sensorIMG: sensor image

In KiranLDA/PAMLr: Suite Of Functions For Manipulating Pressure, Activity, Magnetism, Temperature And Light Data In R

sensor image

Description

This function plots sensor data as an image. An actogram for instance is a type of sensor image.

Usage

sensorIMG(
  date,
  sensor_data,
  tz = "UTC",
  plotx = TRUE,
  ploty = TRUE,
  labelx = TRUE,
  labely = TRUE,
  offset = 0,
  dt = NA,
  xlab = "Hour",
  ylab = "Date",
  cex = 2,
  col = c("black", viridis::magma(90)),
  ...
)

Arguments

date	Date data in POSIXct format, most commonly PAM_data$acceleration$date
sensor_data	sensor data, for example look at PAM_data$acceleration$act
tz	Time zone for POSIXct, default set to "UTC"
plotx	wherether or not to plot the x axis ticks + labels (for instance when compiling multifigures)
ploty	wherether or not to plot the y axis ticks + labels (for instance when compiling multifigures)
labelx	wherether or not to write the name of the x axis (for instance when compiling multifigures)
labely	wherether or not to write the name of the y axis (for instance when compiling multifigures)
offset	This parameter determines where the center of the graph is. When offset = 0, then midday is at the center of the graph. when offset=12 midnight'
dt	the time interval to which the data are resampled (secs). Default is NA
xlab	label for x-axis (as a character string)
ylab	label for y axis (as a character string)
cex	size of labels
col	Colour scheme of plot. Default col = c("black",viridis::magma(90))
...	Any additional parameters used by graphics::image

Value

an image of the sensor data, for instance with activity it would produce an actogram

Examples

##specify the data location
#data(hoopoe)
#start = as.POSIXct("2016-07-01","%Y-%m-%d", tz="UTC")
#end = as.POSIXct("2017-06-01","%Y-%m-%d", tz="UTC")
#PAM_data = cutPAM(hoopoe,start,end)

## Create plots with 3 together (mfrow)
#par( mfrow= c(1,3), oma=c(0,2,0,6))

#par(mar =  c(4,2,4,2))
#sensorIMG(PAM_data$acceleration$date, ploty=FALSE,
#          PAM_data$acceleration$act, main = "Activity",
#          col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2)

#par(mar =  c(4,2,4,2))
#sensorIMG(PAM_data$pressure$date, plotx=TRUE, ploty=FALSE, labely=FALSE,
#          PAM_data$pressure$obs,  main="Pressure",
#          col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2)

#par(mar =  c(4,2,4,2))
#sensorIMG(PAM_data$temperature$date, labely=FALSE,
#          PAM_data$temperature$obs,  main="Temperature",
#          col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2)

######################################################
# Look at a classification output
######################################################

## Classification
#classification  =  classifyFLAP(dta = PAM_data$acceleration, period = 10, toPLOT=FALSE)

#par( mfrow= c(1,3), oma=c(0,2,0,6),mar =  c(4,2,4,2))

#sensorIMG(PAM_data$pressure$date, c(0,abs(diff(PAM_data$pressure$obs))),
#          main="Pressure  difference",
#          ploty=FALSE,
#          col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2)

#sensorIMG(PAM_data$acceleration$date, PAM_data$acceleration$act,  main="Activity",
#          ploty=FALSE, labely=FALSE,
#          col=c(viridis::cividis(90)), cex=1.2, cex.main = 2)

#sensorIMG(PAM_data$acceleration$date,
#          ifelse(classification$classification == classification$migration, 1,2),
#          main="Migration Classification",
#          labely=FALSE,
#          col = c("orange","black"),
#          cex=1.2, cex.main = 2)


#twilights <- GeoLight::twilightCalc(PAM_data$light$date,
#                                    PAM_data$light$obs,
#                                    LightThreshold = 2,
#                                    ask = FALSE)

#addTWL(twilights$tFirst, offset=0,
#       col= ifelse(twilights$type == 1,
#                   "goldenrod","cornflowerblue"),
#       pch=16, cex=0.5)

KiranLDA/PAMLr documentation built on March 6, 2023, 1:40 p.m.