File:Suomen koronavirustapauksia paivassa ennuste sarima 1.svg
Original file (SVG file, nominally 1,080 × 540 pixels, file size: 101 KB)
Captions
Summary
[edit]DescriptionSuomen koronavirustapauksia paivassa ennuste sarima 1.svg |
English: Suomen koronavirustapauksia paivassa ennuste |
Date | |
Source | Own work |
Author | Merikanto |
Asdditional information
[edit]"R" code to produce this covid-19 forecast. Forecast is based on SARIMA procedure.
- calculate forecast of covid-19
- using sarima
- 14.4.2022
- v 0000.0002
- install.packages("astsa", repos ="https://ftp.acc.umu.se/mirror/CRAN/")
- install.packages("MLmetrics", repos ="https://ftp.acc.umu.se/mirror/CRAN/")
- install.packages("rvest")
- install.packages("readtext")
- install.packages("stringi")
- install.packages("datamart")
- install.packages("XML")
- install.packages("svglite")
- install.packages("ggplot2")
- install.packages("tidyr")
- install.packages("stringr")
- install.packages("stringi")
- install.packages("tibble")
Sys.setlocale("LC_ALL","Finnish")
options(encoding = "UTF-8")
library(ggplot2)
library(svglite)
library(rvest)
library(readtext)
library(stringi)
library(stringr)
library(datamart)
library(XML)
library(jsonlite)
library(rjstat)
library(tibble)
library(caTools)
library(mgcv)
library(repmis)
library(lubridate)
library(tidyverse)
library(tidyr)
library(dplyr)
- library(covid19.analytics)
library(R0)
library(EpiEstim)
- library(prophet)
library(forecast)
library(astsa)
library(MLmetrics)
- choices
- 1 finnish wiki data, 2 aggregated cases data
- 3 solanpaa finnish data 4 thl cube json data
load_data_from=3
yala=0
yyla=10000
beginday1='02/11/2021'
forecastendday1<-"2022/07/01"
- 1 from finnish wiki, 2 cases from net, 3 from net 2
plottaa=1 ## must be 1
tulosta_svg=1 # plot to out svg 0, 1 of 2
tulosfilee1="/Users/himot/akor1/sarimaa1.svg"
- ggolot smooth curves pars
- spanni=0.2
spanni=0.5
metodi="loess"
- NOTE date limits change this
- datelimits1=c('1/3/2020', '9/11/2020')
- display date limits
- this
today=Sys.Date()-15
- or this
- today=Sys.Date()
- print(today)
today1=format(today, "%d/%m/%Y")
today2=format(today, "%Y/%m/%d")
- print(today1)
print(today2)
- stop(-1)
datelimits1=c(beginday1, today1)
- dates of dataset
paivat1=seq(as.Date("2020/4/1"), as.Date(today2), "days")
calculate_r0 <- function(time1, time2, val1, val2)
{
td=time2-time1
gr0<-log(val2/val1)
gr=gr0/td
td = log(2)/gr
tau<-5.0
k<-log(2.0)/td
r0<-exp(k*tau)
return(r0)
}
moving_average <- function(x, w, FUN, ...)
{
if (w < 1) {
stop("Window length: mustbe greater than 0")
}
output <- x
for (i in 1:length(x)) {
lower_bound <- i - w + 1
if (lower_bound < 1) {
output[i] <- NA_real_
## !!! assume NA 0
output[i] <- 0
} else {
output[i] <- FUN(x[lower_bound:i, ...])
