Add Spectrogram Annotations to Pamguard Database

Description

Add new annotations to an existing Pamguard Spectrogram Annotations table

Usage

addPgAnno(
  db,
  anno,
  tableName = NULL,
  channel = 1,
  source = c("manual", "aplose", "pammisc", "annomate", "raven"),
  format = c("%m/%d/%Y %H:%M:%OS", "%m-%d-%Y %H:%M:%OS",
    "%Y/%m/%d %H:%M:%OS", "%Y-%m-%d %H:%M:%OS"),
  tz = "UTC"
)

Arguments

Value

Returns a dataframe of the rows added to the database

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
myDb <- 'PamguardDatabase.sqlite3'
myAnno <- data.frame(UTC = '2021/10/23 12:10:10', Duration = .563, f1=2300, f2=3600)
addPgAnno(myDb, myAnno, tableName='Spectrogram_Annotation', source='manual')

## End(Not run)

Add Pamguard Event to Database

Description

Add a new event to an existing Pamguard database in the "OfflineEvents" table. If the specified eventType does not exist in the database, it will be added to the "Lookup" table.

Usage

addPgEvent(
  db,
  UIDs = NULL,
  binary,
  eventType,
  comment = NA,
  tableName = NULL,
  start = NULL,
  end = NULL,
  type = c("click", "dg")
)

Arguments

Value

Adds to the database db, invisibly returns TRUE if successful

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
myDb <- 'PamguardDatabase.sqlite3'
myBinaries <- c('./Binaries/Bin1.pgdf', './Binaries/Bin2.pgdf')
addUIDs <- c(10000001, 10000002, 20000007, 20000008)
addPgEvent(db = myDb, UIDs = addUIDs, binary = myBinaries, eventType = 'MyNewEvent')

## End(Not run)

Add GPS to a Pamguard Database

Description

Add GPS data to an existing Pamguard database

Usage

addPgGps(
  db,
  gps,
  source = c("csv", "SPOTcsv", "SPOTgpx"),
  format = c("%m/%d/%Y %H:%M:%S", "%m-%d-%Y %H:%M:%S",
    "%Y/%m/%d %H:%M:%S", "%Y-%m-%d %H:%M:%S"),
  tz = "UTC"
)

Arguments

Value

Adds to the database db, invisibly returns the Name of the GPS track if successful (NA if not named)

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# not run because example files don't exist
myDb <- 'PamguardDatabase.sqlite3'
# adding from a .gpx file downloaded from SPOT
spotGpx <- 'SpotGPX.gpx'
addPgGps(myDb, spotGpx, source='SPOTgpx')
# adding from a csv file with a Y-M-D H:M date format
gpsCsv <- 'GPS.csv'
addPgGps(myDb, gpsCsv, source='csv', format='%Y-%m-%d %H:%M')

## End(Not run)

Browse a List of Environmental Datasets

Description

This function browses the list of selected environmental datasets that are recommended as a starting point, and prompts the user to select one to use, returning an edinfo object. Also allows user to filter by variable name, matching will be attempted using regex

Usage

browseEdinfo(var = NULL)

Arguments

Value

Returns an edinfo class object that can be used to get environmental data with other functions

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# browse the full list (interactive)
edi <- browseEdinfo()

# search for sst datasets (interactive)
edi <- browseEdinfo(var='sst')

## End(Not run)

Create Sound Speed Profiles

Description

Creates sound speed profiles (Depth vs Sound Speed) using temperature and salinity data downloaded from HYCOM data servers

Usage

createSSP(
  x,
  f = 30000,
  nc = NULL,
  ncVars = c("salinity", "water_temp"),
  dropNA = TRUE,
  progress = TRUE,
  ...
)

Arguments

Value

a list with one element for each row of x, each element is a list containing speed, the sound speed (m/s), and depth (m)

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# examples not run because they require internet connection
coords <- data.frame(UTC=as.POSIXct('2014-07-15 01:00:00', tz='UTC'),
                     Longitude = -119, Latitude = 33)
ssp <- createSSP(coords)
plot(x=ssp[[1]]$speed, y=-ssp[[1]]$depth, type='l')

