From e5dc7878d66f6533aa1e490e5814f555d50edaad Mon Sep 17 00:00:00 2001 From: jeffwalkernoaa Date: Mon, 19 Aug 2024 10:20:52 -0400 Subject: [PATCH] add SWFSC_SAEL_20240814 - added nbhf theme, drifting_buoy platform type - app: legened label for mobile platforms - r: added gps data to external submissions --- .gitignore | 3 +- r/R/external.R | 301 +++++++++++++++++++++++-- r/R/functions.R | 15 +- r/R/pacm.R | 2 +- r/R/refs.R | 3 +- r/_targets.R | 4 +- r/cli/functions.R | 17 ++ r/notes/20240321-si-workshop.R | 65 ++++++ r/notes/20240606-SWFSC-SAEL_20240520.R | 23 ++ r/notes/20240801-SWFSC-SAEL_20240617.R | 100 ++++++++ r/notes/20240806-ORSTD_20240418.R | 100 ++++++++ r/notes/20240815-SWFSC-SAEL_20240814.R | 26 +++ src/components/Legend.vue | 26 +-- src/components/dialogs/About.vue | 5 + src/lib/constants.js | 18 +- src/lib/tour.js | 2 +- 16 files changed, 660 insertions(+), 50 deletions(-) create mode 100644 r/notes/20240321-si-workshop.R create mode 100644 r/notes/20240606-SWFSC-SAEL_20240520.R create mode 100644 r/notes/20240801-SWFSC-SAEL_20240617.R create mode 100644 r/notes/20240806-ORSTD_20240418.R create mode 100644 r/notes/20240815-SWFSC-SAEL_20240814.R diff --git a/.gitignore b/.gitignore index 12144d5..5403a7d 100644 --- a/.gitignore +++ b/.gitignore @@ -25,4 +25,5 @@ public/data/**/*.csv public/data/**/*.json .Rproj.user -dev/ \ No newline at end of file +dev/ +*.tar.gz diff --git a/r/R/external.R b/r/R/external.R index 7cc5056..516ee27 100644 --- a/r/R/external.R +++ b/r/R/external.R @@ -17,7 +17,18 @@ tar_option_set(packages = c( targets_external <- list( tar_target(external_dir, "data/external", format = "file", cue = tar_cue("always")), tar_target(external_db_tables_file, file.path(external_dir, "db-tables.rds"), format = "file"), - tar_target(external_db_tables, read_rds(external_db_tables_file)), + tar_target(external_db_tables, { + x <- read_rds(external_db_tables_file) + swfsc_bw_species_codes <- c("BWBB", "BWMS", "BWMC", "BW43", "BWC", "NBHF") + stopifnot(!any(swfsc_bw_species_codes %in% x$species$SPECIES_CODE)) + + # add beaked species from SWFSC + x$species <- x$species %>% + bind_rows( + tibble(SPECIES_CODE = swfsc_bw_species_codes) + ) + x + }), tar_target(external_submission_groups, { tibble( id = setdiff(list.dirs(external_dir, recursive = FALSE, full.names = FALSE), c("_queue", "_archive")) @@ -34,28 +45,45 @@ targets_external <- list( tibble( id = external_submission_groups$id, metadata = list(x$metadata), - detectiondata = list(x$detectiondata) + detectiondata = list(x$detectiondata), + gpsdata = list(x$gpsdata) ) }, pattern = map(external_submission_groups) ), tar_target(external_submissions_merge, external_submission_branches, iteration = "vector"), tar_target(external_submissions, { - rules <- load_external_rules()$detectiondata + detectiondata_rules <- load_external_rules()$detectiondata + gpsdata_rules <- load_external_rules()$gpsdata codes <- load_codes(external_db_tables) codes[["UNIQUE_ID"]] <- unique(external_metadata$UNIQUE_ID) external_submissions_merge %>% rowwise() %>% mutate( detectiondata = list({ - print(detectiondata) out <- tibble() if (nrow(detectiondata) > 0) { out <- detectiondata %>% rowwise() %>% mutate( validation = list({ - validate_data(joined, rules, codes) + validate_data(joined, detectiondata_rules, codes) + }), + validation_errors = list(extract_validation_errors(validation)), + n_errors = nrow(validation_errors) + ) %>% + ungroup() + } + out + }), + gpsdata = list({ + out <- tibble() + if (nrow(gpsdata) > 0) { + out <- gpsdata %>% + rowwise() %>% + mutate( + validation = list({ + validate_data(joined, gpsdata_rules, codes) }), validation_errors = list(extract_validation_errors(validation)), n_errors = nrow(validation_errors) @@ -94,6 +122,20 @@ targets_external <- list( select(id, filename, validation_errors) %>% unnest(validation_errors) }), + tar_target(external_gpsdata, { + external_submissions %>% + select(gpsdata) %>% + unnest(gpsdata) %>% + select(id, filename, parsed) %>% + unnest(parsed) + }), + tar_target(external_gpsdata_errors, { + external_submissions %>% + select(gpsdata) %>% + unnest(gpsdata) %>% + select(id, filename, validation_errors) %>% + unnest(validation_errors) + }), tar_target(themes_species, { tribble( ~theme, ~species_code, @@ -110,9 +152,16 @@ targets_external <- list( "beaked", "MEME", "beaked", "SOBW", "beaked", "NBWH", + "beaked", "BWBB", + "beaked", "BWMS", + "beaked", "BWMC", + "beaked", "BW43", + "beaked", "BWC", "kogia", "UNKO", + "nbhf", "NBHF", + "dolphin", "UNDO", "risso", "GRAM", @@ -122,11 +171,46 @@ targets_external <- list( "harbor", "HAPO" ) }), + tar_target(external_tracks, { + x <- external_gpsdata %>% + select(UNIQUE_ID, DATETIME, LATITUDE, LONGITUDE) %>% + clean_names() %>% + arrange(unique_id, datetime) %>% + group_by(unique_id, datetime = floor_date(datetime, "hour")) %>% + slice_head(n = 1) %>% + ungroup() + + sf_points <- x %>% + st_as_sf(coords = c("longitude", "latitude"), crs = 4326) + + sf_points %>% + group_by(unique_id) %>% + summarise( + start = min(datetime), + end = max(datetime), + do_union = FALSE, + .groups = "drop" + ) %>% + st_cast("LINESTRING") + }), tar_target(external_analyses, { - x <- external_detectiondata %>% + x_detections <- external_detectiondata %>% select(-id, -filename, -row) %>% - clean_names() %>% + clean_names() + x_calltypes <- x_detections %>% + distinct(unique_id, species_code, call_type_code) %>% + group_by(unique_id, species_code) %>% + summarise( + # n = n(), + call_type_code = str_c(call_type_code, collapse = ", "), + .groups = "drop" + ) + x_analyses <- x_detections %>% + select(-call_type_code) %>% + left_join(x_calltypes, by = c("unique_id", "species_code")) %>% + inner_join(themes_species, by = "species_code") %>% nest_by( + theme, unique_id, species_code, call_type_code, @@ -139,13 +223,12 @@ targets_external <- list( max_analysis_frequency_range_hz, analysis_sampling_rate_hz ) %>% - ungroup() %>% - left_join(themes_species, by = "species_code") + ungroup() stopifnot( - all(count(x, unique_id, species_code)$n == 1), - all(!is.na(x$theme)) + all(count(x_analyses, unique_id, species_code)$n == 1), + all(!is.na(x_analyses$theme)) ) - x + x_analyses }), tar_target(external_deployments, { # deployments with no detection results @@ -163,16 +246,31 @@ targets_external <- list( # by = "unique_id" # ) - external_analyses %>% - rowwise() %>% - mutate( - analysis_start_datetime = min(data$analysis_period_start_datetime), - analysis_start_date = as_date(analysis_start_datetime), - analysis_end_datetime = max(data$analysis_period_start_datetime), - analysis_end_date = as_date(analysis_end_datetime) + # compute start/end by theme (not species!) + x_analyses <- external_analyses %>% + # select(theme, unique_id, data) %>% + unnest(data) %>% + group_by( + theme, unique_id, + analysis_sampling_rate_hz, + qc_data = qc_processing, + call_type_code, + detection_method, + protocol_reference ) %>% - ungroup() %>% - select(theme, unique_id, analysis_sampling_rate_hz, qc_data = qc_processing, call_type_code, detection_method, protocol_reference, analysis_start_date, analysis_end_date) %>% + summarise( + analysis_start_date = as_date(min(analysis_period_start_datetime)), + analysis_end_date = as_date(max(analysis_period_start_datetime)), + .groups = "drop" + ) + stopifnot( + x_analyses %>% + add_count(theme, unique_id) %>% + filter(n > 1) %>% + nrow() == 0 + ) + + x <- x_analyses %>% left_join( external_metadata %>% select(-id, -filename, -row) %>% @@ -191,12 +289,25 @@ targets_external <- list( channel = as.character(channel), platform_type = case_when( platform_type == "BOTTOM-MOUNTED" ~ "mooring", + platform_type == "DRIFTING-BUOY" ~ "drifting_buoy", TRUE ~ platform_type ) ) %>% select(all_of(names(st_drop_geometry(internal$deployments)))) %>% - distinct() %>% + distinct() + + x_stationary <- x %>% + filter(deployment_type == "STATIONARY") %>% st_as_sf(coords = c("longitude", "latitude"), crs = 4326, remove = FALSE) + x_mobile <- x %>% + filter(deployment_type == "MOBILE") %>% + left_join( + external_tracks, + by = c("id" = "unique_id") + ) %>% + st_as_sf() + + bind_rows(x_stationary, x_mobile) }), tar_target(external_detections_raw, { external_analyses %>% @@ -218,8 +329,16 @@ targets_external <- list( ) ) }), - tar_target(external_detections, { + tar_target(external_deployments_types, { + external_deployments %>% + distinct(id, deployment_type) + }), + tar_target(external_detections_stationary, { + ids <- external_deployments_types %>% + filter(deployment_type == "STATIONARY") %>% + pull(id) analysis_periods <- external_analyses %>% + filter(unique_id %in% ids) %>% rowwise() %>% mutate( start = min(as_date(data$analysis_period_start_datetime)), @@ -230,6 +349,7 @@ targets_external <- list( select(-start, -end) %>% unnest(dates) x <- external_detections_raw %>% + filter(id %in% ids) %>% count(theme, id, species_code, date = as_date(datetime), presence) %>% pivot_wider(names_from = "presence", values_from = "n", values_fill = 0) %>% mutate( @@ -243,7 +363,7 @@ targets_external <- list( ) %>% select(-y, -m, -n, -na) analysis_periods %>% - left_join(x, by = c("theme", "id", "date", "species_code")) %>% + left_join(x, by = c("theme", "id", "date", "species_code")) %>% mutate( presence = coalesce(presence, "na"), species_code = case_when( @@ -255,20 +375,151 @@ targets_external <- list( species_code == "BLBW" ~ "Blainville's", species_code == "GEBW" ~ "Gervais'", species_code == "GOBW" ~ "Cuvier's", + species_code == "BWBB" ~ "Baird's", + species_code == "BWMS" ~ "Stejneger's", + species_code == "BWMC" ~ "Hubb's", + species_code == "BW43" ~ "Unid. 43 kHz Beaked Whale", + species_code == "BWC" ~ "Cross Seamount", species_code == "MEME" ~ "Unid. Mesoplodon", species_code == "NBWH" ~ "Northern Bottlenose", species_code == "SOBW" ~ "Sowerby's", # species_code == "UNDO" ~ "Unid. Dolphin", # species_code == "GRAM" ~ "Risso's Dolphin", + TRUE ~ species_code + ) + ) %>% + select(-species_code) + }), + tar_target(external_detections_mobile, { + ids <- external_deployments_types %>% + filter(deployment_type == "MOBILE") %>% + pull(id) + + # create approx functions to linearly interpolate latitude and longitude + x_tracks <- external_gpsdata %>% + clean_names() %>% + select(unique_id, datetime, latitude, longitude) %>% + nest_by(unique_id) %>% + mutate( + data = list({ + data %>% + group_by(datetime) %>% + summarise( + latitude = mean(latitude, na.rm = TRUE), + longitude = mean(longitude, na.rm = TRUE) + ) + }), + approx_lat = list(approxfun(data$datetime, data$latitude, rule = 2)), + approx_lon = list(approxfun(data$datetime, data$longitude, rule = 2)) + ) %>% + select(-data) + + # add coordinates for each detection + x_detections_coord <- external_detections_raw %>% + filter(id %in% ids) %>% + nest_by(id) %>% + left_join(x_tracks, by = c("id" = "unique_id")) %>% + mutate( + data = list({ + data %>% + mutate( + latitude = approx_lat(datetime), + longitude = approx_lon(datetime) + ) + }) + ) %>% + select(-starts_with("approx_")) %>% + unnest(data) %>% + ungroup() + + x_daily <- x_detections_coord %>% + nest_by(theme, id, species_code, date = as_date(datetime), .key = "locations") %>% + mutate( + presence = case_when( + any(locations$presence == "y") ~ "y", + any(locations$presence == "m") ~ "m", + any(locations$presence == "n") ~ "n", + any(locations$presence == "na") ~ "na", + TRUE ~ "na" + ), + locations = list({ + head(locations, 1) %>% + mutate(date = as_date(datetime)) %>% + filter(presence == "y") + }), + species_code = case_when( + theme == "beaked" ~ species_code, + theme == "dolphin" ~ species_code, TRUE ~ NA_character_ + ), + species = case_when( + species_code == "BLBW" ~ "Blainville's", + species_code == "GEBW" ~ "Gervais'", + species_code == "GOBW" ~ "Cuvier's", + species_code == "BWBB" ~ "Baird's", + species_code == "BWMS" ~ "Stejneger's", + species_code == "BWMC" ~ "Hubb's", + species_code == "BW43" ~ "Unid. 43 kHz Beaked Whale", + species_code == "BWC" ~ "Cross Seamount", + species_code == "MEME" ~ "Unid. Mesoplodon", + species_code == "NBWH" ~ "Northern Bottlenose", + species_code == "SOBW" ~ "Sowerby's", + # species_code == "UNDO" ~ "Unid. Dolphin", + # species_code == "GRAM" ~ "Risso's Dolphin", + TRUE ~ species_code ) ) %>% + ungroup() %>% select(-species_code) + + # fill missing dates by theme,id with non-detects + x_deployment_dates <- external_deployments %>% + semi_join(x_daily, by = c("theme", "id")) %>% + st_drop_geometry() %>% + transmute( + theme, id, + start = as_date(monitoring_start_datetime), + end = as_date(monitoring_end_datetime) + ) %>% + rowwise() %>% + mutate( + date = list(seq.Date(start, end, by = "day")) + ) %>% + select(-start, -end) %>% + unnest(date) %>% + distinct() + + x_daily_nondetect <- x_deployment_dates %>% + anti_join(x_daily, by = c("theme", "id", "date")) %>% + mutate( + presence = "n", + locations = list(NULL), + species = NA + ) + + x <- bind_rows( + x_daily, + x_daily_nondetect + ) %>% + arrange(theme, id, date) + + # no duplicate dates + stopifnot( + x %>% + add_count(theme, id, date, species) %>% + filter(n > 1) %>% + nrow() == 0 + ) + x + }), + tar_target(external_detections, { + bind_rows(external_detections_stationary, external_detections_mobile) }), tar_target(external, { stopifnot( nrow(external_metadata_errors) == 0, - nrow(external_detectiondata_errors) == 0 + nrow(external_detectiondata_errors) == 0, + nrow(external_gpsdata_errors) == 0 ) list( deployments = external_deployments, diff --git a/r/R/functions.R b/r/R/functions.R index bb26abb..82d85bb 100644 --- a/r/R/functions.R +++ b/r/R/functions.R @@ -169,10 +169,23 @@ load_external_submission <- function (id, root_dir, db_tables) { join_by = c("UNIQUE_ID" = "METADATA.UNIQUE_ID") ) + gpsdata <- load_submission_files( + id, + files = file.path(raw_dir, raw_files), + pattern = "*_GPSDATA_*", + rules = rules$gpsdata, + codes = codes, + parse = parse_external_gpsdata, + transform = transformers$gpsdata, + join_data = join_metadata, + join_by = c("UNIQUE_ID" = "METADATA.UNIQUE_ID") + ) + list( id = id, metadata = metadata, - detectiondata = detectiondata + detectiondata = detectiondata, + gpsdata = gpsdata ) } diff --git a/r/R/pacm.R b/r/R/pacm.R index e09aa68..be8004f 100644 --- a/r/R/pacm.R +++ b/r/R/pacm.R @@ -1,7 +1,7 @@ targets_pacm <- list( tar_target(pacm_dir, "data/pacm"), tar_target(pacm_themes, { - platform_types <- c("mooring", "buoy", "slocum", "towed") + platform_types <- c("mooring", "buoy", "drifting_buoy", "slocum", "towed") all_datasets <- list( internal, diff --git a/r/R/refs.R b/r/R/refs.R index d8efc07..8524a3c 100644 --- a/r/R/refs.R +++ b/r/R/refs.R @@ -45,7 +45,8 @@ targets_refs <- list( "kogia", "KOSP", "sperm", "PHMA", - "harbor", "PHPH" + "harbor", "PHPH", + "nbhf", "NBHF" ) }), tar_target(refs, { diff --git a/r/_targets.R b/r/_targets.R index dd7230c..c36c800 100644 --- a/r/_targets.R +++ b/r/_targets.R @@ -44,8 +44,8 @@ list( targets_towed, targets_nefsc_20230926, targets_nefsc_20230928, - targets_davis_20230901, - targets_davis_20231122, + # targets_davis_20230901, + # targets_davis_20231122, targets_internal, targets_external, diff --git a/r/cli/functions.R b/r/cli/functions.R index 8bf0bb3..1a176a5 100644 --- a/r/cli/functions.R +++ b/r/cli/functions.R @@ -536,6 +536,23 @@ parse_external_detectiondata <- function (x) { ) } +parse_external_gpsdata <- function (x) { + x %>% + mutate( + across( + c(DATETIME), + ymd_hms + ), + across( + c( + LATITUDE, + LONGITUDE + ), + parse_number + ) + ) +} + parse_internal_header <- function (x) { x %>% mutate( diff --git a/r/notes/20240321-si-workshop.R b/r/notes/20240321-si-workshop.R new file mode 100644 index 0000000..1a3665c --- /dev/null +++ b/r/notes/20240321-si-workshop.R @@ -0,0 +1,65 @@ +# summary stats for 2024 PAM SI workshop + +source("_targets.R") + +tar_load(pacm_themes) + +deployments <- pacm_themes %>% + filter(theme != "deployments") %>% + select(-detections) %>% + unnest(deployments) %>% + select(-geometry) %>% + ungroup() +detections <- pacm_themes %>% + filter(theme != "deployments") %>% + select(-deployments) %>% + unnest(detections) %>% + select(-locations) %>% + ungroup() +species <- detections %>% + mutate( + species = case_when( + is.na(species) & theme == "beaked" ~ NA_character_, + is.na(species) ~ theme, + TRUE ~ species + ) + ) %>% + filter(!is.na(species)) + +tabyl(deployments, data_poc_affiliation) + +tabyl(deployments, platform_type) + +tabyl(species, species) %>% nrow() + +deployments %>% + distinct(id, platform_type) %>% + nrow() + +detections %>% + nrow() + +detections %>% + tabyl(presence) %>% + adorn_percentages("col") + +summary(detections) + +detections %>% + mutate(year = year(date)) %>% + ggplot(aes(year)) + + geom_histogram(binwidth = 1) + +detections %>% + filter(presence == "y") %>% + mutate( + theme = str_to_title(theme), + theme = if_else(theme == "Narw", "NARW", theme) + ) %>% + ggplot(aes(date, fct_rev(fct_inorder(theme)))) + + geom_point(color = "orangered", alpha = 0.25, size = 5) + + scale_x_date(date_breaks = "2 years", date_labels = "%Y") + + labs(y = NULL, x = NULL) + + theme_bw() + + theme(text = element_text(size = 16)) + diff --git a/r/notes/20240606-SWFSC-SAEL_20240520.R b/r/notes/20240606-SWFSC-SAEL_20240520.R new file mode 100644 index 0000000..9469b8e --- /dev/null +++ b/r/notes/20240606-SWFSC-SAEL_20240520.R @@ -0,0 +1,23 @@ +# SWFSC-SAEL_20240520 + +source("_targets.R") + +external_dir <- "data/external" +db_tables <- read_rds(file.path(external_dir, "db-tables.rds")) +x <- load_external_submission( + id = "SWFSC-SAEL_20240520", + root_dir = external_dir, + db_tables = db_tables +) + +x$metadata %>% + filter(n_errors > 