Code
library(tidyverse)
library(leaflet)
library(sf)
library(plotly)
# library(anomalize)
library(slider)
library(anomaly)
# read custom functions
source('../scripts/test_get_tsfeatures.R')voorbereiding workshop
Voorbereiding workshop
Data inlezen
Code
locs <- read_csv2("../data/raw/locatie_mapping.csv")
sensor_lims <- read_csv2("../data/raw/meet_range_sensoren.csv") %>%
filter(path_length %in% c(1, NA))
par_ranges <- readxl::read_excel('../data/raw/validation_master_v1.xlsx', sheet = 'realistic_range')
df <-
read_csv("../data/intermediate/data_voorbereidingworkshop.csv") %>%
mutate(
sensor = case_when(
sensor == 'C4E' ~ 'Ponsel C4E',
sensor == 'PHEHT' ~ 'Ponsel PHEHT',
TRUE ~ sensor
),
datetime = ymd_hms(datetime, tz = 'CET')
)
df_extra <- read_csv("../data/intermediate/data_voorbereidingworkshop_extra.csv") %>%
mutate(
sensor = case_when(
sensor == 'C4E' ~ 'Ponsel C4E',
sensor == 'PHEHT' ~ 'Ponsel PHEHT',
TRUE ~ sensor
),
datetime = ymd_hms(datetime, tz = 'CET')
)
lims <- c(0.0001, 50)
oh <- read_csv('../data/raw/data_oh_voor_workshop.csv') %>%
mutate(datum = ymd_hms(datum, tz = 'CET'))
oh_saqc <- oh %>% select(datum_start = datum) %>%
mutate(maint = datum_start + hours(3))
df_lab_cor <- read_csv('../data/raw/lab_cor_data_workshop.csv') %>%
mutate(datetime_compare = ymd_hms(datetime_compare, tz = 'CET'))criteria tabellen
Code
sensor_lims %>%
knitr::kable(caption = "Sensor ranges, dit geval OTT EcoN NO3 Nnf")| sensor | parameter_code | hoedanigheid_code | eenheid_code | ondergrens | bovengrens | path_length |
|---|---|---|---|---|---|---|
| Ponsel PHEHT | pH | NVT | DIMSLS | 0 | 14 | NA |
| Ponsel PHEHT | T | NVT | oC | -5 | 50 | NA |
| Ponsel C4E | T | NVT | oC | -5 | 50 | NA |
| Ponsel C4E | GELDHD | NVT | uS/cm | 0 | 2000 | NA |
| Ponsel OPTOD | O2 | NVT | mg/l | 0 | 20 | NA |
| Ponsel OPTOD | O2 | NVT | % | 0 | 200 | NA |
| Ponsel OPTOD | T | NVT | oC | -5 | 50 | NA |
| Eureka Manta +35 | pH | NVT | DIMSLS | 0 | 14 | NA |
| Eureka Manta +35 | GELDHD | NVT | uS/cm | 0 | 5000 | NA |
| Eureka Manta +35 | O2 | NVT | mg/l | 0 | 50 | NA |
| Eureka Manta +35 | O2 | NVT | % | 0 | 500 | NA |
| Eureka Manta +35 | T | NVT | oC | -5 | 50 | NA |
| YSI EXO 3 | pH | NVT | DIMSLS | 0 | 14 | NA |
| YSI EXO 3 | GELDHD | NVT | mS/cm | 0 | 100 | NA |
| YSI EXO 3 | GELDHD | NVT | uS/cm | 0 | 100000 | NA |
| YSI EXO 3 | O2 | NVT | mg/l | 0 | 50 | NA |
| YSI EXO 3 | O2 | NVT | % | 0 | 500 | NA |
| YSI EXO 3 | T | NVT | oC | -5 | 50 | NA |
| Trios NICO | NO3 | nf | mg/l | 0 | 266 | 1 |
| Trios NICO | NO3 | Nnf | mg/l | 0 | 60 | 1 |
| OTT EcoN | NO3 | nf | mg/l | 0 | 266 | 1 |
| OTT EcoN | NO3 | Nnf | mg/l | 0 | 60 | 1 |
| YSI EXO 2 | pH | NVT | DIMSLS | 0 | 14 | NA |
| YSI EXO 2 | GELDHD | NVT | uS/cm | 0 | 100000 | NA |
| YSI EXO 2 | O2 | NVT | mg/l | 0 | 50 | NA |
| YSI EXO 2 | O2 | NVT | % | 0 | 500 | NA |
| YSI EXO 2 | T | NVT | oC | -5 | 50 | NA |
Code
par_ranges %>% select(parameter_id, jaar_min, jaar_max) %>% knitr::kable(caption = "Realistische ranges per parameter, dit geval Nitraat_N_mg/l")| parameter_id | jaar_min | jaar_max |
|---|---|---|
| Ammonium_mg/l | 0.00 | 1000.0 |
| Ammonium_N_mg/l_AW | 0.00 | 1000.0 |
| Ammonium_N_mg/l_OW | 0.00 | 1000.0 |
| Chlorofyl-a_blauwalg_ug/l | 0.00 | 5000.0 |
| Chlorofyl-a_groenalg_ug/l | 0.00 | 5000.0 |
| Doorzicht_dm | 0.00 | 617.0 |
| Geleidendheid_25oC_uS/cm | 100.00 | 1000.0 |
| Geleidendheid_25oC_mS/m | 10.00 | 100.0 |
| Lichtintensiteit_1/m | 0.00 | 28000.0 |
| Lichtintensiteit_ref_A_212_nm_SNR | 0.00 | 28000.0 |
| Lichtintensiteit_ref_B_254_nm_SNR | 0.00 | 28000.0 |
| Lichtintensiteit_ref_C_360_nm_SNR | 0.00 | 28000.0 |
| Lichtintensiteit_ref_D_reference_diode_SNR | 0.00 | 28000.0 |
| Nitraat_mg/l | 0.00 | 50.0 |
| Nitraat_N_mg/l | 0.00 | 50.0 |
| pH | 2.00 | 13.0 |
| Ptot_P_mg/l | 0.00 | 4.0 |
| Redoxpotentiaal_mV | -400.00 | 800.0 |
| Relative_fluoresence_units_blauwalg | 0.00 | 100.0 |
| Relative_fluoresence_units_groenalg | 0.00 | 100.0 |
| SAC_254_nm | 0.00 | 1500.0 |
| Saliniteit_PSU | 0.05 | 0.5 |
| SQI | 0.00 | 1.0 |
| Temperatuur_oC_OW | NA | NA |
| Totaal_opgeloste_bestanddelen_mg/l | 0.00 | 500.0 |
| Troebelheid_NTU | 0.00 | 10000.0 |
| TRP_mg/l | 0.00 | 1.0 |
| Zuurstof_verzadiging_% | 0.00 | 300.0 |
| Zuurstof_opgelost_mg/l | NA | NA |
| Chlorofyl-a_blauwalg_ug/l | 0.00 | 400.0 |
| Chlorofyl-a_blauwalg_ug/l | 0.00 | 400.0 |
| Chlorofyl-a_groenalg_ug/l | 0.00 | 400.0 |
| Chlorofyl-a_groenalg_ug/l | 0.00 | 400.0 |
| Geleidendheid_25oC_mS/m | 25.00 | 100.0 |
| Geleidendheid_25oC_mS/m | 25.00 | 250.0 |
| Geleidendheid_25oC_mS/m | 30.00 | 260.0 |
| Geleidendheid_25oC_mS/m | 43.00 | 54.0 |
| Geleidendheid_25oC_mS/m | 20.00 | 83.0 |
| Temperatuur_oC_OW | 1.00 | 28.0 |
| Temperatuur_oC_OW | 6.00 | 28.0 |
| Temperatuur_oC_OW | 9.00 | 30.0 |
| Temperatuur_oC_OW | 4.00 | 28.0 |
| Temperatuur_oC_OW | 4.00 | 28.0 |
| pH | 6.00 | 11.0 |
| pH | 6.00 | 11.0 |
| pH | 6.00 | 9.0 |
| pH | 7.00 | 11.0 |
| pH | 7.00 | 11.0 |
| Zuurstof_verzadiging_% | 0.00 | 180.0 |
| Zuurstof_verzadiging_% | 0.00 | 130.0 |
| Zuurstof_verzadiging_% | 0.00 | 100.0 |
| Zuurstof_verzadiging_% | 0.00 | 250.0 |
| Zuurstof_verzadiging_% | 0.00 | 250.0 |
| Zuurstof_opgelost_mg/l | NA | NA |
| Zuurstof_opgelost_mg/l | NA | NA |
| Zuurstof_opgelost_mg/l | NA | NA |
| Zuurstof_opgelost_mg/l | NA | NA |
| Zuurstof_opgelost_mg/l | NA | NA |
Visualiseer originele data serie
Code
p <- ggplot(df, aes(x = datetime, y = waarde)) +
geom_point(size = 0.1, shape = 1) +
geom_line() +
labs(x = NULL, y = "NO3-N [mg/l]")
#ggplotly(p)
p
Check op onrealistische waarden
Code
df <- df %>% mutate(waarde_realistisch = between(waarde, left = lims[1], right = lims[2]))
ggplot(df, aes(x = datetime, y = waarde)) +
geom_line() +
geom_point(aes(col = waarde_realistisch, shape = waarde_realistisch)) +
labs(x = NULL, y = "NO3-N [mg/l]") +
scale_color_manual(values = c("red", "black")) +
scale_shape_manual(values = c(1, NA))
Check op flatlines
Code
df <- get_flatline(dataset = df, value_col = "waarde", threshold = 0, n_measurements = 10)
ggplot(filter(df, !is.na(flatline)), aes(x = datetime, y = waarde)) +
geom_line() +
geom_point(aes(col = flatline, shape = flatline)) +
labs(x = NULL, y = "NO3-N [mg/l]") +
scale_color_manual(values = c("black", "red")) +
scale_shape_manual(values = c(NA, 1))
Check overige outliers
Code
df_trans <- filter(df, flatline == FALSE, waarde_realistisch == TRUE) %>%
distinct(datetime, waarde) %>%
group_by(datetime) %>%
summarise(waarde = mean(waarde, na.rm = TRUE)) %>%
ungroup() %>%
transform_data_uv(time_col = 'datetime')
df_trans$features <- slide(df_trans, ~get_tsfeatures(.x$waarde), .before = 9, .complete = FALSE)
df_trans <- df_trans %>% unnest_wider(features)
col_keep <- is.na(df_trans) %>% colMeans()
col_keep <- names(col_keep[col_keep < 1])
col_keep <- col_keep[!col_keep %in% c('datetime', 'waarde', 'time')]
df_testout <- df_trans[, col_keep]
out_forest <- isotree::isolation.forest(data = df_testout, ntrees = 500)
df_testout$datetime <- df_trans$datetime
df_testout$waarde <- df_trans$waarde
df_testout$iforest_score <- predict(out_forest, df_testout)
iforest_th <- quantile(df_testout$iforest_score, probs = 0.99, na.rm = TRUE, names = FALSE)
df_testout$iforest_type <- df_testout$iforest_score >= iforest_th
#plot
ggplot(df_testout, aes(x = datetime, y = waarde)) +
geom_line() +
geom_point(aes(col = iforest_type, shape = iforest_type)) +
# labs(title = par) +
scale_color_manual(values = c("black", "red")) +
scale_shape_manual(values = c(NA, 1))
Lab correctie
Code
#plot
ggplot(data = df_lab_cor, aes(x = datetime_compare, y = waarde, size = meet_methode, col = meet_methode)) +
geom_point(alpha = 0.5) +
scale_size_manual(values = c(2, 0.5)) +
geom_vline(data = oh, aes(xintercept = datum), linetype = "dashed", col = "red")
Code
df_lab_cor_wide <- pivot_wider(df_lab_cor, names_from = meet_methode, values_from = waarde, values_fn = mean) %>%
mutate(verschil = trailer - labanalyse)
df_lab_cor_wide_saqc <- df_lab_cor_wide %>% select(datetime = datetime_compare, NO3 = trailer)
lab_corrected_data_mntnc <- NULL
# date <- "2021-06-04 13:00:00 CEST"
for (date in unique(oh$datum)) {
date_idx <- which(date == unique(oh$datum)) -1
prev_date <- ifelse(date_idx == 0, ymd_hms("2021-01-01 08:00:00", tz = "CET"), unique(oh$datum)[date_idx])
df_mntnc <- filter(df_lab_cor_wide, datetime_compare > prev_date, datetime_compare < date)
# test plot #
ggplot(df_mntnc, aes(x = trailer, y = labanalyse)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
geom_abline(col = "red", linetype = "dashed")
# models #
# normal lm model
lm_mod <- lm(labanalyse ~ trailer, data = filter(df_mntnc, !is.na(labanalyse)))
# diff lm model #
lm_mod_diff <- lm(verschil ~ datetime_compare, data = filter(df_mntnc, !is.na(labanalyse)))
df_mntnc <-
mutate(
df_mntnc,
trailer_correctie_lm = predict(lm_mod, newdata = df_mntnc),
trailer_correctie_lm_diff_raw = predict(lm_mod_diff, newdata = df_mntnc)
) %>%
mutate(trailer_correctie_lm_diff = trailer - trailer_correctie_lm_diff_raw,) %>%
select(-trailer_correctie_lm_diff_raw,-verschil)
df_mntnc_corrected <-
pivot_longer(
df_mntnc,
cols = trailer:trailer_correctie_lm_diff,
names_to = "meet_methode",
values_to = "waarde"
)
#plot#
ggplot(df_mntnc_corrected,
aes(x = datetime_compare, y = waarde, col = meet_methode)) +
geom_point(aes(size = meet_methode, alpha = meet_methode)) +
scale_alpha_manual(
values = c(0.2, 1, 0.2, 0.2),
breaks = unique(df_mntnc_corrected$meet_methode)
) +
scale_size_manual(
values = c(0.1, 2, 0.1, 0.1),
breaks = unique(df_mntnc_corrected$meet_methode)
) +
labs(x = NULL)
lab_corrected_data_mntnc <-
bind_rows(lab_corrected_data_mntnc, df_mntnc_corrected)
}
# plot
ggplot(filter(lab_corrected_data_mntnc, meet_methode != 'trailer_correctie_lm'),
aes(x = datetime_compare, y = waarde, col = meet_methode)) +
geom_point(aes(size = meet_methode, alpha = meet_methode)) +
scale_alpha_manual(
values = c(0.2, 1, 0.2, 0.2),
breaks = unique(df_mntnc_corrected$meet_methode)
) +
scale_size_manual(
values = c(0.1, 2, 0.1, 0.1),
breaks = unique(df_mntnc_corrected$meet_methode)
) +
geom_vline(
data = oh,
aes(xintercept = datum),
linetype = "dashed",
col = "red"
) +
facet_wrap( ~ parameter_code, scales = "free", nrow = 3) +
labs(x = NULL)
Code
test <- lab_corrected_data_mntnc %>% filter(meet_methode == "trailer")
df <- get_flatline(dataset = test, value_col = "waarde", threshold = 0, n_measurements = 10)
p <- ggplot(df, aes(x = datetime_compare, y = waarde)) +
geom_line() +
geom_point(aes(col = flatline, shape = flatline)) +
labs(x = NULL, y = "NO3-N [mg/l]") +
scale_color_manual(values = c("black", "red")) +
scale_shape_manual(values = c(NA, 1))
pWarning: Removed 36398 rows containing missing values (`geom_point()`).
Twee variabelen
Code
df_extra2 <- select(df_extra, datetime, parameter_code, waarde) %>%
arrange(parameter_code, datetime)
ggplot(df_extra2, aes(x = datetime, y = waarde, col = parameter_code)) +
geom_line() +
facet_wrap(~parameter_code, scales = 'free')
Code
df_extra2$waarde[df_extra2$parameter_code == 'SQI' & df_extra2$waarde == 0] <- 0.001
df_extra2 <- mutate(df_extra2, waarde_realistisch = between(waarde, left = lims[1], right = lims[2])) %>%
filter(waarde_realistisch == TRUE)
df_etrxa_wide <- select(df_extra2, datetime:waarde) %>% pivot_wider( names_from = 'parameter_code', values_from = 'waarde', values_fn = mean)
df_extra_trans <- oddwater::transform_data(df_etrxa_wide, time_col = 'datetime')
col_keep <- is.na(df_extra_trans) %>% colMeans()
col_keep <- names(col_keep[col_keep < 1])
col_keep <- col_keep[!col_keep %in% c(colnames(df_etrxa_wide), 'time')]
oddwater::NN_HD(na.omit(df_extra_trans[, col_keep])) 1
[1,] 1.178493e-02
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[3,] 2.287910e-03
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[95,] 1.796420e-03
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[100,] 2.555278e-03
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[102,] 3.044725e-03
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[107,] 4.820544e-03
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[131,] 1.818620e-03
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[133,] 9.033249e-04
[134,] 1.890771e-03
[135,] 2.244502e-03
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[139,] 6.552518e-04
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[142,] 2.419512e-03
[143,] 2.133815e-03
[144,] 5.051293e-03
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[146,] 3.540260e-03
[147,] 1.974601e-03
[148,] 8.670257e-04
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[152,] 9.360960e-04
[153,] 1.413504e-03
[154,] 1.752410e-03
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[156,] 5.458284e-04
[157,] 9.360960e-04
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[159,] 1.091592e-03
[160,] 1.417022e-03
[161,] 3.911637e-04
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[164,] 5.458284e-04
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[174,] 2.571757e-03
[175,] 3.496529e-03
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[183,] 5.369430e-03
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[185,] 1.796420e-03
[186,] 1.401710e-03
[187,] 1.980037e-03
[188,] 2.858238e-03
[189,] 2.243988e-03
[190,] 2.239083e-03
[191,] 2.638550e-03
[192,] 6.207069e-04
[193,] 1.031307e-03
[194,] 1.289864e-03
[195,] 1.882885e-03
[196,] 2.024274e-03
[197,] 1.025620e-03
[198,] 1.222829e-03
[199,] 1.222829e-03
[200,] 2.024274e-03
[201,] 1.384067e-03
[202,] 2.163372e-03
[203,] 1.216753e-03
[204,] 3.186855e-03
[205,] 1.384067e-03
[206,] 1.979234e-03
[207,] 1.243838e-03
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[14220,] 3.647524e-03
[14221,] 1.612188e-03
[14222,] 4.851780e-02
[14223,] 6.923512e-02
[14224,] 3.901575e-02
[14225,] 5.993647e-03
[14226,] 1.330172e-02
[14227,] 8.033932e-03
[14228,] 5.838397e-03
[14229,] 5.838397e-03
[14230,] 1.853870e-03
[14231,] 3.665297e-03
[14232,] 2.384381e-03
[14233,] 5.542357e-03
[14234,] 6.678830e-03
[14235,] 5.542357e-03
[14236,] 3.983267e-03
[14237,] 4.683677e-03
[14238,] 2.112859e-03
[14239,] 3.117101e-03
[14240,] 4.307328e-01
[14241,] 2.550106e-01
[14242,] 3.702164e-03
[14243,] 3.101509e-03
[14244,] 1.090647e-02
[14245,] 1.100155e-03
[14246,] 5.928688e-03
[14247,] 9.981007e-03
[14248,] 6.966022e-03
[14249,] 1.023760e-02
[14250,] 9.253491e-03
[14251,] 2.344914e-03
[14252,] 3.484172e-03
[14253,] 3.101509e-03
[14254,] 4.738528e-03
[14255,] 4.062212e-03
[14256,] 6.312466e-03
[14257,] 6.305353e-03
[14258,] 7.315276e-03
[14259,] 3.781475e-03
[14260,] 8.217158e-03
[14261,] 2.050198e-03
[14262,] 2.193815e-03
[14263,] 5.013862e-03
[14264,] 6.217843e-03
[14265,] 7.330722e-03
[14266,] 9.466769e-03
[14267,] 9.727035e-03
[14268,] 8.822071e-03
[14269,] 1.740001e-03
[14270,] 2.545401e-03
[14271,] 3.874962e-03
[14272,] 5.208444e-03
[14273,] 1.740001e-03
[14274,] 7.911924e-03
[14275,] 6.739714e-03
[14276,] 3.874962e-03
[14277,] 9.159293e-03
[14278,] 9.589993e-03
[14279,] 4.514519e-03
[14280,] 4.062212e-03
[14281,] 5.394538e-03
[14282,] 7.849519e-03
[14283,] 7.442265e-04
[14284,] 7.442265e-04
[14285,] 4.816650e-03
[14286,] 3.822866e-03
[14287,] 2.420166e-03
[14288,] 1.903895e-03
[14289,] 7.066289e-03
[14290,] 5.891423e-03
[14291,] 5.154822e-03
[14292,] 5.354966e-03
[14293,] 4.695574e-03
[14294,] 3.255543e-03
[14295,] 1.394063e-03
[14296,] 2.344914e-03
[14297,] 3.255543e-03
[14298,] 3.969153e-03
[14299,] 4.760255e-03
[14300,] 2.420166e-03
[14301,] 1.903895e-03
[14302,] 5.894910e-03
[14303,] 4.718120e-03
[14304,] 1.385528e-03
[14305,] 1.385528e-03
[14306,] 2.079322e-03
[14307,] 4.072213e-03
[14308,] 3.107481e-03
[14309,] 6.938851e-03
[14310,] 1.100155e-03
[14311,] 1.394063e-03
[14312,] 5.465935e-03
[14313,] 1.625661e-03
[14314,] 5.842724e-03
[14315,] 3.785969e-03
[14316,] 4.659543e-03
[14317,] 6.877219e-03
[14318,] 4.003997e-03
[14319,] 3.004512e-03
[14320,] 3.478946e-03
[14321,] 1.625661e-03
[14322,] 5.711720e-03
[14323,] 4.751325e-03
[14324,] 7.785394e-03
[14325,] 8.576608e-03
[14326,] 2.767585e-03
[14327,] 6.070799e-03
[14328,] 5.689073e-03
[14329,] 4.632404e-03
[14330,] 6.948697e-03
[14331,] 6.409366e-03
[14332,] 5.337267e-03
[14333,] 5.134376e-03
[14334,] 3.584336e-03
[14335,] 7.487437e-03
[14336,] 4.329120e-03
[14337,] 8.832537e-03
[14338,] 4.162579e-03
[14339,] 3.852409e-03
[14340,] 2.732518e-03
[14341,] 3.697050e-03
[14342,] 9.330449e-03
[14343,] 1.306113e-02
[14344,] 1.064751e-02
[14345,] 5.150004e-03
[14346,] 6.842065e-03
[14347,] 3.817362e-03
[14348,] 1.932367e-03
[14349,] 1.098269e-02
[14350,] 2.653931e-03
[14351,] 6.122030e-03
[14352,] 5.088916e-03
[14353,] 3.181540e-03
[14354,] 2.425423e-03
[14355,] 4.672080e-03
[14356,] 2.845980e-03
[14357,] 4.633382e-04
[14358,] 2.393343e-03
[14359,] 4.267700e-03
[14360,] 3.061117e-03
[14361,] 3.071779e-03
[14362,] 3.737035e-03
[14363,] 3.442571e-03
[14364,] 3.766902e-03
[14365,] 3.683897e-03
[14366,] 2.321250e-03
[14367,] 4.627160e-03
[14368,] 4.983348e-03
[14369,] 5.926059e-03
[14370,] 9.013437e-03
[14371,] 1.205962e-02
[14372,] 7.679013e-03
[14373,] 1.116685e-02
[14374,] 2.005962e-03
[14375,] 1.112912e-03
[14376,] 1.060824e-03
[14377,] 1.649738e-03
[14378,] 3.739482e-03
[14379,] 3.656108e-03
[14380,] 5.827824e-03
[14381,] 3.216668e-03
[14382,] 3.087944e-03
[14383,] 2.525766e-03
[14384,] 2.314030e-03
[14385,] 3.104708e-03
[14386,] 4.078650e-03
[14387,] 5.042149e-03
[14388,] 2.716371e-03
[14389,] 4.078650e-03
[14390,] 5.042149e-03
[14391,] 2.716371e-03
[14392,] 1.412087e-02
[14393,] 1.434709e-02
[14394,] 6.646029e-03
[14395,] 1.499635e-02
[14396,] 8.233463e-03
[14397,] 6.125572e-03
[14398,] 4.158853e-03
[14399,] 2.247551e-03
[14400,] 1.936446e-03
[14401,] 2.557655e-03
[14402,] 4.099763e-03
[14403,] 8.248541e-03
[14404,] 4.292009e-03
[14405,] 8.312366e-03
[14406,] 7.422208e-03
[14407,] 7.936453e-03
[14408,] 4.292009e-03
[14409,] 4.129716e-03
[14410,] 7.936453e-03
[14411,] 1.109739e-02
[14412,] 2.676599e-03
[14413,] 3.310949e-03
[14414,] 3.323820e-03
[14415,] 4.216682e-03
[14416,] 5.432119e-03
[14417,] 2.445971e-03
[14418,] 8.706668e-03
[14419,] 7.087911e-03
[14420,] 4.597684e-03
[14421,] 7.343570e-03
[14422,] 7.238299e-03
[14423,] 4.410319e-03
[14424,] 4.033103e-03
[14425,] 4.929205e-03
[14426,] 5.241299e-03
[14427,] 2.622830e-03
[14428,] 2.328775e-03
[14429,] 5.631175e-03
[14430,] 6.738106e-03
[14431,] 6.143953e-03
[14432,] 6.870208e-03
[14433,] 5.291879e-03
[14434,] 4.031476e-03
[14435,] 2.565506e-03
[14436,] 2.956599e-03
[14437,] 2.025184e-03
[14438,] 4.183256e-03
[14439,] 2.641605e-03
[14440,] 3.500867e-03
[14441,] 3.942783e-03
[14442,] 9.954266e-03
[14443,] 1.052116e-02
[14444,] 3.042545e-03
[14445,] 6.111521e-03
[14446,] 5.025184e-03
[14447,] 7.803575e-03
[14448,] 5.017148e-03
[14449,] 4.205137e-03
[14450,] 1.172975e-02
[14451,] 3.737399e-03
[14452,] 1.332840e-03
[14453,] 2.113105e-03
[14454,] 9.142566e-03
[14455,] 1.337644e-02
[14456,] 4.256800e-03
[14457,] 5.806147e-03
[14458,] 3.701667e-03
[14459,] 8.610022e-03
[14460,] 3.518041e-03
[14461,] 5.776404e-03
[14462,] 1.711072e-02
[14463,] 1.112106e-02
[14464,] 2.920751e-03
[14465,] 9.920936e-03
[14466,] 4.632404e-03
[14467,] 6.716819e-03
[14468,] 3.753395e-03
[14469,] 3.471992e-03
[14470,] 7.041163e-03
[14471,] 3.869452e-03
[14472,] 1.207545e-02
[14473,] 7.701868e-03
[14474,] 4.840707e-03
[14475,] 1.967302e-03
[14476,] 6.258439e-03
[14477,] 3.745077e-03
[14478,] 3.745077e-03
[14479,] 3.848180e-03
[14480,] 3.004512e-03
[14481,] 3.785969e-03
[14482,] 4.003997e-03
[14483,] 4.840707e-03
[14484,] 4.851628e-03
[14485,] 3.673238e-03
[14486,] 3.478946e-03
[14487,] 3.673238e-03
[14488,] 7.362380e-03
[14489,] 6.453537e-03
[14490,] 5.794207e-03
[14491,] 1.967302e-03
[14492,] 3.933293e-03
[14493,] 2.304850e-03
[14494,] 4.536982e-03
[14495,] 4.458728e-03
[14496,] 3.933293e-03
[14497,] 4.440571e-03
[14498,] 3.869452e-03
[14499,] 4.182692e-03
[14500,] 2.427168e-03
[14501,] 2.427168e-03
[14502,] 5.626617e-03
[14503,] 3.471992e-03
[14504,] 1.038675e-02
[14505,] 2.344128e-03
[14506,] 3.753395e-03
[14507,] 3.854146e-03
[14508,] 3.492746e-03
[14509,] 6.292343e-03
[14510,] 8.034977e-03
[14511,] 3.492746e-03
[14512,] 7.727300e-03
[14513,] 7.670898e-03
[14514,] 5.757984e-03
[14515,] 3.587760e-03
[14516,] 3.587760e-03
[14517,] 5.134376e-03
[14518,] 1.210154e-02
[14519,] 3.702331e-03
[14520,] 4.906526e-03
[14521,] 3.852409e-03
[14522,] 6.604252e-03
[14523,] 5.494705e-03
[14524,] 6.051365e-03
[14525,] 8.619598e-03
[14526,] 6.254161e-03
[14527,] 3.991460e-03
[14528,] 7.732996e-03
[14529,] 1.002841e-02
[14530,] 2.098019e-03
[14531,] 2.098019e-03
[14532,] 2.733105e-03
[14533,] 2.539840e-03
[14534,] 2.733105e-03
[14535,] 2.539840e-03
[14536,] 3.755055e-03
[14537,] 3.289490e-03
[14538,] 4.452121e-04
[14539,] 6.468087e-03
[14540,] 1.591861e-02
[14541,] 3.979754e-03
[14542,] 4.175443e-03
[14543,] 7.782540e-03
[14544,] 5.947863e-03
[14545,] 2.632726e-03
[14546,] 4.176829e-03
[14547,] 2.632726e-03
[14548,] 3.979754e-03
[14549,] 5.602468e-03
[14550,] 3.224484e-03
[14551,] 3.101745e-03
[14552,] 2.653931e-03
[14553,] 3.767875e-03
[14554,] 6.421718e-03
[14555,] 4.213409e-03
[14556,] 7.805341e-03
[14557,] 1.932555e-03
[14558,] 2.064434e-03
[14559,] 2.892058e-03
[14560,] 2.349176e-03
[14561,] 1.123828e-03
[14562,] 5.470745e-03
[14563,] 9.561372e-04
[14564,] 1.303529e-03
[14565,] 6.136906e-04
[14566,] 4.633382e-04
[14567,] 1.240940e-03
[14568,] 3.242310e-03
[14569,] 8.038809e-04
[14570,] 2.292038e-03
[14571,] 1.554255e-03
[14572,] 1.302221e-03
[14573,] 2.392261e-03
[14574,] 1.750066e-03
[14575,] 2.104179e-03
[14576,] 2.046306e-03
[14577,] 4.983348e-03
[14578,] 6.485794e-03
[14579,] 2.179137e-03
[14580,] 2.923386e-03
[14581,] 4.128770e-03
[14582,] 4.378589e-03
[14583,] 3.483852e-02
[14584,] 3.295169e-02
[14585,] 3.206982e-03
[14586,] 2.738331e-03
[14587,] 3.087786e-03
[14588,] 2.240971e-03
[14589,] 3.710010e-03
[14590,] 3.116187e-03
[14591,] 1.070294e-02
[14592,] 4.281488e-02
[14593,] 3.059381e-03
[14594,] 4.448548e-03
[14595,] 8.630874e-04
[14596,] 2.773419e-03
[14597,] 4.010117e-03
[14598,] 2.489261e-03
[14599,] 5.098506e-03
[14600,] 3.322329e-03
[14601,] 2.939319e-03
[14602,] 2.849302e-03
[14603,] 4.460641e-03
[14604,] 3.322329e-03
[14605,] 1.057009e-03
[14606,] 7.066653e-04
[14607,] 1.057003e-03
[14608,] 1.137268e-03
[14609,] 7.066955e-04
[14610,] 1.618472e-03
[14611,] 1.318044e-03
[14612,] 1.821224e-03
[14613,] 0.000000e+00
[14614,] 0.000000e+00
[14615,] 1.057009e-03
[14616,] 1.098270e-03
[14617,] 3.920709e-03
[14618,] 3.962229e-03
[14619,] 3.581983e-03
[14620,] 2.965844e-03
[14621,] 2.461732e-03
[14622,] 3.426700e-03
[14623,] 1.844652e-03
[14624,] 2.060701e-03
[14625,] 3.026695e-03
[14626,] 4.055817e-03
[14627,] 3.715092e-03
[14628,] 2.256958e-03
[14629,] 2.262829e-03
[14630,] 2.152115e-03
[14631,] 2.060123e-03
[14632,] 3.130500e-03
[14633,] 2.665449e-03
[14634,] 4.435092e-03
[14635,] 3.752675e-03
[14636,] 3.903431e-03
[14637,] 3.042534e-03
[14638,] 3.389302e-03
[14639,] 1.575889e-03
[14640,] 4.892066e-03
[14641,] 2.067233e-03
[14642,] 3.727162e-03
[14643,] 5.089389e-03
[14644,] 4.725679e-03
[14645,] 3.727162e-03
[14646,] 4.725679e-03
[14647,] 2.942958e-03
[14648,] 3.322985e-03
[14649,] 2.530705e-03
[14650,] 4.124213e-03
[14651,] 1.825191e-03Code
stray::find_HDoutliers(df_extra_trans[, col_keep])$outliers
integer(0)
$out_scores
[1] 1.129229e-03 1.129622e-03 6.865228e-04 2.915876e-04 2.638970e-04
[6] 3.019399e-04 1.672186e-04 4.914461e-04 4.578757e-04 3.702697e-04
[11] 2.930923e-04 2.015477e-04 2.111811e-04 2.111811e-04 2.650750e-04
[16] 3.237002e-04 3.083032e-04 1.688985e-04 1.636172e-04 3.619906e-04
[21] 5.819301e-04 8.893122e-04 6.943638e-04 2.667384e-04 2.859849e-04
[26] 3.401994e-04 2.264342e-04 1.787455e-04 2.062783e-04 1.901793e-04
[31] 4.942417e-04 2.937310e-04 1.059120e-04 1.655166e-04 1.892245e-04
[36] 2.651338e-04 8.860117e-04 6.066084e-04 1.124296e-04 2.005638e-04
[41] 1.688251e-04 8.380365e-05 1.374731e-04 1.966251e-04 1.467704e-04
[46] 8.635856e-04 1.201811e-03 6.184881e-04 9.105274e-04 6.012160e-04
[51] 7.819607e-04 2.385514e-04 4.752546e-04 4.349076e-04 4.633351e-04
[56] 3.107732e-04 5.506430e-04 7.302000e-04 3.422869e-04 8.120894e-04
[61] 9.434643e-04 1.444188e-04 3.953761e-04 4.571709e-04 9.485859e-04
[66] 8.568176e-04 1.034547e-03 8.643496e-04 9.746076e-04 7.621594e-04
[71] 7.001236e-04 2.767433e-04 6.821721e-04 5.608641e-04 4.070091e-04
[76] 3.198842e-04 3.988800e-04 2.268766e-04 3.379104e-04 2.911657e-04
[81] 3.513882e-04 2.209159e-04 3.563484e-04 3.242753e-04 8.336110e-05
[86] 8.199658e-05 1.251813e-04 1.202836e-04 2.816752e-04 2.244343e-04
[91] 3.099936e-04 3.789083e-04 2.035868e-04 1.640266e-04 1.659553e-04
[96] 1.027227e-04 1.449619e-04 3.640630e-04 4.649061e-04 2.076829e-04
[101] 4.797736e-04 5.865980e-04 1.428583e-04 2.410692e-04 1.428583e-04
[106] 2.356639e-04 3.557524e-04 2.078795e-04 2.078795e-04 1.706216e-04
[111] 2.637512e-04 2.637512e-04 2.637512e-04 3.557524e-04 3.497213e-04
[116] 2.407358e-04 2.792186e-04 2.414248e-04 3.191397e-04 2.930923e-04
[121] 9.695395e-05 1.636172e-04 1.833457e-04 2.083473e-04 1.095525e-04
[126] 2.217933e-04 3.158062e-04 2.157560e-04 1.919423e-04 2.075622e-04
[131] 1.470453e-04 1.606174e-04 3.730779e-04 1.570793e-04 2.509145e-04
[136] 3.231000e-04 1.692317e-04 8.329420e-05 1.864591e-04 1.057761e-04
[141] 1.141216e-04 2.304977e-04 1.824813e-04 4.692768e-04 4.155074e-04
[146] 2.647399e-04 1.736448e-04 1.455160e-04 8.594888e-05 3.012156e-04
[151] 2.048402e-04 2.221418e-04 1.243018e-04 1.461241e-04 9.821587e-05
[156] 2.581284e-04 2.483374e-04 7.409883e-05 1.803116e-04 3.950930e-04
[161] 2.498634e-04 2.047057e-04 2.183287e-04 2.630453e-04 2.658282e-04
[166] 9.830838e-05 3.401166e-04 3.223076e-04 1.877614e-04 1.967050e-04
[171] 2.367697e-04 2.335076e-04 2.989170e-04 1.909691e-04 4.357834e-04
[176] 5.670461e-04 5.352205e-04 3.833601e-04 3.347054e-04 4.605501e-04
[181] 3.699045e-04 3.110208e-04 5.327938e-04 2.160952e-04 1.659553e-04
[186] 1.207209e-04 1.449619e-04 2.656449e-04 3.613727e-04 1.864294e-04
[191] 2.306932e-04 1.289894e-04 1.975370e-04 2.397361e-04 2.299832e-04
[196] 1.787338e-04 9.761542e-05 2.657152e-04 1.167466e-04 1.863796e-04
[201] 1.185644e-04 1.839984e-04 2.086872e-04 2.350804e-04 2.637554e-04
[206] 1.534328e-04 9.254551e-05 1.581170e-04 2.943585e-04 1.441602e-04
[211] 2.458576e-04 1.464200e-04 2.048819e-04 2.357231e-04 1.005126e-04
[216] 1.005126e-04 1.400161e-04 4.131357e-04 2.955131e-04 1.992029e-04
[221] 2.954936e-04 2.764858e-04 3.928656e-04 4.319900e-04 1.951125e-04
[226] 3.505493e-04 8.226895e-04 5.361100e-04 2.937310e-04 4.858303e-04
[231] 7.380408e-04 7.291721e-04 9.049135e-04 1.176431e-03 1.323798e-03
[236] 5.857538e-04 1.317088e-03 1.709610e-03 9.105120e-04 1.123939e-03
[241] 1.650919e-03 2.232897e-03 1.376403e-03 2.276084e-03 3.007326e-03
[246] 2.881042e-03 1.281036e-03 7.797696e-04 1.720667e-03 2.384412e-03
[251] 1.235194e-03 1.000111e-03 3.036594e-04 1.348141e-03 2.168472e-03
[256] 2.071044e-03 2.698366e-03 6.039998e-03 7.808376e-03 3.556178e-03
[261] 7.454506e-03 4.257424e-03 2.254675e-03 1.433848e-03 8.172118e-04
[266] 5.875153e-03 1.530097e-02 6.564680e-03 4.289359e-04 5.382350e-03
[271] 8.619497e-03 1.596383e-03 1.254385e-03 8.546935e-04 1.293833e-03
[276] 2.882900e-04 5.414691e-03 4.437109e-03 1.525646e-03 1.167292e-03
[281] 1.209205e-03 5.296713e-04 5.768330e-04 1.278549e-03 8.748757e-04
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[291] 9.093837e-04 7.483900e-04 5.060618e-04 3.434392e-04 2.761514e-04
[296] 2.201775e-04 2.075708e-03 3.144151e-03 2.126818e-03 2.404745e-04
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[1136] 3.997477e-03 4.740216e-03 4.299245e-04 4.565694e-04 8.082224e-04
[1141] 6.919877e-04 3.992312e-04 4.937045e-04 1.865440e-03 6.455983e-03
[1146] 4.236062e-04 5.931681e-04 6.373576e-04 2.740388e-04 6.120189e-04
[1151] 3.055070e-04 3.953149e-04 2.284006e-04 4.052839e-04 4.406998e-04
[1156] 2.896058e-03 3.705948e-03 1.946576e-04 2.698858e-04 3.593945e-04
[1161] 9.135493e-04 6.879591e-04 2.172816e-04 7.177278e-04 7.465670e-04
[1166] 2.332723e-04 2.625553e-04 2.873716e-04 1.867835e-04 1.303628e-04
[1171] 2.613425e-04 1.330589e-04 2.332723e-04 7.759097e-04 2.401497e-03
[1176] 6.362475e-03 5.211416e-04 3.061000e-04 2.381418e-04 2.949700e-04
[1181] 1.620903e-04 1.620903e-04 2.013272e-04 2.842189e-04 2.516884e-04
[1186] 2.144794e-04 5.747269e-04 2.144794e-04 2.147004e-04 2.147004e-04
[1191] 2.147004e-04 2.265518e-04 5.718221e-04 2.147004e-04 2.683562e-04
[1196] 2.605316e-04 2.605316e-04 2.269198e-03 5.571244e-03 4.545971e-03
[1201] 9.147397e-04 1.608997e-03 1.320141e-03 7.018096e-04 3.901626e-03
[1206] 3.289884e-03 1.130648e-03 1.001390e-03 9.128989e-04 4.769698e-04
[1211] 4.769698e-04 7.576257e-04 6.086835e-04 5.669957e-04 1.027456e-03
[1216] 5.682414e-04 3.832619e-04 3.232378e-04 3.989228e-04 4.664899e-04
[1221] 5.387767e-04 3.222311e-04 2.473322e-04 4.476586e-04 2.925579e-04
[1226] 3.639618e-04 7.141499e-04 4.520941e-04 3.838480e-03 5.184932e-03
[1231] 8.091535e-04 5.567880e-04 3.063367e-04 6.310900e-04 6.255044e-04
[1236] 4.097695e-04 4.594078e-04 4.367900e-04 4.446061e-04 3.743355e-04
[1241] 2.951726e-04 2.235778e-04 1.946576e-04 3.636909e-04 3.636909e-04
[1246] 4.424808e-04 1.709010e-04 1.456480e-04 1.789773e-04 2.593108e-04
[1251] 1.214310e-03 5.124195e-03 7.285106e-04 6.148414e-04 3.500012e-04
[1256] 5.912418e-04 5.834830e-04 4.846738e-04 6.924400e-04 7.156685e-03
[1261] 1.980003e-02 2.089127e-02 2.634064e-02 8.243509e-03 6.498678e-03
[1266] 5.638593e-03 4.681188e-03 4.931435e-03 3.989306e-03 4.294872e-03
[1271] 6.572382e-03 6.482048e-03 2.710108e-03 3.027063e-03 1.998339e-03
[1276] 6.910074e-03 5.113808e-03 2.933888e-03 5.434968e-03 2.803884e-03
[1281] 5.864867e-03 2.258347e-03 2.312537e-03 5.323977e-03 5.435836e-03
[1286] 6.333513e-03 2.692555e-03 1.137988e-03 7.953620e-04 4.060963e-03
[1291] 6.657743e-03 4.500829e-03 5.434968e-03 2.398307e-03 2.803884e-03
[1296] 5.912909e-03 2.087133e-02 4.639270e-02 2.087133e-02 3.484366e-03
[1301] 2.595906e-03 2.194663e-03 6.320500e-03 5.130201e-03 2.726402e-03
[1306] 5.064858e-03 5.790622e-03 3.294745e-03 4.681188e-03 6.931073e-03
[1311] 8.676218e-03 4.683739e-03 5.571490e-03 3.040085e-03 4.701073e-03
[1316] 2.707098e-03 3.215102e-03 7.746959e-03 2.726402e-03 6.086873e-03
[1321] 5.121875e-03 2.635704e-03 5.404266e-03 6.791756e-03 1.056521e-03
[1326] 2.794878e-03 2.927610e-03 3.680339e-03 6.716585e-03 5.316579e-03
[1331] 5.088292e-03 2.526569e-03 5.032039e-03 4.545711e-04 1.006620e-03
[1336] 2.800827e-03 5.672715e-03 7.806783e-03 5.864867e-03 3.484366e-03
[1341] 6.925270e-03 7.116050e-03 4.683739e-03 2.804812e-03 2.190657e-03
[1346] 6.587188e-03 3.215102e-03 7.725997e-03 4.014818e-03 5.270806e-03
[1351] 4.931435e-03 7.070216e-03 6.291456e-03 6.368411e-03 2.901193e-03
[1356] 5.096455e-03 6.959746e-03 5.207674e-03 5.088292e-03 3.617004e-03
[1361] 1.357247e-03 7.635245e-04 3.878075e-03 5.909737e-03 8.633896e-04
[1366] 1.841022e-03 1.855905e-03 2.042325e-03 5.024779e-03 4.892588e-03
[1371] 6.521338e-04 4.095092e-04 5.932477e-04 4.482326e-04 3.957675e-04
[1376] 4.361083e-04 4.644462e-04 5.649313e-04 5.068613e-03 5.071401e-03
[1381] 6.448904e-03 1.450023e-03 5.118876e-03 5.806712e-03 4.718510e-03
[1386] 4.529317e-04 6.680600e-04 5.791382e-04 7.067593e-04 7.844580e-04
[1391] 2.138464e-04 3.121103e-04 7.067593e-04 3.065474e-04 1.012555e-03
[1396] 4.871605e-03 2.806987e-03 3.120286e-03 3.052364e-03 5.677042e-03
[1401] 6.504588e-03 2.321952e-03 7.271171e-04 7.271171e-04 7.971624e-04
[1406] 4.548927e-03 6.606480e-03 1.029170e-03 8.828115e-04 5.143634e-03
[1411] 4.702317e-03 6.521338e-04 7.255126e-04 5.105903e-04 1.064046e-03
[1416] 8.809296e-04 9.829408e-04 3.191343e-03 4.779301e-03 7.589704e-04
[1421] 6.810933e-04 9.202403e-04 7.970961e-04 5.223416e-04 4.893360e-04
[1426] 4.893360e-04 5.520719e-04 4.937045e-04 6.294671e-04 2.707752e-04
[1431] 5.980272e-04 4.955238e-04 5.828815e-04 1.123563e-03 5.828815e-04
[1436] 3.660016e-04 3.963454e-03 3.656746e-03 3.639618e-04 6.485502e-04
[1441] 8.226352e-04 5.653446e-04 3.055070e-04 3.342069e-04 4.706684e-04
[1446] 3.518040e-04 2.284006e-04 3.946042e-04 2.951765e-04 2.951765e-04
[1451] 4.830915e-04 1.060230e-03 1.018444e-03 5.002613e-04 3.463312e-04
[1456] 3.028879e-04 4.561843e-04 3.028879e-04 7.186820e-04 2.744503e-04
[1461] 2.744503e-04 4.951438e-04 5.073771e-03 3.705948e-03 3.041359e-04
[1466] 4.444997e-04 2.835565e-04 3.319459e-04 3.427973e-04 2.771228e-04
[1471] 1.121645e-04 1.051941e-04 2.876007e-04 1.051941e-04 3.958618e-04
[1476] 2.593108e-04 1.909321e-04 1.386095e-04 2.064002e-04 2.284971e-04
[1481] 7.008600e-04 1.963745e-04 2.788332e-04 1.081904e-04 2.395135e-04
[1486] 2.873716e-04 1.216939e-03 7.465199e-04 9.274960e-04 5.110734e-03
[1491] 4.413812e-03 3.581163e-03 3.616869e-03 3.689344e-03 1.114249e-03
[1496] 1.114249e-03 8.838828e-04 1.250998e-03 1.460299e-03 1.150678e-03
[1501] 2.806987e-03 3.935304e-03 1.143025e-03 1.069222e-03 1.069222e-03
[1506] 1.108132e-03 1.257465e-03 1.502584e-03 3.888101e-03 1.915567e-03
