00_FortCollins-POSTCRASH-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Master's Thesis: Investigating Maximum Rainfall Trends in Colorado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Institution:</b> University of Georgia <br>\n",
    "<b>Department:</b> Statistics <br>\n",
    "<b>Student:</b> Jeremy Mulcahey <br>\n",
    "<b>Advisor:</b> Dr. Lynne Seymour <br>\n",
    "<b>File:</b> 02_Boulder<br>\n",
    "<b>Last Edit:</b> 7/4/2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section 0: Preparing data <br>\n",
    "Section 1: Distribution of Max Precipitation and Trends<br>\n",
    "Section 2: Parameter Estimation, Distribution, and Trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Section 0: Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require(fitdistrplus); # required for parameter estimation\n",
    "require(caTools);      # required for smoothing\n",
    "require(repr);         # required for resizing plots in IRKernel\n",
    "require(\"IRdisplay\");  # graphic embedding\n",
    "require(MASS);         # testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# establish working directorys\n",
    "setwd(\"C:/Users/jerem/Desktop/UGA/Master'sThesis/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data files for individual station names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = read.csv(file = \"data/FORT_MonMax_GHCN.csv\", head=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the data for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "11"
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       "11"
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       "11"
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       "[1] 11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an index as x - used as needed\n",
    "month = seq(1,nrow(data),by=1)\n",
    "data = cbind(data,month)\n",
    "\n",
    "# first year of the data (changes by station name)\n",
    "firstYear = data$YEAR[1]\n",
    "\n",
    "# station name for plotting\n",
    "stationName = as.character(data$STATION_NAME[1])\n",
    "\n",
    "# -999 messed with the plotting - change to NA\n",
    "data[data==-999] = NA\n",
    "\n",
    "sum(is.na(data)) #print number of missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check number of years in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "118"
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       "118"
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       "118"
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      "text/plain": [
       "[1] 118"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data$YEAR[nrow(data)]-firstYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4   5   6   7   8   9  10  11  12 \n",
       "119 119 119 119 119 119 119 119 119 119 119 119 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(data$MONTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a data frame of all the 10 year periods following the first year for data collected at the station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#create the first decade (first year and the following 9 years)\n",
    "max_PRCP = data.frame(data[which(data$YEAR < (firstYear + 10)),6])\n",
    "\n",
    "# add all the subsequent decades to the data frame incrementing by 1 year at a time\n",
    "for(year in c((data$YEAR[1] + 1):2005)){\n",
    "    max_PRCP = cbind(max_PRCP,data[which(data$YEAR >= year & data$YEAR < year+10),6])\n",
    "}\n",
    "\n",
    "# name the columns as the first year of the observed 10 year periods\n",
    "colnames(max_PRCP) = as.character(c(firstYear:2005))\n",
    "\n",
    "# set the missing values to NA\n",
    "max_PRCP[max_PRCP==-999] = NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1: Distribution of Max Precipitation and Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2012 = data[which(data$YEAR<2013),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum Rainfall Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create for maximum rainfall\n",
    "maxTS = ts(data$Max.PRCP, start = firstYear, freq=12)\n",
    "\n",
    "# create regression line\n",
    "reg = lm(Max.PRCP. ~ month, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slope of the regression line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>month:</strong> 0.014"
      ],
      "text/latex": [
       "\\textbf{month:} 0.014"
      ],
      "text/markdown": [
       "**month:** 0.014"
      ],
      "text/plain": [
       "month \n",
       "0.014 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(reg$coefficients[2],3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>month:</strong> 19.992"
      ],
      "text/latex": [
       "\\textbf{month:} 19.992"
      ],
      "text/markdown": [
       "**month:** 19.992"
      ],
      "text/plain": [
       " month \n",
       "19.992 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(data$MONTH)*round(reg$coefficients[2],3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although month isn't very predictive of maximum observed rainfall, there does appear to be an increase over the time data was collected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "maxTS2012 = ts(data2012$Max.PRCP, start = firstYear, freq=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxTSNoEvent = maxTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxTSNoEvent[1389] = NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "maxObs = max(na.omit(data2012$Max.PRCP.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smooth = runmean(data2012$Max.PRCP., 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothTS = ts(smooth, start=firstYear, freq=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1219"
      ],
      "text/latex": [
       "1219"
      ],
      "text/markdown": [
       "1219"
      ],
      "text/plain": [
       "[1] 1219"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(data2012$Max.PRCP.==maxObs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>STATION</th><th scope=col>STATION_NAME</th><th scope=col>YEAR</th><th scope=col>MONTH</th><th scope=col>Obs</th><th scope=col>Max.PRCP.</th><th scope=col>month</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1060</th><td>GHCND:USC00053005 </td><td>FORT COLLINS CO US</td><td>1984              </td><td>4                 </td><td>30                </td><td>516               </td><td>1060              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & STATION & STATION\\_NAME & YEAR & MONTH & Obs & Max.PRCP. & month\\\\\n",
       "\\hline\n",
       "\t1060 & GHCND:USC00053005  & FORT COLLINS CO US & 1984               & 4                  & 30                 & 516                & 1060              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "     STATION           STATION_NAME       YEAR MONTH Obs Max.PRCP. month\n",
       "1060 GHCND:USC00053005 FORT COLLINS CO US 1984 4     30  516       1060 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2012[1060,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/boulder2012.eps\")\n",
    "plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,1000))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/boulder2014.eps\")\n",
    "plot(maxTS, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,2500))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setEPS()\n",
    "postscript(\"pics/.eps\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in parse(text = x, srcfile = src): <text>:3:0: unexpected end of input\n1: plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n2:      #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear))\n  ^\n",
     "output_type": "error",
     "traceback": [
      "Error in parse(text = x, srcfile = src): <text>:3:0: unexpected end of input\n1: plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n2:      #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear))\n  ^\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/boulder2012Info.eps\")\n",
    "plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,1000))\n",
    "abline(h=maxObs, col=\"red\", lwd=.5, lty=5)\n",
    "points(smoothTS, col=4, type=\"l\", lwd=2)\n",
    "text(1986,maxObs+50,\n",
    "           sprintf(\"%i\",maxObs))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wqxJuL8Sqe0u+Pd/5JO6mJJXS3UM5pY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\nkACxAAkQC5AAsQAJEAuQALEACf8DSvvqZEvVyC4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(maxTS2012, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,1250))\n",
    "abline(h=maxObs, col=\"red\", lwd=.5, lty=5)\n",
    "points(smoothTS, col=4, type=\"l\", lwd=2)\n",
    "text(1986,maxObs+50,\n",
    "           sprintf(\"%i\",maxObs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the time series plot, we also see that the peaks are gradually increasing over time, which is of specific interest to people working with maximum rainfall estimation. These increasing peaks have been wreaking havoc on Texas and the Eastern US this year. (Template statement - change as needed for individual stations)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce smoothed plots to get a better understanding of the trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JRWn9+mYCwbzttyW274xVaEWEEcOGI1/fpEfw7zNcTaON6FWNscOMcqxek1zbu/\nzeolxFospEiPIdY2wUeF9ksOw08P6yStrymWIhnE2iZwHevjcOPecVM0WWbaLlurrRDrchy4\n8p6y0cI0w4h1dw78rvA9nQxYswxi3ZwDz24QvxUbHn12RIRYl+dIsXg1/Vy6LiDWvTlULO8Q\nEOtyXEOsXwCxgoBYayyPSzTJflSviwCx1pisglg+QKw1ptFKEUt6/KuKXYMDxVKXvZyOCn8B\nxAriQLHe1xeLQyxbjtwVVontJWnOsNEm+yGWD8cukNpeD/4MGw1iBXHs5P0t/bSQKEQ0IFYQ\nOCpcY1JnRaxf1OlCQKxVZrGGRxDLAYi1i0ks7An3gFj7QCwPINY+RrF+WJ9LALH2UcUafm74\n2yqdH4i1D8TyAGLtA7E8gFj7QCwPINY+EMsDiLXP+OUOxHIAYu0jicWlh2ALiLUPxPIAYu0D\nsTyAWDZALGcglhUQyxWIZcV08vt4Gvzpang2IJYVEMsViGWFKtbej4wAxLJE+i0YxLICYtkx\n/xZy/1eRgEMsWyCWIxDLEojlBsSyBGK5AbEsUcX6dW3OD8SyBGK5AbEsgVhuQCxLIJYbEMuS\n2SeIZQPEsgRiuQGxLJHF+nFVLgHEsgZiuQCxrIFYLkAsayCWC4eK9X319zjMyy9ViAOAWDYc\nKFaTspnt6yefetNBLBsOFKtkyV9/bdv6k2xfP/nUmw5i2XCgWIl0yeSKJRQhDgFi2XCgWMoG\n2d46p950EMsGjFjOQCwbjp1jferuEeZY9+fI5YZMOipMG5IQRwCxbDh2Havs1rGS/IV1rLuD\nlXdnIJYNEAuQALEACRALkACxAAmHrrwrUIQAp+FAsXZuNm5tHbgCR+4Kr3WzcRDEoXOsS91s\nHARx7OT9SjcbB0HgqBCQALEACRALkACxAAknFQtcHo+tHl+k39QgVkNQnd9GDDY6uAY0xaA6\nkSD6SueAGtAUg+pE4oCvdKhqQFIMqhOJA77SIasBRTGoTiQO+EqHrgYExaA6kcBRIU05N63O\nmSMS1eCmW/Jk1TlzRKIa3HRLnqw6Z45IVIObbsmTVefMEYlqcNMtebLqnDkiUQ1uuiVPVp0z\nRwSPAGIBEiAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFi\nARIOF+s9RiwTln2mh0nZ6A+di3mnzsUYy2n5skXNnIupCsaKOrg6jXPvmPvBo5NDOFqsavw5\nfn/3sNf8MNUeOhdTdg+TxqEYYzktTcKkV/2q84lTnTrpy6mtyzH3g0cnB3GwWFUydN2bZT6K\ndzIAAAWESURBVA1vCvGb1y9LKvHGV3noXEzFika8UNgXYyxHkPcvh1SHJ23eJhc/Fw+qTtH9\n4Ly0b5a5Hzw6OYxjxWo7jI1jgWhXLXqtZGLM/xOfT+mhczF5/4p4w7IYczldzv7lkOr8dWU1\n4uazQdVhrs0y94N7JwdyrFhtT0kdJf5loifEMF+xXHnoXMz4HrMuZq2cetzAIdUp5gsQBFVn\n2CkLQW3LmYow96xTMd4cK1bF9a5jaw+di+lpxMawLGatnIzV/YOQ6qSMv5JurxRWndewK3zZ\nlyPQ+8G9kwM5/KhwaE/afWy+3m1eFtPzFuO8Q9cZynmxP+4k1kqr+hvQOhVjqs5bzN6Tt1s5\ni354jFgvlje8ykLFkorpqJOc+2xJqZxuH+EpltIqMXkvHEcaQ7Ne0wGiQzmLfniMWLw7is5D\nxZKKETT91eDct6RUTioO1D3FUlol5li1OKoPqs5b7ApbQd8u5Sz74TlitX2VvLpnydzQxHlL\nzsUIsn5xxr6YZTlFd8zUvxxSHebTKkM5KRPTtEYIal/Osh+8OjmEn4nVUYn+6o9S6vmApbY4\nYFkW0+ZLs36h276YZTnydVVDqiMd9YdURxbUthxTP3h1cgi/EivpPohv0bpXN0Z8xJAvPXQu\nps02LjrYF7MsRxYrpDp93lrUKaQ6w/jSrYdZlmPsB69ODuFXYvVLyWl7AOa3KLwspp4XsxzW\nlpflSC+HVSftFs//AqtTMvGtXmm/gm/uh5uvvPOp65r+K7BuOE67h5n20LWYQro0uHUxpupI\nLwdUZziac2uVqZzMsZyVfvDp5BB+Nseq2w7I5e/v9YeuxUi7MPtiTNWRXg6oTruzyZxbZSzH\nsXdW+sGnk0M4XCzwDCAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFiARIgFiABYgESIBYgAWIB\nEiAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFiARIgFiAB\nYgESIBYgAWIBEiAWIAFiARIg1gyTsL5a9cfn4sPStQPvCsSa8RGrZj43lGxY7ZHrUkAsDcfr\n6md+1/IsiS8t+3sgloabWH9eA5YYsv72E10aiKUxijXctuQlbj0i7obbj0zidstvKXWaDbcj\n4eP/OcUnZ8PFiRlrUnFt7U/Gxjs9Z8R3zv05EEtDFau7WPunu9S6UCTXrpD+FfdOal/trsXf\n3bN0TtFf573L1t1vqeTv/pVOuzfx9ft/DsTSUMXKGqFD9zfpbibS8CZj0zFd2d3i6yPuIyGu\n3F/LKZjY2/11xXUliLuXVOKVbqyqiO848nMgloYq1rd7VPPxLkn9nbimFYasn2L1d+jqb82k\npRjE+vaP51WGhvrOEL8GYmlocywu/5Vv+iAnfoud4Le/56WUov68skGsLlk7U8urSotzV27e\nPHd8xBruzVWrKbLp0Zjh1d2Ft1bi3JWbN8+dbbFWEpftTi5NuZKiYOn7UytitdOxMh2OISHW\nw9gSK2faVzHDHKudimdVd9tmKUWXTRdreoI51tPYEutP3OlvuO1mRzkuGqQs6ab4UgoxY6+U\nOVbaHyd2I9YXR4UPY0usYdqUTF/0dRN2wYcNe7g5RTnMtr5TmX/TC2JGhnWsZ7EpllhXZ4X0\nBXI67NAaNio2pygYy76ffvDq3+tW3nuhsPIOtvgMpyl8mNv5CrU+W7sdECuM4eyGjL13Eqrg\n7AawTXc+lvMdlnE+FtjjU4gvAR1PIy3uviOEWIAIiAVIgFiABIgFSIBYgASIBUiAWIAEiAVI\ngFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSIBYgASI\nBUiAWIAEiAVIgFiAhH/jX5vGXWgo4wAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Smoothed 24 hour max rainfall from 1898 to 2012\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 1, main=\"Smoothed 24 hour max rainfall from 1898 to 2012\")\n",
    "    #points(rollLarge, type=\"l\", col=4)\n",
    "    #egend(0,200, c(\"300 window\",\"\", \"900 window\"), col=c(2,0,4), lty=c(1,0,1), bty=\"n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the data shows a slight decrease in average rainfall at the tail end of our data (2014), the long term trend shows that average rainfall has been increasing. Empirically, we have had a wet year in 2016 that we would expect to match/exceed the long term observed increase in average maximum rainfall.(Template statement - change as needed for individual stations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: Parameter Estimation, Distribution, and Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleGamma = data.frame(alpha = double(),\n",
    "                 theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   gamma1 = fitdist(as.vector(testDist),\"gamma\",method = \"mle\")\n",
    "   mleGamma[i,1] = gamma1$estimate[1]\n",
    "   mleGamma[i,2] = gamma1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(gamma1)\n",
    "   mleGamma[i,3] = gof$ks\n",
    "   mleGamma[i,4] = gof$kstest\n",
    "   mleGamma[i,5] = gof$cvm\n",
    "   mleGamma[i,6] = gof$cvmtest\n",
    "   mleGamma[i,7] = gof$ad\n",
    "   mleGamma[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "mleGamma[\"invTheta\"] = 1/(mleGamma$theta)\n",
    "mleGamma[\"mean\"] = mleGamma$invTheta*mleGamma$alpha\n",
    "mleGamma[\"var\"] = mleGamma$alpha*(mleGamma$invTheta^2)\n",
    "mleGamma[\"sd\"] = sqrt(mleGamma$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleGamma2 = data.frame(alpha = double(),\n",
    "                 theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP2)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP2[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   gamma1 = fitdist(as.vector(testDist),\"gamma\",method = \"mge\", gof=\"ADR\")\n",
    "   mleGamma2[i,1] = gamma1$estimate[1]\n",
    "   mleGamma2[i,2] = gamma1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(gamma1)\n",
    "   mleGamma2[i,3] = gof$ks\n",
    "   mleGamma2[i,4] = gof$kstest\n",
    "   mleGamma2[i,5] = gof$cvm\n",
    "   mleGamma2[i,6] = gof$cvmtest\n",
    "   mleGamma2[i,7] = gof$ad\n",
    "   mleGamma2[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "#mleGamma[\"invTheta\"] = 1/(mleGamma$theta)\n",
    "#mleGamma[\"mean\"] = mleGamma$invTheta*mleGamma$alpha\n",
    "#mleGamma[\"var\"] = mleGamma$alpha*(mleGamma$invTheta^2)\n",
    "#mleGamma[\"sd\"] = sqrt(mleGamma$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleGamma2[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleExp = data.frame(theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   exp1 = fitdist(as.vector(testDist),\"exp\",method = \"mle\")\n",
    "   mleExp[i,1] = exp1$estimate[1]\n",
    "   \n",
    "   gof = gofstat(exp1)\n",
    "   mleExp[i,2] = gof$ks\n",
    "   mleExp[i,3] = gof$kstest\n",
    "   mleExp[i,4] = gof$cvm\n",
    "   mleExp[i,5] = gof$cvmtest\n",
    "   mleExp[i,6] = gof$ad\n",
    "   mleExp[i,7] = gof$adtest\n",
    "}\n",
    "\n",
    "mleExp[\"mean\"] = 1/mleExp$theta\n",
    "mleExp[\"square\"] = mleExp$theta^2 \n",
    "mleExp[\"var\"] = 1/mleExp$square\n",
    "mleExp[\"sd\"] = sqrt(mleExp$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleWB = data.frame(alpha = double(),theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   WB1 = fitdist(as.vector(testDist),\"weibull\",method = \"mle\")\n",
    "   mleWB[i,1] = WB1$estimate[1]\n",
    "   mleWB[i,2] = WB1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(WB1)\n",
    "   mleWB[i,3] = gof$ks\n",
    "   mleWB[i,4] = gof$kstest\n",
    "   mleWB[i,5] = gof$cvm\n",
    "   mleWB[i,6] = gof$cvmtest\n",
    "   mleWB[i,7] = gof$ad\n",
    "   mleWB[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "\n",
    "#mleWB[\"mean\"] = 1/mleWB$theta\n",
    "#mleWB[\"square\"] = mleWB$theta^2 \n",
    "#mleWB[\"var\"] = 1/mleWB$square\n",
    "#mleWB[\"sd\"] = sqrt(mleWB$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>alpha</th><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.9012763   </td><td>126.7063    </td><td>0.09708742  </td><td>not rejected</td><td>0.2461638   </td><td>rejected    </td><td>1.916551    </td><td>rejected    </td></tr>\n",
       "\t<tr><td>0.8934700   </td><td>129.2136    </td><td>0.08799946  </td><td>not rejected</td><td>0.2507829   </td><td>rejected    </td><td>1.897670    </td><td>rejected    </td></tr>\n",
       "\t<tr><td>0.8712358   </td><td>127.9071    </td><td>0.09128039  </td><td>not rejected</td><td>0.2549693   </td><td>rejected    </td><td>1.822312    </td><td>rejected    </td></tr>\n",
       "\t<tr><td>0.8612942   </td><td>132.7855    </td><td>0.08917338  </td><td>not rejected</td><td>0.2459454   </td><td>rejected    </td><td>1.751709    </td><td>rejected    </td></tr>\n",
