From 0480888cfcbec0d99e310b6f7ce3eb670322a032 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Wed, 1 Nov 2023 13:32:10 +0000 Subject: [PATCH 01/82] Adding code tab for plots --- R/plot_priors.R | 5 ----- R/shiny_modelviewer.R | 43 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+), 5 deletions(-) diff --git a/R/plot_priors.R b/R/plot_priors.R index 6258df31..418eb871 100644 --- a/R/plot_priors.R +++ b/R/plot_priors.R @@ -4,7 +4,6 @@ #' @param to_plot Type of data to plot, "Range for f" etc #' #' @return ggplot2::ggplot -#' @keywords internal plot_line_comparison <- function(data, to_plot, title) { ar1_data <- purrr::map(data, function(x) as.data.frame(x$pars[[to_plot]])) single_df <- dplyr::bind_rows(ar1_data, .id = "Run") @@ -25,7 +24,6 @@ plot_line_comparison <- function(data, to_plot, title) { #' @param to_plot Type of data to plot, "Range for f" etc #' #' @return ggplot2::ggplot -#' @keywords internal plot_ar1 <- function(data) { ar1_data <- purrr::map(data, function(x) as.data.frame(x$pars$`GroupRho for f`)) single_df <- dplyr::bind_rows(ar1_data, .id = "Run") @@ -43,7 +41,6 @@ plot_ar1 <- function(data) { #' @param data #' #' @return graphics::boxplot -#' @keywords internal plot_priors_boxplot <- function(data) { # TODO - I'm sure this can be done in a nicer functional way fitted_mean_post <- purrr::map(data, function(x) x$fitted_mean_post) @@ -60,7 +57,6 @@ plot_priors_boxplot <- function(data) { #' @param measurement_data Measurement data #' #' @return ggplot2::ggplot -#' @keywords internal plot_priors_density <- function(data, measurement_data) { # Can this be done in a cleaner way? Just create a dataframe from the lists? fitted_values <- unlist(purrr::map(data, function(x) x$fitted_mean_post)) @@ -82,7 +78,6 @@ plot_priors_density <- function(data, measurement_data) { #' @param data #' #' @return ggplot2::ggplot -#' @keywords internal plot_dic <- function(data) { infocri <- base::cbind.data.frame( priors = unlist(purrr::map(seq(1, length(data)), function(x) paste("Run", x))), diff --git a/R/shiny_modelviewer.R b/R/shiny_modelviewer.R index 3468a77a..77a0b818 100644 --- a/R/shiny_modelviewer.R +++ b/R/shiny_modelviewer.R @@ -44,12 +44,14 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri type = "tabs", shiny::tabPanel( "Plots", + class = "p-3 border", shiny::h2("Plot output"), shiny::selectInput(inputId = "plot_type", label = "Plot type:", choices = plot_choices, selected = plot_choices[1]), shiny::plotOutput(outputId = "plot_model_out") ), shiny::tabPanel( "Map", + class = "p-3 border", shiny::fluidRow( shiny::column( 6, @@ -73,8 +75,14 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri ), leaflet::leafletOutput(outputId = "map_out") ), + shiny::tabPanel( + "Code", + class = "p-3 border", + shiny::verbatimTextOutput(outputId = "code_out") + ), shiny::tabPanel( "Help", + class = "p-3 border", shiny::h3("Help"), ) ) @@ -176,6 +184,41 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri } }) + output$code_out <- shiny::reactive({ + code_str <- "" + parsed_model_str <- "parsed_model_out <- fdmr::parse_model_output(model_output = model_output, + measurement_data = measurement_data)" + if (input$plot_type == "Range") { + code_str <- 'fdmr::plot_line_comparison( + data = parsed_model_out, + to_plot = "Range for f", + title = "Range" + )' + } else if (input$plot_type == "Stdev") { + code_str <- 'fdmr::plot_line_comparison( + data = parsed_model_out, + to_plot = "Stdev for f", + title = "Marginal standard deviation" + )' + } else if (input$plot_type == "AR(1)") { + code_str <- 'fdmr::plot_line_comparison( + data = parsed_model_out, + to_plot = "GroupRho for f", + title = "AR(1)" + )' + } else if (input$plot_type == "Boxplot") { + code_str <- "fdmr::plot_priors_boxplot(data = parsed_model_out)" + } else if (input$plot_type == "Density") { + code_str <- "plot_priors_density( + data = parsed_model_out, + measurement_data = measurement_data + )" + } else if (input$plot_type == "DIC") { + code_str <- "fdmr::plot_dic(data = parsed_model_out)" + } + paste0(parsed_model_str, "\n\n", code_str) + }) + output$plot_model_out <- shiny::renderPlot({ model_plot() }) From e8f5dd40988f12e8d1d3fa8b0d003d06976617ca Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Wed, 1 Nov 2023 13:36:43 +0000 Subject: [PATCH 02/82] Add changelog line --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 28349b80..a3990677 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,6 +18,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Allowed reversal of colour palette and added raster plot legend in map plotter - [PR #229](https://github.com/4DModeller/fdmr/pull/229) - Added markers option to mesh plotter - [PR #230](https://github.com/4DModeller/fdmr/pull/230) - Updated the interfaces of the Shiny apps to the use [bslib](https://rstudio.github.io/bslib/index.html) theming - [PR #236](https://github.com/4DModeller/fdmr/pull/236) +- Code tab added to `fdmr::model_viewer` so the user can easily reproduce plots - [PR #237](https://github.com/4DModeller/fdmr/pull/237) ### Removed From 4156fd8c21f60bc3c52d33fe8676b917d476356d Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 10:08:35 +0100 Subject: [PATCH 03/82] Starting a snow data tutorial, including two datasets in lat/lon and UTM --- vignettes/data/Bayelva_snow.nc | Bin 0 -> 589618 bytes vignettes/data/Bayelva_snow_geo.nc | Bin 0 -> 842099 bytes vignettes/snow.Rmd | 69 +++++++++++++++++++++++++++++ 3 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references --- vignettes/snow.Rmd | 50 +++++++++++++++++++++++++++++++--------------- 1 file changed, 34 insertions(+), 16 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index d8691655..011d2f3c 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -1,33 +1,52 @@ --- -editor_options: - markdown: - wrap: 72 +title: "Spatial distribution of snow - exploratory tutorial" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Spatial distribution of snow - exploratory tutorial} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} --- +# Spatial distribution of snow + +This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from timelapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: + +**XXX ADD dois for the datasets** + +The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), there is also a lat/lon version of the same data. + +### Literature references + +Aalstad, K. et al. (2018). . TC. + +Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE. + +Boike, J. et al. (2018). . ESSD. + +Zweigel, R. et al. (2021). . + +## Import and load the data + ```{r modules} +# load modules used in this tutorial library(ncdf4) -library(tidyverse) +#library(tidyverse) ``` -# Import and load the data - ```{r dataload} -# specify where your data is stored locally +# Specify where your data is stored locally ncpath <- "N:/courses_conferences/2023_4DM_hackathon/fdmr/vignettes/data/" ncname <- "Bayelva_snow" ncfname <- paste(ncpath, ncname, ".nc", sep="") #open data ncin <- nc_open(ncfname) -print(ncin) +head(ncin) ``` -# Get relevant features +## Get relevant features -The netcdf file has both point measurements of snow depth (ds, or -translated into SWE: Ds) for several years, and continuous raster data -with snow depth duration for several years and topographic features. We -start with the snow depth points only: +The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We start with the snow depth points only: ```{r dataselect} # specify the longitude and latitude columns. Note that the coordinates are utm! @@ -42,10 +61,9 @@ df <- data.frame(LONG = longitude,LAT = latitude,ds) head(df) ``` -# Build the mesh +## Build the mesh -The shiny app doesn't work with UTM, so we need to do this -non-interactively. +The shiny app doesn't work with UTM, so we need to do this non-interactively. ```{r plotmesh} # This doesn't work with UTM: From 689d50175ba3c71710f57710ea3ccd508ff251b8 Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 11:49:29 +0100 Subject: [PATCH 05/82] Tutorial structure up to the point where things don't work with UTM --- vignettes/snow.Rmd | 36 ++++++++++++++++++++---------------- 1 file changed, 20 insertions(+), 16 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 011d2f3c..a7fdcb21 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -9,11 +9,11 @@ vignette: > # Spatial distribution of snow -This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from timelapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: +This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: **XXX ADD dois for the datasets** -The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), there is also a lat/lon version of the same data. +The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data. ### Literature references @@ -34,14 +34,14 @@ library(ncdf4) ``` ```{r dataload} -# Specify where your data is stored locally -ncpath <- "N:/courses_conferences/2023_4DM_hackathon/fdmr/vignettes/data/" +# Find where your data is stored locally +ncpath <- dirname(rstudioapi::getSourceEditorContext()$path) ncname <- "Bayelva_snow" -ncfname <- paste(ncpath, ncname, ".nc", sep="") +ncfname <- paste(ncpath,'/data/', ncname, ".nc", sep="") #open data ncin <- nc_open(ncfname) -head(ncin) +#head(ncin) ``` ## Get relevant features @@ -63,23 +63,27 @@ head(df) ## Build the mesh -The shiny app doesn't work with UTM, so we need to do this non-interactively. +When trying this whit our UTM projected dataset, we don't get a result: +```{r plotmeshutm} +fdmr::mesh_builder(spatial_data = df) +``` -```{r plotmesh} -# This doesn't work with UTM: -# fdmr::mesh_builder(spatial_data = df, crs="+proj=utm +zone=33") +The shiny app seems not to work with UTM, so we when working with UTM we need to try and do this non-interactively. -# We have to define an UTM +```{r plotmesh} +# Define a proj string for our UTM zone crs <- "+proj=utm +zone=33" -mesh <- fmesher::fm_mesh_2d_inla(loc = -df[,c(1,2)], max.edge = c(20,40),crs = crs ) +# use fmesher directly rather than the fdmr wrapper: +mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], + max.edge = c(20,40), + crs = crs ) + +# plot the result plot(mesh) ``` -# Visualise the mesh: also plot our data points - -This doesn't seem to work with the built-in plot_mesh command in UTM. +We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. This is causing problems - fdmr seems not to be able to deal with our UTM coordinates. ```{r plotmeshandpoints} From 237e0501d0e643cd451cd9a4dc4003bb44d0d892 Mon Sep 17 00:00:00 2001 From: El Date: Fri, 3 Nov 2023 11:51:27 +0100 Subject: [PATCH 06/82] Create Readme.md --- vignettes/Readme.md | 71 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 vignettes/Readme.md diff --git a/vignettes/Readme.md b/vignettes/Readme.md new file mode 100644 index 00000000..49cafe3a --- /dev/null +++ b/vignettes/Readme.md @@ -0,0 +1,71 @@ +# Spatial distribution of snow - exploratory tutorial + +This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from timelapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: + +**XXX ADD dois for the datasets** + +The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), there is also a lat/lon version of the same data. + +### Literature references + +Aalstad, K. et al. (2018). . TC. + +Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE. + +Boike, J. et al. (2018). . ESSD. + +Zweigel, R. et al. (2021). . + +## Import and load the data + +```{r modules} +# load modules used in this tutorial +library(ncdf4) +#library(tidyverse) + +# Specify where your data is stored locally +ncpath <- "N:/courses_conferences/2023_4DM_hackathon/fdmr/vignettes/data/" +ncname <- "Bayelva_snow" +ncfname <- paste(ncpath, ncname, ".nc", sep="") + +#open data +ncin <- nc_open(ncfname) +head(ncin) +``` + +## Get relevant features +The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We start with the snow depth points only: + +```{r modules} +# specify the longitude and latitude columns. Note that the coordinates are utm! +longitude <- ncvar_get(ncin,"xs") +latitude <-ncvar_get(ncin,"ys") + +# Get the first year of snow depth (ds) +ds <- ncvar_get(ncin,"ds")[,1] + +# Create a data frame +df <- data.frame(LONG = longitude,LAT = latitude,ds) +head(df) +``` + +## Build the mesh +The shiny app doesn't work with UTM, so we need to do this non-interactively. + +```{r modules} +# This doesn't work with UTM: +# fdmr::mesh_builder(spatial_data = df, crs="+proj=utm +zone=33") + +# We have to define an UTM +crs <- "+proj=utm +zone=33" +mesh <- fmesher::fm_mesh_2d_inla(loc = +df[,c(1,2)], max.edge = c(20,40),crs = crs ) + +plot(mesh) +``` + +## Visualise the mesh: also plot our data points +This doesn't seem to work with the built-in plot_mesh command in UTM. +```{r modules} +fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) +``` From 2b833e05e9b37a90f3217284a5113f4a2aaf6e6c Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 13:55:13 +0100 Subject: [PATCH 07/82] Commented code up to producing a first mesh attempt in lat/lon --- vignettes/snow.Rmd | 71 +++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 70 insertions(+), 1 deletion(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index a7fdcb21..5b1f870a 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -63,7 +63,8 @@ head(df) ## Build the mesh -When trying this whit our UTM projected dataset, we don't get a result: +When trying this whit our UTM projected dataset, we don't get a result: + ```{r plotmeshutm} fdmr::mesh_builder(spatial_data = df) ``` @@ -89,3 +90,71 @@ We would like to also show our data points and see this in a geographical contex fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) ``` + +The plot_mesh function seems to assume lat/lon coordinates and thus zooms out to max. + +## Redo this with geographical coordinates + +Let's see whether this works if our data is in geographical coordinates instead. Redo all the steps from above: + +```{r latlonload} +ncname_ll <- "Bayelva_snow_geo" +ncfname_ll <- paste(ncpath, '/data/', ncname_ll, ".nc", sep="") +# Open data +ncin_ll <- nc_open(ncfname_ll) + +# Create a data frame with the first year of snow depth (ds) +df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"), + LAT = ncvar_get(ncin_ll,"lats"), + DS = ncvar_get(ncin_ll,"ds")[,1]) + +## Store the point locations in a separate data frame: +location_data_ll = df_ll[,c(1,2)] +``` + +```{r latlonmesh} +# Try with the interactive mesh_builder +fdmr::mesh_builder(location_data_ll) +``` + +The mesh_builder loads the shiny widget and creates a mesh, however there are several issues: + +- the map part of the window covers the entire window, so the rulers disappear + +- the mesh is far too coarse for the small area we are looking at (1km2) + +- closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. + +Let's try to specify the mesh manually using values for max.edge and cutoff that are reasonable for our domain and length scales. + +```{r latlonmesh_manually} +# look at the range of the data to derive the mesh parameters +initial_range <- diff(range(location_data_ll[, "LONG"])) / 5 +max_edge <- initial_range / 8 +offset <- initial_range/2 + +# make the mesh from the locations +mesh <- fmesher::fm_mesh_2d(loc = location_data_ll[, c("LONG", "LAT")], + max.edge = c(1, 2) * max_edge, + offset = c(1,1.5)*offset, + cutoff = max_edge/10 + ) +fdmr::plot_mesh(mesh,spatial_data = location_data_ll) +``` + +This mesh is already a better starting point. There are two things we are not happy about: + +- The mesh is obviously distorted in N/S direction: With latitude/longitude units representing substantially different spatial distances at polar latitudes, edge lengths should be defined differently in E/W and N/S. Or one could simply use UTM (which is currently not supported for the visualisation tools). + +- Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. + + +# TO DO part + +## Get the rasters in and use that information to build the mesh + +## Spatial covariance, and how we could use that + +## Get model results considering some or all data and predictors + +## How to deal with real zeros? (Zero snow depth) From a4ef9a5b2c2c4bc839560720472b4fed35a1f0e3 Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 14:03:15 +0100 Subject: [PATCH 08/82] Disabled cells that break the script (eval=false), added html version of tutorial --- vignettes/snow.Rmd | 6 +- vignettes/snow.html | 4574 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 4577 insertions(+), 3 deletions(-) create mode 100644 vignettes/snow.html diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 5b1f870a..d1e8b422 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -65,7 +65,7 @@ head(df) When trying this whit our UTM projected dataset, we don't get a result: -```{r plotmeshutm} +```{r, eval = FALSE} fdmr::mesh_builder(spatial_data = df) ``` @@ -86,7 +86,7 @@ plot(mesh) We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. This is causing problems - fdmr seems not to be able to deal with our UTM coordinates. -```{r plotmeshandpoints} +```{r, eval = FALSE} fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) ``` @@ -112,7 +112,7 @@ df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"), location_data_ll = df_ll[,c(1,2)] ``` -```{r latlonmesh} +```{r, eval = FALSE} # Try with the interactive mesh_builder fdmr::mesh_builder(location_data_ll) ``` diff --git a/vignettes/snow.html b/vignettes/snow.html new file mode 100644 index 00000000..1d79f60f --- /dev/null +++ b/vignettes/snow.html @@ -0,0 +1,4574 @@ + + + + + + + + + + + + + + +Spatial distribution of snow - exploratory tutorial + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Spatial distribution of snow - exploratory +tutorial

+ + + +
+

Spatial distribution of snow

+

This tutorial explores whether 4Dmodeller can be used to model the +spatial distribution of snow in the Bayelva area on Svalbard, based on +once-a-year point-based manual snow depth measurements for several years +and snow cover duration derived from time lapse camera imagery. This +data has been used before to estimate snow depth and permafrost using +data assimilation methods (see literature references). Additionally, we +included topography and topographic parameters as these are known to +correlate with snow depth distribution. The dataset used in this +tutorial has been prepared by Kristoffer Aalstad based on the following +publicly available data:

+

XXX ADD dois for the datasets

+

The dataset is originally projected (UTM zone 33X) and available in +the data sub-folder in this repository. As UTM was not supported for all +steps we anticipated while developing this tutorial (see below), we also +created also a lat/lon version of the same data.

+
+

Literature references

+

Aalstad, K. et al. (2018). doi:10.5194/tc-12-247-2018. TC.

+

Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE.

+

Boike, J. et al. (2018). doi:10.5194/essd-10-355-2018. ESSD.

+

Zweigel, R. et al. (2021). doi:10.1029/2020JF005673.

+
+
+

Import and load the data

+
# load modules used in this tutorial
+library(ncdf4)
+#library(tidyverse)
+
# Find where your data is stored locally
+ncpath <- dirname(rstudioapi::getSourceEditorContext()$path)
+ncname <- "Bayelva_snow" 
+ncfname <- paste(ncpath,'/data/', ncname, ".nc", sep="")
+
+#open data 
+ncin <- nc_open(ncfname) 
+#head(ncin)
+
+
+

Get relevant features

+

The netcdf file has both point measurements of snow depth (ds, or +translated into SWE: Ds) for several years, and continuous raster data +with snow depth duration for several years and topographic features. We +start with the snow depth points only:

+
# specify the longitude and latitude columns. Note that the coordinates are utm!
+longitude <- ncvar_get(ncin,"xs") 
+latitude <-ncvar_get(ncin,"ys")
+
+# Get the first year of snow depth (ds)
+ds <- ncvar_get(ncin,"ds")[,1]
+
+# Create a data frame
+df <- data.frame(LONG = longitude,LAT = latitude,ds) 
+head(df)
+
##       LONG     LAT   ds
+## 1 432376.2 8762917 0.70
+## 2 432046.8 8763194 0.60
+## 3 432578.7 8763044 0.50
+## 4 432661.3 8763002 0.60
+## 5 432408.1 8762976 0.65
+## 6 432105.0 8763344 0.72
+
+
+

Build the mesh

+

When trying this whit our UTM projected dataset, we don’t get a +result:

+
fdmr::mesh_builder(spatial_data = df)
+

The shiny app seems not to work with UTM, so we when working with UTM +we need to try and do this non-interactively.

+
# Define a proj string for our UTM zone
+crs <- "+proj=utm +zone=33" 
+
+# use fmesher directly rather than the fdmr wrapper:
+mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], 
+                                 max.edge = c(20,40),
+                                 crs = crs ) 
+
+# plot the result
+plot(mesh)
+

+

We would like to also show our data points and see this in a +geographical context to check whether everything is looking OK. This is +causing problems - fdmr seems not to be able to deal with our UTM +coordinates.

+
fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)])
+

The plot_mesh function seems to assume lat/lon coordinates and thus +zooms out to max.

+
+
+

Redo this with geographical coordinates

+

Let’s see whether this works if our data is in geographical +coordinates instead. Redo all the steps from above:

+
ncname_ll <- "Bayelva_snow_geo"
+ncfname_ll <- paste(ncpath, '/data/', ncname_ll, ".nc", sep="")
+# Open data
+ncin_ll <- nc_open(ncfname_ll)
+
+# Create a data frame with the  first year of snow depth (ds)
+df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"),
+                 LAT = ncvar_get(ncin_ll,"lats"),
+                 DS = ncvar_get(ncin_ll,"ds")[,1])
+
+## Store the point locations in a separate data frame:
+location_data_ll = df_ll[,c(1,2)]
+
# Try with the interactive mesh_builder
+fdmr::mesh_builder(location_data_ll)
+

The mesh_builder loads the shiny widget and creates a mesh, however +there are several issues:

+
    +

  • the map part of the window covers the entire window, so the +rulers disappear

    • +

  • the mesh is far too coarse for the small area we are looking at +(1km2)

    • +

  • closing the widget window doesn’t stop the underlying processes +to run, consequently RStudio is blocked and needs to be restarted, as +ctrl + c or interrupting by clicking the STOP button don’t have any +effect.

    • +
+

Let’s try to specify the mesh manually using values for max.edge and +cutoff that are reasonable for our domain and length scales.

+
# look at the range of the data to derive the mesh parameters
+initial_range <- diff(range(location_data_ll[, "LONG"])) / 5
+max_edge <- initial_range / 8
+offset <- initial_range/2
+
+# make the mesh from the locations
+mesh <- fmesher::fm_mesh_2d(loc = location_data_ll[, c("LONG", "LAT")],
+                                 max.edge = c(1, 2) * max_edge,
+                                 offset = c(1,1.5)*offset,
+                                 cutoff = max_edge/10
+                                 )
+fdmr::plot_mesh(mesh,spatial_data = location_data_ll)
+
## Warning in fdmr::plot_mesh(mesh, spatial_data = location_data_ll): Cannot read
+## CRS from mesh, assuming WGS84
+
+ +

This mesh is already a better starting point. There are two things we +are not happy about:

+
    +

  • The mesh is obviously distorted in N/S direction: With +latitude/longitude units representing substantially different spatial +distances at polar latitudes, edge lengths should be defined differently +in E/W and N/S. Or one could simply use UTM (which is currently not +supported for the visualisation tools).

    • +

  • Is the mesh dense enough to represent snow depth variability +between the measurement points, and is the mesh in physically useful +locations? Terrain and derivatives are strong drivers of spatial snow +depth distribution, and they should thus ideally be considered for the +mesh generation.

    • +
+
+
+
+

TO DO part

+
+

Get the rasters in and use that information to build the mesh

+
+
+

Spatial covariance, and how we could use that

+
+
+

Get model results considering some or all data and predictors

+
+
+

How to deal with real zeros? (Zero snow depth)

+
+
+ + + + + + + + + + + From e4d1390f6d4443cab2d1eabcef0c94f6bea5473b Mon Sep 17 00:00:00 2001 From: mmazzolini Date: Fri, 3 Nov 2023 15:03:43 +0100 Subject: [PATCH 09/82] trial to explain the prior design --- vignettes/snow.Rmd | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index d1e8b422..9ecc1994 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -148,13 +148,31 @@ This mesh is already a better starting point. There are two things we are not ha - Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. +## Design the prior and the model + +We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. +The range parameter defines the lenght of the influence of each observation. + +In this small experiment, we have experimented by creating two different priors with two different range. +The model is a stochastic partial differential equation (SPDE). + +```{prior creation} +#julia add code for creating the two models +``` + +## Fitting the model -# TO DO part -## Get the rasters in and use that information to build the mesh ## Spatial covariance, and how we could use that +A future development we would like to work on, is to design the prior to be anisotropic. What we mean, is that the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. + + +# TO DO part + +## Get the rasters in and use that information to build the mesh + ## Get model results considering some or all data and predictors ## How to deal with real zeros? (Zero snow depth) From e8af4e911a17eb7e59c3315eac2d429faa628e6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Julie=20R=C3=B8ste?= Date: Fri, 3 Nov 2023 15:18:42 +0100 Subject: [PATCH 10/82] adding more snow code --- vignettes/snow.Rmd | 119 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 119 insertions(+) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 9ecc1994..833c7b1b 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -176,3 +176,122 @@ A future development we would like to work on, is to design the prior to be anis ## Get model results considering some or all data and predictors ## How to deal with real zeros? (Zero snow depth) + + +```{r} +library(ncdf4) +library(ggplot2) + +# Import the data +ncpath <- "~/" +ncname <- "Bayelva_snow_geo" +ncfname <- paste(ncpath, ncname, ".nc", sep="") + +# Open data +ncin <- nc_open(ncfname) + + + + +# Create a data frame with the first year of snow depth (ds) +df <- data.frame(LONG = ncvar_get(ncin,"lons"), + LAT = ncvar_get(ncin,"lats"), + #elev = ncvar_get(ncin,"zs"), + DS = ncvar_get(ncin,"ds")[,1], + DS_log = log(ncvar_get(ncin,"ds")[,1])) + +## Ready for modelling! + + +# look at the range of the data +initial_range <- diff(range(df[, "LONG"])) / 5 +max_edge <- initial_range / 4 +offset <- initial_range/2 + +mesh <- fmesher::fm_mesh_2d( + loc = location_data[, c("LONG", "LAT")], + max.edge = c(1, 2) * max_edge, + offset = c(1,1.5)*offset, + cutoff = max_edge/10 +) + +fdmr::plot_mesh(mesh,spatial_data = location_data) + +## Start defining the priors +# the prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center + +spde1 <- INLA::inla.spde2.pcmatern( + mesh = mesh, + prior.range = c(initial_range, 0.5), + prior.sigma = c(1, 0.01) +) + +spde2 <- INLA::inla.spde2.pcmatern( + mesh = mesh, + prior.range = c(initial_range*5, 0.5), + prior.sigma = c(1, 0.01) +) + +## Creating our data frame into a SpatialPointsDataFrame () + +sp::coordinates(df) <- c("LONG", "LAT") + +formula1 <- DS ~ 0 + Intercept(1)+ + f( + main = df, + model = spde1 + ) + + +formula2 <- DS ~ 0 + Intercept(1) + + f( + main = df, + model = spde2 + ) + + +inlabru_model1 <- inlabru::bru(formula1, + data = df, + family = "gaussian", + options = list( + verbose = FALSE + ) +) + +inlabru_model2 <- inlabru::bru(formula2, + data = df, + family = "gaussian", + options = list( + verbose = FALSE + ) +) + +summary(inlabru_model1) +summary(inlabru_model2) + + +plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) +plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) + + +plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) +plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) + + +fdmr::model_viewer( + model_output = inlabru_model1, + mesh = mesh, + measurement_data = df, + data_distribution = "Gaussian" +) + +fdmr::model_viewer( + model_output = inlabru_model2, + mesh = mesh, + measurement_data = df, + data_distribution = "Gaussian" +) + + +``` + From 24fd34e95ea6ad7dd1123db013f0fa740be1c1a8 Mon Sep 17 00:00:00 2001 From: Kristoffer Aalstad Date: Fri, 3 Nov 2023 16:12:24 +0100 Subject: [PATCH 11/82] Updated references in snow.Rmd --- vignettes/snow.Rmd | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 833c7b1b..be7a210a 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -9,21 +9,23 @@ vignette: > # Spatial distribution of snow -This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: +This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2018). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): -**XXX ADD dois for the datasets** +Westermann, S. (2020).Snow depth and ground surface temperature at Bayelva research site, Svalbard [Data set]. Norstore. https://doi.org/10.11582/2020.00024 -The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data. +Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5010944 + +Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and multi channel) from aerial overflights in 2008 over Bayelva (Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. https://doi.org/10.1594/PANGAEA.884730 -### Literature references +The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data. -Aalstad, K. et al. (2018). . TC. +### Literature references -Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE. +Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018 -Boike, J. et al. (2018). . ESSD. +Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, 239, https://doi.org/10.1016/j.rse.2019.111618 -Zweigel, R. et al. (2021). . +Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673 ## Import and load the data From 90c545a337029e699f1986011c95bc32f2e8661d Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 16:33:50 +0100 Subject: [PATCH 12/82] Added code for simple spatial prediction --- vignettes/snow.Rmd | 173 +++++++++++++++++++-------------------- vignettes/snow.html | 195 ++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 271 insertions(+), 97 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 833c7b1b..7718c23f 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -106,7 +106,9 @@ ncin_ll <- nc_open(ncfname_ll) # Create a data frame with the first year of snow depth (ds) df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"), LAT = ncvar_get(ncin_ll,"lats"), - DS = ncvar_get(ncin_ll,"ds")[,1]) + #elev = ncvar_get(ncin_ll,"zs"), + DS = ncvar_get(ncin_ll,"ds")[,1], + DS_log = log(ncvar_get(ncin_ll,"ds")[,1])) ## Store the point locations in a separate data frame: location_data_ll = df_ll[,c(1,2)] @@ -148,77 +150,12 @@ This mesh is already a better starting point. There are two things we are not ha - Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. -## Design the prior and the model - -We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. -The range parameter defines the lenght of the influence of each observation. - -In this small experiment, we have experimented by creating two different priors with two different range. -The model is a stochastic partial differential equation (SPDE). - -```{prior creation} -#julia add code for creating the two models -``` - -## Fitting the model - - - -## Spatial covariance, and how we could use that - -A future development we would like to work on, is to design the prior to be anisotropic. What we mean, is that the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. - - -# TO DO part - -## Get the rasters in and use that information to build the mesh - -## Get model results considering some or all data and predictors - -## How to deal with real zeros? (Zero snow depth) +## Start defining the priors +Let's try to predict a spatially distributed snow depth map from our data points. ```{r} -library(ncdf4) -library(ggplot2) - -# Import the data -ncpath <- "~/" -ncname <- "Bayelva_snow_geo" -ncfname <- paste(ncpath, ncname, ".nc", sep="") - -# Open data -ncin <- nc_open(ncfname) - - - - -# Create a data frame with the first year of snow depth (ds) -df <- data.frame(LONG = ncvar_get(ncin,"lons"), - LAT = ncvar_get(ncin,"lats"), - #elev = ncvar_get(ncin,"zs"), - DS = ncvar_get(ncin,"ds")[,1], - DS_log = log(ncvar_get(ncin,"ds")[,1])) - -## Ready for modelling! - - -# look at the range of the data -initial_range <- diff(range(df[, "LONG"])) / 5 -max_edge <- initial_range / 4 -offset <- initial_range/2 - -mesh <- fmesher::fm_mesh_2d( - loc = location_data[, c("LONG", "LAT")], - max.edge = c(1, 2) * max_edge, - offset = c(1,1.5)*offset, - cutoff = max_edge/10 -) - -fdmr::plot_mesh(mesh,spatial_data = location_data) - -## Start defining the priors -# the prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center +# The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above). spde1 <- INLA::inla.spde2.pcmatern( mesh = mesh, @@ -226,72 +163,130 @@ spde1 <- INLA::inla.spde2.pcmatern( prior.sigma = c(1, 0.01) ) +# Try with a different prior range - the entire initial_range spde2 <- INLA::inla.spde2.pcmatern( mesh = mesh, prior.range = c(initial_range*5, 0.5), prior.sigma = c(1, 0.01) ) -## Creating our data frame into a SpatialPointsDataFrame () +``` -sp::coordinates(df) <- c("LONG", "LAT") +We need to convert our data frame into a SpatialPointsDataFrame. +```{r} +sp::coordinates(df_ll) <- c("LONG", "LAT") +``` +Define two models (for the two spde versions defined above, respectively): +```{r} + +# Model 1 is linear and uses the gaussian model 1 defined above: formula1 <- DS ~ 0 + Intercept(1)+ f( - main = df, + main = df_ll, model = spde1 ) - -formula2 <- DS ~ 0 + Intercept(1) + - f( - main = df, - model = spde2 - ) - - inlabru_model1 <- inlabru::bru(formula1, - data = df, + data = df_ll, family = "gaussian", options = list( verbose = FALSE ) ) +# The only difference for Model 2 is the different initial prior range set above (spde2): + +formula2 <- DS ~ 0 + Intercept(1) + + f( + main = df_ll, + model = spde2 + ) + inlabru_model2 <- inlabru::bru(formula2, - data = df, + data = df_ll, family = "gaussian", options = list( verbose = FALSE ) ) +``` + +Show the output of the first model, and plot the fit: +```{r inlabruoutput1} summary(inlabru_model1) -summary(inlabru_model2) -plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) -plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) +plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) + +plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) -plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) -plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df@data)]),exp(df@data$DS)) +``` +In the environment where this tutorial was rendered, the model_viewer widget window unfortunately crashes RStudio. +```{r modelviewer1, eval=FALSE} fdmr::model_viewer( model_output = inlabru_model1, mesh = mesh, - measurement_data = df, + measurement_data = df_ll, data_distribution = "Gaussian" ) + +``` + +Show the output of the second model, and plot the fit: +```{r inlabruoutput2} +summary(inlabru_model2) + +plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) + +plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) +``` + +```{r modelviewer2, eval=FALSE} + fdmr::model_viewer( model_output = inlabru_model2, mesh = mesh, - measurement_data = df, + measurement_data = df_ll, data_distribution = "Gaussian" ) ``` +# TO DO part + + +## Design the prior and the model + +We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. +The range parameter defines the lenght of the influence of each observation. + +In this small experiment, we have experimented by creating two different priors with two different range. +The model is a stochastic partial differential equation (SPDE). + +```{prior creation} +#julia add code for creating the two models +``` + +## Fitting the model + + + +## Spatial covariance, and how we could use that + +A future development we would like to work on, is to design the prior to be anisotropic. What we mean, is that the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. + + + +## Get the rasters in and use that information to build the mesh + +## Get model results considering some or all data and predictors + +## How to deal with real zeros? (Zero snow depth) + diff --git a/vignettes/snow.html b/vignettes/snow.html index 1d79f60f..c729f6be 100644 --- a/vignettes/snow.html +++ b/vignettes/snow.html @@ -4487,10 +4487,12 @@

Redo this with geographical coordinates

# Create a data frame with the first year of snow depth (ds) df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"), LAT = ncvar_get(ncin_ll,"lats"), - DS = ncvar_get(ncin_ll,"ds")[,1]) - -## Store the point locations in a separate data frame: -location_data_ll = df_ll[,c(1,2)] + #elev = ncvar_get(ncin_ll,"zs"), + DS = ncvar_get(ncin_ll,"ds")[,1], + DS_log = log(ncvar_get(ncin_ll,"ds")[,1])) + +## Store the point locations in a separate data frame: +location_data_ll = df_ll[,c(1,2)] 
# Try with the interactive mesh_builder
 fdmr::mesh_builder(location_data_ll)

The mesh_builder loads the shiny widget and creates a mesh, however @@ -4521,8 +4523,8 @@

Redo this with geographical coordinates

fdmr::plot_mesh(mesh,spatial_data = location_data_ll) 
## Warning in fdmr::plot_mesh(mesh, spatial_data = location_data_ll): Cannot read
 ## CRS from mesh, assuming WGS84
-
- +
+

This mesh is already a better starting point. There are two things we are not happy about:

    @@ -4538,14 +4540,191 @@

    Redo this with geographical coordinates

    mesh generation.