[1511] 1.125398e-03 4.467171e-03 6.402161e-03 8.243553e-04 8.314327e-04
[1516] 6.401851e-04 6.012356e-04 8.091448e-04 9.218949e-04 4.955097e-03
[1521] 4.946819e-03 6.976463e-04 5.191166e-04 5.653446e-04 5.701045e-04
[1526] 8.120203e-04 7.372813e-04 3.616869e-03 4.582849e-03 5.177255e-03
[1531] 4.947545e-03 9.367982e-04 1.061491e-03 4.110799e-03 4.511768e-03
[1536] 9.717393e-04 9.717393e-04 1.167247e-03 9.754128e-04 8.683317e-04
[1541] 4.949582e-04 6.609038e-04 8.023001e-04 1.228481e-03 1.028750e-03
[1546] 4.984101e-03 5.198736e-03 3.956014e-04 4.304307e-04 2.915352e-04
[1551] 4.406998e-04 4.759399e-04 5.002613e-04 3.463312e-04 3.057156e-04
[1556] 4.788086e-04 3.395756e-04 8.075188e-04 4.275501e-03 3.865200e-03
[1561] 3.627632e-04 2.172816e-04 3.765976e-04 3.408854e-04 1.330589e-04
[1566] 2.797223e-04 1.867835e-04 2.854612e-04 2.076802e-04 2.839365e-04
[1571] 2.279377e-04 1.986253e-03 6.771672e-03 1.188252e-03 2.718278e-04
[1576] 3.225265e-04 2.655899e-04 4.036042e-04 4.081813e-04 2.783765e-04
[1581] 2.108169e-04 3.074365e-04 3.235950e-04 2.033070e-04 2.075116e-04
[1586] 1.858127e-04 1.909321e-04 3.591586e-04 1.667961e-04 3.844821e-04
[1591] 3.886564e-04 3.225265e-04 2.655899e-04 3.245434e-04 3.503349e-04
[1596] 2.265670e-04 2.967231e-04 2.481830e-04 3.084948e-04 4.448427e-04
[1601] 2.018890e-04 2.382890e-04 1.898267e-04 1.898267e-04 2.528861e-04
[1606] 2.287369e-04 2.829525e-04 7.829135e-04 6.078141e-04 4.066802e-04
[1611] 3.798347e-04 3.525219e-04 4.577858e-04 3.548868e-04 4.020377e-04
[1616] 1.230622e-04 2.115382e-04 2.484565e-04 1.954570e-04 2.513579e-04
[1621] 2.203132e-04 1.230622e-04 3.378073e-04 3.597265e-04 3.804374e-04
[1626] 3.804374e-04 4.284911e-04 5.192224e-04 2.421885e-04 4.396873e-04
[1631] 5.003003e-04 3.178872e-04 2.312320e-04 2.335717e-04 3.489859e-04
[1636] 2.901704e-04 2.382890e-04 9.995690e-04 9.093587e-04 2.908327e-04
[1641] 2.528861e-04 2.287369e-04 2.256263e-04 2.256263e-04 3.619371e-04
[1646] 2.659692e-04 2.080828e-04 1.927783e-04 3.753242e-04 9.683286e-04
[1651] 2.491929e-04 2.659692e-04 1.643364e-04 1.643364e-04 1.285461e-04
[1656] 1.181230e-04 1.446578e-04 1.462136e-04 8.179754e-05 1.446578e-04
[1661] 2.013253e-04 2.143234e-04 1.508531e-04 3.137744e-04 2.293172e-04
[1666] 3.917299e-04 3.886077e-04 2.694938e-04 2.491929e-04 3.422790e-04
[1671] 3.753242e-04 2.936109e-03 3.059753e-03 1.151811e-04 3.437349e-03
[1676] 4.492864e-03 4.644963e-04 1.508531e-04 1.181230e-04 2.009472e-04
[1681] 2.143366e-04 2.396377e-04 1.647264e-04 2.346022e-04 2.006833e-04
[1686] 2.704573e-04 2.347823e-04 3.276206e-04 1.841224e-04 1.841224e-04
[1691] 2.968590e-04 2.968590e-04 2.968590e-04 4.048276e-04 4.066022e-04
[1696] 2.421885e-04 4.602807e-04 3.051051e-04 2.674898e-04 3.995426e-04
[1701] 3.093521e-04 3.093521e-04 4.575519e-04 4.241168e-04 2.294916e-04
[1706] 1.684495e-04 2.354904e-03 2.133157e-03 1.150138e-04 2.045452e-04
[1711] 2.361536e-04 1.098643e-04 9.471207e-05 9.471207e-05 1.146848e-04
[1716] 1.310735e-04 1.292445e-04 1.024485e-04 1.413658e-04 1.623730e-04
[1721] 1.401425e-04 1.172538e-04 2.402336e-04 2.516594e-04 1.209379e-04
[1726] 1.491297e-04 1.179276e-04 1.741785e-04 1.000312e-04 9.015975e-05
[1731] 1.237399e-04 8.703570e-05 2.516594e-04 2.194040e-04 1.904997e-04
[1736] 1.916820e-04 1.555163e-04 1.412018e-04 9.605406e-05 2.278355e-04
[1741] 2.348932e-04 1.523417e-04 1.389819e-04 1.347338e-04 1.316612e-04
[1746] 1.870083e-04 2.232755e-04 6.901727e-05 1.818910e-04 2.187862e-04
[1751] 9.763189e-04 5.998334e-04 7.045316e-04 5.517253e-04 3.781692e-04
[1756] 5.386627e-04 2.962852e-04 2.802499e-03 7.541627e-03 3.171624e-03
[1761] 3.731300e-04 3.174349e-04 2.945007e-04 2.945007e-04 4.433518e-04
[1766] 2.467212e-04 2.467212e-04 4.140440e-04 4.140440e-04 4.086017e-04
[1771] 2.085297e-04 2.085297e-04 2.166042e-04 8.569117e-04 4.202595e-04
[1776] 4.597089e-04 1.712557e-03 5.076373e-03 5.124387e-03 1.838282e-03
[1781] 2.234034e-03 3.098882e-03 1.089122e-03 2.752743e-03 9.325331e-04
[1786] 7.588671e-04 2.432472e-03 1.641681e-03 1.542621e-03 9.188597e-04
[1791] 3.159149e-04 3.693447e-04 8.523631e-04 8.523631e-04 1.047822e-03
[1796] 1.170068e-03 1.013598e-03 5.164573e-04 5.904065e-04 6.489337e-04
[1801] 6.746509e-04 7.650675e-04 2.122067e-03 4.492864e-03 2.783765e-04
[1806] 4.617720e-04 1.963745e-04 1.949671e-04 1.949671e-04 3.464405e-02
[1811] 1.010092e-01 5.269900e-02 2.274736e-04 1.508772e-04 3.071269e-04
[1816] 1.673415e-04 2.554064e-04 1.667961e-04 1.492552e-04 1.442903e-04
[1821] 3.801028e-04 1.973368e-04 2.617442e-04 2.279377e-04 2.017987e-04
[1826] 3.820239e-04 3.851582e-04 4.682381e-04 3.551275e-03 1.455553e-03
[1831] 2.349843e-04 1.666630e-04 2.792126e-04 2.410224e-04 4.146984e-04
[1836] 2.683562e-04 3.078353e-04 6.206831e-03 3.464405e-02 2.956336e-02
[1841] 4.615695e-02 1.136753e-02 1.341682e-02 2.113305e-02 2.113305e-02
[1846] 2.542683e-02 3.051345e-02 1.787147e-02 1.113922e-02 9.700584e-03
[1851] 1.046431e-02 2.686645e-02 1.377155e-02 2.070353e-02 7.375774e-03
[1856] 8.999770e-03 9.229869e-03 1.097548e-02 9.120008e-03 7.395661e-03
[1861] 6.972345e-03 7.934690e-03 5.260897e-03 5.121875e-03 6.789980e-03
[1866] 2.961132e-03 6.664706e-03 2.192783e-03 4.831201e-03 8.071773e-03
[1871] 2.652729e-03 2.589920e-03 2.263445e-03 1.393828e-03 8.205111e-04
[1876] 8.633896e-04 1.075923e-03 4.717656e-03 6.528986e-03 6.315707e-04
[1881] 4.974112e-04 1.031468e-03 4.867103e-04 5.476226e-04 5.476226e-04
[1886] 7.178297e-04 7.730851e-04 2.046860e-03 1.086605e-03 8.417132e-04
[1891] 7.151956e-04 9.345495e-04 6.552777e-04 3.957675e-04 1.783734e-03
[1896] 2.124334e-03 4.836059e-04 1.124704e-03 5.513043e-04 6.441660e-04
[1901] 5.916962e-04 3.065474e-04 1.071012e-03 6.810933e-04 8.495351e-04
[1906] 3.963454e-03 1.468583e-03 3.832619e-04 3.232378e-04 4.664899e-04
[1911] 2.916284e-04 7.144829e-04 2.473322e-04 2.964984e-04 2.925579e-04
[1916] 6.373576e-04 4.520941e-04 3.952949e-03 5.155083e-03 7.461313e-04
[1921] 6.647326e-04 4.152326e-04 4.152326e-04 7.380097e-04 3.125183e-04
[1926] 3.929034e-04 4.210507e-04 5.437997e-04 1.409944e-03 4.367900e-04
[1931] 5.026462e-04 5.615085e-04 4.974776e-04 2.235778e-04 3.278822e-04
[1936] 3.153444e-04 4.499503e-04 2.615002e-04 2.212543e-04 9.946517e-04
[1941] 1.524175e-03 8.086388e-04 3.790977e-04 3.041359e-04 4.108306e-04
[1946] 5.006325e-04 4.270382e-04 2.381372e-04 7.177278e-04 1.524175e-03
[1951] 7.465199e-04 4.204087e-03 4.189596e-03 3.839725e-04 1.505343e-04
[1956] 9.712130e-05 4.073051e-04 3.651254e-04 4.863003e-04 2.930004e-04
[1961] 2.425576e-04 4.781440e-04 1.233702e-03 6.749061e-04 3.428259e-04
[1966] 2.381418e-04 2.692542e-04 2.368558e-04 4.574962e-04 4.781440e-04
[1971] 2.494777e-04 2.842189e-04 1.666630e-04 2.223811e-04 3.511880e-04
[1976] 3.196157e-04 1.777763e-04 1.518598e-04 2.876419e-04 1.555163e-04
[1981] 1.431829e-04 9.965590e-05 2.117304e-04 3.338622e-04 2.263315e-04
[1986] 1.006065e-04 9.965590e-05 3.545930e-04 9.341437e-04 2.961166e-04
[1991] 1.772143e-04 3.588034e-04 2.800213e-04 2.045452e-04 2.834040e-04
[1996] 2.415043e-04 4.074318e-04 1.913935e-04 2.369302e-04 1.437063e-04
[2001] 2.186026e-04 2.130481e-04 2.215707e-04 2.130481e-04 2.525727e-04
[2006] 1.916844e-04 1.880602e-04 1.845460e-04 3.767410e-04 2.098426e-04
[2011] 2.426455e-04 3.424059e-04 2.318083e-04 1.916844e-04 1.918877e-04
[2016] 2.918452e-04 1.218453e-04 2.893125e-04 3.246976e-04 3.757388e-04
[2021] 4.849639e-04 6.917112e-04 5.131258e-03 5.387424e-03 1.014694e-03
[2026] 1.167242e-03 4.981840e-04 2.284854e-04 2.493741e-04 2.988704e-04
[2031] 2.216874e-04 2.445731e-04 2.439853e-04 2.439853e-04 2.486163e-04
[2036] 2.486163e-04 3.376934e-04 4.401259e-04 2.110887e-04 2.110887e-04
[2041] 3.604589e-04 4.150300e-04 7.771091e-04 2.987384e-04 3.984165e-04
[2046] 3.370355e-04 1.739719e-04 1.622194e-04 9.684748e-05 1.531031e-04
[2051] 1.046247e-04 2.431613e-04 2.324580e-04 1.515678e-04 2.289565e-04
[2056] 3.920608e-04 2.371862e-04 2.513433e-04 3.492198e-04 3.874400e-04
[2061] 3.540002e-04 3.540002e-04 3.508329e-04 7.369817e-04 6.816112e-04
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[4811] 2.870603e-03 1.984551e-03 1.557962e-03 1.587090e-03 1.305673e-03
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[4821] 1.902294e-03 1.001959e-03 2.002218e-03 1.211323e-03 1.883106e-03
[4826] 2.387492e-03 2.825775e-03 1.445773e-03 1.314616e-03 7.257724e-04
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[4836] 1.075446e-03 2.714280e-03 2.462387e-03 2.051346e-03 3.204862e-03
[4841] 1.328185e-03 1.110352e-03 9.228746e-04 3.144014e-03 2.281086e-03
[4846] 2.676633e-03 3.144014e-03 3.213553e-03 2.066401e-03 2.955193e-03
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[4901] 2.251704e-03 9.086496e-04 1.001108e-03 1.173766e-03 1.890784e-03
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[5726] 2.080701e-03 1.636322e-03 1.782418e-03 2.269402e-03 1.996424e-03
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[5746] 4.296685e-03 4.291352e-03 4.090996e-03 5.795641e-03 6.548391e-03
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[14621] 2.024574e-04 2.598323e-04 1.455574e-04 1.580504e-04 2.241484e-04
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[14646] 2.390802e-03 1.933348e-03 3.052256e-03 3.355853e-04 9.094347e-04
[14651] 4.031771e-04
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Levels: typicalCode
out_forest_extra <- isotree::isolation.forest(df_extra_trans[, col_keep])SaQC
Code
import saqc
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('../data/raw/hydro_data.csv', index_col=0)
maint = pd.read_csv('../data/raw/hydro_maint.csv', index_col=0)
maint.index = pd.DatetimeIndex(maint.index)
data.index = pd.DatetimeIndex(data.index)
qc = saqc.SaQC([data, maint]) # dataframes "data" and "maint" are integrated internally
qc = qc.flagManual('sac254_raw', mdata='maint', method='closed', label='Maintenance')
qc = qc.flagRange('level_raw', min=0)
qc = qc.flagRange('water_temp_raw', min=-1, max=40)
qc = qc.flagRange('sac254_raw', min=0, max=60)
# qc.plot('sac254_raw')
qc = qc.align(['sac254_raw', 'level_raw', 'water_temp_raw'], freq='15min')
qc = qc.correctDrift('sac254_raw', target='sac254_corrected', maintenance_field='maint', model='exponential')
plt.plot(qc.data['sac254_raw']['2016'], alpha=.5, color='black', label='original')
plt.plot(qc.data['sac254_corrected']['2016'], color='black', label='corrected')
plt.show()
SaQC eigen data
Code
import saqc
import pandas as pd
import matplotlib.pyplot as plt
data_own = r.df_lab_cor_wide_saqc
maint_own = r.oh_saqc
data_own = data_own.set_index('datetime')
maint_own = maint_own.set_index('datum_start')
qc_own = saqc.SaQC([data_own, maint_own])
qc_own = qc_own.flagManual('NO3', mdata='maint', method='closed', label='Maintenance')
qc_own = qc_own.flagRange('NO3', min = 0.0001, max = 50)
# qc_own.plot('NO3')
qc_own = qc_own.align(['NO3'], freq = '10min')
qc_own = qc_own.correctDrift('NO3', target='NO3_corrected', maintenance_field='maint', model='exponential')
plt.plot(qc_own.data['NO3'], alpha=.5, color='grey', label='original')
plt.show()
Code
plt.plot(qc_own.data['NO3_corrected'], color='red', label='corrected')
plt.show()