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# Auto detect text files and perform LF normalization | ||
* text=auto |
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# History files | ||
.Rhistory | ||
.Rapp.history | ||
.DS_Store | ||
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# Session Data files | ||
.RData | ||
.RDataTmp | ||
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# User-specific files | ||
.Ruserdata | ||
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# Example code in package build process | ||
*-Ex.R | ||
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# Output files from R CMD build | ||
/*.tar.gz | ||
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# Output files from R CMD check | ||
/*.Rcheck/ | ||
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# RStudio files | ||
.Rproj.user/ | ||
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# produced vignettes | ||
vignettes/*.html | ||
vignettes/*.pdf | ||
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# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 | ||
.httr-oauth | ||
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# knitr and R markdown default cache directories | ||
*_cache/ | ||
/cache/ | ||
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# Temporary files created by R markdown | ||
*.utf8.md | ||
*.knit.md | ||
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# R Environment Variables | ||
.Renviron | ||
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# pkgdown site | ||
docs/ | ||
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# translation temp files | ||
po/*~ | ||
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# RStudio Connect folder | ||
rsconnect/ | ||
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data/raw/ | ||
data/species/ | ||
data/env/ | ||
data/distances/ | ||
data/log/_log* | ||
data/iucn/ | ||
data/virtual_species/ | ||
data/virtual_species_oldversion/ | ||
_species_info/ | ||
sdmrun/ | ||
sdmrun_b/ | ||
sdmrun_c/ | ||
storr/ | ||
datastd/ | ||
results/ | ||
temp_files/ | ||
codes/drafts/ |
Empty file.
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MIT License | ||
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Copyright (c) 2023 silasprincipe | ||
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Permission is hereby granted, free of charge, to any person obtaining a copy | ||
of this software and associated documentation files (the "Software"), to deal | ||
in the Software without restriction, including without limitation the rights | ||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
copies of the Software, and to permit persons to whom the Software is | ||
furnished to do so, subject to the following conditions: | ||
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The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
SOFTWARE. |
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# Understanding how temperature is changing on the Marine Heritage Sites | ||
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Work in progess. Experimental. | ||
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See the page at https://iobis.github.io/marineheritage_sst | ||
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To add a section with 'climate vulnerability' for the most abundant/present species (?) | ||
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![image](https://github.com/iobis/marineheritage_sst/assets/53846571/db63c648-b78a-4154-9559-c042efd3a607) |
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############# eDNA Expeditions - UNESCO World Heritage Marine Sites ############ | ||
################# Data analysis of the project - Thermal niche ################# | ||
# August of 2023 | ||
# Authors: Silas C. Principe, Pieter Provoost | ||
# Contact: [email protected] | ||
# | ||
################ Retrieve SST satellite data for current period ################ | ||
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# Load packages and define settings ---- | ||
library(terra) | ||
library(sf) | ||
library(dplyr) | ||
library(ggplot2) | ||
library(arrow) | ||
# Path to save files | ||
save_path <- "data/sst/current/" | ||
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# Load Python functions ---- | ||
reticulate::source_python("functions/inspect_cop.py") | ||
