merge all data eps spline gpuc

merge all data

result.gpuc <- lm(apply.quarterly(SP5[k2k],mean)[,1] ~ PAq[k2k] * UCq[k2k] * G[k2k]*CSq[k2k] - UCq[k2k] -G[k2k] - PAq[k2k]*G[k2k] - UCq[k2k]*G[k2k]*CSq[k2k])
result.eps <- lm(apply.quarterly(SP5[,4][k2k],mean) ~ eps_year_xts[k2k]+apply.quarterly(PA[k2k],mean)+apply.quarterly(CS[k2k],mean)+apply.quarterly(UC[k2k],mean))
SP5.result <- merge(residuals(result.gpuc),predict(result.gpuc),residuals(result.eps),predict(result.eps))

GSPC.predict <- merge(to.monthly(GSPC)[substr(k2k,11,23)],last(spline(seq(1,length(SP5.result[,1]),1),as.vector(SP5.result[,2]),n=length(SP5.result[,1])*3+1)$y,n=length(to.monthly(GSPC)[,1][substr(k2k,11,23)])),last(spline(seq(1,length(SP5.result[,1]),1),as.vector(SP5.result[,4]),n=length(SP5.result[,1])*3+1)$y,n=length(to.monthly(GSPC)[,1][substr(k2k,11,23)])),suffixes=c('','spline','eps'))


plot(merge(GSPC.predict[,4],GSPC.predict[,7],GSPC.predict[,8],GSPC.predict[,4]-GSPC.predict[,7],GSPC.predict[,4]-GSPC.predict[,8]),main="GSPC.predict[,4] vs. GSPC.predict[,7]",grid.ticks.on='months')
tmp.legend <- "Black: actual \nRed: spline\nGreen: eps"
addLegend(legend.loc = "topleft", legend.names = tmp.legend,col=3)
tmp.addTA <- as.xts(rep(2800,length(index(GSPC.predict))),index(GSPC.predict))

addSeries(tmp.addTA,on=1,col=6,lwd=1)

SP5,eps,PAYEMS,UC,CS

> summary(lm(apply.quarterly(SP5[,4][k2k],mean) ~ eps_year_xts[k2k]+apply.quarterly(PA[k2k],mean)+apply.quarterly(CS[k2k],mean)+apply.quarterly(UC[k2k],mean)))

Call:
lm(formula = apply.quarterly(SP5[, 4][k2k], mean) ~ eps_year_xts[k2k] +
    apply.quarterly(PA[k2k], mean) + apply.quarterly(CS[k2k],
    mean) + apply.quarterly(UC[k2k], mean))

Residuals:
    Min      1Q  Median      3Q     Max
-178.63  -65.62   14.98   57.55  320.86

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|) 
(Intercept)                    -8.791e+03  4.347e+02 -20.222  < 2e-16 ***
eps_year_xts[k2k]               7.243e+00  6.983e-01  10.373 1.01e-15 ***
apply.quarterly(PA[k2k], mean)  7.740e-02  3.649e-03  21.210  < 2e-16 ***
apply.quarterly(CS[k2k], mean) -4.994e+00  5.692e-01  -8.774 7.71e-13 ***
apply.quarterly(UC[k2k], mean)  1.304e-01  5.309e-02   2.456   0.0166 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 97.83 on 69 degrees of freedom
Multiple R-squared:   0.96, Adjusted R-squared:  0.9577
F-statistic: 413.8 on 4 and 69 DF,  p-value: < 2.2e-16

tmp <- predict(lm(apply.quarterly(SP5[,4][k2k],mean) ~ eps_year_xts[k2k]+apply.quarterly(PA[k2k],mean)+apply.quarterly(CS[k2k],mean)+apply.quarterly(UC[k2k],mean)))
GSPC.predict <- merge(GSPC.predict[,-8],last(spline(seq(1,74,1),tmp,n=220)$y,n=138),suffixes = c("","eps"))

Composite Leading Indicator - OECD

# download csv from oecd(https://data.oecd.org/leadind/composite-leading-indicator-cli.htm)
# store it in CLI3.csv
# this file contains multiple regions data. you have to specify the name of the region.
# extract USA only entries
# sed -n '/USA/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > usa.csv
# extract OECD entries and exclude OECDE
# sed -n '/OECD[^E]/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > oecd.csv

cli_xts <- merge(as.xts(read.zoo(read.csv("~/Downloads/oecd.csv"))),as.xts(read.zoo(read.csv("~/Downloads/usa.csv"))),suffixes = c("oecd","usa"))

plot.default((cli_xts$usa["2012-07::2018-08"]   / as.vector(cli_xts$usa["2012-01::2018-02"])-1)*100,cli_xts$usa["2012-07::2018-08"])
tmp <- par('usr')
plot.default((cli_xts$usa["2012-07::2018-08"] / as.vector(cli_xts$usa["2012-01::2018-02"])-1)*100,cli_xts$usa["2012-07::2018-08"] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]))
plot.default((cli_xts$usa["2012-07::2018-08"] / as.vector(cli_xts$usa["2012-01::2018-02"])-1)*100,cli_xts$usa["2012-07::2018-08"] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]))
par(new=T)
plot.default((cli_xts$usa["2017-09::2018-08"] / as.vector(cli_xts$usa["2017-03::2018-02"])-1)*100,cli_xts$usa["2017-09::2018-08"] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=2)
par(new=T)
plot.default((cli_xts$usa["2016-09::2017-08"] / as.vector(cli_xts$usa["2016-03::2017-02"])-1)*100,cli_xts$usa["2016-09::2017-08"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=3)
par(new=T)
plot.default((cli_xts$usa["2015-09::2016-08"] / as.vector(cli_xts$usa["2015-03::2016-02"])-1)*100,cli_xts$usa["2015-09::2016-08"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=4)

