "0301" "0303" "0304" "0308" "0316" "0324" "0357" "0362"
##  [7631] "0363" "0379" "0386" "0389" "0398" "0412" "0425" "0447" "0449" "0454"
##  [7641] "0474" "0481" "0483" "0490" "0492" "0493" "0494" "0500" "0504" "0505"
##  [7651] "0508" "0509" "0516" "0519" "0531" "0532" "0541" "0544" "0545" "0549"
##  [7661] "0553" "0556" "0557" "0560" "0561" "0562" "0566" "0568" "0569" "0571"
##  [7671] "0575" "0576" "0578" "0580" "0581" "0582" "0584" "0585" "0590" "0593"
##  [7681] "0594" "0602" "0603" "0604" "0605" "0607" "0608" "0609" "0611" "0613"
##  [7691] "0614" "0615" "0617" "0619" "0620" "0623" "9998" "9999" "0000" "0001"
##  [7701] "0003" "0004" "0005" "0006" "0007" "0010" "0012" "0013" "0014" "0016"
##  [7711] "0018" "0021" "0022" "0023" "0025" "0026" "0030" "0031" "0032" "0035"
##  [7721] "0036" "0041" "0044" "0047" "0048" "0051" "0053" "0058" "0078" "0091"
##  [7731] "0096" "0107" "0112" "0121" "0130" "0132" "0138" "9998" "9999" "0000"
##  [7741] "0001" "0003" "0004" "0008" "0013" "0017" "0018" "0023" "0027" "0030"
##  [7751] "0035" "0038" "0041" "9998" "9999" "0000" "0001" "0007" "0008" "0009"
##  [7761] "0011" "0012" "0013" "0014" "0017" "0019" "0027" "0029" "0032" "0038"
##  [7771] "0044" "0054" "0055" "0087" "9998" "0000" "0001" "0008" "0009" "0011"
##  [7781] "0012" "0014" "0015" "0016" "0019" "0021" "0022" "0024" "0025" "0027"
##  [7791] "0029" "0030" "0033" "0037" "0039" "0044" "0046" "0047" "0048" "0050"
##  [7801] "0052" "0053" "0057" "0058" "0059" "0060" "0064" "0069" "0070" "0071"
##  [7811] "0073" "0081" "0085" "0092" "0094" "0095" "0096" "0101" "0104" "0105"
##  [7821] "0107" "0111" "0112" "0113" "0115" "0116" "0120" "0121" "0122" "0125"
##  [7831] "0129" "0130" "0131" "0132" "0135" "0136" "0137" "0138" "0140" "0141"
##  [7841] "0143" "0144" "0150" "0151" "0153" "0155" "0159" "0160" "0165" "0167"
##  [7851] "0173" "0178" "0182" "0185" "0187" "0193" "0198" "0199" "0202" "0204"
##  [7861] "0209" "0213" "0215" "0217" "0218" "0221" "0222" "0223" "0224" "0226"
##  [7871] "0231" "0232" "0237" "0240" "0244" "0246" "0248" "0250" "0252" "0253"
##  [7881] "0255" "0262" "0269" "0272" "0282" "0289" "0290" "0295" "0298" "0299"
##  [7891] "0301" "0302" "0303" "0304" "0308" "0310" "0313" "0318" "0320" "0324"
##  [7901] "0327" "0331" "0332" "0333" "0336" "0337" "0338" "0344" "0346" "0347"
##  [7911] "0348" "0351" "0353" "0355" "0359" "0360" "0361" "0364" "0367" "0373"
##  [7921] "0374" "0375" "0376" "0378" "0381" "9998" "9999" "0000" "0001" "0002"
##  [7931] "0003" "0004" "0006" "0008" "0009" "0010" "0011" "0012" "0014" "0015"
##  [7941] "0016" "0017" "0018" "0019" "0020" "0021" "0022" "0023" "0024" "0025"
##  [7951] "0026" "0027" "0034" "0035" "0037" "0041" "0042" "0043" "0044" "0046"
##  [7961] "0048" "9998" "9999" "0000" "0001" "0002" "0004" "0005" "0008" "0009"
##  [7971] "0012" "0018" "0024" "0029" "0032" "0038" "0039" "0041" "0042" "0044"
##  [7981] "0047" "0049" "0051" "0053" "0054" "0058" "0059" "0062" "0063" "0068"
##  [7991] "0069" "0070" "0071" "0073" "0074" "0075" "0079" "0082" "0087" "0089"
##  [8001] "0095" "0099" "0100" "0103" "0104" "0105" "0109" "0110" "0112" "0113"
##  [8011] "0115" "0117" "0119" "0122" "0128" "0129" "0130" "0131" "0134" "0135"
##  [8021] "0136" "0137" "0138" "0140" "0149" "0151" "0152" "0153" "0154" "0155"
##  [8031] "0157" "0158" "0162" "0167" "0169" "0171" "0174" "0176" "0177" "0178"
##  [8041] "0180" "0181" "0182" "0183" "0185" "0186" "0187" "0188" "0189" "0190"
##  [8051] "0193" "0196" "0197" "0198" "0199" "0200" "0202" "0203" "0204" "0205"
##  [8061] "0208" "0209" "0210" "0211" "0214" "0216" "0218" "0219" "0220" "0223"
##  [8071] "0224" "0226" "0227" "0230" "0241" "0242" "0244" "0250" "0255" "0257"
##  [8081] "0258" "0264" "0265" "0266" "0267" "0268" "0270" "0271" "0273" "0276"
##  [8091] "0277" "0278" "0282" "0285" "0287" "0290" "0291" "0292" "0293" "0295"
##  [8101] "0296" "0297" "0298" "0299" "0300" "0302" "0303" "0305" "0307" "0310"
##  [8111] "0311" "0312" "0315" "0316" "0318" "0320" "0322" "0324" "0325" "0326"
##  [8121] "0327" "0328" "0329" "0330" "0332" "0333" "0334" "0337" "0339" "0340"
##  [8131] "0341" "0344" "0345" "0348" "0358" "0360" "0365" "0371" "0374" "0376"
##  [8141] "0377" "0378" "0382" "0384" "0385" "0387" "0388" "0389" "0391" "0392"
##  [8151] "0394" "0395" "0396" "0399" "0400" "0402" "0403" "0404" "0405" "0406"
##  [8161] "0408" "0412" "0413" "0414" "0418" "0419" "0424" "0425" "0427" "0428"
##  [8171] "0429" "0430" "0431" "0432" "0433" "0435" "0436" "0447" "0461" "0462"
##  [8181] "0463" "0464" "0465" "0468" "0475" "0476" "0481" "0483" "0484" "0485"
##  [8191] "0487" "0488" "0489" "0665" "0666" "0667" "0673" "0676" "0678" "0680"
##  [8201] "0683" "0685" "0694" "0695" "0700" "0702" "0703" "0706" "0707" "0708"
##  [8211] "0710" "0715" "0716" "0717" "0718" "0721" "0722" "0723" "0726" "0730"
##  [8221] "0731" "0732" "0733" "0734" "0736" "0739" "0740" "0741" "0744" "0745"
##  [8231] "0746" "0748" "0749" "0751" "0752" "0755" "0757" "0758" "0759" "0760"
##  [8241] "0761" "0762" "0764" "0765" "0766" "0768" "0769" "0770" "0771" "0774"
##  [8251] "0775" "0777" "0779" "0783" "0784" "0785" "0786" "0787" "0788" "0789"
##  [8261] "0790" "0791" "0793" "0794" "0798" "0800" "0801" "0803" "0805" "0807"
##  [8271] "0808" "0811" "0812" "0813" "0814" "0815" "0816" "0826" "0828" "0831"
##  [8281] "0839" "0841" "0844" "0845" "0848" "0853" "0854" "0857" "0858" "0859"
##  [8291] "0860" "0861" "0862" "0863" "0864" "0866" "0867" "0868" "0869" "0870"
##  [8301] "9998" "9999" "0000" "0001" "0003" "0005" "0012" "0013" "0016" "0017"
##  [8311] "0018" "0019" "0022" "0026" "0027" "0033" "0035" "0039" "0040" "0041"
##  [8321] "0045" "0048" "0056" "0058" "0062" "0063" "0066" "0068" "0069" "0071"
##  [8331] "0077" "0079" "0086" "0088" "0093" "0094" "0095" "0096" "0102" "0109"
##  [8341] "0110" "0118" "0119" "0124" "0127" "0128" "0129" "0133" "0134" "0136"
##  [8351] "0138" "0140" "0141" "0144" "0145" "0152" "0164" "0166" "0169" "0177"
##  [8361] "0180" "0182" "0185" "0188" "0191" "0193" "0197" "0200" "0213" "0219"
##  [8371] "0220" "0221" "0223" "0224" "0227" "0231" "0232" "0233" "0234" "0235"
##  [8381] "0236" "0237" "0252" "0254" "0256" "0265" "0270" "0272" "0280" "0282"
##  [8391] "0284" "0285" "0292" "0295" "0296" "0301" "0303" "0311" "0317" "0417"
##  [8401] "0440" "0447" "0453" "0591" "0596" "0597" "0600" "0605" "0607" "0612"
##  [8411] "0618" "0621" "0623" "0625" "0627" "0634" "0635" "0641" "0642" "0643"
##  [8421] "0646" "0647" "0650" "0651" "0652" "0653" "0656" "0661" "0670" "0673"
##  [8431] "0684" "0686" "0690" "0692" "0694" "0695" "0696" "0700" "0703" "0712"
##  [8441] "0717" "0720" "0722" "0726" "0728" "0729" "0736" "0738" "0741" "0743"
##  [8451] "0746" "0750" "0751" "0754" "0765" "0766" "0767" "0768" "0769" "9998"
##  [8461] "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0011"
##  [8471] "0015" "0026" "0028" "0029" "0038" "0039" "0048" "0054" "0057" "0058"
##  [8481] "0061" "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0006" "0008"
##  [8491] "0009" "0010" "0011" "0013" "0014" "0015" "0016" "0017" "0018" "0023"
##  [8501] "0026" "0027" "0029" "0033" "0035" "0043" "0049" "0050" "9998" "9999"
##  [8511] "0000" "0001" "0003" "0004" "0005" "0006" "0008" "0009" "0011" "0013"
##  [8521] "0014" "0015" "0019" "0020" "0021" "0023" "0024" "0025" "0026" "0028"
##  [8531] "0031" "0032" "0033" "0034" "0035" "0037" "0038" "0039" "0041" "0042"
##  [8541] "0043" "0045" "0046" "0048" "0050" "0051" "0055" "0060" "0075" "0087"
##  [8551] "0089" "0093" "0103" "0105" "0106" "0107" "0110" "0115" "0117" "0119"
##  [8561] "0122" "0125" "0140" "0166" "0169" "0172" "0174" "0177" "0180" "0186"
##  [8571] "0200" "0235" "0237" "0242" "0244" "0325" "0333" "0349" "0372" "0383"
##  [8581] "0394" "0397" "0402" "0403" "0424" "0435" "0439" "0441" "0459" "0461"
##  [8591] "0464" "0466" "0473" "0474" "0487" "0500" "0501" "0502" "0510" "0519"
##  [8601] "0529" "0535" "0538" "0540" "0552" "0560" "0561" "0563" "0569" "0573"
##  [8611] "0575" "0583" "0591" "0606" "0611" "0613" "0617" "0622" "0624" "0626"
##  [8621] "0629" "0632" "0633" "0636" "0637" "0647" "0657" "0658" "0659" "0666"
##  [8631] "0672" "0674" "0676" "0682" "0694" "0695" "0698" "0699" "0701" "0710"
##  [8641] "0714" "0720" "0725" "0729" "0734" "0737" "0738" "0743" "0745" "0750"
##  [8651] "0754" "0756" "0760" "0762" "0763" "0765" "0766" "0767" "0768" "0769"
##  [8661] "0771" "0772" "0773" "0774" "0775" "0777" "0783" "0784" "0787" "0789"
##  [8671] "0790" "0791" "0792" "0793" "0795" "0798" "0799" "0800" "0801" "0802"
##  [8681] "0803" "0806" "0807" "0808" "0809" "0811" "0813" "0814" "0818" "0819"
##  [8691] "0822" "0823" "0827" "0831" "0832" "0833" "0835" "0836" "0837" "0838"
##  [8701] "0842" "0843" "0844" "0845" "0846" "0848" "0852" "0853" "0854" "0856"
##  [8711] "0857" "0858" "0859" "0860" "0861" "0862" "0863" "0864" "0866" "0869"
##  [8721] "0870" "0871" "0872" "0874" "0875" "0877" "0878" "0879" "0880" "0881"
##  [8731] "0882" "0883" "0884" "0890" "0891" "0894" "0895" "0897" "0901" "0902"
##  [8741] "0903" "0904" "0905" "9998" "9999" "0000" "0001" "0003" "0014" "0017"
##  [8751] "0029" "0039" "0049" "0070" "0081" "0083" "0091" "0092" "0095" "0105"
##  [8761] "0108" "0113" "0124" "0126" "0128" "0130" "0131" "0132" "0133" "0134"
##  [8771] "0135" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008"
##  [8781] "0010" "0011" "0012" "0014" "0015" "0017" "0018" "0019" "0020" "0021"
##  [8791] "0023" "0025" "0027" "0029" "0031" "0032" "0036" "0042" "0043" "0044"
##  [8801] "0045" "0058" "0061" "0064" "0071" "0073" "0074" "0077" "0085" "0086"
##  [8811] "0087" "0088" "0089" "9998" "9999" "0000" "0001" "0005" "0009" "0012"
##  [8821] "0013" "0018" "0020" "0021" "0023" "0025" "0026" "0029" "0030" "0033"
##  [8831] "0034" "0038" "0039" "0045" "0046" "0047" "0048" "0050" "0051" "0053"
##  [8841] "0054" "0057" "0058" "0059" "0065" "0067" "0069" "0070" "0073" "0075"
##  [8851] "0076" "0078" "0079" "0080" "0081" "0082" "0086" "0087" "0091" "0092"
##  [8861] "0095" "0096" "0098" "0100" "0101" "0105" "0109" "0110" "0112" "0113"
##  [8871] "0115" "0116" "0120" "0122" "0123" "0125" "0132" "0137" "0138" "0143"
##  [8881] "0145" "0147" "0149" "0152" "0153" "0156" "0157" "0159" "0161" "0165"
##  [8891] "0167" "0168" "0171" "0172" "0180" "0189" "0215" "0217" "0218" "0219"
##  [8901] "0220" "0221" "0230" "0235" "0237" "0242" "0244" "0249" "0250" "0252"
##  [8911] "0261" "0263" "0266" "0267" "0269" "0270" "0271" "0272" "0273" "0275"
##  [8921] "0276" "0280" "0285" "0286" "0287" "0288" "0290" "0292" "0293" "0296"
##  [8931] "0302" "0303" "0306" "0308" "0313" "0314" "0320" "0329" "0338" "0341"
##  [8941] "0343" "0345" "0348" "0351" "0354" "0357" "0358" "0361" "0362" "0363"
##  [8951] "0366" "0367" "0368" "0372" "0374" "0379" "0382" "0383" "0387" "0388"
##  [8961] "0391" "0392" "0393" "0402" "0403" "0406" "0408" "0414" "0418" "0424"
##  [8971] "0427" "0432" "0438" "0452" "0460" "0463" "0473" "0476" "0477" "0479"
##  [8981] "0480" "0482" "0484" "0488" "0490" "0493" "0499" "0509" "0511" "0516"
##  [8991] "0520" "0522" "9998" "9999" "0000" "0001" "0002" "0009" "0012" "0022"
##  [9001] "0023" "0025" "0026" "0027" "0031" "0034" "0038" "0050" "0051" "0056"
##  [9011] "0061" "0073" "0101" "0105" "0109" "0111" "0112" "0114" "0123" "0125"
##  [9021] "0126" "0130" "0133" "0135" "0136" "0154" "0156" "0157" "0164" "0165"
##  [9031] "0169" "0174" "0184" "0188" "0189" "0190" "0192" "0193" "0194" "0195"
##  [9041] "0196" "0197" "0199" "0200" "0204" "0205" "9998" "9999" "0000" "0001"
##  [9051] "0002" "0003" "0009" "0010" "0013" "0015" "0016" "0019" "0022" "0023"
##  [9061] "0025" "0026" "0027" "0028" "0034" "0035" "0037" "0043" "0044" "0047"
##  [9071] "0073" "9998" "9999" "0000" "0001" "0006" "0007" "0009" "0010" "0011"
##  [9081] "0015" "0016" "0019" "0033" "0044" "0048" "0054" "0064" "0065" "0067"
##  [9091] "0068" "0069" "9998" "0000" "0001" "0002" "0003" "0004" "0005" "0006"
##  [9101] "0011" "0013" "0014" "0015" "0016" "0020" "0021" "0023" "0027" "0031"
##  [9111] "0034" "0035" "0037" "0047" "0050" "0052" "0055" "0056" "0061" "0062"
##  [9121] "0063" "0068" "0070" "0071" "0072" "0074" "0075" "0076" "0077" "0078"
##  [9131] "0079" "0082" "0084" "0089" "0104" "0110" "0119" "0135" "0146" "0156"
##  [9141] "0157" "0160" "0161" "0181" "0198" "9998" "9999" "0000" "0001" "0002"
##  [9151] "0005" "0006" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0015"
##  [9161] "0016" "0017" "0018" "0021" "0027" "0029" "0031" "0032" "0034" "0035"