# reticulate::py_help(inspect_cop) # To see help run this line | ||
reticulate::source_python("functions/retrieve_sst_fun.py") | ||
# reticulate::py_help(retrieve_cop) # To see help run this line | ||
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# Get credentials for Copernicus Marine service ---- | ||
cop_user <- rstudioapi::askForPassword("Copernicus username") | ||
cop_pass <- rstudioapi::askForPassword("Copernicus password") | ||
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# Load marine heritage sites shapefile ---- | ||
mhs_path <- "data/shapefiles/WorldMarineHeritageSites_v2.shp" | ||
mhs <- st_read(mhs_path) | ||
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# Limit to those that are confirmed on the website | ||
# (ask for a csv list) | ||
mhs <- mhs[grepl(paste0( | ||
c( | ||
"wadden", "shark", "noronha", "rocas", "french austral", "lord howe", | ||
"sundarbans", "coiba", "revillagigedo", "caledonia", "calanche", | ||
"sanganeb", "everglades", "aldabra", "belize", "tubbataha", | ||
"simangaliso", "banc", "ningaloo", "socotra", "Península Valdés" | ||
), collapse = "|" | ||
), mhs$FULL_NAME, ignore.case = T),] | ||
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mhs_sites <- unique(mhs$MRGID) | ||
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# Oceans (optional) # | ||
oceans <- mregions::mr_shp(key = "MarineRegions:goas", read = TRUE, maxFeatures = 1000) | ||
oceans <- st_as_sf(oceans) | ||
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inter <- st_intersects(mhs, st_make_valid(oceans), sparse = F) | ||
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mhs$OCEAN <- oceans$name[apply(inter, 1, which.max)] | ||
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# Open dataset ---- | ||
# Dataset name | ||
dataset <- "METOFFICE-GLO-SST-L4-REP-OBS-SST" | ||
# Variable of interest | ||
variable <- "analysed_sst" | ||
# Time range | ||
time_window <- 1992:2021 | ||
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# Inspect and open dataset | ||
dataset_info <- inspect_cop(dataset, cop_user, cop_pass, variable = variable, plot = T) | ||
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# Get mean, maximum, minimum and sd for each site and save ---- | ||
# Because all will have the same configurations, we first create a function | ||
get_metric <- function(site, metric, time_window) { | ||
retrieve_cop(c(dataset, dataset_info), # Supplying dataset name and dataset object | ||
variable, # Variable to open | ||
cop_user, cop_pass, # Copernicus access information | ||
shape = mhs_path, # Shapefile path | ||
shape_var = "MRGID", # Shapefile variable to filter | ||
shape_filter = site, # Filter value | ||
time_range = time_window, # Time range | ||
ret_type = "df", # Returning type | ||
metric = metric, # Metric of summary | ||
group_by_lonlat = TRUE, # Group by grouper and summarise by whole raster | ||
res_by_month = TRUE, # Aggregate by month | ||
grouper = "time", # Get for each time step | ||
mask_by_shape = FALSE, # Mask by the shapefile | ||
k_to_celsius = TRUE, # Convert from degree to celsius | ||
plot = F) # Plot during execution | ||
} | ||
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# We run in loop, that way if there is any problem we can try again | ||
sites_metrics <- list() | ||
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for (z in 16:length(mhs_sites)) { | ||
# We load the dataset again at each start to avoid the server disconnecting | ||
dataset_info <- inspect_cop(dataset, cop_user, cop_pass, variable = variable, plot = F) | ||
for (k in 1:length(time_window)) { | ||
tr <- paste0(time_window[k], c("-01-01", "-12-31")) | ||
temp_data <- get_metric(mhs_sites[z], | ||
metric = c("mean", "std", "max", "min", "median"), | ||
time_window = tr) | ||
if (k == 1) { | ||
sites_metrics[[z]] <- temp_data | ||
} else { | ||
sites_metrics[[z]] <- rbind(sites_metrics[[z]], | ||
temp_data) | ||
} | ||
} | ||
} | ||
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# | ||
# sites_metrics <- lapply(mhs_sites, get_metric, | ||
# metric = c("mean", "std", "max", "min", "median")) | ||
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# Convert to a single data frame containing sites info | ||
sites_metrics <- lapply(sites_metrics, function(x){x$time <- rownames(x);x}) | ||
names(sites_metrics) <- mhs_sites | ||
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sites_metrics <- bind_rows(sites_metrics, .id = "MRGID") | ||
