License
@@ -134,31 +65,27 @@License
- - - + + @@ -82,105 +81,137 @@
- outputs with an annual and an hourly time step, -
- synthetic outputs (averaged on several Monte-Carlo years) and detailled outputs (given year by year), +
- synthetic outputs (averaged on several Monte-Carlo years) and +detailled outputs (given year by year),
- outputs for areas, links, clusters and districts.
- Which type of element he wants to study : areas, links or disctricts. +
- Which type of element he wants to study : areas, +links or disctricts.
- Which variable he wants to analyze. -
- With which type of graph : barplot, time series, probability density function, heatmap, etc. +
- With which type of graph : barplot, time series, +probability density function, heatmap, etc.
- For which area(s), link(s), or district(s).
- And during which time period.
- different variables
- different areas/links/district @@ -188,46 +219,67 @@
- Which variable to represent
- With which type of vizualisation
@@ -236,116 +288,125 @@
- links : color, width of the link, popup
- For which time step or date range +
- For which time step or date +range - -
- + Changelog
- - - + + @@ -82,56 +81,76 @@
Quick presentation of antaresViz
-RTE
+Quick presentation of antaresViz
+RTE
-2020-01-31
+2024-06-26
- Source:vignettes/antaresViz.Rmd
+ Source: vignettes/antaresViz.Rmd
antaresViz.RmdThis package works along with RTE’s adequacy software ANTARES : https://antares-simulator.org/
-antaresViz is a package which proposes relevant graphs and maps to vizualize the results of an ANTARES study, with numerous settings and possible customizations.
-Where everything starts
- -The data from an ANTARES study can be imported and easily manipulated using the antaresRead package.
-The examples presented below have been build on a fictionnal study whose output have been loaded with antaresRead. They contains different types of elements :
+This package works along with RTE’s adequacy software ANTARES : https://antares-simulator.org/
+antaresViz is a package which proposes relevant graphs
+and maps to vizualize the results of an ANTARES study, with numerous
+settings and possible customizations.
Where everything starts +
+ +The data from an ANTARES study can be imported and easily manipulated +using the antaresRead +package.
+The examples presented below have been build on a fictionnal study +whose output have been loaded with antaresRead. They contains different +types of elements :
The aim of this vignette is to give a quick overview of the possibilities offered by the package antaresViz. The data with which the following graphs have been plotted are fictionnal.
+
+load("data_for_antaresViz_vignette_extralight.Rdata")The aim of this vignette is to give a quick overview of the +possibilities offered by the package antaresViz. The data with which the +following graphs have been plotted are fictionnal.
-The simple power of the plot function
-The plot() function, used with an antaresDataList object (i.e. an object returned by the antaresRead::readAntares() function) offers different possible vizualisations.
Moreover, this function is really easy to use. It opens a shiny interface which let the user decide :
+The simple power of the plot function
+
+The plot() function, used with an antaresDataList object
+(i.e. an object returned by the antaresRead::readAntares()
+function) offers different possible vizualisations.
Moreover, this function is really easy to use. It +opens a shiny interface which let the user decide :
-
-
+plot(data_hourly_synthesis)
Note that all the interface manipulation can also be set directly in the arguments of the plot function. Some examples of the multiple graphs that can be returned by this function are given below.
Barplot of Loss of Load Duration (LOLD) for several districts of the study
-plot(
- data_annual_synthesis,
- table = "districts",
- variable = "LOLD",
- type = "barplot",
- elements = c("00_a", "00_b", "00_c", "00_d",
- "00_e", "00_f", "00_g", "00_h", "00_i"),
- interactive = FALSE,
- width = "100%",
- height = 400
-)Note that all the interface manipulation can also be set directly in
+the arguments of the plot function. Some examples of the
+multiple graphs that can be returned by this function are given
+below.
Time series of the load in an area with average value and 95% confidence interval.