}
}
return (output)
}
calculate_multiple_r0 <- function(daata1) {
lenu1<-length(daata1)
daata2<-1:lenu1
for (n in 2:lenu1){
valju1=daata1[n-1]
valju2=daata1[n]
timex1=0
timex2=1
r0<-calculate_r0(0, 1, valju1, valju2)
daata2[n]<-r0
#print (r0)
}
return(daata2)
}
load_data_from_finnish_wiki<-function()
{
url1="https://fi.wikipedia.org/wiki/Suomen_koronaviruspandemian_aikajana"
destfile1="./ward0.txt"
download.file(url1, destfile1)
texti000<-readtext(destfile1)
texti0<-texti000$text
etsittava1="1. huhtikuuta 2020 alkaen"
len1=nchar(texti0)
k1=regexpr(pattern=etsittava1, texti0)
k1b=len1-k1
texti1=strtail(texti0,k1b)
sink("out1.txt")
print (texti1)
sink()
etsittava2=""
k2=regexpr(pattern=etsittava2, texti1)
texti2=strhead(texti1,k2)
sample1<-minimal_html(texti2)
tabu1 <- html_table(sample1, fill=TRUE)1
colnames(tabu1) <- c("V1","V2", "V3","V4", "V5","V6", "V7","V8" )
- print(tabu1)
sairaalassa00<-tabu1$V4
sairaalassa=as.integer(sairaalassa00)
teholla00<-tabu1$V5
teholla=as.integer(teholla00)
uusiatapauksia00<-tabu1$V3
uusiatapauksia0<-gsub(" ", "", uusiatapauksia00)
uusia_tapauksia=as.integer(uusiatapauksia0)
uusiakuolleita00<-tabu1$V7
uusiakuolleita1=as.integer(uusiakuolleita00)
uusiakuolleita2<-uusiakuolleita1
uusiakuolleita2[uusiakuolleita2<0]<-0
uusia_kuolleita<-uusiakuolleita2
toipuneita00<-tabu1$V8
toipuneita01<-gsub(" ", "", toipuneita00)
toipuneita0<-gsub("[^0-9.-]", "", toipuneita01)
toipuneita=as.integer(toipuneita0)
tapauksia00<-tabu1$V2
tapauksia01<-gsub(" ", "", tapauksia00)
tapauksia0<-gsub("[^0-9.-]", "", tapauksia01)
tapauksia=as.integer(tapauksia0)
kuolleita00<-tabu1$V6
kuolleita=as.integer(kuolleita00)
aktiivisia_tapauksia=tapauksia-kuolleita-toipuneita
- print (paivat1)
- print (teholla)
- print (sairaalassa)
- print (tapauksia)
- print (kuolleita)
- print (toipuneita)
- print (uusia_tapauksia)
- print (uusia_kuolleita)
- plot(paivat1,aktiivisia_tapauksia)
- xy<-data.frame(paivat1, sairaalassa)
xy<-data.frame(paivat1, uusia_tapauksia)
names(xy)<-c("daate3", "dailycases3")
xyz<-data.frame(paivat1, sairaalassa, teholla)
dfout1<-data.frame(paivat1, aktiivisia_tapauksia, uusia_tapauksia, sairaalassa, teholla, uusia_kuolleita )
names(dfout1)<-c("Pvm", "Aktiivisia_tapauksia","Uusia_tapauksia", "Sairaalassa", "Teholla", "Uusia_kuolleita")
write.csv2(dfout1, "./sairaalassa.csv",row.names=FALSE )
return(xy)
}
load_data_from_aggregated<-function()
{
- fetch the data
dfine <- read.csv(file = 'https://datahub.io/core/covid-19/r/countries-aggregated.csv')
- head(dfine)
- class(dfine)
- tail(dfine, 5)
dfinland <- dfine[ which(dfine$Country=='Finland'), ]
- head(dfinland)
kols <- c("Date", "Confirmed","Recovered","Deaths")
tapaukset <- dfinland[kols]
- head(tapaukset)
len1=nrow(tapaukset)
- len1
len2=len1-1
len3=len2
confirmed<-tapaukset$Confirmed
deaths<-tapaukset$Deaths
dailycases <- vector()
dailycases <- c(dailycases, 0:(len2))
dailydeaths <- vector()
dailydeaths <- c(dailydeaths, 0:(len2))
m=0
