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vlahmvlahm
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resolved wonkitude in konza, hbef kernels. investigated krycklan and suef but found no issues
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Lines changed: 450 additions & 64 deletions

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‎src/acquisition_master.R‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -264,7 +264,7 @@ ms_globals <- c(ls(all.names = TRUE), 'ms_globals')
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dir.create('logs', showWarnings = FALSE)
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# dmnrow = 20
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# dmnrow = 8
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# print(network_domain, n=50)
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for(dmnrow in 1:nrow(network_domain)){
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@@ -281,7 +281,7 @@ for(dmnrow in 1:nrow(network_domain)){
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# held_data = invalidate_tracked_data(network, domain, 'derive')
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# owrite_tracker(network, domain)
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# held_data = invalidate_tracked_data(network, domain, 'munge', 'stream_chemistry')
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# held_data = invalidate_tracked_data(network, domain, 'munge', 'precipitation')
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# owrite_tracker(network, domain)
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# held_data = invalidate_tracked_data(network, domain, 'derive', 'stream_flux_inst')
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# owrite_tracker(network, domain)
@@ -303,7 +303,7 @@ for(dmnrow in 1:nrow(network_domain)){
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# prodname_filter = c('stream_chemistry'),
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domain = domain)
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ms_munge(network = network,
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# prodname_filter = c('discharge'),
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prodname_filter = c('stream_chemistry'),
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domain = domain)
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if(domain != 'mcmurdo'){
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sw(ms_delineate(network = network,

‎src/dev/investigate_pchem_sample_regimes.R‎

Lines changed: 179 additions & 0 deletions
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library(ggplot2)
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#pchem
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d = load_product('precip_chemistry', 'macrosheds_dataset')
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doms = distinct(d, domain) %>% pull()
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for(dom in doms){
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dd = filter(d, domain == !!dom)
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dd = dd %>%
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# filter(ms_interp == 0) %>%
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mutate(val = drop_errors(val)) %>%
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select(-ms_status, -network) %>%
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arrange(domain, site_code, datetime)
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gg = dd %>%
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mutate(val = ifelse(ms_interp == 0, val, -10)) %>%
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filter(datetime > max(dd$datetime) - 60 * 60 * 24 * 100) %>%
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ggplot() +
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geom_line(aes(x = datetime, y = val, color = var)) +
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labs(title = dom) +
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facet_wrap(vars(site_code))
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print(gg)
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readLines(n=1)
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gg = dd %>%
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filter(datetime > max(dd$datetime) - 60 * 60 * 24 * 100) %>%
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ggplot() +
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geom_line(aes(x = datetime, y = val, color = var)) +
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labs(title = dom) +
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facet_wrap(vars(site_code))
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print(gg)
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readLines(n=1)
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gg = dd %>%
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mutate(val = ifelse(ms_interp == 0, val, -10)) %>%
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filter(datetime > max(dd$datetime) - 2 * 60 * 60 * 24 * 365) %>%
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ggplot() +
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geom_line(aes(x = datetime, y = val, color = var)) +
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labs(title = dom) +
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facet_wrap(vars(site_code))
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print(gg)
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# filter(dd, site_code == 'WS78', datetime > as.Date('2015-07-01'), datetime < as.Date('2015-10-01'), var == 'GN_TP') %>%
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# ggplot()+geom_point(aes(datetime, val, color = var))
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readLines(n=1)
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gg = dd %>%
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filter(datetime > max(dd$datetime) - 2 * 60 * 60 * 24 * 365) %>%
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ggplot() +
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geom_line(aes(x = datetime, y = val, color = var)) +
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labs(title = dom) +
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facet_wrap(vars(site_code))
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print(gg)
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readLines(n=1)
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# gg = dd %>%
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# # mutate(val = ifelse(ms_interp == 1, NA_real_, val)) %>%
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# ggplot() +
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# geom_line(aes(x = datetime, y = val, color = var)) +
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# labs(title = dom) +
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# facet_wrap(vars(site_code))
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# print(gg)
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# readLines(n=1)
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}
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#precip
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d = load_product('precipitation', 'macrosheds_dataset')
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doms = distinct(d, domain) %>% pull()
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for(dom in doms){
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dd = filter(d, domain == !!dom)
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dd = dd %>%
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# filter(ms_interp == 0) %>%
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mutate(val = drop_errors(val)) %>%
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select(-ms_status, -network) %>%
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arrange(domain, site_code, datetime)
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# gg = dd %>%
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# mutate(val = ifelse(ms_interp == 0, val, -10)) %>%
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# filter(datetime > max(dd$datetime) - 60 * 60 * 24 * 100) %>%
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# ggplot() +
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# geom_line(aes(x = datetime, y = val)) +
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# labs(title = dom) +
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# facet_wrap(vars(site_code))
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# print(gg)
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# readLines(n=1)
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#
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# gg = dd %>%
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# filter(datetime > max(dd$datetime) - 60 * 60 * 24 * 100) %>%
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# ggplot() +
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# geom_line(aes(x = datetime, y = val)) +
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# labs(title = dom) +
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# facet_wrap(vars(site_code))
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# print(gg)
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# readLines(n=1)
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#
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# gg = dd %>%
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# mutate(val = ifelse(ms_interp == 0, val, -10)) %>%
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# filter(datetime > max(dd$datetime) - 2 * 60 * 60 * 24 * 365) %>%
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# ggplot() +
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# geom_line(aes(x = datetime, y = val)) +
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# labs(title = dom) +
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# facet_wrap(vars(site_code))
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# print(gg)
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# readLines(n=1)
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print(dom)
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qqq = filter(dd, datetime > max(dd$datetime) - 2 * 60 * 60 * 24 * 365)
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print(paste(sum(is.na(qqq$val)), nrow(qqq)))
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gg = dd %>%
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filter(datetime > max(dd$datetime) - 2 * 60 * 60 * 24 * 365) %>%
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ggplot() +
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geom_line(aes(x = datetime, y = val)) +
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labs(title = dom) +
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facet_wrap(vars(site_code))
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print(gg)
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# readLines(n=1)
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#
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# ggsave(glue('~/git/macrosheds/data_acquisition/plots/precip_coverage/{dom}.png'))
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}
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#investigate konza
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bb = read_csv('data/lter/konza/raw/precipitation__4/sitename_NA/APT011.csv') %>%
129+
mutate(ppt = as.numeric(ppt))
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bb %>%
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mutate(RecDate = as.Date(RecDate, format = '%d/%m/%Y')) %>%
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group_by(watershed) %>%
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summarize(n_recs = n(), mindate = min(RecDate, na.rm=T), maxdate = max(RecDate, na.rm=T))
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bb %>%
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filter(! is.na(RecDate),
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duplicated(RecDate)) %>%
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mutate(RecDate = as.Date(RecDate, format = '%d/%m/%Y')) %>%
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arrange(watershed, RecDate) %>%
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ggplot() +
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geom_line(aes(x=RecDate, y=ppt)) +
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facet_wrap(vars(watershed))
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bb %>%
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filter(! is.na(RecDate),
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duplicated(RecDate)) %>%
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mutate(RecDate = as.Date(RecDate, format = '%d/%m/%Y')) %>%
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filter(RecDate > as.Date('2020-01-01')) %>%
149+
arrange(watershed, RecDate) %>%
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ggplot() +
151+
geom_point(aes(x=RecDate, y=ppt)) +
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facet_wrap(vars(watershed))
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dd2 = dd %>%
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filter(site_code == 'N01B') %>%
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mutate(date = as.Date(datetime)) %>%
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select(date, val)
159+
bb2 = bb %>%
160+
filter(! is.na(RecDate),
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duplicated(RecDate)) %>%
162+
mutate(RecDate = as.Date(RecDate, format = '%d/%m/%Y')) %>%
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filter(RecDate > as.Date('2020-01-01')) %>%
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arrange(watershed, RecDate) %>%
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filter(watershed == 'K01B') %>%
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select(RecDate, ppt, Comments) %>%
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full_join(dd2, by = c('RecDate' = 'date')) %>%
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filter(if_any(c(ppt, val), ~!is.na(.))) %>%
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filter(RecDate > as.Date('2020-01-01')) %>%
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arrange(RecDate) %>%
171+
mutate(Comments = ifelse(! is.na(Comments), 'blue', 'black'))
172+
plot(bb2$RecDate, bb2$ppt, col=bb2$Comments)
173+
lines(bb2$RecDate, bb2$val, col = 'red')
174+
bb3 = filter(bb2, RecDate > as.Date('2021-01-01'))
175+
176+
png('~/Desktop/konza_precip.png')
177+
plot(bb3$RecDate, bb3$ppt, col=bb3$Comments)
178+
lines(bb3$RecDate, bb3$val, col = 'red')
179+
dev.off()

‎src/dev/krycklan_stream_chem_investigate.R‎

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#this code is useful after cast_and_reflag (not exactly as-is)
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zz %>%
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select(-`pCO2 µatm`) %>%
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mutate(across(-c(Date, SiteID), as.numeric)) %>%
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pivot_longer(cols = -c(Date, SiteID), names_to = 'var', values_to = 'val') %>%
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ggplot()+
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geom_line(aes(Date, val, color=var))+
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facet_wrap(.~SiteID, scales = 'free')+
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theme(legend.position="none")
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d %>%
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ggplot()+
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geom_line(aes(datetime, val, color=var))+
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facet_wrap(.~site_code, scales = 'free')+
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theme(legend.position="none")
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zz %>%
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filter(SiteID == 54) %>%
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# filter(Date > as.Date('2019-01-01'), Date < as.Date('2019-05-01')) %>%
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select(-`pCO2 µatm`) %>%
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select(Date, SiteID, `Ag µg/l`) %>%
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mutate(across(-c(Date, SiteID), as.numeric)) %>%
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pivot_longer(cols = -c(Date, SiteID), names_to = 'var', values_to = 'val') %>%
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ggplot()+
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geom_line(aes(Date, val, color=var))+
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facet_wrap(.~SiteID, scales = 'free')+
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theme(legend.position="none")
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d %>%
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mutate(val = drop_errors(val)) %>%
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filter(site_code == 'Site54') %>%
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mutate(val = val * 1000) %>%
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filter(var=='GN_Ag') %>%
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# filter(datetime > as.Date('2019-01-01'), datetime < as.Date('2019-05-01'), var == 'GN_Ag')
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ggplot()+
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geom_line(aes(datetime, val, color=var))+
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facet_wrap(.~site_code, scales = 'free')+
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theme(legend.position="none")

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