In rmaestre/variableStars: Efficient software to study patterns on variable stars.

library(variableStars)
library(data.table)
library(ggplot2)
library(ggsci)
library(microbenchmark)
library(RColorBrewer)

Experiment I

# Generate first pattern
dt.spectrum <- data.frame(
  "frequency" = seq(from=0, to=10, by=1) ,
  "amplitude" = 10
)
# Get max amplitude
maxAmplitude <- dt.spectrum[which.max(dt.spectrum$amplitude), ]
# Plot amplitudes
plot_spectrum(min(dt.spectrum$frequency)-1,
              max(dt.spectrum$frequency)+1,
              dt.spectrum)

Experiment execution

result <- process(
  dt.spectrum$frequency,
  dt.spectrum$amplitude,
  filter = "uniform",
  gRegimen = 0,
  minDnu = 15,
  maxDnu = 95,
  dnuValue = -1,
  dnuGuessError = 10,
  dnuEstimation = TRUE,
  numFrequencies = 30,
  debug = TRUE
)

dt <- prepare_periodicities_dataset(result$fresAmps)
# Plot frecuency and amplitude
ggplot(aes(x = fInv, y = b, group=label, colour=label), 
       data = dt) +
  #geom_point(alpha=0.2) +
  geom_line(alpha=0.8) +
  ggtitle(expression(paste("Periodicities (",d^-1,")"))) +
  xlab(expression(paste("Periodicities (",mu,"hz)"))) +
  ylab("Amplitude") +
  theme_bw() + 
  scale_color_lancet() +
  xlim(0, 4)
#dt[which.max(dt$b),]

plot_echelle(dt <- data.frame(
  "x" = result$echelle$modDnuStacked,
  "y" = result$echelle$freMas,
  "h" = result$echelle$amplitudes
))

Experiment II

# Generate first pattern
dt.spectrum <- data.frame(
  "frequency" = seq(from=0, to=10, by=1) ,
  "amplitude" = 10
)
# Generate second pattern as the biased first
dt.spectrum.bias <- data.frame(dt.spectrum)
dt.spectrum.bias$frequency <- dt.spectrum.bias$frequency + 0.25
dt.spectrum.bias$amplitude <- 5

#All together
dt.spectrum <- rbind(dt.spectrum, dt.spectrum.bias)
# Get max amplitude
maxAmplitude <- dt.spectrum[which.max(dt.spectrum$amplitude), ]
# Plot amplitudes
plot_spectrum(min(dt.spectrum$frequency)-1,
              max(dt.spectrum$frequency)+1,
              dt.spectrum)

Experiment execution

result <- process(
  dt.spectrum$frequency,
  dt.spectrum$amplitude,
  filter = "uniform",
  gRegimen = 0,
  minDnu = 15,
  maxDnu = 95,
  dnuValue = -1,
  dnuGuessError = 10,
  dnuEstimation = TRUE,
  numFrequencies = 30,
  debug = TRUE
)

# Plot frecuency and amplitude
ggplot(aes(x = fInv, y = b, group=label, colour=label), 
       data = prepare_periodicities_dataset(result$fresAmps)) +
  #geom_point(alpha=0.2) +
  geom_line(alpha=0.8) +
  ggtitle(expression(paste("Periodicities (",d^-1,")"))) +
  xlab(expression(paste("Periodicities (",mu,"hz)"))) +
  ylab("Amplitude") +
  theme_bw() + 
  scale_color_lancet() +
  xlim(0, 5)

plot_echelle(dt <- data.frame(
  "x" = result$echelle$modDnuStacked,
  "y" = result$echelle$freMas,
  "h" = result$echelle$amplitudes
))