dailycases[1]<-tapaukset$Confirmed[1]
- dailydeaths[1]<-tapaukset$Deaths[1]
dailydeaths[1]<-0
- confirmed
- deaths
m=1
for(n in 2:(len3+1)) {
a<-confirmed[n]
b<-confirmed[m]
#print (a)
#print (b)
cee<- (a-b)
#print(cee)
dailycases[n]=cee
m=m+1
}
mm=1
for(nn in 2:(len3+1)) {
aa<-deaths[nn]
bb<-deaths[mm]
#print ("_")
#print (aa)
#print (bb)
ceb=aa-bb
#if (ceb<0) ceb=0
#print(ceb)
dailydeaths[nn]=ceb
mm=mm+1
}
- deaths
- dailycases
- dailydeaths
dfout1<-dfinland
- print(nrow(dfinland))
- print(length(dailydeaths))
dfout1 <- cbind(dfout1, data.frame(dailycases))
dfout1 <- cbind(dfout1, data.frame(dailydeaths))
- head(dfout1)
dfout2<-within(dfout1, rm(Country))
names(dfout2) <- c('Date','Confirmed','Recovered','Deaths', 'DailyConfirmed','DailyDeaths')
- head(dfout2)
write.csv2(dfout2, "/Users/himot/akor1/finland_data1.csv");
daate1<-dfout2$Date
dailydeaths1<-dfout2$DailyDeaths
dailycases1<-dailycases
- daate1
- daate2<-gsub("2020-", "", daate1)
daate2<-daate1
leenu<-length(daate2)
- alkupvm<-50
alkupvm<-1
daate3<-daate2[alkupvm:leenu]
dailydeaths3<-dailydeaths1[alkupvm:leenu]
dailycases3<-dailycases1[alkupvm:leenu]
- daate3
- dailydeaths3
# barplot(dailydeaths3, main="Koronaviruskuolemat päivittäin vuonna 2020",
# names.arg=daate3)
dataf1 <- data.frame("Date" = daate3, "Paivitt_kuolemat"=dailydeaths3)
- str(dataf1)
dataf2 <- data.frame("Date" = daate3, "Paivitt_tapaukset"=dailycases3)
- str(dataf2)
write.csv(dataf1, "/Users/himot/akor1/dailydeaths1.csv", row.names=T)
write.csv(dataf2, "/Users/himot/akor1/dailycases1.csv", row.names=T)
indf1 <- read.csv(file = '/Users/himot/akor1/dailycases1.csv')
#head(indf1)
cases1<-indf1$Paivitt_tapaukset
dates1<-indf1$Date
len1=length(cases1)
dates2<-as.Date(dates1)
paivat<-1:len1
xy<-data.frame(daate3, dailycases3)
}
calculate_r0_with_r0<-function(xy2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
bekini=as.Date(dates[1])
enti=as.Date(dates[lenu])
#print(bekini)
#print(enti)
#stop(-1)
#enti=lenu
#bekini=enti*0+1
#enti=as.integer(enti)
#bekini=as.integer(bekini)
df1 <- setNames(num, names)
mGT<-generation.time("gamma", c(3, 1.5))
#TD <- est.R0.TD(df1, mGT, begin=1, end=length(dates), nsim=200)
#TD <- est.R0.TD(df1, mGT, begin=bekini, end=enti, nsim=200)
TD <- est.R0.TD(df1, mGT, begin=bekini, end=enti, nsim=200)
TD.5D <- smooth.Rt(TD, 5)
paivat1<-TD.5D$epid$t
paivat2<-as.Date(paivat1)
r0t1<-TD.5D$R
conf1<-TD.5D$conf.int
xypaluu<-data.frame(paivat1,r0t1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
calculate_r0_with_epiestim<-function(xy2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
incid<-cases
bekini=as.Date(dates[1])
enti=as.Date(dates[lenu])
config<-make_config( list(mean_si = 2.6,std_si = 1.5) )
res<-estimate_R(incid,method="parametric_si", config = config)
#plot(res)
resr<-res$R
str(resr)
meanr<-resr$Mean
medianr<-resr$Median
quantile95<-resr$Quantile.0.95
quantile05<-resr$Quantile.0.05
quantile75<-resr$Quantile.0.75
quantile25<-resr$Quantile.0.25
meanr
daydexes<-resr$t_start