+
+

Start defining the priors

+

Let’s try to predict a spatially distributed snow depth map from our +data points.

+
# The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above).
+
+spde1 <- INLA::inla.spde2.pcmatern(
+  mesh = mesh,
+  prior.range = c(initial_range, 0.5),
+  prior.sigma = c(1, 0.01)
+)
+
+# Try with a different prior range - the entire initial_range
+spde2 <- INLA::inla.spde2.pcmatern(
+  mesh = mesh,
+  prior.range = c(initial_range*5, 0.5),
+  prior.sigma = c(1, 0.01)
+)
+

We need to convert our data frame into a SpatialPointsDataFrame.

+
sp::coordinates(df_ll) <- c("LONG", "LAT")
+

Define two models (for the two spde versions defined above, +respectively):

+
# Model 1 is linear and uses the gaussian model 1 defined above:
+formula1 <- DS ~ 0 + Intercept(1)+
+  f(
+    main = df_ll,
+    model = spde1
+  )
+
+inlabru_model1 <- inlabru::bru(formula1,
+                               data = df_ll,
+                               family = "gaussian",
+                               options = list(
+                                 verbose = FALSE
+                               )
+)
+
+# The only difference for Model 2 is the different initial prior range set above (spde2):
+
+formula2 <- DS ~ 0 + Intercept(1)   +
+  f(
+    main = df_ll,
+    model = spde2
+  )
+
+inlabru_model2 <- inlabru::bru(formula2,
+                               data = df_ll,
+                               family = "gaussian",
+                               options = list(
+                                 verbose = FALSE
+                               )
+)
+

Show the output of the first model, and plot the fit:

+
summary(inlabru_model1)
+
## inlabru version: 2.10.0
+## INLA version: 23.09.09
+## Components:
+## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
+## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
+## Likelihoods:
+##   Family: 'gaussian'
+##     Data class: 'SpatialPointsDataFrame'
+##     Predictor: DS ~ .
+## Time used:
+##     Pre = 0.945, Running = 12, Post = 0.375, Total = 13.4 
+## Fixed effects:
+##            mean    sd 0.025quant 0.5quant 0.975quant  mode kld
+## Intercept 0.669 0.059      0.555    0.668       0.79 0.669   0
+## 
+## Random effects:
+##   Name     Model
+##     f SPDE2 model
+## 
+## Model hyperparameters:
+##                                           mean    sd 0.025quant 0.5quant
+## Precision for the Gaussian observations 16.513 5.583      8.301   15.608
+## Range for f                              0.003 0.002      0.001    0.003
+## Stdev for f                              0.191 0.060      0.099    0.182
+##                                         0.975quant   mode
+## Precision for the Gaussian observations     30.012 13.932
+## Range for f                                  0.009  0.002
+## Stdev for f                                  0.334  0.166
+## 
+## Deviance Information Criterion (DIC) ...............: 32.50
+## Deviance Information Criterion (DIC, saturated) ....: 123.35
+## Effective number of parameters .....................: 27.33
+## 
+## Watanabe-Akaike information criterion (WAIC) ...: 46.44
+## Effective number of parameters .................: 34.99
+## 
+## Marginal log-Likelihood:  -39.66 
+##  is computed 
+## Posterior summaries for the linear predictor and the fitted values are computed
+## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
+
plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
+
+plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+

+

In the environment where this tutorial was rendered, the model_viewer +widget window unfortunately crashes RStudio.

+
fdmr::model_viewer(
+  model_output = inlabru_model1,
+  mesh = mesh,
+  measurement_data = df_ll,
+  data_distribution = "Gaussian"
+)
+

Show the output of the second model, and plot the fit:

+
summary(inlabru_model2)
+
## inlabru version: 2.10.0
+## INLA version: 23.09.09
+## Components:
+## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
+## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
+## Likelihoods:
+##   Family: 'gaussian'
+##     Data class: 'SpatialPointsDataFrame'
+##     Predictor: DS ~ .
+## Time used:
+##     Pre = 0.76, Running = 13.5, Post = 0.359, Total = 14.6 
+## Fixed effects:
+##           mean    sd 0.025quant 0.5quant 0.975quant  mode kld
+## Intercept 0.67 0.152      0.325    0.669      1.013 0.669   0
+## 
+## Random effects:
+##   Name     Model
+##     f SPDE2 model
+## 
+## Model hyperparameters:
+##                                           mean    sd 0.025quant 0.5quant
+## Precision for the Gaussian observations 11.626 1.979      8.149   11.484
+## Range for f                              0.017 0.014      0.004    0.013
+## Stdev for f                              0.156 0.075      0.054    0.142
+##                                         0.975quant   mode
+## Precision for the Gaussian observations     15.915 11.244
+## Range for f                                  0.055  0.008
+## Stdev for f                                  0.340  0.115
+## 
+## Deviance Information Criterion (DIC) ...............: 49.65
+## Deviance Information Criterion (DIC, saturated) ....: 108.49
+## Effective number of parameters .....................: 10.74
+## 
+## Watanabe-Akaike information criterion (WAIC) ...: 53.47
+## Effective number of parameters .................: 13.29
+## 
+## Marginal log-Likelihood:  -40.90 
+##  is computed 
+## Posterior summaries for the linear predictor and the fitted values are computed
+## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
+
plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
+plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+

+
fdmr::model_viewer(
+  model_output = inlabru_model2,
+  mesh = mesh,
+  measurement_data = df_ll,
+  data_distribution = "Gaussian"
+)
+

TO DO part

-
-

Get the rasters in and use that information to build the mesh

+
+

Design the prior and the model

+

We create a prior with the Matérn function, a penalized complexity +prior defined by range and standard deviation [cite Finn]. The range +parameter defines the lenght of the influence of each observation.

+

In this small experiment, we have experimented by creating two +different priors with two different range. The model is a stochastic +partial differential equation (SPDE).

+
#julia add code for creating the two models
+
+
+

Fitting the model

Spatial covariance, and how we could use that

+

A future development we would like to work on, is to design the prior +to be anisotropic. What we mean, is that the correlations should be +allowed to behave differently in each dimension of the feature space +where we define distances between points.

+
+
+

Get the rasters in and use that information to build the mesh

Get model results considering some or all data and predictors

From 79a3f3c845e2d979d75c74407d65f5e4609b66aa Mon Sep 17 00:00:00 2001 From: Kristoffer Aalstad Date: Fri, 3 Nov 2023 16:34:41 +0100 Subject: [PATCH 13/82] Update reference.Rmd --- vignettes/snow.Rmd | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index be7a210a..d6758355 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -9,7 +9,7 @@ vignette: > # Spatial distribution of snow -This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2018). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): +This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2019). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): Westermann, S. (2020).Snow depth and ground surface temperature at Bayelva research site, Svalbard [Data set]. Norstore. https://doi.org/10.11582/2020.00024 @@ -23,6 +23,8 @@ The dataset is originally projected (UTM zone 33X) and available in the data sub Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018 +Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models, Earth Syst. Sci. Data, 11, 261–299, https://doi.org/10.5194/essd-11-261-2019, 2019. + Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, 239, https://doi.org/10.1016/j.rse.2019.111618 Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673 From 2f5973fee0fa795e0bf4225aa748349842fe659c Mon Sep 17 00:00:00 2001 From: desireetreichler Date: Fri, 3 Nov 2023 17:08:21 +0100 Subject: [PATCH 14/82] Minor tidy up of typos, heading levels etc. --- vignettes/snow.Rmd | 15 ++++--- vignettes/snow.html | 96 ++++++++++++++++++++++++++++++--------------- 2 files changed, 73 insertions(+), 38 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 86d2ce25..b9c14d7e 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -7,7 +7,7 @@ vignette: > %\VignetteEncoding{UTF-8} --- -# Spatial distribution of snow +## Introduction This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2019). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): @@ -156,8 +156,15 @@ This mesh is already a better starting point. There are two things we are not ha ## Start defining the priors + Let's try to predict a spatially distributed snow depth map from our data points. +We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. +The range parameter defines the lenght of the influence of each observation. + +In this small experiment, we have experimented by creating two different priors with two different range. +The model is a stochastic partial differential equation (SPDE). + ```{r} # The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above). @@ -267,11 +274,7 @@ fdmr::model_viewer( ## Design the prior and the model -We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. -The range parameter defines the lenght of the influence of each observation. - -In this small experiment, we have experimented by creating two different priors with two different range. -The model is a stochastic partial differential equation (SPDE). +Add the rasters ```{prior creation} #julia add code for creating the two models diff --git a/vignettes/snow.html b/vignettes/snow.html index c729f6be..ad6d4a9c 100644 --- a/vignettes/snow.html +++ b/vignettes/snow.html @@ -4387,29 +4387,61 @@

Spatial distribution of snow - exploratory -
-

Spatial distribution of snow

+
+

Introduction

This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years -and snow cover duration derived from time lapse camera imagery. This -data has been used before to estimate snow depth and permafrost using -data assimilation methods (see literature references). Additionally, we -included topography and topographic parameters as these are known to -correlate with snow depth distribution. The dataset used in this -tutorial has been prepared by Kristoffer Aalstad based on the following -publicly available data:

-

XXX ADD dois for the datasets

+and snow cover duration derived from time lapse camera imagery (Aalstad +et al., 2020). This data has been used before for snow data assimilation +(Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) +using modeling and data assimilation methods (see references). +Additionally, we included elevation and other derived topographic +parameters (slope, aspect, topographic position index) as these are +known to influence the snow depth distribution. These topographic +parameters were derived from a high resolution digital elevation model +(Boike et al., 2019). The dataset used in this tutorial has been +prepared by Kristoffer Aalstad as a netCDF by combining and processing +the following publicly available datasets that have been used in several +studies (see literature references):

+

Westermann, S. (2020).Snow depth and ground surface temperature at +Bayelva research site, Svalbard [Data set]. Norstore. https://doi.org/10.11582/2020.00024

+

Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 +high-resolution snow cover maps covering the 2012-2017 snowmelt seasons +in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5010944

+

Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, +P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., +Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and +multi channel) from aerial overflights in 2008 over Bayelva +(Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. +https://doi.org/10.1594/PANGAEA.884730

The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data.

Literature references

-

Aalstad, K. et al. (2018). doi:10.5194/tc-12-247-2018. TC.

-

Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE.

-

Boike, J. et al. (2018). doi:10.5194/essd-10-355-2018. ESSD.

-

Zweigel, R. et al. (2021). doi:10.1029/2020JF005673.

+

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, +L. (2018). Ensemble-based assimilation of fractional snow-covered area +satellite retrievals to estimate the snow distribution at Arctic sites. +The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018

+

Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., +Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., +Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A +16-year record (2002–2017) of permafrost, active-layer, and +meteorological conditions at the Samoylov Island Arctic permafrost +research site, Lena River delta, northern Siberia: an opportunity to +validate remote-sensing data and land surface, snow, and permafrost +models, Earth Syst. Sci. Data, 11, 261–299, https://doi.org/10.5194/essd-11-261-2019, 2019.

+

Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating +satellite retrieved fractional snow-covered area at a high-Arctic site +using terrestrial photography. Remote Sensing of Environment, 239, https://doi.org/10.1016/j.rse.2019.111618

+

Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., +Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow +redistribution and its effect on ground surface temperature at a +high-Arctic site on Svalbard. Journal of Geophysical Research: Earth +Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673

+

Import and load the data

@@ -4523,8 +4555,8 @@

Redo this with geographical coordinates

fdmr::plot_mesh(mesh,spatial_data = location_data_ll) 
## Warning in fdmr::plot_mesh(mesh, spatial_data = location_data_ll): Cannot read
 ## CRS from mesh, assuming WGS84
-
- +
+

This mesh is already a better starting point. There are two things we are not happy about:

    @@ -4544,6 +4576,12 @@

    Redo this with geographical coordinates

    Start defining the priors

    Let’s try to predict a spatially distributed snow depth map from our data points.

    +

    We create a prior with the Matérn function, a penalized complexity +prior defined by range and standard deviation [cite Finn]. The range +parameter defines the lenght of the influence of each observation.

    +

    In this small experiment, we have experimented by creating two +different priors with two different range. The model is a stochastic +partial differential equation (SPDE).

    # The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above).
 
 spde1 <- INLA::inla.spde2.pcmatern(
@@ -4604,7 +4642,7 @@ 
    Start defining the priors
    
 ##     Data class: 'SpatialPointsDataFrame'
 ##     Predictor: DS ~ .
 ## Time used:
-##     Pre = 0.945, Running = 12, Post = 0.375, Total = 13.4 
+##     Pre = 0.953, Running = 12.1, Post = 0.351, Total = 13.4 
 ## Fixed effects:
 ##            mean    sd 0.025quant 0.5quant 0.975quant  mode kld
 ## Intercept 0.669 0.059      0.555    0.668       0.79 0.669   0
@@ -4615,20 +4653,20 @@ 
    Start defining the priors
    
 ## 
 ## Model hyperparameters:
 ##                                           mean    sd 0.025quant 0.5quant
-## Precision for the Gaussian observations 16.513 5.583      8.301   15.608
+## Precision for the Gaussian observations 16.512 5.579      8.304   15.607
 ## Range for f                              0.003 0.002      0.001    0.003
 ## Stdev for f                              0.191 0.060      0.099    0.182
 ##                                         0.975quant   mode
-## Precision for the Gaussian observations     30.012 13.932
+## Precision for the Gaussian observations     30.001 13.933
 ## Range for f                                  0.009  0.002
 ## Stdev for f                                  0.334  0.166
 ## 
 ## Deviance Information Criterion (DIC) ...............: 32.50
-## Deviance Information Criterion (DIC, saturated) ....: 123.35
-## Effective number of parameters .....................: 27.33
+## Deviance Information Criterion (DIC, saturated) ....: 123.34
+## Effective number of parameters .....................: 27.32
 ## 
 ## Watanabe-Akaike information criterion (WAIC) ...: 46.44
-## Effective number of parameters .................: 34.99
+## Effective number of parameters .................: 34.98
 ## 
 ## Marginal log-Likelihood:  -39.66 
 ##  is computed 
@@ -4638,7 +4676,7 @@ 
    Start defining the priors
    
 
 
 plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
    -

    +

    In the environment where this tutorial was rendered, the model_viewer widget window unfortunately crashes RStudio.

    fdmr::model_viewer(
@@ -4659,7 +4697,7 @@ 
    Start defining the priors
    
 ##     Data class: 'SpatialPointsDataFrame'
 ##     Predictor: DS ~ .
 ## Time used:
-##     Pre = 0.76, Running = 13.5, Post = 0.359, Total = 14.6 
+##     Pre = 0.724, Running = 13.5, Post = 0.361, Total = 14.5 
 ## Fixed effects:
 ##           mean    sd 0.025quant 0.5quant 0.975quant  mode kld
 ## Intercept 0.67 0.152      0.325    0.669      1.013 0.669   0
@@ -4674,7 +4712,7 @@ 
    Start defining the priors
    
 ## Range for f                              0.017 0.014      0.004    0.013
 ## Stdev for f                              0.156 0.075      0.054    0.142
 ##                                         0.975quant   mode
-## Precision for the Gaussian observations     15.915 11.244
+## Precision for the Gaussian observations     15.915 11.243
 ## Range for f                                  0.055  0.008
 ## Stdev for f                                  0.340  0.115
 ## 
@@ -4700,17 +4738,11 @@ 
    Start defining the priors
    
   data_distribution = "Gaussian"
 )
-

TO DO part

Design the prior and the model

-

We create a prior with the Matérn function, a penalized complexity -prior defined by range and standard deviation [cite Finn]. The range -parameter defines the lenght of the influence of each observation.

-

In this small experiment, we have experimented by creating two -different priors with two different range. The model is a stochastic -partial differential equation (SPDE).

+

Add the rasters

#julia add code for creating the two models
From 493b3588dff6d2df355ed5d5ec117011b317c567 Mon Sep 17 00:00:00 2001 From: Alexander Minakov Date: Sun, 5 Nov 2023 13:58:09 +0100 Subject: [PATCH 15/82] new geophysics tutorial draft and MT dataset added --- vignettes/data/MT_Svalbard_Z.csv | 37 ++++++++ vignettes/tutorial_geophysics.Rmd | 149 ++++++++++++++++++++++++++++++ 2 files changed, 186 insertions(+) create mode 100644 vignettes/data/MT_Svalbard_Z.csv create mode 100644 vignettes/tutorial_geophysics.Rmd diff --git a/vignettes/data/MT_Svalbard_Z.csv b/vignettes/data/MT_Svalbard_Z.csv new file mode 100644 index 00000000..ca612830 --- /dev/null +++ b/vignettes/data/MT_Svalbard_Z.csv @@ -0,0 +1,37 @@ +T,Code,Lat,Lon,X,Y,Zxx,DZxx +64,W01,79.439116,13.371895,344352.8496,8829185.331,0.52156-1.2687i,0.054863 +128,W01,79.439116,13.371895,344352.8496,8829185.331,0.97673-0.94615i,0.079632 +256,W01,79.439116,13.371895,344352.8496,8829185.331,1.275-0.54561i,0.10554 +64,W02,79.471702,13.403403,345465.1801,8832708.491,0.36581+0.027603i,0.012205 +128,W02,79.471702,13.403403,345465.1801,8832708.491,0.3344+0.13025i,0.019636 +256,W02,79.471702,13.403403,345465.1801,8832708.491,0.33298+0.071971i,0.016023 +64,W03,79.35887,14.091742,357911.5152,8818456.864,0.67767+0.53303i,0.01449 +128,W03,79.35887,14.091742,357911.5152,8818456.864,0.6028+0.52425i,0.022896 +256,W03,79.35887,14.091742,357911.5152,8818456.864,0.46475+0.40616i,0.029995 +64,W04,79.392861,14.0056,356602.2036,8822435.623,-0.44539-1.2946i,0.012704 +128,W04,79.392861,14.0056,356602.2036,8822435.623,-0.12597-0.99152i,0.016897 +256,W04,79.392861,14.0056,356602.2036,8822435.623,0.13967-0.52094i,0.021547 +64,W05,79.372686,13.864863,353455.5873,8820549.936,0.8007+0.63546i,0.018563 +128,W05,79.372686,13.864863,353455.5873,8820549.936,0.90669+0.78722i,0.025465 +256,W05,79.372686,13.864863,353455.5873,8820549.936,0.54578+0.61109i,0.022635 +64,W06,79.337495,13.893527,353563.6793,8816578.44,0.85439+0.74466i,0.039878 +128,W06,79.337495,13.893527,353563.6793,8816578.44,1.0516+1.1238i,0.054053 +256,W06,79.337495,13.893527,353563.6793,8816578.44,0.49092+0.83855i,0.042233 +64,W07,79.42051667,13.736,351485.8998,8826175.357,0.81914+0.80551i,0.023629 +128,W07,79.42051667,13.736,351485.8998,8826175.357,0.78128+0.68671i,0.030426 +256,W07,79.42051667,13.736,351485.8998,8826175.357,0.51883+0.59256i,0.030384 +64,W08,79.431243,13.699478,350892.5935,8827456.802,0.39831-0.1402i,0.01706 +128,W08,79.431243,13.699478,350892.5935,8827456.802,0.44428-0.09936i,0.022626 +256,W08,79.431243,13.699478,350892.5935,8827456.802,0.42242+0.15965i,0.028102 +64,W09,79.49686111,13.91783333,356218.9673,8834177.87,-0.12304-0.52841i,0.00536 +128,W09,79.49686111,13.91783333,356218.9673,8834177.87,-0.011371-0.37717i,0.0055408 +256,W09,79.49686111,13.91783333,356218.9673,8834177.87,0.040909-0.16656i,0.0060876 +64,W10,79.472456,13.944975,356438.2811,8831406.374,0.19744-0.74573i,0.014357 +128,W10,79.472456,13.944975,356438.2811,8831406.374,0.4019-0.45596i,0.021548 +256,W10,79.472456,13.944975,356438.2811,8831406.374,0.50063+0.068497i,0.027314 +64,W11,79.486643,13.235664,342293.8848,8834811.661,0.82246+2.7602i,0.016108 +128,W11,79.486643,13.235664,342293.8848,8834811.661,0.38066+1.8882i,0.02087 +256,W11,79.486643,13.235664,342293.8848,8834811.661,0.17339+1.0451i,0.027693 +64,W12,79.425669,13.378223,344285.4562,8827679.973,0.69293-0.38905i,0.032038 +128,W12,79.425669,13.378223,344285.4562,8827679.973,0.90861-0.030763i,0.0516 +256,W12,79.425669,13.378223,344285.4562,8827679.973,0.813+0.39348i,0.073448 diff --git a/vignettes/tutorial_geophysics.Rmd b/vignettes/tutorial_geophysics.Rmd new file mode 100644 index 00000000..2a6679f0 --- /dev/null +++ b/vignettes/tutorial_geophysics.Rmd @@ -0,0 +1,149 @@ +--- +title: "4DModeller for geophysical signals" +output: html_notebook +--- + +## Introduction +This is a tutorial to apply R-INLA to modeling geophysical data. The data represent time-series at some locations distributed over a line or over an area. There are two possible case studies one is seismic data with seismic stations located along a line. Each station record 4 components of acoustic signal X-Y-Z particle displacement on geophone and presure component on hydrophone sensor. The second case is magnteotelluric data with stations distrubuted over some area. The signal has 4 channels: 2 magnetic and 2 electric field channels. In the dataset only 2-3 stations measure at the same. The spatiotemporal evolution of the field is governed by Maxwell equations. The source of EM field is disturbance of ionosphere due to solar activity, electrical structure of the crust and noise component (cultural EM noise, wind, rain, local conductors). The goal is to describe the source signal components in space in time. +The spatiotemporal evolution of the field is governed by wave equation. The small-scale heterogenetities in earth crust produce multiply scattered wavefield, getting more expressed at a later times and called coda wave. The goal is to learn about correlations between signals at diffent stations and from this predict distribution of heterogeneities. + +## Pre-processing and Import data + +```{r} +# Set the path to the CSV file +data_path <- "data/MT_Svalbard_Z.csv" + +# Read the CSV file into a data frame +d <- read.csv(data_path) + +# Display the first 30 rows of the data frame +print(head(d, 30)) + +# Create a scatter plot +plot(d$X, d$Y, pch = 20, main = 'Svalbard MT sites', xlab = 'X', ylab = 'Y') + +``` + +```{r} +locations = d[, c('Lon', 'Lat')] +locations = unique(locations) +names(locations)=c('LONG','LAT') +``` + +## Meshing +```{r} + +mesh <- fmesher::fm_mesh_2d(loc.domain = locations, + max.edge = 0.05, + cutoff = 1e-3, + offset=0.1 + ) +plot(mesh) +points(locations, col = "red") + +fdmr::plot_mesh(mesh) + +``` + +## Stochastic modeling +```{r} +library(INLA) + +# Synthetic data generation +set.seed(123) # For reproducibility +n <- 100 # Number of time points +stations <- 12 # Number of stations + +# Generate time index +time_index <- 1:n + +# Generate spatial index (station IDs) +space_index <- rep(1:stations, each = n) + +# Simulate some harmonic signals with noise for three stations +harmonic_data <- data.frame( + station = factor(space_index), + time = rep(time_index, stations), + observation = sin(rep(time_index, stations) * 2 * pi / 50) + + rnorm(n * stations, sd = 0.5) + + rep(rnorm(stations, sd = 3), each = n) # Station-specific offset +) + +# Define the model with harmonic terms for time and spatial correlation +formula <- observation ~ f(station, model = "iid") + + f(time, model = "rw1", cyclic = TRUE) + +# Fit the model using INLA +result <- inla(formula, family = "gaussian", data = harmonic_data) + +# Display the summary of the results +summary(result) + +# Visualize the fitted values +plot(harmonic_data$time, harmonic_data$observation, col = harmonic_data$station, pch = 19, cex = 0.5, xlab = "Time", ylab = "Observation", main = "MT signals at 12 stations") +points(harmonic_data$time, result$summary.fitted.values$mean, pch = 4, cex = 0.7, col = "blue") +legend("topright", legend = c("Observations", "Fitted"), col = c("black", "blue"), pch = c(19, 4)) + + +# Extract the hyperparameters of the spatial field +spatial_hyperparams <- result$summary.hyperpar + +# Print the hyperparameters +print(spatial_hyperparams) + +``` +## Observed time series will come here.. + + +## This part doesn't work + +```{r} +simdf <- data.frame( + ID = d$Code, + time = d$T, + datn = Re(d$Zxx), + E = Re(d$DZxx), + LONG = d$Lon, + LAT = d$Lat +) + + +initial_range = 0.1 +spde <- INLA::inla.spde2.pcmatern( + mesh = mesh, + prior.range = c(initial_range, 0.5), + prior.sigma = c(1, 0.01) +) + +sigma0 <- 100 +range0 <- 0.03 +kappa0 <- sqrt(8 / 1) / range0 +tau0 <- 1 / (sqrt(4 * pi) * kappa0 * sigma0) + +inla.seed <- sample.int(n = 1E4, size = length(d$T)) +Q <- INLA::inla.spde.precision(spde, theta = c(log(tau0), log(kappa0))) +x.mat <- matrix(NA, ncol = length(d$T), nrow = mesh$n) +for (co in 1:ncol(x.mat)) { + x.mat[, co] <- INLA::inla.qsample(n = 1, Q) +} + +A <- INLA::inla.spde.make.A(mesh = mesh, loc = locations) +x.dat <- matrix(NA, ncol = n.time, nrow = n) +for (t in 1:ncol(x.dat)) { + x.dat[, t] <- drop(A %*% x.mat[, t]) +} + + +alpha <- 0.9 +sp.mat <- matrix(NA, ncol = n.time, nrow = n) +sp.mat[, 1] <- x.dat[, 1] +for (t in 2:n.time) { + sp.mat[, t] <- alpha * sp.mat[, t - 1] + x.dat[, t] +} + +beta0 <- 0.5 +sigma_e <- 0.1 +lin.pred <- beta0 + sp.mat + matrix(rnorm(n * n.time, 0, sigma_e), ncol = n.time) + +``` + From 5e9078c778921b1f15295556f35826be7721753e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?D=C3=A9sir=C3=A9e=20Treichler?= Date: Mon, 13 Nov 2023 11:23:35 +0100 Subject: [PATCH 16/82] Deleted outdated file vignettes/Readme.md MD version of snow.Rmd, outdated. --- vignettes/Readme.md | 71 --------------------------------------------- 1 file changed, 71 deletions(-) delete mode 100644 vignettes/Readme.md diff --git a/vignettes/Readme.md b/vignettes/Readme.md deleted file mode 100644 index 49cafe3a..00000000 --- a/vignettes/Readme.md +++ /dev/null @@ -1,71 +0,0 @@ -# Spatial distribution of snow - exploratory tutorial - -This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from timelapse camera imagery. This data has been used before to estimate snow depth and permafrost using data assimilation methods (see literature references). Additionally, we included topography and topographic parameters as these are known to correlate with snow depth distribution. The dataset used in this tutorial has been prepared by Kristoffer Aalstad based on the following publicly available data: - -**XXX ADD dois for the datasets** - -The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), there is also a lat/lon version of the same data. - -### Literature references - -Aalstad, K. et al. (2018). . TC. - -Aalstad, K. et al. (2020). doi: 10.1016/j.rse.2019.111618. RSE. - -Boike, J. et al. (2018). . ESSD. - -Zweigel, R. et al. (2021). . - -## Import and load the data - -```{r modules} -# load modules used in this tutorial -library(ncdf4) -#library(tidyverse) - -# Specify where your data is stored locally -ncpath <- "N:/courses_conferences/2023_4DM_hackathon/fdmr/vignettes/data/" -ncname <- "Bayelva_snow" -ncfname <- paste(ncpath, ncname, ".nc", sep="") - -#open data -ncin <- nc_open(ncfname) -head(ncin) -``` - -## Get relevant features -The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We start with the snow depth points only: - -```{r modules} -# specify the longitude and latitude columns. Note that the coordinates are utm! -longitude <- ncvar_get(ncin,"xs") -latitude <-ncvar_get(ncin,"ys") - -# Get the first year of snow depth (ds) -ds <- ncvar_get(ncin,"ds")[,1] - -# Create a data frame -df <- data.frame(LONG = longitude,LAT = latitude,ds) -head(df) -``` - -## Build the mesh -The shiny app doesn't work with UTM, so we need to do this non-interactively. - -```{r modules} -# This doesn't work with UTM: -# fdmr::mesh_builder(spatial_data = df, crs="+proj=utm +zone=33") - -# We have to define an UTM -crs <- "+proj=utm +zone=33" -mesh <- fmesher::fm_mesh_2d_inla(loc = -df[,c(1,2)], max.edge = c(20,40),crs = crs ) - -plot(mesh) -``` - -## Visualise the mesh: also plot our data points -This doesn't seem to work with the built-in plot_mesh command in UTM. -```{r modules} -fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) -``` From 5929dc47dbf0a8a47addc10417bbecbf4a4626c6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?D=C3=A9sir=C3=A9e=20Treichler?= Date: Mon, 13 Nov 2023 11:47:56 +0100 Subject: [PATCH 17/82] Read-through and fix up to line 133 No new code, just some changes in wording. Added links to created issues. --- vignettes/snow.Rmd | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index b9c14d7e..6bcaf589 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -17,7 +17,7 @@ Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 high-resolution s Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and multi channel) from aerial overflights in 2008 over Bayelva (Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. https://doi.org/10.1594/PANGAEA.884730 -The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository. As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data. +The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository (data/Bayelva_snow.nc). As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data (data/Bayelva_snow_geo.nc). ### Literature references @@ -30,11 +30,11 @@ Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrie Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673 ## Import and load the data +We use the library ncdf4 to open the snow dataset: ```{r modules} # load modules used in this tutorial library(ncdf4) -#library(tidyverse) ``` ```{r dataload} @@ -48,7 +48,7 @@ ncin <- nc_open(ncfname) #head(ncin) ``` -## Get relevant features +### Get relevant features The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We start with the snow depth points only: @@ -65,15 +65,14 @@ df <- data.frame(LONG = longitude,LAT = latitude,ds) head(df) ``` -## Build the mesh - -When trying this whit our UTM projected dataset, we don't get a result: +### Build the mesh +When trying to build a mesh whit our UTM projected dataset, we don't get a result as the shiny app does not seem to work with UTM: ```{r, eval = FALSE} fdmr::mesh_builder(spatial_data = df) ``` -The shiny app seems not to work with UTM, so we when working with UTM we need to try and do this non-interactively. +Using fmesher directly (non-interactively, without the fdmr app added functionalities) works and we can plot the resulting mesh outside the geographical context that would have been provided by mesh_builder. ```{r plotmesh} # Define a proj string for our UTM zone @@ -87,19 +86,19 @@ mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], # plot the result plot(mesh) ``` - -We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. This is causing problems - fdmr seems not to be able to deal with our UTM coordinates. +When trying to plot the mesh with fdmr::plot_mesh, we run into the same problem as above: The plot_mesh function seems to assume lat/lon coordinates and thus zooms out to max. ```{r, eval = FALSE} fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) ``` -The plot_mesh function seems to assume lat/lon coordinates and thus zooms out to max. +***TO DO:*** *We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. mesh_builder and plot_mesh suffer from the same problem and can't deal with UTM coordinates. fdmr should be updated to beable to deal with our UTM coordinates. This is especially important in the Arctic, where geographical coordinates result in highly distorted maps in most map grids. (Issue created: https://github.com/4DModeller/fdmr/issues/248#issue-1974643323)* + ## Redo this with geographical coordinates -Let's see whether this works if our data is in geographical coordinates instead. Redo all the steps from above: +Let's see whether our approach works if our data is in geographical coordinates instead. Redo all the steps from above: ```{r latlonload} ncname_ll <- "Bayelva_snow_geo" @@ -125,11 +124,11 @@ fdmr::mesh_builder(location_data_ll) The mesh_builder loads the shiny widget and creates a mesh, however there are several issues: -- the map part of the window covers the entire window, so the rulers disappear +- On several machines where we tried this and the covid tutorial, the map part of the window covers the entire window, so the rulers disappear. ***TO DO:*** *fix this bug (Issue created: https://github.com/4DModeller/fdmr/issues/243#issue-1974424767)* -- the mesh is far too coarse for the small area we are looking at (1km2) +- The mesh is far too coarse for the small area we are looking at (1km2). ***TO DO:*** *Make standard mesh size dependent on area.* -- closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. +- On one machine, closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. *Bug report filed: https://github.com/4DModeller/fdmr/issues/255#issue-1976392121) Let's try to specify the mesh manually using values for max.edge and cutoff that are reasonable for our domain and length scales. From bf21b84fe16ffb4370de41dd71b8b6dc84c49bf4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?D=C3=A9sir=C3=A9e=20Treichler?= Date: Fri, 17 Nov 2023 12:05:22 +0100 Subject: [PATCH 18/82] Added TO DO part with planned outcome/further development to the tutorial --- vignettes/snow.Rmd | 80 ++++++++------ vignettes/snow.html | 250 +++++++++++++++++++++++++++----------------- 2 files changed, 203 insertions(+), 127 deletions(-) diff --git a/vignettes/snow.Rmd b/vignettes/snow.Rmd index 6bcaf589..b1515e9e 100644 --- a/vignettes/snow.Rmd +++ b/vignettes/snow.Rmd @@ -11,25 +11,26 @@ vignette: > This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2019). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): -Westermann, S. (2020).Snow depth and ground surface temperature at Bayelva research site, Svalbard [Data set]. Norstore. https://doi.org/10.11582/2020.00024 +Westermann, S. (2020).Snow depth and ground surface temperature at Bayelva research site, Svalbard [Data set]. Norstore. -Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5010944 +Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. -Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and multi channel) from aerial overflights in 2008 over Bayelva (Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. https://doi.org/10.1594/PANGAEA.884730 +Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and multi channel) from aerial overflights in 2008 over Bayelva (Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository (data/Bayelva_snow.nc). As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data (data/Bayelva_snow_geo.nc). -### Literature references +### Literature references -Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018 +Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere, 12, 247--270, -Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models, Earth Syst. Sci. Data, 11, 261–299, https://doi.org/10.5194/essd-11-261-2019, 2019. +Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A 16-year record (2002--2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models, Earth Syst. Sci. Data, 11, 261--299, , 2019. -Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, 239, https://doi.org/10.1016/j.rse.2019.111618 +Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, 239, -Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673 +Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. ## Import and load the data + We use the library ncdf4 to open the snow dataset: ```{r modules} @@ -66,6 +67,7 @@ head(df) ``` ### Build the mesh + When trying to build a mesh whit our UTM projected dataset, we don't get a result as the shiny app does not seem to work with UTM: ```{r, eval = FALSE} @@ -86,6 +88,7 @@ mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], # plot the result plot(mesh) ``` + When trying to plot the mesh with fdmr::plot_mesh, we run into the same problem as above: The plot_mesh function seems to assume lat/lon coordinates and thus zooms out to max. ```{r, eval = FALSE} @@ -93,8 +96,7 @@ When trying to plot the mesh with fdmr::plot_mesh, we run into the same problem fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) ``` -***TO DO:*** *We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. mesh_builder and plot_mesh suffer from the same problem and can't deal with UTM coordinates. fdmr should be updated to beable to deal with our UTM coordinates. This is especially important in the Arctic, where geographical coordinates result in highly distorted maps in most map grids. (Issue created: https://github.com/4DModeller/fdmr/issues/248#issue-1974643323)* - +***TO DO:*** *We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. mesh_builder and plot_mesh suffer from the same problem and can't deal with UTM coordinates. fdmr should be updated to beable to deal with our UTM coordinates. This is especially important in the Arctic, where geographical coordinates result in highly distorted maps in most map grids. (Issue created: )* ## Redo this with geographical coordinates @@ -122,13 +124,13 @@ location_data_ll = df_ll[,c(1,2)] fdmr::mesh_builder(location_data_ll) ``` -The mesh_builder loads the shiny widget and creates a mesh, however there are several issues: +The mesh_builder loads the shiny widget and creates a mesh, however there are several issues: -- On several machines where we tried this and the covid tutorial, the map part of the window covers the entire window, so the rulers disappear. ***TO DO:*** *fix this bug (Issue created: https://github.com/4DModeller/fdmr/issues/243#issue-1974424767)* +- On several machines where we tried this and the covid tutorial, the map part of the window covers the entire window, so the rulers disappear. ***TO DO:*** *fix this bug (Issue created: )* -- The mesh is far too coarse for the small area we are looking at (1km2). ***TO DO:*** *Make standard mesh size dependent on area.* +- The mesh is far too coarse for the small area we are looking at (1km2). ***TO DO:*** *Make standard mesh size dependent on area.* -- On one machine, closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. *Bug report filed: https://github.com/4DModeller/fdmr/issues/255#issue-1976392121) +- On one machine, closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. *(Bug report filed: )* Let's try to specify the mesh manually using values for max.edge and cutoff that are reasonable for our domain and length scales. @@ -147,22 +149,20 @@ mesh <- fmesher::fm_mesh_2d(loc = location_data_ll[, c("LONG", "LAT")], fdmr::plot_mesh(mesh,spatial_data = location_data_ll) ``` -This mesh is already a better starting point. There are two things we are not happy about: +This mesh is already a better starting point. There are two things we are not happy about: -- The mesh is obviously distorted in N/S direction: With latitude/longitude units representing substantially different spatial distances at polar latitudes, edge lengths should be defined differently in E/W and N/S. Or one could simply use UTM (which is currently not supported for the visualisation tools). - -- Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. +- The mesh is obviously distorted in N/S direction: With latitude/longitude units representing substantially different spatial distances at polar latitudes, edge lengths should be defined differently in E/W and N/S. **\ + *TO DO:*** *Account for unequal distances in x/y. And as above, make it possible to use UTM (which is currently not supported for the visualisation tools).* +- Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. ## Start defining the priors Let's try to predict a spatially distributed snow depth map from our data points. -We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation [cite Finn]. -The range parameter defines the lenght of the influence of each observation. +We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation *(**TO DO**: [cite Finn])*. The range parameter defines the length of the influence of each observation. -In this small experiment, we have experimented by creating two different priors with two different range. -The model is a stochastic partial differential equation (SPDE). +In this small experiment, we have experimented by creating two different priors with two different range. The model is a stochastic partial differential equation (SPDE). ```{r} # The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above). @@ -183,11 +183,13 @@ spde2 <- INLA::inla.spde2.pcmatern( ``` We need to convert our data frame into a SpatialPointsDataFrame. + ```{r} sp::coordinates(df_ll) <- c("LONG", "LAT") ``` Define two models (for the two spde versions defined above, respectively): + ```{r} # Model 1 is linear and uses the gaussian model 1 defined above: @@ -222,8 +224,8 @@ inlabru_model2 <- inlabru::bru(formula2, ) ``` - Show the output of the first model, and plot the fit: + ```{r inlabruoutput1} summary(inlabru_model1) @@ -236,6 +238,7 @@ plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_l ``` In the environment where this tutorial was rendered, the model_viewer widget window unfortunately crashes RStudio. + ```{r modelviewer1, eval=FALSE} fdmr::model_viewer( @@ -249,6 +252,7 @@ fdmr::model_viewer( ``` Show the output of the second model, and plot the fit: + ```{r inlabruoutput2} summary(inlabru_model2) @@ -268,31 +272,41 @@ fdmr::model_viewer( ``` -# TO DO part +# TO DO: planned outcome of the hackathon/tutorial structure ## Design the prior and the model -Add the rasters +- Use the UTM-projected data and do everything in UTM coordinates -```{prior creation} -#julia add code for creating the two models -``` +- Load both the point measurements and the rasters. There are topographic descriptors and snow duration data. Snow data is available for several years. + +- Use both the snow depth measurements and the different raster data to generate the mesh: we want to account for spatial variability of snow depth in the mesh. In areas where the terrain doesn't vary much the mesh doesn't need to be that fine, and the contrary is the case in rough topography with greatly varying snow depth. This information is indirectly available in the topography feature rasters or in a different way in the snow duration rasters. ## Fitting the model +Get model results considering some or all observation data and topographic predictors. Consider that snow duration could be seen as both a predictor and observation. + +-\> do this for one year, and/or several years. + +How do the results look like? Change parameters and adjust until the snow depth map matches snow duration (best validation we have, spatially) and locally the snow depth point measurements. +Do the results for all years look different, or the same? -## Spatial covariance, and how we could use that +## How to deal with real zeros? -A future development we would like to work on, is to design the prior to be anisotropic. What we mean, is that the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. +What happens when/where there is zero snow depth? The model needs to distinguish between no data and zero snow depth, and for temporal evolution also be able to predict zero snow depth after snow melt-out. +## Spatial covariance as prior for data assimilation +We think 4DM could be a nice complementary tool for SWE retrievals, in particular for the prior covariance in spatio-temporal problems. For this -## Get the rasters in and use that information to build the mesh +- we want the prior to be anisotropic. What we mean with this: the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. Features/predictors in this example/tutorial are the provided terrain parameters. -## Get model results considering some or all data and predictors +- we wanted to export the spatial covariance matrix resulting from this tutorial and test wheter we can use it to define the prior covariance in a modelling study, for example within our data assimilation framework MuSA (, see Alonso-González et al., 2022; ). That would necessarily be a separate study from this tutorial but results could eventually be added if this works and results are promising. -## How to deal with real zeros? (Zero snow depth) +## Temporal evolution: snow map time series +How can we get temporal evolution in here? Snow depth/SWE distribution map is a result that's only useful if snow depth measurements are available at maximum snow time (which is the case for this data, but most often unfeasible). If it should be able to compete against other (modelling) approaches, we expect 4Dmodeller to be able to make map time series, as we do with our data assimilation approach. For this to work, what other predictor data needs to be added in addition to meltout date? Adding the day of the first snow fall may lead to linear interpolation until max snow, and then linear interpolation until meltout...? Here, we would expect that some physical/meachanistic model would come in, at least a simple degree day model, using precipitation/temperature from ERA5-land, for example..? +## diff --git a/vignettes/snow.html b/vignettes/snow.html index ad6d4a9c..cb487688 100644 --- a/vignettes/snow.html +++ b/vignettes/snow.html @@ -4416,9 +4416,10 @@