## End(Not run)

Create List of the Ranges of Coordinates

Description

Creates a named list with the ranges of Longitude, Latitude, and Time (UTC) data for use in functions like formatURL. Can also specify an amount to buffer the min and max values by for each coordinate

Usage

dataToRanges(data, buffer = c(0, 0, 0))

Arguments

Value

a list with the ranges of coordinates for Longitude, Latitude, and UTC. Ranges are listed as c(left, right), so if your data spans across the dateline

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

gps <- data.frame(Latitude = c(32, 32.1, 32.2, 32.2, 32.2),
                  Longitude = c(-110, -110.1, -110.2, -110.3, -110.4),
                  UTC = as.POSIXct(c('2000-01-01 00:00:00', '2000-01-01 00:00:10',
                                     '2000-01-01 00:00:20', '2000-01-01 00:00:30',
                                     '2000-01-01 00:00:40')))
dataToRanges(gps)

dataToRanges(gps, buffer = c(.05, .05, 86400))

Decimate Wave Files

Description

Decimate a folder of .wav files or a single .wav file to a new sample rate.

Usage

decimateWavFiles(inDir, outDir, newSr, progress = TRUE)

Arguments

Details

This code is based on R code written by Jay Barlow.

Value

Invisibly returns the names of all files that were successfully decimated

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

# one 20kHz wav file is included in package test data
origDir <- system.file('extdata', package='PAMmisc')
decDir <- file.path(tempdir(), 'decSR')
decWavs <- decimateWavFiles(origDir, decDir, 10000)
file.remove(decWavs)

Download Environmental Data

Description

Downloads environmental data matching the coordinates in a set of data

Usage

downloadEnv(
  data,
  edinfo,
  fileName = NULL,
  buffer = c(0, 0, 0),
  timeout = 120,
  progress = TRUE,
  ...
)

Arguments

Value

if download is successful, invisibly returns the filename. If it fails returns FALSE.

If successful, the file name of downloaded data. If not, returns FALSE

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

data <- data.frame(Latitude = 32, Longitude = -117,
                   UTC = as.POSIXct('2000-01-01 00:00:00', tz='UTC'))
## Not run: 
# not run because download could take time
# download jplMURSST41 dataset
edi <- erddapToEdinfo('jplMURSST41')
ncFile <- downloadEnv(data, edi, 'sstData.nc')

# browse suggested sst datasets, then download
edi <- browseEdinfo(var='sst')
ncFile <- downloadEnv(data, edi, 'sstData.nc')

## End(Not run)

Create a URL for Downloading Data from a edinfo Object

Description

Creates a properly formatted URL (see formatURL) from a datalist either from the package's recommended sources or an ERDDAP dataset id

Usage

edinfoToURL(edinfo, ranges)

Arguments

Value

a properly formatted URL that can be used to download environmental data

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

sstEdi <- getEdinfo()[['jplMURSST41']]
# select all variables for download
sstEdi <- varSelect(sstEdi, TRUE)
edinfoToURL(sstEdi, ranges = list(Latitude = c(32, 33),
                                  Longitude = c(-118, -117),
                                  UTC = as.POSIXct(c('2000-01-01 00:00:00',
                                                     '2000-01-02 00:00:00'), tz='UTC')))

A list of edinfo objects from ERDDAP data sources

Description

A list of edinfo objects, mostly used internally for functions. These objects represent different environmental data sources from ERDDAP servers and are used to download environmental data.

Usage

erddapList

Format

A list with objects of class edinfo

Source

Southwest Fisheries Science Center / NMFS / NOAA

Create an edinfo Object from an ERDDAP Dataset Id

Description

Creates an edinfo object that can be used to create a URL for downloading environmental data using edinfoToURL

Usage

erddapToEdinfo(
  dataset,
  baseurl = c("https://upwell.pfeg.noaa.gov/erddap/",
    "https://coastwatch.pfeg.noaa.gov/erddap/", "https://www.ncei.noaa.gov/erddap/",
    "https://erddap.sensors.ioos.us/erddap"),
  chooseVars = TRUE
)

hycomToEdinfo(
  dataset = "GLBy0.08/expt_93.0",
  baseurl = "https://ncss.hycom.org/thredds/ncss/",
  chooseVars = TRUE
)