daydexes
#plot(daydexes, meanr)
dayss<-as.Date(dates[daydexes])
print (dayss)
#stop(-1)
#plot(dayss, meanr)
xypaluu<-data.frame(dayss,meanr)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
calculate_r0_with_simple_exponent_moving_average<-function(xy2, madays1, madays2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
# compute a MA(7)
ma1<-moving_average(cases,madays1,mean)
r0t1<-calculate_multiple_r0(ma1)
r0avg1<-moving_average(r0t1, madays2, mean)
xypaluu<-data.frame(dates,r0t1)
#plot(r0t1)
#print (r0t1)
#stop(-1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
forecast_profet<-function(xy2, futuredays)
{
## calculate r0 w/r0 package
ds<-as.Date(xy2$Dates)
y<-as.integer(xy2$Cases)
nummeros<-1:length(ds)
lenu=length(ds)
df<-data.frame(ds,y)
m <- prophet(df)
future <- make_future_dataframe(m, periods = futuredays)
forecast <- predict(m, future)
#str(future)
#str(forecast)
futu_days=future$ds
futu_trendi=forecast$trend
futu_trendi_upper=forecast$trend_upper
futu_trendi_lower=forecast$trend_lower
futu_yhat=forecast$yhat
futu_yhat_upper=forecast$yhat_upper
futu_yhat_lower=forecast$yhat_lower
futu_weekly=forecast$weekly
futu_weekly_upper=forecast$weekly_upper
futu_weekly_lower=forecast$weekly_lower
xypaluu<-data.frame(as.Date(futu_days),futu_yhat)
# xypaluu<-data.frame(as.Date(futu_days),futu_weekly)
#plot(r0t1)
#print (r0t1)
#stop(-1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
lataa_thl_tapaukset_kuolleet<-function()
{
url1<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-492118&column=dateweek20200101-508804L"
cube1 <- fromJSONstat(url1, naming = "label", use_factors = F, silent = T)
res01 <- cube11
#res00
url2<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"
cube2 <- fromJSONstat(url2, naming = "label", use_factors = F, silent = T)
res02 <- cube21
#res02
#stop (-1)
paiva=as.Date(res01$dateweek20200101)
kuolleet=as.integer(res01$value)
tapaukset=as.integer(res02$value)
kuolin_prosentit=kuolleet/tapaukset
kuolin_prosentit=kuolin_prosentit*10000
kuolin_prosentit=as.integer(kuolin_prosentit)
kuolin_prosentit=as.double(kuolin_prosentit)
kuolin_prosentit=kuolin_prosentit/100.0
#print (paiva)
#print (kuolleet)
#stop(-1)
#print (tapaukset)
#print (kuolin_prosentit )
df1<-data.frame(paiva,tapaukset, kuolleet, kuolin_prosentit)
names(df1)<-c("Paiva", "Tapauksia", "Kuolleita", "Kuolinprosentti")
#write.csv2(df1, "./kuolleet_ikaryhmittain.csv", sep = ";" )
write.csv(df1, "./thl_tapaukset_kuolleet.csv")
xy0<-data.frame(paiva, tapaukset)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
#return(df1)
}
download_solanpaa_finnish_data<-function()
{
solanpaa_fi="https://covid19.solanpaa.fi/data/fin_cases.json"
cache_file="solanpaa_fi.json"
download.file(solanpaa_fi, cache_file)
j1 <- fromJSON(cache_file)
## maybe errori
dates<-as.Date(j1$date)
dailycases<-j1$new_cases
dailydeaths<-j1$new_deaths
dataf1 <- data.frame("Date" = dates, "Paivitt_kuolemat"=dailydeaths)
