circleCorrect: Shift localization responses so they are centred on the...

In thartbm/SMCL: Utilities for common tasks in the SensoriMotor Control Lab.

Shift localization responses so they are centred on the origin

Description

Shift localization responses so they are centred on the origin

Usage

circleCorrect(df, unit = "cm", vrbl = "tap", r = 1, fitr = FALSE)

Arguments

df	Data frame with localization coordinates (X,Y).
unit	Unit of the coordinates (default: 'cm')
vrbl	Variable of coordinates (default: 'tap')
r	Radius of the circle the coordinates should be on (default: 1).
fitr	(boolean) Should radius be fit? (default: FALSE)

Details

The parameters vrbl and unit are combined with a lower case x and y: tapx_cm and tapy_cm with default settings. These should be columns in the data frame (df).

Value

The data frame with corrected tapx_cm and tapy_cm columns. The corrected localization responses fall closest to a circle with radius r (in unit) and origin (0,0). Only response with df$selected == 1 are used for this correction.

Examples

data("localization_aligned")
data("localization_unaligned")

localization_aligned <- convert2cm(localization_aligned, from='r')
localization_aligned <- convert2cm(localization_aligned, from='t')

par(mfrow=c(1,2))

plot(localization_aligned$tapx_cm, localization_aligned$tapy_cm, 
     main='aligned',xlab='cm', ylab='cm', 
     asp=1,bty='n', xlim=c(-2,12),ylim=c(-2,12),col='blue')
segments(localization_aligned$tapx_cm, localization_aligned$tapy_cm, 
         localization_aligned$handx_cm, localization_aligned$handy_cm, col='blue')
lines(c(-1,11),c(0,0),col='gray')
lines(c(0,0),c(-1,11),col='gray')
lines(cos(seq(0,pi/2,pi/200))*10,sin(seq(0,pi/2,pi/200))*10, col='gray')
centre <- circleFit(localization_aligned$tapx_cm, localization_aligned$tapy_cm, r=10)$par
points(centre['xc'], centre['yc'], col='red')
lines( (cos(seq(0,pi/2,pi/200))*10) + centre['xc'], (sin(seq(0,pi/2,pi/200))*10) + centre['yc'], col='red', lty=2)
points(localization_aligned$tapx_cm - centre['xc'], localization_aligned$tapy_cm - centre['yc'], col='green')
segments(localization_aligned$tapx_cm - centre['xc'], localization_aligned$tapy_cm - centre['yc'], 
         localization_aligned$handx_cm, localization_aligned$handy_cm, col='green')


localization_unaligned <- convert2cm(localization_unaligned, from='r')
localization_unaligned <- convert2cm(localization_unaligned, from='t')

plot(localization_unaligned$tapx_cm, localization_unaligned$tapy_cm, 
     main='unaligned',xlab='cm', ylab='cm', 
     asp=1,bty='n', xlim=c(-2,12),ylim=c(-2,12),col='blue')
segments(localization_unaligned$tapx_cm, localization_unaligned$tapy_cm, 
         localization_unaligned$handx_cm, localization_unaligned$handy_cm, col='blue')
lines(c(-1,11),c(0,0),col='gray')
lines(c(0,0),c(-1,11),col='gray')
lines(cos(seq(0,pi/2,pi/200))*10,sin(seq(0,pi/2,pi/200))*10, col='gray')
centre <- circleFit(localization_unaligned$tapx_cm, localization_unaligned$tapy_cm, r=10)$par
points(centre['xc'], centre['yc'], col='red')
lines( (cos(seq(0,pi/2,pi/200))*10) + centre['xc'], (sin(seq(0,pi/2,pi/200))*10) + centre['yc'], col='red', lty=2)
points(localization_unaligned$tapx_cm - centre['xc'], localization_unaligned$tapy_cm - centre['yc'], col='green')
segments(localization_unaligned$tapx_cm - centre['xc'], localization_unaligned$tapy_cm - centre['yc'], 
         localization_unaligned$handx_cm, localization_unaligned$handy_cm,, col='green')

thartbm/SMCL documentation built on Oct. 23, 2022, 5:17 a.m.