       "\t<tr><td>0.9055221   </td><td>139.7309    </td><td>0.09486993  </td><td>not rejected</td><td>0.1833747   </td><td>rejected    </td><td>1.108900    </td><td>rejected    </td></tr>\n",
       "\t<tr><td>0.9162238   </td><td>142.1785    </td><td>0.08816377  </td><td>not rejected</td><td>0.1860520   </td><td>rejected    </td><td>1.141164    </td><td>rejected    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " alpha & theta & KS & KStest & CvM & CvMtest & AD & ADtest\\\\\n",
       "\\hline\n",
       "\t 0.9012763    & 126.7063     & 0.09708742   & not rejected & 0.2461638    & rejected     & 1.916551     & rejected    \\\\\n",
       "\t 0.8934700    & 129.2136     & 0.08799946   & not rejected & 0.2507829    & rejected     & 1.897670     & rejected    \\\\\n",
       "\t 0.8712358    & 127.9071     & 0.09128039   & not rejected & 0.2549693    & rejected     & 1.822312     & rejected    \\\\\n",
       "\t 0.8612942    & 132.7855     & 0.08917338   & not rejected & 0.2459454    & rejected     & 1.751709     & rejected    \\\\\n",
       "\t 0.9055221    & 139.7309     & 0.09486993   & not rejected & 0.1833747    & rejected     & 1.108900     & rejected    \\\\\n",
       "\t 0.9162238    & 142.1785     & 0.08816377   & not rejected & 0.1860520    & rejected     & 1.141164     & rejected    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha     theta    KS         KStest       CvM       CvMtest  AD      \n",
       "1 0.9012763 126.7063 0.09708742 not rejected 0.2461638 rejected 1.916551\n",
       "2 0.8934700 129.2136 0.08799946 not rejected 0.2507829 rejected 1.897670\n",
       "3 0.8712358 127.9071 0.09128039 not rejected 0.2549693 rejected 1.822312\n",
       "4 0.8612942 132.7855 0.08917338 not rejected 0.2459454 rejected 1.751709\n",
       "5 0.9055221 139.7309 0.09486993 not rejected 0.1833747 rejected 1.108900\n",
       "6 0.9162238 142.1785 0.08816377 not rejected 0.1860520 rejected 1.141164\n",
       "  ADtest  \n",
       "1 rejected\n",
       "2 rejected\n",
       "3 rejected\n",
       "4 rejected\n",
       "5 rejected\n",
       "6 rejected"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleWB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          98           12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleWB$KStest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>30</li>\n",
       "\t<li>31</li>\n",
       "\t<li>32</li>\n",
       "\t<li>33</li>\n",
       "\t<li>34</li>\n",
       "\t<li>35</li>\n",
       "\t<li>36</li>\n",
       "\t<li>37</li>\n",
       "\t<li>38</li>\n",
       "\t<li>39</li>\n",
       "\t<li>52</li>\n",
       "\t<li>53</li>\n",
       "\t<li>54</li>\n",
       "\t<li>55</li>\n",
       "\t<li>56</li>\n",
       "\t<li>57</li>\n",
       "\t<li>61</li>\n",
       "\t<li>62</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 30\n",
       "\\item 31\n",
       "\\item 32\n",
       "\\item 33\n",
       "\\item 34\n",
       "\\item 35\n",
       "\\item 36\n",
       "\\item 37\n",
       "\\item 38\n",
       "\\item 39\n",
       "\\item 52\n",
       "\\item 53\n",
       "\\item 54\n",
       "\\item 55\n",
       "\\item 56\n",
       "\\item 57\n",
       "\\item 61\n",
       "\\item 62\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 30\n",
       "2. 31\n",
       "3. 32\n",
       "4. 33\n",
       "5. 34\n",
       "6. 35\n",
       "7. 36\n",
       "8. 37\n",
       "9. 38\n",
       "10. 39\n",
       "11. 52\n",
       "12. 53\n",
       "13. 54\n",
       "14. 55\n",
       "15. 56\n",
       "16. 57\n",
       "17. 61\n",
       "18. 62\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 30 31 32 33 34 35 36 37 38 39 52 53 54 55 56 57 61 62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleGamma$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleExp$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>alpha</th><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>invTheta</th><th scope=col>mean</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.7702459   </td><td>0.005838670 </td><td>0.11823885  </td><td>not rejected</td><td>0.3945848   </td><td>NA          </td><td>2.578290    </td><td>NA          </td><td>171.2719    </td><td>131.9215    </td><td>22594.44    </td><td>150.3145    </td></tr>\n",
       "\t<tr><td>0.7592384   </td><td>0.005623842 </td><td>0.10957672  </td><td>not rejected</td><td>0.3993983   </td><td>NA          </td><td>2.545615    </td><td>NA          </td><td>177.8144    </td><td>135.0035    </td><td>24005.57    </td><td>154.9373    </td></tr>\n",
       "\t<tr><td>0.7329028   </td><td>0.005426065 </td><td>0.11023874  </td><td>not rejected</td><td>0.3867091   </td><td>NA          </td><td>2.377635    </td><td>NA          </td><td>184.2956    </td><td>135.0708    </td><td>24892.95    </td><td>157.7750    </td></tr>\n",
       "\t<tr><td>0.7228140   </td><td>0.005126209 </td><td>0.10648805  </td><td>not rejected</td><td>0.3718661   </td><td>NA          </td><td>2.266249    </td><td>NA          </td><td>195.0759    </td><td>141.0036    </td><td>27506.41    </td><td>165.8506    </td></tr>\n",
       "\t<tr><td>0.7894197   </td><td>0.005429997 </td><td>0.10174311  </td><td>not rejected</td><td>0.2569872   </td><td>NA          </td><td>1.422317    </td><td>NA          </td><td>184.1622    </td><td>145.3812    </td><td>26773.72    </td><td>163.6268    </td></tr>\n",
       "\t<tr><td>0.7953316   </td><td>0.005407428 </td><td>0.09718581  </td><td>not rejected</td><td>0.2725329   </td><td>NA          </td><td>1.497542    </td><td>NA          </td><td>184.9308    </td><td>147.0813    </td><td>27199.86    </td><td>164.9238    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " alpha & theta & KS & KStest & CvM & CvMtest & AD & ADtest & invTheta & mean & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.7702459    & 0.005838670  & 0.11823885   & not rejected & 0.3945848    & NA           & 2.578290     & NA           & 171.2719     & 131.9215     & 22594.44     & 150.3145    \\\\\n",
       "\t 0.7592384    & 0.005623842  & 0.10957672   & not rejected & 0.3993983    & NA           & 2.545615     & NA           & 177.8144     & 135.0035     & 24005.57     & 154.9373    \\\\\n",
       "\t 0.7329028    & 0.005426065  & 0.11023874   & not rejected & 0.3867091    & NA           & 2.377635     & NA           & 184.2956     & 135.0708     & 24892.95     & 157.7750    \\\\\n",
       "\t 0.7228140    & 0.005126209  & 0.10648805   & not rejected & 0.3718661    & NA           & 2.266249     & NA           & 195.0759     & 141.0036     & 27506.41     & 165.8506    \\\\\n",
       "\t 0.7894197    & 0.005429997  & 0.10174311   & not rejected & 0.2569872    & NA           & 1.422317     & NA           & 184.1622     & 145.3812     & 26773.72     & 163.6268    \\\\\n",
       "\t 0.7953316    & 0.005407428  & 0.09718581   & not rejected & 0.2725329    & NA           & 1.497542     & NA           & 184.9308     & 147.0813     & 27199.86     & 164.9238    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha     theta       KS         KStest       CvM       CvMtest AD      \n",
       "1 0.7702459 0.005838670 0.11823885 not rejected 0.3945848 NA      2.578290\n",
       "2 0.7592384 0.005623842 0.10957672 not rejected 0.3993983 NA      2.545615\n",
       "3 0.7329028 0.005426065 0.11023874 not rejected 0.3867091 NA      2.377635\n",
       "4 0.7228140 0.005126209 0.10648805 not rejected 0.3718661 NA      2.266249\n",
       "5 0.7894197 0.005429997 0.10174311 not rejected 0.2569872 NA      1.422317\n",
       "6 0.7953316 0.005407428 0.09718581 not rejected 0.2725329 NA      1.497542\n",
       "  ADtest invTheta mean     var      sd      \n",
       "1 NA     171.2719 131.9215 22594.44 150.3145\n",
       "2 NA     177.8144 135.0035 24005.57 154.9373\n",
       "3 NA     184.2956 135.0708 24892.95 157.7750\n",
       "4 NA     195.0759 141.0036 27506.41 165.8506\n",
       "5 NA     184.1622 145.3812 26773.72 163.6268\n",
       "6 NA     184.9308 147.0813 27199.86 164.9238"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleGamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>mean</th><th scope=col>square</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.007582251 </td><td>0.06135990  </td><td>not rejected</td><td>0.07771479  </td><td>not rejected</td><td>1.154525    </td><td>not rejected</td><td>131.8870    </td><td>5.749053e-05</td><td>17394.17    </td><td>131.8870    </td></tr>\n",
       "\t<tr><td>0.007406461 </td><td>0.05409077  </td><td>not rejected</td><td>0.07202469  </td><td>not rejected</td><td>1.120545    </td><td>not rejected</td><td>135.0172    </td><td>5.485567e-05</td><td>18229.66    </td><td>135.0172    </td></tr>\n",
       "\t<tr><td>0.007404126 </td><td>0.05999186  </td><td>not rejected</td><td>0.06899385  </td><td>not rejected</td><td>1.086094    </td><td>not rejected</td><td>135.0598    </td><td>5.482108e-05</td><td>18241.16    </td><td>135.0598    </td></tr>\n",
       "\t<tr><td>0.007092198 </td><td>0.05165822  </td><td>not rejected</td><td>0.05439701  </td><td>not rejected</td><td>1.053790    </td><td>not rejected</td><td>141.0000    </td><td>5.029928e-05</td><td>19881.00    </td><td>141.0000    </td></tr>\n",
       "\t<tr><td>0.006879263 </td><td>0.07186013  </td><td>not rejected</td><td>0.08074364  </td><td>not rejected</td><td>0.770200    </td><td>not rejected</td><td>145.3644    </td><td>4.732426e-05</td><td>21130.81    </td><td>145.3644    </td></tr>\n",
       "\t<tr><td>0.006798410 </td><td>0.06769829  </td><td>not rejected</td><td>0.08938361  </td><td>not rejected</td><td>0.817524    </td><td>not rejected</td><td>147.0932    </td><td>4.621838e-05</td><td>21636.42    </td><td>147.0932    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " theta & KS & KStest & CvM & CvMtest & AD & ADtest & mean & square & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.007582251  & 0.06135990   & not rejected & 0.07771479   & not rejected & 1.154525     & not rejected & 131.8870     & 5.749053e-05 & 17394.17     & 131.8870    \\\\\n",
       "\t 0.007406461  & 0.05409077   & not rejected & 0.07202469   & not rejected & 1.120545     & not rejected & 135.0172     & 5.485567e-05 & 18229.66     & 135.0172    \\\\\n",
       "\t 0.007404126  & 0.05999186   & not rejected & 0.06899385   & not rejected & 1.086094     & not rejected & 135.0598     & 5.482108e-05 & 18241.16     & 135.0598    \\\\\n",
       "\t 0.007092198  & 0.05165822   & not rejected & 0.05439701   & not rejected & 1.053790     & not rejected & 141.0000     & 5.029928e-05 & 19881.00     & 141.0000    \\\\\n",
       "\t 0.006879263  & 0.07186013   & not rejected & 0.08074364   & not rejected & 0.770200     & not rejected & 145.3644     & 4.732426e-05 & 21130.81     & 145.3644    \\\\\n",
       "\t 0.006798410  & 0.06769829   & not rejected & 0.08938361   & not rejected & 0.817524     & not rejected & 147.0932     & 4.621838e-05 & 21636.42     & 147.0932    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  theta       KS         KStest       CvM        CvMtest      AD      \n",
       "1 0.007582251 0.06135990 not rejected 0.07771479 not rejected 1.154525\n",
       "2 0.007406461 0.05409077 not rejected 0.07202469 not rejected 1.120545\n",
       "3 0.007404126 0.05999186 not rejected 0.06899385 not rejected 1.086094\n",
       "4 0.007092198 0.05165822 not rejected 0.05439701 not rejected 1.053790\n",
       "5 0.006879263 0.07186013 not rejected 0.08074364 not rejected 0.770200\n",
       "6 0.006798410 0.06769829 not rejected 0.08938361 not rejected 0.817524\n",
       "  ADtest       mean     square       var      sd      \n",
       "1 not rejected 131.8870 5.749053e-05 17394.17 131.8870\n",
       "2 not rejected 135.0172 5.485567e-05 18229.66 135.0172\n",
       "3 not rejected 135.0598 5.482108e-05 18241.16 135.0598\n",
       "4 not rejected 141.0000 5.029928e-05 19881.00 141.0000\n",
       "5 not rejected 145.3644 4.732426e-05 21130.81 145.3644\n",
       "6 not rejected 147.0932 4.621838e-05 21636.42 147.0932"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleExp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trueEst = NULL\n",
    "for(i in 1:nrow(mleExp)){\n",
    "    if(mleExp$KStest[i] == \"not rejected\" & mleGamma$KStest[i] == \"not rejected\"){\n",
    "        trueEst = c(trueEst,\"plot\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"not rejected\" & mleGamma$KStest[i] == \"rejected\"){\n",
    "        trueEst = c(trueEst,\"exp\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"rejected\" & mleGamma$KStest[i] == \"not rejected\"){\n",
    "        trueEst = c(trueEst,\"gamma\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"rejected\" & mleGamma$KStest[i] == \"rejected\"){\n",
    "        trueEst = c(trueEst,\"fail\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trueEst\n",
       " exp plot \n",
       "  18   92 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(trueEst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(trueEst==\"fail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>4</li>\n",
       "\t<li>5</li>\n",
       "\t<li>6</li>\n",
       "\t<li>7</li>\n",
       "\t<li>8</li>\n",
       "\t<li>9</li>\n",
       "\t<li>10</li>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "\t<li>15</li>\n",
       "\t<li>16</li>\n",
       "\t<li>17</li>\n",
       "\t<li>18</li>\n",
       "\t<li>19</li>\n",
       "\t<li>20</li>\n",
       "\t<li>21</li>\n",
       "\t<li>22</li>\n",
       "\t<li>23</li>\n",
       "\t<li>24</li>\n",
       "\t<li>25</li>\n",
       "\t<li>26</li>\n",
       "\t<li>27</li>\n",
       "\t<li>28</li>\n",
       "\t<li>29</li>\n",
       "\t<li>40</li>\n",
       "\t<li>41</li>\n",
       "\t<li>42</li>\n",
       "\t<li>43</li>\n",
       "\t<li>44</li>\n",
       "\t<li>45</li>\n",
       "\t<li>46</li>\n",
       "\t<li>47</li>\n",
       "\t<li>48</li>\n",
       "\t<li>49</li>\n",
       "\t<li>50</li>\n",
       "\t<li>51</li>\n",
       "\t<li>58</li>\n",
       "\t<li>59</li>\n",
       "\t<li>60</li>\n",
       "\t<li>63</li>\n",
       "\t<li>64</li>\n",
       "\t<li>65</li>\n",
       "\t<li>66</li>\n",
       "\t<li>67</li>\n",
       "\t<li>68</li>\n",
       "\t<li>69</li>\n",
       "\t<li>70</li>\n",
       "\t<li>71</li>\n",
       "\t<li>72</li>\n",
       "\t<li>73</li>\n",
       "\t<li>74</li>\n",
       "\t<li>75</li>\n",
       "\t<li>76</li>\n",
       "\t<li>77</li>\n",
       "\t<li>78</li>\n",
       "\t<li>79</li>\n",
       "\t<li>80</li>\n",
       "\t<li>81</li>\n",
       "\t<li>82</li>\n",
       "\t<li>83</li>\n",
       "\t<li>84</li>\n",
       "\t<li>85</li>\n",
       "\t<li>86</li>\n",
       "\t<li>87</li>\n",
       "\t<li>88</li>\n",
       "\t<li>89</li>\n",
       "\t<li>90</li>\n",
       "\t<li>91</li>\n",
       "\t<li>92</li>\n",
       "\t<li>93</li>\n",
       "\t<li>94</li>\n",
       "\t<li>95</li>\n",
       "\t<li>96</li>\n",
       "\t<li>97</li>\n",
       "\t<li>98</li>\n",
       "\t<li>99</li>\n",
       "\t<li>100</li>\n",
       "\t<li>101</li>\n",
       "\t<li>102</li>\n",
       "\t<li>103</li>\n",
       "\t<li>104</li>\n",
       "\t<li>105</li>\n",
       "\t<li>106</li>\n",
       "\t<li>107</li>\n",
       "\t<li>108</li>\n",
       "\t<li>109</li>\n",
       "\t<li>110</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
       "\\item 5\n",
       "\\item 6\n",
       "\\item 7\n",
       "\\item 8\n",
       "\\item 9\n",
       "\\item 10\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\item 15\n",
       "\\item 16\n",
       "\\item 17\n",
       "\\item 18\n",
       "\\item 19\n",
       "\\item 20\n",
       "\\item 21\n",
       "\\item 22\n",
       "\\item 23\n",
       "\\item 24\n",
       "\\item 25\n",
       "\\item 26\n",
       "\\item 27\n",
       "\\item 28\n",
       "\\item 29\n",
       "\\item 40\n",
       "\\item 41\n",
       "\\item 42\n",
       "\\item 43\n",
       "\\item 44\n",
       "\\item 45\n",
       "\\item 46\n",
       "\\item 47\n",
       "\\item 48\n",
       "\\item 49\n",
       "\\item 50\n",
       "\\item 51\n",
       "\\item 58\n",
       "\\item 59\n",
       "\\item 60\n",
       "\\item 63\n",
       "\\item 64\n",
       "\\item 65\n",
       "\\item 66\n",
       "\\item 67\n",
       "\\item 68\n",
       "\\item 69\n",
       "\\item 70\n",
       "\\item 71\n",
       "\\item 72\n",
       "\\item 73\n",
       "\\item 74\n",
       "\\item 75\n",
       "\\item 76\n",
       "\\item 77\n",
       "\\item 78\n",
       "\\item 79\n",
       "\\item 80\n",
       "\\item 81\n",
       "\\item 82\n",
       "\\item 83\n",
       "\\item 84\n",
       "\\item 85\n",
       "\\item 86\n",
       "\\item 87\n",
       "\\item 88\n",
       "\\item 89\n",
       "\\item 90\n",
       "\\item 91\n",
       "\\item 92\n",
       "\\item 93\n",
       "\\item 94\n",
       "\\item 95\n",
       "\\item 96\n",
       "\\item 97\n",
       "\\item 98\n",
       "\\item 99\n",
       "\\item 100\n",
       "\\item 101\n",
       "\\item 102\n",
       "\\item 103\n",
       "\\item 104\n",
       "\\item 105\n",
       "\\item 106\n",
       "\\item 107\n",
       "\\item 108\n",
       "\\item 109\n",
       "\\item 110\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "5. 5\n",
       "6. 6\n",
       "7. 7\n",
       "8. 8\n",
       "9. 9\n",
       "10. 10\n",
       "11. 11\n",
       "12. 12\n",
       "13. 13\n",
       "14. 14\n",
       "15. 15\n",
       "16. 16\n",
       "17. 17\n",
       "18. 18\n",
       "19. 19\n",
       "20. 20\n",
       "21. 21\n",
       "22. 22\n",
       "23. 23\n",
       "24. 24\n",
       "25. 25\n",
       "26. 26\n",
       "27. 27\n",
       "28. 28\n",