Introduction

(Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. https://doi.org/10.1594/PANGAEA.884730

The dataset is originally projected (UTM zone 33X) and available in -the data sub-folder in this repository. As UTM was not supported for all -steps we anticipated while developing this tutorial (see below), we also -created also a lat/lon version of the same data.

+the data sub-folder in this repository (data/Bayelva_snow.nc). As UTM +was not supported for all steps we anticipated while developing this +tutorial (see below), we also created also a lat/lon version of the same +data (data/Bayelva_snow_geo.nc).

Literature references

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, @@ -4445,9 +4446,9 @@

Literature references

Import and load the data

+

We use the library ncdf4 to open the snow dataset:

# load modules used in this tutorial
-library(ncdf4)
-#library(tidyverse)
+library(ncdf4)
# Find where your data is stored locally
 ncpath <- dirname(rstudioapi::getSourceEditorContext()$path)
 ncname <- "Bayelva_snow" 
@@ -4456,9 +4457,8 @@ 
Import and load the data

 #open data 
 ncin <- nc_open(ncfname) 
 #head(ncin)
-
-
-

Get relevant features

+
+

Get relevant features

The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We @@ -4481,13 +4481,14 @@

Get relevant features

## 5 432408.1 8762976 0.65 ## 6 432105.0 8763344 0.72
-
-

Build the mesh

-

When trying this whit our UTM projected dataset, we don’t get a -result:

+
+

Build the mesh

+

When trying to build a mesh whit our UTM projected dataset, we don’t +get a result as the shiny app does not seem to work with UTM:

fdmr::mesh_builder(spatial_data = df)
-

The shiny app seems not to work with UTM, so we when working with UTM -we need to try and do this non-interactively.

+

Using fmesher directly (non-interactively, without the fdmr app added +functionalities) works and we can plot the resulting mesh outside the +geographical context that would have been provided by mesh_builder.

# Define a proj string for our UTM zone
 crs <- "+proj=utm +zone=33" 
 
@@ -4499,17 +4500,22 @@ 
Build the mesh

 # plot the result
 plot(mesh)

-

We would like to also show our data points and see this in a -geographical context to check whether everything is looking OK. This is -causing problems - fdmr seems not to be able to deal with our UTM -coordinates.

+

When trying to plot the mesh with fdmr::plot_mesh, we run into the +same problem as above: The plot_mesh function seems to assume lat/lon +coordinates and thus zooms out to max.

fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)])
-

The plot_mesh function seems to assume lat/lon coordinates and thus -zooms out to max.

+

TO DO: We would like to also show our +data points and see this in a geographical context to check whether +everything is looking OK. mesh_builder and plot_mesh suffer from the +same problem and can’t deal with UTM coordinates. fdmr should be updated +to beable to deal with our UTM coordinates. This is especially important +in the Arctic, where geographical coordinates result in highly distorted +maps in most map grids. (Issue created: https://github.com/4DModeller/fdmr/issues/248#issue-1974643323)

+

Redo this with geographical coordinates

-

Let’s see whether this works if our data is in geographical +

Let’s see whether our approach works if our data is in geographical coordinates instead. Redo all the steps from above:

ncname_ll <- "Bayelva_snow_geo"
 ncfname_ll <- paste(ncpath, '/data/', ncname_ll, ".nc", sep="")
@@ -4530,14 +4536,17 @@ 
Redo this with geographical coordinates

 
The mesh_builder loads the shiny widget and creates a mesh, however
 there are several issues:

 
    
-
  • the map part of the window covers the entire window, so the
-rulers disappear
    • 
-
  • the mesh is far too coarse for the small area we are looking at
-(1km2)
    • 
-
  • closing the widget window doesn’t stop the underlying processes
-to run, consequently RStudio is blocked and needs to be restarted, as
-ctrl + c or interrupting by clicking the STOP button don’t have any
-effect.
    • 
+
  • On several machines where we tried this and the covid tutorial,
+the map part of the window covers the entire window, so the rulers
+disappear. TO DO: fix this bug (Issue
+created: https://github.com/4DModeller/fdmr/issues/243#issue-1974424767)
    • 
+
  • The mesh is far too coarse for the small area we are looking at
+(1km2). TO DO: Make standard mesh size
+dependent on area.
    • 
+
  • On one machine, closing the widget window doesn’t stop the
+underlying processes to run, consequently RStudio is blocked and needs
+to be restarted, as ctrl + c or interrupting by clicking the STOP button
+don’t have any effect. (Bug report filed: https://github.com/4DModeller/fdmr/issues/255#issue-1976392121)
    • 
 

 
Let’s try to specify the mesh manually using values for max.edge and
 cutoff that are reasonable for our domain and length scales.

@@ -4555,16 +4564,18 @@ 
Redo this with geographical coordinates

 fdmr::plot_mesh(mesh,spatial_data = location_data_ll) 
## Warning in fdmr::plot_mesh(mesh, spatial_data = location_data_ll): Cannot read
 ## CRS from mesh, assuming WGS84
-
- +
+

This mesh is already a better starting point. There are two things we are not happy about:

  • The mesh is obviously distorted in N/S direction: With latitude/longitude units representing substantially different spatial distances at polar latitudes, edge lengths should be defined differently -in E/W and N/S. Or one could simply use UTM (which is currently not -supported for the visualisation tools).

    • +in E/W and N/S.
    +TO DO:     Account for unequal distances in x/y. And +as above, make it possible to use UTM (which is currently not supported +for the visualisation tools).

  • Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow @@ -4577,8 +4588,9 @@

    Start defining the priors

    Let’s try to predict a spatially distributed snow depth map from our data points.

    We create a prior with the Matérn function, a penalized complexity -prior defined by range and standard deviation [cite Finn]. The range -parameter defines the lenght of the influence of each observation.

    +prior defined by range and standard deviation (TO +DO: [cite Finn]). The range parameter defines the length +of the influence of each observation.

    In this small experiment, we have experimented by creating two different priors with two different range. The model is a stochastic partial differential equation (SPDE).

    @@ -4613,27 +4625,28 @@

    Start defining the priors

    options = list( verbose = FALSE ) -) - -# The only difference for Model 2 is the different initial prior range set above (spde2): - -formula2 <- DS ~ 0 + Intercept(1) + - f( - main = df_ll, - model = spde2 - ) - -inlabru_model2 <- inlabru::bru(formula2, - data = df_ll, - family = "gaussian", - options = list( - verbose = FALSE - ) -)
+)
+
## Warning in .recacheSubclasses(def@className, def, env): undefined subclass
+## "ndiMatrix" of class "replValueSp"; definition not updated
+
# The only difference for Model 2 is the different initial prior range set above (spde2):
+
+formula2 <- DS ~ 0 + Intercept(1)   +
+  f(
+    main = df_ll,
+    model = spde2
+  )
+
+inlabru_model2 <- inlabru::bru(formula2,
+                               data = df_ll,
+                               family = "gaussian",
+                               options = list(
+                                 verbose = FALSE
+                               )
+)

Show the output of the first model, and plot the fit:

-
summary(inlabru_model1)
+
summary(inlabru_model1)
## inlabru version: 2.10.0
-## INLA version: 23.09.09
+## INLA version: 23.11.15
 ## Components:
 ## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
 ## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
@@ -4642,7 +4655,7 @@ 
Start defining the priors

 ##     Data class: 'SpatialPointsDataFrame'
 ##     Predictor: DS ~ .
 ## Time used:
-##     Pre = 0.953, Running = 12.1, Post = 0.351, Total = 13.4 
+##     Pre = 0.748, Running = 17.7, Post = 0.494, Total = 19 
 ## Fixed effects:
 ##            mean    sd 0.025quant 0.5quant 0.975quant  mode kld
 ## Intercept 0.669 0.059      0.555    0.668       0.79 0.669   0
@@ -4672,23 +4685,23 @@ 
Start defining the priors

 ##  is computed 
 ## Posterior summaries for the linear predictor and the fitted values are computed
 ## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
-
plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-
-
-plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
+
+plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))

In the environment where this tutorial was rendered, the model_viewer widget window unfortunately crashes RStudio.

-
fdmr::model_viewer(
-  model_output = inlabru_model1,
-  mesh = mesh,
-  measurement_data = df_ll,
-  data_distribution = "Gaussian"
-)
+
fdmr::model_viewer(
+  model_output = inlabru_model1,
+  mesh = mesh,
+  measurement_data = df_ll,
+  data_distribution = "Gaussian"
+)

Show the output of the second model, and plot the fit:

-
summary(inlabru_model2)
+
summary(inlabru_model2)
## inlabru version: 2.10.0
-## INLA version: 23.09.09
+## INLA version: 23.11.15
 ## Components:
 ## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
 ## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
@@ -4697,7 +4710,7 @@ 
Start defining the priors

 ##     Data class: 'SpatialPointsDataFrame'
 ##     Predictor: DS ~ .
 ## Time used:
-##     Pre = 0.724, Running = 13.5, Post = 0.361, Total = 14.5 
+##     Pre = 0.606, Running = 16.2, Post = 0.427, Total = 17.2 
 ## Fixed effects:
 ##           mean    sd 0.025quant 0.5quant 0.975quant  mode kld
 ## Intercept 0.67 0.152      0.325    0.669      1.013 0.669   0
@@ -4712,7 +4725,7 @@ 
Start defining the priors

 ## Range for f                              0.017 0.014      0.004    0.013
 ## Stdev for f                              0.156 0.075      0.054    0.142
 ##                                         0.975quant   mode
-## Precision for the Gaussian observations     15.915 11.243
+## Precision for the Gaussian observations     15.915 11.244
 ## Range for f                                  0.055  0.008
 ## Stdev for f                                  0.340  0.115
 ## 
@@ -4727,42 +4740,91 @@ 
Start defining the priors

 ##  is computed 
 ## Posterior summaries for the linear predictor and the fitted values are computed
 ## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
-
plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-
-plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
+
+plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))

-
fdmr::model_viewer(
-  model_output = inlabru_model2,
-  mesh = mesh,
-  measurement_data = df_ll,
-  data_distribution = "Gaussian"
-)
+
fdmr::model_viewer(
+  model_output = inlabru_model2,
+  mesh = mesh,
+  measurement_data = df_ll,
+  data_distribution = "Gaussian"
+)
-
-

TO DO part

+
+

TO DO: planned outcome of the hackathon/tutorial structure

Design the prior and the model

-

Add the rasters

-
#julia add code for creating the two models
+
    +

  • Use the UTM-projected data and do everything in UTM +coordinates

    • +

  • Load both the point measurements and the rasters. There are +topographic descriptors and snow duration data. Snow data is available +for several years.

    • +

  • Use both the snow depth measurements and the different raster +data to generate the mesh: we want to account for spatial variability of +snow depth in the mesh. In areas where the terrain doesn’t vary much the +mesh doesn’t need to be that fine, and the contrary is the case in rough +topography with greatly varying snow depth. This information is +indirectly available in the topography feature rasters or in a different +way in the snow duration rasters.

    • +

Fitting the model

+

Get model results considering some or all observation data and +topographic predictors. Consider that snow duration could be seen as +both a predictor and observation.

+

-> do this for one year, and/or several years.

+

How do the results look like? Change parameters and adjust until the +snow depth map matches snow duration (best validation we have, +spatially) and locally the snow depth point measurements.

+

Do the results for all years look different, or the same?

-
-

Spatial covariance, and how we could use that

-

A future development we would like to work on, is to design the prior -to be anisotropic. What we mean, is that the correlations should be -allowed to behave differently in each dimension of the feature space -where we define distances between points.

+
+

How to deal with real zeros?

+

What happens when/where there is zero snow depth? The model needs to +distinguish between no data and zero snow depth, and for temporal +evolution also be able to predict zero snow depth after snow +melt-out.

-
-

Get the rasters in and use that information to build the mesh

+
+

Spatial covariance as prior for data assimilation

+

We think 4DM could be a nice complementary tool for SWE retrievals, +in particular for the prior covariance in spatio-temporal problems. For +this

+
    +

  • we want the prior to be anisotropic. What we mean with this: the +correlations should be allowed to behave differently in each dimension +of the feature space where we define distances between points. +Features/predictors in this example/tutorial are the provided terrain +parameters.

    • +

  • we wanted to export the spatial covariance matrix resulting from +this tutorial and test wheter we can use it to define the prior +covariance in a modelling study, for example within our data +assimilation framework MuSA (https://github.com/ealonsogzl/MuSA, see Alonso-González +et al., 2022; https://doi.org/10.5194/gmd-15-9127-2022 ). That would +necessarily be a separate study from this tutorial but results could +eventually be added if this works and results are promising.

    • +
-
-

Get model results considering some or all data and predictors

+
+

Temporal evolution: snow map time series

+

How can we get temporal evolution in here? Snow depth/SWE +distribution map is a result that’s only useful if snow depth +measurements are available at maximum snow time (which is the case for +this data, but most often unfeasible). If it should be able to compete +against other (modelling) approaches, we expect 4Dmodeller to be able to +make map time series, as we do with our data assimilation approach. For +this to work, what other predictor data needs to be added in addition to +meltout date? Adding the day of the first snow fall may lead to linear +interpolation until max snow, and then linear interpolation until +meltout…? Here, we would expect that some physical/meachanistic model +would come in, at least a simple degree day model, using +precipitation/temperature from ERA5-land, for example..?

-
-

How to deal with real zeros? (Zero snow depth)

