---
title: "Use h5 file format with 'antaresRead'"
author: "Titouan Robert"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
css: vignette.css
vignette: >
  %\VignetteIndexEntry{Use h5 file format with 'antaresRead'}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


This document describes use of the `antaresRead` package with h5 file format.


## Installation

To use **h5** file format, you must install packages ``zlibbioc`` and ``rhdf5 (>= 2.20.0)`` from **bioconductor**. If your proxy allows it, you can use :

With R < 3.5.0, use:

```r
source("https://bioconductor.org/biocLite.R')
biocLite("rhdf5')
```

With R >= 3.5.0 :

```r
install.packages("BiocManager")
BiocManager::install("rhdf5")
```


An other solution is to install manualy this two packages from zip file. They are available here :
https://bioconductor.org/packages/release/bioc/html/zlibbioc.html
http://bioconductor.org/packages/release/bioc/html/rhdf5.html


## Avantages of h5 transformation

With h5 file, ``antaresRead`` will be running faster. Your reading will take between **2 and 4 times** less time. An other avantage is the **compression**, a study can pass from **15Go** in txt to **2.5Go** in h5. Furthermore, a h5 transformation convert lot of txt file in a single h5 file, it will be **easier to move** it, a copy/paste of a study with 137 thousand files (15Go) takes 45 minutes in txt and 30 seconds in h5. 

Finally, you can use in the same way function of ``antaresProcessing`` and ``antaresViz`` packages. You can also use ``addProcessingH5`` function to add columns to your h5 file.


## Comparaison between Conventional and H5

```{r, echo=FALSE}
  siZ <- readRDS(system.file("vignetteData/size.rds",package = "antaresRead"))
  TimeSel <- readRDS(system.file("vignetteData/Time.rds",package = "antaresRead"))

library(DT)
sketch = htmltools::withTags(table(
  class = 'display',
  thead(
    tr(
      th(colspan = 3, 'Advantage for storage')
    ),
    tr(lapply(c("", names(siZ)), th))
  )
  
))
sketch2 = htmltools::withTags(table(
  class = 'display',
  thead(
    tr(
      th(colspan = 3, 'Speed of reading')
    ),
    tr(lapply(c("", names(TimeSel)), th))
  )
  
))


datatable(siZ,container = sketch, options = list(dom = 't',
                              columnDefs = list(list(className = 'dt-center', targets = 0:2))), 
          class = 'cell-border stripe')
datatable(TimeSel,container = sketch2,  options = list(dom = 't',
                              columnDefs = list(list(className = 'dt-center', targets = 0:2))), 
          class = 'cell-border stripe')


```

## Write h5 file from antares study

The function ```writeAntaresH5``` is used to write h5 file from antares output. You can add inputs data like in ``readAntares``.

### Basic use

```r
setSimulationPath("study_path/output/simulation_name")
writeAntaresH5()
```

### Add intput data

```r
setSimulationPath("study_path/output/simulation_name")
writeAntaresH5(misc = TRUE, thermalAvailabilities = TRUE,
  hydroStorage = TRUE, hydroStorageMaxPower = TRUE, reserve = TRUE,
  linkCapacity = TRUE, mustRun = TRUE, thermalModulation = TRUE,
  writeAllSimulations = TRUE)

#with a shorcut 
writeAntaresH5(allData = TRUE,
  writeAllSimulations = TRUE)

```

### Overwrite 

By default ``writeAntaresH5`` returns an error if the **h5** file already exists. You can use ``overwrite = TRUE``.
```r
setSimulationPath("study_path/output/simulation_name")
writeAntaresH5(overwrite = TRUE)
```

### Write all your simulations in parallel

```r
setSimulationPath("study_path/output/simulation_name")
writeAntaresH5(nbCores = 5, writeAllSimulations = TRUE)
```

## Read data from your h5 file

You can use ```setSimulationPath``` and ```readAntares``` in the same way than on normal study.

```r
setSimulationPath("mySim.h5")
readAntares()
readAntares(areas = "all", links = "all")
readAntares(areas = "all", mcYears = "all")
readAntares(timeStep = "weekly")
```



## Add columns to your h5 file (hourly data only)

You can use function ```addProcessingH5``` from ``antaresProcessing`` package. 

```
 library(antaresProcessing)
 opts <- setSimulationPath("mySim.h5")
 addProcessingH5(opts = opts,  mcY = "mcInd",
                addDownwardMargin = TRUE,
                evalAreas = list(Tota = "`H. STOR` + `MISC. DTG`",
                                 Tota2 = "`NODU` + `NP COST` + 1")
                )
```

For read data after adding **addDownwardMargin**, you can use alias ``Out_addDownwardMargin``, use ```showAliases()``` to see them.

```
readAntares(mcYears = "all", select = c("Tota", "Tota2"))
readAntares(mcYears = "all", select = "Out_addDownwardMargin")
```

## Use h5 opts for vizualisation

It's possible to use opts from **h5** with all ``antaresViz`` modules.

```
library(antaresViz)
opts <- setSimulationPath("mySim.h5")

prodStack(opts)
plot(opts)
exchangesStack(opts)

#Run on more than one opts :
opts2 <- setSimulationPath("mySim2.h5")
plot(list(opts, opts2))
```