       "29. 29\n",
       "30. 40\n",
       "31. 41\n",
       "32. 42\n",
       "33. 43\n",
       "34. 44\n",
       "35. 45\n",
       "36. 46\n",
       "37. 47\n",
       "38. 48\n",
       "39. 49\n",
       "40. 50\n",
       "41. 51\n",
       "42. 58\n",
       "43. 59\n",
       "44. 60\n",
       "45. 63\n",
       "46. 64\n",
       "47. 65\n",
       "48. 66\n",
       "49. 67\n",
       "50. 68\n",
       "51. 69\n",
       "52. 70\n",
       "53. 71\n",
       "54. 72\n",
       "55. 73\n",
       "56. 74\n",
       "57. 75\n",
       "58. 76\n",
       "59. 77\n",
       "60. 78\n",
       "61. 79\n",
       "62. 80\n",
       "63. 81\n",
       "64. 82\n",
       "65. 83\n",
       "66. 84\n",
       "67. 85\n",
       "68. 86\n",
       "69. 87\n",
       "70. 88\n",
       "71. 89\n",
       "72. 90\n",
       "73. 91\n",
       "74. 92\n",
       "75. 93\n",
       "76. 94\n",
       "77. 95\n",
       "78. 96\n",
       "79. 97\n",
       "80. 98\n",
       "81. 99\n",
       "82. 100\n",
       "83. 101\n",
       "84. 102\n",
       "85. 103\n",
       "86. 104\n",
       "87. 105\n",
       "88. 106\n",
       "89. 107\n",
       "90. 108\n",
       "91. 109\n",
       "92. 110\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
       "[20]  20  21  22  23  24  25  26  27  28  29  40  41  42  43  44  45  46  47  48\n",
       "[39]  49  50  51  58  59  60  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
       "[58]  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94\n",
       "[77]  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(trueEst==\"plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ia2z0LHOLSCceyFLYW6zk8JfIJxaAXj2AubCnUa3JEwB8ahlYAJYN9/kE+kLewD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\nwMKsAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Histogram of max_PRCP[, 13]\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plot(density(rgamma(10000,mleGamma$alpha[13],mleGamma$theta[13])))\n",
    "hist(max_PRCP[,13], breaks=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Awi44U4tBIwACzCjXwQRzpExgtxaMU3NHFu5IM40pEuXg5XYRCHVnxDE+fZsUGJ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",
      "text/plain": [
       "Plot with title \"Histogram of max_PRCP[, 14]\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plot(density(rgamma(10000,mleGamma$alpha[14],mleGamma$theta[14])))\n",
    "hist(max_PRCP[,14], breaks=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TiOKL3aHsdBml0QZEwRh1RCHDlKml0QZEwRh1QQx1kIMqaIQyohDjknzS4IMqaI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\n",
      "text/plain": [
       "Plot with title \"Histogram of max_PRCP[, 3]\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plot(density(rgamma(10000,mleGamma$alpha[3],mleGamma$theta[3])))\n",
    "hist(max_PRCP[,3], breaks=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewFkfFBHFr5HXHQyvOIDA/i0Ari2Asiw4M4tPJD4qCZZxEZHcShlV8SB+08h8jg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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Histogram of max_PRCP[, 14]\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plot(density(rgamma(10000,mleGamma$alpha[14],mleGamma$theta[14])))\n",
    "hist(max_PRCP[,14], breaks=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "110"
      ],
      "text/latex": [
       "110"
      ],
      "text/markdown": [
       "110"
      ],
      "text/plain": [
       "[1] 110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(trueEst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxObs2 = max(na.omit(data$Max.PRCP.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smooth2 = runmean(data$Max.PRCP., 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothTS2 = ts(smooth2, start=firstYear, freq=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/boulder2014Info.eps\")\n",
    "plot(maxTS, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,2500))\n",
    "abline(h=maxObs2, col=\"red\", lwd=.5, lty=5)\n",
    "points(smoothTS2, col=4, type=\"l\", lwd=2)\n",
    "text(2013,maxObs2+100,sprintf(\"%i\",maxObs2))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aGJ8uwJX+b+z06iHi22jK1BU7f/2HxkX8sW8MocDMrNevYUjZBkgSKac4F6jNZN\n96/p5eqfgQYL2t5JFTqmd7iE/rDkzz8sod3V35xXFXKn99sBludxxGFLKvCX/fVef6sNUASs\nWW24rTehmPbvc1Aczw8LE7K1WlE0p8i+qDVft5V3YN1v/+r/Cu82VbBSFW3uTe/Yg9vJ3vqo\nMrpqXS1p5ez+cY0xRSMkWaCIjmYbak17DLSjuekU601rkDh4ezrL6nlTQVvwNHG0Y8wSR/On\nPP+Xi3YrgmOybqnplZiT+N+z49ox7r8bKqKnYzpzd/o3KI7m3KhLHF4GhdN1TW62ONre8Yed\nJw6/Dn4lrSSvz95NZm11PXE0Ac+dohGSLFDEv2qI97Xba+i4neqJQzZX27K516l9tKPa6bln\nPu1Itjtp3EmpO7VfmRdSHK/9hRRHXkycsn7FZUvZw68o7EYtmkD8/mmnvXN9yf6o4jW1K8rB\nprOmSB1KZye2dQhWVDSmtTVFHPb/8vniaHcHPqrYoTnlhlr5a8Tx73U21dYyfw2mXLSP36mD\n4rA6c5I4nG5o+7TuL2eIeicOsqhJ0z4wlXo/W/1zryUEKj5aHO41jleuudc0Mj/nr7vzpieO\n9hrHK+B/gVP7JktHHHYRMcTRPTqiiqMZlzPF4RfwNeIoWl1Y4pCNVlh7CqtJRMP67WeNI08c\nHaO+PS4sjnZLIY76dT21rY9ZXWoIu8EPusOG9cD1jim8g0eKQ6TrbUK/3vJNd7YUrTiCR8vt\nzj/gzXuF0yvO8TPFYYfri0NqrogiDid40RUd4rCPiTFFIyRZoIhY4ghOiOni8DtytjjcfN16\nyuOabNpjVeJw/rDHFEcfbxKHNV9iiCOQhT9Xh8ThT3T7jEkOgVHiyN38vl0c9pxzxVHkVg9J\ncch3VOJ4leOmbM8F7eJGi6NOExaHKyS7Vm7dci/zsDjcCohwpoqjkAE5OwcYJQ47kICTAg3m\nvOXkKOdzRHH4YpbiaN6NKo6ehi5EfiFxOMfEmKIRkkQvoql+0y4accg/VimJw+1eJzznr2y/\nOGSFgvSKw446cLD8a920gNW8Th/0MkIcbd9YsboBBeIM5RldHHI6O6ephSzIKscThxwjGnEU\nfrvZ8SOOJ3X1RcNYI+vN4vD7Nv9kcchwHXGMwq5VlzgCAY8VR1eOq4nDn/O2OMQOjThENcIV\ntzvVHlnfLQ6rnrnohg5xFGFxeFqZKY5A39YxiT4L9+Zocci6+cWG47KOCr4TKjdwnPt3q2j7\nIyCO7hjc7aJjAoSyWUwcIUnKrQXF0R7b5hdfHN5xdgfGmKIxksQvoql+vS1N4XmkTxyFez5S\n2Bn2iSM0xZOW/woAAAxmSURBVKUVAt3jdWqbSVOqVZhrOKU4nBr0Bife6xCH3W7OS6s2zXu9\n4ijcuuVeEwdDC2VgGa8vAyefsDgK/8xuUBxOz9kH11VyT+W8dp4sDlFoqKqFk1/oOOuYKFM0\nRpL4RTi9UgyKo2356eIoxKHWkAuLI9Q/RSFGmNPDhTP8nNHpi8P5q+/k6YhjUAUyFq8qMppX\nO8mXrjiaw/04vQaKIA5rkvTLo5miXeIQWYsesCs4QxyFHDWiqBHi8GvU1W6IQ+D0SuH0vueR\naeKwc6zbU3RHnYGV1hdHYHSHxNEkKdrSRC/PFoespxOlm8kC4hBV8cb6JHEU/ivRwl6a9ti6\n7bxuaoeYm0M7ozvEIQeEJw7RQkuLQ45UxPHE6ZV6T784iqZju6zSKY56Z684mtByu7RucXT8\nDS6cf+pMAgNNzl33fZ04nEk8ThxtOqvmbpPkjjjqQ4vCKdNOEWSkOKTRQvkU9bgR4vD60cnB\nEYeovOy5ercTmKynXW2nKCEm51gZWCHy6+h9UTTiaHq57tPXtiUO0XX2ux1vKsRhFTYgDuGK\nJvY2vd15IqBCZu4PHmcIWOVYo8Uafnb8dpFtkrZ67kFNC7jTX74MNInTXm7DW+W74mhicsO2\n28RpoVe7iUbxtorCqpNzQGFHLRtrLXFYFSvsDKxcRfPkTsT1YTKuQXHUDReYcC6fIw77rfBY\nsgas/2b9ailxiIja3GRnF3Y3FyIQt2bBIVDnZlfXic7VT+FUUFRaeEC2cKgZ24xlod4gFl0X\nFkcbph11U3jzjuiisDgKWYJojDo2UW3ZM1YhQXF4XSZ6XgxKe2yEms0Zon7LBSokG7V9b0Ac\nnjushi9GsKo4fs8HU3I4/c4tItRi7VsriMMuyx9w7oxtOsuKsj3a7jw5Coui8KoiB6zs/HZc\n+v+V2Ynx4tTaLXOuOEQLOz1mHStHbNP6HU1thx7obFHBOve2kexiHHHIdwKT2CmrrZusqNfo\nVgvJRrD6Q2QfbDm71nbHNce0FR8Qh6WfQhY2ghXF8diZlv3MIuputl6Jt9pubf8tCtmB9lH1\noc4UEqPSOajoyKdzxopu6RaHVyNnWsjOzm2aGSJcEBSHNe7EyHcr1Jbt52BXv3lpjX9/FIZ7\nrKlYoLHspnbDb3Z7AVkN46b1R4u1T/ajFIfVRE4V/BbyJqDTQvKg1gKiB7pz7xVH6yC3c2ph\nCLtJcYxmRXGcTPZzq7bu18yc5hUhR4A7FJ7/1C/9hOPFYf0rMnE2ZC6F02d+OielGE5ejQKT\n2L2kY42VoHmcFnDF4QUu5mtAHOE3hTj82tu7QrUNDt5AU+deoF4LNdUbKw6nQnXO7hwPi2ME\nQXGE6xoeNG4fNjX0jmyHhyOOUJ55c7CSFcWRmVuzfTPZvCLkSOucc+57bud449dqZDmEesLo\n3tcztgY6KzhPQ9nac0KKoztre+KPD2kgyvr1yuLw8s8Dr2SFu23e3QaBVtU11xhxhPMNiUMT\nQ2hASPUnLg5jul689lhMLKJC0ZhdkpnSkqHyZ+UzqgQxn4rQXBkf33xxjM8nNHnHle+f/8k3\nNcWuiPUHSxlE3+GKrAaUqGWrZxwA8EbWvcZxvVdbEa5xAMAbWfN27N76LLJ7LFIEAKzBuus4\nTtU6juxwnr2OAwDeyFZXjgLAG0EcAKAGcQCAGsQBAGoQBwCoQRwAoAZxAIAaxAEAahAHAKhJ\nVBwAkDQTZnV8UUQmuQgJaAgCGiK5gNSkX4PkIiSgIQhoiOQCUpN+DZKLkICGIKAhkgtITfo1\nSC5CAhqCgIZILiA16dcguQgJaAgCGiK5gNSkX4PkIiSgIQhoiOQCUpN+DZKLkICGIKAhkgtI\nTfo1SC5CAhqCgIZILiA16dcguQgJaAgCGiK5gNSkX4PkIiSgIQhoiOQCUpN+DZKLkICGIKAh\nkgtITfo1SC5CAhqCgIZILiA1268BAKwO4gAANYgDANQgDgBQgzgAQA3iAAA1iAMA1CAOAFCD\nOABADeIAADWIAwDUIA4AUIM4AEAN4gAANYgDANQgDgBQk5w4LnVEp8zsr81mdnq4m28M6LJL\nLKA/fk2zN4GAbkdjjvd0Anq8cQyFR8tbB/V8UhPHrf7l7H31K9rndnPnbL4xoFO1mT2SCeiP\nR2asve8O6JpYC92zZ0D3NwQUHi1vHdQRSEwct+zV6RezfxSPo7mVf0qzW/nGr9h8Y0A3c3yU\nO46pBFRyeO5OJKDsL4rHwZxSCehYhvI3hd/QZeHR8tZBHYO0xPHX1ab+w1m2473s75MpzzZ/\nyr8c1uYbAzo895RvpBFQFcJzdxoB/VRRPUyWSkDmfV0WHi3vHNRRSEscf31sdXH5z75s+fIE\n82YOYvONAdXvmXQCutezJY2AmvOgVAJ6fY4rTbZ6QEUTTHgkvymgmaQljlvhdrrp2nxjQE8e\n5YBMJaC9uT830ghoZ4pzVp2iJxLQ+fVR5fyGgCrc0fLOQR2F5KJ9td+u0vBvAm3sB/TkUp5h\nJhLQ2fwUbxJHMCBjDtUFwWQCKi7l1dHs8qaA/NGCOGLzar+zOTyK2z6BNvYDqrhnhyKVgKrT\n3LeLQ3RZeXH0+K4/8IEuOzc3WN4yT73RgjhiU7dfdf/skEAb+wGVPLK99eabA9qV9/reLg7R\nZeU1jnt5izGNgC7lR5U/k13eE5A/WhBHbJqZeTTZuXqVtQ2bvXEUtgGV7J+33ZMI6Fhdln/u\nTiIgey6kEdDOlNdbHqXJ3hGQP1rePKjnk1y0ov1uZU8/rzrf2wvQ91UvQPsB/UWw2z9XRSYR\nkGlIJCD7FmQaAZl3BhQaLW8e1PNJVRxZ9SfiUrbmufqDei1PNq3NNwb0F0B9UzaJgGxxJBHQ\nK4p72UxpBPT8q14tLFk9oOBoefOgnk+q4ngu8tuZn7cvsvMDureLOdIIyNqdRkB3s6vWbP6k\nEtDJlN8FOb1jKWt4tLByNDavTn88v1xQnb7tqs29s/m+gI7tH/g0ArJ3pxHQObEue30f5B0B\ndYyW9w7q+aQqjuL+1+AH+5uN7ub7ArI+GaQRkL07kYCu+6S67PUN1HcE1DFa3juo55OcOAAg\nfRAHAKhBHACgBnEAgBrEAQBqEAcAqEEcAKAGcQCAGsQBAGoQBwCoQRwAoAZxAIAaxAEAahAH\nAKhBHACgBnEAgBrEAQBqEAcAqEEcAKAGcQCAGsQBAGoQBwCoQRwAoAZxAIAaxAEAahAHAKhB\nHACgBnEAgBrEAQBqEAcAqEEcAKAGcQCAGsQBAGoQx/diLP5ejEt0PUwo6XCdkAhSBnF8L1PE\ncTePCSU9zH1CKkgYxPHljD3TeLE/TSrltJ+UDJIFcXw5OnH8TDrhKE85fialg1RBHF9OLY7y\n37//n012/jtBMOZ5ZnHZmexiHb3blxLYVdvPf9sjrgdjstMzr8fOHP727I3ZPy9v7HfrVAdW\nAnF8OVIc5/J6Rznfn+Y4VNc/2o8Zv+ZS7f0tX/yYs33E+Xmx5FTldSg3Ls89lVYuzzTwKSCO\nL0eKY/8op3v13+zvjKHceuxNc0/kZG5FuftYvjiau32EKT+N/FTZVTkURVYe/fM8P7mZaRdH\nIFEQx5cjxfFbbd1frw/VFY2Hae7A7p+XOHbVP6URvCNe4vh9brd3YR+Gy6MfBeL4cpxrHIX9\n3/ZmrTz4Un5I+S3/I464X8/7lziqw05/H1luN6cc+Azozi9nijge5eeYc3liYh+xb7bqBOfs\n73V2F+XAZ0B3fjn94ug4+PT3IWS3K8QRR7O7XO9CHEVxPe1e92AQx2dBd345feI4GGep+Osa\nR3Ez+1v5ScU+okrmiqN5wTWODwNxfDl94vgx2a28otFc+jzVN1V3JqsuoVpHlFdEb+Iax+55\nn6U64/jlrspngTi+nD5xvC5bZM0XTaoLoiVX8/oE0h5xel3t+G3y/Gl2lFdEWMfxUSCOL6dX\nHOW6UHO0vqC2e33geJhaIe0RR2P2v9fnycfzvWrl6FMYrBz9MBAHKLi+vuZ6Nbrvu97dqyWw\ncRAHaHh9O3ZvLgMHSvh27KeBOEBD9TwO8f2VMfA8jo8DcYCK67H8EoryMWBHPqh8GogDANQg\nDgBQgzgAQA3iAAA1iAMA1CAOAFCDOABADeIAADWIAwDUIA4AUIM4AEAN4gAANYgDANQgDgBQ\ngzgAQA3iAAA1iAMA1CAOAFCDOABADeIAADWIAwDUIA4AUIM4AEAN4gAANYgDANQgDgBQgzgA\nQM3/e61G4mQwIeEAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(maxTS, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Precipitation (1/10mm)\", \n",
    "     #main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2012 by Monthly Max\", stationName,firstYear),\n",
    "     ylim=c(0,2500))\n",
    "abline(h=maxObs2, col=\"red\", lwd=.5, lty=5)\n",
    "points(smoothTS2, col=4, type=\"l\", lwd=2)\n",
    "text(2013,maxObs2+100,sprintf(\"%i\",maxObs2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxObs3 = maxObs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxTSNoEvent = ts(maxTSNoEvent, start=1898, freq=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setEPS()\n",
    "postscript(\"pics/.eps\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/ts2012.eps\")\n",
    "plot(smoothTS, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 1\n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AaUrERpUepUQKwJxOfqZ3bP8iyYHy17PBBrAk2sP6sDVn3I+ttf6NRArAm9WN2w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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 1\n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/tsEvent.eps\")\n",
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 4, ylim=c(130,220)\n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jQ/k2ZKkUpUGRvu6Ypmmq93PZlEPX5/Ka/B6qvdXxOuiVBPYfKtX2nOVd8g844c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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 4\n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7PsRqiEl+q8CFm0TxSxlIWOIVbAhOdQ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\nsACxAAsQC7AAsQAL/5VvNWpUlmhtAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Smoothed 24 hour max rainfall from 1898 to 2014\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=5,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 1, main=\"Smoothed 24 hour max rainfall from 1898 to 2014\")\n",
    "lines(smoothTS, col=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "means = apply(max_PRCP,2,mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "76"
      ],
      "text/latex": [
       "76"
      ],
      "text/markdown": [
       "76"
      ],
      "text/plain": [
       "[1] 76"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(is.na(max_PRCP))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can't use the apply function with all the NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "means = NULL\n",
    "for(i in 1:ncol(max_PRCP)){\n",
    "    means = c(means,mean(na.omit(max_PRCP[,i])))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meanTS = ts(means,start=firstYear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YLshEBYFox2FDsMHKFOxlelbq9vPIbr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\nYII=",
      "text/plain": [
       "Plot with title \"FORT COLLINS CO US \n",
       "Observed 24 Hour Max Rainfall \n",
       "from 1896 to 2014 by Monthly Max without Event\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(maxTSNoEvent, xlab = \"Time (years)\", col = \"darkgrey\", lwd=1.75, ylab = \"Observed Max Precipitation (1/10mm)\", \n",
    "     main=sprintf(\"%s \\nObserved 24 Hour Max Rainfall \\nfrom %i to 2014 by Monthly Max without Event\", \n",
    "     stationName,firstYear), ylim=c(0,1000), type=\"l\")\n",
    "abline(h=maxObs, col=\"red\", lwd=.5, lty=5)\n",
    "#points(smoothTS, col=4, type=\"l\", lwd=2)\n",
    "text(1985,maxObs3+50,sprintf(\"%i\",maxObs3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in which(data$Max.PRCP. == maxObs2): object 'maxObs2' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in which(data$Max.PRCP. == maxObs2): object 'maxObs2' not found\nTraceback:\n",
      "1. which(data$Max.PRCP. == maxObs2)"
     ]
    }
   ],
   "source": [
    "which(data$Max.PRCP.==maxObs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2= data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data2$Max.PRCP.[1389]=NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "smooth3 = runmean(data2$Max.PRCP., 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothTS3 = ts(smooth3, start = 1898, freq=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create the first decade (first year and the following 9 years)\n",
    "max_PRCP2 = data.frame(data2[which(data2$YEAR < (firstYear + 10)),6])\n",
    "\n",
    "# add all the subsequent decades to the data frame incrementing by 1 year at a time\n",
    "# i.e. 1898-1907, 1898-1908, etc\n",
    "for(year in c((data2$YEAR[1] + 1):2005)){\n",
    "    max_PRCP2 = cbind(max_PRCP2,data2[which(data2$YEAR >= year & data2$YEAR < year+10),6])\n",
    "}\n",
    "\n",
    "# name the columns as the first year of the observed 10 year periods\n",
    "colnames(max_PRCP2) = as.character(c(firstYear:2005))\n",
    "\n",
    "# set the missing values to NA\n",
    "max_PRCP2[max_PRCP2==-999] = NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "means1 = NULL\n",
    "for(i in 1:(length(max_PRCP)-2)){\n",
    "    means1 = c(means1,mean(na.omit(max_PRCP[,i])))\n",
    "}\n",
    "means1TS = ts(means1, start = 1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vars1 = NULL\n",
    "for(i in 1:(length(max_PRCP)-2)){\n",
    "    vars1 = c(vars1,var(na.omit(max_PRCP2[,i])))\n",
    "}\n",
    "vars1TS = ts(vars1, start = 1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means3 = NULL\n",
    "for(i in 1:(length(max_PRCP))){\n",
    "    means3 = c(means3,mean(na.omit(max_PRCP[,i])))\n",
    "}\n",
    "means3TS = ts(means3, start = 1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means2 = NULL\n",
    "for(i in 1:length(max_PRCP2)){\n",
    "    means2 = c(means2,mean(na.omit(max_PRCP2[,i])))\n",
    "}\n",
    "means2TS = ts(means2, start = 1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time Series:\n",
       "Start = 1898 \n",
       "End = 2005 \n",
       "Frequency = 1 \n",
       "  [1] 17132.24 17677.51 17912.33 19677.28 19770.37 18721.59 16125.19 16460.47\n",
       "  [9] 17512.64 14540.66 14689.79 15050.07 14845.88 15935.55 15455.63 14519.03\n",
       " [17] 14753.16 14319.76 18535.31 18501.20 17857.39 16471.50 16521.46 13537.75\n",
       " [25] 13761.54 14640.60 14634.97 15801.85 11707.51 11468.71 12026.85 11840.48\n",
       " [33] 11595.92 16297.79 15885.14 15293.37 15658.78 18373.73 17304.37 17652.75\n",
       " [41] 16094.08 17579.49 20111.56 15176.31 20985.87 21969.79 25372.25 22826.83\n",
       " [49] 23472.03 22848.96 22830.26 21355.61 21017.81 21669.77 16268.04 15745.51\n",
       " [57] 18597.63 17659.00 19113.17 19469.10 22040.06 22215.61 22316.76 22566.00\n",
       " [65] 22637.99 23726.30 17990.42 17754.49 15277.50 18127.67 18185.35 17935.23\n",
       " [73] 24621.77 25441.96 26969.18 25023.21 24271.05 29196.00 30871.52 29043.74\n",
       " [81] 27469.42 27315.29 18827.34 18181.66 16405.06 21490.26 21483.60 20336.29\n",
       " [89] 18230.95 18051.63 17934.97 18622.73 30068.36 30474.60 32557.70 27551.27\n",
       " [97] 27679.00 24523.59 31476.80 30487.58 30590.05 30349.90 19850.87 19495.36\n",
       "[105] 19264.17 19609.30 20479.45 21505.12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vars1TS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>1896</th><th scope=col>1897</th><th scope=col>1898</th><th scope=col>1899</th><th scope=col>1900</th><th scope=col>1901</th><th scope=col>1902</th><th scope=col>1903</th><th scope=col>1904</th><th scope=col>1905</th><th scope=col>...</th><th scope=col>1996</th><th scope=col>1997</th><th scope=col>1998</th><th scope=col>1999</th><th scope=col>2000</th><th scope=col>2001</th><th scope=col>2002</th><th scope=col>2003</th><th scope=col>2004</th><th scope=col>2005</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 94</td><td> 36</td><td> 20</td><td> 56</td><td> 25</td><td> 33</td><td> 46</td><td> 20</td><td>  5</td><td> 20</td><td>...</td><td> 64</td><td> 51</td><td> 51</td><td> 61</td><td> 20</td><td> 48</td><td> 61</td><td>  8</td><td> 41</td><td> 53</td></tr>\n",