##  [9171] "0037" "0038" "0039" "0040" "0041" "0044" "0045" "0046" "0047" "0049"
##  [9181] "0050" "0053" "0054" "0058" "0061" "0062" "0063" "0065" "0067" "0068"
##  [9191] "0070" "0074" "0075" "0076" "0105" "0106" "0107" "0113" "0114" "0115"
##  [9201] "0116" "0117" "0118" "0119" "0120" "0127" "0131" "0134" "0136" "0138"
##  [9211] "0140" "0141" "0144" "0148" "0149" "0154" "0155" "0160" "0162" "0165"
##  [9221] "0166" "0170" "0176" "0180" "0181" "0183" "0184" "0185" "0187" "0191"
##  [9231] "0194" "0196" "0199" "0201" "0202" "0206" "0207" "0212" "0215" "0216"
##  [9241] "0222" "0226" "0230" "0232" "9998" "9999" "0000" "0001" "0003" "0006"
##  [9251] "0007" "0008" "0010" "0011" "0013" "0014" "0016" "0019" "0020" "0021"
##  [9261] "0022" "0023" "0024" "0025" "0033" "0036" "0039" "0042" "0046" "9998"
##  [9271] "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0008"
##  [9281] "0009" "0010" "0014" "0019" "0020" "0022" "0024" "0040" "0042" "0045"
##  [9291] "0047" "0048" "9998" "9999" "0000" "0001" "0003" "0005" "0006" "0007"
##  [9301] "0010" "0012" "0014" "0022" "0024" "0026" "0028" "0029" "0031" "0032"
##  [9311] "0034" "0037" "0038" "0041" "0042" "0046" "0047" "0048" "0050" "0051"
##  [9321] "0053" "0056" "0058" "0059" "0062" "0065" "0066" "0072" "0086" "0093"
##  [9331] "0101" "0109" "0111" "0112" "0122" "0125" "0126" "0127" "0131" "0132"
##  [9341] "0134" "0136" "0260" "0262" "0270" "0271" "0276" "0280" "0285" "0288"
##  [9351] "0290" "0293" "0294" "0295" "0297" "0298" "0300" "0301" "0303" "0306"
##  [9361] "0309" "0312" "0315" "0324" "0327" "0331" "0333" "0334" "0338" "0340"
##  [9371] "0345" "0346" "0349" "0355" "0359" "0362" "9998" "9999" "0000" "0001"
##  [9381] "0002" "0005" "0006" "0008" "0011" "0014" "0015" "0017" "0020" "0022"
##  [9391] "0023" "0027" "0030" "0032" "0036" "0037" "0044" "0045" "0046" "0047"
##  [9401] "0049" "0050" "0060" "0061" "0066" "0071" "0072" "0074" "0075" "0076"
##  [9411] "0081" "0082" "0084" "0086" "0088" "0089" "0091" "0092" "0094" "0098"
##  [9421] "0100" "0101" "0102" "0106" "0107" "0108" "0109" "0110" "0111" "0112"
##  [9431] "0117" "0118" "0120" "0121" "0125" "0126" "0128" "0129" "0130" "0140"
##  [9441] "0144" "0146" "0151" "0152" "0153" "0154" "0157" "0158" "0161" "0167"
##  [9451] "0168" "0170" "0176" "0183" "0238" "0244" "0245" "0246" "0247" "0249"
##  [9461] "0255" "0256" "0265" "0267" "0270" "0271" "0278" "0344" "0362" "0378"
##  [9471] "0380" "0382" "0385" "0392" "0394" "0395" "0399" "0400" "0402" "0403"
##  [9481] "0404" "0405" "0407" "0409" "0412" "0417" "0419" "0420" "0423" "0427"
##  [9491] "0429" "0431" "0432" "0434" "0435" "0438" "0440" "0441" "0443" "0444"
##  [9501] "0446" "0448" "0451" "0452" "0453" "0455" "0458" "0461" "0462" "0463"
##  [9511] "0464" "0466" "0467" "0469" "0471" "0472" "9998" "9999" "0000" "0001"
##  [9521] "0003" "0006" "0008" "0014" "0021" "0022" "0027" "0032" "0034" "0036"
##  [9531] "0038" "0039" "0040" "0041" "0042" "0044" "0045" "0047" "0048" "0049"
##  [9541] "0050" "0058" "0060" "0064" "0066" "0068" "0073" "0086" "0089" "0091"
##  [9551] "0110" "0125" "0135" "0138" "0143" "0155" "0156" "9998" "9999" "0000"
##  [9561] "0001" "0005" "0008" "0009" "0011" "0012" "0014" "0016" "0017" "0018"
##  [9571] "0019" "0020" "0022" "0029" "0035" "0036" "0039" "0040" "0047" "0048"
##  [9581] "0054" "0055" "0057" "0060" "0062" "0064" "0065" "0067" "0070" "0071"
##  [9591] "0072" "0075" "0076" "0078" "0080" "0081" "0085" "0086" "0091" "0092"
##  [9601] "0101" "0104" "0105" "0106" "0107" "0125" "0132" "0135" "0147" "0149"
##  [9611] "0150" "0152" "0155" "0156" "0157" "0160" "0162" "0163" "0164" "0165"
##  [9621] "0167" "0168" "0169" "0170" "0173" "0174" "0176" "0177" "0178" "0182"
##  [9631] "0183" "9998" "9999" "0000" "0001" "0002" "0005" "0006" "0009" "0012"
##  [9641] "0013" "0014" "0021" "0026" "0028" "0029" "0030" "0031" "0032" "0033"
##  [9651] "0035" "0036" "0037" "0041" "0043" "0045" "0046" "0053" "0054" "0055"
##  [9661] "0056" "0057" "0063" "0064" "0067" "0071" "0072" "0077" "0078" "0079"
##  [9671] "0080" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0009"
##  [9681] "0010" "0011" "0012" "0013" "0018" "0020" "0021" "0023" "0024" "0026"
##  [9691] "0027" "0028" "0029" "0030" "0033" "0034" "0040" "0041" "0042" "0043"
##  [9701] "0046" "0047" "0051" "0052" "0053" "0055" "0058" "0059" "0060" "0061"
##  [9711] "0062" "0064" "0066" "0068" "0080" "0081" "0084" "0092" "0093" "0096"
##  [9721] "0097" "0104" "0115" "0116" "0119" "0124" "0125" "0126" "0137" "0138"
##  [9731] "0141" "0143" "0151" "0173" "0178" "0187" "0189" "9998" "9999" "0000"
##  [9741] "0001" "0002" "0003" "0004" "0005" "0006" "0008" "0009" "0017" "0018"
##  [9751] "0019" "0020" "0022" "0024" "0025" "0026" "0028" "0029" "0030" "0032"
##  [9761] "0033" "0036" "0037" "0040" "0041" "0043" "0044" "0047" "0050" "9998"
##  [9771] "9999" "0000" "0001" "0003" "0008" "0009" "0011" "0013" "0015" "0018"
##  [9781] "0021" "0028" "0033" "0035" "0040" "0041" "0042" "0045" "0046" "0048"
##  [9791] "0058" "0060" "0061" "0066" "0069" "0070" "0080" "0083" "0084" "0086"
##  [9801] "0088" "0090" "0094" "0097" "0099" "0116" "0130" "0136" "0149" "0150"
##  [9811] "0152" "0156" "0163" "0164" "0165" "0170" "0208" "0231" "0234" "0260"
##  [9821] "0270" "0275" "0276" "9998" "9999" "0000" "0001" "0002" "0004" "0006"
##  [9831] "0012" "0013" "0016" "0018" "0019" "0020" "0023" "0024" "0025" "0027"
##  [9841] "0028" "0031" "0032" "0036" "0044" "0045" "0046" "0048" "0052" "0055"
##  [9851] "0067" "0074" "0085" "0086" "0091" "0093" "0094" "0097" "0099" "0102"
##  [9861] "0103" "0104" "0107" "0109" "0110" "0112" "0113" "0119" "0121" "0123"
##  [9871] "0124" "0129" "0130" "0131" "9998" "9999" "0000" "0001" "0002" "0003"
##  [9881] "0004" "0007" "0013" "0015" "0020" "0021" "0023" "0026" "0027" "0032"
##  [9891] "0041" "0047" "0051" "0052" "0053" "0054" "0057" "0058" "0062" "0063"
##  [9901] "0064" "0065" "0066" "0067" "0073" "0074" "0075" "0076" "0080" "0089"
##  [9911] "0092" "0101" "0102" "0109" "0116" "0120" "0124" "0130" "0132" "0133"
##  [9921] "0135" "0136" "9998" "9999" "0000" "0001" "0002" "0005" "0006" "0009"
##  [9931] "0010" "0012" "0013" "0019" "0021" "0024" "0025" "0026" "0027" "0028"
##  [9941] "0031" "0032" "0036" "0046" "0047" "0049" "0052" "0056" "0058" "0059"
##  [9951] "0061" "0062" "0066" "0068" "0069" "0071" "0074" "0075" "0076" "0077"
##  [9961] "0079" "0081" "0083" "0084" "0087" "0088" "0091" "0095" "0097" "0098"
##  [9971] "0101" "0103" "0105" "0108" "0110" "0112" "0114" "0118" "0120" "0121"
##  [9981] "0122" "0124" "0125" "0128" "0130" "0131" "0133" "0135" "0137" "0140"
##  [9991] "0141" "0143" "0147" "0153" "0154" "0164" "0165" "0169" "0172" "0177"
## [10001] "0180" "0183" "0186" "0187" "0189" "0193" "0195" "0199" "0209" "0210"
## [10011] "0213" "0222" "0224" "0225" "0229" "0230" "0232" "0233" "0235" "0236"
## [10021] "0239" "0240" "0244" "0247" "0248" "0249" "0250" "0252" "0253" "0254"
## [10031] "0255" "0257" "0260" "0264" "0267" "0271" "0273" "0278" "0280" "0282"
## [10041] "0283" "0286" "0289" "0291" "0309" "0310" "0314" "0317" "0318" "0321"
## [10051] "0322" "0324" "0331" "0332" "0333" "0334" "0335" "0340" "0345" "0346"
## [10061] "0347" "0348" "0350" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
## [10071] "0005" "0006" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0014"
## [10081] "0015" "0016" "0017" "0018" "0022" "0024" "0025" "0026" "0027" "0035"
## [10091] "0036" "0042" "0043" "0044" "0057" "0059" "0060" "9998" "9999" "0000"
## [10101] "0001" "0004" "0006" "0018" "0020" "0022" "0027" "0029" "0033" "0038"
## [10111] "0047" "0050" "0056" "0057" "0062" "0064" "0070" "0078" "0089" "0095"
## [10121] "0097" "0099" "0111" "0124" "0129" "0133" "0134" "0139" "0143" "0151"
## [10131] "0153" "0168" "0183" "0190" "0194" "0198" "0201" "0202" "0206" "0210"
## [10141] "0213" "0214" "0218" "0227" "0230" "0231" "0232" "0235" "0242" "0243"
## [10151] "0249" "0250" "0253" "0255" "0258" "0259" "0263" "0264" "0268" "0270"
## [10161] "0271" "0272" "0273" "0274" "0276" "0277" "0279" "0280" "0286" "0288"
## [10171] "0297" "0300" "0302" "0312" "0313" "0314" "0315" "0317" "0318" "0319"
## [10181] "0324" "0329" "0334" "0342" "0343" "0350" "0355" "0356" "0360" "0369"
## [10191] "0376" "0377" "0378" "0380" "0391" "0396" "0405" "0407" "0415" "0417"
## [10201] "0430" "0435" "0440" "0444" "0449" "0450" "0451" "0452" "0453" "0458"
## [10211] "0460" "0463" "0464" "0468" "0470" "0471" "0476" "0486" "0511" "0512"
## [10221] "0513" "0516" "0522" "0526" "0528" "0529" "0535" "0536" "0537" "0539"
## [10231] "0540" "0542" "0544" "0547" "0550" "0560" "0565" "0566" "0568" "0572"
## [10241] "0573" "0574" "0575" "0577" "0583" "0587" "0588" "0591" "0592" "0593"
## [10251] "0594" "0595" "0596" "0597" "0600" "0605" "0608" "0610" "0611" "0613"
## [10261] "0615" "0620" "0621" "0622" "0626" "0632" "0660" "0668" "0683" "0684"
## [10271] "0690" "0697" "0703" "0706" "0708" "0709" "0712" "0714" "0715" "0718"
## [10281] "0721" "0722" "0724" "0726" "0727" "0728" "0735" "0737" "0738" "0739"
## [10291] "0741" "0747" "0748" "0749" "0750" "0752" "0753" "0754" "0755" "0756"
## [10301] "0757" "0758" "0760" "0761" "0763" "0764" "0766" "0767" "0770" "0771"
## [10311] "9998" "9999" "0000" "0001" "0005" "0009" "0010" "0011" "0012" "0013"
## [10321] "0018" "0020" "0021" "0022" "0023" "0024" "0025" "0029" "0031" "0037"
## [10331] "0052" "0053" "0057" "0059" "0067" "0071" "0072" "0074" "0084" "0086"
## [10341] "9998" "9999" "0000" "0001" "0009" "0010" "0011" "0014" "0016" "0017"
## [10351] "0018" "0019" "0020" "0021" "0023" "0025" "0027" "0028" "0029" "0031"
## [10361] "0033" "0034" "0035" "0036" "0037" "0038" "0039" "0040" "0041" "0042"
## [10371] "0046" "0047" "0049" "0053" "0054" "0068" "0069" "0071" "0072" "0081"
## [10381] "0082" "0083" "0084" "9998" "9999" "0000" "0001" "0002" "0003" "0005"
## [10391] "0008" "0009" "0011" "0014" "0015" "0016" "0018" "0019" "0020" "0024"
## [10401] "0026" "0027" "0028" "0029" "0030" "0031" "0034" "0036" "0037" "0038"
## [10411] "0039" "0041" "0042" "0043" "0044" "0045" "0046" "0047" "0051" "0052"
## [10421] "0053" "0069" "0070" "0071" "0077" "0079" "0083" "0084" "0085" "0086"
## [10431] "0087" "0090" "0096" "0112" "0118" "0127" "0132" "0134" "0141" "0144"
## [10441] "0150" "0151" "0152" "0153" "9998" "9999" "0000" "0001" "0003" "0004"
## [10451] "0006" "0007" "0009" "0010" "0015" "0016" "0017" "0018" "0019" "0020"
## [10461] "0025" "0026" "0027" "0030" "0031" "0034" "0036" "0039" "0042" "0043"
## [10471] "0044" "0045" "0046" "0047" "0049" "0050" "0051" "0052" "0054" "0055"
## [10481] "0058" "0059" "0060" "0061" "0062" "0063" "0064" "0067" "0068" "0070"
## [10491] "0071" "0073" "0075" "0081" "0082" "0086" "0088" "0089" "0092" "0093"
## [10501] "0094" "0095" "0096" "0097" "0100" "0102" "0104" "0105" "0107" "0109"
## [10511] "0114" "0115" "0116" "0117" "0120" "0122" "0124" "0125" "0127" "0129"
## [10521] "0131" "0133" "0134" "0135" "0139" "0143" "0147" "0149" "0154" "0155"
## [10531] "0156" "0158" "0161" "0162" "0163" "0167" "0168" "0174" "0175" "0176"
## [10541] "0179" "0180" "0181" "0182" "0184" "0185" "0187" "0189" "0191" "0192"
## [10551] "0195" "0196" "0197" "0198" "0199" "0200" "0201" "0203" "0205" "0209"
## [10561] "0210" "0212" "0214" "0216" "0217" "0218" "0221" "0223" "0226" "0228"
## [10571] "0233" "0235" "0237" "0240" "0244" "0246" "0247" "0248" "0250" "0251"
## [10581] "0253" "0255" "0258" "0260" "0271" "0272" "0273" "0276" "0277" "0284"
## [10591] "0286" "0288" "0289" "0290" "0291" "0293" "0295" "0297" "0302" "0303"
## [10601] "0307" "0310" "0312" "0314" "0317" "0318" "0322" "0325" "0326" "0327"
## [10611] "0331" "0332" "0333" "0337" "0338" "0340" "0342" "0343" "0344" "0346"
## [10621] "0348" "0349" "0350" "0351" "0352" "0355" "0357" "0358" "0359" "0360"
## [10631] "0361" "0362" "0365" "9998" "9999" "0000" "0001" "0003" "0004" "0006"
## [10641] "0007" "0009" "0010" "0011" "0012" "0013" "0014" "0015" "0016" "0018"
## [10651] "0019" "0020" "0022" "0023" "0024" "0025" "0026" "0027" "0028" "0029"
## [10661] "0030" "0031" "0032" "0033" "0034" "0035" "0036" "0037" "0039" "0040"
## [10671] "0041" "0042" "0043" "0044" "0045" "0047" "0050" "0051" "0052" "0053"
## [10681] "0055" "0056" "0057" "0058" "0059" "0060" "0062" "0063" "0064" "0066"
## [10691] "0067" "0068" "0069" "0070" "0072" "0073" "0074" "0075" "0076" "0077"
## [10701] "0078" "0079" "0080" "0081" "0082" "0083" "0085" "0086" "0087" "0088"
## [10711] "0089" "0090" "0091" "9998" "9999"