-plot(
- data_hourly_allmc,
- table = "areas",
- variable = "LOAD",
- type = "ts",
- elements = "23_b",
- confInt = 0.95,
- dateRange = c("2018-01-08", "2018-01-14"),
- width = "100%",
- height = 400,
- interactive = FALSE
-)Probability density function of the wind power generation in two areas
-plot(
- data_hourly_synthesis,
- table = "areas",
- variable = "WIND",
- type = "density",
- elements = c("01_a", "02_a"),
- interactive = FALSE,
- width = "100%",
- height = 400
-)Heatmap of the congestion probability of one interconnection
-plot(
- data_hourly_synthesis_1year,
- table = "links",
- variable = "CONG. PROB +",
- type = "heatmap",
- elements = "25_c - 26_d",
- interactive = FALSE,
- width = "100%",
- height = 400,
- main = "Congestion probability"
-)Note that the plot() function also contains a compare argument which allows comparisons between :
Heatmap of the congestion probability of one interconnection +
+
+plot(
+ data_hourly_synthesis_1year,
+ table = "links",
+ variable = "CONG. PROB +",
+ type = "heatmap",
+ elements = "25_c - 26_d",
+ interactive = FALSE,
+ width = "100%",
+ height = 400,
+ main = "Congestion probability"
+)Note that the plot() function also contains a
+compare argument which allows comparisons between :
Heatmap of the congestion probability of one interconnection
-When the production meets the demand
-The prodStack function builds a graph which contains the time series of demand in one area (or the sum of the demand of a set of areas) along with the generation of this area (or set of areas), divided between the different fuel types (e.g. nuclear, gas, wind, etc).
Once again, this function is easy to use and opens a shiny interface which let the user manipulate the selected areas and time range. Some settings can also be passed to the function through its (optionnal) arguments.
-prodStack(
- data_hourly_synthesis,
- stack = "eco2mix",
- areas = "37_h",
- dateRange = c("2018-01-08", "2018-01-21"),
- main = "Production stack",
- unit = "GWh",
- interactive = FALSE,
- width = "100%",
- height = 500
-)The graphical template used by default is the one of the RTE’s application eco2mix. This template can though be redefined completely by the user with the function setProdStackAlias().
The exchangesStack() function proposes similar graphs with a superposition of all the imports and exports of an area.
exchangesStack(
- data_hourly_synthesis,
- area = "37_h",
- dateRange = c("2018-01-08", "2018-01-21"),
- main = "Import/Export of area 37_h",
- unit = "GWh",
- interactive = FALSE,
- width = "100%",
- height = 500
-)When the production meets the demand +
+The prodStack function builds a graph which contains the
+time series of demand in one area (or the sum of the demand of a set of
+areas) along with the generation of this area (or set of areas), divided
+between the different fuel types (e.g. nuclear, gas, wind, etc).
+prodStack(data_hourly_synthesis)Once again, this function is easy to use and opens a shiny interface +which let the user manipulate the selected areas and time range. Some +settings can also be passed to the function through its (optionnal) +arguments.
+
+prodStack(
+ data_hourly_synthesis,
+ stack = "eco2mix",
+ areas = "37_h",
+ dateRange = c("2018-01-08", "2018-01-21"),
+ main = "Production stack",
+ unit = "GWh",
+ interactive = FALSE,
+ width = "100%",
+ height = 500
+)The graphical template used by default is the one of the RTE’s +application eco2mix
+ +This template can though be redefined completely by the user with the
+function setProdStackAlias().
The exchangesStack() function proposes similar graphs
+with a superposition of all the imports and exports of an area.
+exchangesStack(
+ data_hourly_synthesis,
+ area = "37_h",
+ dateRange = c("2018-01-08", "2018-01-21"),
+ main = "Import/Export of area 37_h",
+ unit = "GWh",
+ interactive = FALSE,
+ width = "100%",
+ height = 500
+)-Everything looks better on a map
-Last but not least, antaresViz proposes several function to vizualise the results of a study on a map.
To do so, the first function to use is mapLayout(). This function launches an interactive application that let the user place areas of the ANTARES study on a map.
antares_layout <- antaresRead::readLayout(opts = antaresRead::setSimulationPath(study_path))
-map_layout <- mapLayout(layout = antares_layout)Everything looks better on a map +
+Last but not least, antaresViz proposes several function
+to vizualise the results of a study on a map.