       "\t<tr><td>  5</td><td> 76</td><td> 10</td><td> 99</td><td> 74</td><td> 38</td><td> 20</td><td>147</td><td> 53</td><td> 38</td><td>...</td><td> 18</td><td> 36</td><td> 94</td><td>  3</td><td> 81</td><td> 33</td><td> 41</td><td> 58</td><td> 71</td><td> 51</td></tr>\n",
       "\t<tr><td>107</td><td>251</td><td> 43</td><td>147</td><td>145</td><td>203</td><td>114</td><td>206</td><td> 43</td><td>191</td><td>...</td><td>333</td><td> 43</td><td>465</td><td> 79</td><td>203</td><td> 46</td><td>135</td><td>838</td><td> 71</td><td>150</td></tr>\n",
       "\t<tr><td>170</td><td> 91</td><td> NA</td><td>114</td><td>607</td><td>404</td><td> 64</td><td>173</td><td> 97</td><td>442</td><td>...</td><td> 91</td><td>536</td><td>155</td><td>612</td><td> 43</td><td>267</td><td> 33</td><td>269</td><td>147</td><td>140</td></tr>\n",
       "\t<tr><td>244</td><td> NA</td><td>211</td><td>112</td><td>307</td><td>589</td><td>117</td><td> 56</td><td>767</td><td>305</td><td>...</td><td>343</td><td>157</td><td>218</td><td>127</td><td>208</td><td>229</td><td>315</td><td>414</td><td> 99</td><td>381</td></tr>\n",
       "\t<tr><td>414</td><td> 79</td><td>198</td><td> 91</td><td>130</td><td>371</td><td>259</td><td>124</td><td>102</td><td> 56</td><td>...</td><td>104</td><td>300</td><td>117</td><td>216</td><td>130</td><td> 51</td><td>102</td><td> 56</td><td>216</td><td>246</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " 1896 & 1897 & 1898 & 1899 & 1900 & 1901 & 1902 & 1903 & 1904 & 1905 & ... & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 & 2002 & 2003 & 2004 & 2005\\\\\n",
       "\\hline\n",
       "\t  94 &  36 &  20 &  56 &  25 &  33 &  46 &  20 &   5 &  20 & ... &  64 &  51 &  51 &  61 &  20 &  48 &  61 &   8 &  41 &  53\\\\\n",
       "\t   5 &  76 &  10 &  99 &  74 &  38 &  20 & 147 &  53 &  38 & ... &  18 &  36 &  94 &   3 &  81 &  33 &  41 &  58 &  71 &  51\\\\\n",
       "\t 107 & 251 &  43 & 147 & 145 & 203 & 114 & 206 &  43 & 191 & ... & 333 &  43 & 465 &  79 & 203 &  46 & 135 & 838 &  71 & 150\\\\\n",
       "\t 170 &  91 &  NA & 114 & 607 & 404 &  64 & 173 &  97 & 442 & ... &  91 & 536 & 155 & 612 &  43 & 267 &  33 & 269 & 147 & 140\\\\\n",
       "\t 244 &  NA & 211 & 112 & 307 & 589 & 117 &  56 & 767 & 305 & ... & 343 & 157 & 218 & 127 & 208 & 229 & 315 & 414 &  99 & 381\\\\\n",
       "\t 414 &  79 & 198 &  91 & 130 & 371 & 259 & 124 & 102 &  56 & ... & 104 & 300 & 117 & 216 & 130 &  51 & 102 &  56 & 216 & 246\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 ... 1996 1997 1998 1999\n",
       "1  94   36   20   56   25   33   46   20    5   20  ...  64   51   51   61 \n",
       "2   5   76   10   99   74   38   20  147   53   38  ...  18   36   94    3 \n",
       "3 107  251   43  147  145  203  114  206   43  191  ... 333   43  465   79 \n",
       "4 170   91   NA  114  607  404   64  173   97  442  ...  91  536  155  612 \n",
       "5 244   NA  211  112  307  589  117   56  767  305  ... 343  157  218  127 \n",
       "6 414   79  198   91  130  371  259  124  102   56  ... 104  300  117  216 \n",
       "  2000 2001 2002 2003 2004 2005\n",
       "1  20   48   61    8   41   53 \n",
       "2  81   33   41   58   71   51 \n",
       "3 203   46  135  838   71  150 \n",
       "4  43  267   33  269  147  140 \n",
       "5 208  229  315  414   99  381 \n",
       "6 130   51  102   56  216  246 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(max_PRCP2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>alpha</th><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>invTheta</th><th scope=col>mean</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.7702459   </td><td>0.005838670 </td><td>0.11823885  </td><td>not rejected</td><td>0.3945848   </td><td>NA          </td><td>2.578290    </td><td>NA          </td><td>171.2719    </td><td>131.9215    </td><td>22594.44    </td><td>150.3145    </td></tr>\n",
       "\t<tr><td>0.7592384   </td><td>0.005623842 </td><td>0.10957672  </td><td>not rejected</td><td>0.3993983   </td><td>NA          </td><td>2.545615    </td><td>NA          </td><td>177.8144    </td><td>135.0035    </td><td>24005.57    </td><td>154.9373    </td></tr>\n",
       "\t<tr><td>0.7329028   </td><td>0.005426065 </td><td>0.11023874  </td><td>not rejected</td><td>0.3867091   </td><td>NA          </td><td>2.377635    </td><td>NA          </td><td>184.2956    </td><td>135.0708    </td><td>24892.95    </td><td>157.7750    </td></tr>\n",
       "\t<tr><td>0.7228140   </td><td>0.005126209 </td><td>0.10648805  </td><td>not rejected</td><td>0.3718661   </td><td>NA          </td><td>2.266249    </td><td>NA          </td><td>195.0759    </td><td>141.0036    </td><td>27506.41    </td><td>165.8506    </td></tr>\n",
       "\t<tr><td>0.7894197   </td><td>0.005429997 </td><td>0.10174311  </td><td>not rejected</td><td>0.2569872   </td><td>NA          </td><td>1.422317    </td><td>NA          </td><td>184.1622    </td><td>145.3812    </td><td>26773.72    </td><td>163.6268    </td></tr>\n",
       "\t<tr><td>0.7953316   </td><td>0.005407428 </td><td>0.09718581  </td><td>not rejected</td><td>0.2725329   </td><td>NA          </td><td>1.497542    </td><td>NA          </td><td>184.9308    </td><td>147.0813    </td><td>27199.86    </td><td>164.9238    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " alpha & theta & KS & KStest & CvM & CvMtest & AD & ADtest & invTheta & mean & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.7702459    & 0.005838670  & 0.11823885   & not rejected & 0.3945848    & NA           & 2.578290     & NA           & 171.2719     & 131.9215     & 22594.44     & 150.3145    \\\\\n",
       "\t 0.7592384    & 0.005623842  & 0.10957672   & not rejected & 0.3993983    & NA           & 2.545615     & NA           & 177.8144     & 135.0035     & 24005.57     & 154.9373    \\\\\n",
       "\t 0.7329028    & 0.005426065  & 0.11023874   & not rejected & 0.3867091    & NA           & 2.377635     & NA           & 184.2956     & 135.0708     & 24892.95     & 157.7750    \\\\\n",
       "\t 0.7228140    & 0.005126209  & 0.10648805   & not rejected & 0.3718661    & NA           & 2.266249     & NA           & 195.0759     & 141.0036     & 27506.41     & 165.8506    \\\\\n",
       "\t 0.7894197    & 0.005429997  & 0.10174311   & not rejected & 0.2569872    & NA           & 1.422317     & NA           & 184.1622     & 145.3812     & 26773.72     & 163.6268    \\\\\n",
       "\t 0.7953316    & 0.005407428  & 0.09718581   & not rejected & 0.2725329    & NA           & 1.497542     & NA           & 184.9308     & 147.0813     & 27199.86     & 164.9238    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha     theta       KS         KStest       CvM       CvMtest AD      \n",
       "1 0.7702459 0.005838670 0.11823885 not rejected 0.3945848 NA      2.578290\n",
       "2 0.7592384 0.005623842 0.10957672 not rejected 0.3993983 NA      2.545615\n",
       "3 0.7329028 0.005426065 0.11023874 not rejected 0.3867091 NA      2.377635\n",
       "4 0.7228140 0.005126209 0.10648805 not rejected 0.3718661 NA      2.266249\n",
       "5 0.7894197 0.005429997 0.10174311 not rejected 0.2569872 NA      1.422317\n",
       "6 0.7953316 0.005407428 0.09718581 not rejected 0.2725329 NA      1.497542\n",
       "  ADtest invTheta mean     var      sd      \n",
       "1 NA     171.2719 131.9215 22594.44 150.3145\n",
       "2 NA     177.8144 135.0035 24005.57 154.9373\n",
       "3 NA     184.2956 135.0708 24892.95 157.7750\n",
       "4 NA     195.0759 141.0036 27506.41 165.8506\n",
       "5 NA     184.1622 145.3812 26773.72 163.6268\n",
       "6 NA     184.9308 147.0813 27199.86 164.9238"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleGamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleGam1 = ts(mleGamma$var, start=1898, freq=12)\n",
    "mleGamSD = ts(mleGamma$sd, start=1898, freq=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+ddd6N0iiUpIhweNl7OAKnSK9Nm+lOo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\nYhEIFItAoFgEAsUiEP4DI5NDv5/Bs20AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(mleGam1,type=\"l\")\n",
    "lines(vars1TS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lrer0cVy6UC+jIosZTl9hSr/83MMCTF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\nYIxAgDZGAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(mleGamSD,type=\"l\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K6YJ6ccsUKIJdgDuxbr41KRpoglFGvw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\nLFABsUAFxAIVEAtU+A/j7199c1DQZQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(vars1TS, type=\"l\", xlab = \"Time (years)\", lwd=1,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/eventComp1.eps\")\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 4, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "#lines(smoothTS3, col=2, lwd=2)\n",
    "lines(smoothTS, col=1, lwd=2)\n",
    "legend(\"topleft\",c(\"TS with event\",\"\", \"TS prior to event\"), lty = c(1,0,1),col=c(4,0,1),\n",
    "      lwd=c(2,0,2), bty=\"n\", cex = 1, pt.cex=1)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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49VNPNYSf7EPNbVwcy7JRDLDIgFWIBYgAWIBViAWICFqDPvChxVgMMQUayNl40b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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 4, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "#lines(smoothTS3, col=2, lwd=2)\n",
    "lines(smoothTS, col=1, lwd=2)\n",
    "legend(\"topleft\",c(\"TS with event\",\"\", \"TS omitted event\",\"\", \"TS prior to event\"), lty = c(1,0,1,0,1),col=c(4,0,2,0,1),\n",
    "      lwd=c(2,0,2,0,2), bty=\"n\", cex = 1, pt.cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "144"
      ],
      "text/latex": [
       "144"
      ],
      "text/markdown": [
       "144"
      ],
      "text/plain": [
       "[1] 144"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothTS2)-1284"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "truncSmoothTS = ts(smoothTS[1284:1320], start=2004, freq=12)\n",
    "truncSmoothTS2 = ts(smoothTS2[1284:length(smoothTS2)], start=2004, freq=12)\n",
    "truncSmoothTS3 = ts(smoothTS3[1284:length(smoothTS3)], start=2004, freq=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/eventComp2.eps\")\n",
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(truncSmoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=4,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 4, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "lines(truncSmoothTS3, col=2, lwd=3)\n",
    "lines(truncSmoothTS, col=1, lwd=2)\n",
    "legend(\"topleft\",c(\"TS with event\",\"\", \"TS omitted event\",\"\", \"TS prior to event\"), lty = c(1,0,1,0,1),col=c(4,0,2,0,1),\n",
    "      lwd=c(2,0,2,0,2), bty=\"n\", cex = 1, pt.cex=1)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0KSKQdTsbpBuKKQJr2LWM1trO2UfNipcohlmqWZx3pZbNzrUIVAOLHE8fr8tLAT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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(truncSmoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=4,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 4, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2014\"\n",
    "    )\n",
    "lines(truncSmoothTS3, col=2, lwd=3)\n",
    "lines(truncSmoothTS, col=1, lwd=2)\n",
    "legend(\"topleft\",c(\"TS with event\",\"\", \"TS omitted event\",\"\", \"TS prior to event\"), lty = c(1,0,1,0,1),col=c(4,0,2,0,1),\n",
    "      lwd=c(2,0,2,0,2), bty=\"n\", cex = 1, pt.cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AFXxPIe2xn9dLh/EtL9D7gilvdYnwqP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\nIBaIgFggAmKBCIgFIiAWiIBYIMI/LLZ5Y5BbenoAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Smoothed 24 hour max rainfall from 1898 to 2014\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(means2[85:length(means2)], type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 2, main=\"Smoothed 24 hour max rainfall from 1898 to 2014\",   ylim=c(170,195))\n",
    "lines(means3[85:length(means2)], col=4, lwd=2)\n",
    "lines(means1[85:length(means2)], col=1, lwd=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothALL = runmean(data2$Max.PRCP., 1404/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothAllTS = ts(smoothALL, start=1898, freq = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9bjJHL5cAIIsYLHo1jlcVz1/O75d/gu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\nA134rOuMQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothAllTS, type=\"l\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothALLEvent = runmean(data$Max.PRCP., 1404/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothAllEventTS = ts(smoothALLEvent, start = 1898, freq = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/longTerm.eps\")\n",
    "plot(smoothAllEventTS, type=\"l\", col=\"blue\", ylab=\"Smoothed Precipitation (1/10mm)\", xlab=\"Time (years)\")\n",
    "lines(smoothAllTS)\n",
    "legend(\"topleft\",c(\"Smoothing with Event\",\"\", \"Smoothing without Event\"), col = c(4,0,1),, lty=c(1,0,1), bty=\"n\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1428"
      ],
      "text/latex": [
       "1428"
      ],
      "text/markdown": [
       "1428"
      ],
      "text/plain": [
       "[1] 1428"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothAllTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "702"
      ],
      "text/latex": [
       "702"
      ],
      "text/markdown": [
       "702"
      ],
      "text/plain": [
       "[1] 702"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1404/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q4K1TJara+9E235T8W6KjuK1XQ1z7UPwz8zjSXbF4il2F2sP3UP3OeVzgFoYxYh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\nA+LWAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothAllEventTS, type=\"l\", col=\"blue\", ylab=\"Smoothed Precipitation (1/10mm)\", xlab=\"Time (years)\")\n",
    "lines(smoothAllTS)\n",
    "legend(\"topleft\",c(\"Smoothing with Event\",\"\", \"Smoothing without Event\"), col = c(4,0,1),, lty=c(1,0,1), bty=\"n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3OxzDLxLVY5THLYlucjTyGWPT8mVjKO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\nuwGmOKTVK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      "text/plain": [
       "Plot with title \"Smoothed 24 hour max rainfall from 1898 to 2014\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(means1TS[70:90], type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 2, main=\"Smoothed 24 hour max rainfall from 1898 to 2014\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time Series:\n",
       "Start = 1898 \n",
       "End = 2005 \n",
       "Frequency = 1 \n",
       "  [1] 131.8870 135.0172 135.0598 141.0000 145.3644 147.0932 139.4153 145.1345\n",
       "  [9] 147.9076 145.6891 148.6975 146.1681 144.4370 148.0417 140.5417 135.0917\n",
       " [17] 137.6083 129.6917 141.7583 137.7167 132.7417 133.2417 136.4583 128.1667\n",
       " [25] 130.7083 131.8500 128.1500 131.1833 118.9500 118.5167 118.8083 115.4083\n",
       " [33] 114.6167 119.9333 114.6833 114.1500 117.8750 126.3250 122.1417 124.5917\n",
       " [41] 122.6583 125.3667 130.1750 123.2583 135.9250 136.8250 142.6833 133.3500\n",
       " [49] 137.7583 135.6333 136.7167 133.5750 131.6833 135.3333 127.6583 127.4833\n",
       " [57] 134.2773 134.2521 134.2773 134.8487 137.2605 134.2773 138.3109 137.2521\n",
       " [65] 141.9748 146.5508 132.0504 129.5630 126.3814 132.4407 136.6017 139.8390\n",
       " [73] 135.8559 135.5424 141.4068 137.6723 139.3109 149.6017 157.6271 157.4576\n",
       " [81] 153.7034 154.4153 152.8475 156.9831 147.7542 154.7712 151.5678 152.9664\n",
       " [89] 148.7750 148.5583 144.0083 143.9250 158.2167 159.6833 165.3250 157.9583\n",
       " [97] 156.1917 144.4583 151.1000 149.9833 152.2333 151.7250 139.5167 135.3583\n",
       "[105] 136.3250 137.3750 146.1167 147.5833"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "means1TS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "153.703389830508"
      ],
      "text/latex": [
       "153.703389830508"
      ],
      "text/markdown": [
       "153.703389830508"
      ],
      "text/plain": [
       "[1] 153.7034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "means1TS[81]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1st = data[which(data$YEAR<=1981),]\n",
    "data2nd = data[-which(data$YEAR<=1981),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean1sthalf = mean(na.omit(data1st$Max.PRCP.))\n",
    "v1 = var(na.omit(data1st$Max.PRCP.))\n",
    "mean2ndhalf = mean(na.omit(data2nd$Max.PRCP.))\n",
    "v2 = var(na.omit(data2nd$Max.PRCP.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1896</li>\n",
       "\t<li>1897</li>\n",
       "\t<li>1898</li>\n",
       "\t<li>1899</li>\n",
       "\t<li>1900</li>\n",
       "\t<li>1901</li>\n",
       "\t<li>1902</li>\n",
       "\t<li>1903</li>\n",
       "\t<li>1904</li>\n",
       "\t<li>1905</li>\n",
       "\t<li>1906</li>\n",
       "\t<li>1907</li>\n",
       "\t<li>1908</li>\n",
       "\t<li>1909</li>\n",
       "\t<li>1910</li>\n",
       "\t<li>1911</li>\n",
       "\t<li>1912</li>\n",
       "\t<li>1913</li>\n",
       "\t<li>1914</li>\n",
       "\t<li>1915</li>\n",
       "\t<li>1916</li>\n",
       "\t<li>1917</li>\n",
       "\t<li>1918</li>\n",
       "\t<li>1919</li>\n",
       "\t<li>1920</li>\n",
       "\t<li>1921</li>\n",
       "\t<li>1922</li>\n",
       "\t<li>1923</li>\n",
       "\t<li>1924</li>\n",
       "\t<li>1925</li>\n",
       "\t<li>1926</li>\n",
       "\t<li>1927</li>\n",
       "\t<li>1928</li>\n",