datos_inegi_jal2$LOC

##     [1]    0 9998 9999    0    1    2    3    6    7    9   10   11   12   13
##    [15]   14   16   18   19   20   21   22   23   24   26   27   28   29   30
##    [29]   31   32   33   34   35   36   37   38   40   41   42   43   44   46
##    [43]   47   48   49   52   53   54   55   56   57   59   60   62   63   66
##    [57]   67   68   69   70   71   72   73   74   75   78   79   81   84   86
##    [71]   87   88   94  102  104  106  107  108  109  111  112  113  115  116
##    [85]  118  119  122  123  128  129  131  132  133  135  137  139  141  142
##    [99] 9998 9999    0    1    2    4    5    7    8    9   10   13   19   21
##   [113]   24   25   27   30   34   35   36   37   39   40   41   43   48   53
##   [127]   55   58   61   62   63   64   65 9998 9999    0    1    4    7   19
##   [141]   21   28   30   31   32   38   51   56   57   58   59   68   74   80
##   [155]   82   83   86   94   96   97   99 9998 9999    0    1    3    4    5
##   [169]    7    8   10   14   17   20   22   23   25   26   30   32   39   53
##   [183]   61   64   66 9998 9999    0    1    2    3    4    5    7    8    9
##   [197]   10   13   15   20   22   26   29   31   32   35   36   39   40   43
##   [211]   45   56   65   70   71   74   75 9998 9999    0    1    2    3    4
##   [225]    5    7   10   11   12   13   14   15   17   18   19   20   22   23
##   [239]   24   25   26   27   28   30   31   32   33   34   37   38   39   40
##   [253]   41   42   43   44   45   46   47   48   49   50   51   52   53   54
##   [267]   57   58   59   61   62   70   73   75   76   77   79   80   82   83
##   [281]   84   85   87   88   89   91   92   95   97   98  100  102  107  109
##   [295]  111  112  116  121  122  123  130  133  137  140  142  147  148  149
##   [309]  153  156  157  158  160  162  165  167  171  174  182  185  187  189
##   [323]  190  192  193  194 9998 9999    0    1    3    4    8    9   10   12
##   [337]   13   14   15   22   76   86 9998    0    1    2    3    4    5    6
##   [351]    7    9   11   14   16   21   22   24   26   28   32   36   45   46
##   [365]   48   50   51   54   56   57   58   61   63   64   65   66   68   72
##   [379]   73   76   78   83   89   90   93   95   96   97  101  102  103  106
##   [393]  108  110  113  115  116  117  118  120  127  130  131  133  137  139
##   [407]  141  142  143  145  146  147  150  151  153  154  156  158  160  161
##   [421]  162  167  171  172  175  177  178  181  184  187  188  189  191  193
##   [435]  194  196  198  201  202  203  207  210  212  213  214  216  217  222
##   [449]  225  228  230  231  234  238  239  241  243  244  245  247  248  249
##   [463]  250  257  258  261  262  263  266  267  269  271  272  274  275  277
##   [477]  279  280  281  283  287  288  289  294  295  301  303  307  311  317
##   [491]  318  320  321  322  327  328  331  337  339  342  343  345  346  348
##   [505]  351  352  353  358  360  361  371  372  375  376  378  379  381  382
##   [519]  390  393  394  395  396  403  405  406  408  409  411  412  415  417
##   [533]  418  422  423  424  425  494  497  500  501  503  509  511  512  513
##   [547]  516  519  520  521  523  525  532  533  536  537  538  540  541  543
##   [561]  548  552  553  557  560  567  570  571  574  575  576  579  582  585
##   [575]  586  587  589  590  592  593  596  602  606  607  610  611  612  614
##   [589]  617  621  624  626  627  631  632  633  635  637  638  639  641  643
##   [603]  645  646  649  650  657  659  661  662  664  665  666  667  668  669
##   [617]  672  673  675  676  677  681  682  683  684  686  687  688  689  690
##   [631]  691  694  695  696  697  698  701 9998 9999    0    1    3    4    5
##   [645]    6    7    9   12   14   20   21   24   30   32   34   41   43   44
##   [659]   48   54   55   57   60   65   66   67   68   69   70   71   72   73
##   [673]   74   75   76   77   78   79   80   81 9998 9999    0    1    3   16
##   [687]   21   23   24   25   26   27   28   29   32   33   46   51   52   55
##   [701]   58   60   61  107 9998    0    1    3    8    9   11   15   16   17
##   [715]   21   22   24   26   29   30   33   41   47   54   68 9998 9999    0
##   [729]    1    2    3    4    5   10   12   13   16   21   32   38   39   40
##   [743]   41   44   45   46   49   50   53   56   58   59   60   61   63   64
##   [757]   66   67   73   74   76   90   95  103  104  107  109  111  114  116
##   [771]  119  130  139  141  142  148  149  152  155  156 9998 9999    0    1
##   [785]    2    3    4    5    6    7    8   10   11   12   13   14   16   17
##   [799]   18   19   23   24   25   26   28   29   30   32   35   36   37   38
##   [813]   39   41   43   44   45   46   47   48   50   51   52   54   55   57
##   [827]   58   60   61   65   67   68   69   70   74   75   76   77   78   80
##   [841]   82   83   84   85   87   89   90   91   92   93   94   96   98   99
##   [855]  101  105  106  109  114  121  123  125  127  128  130  131  134  136
##   [869]  137  141  142  145  146  147  148  151  154  155  160  165  166  167
##   [883]  169  173  175  177  178  180  185  188  191  195  203  204  205  207
##   [897]  211  212  213  215  216  218  219  220  221  224  225  227  230  232
##   [911]  235  240  241  242  243  245  246  248 9998 9999    0    1    3    4
##   [925]    5    6    8    9   10   13   15   16   19   20   21   23   24   25
##   [939]   26   28   29   31   45   47   56   57   62   63   68   71   73   79
##   [953] 9998 9999    0    1    2    3    5    7    9   11   12   13   14   15
##   [967]   17   19   20   22   23   24   25   26   27   28   31   33   36   38
##   [981]   41   42   46   47   48   51   53   55   58   60   63   64   68   69
##   [995]   71   75   76   77   79   81   82   84   85   86   88   97  102  104
##  [1009]  105  107  108  112  116  119  120  125  130  131  132  133  134  136
##  [1023]  138  139  140  142  144  146  152  154  156  158  160  161  163  165
##  [1037]  175  176  178  186  190  194  195  203  204  207  208  212  213  216
##  [1051]  225  227  237  239  242  244  247  248  251  252  253  255  257  258
##  [1065]  259  264  265  266  267  270  273  275  276  283  287  296  298  301
##  [1079]  302  304  309  314  333  337  347  348  358  360  369  372  375  378
##  [1093]  379  382  383 9998 9999    0    1    2    3    4    5    8   10   11
##  [1107]   12   14   15   17   18   20   24   27   29   30   33   35   37   38
##  [1121]   39   43   46   47   48   49   50   51   52   53   54   55   69   70
##  [1135]   71   72   73   75   77   79   80   82   83   85   86   87   89   93
##  [1149]   95  100  101  103  104  106  108  112  114  115  116  121  122  124
##  [1163]  128  133  141  142  145  152  156  160  161  162  164  170 9998 9999
##  [1177]    0    1    5    6   12   13   17   18   20   26   37   41   42   54
##  [1191]   56   58   64   66   76   78   83   84   85   86   87   88   93   96
##  [1205]   97  100  102  103  104  105  110  111  112  116  119  120  121  123
##  [1219]  124  125  130  133  134  135  139  141  145  150  181  182  188  200
##  [1233]  203  205  206  211  214  215  227  230  233  242  245  247  250  251
##  [1247]  253 9998 9999    0    1    3    5    7    8    9   10   11   12   13
##  [1261]   15   17   18   19   22   23   24   26   27   28   29   31   32   33
##  [1275]   34   36   37   38   39   40   41   42   55   60   61   62   71   74
##  [1289]   75   86   89   95  109  112  123  129  135  139  144  149  154  174
##  [1303]  178  180  191  195  204  210  211  214  220  230  231  232  240  243
##  [1317]  244  245  246  251  257  259  262  264  265  266 9998 9999    0    1
##  [1331]    2    7   10   13   17   18   20   23   26   27   32   35   36   40
##  [1345]   44   48   50   52   61   63   67   70   72   73   75   76   81   85
##  [1359]   92   94   96   98  100  101  102  103  105  109  110  111  114  119
##  [1373]  131  133  136  137  138  152  153  158  162  165  167  174  177  187
##  [1387]  188  193  197  200  201  217  221  238  239  243  244  249  261  262
##  [1401]  263  266  275  276  277  285  286  287  292  293  296  307  315  334
##  [1415]  335  336  340  345  350  351  352  358  359  362  366  367  368  375
##  [1429]  377  381  384  387  389  390  391  394  396  397  400  405  407  411
##  [1443]  412  413  415  417  420  421  423  425  427  428  429  430  431  432
##  [1457]  436  438  440  441  443  444 9998 9999    0    1    7   10   13   20
##  [1471]   21   22   32   33   34   35   37   38   41   43   47   53   55   57
##  [1485]   60   62   63   64   65   66   69   70   73   74   75   76   78   79
##  [1499]   82   86   87   88   90   91   92   94   95   96   97   99  100  101
##  [1513]  102  106  107  109  110  111  114  116  117  121  123  125  127  128
##  [1527]  129  130  131  134  135  136  138  148  152  153  155  161  164  177
##  [1541]  191  193  194  196  203  207  208  211  212  213  216  217  219  220
##  [1555]  225  226  228  234  237  238  240  241  242  245  248  249  251  258
##  [1569]  260  263  264  267  271  273  279  282  284  285  286  287  289  293
##  [1583]  295  296  297  298  299  300  302  303  309  311  312  313 9998 9999
##  [1597]    0    1    2    3    8   11   15   18   20   21   24   29   33   34
##  [1611]   35   42   43   44   46   49   50   51   53   57   60   61   62   64
##  [1625]   68   75   78   84   89   92   94  103  104  109  131  135  136  140
##  [1639]  162  164 9998 9999    0    1    2    3    4    6    7    9   12   16
##  [1653]   17   24   30   33   34   36   47   49   51   58   59   62   68   69
##  [1667]   71   77   82   87   94  100  102  103  104  107  108  109  110  112
##  [1681]  113  115  118  119  120  123  124  133  134  136  138  139  141  143
##  [1695]  144  145  146  147  150  151  154  157  159  162  163 9998 9999    0
##  [1709]    1    3    5    8   11   14   21   24   28   37   38   42   69   71
##  [1723]   74   76   93  101  102  125  137  139  149  152  167  171  174  194
##  [1737]  206  207  211  222  232  234  235  236  237  238 9998 9999    0    1
##  [1751]    2    3    4    5    7    8   10   11   13   15   16   17   18   19
##  [1765]   20   21   22   23   24   32   37   38   40   41   43   45   47   52
##  [1779]   53   55   56   58   60   62   66   67   68   69 9998 9999    0    1
##  [1793]    5    6    7    8   12   13   14   16   18   20   23   26   28   31
##  [1807]   32   35   37   42   43   45   51   58   61   62   63   65   66   67
##  [1821]   68   70   74   75   76   79   80   85   89   90   99  103  107  112
##  [1835]  119  120  128  130  133  135  136  140  143  144  145  147  149  153
##  [1849]  154  169  178  179  190  193  195  196  200  204  207 9998 9999    0
##  [1863]    1    5    9   14   18   21   24   25   30   31   32  223  228  233
##  [1877]  235  238  239  241  242  243  244 9998    0    1    5    8   10   12
##  [1891]   14   21   25   29   30   31   32   33   34   35   38   40   41   42
##  [1905]   43   44   49   51   54   55   56   59   60   61   63   67   68   71
##  [1919]   76   78   80   81   84   87   92   93   95   96   99  101  103  104
##  [1933]  106  110  111  114  115  116  121  128  129  132  134  138  140  148
##  [1947]  150  152  153  162  163  164  165  167  168  174  176  181  188  191
##  [1961]  192  195  196  199  204  205  208  209  210  212  213  215  216  218
##  [1975]  220  221  227  228  231  232  237  238  239  240  246  247  248  252
##  [1989]  254  255  256  259  262  264  273  279  282  283  284  286  287  288
##  [2003]  294  295  296  297 9998 9999    0    1    4    5    7    8    9   10
##  [2017]   16   17   19   21   22   23   26   28   29   31   52   60   61   62
##  [2031]   64   65   68   69   73 9998 9999    0    1    2    3    4    5    6
##  [2045]    8   10   12   13   15   17   18   19   20   22   24   25   27   28
##  [2059]   29   30   31   35   36   40   42   43   44   45   47   48   49   50
##  [2073]   51   54   57   58   59   60   62   63   66   69   71   72   74   76
##  [2087]   77   79   80   81   83   84   85   86   88   89   91   92   93   94
##  [2101]   95   97   99  101  102  103  104  105  106  107  108  109  110  111
##  [2115]  112  113  115  116  117  118  120  122  123  124  126  128  134  135
##  [2129]  138  144  145  146  152  154  155  156  167  168  169  170  