Arguments

Value

an edinfo list object that can be used to download environmental data

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# examples not run because they require internet connection
sstEdi <- erddapToEdinfo('jplMURSST41')
# dataset from a diferent erddap server
sshEdi <- erddapToEdinfo('hawaii_soest_2ee3_0bfa_a8d6',
                          baseurl = 'http://apdrc.soest.hawaii.edu/erddap/')
# THese work the same - erddap function will pass to hycom if appears to be hycom dataset
hycomEdi <- hycomToEdinfo('GLBy0.08/expt_93.0')
hycomEdi <- erddapToEdinfo('GLBy0.08/expt_93.0')

## End(Not run)

Load Wave File

Description

loads data from a WAVE file

Usage

fastReadWave(where, from = 0, to = NA_real_, header = FALSE, toWaveMC = FALSE)

Arguments

Details

WAVE is a RIFF (Resource Interchange File Format) widely used for storage of uncompressed audio data. It is often identified by the extension .WAV on DOS-legacy systems (such as Windows). Although WAVE files may contain compressed data, the above functions only support plain, uncompressed PCM data.

This function was originally written Simon Urbanek, all credit for the bulk of the C programming goes to him. Adapted by Taiki Sakai to add additional features and fix some bugs. See additional license comments in file.c

Value

returns an object of the class audioSample as loaded from the WAVE file, or if header=TRUE a named list with the sample.rate, num channels, bit rate, and sample length. audioSample objects store the wav data as a matrix wth one row for every channel and attributes "rate" and "bits"

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Find Estimated Echo Times

Description

Finds the estimated times of echoes in a waveform clip. This function was developed to estimate the time of a surface reflected echo of echolocation clicks of deep diving marine mammals. The times of echoes are estimated by finding peaks in the autocorrelation of a signal.

Usage

findEchoTimes(
  wav,
  sr = NULL,
  filter = NULL,
  clipLen = 0.03,
  peakMin = 0.01,
  minTime = 0.001,
  maxTime = NULL,
  channel = NULL,
  n = 3,
  plot = TRUE,
  plotText = NULL
)

Arguments

Value

a list with elements mag, time and wav

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Format URL for Environmental Data Download

Description

This creates a properly formatted URL for downloading environmental data either from an ERDDAP or HYCOM server. This URL can be pasted into a browser or submitted to something like httr::GET to actually download the data. Also see edinfoToURL

Usage

formatURL(
  base,
  dataset,
  fileType,
  vars,
  ranges,
  stride = 1,
  style = c("erddap", "hycom")
)

Arguments

Value

a properly formatted URL that can be used to download environmental data

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

formatURL(
    base = "https://upwell.pfeg.noaa.gov/erddap/griddap/",
    dataset = "jplMURSST41",
    fileType = "nc",
    vars = "analysed_sst",
    ranges = list(
               Latitude = c(30, 31),
               Longitude = c(-118, -117),
               UTC = as.POSIXct(c('2005-01-01 00:00:00', '2005-01-02 00:00:00'), tz='UTC')
             ),
    stride=1,
    style = 'erddap'
)

Browse a List of Curated Environmental Datasets

Description

This function gets the list of environmental datasets provided as a recommended starting point for various measures

Usage

getEdinfo()

Value

a list of edinfo list objects

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

ediList <- getEdinfo()
ediList[[1]]
ediList[['jplMURSST41']]

getFigshareInfo

Description

downloads filename and recording URL information from a Figshare article. Requires a users API token from their figshare account

Usage

getFigshareInfo(token, id)

Arguments

Value

dataframe with columns filename and recording_url

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

A list of edinfo objects from HYCOM data sources

Description

A list of edinfo objects, mostly used internally for functions. These objects represent different environmental data sources from HYCOM servers and are used to download environmental data.