macos mediawiki

ファイルの場所

ドキュメントルートは。

  /Library/WebServer/Documents/

wikiのルートは1.29.0ならこんな感じ。update.phpや LocalSettings.php もここで見つかる。

   /Library/WebServer/Documents/mediawiki-1.29.0

httpd.confは

  /etc/apache2/

OSのアップグレード時に先祖がえりしていることがあるので、忘れずに

   LoadModule php7_module libexec/apache2/libphp7.so

をコメントアウトすること。

アップグレードの注意

mediawikiのアップグレードをしたら、

 $ sudo php update.php

を忘れないこと。update.phpはmediawiki配布パッケージの直下にあるはず。実行はこんな感じで。

   sudo apachectl stop
   sudo php update.php
   sudo apachectl start

エラーが起きたら

LocalSettings.php の末尾に以下の3行を追加すること。エラー・メッセージが詳細になります。

   $wgShowExceptionDetails = true;
   $wgShowDBErrorBacktrace = true;
   $wgShowSQLErrors = true;

Hiroyuki Sawano - Best of "Epic BGM Music" 澤野弘之【Best 3 Hour Mix】HQ

https://www.youtube.com/watch?v=qAxg37wrVCE

( 0:00 ) 蔑、guy
( 2:49 ) This Is A Fight To Change The World ft. Mika Kobayashi
( 6:17 ) CR€SC∃NT ft. Naoshi Jimbow & Mika Kobayashi
( 8:55 ) Melancholia ft. David Whitaker & Aimee Blackschleger
( 13:01 ) No Naming Sense Type Five-Star Goku Uniform
( 18:11 ) raTEoREkiSImeAra
( 22:06 ) Important Event Highlight Type Twelve-Star Goku Uniform
( 23:59 ) KiryuG@kiLL
( 26:17 ) goriLLAjaL
( 28:15 ) KEKKAI ft. Mpi & Mika Kobayashi
( 33:56 ) RE:ARR.X
( 38:46 ) CODENAMEZ
( 43:56 ) NO.EX01
( 48:08 ) MKAlieZ ft. Mika Kobayashi ＜a0v＞
( 51:45 ) α≠a ft. Mika Kobayashi
( 55:56 ) G-LOW-S→F.S.K.O
( 1:00:50 ) 1st-Mov. : E
( 1:05:47 ) z37b20a13t01t08le
( 1:08:53 ) The Second Movement: A-maimon
( 1:12:27 ) Genesi§
( 1:15:40 ) AcE & ArMs
( 1:20:54 ) [104EYES-29CA2]suite-2楽章
( 1:22:47 ) ətˈæk 0N tάɪtn + ətˈæk 0N tάɪtn ＜3Tv＞ ft. Mika Kobayashi
( 1:27:01 ) Shingeki st-hrn-egt20130629 Kyojin + TheWeightOfLives
( 1:30:50 ) EREN The Coordinate + ERENthe標 ＜MOVIEver.＞
( 1:37:09 ) EMAymniam
( 1:40:34 ) E.M.A
( 1:44:20 ) YAMANAIAME ft. Mica Caldito / Mpi / Mika Kobayashi
( 1:48:44 ) 4GL4yu8RE:E + NeLLnaki9 + 高度8b6n + RE:3343 + 音:9RE:eita-zu
( 2:00:36 ) FANTASIA 1st Mov:[Open a title page] Reprogramming
( 2:05:41 ) 横浜-BIGMAN
( 2:07:22 ) MURDER CASE
( 2:10:43 ) BaNG!!
( 2:14:20 ) BOXX!!
( 2:17:42 ) LINK01BPM130KINPAKU
( 2:20:40 ) Dragon Demon ＜Scroll of the Unification of the Land＞
( 2:26:27 ) The First Movement: Mephistopheles
( 2:27:38 ) BLUe-eXOSUiTe-toKYOto-One + BLUe-eXOSUiTe-toKYOto-Two
( 2:37:38 ) ymniam-orch ft. Mika Kobayshi
( 2:40:10 ) ThreeFiveNineFourε ft. Mika Kobayashi
( 2:44:12 ) The Brave ft. Yosh
( 2:47:46 ) Seek Your Fate
( 2:51:03 ) OH92&HrBrM
( 2:53:32 ) MOBILE SUIT ＜W-REC MIX＞
( 2:57:38 ) JAILBREAK

read file and normalize data to adjust TZ.

THIS ENTRY IS OBSOLETE. PLEASE GO TO THIS PAGE

# read data as csv format and convert to xts to plot
#
Sys.setenv(TZ=Sys.timezone())
bp <- read.csv("~/Downloads/bp - シート1.csv")
system("rm \"$HOME/Downloads/bp - シート1.csv\"")
bp.xts <- xts(bp[,c(-1,-2,-6)],as.POSIXct(paste(bp$Date,bp$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
# weekly average
apply.weekly(bp.xts[bp.xts$High > 95],mean)
#
#
# prepare data according to system timezone. "Asia/Tokyo" in most cases.
#
bp.day <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz=tzone(bp.xts))),as.vector(bp.xts[,2]))
colnames(bp.day)[1] <- "high"
colnames(bp.day)[2] <- "low"
#
# prepare timezone 2 hours behind "Asia/Tokyo".
#
bp.bangkok <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz="Asia/Bangkok")),as.vector(bp.xts[,2]))
colnames(bp.bangkok)[1] <- "high"
colnames(bp.bangkok)[2] <- "low"
apply.weekly(bp.bangkok,mean)