dataf2 <- data.frame("Date" = dates, "Paivitt_tapaukset"=dailycases)
write.csv(dataf1, "./dailydeaths1.csv", row.names=T)
write.csv(dataf2, "./dailycases1.csv", row.names=T)
xy0<-data.frame(dates, dailycases)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
return(xy)
}
if(load_data_from==1)
{
xy<-load_data_from_finnish_wiki()
print (xy)
}
if(load_data_from==2)
{
xy<-load_data_from_aggregated()
}
if(load_data_from==3)
{
xy<-download_solanpaa_finnish_data()
}
if(load_data_from==4)
{
xy<-lataa_thl_tapaukset_kuolleet()
}
names(xy)<-c("Dates","Cases")
#print (xy)
#print (beginday1)
select_datelimit_begin=as.Date(beginday1,format="%d/%m/%Y")
select_datelimit_end=as.Date(today1,format="%d/%m/%Y" )
#format(select_datelimit_begin, "%Y-%m-%d")
#print(select_datelimit_begin)
#print(select_datelimit_end)
#2020-12-16
##xy2<-xy[xy$Dates >= select_datelimit_begin,]
- xy2<-xy[xy$Dates >= select_datelimit_begin & xy$Dates <= select_datelimit_end]
xy2a<-xy[xy$Dates >= select_datelimit_begin, ]
xy2<-xy2a[ xy2a$Dates <= select_datelimit_end,]
print("Sel")
print(today1)
print(select_datelimit_begin)
print(select_datelimit_end)
print(xy2)
- stop(-1)
cases1<-xy2$Cases
dates1<-xy2$Dates
xy3<-data.frame( as.Date(dates1),as.integer(cases1) )
names(xy3)<-c("Dates", "Cases")
len1=length(cases1)
dates2<-as.Date(dates1)
paivat<-1:len1
## test code
arrat0<-calculate_r0_with_simple_exponent_moving_average(xy2, 14,7)
arrat1<-calculate_r0_with_r0(xy2)
arrat2<-calculate_r0_with_epiestim(xy2)
#plot(arrat$paivat, arrat$r0)
arrat<-arrat2
#str(arrat)
#head(arrat)
sarrat1<-arrat1
sarrat2<-sarrat1
names(sarrat1)<-c("Dates","Cases")
datelimits2=c(today1, as.Date(forecastendday1,"%Y/%m/%d"))
datelimits3=c(as.Date(beginday1, "%d/%m/%Y" ), as.Date(forecastendday1,"%Y/%m/%d"))
daysek1<-seq(today, as.Date(forecastendday1), "days")
forecastdaynum1<-length(daysek1)
lendaysek1<-length(daysek1)
daysek2<-seq(as.Date(beginday1),as.Date(forecastendday1), "days")
daysek3<-seq(as.Date(beginday1),as.Date(forecastendday1), "days")
daysek4<-seq(as.Date(today), as.Date(forecastendday1,"%Y/%m/%d"),"days" )
# farrat1<-forecast_profet(xy2, lendaysek1)
# names(farrat1)<-c("Dates", "Cases")
# print(datelimits3)
#print(farrat1)
# stop(-1)
#plot(arrat$paivat, arrat$r0)
# plot(farrat1$paivat, farrat1$r0)
# lines(farrat1$paivat, farrat1$r0 )
# lines(sarrat2$paivat, sarrat1$r0 )
- stop(-1)
#arrat<-farrat1
# names(arrat)<-c("Dates","Forecast")
# arrat$Forecast<-as.integer(arrat$Forecast)
#marrat <- left_join(farrat1, xy3, by=c("Dates"))
# names(marrat)<-c("Dates","Forecast","Cases")
- print (marrat)
#stop(-1)
- days2 <- seq(ISOdate(2020,11,22), by = "day", length.out = daynum)
- days3 <- seq(ISOdate(2020,10,1), by = "day", length.out = daynum2)
y=xy3$Cases
y[y == 0] <- 1
yy=y
print (yy)
- stop(-1)
logy=log(yy)
y=logy
print (length(y))
print (forecastdaynum1)
- sarima_forecast = sarima.for(y, n.ahead=daynum,p=0,d=1,q=1,P=1,D=1,Q=0,S=7)
- sarima_forecast = sarima.for(y, n.ahead=daynum,p=0,d=1,q=1,P=1,D=1,Q=0,S=7)
