Nothing

```
#' A function to search the Kepler data for periodically recurring dips in luminosity.
#'
#' @name period_average
#'
#' @description The signal of transiting planets is very weak, especially if the planet is small. This function amplifies it by exploiting the periodicity of the signal. All observations
#' times are taken modulo the period and then binned. An average is then taken within each bin, those averages then stored as a vector and returned. If the orbital period of an
#' exoplanet (or an integer fraction thereof) is used as argument for "period", the signal to noise ratio of the transit is improved, which can allow for the planet's detection.
#'
#' @param data A dataframe with one column named "Day" and the other "Brightness", such as Kepler10965588 (included in the package).
#' @param period A numeric which is larger than 0 representing the period (in days) which is to be examined.
#'
#' @return A vector of numerics.
#'
#' @examples
#' library(anomaly)
#' data(Lightcurves)
#' ### Plot the data for Kepler 10965588: No transit apparent
#' plot(Lightcurves$Kepler10965588$Day,Lightcurves$Kepler10965588$Brightness,xlab = "Day",pch=".")
#' ### Examine a period of 62.9 days for Kepler 10965588
#' binned_data = period_average(Lightcurves$Kepler10965588,62.9)
#' inferred_anomalies = capa.uv(binned_data)
#' plot(inferred_anomalies)
#'
#' @export
#'
period_average = function(data,period){
period = tryCatch({as.numeric(period)},
error = function(e){stop("period has to be a positive numeric")},
warning = function(w){stop("period has to be a positive numeric")})
if (length(period)>1){
warning("The input for period has multiple entries. Only the first one is kept")
period = period[1]
}
if(is.na(period)){
stop("period has to be a double. It is NA.")
}
if((period)<0){
stop("period has to be positive.")
}
if(is.infinite(period)){
stop("period is infinite.")
}
if(is.nan(period)){
stop("period has to be a double. It is NaN.")
}
data = tryCatch({as.data.frame(data)},
error = function(e){stop("data has to be a data frame or something which can be converted into one")},
warning = function(e){stop("data has to be a data frame or something which can be converted into one")})
given_names = colnames(data)
if (!("Day" %in% given_names)){
stop("Column Day is missing in data")
}
if (!("Day" %in% given_names)){
stop("Column Brightness is missing in data")
}
if(length(given_names) > 2){
warning("data was expected to have 2 columns named Day and Brightness. Addidtional columns are ignored")
}
if (sum(is.infinite(data$Day)) > 0){
warning("data$Day contains infinite values. We removed them and continued our analysis on the rest.")
data = data[which(!is.infinite(data$Day)),]
}
if (sum(is.na(data$Day)) > 0){
warning("data$Day contains NAs. We removed them and continued our analysis on the rest.")
data = data[which(!is.na(data$Day)),]
}
if (sum(is.nan(data$Day)) > 0){
warning("data$Day contains NaNs. We removed them and continued our analysis on the rest.")
data = data[which(!is.nan(data$Day)),]
}
if (!(is.numeric(data$Day) + is.integer(data$Day))){
stop("Column Day of data must contain numbers.")
}
if (sum(is.infinite(data$Brightness)) > 0){
warning("data$Brightness contains infinite values. We removed them and continued our analysis on the rest.")
data = data[which(!is.infinite(data$Brightness)),]
}
if (sum(is.na(data$Brightness)) > 0){
warning("data$Brightness contains NAs. We removed them and continued our analysis on the rest.")
data = data[which(!is.na(data$Brightness)),]
}
if (sum(is.nan(data$Brightness)) > 0){
warning("data$Brightness contains NaNs. We removed them and continued our analysis on the rest.")
data = data[which(!is.nan(data$Brightness)),]
}
if (!(is.numeric(data$Brightness) + is.integer(data$Brightness))){
stop("Column Brightness of data must contain numbers.")
}
if(nrow(data)<3){
stop("data has fewer than 3 rows.")
}
times = data$Day
obs = data$Brightness
modtimes = times %% period
numdatapoints = round(period/median(times[2:length(times)]-times[1:(length(times)-1)]))
step = period/numdatapoints
indices = ceiling(modtimes/step)
tmp = data.frame(index = indices, observation = obs)
planets <- group_by(tmp, .data$index)
delay2 <- summarize(planets, avg = mean(.data$observation, na.rm = T))
observationsavg = delay2$avg
return(observationsavg)
}
```

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