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Merge 00561e7 into c7bfa8c
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@vincentvanhees
vincentvanhees authored Sep 13, 2023
2 parents c7bfa8c + 00561e7 commit f7b53e3
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Showing 16 changed files with 502 additions and 84 deletions.
4 changes: 3 additions & 1 deletion NAMESPACE
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Expand Up @@ -39,7 +39,9 @@ export(g.analyse, g.calibrate,
ShellDoc2Vignette, parametersVignette,
correctOlderMilestoneData,
convertEpochData, appendRecords, extractID,
g.part5_analyseSegment, g.part5_initialise_ts)
g.part5_analyseSegment, g.part5_initialise_ts,
g.part5.analyseRest)

importFrom("grDevices", "colors", "dev.off", "pdf","rainbow","rgb")
importFrom("graphics", "abline", "axis", "par", "plot", "plot.new",
"rect","axis.POSIXct", "barplot", "box", "legend",
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3 changes: 3 additions & 0 deletions NEWS.md
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@@ -1,5 +1,8 @@
# CHANGES IN GGIR VERSION 2.10-3

- Part 5: Now able to assess overlap between self-reported naps and nonwear
with sustained inactivity bouts in order to facilitate research on nap detection. Fixes #687

- Part 5: Time series now also exported if recording only includes one night, even though this is not sufficient for the main part 5 analyses. #894 Further, the time series now also come with lightpeak (LUX).

- Visualreport: Bug fixed extracting the numeric value of the days to exclude in g.plot5 #879
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2 changes: 1 addition & 1 deletion R/check_params.R
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Expand Up @@ -30,7 +30,7 @@ check_params = function(params_sleep = c(), params_metrics = c(),
#-----------------------------------------------------------------------------------------
if (length(params_sleep) > 0) { # Check class of sleep parameters
numeric_params = c("anglethreshold", "timethreshold", "longitudinal_axis", "possible_nap_window", "possible_nap_dur",
"colid", "coln1", "def.noc.sleep", "nnights", "sleepefficiency.metric")
"colid", "coln1", "def.noc.sleep", "nnights", "sleepefficiency.metric", "possible_nap_edge_acc")
boolean_params = c("ignorenonwear", "constrain2range", "HASPT.ignore.invalid",
"relyonguider", "sleeplogidnum")
character_params = c("HASPT.algo", "HASIB.algo", "Sadeh_axis", "nap_model",
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113 changes: 63 additions & 50 deletions R/g.part5.R
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Expand Up @@ -228,6 +228,7 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
}

for (sibDef in def) { # loop through sleep definitions (defined by angle and time threshold in g.part3)

ws3new = ws3 # reset wse3new, because if part5_agg2_60seconds is TRUE then this will have been change in the previous iteration of the loop
if (params_general[["part5_agg2_60seconds"]] == TRUE) {
ts = ts_backup
Expand Down Expand Up @@ -315,7 +316,7 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
#===============================================
# Use sib.report to classify naps, non-wear and integrate these in time series
# does not depend on bout detection criteria or window definitions.
if (params_output[["do.sibreport"]] == TRUE & length(params_sleep[["nap_model"]]) > 0) {
if (params_output[["do.sibreport"]] == TRUE) {
IDtmp = as.character(ID)
sibreport = g.sibreport(ts, ID = IDtmp, epochlength = ws3new, logs_diaries,
desiredtz = params_general[["desiredtz"]])
Expand All @@ -325,66 +326,66 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
dir.create(file.path(metadatadir, ms5.sibreport))
}
shortendFname = gsub(pattern = "[.]|RData|csv|cwa|bin", replacement = "", x = fnames.ms3[i], ignore.case = TRUE)

sibreport_fname = paste0(metadatadir,ms5.sibreport,"/sib_report_", shortendFname, "_",sibDef,".csv")
data.table::fwrite(x = sibreport, file = sibreport_fname, row.names = FALSE,
sep = params_output[["sep_reports"]])
# nap/sib/nonwear overlap analysis