Usage

hycomList

Format

A list with objects of class edinfo

Source

Southwest Fisheries Science Center / NMFS / NOAA

Match Data From an Existing Netcdf File or Download and Match

Description

Extracts all variables from a netcdf file matching Longitude, Latitude, and UTC coordinates in given dataframe

Usage

matchEnvData(
  data,
  nc = NULL,
  var = NULL,
  buffer = c(0, 0, 0),
  FUN = c(mean),
  fileName = NULL,
  progress = TRUE,
  depth = 0,
  ...
)

## S4 method for signature 'data.frame'
matchEnvData(
  data,
  nc = NULL,
  var = NULL,
  buffer = c(0, 0, 0),
  FUN = c(mean),
  fileName = NULL,
  progress = TRUE,
  depth = 0,
  ...
)

Arguments

Value

original dataframe with three attached columns for each variable in the netcdf file, one for each of mean, median, and standard deviation of all values within the buffer

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

data <- data.frame(Latitude = 32, Longitude = -117,
                   UTC = as.POSIXct('2004-12-31 09:00:00', tz='UTC'))
## Not run: 
# Not run because downloads files
# default calculates mean, median, and standard deviation
matchEnvData(data, nc='jplMURSST41', var=c('analysed_sst', 'analysis_error'))
# get just mean within a buffer around coordinates
matchEnvData(data, nc='jplMURSST41', var=c('analysed_sst', 'analysis_error'),
             FUN = mean, buffer = c(.01, .01, 86400))

## End(Not run)
# Can also work from an existing nc file
nc <- system.file('extdata', 'sst.nc', package='PAMmisc')
matchEnvData(data, nc = nc)
# Using a custom function
meanPlusOne <- function(x) {
  mean(x, na.rm=TRUE) + 1
}
matchEnvData(data, nc=nc, FUN=c(mean, meanPlusOne))

Match Data From a Netcdf File

Description

Extracts all variables from a netcdf file matching Longitude, Latitude, and UTC coordinates in given dataframe

Usage

ncToData(
  data,
  nc,
  var = NULL,
  buffer = c(0, 0, 0),
  FUN = c(mean),
  raw = FALSE,
  keepMatch = TRUE,
  progress = TRUE,
  depth = 0,
  verbose = TRUE,
  ...
)

Arguments

Value

original dataframe with three attached columns for each variable in the netcdf file, one for each of mean, median, and standard deviation of all values within the buffer

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

data <- data.frame(Latitude = 32, Longitude = -117,
                   UTC = as.POSIXct('2005-01-01 00:00:00', tz='UTC'))
nc <- system.file('extdata', 'sst.nc', package='PAMmisc')
# default calculates mean
ncToData(data, nc = nc)
# calculate mean, median, and sd
ncToData(data, nc=nc, FUN=c(mean, median, sd), buffer = c(.01, .01, 86400))
# custom function
meanPlusOne <- function(x) {
    mean(x, na.rm=TRUE) + 1
}
ncToData(data, nc=nc, FUN=c(mean, meanPlusOne))

Find Peaks and Troughs in a Spectrum

Description

Finds up to three peaks in a spectrum, as well as the troughs between those peaks.

Usage

peakTrough(spec, freqBounds = c(10, 30), dbMin = -15, smooth = 5, plot = FALSE)

Arguments

Details

The first peak is the frequency with the highest dB level (first and last frequency points are ignored). Then this uses a very simple algorithm to find second and third peaks in a spectrum. Peak candidates are identified with a few simple steps:

Step 1

Use a local average of (smooth) points to smooth the spectrum.

Step 2

Check if a point is larger than both its neighbors.

Step 3

Check if points are within the frequency range specified by freqBounds. Points must be at least f1 kHz away from the frequency , but no further than f2 kHz away.

Step 4

Check if points are above the minimum dB level specified by dbMin.

From the remaining points the point with the highest dB level is selected as the second peak, then the frequency range filter of Step 3 is applied again around this second peak before attempting to find a third peak. If no second or third peak is found (ie. no values fall within the specified frequency and dB search ranges), then it will be set to 0. The trough values are set as the frequency with the lowest dB level between any peaks that were found. The trough values will be 0 for any peaks that were not found.

If you are unsure of what levels to specify for freqBounds and dbMin, setting plot=TRUE will show a visualization of the search range and selected peaks so you can easily see if the selected parameters are capturing the behavior you want.

Value

a dataframe with the frequencies (in kHz) of up to 3 peaks and 2 troughs between those peaks. Also reports the peak-to-peak distance. Any peaks / troughs that were not able to be found (based on freqBounds and dbMin parameters) will be 0.

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

clickWave <- createClickWave(signalLength = .1, clickLength = 1000, clicksPerSecond = 200,
                             frequency = 3e3, sampleRate = 10e3)
peakTrough(seewave::spec(clickWave, plot=FALSE), plot=TRUE)

plotPresBar

Description

Creates a bar plot of the presence or density of detections across time

Usage

plotPresBar(
  x,
  start = NULL,
  end = NULL,
  bin = "hour/day",
  by = NULL,
  title = TRUE,
  fill = "grey35",
  format = c("%m/%d/%Y %H:%M:%S", "%m-%d-%Y %H:%M:%S",
    "%Y/%m/%d %H:%M:%S", "%Y-%m-%d %H:%M:%S"),
  plotTz = "UTC"
)

Arguments

Value

a ggplot2 object

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

df <- data.frame(UTC = as.POSIXct(runif(1e2, min=0, max=7*24*3600),
                                  origin='1970-01-01 00:00:00', tz='UTC'),
                 label = sample(letters[1:3], 1e2, replace=TRUE))
# hours per day with detections
plotPresBar(df, bin='hour/day')
# calls per day - these options are identical
plotPresBar(df, bin='day')
plotPresBar(df, bin='call/day')
plotPresBar(df, bin='calls/day')
# calls per day, colored by 'label'
plotPresBar(df, bin='day', by='label')

plotPresGrid

Description

Creates a grid plot of the presence or density of detections across time where the x-axis is the hour of the day and the y-axis is the date

Usage

plotPresGrid(
  x,
  start = NULL,
  end = NULL,
  bin = c("hour", "minute", "30min", "15min"),
  type = c("presence", "density"),
  by = NULL,
  alpha = 0.5,
  gps = NULL,
  format = c("%m/%d/%Y %H:%M:%S", "%m-%d-%Y %H:%M:%S",
    "%Y/%m/%d %H:%M:%S", "%Y-%m-%d %H:%M:%S"),
  fill = "blue",
  color = NA,
  cmap = viridis_pal()(25),
  title = TRUE,
  plotTz = "UTC"
)

Arguments

Value

a ggplot2 object

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

df <- data.frame(UTC = as.POSIXct(runif(1e2, min=0, max=7*24*3600),
                                  origin='1970-01-01 00:00:00', tz='UTC'),
                 label = sample(letters[1:3], 1e2, replace=TRUE))
plotPresGrid(df, type='presence', bin='hour')
plotPresGrid(df, type='density', bin='hour')
plotPresGrid(df, type='density', bin='30min')
gps <- data.frame(UTC = as.POSIXct('1970-01-01 00:00:00', tz='UTC'),
                 Latitude=32.4,
                 Longitude = -118)
plotPresGrid(df, gps=gps, bin='hour')
# coloring presence grid by label column
plotPresGrid(df, gps=gps, by='label')
# can be confusing if there is a lot of overlap, ggplot output can be split
library(ggplot2)
plotPresGrid(df, gps=gps, by='label') + facet_wrap(vars(label), ncol=2)
# using "by" with type="density" defaults to this facet_wrap behavior
# since color is already being used to represent density
plotPresGrid(df, gps=gps, by='label', type='density')
# can adjust facet_wrap parameters by adding it again
plotPresGrid(df, gps=gps, by='label', type='density') + facet_wrap(vars(label), ncol=2)

Estimate Power Spectral Density Using Welch's Method

Description

Estimates the power spectral density (PSD) of an input signal using Welch's method. This should function similarly to the Matlab function pwelch, but results may not be identical. Breaks the input signal into (usually) overlapping frames and averages the resulting PSD estimates

Usage

pwelch(
  x,
  nfft,
  noverlap = 0,
  sr = NULL,
  window = NULL,
  demean = c("long", "short", "none"),
  channel = 1
)

Arguments

Value

returns a list with items spec, the PSD estimate of the input signal, and freq, the frequency values (Hz) at each value of spec

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

# wav example is synthetic echolocation clicks at 4kHz
wavFile <- system.file('extdata/testWav.wav', package='PAMmisc')
psd <- pwelch(wavFile, nfft=1e3, noverlap=500, demean='long')
plot(x=psd$freq, y=10*log10(psd$spec), type='l')

Raytrace Through a Soundspeed Profile

Description

Traces the ray of a sound through a varying soundspeed profile for a fixed amount of time. Also plots the provided sound speed profile and all traces generated. All code here is based on MATLAB code originally written by Val Schmidt from the University of New Hampshire Val Schmidt (2021). raytrace https://www.mathworks.com/matlabcentral/fileexchange/26253-raytrace), MATLAB Central File Exchange. Retrieved June 29, 2021.

Usage

raytrace(x0, z0, theta0, tt, zz, cc, plot = TRUE, progress = FALSE)

Arguments

Value

A list with four elements: x, the horizontal coordinates of ray path, z the vertical coordinates of ray path, t actual travel time of ray in seconds, and d the total distance the ray traveled. Each individual item in the output is a list with one entry for each theta0 provided.

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

#  Setup the sound speed profile
zz <- seq(from=0, to=5000, by=1)
cc <- 1520 + zz * -.05
cc[751:length(cc)] <- cc[750] + (zz[751:length(zz)] - zz[750])*.014
rt <- raytrace(0, 0, 5, 120, zz, cc, TRUE)

Read Tracks from a GPX File

Description

Read in a GPX file and convert the tracks to a dataframe

Usage

readGPXTrack(x)

Arguments

Value

a dataframe with columns Latitude, Longitude, UTC, and Name

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

gpxFile <- system.file('extdata', 'GPX.gpx', package='PAMmisc')
gpxData <- readGPXTrack(gpxFile)
str(gpxData)

Read Pamguard Spectrogram Annotation Table

Description

Reads the Spectrogram Annotation table from a PAMGuard database and applies some minor formatting

Usage

readSpecAnno(db, table = "Spectrogram_Annotation")

Arguments

Value

a dataframe containing spectrogram annotation data

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
myDb <- 'PamguardDatabase.sqlite3'
specAnno <- readSpecAnno(db)

## End(Not run)

Perform QA/QC on Soundtrap Files

Description

Gathers data from Soundtrap XML log files to perform QA/QC on a set of recordings.

Usage

soundtrapQAQC(
  dir,
  outDir = NULL,
  xlim = NULL,
  label = NULL,
  voltSelect = c("internal", "external"),
  plot = TRUE
)

processSoundtrapLogs(dir, voltSelect = c("internal", "external"))

Arguments

Value

list of dataframes with summary data for $xmlInfo, $sudInfo, and $wavInfo

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# not run
stDir <- './Data/SoundtrapFiles/'
stData <- soundtrapQAQC(stDir, plot=TRUE)
# save data
stData <- soundtrapQAQC(stDir, outDir='./Data/SoundtrapFiles/QAQC', plot=TRUE)
# or provide separate folders of data
stDirs <- c('./Data/SoundtrapFiles/SUDFiles',
            './Data/SoundtrapFiles/WavFiles',
            './Data/SoundtrapFiles/XMLFiles')
stData <- soundtrapQAQC(stDirs, plot=TRUE)

## End(Not run)

Compress a List by Name

Description

Attempts to compress a list by combining elements with the same name, searching recursively if there are lists in your list

Usage

squishList(myList, unique = FALSE)

Arguments

Details

items with the same name are assumed to have the same structure and will be combined. Dataframes will be combined with bind_rows, vectors just be collapsed into one vector, matrices will be combined with rbind, lists will be combined recursively with another call to squishList

Value

a list with one element for every unique name in the original list

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

myList <- list(a=1:3, b=letters[1:4], a=5:6, b=letters[4:10])
squishList(myList)

myList <- list(a=1:3, b=data.frame(x=1:3, y=4:6), b=data.frame(x=10:14, y=1:5))
squishList(myList)

myList <- list(a=list(c=1:2, d=2), b=letters[1:3], a=list(c=4:5, d=6:9))
squishList(myList)

Mark Straight Path Segments in GPS Track

Description

This function attempts to mark portions of a GPS track where a ship is traveling in a straight line by comparing the recent average heading with a longer term average heading. If these are different, then the ship should be turning. Note this currently does not take in to account time, only number of points

Usage

straightPath(gps, nSmall = 10, nLarge = 60, thresh = 10, plot = FALSE)

Arguments

Value

the original dataframe gps with an added logical column straight indicating which portions are approximately straight

