README.md

In davidjayjackson/LC:

Title: Analyzing Light Curves, A Practial Guide

Author: Grant Foster

Library creater: David Jackson

Install: devtools::install_github("davidjayjackson/LC")

R scripts and data for book: https://github.com/davidjayjackson/LC-Demo

List of functions to assist in the analyzing of light curves of variable stars.

Function list:

• anova1 == Anova1 1-way ANOVA
• aovper == Period search by Analysis of Variance
• dcdft == date-compensated discrete Fourier transform
• findstart == find starting index of a particular value
• foldit == fold a light curve with a trial period
• fourfit == fit a Fourier series
• movave == compute a moving average
• peak1 == find the main peak (only) in DCDFT or AoVper
• timave == time-based averaging

Apply ANOVA test to data based on a single categorical (factor) variable.

anova1(x,fac,sort=F,progress=F)

• x dependent variable (to be tested for dependence)
• fac factor on which the dependent variable may depend Note: if the variable is numeric, then it is converted to catagorical (a factor) for the test
• sort if TRUE, then the output is sorted into descending order of the number of occurences of each factor
• progress if TRUE, a graph is displayed showing the progress of the computation

Example:

anova1(mag,floor(t/10))

test for changes in "mag" dependent on floor(t/10) as a categorical (rather than numerical) variable. This is quivalent to using 10-day wide bins

anova1(mag,obs)

test for changes in "mag" dependent on the variable "obs" -- which might be observer I.D. In this case it's OK if "obs" is a string variable -- since the anova1 routine converts the variable to a factor anyway, numeric variables are treated the same as non-numerics.

aovper

                    **********
                    * aovper *
                    **********

aovper(t,x,lowfreq=0,hifreq=0,bins=4,resmag=1,plot=T,outfile=NULL)

• t vector of times of observation
• x vector of data values
• lowfreq lowest frequency to test
• hifreq highest frequency to test
• bins number of bins to use in AoV computation
• resmag resolution magnification. The number of frequencies tested for a given frequency range is multiplied by this parameter
• plot if TRUE, the periodogram is plotted outfile if a string variable rather than null, the results are written to a file of this name

Examples:

• aovper(t,x) # compute the AoV periodogram # for x as a function of t
• aovper(t,x,0,.02) # compute the AoV periodogram # for frequencies from 0 to 0.02
• aovper(t,x,bins=12) # compute the AoV periodogram using # 12 bins rather than the default 4
• aovper(t,x,0,.01,resmag=10) # compute the AoV periodogram for # frequencies from 0 to 0.01, scanning # frequency space at 10 times higher # than the usual resolution (i.e., # oversampling by a factor of 40 # rather than the default factor of 4

dcdft Compute the DCDFT periodogram.

dcdft(t,x,lowfreq=0,hifreq=0,resmag=1,plot=T,outfile=NULL)

t vector of times of observation x vector of observed data lowfreq lowest frequency to test hifreq highest frequency to test resmag resolution magnification. The number of frequencies tested for a given frequency range is multiplied by this parameter plot if TRUE, the periodogram is plotted outfile if a string variable rather than null, the results are written to a file of this name

Examples:

dcdft(t,x) # compute the DCDFT periodogram # for x as a function of t dcdft(t,x,0,.02) # compute the DCDFT periodogram # for frequencies from 0 to 0.02 dcd = dcdft(t,x,plot=F) # compute the DCDFT and assign # the output to "dcd" # but don't plot the periodogram dcdft(t,x,0,.01,resmag=10) # compute the DCDFT periodogram for # frequencies from 0 to 0.01, scanning # frequency space at 10 times higher # than the usual resolution (i.e., # oversampling by a factor of 40 # rather than the default factor of 4

                    *************

#### * findstart ** Find starting index for a given value.

findstart(X,start)

X vector of values to be scanned. Must be sorted into ascending order start starting value to be located

Examples:

n1 = findstart(t,40000) # find the index for the first # t value which is at least 40000

restrict data for t,x to the time range 40000 to 45000

n1 = findstart(t,40000) # 1st value to include n2 = findstart(t,45000) - 1 # last value to include t = t[n1:n2] # keep only t values from n1 to n2 x = x[n1:n2] # keep only x values from n1 to n2

                    **********

* foldit *

                    **********

Compute and plot a folded light curve.

