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# install/load dependent libraries
if(!require(zoo)) install.packages("zoo")
library(zoo)
if(!require(tgp)) install.packages("tgp")
library(tgp)
if(!require(qualV)) install.packages("qualV")
library(qualV)
if(!require(hydromad)) install.packages("hydromad",repos="http://hydromad.catchment.org")
library(hydromad)
if(!require(devtools)) install.packages("devtools")

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if(!require(RHydro)) install.packages("RHydro",repos="http://R-Forge.R-project.org")
if(!require(fuse)) install.packages("fuse", repos="http://R-Forge.R-project.org")

library(RHydro)
library(fuse)

data(DATA)
data(modlist)

# Set parameter space

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#' Generate map of gauging stations.
#'
#' @author Claudia Vitolo <cvitolodev@gmail.com>
#'
#' @description This function takes as input a table of UK NRFA stations (output of getStationSummary() function) and generates a map using leaflet javascript library. 
#'
#' @param selectedStationSummary This is the data.frame containing at least a column called "gridReference" (in which OS Grid references are stored) with 1 row for each station. Alternatively the table could contain 2 columns called "Latitude" and "Longitude" containing the coordinates of stations in WGS84, with 1 row for each station.
#'
#' @return displays a map 
#' 

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# Load library
library(xts)

# Generate dummy time series
from <- as.Date("1950-01-01")
to <- as.Date("1990-12-31")
myDates <- seq.Date(from=from,to=to,by="day")
myTS <- as.xts(runif(length(myDates)),order.by=myDates)

# SPLIT THE TIME SERIES INTO CALENDAR YEARS

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#' This function returns information on model structure components and parameters used, given FUSE model.
#'
#' @param mid FUSE model structure ID number (integer from 1 to 1248).
#' @number default is TRUE (returns the model options numbers, if FALSE it returns model options names)
#'
#' @examples
#' # FUSEinfo(mid=60)
#'

FUSEinfo <- function(mid, number=TRUE) {

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#' This function generates parameter sets for FUSE (Clark et al., 2008).
#'
#' @param NumberOfRuns number of samples to generate, can be any integer
#' @param SamplingType sampling procedure to use, can be "URS" or "LHS"
#' @param rferr_add range of the additive rainfall error (mm), default is c(0,0)
#' @param rferr_mlt range of the multiplicative rainfall error (-), default is c(1,1)
#' @param maxwatr_1 range of the depth of the upper soil layer (mm), default is c(25,500)
#' @param maxwatr_2 range of the depth of the lower soil layer (mm), default is c(50,5000)
#' @param fracten range of the fraction total storage in tension storage (-), default is c(0.05,0.95)
#' @param frchzne range of the fraction tension storage in recharge zone (-), default is c(0.05,0.95)

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---
title: "RNRFA: an R package to interact with the UK National River Flow Archive"
author: "Claudia Vitolo"
date: "18 November 2015"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
```

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# Load the package and prepare the list of models
library(hydromad)
library(RHydro)
data(modlist)
 
# read data in
temp <- read.csv("dummyData.csv")

# Convert to date
temp[,1] <- as.Date(temp[,1],format="%Y-%m-%d")

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if(!require(RHydro)) install.packages("RHydro",repos="http://R-Forge.R-project.org")

library(RHydro)

temp <- read.csv("dummyData.csv") 

DATA <- zooreg(temp[,2:4], order.by=temp[,1])

myDELTIM <- 1
 

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if(!require(RHydro)) install.packages("RHydro",repos="http://R-Forge.R-project.org")

library(RHydro)
temp <- read.csv("dummyData.csv") 
DATA <- zooreg(temp[,2:4], order.by=temp[,1])
myDELTIM <- 1
myMID <- 60

# Sample parameter space using the Latin Hypercube Sampling method