Experiment III

# Generate first pattern
dt.spectrum <- data.frame(
  "frequency" = seq(from=0, to=10, by=0.25) ,
  "amplitude" = 10 
)
dt.spectrum$amplitude <- dt.spectrum$amplitude + rnorm(nrow(dt.spectrum),0,1.0)
# Generate second pattern as the biased first
dt.spectrum.bias <- data.frame(dt.spectrum)
dt.spectrum.bias$frequency <- dt.spectrum.bias$frequency + 0.15
dt.spectrum.bias$amplitude <- 5 + rnorm(nrow(dt.spectrum.bias),0,1.0)

#All together
dt.spectrum <- rbind(dt.spectrum, dt.spectrum.bias)
# Get max amplitude
maxAmplitude <- dt.spectrum[which.max(dt.spectrum$amplitude), ]
# Plot amplitudes
plot_spectrum(min(dt.spectrum$frequency)-1,
              max(dt.spectrum$frequency)+1,
              dt.spectrum)

Experiment execution

result <- process(
  dt.spectrum$frequency,
  dt.spectrum$amplitude,
  filter = "uniform",
  gRegimen = 0,
  minDnu = 15,
  maxDnu = 95,
  dnuValue = -1,
  dnuGuessError = 10,
  dnuEstimation = TRUE,
  numFrequencies = 30,
  debug = TRUE
)

# Plot frecuency and amplitude
ggplot(aes(x = fInv, y = b, group=label, colour=label), 
       data = prepare_periodicities_dataset(result$fresAmps)) +
  #geom_point(alpha=0.2) +
  geom_line(alpha=0.8) +
  ggtitle(expression(paste("Periodicities (",d^-1,")"))) +
  xlab(expression(paste("Periodicities (",mu,"hz)"))) +
  ylab("Amplitude") +
  theme_bw() + 
  scale_color_lancet() +
  xlim(0, 5)

plot_echelle(dt <- data.frame(
  "x" = result$echelle$modDnuStacked,
  "y" = result$echelle$freMas,
  "h" = result$echelle$amplitudes
))

Experiment IV

# Generate first pattern
dt.spectrum <- data.frame(
  "frequency" = seq(from=0, to=10, by=0.25) + rnorm(nrow(dt.spectrum.bias),0,0.1) ,
  "amplitude" = 10 
)
# Generate second pattern as the biased first
dt.spectrum.bias <- data.frame(dt.spectrum)
dt.spectrum.bias$frequency <- dt.spectrum.bias$frequency + rnorm(nrow(dt.spectrum.bias),0,0.1)
dt.spectrum.bias$amplitude <- 5 + rnorm(nrow(dt.spectrum.bias),0,1.0)

#All together
dt.spectrum <- rbind(dt.spectrum, dt.spectrum.bias)
# Get max amplitude
maxAmplitude <- dt.spectrum[which.max(dt.spectrum$amplitude), ]
# Plot amplitudes
plot_spectrum(min(dt.spectrum$frequency)-1,
              max(dt.spectrum$frequency)+1,
              dt.spectrum)

Experiment execution

result <- process(
  dt.spectrum$frequency,
  dt.spectrum$amplitude,
  filter = "uniform",
  gRegimen = 0,
  minDnu = 15,
  maxDnu = 95,
  dnuValue = -1,
  dnuGuessError = 10,
  dnuEstimation = TRUE,
  numFrequencies = 30,
  debug = TRUE
)

# Plot frecuency and amplitude
ggplot(aes(x = fInv, y = b, group=label, colour=label), 
       data = prepare_periodicities_dataset(result$fresAmps)) +
  #geom_point(alpha=0.2) +
  geom_line(alpha=0.8) +
  ggtitle(expression(paste("Periodicities (",d^-1,")"))) +
  xlab(expression(paste("Periodicities (",mu,"hz)"))) +
  ylab("Amplitude") +
  theme_bw() + 
  scale_color_lancet() +
  xlim(0, 5)

plot_echelle(dt <- data.frame(
  "x" = result$echelle$modDnuStacked,
  "y" = result$echelle$freMas,
  "h" = result$echelle$amplitudes
))

rmaestre/variableStars documentation built on April 11, 2020, 11:10 p.m.