To do so, the first function to use is mapLayout(). This
+function launches an interactive application that let the user place
+areas of the ANTARES study on a map.
+antares_layout <- antaresRead::readLayout(opts = antaresRead::setSimulationPath(study_path))
+map_layout <- mapLayout(layout = antares_layout)
(Once again : the study presented here is fictionnal !)
-The function plotMap() then generates an interactive map that let the user visually explore the results of an Antares simulation. By default the function starts a Shiny gadget that let the user choose :
The function plotMap() then generates an interactive map
+that let the user visually explore the results of an Antares simulation.
+By default the function starts a Shiny gadget that let the user choose
+:
Some examples of results returned by the plotMap() function are depicted below.
C02 emissions
-This first map depicts the annual CO2 emissions of all the areas of the ANTARES study.
-plotMap(
- data_annual_filtered, map_layout,
- showLabels = TRUE,
- sizeAreaVar = "CO2 EMIS.",
- interactive = FALSE,
- labelAreaVar = "CO2 EMIS.",
- colAreaVar = "CO2 EMIS.",
- type = "avg",
- options = plotMapOptions(
- areaDefaultSize = 30,
- labelMaxSize = 8,
- labelMinSize = 14,
- areaColorScaleOpts = colorScaleOptions(
- zeroCol = "white",
- posCol = "red2"
- )
- ),
- width = "100%",
- height = 600
-)
+plotMap(data_hourly_synthesis, map_layout)Some examples of results returned by the plotMap()
+function are depicted below.
C02 emissions +
+This first map depicts the annual CO2 emissions of all the areas of +the ANTARES study.
+
+plotMap(
+ data_annual_filtered, map_layout,
+ showLabels = TRUE,
+ sizeAreaVar = "CO2 EMIS.",
+ interactive = FALSE,
+ labelAreaVar = "CO2 EMIS.",
+ colAreaVar = "CO2 EMIS.",
+ type = "avg",
+ options = plotMapOptions(
+ areaDefaultSize = 30,
+ labelMaxSize = 8,
+ labelMinSize = 14,
+ areaColorScaleOpts = colorScaleOptions(
+ zeroCol = "white",
+ posCol = "red2"
+ )
+ ),
+ width = "100%",
+ height = 600
+)Average balance and flows in the system
-This map illustrates the annual balance (MWh exported if positive - or imported if negative - during an hour) of each area and the annual flows of each link.
-plotMap(
- data_annual_synthesis,
- map_layout,
- showLabels = TRUE,
- colAreaVar = "BALANCE",
- interactive = FALSE,
- labelAreaVar = "BALANCE",
- sizeLinkVar = "FLOW LIN.",
- type = "avg",
- options = plotMapOptions(
- areaDefaultSize = 30,
- labelMaxSize = 10,
- labelMinSize = 8,
- areaColorScaleOpts = colorScaleOptions(
- negCol = "tomato3",
- zeroCol = "white",
- posCol = "blue3"
- )
- ),
- width = "100%",
- height = 600
-)Average balance and flows in the system +
+This map illustrates the annual balance (MWh exported if positive - +or imported if negative - during an hour) of each area and the annual +flows of each link.
+
+plotMap(
+ data_annual_synthesis,
+ map_layout,
+ showLabels = TRUE,
+ colAreaVar = "BALANCE",
+ interactive = FALSE,
+ labelAreaVar = "BALANCE",
+ sizeLinkVar = "FLOW LIN.",
+ type = "avg",
+ options = plotMapOptions(
+ areaDefaultSize = 30,
+ labelMaxSize = 10,
+ labelMinSize = 8,
+ areaColorScaleOpts = colorScaleOptions(
+ negCol = "tomato3",
+ zeroCol = "white",
+ posCol = "blue3"
+ )
+ ),
+ width = "100%",
+ height = 600
+)Energy mixes
-The proportion of each energy type in the annual production of each area is illustrated on this next map. The actual generated energies (in MWh) can be known by clicking on the pie charts.
