Last updated: 2020-12-21
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Knit directory: MHWflux/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 65f38bf | robwschlegel | 2020-12-21 | Build site. |
| html | 33f4595 | robwschlegel | 2020-11-10 | Build site. |
| html | a438235 | robwschlegel | 2020-11-10 | Build site. |
| Rmd | cbc5b74 | robwschlegel | 2020-11-10 | Re-built site. |
| Rmd | e513e01 | robwschlegel | 2020-10-09 | Added Qsw* to the SOM data and removed the SOM correlation figure |
| Rmd | 54144a7 | robwschlegel | 2020-09-28 | Working on non-numeric node labels |
| Rmd | 5bfa2a7 | robwschlegel | 2020-09-03 | Push before beginning large structural additions to app |
| html | 8d65758 | robwschlegel | 2020-09-03 | Build site. |
| Rmd | d1c9bad | robwschlegel | 2020-09-03 | Re-built site. |
| Rmd | 66f3736 | robwschlegel | 2020-08-26 | More edits to the figures |
| Rmd | 4b04d7a | robwschlegel | 2020-08-14 | Renamed some files in preparation for the file runs on the SOM sized data |
| Rmd | c0c599d | robwschlegel | 2020-08-12 | Combining the MHWNWA and MHWflux code bases |
This vignette contains the code used to perform the self-organising map (SOM) analysis on the mean synoptic states created in the data preparation vignette. We’ll start by creating custom packets that meet certain experimental criteria before feeding them into a SOM.
# Load functions and objects to be used below
source("code/functions.R")In this step we will create a data packet that can be fed directly into the SOM algorithm. This means that it must be converted into a super-wide matrix format. In the first run of this analysis on the NAPA model data it was found that the inclusion of the Labrador Sea complicated the results quite a bit. It was also unclear whether or not the Gulf of St Lawrence (GSL) region should be included in the analysis. So in the second run of this analysis multiple different SOM variations were employed and it was decided that the GSL region should be included.
Up first we must create the synoptic state packets.
# Set number of cores
# NB: 50 cores uses too much RAM
registerDoParallel(cores = 20)
# Load needed data
ALL_anom <- readRDS("data/ALL_anom.Rda")
ALL_other <- readRDS("data/ALL_other.Rda")
# Create one big anomaly packet from OISST data
system.time(synoptic_states <- plyr::ddply(OISST_MHW_event, c("region", "event_no"),
data_packet_func, .parallel = T, df = ALL_anom)) # 129 seconds
# Save
saveRDS(synoptic_states, "data/synoptic_states.Rda")
# Create other synoptic states per MHW per variable
doParallel::registerDoParallel(cores = 10) # NB: Be careful here...
system.time(synoptic_states_other <- plyr::ddply(OISST_MHW_event, c("region", "event_no"),
data_packet_func, .parallel = T, df = ALL_other)) # 212 seconds
# Save
saveRDS(synoptic_states_other, "data/synoptic_states_other.Rda")With all of our data ready we may now prepare and save them for the SOM.
## Create wide data packet that is fed to SOM
system.time(packet <- synoptic_states %>%
select(region, event_no, synoptic) %>%
unnest(cols = "synoptic") %>%
wide_packet_func()) # 79 seconds
# Save
saveRDS(packet, "data/packet.Rda")Now we feed the SOM with a function that ingests the data packet and produces results for us. The function below has been greatly expanded on from the previous version of this project and now performs all of the SOM related work in one go. This allowed me to remove a couple hundreds lines of code and text from this vignette.
# # OISST SOM analysis
packet <- readRDS("data/packet.Rda")
synoptic_states_other <- readRDS("data/synoptic_states_other.Rda")
system.time(som <- som_model_PCI(packet, synoptic_states_other)) # 176 seconds
saveRDS(som, file = "data/som.Rda")
# saveRDS(som, file = "shiny/som.Rda")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] doParallel_1.0.16 iterators_1.0.13 foreach_1.5.1
[4] Metrics_0.1.4 yasomi_0.3 proxy_0.4-24
[7] e1071_1.7-4 ggraph_2.0.3 correlation_0.4.0
[10] tidync_0.2.4 heatwaveR_0.4.4.9004 lubridate_1.7.9.2
[13] data.table_1.13.2 forcats_0.5.0 stringr_1.4.0
[16] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[19] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[22] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] fs_1.5.0 insight_0.10.0 httr_1.4.2 rprojroot_2.0.2
[5] tools_4.0.3 backports_1.2.0 R6_2.5.0 DBI_1.1.0
[9] lazyeval_0.2.2 colorspace_2.0-0 withr_2.3.0 gridExtra_2.3
[13] tidyselect_1.1.0 compiler_4.0.3 git2r_0.27.1 cli_2.1.0
[17] rvest_0.3.6 RNetCDF_2.4-2 xml2_1.3.2 plotly_4.9.2.1
[21] bayestestR_0.7.5 scales_1.1.1 digest_0.6.27 rmarkdown_2.5
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_2.0.0 htmlwidgets_1.5.2
[29] rlang_0.4.8 readxl_1.3.1 rstudioapi_0.13 generics_0.1.0
[33] farver_2.0.3 jsonlite_1.7.1 magrittr_2.0.1 ncmeta_0.3.0
[37] parameters_0.9.0 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[41] viridis_0.5.1 lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
[45] yaml_2.2.1 MASS_7.3-53 grid_4.0.3 promises_1.1.1
[49] ggrepel_0.8.2 crayon_1.3.4 graphlayouts_0.7.1 haven_2.3.1
[53] hms_0.5.3 knitr_1.30 pillar_1.4.6 igraph_1.2.6
[57] effectsize_0.4.0 codetools_0.2-18 reprex_0.3.0 glue_1.4.2
[61] evaluate_0.14 modelr_0.1.8 vctrs_0.3.5 tweenr_1.0.1
[65] httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[69] assertthat_0.2.1 xfun_0.19 ggforce_0.3.2 broom_0.7.2
[73] tidygraph_1.2.0 later_1.1.0.1 class_7.3-17 ncdf4_1.17
[77] viridisLite_0.3.0 workflowr_1.6.2 ellipsis_0.3.1