173  174
##  [2143]  176  178  180  183  188  189  191  192  194  195  196  197  198  200
##  [2157]  203  205  206  212  216  217  219  222  227  228  231  232  234  236
##  [2171]  237  239  242  243  245  247  248  249  251  253  259  260  261  262
##  [2185] 9998 9999    0    1    2    3    4    5    6    7   18   24   25   26
##  [2199]   27   31   38   39   41   42   44   48   50   51   52   53   54   55
##  [2213]   56   57   58   60   64   65   66   67   72   74   77   78   81   82
##  [2227]   86   87   92   93   94   96   99  100  101  102  104 9998 9999    0
##  [2241]    1    3    7   12   13   14   16   18   20   21   22   24   29   30
##  [2255]   35   37   39   40   45   50   51   55   58   60   64   66   69   70
##  [2269]   71   73   74   88   89   90   91   93   94   96   98  109  111  118
##  [2283]  119  120  124  126  137  140  141  152  160  161  176  179  189  190
##  [2297]  206  211  214  215  223  234 9998 9999    0    1    2    3    4    5
##  [2311]    6    8    9   10   12   14   16   20   24   25   27   34   40   45
##  [2325]   47   53   54   55 9998 9999    0    1    2    6    7    8   10   11
##  [2339]   13   14   19   22   23   24   26   27   29   30   33   35   36   37
##  [2353]   38   39   44   46   48   49   51   53   54   56   58   59   60   61
##  [2367]   62   64   65   67   68   69   70   71   72   73   74   76   77   78
##  [2381]   79   80   81   85   91   92   94   95   97  101  103  108  110  119
##  [2395]  122  132  133  137  139  141  142  144  146  151  152  153  159  160
##  [2409]  163  167  168  169  171  172  174  176  177 9998 9999    0    1    3
##  [2423]    8   13   15   17   20   21   23   34   42   50   57   65   69   73
##  [2437]   74   77 9998    0    1    2    3   17   21   25   32   33   34   35
##  [2451]   36   39   40   44   48   50   51   54   61   62   66   69   73   75
##  [2465]   79   81   82   85   86   87   90  100  104  108  110  117  123  125
##  [2479]  127  128  132  134  136  138  139  140  148  150  156  158  161  162
##  [2493]  169  172  176  178  181  184  190  194  202  203  207  210  213  216
##  [2507]  217  218  222  225  226  228  230  235  236  238  241  243  244  245
##  [2521]  246  247  249  251  252  259  260  261  264  268  269  270  273  277
##  [2535]  278  283  284  286  292  293  295  300  301  302  303  305  313  314
##  [2549]  320  329  331  332  350  351  355  357  359  363  375  379  381  382
##  [2563]  383  385  386  395  398  399  406  407  409  410  412  413  414  415
##  [2577]  416  417  419  421  422  423  425  427  428  429  430  431  432  433
##  [2591]  434  435  438  440  442  443  446  449  452  453  454  460  463  466
##  [2605]  468  471  472  473  474  475  476  478  481  483  487  491  492  495
##  [2619]  497  498  500  501  502  504  507  508  509  510  516  521  523  524
##  [2633]  525  526  527  529  534  536  542  546  549  551  552  563  564  566
##  [2647]  568  569  571  573  575  576  577  590  591  594  598  599  602  603
##  [2661]  604  605  606  608  609  610  611  612  613  617  619  620  622  624
##  [2675]  625  627  631  632  633  634  635  636  637  638  639  640  641  642
##  [2689]  643  646  647  650  653  655  656  657  659  661  663  666  671  672
##  [2703]  673  677  678  679  682  683  684  685  687  691  692  693  694  696
##  [2717]  699  705  706  707  710  711  713  714  716  717  719  720  721  722
##  [2731]  723  724  727  728  731  732  734  736  737  738  739  740  741  743
##  [2745]  744  750  755  757  763  764  765  766  769  774  776  777  778  779
##  [2759]  781  782  786  787  789  790  791  792  793  794  795  796  797  800
##  [2773]  802 9998 9999    0    1    3   12   14   19   20   25   27   30   31
##  [2787]   32   36   37   39   46   47   52   55   57   58   59   60   66   76
##  [2801]   83   86   90  102  127  132  133 9998 9999    0    1    2    7    9
##  [2815]   11   12   18   20   24   25   26   27   31   43   48   54   57   60
##  [2829]   76   79   80   81   82   87   88   89   90   93   98  115  118  119
##  [2843]  126  133  135  138  161  162  164  167  168 9998 9999    0    1   15
##  [2857]   16   22   27   28   29   30   32   33   34   42   44   45   49   53
##  [2871]   61   65   70   72   74   76   77   78   84   90   95   97   98   99
##  [2885]  100  113  115  116  117  119  121  126  130  131  132  155  166  170
##  [2899]  171  175  183  234  260  261  263  264 9998 9999    0    1    2 9998
##  [2913]    0    1    8   10   11   13   15   16   18   19   21   23   25   30
##  [2927]   31   35   38   39   43   44   46   47   48   49   57   60   64   65
##  [2941]   67   70   71   72   74   77   78   82   85   86   95  100  121  122
##  [2955]  123  130  147  149  156  163 9998 9999    0    1    2    3    4    5
##  [2969]    6    7    9   10   12   13   14   15   17   18   19   20   21   22
##  [2983]   23   26   44   45   46   52   55   62   63   68   74   77   78   84
##  [2997]   93   95  117 9998 9999    0    1    2    3    6    7    8    9   10
##  [3011]   12   13   14   15   16   18   19   20   21   22   23   24   26   27
##  [3025]   28   29   30   31   33   34   35   36   37   38   39   41   42   43
##  [3039]   44   46   47   48   49   61   63   67   82  105  110  117  130  132
##  [3053]  133  134  140  148  151  163  172  173  175  176  183  199  200 9998
##  [3067] 9999    0    1    4    6    7    9   10   12   15   16   22   24   25
##  [3081]   26   30   31   33   36   39   40   41   42   48   52   57   60   62
##  [3095]   63   66   67   68   74   81   85   86   91   92   94   95   97   99
##  [3109]  101  102  105  110  111  114  115  117  123  124  125  126  132  135
##  [3123]  136  137  139  140  141  146  147  148  150  152  154  159  161  165
##  [3137]  166  173  176  182  183  186  190  191  202  212  213  216  220  226
##  [3151]  227  232  236  242  247  249  252  253  257  263  276  281  284  290
##  [3165]  291  306  308  325  329  330  331  338  345  350  358  363 9998 9999
##  [3179]    0    1    2    3    4    5    6    7    9   10   11   12   14   18
##  [3193]   19   20   27   30   33   34   35   41   42   43   48   49   50   51
##  [3207]   54   57   59   60   62   64   67   69   70   71   73   74   75   77
##  [3221]   78   79   80   82   83   84   85   89   91   94   96   97   99  101
##  [3235]  103  105  106  109  110  113  114  116  120  127  128  129  130  131
##  [3249]  134  135  137  139  141  142  145  147  152  153  154  155  156  157
##  [3263]  161  162  163  164  165  166  168  169  170  171  172  173  174  175
##  [3277]  176  177  178  179  180  181 9998 9999    0    1    5    7    8    9
##  [3291]   10   11   13   16   21   22   24   25   26   27   28   30   32   33
##  [3305]   34   35   40   43   45   46   47   48   50   51   54   56   57   60
##  [3319]   63   65   71   72   76   77   78   79   80   81   83   84   88   89
##  [3333]   93   95   98   99  100  101  104  106  107  108  109  110  114  115
##  [3347]  116  117  119  120  121  122  123  124  126  127  130  131  132  135
##  [3361]  136  137  139  140  141  142  144  147  148  151  157  158  159  164
##  [3375]  165  169  174  179  181  186  199  203  205  206  207  208  210  211
##  [3389]  212  214  217  218  221  226  227  231  232  233  234  236  239  240
##  [3403]  241  242  250  253  255  258  264  265  266  268  272  273  274  278
##  [3417]  279  280  281  289  292  295  297  298  302  306  308  310  317  319
##  [3431]  320  327  330  333  337  341  344  345  346  347  351  352 9998 9999
##  [3445]    0    1    2    3    6    8   12   13   14   17   18   20   21   24
##  [3459]   25   26   27   28   29   31   32   34   35   37   38   41   42   45
##  [3473]   46   47   51   52   55   56   57   59   61   62   64   65   66   67
##  [3487]   68   69   70   71   72   74   76   78   80   81   87   88   91   92
##  [3501]   93   95   98  100  101  102  103  104  108  110  111  112  113  114
##  [3515]  115  116  117  118  120  121  122  123  130  133  136  139  142  143
##  [3529]  144  147  149  151  152  153  154  163  177  178  181  182  183  189
##  [3543]  192  193  194  195  198  199  203  206  207  210  213  217  223  224
##  [3557]  228  230  232  235  238  239  241  242  243  244  245  246  249  251
##  [3571]  252  253  254  255  256  257  258  259  260  261  262  265  266  268
##  [3585]  271  273  274  275  277  283  284  285  286  291  292  293  295  296
##  [3599]  297  298  299  300  301  302  303  304  307  309 9998 9999    0    1
##  [3613]    2    3    4    5    6    7   28   29   33   38   39   44   45   56
##  [3627]   57   58   61   63 9998 9999    0    1    3    4    6    7    9   11
##  [3641]   15   17   18   19   21   22   23   24   25   26   27   29   30   32
##  [3655]   33   34   37   38   39   40   42   44   46   48   49   50   52   53
##  [3669]   55   56   58   60   61   63   65   66   67   69   70   71   74   75
##  [3683]   76   77   79   80   82   83   84   86   87   89   90   93   96   97
##  [3697]   99  101  102  103  104  105  106  109  110  111  113  114  116  118
##  [3711]  120  121  122  124  126  128  129  132  133  134  136  137  138  139
##  [3725]  140  142  143  145  146  149  150  151  152  159  164  169  175  176
##  [3739]  180  181  183  187  192  202  203  204  205  206  207  214  215  217
##  [3753]  218  219  220  221  222  223  224  225  226  227  228  229  232  237
##  [3767]  238  239  243  245  249  250  251  254  256  259  260  263  264  265
##  [3781]  266  267  271  272  273  274  275  276  278  286  288  293  294  295
##  [3795]  296  297 9998 9999    0    1    5    6    8    9   11   14   15   19
##  [3809]   21   23   26   28   30   31   33   37   38   39   40   42   49   53
##  [3823]   55   58   62   64   69   73   78   79   86   87   88   89   90   91
##  [3837]   96   97   98   99  100  108  109  112  113  116  117  118  126  128
##  [3851]  130  131  134  136  137  139  140  148  153  154  157  159  163  165
##  [3865]  170  173  174  177  178  179  185  187  188  189  191  195  197  199
##  [3879]  200  210  219  220  221  222  226  227  230  235  245  248  250  253
##  [3893]  256  258  260  261  266  267  268  269  270  274  276  278  283  286
##  [3907]  289  291  294  304  306  307  308  310  312  313  314  317  318  321
##  [3921]  345  347  355  358  362  363  368  427  435  437  442  448  452  458
##  [3935]  461  464  466  467  474  475  476  479  480  481  483  485  496  497
##  [3949]  503  505  510  511  512  513 9998 9999    0    1    2    3    4    5
##  [3963]    7    8   10   11   12   13   14   15   16   17   19   20   21   22
##  [3977]   24   25   27   29   31   39   45   46   47   48   50   51   53   54
##  [3991]   57   66   70   71   76   77   78   79   80   89   90   91   98  101
##  [4005]  103  105  108  109 9998 9999    0    1    2    4    7    8    9   10
##  [4019]   11   13   14   15   16   17   19   21   24   27   28   29   30   31
##  [4033]   35   36   39   43   44   48 9998 9999    0    1    2    5    7    8
##  [4047]    9   12   14   18   23   25   28   32   33   35   43   51   56   57
##  [4061]   59   64   66 9998 9999    0    1    2    6    7   10   19   20   21
##  [4075]   22   24   25   26   27   28   30   31   32   34   35   36   37   38
##  [4089]   42   44   46   48   50   51   53   55   57   59   63   64   65   66
##  [4103]   67   69   70   71   72   75   76   77   79   81   82   84   85   86
##  [4117]   90   91   93   94   95   96   98   99  101  102  103  104  107  110
##  [4131]  113  115  117  118  119  120  121  122  124  125  127  129  131  132
##  [4145]  133  135  138  142  144  145  153  154  156  158  159  161  165  166
##  [4159]  167  168  171  176  177  178  179  183  184  185  188  189  190  193
##  [4173]  194  195  196  198  199  201  211  213  215  216  219  222  224  225
##  [4187]  228  230  233  234  237  240  241  242  245  246  248  249  250  255
##  [4201]  256  258  260  261  265  268  270  273  275  277  278  279  281  284
##  [4215]  287  291  292  293  299  301  307  308  309  310  311  313  317  322
##  [4229]  323  324  326  328  332  333  334  336  337  338  342  344  345  347
##  [4243]  348  351  360  361  363  365  367  369  375  378  386  387  388  390
##  [4257]  391  392  393  400  401  404  408  410  415  419  431  433  434  446
##  [4271]  458  461  464  467  468  473  474  475  476  478  479  483  484  488