       "\t<li>1929</li>\n",
       "\t<li>1930</li>\n",
       "\t<li>1931</li>\n",
       "\t<li>1932</li>\n",
       "\t<li>1933</li>\n",
       "\t<li>1934</li>\n",
       "\t<li>1935</li>\n",
       "\t<li>1936</li>\n",
       "\t<li>1937</li>\n",
       "\t<li>1938</li>\n",
       "\t<li>1939</li>\n",
       "\t<li>1940</li>\n",
       "\t<li>1941</li>\n",
       "\t<li>1942</li>\n",
       "\t<li>1943</li>\n",
       "\t<li>1944</li>\n",
       "\t<li>1945</li>\n",
       "\t<li>1946</li>\n",
       "\t<li>1947</li>\n",
       "\t<li>1948</li>\n",
       "\t<li>1949</li>\n",
       "\t<li>1950</li>\n",
       "\t<li>1951</li>\n",
       "\t<li>1952</li>\n",
       "\t<li>1953</li>\n",
       "\t<li>1954</li>\n",
       "\t<li>1955</li>\n",
       "\t<li>1956</li>\n",
       "\t<li>1957</li>\n",
       "\t<li>1958</li>\n",
       "\t<li>1959</li>\n",
       "\t<li>1960</li>\n",
       "\t<li>1961</li>\n",
       "\t<li>1962</li>\n",
       "\t<li>1963</li>\n",
       "\t<li>1964</li>\n",
       "\t<li>1965</li>\n",
       "\t<li>1966</li>\n",
       "\t<li>1967</li>\n",
       "\t<li>1968</li>\n",
       "\t<li>1969</li>\n",
       "\t<li>1970</li>\n",
       "\t<li>1971</li>\n",
       "\t<li>1972</li>\n",
       "\t<li>1973</li>\n",
       "\t<li>1974</li>\n",
       "\t<li>1975</li>\n",
       "\t<li>1976</li>\n",
       "\t<li>1977</li>\n",
       "\t<li>1978</li>\n",
       "\t<li>1979</li>\n",
       "\t<li>1980</li>\n",
       "\t<li>1981</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1896\n",
       "\\item 1897\n",
       "\\item 1898\n",
       "\\item 1899\n",
       "\\item 1900\n",
       "\\item 1901\n",
       "\\item 1902\n",
       "\\item 1903\n",
       "\\item 1904\n",
       "\\item 1905\n",
       "\\item 1906\n",
       "\\item 1907\n",
       "\\item 1908\n",
       "\\item 1909\n",
       "\\item 1910\n",
       "\\item 1911\n",
       "\\item 1912\n",
       "\\item 1913\n",
       "\\item 1914\n",
       "\\item 1915\n",
       "\\item 1916\n",
       "\\item 1917\n",
       "\\item 1918\n",
       "\\item 1919\n",
       "\\item 1920\n",
       "\\item 1921\n",
       "\\item 1922\n",
       "\\item 1923\n",
       "\\item 1924\n",
       "\\item 1925\n",
       "\\item 1926\n",
       "\\item 1927\n",
       "\\item 1928\n",
       "\\item 1929\n",
       "\\item 1930\n",
       "\\item 1931\n",
       "\\item 1932\n",
       "\\item 1933\n",
       "\\item 1934\n",
       "\\item 1935\n",
       "\\item 1936\n",
       "\\item 1937\n",
       "\\item 1938\n",
       "\\item 1939\n",
       "\\item 1940\n",
       "\\item 1941\n",
       "\\item 1942\n",
       "\\item 1943\n",
       "\\item 1944\n",
       "\\item 1945\n",
       "\\item 1946\n",
       "\\item 1947\n",
       "\\item 1948\n",
       "\\item 1949\n",
       "\\item 1950\n",
       "\\item 1951\n",
       "\\item 1952\n",
       "\\item 1953\n",
       "\\item 1954\n",
       "\\item 1955\n",
       "\\item 1956\n",
       "\\item 1957\n",
       "\\item 1958\n",
       "\\item 1959\n",
       "\\item 1960\n",
       "\\item 1961\n",
       "\\item 1962\n",
       "\\item 1963\n",
       "\\item 1964\n",
       "\\item 1965\n",
       "\\item 1966\n",
       "\\item 1967\n",
       "\\item 1968\n",
       "\\item 1969\n",
       "\\item 1970\n",
       "\\item 1971\n",
       "\\item 1972\n",
       "\\item 1973\n",
       "\\item 1974\n",
       "\\item 1975\n",
       "\\item 1976\n",
       "\\item 1977\n",
       "\\item 1978\n",
       "\\item 1979\n",
       "\\item 1980\n",
       "\\item 1981\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1896\n",
       "2. 1897\n",
       "3. 1898\n",
       "4. 1899\n",
       "5. 1900\n",
       "6. 1901\n",
       "7. 1902\n",
       "8. 1903\n",
       "9. 1904\n",
       "10. 1905\n",
       "11. 1906\n",
       "12. 1907\n",
       "13. 1908\n",
       "14. 1909\n",
       "15. 1910\n",
       "16. 1911\n",
       "17. 1912\n",
       "18. 1913\n",
       "19. 1914\n",
       "20. 1915\n",
       "21. 1916\n",
       "22. 1917\n",
       "23. 1918\n",
       "24. 1919\n",
       "25. 1920\n",
       "26. 1921\n",
       "27. 1922\n",
       "28. 1923\n",
       "29. 1924\n",
       "30. 1925\n",
       "31. 1926\n",
       "32. 1927\n",
       "33. 1928\n",
       "34. 1929\n",
       "35. 1930\n",
       "36. 1931\n",
       "37. 1932\n",
       "38. 1933\n",
       "39. 1934\n",
       "40. 1935\n",
       "41. 1936\n",
       "42. 1937\n",
       "43. 1938\n",
       "44. 1939\n",
       "45. 1940\n",
       "46. 1941\n",
       "47. 1942\n",
       "48. 1943\n",
       "49. 1944\n",
       "50. 1945\n",
       "51. 1946\n",
       "52. 1947\n",
       "53. 1948\n",
       "54. 1949\n",
       "55. 1950\n",
       "56. 1951\n",
       "57. 1952\n",
       "58. 1953\n",
       "59. 1954\n",
       "60. 1955\n",
       "61. 1956\n",
       "62. 1957\n",
       "63. 1958\n",
       "64. 1959\n",
       "65. 1960\n",
       "66. 1961\n",
       "67. 1962\n",
       "68. 1963\n",
       "69. 1964\n",
       "70. 1965\n",
       "71. 1966\n",
       "72. 1967\n",
       "73. 1968\n",
       "74. 1969\n",
       "75. 1970\n",
       "76. 1971\n",
       "77. 1972\n",
       "78. 1973\n",
       "79. 1974\n",
       "80. 1975\n",
       "81. 1976\n",
       "82. 1977\n",
       "83. 1978\n",
       "84. 1979\n",
       "85. 1980\n",
       "86. 1981\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910\n",
       "[16] 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925\n",
       "[31] 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940\n",
       "[46] 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955\n",
       "[61] 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970\n",
       "[76] 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(data1st$YEAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anotherTS = smoothTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require(changepoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cpTest = sort(smoothTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "136.525"
      ],
      "text/latex": [
       "136.525"
      ],
      "text/markdown": [
       "136.525"
      ],
      "text/plain": [
       "[1] 136.525"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpTest[652]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>136.428571428571</li>\n",
       "\t<li>136.458333333333</li>\n",
       "\t<li>136.466666666667</li>\n",
       "\t<li>136.466666666667</li>\n",
       "\t<li>136.475</li>\n",
       "\t<li>136.478991596639</li>\n",
       "\t<li>136.507936507937</li>\n",
       "\t<li>136.525</li>\n",
       "\t<li>136.533333333333</li>\n",
       "\t<li>136.583333333333</li>\n",
       "\t<li>136.584745762712</li>\n",
       "\t<li>136.588235294118</li>\n",
       "\t<li>136.591666666667</li>\n",
       "\t<li>136.601694915254</li>\n",
       "\t<li>136.65</li>\n",
       "\t<li>136.675</li>\n",
       "\t<li>136.716666666667</li>\n",
       "\t<li>136.775</li>\n",
       "\t<li>136.790322580645</li>\n",
       "\t<li>136.8</li>\n",
       "\t<li>136.825</li>\n",
       "\t<li>136.833333333333</li>\n",
       "\t<li>136.908333333333</li>\n",
       "\t<li>136.924369747899</li>\n",
       "\t<li>136.925</li>\n",
       "\t<li>136.941176470588</li>\n",
       "\t<li>136.941666666667</li>\n",
       "\t<li>136.958333333333</li>\n",
       "\t<li>136.991452991453</li>\n",
       "\t<li>137.008474576271</li>\n",
       "\t<li>137.033613445378</li>\n",
       "\t<li>137.042372881356</li>\n",
       "\t<li>137.058333333333</li>\n",
       "\t<li>137.0625</li>\n",
       "\t<li>137.075</li>\n",
       "\t<li>137.09243697479</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 136.428571428571\n",
       "\\item 136.458333333333\n",
       "\\item 136.466666666667\n",
       "\\item 136.466666666667\n",
       "\\item 136.475\n",
       "\\item 136.478991596639\n",
       "\\item 136.507936507937\n",
       "\\item 136.525\n",
       "\\item 136.533333333333\n",
       "\\item 136.583333333333\n",
       "\\item 136.584745762712\n",
       "\\item 136.588235294118\n",
       "\\item 136.591666666667\n",
       "\\item 136.601694915254\n",
       "\\item 136.65\n",
       "\\item 136.675\n",
       "\\item 136.716666666667\n",
       "\\item 136.775\n",
       "\\item 136.790322580645\n",
       "\\item 136.8\n",
       "\\item 136.825\n",
       "\\item 136.833333333333\n",
       "\\item 136.908333333333\n",
       "\\item 136.924369747899\n",
       "\\item 136.925\n",
       "\\item 136.941176470588\n",
       "\\item 136.941666666667\n",
       "\\item 136.958333333333\n",
       "\\item 136.991452991453\n",
       "\\item 137.008474576271\n",
       "\\item 137.033613445378\n",
       "\\item 137.042372881356\n",
       "\\item 137.058333333333\n",
       "\\item 137.0625\n",
       "\\item 137.075\n",
       "\\item 137.09243697479\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 136.428571428571\n",
       "2. 136.458333333333\n",
       "3. 136.466666666667\n",
       "4. 136.466666666667\n",
       "5. 136.475\n",
       "6. 136.478991596639\n",
       "7. 136.507936507937\n",
       "8. 136.525\n",
       "9. 136.533333333333\n",
       "10. 136.583333333333\n",
       "11. 136.584745762712\n",
       "12. 136.588235294118\n",
       "13. 136.591666666667\n",
       "14. 136.601694915254\n",
       "15. 136.65\n",
       "16. 136.675\n",
       "17. 136.716666666667\n",
       "18. 136.775\n",
       "19. 136.790322580645\n",
       "20. 136.8\n",
       "21. 136.825\n",
       "22. 136.833333333333\n",
       "23. 136.908333333333\n",
       "24. 136.924369747899\n",
       "25. 136.925\n",
       "26. 136.941176470588\n",
       "27. 136.941666666667\n",
       "28. 136.958333333333\n",
       "29. 136.991452991453\n",
       "30. 137.008474576271\n",
       "31. 137.033613445378\n",
       "32. 137.042372881356\n",
       "33. 137.058333333333\n",
       "34. 137.0625\n",
       "35. 137.075\n",
       "36. 137.09243697479\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 136.4286 136.4583 136.4667 136.4667 136.4750 136.4790 136.5079 136.5250\n",
       " [9] 136.5333 136.5833 136.5847 136.5882 136.5917 136.6017 136.6500 136.6750\n",
       "[17] 136.7167 136.7750 136.7903 136.8000 136.8250 136.8333 136.9083 136.9244\n",
       "[25] 136.9250 136.9412 136.9417 136.9583 136.9915 137.0085 137.0336 137.0424\n",
       "[33] 137.0583 137.0625 137.0750 137.0924"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpTest[645:680]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class 'cpt' : Changepoint Object\n",
       "       ~~   : S4 class containing 12 slots with names\n",
       "              date version data.set cpttype method test.stat pen.type pen.value minseglen cpts ncpts.max param.est \n",
       "\n",
       "Created on  : Tue Sep 13 17:22:34 2016 \n",
       "\n",
       "summary(.)  :\n",
       "----------\n",
       "Created Using changepoint version 2.2.1 \n",
       "Changepoint type      : Change in mean \n",
       "Method of analysis    : AMOC \n",
       "Test Statistic  : Normal \n",
       "Type of penalty       : MBIC with value, 21.74124 \n",
       "Minimum Segment Length : 1 \n",
       "Maximum no. of cpts   : 1 \n",
       "Changepoint Locations : 840 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpt.mean(cpTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class 'cpt' : Changepoint Object\n",
       "       ~~   : S4 class containing 12 slots with names\n",
       "              date version data.set cpttype method test.stat pen.type pen.value minseglen cpts ncpts.max param.est \n",
       "\n",
       "Created on  : Tue Sep 13 17:22:34 2016 \n",
       "\n",
       "summary(.)  :\n",
       "----------\n",
       "Created Using changepoint version 2.2.1 \n",
       "Changepoint type      : Change in mean \n",
       "Method of analysis    : AMOC \n",
       "Test Statistic  : Normal \n",
       "Type of penalty       : MBIC with value, 21.74124 \n",
       "Minimum Segment Length : 1 \n",
       "Maximum no. of cpts   : 1 \n",
       "Changepoint Locations : 976 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpt.mean(anotherTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/qq1.eps\")\n",
    "qqnorm(smoothTS);qqline(smoothTS)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  smoothTS\n",
       "W = 0.99083, p-value = 1.096e-07\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shapiro.test(smoothTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fBoKT1aaLhU/hCFV7oXoUv+2E1eml5F\nINDKFsThFV5ua5u1V6EeKsSySyxeQSy7xKIVxLJMNF5BLKtEoxXEskpEXkEsi0SkFcSyR0zd\nVWdArCrntZQ3O1++R4D1HplX2mJlfTWxxKhZ4VV8PKeDA5pilSxreTWVeI3REfGEryY0xUrY\n8Pgp7iA9IEKv9F8K0kGsE9ZaBXVwu2iKlQ49Vo2ndHb4p1VAx3aEmTlWlTCF9xjdy8JvItVK\n/6wwH+rL6KpywYgVa3fVGYpjsfxjqDibWXhLxF4h8k5IxFpBLEKi9krvSWiyaguhDaLWCmKR\nEbdWGArJiNwriEVE5FoZEOvDww2vC+/zu56Fh0TvlZnbZn5g9S+J2IdBjqZYRf90PS7pyMCr\nzsBtM/3LGHARegZecQzcNrP8YASfmwNeCbSHwrHHMjrJ8rg94FWP7uT9LeZYX+VXqt3Iwiti\nj4tOaA+FJPXobYPAqxGIZRRoNYLIu0ng1QTEMgi8mrEq1vfd38mci0XDSLJ4EngloStWkSjX\nZJtK9X58Fulls8ArGe04lnpVFiz59FGvpkqOX8ztZbvAKxnts0L1a4SJ9C7umiWqWfgCvFpg\n6pKOSjr1hB62DAbCJdpDofor3oPuseDVCu37sTLl9xfxW2z6Lwc3x4JW/9AVq7pQn5lU++lh\nT+dZ4zCI9Q9Nsd6X6vNbiDhWkr+DimPBqw20b/QzeufoVhbOwyDWBhbPCm9m4TrwahPtofD6\nwl9lwtKTfs6j5oFW22jf6JedTJck6pwl5TAtC+WSDrzawdz9WKfpavG1gr3arsmP52betBC8\n2sOiWC8euyr6yGh7/FSPL00Er3axeNtMX/PDUxf/m8HDNvKwyNawLtanHwNDuKSD/uoAU2J9\nzx//evHZVU/7CuGSDrw6QPtGP/WabRPp8dbDDssPseDVEcZu9FN530wx6pQc9ld+iAWvDtG+\npPPpMtY0GVMPZ13MwlXg1SEGLum8f71VbfZF7x60FLw6xoBYFQ92RvdSEHh1jKZY+W8obFja\nfWMTC16doClWxetV3MAX13qFGAjP0L4IzX97seOw1JBOPaDoemPBq1MsRt7LYMSCV+dYFKur\nld+i5XhzQaxzdMRqC/Hxm/LbrFSoVUbMm6WyCLxSQEes/hJN/5yOWl9USo8WGi6VReCVAhpi\n8dVVfz+SpO7ajBldsdDpBoNXKmiIlTH++OmXvcWf8UTeIZYKGmL19Vr0VwkjCpDCKxW0xUqZ\n9IspXG4yeKWEhlgpHwqbPuTentxgdTMLF4FYSmiIVfDJ+6u/EauM5pIOZlhqaIjVJlOcoWSK\ncYSLWTgIvFJDK0A6XiNkStcK72ThHOiwFDFySYflRu8f9UOsp0viODavFTqVxT3QYakCsa4A\nr5SBWBfA1Wd1IJY68OoCEEud0Sl4pQDEUgYd1hUglirw6hIQSxEGsS4BsdSAVxeBWErAq6tA\nLBXg1WUglgoQ6zIQSwFodR2IdQ76qxtArFPg1R0g1hkM9zTcAWKdgf7qFhDrBHh1D4h1DCZY\nN4FYh8Cru0CsIxjEugvEOgBe3Qdi7cMwEt4HYu0Cr3SAWHswhBp0sCrW952LRsqLkyenXWhG\nBq90sChWm0oN5fxi48sO6+nS+IdFsQqWfPp30jRV4vpCmLwE8EoDi2Il0quOaseX7oVXulgU\na9FAbq9MIfKHWDqgx9rLHl5pYXeOVTXik+NzrD53iKWFzXBDJp1mpe3RNx9tyyFzoRTEuovd\nOFYh4lhJ/nY4jiV5hQ7rPoi872QNsfSAWNtZwytNbIrF37KcVcNOHA03wCtD2Lykk/QXCvud\nuCnWP68g1k2shhvKn11lv8yqm2L9n2BBrJtYDZCKH02SNo6KteEVxLrJA5d02izbEos93pxT\nrk8XJAQsipWyMSiaZi72WHOmEEsfi2LNK4Q1LHNPLHhlFJvhhmJqqOqkzR5oUHhlFqsB0jof\nPzUvV8V6fKYXCIi8L3N8/hQiECDWMkOIZQiIJefH4JUpIFa35xXE0gFi7Y2D8EoLq5F35Xaz\n2qiYX1FgNUDqpFibASx4pYvNobBOjp9/NpDFdeAVDXYDpMfP5pjI4ioYB4mwO3kvpUcLibK4\nCAKjRER+VgivqIhbrMVA2EEsg0QtFryiI2ax/k3cO3hljIjFkgMNHbwyTLxi/fMK46BJohVL\nygLDIAGxioU7kYmJXiwEsGiIVCzc2UdNnGLhzj5yohQLXtETo1gLr/DGIhoiFGvYOVtDmWd8\nxCdWv29ZKGhFQHRisf9dFbwiIDaxNq2CVuaJSqy1TtCKjojE2u6sIBYNcYi17KHglQViEGu3\nq4JWdIQv1tRVbQpmMCMgE4VYGAXtE6xYsj7yrcc4F7RDoGKt+yjcd2Wb8MQaOqU5xL4Kthsr\nJDgiLLGmzknyC5OqRwhJrMmnbikWZlUPEIxYQ2c16tX1I+A0bweW8Vas2ZbhvG/orLrx/+kU\nsINYD+CpWEKV9R+dJNZickVbWLCFr2JNf4pPbOylunEchFcP46dY0yg4GTZ1WePQN4+URGUE\nhwQl1hgIlWdV8OoZrIr1fedibMqLr14Wa7GG/mlj3INXD2FRrDaV5j3H70++OseSpvAXdwSI\nsChWwZJP/27bpkqO359846xwc5IOrx7DoliJ9MrkmiWaWSzjWPf3A2iwKNai/a2sTAGvnsOP\nHuteMApePYjdOVbViE8X51h7M/MT4NWT2Aw3ZNJZYdqqZ8E2/u4cePUoduNYhYhjJfn7Shzr\nXggdXj2LB5H3W2LBq4cJVCx49TQeiHVjjgWvHscLsS6fFUKsx/FBrMtxLHj1PFYj78oPzOiZ\nAa8cwKJYJ4uNG3tMC165gM2h0M5i4/DKCazOsWwsNg6v3MDu5J1+sXF45Qh+nBXSJwSGCUss\neOUMQYkFr9wBYgESHBULeM+NVjcv0l2ejNA/GMT1NzXJrrWNNleUx1N7W3AXxTq5pGOzKI+n\n9rbgLop14ZKOKt7WsLcFd1Is9Us6qnhbw94W3E2xlC/pqOJtDXtbcEfFMo23NextwSGW26m9\nLTjEcju1twWHWG6n9rbgEMvt1N4WHGK5ndrbgkciFggJiAVIgFiABIgFSIBYgASIBUiAWIAE\niAVIgFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSHBIrPbF2Ov+E2VlypLicLWok/T3qqJI\nHsm2T6tzyJrVfYZDYiXiYf27h1qI1MntJq7vvSegXwQtvZvr3WwFeoesV92nuCNWwV78j/xe\n6pq9Wv7P/3Uz9zq51cJfltQ87ckCaKaz7dNqHbJedZ/jjlgJ4//07tZz3qe7m7xk2a2kBat+\nf37Y2262Ar1D1qvuc9wRq+d4FeDz5DePhxX3kuaMLzNb3/2HfzfbxT50dqBZ3Ud7ptrxPQpW\n6iRv2c1X4NQ3G4jpdZR3s5W4fcgczeo+wimxPkzzDTalGJnu8YRYekkFGoesXd1HOCVWmSd3\nZyuCJtGYi/opls4h61b3IU6J9eOl0Tm3Wu+C81IsvUPWq+5jnhdr+a7J9uJ0Uk6dXQ4nyalv\ntXDysFjXD3nJ1epWxzWxrlb0nLpJs0Ynb42zwkYjHKQj1p1DNpj98Y6J9nudPrDS3I1iVzpn\nR5xbVfwWU+dKYxKs0bJah6xZ3ae4I5YIBbf5zUG/0fXqXgvrRt51xNI7ZL3qPscdsYaLVzcr\n66X9YvB7SVOdQt/PlqN5yFrVfY5DYvEbBdK7/4D03zh/L2kr7m64namOWLqHrFPd57gkFggI\niAVIgFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSIBY\ngASIBUiAWIAEiAVIgFiABIgFSIBYgASIBUiAWIAEiAVIgFiABIgFSIBYgASIBUiAWIAEiAVI\ngFiAhEjEYhJ3XqJX9TvZ3bv0S1ukjKWKywhO+6VbLekhQjueHTTFSlm/k929z58/YzYqb2Gc\n9wuxPOb+G91PUkib+fo0Tdc1hZJZc0KI5TEWxGqTcc2kirHz0RBiBYEkVsGSfnkivqzy0LX8\nPvZvEf4pkYq1JsaNw7uJ+/RFwvr1IKqcDS9Mnq0o56UECr4AEpvHUPnrTS7yl/bbf3EuTZUx\nlt1fyux54hQrHydB+fyy82z6KLYX0kZZrGxch/ndT6WKThYrnxdZ/vYpx4wXXxfvWH//E2su\nTak+T3OUOMXK2l/TpWLRkLZrMz58fYY1Jj7D9sXG2Y8P/8sX94Pxr37Elu0Rbe6I+k/S18f8\n5c2LDBMu6IdsPRILxCnWd/gtF/Oglo97+bAqTjZulzfOAuR8m7RmlrJYi69//21elYZprOjp\nBnGKNf4pLe4grTw4fVxtXIcFmuqdXRBr9fX/+5Uy/J1U5nXdeQzEui1WNi04sjnHqpdd3b+v\nH4rVvfk0LNFdNO5BYhdrvXHlWLef4vU7h6yalVjDWWHd8E5nMTn79/UtseTSVkWKOZYf/Bcr\nn6cy4xxr6mbkjXOKbJpjib9bizXEsXKW9zNvseU7WXMsVr6eWDGPW8fjol/mv1jiVPDXzeSr\ns0LxNWkjEwup8r8v+Zlb0Z8Vfrt6PcficVEeeX8P41jKSn6exza+3v9l081iSRmm/Tkkeiwv\n+C/WMPERDshxrP5788aUTX3UFMcqhgnRd9mzVNNMiQehRDwqH6bjy6/zP+f9rkrzmb7sK5GL\nxWPd7NXPkctkirwPCaaN33QSS5ywiQSvn4ZfeezsGe5uqDIRvP9Nwl/99vXX+Z/zftelEZF3\nj72KSizLVO+nS/AkEAuQALEACRALkACxAAkQC5AAsQAJEAuQALEACRALkACxAAkQC5AAsQAJ\nEAuQALEACRALkACxAAkQC5AAsQAJEAuQALEACRALkACxAAkQC5AAsQAJEAuQALEACRALkACx\nAAkQC5AAsQAJf26t7U4d5y8EAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Normal Q-Q Plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqnorm(smoothTS);qqline(smoothTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/tri.eps\")\n",