+
+

From 3a4f287c0c1689006708476370f164eac789e7ce Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:19:46 +0000 Subject: [PATCH 19/82] Add coordinates to shiny_meshbuilder.R Adds mouse-over coordinate header and measurement tool to Shiny app map --- R/shiny_meshbuilder.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index d72711d4..81bea16c 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -183,6 +183,8 @@ meshbuilder_shiny <- function( m <- leaflet::leaflet() m <- leaflet::addTiles(m, group = "OSM") m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") + m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') + m <- leafem::addMouseCoordinates(m, native.crs = TRUE) if (plot_polygons) { m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") } else if (plot_points) { From 0fd5a404bda48798466ae79487a0158fb60e159a Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:26:59 +0000 Subject: [PATCH 20/82] Add coordinates to shiny_modelbuilder.R map Added mouse-over coordinate header and measurement tool --- R/shiny_modelbuilder.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/shiny_modelbuilder.R b/R/shiny_modelbuilder.R index 34b4b6da..fc395d31 100644 --- a/R/shiny_modelbuilder.R +++ b/R/shiny_modelbuilder.R @@ -604,7 +604,9 @@ model_builder_shiny <- function(spatial_data, leaflet::leaflet() %>% leaflet::addTiles(group = "OSM") %>% leaflet::addRasterImage(map_raster(), colors = map_colours(), opacity = 0.9, group = "Raster") %>% - leaflet::addLegend(position = "topright", pal = map_colours(), values = legend_values()) + leaflet::addLegend(position = "topright", pal = map_colours(), values = legend_values()) %>% + leaflet::addMeasure(position = "bottomleft", primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') %>% + leafem::addMouseCoordinates(native.crs = TRUE) }) model_plot <- shiny::eventReactive(input$plot_type, ignoreNULL = FALSE, { From f2e1409e3b160ed66140ae5a7290829c1972e5be Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:29:23 +0000 Subject: [PATCH 21/82] Added coordinates to plot_mapping.R Adds mouse-over coordinate header and measurement tool --- R/plot_mapping.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index c5ebb625..ec84fa05 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -34,6 +34,7 @@ plot_map <- function(polygon_data = NULL, m <- leaflet::leaflet() m <- leaflet::addTiles(m) m <- leaflet::addProviderTiles(m, leaflet::providers$Esri.WorldImagery, group = "Satellite") + m <- leafem::addMouseCoordinates(m, native.crs = TRUE) # Store a vector of layers we add to the map, # used later to create the layers control object @@ -97,5 +98,7 @@ plot_map <- function(polygon_data = NULL, m <- leaflet::addScaleBar(m, position = "bottomleft") } + m <- leaflet::addMeasure(m, position = "bottomleft", primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') + return(m) } From 36bb35dccb0fb135a4ab0ed4b718e9e2b09791e4 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:36:02 +0000 Subject: [PATCH 22/82] Add coordinates to plot_mesh.R Adds mouse-over coordinate header and measurement tool --- R/plot_mesh.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/plot_mesh.R b/R/plot_mesh.R index f83144ca..98f1e243 100644 --- a/R/plot_mesh.R +++ b/R/plot_mesh.R @@ -60,6 +60,8 @@ plot_mesh <- function(mesh, spatial_data = NULL, longitude_column = "LONG", lati m <- leaflet::leaflet() m <- leaflet::addTiles(m, group = "OSM") m <- leaflet::addPolygons(m, data = spatial_mesh, weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") + m <- leaflet::addMeasure(m, position = "bottomleft", primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') + m <- leafem::addMouseCoordinates(m, native.crs = TRUE) if (plot_polygons) { m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") From 07738ccc647a992039c47426371d11fd2144bdcc Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:44:31 +0000 Subject: [PATCH 23/82] Update DESCRIPTION Adds `leafem` package --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e2dcf469..8d01789e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -55,7 +55,8 @@ Imports: fmesher, purrr, shinyjs, - bslib + bslib, + leafem Suggests: bookdown, knitr, From f22ff4a5baeabe2c4461a0f72dd42a0953429638 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Fri, 17 Nov 2023 11:49:07 +0000 Subject: [PATCH 24/82] Update CHANGELOG.md --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index f18fd8cd..b06df315 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) +### Added +- Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) + ## [0.1.1] - 2023-11-01 ### Added From 6ac5c376d4af3586262dd255ebfed3d5ab93588c Mon Sep 17 00:00:00 2001 From: John Aiken Date: Fri, 17 Nov 2023 15:44:02 +0100 Subject: [PATCH 25/82] Update README.md to have contributor guidelines --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3b1176fd..09939cba 100644 --- a/README.md +++ b/README.md @@ -58,4 +58,14 @@ sudo apt-get install libharfbuzz-dev libfribidi-dev libfreetype6-dev \ libpng-dev libtiff5-dev libjpeg-dev libudunits2-dev libgdal-dev ``` -Note that on other Linux distributions the names of these packages may differ. \ No newline at end of file +Note that on other Linux distributions the names of these packages may differ. + +## Want to contribute? + +You can contribute to 4D-Modeller in a variety of ways including: responding to issues, introducing new features such as new tutorials or core functionality, or helping to plan a future 4D-Modeller hackathon. See below how to do each: + +1. *Issues:* Please [checkout our issues page](https://github.com/4DModeller/fdmr/issues). if you see something you can solve then [fork the repo, make the changes, then make a pull request](https://stackoverflow.com/questions/14587045/how-to-merge-branch-of-forked-repo-into-master-branch-of-original-repo#14587354). If you have an issue with 4D-Modeller, please open an issue instead. +2. *New Features:* new features can be handled in two ways. First, you can suggest new features [using the github issue tracker](https://github.com/4DModeller/fdmr/issues). Second, you can contribute new features by [forking the repo, creating the new tutorial or core functionality, then making a pull request](https://github.com/4DModeller/fdmr/issues). +3. *Hackathon Planning:* If you would like to help organize a 4D-Modeller hackathon either by helping organize a core hackathon or by organizing one yourself at your institution, then please reach out to one of the 4D-Modeller developers. + +If you make regular contributions through issues and new features then we would be happy to include you in the core group as a developer of 4D-Modeller. From 254462a355d62cb2c2ab79be6ac7808713522173 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Fri, 17 Nov 2023 14:56:19 +0000 Subject: [PATCH 26/82] Add missing libraries to DESC --- CHANGELOG.md | 1 + DESCRIPTION | 2 ++ 2 files changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b06df315..210efb8c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) ### Added + - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) ## [0.1.1] - 2023-11-01 diff --git a/DESCRIPTION b/DESCRIPTION index 8d01789e..f3c7f164 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -56,6 +56,8 @@ Imports: purrr, shinyjs, bslib, + bsicons, + future, leafem Suggests: bookdown, From 06e1b5eff044a9d419da8f6f4a2217854b087738 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Fri, 17 Nov 2023 15:04:48 +0000 Subject: [PATCH 27/82] Small changes to text --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 09939cba..15e1b3a7 100644 --- a/README.md +++ b/README.md @@ -62,10 +62,10 @@ Note that on other Linux distributions the names of these packages may differ. ## Want to contribute? -You can contribute to 4D-Modeller in a variety of ways including: responding to issues, introducing new features such as new tutorials or core functionality, or helping to plan a future 4D-Modeller hackathon. See below how to do each: +You can contribute to 4DModeller in a variety of ways including: responding to issues, introducing new features such as new tutorials or core functionality, or helping to plan a future 4DModeller hackathon. See below how to do each: -1. *Issues:* Please [checkout our issues page](https://github.com/4DModeller/fdmr/issues). if you see something you can solve then [fork the repo, make the changes, then make a pull request](https://stackoverflow.com/questions/14587045/how-to-merge-branch-of-forked-repo-into-master-branch-of-original-repo#14587354). If you have an issue with 4D-Modeller, please open an issue instead. -2. *New Features:* new features can be handled in two ways. First, you can suggest new features [using the github issue tracker](https://github.com/4DModeller/fdmr/issues). Second, you can contribute new features by [forking the repo, creating the new tutorial or core functionality, then making a pull request](https://github.com/4DModeller/fdmr/issues). -3. *Hackathon Planning:* If you would like to help organize a 4D-Modeller hackathon either by helping organize a core hackathon or by organizing one yourself at your institution, then please reach out to one of the 4D-Modeller developers. +1. *Issues:* Please [checkout our issues page](https://github.com/4DModeller/fdmr/issues). If you see something you can solve then [fork the repo, make the changes, then make a pull request](https://stackoverflow.com/questions/14587045/how-to-merge-branch-of-forked-repo-into-master-branch-of-original-repo#14587354). If you have an issue with 4DModeller, please open an issue instead. +2. *New Features:* new features can be handled in two ways. First, you can suggest new features [using the GitHub issue tracker](https://github.com/4DModeller/fdmr/issues). Second, you can contribute new features by [forking the repo, creating the new tutorial or core functionality, then making a pull request](https://github.com/4DModeller/fdmr/issues). +3. *Hackathon Planning:* If you would like to help organize a 4DModeller hackathon either by helping organize a core hackathon or by organizing one yourself at your institution, then please reach out to one of the 4DModeller developers. -If you make regular contributions through issues and new features then we would be happy to include you in the core group as a developer of 4D-Modeller. +If you make regular contributions through issues and new features then we would be happy to include you in the core group as a developer of 4DModeller. From 104d072a789eb90737f8b2a4bdd51730f7e66cec Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Fri, 17 Nov 2023 15:15:59 +0000 Subject: [PATCH 28/82] Ran styler over the geophysical tutorial --- vignettes/tutorial_geophysics.Rmd | 91 +++++++------------------------ 1 file changed, 20 insertions(+), 71 deletions(-) diff --git a/vignettes/tutorial_geophysics.Rmd b/vignettes/tutorial_geophysics.Rmd index 2a6679f0..540be810 100644 --- a/vignettes/tutorial_geophysics.Rmd +++ b/vignettes/tutorial_geophysics.Rmd @@ -1,9 +1,14 @@ --- title: "4DModeller for geophysical signals" -output: html_notebook +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{4DModeller for geophysical signals} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} --- ## Introduction + This is a tutorial to apply R-INLA to modeling geophysical data. The data represent time-series at some locations distributed over a line or over an area. There are two possible case studies one is seismic data with seismic stations located along a line. Each station record 4 components of acoustic signal X-Y-Z particle displacement on geophone and presure component on hydrophone sensor. The second case is magnteotelluric data with stations distrubuted over some area. The signal has 4 channels: 2 magnetic and 2 electric field channels. In the dataset only 2-3 stations measure at the same. The spatiotemporal evolution of the field is governed by Maxwell equations. The source of EM field is disturbance of ionosphere due to solar activity, electrical structure of the crust and noise component (cultural EM noise, wind, rain, local conductors). The goal is to describe the source signal components in space in time. The spatiotemporal evolution of the field is governed by wave equation. The small-scale heterogenetities in earth crust produce multiply scattered wavefield, getting more expressed at a later times and called coda wave. The goal is to learn about correlations between signals at diffent stations and from this predict distribution of heterogeneities. @@ -20,29 +25,27 @@ d <- read.csv(data_path) print(head(d, 30)) # Create a scatter plot -plot(d$X, d$Y, pch = 20, main = 'Svalbard MT sites', xlab = 'X', ylab = 'Y') - +plot(d$X, d$Y, pch = 20, main = "Svalbard MT sites", xlab = "X", ylab = "Y") ``` ```{r} -locations = d[, c('Lon', 'Lat')] -locations = unique(locations) -names(locations)=c('LONG','LAT') +locations <- d[, c("Lon", "Lat")] +locations <- unique(locations) +names(locations) <- c("LONG", "LAT") ``` ## Meshing ```{r} - -mesh <- fmesher::fm_mesh_2d(loc.domain = locations, - max.edge = 0.05, - cutoff = 1e-3, - offset=0.1 - ) +mesh <- fmesher::fm_mesh_2d( + loc.domain = locations, + max.edge = 0.05, + cutoff = 1e-3, + offset = 0.1 +) plot(mesh) points(locations, col = "red") fdmr::plot_mesh(mesh) - ``` ## Stochastic modeling @@ -64,9 +67,9 @@ space_index <- rep(1:stations, each = n) harmonic_data <- data.frame( station = factor(space_index), time = rep(time_index, stations), - observation = sin(rep(time_index, stations) * 2 * pi / 50) + - rnorm(n * stations, sd = 0.5) + - rep(rnorm(stations, sd = 3), each = n) # Station-specific offset + observation = sin(rep(time_index, stations) * 2 * pi / 50) + + rnorm(n * stations, sd = 0.5) + + rep(rnorm(stations, sd = 3), each = n) # Station-specific offset ) # Define the model with harmonic terms for time and spatial correlation @@ -90,60 +93,6 @@ spatial_hyperparams <- result$summary.hyperpar # Print the hyperparameters print(spatial_hyperparams) - -``` -## Observed time series will come here.. - - -## This part doesn't work - -```{r} -simdf <- data.frame( - ID = d$Code, - time = d$T, - datn = Re(d$Zxx), - E = Re(d$DZxx), - LONG = d$Lon, - LAT = d$Lat -) - - -initial_range = 0.1 -spde <- INLA::inla.spde2.pcmatern( - mesh = mesh, - prior.range = c(initial_range, 0.5), - prior.sigma = c(1, 0.01) -) - -sigma0 <- 100 -range0 <- 0.03 -kappa0 <- sqrt(8 / 1) / range0 -tau0 <- 1 / (sqrt(4 * pi) * kappa0 * sigma0) - -inla.seed <- sample.int(n = 1E4, size = length(d$T)) -Q <- INLA::inla.spde.precision(spde, theta = c(log(tau0), log(kappa0))) -x.mat <- matrix(NA, ncol = length(d$T), nrow = mesh$n) -for (co in 1:ncol(x.mat)) { - x.mat[, co] <- INLA::inla.qsample(n = 1, Q) -} - -A <- INLA::inla.spde.make.A(mesh = mesh, loc = locations) -x.dat <- matrix(NA, ncol = n.time, nrow = n) -for (t in 1:ncol(x.dat)) { - x.dat[, t] <- drop(A %*% x.mat[, t]) -} - - -alpha <- 0.9 -sp.mat <- matrix(NA, ncol = n.time, nrow = n) -sp.mat[, 1] <- x.dat[, 1] -for (t in 2:n.time) { - sp.mat[, t] <- alpha * sp.mat[, t - 1] + x.dat[, t] -} - -beta0 <- 0.5 -sigma_e <- 0.1 -lin.pred <- beta0 + sp.mat + matrix(rnorm(n * n.time, 0, sigma_e), ncol = n.time) - ``` +## Observed time series will be added soon From 4ce889471c9f868e75726df758ce070d544771b9 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Fri, 17 Nov 2023 15:31:46 +0000 Subject: [PATCH 29/82] Added note to changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b06df315..62da17b6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) ### Added + +- Added new geophysical processes tutorial thanks to Alexander Minakov (4minakov) - [PR #257](https://github.com/4DModeller/fdmr/pull/257) - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) ## [0.1.1] - 2023-11-01 From 9cb0d56d6787e7dff88babdb4c7eb22420b01814 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 20 Nov 2023 13:48:06 +0100 Subject: [PATCH 30/82] Ran styler over vignette [skip ci] --- vignettes/data_loading.Rmd | 385 +++++++++++++++++++------------------ 1 file changed, 193 insertions(+), 192 deletions(-) diff --git a/vignettes/data_loading.Rmd b/vignettes/data_loading.Rmd index 1de3655f..5983b607 100644 --- a/vignettes/data_loading.Rmd +++ b/vignettes/data_loading.Rmd @@ -1,192 +1,193 @@ ---- -title: "Data loading" -output: - bookdown::html_document2: - base_format: rmarkdown::html_vignette - fig_caption: yes -link-citations: yes -vignette: > - %\VignetteIndexEntry{Data loading} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -In this tutorial, we'll show the steps of importing netCDF and raster data into R and preparing for the modelling in the fdmr package. - -# Import netCDF data and prepare for fdmr -To begin, we'll demonstrate how to import netCDF data into R. Now we create a netCDF file which stores the temperature values at a number of geographical locations provided with longitude and latitude information, at 5 time points. - -```{r createNetCDF} -library(ncdf4) -filename="temp.nc" -xvals <- seq(-177.5, 177.5, 10) -yvals <- seq(-87.5, 87.5, 10) -nx <- length(xvals) -ny <- length(yvals) -lon <- ncdim_def("longitude", "degrees_east", xvals) -lat <- ncdim_def("latitude", "degrees_north", yvals) - -time <- ncdim_def("Time","months", 1:5, unlim=TRUE) -var_temp <- ncvar_def("temperature", "celsius", - list(lon, lat, time), - longname="value") - -ncnew <- nc_create(filename, list(var_temp)) -data <- runif(nx*ny*5, 0,1) -ncvar_put(nc=ncnew, - varid=var_temp, - data, - start=c(1,1,1),count=c(nx,ny,5)) - -``` - -`ncnew` is a netCDF file, whose class is `ncdf4`. This file has 1 variable (temperature), and 3 dimensions (longitude, latitude and time). - -```{r} -class(ncnew) -print(paste("The file has", ncnew$nvars,"variables")) -print(paste("The file has", ncnew$ndim,"dimensions")) -``` - -A summary of `ncnew` is -```{r} -print(ncnew) -``` - -```{r closenetCDF} -nc_close(ncnew) -``` - -Now the netCDF file `ncnew` with the name `temp.nc` is created, and we can read the values we put in. - -```{r opennetCDF} -ncnew <- nc_open('temp.nc') -time <- ncvar_get(ncnew,"Time") -nt <- dim(time) -tmp_vec_long <- as.vector(ncvar_get(ncnew,"temperature")) -tmp_mat <- matrix(tmp_vec_long, ncol=nt) -lon <- ncvar_get(ncnew, "longitude") -lat <- ncvar_get(ncnew, "latitude") -lonlat <- as.matrix(expand.grid(lon,lat)) - -``` - -We store the data in a data frame with a structure that is expected by `fdmr`. - -```{r storedat} -tmp_df <- data.frame(cbind(lonlat,tmp_mat)) -names(tmp_df) <- c("lon","lat","tmp_time1","tmp_time2","tmp_time3", - "tmp_time4","tmp_time5") - -tmp_df<-reshape(tmp_df, - varying = c("tmp_time1","tmp_time2","tmp_time3", - "tmp_time4","tmp_time5"), - v.names = "temperature value", - timevar = "time", - times = c("1", "2", "3","4","5"), - idvar= 'location ID', - new.row.names = 1:(nx*ny*nt), - direction = "long") - -``` - -Then the first 6 rows of the data frame can be viewed using the following code. - -```{r headat} -utils::head(tmp_df) -``` - -Here we give another example of importing the netCDF file named `oisst-sst.nc`, which is available at [https://github.com/rstudio/leaflet/tree/main/docs/nc](https://github.com/rstudio/leaflet/tree/main/docs/nc), into R. We first download it from the above link, and then save it to the computer. Likewise, we use the `nc_open()` function from the `ncdf4` package to open and import this netCDF file into R. Ensure that the R working directory is set to the location of `oisst-sst.nc`, and then pass in the filename (including the extension) of the netCDF file as the first argument to the `nc_open()` function. - - -```{r imporoisst, eval=FALSE} -oisst<-nc_open('oisst-sst.nc') -``` - -A summary of `oisst` is -```{r, eval=FALSE} -print(oisst) -``` - -`oisst` is a `ncdf4` object, which contains one variable named `Daily.sea.surface.temperature` and two dimensions, i.e., longitude and latitude. Now we can read the values, and store them in a data frame with a structure that is expected by `fdmr`. - -```{r, eval=FALSE} -Daily_sea_surface_temperature <- as.vector(ncvar_get(oisst,"Daily.sea.surface.temperature")) -lon <- ncvar_get(oisst, "longitude") -lat <- ncvar_get(oisst, "latitude") -lonlat <- as.matrix(expand.grid(lon,lat)) -tmp_df <- data.frame(cbind(lonlat,Daily_sea_surface_temperature)) -colnames(tmp_df)<-c('lon', 'lat', 'Daily.sea.surface.temperature') -``` - -The first 6 rows of the data frame can be viewed using the following code. - -```{r headoisst, eval=FALSE} -utils::head(tmp_df) -``` - -# Import raster data and prepare for fdmr - -In this section we'll demonstrate how to import raster data into R. Now we create a raster file which stores the temperature values at a number of geographical locations provided with longitude and latitude information, at 3 time points. - - -```{r createRaster} -library(raster) -r1 <- raster(ncol=30, nrow=30, xmn=-180, xmx=180, ymn=-90, ymx=90) -projection(r1) <- "+proj=longlat +datum=WGS84" -values(r1) <- runif(length(values(r1)),0,1) - -r2 <- raster(ncol=30, nrow=30, xmn=-180, xmx=180, ymn=-90, ymx=90) -projection(r2) <- "+proj=longlat +datum=WGS84" -values(r2) <- runif( length(values(r2)),0,1) - - -r3 <- raster(ncol=30, nrow=30, xmn=-180, xmx=180, ymn=-90, ymx=90) -projection(r3) <- "+proj=longlat +datum=WGS84" -values(r3) <- runif( length(values(r3)),0,1) -r_stack = stack(list(r1=r1, r2=r2, r3=r3)) - -``` - - -The class of `r_stack` is a `raster`. - -```{r classraster} -class(r_stack) -``` - -Note that here we create a raster object directly in the R environment, but raster files are most easily read into R with the `raster()` function from the `raster` package. You simply pass in the filename (including the extension) of the raster file as the first argument. For example, if the raster file is a netCDF file, it can be loaded into R by - -```{r importraster, eval=FALSE} -r_stack <- raster::raster('filename.nc') - -``` - -We can plot the raster data at each time point. - -```{r plotraster, fig.cap="Plots of the raster data at each time point.", fig.width=8, fig.height=4, fig.align='center'} - -plot(r_stack) - -``` - -Then we extract the data values in `r_stack`, and save them in a data frame with a structure that is expected by `fdmr`. - -```{r storedata} -r_df <-data.frame(raster::rasterToPoints(r_stack)) -r_df<-reshape(r_df, - varying = c("r1", "r2", "r3"), - v.names = "temperature value", - timevar = "time", - times = c("1", "2", "3"), - idvar= 'location ID', - new.row.names = 1:(nx*ny*nt), - direction = "long") - -``` - -Then the first 6 rows of the data frame can be viewed using the following code. - -```{r headata} -utils::head(r_df) -``` +--- +title: "Data loading" +output: + bookdown::html_document2: + base_format: rmarkdown::html_vignette + fig_caption: yes +link-citations: yes +vignette: > + %\VignetteIndexEntry{Data loading} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +In this tutorial, we'll show the steps of importing netCDF and raster data into R and preparing for the modelling in the fdmr package. + +# Import netCDF data and prepare for fdmr +To begin, we'll demonstrate how to import netCDF data into R. Now we create a netCDF file which stores the temperature values at a number of geographical locations provided with longitude and latitude information, at 5 time points. + +```{r createNetCDF} +library(ncdf4) +filename <- "temp.nc" +xvals <- seq(-177.5, 177.5, 10) +yvals <- seq(-87.5, 87.5, 10) +nx <- length(xvals) +ny <- length(yvals) +lon <- ncdim_def("longitude", "degrees_east", xvals) +lat <- ncdim_def("latitude", "degrees_north", yvals) + +time <- ncdim_def("Time", "months", 1:5, unlim = TRUE) +var_temp <- ncvar_def("temperature", "celsius", + list(lon, lat, time), + longname = "value" +) + +ncnew <- nc_create(filename, list(var_temp)) +data <- runif(nx * ny * 5, 0, 1) +ncvar_put( + nc = ncnew, + varid = var_temp, + data, + start = c(1, 1, 1), count = c(nx, ny, 5) +) +``` + +`ncnew` is a netCDF file, whose class is `ncdf4`. This file has 1 variable (temperature), and 3 dimensions (longitude, latitude and time). + +```{r} +class(ncnew) +print(paste("The file has", ncnew$nvars, "variables")) +print(paste("The file has", ncnew$ndim, "dimensions")) +``` + +A summary of `ncnew` is +```{r} +print(ncnew) +``` + +```{r closenetCDF} +nc_close(ncnew) +``` + +Now the netCDF file `ncnew` with the name `temp.nc` is created, and we can read the values we put in. + +```{r opennetCDF} +ncnew <- nc_open("temp.nc") +time <- ncvar_get(ncnew, "Time") +nt <- dim(time) +tmp_vec_long <- as.vector(ncvar_get(ncnew, "temperature")) +tmp_mat <- matrix(tmp_vec_long, ncol = nt) +lon <- ncvar_get(ncnew, "longitude") +lat <- ncvar_get(ncnew, "latitude") +lonlat <- as.matrix(expand.grid(lon, lat)) +``` + +We store the data in a data frame with a structure that is expected by `fdmr`. + +```{r storedat} +tmp_df <- data.frame(cbind(lonlat, tmp_mat)) +names(tmp_df) <- c( + "lon", "lat", "tmp_time1", "tmp_time2", "tmp_time3", + "tmp_time4", "tmp_time5" +) + +tmp_df <- reshape(tmp_df, + varying = c( + "tmp_time1", "tmp_time2", "tmp_time3", + "tmp_time4", "tmp_time5" + ), + v.names = "temperature value", + timevar = "time", + times = c("1", "2", "3", "4", "5"), + idvar = "location ID", + new.row.names = 1:(nx * ny * nt), + direction = "long" +) +``` + +Then the first 6 rows of the data frame can be viewed using the following code. + +```{r headat} +utils::head(tmp_df) +``` + +Here we give another example of importing the netCDF file named `oisst-sst.nc`, which is available at [https://github.com/rstudio/leaflet/tree/main/docs/nc](https://github.com/rstudio/leaflet/tree/main/docs/nc), into R. We first download it from the above link, and then save it to the computer. Likewise, we use the `nc_open()` function from the `ncdf4` package to open and import this netCDF file into R. Ensure that the R working directory is set to the location of `oisst-sst.nc`, and then pass in the filename (including the extension) of the netCDF file as the first argument to the `nc_open()` function. + + +```{r imporoisst, eval=FALSE} +oisst <- nc_open("oisst-sst.nc") +``` + +A summary of `oisst` is +```{r, eval=FALSE} +print(oisst) +``` + +`oisst` is a `ncdf4` object, which contains one variable named `Daily.sea.surface.temperature` and two dimensions, i.e., longitude and latitude. Now we can read the values, and store them in a data frame with a structure that is expected by `fdmr`. + +```{r, eval=FALSE} +Daily_sea_surface_temperature <- as.vector(ncvar_get(oisst, "Daily.sea.surface.temperature")) +lon <- ncvar_get(oisst, "longitude") +lat <- ncvar_get(oisst, "latitude") +lonlat <- as.matrix(expand.grid(lon, lat)) +tmp_df <- data.frame(cbind(lonlat, Daily_sea_surface_temperature)) +colnames(tmp_df) <- c("lon", "lat", "Daily.sea.surface.temperature") +``` + +The first 6 rows of the data frame can be viewed using the following code. + +```{r headoisst, eval=FALSE} +utils::head(tmp_df) +``` + +# Import raster data and prepare for fdmr + +In this section we'll demonstrate how to import raster data into R. Now we create a raster file which stores the temperature values at a number of geographical locations provided with longitude and latitude information, at 3 time points. + + +```{r createRaster} +library(raster) +r1 <- raster(ncol = 30, nrow = 30, xmn = -180, xmx = 180, ymn = -90, ymx = 90) +projection(r1) <- "+proj=longlat +datum=WGS84" +values(r1) <- runif(length(values(r1)), 0, 1) + +r2 <- raster(ncol = 30, nrow = 30, xmn = -180, xmx = 180, ymn = -90, ymx = 90) +projection(r2) <- "+proj=longlat +datum=WGS84" +values(r2) <- runif(length(values(r2)), 0, 1) + + +r3 <- raster(ncol = 30, nrow = 30, xmn = -180, xmx = 180, ymn = -90, ymx = 90) +projection(r3) <- "+proj=longlat +datum=WGS84" +values(r3) <- runif(length(values(r3)), 0, 1) +r_stack <- stack(list(r1 = r1, r2 = r2, r3 = r3)) +``` + + +The class of `r_stack` is a `raster`. + +```{r classraster} +class(r_stack) +``` + +Note that here we create a raster object directly in the R environment, but raster files are most easily read into R with the `raster()` function from the `raster` package. You simply pass in the filename (including the extension) of the raster file as the first argument. For example, if the raster file is a netCDF file, it can be loaded into R by + +```{r importraster, eval=FALSE} +r_stack <- raster::raster("filename.nc") +``` + +We can plot the raster data at each time point. + +```{r plotraster, fig.cap="Plots of the raster data at each time point.", fig.width=8, fig.height=4, fig.align='center'} +plot(r_stack) +``` + +Then we extract the data values in `r_stack`, and save them in a data frame with a structure that is expected by `fdmr`. + +```{r storedata} +r_df <- data.frame(raster::rasterToPoints(r_stack)) +r_df <- reshape(r_df, + varying = c("r1", "r2", "r3"), + v.names = "temperature value", + timevar = "time", + times = c("1", "2", "3"), + idvar = "location ID", + new.row.names = 1:(nx * ny * nt), + direction = "long" +) +``` + +Then the first 6 rows of the data frame can be viewed using the following code. + +```{r headata} +utils::head(r_df) +``` From a96f23651b3d40d92916ad03dba394166f659e0f Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 20 Nov 2023 14:08:28 +0100 Subject: [PATCH 31/82] Remove the html file --- vignettes/snow.html | 4847 ------------------------------------------- 1 file changed, 4847 deletions(-) delete mode 100644 vignettes/snow.html diff --git a/vignettes/snow.html b/vignettes/snow.html deleted file mode 100644 index cb487688..00000000 --- a/vignettes/snow.html +++ /dev/null @@ -1,4847 +0,0 @@ - - - - - - - - - - - - - - -Spatial distribution of snow - exploratory tutorial - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Spatial distribution of snow - exploratory -tutorial

- - - -
-

Introduction

-

This tutorial explores whether 4Dmodeller can be used to model the -spatial distribution of snow in the Bayelva area on Svalbard, based on -once-a-year point-based manual snow depth measurements for several years -and snow cover duration derived from time lapse camera imagery (Aalstad -et al., 2020). This data has been used before for snow data assimilation -(Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) -using modeling and data assimilation methods (see references). -Additionally, we included elevation and other derived topographic -parameters (slope, aspect, topographic position index) as these are -known to influence the snow depth distribution. These topographic -parameters were derived from a high resolution digital elevation model -(Boike et al., 2019). The dataset used in this tutorial has been -prepared by Kristoffer Aalstad as a netCDF by combining and processing -the following publicly available datasets that have been used in several -studies (see literature references):

-

Westermann, S. (2020).Snow depth and ground surface temperature at -Bayelva research site, Svalbard [Data set]. Norstore. https://doi.org/10.11582/2020.00024

-

Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 -high-resolution snow cover maps covering the 2012-2017 snowmelt seasons -in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5010944

-

Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, -P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., -Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and -multi channel) from aerial overflights in 2008 over Bayelva -(Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. -https://doi.org/10.1594/PANGAEA.884730

-

The dataset is originally projected (UTM zone 33X) and available in -the data sub-folder in this repository (data/Bayelva_snow.nc). As UTM -was not supported for all steps we anticipated while developing this -tutorial (see below), we also created also a lat/lon version of the same -data (data/Bayelva_snow_geo.nc).

-
-

Literature references

-

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, -L. (2018). Ensemble-based assimilation of fractional snow-covered area -satellite retrievals to estimate the snow distribution at Arctic sites. -The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018

-

Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., -Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., -Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A -16-year record (2002–2017) of permafrost, active-layer, and -meteorological conditions at the Samoylov Island Arctic permafrost -research site, Lena River delta, northern Siberia: an opportunity to -validate remote-sensing data and land surface, snow, and permafrost -models, Earth Syst. Sci. Data, 11, 261–299, https://doi.org/10.5194/essd-11-261-2019, 2019.

-

Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating -satellite retrieved fractional snow-covered area at a high-Arctic site -using terrestrial photography. Remote Sensing of Environment, 239, https://doi.org/10.1016/j.rse.2019.111618

-

Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., -Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow -redistribution and its effect on ground surface temperature at a -high-Arctic site on Svalbard. Journal of Geophysical Research: Earth -Surface, 126, e2020JF005673. https://doi.org/10.1029/2020JF005673

-
-
-
-

Import and load the data

-

We use the library ncdf4 to open the snow dataset:

-
# load modules used in this tutorial
-library(ncdf4)
-
# Find where your data is stored locally
-ncpath <- dirname(rstudioapi::getSourceEditorContext()$path)
-ncname <- "Bayelva_snow" 
-ncfname <- paste(ncpath,'/data/', ncname, ".nc", sep="")
-
-#open data 
-ncin <- nc_open(ncfname) 
-#head(ncin)
-
-

Get relevant features

-

The netcdf file has both point measurements of snow depth (ds, or -translated into SWE: Ds) for several years, and continuous raster data -with snow depth duration for several years and topographic features. We -start with the snow depth points only:

-
# specify the longitude and latitude columns. Note that the coordinates are utm!
-longitude <- ncvar_get(ncin,"xs") 
-latitude <-ncvar_get(ncin,"ys")
-
-# Get the first year of snow depth (ds)
-ds <- ncvar_get(ncin,"ds")[,1]
-
-# Create a data frame
-df <- data.frame(LONG = longitude,LAT = latitude,ds) 
-head(df)
-
##       LONG     LAT   ds
-## 1 432376.2 8762917 0.70
-## 2 432046.8 8763194 0.60
-## 3 432578.7 8763044 0.50
-## 4 432661.3 8763002 0.60
-## 5 432408.1 8762976 0.65
-## 6 432105.0 8763344 0.72
-
-
-

Build the mesh

-

When trying to build a mesh whit our UTM projected dataset, we don’t -get a result as the shiny app does not seem to work with UTM:

-
fdmr::mesh_builder(spatial_data = df)
-

Using fmesher directly (non-interactively, without the fdmr app added -functionalities) works and we can plot the resulting mesh outside the -geographical context that would have been provided by mesh_builder.

-
# Define a proj string for our UTM zone
-crs <- "+proj=utm +zone=33" 
-
-# use fmesher directly rather than the fdmr wrapper:
-mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], 
-                                 max.edge = c(20,40),
-                                 crs = crs ) 
-
-# plot the result
-plot(mesh)
-

-

When trying to plot the mesh with fdmr::plot_mesh, we run into the -same problem as above: The plot_mesh function seems to assume lat/lon -coordinates and thus zooms out to max.

-
fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)])
-

TO DO: We would like to also show our -data points and see this in a geographical context to check whether -everything is looking OK. mesh_builder and plot_mesh suffer from the -same problem and can’t deal with UTM coordinates. fdmr should be updated -to beable to deal with our UTM coordinates. This is especially important -in the Arctic, where geographical coordinates result in highly distorted -maps in most map grids. (Issue created: https://github.com/4DModeller/fdmr/issues/248#issue-1974643323)

-
-
-
-

Redo this with geographical coordinates

-

Let’s see whether our approach works if our data is in geographical -coordinates instead. Redo all the steps from above:

-
ncname_ll <- "Bayelva_snow_geo"
-ncfname_ll <- paste(ncpath, '/data/', ncname_ll, ".nc", sep="")
-# Open data
-ncin_ll <- nc_open(ncfname_ll)
-
-# Create a data frame with the  first year of snow depth (ds)
-df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"),
-                 LAT = ncvar_get(ncin_ll,"lats"),
-                 #elev = ncvar_get(ncin_ll,"zs"),
-                 DS = ncvar_get(ncin_ll,"ds")[,1],
-                 DS_log = log(ncvar_get(ncin_ll,"ds")[,1]))
-
-## Store the point locations in a separate data frame:
-location_data_ll = df_ll[,c(1,2)]
-
# Try with the interactive mesh_builder
-fdmr::mesh_builder(location_data_ll)
-

The mesh_builder loads the shiny widget and creates a mesh, however -there are several issues:

-
    -

  • On several machines where we tried this and the covid tutorial, -the map part of the window covers the entire window, so the rulers -disappear. TO DO: fix this bug (Issue -created: https://github.com/4DModeller/fdmr/issues/243#issue-1974424767)

    • -

  • The mesh is far too coarse for the small area we are looking at -(1km2). TO DO: Make standard mesh size -dependent on area.

    • -

  • On one machine, closing the widget window doesn’t stop the -underlying processes to run, consequently RStudio is blocked and needs -to be restarted, as ctrl + c or interrupting by clicking the STOP button -don’t have any effect. (Bug report filed: https://github.com/4DModeller/fdmr/issues/255#issue-1976392121)

    • -
-

Let’s try to specify the mesh manually using values for max.edge and -cutoff that are reasonable for our domain and length scales.

-
# look at the range of the data to derive the mesh parameters
-initial_range <- diff(range(location_data_ll[, "LONG"])) / 5
-max_edge <- initial_range / 8
-offset <- initial_range/2
-
-# make the mesh from the locations
-mesh <- fmesher::fm_mesh_2d(loc = location_data_ll[, c("LONG", "LAT")],
-                                 max.edge = c(1, 2) * max_edge,
-                                 offset = c(1,1.5)*offset,
-                                 cutoff = max_edge/10
-                                 )
-fdmr::plot_mesh(mesh,spatial_data = location_data_ll)
-
## Warning in fdmr::plot_mesh(mesh, spatial_data = location_data_ll): Cannot read
-## CRS from mesh, assuming WGS84
-
- -

This mesh is already a better starting point. There are two things we -are not happy about:

-
    -

  • The mesh is obviously distorted in N/S direction: With -latitude/longitude units representing substantially different spatial -distances at polar latitudes, edge lengths should be defined differently -in E/W and N/S.
    -TO DO:     Account for unequal distances in x/y. And -as above, make it possible to use UTM (which is currently not supported -for the visualisation tools).

    • -

  • Is the mesh dense enough to represent snow depth variability -between the measurement points, and is the mesh in physically useful -locations? Terrain and derivatives are strong drivers of spatial snow -depth distribution, and they should thus ideally be considered for the -mesh generation.

    • -
-
-
-

Start defining the priors

-

Let’s try to predict a spatially distributed snow depth map from our -data points.

-

We create a prior with the Matérn function, a penalized complexity -prior defined by range and standard deviation (TO -DO: [cite Finn]). The range parameter defines the length -of the influence of each observation.

-

In this small experiment, we have experimented by creating two -different priors with two different range. The model is a stochastic -partial differential equation (SPDE).

-
# The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above).
-
-spde1 <- INLA::inla.spde2.pcmatern(
-  mesh = mesh,
-  prior.range = c(initial_range, 0.5),
-  prior.sigma = c(1, 0.01)
-)
-
-# Try with a different prior range - the entire initial_range
-spde2 <- INLA::inla.spde2.pcmatern(
-  mesh = mesh,
-  prior.range = c(initial_range*5, 0.5),
-  prior.sigma = c(1, 0.01)
-)
-

We need to convert our data frame into a SpatialPointsDataFrame.

-
sp::coordinates(df_ll) <- c("LONG", "LAT")
-

Define two models (for the two spde versions defined above, -respectively):

-
# Model 1 is linear and uses the gaussian model 1 defined above:
-formula1 <- DS ~ 0 + Intercept(1)+
-  f(
-    main = df_ll,
-    model = spde1
-  )
-
-inlabru_model1 <- inlabru::bru(formula1,
-                               data = df_ll,
-                               family = "gaussian",
-                               options = list(
-                                 verbose = FALSE
-                               )
-)
-
## Warning in .recacheSubclasses(def@className, def, env): undefined subclass
-## "ndiMatrix" of class "replValueSp"; definition not updated
-
# The only difference for Model 2 is the different initial prior range set above (spde2):
-
-formula2 <- DS ~ 0 + Intercept(1)   +
-  f(
-    main = df_ll,
-    model = spde2
-  )
-
-inlabru_model2 <- inlabru::bru(formula2,
-                               data = df_ll,
-                               family = "gaussian",
-                               options = list(
-                                 verbose = FALSE
-                               )
-)
-

Show the output of the first model, and plot the fit:

-
summary(inlabru_model1)
-
## inlabru version: 2.10.0
-## INLA version: 23.11.15
-## Components:
-## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
-## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
-## Likelihoods:
-##   Family: 'gaussian'
-##     Data class: 'SpatialPointsDataFrame'
-##     Predictor: DS ~ .
-## Time used:
-##     Pre = 0.748, Running = 17.7, Post = 0.494, Total = 19 
-## Fixed effects:
-##            mean    sd 0.025quant 0.5quant 0.975quant  mode kld
-## Intercept 0.669 0.059      0.555    0.668       0.79 0.669   0
-## 
-## Random effects:
-##   Name     Model
-##     f SPDE2 model
-## 
-## Model hyperparameters:
-##                                           mean    sd 0.025quant 0.5quant
-## Precision for the Gaussian observations 16.512 5.579      8.304   15.607
-## Range for f                              0.003 0.002      0.001    0.003
-## Stdev for f                              0.191 0.060      0.099    0.182
-##                                         0.975quant   mode
-## Precision for the Gaussian observations     30.001 13.933
-## Range for f                                  0.009  0.002
-## Stdev for f                                  0.334  0.166
-## 
-## Deviance Information Criterion (DIC) ...............: 32.50
-## Deviance Information Criterion (DIC, saturated) ....: 123.34
-## Effective number of parameters .....................: 27.32
-## 
-## Watanabe-Akaike information criterion (WAIC) ...: 46.44
-## Effective number of parameters .................: 34.98
-## 
-## Marginal log-Likelihood:  -39.66 
-##  is computed 
-## Posterior summaries for the linear predictor and the fitted values are computed
-## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
-
plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-
-
-plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-

-

In the environment where this tutorial was rendered, the model_viewer -widget window unfortunately crashes RStudio.

-
fdmr::model_viewer(
-  model_output = inlabru_model1,
-  mesh = mesh,
-  measurement_data = df_ll,
-  data_distribution = "Gaussian"
-)
-

Show the output of the second model, and plot the fit:

-
summary(inlabru_model2)
-
## inlabru version: 2.10.0
-## INLA version: 23.11.15
-## Components:
-## Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
-## f: main = spde(df_ll), group = exchangeable(1L), replicate = iid(1L)
-## Likelihoods:
-##   Family: 'gaussian'
-##     Data class: 'SpatialPointsDataFrame'
-##     Predictor: DS ~ .
-## Time used:
-##     Pre = 0.606, Running = 16.2, Post = 0.427, Total = 17.2 
-## Fixed effects:
-##           mean    sd 0.025quant 0.5quant 0.975quant  mode kld
-## Intercept 0.67 0.152      0.325    0.669      1.013 0.669   0
-## 
-## Random effects:
-##   Name     Model
-##     f SPDE2 model
-## 
-## Model hyperparameters:
-##                                           mean    sd 0.025quant 0.5quant
-## Precision for the Gaussian observations 11.626 1.979      8.149   11.484
-## Range for f                              0.017 0.014      0.004    0.013
-## Stdev for f                              0.156 0.075      0.054    0.142
-##                                         0.975quant   mode
-## Precision for the Gaussian observations     15.915 11.244
-## Range for f                                  0.055  0.008
-## Stdev for f                                  0.340  0.115
-## 
-## Deviance Information Criterion (DIC) ...............: 49.65
-## Deviance Information Criterion (DIC, saturated) ....: 108.49
-## Effective number of parameters .....................: 10.74
-## 
-## Watanabe-Akaike information criterion (WAIC) ...: 53.47
-## Effective number of parameters .................: 13.29
-## 
-## Marginal log-Likelihood:  -40.90 
-##  is computed 
-## Posterior summaries for the linear predictor and the fitted values are computed
-## (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
-
plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-
-plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS))
-

-
fdmr::model_viewer(
-  model_output = inlabru_model2,
-  mesh = mesh,
-  measurement_data = df_ll,
-  data_distribution = "Gaussian"
-)
-
-
-

TO DO: planned outcome of the hackathon/tutorial structure

-
-

Design the prior and the model

-
    -

  • Use the UTM-projected data and do everything in UTM -coordinates

    • -

  • Load both the point measurements and the rasters. There are -topographic descriptors and snow duration data. Snow data is available -for several years.

    • -

  • Use both the snow depth measurements and the different raster -data to generate the mesh: we want to account for spatial variability of -snow depth in the mesh. In areas where the terrain doesn’t vary much the -mesh doesn’t need to be that fine, and the contrary is the case in rough -topography with greatly varying snow depth. This information is -indirectly available in the topography feature rasters or in a different -way in the snow duration rasters.

    • -
-
-
-

Fitting the model

-

Get model results considering some or all observation data and -topographic predictors. Consider that snow duration could be seen as -both a predictor and observation.

-

-> do this for one year, and/or several years.

-

How do the results look like? Change parameters and adjust until the -snow depth map matches snow duration (best validation we have, -spatially) and locally the snow depth point measurements.

-

Do the results for all years look different, or the same?

-
-
-

How to deal with real zeros?

-

What happens when/where there is zero snow depth? The model needs to -distinguish between no data and zero snow depth, and for temporal -evolution also be able to predict zero snow depth after snow -melt-out.

-
-
-

Spatial covariance as prior for data assimilation

-

We think 4DM could be a nice complementary tool for SWE retrievals, -in particular for the prior covariance in spatio-temporal problems. For -this

-
    -

  • we want the prior to be anisotropic. What we mean with this: the -correlations should be allowed to behave differently in each dimension -of the feature space where we define distances between points. -Features/predictors in this example/tutorial are the provided terrain -parameters.

    • -

  • we wanted to export the spatial covariance matrix resulting from -this tutorial and test wheter we can use it to define the prior -covariance in a modelling study, for example within our data -assimilation framework MuSA (https://github.com/ealonsogzl/MuSA, see Alonso-González -et al., 2022; https://doi.org/10.5194/gmd-15-9127-2022 ). That would -necessarily be a separate study from this tutorial but results could -eventually be added if this works and results are promising.

    • -
-
-
-

Temporal evolution: snow map time series

-

How can we get temporal evolution in here? Snow depth/SWE -distribution map is a result that’s only useful if snow depth -measurements are available at maximum snow time (which is the case for -this data, but most often unfeasible). If it should be able to compete -against other (modelling) approaches, we expect 4Dmodeller to be able to -make map time series, as we do with our data assimilation approach. For -this to work, what other predictor data needs to be added in addition to -meltout date? Adding the day of the first snow fall may lead to linear -interpolation until max snow, and then linear interpolation until -meltout…? Here, we would expect that some physical/meachanistic model -would come in, at least a simple degree day model, using -precipitation/temperature from ERA5-land, for example..?