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

gps <- data.frame(Latitude = c(32, 32.1, 32.2, 32.2, 32.2),
                  Longitude = c(-110, -110.1, -110.2, -110.3, -110.4),
                  UTC = as.POSIXct(c('2000-01-01 00:00:00', '2000-01-01 00:00:10',
                                     '2000-01-01 00:00:20', '2000-01-01 00:00:30',
                                     '2000-01-01 00:00:40')),
                  Heading = c(320, 320, 270, 270, 270),
                  Speed = c(.8, .8, .5, .5, .5))

straightPath(gps, nSmall=1, nLarge=2)

straightPath(gps, nSmall=1, nLarge=4)

trainSplitPermute

Description

Find a desired train/val/test split of a dataset through random permutation. Uses a variable in your dataset to randomly split by (for example, could be the location of different sites, or different months of data), then tries to find the split that most closesly matches your desired distribution of data for a set of labels. It can often be difficult to find a good split if the distribution of your labels is not consistent across sites, so this function tries a bunch of random splits then uses a score to find the best one.

Usage

trainSplitPermute(
  x,
  probs = c(0.7, 0.15, 0.15),
  n = 1000,
  splitBy = "drift",
  label = "species",
  countCol = NULL,
  minCount = c(1, 1, 1),
  top = 3,
  seed = 112188
)

Arguments

Value

a list of the top results. Each individual result contains $splitMap containing the random split marked as integer 1, 2, 3 corresponding to train, val, test and $splitVec a vector marking each row of x with its category. These two results are named by the levels of splitBy. $distribution a table of the distribution of label in the split, and $score the split score (lower is closer to desired probs)

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

# making some dummy data
df <- data.frame(
    species = sample(letters[1:5], prob=c(.4, .2, .1, .1, .2), 1e3, replace=TRUE),
    site = sample(LETTERS[1:12], 1e3, replace=TRUE),
    event = 1:1e3
)
# try a split with n=3
split <- trainSplitPermute(df, probs=c(.7, .15, .15), n=3, label='species', splitBy='site')
# assign the best split as the split cateogry
df$split <- split[[1]]$splitVec
# distribution is not close to our desired .7, .15, .15 split because n is too low
round(table(df$species, df$split) /
    matrix(rep(table(df$species), 3), nrow=5), 2)

# rerun with higher n to get closer to desired distribution
split <- trainSplitPermute(df, probs=c(.7, .15, .15), n=1e3, label='species', splitBy='site')
df$split <- split[[1]]$splitVec
round(table(df$species, df$split) /
    matrix(rep(table(df$species), 3), nrow=5), 2)
    
# adding a new site that has significantly more detections than others
addSite <- data.frame(
    species = sample(letters[1:5], 500, replace=TRUE),
    site = rep(LETTERS[13], 500),
    event = 1001:1500)
df$split <- NULL
df <- rbind(df, addSite)

# now just splitting by site does not result in a balanced split for our number of species
# it splits the sites to approx .7, .15, .15 but this does not result in balanced species
split <- trainSplitPermute(df, probs=c(.7, .15, .15), n=1e3, label='species', splitBy='site')
df$split <- split[[1]]$splitVec
round(table(df$species, df$split) /
    matrix(rep(table(df$species), 3), nrow=5), 2)

# adding 'event' as a countCol fixes this
split <- trainSplitPermute(df, probs=c(.7, .15, .15), n=1e3, label='species', 
    splitBy='site', countCol='event')
df$split <- split[[1]]$splitVec
round(table(df$species, df$split) /
    matrix(rep(table(df$species), 3), nrow=5), 2)

Update Detection UIDs

Description

Update the UIDs of detections in a Pamguard database. UIDs can become mismatched when re-running data, this will attempt to re-associate the new UIDs in binary files with detections in the database

Usage

updateUID(db, binaries, verbose = TRUE, progress = TRUE)

Arguments

Value

Same database as db, but with an additional column "newUID" added to each detection table with updated UIDs if found. "newUID" will be -1 for any detections where no match was found

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

## Not run: 
# not run because sample data does not exist
db <- 'MismatchedUid.sqlite3'
bin <- './BinaryFolder'
updateUID(db, bin)

## End(Not run)

Utility for Selecting Variables to Download

Description

Loops through the available variables in an edinfo object and asks whether or not each should be downloaded, then stores the result for passing on to formatURL