    "hist(smoothTS,breaks=40,xlab=\"Precipitation (1/10mm)\", main=\"\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ja+jEusWVc+pZ79PGdG3u03BcUyiRgn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\nDVDePQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Histogram of smoothTS\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothTS,breaks=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>158.296610169492</li>\n",
       "\t<li>159.050847457627</li>\n",
       "\t<li>161.78813559322</li>\n",
       "\t<li>163.008474576271</li>\n",
       "\t<li>158.338983050847</li>\n",
       "\t<li>158.677966101695</li>\n",
       "\t<li>158.728813559322</li>\n",
       "\t<li>157.525423728814</li>\n",
       "\t<li>158</li>\n",
       "\t<li>157.177966101695</li>\n",
       "\t<li>157.457627118644</li>\n",
       "\t<li>157.720338983051</li>\n",
       "\t<li>157.398305084746</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 158.296610169492\n",
       "\\item 159.050847457627\n",
       "\\item 161.78813559322\n",
       "\\item 163.008474576271\n",
       "\\item 158.338983050847\n",
       "\\item 158.677966101695\n",
       "\\item 158.728813559322\n",
       "\\item 157.525423728814\n",
       "\\item 158\n",
       "\\item 157.177966101695\n",
       "\\item 157.457627118644\n",
       "\\item 157.720338983051\n",
       "\\item 157.398305084746\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 158.296610169492\n",
       "2. 159.050847457627\n",
       "3. 161.78813559322\n",
       "4. 163.008474576271\n",
       "5. 158.338983050847\n",
       "6. 158.677966101695\n",
       "7. 158.728813559322\n",
       "8. 157.525423728814\n",
       "9. 158\n",
       "10. 157.177966101695\n",
       "11. 157.457627118644\n",
       "12. 157.720338983051\n",
       "13. 157.398305084746\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 158.2966 159.0508 161.7881 163.0085 158.3390 158.6780 158.7288 157.5254\n",
       " [9] 158.0000 157.1780 157.4576 157.7203 157.3983"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoothTS[998:1010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>158.296610169492</li>\n",
       "\t<li>159.050847457627</li>\n",
       "\t<li>161.78813559322</li>\n",
       "\t<li>163.008474576271</li>\n",
       "\t<li>158.338983050847</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 158.296610169492\n",
       "\\item 159.050847457627\n",
       "\\item 161.78813559322\n",
       "\\item 163.008474576271\n",
       "\\item 158.338983050847\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 158.296610169492\n",
       "2. 159.050847457627\n",
       "3. 161.78813559322\n",
       "4. 163.008474576271\n",
       "5. 158.338983050847\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 158.2966 159.0508 161.7881 163.0085 158.3390"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anotherTS[998:1002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "83"
      ],
      "text/latex": [
       "83"
      ],
      "text/markdown": [
       "83"
      ],
      "text/plain": [
       "[1] 83"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "996/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1980"
      ],
      "text/latex": [
       "1980"
      ],
      "text/markdown": [
       "1980"
      ],
      "text/plain": [
       "[1] 1980"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1897+83"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "117"
      ],
      "text/latex": [
       "117"
      ],
      "text/markdown": [
       "117"
      ],
      "text/plain": [
       "[1] 117"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothTS)/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anotherTS[1:1007] = mean1sthalf\n",
    "anotherTS[1008:length(smoothTS)] = mean2ndhalf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S0pthzlGY1W4PSWFFuO8oxGK3B6S4ot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\nmwemiDIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(anotherTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vl5c5a39D30PvZ7uNhiXdEsGrHWJ2NO\nJZYj1ns4n1qxeYmJvxDrKWJZ7+Eg1knFWhlXxRUrLXcuMPYHYsURq40slrySTiweJbO6rsE7\nhEsWVSz59Of+Yon/kcSaU5vqdIBYzafo78Wdv6Pf1A9i/UwsKeOyAseIxan/xBVZxV/Ue5FG\nEstyGHpO7KtNIZYxSSuXQD94r15Z7G3nXhhTxWL3EMuu3hti6eVdSizOt9z8cUSMEDs5jGKp\nB8VrcWaxWHuUWJGBWPHFauOKpbwJsS7EucViTxdr7ZLs80MllmGINJayI5byUX2QWExahFjr\nBZ5eLKZtwYi4FTY21lgviKUX6CuWOrjQxFoeNa2qbsxymgv9IJb4fxexXC/08wlhl3oWS2r3\nuBJiaQWOfbMs5SxiOV7o5xXCKvWeWKYxwOl5sliOF/p5hbBJzZhZrKG3IJZS4DhCWJZyHrGo\ncArBJJP0Y9+KWFcQzf7zALFoQkAsD7GWKizXQax1sVYGFLcSa0q1I9a4/U8vVq5fKdMU7oVt\nh7BLrIqlthpiKSmlPjK8eRKxPky5Z0xdsmiXKvuKxaTl6c32kmIZz9xWU/bcRay2zlj2rrhc\nzffVLce7kBRinVSs9hCx2vZvfoZAGvOXFRDLRqzhbXuxxmznF4tf4ccvH80iP/MEYtmJxcaU\nPceKZaiJssqmKsFZSENArOG1tVhtqFj6ew8RS242xFop2V8sQ48+Riz5zfmvvvbU0IklZdVW\nQSw1sfo8NIhlVbqhaIilJYZYgl+KtSzMtipBWUhD6GKZk2y+PiE/EWvz9SiUyaIHi6XNu4TV\nz6pS4QUcLJapCvrLB4kldtDSDIsxjdqVtxCLQSzSEOPZM2PrG2MpFrVazxLLlNm2KitZXtOX\nOu4FWYbYS3tSsQIDnFYsy4YFivWaJpHOItZaGoil5bWrgv7qKLGS7Ufwanxf/U2PWL731aJD\nrZiHWNE/CCvVCivg0WK5dF8zXwrB2PYPxyDW08XKmf0N10qW/Ikf9ew+6vcWYoVFeLhYdZJZ\nXzDj8HDyx4s1TsvZhLilWNLBbT+f/TE0tliaShDrVmL9cI8FsZZ5bRItX1k3K1AsF7ox1kdc\nFh9xjMUg1uPFajNp/5ZGetg4xPIUyznR0WL9cV3yP6uc31LcvDvfe+wAxCIWy7ISy1eHiTXu\nheLe0IhcLLuPbAAQyyOetPzuxk3dv4/bDLxTiJ2UHmLtb7NwDhNLCnSEWNYdFyhWOpzpVczl\nPu+7lYNYTxdrCuPUjRDLLvuyCHnNrcWa91ib81JDlRTi1Go+8m8Wqlh3CbHmv1qxWoibiuUy\nxvomEMs6t/xPLVYLcVOxnM4Km3y4cYixctbWabnGvxDrVmK1/XPlLOex2j/GeMqoY6zx76nE\nYvpDQZyzS/+UNx4jlht1xvIGYllkl/4pb8jNgFgSr25URibWVrLjxBpPUf1CSLWzEIuRimWY\nADhArGk34TYsqtL9xK5iiaWTiMXoxGJHi8XkV+Y6rWX2iCdF8hlvtwWRWNvJjhbLNwSb63oa\nsTb3oiuZPeK5Z6ELYSnWIiXEsqrJJNZ6ndYye8Rzz0IXwn7rXUgsZiHWkOwAsRj7gVhTmGR/\n5t0zxE5Kb7HIzKIWi8mLdxerjrudKMSak/5CLJcPwJ5YTEp2R7E+TGb/6gamEqdWEKu9nVit\n/APUdP9XYA4PznygWOrYaVWsaUQ9izVxG7EcwvTYPzjzNmIpMS1qp37sfizWUIGfiOWG9YMz\nbyfWfjg/sdghYrEfivXNbbLaPjjzELGozFLFms7hrGqn2aLnOl6s8fyAzR8Ry6we0eQXpdWY\nKSjEdkqnpOM2OUosad9jLZZctR2x2vuKNXsV81E6hGKxMQ+pWMwg1vYvg5iXWGorDhHLOqtH\nNGk5YX9txuo6Y1EfpkMkFlsTK6pkoWK1EEu0/NXtraq4Pyy0rpVLe5m0xVt1oxGLZTFcmFPK\ntdkWS09xN7E+/Hr3H42xbiGWNObT0mg55MGhoTBSsZyzekSTlvPuUFiztP1eRKz2nGIxe7Hm\nlIty7iRWfzFo/4OKaM+D1kOIFRsfdutCLyGW9s7y5U/E8sgamOXFXxXMdubTJ0S/gkIsJr8R\nj/Hsc02sRbBpt7AtllSYqZSbiUXD/cRqF2LNUSGWd5bgEGtt8xBr+eXE78SallrFebkorf7j\nP1OVacTyGmFFOSvsIb7Qb6VtTk3+mVjMVqzFVNeqWMYq31Es6gv9riPWFKWd9kOqWCyCWOYa\n30Us1wv9QmplbJtjkyGWV2M8eyfERccL/UJqRSTWvKV8a7pSJ1ksNos1DrjU2ruKtVbj24jV\nug2ePUMMcSKJ1WpisaPFknWCWKFZgkMYWwexVO4i1rRRvIPb18oQwDnsD8WaqrsmFoNYEGs3\nCBsXp5F7uyWWtE+DWMTYiuVWqFksw/YMwCTW/MaeWHqTIFZk9sXyb/BSrJjnIIpYzEksgy+6\nWHO1jxHL/bAw5wzN0t/Rr4h6YfLVxZKraCfW2jH5DGK1nv0cmGW8B6nVj3T8QvQrmN7HpxVL\nWrQVq4VYepbyoCdTXFWsVheLqWLNFXARa2Vbk4jl2zmBYiVeT6ZwCiFW3Ees9mpieeYMyyIN\nKHxrsBei5x+wIuZGCCP4UDjusaIOsvRa/Xp7XYaYGyGM0MH7qx9jfa1v9+ERgvPrDXYRom6E\nMIIPhQpktTKOsfyK/tEYazpJVMNvt2NtjHUBHi8WM4gV0BA6scaUzxCLiLuI1UpitRArfpbg\nEGr/h+wfjxNrXCGrBrEsskxTL3EPg8ZaSQFsNshW0WtiqQL4sp113Gkp4SGWluVnYgVFXM5Q\nHitW6yaWPmR7gliE/EqseZv7Eles5bkAxAoCYi2KYw8Tqyn5L1WTsrHJ+X3lff/k5c5ven4j\n1vKHYV7Fb75rEMuuuIeJVSfDOXVS7+Zr5J+Lbc/Um8UaOzayWK0s1vTSE4g1EChWxgq+r2pK\ni+8KS5b8iW8W60+yfXsaUrHGjMpe8Fdi7WaQ6/0gsZRNs0Mi3Yu7Ypv3ejharHkrQ6xIBIqV\nMDG4aiyay4xG2tXKJJZNXU21OJNY+xnkej9IrJJlfBz+zSzuvBZ5j2VbXVNJ2pJ8inakWDYZ\npKwXUUoQelY4XvNucdkMv4xZDPH9xljsQLG8NyHEGggVS/xKJ7O64n2UkJNuzk9siBV4JLyc\nWPMg9mFiufAt+3msJH/5zGMxxpjNKfoOvxWr9RRrd5h/Ng4Vyz/EBcSyGZ4FiXUtgsX65LzN\n+f78qH+IVtrwZGKxCGLtnuQNCQ3/NgvdTXZCogzeu3UWM+8j74SlO2Oyq4pldbxyUWVq7NPE\nerOsn8J62zxAoMpZ8m5ffWc5f6VzjFjtycRqnyoWnyC1nfqp+q4v+ZdAdb79y+l1sRixWMOk\nJcQKJVCsaVhh0fCCz12VYma02f7l9FFiySshVlQCxUqHPZbNT+yHHsqlF+rbG+IcIdY8y+Qp\nluWcAMSyyDKMsaxuCiL65k8kdP5KR51xcKqwXtSeWH5zRhBLIfSsMB+2tsVXOoW4xIbTFM5f\n6VCIpaw8p1ijWY8Tq5/HYvmfRb4mmXqHbe+wVsRq44qlrTyxWHbzY+ciWCwXylGnZOdaCJNY\nw/8IYhlXHi2WbYSHipXHfU6hKYRYMe/rINYliDDdQAClWOaApxWrfaZYKbP6eU5ICLHiQLEs\np6OWJUAsiUCxmjyL+nQmQwix4rlitY8UK86c5WaIIc4ckKKTIVZsDhSLqTjVCmI9TCwX3hBr\nSDn/tS0ZYm1RWd+p9Idi+YagE6t9nFh1mTDLuzYIKosfiakhphXEYrWSFLYh1DTzVMV+PkdT\nniYWv28Ds7prw8Rb+mmhU60UsUiYy7XdjiFiOX06niZWwbKmbTKba0d9Q8wryMVaiWedBmKp\nBIglfl5f73yf7MUVxGJBYrm042liTUOSeLXRQ8wrIJZ18nMAscwVgFiBQCxzBejFcqsMxIrC\nBcTS3YBYKkFiWc+kB9fqdGItmgyxVCCWuQIQK5AAsQiBWMsM9ulPAcQyV2BfrLAJUogVJUtw\nCIgFsWJwMbHGQRDEmoFY5go4iiWqZSvWYq5iLwPEisS6WAedHlGK1S4nwfZTQ6wo6CGkbj2b\nWOIQ6CiW244XYkXjKmINWxxiLYFY5gpArEACxZruH1PbXs7uHKJ/fTaxmCQWg1gGAsViibjP\nzJv2K51zi8VmsWxrB7F2snwTltfd7oolUX8Q/QCxnCrzPLFafhPkkrFXpOoYQ9xQLNfKPFCs\n/neoVo/S8Q9xkE0OAXWxWoilEWmPFfk2WRBLq8zjxOrGWFnNb9t+2BjrGGzEkg+E01oysQjK\nJSX0rHA4Cv4lR50VHoO1WO0BYl2SQLGy8WfQTdSfrUKsy3O5mfdjgFihxBLrm4fWZCsExLoc\noWKV2vg1DhDr8gSKNXv1iVal9kJiadPiEGsiUKyE/bUZq+uMPXO6Qf++BWKNBE838CnST1vZ\nPEzHL8QY5lCGgKt7oFks9QAIsUYiiPXhc1n3HGM5iwVGAsXKu0NhzdL2C7GAQqBYH96n/QPH\nbSZIvy/xFLq83BmRQazLEzrd8OKvCqtvoZtU+nLN7WHjEOtyHDjzXrLkT9zbtv4kbg/CPKdY\nU7Ug1pIDxUqkWyZXbo/u/YFYTPq39j6EWudAsZTNsL1NziCWMAdieRIgVsIUdvNdbI8FsYII\nECt3FKsbY33EVTZXGGNtXxIKsfYIEOvN0vLP4bEUYlpiIN18UspFxALrBIhVF/xgmBT2cn3L\nfi+X5K/zz2NBrDDCBu/VW5jiIJdrCPEaYl2N8LPC76s/xEV98MnPxWp3xTq2OtcjynRDU1p9\ngpuCsWy4buvk0w0QK5QD91iNmJ8Q1zBfQayNs12ItceBY6ySX17TvMVjVi8g1mIaRVqGWHsE\nnxXaTzkMPz2sk7S+oliKZBBrj8B5rI/Dg3vHjdFkmWnLbM22QqzLceDMe8pGC9MMe6y7c+B3\nhe/pYsCaZRDr5hx4dQP/rdiw9NkREWJdniPFaqvp59J1AbHuzaFieYeAWJfjGmIdD8QKBGKt\n1GBhlibZj+p1GSDWSg1GqyCWHxBrpQbj3koRS1r+VcWuwoFiqdNeTmeFxwOxAjlQrPfVxWoh\nlj1HHgqrxPaWNL/fbJP7EMuPYydIbe8H//vNtinW3g4XHD14f0s/LSQKEYk9sX5UreuAs8KV\nGswSTWvaxTqwCsRaq4J+2INYTkCsHQxi4UhoAcTaAWL5AbF2MIr1ywpdBIi1gyaWMmcK1oFY\nO0AsPyDWDhDLD4i1A8TyA2LtALH8gFg7yN/t6Bc8gHUg1g7TNT4QywmItQPE8gNi7SB8aiGW\nIxBrH4jlAcSyAGK5A7EsmHQafhB2suqdEohlAcRyB2JZoIiFC96tgFgWSL8Fg1iWQCwbZp0g\nliUQywaI5QzEsgFiOQOxbIBYzkAsGzSxfl2dKwCxrIBYrkAsKyCWKxDLCojlCsSyYvYJYtkB\nsaxQxPpxXa4BxLJCFuvHVbkIEMsSiOUGxLIEYrkBsSyBWG4cKtb3JZ5xmJdfqhDkQCw7DhSr\nSdnM9v2TT7zxIJYdB4pVsuRP3Nu2/iTb908+8caDWHYcKFYi3TK5YglFiAOAWHYcKJaySba3\nz4k3HsSyA3ssRyCWHceOsT51v4Qx1v05crohk84K04YkBD0Qy45j57HKfh4ryV+Yx7o7mHl3\nBGLZAbEACRALkACxAAkQC5Bw6My7AkUIcBoOFGvnYePW1oErcOSh8EoPGweBHDrGutDDxkEg\nxw7er/OwcRAIzgoBCRALkACxAAkQC5BwUrHA5fHY6vFF+kkN7lfKuSpzWNxgo4NrcPdSzlWZ\nw+LufKVzQA3uXsq5KnNcXPuvdKhqcPNSzlWZA+Naf6VDVoN7l3KuyhwZ1/YrHboa3LqUc1Xm\nMnFj1+B+pZyrMpeJG7sG9yvlXJW5TNzYNbhfKeeqzGXixq7B/Uo5V2UuEzd2De5Xyrkqc5m4\nsWtwv1LOVZnLxAU3B2IBEiAWIAFiARIgFiABYgESIBYgAWIBEiAWIAFiARIgFiABYgESIBYg\nAWIBEiAWIAFiARIOF+s9RiwTln2mxaRs9EXXUt5pjFI6vmxRLddSqoKxonYpxVRM49ox5i5w\n794IHC1WNf4cXzw97DUvptqiayllv5g0YaV0NAmT1nqV8nGui6mYOhHF1LbFmLvAvXtjcLBY\nVZlo7mYAAAV1SURBVDL035tlTdsU/DevX5ZU/I2vsuhaSsWKhq8ogkrh5GJ1SClJl7XJ+U/F\nLUsxFlP0vzUvrZtk7gL37o3CsWJ1vcbG3QFvXc27rmR8x//HP6TSomspuVjD3wgopc8nVgeU\n8tcX1fAHz9qVYi6GOTbJ3AXO3RuHY8XqukvqLf4v4/3B9/UVy5VF11LG91hgKfW4iQNKKeab\nD9iVYi5mOCRzPy2LmUow96lLKaEcK1bV6v3H1hZdSxE0fIMElZKxWiwElJKy9pX0xyXLUszF\nvIZD4cu6GI7eBc7dG4fDzwqHVqX9h+fr2/JlKYI339mHlPJif62LWCstEg+fdSnFVJk3H70n\nb6diFl3wMLFeLG/aKgsUSyqlp07yNqiU/jDhJ5bSIj54L9x2NYYmvaYTRPtiFl3wMLHa/lQ6\nDxRLKoXTiJvBBZSS8nN1P7GUFvExVs3P653Fkop580Nh5+fboZhlFzxNrK7Dklf/Kpmbm7hu\nzLkUTiZmaPxLKfrTJrE6oC7Mo0WGYlLGR2kN99O6mGUX+HRvBH4mVk/FO02cq9TzaUu9f9qy\nLKXLlmZirtu/FPmmqgF1kc77rUsxFMOcizF1gU/3RuBXYiX9p/HN2/jqdxMfvt+XFl1L6XKN\nkw7+pchiBdRFZK15haxLMRQjdjD9dJhdMcYu8OneCPxKLDGfnHbnYF5Tw8tS6nkyK6AUaXVQ\nXdJ+9vzPZbJ7WUzJ+Nd6pfUEvrkLHjHz3k7914jvwfqdctovZtqiYymFdGdw/1Lk1QGlvNxb\nZComcytmpQs8ujcCPxtj1V035PKX+PqiYynSQSygFHl1SCmfzLVFxmLcOmalCzy6NwKHiwWe\nAcQCJEAsQALEAiRALEACxAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxAAsQCJEAsQALEAiRA\nLEACxAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxAAsQC\nJEAsQALEmmES1ves/vjcgli6feBdgVgzPmLVzOexkg2rPXJdCoil4Xh3/czvjp7lITeY/SUQ\nS8NNrD+vHRbfZf3tJ7o0EEtjFGt4dMmLP3+EPxNX7Jn4Q5ffUuo0G55J0o7/5xSfnA23KGas\nSfkNtj8ZGx/3nB3y/NwfArE0VLH6G7Z/+vutc0Vy7T7pX/4ApW5tf0f+/smlcwpxr/c+W//Q\npbJ9izW9du9D7uL/QyCWhipW1nAd+r9J/0iRpm0yNp3Tlf1jvj78YRL8/v21nILxo91fX1xf\nAn+EScXX9Puq6pDnjvwQiKWhivXtl+p2fFSSeBzXNMOQiSGWeEyXeDyTlmIQ6yuW51mG5pjn\nQ/wOiKWhjbFa+a/86Ac58ZsfBL/iuZdSivrzygax+mTdSC2vKi3OXbl589zxEWt4QFetpsim\npTHDq38Ub63EuSs3b54722KtJC67g1yatkqKgqXvT62I1Q3HynQ4h4RYD2NLrJxpX8UMY6xu\nKJ5V/bObpRR9Nl2s6QXGWE9jS6w//ry/4bmbPeU4aZCypB/iSyn4iL1SxlipOE/s91hfnBU+\njC2xhmFTMn3R1w/YOR82HOHmFOUw2vpOZf5NK/iIDPNYz2JTLD6vzgrpC+R0OKA1bFRsTlEw\nln0/Yucl3utn3oVQmHkHW3yGyxQ+zO16hVofrd0OiBXGcHVDxt47CVVwdQPYpr8ey/k5y7ge\nC+zxKfiXgI6XkRZ3PxBCLEAExAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxAAsQCJEAsQALE\nAiRALEACxAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxAAsQCJEAsQALEAiRALEACxAIkQCxA\nwn8qwbzQypRwWwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Smoothed 24 hour max rainfall from 1898 to 2012\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS2, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 1, main=\"Smoothed 24 hour max rainfall from 1898 to 2012\")\n",
    "    #points(rollLarge, type=\"l\", col=4)\n",
    "    #egend(0,200, c(\"300 window\",\"\", \"900 window\"), col=c(2,0,4), lty=c(1,0,1), bty=\"n\")\n",
    "    lines(anotherTS,col=2,lwd=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1st2 = data2[which(data2$YEAR<=1981),]\n",
    "data2nd2 = data2[-which(data2$YEAR<=1981),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean1sthalf2 = mean(na.omit(data1st2$Max.PRCP.))\n",
    "mean2ndhalf2 = mean(na.omit(data2nd2$Max.PRCP.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "150.208121827411"
      ],
      "text/latex": [
       "150.208121827411"
      ],
      "text/markdown": [
       "150.208121827411"
      ],
      "text/plain": [
       "[1] 150.2081"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean2ndhalf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "149.923664122137"
      ],
      "text/latex": [
       "149.923664122137"
      ],
      "text/markdown": [
       "149.923664122137"
      ],
      "text/plain": [
       "[1] 149.9237"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean2ndhalf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anotherTS2 = anotherTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anotherTS2[1:1007] = mean1sthalf2\n",
    "anotherTS2[1008:length(smoothTS)] = mean2ndhalf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "133.890518084066"
      ],
      "text/latex": [
       "133.890518084066"
      ],
      "text/markdown": [
       "133.890518084066"
      ],
      "text/plain": [
       "[1] 133.8905"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean1sthalf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "149.923664122137"
      ],
      "text/latex": [
       "149.923664122137"
      ],
      "text/markdown": [
       "149.923664122137"
      ],
      "text/plain": [
       "[1] 149.9237"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean2ndhalf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "154.194915254237"
      ],
      "text/latex": [
       "154.194915254237"
      ],
      "text/markdown": [
       "154.194915254237"
      ],
      "text/plain": [
       "[1] 154.1949"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoothTS[1048]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setEPS()\n",
    "postscript(\"pics/.eps\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/changeTS.eps\")\n",
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Precipitation (1/10mm)\", col = 1, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2012\"\n",
    "    )\n",
    "    #points(rollLarge, type=\"l\", col=4)\n",
    "    #egend(0,200, c(\"300 window\",\"\", \"900 window\"), col=c(2,0,4), lty=c(1,0,1), bty=\"n\")\n",
    "    lines(anotherTS2,col=2,lwd=3)\n",
    "    legend(\"topleft\",c(\"Trend Line\",\"\",\"Means left and right of change point\"), lty=c(1,0,1), lwd=c(2,0,2), col=c(1,0,2),\n",
    "    bty=\"n\", cex = .8, pt.cex=.8)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kUQa4EHl1gb5ZT1EUMs5xh9/XZ6/uVO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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rollSmall = runmean(data$Max.PRCP., 300)\n",
    "plot(smoothTS, type=\"l\", xlab = \"Time (years)\", lwd=2,\n",
    "    ylab = \"Max Precipitation (1/10mm)\", col = 1, \n",
    "     #main=\"Smoothed 24 hour max rainfall from 1898 to 2012\"\n",
    "    )\n",
    "    #points(rollLarge, type=\"l\", col=4)\n",
    "    #egend(0,200, c(\"300 window\",\"\", \"900 window\"), col=c(2,0,4), lty=c(1,0,1), bty=\"n\")\n",
    "    lines(anotherTS2,col=2,lwd=3)\n",
    "    legend(\"topleft\",c(\"Trend Line\",\"\",\"Mean prior and post change point\"), lty=c(1,0,1), lwd=c(2,0,2), col=c(1,0,2),\n",
    "    bty=\"n\", cex = .8, pt.cex=.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1404"
      ],
      "text/latex": [
       "1404"
      ],
      "text/markdown": [
       "1404"
      ],
      "text/plain": [
       "[1] 1404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1428"
      ],
      "text/latex": [
       "1428"
      ],
      "text/markdown": [
       "1428"
      ],
      "text/plain": [
       "[1] 1428"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothTS2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"box\"] = as.numeric(data$YEAR>=1981)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2[\"box\"] = as.numeric(data2$YEAR>=1981)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "133.851632047478"
      ],
      "text/latex": [
       "133.851632047478"
      ],
      "text/markdown": [
       "133.851632047478"
      ],
      "text/plain": [
       "[1] 133.8516"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(na.omit(data2[which(data2$box == 0),\"Max.PRCP.\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "149.545679012346"
      ],
      "text/latex": [