# TO DO

# nap detection
if (params_general[["acc.metric"]] != "ENMO" |
params_sleep[["HASIB.algo"]] != "vanHees2015") {
warning("\nNap classification currently assumes acc.metric = ENMO and HASIB.algo = vanHees2015, so output may not be meaningful")
}
naps_nonwear = g.part5.classifyNaps(sibreport = sibreport,
desiredtz = params_general[["desiredtz"]],
possible_nap_window = params_sleep[["possible_nap_window"]],
possible_nap_dur = params_sleep[["possible_nap_dur"]],
nap_model = params_sleep[["nap_model"]],
HASIB.algo = params_sleep[["HASIB.algo"]])
# store in ts object, such that it is exported in as time series
ts$nap1_nonwear2 = 0
# napsindices = which(naps_nonwear$probability_nap == 1)
# if (length(napsindices) > 0) {
if (length(naps_nonwear) > 0) {
for (nni in 1:nrow(naps_nonwear)) {
nnc_window = which(time_POSIX >= naps_nonwear$start[nni] & time_POSIX <= naps_nonwear$end[nni] & ts$diur == 0)
if (length(nnc_window) > 0) {
if (naps_nonwear$probability_nap[nni] == 1) {
ts$nap1_nonwear2[nnc_window] = 1 # nap
} else if (naps_nonwear$probability_nap[nni] == 0) {
ts$nap1_nonwear2[nnc_window] = 2 # nonwear
if (length(params_sleep[["nap_model"]]) > 0) {
# nap detection
if (params_general[["acc.metric"]] != "ENMO" |
params_sleep[["HASIB.algo"]] != "vanHees2015") {
warning("\nNap classification currently assumes acc.metric = ENMO and HASIB.algo = vanHees2015, so output may not be meaningful")
}
naps_nonwear = g.part5.classifyNaps(sibreport = sibreport,
desiredtz = params_general[["desiredtz"]],
possible_nap_window = params_sleep[["possible_nap_window"]],
possible_nap_dur = params_sleep[["possible_nap_dur"]],
nap_model = params_sleep[["nap_model"]],
HASIB.algo = params_sleep[["HASIB.algo"]])
# store in ts object, such that it is exported in as time series
ts$nap1_nonwear2 = 0
# napsindices = which(naps_nonwear$probability_nap == 1)
# if (length(napsindices) > 0) {
if (length(naps_nonwear) > 0) {
for (nni in 1:nrow(naps_nonwear)) {
nnc_window = which(time_POSIX >= naps_nonwear$start[nni] & time_POSIX <= naps_nonwear$end[nni] & ts$diur == 0)
if (length(nnc_window) > 0) {
if (naps_nonwear$probability_nap[nni] == 1) {
ts$nap1_nonwear2[nnc_window] = 1 # nap
} else if (naps_nonwear$probability_nap[nni] == 0) {
ts$nap1_nonwear2[nnc_window] = 2 # nonwear
}
}
}
}
}
# impute non-naps episodes (non-wear)
nonwearindices = which(naps_nonwear$probability_nap == 0)
if (length(nonwearindices) > 0) {
for (nni in nonwearindices) {
nwwindow_start = which(time_POSIX >= naps_nonwear$start[nni] & time_POSIX <= naps_nonwear$end[nni] & ts$diur == 0)
if (length(nwwindow_start) > 0) {
Nepochsin24Hours = (60/ws3new) * 60 * 24
if (nwwindow_start[1] > Nepochsin24Hours) {
nwwindow = nwwindow_start - Nepochsin24Hours # impute time series with preceding day
if (length(which(ts$nap1_nonwear2[nwwindow] == 2)) / length(nwwindow) > 0.5) {
# if there is also a lot of overlap with non-wear there then do next day
nwwindow = nwwindow_start + Nepochsin24Hours
# impute non-naps episodes (non-wear)
nonwearindices = which(naps_nonwear$probability_nap == 0)
if (length(nonwearindices) > 0) {
for (nni in nonwearindices) {
nwwindow_start = which(time_POSIX >= naps_nonwear$start[nni] & time_POSIX <= naps_nonwear$end[nni] & ts$diur == 0)
if (length(nwwindow_start) > 0) {
Nepochsin24Hours = (60/ws3new) * 60 * 24
if (nwwindow_start[1] > Nepochsin24Hours) {
nwwindow = nwwindow_start - Nepochsin24Hours # impute time series with preceding day
if (length(which(ts$nap1_nonwear2[nwwindow] == 2)) / length(nwwindow) > 0.5) {
# if there is also a lot of overlap with non-wear there then do next day
nwwindow = nwwindow_start + Nepochsin24Hours
}
} else {
nwwindow = nwwindow_start + Nepochsin24Hours # if there is not preceding day use next day
}
} else {
nwwindow = nwwindow_start + Nepochsin24Hours # if there is not preceding day use next day
}
if (max(nwwindow) <= nrow(ts)) { # only attempt imputation if possible
# check again that there is not a lot of overlap with non-wear
if (length(which(ts$nap1_nonwear2[nwwindow] == 2)) / length(nwwindow) > 0.5) {
ts$ACC[nwwindow_start] = ts$ACC[nwwindow] # impute
if (max(nwwindow) <= nrow(ts)) { # only attempt imputation if possible
# check again that there is not a lot of overlap with non-wear
if (length(which(ts$nap1_nonwear2[nwwindow] == 2)) / length(nwwindow) > 0.5) {
ts$ACC[nwwindow_start] = ts$ACC[nwwindow] # impute
}
}
}
}
}
}
} else {
sibreport = NULL
}
ts$window = 0
# backup of nightsi outside threshold defintions to avoid
Expand All @@ -395,7 +396,7 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
for (TRVi in params_phyact[["threshold.vig"]]) {
# derive behavioral levels (class), e.g. MVPA, inactivity bouts, etc.
levelList = identify_levels(ts = ts, TRLi = TRLi, TRMi = TRMi, TRVi = TRVi,
ws3 = ws3new, params_phyact = params_phyact)
ws3 = ws3new, params_phyact = params_phyact)
LEVELS = levelList$LEVELS
OLEVELS = levelList$OLEVELS
Lnames = levelList$Lnames
Expand Down Expand Up @@ -503,7 +504,8 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
fullFilename = fullfilenames[i],
add_one_day_to_next_date,
lightpeak_available, tail_expansion_log,
foldernamei = foldername[i])
foldernamei = foldername[i],
sibreport = sibreport)
# Extract essential object to be used as input for the next
# segment
indexlog = gas$indexlog
Expand All @@ -523,6 +525,17 @@ g.part5 = function(datadir = c(), metadatadir = c(), f0=c(), f1=c(),
ws3new = timeList$epochSize
if (doNext == TRUE) next
}
#===============================================
# FOLDER STRUCTURE
if (params_output[["storefolderstructure"]] == TRUE) {
if ("filename_dir" %in% ds_names) fi = which( ds_names == "filename_dir")
dsummary[di,fi] = fullfilenames[i] #full filename structure
ds_names[fi] = "filename_dir"; fi = fi + 1
dsummary[di,fi] = foldername[i] #store the lowest foldername
if ("foldername" %in% ds_names) fi = which( ds_names == "foldername")
ds_names[fi] = "foldername"; fi = fi + 1
}
di = di + 1
}
}
di = di + 1
Expand Down
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