-
-
-

-
-
- - - - - - - - - - - From b0d3740a11413430fda960ce203fb4b8b157defe Mon Sep 17 00:00:00 2001 From: John Aiken Date: Mon, 20 Nov 2023 13:13:20 +0000 Subject: [PATCH 32/82] added small texts describing each step --- vignettes/tutorial_geophysics.Rmd | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/vignettes/tutorial_geophysics.Rmd b/vignettes/tutorial_geophysics.Rmd index 540be810..ccc31fea 100644 --- a/vignettes/tutorial_geophysics.Rmd +++ b/vignettes/tutorial_geophysics.Rmd @@ -14,6 +14,8 @@ The spatiotemporal evolution of the field is governed by wave equation. The smal ## Pre-processing and Import data +Geophysical data can come in lots of different formats but here will will start with a csv. + ```{r} # Set the path to the CSV file data_path <- "data/MT_Svalbard_Z.csv" @@ -28,12 +30,15 @@ print(head(d, 30)) plot(d$X, d$Y, pch = 20, main = "Svalbard MT sites", xlab = "X", ylab = "Y") ``` +Here we create the locations of the data for the mesh creation. This is the locations of the data, not the locations of the mesh nodes. ```{r} locations <- d[, c("Lon", "Lat")] locations <- unique(locations) names(locations) <- c("LONG", "LAT") ``` +Here we use the locations to help specify the best fit mesh to our data. + ## Meshing ```{r} mesh <- fmesher::fm_mesh_2d( @@ -48,7 +53,10 @@ points(locations, col = "red") fdmr::plot_mesh(mesh) ``` -## Stochastic modeling +## Stochastic modeling with INLA + +Now we can create a model that is solved on the mesh. + ```{r} library(INLA) From 566f5a6363dda299e5ec66d3167ede83714da6ff Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 21 Nov 2023 11:25:39 +0100 Subject: [PATCH 33/82] WIP: Testing use of mapview --- vignettes/using_mapview.Rmd | 53 +++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 vignettes/using_mapview.Rmd diff --git a/vignettes/using_mapview.Rmd b/vignettes/using_mapview.Rmd new file mode 100644 index 00000000..b4d44208 --- /dev/null +++ b/vignettes/using_mapview.Rmd @@ -0,0 +1,53 @@ +--- +title: "Using mapview" +output: + bookdown::html_document2: + base_format: rmarkdown::html_vignette + fig_caption: yes +bibliography: "covid.bib" +link-citations: yes +vignette: > + %\VignetteIndexEntry{Using mapview} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +Here we'll use mapview to create some plots of things and then use the plotting code in the +Shiny apps (maybe). + +```{r} +library(mapview) +``` + +Let's get some of the UTM data they wanted to load in. + +```{r} +filepath = "/Users/gar/Documents/Devel/global_mass/fdmr/vignettes/data/Bayelva_snow.nc" +``` + +```{r} +ncin <- ncdf4::nc_open(filepath) +longitude <- ncdf4::ncvar_get(ncin,"xs") +latitude <-ncdf4::ncvar_get(ncin,"ys") + +# Get the first year of snow depth (ds) +ds <- ncdf4::ncvar_get(ncin,"ds")[,1] + +# Create a data frame +df <- data.frame(LONG = longitude,LAT = latitude,ds) +head(df) +``` + +```{r} +locations <- sf::st_as_sf( + df, + coords = c("LONG","LAT"), + crs = "+proj=utm +zone=33" +) +``` + +Now let's plot the map + +```{r} +mapview::mapview(locations) +``` \ No newline at end of file From 8990240fd0b1ba5c3bec1b7a0e3620207efa7679 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Wed, 22 Nov 2023 12:13:08 +0100 Subject: [PATCH 34/82] WIP: mapview basic working but breaking out of div --- R/shiny_meshbuilder.R | 40 ++++++++++++++++++++++--------------- vignettes/using_mapview.Rmd | 25 +++++++++++++++++++++++ 2 files changed, 49 insertions(+), 16 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 81bea16c..d17db3ec 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -180,23 +180,31 @@ meshbuilder_shiny <- function( ) output$map <- leaflet::renderLeaflet({ - m <- leaflet::leaflet() - m <- leaflet::addTiles(m, group = "OSM") - m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") - m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') - m <- leafem::addMouseCoordinates(m, native.crs = TRUE) - if (plot_polygons) { - m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") - } else if (plot_points) { - m <- leaflet::addCircles(m, data = spatial_points, group = "Spatial", fillColor = "#b9220b", color = "#b9220b") - } - - m <- leaflet::addLayersControl(m, - position = "topright", - baseGroups = c("OSM"), - overlayGroups = c("Mesh", "Spatial"), - options = leaflet::layersControlOptions(collapsed = FALSE) + spatial <- sf::st_as_sf( + spatial_data, + coords = c("LONG", "LAT"), + crs = "+proj=utm +zone=33" ) + + m <- mapview::mapview(spatial) + m@map + # m <- leaflet::leaflet() + # m <- leaflet::addTiles(m, group = "OSM") + # m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") + # m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') + # m <- leafem::addMouseCoordinates(m, native.crs = TRUE) + # if (plot_polygons) { + # m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") + # } else if (plot_points) { + # m <- leaflet::addCircles(m, data = spatial_points, group = "Spatial", fillColor = "#b9220b", color = "#b9220b") + # } + + # m <- leaflet::addLayersControl(m, + # position = "topright", + # baseGroups = c("OSM"), + # overlayGroups = c("Mesh", "Spatial"), + # options = leaflet::layersControlOptions(collapsed = FALSE) + # ) }) output$mesh_code <- shiny::reactive( diff --git a/vignettes/using_mapview.Rmd b/vignettes/using_mapview.Rmd index b4d44208..3eee855d 100644 --- a/vignettes/using_mapview.Rmd +++ b/vignettes/using_mapview.Rmd @@ -46,8 +46,33 @@ locations <- sf::st_as_sf( ) ``` +```{r} +fdmr::mesh_builder(spatial_data=df, crs= "+proj=utm +zone=33") +``` + Now let's plot the map ```{r} mapview::mapview(locations) +``` + +Now let's create a mesh from the spatial data and plot that + +```{r} +coords_only = df[, c("LONG", "LAT")] +crs = "+proj=utm +zone=33" +mesh <- fmesher::fm_mesh_2d(coords_only, crs=crs) +``` + +Now let's plot it + +```{r} +spatial_mesh = fdmr::mesh_to_spatial(mesh, crs=crs) +mapview::mapview(spatial_mesh) +``` + +We'll try the fmesher plotting functions + +```{r} +inlabru::gg(mesh) ``` \ No newline at end of file From d6d9ee1d5ac12435c187994a98c978492074001c Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Wed, 22 Nov 2023 13:54:41 +0100 Subject: [PATCH 35/82] Looking at reason for div breakout of maps --- R/shiny_meshbuilder.R | 3 +-- vignettes/using_mapview.Rmd | 31 +++++++++++++++++++++++++++++++ 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index d17db3ec..d79b6b10 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -89,8 +89,7 @@ meshbuilder_shiny <- function( busy_spinner <- get_busy_spinner() - ui <- bslib::page_fluid( - theme = bslib::bs_theme(bootswatch = "cosmo"), + ui <- shiny::fluidPage( busy_spinner, shinybusy::add_loading_state( "#map", diff --git a/vignettes/using_mapview.Rmd b/vignettes/using_mapview.Rmd index 3eee855d..9d0bfe64 100644 --- a/vignettes/using_mapview.Rmd +++ b/vignettes/using_mapview.Rmd @@ -75,4 +75,35 @@ We'll try the fmesher plotting functions ```{r} inlabru::gg(mesh) +``` + +```{r} +library(shiny) +library(leaflet) + +coords_only = df[, c("LONG", "LAT")] +crs = "+proj=utm +zone=33" +mesh <- fmesher::fm_mesh_2d(coords_only, crs=crs) +spatial_mesh = fdmr::mesh_to_spatial(mesh, crs=crs) +# mapview::mapview(spatial_mesh) + +ui <- fluidPage( + leafletOutput("mymap"), + p(), + actionButton("recalc", "New points") +) + +server <- function(input, output, session) { + + points <- eventReactive(input$recalc, { + cbind(rnorm(40) * 2 + 13, rnorm(40) + 48) + }, ignoreNULL = FALSE) + + output$mymap <- renderLeaflet({ + m <- mapview::mapview(spatial_mesh) + m@map + }) +} + +shinyApp(ui, server) ``` \ No newline at end of file From d004f030298a57be074109f674f5295f2f5130d0 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 23 Nov 2023 15:50:19 +0100 Subject: [PATCH 36/82] Testing --- R/shiny_meshbuilder.R | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index d79b6b10..f0ff398b 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -123,27 +123,27 @@ meshbuilder_shiny <- function( # shiny::actionButton("check_button", "Check mesh"), ), shiny::mainPanel( - shiny::tabsetPanel( - type = "tabs", - shiny::tabPanel( - "Plot", - class = "p-3 border", - shiny::div(id = "map_div", leaflet::leafletOutput("map", height = "80vh")) - ), - shiny::tabPanel("Code", class = "p-3 border", shiny::verbatimTextOutput("mesh_code")), - shiny::tabPanel( - "Help", - class = "p-3 border", - shiny::h3("Help"), - shiny::h4("Max edge"), - shiny::p("Determines the maximum permitted length for a triangle (lower values for max.edge result in higher mesh resolution). This parameter can take either a scalar value, which controls the triangle edge lengths in the inner domain, - or a length-two vector that controls edge lengths both in the inner domain and in the outer extension to avoid the boundary effect."), - shiny::h4("Offset"), - shiny::p("Specifies the size of the inner and outer extensions around data locations."), - shiny::h4("Cutoff"), - shiny::p("Minimum allowed distance between data points."), - ) - ) + shiny::div(leaflet::leafletOutput("map", height = "80vh")) + # shiny::tabsetPanel( + # type = "tabs", + # shiny::tabPanel( + # "Plot", + # class = "p-3 border", + # ), + # shiny::tabPanel("Code", class = "p-3 border", shiny::verbatimTextOutput("mesh_code")), + # shiny::tabPanel( + # "Help", + # class = "p-3 border", + # shiny::h3("Help"), + # shiny::h4("Max edge"), + # shiny::p("Determines the maximum permitted length for a triangle (lower values for max.edge result in higher mesh resolution). This parameter can take either a scalar value, which controls the triangle edge lengths in the inner domain, + # or a length-two vector that controls edge lengths both in the inner domain and in the outer extension to avoid the boundary effect."), + # shiny::h4("Offset"), + # shiny::p("Specifies the size of the inner and outer extensions around data locations."), + # shiny::h4("Cutoff"), + # shiny::p("Minimum allowed distance between data points."), + # ) + # ) ) ) ) From 3bdc2b34758ae209e0b21627fcc1eaa6a4cc49c7 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:38:42 +0000 Subject: [PATCH 37/82] Added functions to util_coords.R Adds converter from 0/360 to -180/180 and a function that splits the polygon along the dateline --- R/util_coords.R | 104 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) diff --git a/R/util_coords.R b/R/util_coords.R index 07c7a649..fab5f7cb 100644 --- a/R/util_coords.R +++ b/R/util_coords.R @@ -40,3 +40,107 @@ has_coords <- function(spatial_data) { } ) } + +#' Convert longitudes from 0 to 360 degrees to -180 to 180 degrees +#' @param sf_data An sf object; does not accept SpatialPolygon* objects +#' @param crs CRS as a proj4string or EPSG code +#' @param add_data Select if data associated with the object are carried forward by the transformed version, defaults to FALSE +#' @param longitude_column Name of longitude, defaults to LONG +convert_from_lon_360 <- function(sf_data, crs = 4326, add_data = TRUE, longitude_column = "LONG") { + # Get polygon coordinates + coords <- sf::st_coordinates(sf_data) + + # Check if POLYGON contains holes, remove if so + if ((base::length(base::unique(coords[, "L1"])) > 1) & (base::ncol(coords) > 3)) { + warning("Converter does not define the interior holes. Please convert your data yourself if these features are crucial") + sf_data <- sfheaders::sf_remove_holes(obj = sf_data) + coords <- sf::st_coordinates(sf_data) + } + + coords_conv <- coords + + # Convert data coordinates if selected + if (base::isTRUE(add_data) & (!base::is.null(longitude_column))) { + if (base::is.null(sf_data[["LONG"]]) & (longitude_column == "LONG")) { + warning("`LONG` not found. Please change the default `longitude_column` name to the longitude name in your dataset") + } else { + sf_data[[longitude_column]] <- base::ifelse(sf_data[[longitude_column]] > 180, + sf_data[[longitude_column]] - 360, + sf_data[[longitude_column]]) + } + } + + # Convert polygon longitudes + coords_conv[, "X"] <- base::ifelse(coords[, "X"] > 180, coords[, "X"] - 360, coords[, "X"]) + + # Define the feature column and convert to POLYGON + if (base::ncol(coords_conv) == 4) { + poly <- "L2" + conv <- sfheaders::sf_polygon(obj = coords_conv, x = 'X', y = 'Y', polygon_id = poly) + conv <- sf::st_sf(conv, crs = crs) + } else if (base::ncol(coords_conv) == 5) { + poly <- "L3" + multipoly <- "L2" + conv <- sfheaders::sf_multipolygon(obj = coords_conv, x = 'X', y = 'Y', multipolygon_id = multipoly, polygon_id = poly) + conv <- sf::st_sf(conv, crs = crs) + } else { + stop("Object type not supported (must be POLYGON or MULTIPOLYGON)") + } + + # Add the associated data if requested + if (base::isTRUE(add_data)) { + r_conv <- sf_data + r_conv$geometry <- conv$geometry + } else { + r_conv <- conv + } + + # Return a list of POLYGON objects with shifted longitudes + return(r_conv) +} + +#' Split polygons into two if crossing the dateline (antimeridian) +#' +#' @param sp_data SpatialPolygon or SpatialPolygonDataFrame object +#' @param crs CRS as a proj4string or EPSG code, default EPSG:4326 (datum=WGS84) +#' @param unique_inst Option to remove duplicate polygons (with the same coordinates), default TRUE +#' @param to_sp Option to convert POLYGON object into SpatialPolygon*, default TRUE +#' @return SpatialPolygon or SpatialPolygonDataFrame split along the antimeridian +#' @export +antimeridian_wrapping <- function(polygon_data, crs = 4326, unique_inst = TRUE, to_sp = TRUE) { + # Convert to sf object if needed + if (!is(polygon_data, 'sf')) { + if (isTRUE(unique_inst)) { + all_coords <- base::lapply(polygon_data@polygons, function(x) { + base::lapply(x@Polygons, function(x) { + sp::coordinates(x) + }) + }) + polygon_data <- polygon_data[!base::duplicated(all_coords),] + } + sf_data <- sf::st_as_sf(polygon_data) + if (!isTRUE(sf::st_is_longlat(sf_data))) { + stop("Projected coordinates not allowed, please choose a different projection to avoid the antimeridian problem") + } + } else { + sf_data <- polygon_data + } + + # Check if longitudes go from -180 to 180 rather than 0 to 360 + if (sf::st_bbox(sf_data)[['xmin']] > 0) { + polygon_data_conv <- convert_from_lon_360(sf_data, crs = crs, add_data = FALSE) + warning("Polygon coordinates [0;360] have been converted to [-180;180]") + } + + # Apply the function + sf_split <- sf::st_wrap_dateline(sf_data) + + # Revert sf objects back to sp + if (isTRUE(to_sp)) { + polygon_data_conv <- sf::as_Spatial(sf_split) + } else { + polygon_data_conv <- sf_split + } + + return(polygon_data_conv) +} From d4abfc34ab2b92a198297f3361005a1a22f3614d Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:40:25 +0000 Subject: [PATCH 38/82] Added support for antimeridian wrapping in plot_mesh.R --- R/plot_mesh.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/plot_mesh.R b/R/plot_mesh.R index 98f1e243..617ad9a4 100644 --- a/R/plot_mesh.R +++ b/R/plot_mesh.R @@ -24,7 +24,9 @@ plot_mesh <- function(mesh, spatial_data = NULL, longitude_column = "LONG", lati ) } - spatial_mesh <- fdmr::mesh_to_spatial(mesh = mesh, crs = expected_crs) + spatial_mesh_original <- fdmr::mesh_to_spatial(mesh = mesh, crs = expected_crs) + + spatial_mesh <- fdmr::antimeridian_wrapping(spatial_mesh_original, crs = expected_crs, unique_inst = FALSE, to_sp = FALSE) plot_polygons <- FALSE plot_points <- FALSE From e5b182596d7acd7973ddb982537f857420a1bb28 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:40:47 +0000 Subject: [PATCH 39/82] Added optional support for antimeridian wrapping in plot_mapping.R --- R/plot_mapping.R | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index ec84fa05..1e52c89a 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -12,6 +12,7 @@ #' @param polygon_line_colour Polygon surrounding line colour #' @param polygon_line_weight Polygon surrounding line weight #' @param reverse Reverse the colour palette if TRUE +#' @param wrapping Split polygons along the antimeridian (-180/180 boundary) if TRUE #' #' @return leaflet::leaflet #' @export @@ -26,7 +27,8 @@ plot_map <- function(polygon_data = NULL, polygon_line_colour = "grey", polygon_line_weight = 1, polygon_fill_opacity = 0.6, - reverse = FALSE) { + reverse = FALSE, + wrapping = FALSE) { if (is.null(polygon_data) && is.null(raster_data)) { stop("Polygon or raster data must be given.") } @@ -41,6 +43,9 @@ plot_map <- function(polygon_data = NULL, layers <- c() if (!is.null(polygon_data)) { + if (isTRUE(wrapping)) { + polygon_data <- fdmr::antimeridian_wrapping(polygon_data, crs = "+proj=longlat +datum=WGS84", unique_inst = FALSE, to_sp = FALSE) + } if (!is.null(domain)) { colours <- leaflet::colorNumeric(palette = palette, domain = domain, reverse = reverse) polygon_fill_colour <- ~ colours(domain) From 759a03124908a2de5a05fa73f452383a31a3d2d4 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:41:38 +0000 Subject: [PATCH 40/82] Update shiny_meshbuilder.R Added support for antimeridian wrapping. --- R/shiny_meshbuilder.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 81bea16c..c7b1dc1a 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -179,10 +179,12 @@ meshbuilder_shiny <- function( ) ) + spatial_mesh <- fdmr::antimeridian_wrapping(mesh_spatial(), crs = crs, unique_inst = FALSE, to_sp = FALSE) + output$map <- leaflet::renderLeaflet({ m <- leaflet::leaflet() m <- leaflet::addTiles(m, group = "OSM") - m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") + m <- leaflet::addPolygons(m, data = spatial_mesh, weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') m <- leafem::addMouseCoordinates(m, native.crs = TRUE) if (plot_polygons) { From fa828f88e7278e9fa4907b538dbcadfec935fdb8 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:53:53 +0000 Subject: [PATCH 41/82] Update CHANGELOG.md --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 210efb8c..d44f0668 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) +### Fixed +- Added a function to ensure correct polygon display across the dateline - [PR #225](https://github.com/4DModeller/fdmr/pull/225) + ### Added - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) From 3933ba010bc80e955bd01e29567bdc53b2565780 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 08:20:05 +0000 Subject: [PATCH 42/82] Update util_coords.R --- R/util_coords.R | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/R/util_coords.R b/R/util_coords.R index fab5f7cb..ebfbd754 100644 --- a/R/util_coords.R +++ b/R/util_coords.R @@ -42,10 +42,14 @@ has_coords <- function(spatial_data) { } #' Convert longitudes from 0 to 360 degrees to -180 to 180 degrees +#' #' @param sf_data An sf object; does not accept SpatialPolygon* objects #' @param crs CRS as a proj4string or EPSG code #' @param add_data Select if data associated with the object are carried forward by the transformed version, defaults to FALSE #' @param longitude_column Name of longitude, defaults to LONG +#' +#' @return polygons with converted coordinates +#' @export convert_from_lon_360 <- function(sf_data, crs = 4326, add_data = TRUE, longitude_column = "LONG") { # Get polygon coordinates coords <- sf::st_coordinates(sf_data) @@ -101,16 +105,17 @@ convert_from_lon_360 <- function(sf_data, crs = 4326, add_data = TRUE, longitude #' Split polygons into two if crossing the dateline (antimeridian) #' -#' @param sp_data SpatialPolygon or SpatialPolygonDataFrame object +#' @param polygon_data SpatialPolygon or SpatialPolygonDataFrame object #' @param crs CRS as a proj4string or EPSG code, default EPSG:4326 (datum=WGS84) #' @param unique_inst Option to remove duplicate polygons (with the same coordinates), default TRUE #' @param to_sp Option to convert POLYGON object into SpatialPolygon*, default TRUE -#' @return SpatialPolygon or SpatialPolygonDataFrame split along the antimeridian + +#' @return SpatialPolygon or SpatialPolygonDataFrame split along the antimeridian #' @export antimeridian_wrapping <- function(polygon_data, crs = 4326, unique_inst = TRUE, to_sp = TRUE) { # Convert to sf object if needed if (!is(polygon_data, 'sf')) { - if (isTRUE(unique_inst)) { + if (base::isTRUE(unique_inst)) { all_coords <- base::lapply(polygon_data@polygons, function(x) { base::lapply(x@Polygons, function(x) { sp::coordinates(x) @@ -136,7 +141,7 @@ antimeridian_wrapping <- function(polygon_data, crs = 4326, unique_inst = TRUE, sf_split <- sf::st_wrap_dateline(sf_data) # Revert sf objects back to sp - if (isTRUE(to_sp)) { + if (base::isTRUE(to_sp)) { polygon_data_conv <- sf::as_Spatial(sf_split) } else { polygon_data_conv <- sf_split From cdafc3ed3388f9f956605312610e9ad936292a8a Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 08:22:25 +0000 Subject: [PATCH 43/82] Do not plot duplicate polygons in plot_mapping.R --- R/plot_mapping.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index 1e52c89a..9e9a518e 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -44,7 +44,7 @@ plot_map <- function(polygon_data = NULL, if (!is.null(polygon_data)) { if (isTRUE(wrapping)) { - polygon_data <- fdmr::antimeridian_wrapping(polygon_data, crs = "+proj=longlat +datum=WGS84", unique_inst = FALSE, to_sp = FALSE) + polygon_data <- fdmr::antimeridian_wrapping(polygon_data, crs = "+proj=longlat +datum=WGS84", unique_inst = TRUE, to_sp = FALSE) } if (!is.null(domain)) { colours <- leaflet::colorNumeric(palette = palette, domain = domain, reverse = reverse) From 613d9260cb779f9a3a51138739e24256a1dd08a1 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 11:32:03 +0000 Subject: [PATCH 44/82] Fixing shiny_meshbuilder.R error --- R/shiny_meshbuilder.R | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index c7b1dc1a..a9d9ec36 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -174,17 +174,15 @@ meshbuilder_shiny <- function( mesh_spatial <- shiny::reactive( suppressMessages( suppressWarnings( - fdmr::mesh_to_spatial(mesh = mesh(), crs = crs) + fdmr::antimeridian_wrapping(fdmr::mesh_to_spatial(mesh = mesh(), crs = crs), crs = crs, unique_inst = FALSE, to_sp = FALSE) ) ) ) - spatial_mesh <- fdmr::antimeridian_wrapping(mesh_spatial(), crs = crs, unique_inst = FALSE, to_sp = FALSE) - output$map <- leaflet::renderLeaflet({ m <- leaflet::leaflet() m <- leaflet::addTiles(m, group = "OSM") - m <- leaflet::addPolygons(m, data = spatial_mesh, weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") + m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') m <- leafem::addMouseCoordinates(m, native.crs = TRUE) if (plot_polygons) { From 9254c50ca820bc196e71b0341b0528653cc38509 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 14:15:45 +0000 Subject: [PATCH 45/82] Fixed the addMeasure --- R/shiny_meshbuilder.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index a9d9ec36..d818fa25 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -183,7 +183,7 @@ meshbuilder_shiny <- function( m <- leaflet::leaflet() m <- leaflet::addTiles(m, group = "OSM") m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") - m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') + m <- leaflet::addMeasure(m, position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') m <- leafem::addMouseCoordinates(m, native.crs = TRUE) if (plot_polygons) { m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") From 129fab466e8def86c0684dcd8ee6c14a55af95a8 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 14:20:26 +0000 Subject: [PATCH 46/82] Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d44f0668..ca19bc78 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) ### Fixed -- Added a function to ensure correct polygon display across the dateline - [PR #225](https://github.com/4DModeller/fdmr/pull/225) +- Added a function to ensure correct polygon display across the dateline - [PR #285](https://github.com/4DModeller/fdmr/pull/285) ### Added From a76f56546717ea3e14a2c18a5e82745fb8691f97 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 14:33:36 +0000 Subject: [PATCH 47/82] Update NAMESPACE --- NAMESPACE | 2 ++ 1 file changed, 2 insertions(+) diff --git a/NAMESPACE b/NAMESPACE index fb7e01c5..dcf9a937 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,8 @@ # Generated by roxygen2: do not edit by hand +export(antimeridian_wrapping) export(clear_caches) +export(convert_from_lon_360) export(create_prediction_field) export(get_tutorial_datapath) export(latlong_to_utm) From 4b1298185f4325880637e7f7df6443bb323450b6 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 14:44:50 +0000 Subject: [PATCH 48/82] Update DESCRIPTION Added package `sfheaders` --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index f3c7f164..121a2d03 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -58,7 +58,8 @@ Imports: bslib, bsicons, future, - leafem + leafem, + sfheaders Suggests: bookdown, knitr, From cf815eb82f425fd79234ffe6fb2cdbef682d06f7 Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:07:43 +0000 Subject: [PATCH 49/82] Change the default offset for st_wrap_dateline Polar meshes are shown incorrectly north/south of 70 degree latitude if default offset (10 degrees) is used. Increasing the offset to 90 degrees does not negatively impact polygon splitting north/south of 70 degrees and allows to properly plot polar polygons. --- R/util_coords.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/util_coords.R b/R/util_coords.R index ebfbd754..def37fa6 100644 --- a/R/util_coords.R +++ b/R/util_coords.R @@ -138,7 +138,7 @@ antimeridian_wrapping <- function(polygon_data, crs = 4326, unique_inst = TRUE, } # Apply the function - sf_split <- sf::st_wrap_dateline(sf_data) + sf_split <- sf::st_wrap_dateline(sf_data, options = c("WRAPDATELINE=YES", "DATELINEOFFSET=90")) # Revert sf objects back to sp if (base::isTRUE(to_sp)) { From d3a8ab44a73591083c595c1e41f2834bae0689ce Mon Sep 17 00:00:00 2001 From: "Anrijs K. Abele" <57137242+aabelean@users.noreply.github.com> Date: Mon, 4 Dec 2023 11:16:00 +0000 Subject: [PATCH 50/82] Removed the `Comparing the output` section in decision_tree.Rmd --- vignettes/decision_tree.Rmd | 8 -------- 1 file changed, 8 deletions(-) diff --git a/vignettes/decision_tree.Rmd b/vignettes/decision_tree.Rmd index 09a92854..7ea02596 100644 --- a/vignettes/decision_tree.Rmd +++ b/vignettes/decision_tree.Rmd @@ -107,11 +107,3 @@ fit <- bru(value ~ 1 + observed(x + y, model = "linear"), data = df) # Print the model summary print(summary(fit)) ``` - -### Comparing the output - -Finally, we can compare the output of all the models using the DIC. This is important as we would like to know the fittedness of each model even if we have some theoretical belief that a model should be specificied in a certain way. - -```{r error=TRUE} -plot(DICs) -``` From 8d0bb8022750aeac4076e35ae7c032d6c2f466e7 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 12:12:57 +0000 Subject: [PATCH 51/82] WIP: Adding multiple layers --- R/shiny_meshbuilder.R | 61 +++++++++++++++++++++---------------------- 1 file changed, 30 insertions(+), 31 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index f0ff398b..816deea3 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -52,9 +52,6 @@ meshbuilder_shiny <- function( stop("Cannot read latitude and longitude data from spatial data. Please ensure given names are correct.") } - # The number of nodes we count as being a big mesh - n_nodes_big_mesh <- 10000 - default_max_edge <- c(0.1, 0.3) default_offset <- c(0.2, 0.7) default_cutoff <- 0.2 @@ -91,12 +88,6 @@ meshbuilder_shiny <- function( ui <- shiny::fluidPage( busy_spinner, - shinybusy::add_loading_state( - "#map", - timeout = 600, - text = "Calculating mesh...", - svgColor = "steelblue" - ), shiny::headerPanel(title = "Creating a mesh"), shiny::sidebarLayout( shiny::sidebarPanel( @@ -123,27 +114,27 @@ meshbuilder_shiny <- function( # shiny::actionButton("check_button", "Check mesh"), ), shiny::mainPanel( - shiny::div(leaflet::leafletOutput("map", height = "80vh")) - # shiny::tabsetPanel( - # type = "tabs", - # shiny::tabPanel( - # "Plot", - # class = "p-3 border", - # ), - # shiny::tabPanel("Code", class = "p-3 border", shiny::verbatimTextOutput("mesh_code")), - # shiny::tabPanel( - # "Help", - # class = "p-3 border", - # shiny::h3("Help"), - # shiny::h4("Max edge"), - # shiny::p("Determines the maximum permitted length for a triangle (lower values for max.edge result in higher mesh resolution). This parameter can take either a scalar value, which controls the triangle edge lengths in the inner domain, - # or a length-two vector that controls edge lengths both in the inner domain and in the outer extension to avoid the boundary effect."), - # shiny::h4("Offset"), - # shiny::p("Specifies the size of the inner and outer extensions around data locations."), - # shiny::h4("Cutoff"), - # shiny::p("Minimum allowed distance between data points."), - # ) - # ) + shiny::tabsetPanel( + type = "tabs", + shiny::tabPanel( + "Plot", + class = "p-3 border", + shiny::div(leaflet::leafletOutput("map", height = "80vh")) + ), + shiny::tabPanel("Code", class = "p-3 border", shiny::verbatimTextOutput("mesh_code")), + shiny::tabPanel( + "Help", + class = "p-3 border", + shiny::h3("Help"), + shiny::h4("Max edge"), + shiny::p("Determines the maximum permitted length for a triangle (lower values for max.edge result in higher mesh resolution). This parameter can take either a scalar value, which controls the triangle edge lengths in the inner domain, + or a length-two vector that controls edge lengths both in the inner domain and in the outer extension to avoid the boundary effect."), + shiny::h4("Offset"), + shiny::p("Specifies the size of the inner and outer extensions around data locations."), + shiny::h4("Cutoff"), + shiny::p("Minimum allowed distance between data points."), + ) + ) ) ) ) @@ -185,7 +176,15 @@ meshbuilder_shiny <- function( crs = "+proj=utm +zone=33" ) - m <- mapview::mapview(spatial) + layers <- list(spatial) + layer_names <- c("Spatial") + if (!is.null(mesh_spatial())) { + layers <- list(spatial, mesh_spatial()) + layer_names <- list("Spatial", "Mesh") + } + + m <- mapview::mapview(layers, layer.names = layer_names, legend = TRUE, map.types = "OpenStreetMap") + m@map # m <- leaflet::leaflet() # m <- leaflet::addTiles(m, group = "OSM") From 039a4fd474a79092173b9d337c9fc80985e14839 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 12:56:32 +0000 Subject: [PATCH 52/82] UTM data and mesh being plotted with mapview --- R/shiny_meshbuilder.R | 118 +++++++++++------------------------------- 1 file changed, 31 insertions(+), 87 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 816deea3..cab6c792 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -64,54 +64,36 @@ meshbuilder_shiny <- function( coords_only <- spatial_data[, c(longitude_column, latitude_column)] names(coords_only) <- c("LONG", "LAT") - plot_polygons <- FALSE - plot_points <- FALSE - # We may not use this - spatial_points <- NULL - # Do some checks to see what kind of data we have - # If we have a SpatialPolygonsDataFrame, we can plot the polygons - # otherwise if we just have SpatialPoints we can plot the points - # otherwise we don't plot anything - if (class(spatial_data) == "SpatialPolygonsDataFrame") { - plot_polygons <- TRUE - } else { - plot_points <- TRUE - - spatial_points <- sp::SpatialPointsDataFrame( - coords = coords_only, - data = spatial_data, - proj4string = sp::CRS(crs) - ) - } busy_spinner <- get_busy_spinner() ui <- shiny::fluidPage( busy_spinner, + shinyjs::useShinyjs(), shiny::headerPanel(title = "Creating a mesh"), shiny::sidebarLayout( shiny::sidebarPanel( + shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = FALSE), shiny::sliderInput( inputId = "max_edge", label = "Max edge:", - min = 0.02, value = c(0.1, 0.3), max = 10 + min = 0.02, value = 0.1, max = 10 ), shiny::p("Max permitted edge length for a triangle"), shiny::sliderInput( inputId = "offset", label = "Offset:", - min = 0.02, value = c(0.2, 0.7), max = 10 + min = 0.02, value = 0.1, max = 10 ), shiny::p("Specifies the size of the inner and outer extensions around data locations."), shiny::sliderInput( inputId = "cutoff", label = "Cutoff:", - min = 0.005, value = 0.2, max = 0.9 + min = 0.005, value = 0.1, max = 0.9 ), shiny::p("Minimum allowed distance between data points."), shiny::actionButton("plot_mesh", label = "Plot mesh"), shiny::actionButton("reset_mesh", label = "Reset"), - # shiny::actionButton("check_button", "Check mesh"), ), shiny::mainPanel( shiny::tabsetPanel( @@ -141,6 +123,18 @@ meshbuilder_shiny <- function( # Define server logic required to draw a histogram server <- function(input, output, session) { + shiny::observeEvent(input$enable_inputs, { + if (input$enable_inputs) { + shinyjs::enable("max_edge") + shinyjs::enable("offset") + shinyjs::enable("cutoff") + } else { + shinyjs::disable("max_edge") + shinyjs::disable("offset") + shinyjs::disable("cutoff") + } + }) + shiny::observeEvent(input$reset_mesh, { shiny::updateSliderInput(session, inputId = "max_edge", value = default_max_edge) shiny::updateSliderInput(session, inputId = "offset", value = default_offset) @@ -148,19 +142,25 @@ meshbuilder_shiny <- function( }) mesh <- shiny::eventReactive(input$plot_mesh, ignoreNULL = FALSE, { + if (input$enable_inputs) { + max_edge <- input$max_edge + offset <- input$offset + cutoff <- input$cutoff + } else { + max_edge <- NULL + offset <- NULL + cutoff <- NULL + } + fmesher::fm_mesh_2d_inla( loc = coords_only, - max.edge = input$max_edge, - cutoff = input$cutoff, - offset = input$offset, + max.edge = max_edge, + cutoff = cutoff, + offset = offset, crs = crs, ) }) - # large_mesh <- shiny::reactive({ - # mesh()$n > n_nodes_big_mesh - # }) - mesh_spatial <- shiny::reactive( suppressMessages( suppressWarnings( @@ -176,33 +176,8 @@ meshbuilder_shiny <- function( crs = "+proj=utm +zone=33" ) - layers <- list(spatial) - layer_names <- c("Spatial") - if (!is.null(mesh_spatial())) { - layers <- list(spatial, mesh_spatial()) - layer_names <- list("Spatial", "Mesh") - } - - m <- mapview::mapview(layers, layer.names = layer_names, legend = TRUE, map.types = "OpenStreetMap") - + m <- mapview::mapview(list(spatial, mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) m@map - # m <- leaflet::leaflet() - # m <- leaflet::addTiles(m, group = "OSM") - # m <- leaflet::addPolygons(m, data = mesh_spatial(), weight = 0.5, fillOpacity = 0.2, fillColor = "#5252ca", group = "Mesh") - # m <- leaflet::addMeasure(position = 'bottomleft', primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') - # m <- leafem::addMouseCoordinates(m, native.crs = TRUE) - # if (plot_polygons) { - # m <- leaflet::addPolygons(m, data = spatial_data, fillColor = "#d66363", color = "green", weight = 1, group = "Spatial") - # } else if (plot_points) { - # m <- leaflet::addCircles(m, data = spatial_points, group = "Spatial", fillColor = "#b9220b", color = "#b9220b") - # } - - # m <- leaflet::addLayersControl(m, - # position = "topright", - # baseGroups = c("OSM"), - # overlayGroups = c("Mesh", "Spatial"), - # options = leaflet::layersControlOptions(collapsed = FALSE) - # ) }) output$mesh_code <- shiny::reactive( @@ -215,37 +190,6 @@ meshbuilder_shiny <- function( offset=c(", paste0(input$offset, collapse = ", "), "))\n" ) ) - - # shiny::observe({ - # if (large_mesh() && !modal_shown()) { - # shiny::showModal(shiny::modalDialog( - # "Mesh is large, plotting may be slow.", - # title = "Mesh warning", - # easyClose = TRUE, - # footer = NULL - # )) - # } - # modal_shown(TRUE) - # }) - - - shiny::observeEvent(input$check_button, { - if (is.null(obs_data) || is.null(mesh())) { - errors <- "No observation data. Cannot check mesh." - } else { - errors <- fdmr::mesh_checker(mesh = mesh(), observations = obs_data) - if (!length(errors)) { - errors <- "No errors found." - } - } - - shiny::showModal(shiny::modalDialog( - stringr::str_flatten(errors, collapse = "\n"), - title = "Mesh check", - easyClose = TRUE, - footer = NULL - )) - }) } # Run the application From bd1c75e40a9f72fe9c2054afed11ea2defe51759 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 15:23:59 +0000 Subject: [PATCH 53/82] Fix code output for NULL maxedge etc --- R/shiny_meshbuilder.R | 37 +++++++++++++++++++++++++------------ vignettes/meshbuilder.Rmd | 19 ++----------------- 2 files changed, 27 insertions(+), 29 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index cab6c792..0d9e4bd3 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -47,14 +47,17 @@ meshbuilder_shiny <- function( ) } + # If the user passes in any of these then we enable the sliders + enable_inputs <- (!is.null(max_edge) || !is.null(offset) || !is.null(cutoff)) + got_lat_long <- all(c(longitude_column, latitude_column) %in% names(spatial_data)) if (!got_lat_long) { stop("Cannot read latitude and longitude data from spatial data. Please ensure given names are correct.") } - default_max_edge <- c(0.1, 0.3) - default_offset <- c(0.2, 0.7) - default_cutoff <- 0.2 + default_max_edge <- c(0.01, 0.3) + default_offset <- c(0.02, 0.7) + default_cutoff <- 0.02 # TODO - these defaults need changing? if (is.null(max_edge)) max_edge <- default_max_edge if (is.null(offset)) offset <- default_offset @@ -73,7 +76,7 @@ meshbuilder_shiny <- function( shiny::headerPanel(title = "Creating a mesh"), shiny::sidebarLayout( shiny::sidebarPanel( - shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = FALSE), + shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = enable_inputs), shiny::sliderInput( inputId = "max_edge", label = "Max edge:", @@ -180,16 +183,26 @@ meshbuilder_shiny <- function( m@map }) - output$mesh_code <- shiny::reactive( + output$mesh_code <- shiny::reactive({ + if (input$enable_inputs) { + max_edge_str <- paste0("max.edge = c(", paste0(input$max_edge, collapse = ","), "),") + offset_str <- paste0("offset = c(", paste0(input$offset, collapse = ", "), "),") + cutoff_str <- paste0("cutoff = ", input$cutoff, ",") + } else { + max_edge_str <- "max.edge = NULL," + offset_str <- "offset = NULL," + cutoff_str <- "cutoff = NULL," + } + paste0( - "location_data <- spatial_data[, c('", longitude_column, "', '", latitude_column, "')]\n\n", - "names(location_data) <- c('LONG', 'LAT')\n\n", - "mesh <- fmesher::fm_mesh_2d_inla(loc = location_data, - max.edge = c(", paste0(input$max_edge, collapse = ", "), "), - cutoff = ", input$cutoff, ", - offset=c(", paste0(input$offset, collapse = ", "), "))\n" + "location_data <- spatial_data[, c('", longitude_column, "', '", latitude_column, "')],\n", + "names(location_data) <- c('LONG', 'LAT')\n", + "mesh <- fmesher::fm_mesh_2d_inla(loc = location_data,\n\t", + max_edge_str, "\n\t", + cutoff_str, "\n\t", + offset_str, ")\n" ) - ) + }) } # Run the application diff --git a/vignettes/meshbuilder.Rmd b/vignettes/meshbuilder.Rmd index 70bf7b92..1abdb03b 100644 --- a/vignettes/meshbuilder.Rmd +++ b/vignettes/meshbuilder.Rmd @@ -81,29 +81,14 @@ offset <- c(initial_range / 4, initial_range) cutoff <- max_edge / 7 ``` +> **_NOTE:_** By default we let `fmesher` select the defaults for these values itself. If you encounter long mesh build times try using the `meshbuilder` defaults of NULL values for `max_edge` etc. + Now we're ready to start the app. ```{r eval=FALSE} fdmr::mesh_builder(spatial_data = sp_data, max_edge = max_edge_fin, offset = offset, cutoff = cutoff) ``` -## Run checks on mesh - -We provide a simple function to check meshes created using the mesh builder tool. From with the user interface click "Check mesh" to run a number of tests on the mesh. -Currently these are: - -1. Check that the number of mesh nodes isn't greater than the number of measurments -2. Check that the number of triangles isn't greater than the number of measurements -3. Check that there are no isolated triangles - -To use the mesh checking functionality you must pass your measuremnet data to the `fdmr::mesh_builder` function. Here we use the COVID-19 data. Create a mesh of your design and when you're finished click the "Check mesh" button. This passes the created mesh -to the `fdmr::mesh_checker` function and returns a list containing any errors found with the mesh. - -```{r eval=FALSE} -covid19_data <- fdmr::load_tutorial_data(dataset = "covid", filename = "covid19_data.rds") -fdmr::mesh_builder(spatial_data = sp_data, obs_data = covid19_data) -``` - ## Exporting your mesh To export your mesh click on the Code tab and copy and paste the code used to created the mesh. From 94cd220abba370249aa33e4aa0e272b14fdcc8aa Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 15:31:06 +0000 Subject: [PATCH 54/82] Add text output to tell user of CRS, correct indentation of code output --- R/shiny_meshbuilder.R | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 0d9e4bd3..73aa8d63 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -104,7 +104,9 @@ meshbuilder_shiny <- function( shiny::tabPanel( "Plot", class = "p-3 border", - shiny::div(leaflet::leafletOutput("map", height = "80vh")) + shiny::div(leaflet::leafletOutput("map", height = "80vh")), + shiny::br(), + shiny::textOutput(outputId = "mesh_crs") ), shiny::tabPanel("Code", class = "p-3 border", shiny::verbatimTextOutput("mesh_code")), shiny::tabPanel( @@ -144,6 +146,10 @@ meshbuilder_shiny <- function( shiny::updateSliderInput(session, inputId = "cutoff", value = default_cutoff) }) + output$mesh_crs <- shiny::renderText({ + paste("Mesh CRS: ", crs) + }) + mesh <- shiny::eventReactive(input$plot_mesh, ignoreNULL = FALSE, { if (input$enable_inputs) { max_edge <- input$max_edge @@ -196,11 +202,11 @@ meshbuilder_shiny <- function( paste0( "location_data <- spatial_data[, c('", longitude_column, "', '", latitude_column, "')],\n", - "names(location_data) <- c('LONG', 'LAT')\n", - "mesh <- fmesher::fm_mesh_2d_inla(loc = location_data,\n\t", - max_edge_str, "\n\t", - cutoff_str, "\n\t", - offset_str, ")\n" + "names(location_data) <- c('LONG', 'LAT')\n", + "mesh <- fmesher::fm_mesh_2d_inla(loc = location_data,\n\t", + max_edge_str, "\n\t", + cutoff_str, "\n\t", + offset_str, ")\n" ) }) } From d5302fe9ecc5f5e12d6d31c61237c1af3319c2fb Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 15:35:56 +0000 Subject: [PATCH 55/82] Rename col usage --- R/shiny_meshbuilder.R | 32 +++++++++++++------------------- vignettes/using_mapview.Rmd | 2 +- 2 files changed, 14 insertions(+), 20 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 73aa8d63..34475d65 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -6,8 +6,8 @@ #' @param offset Specifies the size of the inner and outer extensions around data locations, passed to fmesher::fm_mesh_2d_inla #' @param max_edge The largest allowed triangle edge length. One or two values, passed to fmesher::fm_mesh_2d_inla #' @param cutoff The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla -#' @param latitude_column Name of the latitude column in the spatial data -#' @param longitude_column Name of the longitude column in the spatial data +#' @param y_coord Name of the y coordinate (for example latitude) column in the spatial data +#' @param x_coord Name of the x coordinate (for example longitude) column in the spatial data #' #' @importFrom magrittr %>% #' @@ -21,8 +21,8 @@ meshbuilder_shiny <- function( offset = NULL, cutoff = NULL, plot_poly = FALSE, - latitude_column = "LAT", - longitude_column = "LONG") { + y_coord = "LAT", + x_coord = "LONG") { if (!is.data.frame(spatial_data) && !is(spatial_data, "SpatialPolygonsDataFrame") && !is(spatial_data, "SpatialPointsDataFrame")) { stop("spatial_data must be a data.frame or tibble containing columns with latitude and longitude data.") } @@ -50,7 +50,7 @@ meshbuilder_shiny <- function( # If the user passes in any of these then we enable the sliders enable_inputs <- (!is.null(max_edge) || !is.null(offset) || !is.null(cutoff)) - got_lat_long <- all(c(longitude_column, latitude_column) %in% names(spatial_data)) + got_lat_long <- all(c(x_coord, y_coord) %in% names(spatial_data)) if (!got_lat_long) { stop("Cannot read latitude and longitude data from spatial data. Please ensure given names are correct.") } @@ -64,7 +64,7 @@ meshbuilder_shiny <- function( if (is.null(cutoff)) cutoff <- default_cutoff # Make sure we have our own internal correctly formatted version of the data - coords_only <- spatial_data[, c(longitude_column, latitude_column)] + coords_only <- spatial_data[, c(x_coord, y_coord)] names(coords_only) <- c("LONG", "LAT") @@ -179,13 +179,7 @@ meshbuilder_shiny <- function( ) output$map <- leaflet::renderLeaflet({ - spatial <- sf::st_as_sf( - spatial_data, - coords = c("LONG", "LAT"), - crs = "+proj=utm +zone=33" - ) - - m <- mapview::mapview(list(spatial, mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) + m <- mapview::mapview(list(spatial_data, mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) m@map }) @@ -201,7 +195,7 @@ meshbuilder_shiny <- function( } paste0( - "location_data <- spatial_data[, c('", longitude_column, "', '", latitude_column, "')],\n", + "location_data <- spatial_data[, c('", x_coord, "', '", y_coord, "')],\n", "names(location_data) <- c('LONG', 'LAT')\n", "mesh <- fmesher::fm_mesh_2d_inla(loc = location_data,\n\t", max_edge_str, "\n\t", @@ -224,12 +218,12 @@ meshbuilder_shiny <- function( #' @param offset Specifies the size of the inner and outer extensions around data locations, passed to fmesher::fm_mesh_2d_inla #' @param max_edge The largest allowed triangle edge length. One or two values, passed to fmesher::fm_mesh_2d_inla #' @param cutoff The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla -#' @param latitude_column Name of the latitude column in the spatial data -#' @param longitude_column Name of the longitude column in the spatial data +#' @param y_coord Name of the latitude column in the spatial data +#' @param x_coord Name of the longitude column in the spatial data #' #' @return shiny::app #' @export -mesh_builder <- function(spatial_data, obs_data = NULL, crs = NULL, max_edge = NULL, offset = NULL, cutoff = NULL, latitude_column = "LAT", longitude_column = "LONG") { +mesh_builder <- function(spatial_data, obs_data = NULL, crs = NULL, max_edge = NULL, offset = NULL, cutoff = NULL, y_coord = "LAT", x_coord = "LONG") { shiny::runApp(meshbuilder_shiny( spatial_data = spatial_data, obs_data = obs_data, @@ -237,7 +231,7 @@ mesh_builder <- function(spatial_data, obs_data = NULL, crs = NULL, max_edge = N max_edge = max_edge, offset = offset, cutoff = cutoff, - latitude_column = latitude_column, - longitude_column = longitude_column + y_coord = y_coord, + x_coord = x_coord )) } diff --git a/vignettes/using_mapview.Rmd b/vignettes/using_mapview.Rmd index 9d0bfe64..35604e90 100644 --- a/vignettes/using_mapview.Rmd +++ b/vignettes/using_mapview.Rmd @@ -47,7 +47,7 @@ locations <- sf::st_as_sf( ``` ```{r} -fdmr::mesh_builder(spatial_data=df, crs= "+proj=utm +zone=33") +fdmr::mesh_builder(spatial_data=locations, crs= "+proj=utm +zone=33") ``` Now let's plot the map From 92976879f57c35e0b863f94f11a5367338642f7a Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 15:47:34 +0000 Subject: [PATCH 56/82] Convert spatial data to something mapview expects and ensure crs is correct --- R/shiny_meshbuilder.R | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 34475d65..9a683608 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -178,8 +178,20 @@ meshbuilder_shiny <- function( ) ) + spatial <- shiny::reactive({ + if (is.data.frame(spatial_data)) { + sf::st_as_sf( + spatial_data, + coords = c(x_coord, y_coord), + crs = crs + ) + } else { + spatial_data + } + }) + output$map <- leaflet::renderLeaflet({ - m <- mapview::mapview(list(spatial_data, mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) + m <- mapview::mapview(list(spatial(), mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) m@map }) From 8c1daa715ee8af766b0ac2d09ad18e40055c48d8 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 16:05:43 +0000 Subject: [PATCH 57/82] Update arguments used --- R/shiny_meshbuilder.R | 3 ++- vignettes/meshbuilder.Rmd | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 9a683608..76aa29ba 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -191,7 +191,8 @@ meshbuilder_shiny <- function( }) output$map <- leaflet::renderLeaflet({ - m <- mapview::mapview(list(spatial(), mesh_spatial()), layer.name = (c("Spatial data", "Mesh"))) + map_tiles <- c("OpenStreetMap", "Esri.WorldImagery", "OpenTopoMap") + m <- mapview::mapview(mesh_spatial(), layer.name = "Mesh", col.regions = "#548C2F", map.types = map_tiles) + mapview::mapview(spatial(), layer.name = "Spatial") m@map }) diff --git a/vignettes/meshbuilder.Rmd b/vignettes/meshbuilder.Rmd index 1abdb03b..7a029780 100644 --- a/vignettes/meshbuilder.Rmd +++ b/vignettes/meshbuilder.Rmd @@ -41,7 +41,7 @@ Let's call the `mesh_builder` function and pass in the data and tell it to use ` > **_NOTE:_** The mesh builder may take a short time (~ 20s) to build this mesh due to its size. ```{r eval=FALSE} -fdmr::mesh_builder(spatial_data = lakes_data, longitude_column = "centroid_lon", latitude_column = "centroid_lat") +fdmr::mesh_builder(spatial_data = lakes_data, x_coord = "centroid_lon", y_coord = "centroid_lat") ``` ## Loading data - `rds` file From c863f42889181a9e0c939d4334e0fa3a8edbc029 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 16:07:13 +0000 Subject: [PATCH 58/82] Remove section of vignette about mesh checking code we don't have --- vignettes/simulation_poisson_data.Rmd | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/vignettes/simulation_poisson_data.Rmd b/vignettes/simulation_poisson_data.Rmd index b1656e63..c4f22e6b 100644 --- a/vignettes/simulation_poisson_data.Rmd +++ b/vignettes/simulation_poisson_data.Rmd @@ -227,11 +227,7 @@ fdmr::plot_mesh(mesh = mesh, spatial_data = simdf@coords) ```{r meshbuilder,eval=FALSE} fdmr::mesh_builder(spatial_data = sp_data) ``` -The interactive map allows you to customise the initial parameters such as `max_edge`, `offset` and `cutoff` to change the shape and size of the mesh. We also provide a simple function to check if there are any errors with the meshes created using the mesh builder tool. To use the mesh checking functionality you must pass your measuremnet data to the `fdmr::mesh_builder` function. Specifically, create a mesh of your design using the meshbuilder tool. When you’re finished, click the “Check mesh” button on the user interface. This passes the created mesh to the `fdmr::mesh_checker` function and returns a list containing any errors found with the mesh. - -```{r meshchecking,eval=FALSE} -fdmr::mesh_builder(spatial_data = sp_data, obs_data = simdf) -``` +The interactive map allows you to customise the initial parameters such as `max_edge`, `offset` and `cutoff` to change the shape and size of the mesh. ## Model builder Shiny app `fdmr` provides a Priors Shiny app which allows you to interactively set and see the model fitting results of different priors. You can launch this app by passing the spatial and observation data to the `fdmr::model_builder` function. From af5f38865d48e141b2994d85c58ac32a04f95899 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 16:15:03 +0000 Subject: [PATCH 59/82] Add line to changelog --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index f697662a..c093cfff 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added new geophysical processes tutorial thanks to Alexander Minakov (4minakov) - [PR #257](https://github.com/4DModeller/fdmr/pull/257) - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) +### Changed + +- Moved from using `leaflet` to using (`mapview`)[https://r-spatial.github.io/mapview/index.html] for plotting the mesh and spatial data in the `mesh_builder` Shiny app. This enables use of UTM coordinates - [PR #288](https://github.com/4DModeller/fdmr/pull/288) + ## [0.1.1] - 2023-11-01 ### Added From 24ad5b9d484c10925f3ae573af7abcab012e55d9 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 16:56:31 +0000 Subject: [PATCH 60/82] Remove snow tutorial work until it can be looked over --- vignettes/data/Bayelva_snow.nc | Bin 589618 -> 0 bytes vignettes/data/Bayelva_snow_geo.nc | Bin 842099 -> 0 bytes vignettes/snow.Rmd | 312 ----------------------------- vignettes/using_mapview.Rmd | 109 ---------- 4 files changed, 421 deletions(-) delete mode 100644 vignettes/data/Bayelva_snow.nc delete mode 100644 vignettes/data/Bayelva_snow_geo.nc delete mode 100644 vignettes/snow.Rmd delete mode 100644 vignettes/using_mapview.Rmd diff --git a/vignettes/data/Bayelva_snow.nc b/vignettes/data/Bayelva_snow.nc deleted file mode 100644 index 6de7ac10cb999d6b47e2d96c55d0a27a8092c542..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 589618 zcmeFXcT`i&*Dp>-0jVOLpa>CZ(mMzUh%^=Hy-V*sbde$*0g?779ccmSozPoAT96hX 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%\VignetteEncoding{UTF-8} ---- - -## Introduction - -This tutorial explores whether 4Dmodeller can be used to model the spatial distribution of snow in the Bayelva area on Svalbard, based on once-a-year point-based manual snow depth measurements for several years and snow cover duration derived from time lapse camera imagery (Aalstad et al., 2020). This data has been used before for snow data assimilation (Aalstad et al., 2018) and permafrost modeling (Zweigel et al., 2021) using modeling and data assimilation methods (see references). Additionally, we included elevation and other derived topographic parameters (slope, aspect, topographic position index) as these are known to influence the snow depth distribution. These topographic parameters were derived from a high resolution digital elevation model (Boike et al., 2019). The dataset used in this tutorial has been prepared by Kristoffer Aalstad as a netCDF by combining and processing the following publicly available datasets that have been used in several studies (see literature references): - -Westermann, S. (2020).Snow depth and ground surface temperature at Bayelva research site, Svalbard [Data set]. Norstore. - -Aalstad, K., and Westermann. S. (2021). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway) (2.0) [Data set]. Zenodo. - -Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E., & Westermann, S. (2018). HRSC-AX data products (DEM and multi channel) from aerial overflights in 2008 over Bayelva (Brøggerhalvøya peninsula, Spitsbergen). (Version 1) [Data set]. Zenodo. - -The dataset is originally projected (UTM zone 33X) and available in the data sub-folder in this repository (data/Bayelva_snow.nc). As UTM was not supported for all steps we anticipated while developing this tutorial (see below), we also created also a lat/lon version of the same data (data/Bayelva_snow_geo.nc). - -### Literature references - -Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere, 12, 247--270, - -Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., Wille, C., Chadburn, S., Gouttevin, I., Burke, E., and Kutzbach, L.: A 16-year record (2002--2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models, Earth Syst. Sci. Data, 11, 261--299, , 2019. - -Aalstad, K., Westermann, S., and Bertino, L. (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, 239, - -Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T. (2021). Simulating snow redistribution and its effect on ground surface temperature at a high-Arctic site on Svalbard. Journal of Geophysical Research: Earth Surface, 126, e2020JF005673. - -## Import and load the data - -We use the library ncdf4 to open the snow dataset: - -```{r modules} -# load modules used in this tutorial -library(ncdf4) -``` - -```{r dataload} -# Find where your data is stored locally -ncpath <- dirname(rstudioapi::getSourceEditorContext()$path) -ncname <- "Bayelva_snow" -ncfname <- paste(ncpath,'/data/', ncname, ".nc", sep="") - -#open data -ncin <- nc_open(ncfname) -#head(ncin) -``` - -### Get relevant features - -The netcdf file has both point measurements of snow depth (ds, or translated into SWE: Ds) for several years, and continuous raster data with snow depth duration for several years and topographic features. We start with the snow depth points only: - -```{r dataselect} -# specify the longitude and latitude columns. Note that the coordinates are utm! -longitude <- ncvar_get(ncin,"xs") -latitude <-ncvar_get(ncin,"ys") - -# Get the first year of snow depth (ds) -ds <- ncvar_get(ncin,"ds")[,1] - -# Create a data frame -df <- data.frame(LONG = longitude,LAT = latitude,ds) -head(df) -``` - -### Build the mesh - -We'll use the `mesh_builder` Shiny app to help us create a mesh. - -```{r, eval = FALSE} -fdmr::mesh_builder(spatial_data = df, crs="+proj=utm +zone=33") -``` - -Using fmesher directly (non-interactively, without the fdmr app added functionalities) works and we can plot the resulting mesh outside the geographical context that would have been provided by mesh_builder. - -```{r plotmesh} -# Define a proj string for our UTM zone -crs <- "+proj=utm +zone=33" - -# use fmesher directly rather than the fdmr wrapper: -mesh <- fmesher::fm_mesh_2d_inla(loc = df[,c(1,2)], - max.edge = c(20,40), - crs = crs ) - -# plot the result -plot(mesh) -``` - -When trying to plot the mesh with fdmr::plot_mesh, we run into the same problem as above: The plot_mesh function seems to assume lat/lon coordinates and thus zooms out to max. - -```{r, eval = FALSE} - -fdmr::plot_mesh(mesh=mesh, spatial_data = df[,c(1,2)]) -``` - -***TO DO:*** *We would like to also show our data points and see this in a geographical context to check whether everything is looking OK. mesh_builder and plot_mesh suffer from the same problem and can't deal with UTM coordinates. fdmr should be updated to beable to deal with our UTM coordinates. This is especially important in the Arctic, where geographical coordinates result in highly distorted maps in most map grids. (Issue created: )* - -## Redo this with geographical coordinates - -Let's see whether our approach works if our data is in geographical coordinates instead. Redo all the steps from above: - -```{r latlonload} -ncname_ll <- "Bayelva_snow_geo" -ncfname_ll <- paste(ncpath, '/data/', ncname_ll, ".nc", sep="") -# Open data -ncin_ll <- nc_open(ncfname_ll) - -# Create a data frame with the first year of snow depth (ds) -df_ll <- data.frame(LONG = ncvar_get(ncin_ll,"lons"), - LAT = ncvar_get(ncin_ll,"lats"), - #elev = ncvar_get(ncin_ll,"zs"), - DS = ncvar_get(ncin_ll,"ds")[,1], - DS_log = log(ncvar_get(ncin_ll,"ds")[,1])) - -## Store the point locations in a separate data frame: -location_data_ll = df_ll[,c(1,2)] -``` - -```{r, eval = FALSE} -# Try with the interactive mesh_builder -fdmr::mesh_builder(location_data_ll) -``` - -The mesh_builder loads the shiny widget and creates a mesh, however there are several issues: - -- On several machines where we tried this and the covid tutorial, the map part of the window covers the entire window, so the rulers disappear. ***TO DO:*** *fix this bug (Issue created: )* - -- The mesh is far too coarse for the small area we are looking at (1km2). ***TO DO:*** *Make standard mesh size dependent on area.* - -- On one machine, closing the widget window doesn't stop the underlying processes to run, consequently RStudio is blocked and needs to be restarted, as ctrl + c or interrupting by clicking the STOP button don't have any effect. *(Bug report filed: )* - -Let's try to specify the mesh manually using values for max.edge and cutoff that are reasonable for our domain and length scales. - -```{r latlonmesh_manually} -# look at the range of the data to derive the mesh parameters -initial_range <- diff(range(location_data_ll[, "LONG"])) / 5 -max_edge <- initial_range / 8 -offset <- initial_range/2 - -# make the mesh from the locations -mesh <- fmesher::fm_mesh_2d(loc = location_data_ll[, c("LONG", "LAT")], - max.edge = c(1, 2) * max_edge, - offset = c(1,1.5)*offset, - cutoff = max_edge/10 - ) -fdmr::plot_mesh(mesh,spatial_data = location_data_ll) -``` - -This mesh is already a better starting point. There are two things we are not happy about: - -- The mesh is obviously distorted in N/S direction: With latitude/longitude units representing substantially different spatial distances at polar latitudes, edge lengths should be defined differently in E/W and N/S. **\ - *TO DO:*** *Account for unequal distances in x/y. And as above, make it possible to use UTM (which is currently not supported for the visualisation tools).* - -- Is the mesh dense enough to represent snow depth variability between the measurement points, and is the mesh in physically useful locations? Terrain and derivatives are strong drivers of spatial snow depth distribution, and they should thus ideally be considered for the mesh generation. - -## Start defining the priors - -Let's try to predict a spatially distributed snow depth map from our data points. - -We create a prior with the Matérn function, a penalized complexity prior defined by range and standard deviation *(**TO DO**: [cite Finn])*. The range parameter defines the length of the influence of each observation. - -In this small experiment, we have experimented by creating two different priors with two different range. The model is a stochastic partial differential equation (SPDE). - -```{r} -# The prior range is the distance that the process should stop effecting, so in this case it is currently 20km away from the node center (initial_range defined above). - -spde1 <- INLA::inla.spde2.pcmatern( - mesh = mesh, - prior.range = c(initial_range, 0.5), - prior.sigma = c(1, 0.01) -) - -# Try with a different prior range - the entire initial_range -spde2 <- INLA::inla.spde2.pcmatern( - mesh = mesh, - prior.range = c(initial_range*5, 0.5), - prior.sigma = c(1, 0.01) -) - -``` - -We need to convert our data frame into a SpatialPointsDataFrame. - -```{r} -sp::coordinates(df_ll) <- c("LONG", "LAT") -``` - -Define two models (for the two spde versions defined above, respectively): - -```{r} - -# Model 1 is linear and uses the gaussian model 1 defined above: -formula1 <- DS ~ 0 + Intercept(1)+ - f( - main = df_ll, - model = spde1 - ) - -inlabru_model1 <- inlabru::bru(formula1, - data = df_ll, - family = "gaussian", - options = list( - verbose = FALSE - ) -) - -# The only difference for Model 2 is the different initial prior range set above (spde2): - -formula2 <- DS ~ 0 + Intercept(1) + - f( - main = df_ll, - model = spde2 - ) - -inlabru_model2 <- inlabru::bru(formula2, - data = df_ll, - family = "gaussian", - options = list( - verbose = FALSE - ) -) -``` - -Show the output of the first model, and plot the fit: - -```{r inlabruoutput1} -summary(inlabru_model1) - - -plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) - - -plot(exp(inlabru_model1$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) - -``` - -In the environment where this tutorial was rendered, the model_viewer widget window unfortunately crashes RStudio. - -```{r modelviewer1, eval=FALSE} - -fdmr::model_viewer( - model_output = inlabru_model1, - mesh = mesh, - measurement_data = df_ll, - data_distribution = "Gaussian" -) - - -``` - -Show the output of the second model, and plot the fit: - -```{r inlabruoutput2} -summary(inlabru_model2) - -plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) - -plot(exp(inlabru_model2$summary.fitted.values$mean[1:nrow(df_ll@data)]),exp(df_ll@data$DS)) -``` - -```{r modelviewer2, eval=FALSE} - -fdmr::model_viewer( - model_output = inlabru_model2, - mesh = mesh, - measurement_data = df_ll, - data_distribution = "Gaussian" -) - - -``` - -# TO DO: planned outcome of the hackathon/tutorial structure - -## Design the prior and the model - -- Use the UTM-projected data and do everything in UTM coordinates - -- Load both the point measurements and the rasters. There are topographic descriptors and snow duration data. Snow data is available for several years. - -- Use both the snow depth measurements and the different raster data to generate the mesh: we want to account for spatial variability of snow depth in the mesh. In areas where the terrain doesn't vary much the mesh doesn't need to be that fine, and the contrary is the case in rough topography with greatly varying snow depth. This information is indirectly available in the topography feature rasters or in a different way in the snow duration rasters. - -## Fitting the model - -Get model results considering some or all observation data and topographic predictors. Consider that snow duration could be seen as both a predictor and observation. - --\> do this for one year, and/or several years. - -How do the results look like? Change parameters and adjust until the snow depth map matches snow duration (best validation we have, spatially) and locally the snow depth point measurements. - -Do the results for all years look different, or the same? - -## How to deal with real zeros? - -What happens when/where there is zero snow depth? The model needs to distinguish between no data and zero snow depth, and for temporal evolution also be able to predict zero snow depth after snow melt-out. - -## Spatial covariance as prior for data assimilation - -We think 4DM could be a nice complementary tool for SWE retrievals, in particular for the prior covariance in spatio-temporal problems. For this - -- we want the prior to be anisotropic. What we mean with this: the correlations should be allowed to behave differently in each dimension of the feature space where we define distances between points. Features/predictors in this example/tutorial are the provided terrain parameters. - -- we wanted to export the spatial covariance matrix resulting from this tutorial and test wheter we can use it to define the prior covariance in a modelling study, for example within our data assimilation framework MuSA (, see Alonso-González et al., 2022; ). That would necessarily be a separate study from this tutorial but results could eventually be added if this works and results are promising. - -## Temporal evolution: snow map time series - -How can we get temporal evolution in here? Snow depth/SWE distribution map is a result that's only useful if snow depth measurements are available at maximum snow time (which is the case for this data, but most often unfeasible). If it should be able to compete against other (modelling) approaches, we expect 4Dmodeller to be able to make map time series, as we do with our data assimilation approach. For this to work, what other predictor data needs to be added in addition to meltout date? Adding the day of the first snow fall may lead to linear interpolation until max snow, and then linear interpolation until meltout...? Here, we would expect that some physical/meachanistic model would come in, at least a simple degree day model, using precipitation/temperature from ERA5-land, for example..? - -## diff --git a/vignettes/using_mapview.Rmd b/vignettes/using_mapview.Rmd deleted file mode 100644 index 35604e90..00000000 --- a/vignettes/using_mapview.Rmd +++ /dev/null @@ -1,109 +0,0 @@ ---- -title: "Using mapview" -output: - bookdown::html_document2: - base_format: rmarkdown::html_vignette - fig_caption: yes -bibliography: "covid.bib" -link-citations: yes -vignette: > - %\VignetteIndexEntry{Using mapview} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -Here we'll use mapview to create some plots of things and then use the plotting code in the -Shiny apps (maybe). - -```{r} -library(mapview) -``` - -Let's get some of the UTM data they wanted to load in. - -```{r} -filepath = "/Users/gar/Documents/Devel/global_mass/fdmr/vignettes/data/Bayelva_snow.nc" -``` - -```{r} -ncin <- ncdf4::nc_open(filepath) -longitude <- ncdf4::ncvar_get(ncin,"xs") -latitude <-ncdf4::ncvar_get(ncin,"ys") - -# Get the first year of snow depth (ds) -ds <- ncdf4::ncvar_get(ncin,"ds")[,1] - -# Create a data frame -df <- data.frame(LONG = longitude,LAT = latitude,ds) -head(df) -``` - -```{r} -locations <- sf::st_as_sf( - df, - coords = c("LONG","LAT"), - crs = "+proj=utm +zone=33" -) -``` - -```{r} -fdmr::mesh_builder(spatial_data=locations, crs= "+proj=utm +zone=33") -``` - -Now let's plot the map - -```{r} -mapview::mapview(locations) -``` - -Now let's create a mesh from the spatial data and plot that - -```{r} -coords_only = df[, c("LONG", "LAT")] -crs = "+proj=utm +zone=33" -mesh <- fmesher::fm_mesh_2d(coords_only, crs=crs) -``` - -Now let's plot it - -```{r} -spatial_mesh = fdmr::mesh_to_spatial(mesh, crs=crs) -mapview::mapview(spatial_mesh) -``` - -We'll try the fmesher plotting functions - -```{r} -inlabru::gg(mesh) -``` - -```{r} -library(shiny) -library(leaflet) - -coords_only = df[, c("LONG", "LAT")] -crs = "+proj=utm +zone=33" -mesh <- fmesher::fm_mesh_2d(coords_only, crs=crs) -spatial_mesh = fdmr::mesh_to_spatial(mesh, crs=crs) -# mapview::mapview(spatial_mesh) - -ui <- fluidPage( - leafletOutput("mymap"), - p(), - actionButton("recalc", "New points") -) - -server <- function(input, output, session) { - - points <- eventReactive(input$recalc, { - cbind(rnorm(40) * 2 + 13, rnorm(40) + 48) - }, ignoreNULL = FALSE) - - output$mymap <- renderLeaflet({ - m <- mapview::mapview(spatial_mesh) - m@map - }) -} - -shinyApp(ui, server) -``` \ No newline at end of file From 43cc5628486a94011c76e65ff1da7f5997e97ed7 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 17:20:38 +0000 Subject: [PATCH 61/82] Add new plot_map_mapview function for simple map plots --- NAMESPACE | 3 +- R/plot_mapping.R | 71 +++++++++++++++++--------- man/plot_map.Rd | 49 ------------------ man/plot_mesh.Rd | 6 ++- vignettes/covid.Rmd | 4 +- vignettes/covid_ST_mcmc.Rmd | 4 +- vignettes/hydro.Rmd | 4 +- vignettes/modelbuilder.Rmd | 2 +- vignettes/priors.Rmd | 2 +- vignettes/simulation_gaussian_data.Rmd | 10 ++-- vignettes/simulation_poisson_data.Rmd | 10 ++-- 11 files changed, 73 insertions(+), 92 deletions(-) delete mode 100644 man/plot_map.Rd diff --git a/NAMESPACE b/NAMESPACE index dcf9a937..93cea5d5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,7 +17,8 @@ export(parse_model_output) export(plot_barchart) export(plot_boxplot) export(plot_line_average) -export(plot_map) +export(plot_map_leaflet) +export(plot_map_mapview) export(plot_mesh) export(plot_timeseries) export(retrieve_tutorial_data) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index 9e9a518e..1618dae9 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -16,26 +16,26 @@ #' #' @return leaflet::leaflet #' @export -plot_map <- function(polygon_data = NULL, - raster_data = NULL, - domain = NULL, - markers = NULL, - palette = "viridis", - legend_title = NULL, - add_scale_bar = FALSE, - polygon_fill_colour = "#E4572E", - polygon_line_colour = "grey", - polygon_line_weight = 1, - polygon_fill_opacity = 0.6, - reverse = FALSE, - wrapping = FALSE) { +plot_map_leaflet <- function(polygon_data = NULL, + raster_data = NULL, + domain = NULL, + markers = NULL, + palette = "viridis", + legend_title = NULL, + add_scale_bar = FALSE, + polygon_fill_colour = "#E4572E", + polygon_line_colour = "grey", + polygon_line_weight = 1, + polygon_fill_opacity = 0.6, + reverse = FALSE, + wrapping = FALSE) { if (is.null(polygon_data) && is.null(raster_data)) { stop("Polygon or raster data must be given.") } library(leaflet) m <- leaflet::leaflet() m <- leaflet::addTiles(m) - m <- leaflet::addProviderTiles(m, leaflet::providers$Esri.WorldImagery, group = "Satellite") + m <- leaflet::addProviderTiles(m, leaflet::providers$Openstreetmap, group = "Satellite") m <- leafem::addMouseCoordinates(m, native.crs = TRUE) # Store a vector of layers we add to the map, @@ -45,7 +45,7 @@ plot_map <- function(polygon_data = NULL, if (!is.null(polygon_data)) { if (isTRUE(wrapping)) { polygon_data <- fdmr::antimeridian_wrapping(polygon_data, crs = "+proj=longlat +datum=WGS84", unique_inst = TRUE, to_sp = FALSE) - } + } if (!is.null(domain)) { colours <- leaflet::colorNumeric(palette = palette, domain = domain, reverse = reverse) polygon_fill_colour <- ~ colours(domain) @@ -69,7 +69,7 @@ plot_map <- function(polygon_data = NULL, } if (!is.null(raster_data)) { - colours <- leaflet::colorNumeric(palette = palette, domain = raster::values(raster_data), na.color=rgb(0,0,0,0), reverse = reverse) + colours <- leaflet::colorNumeric(palette = palette, domain = raster::values(raster_data), na.color = rgb(0, 0, 0, 0), reverse = reverse) m <- leaflet::addRasterImage(m, x = raster_data, opacity = 0.75, @@ -78,11 +78,11 @@ plot_map <- function(polygon_data = NULL, colors = colours, ) m <- leaflet::addLegend(m, - pal = colours, - values = raster::values(raster_data), - opacity = 0.75, - title = legend_title, - na.label = "" + pal = colours, + values = raster::values(raster_data), + opacity = 0.75, + title = legend_title, + na.label = "" ) layers <- append(layers, "Raster") } @@ -103,7 +103,32 @@ plot_map <- function(polygon_data = NULL, m <- leaflet::addScaleBar(m, position = "bottomleft") } - m <- leaflet::addMeasure(m, position = "bottomleft", primaryLengthUnit = 'kilometers', primaryAreaUnit = 'sqmeters') - + m <- leaflet::addMeasure(m, position = "bottomleft", primaryLengthUnit = "kilometers", primaryAreaUnit = "sqmeters") + + return(m) +} + + +#' A simple map plotter using mapview. This is only intended for very quick viewing of data. +#' +#' @param spatial_data Spatial data to plot +#' @param raster_data Raster data to plot +#' +#' @return mapview::mapview +#' @export +plot_map_mapview <- function(spatial_data = NULL, raster_data = NULL) { + if (is.null(spatial_data) && is.null(raster_data)) { + stop("Spatial or raster data must be given.") + } + + map_types <- c("OpenStreetMap", "Esri.WorldImagery", "OpenTopoMap") + + m <- mapview::mapview(map.types = map_types) + if (!is.null(spatial_data)) { + m <- m + mapview::mapview(spatial_data) + } + if (!is.null(raster_data)) { + m <- m + mapview::mapview(raster_data) + } return(m) } diff --git a/man/plot_map.Rd b/man/plot_map.Rd deleted file mode 100644 index 6daa45b9..00000000 --- a/man/plot_map.Rd +++ /dev/null @@ -1,49 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plot_mapping.R -\name{plot_map} -\alias{plot_map} -\title{Create a simple Leaflet map from data} -\usage{ -plot_map( - polygon_data = NULL, - raster_data = NULL, - domain = NULL, - markers = NULL, - palette = "viridis", - legend_title = NULL, - add_scale_bar = FALSE, - polygon_fill_colour = "#E4572E", - polygon_line_colour = "grey", - polygon_line_weight = 1, - polygon_fill_opacity = 0.6 -) -} -\arguments{ -\item{polygon_data}{Polygon data} - -\item{raster_data}{Raster datas} - -\item{domain}{Domain data to be passed to leaflet::colorNumeric and leaflet::addLegend} - -\item{markers}{Markers to display on map. A named list with latitude, longitude and label names must be given.