Usage

varSelect(edinfo, select = NULL)

Arguments

Value

the same object as edinfo with an updated varSelect field

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

sstEdi <- getEdinfo()[['jplMURSST41']]
## Not run: 
# interactively select
sstEdi <- varSelect(sstEdi)

## End(Not run)
# select all variables
sstEdi <- varSelect(sstEdi, TRUE)
# select the first two of four
sstEdi <- varSelect(sstEdi, c(TRUE, TRUE, FALSE, FALSE))

Calculate the Wigner-Ville Transform of a Signal

Description

Calculates the Wigner-Ville transform a signal. By default, the signal will be zero-padded to the next power of two before computing the transform, and creates an NxN matrix where N is the zero-padded length. Note that this matrix can get very large for larger N, consider shortening longer signals.

Usage

wignerTransform(signal, n = NULL, sr, plot = FALSE)

Arguments

Details

This code mostly follows Pamguard's Java code for computing the Wigner-Ville and Hilbert transforms.

Value

a list with three items. tfr, the real values of the wigner transform as a matrix with n rows and number of columns equal to the next power of two from the length of the input signal. f and t the values of the frequency and time axes.

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

clickWave <- createClickWave(signalLength = .05, clickLength = 1000, clicksPerSecond = 200,
                             frequency = 3e3, sampleRate = 10e3)
wt <- wignerTransform(clickWave@left, n = 1000, sr = 10e3, plot=TRUE)

Write Amplitude Modulated Waveform

Description

Write a wave file for a synthesized amplitude modulated call

Usage

writeAMWave(
  fileName,
  outDir,
  signalLength,
  modFrequency,
  frequency,
  sampleRate,
  window = c(0.55, 0.45),
  silence = c(0, 0),
  gainFactor = 0.1
)

createAMWave(
  signalLength,
  modFrequency,
  frequency,
  sampleRate,
  window = c(0.55, 0.45),
  silence = c(0, 0),
  gainFactor = 0.1
)

Arguments

Details

Amplitude modulated signals are modelled as an ideal sinusoid multiplied by a window function. The window function is an offset sinusoid with frequency equal to the modulation frequency:

W = .5 + .45 * sin(2 \pi mf t)

See example(writeAMWave) for a plot showing how this works.

Value

writeAMWave invisibly returns the file name, createAMWave returns a Wave class object

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

# Visualisation of modelled AM wave
signal <- sin(2*pi*100*(1:1000)/1000)
window <- .55 + .45 * sin(2*pi*15*(1:1000)/1000)
oldMf <- par()$mfrow
par(mfrow=c(3,1))
plot(signal, type='l')
plot(window, type='l')
plot(window*signal, type='l')
tmpFile <- file.path(tempdir(), 'tempWav.wav')
writeAMWave(tmpFile, signalLength = 1, modFrequency = 1000,
               frequency = 30000, sampleRate = 100000)
file.remove(tmpFile)
amWave <- createAMWave(signalLength = 1, modFrequency = 1000,
                             frequency = 30e3, sampleRate = 100e3)
par(mfrow=oldMf)

Write Click Waveform

Description

Write a wave file for a synthesized delphinid click

Usage

writeClickWave(
  fileName,
  outDir,
  signalLength,
  clickLength,
  clicksPerSecond,
  frequency,
  sampleRate,
  silence = c(0, 0),
  gainFactor = 0.1
)

createClickWave(
  signalLength,
  clickLength,
  clicksPerSecond,
  frequency,
  sampleRate,
  silence = c(0, 0),
  gainFactor = 0.1
)

Arguments

Details

This code is based on Matlab code by Julie Oswald (2004). Clicks are simulated as an exponentially damped sinusoid.

Value

writeClickWave invisibly returns the file name, createClickWave returns a Wave class object

Author(s)

Taiki Sakai taiki.sakai@noaa.gov

Examples

tmpFile <- file.path(tempdir(), 'tempWav.wav')
writeClickWave(tmpFile, signalLength = 1, clickLength = 100, clicksPerSecond = 200,
               frequency = 30000, sampleRate = 100000)
file.remove(tmpFile)
clickWave <- createClickWave(signalLength = 1, clickLength = 100, clicksPerSecond = 200,
                             frequency = 30e3, sampleRate = 100e3)