0) %>% + pull(filename) + + +# duplicate UNIQUE_IDs (different recorders): +# - SWFSC_NEPac-MBY_201608_Pascal_009 +# - SWFSC_NEPac-SND_201609_Pascal_024 +# - SWFSC_NEPac-CHI_201609_Pascal_026 +# - SWFSC_NEPac-CHI_201609_Pascal_030 + diff --git a/r/notes/20240801-SWFSC-SAEL_20240617.R b/r/notes/20240801-SWFSC-SAEL_20240617.R new file mode 100644 index 0000000..d956134 --- /dev/null +++ b/r/notes/20240801-SWFSC-SAEL_20240617.R @@ -0,0 +1,100 @@ +# SWFSC-SAEL_20240617 (replaces SWFSC-SAEL_20240520) + +source("_targets.R") + +external_dir <- "data/external" +db_tables <- read_rds(file.path(external_dir, "db-tables.rds")) +db_tables$species <- db_tables$species %>% + bind_rows( + tibble(SPECIES_CODE = c("BWBB", "BWMS", "BWMC", "BW43", "BWC")) + ) + +x <- load_external_submission( + id = "SWFSC-SAEL_20240617", + root_dir = external_dir, + db_tables = db_tables +) + +x$metadata %>% + filter(n_errors > 0) + +x$detectiondata %>% + filter(n_errors > 0) + +x$gpsdata %>% + filter(n_errors > 0) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name == "MAX_ANALYSIS_FREQUENCY_RANGE_HZ.out_of_range") %>% + left_join( + x$detectiondata %>% + select(filename, joined) %>% + unnest(joined), + by = c("filename", "row") + ) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name == "ANALYSIS_PERIOD_START_DATETIME.outside_monitoring_period") %>% + left_join( + x$detectiondata %>% + select(filename, joined) %>% + unnest(joined), + by = c("filename", "row") + ) %>% + view() + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name %in% c("ANALYSIS_PERIOD_START_DATETIME.outside_monitoring_period", "ANALYSIS_PERIOD_END_DATETIME.outside_monitoring_period")) %>% + distinct(filename, row) %>% + mutate(row = row + 1) %>% + group_by(filename) %>% + summarise(row = str_c(row, collapse = ",")) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name %in% c("MAX_ANALYSIS_FREQUENCY_RANGE_HZ.out_of_range")) %>% + left_join( + x$detectiondata %>% + select(filename, joined) %>% + unnest(joined), + by = c("filename", "row") + ) %>% + tabyl(METADATA.SAMPLING_RATE_HZ, MAX_ANALYSIS_FREQUENCY_RANGE_HZ) + # view() + + +x$detectiondata %>% + select(filename, joined) %>% + unnest(joined) %>% + filter(DETECTION_SOFTWARE_NAME == "PAMGuard", MAX_ANALYSIS_FREQUENCY_RANGE_HZ == 288000) %>% + filter(METADATA.SAMPLING_RATE_HZ < 288000, METADATA.SAMPLING_RATE_HZ == 256000) %>% + distinct(UNIQUE_ID) %>% + pull(UNIQUE_ID) %>% + dput() + +x$detectiondata %>% + select(filename, joined) %>% + unnest(joined) %>% + filter( + DETECTION_SOFTWARE_NAME == "PAMGuard" & + MAX_ANALYSIS_FREQUENCY_RANGE_HZ == 288000 & + UNIQUE_ID %in% c("SWFSC_NEPac-BCN_201810_CCES_017", "SWFSC_NEPac-HUM_201807_CCES_004", + "SWFSC_NEPac-BCN_201608_Pascal_007", "SWFSC_NEPac-CHI_201608_Pascal_008", + "SWFSC_NEPac-COL_201608_Pascal_013", "SWFSC_NEPac-MBY_201609_Pascal_018", + "SWFSC_NEPac-MND_201608_Pascal_010", "SWFSC_NEPac-ORE_201608_Pascal_011", + "SWFSC_NEPac-PTA_201609_Pascal_017") + ) %>% + tabyl(METADATA.SAMPLING_RATE_HZ) + view() + +x$detectiondata %>% + select(filename, joined) %>% + unnest(joined) %>% + tabyl(CALL_TYPE_CODE, SPECIES_CODE) \ No newline at end of file diff --git a/r/notes/20240806-ORSTD_20240418.R b/r/notes/20240806-ORSTD_20240418.R new file mode 100644 index 0000000..e5443eb --- /dev/null +++ b/r/notes/20240806-ORSTD_20240418.R @@ -0,0 +1,100 @@ +# ORSTD_20240418 + +source("_targets.R") + +external_dir <- "data/external" +db_tables <- read_rds(file.path(external_dir, "db-tables.rds")) + +x <- load_external_submission( + id = "ORSTD_20240418", + root_dir = external_dir, + db_tables = db_tables +) + +x$metadata %>% + filter(n_errors > 0) + +x$detectiondata %>% + filter(n_errors > 0) + +x$gpsdata %>% + filter(n_errors > 0) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + tabyl(name) + # filter(name == "MAX_ANALYSIS_FREQUENCY_RANGE_HZ.out_of_range") %>% + # left_join( + # x$detectiondata %>% + # select(filename, joined) %>% + # unnest(joined), + # by = c("filename", "row") + # ) + +x$detectiondata$parsed[[1]] %>% + tabyl(CALL_TYPE_CODE, SPECIES_CODE, ACOUSTIC_PRESENCE) + +x$detectiondata$parsed[[1]] %>% + distinct(SPECIES_CODE, CALL_TYPE_CODE) %>% + anti_join(db_tables$call_type, by = c("SPECIES_CODE", "CALL_TYPE_CODE")) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name == "ANALYSIS_PERIOD_START_DATETIME.outside_monitoring_period") %>% + left_join( + x$detectiondata %>% + select(filename, joined) %>% + unnest(joined), + by = c("filename", "row") + ) %>% + view() + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name %in% c("ANALYSIS_PERIOD_START_DATETIME.outside_monitoring_period", "ANALYSIS_PERIOD_END_DATETIME.outside_monitoring_period")) %>% + distinct(filename, row) %>% + mutate(row = row + 1) %>% + group_by(filename) %>% + summarise(row = str_c(row, collapse = ",")) + +x$detectiondata %>% + select(filename, validation_errors) %>% + unnest(validation_errors) %>% + filter(name %in% c("MAX_ANALYSIS_FREQUENCY_RANGE_HZ.out_of_range")) %>% + left_join( + x$detectiondata %>% + select(filename, joined) %>% + unnest(joined), + by = c("filename", "row") + ) %>% + tabyl(METADATA.SAMPLING_RATE_HZ, MAX_ANALYSIS_FREQUENCY_RANGE_HZ) + # view() + + +x$detectiondata %>% + select(filename, joined) %>% + unnest(joined) %>% + filter(DETECTION_SOFTWARE_NAME == "PAMGuard", MAX_ANALYSIS_FREQUENCY_RANGE_HZ == 288000) %>% + filter(METADATA.SAMPLING_RATE_HZ < 288000, METADATA.SAMPLING_RATE_HZ == 256000) %>% + distinct(UNIQUE_ID) %>% + pull(UNIQUE_ID) %>% + dput() + +x$detectiondata %>% + select(filename, joined) %>% + unnest(joined) %>% + filter( + DETECTION_SOFTWARE_NAME == "PAMGuard" & + MAX_ANALYSIS_FREQUENCY_RANGE_HZ == 288000 & + UNIQUE_ID %in% c("SWFSC_NEPac-BCN_201810_CCES_017", "SWFSC_NEPac-HUM_201807_CCES_004", + "SWFSC_NEPac-BCN_201608_Pascal_007", "SWFSC_NEPac-CHI_201608_Pascal_008", + "SWFSC_NEPac-COL_201608_Pascal_013", "SWFSC_NEPac-MBY_201609_Pascal_018", + "SWFSC_NEPac-MND_201608_Pascal_010", "SWFSC_NEPac-ORE_201608_Pascal_011", + "SWFSC_NEPac-PTA_201609_Pascal_017") + ) %>% + tabyl(METADATA.SAMPLING_RATE_HZ) + view() + \ No newline at end of file diff --git a/r/notes/20240815-SWFSC-SAEL_20240814.R b/r/notes/20240815-SWFSC-SAEL_20240814.R new file mode 100644 index 0000000..9567585 --- /dev/null +++ b/r/notes/20240815-SWFSC-SAEL_20240814.R @@ -0,0 +1,26 @@ +# SWFSC-SAEL_20240814 (replaces SWFSC-SAEL_20240617) + +source("_targets.R") + +external_dir <- "data/external" +db_tables <- read_rds(file.path(external_dir, "db-tables.rds")) +db_tables$species <- db_tables$species %>% + bind_rows( + tibble(SPECIES_CODE = c("BWBB", "BWMS", "BWMC", "BW43", "BWC", "NBHF")) + ) + +x <- load_external_submission( + id = "SWFSC-SAEL_20240814", + root_dir = external_dir, + db_tables = db_tables +) + +x$metadata %>% + filter(n_errors > 0) + +x$detectiondata %>% + filter(n_errors > 0) + +x$gpsdata %>% + filter(n_errors > 0) + diff --git a/src/components/Legend.vue b/src/components/Legend.vue index f185ccc..21b2de2 100644 --- a/src/components/Legend.vue +++ b/src/components/Legend.vue @@ -50,7 +50,7 @@ -
+

Stationary Platforms

@@ -122,16 +122,16 @@
-
-

Gliders

+
+

Mobile Platforms

- {{detectionTypes[0].label}} (Daily) + {{detectionTypes[0].label}} - {{detectionTypes[1].label}} (Daily) + {{detectionTypes[1].label}} @@ -144,7 +144,7 @@
-
+ @@ -187,15 +187,15 @@ export default { }, computed: { ...mapGetters(['deployments', 'theme']), - hasStation () { + hasStationary () { return this.deployments && this.deployments.some(d => d.properties.deployment_type === 'stationary') }, - hasGlider () { - return this.deployments && this.deployments.some(d => d.properties.platform_type === 'slocum' || d.properties.platform_type === 'wave') - }, - hasTowed () { - return this.deployments && this.deployments.some(d => d.properties.platform_type === 'towed') + hasMobile () { + return this.deployments && this.deployments.some(d => d.properties.deployment_type === 'mobile') }, + // hasTowed () { + // return this.deployments && this.deployments.some(d => d.properties.platform_type === 'towed') + // }, normalizeEffort: { get () { return this.$store.state.normalizeEffort diff --git a/src/components/dialogs/About.vue b/src/components/dialogs/About.vue index ddba692..377357d 100644 --- a/src/components/dialogs/About.vue +++ b/src/components/dialogs/About.vue @@ -86,6 +86,11 @@

Latest Updates

+
+ v1.1.9 | Aug 1, 2024 +
+

Fixed user-defined spatial filter when switching species. Added SWFSC-SAEL_20240814 dataset.

+
v1.1.8 | Mar 22, 2024
diff --git a/src/lib/constants.js b/src/lib/constants.js index ece6345..532b8a0 100644 --- a/src/lib/constants.js +++ b/src/lib/constants.js @@ -37,21 +37,25 @@ export const themes = [ label: 'Beaked Whale Species', showSpeciesFilter: true }, - { - id: 'kogia', - label: 'Kogia Species' - }, { id: 'sperm', label: 'Sperm Whale' }, + { + id: 'kogia', + label: 'Kogia Species' + }, { id: 'harbor', label: 'Harbor Porpoise' }, + { + id: 'nbhf', + label: 'Unidentified High-Frequency Species' + }, { id: 'dolphin', - label: 'Dolphin (Unidentified)' + label: 'Unidentified Dolphin' }, { id: 'risso', @@ -80,6 +84,10 @@ export const platformTypes = [ id: 'buoy', label: 'Surface Buoy' }, + { + id: 'drifting_buoy', + label: 'Drifting Buoy' + }, { id: 'slocum', label: 'Glider (Slocum)' diff --git a/src/lib/tour.js b/src/lib/tour.js index 7471e19..6db1f28 100644 --- a/src/lib/tour.js +++ b/src/lib/tour.js @@ -10,7 +10,7 @@ export default [ Detections are shown using different symbols for each type of monitoring platform:
  • Stationary Platforms: Bottom-mounted moorings and surface buoys are represented using circles. The size and color of each circle reflect the number days and type of detections observed.
    • -
  • Mobile Platforms: Gliders and towed arrays are represented using lines for their tracks and and square symbols to indicate the specific locations where a detection was observed. For gliders, only the first detection of each day is shown on the map. For towed arrays, all detections are shown and may include more than one on a given day.
    • +
  • Mobile Platforms: Drifting buoys, gliders and towed arrays are represented using lines for their tracks and and square symbols to indicate the specific locations where a detection was observed. For gliders, only the first detection of each day is shown on the map. For towed arrays, all detections are shown and may include more than one on a given day.

Hover over a point to view a brief summary of that deployment. Click on a point or track line to view the complete metadata and a timeseries chart of daily detection results for the corresponding deployment.