##  [4285]  489  490  492  494  495  496  498  502  503  506  515  517  518  520
##  [4299]  521  525  527  530  531  532  537  540  541  545  551  557  559  560
##  [4313]  565  566  570  573  579  580  581  582  583  587  589  590  591  592
##  [4327]  594  600  604  605  606  613  617  619  622  623  624  625  626  627
##  [4341]  628  632  633  634  637  638  641  643  649  650  651  656  657  658
##  [4355]  659  663  665  667  668  671  672  675  677  680  683  684  686  687
##  [4369]  688  689  690  691  692  694  695  696  697  699  701  702  705  706
##  [4383]  707  709  710  711  712  713  715  716  718  721  722  723  724  726
##  [4397]  729  730  732  734  735  736  737  738  739  740  742  744  745  749
##  [4411]  750  751  752  754  755  757  759  763  765  766  767  768  771  772
##  [4425]  773  776  777  779  783  784  785  793  794  795  799  800  801  802
##  [4439]  805  806  807  808  811  812  813  814  815  816  817  818  819  820
##  [4453]  821  823  824  825  826  828  829  830  831  832  833  834  837  838
##  [4467]  840  841  842  843  845  846  847  849  852  857  859  861  862  863
##  [4481]  865  866  867  869  870  871  873  874  878  880  883  884  885  887
##  [4495]  888  891  893  895  896  898  900  901  903  904  907  908  909  910
##  [4509]  912  914  916  917  918  919  920  921  922  923  926  927  928  929
##  [4523]  931 9998 9999    0    1    2    3    4    5    6    7    8   10   43
##  [4537]   49 9998    0    1    2    3    4    6    7    8    9   10   15   21
##  [4551]   37   39   43   48   49   52   62   63 9998    0    1    2    3    5
##  [4565]    6   17   21   24   27   32   39   41   43   44   45   47   49   52
##  [4579]   54   55   59   66   69   70   71   78   82   87   89   92   95  106
##  [4593]  108  109  110  112  120  121  125  133  135  136  137  139  141  142
##  [4607]  144  145  146  147  163  171  175  177  192  196  197  199  204  208
##  [4621]  214  215  219  227  232  240  252  255  257 9998 9999    0    1    2
##  [4635]    3   12   13   14   16   18   24   38   40 9998 9999    0    1    2
##  [4649]    6    8   14   17   18   20   21   23   30   32   35   38   43   44
##  [4663]   47   48   49   52   55   56   59   60   64   66   67   68   69   72
##  [4677]   75   76   78   79   83   85   86   87   89   94   97  100  101  106
##  [4691]  112  113  114  116  117  119  120  122  125  126  127  131  133  134
##  [4705]  136  137  138  140  142  146  152  154  156  160  161  162  163  164
##  [4719]  165  166  168  169  174  178  182  187  190  192  193  194  195  196
##  [4733]  197  198  201  202  205  208  210  211  214  215  216  217  218  221
##  [4747]  224  225  226  231  234  241  242  249  251  252  253  257  258  260
##  [4761]  263  264  267  268  272  279  290  291  294  295  299  300  302  303
##  [4775]  304  305  306  308  309  315  316  319  323  324  328  329  330  333
##  [4789]  334  335  340  341  342  362  363  366  367  368  379  380  383 9998
##  [4803] 9999    0    1    6    7    8    9   12   13   15   16   17   22   23
##  [4817]   25   26   28   29   30   32   35   38   39   41   42   43   44   45
##  [4831]   47   48   53   55   56   58   65   68   69   71   72   74   76   77
##  [4845]   84   89   90   95   97   99  100  104  105  107  108  109  110  113
##  [4859]  114  115  116  118 9998 9999    0    1    2    8    9   11   12   13
##  [4873]   17   19   20   21   26   27   29   33   34   39   41   50   53   54
##  [4887]   56   57   58   60   63   65   71   74   78   79   95   97  100  103
##  [4901]  105  108  115  118  133  135  136  137  146  156  157  167  169  170
##  [4915] 9998 9999    0    1    3    5    6    8   11   13   15   24   25   27
##  [4929]   31   33   36   37   39   40   42   45   48   51   53   56   58   65
##  [4943]   66   67   77   80   82   87   88   91   95   98  100  104  105  109
##  [4957]  110  117  120  129  130  131  132  139  140  142  143  146  149  150
##  [4971]  151  152  158  163  164  165  166  168  177  178  186  187  190  191
##  [4985]  193  195  197  198  201  202  204  205  207  208  213  221  227  228
##  [4999]  229  230  235  236  238  240  241  245  248  251  259  268  293  294
##  [5013]  298  302  309  325  326  328  330  331  335  336  337  350  351  358
##  [5027]  363  365  367  373  374  376  377  380  384  385  388  393  394  402
##  [5041]  404  406  412  413  416  419  420  421  423  425  426  427  428  430
##  [5055]  432  434  435  441  442  444  446  448  451  454  455  457  458  462
##  [5069]  465  467  468  470  472  474  475  477  478  480  482  483  484  486
##  [5083]  488  489  490  491  492  493  494  495  499  500  503  506  508  510
##  [5097]  514  515  517  520  521  522  524  525  528  532  533  535  538  539
##  [5111]  540  541  542  544  545  547  548  552  555  556  557  560  563  564
##  [5125]  570  571  573  575  589  590  591  592  593  595  596  598  600  601
##  [5139]  603  605  606  608  609  610  611  615  617  618  619  621  622  626
##  [5153]  627  629  632  634  637  646  648  649  651  661  662  663  666  668
##  [5167]  679  684  703  704  705  731  737  745  749  760  763  765  768  773
##  [5181]  775  777  778  780  784  787  793  799  802  807  810  815  818  821
##  [5195]  822  824  831  835  839  844  848  850  851  854  855  857  858  859
##  [5209]  861  862  863  865  867  870  873  874  877  879  880  883  885  887
##  [5223]  891  892  895  902  906  908  913  917  920  922  923  926  927  929
##  [5237]  931  932  933  935  937  940  942  945  948  952  953  954  958  959
##  [5251]  960  961  963  966  967  968  971  974  976  981  983  984  985  990
##  [5265]  992  994  999 1000 1004 1006 1008 1014 1019 1024 1025 1026 1028 1029
##  [5279] 1031 1032 1034 1035 1036 1037 1039 1040 1041 1042 1044 1045 1046 1052
##  [5293] 1053 1055 1058 1059 1062 1063 1064 1067 1068 1069 1070 1073 1074 1078
##  [5307] 1079 1080 1082 1084 1087 1095 1096 1099 1100 1101 1102 1103 1104 1106
##  [5321] 1107 1108 1110 1111 1112 1115 1116 1124 1127 1128 1130 1145 1146 1147
##  [5335] 1148 1151 1153 1154 1155 1157 1158 1159 1160 1161 1162 1163 1164 1165
##  [5349] 1166 1167 1168 1170 1171 1172 1173 1174 1176 1178 1179 1181 1182 1183
##  [5363] 1185 1186 1188 1189 1193 1194 1196 1197 1199 1200 1203 1206 1208 1209
##  [5377] 1213 1214 1216 1217 1218 1220 1221 1222 1224 1225 1227 1229 1235 1236
##  [5391] 1238 1239 1242 1243 1244 1246 1248 1249 1251 1252 1253 1254 1256 1260
##  [5405] 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1273 1274 1275
##  [5419] 9998 9999    0    1    3    4    7    8   10   11   12   15   16   19
##  [5433]   21   22   23   24   25   27   30   33   34   36   43   54   66   73
##  [5447]   75   79   84   92   95   96   99  103  107  108  109 9998 9999    0
##  [5461]    1    9   10   11   12   14   16   17   18   19   21   22   23   24
##  [5475]   26   28   29   31   32   40   41   43   45   47   60   62   63   66
##  [5489]   69   72   75   77   80   81   82   90   92   98  100  102  106  109
##  [5503]  112  113  117  118  121  122  123  125  126 9998 9999    0    1    3
##  [5517]    7    9   15   17   19   20   21   24   25   26   28   29   32   33
##  [5531]   35   38   39   45   46   48   49   53   57   58   63   65   67   71
##  [5545]   72   90   92   96   97  105  108  114  117  120  123  127  128  130
##  [5559]  140  142  144  149  151  155  156  163  164  168  171  183  184 9998
##  [5573] 9999    0    1    3    5    9   11   18   19   20   21   24   25   31
##  [5587]   32   34   35   37   38   40   42   44   45   49   50   51   52   63
##  [5601]   65   66   67   69   70   72   73   74   75   81   83   84   86   87
##  [5615]   93   96   98  100  104  108  109  121  127  132  134  136  137  141
##  [5629]  144  147  148  149  151  153  154  156  161  162  164  175  187  195
##  [5643]  196  198  200  202  207  210  230  238  254  255  256  269  335  336
##  [5657]  346  349  354  366  373  379  394  396  398  401  406  407  408  416
##  [5671]  419  420  427  430  432  449  451  461 9998 9999    0    1    2    4
##  [5685]    6    7   10   11   13   15   16   17   19   20   21   22   25   26
##  [5699]   27   28   30   31   32   33   36   37   40   41   42   43   44   45
##  [5713]   48   51   52   54   56   57   58   59   60   62   67   75   76   79
##  [5727]   80   81   89   92   96  102  106  109  114  122  123  124  133  136
##  [5741]  142  145  147  152  153 9998 9999    0    1    4    7    8    9   11
##  [5755]   14   15   22   23   24   27   28   30   31   38   43   44   45   48
##  [5769]   49   50   52   57   58   59   61   66   68   73   77   78   85   86
##  [5783]  103  104  109  112  115  117  120  121  126  127  128  130  131  133
##  [5797]  134  141  142  148  156  157  158  162  164  166  167  168  169  173
##  [5811]  178  179  180  182  183  190  191  194  195  198  202  204  205  208
##  [5825]  222  227  231  232  235  239  240  242  244  245  246  247  248  249
##  [5839]  251  252  255  257  258  260  261  262  267  268 9998 9999    0    1
##  [5853]    2    5    8    9   10   21   23   25   27   30   32   39   40   42
##  [5867]   43   44   49   52   53   55   57   63   64   65   66   69   76   82
##  [5881]   84   87   91  100  101  105  107  115  116  119  123  130  131  132
##  [5895]  136  139  141  145  146  147  148  153  157  160  163  164  165  166
##  [5909]  167  168  169  170  172  173  174  175  178  180  182  185  186  190
##  [5923]  191  192  193  204  206  208  215  217  219  228  233  240  242  244
##  [5937]  245  254  255  256  266  269  270  274  429  437  447  449  451  471
##  [5951]  479  483  488  490  494  496  500  506  508  512  513  518  521  526
##  [5965]  527  530  534  540  542  544  545  549  551  558  571  573  574  580
##  [5979]  593  597  598  602  603  607  608  619 9998 9999    0    1    2    6
##  [5993]   10   13   18   20   23   24   28   30   31   33   40   41   43   44
##  [6007]   45   47   49   54   57   59   61   63   64   68   73   77   79   80
##  [6021]   82   83   86   87   88   90   91   92   95   97   98   99  101  106
##  [6035]  107  108  111  117  118  119  123  129  132  136  137  138  139  140
##  [6049]  142  143  145  148  149  151  153  154  155  156  161  163  164  165
##  [6063]  166  167  169  170  173  175  189  193  201  202  203  204  212  216
##  [6077]  217  220  221  222  224  228  232  237  239  240  243  245  257  263
##  [6091]  267  268  270  271  278  279 9998 9999    0    1    2    4    9   11
##  [6105]   12   13   14   20   21   26   27   34   35   43   44   51   61   64
##  [6119]   83   86   89   96  105  111  114  115  116  117  118  120  121  124
##  [6133]  125  126  127  128  129  130  131  132  133  134 9998 9999    0    1
##  [6147]    3    4   10   12   13   17   18   23   24   28   29   30   35   36
##  [6161]   42   44   45   49   50   54   56   60   61   66   67   69   71   73
##  [6175]   74   75   76   79   81   83   84   87   89   90   91   95   96   98
##  [6189]   99  109  112  115  117  118  121  162  167  168  170  178  179  180
##  [6203]  181  201  209  211  212  216  218  219  226  231  235  239  251  263
##  [6217]  267  269  272 9998 9999    0    1    2    5    6   11   12   14   15
##  [6231]   16   18   21   22   24   25   32   33   36   43   46   49   53   54
##  [6245]   56   57   63   64   69   74   75   76   78   79   81   82   83   86
##  [6259]   90   91   92   94   98  100  102  103  105  107  111  120  126  130
##  [6273]  131  147  148  151  153  154  155  156  161  162  166  167  168  197
##  [6287]  209  213  219  220  223  224  225  226  227  228  229  230  231  232
##  [6301]  234 9998 9999    0    1    2    3    4    6    7    8   10   11   12
##  [6315]   13   19   20   25   26   27   28   31   38   40   41   45   46   47
##  [6329]   48   49   51   52   53   55   56   57   58   59   64   65   66   67
##  [6343]   70   71   72   75   78   81   82   85   87   92   97  101  103  107
##  [6357]  108  111  112  113  115  116  117  118  120  121  122  124  127  128
##  [6371]  133  134  135  136  138  140  142  148  149  150  152  154  157  158
##  [6385]  160  161  162  165  166  167  170  173  174  178  179  180  181  182
##  [6399]  183  185  186  187  188  189  190  194  195  196  197  198  200  204
##  [6413]  206  208  209  210  212  214  215  216  218  219  220  221  223  224
##  [6427]  