sarima_forecast = sarima.for(y, n.ahead= forecastdaynum1,p=0,d=1,q=1,P=1,D=1,Q=0, S=6)
str(sarima_forecast)
y1=sarima_forecast$pred
y2=sarima_forecast$se
y1=exp(y1)
y2=exp(y2)
days2<-daysek4
print (length(y2))
print (length(days2))
- stop(-1)
- print (length(days3))
- print (length(y1))
ydelta1=y2*50
ydelta2=y2*25
ydelta3=y2*10
ydelta4=y2*5
ya1=y1+ydelta1
ya2=y1-ydelta1
ya3=y1+ydelta2
ya4=y1-ydelta2
ya5=y1+ydelta3
ya6=y1-ydelta3
ya7=y1+ydelta4
ya8=y1-ydelta4
lines(days2, ya1)
lines(days2, ya2)
plot(days2, y1, type="l", main="Koronatapaukset ennuste",xlabel="Pvm", ylabel="Tapauksia" )
print (y2)
data<-data.frame(days2,y1,ya1,ya2,ya3,ya4)
if(tulosta_svg==1)
{
svg(filename=tulosfilee1, width=12, height=6, pointsize=12)
}
if(plottaa==1)
{
metodi="loess"
- spanni=0.1
- metodi="loess"
ggplot(data, aes(x =days2 , y = y1) ) +
ylim(yala, yyla) +
geom_line()+
#ggtitle(" Ennuste - koronavirustapauksia Suomessa") +
labs(title = "Ennuste: koronavirustapauksia Suomessa",
subtitle = "jos muutos jatkuu samaa vauhtia",
caption = "")+
xlab("Kuukausi") + ylab("Tapauksia")+
theme(title=element_text(size=15), axis.text=element_text(size=12,face="bold"),axis.title=element_text(size=14,face="bold"))+
#geom_smooth( fill="#a0a0ff",span=spanni, method=metodi, level=0.9999, size=2)+
geom_smooth( fill="#9090ff", span=spanni,method=metodi, level=0.7) +
geom_smooth( fill="#8a08af", span=spanni, method=metodi,level=0.5) +
geom_ribbon( aes(ymin=ya1,ymax=ya2), fill="blue", alpha=0.5) +
geom_ribbon( aes(ymin=ya3,ymax=ya4), fill="blue", alpha=0.25)+
geom_ribbon( aes(ymin=ya5,ymax=ya6), fill="blue", alpha=0.1) +
geom_ribbon( aes(ymin=ya7,ymax=ya6), fill="blue", alpha=0.5)
}
if(tulosta_svg==1)
{
dev.off()
}
Licensing
[edit]- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
File history
Click on a date/time to view the file as it appeared at that time.
Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 07:42, 14 April 2022 | 1,080 × 540 (101 KB) | Merikanto (talk | contribs) | update | |
09:53, 8 December 2021 | 1,080 × 540 (111 KB) | Merikanto (talk | contribs) | update | ||
08:19, 25 July 2021 | 1,080 × 540 (83 KB) | Merikanto (talk | contribs) | update | ||
11:46, 19 June 2021 | 1,080 × 540 (81 KB) | Merikanto (talk | contribs) | update | ||
12:02, 13 May 2021 | 1,080 × 540 (90 KB) | Merikanto (talk | contribs) | upload | ||
18:41, 7 May 2021 | 1,080 × 540 (91 KB) | Merikanto (talk | contribs) | Update | ||
18:39, 7 May 2021 | 1,080 × 540 (93 KB) | Merikanto (talk | contribs) | upload | ||
12:43, 16 April 2021 | 1,080 × 540 (91 KB) | Merikanto (talk | contribs) | update | ||
10:28, 23 March 2021 | 1,080 × 540 (85 KB) | Merikanto (talk | contribs) | update | ||
18:26, 26 February 2021 | 1,080 × 540 (94 KB) | Merikanto (talk | contribs) | Upload |
You cannot overwrite this file.
File usage on Commons
There are no pages that use this file.
Metadata
This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.
Width | 864pt |
---|---|
Height | 432pt |