} - -\item{palette}{Palette to be used for colours, defaults to viridis} - -\item{legend_title}{Title for legend} - -\item{add_scale_bar}{Add scale bar if TRUE} - -\item{polygon_fill_colour}{Polygon fill colour} - -\item{polygon_line_colour}{Polygon surrounding line colour} - -\item{polygon_line_weight}{Polygon surrounding line weight} - -\item{polygon_fill_opacity}{Leaflet polygon fill opacity, float from 0 to 1.0, passed to fillOpacity of leaflet::addPolygons} -} -\value{ -leaflet::leaflet -} -\description{ -Create a simple Leaflet map from data -} diff --git a/man/plot_mesh.Rd b/man/plot_mesh.Rd index 928db33e..9656973d 100644 --- a/man/plot_mesh.Rd +++ b/man/plot_mesh.Rd @@ -8,7 +8,8 @@ plot_mesh( mesh, spatial_data = NULL, longitude_column = "LONG", - latitude_column = "LAT" + latitude_column = "LAT", + markers = NULL ) } \arguments{ @@ -20,6 +21,9 @@ that can be converted to a data.frame with longitude and latitude columns} \item{longitude_column}{Longitude column in spatial_data} \item{latitude_column}{Latitude column in spatial_data name} + +\item{markers}{Markers to display on top of mesh. A named list with latitude, longitude and label names must be given. +Expects longitude name to be longitude, latitude name to be latitude, label name to be label.} } \value{ leaflet::leaflet diff --git a/vignettes/covid.Rmd b/vignettes/covid.Rmd index c6be9b43..8db3b50c 100644 --- a/vignettes/covid.Rmd +++ b/vignettes/covid.Rmd @@ -47,7 +47,7 @@ Now we make a map of the study region sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` The COVID-19 data and the related covariate information are included in our tutorial data package. We'll load in the data using same process we used above. @@ -414,7 +414,7 @@ sp_data@data$ave.risk <- average_risk_by_nb$ave.risk domain <- sp_data@data$ave.risk -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", diff --git a/vignettes/covid_ST_mcmc.Rmd b/vignettes/covid_ST_mcmc.Rmd index 2acdb33d..016cc54f 100644 --- a/vignettes/covid_ST_mcmc.Rmd +++ b/vignettes/covid_ST_mcmc.Rmd @@ -251,7 +251,7 @@ mcmc_mean_rate <- dplyr::group_by(mergedat, MSOA11CD) %>% dplyr::summarize( sp_data@data$mcmc_mean_rate <- mcmc_mean_rate$mean.rate domain <- sp_data@data$mcmc_mean_rate -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", @@ -270,7 +270,7 @@ INLA_mean_rate <- dplyr::group_by(mergedat, MSOA11CD) %>% dplyr::summarize( sp_data@data$INLA_mean_rate <- INLA_mean_rate$mean.rate domain <- sp_data@data$INLA_mean_rate -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", diff --git a/vignettes/hydro.Rmd b/vignettes/hydro.Rmd index f4b55f26..a168c469 100644 --- a/vignettes/hydro.Rmd +++ b/vignettes/hydro.Rmd @@ -31,7 +31,7 @@ norway_polygon <- sf::read_sf(norway_polygon_path) %>% sf::st_zm() sfc <- sf::st_transform(norway_polygon, crs = "+proj=longlat +datum=WGS84") ``` -We want to mark the position of the dam and the stream gauges on the map so we'll create a `data.frame` to pass into the `plot_map` +We want to mark the position of the dam and the stream gauges on the map so we'll create a `data.frame` to pass into the `plot_map_leaflet` function with the names and positions. We'll create a list for each of the points and then use the (`dplyr::bind_rows`)[https://dplyr.tidyverse.org/reference/bind.html] function to create a `data.frame` from these. @@ -47,7 +47,7 @@ markers <- dplyr::bind_rows(points) We're now ready to plot the map, passing in the polygon data, markers and optionally the fill opacity. ```{r} -fdmr::plot_map(polygon_data = sfc, markers = markers, polygon_fill_opacity = 0.5) +fdmr::plot_map_leaflet(polygon_data = sfc, markers = markers, polygon_fill_opacity = 0.5) ``` On the map you can see the dam, the resevoir, the catchment area, and the two stream gauges. diff --git a/vignettes/modelbuilder.Rmd b/vignettes/modelbuilder.Rmd index ceee0acf..68e6a437 100644 --- a/vignettes/modelbuilder.Rmd +++ b/vignettes/modelbuilder.Rmd @@ -24,7 +24,7 @@ load ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align='center'} sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` The COVID-19 data in Bristol are included in our tutorial data package. We'll load in the data using same process we used above diff --git a/vignettes/priors.Rmd b/vignettes/priors.Rmd index 92c44c07..7ca09010 100644 --- a/vignettes/priors.Rmd +++ b/vignettes/priors.Rmd @@ -39,7 +39,7 @@ Now we make a map of the study region. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align='center'} sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` diff --git a/vignettes/simulation_gaussian_data.Rmd b/vignettes/simulation_gaussian_data.Rmd index a93efc6e..0af2a70c 100644 --- a/vignettes/simulation_gaussian_data.Rmd +++ b/vignettes/simulation_gaussian_data.Rmd @@ -90,10 +90,10 @@ A histogram is created to visualize the distribution of the simulated Gaussian d hist(simdf$datn, main = "", xlab = "Data") ``` -We can also map the simulated data using the `fdmr::plot_map` function. For example, a map of the simulated data at the first time point is given as +We can also map the simulated data using the `fdmr::plot_map_leaflet` function. For example, a map of the simulated data at the first time point is given as ```{r mapsimdat,error=TRUE,fig.cap="A map of the simulated data in Bristol at the first time point.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = simdf[simdf$time == 1, "datn"], palette = "Reds", @@ -256,10 +256,10 @@ fdmr::model_viewer( ) ``` -## fdmr::plot_map +## fdmr::plot_map_leaflet -The `fdmr::plot_map` function generates a map of the region based on the provided SpatialPolygonDataFrame. +The `fdmr::plot_map_leaflet` function generates a map of the region based on the provided SpatialPolygonDataFrame. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map(polygon_data = sp_data) +fdmr::plot_map_leaflet(polygon_data = sp_data) ``` diff --git a/vignettes/simulation_poisson_data.Rmd b/vignettes/simulation_poisson_data.Rmd index b1656e63..9c4d8d3e 100644 --- a/vignettes/simulation_poisson_data.Rmd +++ b/vignettes/simulation_poisson_data.Rmd @@ -95,11 +95,11 @@ A histogram is created to visualize the distribution of the simulated data. hist(simdf$datn, main = "", xlab = "Data") ``` -We can also map the simulated data using the `fdmr::plot_map` function. +We can also map the simulated data using the `fdmr::plot_map_leaflet` function. . ```{r mapsimdat,error=TRUE,fig.cap="A map of the simulated data in Bristol over all time periods.", fig.width=8, fig.height=4, fig.align = "center"} dat_by_ID <- dplyr::group_by(simdf, ID) %>% dplyr::summarize(sum.dat = sum(datn)) -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = dat_by_ID$sum.dat, palette = "Reds", @@ -260,10 +260,10 @@ fdmr::model_viewer( ) ``` -## fdmr::plot_map +## fdmr::plot_map_leaflet -The `fdmr::plot_map` function generates a map of the region based on the provided SpatialPolygonDataFrame. +The `fdmr::plot_map_leaflet` function generates a map of the region based on the provided SpatialPolygonDataFrame. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map(polygon_data = sp_data) +fdmr::plot_map_leaflet(polygon_data = sp_data) ``` From d005bacdfd26848038622c0b97e29f902ee88d71 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 17:24:04 +0000 Subject: [PATCH 62/82] Add lines to changelog --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5a5d9d7e..ca912ef2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) ### Fixed + - Added a function to ensure correct polygon display across the dateline - [PR #285](https://github.com/4DModeller/fdmr/pull/285) ### Added @@ -15,6 +16,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added a new tutorial on loading data from different sources - [PR #266](https://github.com/4DModeller/fdmr/pull/266/) - Added new geophysical processes tutorial thanks to Alexander Minakov (4minakov) - [PR #257](https://github.com/4DModeller/fdmr/pull/257) - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) +- New plotting function `plot_map_mapview` to use `mapview` to plot raster and spatial data - [#291](https://github.com/4DModeller/fdmr/pull/291) + +### Changed + +- Renamed `plot_map` to `plot_map_leaflet` with the addition of `plot_map_mapview` - [#291](https://github.com/4DModeller/fdmr/pull/291) ## [0.1.1] - 2023-11-01 From a07cb922a03f1d79183b461d73460d0b82272b31 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 17:24:29 +0000 Subject: [PATCH 63/82] Ensure mapview is in desc --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 121a2d03..e9591b45 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -59,7 +59,8 @@ Imports: bsicons, future, leafem, - sfheaders + sfheaders, + mapview Suggests: bookdown, knitr, From 83d806decc05d43be6f4acbca7a293795a6496e5 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Thu, 7 Dec 2023 17:27:03 +0000 Subject: [PATCH 64/82] Rename bibtex --- vignettes/covid.Rmd | 2 +- vignettes/covid_ST_mcmc.Rmd | 2 +- vignettes/covid_mcmc.Rmd | 8 ++++---- vignettes/data_preprocessing.Rmd | 2 +- vignettes/priors.Rmd | 2 +- vignettes/{covid.bib => references.bib} | 0 6 files changed, 8 insertions(+), 8 deletions(-) rename vignettes/{covid.bib => references.bib} (100%) diff --git a/vignettes/covid.Rmd b/vignettes/covid.Rmd index c6be9b43..23123147 100644 --- a/vignettes/covid.Rmd +++ b/vignettes/covid.Rmd @@ -4,7 +4,7 @@ output: bookdown::html_document2: base_format: rmarkdown::html_vignette fig_caption: yes -bibliography: "covid.bib" +bibliography: "references.bib" link-citations: yes vignette: > %\VignetteIndexEntry{COVID-19} diff --git a/vignettes/covid_ST_mcmc.Rmd b/vignettes/covid_ST_mcmc.Rmd index 2acdb33d..003c1fe9 100644 --- a/vignettes/covid_ST_mcmc.Rmd +++ b/vignettes/covid_ST_mcmc.Rmd @@ -4,7 +4,7 @@ output: bookdown::html_document2: base_format: rmarkdown::html_vignette fig_caption: yes -bibliography: "covid.bib" +bibliography: "references.bib" link-citations: yes vignette: > %\VignetteIndexEntry{COVID-19 MCMC (Space-time)} diff --git a/vignettes/covid_mcmc.Rmd b/vignettes/covid_mcmc.Rmd index 95d1e832..e86e16c5 100644 --- a/vignettes/covid_mcmc.Rmd +++ b/vignettes/covid_mcmc.Rmd @@ -4,7 +4,7 @@ output: bookdown::html_document2: base_format: rmarkdown::html_vignette fig_caption: yes -bibliography: "covid.bib" +bibliography: "references.bib" link-citations: yes vignette: > %\VignetteIndexEntry{COVID-19 MCMC} @@ -218,7 +218,7 @@ sp_data@data$est.rate.mcmc <- mcmc_fitted_prev domain <- sp_data@data$est.rate.mcmc legend_values <- sp_data@data$est.rate.mcmc -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", @@ -236,7 +236,7 @@ sp_data@data$est.rate.inlabym <- INLA_model$summary.fitted.values$mean domain <- sp_data@data$est.rate.inlabym legend_values <- sp_data@data$est.rate.inlabym -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", @@ -254,7 +254,7 @@ sp_data@data$est.rate.inlaspde <- inlabru_model$summary.fitted.values$mean[1:nro domain <- sp_data@data$est.rate.inlaspde legend_values <- sp_data@data$est.rate.inlaspde -fdmr::plot_map( +fdmr::plot_map_leaflet( polygon_data = sp_data, domain = domain, palette = "Reds", diff --git a/vignettes/data_preprocessing.Rmd b/vignettes/data_preprocessing.Rmd index 9403597c..3f373109 100644 --- a/vignettes/data_preprocessing.Rmd +++ b/vignettes/data_preprocessing.Rmd @@ -4,7 +4,7 @@ output: bookdown::html_document2: base_format: rmarkdown::html_vignette fig_caption: yes -bibliography: "covid.bib" +bibliography: "references.bib" link-citations: yes vignette: > %\VignetteIndexEntry{Data pre-processing} diff --git a/vignettes/priors.Rmd b/vignettes/priors.Rmd index 92c44c07..3f131a17 100644 --- a/vignettes/priors.Rmd +++ b/vignettes/priors.Rmd @@ -4,7 +4,7 @@ output: bookdown::html_document2: base_format: rmarkdown::html_vignette fig_caption: yes -bibliography: "covid.bib" +bibliography: "references.bib" link-citations: yes vignette: > %\VignetteIndexEntry{Priors exploration} diff --git a/vignettes/covid.bib b/vignettes/references.bib similarity index 100% rename from vignettes/covid.bib rename to vignettes/references.bib From 0b7f017c3b6531f9562f181fd99e522a9f56dc21 Mon Sep 17 00:00:00 2001 From: Gareth Jones <8915182+gareth-j@users.noreply.github.com> Date: Thu, 7 Dec 2023 17:52:04 +0000 Subject: [PATCH 65/82] Update model_viewer clip --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 15e1b3a7..7a25c950 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ ### - evaluate the fully trained model output -![](https://github.com/4DModeller/fdmr/assets/8915182/fe791f74-c9b4-4db0-a52e-ffa018b12b41) +![](https://github.com/4DModeller/fdmr/assets/8915182/f2b68ccc-cfba-4393-b2f4-6cecdad332c0) ## Quickstart From f62dffad9d19dd8bfd1b71eb19a86d267333a90e Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 14:43:00 +0000 Subject: [PATCH 66/82] Adding backend argument to tidy mapping function calls --- NAMESPACE | 3 +- R/plot_mapping.R | 80 +++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 73 insertions(+), 10 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 93cea5d5..dcf9a937 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,8 +17,7 @@ export(parse_model_output) export(plot_barchart) export(plot_boxplot) export(plot_line_average) -export(plot_map_leaflet) -export(plot_map_mapview) +export(plot_map) export(plot_mesh) export(plot_timeseries) export(retrieve_tutorial_data) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index 1618dae9..76d78d59 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -1,4 +1,7 @@ -#' Create a simple Leaflet map from data +#' Create a simple map from data using either the leaflet or mapview packages +#' +#' NOTE that the mapview backend is only intended for quick viewing of data, +#' most of the customisation arguments are not available. #' #' @param polygon_data Polygon data #' @param raster_data Raster datas @@ -13,9 +16,70 @@ #' @param polygon_line_weight Polygon surrounding line weight #' @param reverse Reverse the colour palette if TRUE #' @param wrapping Split polygons along the antimeridian (-180/180 boundary) if TRUE +#' @param backend Backend package to use for plotting, either "leaflet" or "mapview" +#' +#' @return leaflet::leaflet or mapview::mapview #' -#' @return leaflet::leaflet #' @export +plot_map <- function(polygon_data = NULL, + raster_data = NULL, + domain = NULL, + markers = NULL, + palette = "viridis", + legend_title = NULL, + add_scale_bar = FALSE, + polygon_fill_colour = "#E4572E", + polygon_line_colour = "grey", + polygon_line_weight = 1, + polygon_fill_opacity = 0.6, + reverse = FALSE, + wrapping = FALSE, + backend = "leaflet") { + if (backend == "leaflet") { + plot_map_leaflet( + polygon_data = polygon_data, + raster_data = raster_data, + domain = domain, + markers = markers, + palette = palette, + legend_title = legend_title, + add_scale_bar = add_scale_bar, + polygon_fill_colour = polygon_fill_colour, + polygon_line_colour = polygon_line_colour, + polygon_line_weight = polygon_line_weight, + polygon_fill_opacity = polygon_fill_opacity, + reverse = reverse, + wrapping = wrapping + ) + } else if (backend == "mapview") { + plot_map_mapview( + polygon_data = polygon_data, + raster_data = raster_data + ) + } else { + stop("Invalid backend given, must be either 'leaflet' or 'mapview'.") + } +} + + +#' Create a simple Leaflet map from data +#' +#' @param polygon_data Polygon data +#' @param raster_data Raster data +#' @param domain Domain data to be passed to leaflet::colorNumeric and leaflet::addLegend +#' @param markers Markers to display on map. A named list with latitude, longitude and label names must be given. +#' @param palette Palette to be used for colours, defaults to viridis +#' @param legend_title Title for legend +#' @param add_scale_bar Add scale bar if TRUE +#' @param polygon_fill_opacity Leaflet polygon fill opacity, float from 0 to 1.0, passed to fillOpacity of leaflet::addPolygons +#' @param polygon_fill_colour Polygon fill colour +#' @param polygon_line_colour Polygon surrounding line colour +#' @param polygon_line_weight Polygon surrounding line weight +#' @param reverse Reverse the colour palette if TRUE +#' @param wrapping Split polygons along the antimeridian (-180/180 boundary) if TRUE +#' +#' @return leaflet::leaflet +#' @keywords internal plot_map_leaflet <- function(polygon_data = NULL, raster_data = NULL, domain = NULL, @@ -111,21 +175,21 @@ plot_map_leaflet <- function(polygon_data = NULL, #' A simple map plotter using mapview. This is only intended for very quick viewing of data. #' -#' @param spatial_data Spatial data to plot +#' @param polygon_data Spatial data to plot #' @param raster_data Raster data to plot #' #' @return mapview::mapview -#' @export -plot_map_mapview <- function(spatial_data = NULL, raster_data = NULL) { - if (is.null(spatial_data) && is.null(raster_data)) { +#' @keywords internal +plot_map_mapview <- function(polygon_data = NULL, raster_data = NULL) { + if (is.null(polygon_data) && is.null(raster_data)) { stop("Spatial or raster data must be given.") } map_types <- c("OpenStreetMap", "Esri.WorldImagery", "OpenTopoMap") m <- mapview::mapview(map.types = map_types) - if (!is.null(spatial_data)) { - m <- m + mapview::mapview(spatial_data) + if (!is.null(polygon_data)) { + m <- m + mapview::mapview(polygon_data) } if (!is.null(raster_data)) { m <- m + mapview::mapview(raster_data) From 8b8efc70988159ddc97b55a7c99de313b1864cc4 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 15:23:31 +0000 Subject: [PATCH 67/82] Update docstring for map plotting fn --- R/plot_mapping.R | 15 +++++++-------- _pkgdown.yml | 4 +++- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/R/plot_mapping.R b/R/plot_mapping.R index 76d78d59..4ea0a1ba 100644 --- a/R/plot_mapping.R +++ b/R/plot_mapping.R @@ -3,6 +3,9 @@ #' NOTE that the mapview backend is only intended for quick viewing of data, #' most of the customisation arguments are not available. #' +#' The leaflet backend will work for most use cases and is recommended. +#' For plotting of maps with UTM coordinates, the mapview backend is recommended. +#' #' @param polygon_data Polygon data #' @param raster_data Raster datas #' @param domain Domain data to be passed to leaflet::colorNumeric and leaflet::addLegend @@ -35,6 +38,10 @@ plot_map <- function(polygon_data = NULL, reverse = FALSE, wrapping = FALSE, backend = "leaflet") { + if (is.null(polygon_data) && is.null(raster_data)) { + stop("Polygon or raster data must be given.") + } + if (backend == "leaflet") { plot_map_leaflet( polygon_data = polygon_data, @@ -93,10 +100,6 @@ plot_map_leaflet <- function(polygon_data = NULL, polygon_fill_opacity = 0.6, reverse = FALSE, wrapping = FALSE) { - if (is.null(polygon_data) && is.null(raster_data)) { - stop("Polygon or raster data must be given.") - } - library(leaflet) m <- leaflet::leaflet() m <- leaflet::addTiles(m) m <- leaflet::addProviderTiles(m, leaflet::providers$Openstreetmap, group = "Satellite") @@ -181,10 +184,6 @@ plot_map_leaflet <- function(polygon_data = NULL, #' @return mapview::mapview #' @keywords internal plot_map_mapview <- function(polygon_data = NULL, raster_data = NULL) { - if (is.null(polygon_data) && is.null(raster_data)) { - stop("Spatial or raster data must be given.") - } - map_types <- c("OpenStreetMap", "Esri.WorldImagery", "OpenTopoMap") m <- mapview::mapview(map.types = map_types) diff --git a/_pkgdown.yml b/_pkgdown.yml index cfebb6a8..4abdf5c0 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -86,10 +86,12 @@ reference: desc: Functions to help convert coordinates and units contents: - latlong_to_utm - - to_dates + - antimeridian_wrapping + - convert_from_lon_360 - title: Utility functions desc: These help us handle paths, retrieve data etc contents: + - to_dates - retrieve_tutorial_data - get_tutorial_datapath - load_tutorial_data From e57c713e29530fca3fe24fee5ac7bb81dccd8e05 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 15:23:54 +0000 Subject: [PATCH 68/82] Add map plot r doc --- man/plot_map.Rd | 63 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 man/plot_map.Rd diff --git a/man/plot_map.Rd b/man/plot_map.Rd new file mode 100644 index 00000000..75c2f3d9 --- /dev/null +++ b/man/plot_map.Rd @@ -0,0 +1,63 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_mapping.R +\name{plot_map} +\alias{plot_map} +\title{Create a simple map from data using either the leaflet or mapview packages} +\usage{ +plot_map( + polygon_data = NULL, + raster_data = NULL, + domain = NULL, + markers = NULL, + palette = "viridis", + legend_title = NULL, + add_scale_bar = FALSE, + polygon_fill_colour = "#E4572E", + polygon_line_colour = "grey", + polygon_line_weight = 1, + polygon_fill_opacity = 0.6, + reverse = FALSE, + wrapping = FALSE, + backend = "leaflet" +) +} +\arguments{ +\item{polygon_data}{Polygon data} + +\item{raster_data}{Raster datas} + +\item{domain}{Domain data to be passed to leaflet::colorNumeric and leaflet::addLegend} + +\item{markers}{Markers to display on map. A named list with latitude, longitude and label names must be given.} + +\item{palette}{Palette to be used for colours, defaults to viridis} + +\item{legend_title}{Title for legend} + +\item{add_scale_bar}{Add scale bar if TRUE} + +\item{polygon_fill_colour}{Polygon fill colour} + +\item{polygon_line_colour}{Polygon surrounding line colour} + +\item{polygon_line_weight}{Polygon surrounding line weight} + +\item{polygon_fill_opacity}{Leaflet polygon fill opacity, float from 0 to 1.0, passed to fillOpacity of leaflet::addPolygons} + +\item{reverse}{Reverse the colour palette if TRUE} + +\item{wrapping}{Split polygons along the antimeridian (-180/180 boundary) if TRUE} + +\item{backend}{Backend package to use for plotting, either "leaflet" or "mapview"} +} +\value{ +leaflet::leaflet or mapview::mapview +} +\description{ +NOTE that the mapview backend is only intended for quick viewing of data, +most of the customisation arguments are not available. +} +\details{ +The leaflet backend will work for most use cases and is recommended. +For plotting of maps with UTM coordinates, the mapview backend is recommended. +} From 77050ee95b361938709cbc56da18cdecc6a12614 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 15:48:51 +0000 Subject: [PATCH 69/82] Add numeric inputs instead of sliders for mesh builder --- R/shiny_meshbuilder.R | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 63534d66..3e25e4c0 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -77,22 +77,22 @@ meshbuilder_shiny <- function( shiny::sidebarLayout( shiny::sidebarPanel( shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = enable_inputs), - shiny::sliderInput( + shiny::numericInput( inputId = "max_edge", label = "Max edge:", - min = 0.02, value = 0.1, max = 10 + value = 0.1 ), shiny::p("Max permitted edge length for a triangle"), - shiny::sliderInput( + shiny::numericInput( inputId = "offset", label = "Offset:", - min = 0.02, value = 0.1, max = 10 + value = 0.1 ), shiny::p("Specifies the size of the inner and outer extensions around data locations."), - shiny::sliderInput( + shiny::numericInput( inputId = "cutoff", label = "Cutoff:", - min = 0.005, value = 0.1, max = 0.9 + value = 0.1 ), shiny::p("Minimum allowed distance between data points."), shiny::actionButton("plot_mesh", label = "Plot mesh"), @@ -192,7 +192,7 @@ meshbuilder_shiny <- function( output$map <- leaflet::renderLeaflet({ map_tiles <- c("OpenStreetMap", "Esri.WorldImagery", "OpenTopoMap") - m <- mapview::mapview(mesh_spatial(), layer.name = "Mesh", col.regions = "#548C2F", map.types = map_tiles) + mapview::mapview(spatial(), layer.name = "Spatial") + m <- mapview::mapview(mesh_spatial(), layer.name = "Mesh", col.regions = "#548C2F", map.types = map_tiles) + mapview::mapview(spatial(), layer.name = "Spatial") m@map }) From cdaa6cd6d0ea598e8112fd285fd0ca98dd06e9cc Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 16:26:44 +0000 Subject: [PATCH 70/82] WIP: Updating meshbuilder with custom inputs --- R/shiny_meshbuilder.R | 91 +++++++++++++++++++++++++++++-------------- 1 file changed, 62 insertions(+), 29 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index 3e25e4c0..a42b7fb0 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -1,13 +1,13 @@ #' Mesh building shiny app #' -#' @param spatial_data a data.frame or tibble containing spatial data -#' @param data Observations data, for use with the check_mesh functionality +#' @param spatial_data Spatial data +#' @param obs_data Measurement data #' @param crs CRS as a proj4string #' @param offset Specifies the size of the inner and outer extensions around data locations, passed to fmesher::fm_mesh_2d_inla #' @param max_edge The largest allowed triangle edge length. One or two values, passed to fmesher::fm_mesh_2d_inla #' @param cutoff The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla -#' @param y_coord Name of the y coordinate (for example latitude) column in the spatial data -#' @param x_coord Name of the x coordinate (for example longitude) column in the spatial data +#' @param y_coord Name of the latitude column in the spatial data +#' @param x_coord Name of the longitude column in the spatial data #' #' @importFrom magrittr %>% #' @@ -17,8 +17,8 @@ meshbuilder_shiny <- function( spatial_data, obs_data = NULL, crs = NULL, - max_edge = NULL, offset = NULL, + max_edge = NULL, cutoff = NULL, plot_poly = FALSE, y_coord = "LAT", @@ -55,13 +55,23 @@ meshbuilder_shiny <- function( stop("Cannot read latitude and longitude data from spatial data. Please ensure given names are correct.") } - default_max_edge <- c(0.01, 0.3) - default_offset <- c(0.02, 0.7) + default_max_edge_min <- 0.01 + default_max_edge_max <- 0.3 + default_offset_min <- 0.02 + default_offset_max <- 0.2 default_cutoff <- 0.02 # TODO - these defaults need changing? - if (is.null(max_edge)) max_edge <- default_max_edge - if (is.null(offset)) offset <- default_offset - if (is.null(cutoff)) cutoff <- default_cutoff + if (!is.null(max_edge)) { + default_max_edge_min <- max_edge[1] + default_max_edge_max <- max_edge[2] + } + + if (!is.null(offset)) { + default_offset_min <- offset[1] + default_offset_max <- offset[2] + } + + if (!is.null(cutoff)) default_cutoff <- cutoff # Make sure we have our own internal correctly formatted version of the data coords_only <- spatial_data[, c(x_coord, y_coord)] @@ -77,22 +87,39 @@ meshbuilder_shiny <- function( shiny::sidebarLayout( shiny::sidebarPanel( shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = enable_inputs), - shiny::numericInput( - inputId = "max_edge", - label = "Max edge:", - value = 0.1 + shiny::h4("Max edge"), + shiny::fluidRow( + shiny::numericInput( + inputId = "max_edge_min", + label = "Min:", + value = default_max_edge_min + ), + shiny::numericInput( + inputId = "max_edge_max", + label = "Max:", + value = default_max_edge_max + ) ), shiny::p("Max permitted edge length for a triangle"), - shiny::numericInput( - inputId = "offset", - label = "Offset:", - value = 0.1 + shiny::h4("Offset"), + shiny::fluidRow( + shiny::numericInput( + inputId = "offset_min", + label = "Min:", + value = default_offset_min + ), + shiny::numericInput( + inputId = "offset_max", + label = "Max:", + value = default_offset_max + ) ), shiny::p("Specifies the size of the inner and outer extensions around data locations."), + shiny::h4("Cutoff"), shiny::numericInput( inputId = "cutoff", - label = "Cutoff:", - value = 0.1 + label = NULL, + value = default_cutoff ), shiny::p("Minimum allowed distance between data points."), shiny::actionButton("plot_mesh", label = "Plot mesh"), @@ -130,20 +157,26 @@ meshbuilder_shiny <- function( server <- function(input, output, session) { shiny::observeEvent(input$enable_inputs, { if (input$enable_inputs) { - shinyjs::enable("max_edge") - shinyjs::enable("offset") + shinyjs::enable("max_edge_min") + shinyjs::enable("max_edge_max") + shinyjs::enable("offset_min") + shinyjs::enable("offset_max") shinyjs::enable("cutoff") } else { - shinyjs::disable("max_edge") - shinyjs::disable("offset") + shinyjs::disable("max_edge_min") + shinyjs::disable("max_edge_max") + shinyjs::disable("offset_min") + shinyjs::disable("offset_max") shinyjs::disable("cutoff") } }) shiny::observeEvent(input$reset_mesh, { - shiny::updateSliderInput(session, inputId = "max_edge", value = default_max_edge) - shiny::updateSliderInput(session, inputId = "offset", value = default_offset) - shiny::updateSliderInput(session, inputId = "cutoff", value = default_cutoff) + shiny::updateNumericInput(session, inputId = "max_edge_min", value = default_max_edge_min) + shiny::updateNumericInput(session, inputId = "max_edge_max", value = default_max_edge_max) + shiny::updateNumericInput(session, inputId = "offset_min", value = default_offset_min) + shiny::updateNumericInput(session, inputId = "offset_max", value = default_offset_max) + shiny::updateNumericInput(session, inputId = "cutoff", value = default_cutoff) }) output$mesh_crs <- shiny::renderText({ @@ -152,8 +185,8 @@ meshbuilder_shiny <- function( mesh <- shiny::eventReactive(input$plot_mesh, ignoreNULL = FALSE, { if (input$enable_inputs) { - max_edge <- input$max_edge - offset <- input$offset + max_edge <- c(input$max_edge_min, input$max_edge_max) + offset <- c(input$offset_min, input$offset_max) cutoff <- input$cutoff } else { max_edge <- NULL From 6e93c17a41a21ec48c26a7d721b57536a91a66b2 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 16:36:00 +0000 Subject: [PATCH 71/82] Added inputs to rows --- R/shiny_meshbuilder.R | 50 +++++++++++++++++++++++++++---------------- 1 file