225  227  228  229  232  233  234  236  237  239  241  242  243  244
##  [6441]  246  247  251  252  253  254  256  258  260  264  268  269  270  271
##  [6455]  273  275  276  277  281  282  291  292  294  295  296  297  300  302
##  [6469]  303  304  305  311  313  314  316  317  319  321  323  329  332  333
##  [6483]  335  336  337  338  340  343  345  346  347  349  350  351  352  354
##  [6497]  356  357  358  360  362  363  364  365  366  367  368  369  371  372
##  [6511]  373  374  375  376  378  379  380  381  382  384  385  387  389  390
##  [6525]  391  392  393  394  395  396  398  399  401  402  403  404  405  408
##  [6539]  409  414  417  418  419  420  421  423  424  425  426  427  428  429
##  [6553]  430  431  432  433  434  435  437  438  439  440  441  445 9998 9999
##  [6567]    0    1    2    3    6    9   11   13   14   15   17   19   20   21
##  [6581]   22   25   30   31   32   34   36   38   39   41   42   43   44   45
##  [6595]   46   48   52   53   54   56   57   61   62   65   72   73   75   78
##  [6609]   90  103  109  114  117  122  123  127 9998 9999    0    1    3   12
##  [6623]   19   20   22   39   44   47   48   49   51   52   60   68   71   76
##  [6637]   82   86   88   90   93  112  195  196  197  198  199 9998 9999    0
##  [6651]    1    2    3    7   15   27   28   32   33   34   36   37   40   41
##  [6665]   47   57   64   67   73   74   83   85   86   92   93   96   97   98
##  [6679]   99  103  105  110  112  128  131  133  139  140  141  153  158  174
##  [6693]  185  188  190  191  202  207  210  214  220  227  229  236  237  241
##  [6707]  244  250  253  271  274  281  283  285  286  289 9998 9999    0    1
##  [6721]    2    3    4    5    6    7    9   10   11   12   13   14   15   16
##  [6735]   17   18   19   20   22   23   24   27   32   37   40   51   53   54
##  [6749]   63   64   65   66   74   75   83   84   86 9998 9999    0    1    3
##  [6763]    5    6    7    9   12   13   14   20   22   23   26   27   34   37
##  [6777]   39   41   45   46   47   49   51   54   55   56   57   58   59   60
##  [6791]   61   62   63   64   65   66   68   70   74   80   81   82   86   87
##  [6805]   88   93   95   96  100  103  104  105  108  112  115  117  120  121
##  [6819]  122  123  125  126  129  131  133  134  135  137  138  139  141  144
##  [6833]  146  148  153  155  157  158  164  165  167  170  173  175  176  178
##  [6847]  180  181  182  183  184  185  186  187  189  190  191  195  196  198
##  [6861]  202  203  204  216  217  218  220  224  225  226  227  228  233  240
##  [6875]  241  245  250  253  254  258  261  267  270  274  307  308  309  310
##  [6889]  313  317  320  324  327  328  330  333  336  338  339  346  350  352
##  [6903]  354  355  357  358  359  360  361  363  364  368  370  371  378 9998
##  [6917] 9999    0    1    8   11   13   14   26   29   30   32   65   73   87
##  [6931] 9998    0    1    5   14   15   16   19   20   21   28   31   32   33
##  [6945]   34   35   37   40   41   45   61   62   65   67   68   69   70   71
##  [6959]   79   82   84   85   86   87   92   95  101  103  104  108  111  112
##  [6973]  114  115  120  122  124  127  129  137  141  146  148  150  156  157
##  [6987]  161  167  170  171  172  180  181  185  191  198  200  215  227  229
##  [7001] 9998 9999    0    1    2    3    6    7    8    9   11   12   14   16
##  [7015]   22   23   26   27   28   30   34   38   46   54   56 9999    0    1
##  [7029]    3    4   10   20   25   26   31   41   46   49   55   62   65   67
##  [7043]   73   78   80   86  102  114  116  117  134  136  146  151  159 9998
##  [7057] 9999    0    1    2    4    5    7    8    9   10   11   12   13   14
##  [7071]   15   16   17   18   24   25   26   33   34   35   39   43   49   54
##  [7085]   75   82   85   92  104  107  110  112  117  118  119  123  125  128
##  [7099]  130  132  134  138  141  142  144  153  154  156  159  163  166  168
##  [7113]  176  177  179  180  181  182  188  191  192  198  205  207  209  210
##  [7127]  212  213  215  217  219  220  221  222  228  229  236  240  248  249
##  [7141]  252  253  255 9998 9999    0    1    2    3    4    6    7    8   10
##  [7155]   15   24   26   28   29   30   33   38   41   44   45   50   53   54
##  [7169]   63   66   70   72   77   79   81   82   83   84   86   90   98  100
##  [7183]  103  106  111  113  115  119  120  129  131  134  136  144  146  151
##  [7197]  155  156  160  163  167  169  172  174  175  181  183  188  191  204
##  [7211]  206  209  215  217  218  223  224  227  228  233  234  235  237  241
##  [7225]  247  249  251  252  257  264  266  268  269  270  271  272  276  277
##  [7239]  278  279  280  284  285  287  288  289  292  299  300  305  311  406
##  [7253]  410  411  417  429  430  442  452  453  454  455  460  464  476  488
##  [7267]  502  504  505  513  517  519  523  524  527  541  549  551  553  554
##  [7281]  555  556  561  566  568  575  589  592  594  596  760  766  767 9998
##  [7295] 9999    0    1    2    5    6   10   11   13   20   24   25   31   32
##  [7309]   33   34   35   37   38   40   42   50   51   52   53   58   60   61
##  [7323]   63   66   71   72   76   77   80   83   84   87   88   89   90   94
##  [7337]   95   96   98  103  104  105  106  111  112  116  118  120  123  124
##  [7351]  126  128  129  131  133  135  137  140  141  142  146  148  152  153
##  [7365]  154  155  156  157  161  163  164  170  172  173  174  179  183  184
##  [7379]  188  189  190  195  205  222  229  230  231  232  235  239  242  248
##  [7393]  251  252  259  260  261  264  266  272  275  276  288  302  304  310
##  [7407]  315  319  322  323  324  332  335  339  345  346  352  357  361  385
##  [7421]  391  398  400  412  418  422  423  432  433  435  442  443  444  447
##  [7435]  449  453  455  458  460  461  462  463  464  466  467  468  469  470
##  [7449]  473  477 9998 9999    0    1    3    4    5    6    7    9   10   12
##  [7463]   15   16   17   19   20   24   25   26   30   31   32   33   34   35
##  [7477]   36   38   40   42   43   44   47   50   51   52   59   62   75   87
##  [7491]   89   92   93   94   97   98  102  105  111  113  115  119  125  127
##  [7505]  130  132  135  137  140  143  147  148  150  151  152  154  156  157
##  [7519]  161  162  164  167  169  172  180  185  194  195  196  197  198  201
##  [7533]  202  206  207  209  210  211  215  223  229  230  232  233  234  239
##  [7547]  240 9998 9999    0    1    8    9   15   17   27   31   32   43   47
##  [7561]   50   70   72   84   87   89   95   96  100  102  103  107  108  111
##  [7575]  116  132  144  145  148  149  155  156  160  161  169  172  177  178
##  [7589]  180  182  185  190  198  207  209  210  212  213  220  227  233  238
##  [7603]  239  242  244  246  250  251  253  256  261  262  272  273  274  280
##  [7617]  287  289  290  291  296  298  301  303  304  308  316  324  357  362
##  [7631]  363  379  386  389  398  412  425  447  449  454  474  481  483  490
##  [7645]  492  493  494  500  504  505  508  509  516  519  531  532  541  544
##  [7659]  545  549  553  556  557  560  561  562  566  568  569  571  575  576
##  [7673]  578  580  581  582  584  585  590  593  594  602  603  604  605  607
##  [7687]  608  609  611  613  614  615  617  619  620  623 9998 9999    0    1
##  [7701]    3    4    5    6    7   10   12   13   14   16   18   21   22   23
##  [7715]   25   26   30   31   32   35   36   41   44   47   48   51   53   58
##  [7729]   78   91   96  107  112  121  130  132  138 9998 9999    0    1    3
##  [7743]    4    8   13   17   18   23   27   30   35   38   41 9998 9999    0
##  [7757]    1    7    8    9   11   12   13   14   17   19   27   29   32   38
##  [7771]   44   54   55   87 9998    0    1    8    9   11   12   14   15   16
##  [7785]   19   21   22   24   25   27   29   30   33   37   39   44   46   47
##  [7799]   48   50   52   53   57   58   59   60   64   69   70   71   73   81
##  [7813]   85   92   94   95   96  101  104  105  107  111  112  113  115  116
##  [7827]  120  121  122  125  129  130  131  132  135  136  137  138  140  141
##  [7841]  143  144  150  151  153  155  159  160  165  167  173  178  182  185
##  [7855]  187  193  198  199  202  204  209  213  215  217  218  221  222  223
##  [7869]  224  226  231  232  237  240  244  246  248  250  252  253  255  262
##  [7883]  269  272  282  289  290  295  298  299  301  302  303  304  308  310
##  [7897]  313  318  320  324  327  331  332  333  336  337  338  344  346  347
##  [7911]  348  351  353  355  359  360  361  364  367  373  374  375  376  378
##  [7925]  381 9998 9999    0    1    2    3    4    6    8    9   10   11   12
##  [7939]   14   15   16   17   18   19   20   21   22   23   24   25   26   27
##  [7953]   34   35   37   41   42   43   44   46   48 9998 9999    0    1    2
##  [7967]    4    5    8    9   12   18   24   29   32   38   39   41   42   44
##  [7981]   47   49   51   53   54   58   59   62   63   68   69   70   71   73
##  [7995]   74   75   79   82   87   89   95   99  100  103  104  105  109  110
##  [8009]  112  113  115  117  119  122  128  129  130  131  134  135  136  137
##  [8023]  138  140  149  151  152  153  154  155  157  158  162  167  169  171
##  [8037]  174  176  177  178  180  181  182  183  185  186  187  188  189  190
##  [8051]  193  196  197  198  199  200  202  203  204  205  208  209  210  211
##  [8065]  214  216  218  219  220  223  224  226  227  230  241  242  244  250
##  [8079]  255  257  258  264  265  266  267  268  270  271  273  276  277  278
##  [8093]  282  285  287  290  291  292  293  295  296  297  298  299  300  302
##  [8107]  303  305  307  310  311  312  315  316  318  320  322  324  325  326
##  [8121]  327  328  329  330  332  333  334  337  339  340  341  344  345  348
##  [8135]  358  360  365  371  374  376  377  378  382  384  385  387  388  389
##  [8149]  391  392  394  395  396  399  400  402  403  404  405  406  408  412
##  [8163]  413  414  418  419  424  425  427  428  429  430  431  432  433  435
##  [8177]  436  447  461  462  463  464  465  468  475  476  481  483  484  485
##  [8191]  487  488  489  665  666  667  673  676  678  680  683  685  694  695
##  [8205]  700  702  703  706  707  708  710  715  716  717  718  721  722  723
##  [8219]  726  730  731  732  733  734  736  739  740  741  744  745  746  748
##  [8233]  749  751  752  755  757  758  759  760  761  762  764  765  766  768
##  [8247]  769  770  771  774  775  777  779  783  784  785  786  787  788  789
##  [8261]  790  791  793  794  798  800  801  803  805  807  808  811  812  813
##  [8275]  814  815  816  826  828  831  839  841  844  845  848  853  854  857
##  [8289]  858  859  860  861  862  863  864  866  867  868  869  870 9998 9999
##  [8303]    0    1    3    5   12   13   16   17   18   19   22   26   27   33
##  [8317]   35   39   40   41   45   48   56   58   62   63   66   68   69   71
##  [8331]   77   79   86   88   93   94   95   96  102  109  110  118  119  124
##  [8345]  127  128  129  133  134  136  138  140  141  144  145  152  164  166
##  [8359]  169  177  180  182  185  188  191  193  197  200  213  219  220  221
##  [8373]  223  224  227  231  232  233  234  235  236  237  252  254  256  265
##  [8387]  270  272  280  282  284  285  292  295  296  301  303  311  317  417
##  [8401]  440  447  453  591  596  597  600  605  607  612  618  621  623  625
##  [8415]  627  634  635  641  642  643  646  647  650  651  652  653  656  661
##  [8429]  670  673  684  686  690  692  694  695  696  700  703  712  717  720
##  [8443]  722  726  728  729  736  738  741  743  746  750  751  754  765  766
##  [8457]  767  768  769 9998 9999    0    1    2    3    4    5    6    7   11
##  [8471]   15   26   28   29   38   39   48   54   57   58   61 9998 9999    0
##  [8485]    1    3    4    5    6    8    9   10   11   13   14   15   16   17
##  [8499]   18   23   26   27   29   33   35   43   49   50 9998 9999    0    1
##  [8513]    3    4    5    6    8    9   11   13   14   15   19   20   21   23
##  [8527]   24   25   26   28   31   32   33   34   35   37   38   39   41   42
##  [8541]   43   45   46   48   50   51   55   60   75   87   89   93  103  105
##  [8555]  106  107  110  115  117  119  122  125  140  166  169  172  174  177
##  [8569]  180  186  200  235  237  242  244  325  333  349  372  383  394  397
##  [8583]  402  403  424  435  439  441  459  461  464  466  473  474  487  500
##  [8597]  501  502  510  519  529  535  538  540  552  560  561  563  569  573
##  [8611]  575  583  591  606  611  613  617  622  624  626  629  632  633  636
##  [8625]  637  647  657  658  659  666  672  674  676  682  694  695  698  699
##  [8639]  701  710  714  720  725  729  734  737  738  743  745  750  754  756
##  [8653]  760  762  763  765  766  767  768  769  771  772  773  774  775  777
##  [8667]  783  784  787  789  790  791  792  793  795  798  799  800  801  802
##  [8681]  803  806  807  808  809  811  813  814  818  819  822  823  827  831
##  [8695]  832  833  835  836  837  838  842  843  844  845  846  848  852  853
##  [8709]  854  856  857  858  859  860  861  862  863  864  866  869  870  871
##  [8723]  872  874  875  877  878  879  880  881  882  883  884  890  891  894
##  [8737]  895  897  901  902  903  904  905 9998 9999    0    1    3   14   17
##  [8751]   29   39   49   70   81   83   91   92   95  105  108  113  124  126
##  [8765]  128  130  131  132  133  134  135 9998 9999    0    1    2    3    6
##  [8779]    7    8   10   11   12   14   15   17   18   19   20   21   23   25
##  [8793]   27   29   31   32   36   42   43   44   45   58   61   64   71   73
##  [8807]   74   77   85   86   87   88   89 9998 9999    0    1    5    9   12
##  [8821]   13   18   20   21   23   25   26   29   30   33   34   38   39   45
##  [8835]   46   47   48   50   51   53   54   57   58   59   65   67   69   70
##  [8849]   73   75   76   78   79   80   81   82   86   87   91   92   95   96
##  [8863]   98  100  101  105  109  110  112  113  115  116  120  122  123  125
##  [8877]  132  137  138  143  145  147  149  152  153  156  157  159  161  165
##  [8891]  167  168  171  172  180  189  215  217  218  219  220  221  230  235
##  [8905]  237  242  244  249  250  252  261  263  266  267  269  270  271  272
##  [8919]  273  275  276  280  285  286  287  288  290  292  293  296  302  303
##  [8933]  306  308  313  314  320  329  338  341  343  345  348  351  354  357
##  [8947]  358  361  362  363  366  367  368  372  374  379  382  383  387  388
##  [8961]  391  392  393  402  403  406  408  414  418  424  427  432  438  452
##  [8975]  460  463  473  476  477  479  480  482  484  488  490  493  499  509
##  [8989]  511  516  520  522 9998 9999    0    1    2    9   12   22   23   25
##  [9003]   26   27   31   34   38   50   51   56   61   73  101  105  109  111
##  [9017]  112  114  123  125  126  130  133  135  136  154  156  157  164  165
##  [9031]  169  174  184  188  189  190  192  193  194  195  196  197  199  200
##  [9045]  204  205 9998 9999    0    1    2    3    9   10   13   15   16   19
##  [9059]   22   23   25   26   27   28   34   35   37   43   44   47   73 9998
##  [9073] 9999    0    1    6    7    9   10   11   15   16   19   33   44   48
##  [9087]   54   64   65   67   68   69 9998    0    1    2    3    4    5    6
##  [9101]   11   13   14   15   16   20   21   23   27   31   34   35   37   47
##  [9115]   50   52   55   56   61   62   63   68   70   71   72   74   75   76
##  [9129]   77   78   79   82   84   89  104  110  119  135  146  156  157  160
##  [9143]  161  181  198 9998 9999    0    1    2    5    6    7    8    9   10
##  [9157]   11   12   13   15   16   17   18   21   27   29   31   32   34   35
##  [9171]   37   38   39   40   41   44   45   46   47   49   50   53   54   58
##  [9185]   61   62   63   65   67   68   70   74   75   76  105  106  107  113
##  [9199]  114  115  116  117  118  119  120  127  131  134  136  138  140  141
##  [9213]  144  148  149  154  155  160  162  165  166  170  176  180  181  183
##  [9227]  184  185  187  191  194  196  199  201  202  206  207  212  215  216
##  [9241]  222  226  230  232 9998 9999    0    1    3    6    7    8   10   11
##  [9255]   13   14   16   19   20   21   22   23   24   25   33   36   39   42
##  [9269]   46 9998 9999    0    1    2    3    4    5    6    7    8    9   10
##  [9283]   14   19   20   22   24   40   42   45   47   48 9998 9999    0    1
##  [9297]    3    5    6    7   10   12   14   22   24   26   28   29   31   32
##  [9311]   34   37   38   41   42   46   47   48   50   51   53   56   58   59
##  [9325]   62   65   66   72   86   93  101  109  111  112  122  125  126  127
##  [9339]  131  132  134  136  260  262  270  271  276  280  285  288  290  293
##  [9353]  294  295  297  298  300  301  303  306  309  312  315  324  327  331
##  [9367]  333  334  338  340  345  346  349  355  359  362 9998 9999    0    1
##  [9381]    2    5    6    8   11   14   15   17   20   22   23   27   30   32
##  [9395]   36   37   44   45   46   47   49   50   60   61   66   71   72   74
##  [9409]   75   76   81   82   84   86   88   89   91   92   94   98  100  101
##  [9423]  102  106  107  108  109  110  111  112  117  118  120  121  125  126
##  [9437]  128  129  130  140  144  146  151  152  153  154  157  158  161  167
##  [9451]  168  170  176  183  238  244  245  246  247  249  255  256  265  267
##  [9465]  270  271  278  344  362  378  380  382  385  392  394  395  399  400
##  [9479]  402  403  404  405  407  409  412  417  419  420  423  427  429  431
##  [9493]  432  434  435  438  440  441  443  444  446  448  451  452  453  455
##  [9507]  458  461  462  463  464  466  467  469  471  472 9998 9999    0    1
##  [9521]    3    6    8   14   21   22   27   32   34   36   38   39   40   41
##  [9535]   42   44   45   47   48   49   50   58   60   64   66   68   73   86
##  [9549]   89   91  110  125  135  138  143  155  156 9998 9999    0    1    5
##  [9563]    8    9   11   12   14   16   17   18   19   20   22   29   35   36
##  [9577]   39   40   47   48   54   55   57   60   62   64   65   67   70   71
##  [9591]   72   75   76   78   80   81   85   86   91   92  101  104  105  106
##  [9605]  107  125  132  135  147  149  150  152  155  156  157  160  162  163
##  [9619]  164  165  167  168  169  170  173  174  176  177  178  182  183 9998
##  [9633] 9999    0    1    2    5    6    9   12   13   14   21   26   28   29
##  [9647]   30   31   32   33   35   36   37   41   43   45   46   53   54   55
##  [9661]   56   57   63   64   67   71   72   77   78   79   80 9998 9999    0
##  [9675]    1    2    3    4    6    9   10   11   12   13   18   20   21   23
##  [9689]   24   26   27   28   29   30   33   34   40   41   42   43   46   47
##  [9703]   51   52   53   55   58   59   60   61   62   64   66   68   80   81
##  [9717]   84   92   93   96   97  104  115  116  119  124  125  126  137  138
##  [9731]  141  143  151  173  178  187  189 9998 9999    0    1    2    3    4
##  [9745]    5    6    8    9   17   18   19   20   22   24   25   26   28   29
##  [9759]   30   32   33   36   37   40   41   43   44   47   50 9998 9999    0
##  [9773]    1    3    8    9   11   13   15   18   21   28   33   35   40   41
##  [9787]   42   45   46   48   58   60   61   66   69   70   80   83   84   86
##  [9801]   88   90   94   97   99  116  130  136  149  150  152  156  163  164
##  [9815]  165  170  208  231  234  260  270  275  276 9998 9999    0    1    2
##  [9829]    4    6   12   13   16   18   19   20   23   24   25   27   28   31
##  [9843]   32   36   44   45   46   48   52   55   67   74   85   86   91   93
##  [9857]   94   97   99  102  103  104  107  109  110  112  113  119  121  123
##  [9871]  124  129  130  131 9998 9999    0    1    2    3    4    7   13   15
##  [9885]   20   21   23   26   27   32   41   47   51   52   53   54   57   58
##  [9899]   62   63   64   65   66   67   73   74   75   76   80   89   92  101
##  [9913]  102  109  116  120  124  130  132  133  135  136 9998 9999    0    1
##  [9927]    2    5    6    9   10   12   13   19   21   24   25   26   27   28
##  [9941]   31   32   36   46   47   49   52   56   58   59   61   62   66   68
##  [9955]   69   71   74   75   76   77   79   81   83   84   87   88   91   95
##  [9969]   97   98  101  103  105  108  110  112  114  118  120  121  122  124
##  [9983]  125  128  130  131  133  135  137  140  141  143  147  153  154  164
##  [9997]  165  169  172  177  180  183  186  187  189  193  195  199  209  210
## [10011]  213  222  224  225  229  230  232  233  235  236  239  240  244  247
## [10025]  248  249  250  252  253  254  255  257  260  264  267  271  273  278
## [10039]  280  282  283  286  289  291  309  310  314  317  318  321  322  324
## [10053]  331  332  333  334  335  340  345  346  347  348  350 9998 9999    0
## [10067]    1    2    3    4    5    6    7    8    9   10   11   12   13   14
## [10081]   15   16   17   18   22   24   25   26   27   35   36   42   43   44
## [10095]   57   59   60 9998 9999    0    1    4    6   18   20   22   27   29
## [10109]   33   38   47   50   56   57   62   64   70   78   89   95   97   99
## [10123]  111  124  129  133  134  139  143  151  153  168  183  190  194  198
## [10137]  201  202  206  210  213  214  218  227  230  231  232  235  242  243
## [10151]  249  250  253  255  258  259  263  264  268  270  271  272  273  274
## [10165]  276  277  279  280  286  288  297  300  302  312  313  314  315  317
## [10179]  318  319  324  329  334  342  343  350  355  356  360  369  376  377
## [10193]  378  380  391  396  405  407  415  417  430  435  440  444  449  450
## [10207]  451  452  453  458  460  463  464  468  470  471  476  486  511  512
## [10221]  513  516  522  526  528  529  535  536  537  539  540  542  544  547
## [10235]  550  560  565  566  568  572  573  574  575  577  583  587  588  591
## [10249]  592  593  594  595  596  597  600  605  608  610  611  613  615  620
## [10263]  621  622  626  632  660  668  683  684  690  697  703  706  708  709
## [10277]  712  714  715  718  721  722  724  726  727  728  735  737  738  739
## [10291]  741  747  748  749  750  752  753  754  755  756  757  758  760  761
## [10305]  763  764  766  767  770  771 9998 9999    0    1    5    9   10   11
## [10319]   12   13   18   20   21   22   23   24   25   29   31   37   52   53
## [10333]   57   59   67   71   72   74   84   86 9998 9999    0    1    9   10
## [10347]   11   14   16   17   18   19   20   21   23   25   27   28   29   31
## [10361]   33   34   35   36   37   38   39   40   41   42   46   47   49   53
## [10375]   54   68   69   71   72   81   82   83   84 9998 9999    0    1    2
## [10389]    3    5    8    9   11   14   15   16   18   19   20   24   26   27
## [10403]   28   29   30   31   34   36   37   38   39   41   42   43   44   45
## [10417]   46   47   51   52   53   69   70   71   77   79   83   84   85   86
## [10431]   87   90   96  112  118  127  132  134  141  144  150  151  152  153
## [10445] 9998 9999    0    1    3    4    6    7    9   10   15   16   17   18
## [10459]   19   20   25   26   27   30   31   34   36   39   42   43   44   45
## [10473]   46   47   49   50   51   52   54   55   58   59   60   61   62   63
## [10487]   64   67   68   70   71   73   75   81   82   86   88   89   92   93
## [10501]   94   95   96   97  100  102  104  105  107  109  114  115  116  117
## [10515]  120  122  124  125  127  129  131  133  134  135  139  143  147  149
## [10529]  154  155  156  158  161  162  163  167  168  174  175  176  179  180
## [10543]  181  182  184  185  187  189  191  192  195  196  197  198  199  200
## [10557]  201  203  205  209  210  212  214  216  217  218  221  223  226  228
## [10571]  233  235  237  240  244  246  247  248  250  251  253  255  258  260
## [10585]  271  272  273  276  277  284  286  288  289  290  291  293  295  297
## [10599]  302  303  307  310  312  314  317  318  322  325  326  327  331  332
## [10613]  333  337  338  340  342  343  344  346  348  349  350  351  352  355
## [10627]  357  358  359  360  361  362  365 9998 9999    0    1    3    4    6
## [10641]    7    9   10   11   12   13   14   15   16   18   19   20   22   23
## [10655]   24   25   26   27   28   29   30   31   32   33   34   35   36   37
## [10669]   39   40   41   42   43   44   45   47   50   51   52   53   55   56
## [10683]   57   58   59   60   62   63   64   66   67   68   69   70   72   73
## [10697]   74   75   76   77   78   79   80   81   82   83   85   86   87   88
## [10711]   89   90   91 9998 9999

########## ARREGLAR LA BASE CSV

library(stringr)
datos_inegi_jal2$LOC<-str_pad(datos_inegi_jal$LOC,3,side="left",pad="0")

#Datos en xlsx, cortar el texto
datos_inegi_jal$LOC<-substr(datos_inegi_jal$LOC,2,4)

        
#which(datos_inegi_jal$LOC=="000")

posis_mun<-which(datos_inegi_jal$NOM_LOC=="Total del Municipio")
datos_inegi_jal_mun<-datos_inegi_jal[posis_mun,]
dim(datos_inegi_jal_mun)

## [1] 125 232

######################

# ¿% personas con  discapacidad en cada municipio?
# ¿% personas con 65 o más años de cada municipio?

datos_inegi_jal_mun$Prop_disc<-as.numeric(datos_inegi_jal_mun$PCON_DISC)/as.numeric(datos_inegi_jal_mun$POBTOT)
datos_inegi_jal_mun$Prop_adulto<-as.numeric(datos_inegi_jal_mun$POB65_MAS)/as.numeric(datos_inegi_jal_mun$POBTOT)

red_jal_mun<-datos_inegi_jal_mun[,c(3,233,234)]

mapa_jalisco@data

##     CVEGEO CVE_ENT CVE_MUN                           NOMGEO
## 0    14073      14     073            San Juan de los Lagos
## 1    14074      14     074                      San Julián
## 2    14075      14     075                       San Marcos
## 3    14076      14     076          San Martín de Bolaños
## 4    14077      14     077              San Martín Hidalgo
## 5    14078      14     078               San Miguel el Alto
## 6    14079      14     079                   Gómez Farías
## 7    14080      14     080         San Sebastián del Oeste
## 8    14081      14     081     Santa María de los Ã\201ngeles
## 9    14082      14     082                           Sayula
## 10   14083      14     083                             Tala
## 11   14084      14     084                 Talpa de Allende
## 12   14085      14     085             Tamazula de Gordiano
## 13   14086      14     086                          Tapalpa
## 14   14087      14     087                      Tecalitlán
## 15   14006      14     006                            Ameca
## 16   14007      14     007          San Juanito de Escobedo
## 17   14008      14     008                          Arandas
## 18   14009      14     009                        El Arenal
## 19   14010      14     010             Atemajac de Brizuela
## 20   14011      14     011                           Atengo
## 21   14012      14     012                       Atenguillo
## 22   14013      14     013               Atotonilco el Alto
## 23   14014      14     014                           Atoyac
## 24   14015      14     015               Autlán de Navarro
## 25   14016      14     016                         Ayotlán
## 26   14017      14     017                           Ayutla
## 27   14018      14     018                         La Barca
## 28   14019      14     019                         Bolaños
## 29   14020      14     020                  Cabo Corrientes
## 30   14021      14     021                Casimiro Castillo
## 31   14022      14     022                       Cihuatlán
## 32   14023      14     023              Zapotlán el Grande
## 33   14024      14     024                           Cocula
## 34   14025      14     025                        Colotlán
## 35   14026      14     026      Concepción de Buenos Aires
## 36   14027      14     027 Cuautitlán de García Barragán
## 37   14028      14     028                          Cuautla
## 38   14029      14     029                          Cuquío
## 39   14030      14     030                          Chapala
## 40   14031      14     031                     Chimaltitán
## 41   14032      14     032                   Chiquilistlán
## 42   14033      14     033                        Degollado
## 43   14034      14     034                           Ejutla
## 44   14035      14     035            Encarnación de Díaz
## 45   14036      14     036                        Etzatlán
## 46   14037      14     037                        El Grullo
## 47   14038      14     038                      Guachinango
## 48   14039      14     039                      Guadalajara
## 49   14040      14     040                  Hostotipaquillo
## 50   14041      14     041                        Huejúcar
## 51   14042      14     042              Huejuquilla el Alto
## 52   14043      14     043                        La Huerta
## 53   14044      14     044   Ixtlahuacán de los Membrillos
## 54   14045      14     045            Ixtlahuacán del Río
## 55   14046      14     046                   Jalostotitlán
## 56   14047      14     047                            Jamay
## 57   14048      14     048                    Jesús María
## 58   14049      14     049         Jilotlán de los Dolores
## 59   14050      14     050                        Jocotepec
## 60   14051      14     051                     Juanacatlán
## 61   14052      14     052                       Juchitlán
## 62   14053      14     053                  Lagos de Moreno
## 63   14054      14     054                        El Limón
## 64   14055      14     055                        Magdalena
## 65   14056      14     056             Santa María del Oro
## 66   14057      14     057          La Manzanilla de la Paz
## 67   14058      14     058                          Mascota
## 68   14059      14     059                        Mazamitla
## 69   14060      14     060                      Mexticacán
## 70   14061      14     061                        Mezquitic
## 71   14062      14     062                         Mixtlán
## 72   14063      14     063                         Ocotlán
## 73   14064      14     064               Ojuelos de Jalisco
## 74   14065      14     065                          Pihuamo
## 75   14066      14     066                       Poncitlán
## 76   14067      14     067                  Puerto Vallarta
## 77   14068      14     068              Villa Purificación
## 78   14069      14     069                         Quitupan
## 79   14070      14     070                         El Salto
## 80   14071      14     071    San Cristóbal de la Barranca
## 81   14072      14     072         San Diego de Alejandría
## 82   14088      14     088                      Tecolotlán
## 83   14089      14     089          Techaluta de Montenegro
## 84   14090      14     090                     Tenamaxtlán
## 85   14091      14     091                      Teocaltiche
## 86   14092      14     092          Teocuitatlán de Corona
## 87   14093      14     093           Tepatitlán de Morelos
## 88   14094      14     094                          Tequila
## 89   14095      14     095                      Teuchitlán
## 90   14096      14     096                 Tizapán el Alto
## 91   14097      14     097           Tlajomulco de Zúñiga
## 92   14098      14     098            San Pedro Tlaquepaque
## 93   14099      14     099                         Tolimán
## 94   14100      14     100                        Tomatlán
## 95   14101      14     101                          Tonalá
## 96   14102      14     102                           Tonaya
## 97   14103      14     103                           Tonila
## 98   14104      14     104                        Totatiche
## 99   14105      14     105                        Tototlán
## 100  14106      14     106                      Tuxcacuesco
## 101  14107      14     107                         Tuxcueca
## 102  14108      14     108                           Tuxpan
## 103  14109      14     109            Unión de San Antonio
## 104  14110      14     110                   Unión de Tula
## 105  14111      14     111               Valle de Guadalupe
## 106  14112      14     112                 Valle de Juárez
## 107  14113      14     113                      San Gabriel
## 108  14114      14     114                     Villa Corona
## 109  14115      14     115                   Villa Guerrero
## 110  14116      14     116                    Villa Hidalgo
## 111  14117      14     117             Cañadas de Obregón
## 112  14118      14     118     Yahualica de González Gallo
## 113  14119      14     119               Zacoalco de Torres
## 114  14120      14     120                          Zapopan
## 115  14121      14     121                       Zapotiltic
## 116  14122      14     122           Zapotitlán de Vadillo
## 117  14123      14     123                Zapotlán del Rey
## 118  14124      14     124                      Zapotlanejo
## 119  14001      14     001                           Acatic
## 120  14002      14     002              Acatlán de Juárez
## 121  14003      14     003             Ahualulco de Mercado
## 122  14004      14     004                         Amacueca
## 123  14005      14     005                        Amatitán
## 124  14125      14     125          San Ignacio Cerro Gordo

tabla11<-merge(x=mapa_jalisco@data,y=red_jal_mun,by.x="CVE_MUN",by.y="MUN",sort=FALSE)

mapa_jalisco@data$Discapa<-tabla11$Prop_disc
mapa_jalisco@data$Adultos<-tabla11$Prop_adulto


#################### Termina clase martes 16.11.21 ######

clase martes 23.11.21 (mapa interactivo)

#Generar Mapa "estático"
plot(mapa_jalisco)

summary(mapa_jalisco@data$Discapa)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.03390 0.04715 0.05497 0.05867 0.06842 0.11590

cut(mapa_jalisco@data$Discapa,4)

##   [1] (0.0338,0.0544] (0.0749,0.0954] (0.0749,0.0954] (0.0749,0.0954]
##   [5] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
##   [9] (0.0749,0.0954] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
##  [13] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
##  [17] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
##  [21] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
##  [25] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0544,0.0749]
##  [29] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
##  [33] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
##  [37] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0338,0.0544]
##  [41] (0.0954,0.116]  (0.0338,0.0544] (0.0338,0.0544] (0.0749,0.0954]
##  [45] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749]
##  [49] (0.0338,0.0544] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749]
##  [53] (0.0749,0.0954] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
##  [57] (0.0544,0.0749] (0.0749,0.0954] (0.0544,0.0749] (0.0338,0.0544]
##  [61] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0954,0.116] 
##  [65] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0749,0.0954]
##  [69] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0544,0.0749]
##  [73] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
##  [77] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
##  [81] (0.0749,0.0954] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
##  [85] (0.0749,0.0954] (0.0544,0.0749] (0.0749,0.0954] (0.0338,0.0544]
##  [89] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
##  [93] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
##  [97] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [101] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [105] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749]
## [109] (0.0544,0.0749] (0.0749,0.0954] (0.0338,0.0544] (0.0954,0.116] 
## [113] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
## [117] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
## [121] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
## [125] (0.0338,0.0544]
## Levels: (0.0338,0.0544] (0.0544,0.0749] (0.0749,0.0954] (0.0954,0.116]

library(RColorBrewer)

my_colors <- brewer.pal(5, "RdPu") 
my_colors <- colorRampPalette(my_colors)(4)

cuantil <- cut(mapa_jalisco@data$Discapa, 4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=my_colors ,  bg = "white")

intermedio <-rep(NA,125)

intermedio[which(as.numeric(cuantil)==1)]<-"#AE017E"
intermedio[which(as.numeric(cuantil)==2)]<-"#F768A1"
intermedio[which(as.numeric(cuantil)==3)]<-"#FBB4B9"
intermedio[which(as.numeric(cuantil)==4)]<-"#FEEBE2"

#my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=intermedio ,  bg = "white")

#### Variable "Adulto", 5 cortes, Jalisco también... ¿paleta? la que quieran


summary(mapa_jalisco@data$Adultos)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.03587 0.08596 0.10427 0.10725 0.12842 0.19182

cut(mapa_jalisco@data$Adultos,5)

##   [1] (0.0671,0.0983] (0.0983,0.129]  (0.0983,0.129]  (0.0983,0.129] 
##   [5] (0.129,0.161]   (0.0671,0.0983] (0.0671,0.0983] (0.129,0.161]  
##   [9] (0.161,0.192]   (0.0671,0.0983] (0.0671,0.0983] (0.0983,0.129] 
##  [13] (0.0983,0.129]  (0.0357,0.0671] (0.0983,0.129]  (0.0983,0.129] 
##  [17] (0.0983,0.129]  (0.0671,0.0983] (0.0671,0.0983] (0.0671,0.0983]
##  [21] (0.129,0.161]   (0.129,0.161]   (0.0671,0.0983] (0.129,0.161]  
##  [25] (0.0671,0.0983] (0.0671,0.0983] (0.0983,0.129]  (0.0671,0.0983]
##  [29] (0.0357,0.0671] (0.0983,0.129]  (0.0983,0.129]  (0.0671,0.0983]
##  [33] (0.0671,0.0983] (0.0983,0.129]  (0.0983,0.129]  (0.0983,0.129] 
##  [37] (0.0983,0.129]  (0.129,0.161]   (0.0983,0.129]  (0.129,0.161]  
##  [41] (0.0983,0.129]  (0.0671,0.0983] (0.0983,0.129]  (0.161,0.192]  
##  [45] (0.0671,0.0983] (0.0983,0.129]  (0.0983,0.129]  (0.129,0.161]  
##  [49] (0.0983,0.129]  (0.0983,0.129]  (0.161,0.192]   (0.0983,0.129] 
##  [53] (0.0983,0.129]  (0.0357,0.0671] (0.0983,0.129]  (0.0671,0.0983]
##  [57] (0.0671,0.0983] (0.0983,0.129]  (0.0671,0.0983] (0.0671,0.0983]
##  [61] (0.0357,0.0671] (0.129,0.161]   (0.0671,0.0983] (0.161,0.192]  
##  [65] (0.0671,0.0983] (0.129,0.161]   (0.0983,0.129]  (0.129,0.161]  
##  [69] (0.0671,0.0983] (0.129,0.161]   (0.0357,0.0671] (0.129,0.161]  
##  [73] (0.0671,0.0983] (0.0671,0.0983] (0.129,0.161]   (0.0671,0.0983]
##  [77] (0.0357,0.0671] (0.129,0.161]   (0.161,0.192]   (0.0357,0.0671]
##  [81] (0.0983,0.129]  (0.0671,0.0983] (0.129,0.161]   (0.0983,0.129] 
##  [85] (0.129,0.161]   (0.0983,0.129]  (0.129,0.161]   (0.0671,0.0983]
##  [89] (0.0671,0.0983] (0.0983,0.129]  (0.0983,0.129]  (0.0357,0.0671]
##  [93] (0.0357,0.0671] (0.0983,0.129]  (0.0983,0.129]  (0.0357,0.0671]
##  [97] (0.129,0.161]   (0.0983,0.129]  (0.161,0.192]   (0.0671,0.0983]
## [101] (0.0983,0.129]  (0.0983,0.129]  (0.0983,0.129]  (0.0671,0.0983]
## [105] (0.129,0.161]   (0.0983,0.129]  (0.129,0.161]   (0.0983,0.129] 
## [109] (0.0983,0.129]  (0.129,0.161]   (0.0671,0.0983] (0.129,0.161]  
## [113] (0.129,0.161]   (0.0983,0.129]  (0.0671,0.0983] (0.0671,0.0983]
## [117] (0.0983,0.129]  (0.0671,0.0983] (0.0671,0.0983] (0.0671,0.0983]
## [121] (0.0671,0.0983] (0.0983,0.129]  (0.0983,0.129]  (0.0671,0.0983]
## [125] (0.0671,0.0983]
## 5 Levels: (0.0357,0.0671] (0.0671,0.0983] (0.0983,0.129] ... (0.161,0.192]

my_colors <- brewer.pal(5, "BuGn") 
my_colors <- colorRampPalette(my_colors)(5)

cuantil <- cut(mapa_jalisco@data$Adultos, 5)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=my_colors ,  bg = "yellow")
legend("topright",levels(cut(mapa_jalisco@data$Adultos, 5)),
       cex = 0.6)

# imagen

#jpeg(filename = "mapa_jalisco1222.jpg",
#width = 480, height = 480, units = "px", pointsize = 12,
#quality = 100,
#bg = "white", res = NA, family = "", restoreConsole = TRUE,
#type = c("windows", "cairo"))

#plot(mapa_jalisco , col=my_colors ,  bg = "white")
#legend("topleft",texto5,cex = 1.2,bty = "n",
 #      col = c("#FEE5D9","#FCBBA1","#FC9272","#FB6A4A","#DE2D26"),pch = "@")

#dev.off()





library(leaflet)

## Warning: package 'leaflet' was built under R version 4.1.2

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

leaflet(data=mapa_jalisco) %>% addTiles() %>% addPolygons()

#####

cortess <- c(0,0.0280,0.0566,0.0849,Inf)
colores <- colorBin( palette="Reds", domain=mapa_jalisco$Discapa, na.color="transparent", bins=cortess)


leaflet(data=mapa_jalisco) %>%
addTiles() %>%
addPolygons(fillColor = colores(mapa_jalisco$Discapa),
            fillOpacity = 0.9)  

library(htmltools)
textoss <- paste(
  "Municipio : ",mapa_jalisco$NOMGEO,"<br/>",
  "% Discapacidad: ", round(mapa_jalisco$Discapa*100,2),"<br/>", 
  "% Ancianos: ", round(mapa_jalisco$Adultos*100,2) )  %>% lapply(htmltools::HTML)

leaflet(data=mapa_jalisco) %>% 
  addTiles() %>% 
  addPolygons(label = textoss,fillColor = colores(mapa_jalisco$Discapa),
              fillOpacity = 0.9)