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sites_info <- st_drop_geometry(mhs[,c("FULL_NAME", "COUNTRY", "MRGID")]) | ||
sites_info <- distinct(sites_info, MRGID, .keep_all = T) | ||
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sites_metrics <- left_join(sites_metrics, sites_info, by = "MRGID") | ||
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# Save results ---- | ||
write_parquet(sites_metrics, paste0(save_path, "mhs_sst_current.parquet")) | ||
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# Plot results ---- | ||
sites_metrics$time <- lubridate::as_date(sites_metrics$time) | ||
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sites_metrics <- sites_metrics %>% | ||
group_by(MRGID) %>% | ||
mutate(general_mean = mean(mean)) %>% | ||
mutate(detrend = mean - general_mean) %>% | ||
select(-general_mean) | ||
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ggplot(sites_metrics, aes(x = time, y = mean))+ | ||
geom_line() + | ||
geom_ribbon(aes(ymin = mean-sd, ymax = mean+sd), alpha = .3)+ | ||
theme_light() + | ||
facet_wrap(~ MRGID, scales = "free_y") | ||
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sites_metrics$state <- ifelse(sites_metrics$detrend > 0, "Higher", "Lower") | ||
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ggplot(sites_metrics, aes(x = time, y = detrend))+ | ||
geom_hline(yintercept = 0) + | ||
#geom_line(aes(color = state)) + | ||
geom_area(aes(x=time, y=ifelse(detrend<0, detrend, 0)), fill="#1093C8") + | ||
geom_area(aes(x=time, y=ifelse(detrend>0, detrend, 0)), fill="#C72B10") + | ||
theme_bw() + | ||
facet_wrap(~ MRGID, scales = "free_y") | ||
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#### | ||
# Animated grap | ||
library(dygraphs) | ||
library(xts) | ||
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wide_metrics <- sites_metrics %>% | ||
select(time, MRGID, mean, sd) %>% | ||
filter(MRGID %in% c(26836, 64215)) %>% | ||
mutate(upr = mean+sd, lwr = mean-sd) %>% | ||
select(-sd) %>% | ||
tidyr::pivot_wider(names_from = MRGID, values_from = c(mean, upr, lwr)) | ||
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smetric <- xts(x = wide_metrics, order.by = wide_metrics$time) | ||
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# Finally the plot | ||
(p <- dygraph(smetric) %>% | ||
dySeries(c("lwr_26836", "mean_26836", "upr_26836"), label = "26836") %>% | ||
dySeries(c("lwr_64215", "mean_64215", "upr_64215"), label = "64215") %>% | ||
dyOptions(labelsUTC = TRUE, fillGraph=FALSE, fillAlpha=0.1, drawGrid = FALSE, | ||
colors = RColorBrewer::brewer.pal(3, "Set2")) %>% | ||
dyAxis("y", label = "Temperature (°C)") %>% | ||
dyRangeSelector() %>% | ||
dyCrosshair(direction = "vertical") %>% | ||
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE, | ||
highlightSeriesOpts = list(strokeWidth = 2)) #%>% | ||
#dyRoller(rollPeriod = 1) | ||
) | ||
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smetric_a <- xts(x = wide_metrics[,c("time", "mean_26836", "lwr_26836", "upr_26836")], order.by = wide_metrics$time) | ||
smetric_b <- xts(x = wide_metrics[,c("time", "mean_64215", "lwr_64215", "upr_64215")], order.by = wide_metrics$time) | ||
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(p1 <- dygraph(smetric_a, group = "sst") %>% | ||
dySeries(c("lwr_26836", "mean_26836", "upr_26836"), label = "26836") %>% | ||
dyOptions(labelsUTC = TRUE, fillGraph=FALSE, fillAlpha=0.1, drawGrid = FALSE, | ||
colors = RColorBrewer::brewer.pal(3, "Set2")) %>% | ||
dyAxis("y", label = "Temperature (°C)") %>% | ||
dyRangeSelector() %>% | ||
dyCrosshair(direction = "vertical") %>% | ||
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE, | ||
highlightSeriesOpts = list(strokeWidth = 2)) #%>% | ||
#dyRoller(rollPeriod = 1) | ||
) | ||
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(p2 <- dygraph(smetric_b, group = "sst") %>% | ||
dySeries(c("lwr_64215", "mean_64215", "upr_64215"), label = "64215") %>% | ||
dyOptions(labelsUTC = TRUE, fillGraph=FALSE, fillAlpha=0.1, drawGrid = FALSE, | ||
colors = RColorBrewer::brewer.pal(3, "Set2")) %>% | ||
dyAxis("y", label = "Temperature (°C)") %>% | ||
dyRangeSelector() %>% | ||
dyCrosshair(direction = "vertical") %>% | ||
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE, | ||
highlightSeriesOpts = list(strokeWidth = 2)) #%>% | ||
#dyRoller(rollPeriod = 1) | ||
) |
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