changed, 31 insertions(+), 19 deletions(-) diff --git a/R/shiny_meshbuilder.R b/R/shiny_meshbuilder.R index a42b7fb0..8a22be0d 100644 --- a/R/shiny_meshbuilder.R +++ b/R/shiny_meshbuilder.R @@ -89,29 +89,41 @@ meshbuilder_shiny <- function( shiny::checkboxInput(inputId = "enable_inputs", label = "Enable customisation", value = enable_inputs), shiny::h4("Max edge"), shiny::fluidRow( - shiny::numericInput( - inputId = "max_edge_min", - label = "Min:", - value = default_max_edge_min + shiny::column( + 6, + shiny::numericInput( + inputId = "max_edge_min", + label = "Min:", + value = default_max_edge_min + ) ), - shiny::numericInput( - inputId = "max_edge_max", - label = "Max:", - value = default_max_edge_max + shiny::column( + 6, + shiny::numericInput( + inputId = "max_edge_max", + label = "Max:", + value = default_max_edge_max + ) ) ), shiny::p("Max permitted edge length for a triangle"), shiny::h4("Offset"), shiny::fluidRow( - shiny::numericInput( - inputId = "offset_min", - label = "Min:", - value = default_offset_min + shiny::column( + 6, + shiny::numericInput( + inputId = "offset_min", + label = "Min:", + value = default_offset_min + ), ), - shiny::numericInput( - inputId = "offset_max", - label = "Max:", - value = default_offset_max + shiny::column( + 6, + shiny::numericInput( + inputId = "offset_max", + label = "Max:", + value = default_offset_max + ) ) ), shiny::p("Specifies the size of the inner and outer extensions around data locations."), @@ -231,13 +243,13 @@ meshbuilder_shiny <- function( output$mesh_code <- shiny::reactive({ if (input$enable_inputs) { - max_edge_str <- paste0("max.edge = c(", paste0(input$max_edge, collapse = ","), "),") - offset_str <- paste0("offset = c(", paste0(input$offset, collapse = ", "), "),") + max_edge_str <- paste0("max.edge = c(", input$max_edge_min, ",", input$max_edge_max, "),") cutoff_str <- paste0("cutoff = ", input$cutoff, ",") + offset_str <- paste0("offset = c(", input$offset_min, ",", input$max_edge_max, ")") } else { max_edge_str <- "max.edge = NULL," - offset_str <- "offset = NULL," cutoff_str <- "cutoff = NULL," + offset_str <- "offset = NULL" } paste0( From 4db11d988ebe3dd3df7ce265ea1b1200bc726b49 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 16:40:23 +0000 Subject: [PATCH 72/82] Update docs --- man/mesh_builder.Rd | 8 ++++---- man/meshbuilder_shiny.Rd | 20 ++++++++++---------- man/plot_map.Rd | 8 +++++++- man/plot_mesh.Rd | 6 +++++- 4 files changed, 26 insertions(+), 16 deletions(-) diff --git a/man/mesh_builder.Rd b/man/mesh_builder.Rd index 30ca5dde..b8b00eee 100644 --- a/man/mesh_builder.Rd +++ b/man/mesh_builder.Rd @@ -11,8 +11,8 @@ mesh_builder( max_edge = NULL, offset = NULL, cutoff = NULL, - latitude_column = "LAT", - longitude_column = "LONG" + y_coord = "LAT", + x_coord = "LONG" ) } \arguments{ @@ -28,9 +28,9 @@ mesh_builder( \item{cutoff}{The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla} -\item{latitude_column}{Name of the latitude column in the spatial data} +\item{y_coord}{Name of the latitude column in the spatial data} -\item{longitude_column}{Name of the longitude column in the spatial data} +\item{x_coord}{Name of the longitude column in the spatial data} } \value{ shiny::app diff --git a/man/meshbuilder_shiny.Rd b/man/meshbuilder_shiny.Rd index 673598a1..27656e22 100644 --- a/man/meshbuilder_shiny.Rd +++ b/man/meshbuilder_shiny.Rd @@ -8,30 +8,30 @@ meshbuilder_shiny( spatial_data, obs_data = NULL, crs = NULL, - max_edge = NULL, offset = NULL, + max_edge = NULL, cutoff = NULL, plot_poly = FALSE, - latitude_column = "LAT", - longitude_column = "LONG" + y_coord = "LAT", + x_coord = "LONG" ) } \arguments{ -\item{spatial_data}{a data.frame or tibble containing spatial data} +\item{spatial_data}{Spatial data} -\item{crs}{CRS as a proj4string} +\item{obs_data}{Measurement data} -\item{max_edge}{The largest allowed triangle edge length. One or two values, passed to fmesher::fm_mesh_2d_inla} +\item{crs}{CRS as a proj4string} \item{offset}{Specifies the size of the inner and outer extensions around data locations, passed to fmesher::fm_mesh_2d_inla} -\item{cutoff}{The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla} +\item{max_edge}{The largest allowed triangle edge length. One or two values, passed to fmesher::fm_mesh_2d_inla} -\item{latitude_column}{Name of the latitude column in the spatial data} +\item{cutoff}{The minimum allowed distance between points, passed to fmesher::fm_mesh_2d_inla} -\item{longitude_column}{Name of the longitude column in the spatial data} +\item{y_coord}{Name of the latitude column in the spatial data} -\item{data}{Observations data, for use with the check_mesh functionality} +\item{x_coord}{Name of the longitude column in the spatial data} } \value{ shiny::app diff --git a/man/plot_map.Rd b/man/plot_map.Rd index 6daa45b9..e30158d8 100644 --- a/man/plot_map.Rd +++ b/man/plot_map.Rd @@ -15,7 +15,9 @@ plot_map( polygon_fill_colour = "#E4572E", polygon_line_colour = "grey", polygon_line_weight = 1, - polygon_fill_opacity = 0.6 + polygon_fill_opacity = 0.6, + reverse = FALSE, + wrapping = FALSE ) } \arguments{ @@ -40,6 +42,10 @@ plot_map( \item{polygon_line_weight}{Polygon surrounding line weight} \item{polygon_fill_opacity}{Leaflet polygon fill opacity, float from 0 to 1.0, passed to fillOpacity of leaflet::addPolygons} + +\item{reverse}{Reverse the colour palette if TRUE} + +\item{wrapping}{Split polygons along the antimeridian (-180/180 boundary) if TRUE} } \value{ leaflet::leaflet diff --git a/man/plot_mesh.Rd b/man/plot_mesh.Rd index 928db33e..9656973d 100644 --- a/man/plot_mesh.Rd +++ b/man/plot_mesh.Rd @@ -8,7 +8,8 @@ plot_mesh( mesh, spatial_data = NULL, longitude_column = "LONG", - latitude_column = "LAT" + latitude_column = "LAT", + markers = NULL ) } \arguments{ @@ -20,6 +21,9 @@ that can be converted to a data.frame with longitude and latitude columns} \item{longitude_column}{Longitude column in spatial_data} \item{latitude_column}{Latitude column in spatial_data name} + +\item{markers}{Markers to display on top of mesh. A named list with latitude, longitude and label names must be given. +Expects longitude name to be longitude, latitude name to be latitude, label name to be label.} } \value{ leaflet::leaflet From 204863c5b489cd04124c5d702af1e70ab8465d97 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Mon, 18 Dec 2023 16:41:07 +0000 Subject: [PATCH 73/82] Add new internal fn docs --- man/plot_map_leaflet.Rd | 56 +++++++++++++++++++++++++++++++++++++++++ man/plot_map_mapview.Rd | 20 +++++++++++++++ 2 files changed, 76 insertions(+) create mode 100644 man/plot_map_leaflet.Rd create mode 100644 man/plot_map_mapview.Rd diff --git a/man/plot_map_leaflet.Rd b/man/plot_map_leaflet.Rd new file mode 100644 index 00000000..b6e583c5 --- /dev/null +++ b/man/plot_map_leaflet.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_mapping.R +\name{plot_map_leaflet} +\alias{plot_map_leaflet} +\title{Create a simple Leaflet map from data} +\usage{ +plot_map_leaflet( + polygon_data = NULL, + raster_data = NULL, + domain = NULL, + markers = NULL, + palette = "viridis", + legend_title = NULL, + add_scale_bar = FALSE, + polygon_fill_colour = "#E4572E", + polygon_line_colour = "grey", + polygon_line_weight = 1, + polygon_fill_opacity = 0.6, + reverse = FALSE, + wrapping = FALSE +) +} +\arguments{ +\item{polygon_data}{Polygon data} + +\item{raster_data}{Raster data} + +\item{domain}{Domain data to be passed to leaflet::colorNumeric and leaflet::addLegend} + +\item{markers}{Markers to display on map. A named list with latitude, longitude and label names must be given.} + +\item{palette}{Palette to be used for colours, defaults to viridis} + +\item{legend_title}{Title for legend} + +\item{add_scale_bar}{Add scale bar if TRUE} + +\item{polygon_fill_colour}{Polygon fill colour} + +\item{polygon_line_colour}{Polygon surrounding line colour} + +\item{polygon_line_weight}{Polygon surrounding line weight} + +\item{polygon_fill_opacity}{Leaflet polygon fill opacity, float from 0 to 1.0, passed to fillOpacity of leaflet::addPolygons} + +\item{reverse}{Reverse the colour palette if TRUE} + +\item{wrapping}{Split polygons along the antimeridian (-180/180 boundary) if TRUE} +} +\value{ +leaflet::leaflet +} +\description{ +Create a simple Leaflet map from data +} +\keyword{internal} diff --git a/man/plot_map_mapview.Rd b/man/plot_map_mapview.Rd new file mode 100644 index 00000000..85e024e4 --- /dev/null +++ b/man/plot_map_mapview.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_mapping.R +\name{plot_map_mapview} +\alias{plot_map_mapview} +\title{A simple map plotter using mapview. This is only intended for very quick viewing of data.} +\usage{ +plot_map_mapview(polygon_data = NULL, raster_data = NULL) +} +\arguments{ +\item{polygon_data}{Spatial data to plot} + +\item{raster_data}{Raster data to plot} +} +\value{ +mapview::mapview +} +\description{ +A simple map plotter using mapview. This is only intended for very quick viewing of data. +} +\keyword{internal} From fd50b829d853102765cf589d191949906cdc2f98 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 10:58:31 +0000 Subject: [PATCH 74/82] Update plot map function calls --- CHANGELOG.md | 2 +- vignettes/covid.Rmd | 4 ++-- vignettes/covid_ST_mcmc.Rmd | 4 ++-- vignettes/hydro.Rmd | 4 ++-- vignettes/modelbuilder.Rmd | 2 +- vignettes/priors.Rmd | 2 +- vignettes/simulation_gaussian_data.Rmd | 10 +++++----- vignettes/simulation_poisson_data.Rmd | 10 +++++----- 8 files changed, 19 insertions(+), 19 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ca912ef2..f20fc496 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -20,7 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Changed -- Renamed `plot_map` to `plot_map_leaflet` with the addition of `plot_map_mapview` - [#291](https://github.com/4DModeller/fdmr/pull/291) +- Updated `plot_map` to allow use of both `leaflet` and `mapview` packages - [#291](https://github.com/4DModeller/fdmr/pull/291) ## [0.1.1] - 2023-11-01 diff --git a/vignettes/covid.Rmd b/vignettes/covid.Rmd index 8db3b50c..c6be9b43 100644 --- a/vignettes/covid.Rmd +++ b/vignettes/covid.Rmd @@ -47,7 +47,7 @@ Now we make a map of the study region sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` The COVID-19 data and the related covariate information are included in our tutorial data package. We'll load in the data using same process we used above. @@ -414,7 +414,7 @@ sp_data@data$ave.risk <- average_risk_by_nb$ave.risk domain <- sp_data@data$ave.risk -fdmr::plot_map_leaflet( +fdmr::plot_map( polygon_data = sp_data, domain = domain, palette = "Reds", diff --git a/vignettes/covid_ST_mcmc.Rmd b/vignettes/covid_ST_mcmc.Rmd index 016cc54f..2acdb33d 100644 --- a/vignettes/covid_ST_mcmc.Rmd +++ b/vignettes/covid_ST_mcmc.Rmd @@ -251,7 +251,7 @@ mcmc_mean_rate <- dplyr::group_by(mergedat, MSOA11CD) %>% dplyr::summarize( sp_data@data$mcmc_mean_rate <- mcmc_mean_rate$mean.rate domain <- sp_data@data$mcmc_mean_rate -fdmr::plot_map_leaflet( +fdmr::plot_map( polygon_data = sp_data, domain = domain, palette = "Reds", @@ -270,7 +270,7 @@ INLA_mean_rate <- dplyr::group_by(mergedat, MSOA11CD) %>% dplyr::summarize( sp_data@data$INLA_mean_rate <- INLA_mean_rate$mean.rate domain <- sp_data@data$INLA_mean_rate -fdmr::plot_map_leaflet( +fdmr::plot_map( polygon_data = sp_data, domain = domain, palette = "Reds", diff --git a/vignettes/hydro.Rmd b/vignettes/hydro.Rmd index a168c469..f4b55f26 100644 --- a/vignettes/hydro.Rmd +++ b/vignettes/hydro.Rmd @@ -31,7 +31,7 @@ norway_polygon <- sf::read_sf(norway_polygon_path) %>% sf::st_zm() sfc <- sf::st_transform(norway_polygon, crs = "+proj=longlat +datum=WGS84") ``` -We want to mark the position of the dam and the stream gauges on the map so we'll create a `data.frame` to pass into the `plot_map_leaflet` +We want to mark the position of the dam and the stream gauges on the map so we'll create a `data.frame` to pass into the `plot_map` function with the names and positions. We'll create a list for each of the points and then use the (`dplyr::bind_rows`)[https://dplyr.tidyverse.org/reference/bind.html] function to create a `data.frame` from these. @@ -47,7 +47,7 @@ markers <- dplyr::bind_rows(points) We're now ready to plot the map, passing in the polygon data, markers and optionally the fill opacity. ```{r} -fdmr::plot_map_leaflet(polygon_data = sfc, markers = markers, polygon_fill_opacity = 0.5) +fdmr::plot_map(polygon_data = sfc, markers = markers, polygon_fill_opacity = 0.5) ``` On the map you can see the dam, the resevoir, the catchment area, and the two stream gauges. diff --git a/vignettes/modelbuilder.Rmd b/vignettes/modelbuilder.Rmd index 68e6a437..ceee0acf 100644 --- a/vignettes/modelbuilder.Rmd +++ b/vignettes/modelbuilder.Rmd @@ -24,7 +24,7 @@ load ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align='center'} sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` The COVID-19 data in Bristol are included in our tutorial data package. We'll load in the data using same process we used above diff --git a/vignettes/priors.Rmd b/vignettes/priors.Rmd index 7ca09010..92c44c07 100644 --- a/vignettes/priors.Rmd +++ b/vignettes/priors.Rmd @@ -39,7 +39,7 @@ Now we make a map of the study region. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align='center'} sp_data@data$mapp <- 0 domain <- sp_data@data$mapp -fdmr::plot_map_leaflet(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") +fdmr::plot_map(polygon_data = sp_data, domain = domain, add_scale_bar = TRUE, polygon_fill_opacity = 0.5, palette = "YlOrRd") ``` diff --git a/vignettes/simulation_gaussian_data.Rmd b/vignettes/simulation_gaussian_data.Rmd index 0af2a70c..a93efc6e 100644 --- a/vignettes/simulation_gaussian_data.Rmd +++ b/vignettes/simulation_gaussian_data.Rmd @@ -90,10 +90,10 @@ A histogram is created to visualize the distribution of the simulated Gaussian d hist(simdf$datn, main = "", xlab = "Data") ``` -We can also map the simulated data using the `fdmr::plot_map_leaflet` function. For example, a map of the simulated data at the first time point is given as +We can also map the simulated data using the `fdmr::plot_map` function. For example, a map of the simulated data at the first time point is given as ```{r mapsimdat,error=TRUE,fig.cap="A map of the simulated data in Bristol at the first time point.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map_leaflet( +fdmr::plot_map( polygon_data = sp_data, domain = simdf[simdf$time == 1, "datn"], palette = "Reds", @@ -256,10 +256,10 @@ fdmr::model_viewer( ) ``` -## fdmr::plot_map_leaflet +## fdmr::plot_map -The `fdmr::plot_map_leaflet` function generates a map of the region based on the provided SpatialPolygonDataFrame. +The `fdmr::plot_map` function generates a map of the region based on the provided SpatialPolygonDataFrame. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map_leaflet(polygon_data = sp_data) +fdmr::plot_map(polygon_data = sp_data) ``` diff --git a/vignettes/simulation_poisson_data.Rmd b/vignettes/simulation_poisson_data.Rmd index 9c4d8d3e..b1656e63 100644 --- a/vignettes/simulation_poisson_data.Rmd +++ b/vignettes/simulation_poisson_data.Rmd @@ -95,11 +95,11 @@ A histogram is created to visualize the distribution of the simulated data. hist(simdf$datn, main = "", xlab = "Data") ``` -We can also map the simulated data using the `fdmr::plot_map_leaflet` function. +We can also map the simulated data using the `fdmr::plot_map` function. . ```{r mapsimdat,error=TRUE,fig.cap="A map of the simulated data in Bristol over all time periods.", fig.width=8, fig.height=4, fig.align = "center"} dat_by_ID <- dplyr::group_by(simdf, ID) %>% dplyr::summarize(sum.dat = sum(datn)) -fdmr::plot_map_leaflet( +fdmr::plot_map( polygon_data = sp_data, domain = dat_by_ID$sum.dat, palette = "Reds", @@ -260,10 +260,10 @@ fdmr::model_viewer( ) ``` -## fdmr::plot_map_leaflet +## fdmr::plot_map -The `fdmr::plot_map_leaflet` function generates a map of the region based on the provided SpatialPolygonDataFrame. +The `fdmr::plot_map` function generates a map of the region based on the provided SpatialPolygonDataFrame. ```{r rmap, error=TRUE,fig.cap="A map of the study region.", fig.width=8, fig.height=4, fig.align = "center"} -fdmr::plot_map_leaflet(polygon_data = sp_data) +fdmr::plot_map(polygon_data = sp_data) ``` From 8b3f3a5ffaf54fd266ac395d2c39d50408c1c791 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 14:35:14 +0000 Subject: [PATCH 75/82] Added graph plotting code to code tab --- R/plot_priors.R | 23 ++---------- R/shiny_modelviewer.R | 70 ++++++++++++++++++++++++------------- man/plot_ar1.Rd | 20 ----------- man/plot_dic.Rd | 1 - man/plot_line_comparison.Rd | 1 - man/plot_priors_boxplot.Rd | 1 - man/plot_priors_density.Rd | 1 - 7 files changed, 48 insertions(+), 69 deletions(-) delete mode 100644 man/plot_ar1.Rd diff --git a/R/plot_priors.R b/R/plot_priors.R index 418eb871..7c8524a0 100644 --- a/R/plot_priors.R +++ b/R/plot_priors.R @@ -5,8 +5,8 @@ #' #' @return ggplot2::ggplot plot_line_comparison <- function(data, to_plot, title) { - ar1_data <- purrr::map(data, function(x) as.data.frame(x$pars[[to_plot]])) - single_df <- dplyr::bind_rows(ar1_data, .id = "Run") + parsed_data <- purrr::map(data, function(x) as.data.frame(x$pars[[to_plot]])) + single_df <- dplyr::bind_rows(parsed_data, .id = "Run") if (nrow(single_df) == 0) { return("No pars data.") } @@ -17,25 +17,6 @@ plot_line_comparison <- function(data, to_plot, title) { ggplot2::theme(text = ggplot2::element_text(size = 16)) } - -#' Plot AR(1) -#' -#' @param data Parsed model output -#' @param to_plot Type of data to plot, "Range for f" etc -#' -#' @return ggplot2::ggplot -plot_ar1 <- function(data) { - ar1_data <- purrr::map(data, function(x) as.data.frame(x$pars$`GroupRho for f`)) - single_df <- dplyr::bind_rows(ar1_data, .id = "Run") - if (nrow(single_df) == 0) { - return("No pars data.") - } - - ggplot2::ggplot(single_df, ggplot2::aes(x = x, y = y, color = Run)) + - ggplot2::geom_line() + - ggplot2::theme(text = ggplot2::element_text(size = 16)) -} - #' Create boxplots from priors run data #' #' @param data diff --git a/R/shiny_modelviewer.R b/R/shiny_modelviewer.R index ea1f8a7e..79e7af00 100644 --- a/R/shiny_modelviewer.R +++ b/R/shiny_modelviewer.R @@ -215,34 +215,56 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri code_str <- "" parsed_model_str <- "parsed_model_out <- fdmr::parse_model_output(model_output = model_output, measurement_data = measurement_data)" - if (input$plot_type == "Range") { - code_str <- 'fdmr::plot_line_comparison( - data = parsed_model_out, - to_plot = "Range for f", - title = "Range" - )' - } else if (input$plot_type == "Stdev") { - code_str <- 'fdmr::plot_line_comparison( - data = parsed_model_out, - to_plot = "Stdev for f", - title = "Marginal standard deviation" - )' - } else if (input$plot_type == "AR(1)") { - code_str <- 'fdmr::plot_line_comparison( - data = parsed_model_out, - to_plot = "GroupRho for f", - title = "AR(1)" - )' + + if (input$plot_type %in% c("Range", "Stdev", "AR(1)")) { + if (input$plot_type == "Range") { + to_plot <- "Range for f" + title <- "Range" + } else if (input$plot_type == "Stdev") { + to_plot <- "Stdev for f" + title <- "Marginal standard deviation" + } else if (input$plot_type == "AR(1)") { + to_plot <- "GroupRho for f" + title <- "AR(1)" + } + + code_str <- paste0("parsed_data <- purrr::map(parsed_model_out, function(x) as.data.frame(x$pars[[", to_plot, ']])) + single_df <- dplyr::bind_rows(parsed_data, .id = "Run") + + ggplot2::ggplot(single_df, ggplot2::aes(x = x, y = y, color = Run)) + + ggplot2::geom_line() + + ggplot2::ggtitle(', title, ") + + ggplot2::theme(text = ggplot2::element_text(size = 16))") } else if (input$plot_type == "Boxplot") { - code_str <- "fdmr::plot_priors_boxplot(data = parsed_model_out)" + code_str <- 'fitted_mean_post <- purrr::map(parsed_model_out, function(x) x$fitted_mean_post) + names(fitted_mean_post) <- purrr::map(seq(1, length(parsed_model_out)), function(x) paste("Run", x)) + post_rate <- cbind.data.frame(fitted_mean_post) + graphics::boxplot(post_rate, xlab = "Prior scenario", ylab = "Fitted values")' } else if (input$plot_type == "Density") { - code_str <- "plot_priors_density( - data = parsed_model_out, - measurement_data = measurement_data - )" + code_str <- 'fitted_values <- unlist(purrr::map(parsed_model_out, function(x) x$fitted_mean_post)) + run_strings <- unlist(purrr::map(seq(1, length(data)), function(x) paste("Run", x))) + + post_rate <- base::cbind.data.frame( + "Prior scenario" = rep(run_strings, each = nrow(measurement_data)), + "Fitted values" = fitted_values + ) + + ggplot2::ggplot(post_rate, ggplot2::aes(x = `Fitted values`, color = `Prior scenario`)) + + ggplot2::geom_density() + + ggplot2::theme(text = ggplot2::element_text(size = 16))' } else if (input$plot_type == "DIC") { - code_str <- "fdmr::plot_dic(data = parsed_model_out)" + code_str <- 'infocri <- base::cbind.data.frame( + priors = unlist(purrr::map(seq(1, length(data)), function(x) paste("Run", x))), + DIC = unlist(purrr::map(data, function(x) x$dic)) + ) + + infocri$priors <- base::as.factor(infocri$priors) + + ggplot2::ggplot(infocri, ggplot2::aes(x = priors, y = DIC)) + + ggplot2::geom_point() + + ggplot2::theme(text = ggplot2::element_text(size = 16))' } + paste0(parsed_model_str, "\n\n", code_str) }) diff --git a/man/plot_ar1.Rd b/man/plot_ar1.Rd deleted file mode 100644 index f5b26b62..00000000 --- a/man/plot_ar1.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plot_priors.R -\name{plot_ar1} -\alias{plot_ar1} -\title{Plot AR(1)} -\usage{ -plot_ar1(data) -} -\arguments{ -\item{data}{Parsed model output} - -\item{to_plot}{Type of data to plot, "Range for f" etc} -} -\value{ -ggplot2::ggplot -} -\description{ -Plot AR(1) -} -\keyword{internal} diff --git a/man/plot_dic.Rd b/man/plot_dic.Rd index 69fff308..b92fa7d9 100644 --- a/man/plot_dic.Rd +++ b/man/plot_dic.Rd @@ -15,4 +15,3 @@ ggplot2::ggplot \description{ Plot Deviance Information Criterion (DIC) values } -\keyword{internal} diff --git a/man/plot_line_comparison.Rd b/man/plot_line_comparison.Rd index f953e712..b185b7d4 100644 --- a/man/plot_line_comparison.Rd +++ b/man/plot_line_comparison.Rd @@ -17,4 +17,3 @@ ggplot2::ggplot \description{ Plot line comparison for stdev etc } -\keyword{internal} diff --git a/man/plot_priors_boxplot.Rd b/man/plot_priors_boxplot.Rd index 2ca7e7cb..e7ee38dc 100644 --- a/man/plot_priors_boxplot.Rd +++ b/man/plot_priors_boxplot.Rd @@ -15,4 +15,3 @@ graphics::boxplot \description{ Create boxplots from priors run data } -\keyword{internal} diff --git a/man/plot_priors_density.Rd b/man/plot_priors_density.Rd index ef702e46..c840ab5c 100644 --- a/man/plot_priors_density.Rd +++ b/man/plot_priors_density.Rd @@ -17,4 +17,3 @@ ggplot2::ggplot \description{ Plot density function } -\keyword{internal} From e380bde527aad2c9f13780ac6020352c201a02b6 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 14:58:19 +0000 Subject: [PATCH 76/82] Fixes to plot code --- R/shiny_modelviewer.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/shiny_modelviewer.R b/R/shiny_modelviewer.R index 79e7af00..b69348c8 100644 --- a/R/shiny_modelviewer.R +++ b/R/shiny_modelviewer.R @@ -228,13 +228,13 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri title <- "AR(1)" } - code_str <- paste0("parsed_data <- purrr::map(parsed_model_out, function(x) as.data.frame(x$pars[[", to_plot, ']])) + code_str <- paste0('parsed_data <- purrr::map(parsed_model_out, function(x) as.data.frame(x$pars[["', to_plot, '"]])) single_df <- dplyr::bind_rows(parsed_data, .id = "Run") ggplot2::ggplot(single_df, ggplot2::aes(x = x, y = y, color = Run)) + ggplot2::geom_line() + - ggplot2::ggtitle(', title, ") + - ggplot2::theme(text = ggplot2::element_text(size = 16))") + ggplot2::ggtitle("', title, '") + + ggplot2::theme(text = ggplot2::element_text(size = 16))') } else if (input$plot_type == "Boxplot") { code_str <- 'fitted_mean_post <- purrr::map(parsed_model_out, function(x) x$fitted_mean_post) names(fitted_mean_post) <- purrr::map(seq(1, length(parsed_model_out)), function(x) paste("Run", x)) @@ -242,7 +242,7 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri graphics::boxplot(post_rate, xlab = "Prior scenario", ylab = "Fitted values")' } else if (input$plot_type == "Density") { code_str <- 'fitted_values <- unlist(purrr::map(parsed_model_out, function(x) x$fitted_mean_post)) - run_strings <- unlist(purrr::map(seq(1, length(data)), function(x) paste("Run", x))) + run_strings <- unlist(purrr::map(seq(1, length(parsed_model_out)), function(x) paste("Run", x))) post_rate <- base::cbind.data.frame( "Prior scenario" = rep(run_strings, each = nrow(measurement_data)), @@ -254,8 +254,8 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri ggplot2::theme(text = ggplot2::element_text(size = 16))' } else if (input$plot_type == "DIC") { code_str <- 'infocri <- base::cbind.data.frame( - priors = unlist(purrr::map(seq(1, length(data)), function(x) paste("Run", x))), - DIC = unlist(purrr::map(data, function(x) x$dic)) + priors = unlist(purrr::map(seq(1, length(parsed_model_out)), function(x) paste("Run", x))), + DIC = unlist(purrr::map(parsed_model_out, function(x) x$dic)) ) infocri$priors <- base::as.factor(infocri$priors) From 56e6d17883d6afb75583de9e52c1d24efb9024ba Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:11:38 +0000 Subject: [PATCH 77/82] Added explainer text for the plots --- R/shiny_modelviewer.R | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/R/shiny_modelviewer.R b/R/shiny_modelviewer.R index b69348c8..25be16e7 100644 --- a/R/shiny_modelviewer.R +++ b/R/shiny_modelviewer.R @@ -90,7 +90,21 @@ model_viewer_shiny <- function(model_output, mesh, measurement_data, data_distri shiny::tabPanel( "Help", class = "p-3 border", - shiny::h3("Help"), + shiny::h4("Range"), + shiny::p("The Range plot provides a plot of the posterior distribution of the spatial range parameter ρ, the distance at which the correlation between two points is approximately 0."), + shiny::h4("Stdev"), + shiny::p("The Stdev plot provides a plot of the posterior distribution of the marginal standard deviation σ of the spatial field."), + shiny::h4("AR(1)"), + shiny::p("The AR(1) plot provides a plot of the posterior distribution of the temporal autocorrelation α. A value close to 1 indicates strong positive temporal dependence, a value of 0 indicates independence across time, and a value close to -1 indicates strong negative temporal dependence."), + shiny::h4("Boxplot"), + shiny::p("The Boxplot displays the predicted (or fitted) values across the observed locations and time points, showing how the predictions are distributed."), + shiny::h4("Density"), + shiny::p("The Density plot displays the density curve of the predicted (or fitted) values across the observed locations and time points."), + shiny::h4("DIC"), + shiny::p("The DIC plot displays the Deviance Information Criterion (DIC) value for the model. DIC is a model selection criterion, with lower values indicating better model fit."), + shiny::h4("Map"), + shiny::p("The plot of predicted mean fields maps the predicted values across a number of grid locations that cover the study region. It helps to identify areas with high predictions and those with low predictions. The predicted values at observed locations and time points can be obtained by using the syntax model_output$summary.fitted.values$mean[1:nrow(observed_data)]. The predicted values at the grid locations are calculated using the method described in the priors tutorial "), + shiny::p("The plot of random effect fields maps the random effect values (the f() values) across a number of grid locations that cover the study region. The random effect values at the mesh nodes and time points can be obtained by using the syntax model_output$summary.random$f$mean.") ) ) ) From aa48e831ae40732d059bff08d2b86708086b16c0 Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:14:39 +0000 Subject: [PATCH 78/82] Add changelog line --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3121feec..04cb564a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,7 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added a new tutorial on loading data from different sources - [PR #266](https://github.com/4DModeller/fdmr/pull/266/) - Added new geophysical processes tutorial thanks to Alexander Minakov (4minakov) - [PR #257](https://github.com/4DModeller/fdmr/pull/257) - Added mouse pointer coordinates header and standard measurement tool - [PR #260](https://github.com/4DModeller/fdmr/pull/260) -- New plotting function `plot_map_mapview` to use `mapview` to plot raster and spatial data - [#291](https://github.com/4DModeller/fdmr/pull/291) +- Added new help explainer to the `Help` tab of the `model_viewer` Shiny app - [PR #295](https://github.com/4DModeller/fdmr/pull/295) ### Changed From 42248161d8de2795437f5f8e7f34fbad19c01d7f Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:17:11 +0000 Subject: [PATCH 79/82] Remove reference to check mesh [skip ci] --- vignettes/simulation_gaussian_data.Rmd | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/vignettes/simulation_gaussian_data.Rmd b/vignettes/simulation_gaussian_data.Rmd index a93efc6e..8e528241 100644 --- a/vignettes/simulation_gaussian_data.Rmd +++ b/vignettes/simulation_gaussian_data.Rmd @@ -225,11 +225,7 @@ fdmr::plot_mesh(mesh = mesh, spatial_data = simdf@coords) fdmr::mesh_builder(spatial_data = sp_data) ``` -The interactive map allows you to customise the initial parameters such as `max_edge`, `offset` and `cutoff` to change the shape and size of the mesh. We also provide a simple function to check if there are any errors with the meshes created using the mesh builder tool. To use the mesh checking functionality you must pass your measuremnet data to the `fdmr::mesh_builder` function. Specifically, create a mesh of your design using the meshbuilder tool. When you’re finished, click the “Check mesh” button on the user interface. This passes the created mesh to the `fdmr::mesh_checker` function and returns a list containing any errors found with the mesh. - -```{r meshchecking,eval=FALSE} -fdmr::mesh_builder(spatial_data = sp_data, obs_data = simdf) -``` +The interactive map allows you to customise the initial parameters such as `max_edge`, `offset` and `cutoff` to change the shape and size of the mesh. ## Model builder Shiny app `fdmr` provides a Priors Shiny app which allows you to interactively set and see the model fitting results of different priors. You can launch this app by passing the spatial and observation data to the `fdmr::model_builder` function. From 9aa803189edfceb641ee0976e48822a03b4a7f5a Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:19:23 +0000 Subject: [PATCH 80/82] Release 0.2.0 of fdmr --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e9591b45..c654b6b7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: fdmr Title: 4D Modeller project -Version: 0.1.1 +Version: 0.2.0 Authors@R: c(person("Gareth", "Jones", , "g.m.jones@bristol.ac.uk", role = c("aut", "cre"), comment = ""), From 3ea3fdfe6bcdec2aaeecb2fabf5629014658d63b Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:22:49 +0000 Subject: [PATCH 81/82] Update changelog with release date --- CHANGELOG.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 04cb564a..7ebd9558 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,9 @@ All notable changes to `fdmr` will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [Unreleased](https://github.com/openghg/openghg/compare/0.1.1...HEAD) +## [Unreleased](https://github.com/openghg/openghg/compare/0.2.0...HEAD) + +## [0.2.0] - 2023-12-19 ### Fixed @@ -21,9 +23,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Changed - Updated `plot_map` to allow use of both `leaflet` and `mapview` packages - [#291](https://github.com/4DModeller/fdmr/pull/291) - -### Changed - - Moved from using `leaflet` to using (`mapview`)[https://r-spatial.github.io/mapview/index.html] for plotting the mesh and spatial data in the `mesh_builder` Shiny app. This enables use of UTM coordinates - [PR #288](https://github.com/4DModeller/fdmr/pull/288) ## [0.1.1] - 2023-11-01 From b79bf89ca40236453073819fa947c4630c29b12a Mon Sep 17 00:00:00 2001 From: Gareth Jones Date: Tue, 19 Dec 2023 15:30:51 +0000 Subject: [PATCH 82/82] Add missing function doc files --- man/antimeridian_wrapping.Rd | 28 ++++++++++++++++++++++++++++ man/convert_from_lon_360.Rd | 28 ++++++++++++++++++++++++++++ 2 files changed, 56 insertions(+) create mode 100644 man/antimeridian_wrapping.Rd create mode 100644 man/convert_from_lon_360.Rd diff --git a/man/antimeridian_wrapping.Rd b/man/antimeridian_wrapping.Rd new file mode 100644 index 00000000..2eaf85e4 --- /dev/null +++ b/man/antimeridian_wrapping.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/util_coords.R +\name{antimeridian_wrapping} +\alias{antimeridian_wrapping} +\title{Split polygons into two if crossing the dateline (antimeridian)} +\usage{ +antimeridian_wrapping( + polygon_data, + crs = 4326, + unique_inst = TRUE, + to_sp = TRUE +) +} +\arguments{ +\item{polygon_data}{SpatialPolygon or SpatialPolygonDataFrame object} + +\item{crs}{CRS as a proj4string or EPSG code, default EPSG:4326 (datum=WGS84)} + +\item{unique_inst}{Option to remove duplicate polygons (with the same coordinates), default TRUE} + +\item{to_sp}{Option to convert POLYGON object into SpatialPolygon*, default TRUE} +} +\value{ +SpatialPolygon or SpatialPolygonDataFrame split along the antimeridian +} +\description{ +Split polygons into two if crossing the dateline (antimeridian) +} diff --git a/man/convert_from_lon_360.Rd b/man/convert_from_lon_360.Rd new file mode 100644 index 00000000..0bb32900 --- /dev/null +++ b/man/convert_from_lon_360.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/util_coords.R +\name{convert_from_lon_360} +\alias{convert_from_lon_360} +\title{Convert longitudes from 0 to 360 degrees to -180 to 180 degrees} +\usage{ +convert_from_lon_360( + sf_data, + crs = 4326, + add_data = TRUE, + longitude_column = "LONG" +) +} +\arguments{ +\item{sf_data}{An sf object; does not accept SpatialPolygon* objects} + +\item{crs}{CRS as a proj4string or EPSG code} + +\item{add_data}{Select if data associated with the object are carried forward by the transformed version, defaults to FALSE} + +\item{longitude_column}{Name of longitude, defaults to LONG} +} +\value{ +polygons with converted coordinates +} +\description{ +Convert longitudes from 0 to 360 degrees to -180 to 180 degrees +}

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