-data_annual_synthesis$areas <- data_annual_synthesis$areas[, `:=`(
- THERMAL = NUCLEAR + LIGNITE + COAL + GAS + OIL + `MIX. FUEL` + `MISC. DTG`, HYDRO =`H. ROR` + `H. STOR`
-)]
-
-plotMap(
- data_annual_synthesis,
- map_layout,
- interactive = FALSE,
- sizeAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"),
- popupAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"),
- areaChartType = "pie", type = "avg",
- options = plotMapOptions(
- areaDefaultSize = 25
- ),
- width = "100%",
- height = 600
-)Energy mixes +
+The proportion of each energy type in the annual production of each +area is illustrated on this next map. The actual generated energies (in +MWh) can be known by clicking on the pie charts.
+
+data_annual_synthesis$areas <- data_annual_synthesis$areas[, `:=`(
+ THERMAL = NUCLEAR + LIGNITE + COAL + GAS + OIL + `MIX. FUEL` + `MISC. DTG`, HYDRO =`H. ROR` + `H. STOR`
+)]
+
+plotMap(
+ data_annual_synthesis,
+ map_layout,
+ interactive = FALSE,
+ sizeAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"),
+ popupAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"),
+ areaChartType = "pie", type = "avg",
+ options = plotMapOptions(
+ areaDefaultSize = 25
+ ),
+ width = "100%",
+ height = 600
+)-Let’s get started
-The antaresViz package is available on the CRAN and can be installed with :
Let’s get started +
+The antaresViz package is available on the CRAN and can
+be installed with :
+install.packages("antaresViz")
+
+
diff --git a/docs/articles/article/creating_new_layouts.html b/docs/articles/article/creating_new_layouts.html
index 49fa88ba..50de1a86 100644
--- a/docs/articles/article/creating_new_layouts.html
+++ b/docs/articles/article/creating_new_layouts.html
@@ -6,19 +6,21 @@
Creating new layouts • antaresViz
-
-
-
-
+
+
+
+
+
-
-
+
-
+
+
+
@@ -31,18 +33,12 @@
+
+
- Creating new layouts
- RTE
+ Creating new layouts
+ RTE
- 2020-01-31
+ 2024-06-26
- Source: vignettes/article/creating_new_layouts.Rmd
+ Source: vignettes/article/creating_new_layouts.Rmd
creating_new_layouts.Rmd
-The antaresViz package allows to visualize simulation results. Several default layouts are available in the package, but, in some cases, the user will need to create a custom layout.
-In this vignette we illustrate how to create a custom layout along the German inner borders.
-
-The coordinates of administratives regions of most countries can be found in the rnaturalearth package. Grouping these regions together often allows to obtain the desired layout. For instance:
-germany <- rnaturalearth::ne_states(
- geounit = "germany",
- returnclass = "sf"
-)
-
-german_zones <- germany %>%
- select(iso_3166_2, geometry) %>%
- transmute(zone = case_when(
- iso_3166_2 %in% c("DE-SL", "DE-RP", "DE-NW", "DE-HE") ~ "34_DE",
- iso_3166_2 %in% c("DE-BW", "DE-BY") ~ "35_DE",
- iso_3166_2 %in% c("DE-NI", "DE-HH", "DE-HB", "DE-SH") ~ "36_DE",
- TRUE ~ "37_DE"
- )) %>%
- group_by(zone) %>%
- summarise()
-
-ggplot(german_zones) +
- geom_sf(aes()) +
- theme_minimal() +
- theme(
- legend.position = "none",
- axis.title.x = element_blank(),
- axis.title.y = element_blank()
- )
+The antaresViz package allows to visualize simulation
+results. Several default layouts are available in the package, but, in
+some cases, the user will need to create a custom layout.
+In this vignette we illustrate how to create a custom layout along
+the German inner borders.