       "149.545679012346"
      ],
      "text/markdown": [
       "149.545679012346"
      ],
      "text/plain": [
       "[1] 149.5457"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(na.omit(data2[which(data2$box == 1),\"Max.PRCP.\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "18336.7066780269"
      ],
      "text/latex": [
       "18336.7066780269"
      ],
      "text/markdown": [
       "18336.7066780269"
      ],
      "text/plain": [
       "[1] 18336.71"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var(na.omit(data2[which(data2$box == 0),\"Max.PRCP.\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "22203.050507273"
      ],
      "text/latex": [
       "22203.050507273"
      ],
      "text/markdown": [
       "22203.050507273"
      ],
      "text/plain": [
       "[1] 22203.05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var(na.omit(data2[which(data2$box == 1),\"Max.PRCP.\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzbIJL3vYn0w4q0rTNsy/7917eya70h\nZl7LmrDa25SPO0gdyqb1hpgp24m79zkWaVtn+PoZhPpR/O4QbG8ibesM+cI6B9ubSNs6A2G1\nFmlbZyCs1iJt6wwDYTUWaVtn+HxXeEz0rvBMWB7sLutY+99vVh0NYbWXcOWdsFxYXa4Vrltv\nhinCcuB0+XRD662wRVgAYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgL\nEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRh\nQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAg\nLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsS\nhAUJwoIEYUGCsCBBWJAgLEgQFiQICxL/AZid9Q4LVx5rAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(data$Max.PRCP.~data$box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzbIJL3vYn0w4q0rTNsy/7917eya70h\nZl7LmrDa25SPO0gdyqb1hpgp24m79zkWaVtn+PoZhPpR/O4QbG8ibesM+cI6B9ubSNs6A2G1\nFmlbZyCs1iJt6wwDYTUWaVtn+HxXeEz0rvBMWB7sLutY+99vVh0NYbWXcOWdsFxYXa4Vrltv\nhinCcuB0+XRD662wRVgAYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgL\nEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRh\nQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAg\nLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsS\nhAUJwoIEYUGCsCBBWJAgLEgQFiQICxL/AXF09R3xx5cDAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(data2$Max.PRCP.~data2$box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "indicator = c(rep(0,1007),rep(1,397))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1404"
      ],
      "text/latex": [
       "1404"
      ],
      "text/markdown": [
       "1404"
      ],
      "text/plain": [
       "[1] 1404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(indicator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1404"
      ],
      "text/latex": [
       "1404"
      ],
      "text/markdown": [
       "1404"
      ],
      "text/plain": [
       "[1] 1404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(smoothTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "117"
      ],
      "text/latex": [
       "117"
      ],
      "text/markdown": [
       "117"
      ],
      "text/plain": [
       "[1] 117"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1404/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meansBox = as.data.frame(cbind(smoothTS,indicator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in .cbind.ts(list(...), .makeNamesTs(...), dframe = FALSE, union = TRUE): non-time series not of the correct length\n",
     "output_type": "error",
     "traceback": [
      "Error in .cbind.ts(list(...), .makeNamesTs(...), dframe = FALSE, union = TRUE): non-time series not of the correct length\nTraceback:\n",
      "1. as.data.frame(cbind(smoothTS, rep(c(1, 2, 3, 4, 5, 6, 7, 8, 9, \n .     10, 11, 12), 115)))",
      "2. cbind(smoothTS, rep(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), \n .     115))",
      "3. cbind(deparse.level, ...)",
      "4. .cbind.ts(list(...), .makeNamesTs(...), dframe = FALSE, union = TRUE)",
      "5. stop(\"non-time series not of the correct length\")"
     ]
    }
   ],
   "source": [
    "meansBoxSea = as.data.frame(cbind(smoothTS, rep(c(1,2,3,4,5,6,7,8,9,10,11,12),115)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in names(meansBoxSea) = c(\"precipitation\", \"month\"): object 'meansBoxSea' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in names(meansBoxSea) = c(\"precipitation\", \"month\"): object 'meansBoxSea' not found\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "names(meansBoxSea) = c(\"precipitation\",\"month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "setEPS()\n",
    "postscript(\"pics/box.eps\")\n",
    "boxplot(meansBox$smoothTS ~ meansBox$indicator, xlab = \"Group (0=pre change point, 1=post change point)\",\n",
    "       col = c(\"blue\",\"green\"), ylab = \"Precipitation (1/10mm)\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(meansBox$smoothTS ~ meansBox$indicator, xlab = \"Group (0=pre change point, 1=post change point)\",\n",
    "       col = c(\"blue\",\"green\"), ylab = \"Precipitation (1/10mm)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in eval(expr, envir, enclos): object 'meansBoxSea' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in eval(expr, envir, enclos): object 'meansBoxSea' not found\nTraceback:\n",
      "1. boxplot(meansBoxSea$precipitation ~ meansBoxSea$month, xlab = \"Group (0=pre change point, 1=post change point)\", \n .     col = c(\"blue\", \"green\"), ylab = \"Precipitation (1/10mm)\")",
      "2. boxplot.formula(meansBoxSea$precipitation ~ meansBoxSea$month, \n .     xlab = \"Group (0=pre change point, 1=post change point)\", \n .     col = c(\"blue\", \"green\"), ylab = \"Precipitation (1/10mm)\")",
      "3. eval(m, parent.frame())",
      "4. eval(expr, envir, enclos)",
      "5. stats::model.frame(formula = meansBoxSea$precipitation ~ meansBoxSea$month)",
      "6. model.frame.default(formula = meansBoxSea$precipitation ~ meansBoxSea$month)",
      "7. eval(predvars, data, env)",
      "8. eval(expr, envir, enclos)"
     ]
    }
   ],
   "source": [
    "boxplot(meansBoxSea$precipitation ~ meansBoxSea$month, xlab = \"Group (0=pre change point, 1=post change point)\",\n",
    "       col = c(\"blue\",\"green\"), ylab = \"Precipitation (1/10mm)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "monthly = NULL\n",
    "for(i in 1:12){\n",
    "    monthly = cbind(monthly,data2[which(data2$MONTH==i),\"Max.PRCP.\"])\n",
    "}\n",
    "monthlyDF = as.data.frame(monthly)\n",
    "colnames(monthlyDF) = c(\"jan\", \"feb\",\"mar\",\"apr\",\"may\",\"june\",\"july\",\"aug\",\"sept\",\"oct\",\"nov\",\"dec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = seq(1:nrow(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "smoothJan = ts(monthlyDF$jan,start=1898)\n",
    "smoothFeb = ts(monthlyDF$feb,start=1898)\n",
    "smoothMar = ts(monthlyDF$mar,start=1898)\n",
    "smoothApr = ts(monthlyDF$apr,start=1898)\n",
    "\n",
    "smoothMay = ts(monthlyDF$may,start=1898)\n",
    "smoothJune = ts(monthlyDF$june,start=1898)\n",
    "smoothJuly = ts(monthlyDF$july,start=1898)\n",
    "smoothAug = ts(monthlyDF$aug,start=1898)\n",
    "\n",
    "smoothSept = ts(monthlyDF$sept,start=1898)\n",
    "smoothOct = ts(monthlyDF$oct,start=1898)\n",
    "smoothNov = ts(monthlyDF$nov,start=1898)\n",
    "smoothDec = ts(monthlyDF$dec,start=1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AtSofdF39p7yB12cVD/jwmSbaasdgFI3GpGzT8UpFhHWg/SeJWLQVqwrcsUSDk/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\nToBdF07dhfcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJan,col=2,ylim=c(25,900),ylab=\"Smooth Seasons\", lwd=2, lty=1,xlim=c(1870,2014))\n",
    "lines(smoothFeb,col=3, lwd=2, lty=2)\n",
    "lines(smoothMar, lwd=2, lty=3)\n",
    "lines(smoothApr,col=4, lwd=2, lty=4)\n",
    "\n",
    "lines(smoothMay,col=5, lwd=2, lty=5)\n",
    "lines(smoothJune,col=6, lwd=2, lty=6)\n",
    "lines(smoothJuly,col=7, lwd=2, lty=7)\n",
    "lines(smoothAug,col=8, lwd=2, lty=8)\n",
    "\n",
    "lines(smoothSept,col=9, lwd=2, lty=9)\n",
    "lines(smoothOct,col=10, lwd=2, lty=10)\n",
    "lines(smoothNov,col=11, lwd=2, lty=11)\n",
    "lines(smoothDec,col=12, lwd=2, lty=12)\n",
    "\n",
    "legend(\"left\",c(\"jan\", \"feb\",\"mar\",\"apr\",\"may\",\"june\",\"july\",\"aug\",\"sept\",\"oct\",\"nov\",\"dec\"), \n",
    "       col=c(2,3,1,4,5,6,7,8,9,10,11,12), lty=c(1,2,3,4,5,6,7,8,9,10,11,12),inset=c(0,0),\n",
    "      lwd=c(rep(2,12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OpVn71lycsjnBex7OhFzWAlGxVryKyV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\nRPh/kgjZpamI28sAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJan,col=2,ylim=c(500,900),ylab=\"Smooth Seasons\", lwd=2, lty=1,xlim=c(1870,2014))\n",
    "lines(smoothFeb,col=3, lwd=2, lty=1)\n",
    "lines(smoothMar, lwd=2, lty=1)\n",
    "lines(smoothApr,col=4, lwd=2, lty=1)\n",
    "\n",
    "lines(smoothMay,col=5, lwd=2, lty=1)\n",
    "lines(smoothJune,col=6, lwd=2, lty=1)\n",
    "lines(smoothJuly,col=7, lwd=2, lty=1)\n",
    "lines(smoothAug,col=8, lwd=2, lty=1)\n",
    "\n",
    "lines(smoothSept,col=9, lwd=2, lty=1)\n",
    "lines(smoothOct,col=10, lwd=2, lty=1)\n",
    "lines(smoothNov,col=11, lwd=2, lty=1)\n",
    "lines(smoothDec,col=12, lwd=2, lty=1)\n",
    "\n",
    "legend(\"left\",c(\"jan\", \"feb\",\"mar\",\"apr\",\"may\",\"june\",\"july\",\"aug\",\"sept\",\"oct\",\"nov\",\"dec\"), \n",
    "       col=c(2,3,1,4,5,6,7,8,9,10,11,12), lty=(c(rep(1,8),2,2,2,2)),inset=c(0,0),\n",
    "      lwd=c(rep(2,12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothJan = ts(runmean(monthlyDF$jan,10),start=1898)\n",
    "smoothFeb = ts(runmean(monthlyDF$feb,10),start=1898)\n",
    "smoothMar = ts(runmean(monthlyDF$mar,10),start=1898)\n",
    "smoothApr = ts(runmean(monthlyDF$apr,10),start=1898)\n",
    "\n",
    "smoothMay = ts(runmean(monthlyDF$may,10),start=1898)\n",
    "smoothJune = ts(runmean(monthlyDF$june,10),start=1898)\n",
    "smoothJuly = ts(runmean(monthlyDF$july,10),start=1898)\n",
    "smoothAug = ts(runmean(monthlyDF$aug,10),start=1898)\n",
    "\n",
    "smoothSept = ts(runmean(monthlyDF$sept,10),start=1898)\n",
    "smoothOct = ts(runmean(monthlyDF$oct,10),start=1898)\n",
    "smoothNov = ts(runmean(monthlyDF$nov,10),start=1898)\n",
    "smoothDec = ts(runmean(monthlyDF$dec,10),start=1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OgQkBJfdIGEJZoFKSJJcTRK0/ogvT9N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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJan,col=2,ylim=c(25,325),ylab=\"Smooth Seasons\", lwd=2, lty=1,xlim=c(1870,2014))\n",
    "lines(smoothFeb,col=3, lwd=2, lty=2)\n",
    "lines(smoothMar, lwd=2, lty=3)\n",
    "lines(smoothApr,col=4, lwd=2, lty=4)\n",
    "\n",
    "lines(smoothMay,col=5, lwd=2, lty=5)\n",
    "lines(smoothJune,col=6, lwd=2, lty=6)\n",
    "lines(smoothJuly,col=7, lwd=2, lty=7)\n",
    "lines(smoothAug,col=8, lwd=2, lty=8)\n",
    "\n",
    "lines(smoothSept,col=9, lwd=2, lty=9)\n",
    "lines(smoothOct,col=10, lwd=2, lty=10)\n",
    "lines(smoothNov,col=11, lwd=2, lty=11)\n",
    "lines(smoothDec,col=12, lwd=2, lty=12)\n",
    "\n",
    "legend(\"left\",c(\"jan\", \"feb\",\"mar\",\"apr\",\"may\",\"june\",\"july\",\"aug\",\"sept\",\"oct\",\"nov\",\"dec\"), \n",
    "       col=c(2,3,1,4,5,6,7,8,9,10,11,12), lty=c(1,1,1,1,1,1,1,1,2,2,2,2),inset=c(0,0),\n",
    "      lwd=c(rep(2,12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "smoothJan = ts(runmean(monthlyDF$jan,40),start=1898)\n",
    "smoothFeb = ts(runmean(monthlyDF$feb,40),start=1898)\n",
    "smoothMar = ts(runmean(monthlyDF$mar,40),start=1898)\n",
    "smoothApr = ts(runmean(monthlyDF$apr,40),start=1898)\n",
    "\n",
    "smoothMay = ts(runmean(monthlyDF$may,40),start=1898)\n",
    "smoothJune = ts(runmean(monthlyDF$june,40),start=1898)\n",
    "smoothJuly = ts(runmean(monthlyDF$july,40),start=1898)\n",
    "smoothAug = ts(runmean(monthlyDF$aug,40),start=1898)\n",
    "\n",
    "smoothSept = ts(runmean(monthlyDF$sept,40),start=1898)\n",
    "smoothOct = ts(runmean(monthlyDF$oct,40),start=1898)\n",
    "smoothNov = ts(runmean(monthlyDF$nov,40),start=1898)\n",
    "smoothDec = ts(runmean(monthlyDF$dec,40),start=1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=7, repr.plot.height=6) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xkHAF0wp5KivQ48cUf+uJoytVvEKbEFJH4He+9SccCV3eJaIOz5iuNtUUKjp6t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\nBEAkAAiASAAQAJEAIAAiAUDA/4KvIBZDAA4NAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJan,col=2,ylim=c(25,325),ylab=\"Smooth Seasons\", lwd=2, lty=1,xlim=c(1870,2014))\n",
    "lines(smoothFeb,col=3, lwd=2, lty=1)\n",
    "lines(smoothMar, lwd=2, lty=1)\n",
    "lines(smoothApr,col=4, lwd=2, lty=1)\n",
    "\n",
    "lines(smoothMay,col=5, lwd=2, lty=1)\n",
    "lines(smoothJune,col=6, lwd=2, lty=1)\n",
    "lines(smoothJuly,col=7, lwd=2, lty=2)\n",
    "lines(smoothAug,col=8, lwd=2, lty=2)\n",
    "\n",
    "lines(smoothSept,col=9, lwd=2, lty=2)\n",
    "lines(smoothOct,col=10, lwd=2, lty=2)\n",
    "lines(smoothNov,col=11, lwd=2, lty=2)\n",
    "lines(smoothDec,col=12, lwd=2, lty=2)\n",
    "\n",
    "legend(\"left\",c(\"jan\", \"feb\",\"mar\",\"apr\",\"may\",\"june\",\"july\",\"aug\",\"sept\",\"oct\",\"nov\",\"dec\"), \n",
    "       col=c(2,3,1,4,5,6,7,8,9,10,11,12), lty=c(1,1,1,1,1,1,1,1,2,2,2,2),inset=c(0,0),\n",
    "      lwd=c(rep(2,12)), bty=\"n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      ": Removed 12 rows containing missing values (geom_point)."
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gLZFwHptbN2hxIgTcjabQ2kvyylyfD4igZCv/wAI7IDjzYfVLpc0Vc/ZRypBEev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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data2, aes(x, Max.PRCP.,colour=MONTH)) + \n",
    "    geom_line() + \n",
    "    geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothJan = ts(monthlyDF$jan,start=1898)\n",
    "smoothFeb = ts(monthlyDF$feb,start=1898)\n",
    "smoothMar = ts(monthlyDF$mar,start=1898)\n",
    "smoothApr = ts(monthlyDF$apr,start=1898)\n",
    "\n",
    "smoothMay = ts(monthlyDF$may,start=1898)\n",
    "smoothJune = ts(monthlyDF$june,start=1898)\n",
    "smoothJuly = ts(monthlyDF$july,start=1898)\n",
    "smoothAug = ts(monthlyDF$aug,start=1898)\n",
    "\n",
    "smoothSept = ts(monthlyDF$sept,start=1898)\n",
    "smoothOct = ts(monthlyDF$oct,start=1898)\n",
    "smoothNov = ts(monthlyDF$nov,start=1898)\n",
    "smoothDec = ts(monthlyDF$dec,start=1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothJan = ts(monthlyDF$jan,start=1898)\n",
    "smoothFeb = ts(monthlyDF$feb,start=1898)\n",
    "smoothMar = ts(monthlyDF$mar,start=1898)\n",
    "smoothApr = ts(monthlyDF$apr,start=1898)\n",
    "\n",
    "smoothMay = ts(monthlyDF$may,start=1898)\n",
    "smoothJune = ts(monthlyDF$june,start=1898)\n",
    "smoothJuly = ts(monthlyDF$july,start=1898)\n",
    "smoothAug = ts(monthlyDF$aug,start=1898)\n",
    "\n",
    "smoothSept = ts(monthlyDF$sept,start=1898)\n",
    "smoothOct = ts(monthlyDF$oct,start=1898)\n",
    "smoothNov = ts(monthlyDF$nov,start=1898)\n",
    "smoothDec = ts(monthlyDF$dec,start=1898)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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40amVU9y+py+QkEYdFqoeHPk7YPfcnr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\nEBqkQGiQ4j9tiB88jpoM8AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJan, ylim=c(0,900), lwd=2, lty=1, col=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OtKlyYwCkLrjP9+AqMgtM54hBaC0Drj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\nSUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothJan\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothJan,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M9DhLauU2vTo1mH0P5bD182+07bgYLQ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothFeb, ylim=c(0,900), lwd=2, lty=1, col=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JPBHoSOMBnsQegIk8EehI4wGexB6AiT\nwR6EjjAZ7EHoCJPBHoSOMBnsQegIk8EehI4wGexB6AiTwR6EDpi8P99G/x8Q+sDJnPOXIPSB\nkxF6CUIfOBmhlyD0gZMReglCHzgZoZcg9IGTEXoJQh84GaGXIPSBkxF6CUIfOHn/p5UdH4Q+\ncPL+u3h8EPrAyQi9BKEPnIzQSxD6wMkIvQShD5yM0EsQ+sDJCL0EoQ+cjNBLEPrAyQi9BKEP\nnPz9Lh6Ab495p7dk7whBWh1BaEHJgerZGnGAdy7+5APsIkLbFpTUZoJ28UBC38552/fJi5tF\nxAHeufiTD7CLhxG6So3+fLY94gDvXPzJB9jFwwhdqORyb5ce14R5CiMvKCnZUz2Juj+X78wk\nG3lBScme6pmMG9rM9Q0wwVLEhViW2204QwOEw6EPfX20Sx/70ADhsD7TZ8Zvi3R9bmSAYDiM\nQxftOHSSnz+MQwOEI8CVQoBwIDSIAqFBFAgNokBoEMWvhP7R5SiIAp9ieaz797lBUgQdioAU\nhD5GCCkR1P37XIT+dykIfYwQUiKo+/e5CP3vUhD6GCGkRFD373MR+t+lIPQxQkiJoO7f5yL0\nv0tB6GOEkBJB3b/PReh/l4LQxwghJYK6AYKD0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB\n0CAKhAZRIDSIAqFBFAgNokBoEAVCgyh+InSRqKTwOC+L+UhAP1nl8L4Z1e+fNKT4PJ4yfXUA\n/lJ8t80vhO7mG0q91X833jQ/WffhAZpG9fsnDSk+j6do60uq2uuxjCne2+YHQt9Ucq/vifI2\n19Bd5X6zmgrVvPr9k54pHo/nrk6V/k1w8nosRor3tvmB0IW6Nl8v6uwroByr9pJVqmz4nTlW\nv3vSmOLxePIuQQd5PBYjxXfb/ELoXOkZO43/qntTqtJrliqGyaCN6ndPGlN8H0+nms9jGVO8\nH8sPhFbKfPFArq6n5tOGt6z7vF79snvSmOL7eOpKZX6PZUzxfiwyhW7JPGYFELo2hPZ8PKX+\n5e9d6DbF+7FIFFqpS3M+KPQvNxFC+z6eR5LX/oUeUjy3jUShOyo9ICRC6A5vx1MlmVGnr2Pp\nU/pv/LXND4ROwgjdBvjK6is0qveRNK3MV0rWjQR7PpZsMt7sr21+Nsrx8DfK0TN+dN8/azLK\n8RhHBvZNWgq9f8ojzR7tgtdjeab0+GubHwh9bscfr6rwFZAofeGrfad8ZfWqGdX7SHr+HvB3\nPFc19AR8HsuY4r1tJF4pLPR7VLXj9r6yQlwpfKZ4PJ7H0zSfx2KkeG+bHwhdp8+RGz9USRtQ\neMwaOgNG9R6S+hSPx3My5nb1dyxGive2+YXQVXuXleeAtPSZNQhtVO8hyUzxczzmZMX+jmWe\n4rNtfiE0gDcQGkSB0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB0CAKhAZRIDSIAqFBFAgN\nokBoEAVCgygQGkSB0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB0CAKhAZRIDSIAqFBFAgN\nokBoEAVCgygQ+sfoiXPM6a7Mp93Xxtqwe3VceKN+S9o2AELvBm/Ub1FLod8Xg8/wRv0WhN4Z\n3qiduWZKZbpj3Dh4VslZT83Xzy1ZpsP8T8/FvnPR/CvasjN1m2JJ2a8t/M4cJgWE3pey6wCX\n2sGzXtKCd0Zn47R8z8Wn0Hm/1UTo/LlFX8Df3I5iQOh9SdS9ri8q1Q5mlfa7/Zrold3EqZfJ\n4tDlaEul9fihsG4nFK7qKtOzrqpxC1gFofdFtRNYd0u39uujHuZq76a2ziaLg9C3vpQpdN7O\ni13pebFVv8W+E71LBKH3pekw5/e7XjI/7vX95LeLxtfXI3jGFrAK79DOnPVk1skDoX8E79Du\nXIu060Prb9yEni8i9Ed4h3zwStV87AYbi2tC58/+eN/Jpg/9GYTel7QbxXhxhn43yvGo3wnd\nFqvLzvtui+uLSDBB6H25dL3e24tz76tx6OY/gB7Sey10X6zrkJ/0IifojyD0zrRXCp+DcJOv\nZTJeKRwWb+mK0PpKoTr1p/DhUiKsgtAgCoQGUSA0iAKhQRQIDaJAaBAFQoMoEBpEgdAgCoQG\nUSA0iAKhQRQIDaJAaBAFQoMoEBpEgdAgCoQGUSA0iAKhQRQIDaJAaBAFQoMoEBpEgdAgCoQG\nUSA0iAKhQRQIDaJAaBAFQoMo/gAdaWL4U/y7TAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Histogram of smoothFeb\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothFeb,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VOFAoXuv2G7iexCu1+es2Yn6xNaW0Fv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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothMar, ylim=c(0,900), lwd=2, lty=1, col=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VDwCUI7Dwo+QWjnQcEnCO08KPgEoZ0H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\nCoQGUSA0iAKhQRR/eeSCDKzublEAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothMar\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothMar,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hWh7dcBM8QsoTuRLSkYJdTjVZl0YuV+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\nWCzAl/12AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothApr, ylim=c(0,900), lwd=2, lty=1, col=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Histogram of smoothApr\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothApr,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MWohR4fhbbS5uD6irgLrcu3UdcoidC9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\nCoQGUSA0iAKhQRQIDaJAaBAFQoMo/gPLctdQODL3lgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothMay, ylim=c(0,900), lwd=2, lty=1, col=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bRWPlp5Edo2nbr/N3ocv82SFIdNemh+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\nVPFFeKQAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothMay\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothMay,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eZJJnSjFbOd4mRTM4XZhTY6ab7QXa97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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJune, ylim=c(0,900), lwd=2, lty=1, col=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tDrQWiPgyL0ehDa46AIvR6E9jgoQq8H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\nRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Histogram of smoothJune\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothJune,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothJuly, ylim=c(0,900), lwd=2, lty=1, col=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+wMpkf9RmihWITeMU9o7KMeGTmL7fSo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\nhAZV/AE0B3EVNkh+pgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Histogram of smoothJuly\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothJuly,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothAug, ylim=c(0,900), lwd=2, lty=1, col=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Histogram of smoothAug\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothAug,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothSept, ylim=c(0,900), lwd=2, lty=1, col=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IYg0MsRPChIgtDBg4IkCB08KEiC0MGD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\nSUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothSept\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothSept,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tq/HFfocQbWFnpQjsMftjsPZqE9VvS/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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothOct, ylim=c(0,900), lwd=2, lty=1, col=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O0AQocTi9AOIHQ4sQjtAEKHE7su8X96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\nYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothOct\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothOct,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5NRPShqd70G9o7fF6+sTkX3E9pwzEvo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\ngygQGkSB0CAKhAZRIDSIAqFBFP8BYORHhdw429cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothNov, ylim=c(0,900), lwd=2, lty=1, col=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Histogram of smoothNov\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothNov,breaks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qENTrnS10ZPxRg/ptDmLI6EHwtt3Fj/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\nM3Ug6dqbAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothDec, ylim=c(0,900), lwd=2, lty=1, col=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XbVAxDa46CX21UPMK/9byWvv1p9EXon\nv5a8/nId4XN29fyeu2k5v5a8/mr1/aVd/Uo5v5Ya+Wr1/aVd/Uo5K8nrVz9Mjnw4gUQMRfwn\nluF2O47QAO440IfemrwewB3GR/rtyesB3HFgHHpr8noAd1x7FB1gBEKDKBAaRIHQIAqEBlGc\nJfRJ01Fgl5Ns6ov1U3FPicqu/kIVaGWJURH6F6Kyq79QBVpZYlSE/oWo7OovVIFWlhgVoX8h\nKrv6C1WglSVGRehfiMqu/kIVaGWJURH6F6Kyq79QBVpZYtQfFhrACggNokBoEAVCgygQGkSB\n0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB0CAKhAZRIDSI4hSh00AFqau8LFm3i72oliuQ\nhVORLActEqWSNjWZu6gVT3VC0FnOELrJNxS6CfbqLonZi2q5AmldfFA4DVoGdfmvUSj7r3UR\nNC+w06DznCD0UwWv8hUoJ7mGqjhqHNVyBV4qKfQHQ+IyaPUuSvS/uHQatSJuXmC3Qec5QehU\nPar/d3VzECtTUSt0L6rlCsRNQB3XXdDqAF20QV1G1WU3L7DToAucIHSsdMbOV30wsY1Ku7zN\nvahuKqDjug+qc/q6jJp3RwznuzrDCUIr1b+xy2scTt84qUChIvdBU5WVbqNGKm8Kd72rcwgX\n+l84Zy94pj933QatPv3TXgwXUW/qXiL0LwidB7HzoFkc1P1Wd1HrbgVC/4DQRRC5D1qR6D6H\nu6ihHpv8eaGDU4TuRXVQgSg8IWipe+6Bw6hJPZzRFO58V2c4bZQjd/UleDDKkX++hVusQB5G\nufOgDZ+xFQdR++nc3O/qTJ3ch7zV7+tH8wXGPq3Qvai2K/BQUbvkMGgzDp3r+TlnUftCO9zV\n5Tq5D+l4Isn5TGH+9tn5TGER6z6040m7n58pLMP6TR2tP/ErdF25XlS7FUh6aVWdBe3O5RiH\ncvBaty+w26DztTkhZlGfjOUqWid0L6rdCvTzBDsLWtanuIXZOJSD17p9gd0Gna/NOWEB7IDQ\nIAqEBlEgNIgCoUEUCA2iQGgQBUKDKBAaRIHQIAqEBlEgNIgCoUEUCA2iQGgQBUKDKBAaRIHQ\nIAqEBlEgNIgCoUEUCA2iQGgQBUKDKBAaRIHQIAqEBlEgNIgCoUEUCA2iQGgQBUKDKBAaRIHQ\n56LT6/QToDXX/g/T4rQaXRyEPpWwn7SyWWwI8tPqdG0Q+lTUf6H1/zw6KeXO9UHoU5kRWh+6\nH6dU6PIg9Hd5REpFbcLgmwpuOoFgm4EyC7ssVe/FNllW9ZfWz/0I/dBpB+snBu02aaAi+iGr\nIPRXyZoecJ1B/qaXtOCN0dEned978S103G71SUKnU8KW8WibgO+KayD0VwnUqyzvWsbKw0L7\nXf8P9Momvep9sNh1OepnhWWv96EXHnp9Eenex10vJudkG74UCP1V1Lvrq+rMwKrO494kd28S\nYEeDxU7oZ/usodBxnb+7aBLCP/VS4Hh/rgdCf5Wqwxy/Xnqp/3Wv7SfPLvb+D4Tu5aRVNNQ2\neJ2+yy1oB5EPCp2/u9gIvQtep2/zSMOmD63vGAt9193lieE8WIHXyQJTqnYd53iwOCt0qPvM\n8WcwOqIPvQ2E/iphM4oxcYSeG+XIywmh25nC+ollps3P9ChHyijHKgj9Ve5Nn/c5ceydGoeu\n3gD6oDsQun8uR/RZZhx6Gwj9XeqZwvcg3OB/FnxmCrvFZzgpdHRri8sq45NmflAPoDBTuApC\ngygQGkSB0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB0CAKhAZRIDSIAqFBFAgNokBoEAVC\ngygQGkSB0CAKhAZRIDSIAqFBFAgNokBoEAVCgygQGkSB0CAKhAZRIDSIAqFBFAgNovgD3wmG\nOCZJSEUAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of smoothDec\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(smoothDec,breaks=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flood tolerance, probabilities from selected distributions, bayesian updated (last add)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change point detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zy5sNCef1kxQkdXEkIHJ2lJe4Z2px/P\nVFzoTkp3HuFWRfuI0BFJWpJOfc9IP35R8BV6xpXFo3MaYVXsUDlCRyR5kXLq251+4iItKXR/\nGHyG0BElRegx8o5Dp5v6dqef6EZ5ims1sD8OPqMYy0mF0BFJcoeYFHfwxXSkiSb3HKE9xokX\nFFqlz1sTevCNp9D209pXd3CeMAtahdDhSX5JOPXtymCqFZBM6JlFQGhhMgqddurbkcFUR81b\naNu+mdFFKwgtTN5hu4RT344MZISe6Fi6M5NMAjZ0TqzYWhbGuqH9G3mhQZiMQjumvk2XwBCf\nzHqBEHoraD9Dv4YbzOh3jkjDr/p/G+4UkJu8behcU9/vQTrT/TWWuaTQ+FwAOfsx+aa+7e6Z\nwc95O+FKgdAFkHccOtfUt31mo20JG1eD+Ou7TsseoYumzJEmAaFtHcvPqyZnCm3GV08ngKVQ\nK/ScL+fO+yH0esgv9GVnzOGaNMRsZv7fzMxuJEIXQPZx6N+eoXWQI58aCK2N3EKfzOlR1/eT\nuaQIIYvp/R7/OOMLyEduoSvTjtc9zC5FCFkQeoXkFvqv1Zr0v76FMF+/+qsnt4clyS308c+S\ntFPfIiD0Cskq9OF8uZp/z8XHKfHUtwgTEym2+RpYmqxCv+fbjKkST32LMD5/jtAlk3Mc+na7\nXA6Htmt4svpcihvj8+cIXTJaZwpFGJ8/R+iSQWgL47uB0CWD0N4gdMkgtDcIXTII7Q1Cl8wC\nw3Yz/g+2bDcm9q7snd4KGYW+IDQkJ+s4dGV/XpJAiCwgdMFkbUPfHLdBC4TIgd9oHmQlb6fw\n0nk0R6IQGUDogmGUwx+ELhiE9gehCwah/UHogkFofxC6YBDaH4QuGIT2B6ELBqH9QeiCQegA\nRnev8H3eCggdwGf3PrekFL7LmwGhA+ha/H7QyFI7A18gdADm6/fgLS6wIAgdQP+k3Chd+C5v\nBoQOYPgsf4EXd4EICB3E8Jxc/C5vBIQOhFNymSA0qAKhQRUIDapAaFAFQoMqEBpUgdCgCoQG\nVSA0qKJQoQECCbBNXuDy9oAQpURIHwKhCZExAkITImMIDYVAaEJkjIDQhMgYQkMhEJoQGSMg\nNCEyhtBQCIQmRMYICE2IjCE0FAKhCZExAkITImMIDYVAaEJkjLAFoQEEQWhQBUKDKhAaVIHQ\noAqEBlUgNKgCoUEVCA2qQGhQBUKDKhAaVIHQoAqEBlUgNKgCoUEV2YW+/EU8VWZ/fS9Wp0d/\nUTTEZZc8xJMf816bJMTtaMzxLhViJMJD9FCMV7rw0e6RW+jb3xMl9+3TJc+fxV1vUTTEqV2s\nHglDPHlUprNWPsRVtBQjEe7VK8JdJMJ4pQsf7T6Zhb5Vv7V4MftH/TiaW3Naq27NFz9fi6Ih\nbub4aFYc04VoOLxWJwtRPfN9HMxJJMRYhGOT91NEkXoar3Thoz0gr9DPujN/J7GmGPemAk+m\nudz9a04RnUXREIfXmuaLVCHaTF+rU4X418Z5mEoixGgEI1lP45Uue7SH5BX6WWmdOmt+7ZuC\nN1e4mzl8LYqG+PvOpAxx/1MkVYj3lUAixGiE3yZT8ycjUIj6nf34IRYL8RVOMjMnt7pfi2Zq\nUTTEi0dzzNKF2Jv7ayFViJ2pz1V7HRcIMRrh/NvkOIsUoqVf6bJHe0j2UY7f3d+1f50/SYo4\nDPHi0lzikoU4m3+1mNCjIYw5tL0sqRAjhbg0vcLqIhWhHla6VqHP5vCob/uUQndCtNyrQ50u\nRHvlTCD0V0U1ncKj2PlzpJ7O7wEPIdsGla5V6LodIDqkFLoTouFR7TtfiofYNcNTCYT+qqim\nDX1vxrkkhe5EuDRNjuefzEWqEMNKVyv0s9qqc/up+pSrElXhE6Jh/xrvTBTi2PbYX6tTlcII\nV9Qwws40DfRH8ycjU4hhpYsf7R6LCd1ya6ru1dm9f/q998h+7zDEM8/d/jXDlihE901kqUrR\nGQcTCTGMYGQjjFW6+NHusZTQVXsuuDSFObcnt2tztessioZ4Zvk3eJcoRFfoVKV45XtviiIS\nYhjhdc5sR7oFIoxWuvjR7rGU0K/ZqJ35l2DuaBji/hmMThWiszpdKXbtlN4/oRDDCCfT3Flx\nkpmLHK90XTOF9bsWH6+7Btqrza5d3PcWJUMcO28mTRSiuzpViLNsRY1E2AtGmKh06aPdY7E2\n9P1Z3kP3Fq/+omSITnsgVYju6mQhrnvJihqLIHgoJipd+mj3o8pmB7AsCA2qQGhQBUKDKhAa\nVIHQoAqEBlUgNKgCoUEVCA2qQGhQBUKDKhAaVIHQoAqEBlUgNKgCoUEVCA2qQGhQBUKDKhAa\nVIHQoAqEBlUgNKgCoUEVCA2qQGhQBUKDKhAaVIHQoAqEBlUgNKgCoUEVCJ0L00H4VWbwgYrN\nBUJngYrNCiKnhgrOCkKnhgrOyp/Qv286PjfvJK5P5vfdk5edqS4L7p0GEDor30K37x28tu8G\nbIw+JHht3+ZA6Kx8C71/1Jffn1X7JuFH/dibqz0LsILQWfkW+qddutd/r4tv3rn9kH2X++ZA\n6Kz02tB192f3zasQCrWXFYRODbWXFbvQy+2XHqjErNiEPtAdFAChs2IT+p+pbnV9oVMYBUJn\nxSZ03Q5Im+q+2N5pAKGzYhW6mSk0R3yOAqFBFQgNqkBoUAVCgyoQGlSB0KAKhAZVIDSoAqFB\nFQgNqkBoUAVCgyoQGlSB0KAKhAZVIDSoAqFBFQgNqkBoUAVCgyoQGlSB0KAKhAZVIDSoAqFB\nFQgNqkBoUAVCgyoQGlSB0KAKhAZV/Aen4qH9y2bFlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(vars1TS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>alpha</th><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>invTheta</th><th scope=col>mean</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.7702459   </td><td>0.005838670 </td><td>0.11823885  </td><td>not rejected</td><td>0.3945848   </td><td>NA          </td><td>2.578290    </td><td>NA          </td><td>171.2719    </td><td>131.9215    </td><td>22594.44    </td><td>150.3145    </td></tr>\n",
       "\t<tr><td>0.7592384   </td><td>0.005623842 </td><td>0.10957672  </td><td>not rejected</td><td>0.3993983   </td><td>NA          </td><td>2.545615    </td><td>NA          </td><td>177.8144    </td><td>135.0035    </td><td>24005.57    </td><td>154.9373    </td></tr>\n",
       "\t<tr><td>0.7329028   </td><td>0.005426065 </td><td>0.11023874  </td><td>not rejected</td><td>0.3867091   </td><td>NA          </td><td>2.377635    </td><td>NA          </td><td>184.2956    </td><td>135.0708    </td><td>24892.95    </td><td>157.7750    </td></tr>\n",
       "\t<tr><td>0.7228140   </td><td>0.005126209 </td><td>0.10648805  </td><td>not rejected</td><td>0.3718661   </td><td>NA          </td><td>2.266249    </td><td>NA          </td><td>195.0759    </td><td>141.0036    </td><td>27506.41    </td><td>165.8506    </td></tr>\n",
       "\t<tr><td>0.7894197   </td><td>0.005429997 </td><td>0.10174311  </td><td>not rejected</td><td>0.2569872   </td><td>NA          </td><td>1.422317    </td><td>NA          </td><td>184.1622    </td><td>145.3812    </td><td>26773.72    </td><td>163.6268    </td></tr>\n",
       "\t<tr><td>0.7953316   </td><td>0.005407428 </td><td>0.09718581  </td><td>not rejected</td><td>0.2725329   </td><td>NA          </td><td>1.497542    </td><td>NA          </td><td>184.9308    </td><td>147.0813    </td><td>27199.86    </td><td>164.9238    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " alpha & theta & KS & KStest & CvM & CvMtest & AD & ADtest & invTheta & mean & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.7702459    & 0.005838670  & 0.11823885   & not rejected & 0.3945848    & NA           & 2.578290     & NA           & 171.2719     & 131.9215     & 22594.44     & 150.3145    \\\\\n",
       "\t 0.7592384    & 0.005623842  & 0.10957672   & not rejected & 0.3993983    & NA           & 2.545615     & NA           & 177.8144     & 135.0035     & 24005.57     & 154.9373    \\\\\n",
       "\t 0.7329028    & 0.005426065  & 0.11023874   & not rejected & 0.3867091    & NA           & 2.377635     & NA           & 184.2956     & 135.0708     & 24892.95     & 157.7750    \\\\\n",
       "\t 0.7228140    & 0.005126209  & 0.10648805   & not rejected & 0.3718661    & NA           & 2.266249     & NA           & 195.0759     & 141.0036     & 27506.41     & 165.8506    \\\\\n",
       "\t 0.7894197    & 0.005429997  & 0.10174311   & not rejected & 0.2569872    & NA           & 1.422317     & NA           & 184.1622     & 145.3812     & 26773.72     & 163.6268    \\\\\n",
       "\t 0.7953316    & 0.005407428  & 0.09718581   & not rejected & 0.2725329    & NA           & 1.497542     & NA           & 184.9308     & 147.0813     & 27199.86     & 164.9238    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha     theta       KS         KStest       CvM       CvMtest AD      \n",
       "1 0.7702459 0.005838670 0.11823885 not rejected 0.3945848 NA      2.578290\n",
       "2 0.7592384 0.005623842 0.10957672 not rejected 0.3993983 NA      2.545615\n",
       "3 0.7329028 0.005426065 0.11023874 not rejected 0.3867091 NA      2.377635\n",
       "4 0.7228140 0.005126209 0.10648805 not rejected 0.3718661 NA      2.266249\n",
       "5 0.7894197 0.005429997 0.10174311 not rejected 0.2569872 NA      1.422317\n",
       "6 0.7953316 0.005407428 0.09718581 not rejected 0.2725329 NA      1.497542\n",
       "  ADtest invTheta mean     var      sd      \n",
       "1 NA     171.2719 131.9215 22594.44 150.3145\n",
       "2 NA     177.8144 135.0035 24005.57 154.9373\n",
       "3 NA     184.2956 135.0708 24892.95 157.7750\n",
       "4 NA     195.0759 141.0036 27506.41 165.8506\n",
       "5 NA     184.1622 145.3812 26773.72 163.6268\n",
       "6 NA     184.9308 147.0813 27199.86 164.9238"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleGamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = seq(.00001,2500,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "66"
      ],
      "text/latex": [
       "66"
      ],
      "text/markdown": [
       "66"
      ],
      "text/plain": [
       "[1] 66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1963-1897"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = dgamma(x,mleGamma$alpha[66], scale=(1/mleGamma$theta[66]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ni2OT/5rbBU/VZYhlF5P2f2kf2TVCvn\n3KyfSAc5wRzPYaXN/9vzkWdqGyq/nL+5N2f2kf2wlHPOd1nWP5V0ULttatbLZpuzppX38npe\ncPnK+9HHyntZn9LutO+zFw9KXP2KYDLrCr/v1wrDb04MP9Cudx2F3hzNyO7pQzsnfXrv8w+K\n5p/jO6z2dXTNffcv0NGboxnZhSWek0dP7l0w6OmdT5rjOyxsBGFBgrAgQViQICxIEBYkCAsS\nhAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFB\ngrAgQViQICxIEBYkCAsShAUJwoLEVsPqzv0X3Z9g8+7k09XO3ZzctXf+81benVM4O99Ydddh\nu9ww+KJ/cT7sW7dh42G5+1O33oUVdxds6H+5/0dK1yZTXG7szjJ8uWH4Rf8qV474AZZuu2E1\n/5N8OHeru3uw78OK2s1ZEVxubC8vcLlhzKmHE805f39j42F9uu7C/Zfvwtq3G6zsfJW5Y3fO\n9OsNYy76V9Xn3V4Lwqr/m5+e8trzA7fllLEL0vPz5eOXL/cSttuY07Pf+cb2XPPXG3oX/Tvd\nkjZ3m7T7bfefX67XsgobD6t9KmxPp35+tOuLbrUXw+rCevjy+U4O9WWNTorrje226XrDzUWP\nmjvKo+7O7j+vN3SSCyv8xHbD6hTNJ/vmarzHcwFRdXqQw+7Txy+f7yS5XgH1fGN8buNZWM3d\ntv8NHj+vN2yiM+D/wMbDior+TcdzAYf+p49fPt8Uuermq/2/8Cys9m7LF593F4ddie2Gdawv\nPHheOirzNLorpx/Wky8fj4/Z9a+g/Cys6w3PPr+9Q+vW85OM0zyEh/NqQnS5VMrTsJ59+fJH\nbj5Mu72uyw0BYW1L+xDG7QZm58IsL1+G9fTL1zvpfxhd1r1ufiss7y/2S1hr1T6ExXnn/Vg/\n+K/Cevrl2uM+VrsT3ruhd9G/z2Gxj2XfZYPSXpr8cCxe72M9/XItuS4PtDce3O5uQG/l/XNY\nB34rNK973Ktmk5VcXzd8FtbTL9cO1wX19sbkunZ+/lPXi/59DitlHcu88+OeNJusXX2cQ37e\nEXrYeX/25UYY3d5f+Lj+cL3o3+ewWHlHK5/1gITSrejAGcKaJJpzp4ijG3BWuiGH8A3D8Vi4\nynef/8xAuxU9ERIWRAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAg\nLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFiX9G120yivL9uAAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y, col = \"darkblue\", type =\"l\", xlim=c(0,800), ylab=\"Density\", xlab=\"Rainfall (1/10mm)\", lwd=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = dexp(1/mleExp$theta[20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "2"
      ],
      "text/latex": [
       "2"
      ],
      "text/markdown": [
       "2"
      ],
      "text/plain": [
       "[1] 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(na.omit(data2$Max.PRCP>914))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in xy.coords(x, y, xlabel, ylabel, log): 'x' and 'y' lengths differ\n",
     "output_type": "error",
     "traceback": [
      "Error in xy.coords(x, y, xlabel, ylabel, log): 'x' and 'y' lengths differ\nTraceback:\n",
      "1. plot(x, y, col = \"darkblue\", type = \"l\", xlim = c(0, 800), ylab = \"Density\", \n .     xlab = \"Rainfall (1/10mm)\", lwd = 2)",
      "2. plot(x, y, col = \"darkblue\", type = \"l\", xlim = c(0, 800), ylab = \"Density\", \n .     xlab = \"Rainfall (1/10mm)\", lwd = 2)",
      "3. plot.default(x, y, col = \"darkblue\", type = \"l\", xlim = c(0, \n .     800), ylab = \"Density\", xlab = \"Rainfall (1/10mm)\", lwd = 2)",
      "4. xy.coords(x, y, xlabel, ylabel, log)",
      "5. stop(\"'x' and 'y' lengths differ\")"
     ]
    }
   ],
   "source": [
    "plot(x,y, col = \"darkblue\", type =\"l\", xlim=c(0,800), ylab=\"Density\", xlab=\"Rainfall (1/10mm)\", lwd=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "jpeg(file = “C://R//SAVEHERE//myplot.jpeg”)\n",
    "plot(x,y)\n",
    "dev.off()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}