foldit(t,x,period,epoch=0,plot=T,bin=.05)

t vector of times x vector of data values (magnitudes) period period with which to fold the data epoch epoch to use when folding the data. Default is zero. plot if TRUE, produce a plot of the folded light curve bin size (in phase) of bins to use for averaging. default is bins of width 0.05 (i.e., 20 bins per cycle)

Examples:

foldit(t,x,332) # fold time series (t,x) using # a period of 332 folded = foldit(t,x,per) # fold the time series (t,x) using # period "per" and assign the result # to an object called "folded" names(folded) # show the names of the object "folded"

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* fourfit *

                    ***********

Fit a Fourier series to data.

fourfit(t,x,p)

t vector of times x vector of data values p period to fit to the data

Examples:

xfit = fourfit(t,x,332) # fit a Fourier series to (t,x) # using the single period 332 pers = c(332,166,110.667,83) # create a vector "pers" consisting # of the numbers 332, 166, 110.667, and 83 xfit = fourfit(t,x,pers) # fit a Fourier series to (t,x) # using periods 332, 166, 110.667, and 83 names(xfit) # show the names of the object "xfit"

                    **********

### * movave *** Compute moving averages.

movave(X,L=12)

X quantity for which to compute a moving average L number of points to include in each moving average

Examples:

tave = movave(t,30) # compute a 30-point moving average of t # and assign it to a variable "tave" xave = movave(x,30) # compute a 30-point moving average of x # and assign it to a variable "xave" plot(tave,xave,type="l") # make a line plot of xave vs tave

                    *********

* peak1 *

                    *********

Find the strongest peak in a periodogram.

peak1(dcd,t=NULL,x=NULL)

dcd output from the "dcdft" or "aovper" scripts t, x times and data values used to create the "dcd" object. If these are given, and the periodogram is a DCDFT rather than AoV periodogram, then standard errors are computed for the peak frequency and (semi-)amplitude

Examples:

dcd = dcdft(t,x,0,.01) # compute the DCDFT of (t,x) for # frequencies from 0 to 0.01, and # assign it to an object called "dcd" peak1(dcd) # find the strongest peak in the # object "dcd" and the upper and lower # FWHM limits for frequency and period pk1 = peak1(dcd,t,x) # find the strongest peak in the # object "dcd" and the upper and lower # FWHM limits for frequency and period # as well as the standard errors for # period and amplitude, and assign the # result to an object called "pk1"

                    *********

* peaks *

                    *********

Find all peaks in a periodogram.

peaks(dcd,lofre=0,hifre=0,maxpeak=0,plot=T)

dcd output from the "dcdft" or "aovper" scripts lofre lowest frequency to include hifre highest frequency to include maxpeak maximum number of peaks to report. If specified, only this many strongest peaks will be returned plot if TRUE, a plot is made of the observed chi-square values of the peaks vs the theoretical chi-square peak values (based on treating peaks as chi-square with 3 degrees of freedom)

Examples:

dcd = dcdft(t,x,0,.01) # compute the DCDFT of (t,x) for # frequencies from 0 to 0.01, and # assign it to an object called "dcd" peaks(dcd) # find all the peaks in the periodogram # saved as "dcd" pp = peaks(dcd) # find all the peaks in the periodogram # saved as "dcd" and store them in an # object called "pp"

                    **********

* timave *

                    **********

Average data over bins of a fixed time width.

timave(t,x,t2ave=-1,t.off=0,n.min=2,wide=2,plot=T,lines=T,tit=NULL,big=F)

t vector of times of observations x vector of data values t2ave width of time spans over which to average. If not specified, the width defaults to 1 time unit t.off time offset. Normally, time bins start at zero, but if t.off is specified they start at t.off n.min minimum number of data points required for a given bin wide width of error bars (in units of the standard error) plot if TRUE, a plot of the averages, with error bars, is created lines if TRUE, the plot is a points-and-lines plot; otherwise only points are plotted tit title to be used for the plot big if TRUE, then on the plot those values that are significantly different from the overall mean are circled in red, those which are more than 2.5 standard deviations from the overall mean (whether significant or not) are circled in blue

Examples:

timave(t,x,10) # average the time series (t,x) # using 10-day wide time bins # and plot the result. Note: this # plot uses a "normal" (not inverted) # y-axis, so it's "upside-down" when # the "x" variable is magnitude q = timave(t,x,10) # average the time series (t,10) # using 10-day wide time bins # and store the result in an object # called "q"

davidjayjackson/LC documentation built on May 18, 2020, 3:20 p.m.