+
+library(antaresViz)
+library(dplyr)
+library(rnaturalearth)
+library(ggplot2)
+library(sf)
+The coordinates of administratives regions of most countries can be
+found in the rnaturalearth package. Grouping these regions
+together often allows to obtain the desired layout. For instance:
+
+germany <- rnaturalearth::ne_states(
+ geounit = "germany",
+ returnclass = "sf"
+)
+
+german_zones <- germany %>%
+ select(iso_3166_2, geometry) %>%
+ transmute(zone = case_when(
+ iso_3166_2 %in% c("DE-SL", "DE-RP", "DE-NW", "DE-HE") ~ "34_DE",
+ iso_3166_2 %in% c("DE-BW", "DE-BY") ~ "35_DE",
+ iso_3166_2 %in% c("DE-NI", "DE-HH", "DE-HB", "DE-SH") ~ "36_DE",
+ TRUE ~ "37_DE"
+ )) %>%
+ group_by(zone) %>%
+ summarise()
+
+ggplot(german_zones) +
+ geom_sf(aes()) +
+ theme_minimal() +
+ theme(
+ legend.position = "none",
+ axis.title.x = element_blank(),
+ axis.title.y = element_blank()
+ )
-Then the layout map can be generated from german_zones with a single line of code:
-
+Then the layout map can be generated from
+german_zones with a single line of code:
+
+map <- as(st_transform(german_zones, crs = 4326), "Spatial")
+This map is then used to generate a
+mapLayout object:
+
-
Creating new layouts
-RTE
+Creating new layouts
+RTE
-2020-01-31
+2024-06-26
- Source:vignettes/article/creating_new_layouts.Rmd
+ Source: vignettes/article/creating_new_layouts.Rmd
creating_new_layouts.RmdThe antaresViz package allows to visualize simulation results. Several default layouts are available in the package, but, in some cases, the user will need to create a custom layout.
In this vignette we illustrate how to create a custom layout along the German inner borders.
- -The coordinates of administratives regions of most countries can be found in the rnaturalearth package. Grouping these regions together often allows to obtain the desired layout. For instance:
germany <- rnaturalearth::ne_states(
- geounit = "germany",
- returnclass = "sf"
-)
-
-german_zones <- germany %>%
- select(iso_3166_2, geometry) %>%
- transmute(zone = case_when(
- iso_3166_2 %in% c("DE-SL", "DE-RP", "DE-NW", "DE-HE") ~ "34_DE",
- iso_3166_2 %in% c("DE-BW", "DE-BY") ~ "35_DE",
- iso_3166_2 %in% c("DE-NI", "DE-HH", "DE-HB", "DE-SH") ~ "36_DE",
- TRUE ~ "37_DE"
- )) %>%
- group_by(zone) %>%
- summarise()
-
-ggplot(german_zones) +
- geom_sf(aes()) +
- theme_minimal() +
- theme(
- legend.position = "none",
- axis.title.x = element_blank(),
- axis.title.y = element_blank()
- )The antaresViz package allows to visualize simulation
+results. Several default layouts are available in the package, but, in
+some cases, the user will need to create a custom layout.
In this vignette we illustrate how to create a custom layout along +the German inner borders.
+
+library(antaresViz)
+library(dplyr)
+library(rnaturalearth)
+library(ggplot2)
+library(sf)The coordinates of administratives regions of most countries can be
+found in the rnaturalearth package. Grouping these regions
+together often allows to obtain the desired layout. For instance:
+germany <- rnaturalearth::ne_states(
+ geounit = "germany",
+ returnclass = "sf"
+)
+
+german_zones <- germany %>%
+ select(iso_3166_2, geometry) %>%
+ transmute(zone = case_when(
+ iso_3166_2 %in% c("DE-SL", "DE-RP", "DE-NW", "DE-HE") ~ "34_DE",
+ iso_3166_2 %in% c("DE-BW", "DE-BY") ~ "35_DE",
+ iso_3166_2 %in% c("DE-NI", "DE-HH", "DE-HB", "DE-SH") ~ "36_DE",
+ TRUE ~ "37_DE"
+ )) %>%
+ group_by(zone) %>%
+ summarise()
+
+ggplot(german_zones) +
+ geom_sf(aes()) +
+ theme_minimal() +
+ theme(
+ legend.position = "none",
+ axis.title.x = element_blank(),
+ axis.title.y = element_blank()
+ )
Then the layout map can be generated from german_zones with a single line of code:
Then the layout map can be generated from
+german_zones with a single line of code:
+map <- as(st_transform(german_zones, crs = 4326), "Spatial")This map is then used to generate a
+mapLayout object:
