sunspots: Recorded sunspots births/deaths during 1872-2018
In rotasym: Tests for Rotational Symmetry on the Hypersphere
sunspots R Documentation
Recorded sunspots births/deaths during 1872–2018
Description
Processed version of the
Debrecen Photoheliographic
Data (DPD) sunspot catalog and the revised version of the
Greenwich Photoheliographic
Results (GPR) sunspot catalog. The two sources contain the records of
sunspots appeared during 1872–2018 (GPR for 1872–1976; DPD for 1974–2018).
Sunspots appear in groups and have a variable lifetime. This dataset has been
processed to account for the births (first observations) and
deaths (last observations) of groups of sunspots.
Usage
sunspots_births
sunspots_deaths
Format
A data frame with 51303 rows and 7 variables:
- NOAA
NOAA identifier of the group of sunspots, a factor.
- date
UTC date, as POSIXct, of
the first observation of a group of sunspots.
- cycle
solar cycle in which the group of sunspots was observed.
- total_area
total whole spot area of the group, measured in
millionths of the solar hemisphere.
- dist_sun_disc
distance from the center of Sun's disc, measured in
units of the solar radius.
- theta
mean longitude angle \theta \in [0, 2\pi) of the group
position.
- phi
mean latitude angle \phi \in [-\pi/2, \pi/2) of the group
position.
An object of class data.frame with 51303 rows and 7 columns.
Details
The mean position of the group of sunspots is obtained by a weighted
average of the positions of the single sunspots by the whole spot area of the
single spots. The areas are corrected to account for foreshortening.
The (\theta, \phi) angles are such their associated Cartesian
coordinates are:
(\cos(\phi) \cos(\theta), \cos(\phi) \sin(\theta), \sin(\phi)),
with (0, 0, 1) denoting the north pole.
The DPD data has
different
states of completeness and quality control. The longest span of "final
complete data" (no missing observation days and the data has undergone a
systematic quality control) is from 2005 to 2015.
The data has been preprocessed using the following pipeline:
Retrieve data from the GPR and DPD sunspot catalogs.
Omit observations with NAs in the sunspot positions.
Filter for sunspot groups.
Relabel the NOAA identifier for the sunspot group for records before
1974, prefixing the "GPR" string. Otherwise, very different groups of
sunspots from the two catalogs may share the same identifier.
Keep only the first row of each NOAA instance, the first-ever
observation of each sunspot group.
The script performing the preprocessing is available at
sunspots.R
Source
http://fenyi.solarobs.epss.hun-ren.hu
References
Baranyi, T., Győri, L., Ludmány, A. (2016) On-line tools for
solar data compiled at the Debrecen observatory and their extensions with the
Greenwich sunspot data. Solar Physics, 291(9–10):3081–3102.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11207-016-0930-1")}
Győri, L., Ludmány, A., Baranyi, T. (2019) Comparative analysis of Debrecen
sunspot catalogues. Monthly Notices of the Royal Astronomical Society,
465(2):1259–1273. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/mnras/stw2667")}
See Also
test_rotasym.
Examples
# Load births data of the 23rd cycle
data("sunspots_births")
sp_bir_23 <- subset(sunspots_births, cycle == 23)
# Transform to Cartesian coordinates
sp_bir_23$X_bir <-
cbind(cos(sp_bir_23$phi) * cos(sp_bir_23$theta),
cos(sp_bir_23$phi) * sin(sp_bir_23$theta),
sin(sp_bir_23$phi))
# Plot births
n_cols <- 100
cuts <- cut(x = sp_bir_23$date, include.lowest = TRUE,
breaks = quantile(sp_bir_23$date,
probs = seq(0, 1, l = n_cols + 1)))
scatterplot3d::scatterplot3d(sp_bir_23$X_bir, xlim = c(-1, 1),
ylim = c(-1, 1), zlim = c(-1, 1),
color = viridisLite::viridis(n_cols)[cuts],
pch = 16, cex.symbols = 0.5, xlab = "",
ylab = "", zlab = "", angle = 30)
# Spörer's law: sunspots at the beginning of the solar cycle (dark blue
# color) tend to appear at higher latitudes, gradually decreasing to the
# equator as the solar cycle advances (yellow color)
# Estimation of the density of the cosines
V <- cosines(X = sp_bir_23$X_bir, theta = c(0, 0, 1))
h <- bw.SJ(x = V, method = "dpi")
plot(kde <- density(x = V, bw = h, n = 2^13, from = -1, to = 1), col = 1,
xlim = c(-1, 1), ylim = c(0, 3), axes = FALSE, main = "",
xlab = "Cosines (latitude angles)", lwd = 2)
at <- seq(-1, 1, by = 0.25)
axis(2); axis(1, at = at)
axis(1, at = at, line = 1, tick = FALSE,
labels = paste0("(", 90 - round(acos(at) / pi * 180, 1), "º)"))
rug(V)
legend("topright", legend = c("Full cycle", "Initial 25% cycle",
"Final 25% cycle"),
lwd = 2, col = c(1, viridisLite::viridis(12)[c(3, 8)]))
# Density for the observations within the initial 25% of the cycle
part1 <- sp_bir_23$date < quantile(sp_bir_23$date, 0.25)
V1 <- cosines(X = sp_bir_23$X[part1, ], theta = c(0, 0, 1))
h1 <- bw.SJ(x = V1, method = "dpi")
lines(kde1 <- density(x = V1, bw = h1, n = 2^13, from = -1, to = 1),
col = viridisLite::viridis(12)[3], lwd = 2)
# Density for the observations within the final 25% of the cycle
part2 <- sp_bir_23$date > quantile(sp_bir_23$date, 0.75)
V2 <- cosines(X = sp_bir_23$X[part2, ], theta = c(0, 0, 1))
h2 <- bw.SJ(x = V2, method = "dpi")
lines(kde2 <- density(x = V2, bw = h2, n = 2^13, from = -1, to = 1),
col = viridisLite::viridis(12)[8], lwd = 2)
# Computation the level set of a kernel density estimator that contains
# at least 1 - alpha of the probability (kde stands for an object
# containing the output of density(x = data))
kde_level_set <- function(kde, data, alpha) {
# Estimate c from alpha
c <- quantile(approx(x = kde$x, y = kde$y, xout = data)$y, probs = alpha)
# Begin and end index for the potentially many intervals in the level sets
kde_larger_c <- kde$y >= c
run_length_kde <- rle(kde_larger_c)
begin <- which(diff(kde_larger_c) > 0) + 1
end <- begin + run_length_kde$lengths[run_length_kde$values] - 1
# Return the [a_i, b_i], i = 1, ..., K in the K rows
return(cbind(kde$x[begin], kde$x[end]))
}
# Level set containing the 90% of the probability, in latitude angles
90 - acos(kde_level_set(kde = kde, data = V, alpha = 0.10)) / pi * 180
# Modes (in cosines and latitude angles)
modes <- c(kde$x[kde$x < 0][which.max(kde$y[kde$x < 0])],
kde$x[kde$x > 0][which.max(kde$y[kde$x > 0])])
90 - acos(modes) / pi * 180
# Load deaths data of the 23rd cycle
data("sunspots_deaths")
sp_dea_23 <- subset(sunspots_deaths, cycle == 23)
# Transform to Cartesian coordinates
sp_dea_23$X_dea <-
cbind(cos(sp_dea_23$phi) * cos(sp_dea_23$theta),
cos(sp_dea_23$phi) * sin(sp_dea_23$theta),
sin(sp_dea_23$phi))
# Match births and deaths, and exclude single-day observations
sp_23 <- dplyr::full_join(sp_bir_23, sp_dea_23, by = "NOAA",
suffix = c("_bir", "_dea"))
sp_23 <- sp_23[sp_23$date_bir != sp_23$date_dea, ]
# Plot births and deaths associated to the 23rd cycle, and color according
# to movement direction of sunspots
sc <- scatterplot3d::scatterplot3d(sp_23$X_bir, xlim = c(-1, 1),
ylim = c(-1, 1), zlim = c(-1, 1),
color = 1, cex.symbols = 0.1,
pch = 16, xlab = "", ylab = "", zlab = "",
angle = 30)
sc$points3d(sp_23$X_dea, col = 1, cex = 0.1)
for (i in seq_len(nrow(sp_23))) {
sc$points3d(rbind(sp_23$X_bir[i, ], sp_23$X_dea[i, ]),
col = ifelse((sp_23$theta_bir[i] - sp_23$theta_dea[i]) %%
(2 * pi) < pi, 2, 3), type = "l")
}
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| sunspots | R Documentation |
Recorded sunspots births/deaths during 1872–2018
Description
Processed version of the Debrecen Photoheliographic Data (DPD) sunspot catalog and the revised version of the Greenwich Photoheliographic Results (GPR) sunspot catalog. The two sources contain the records of sunspots appeared during 1872–2018 (GPR for 1872–1976; DPD for 1974–2018).
Sunspots appear in groups and have a variable lifetime. This dataset has been processed to account for the births (first observations) and deaths (last observations) of groups of sunspots.
Usage
sunspots_births
sunspots_deaths
Format
A data frame with 51303 rows and 7 variables:
- NOAA
NOAA identifier of the group of sunspots, a factor.
- date
UTC date, as
POSIXct, of the first observation of a group of sunspots.- cycle
solar cycle in which the group of sunspots was observed.
- total_area
total whole spot area of the group, measured in millionths of the solar hemisphere.
- dist_sun_disc
distance from the center of Sun's disc, measured in units of the solar radius.
- theta
mean longitude angle
\theta \in [0, 2\pi)of the group position.- phi
mean latitude angle
\phi \in [-\pi/2, \pi/2)of the group position.
An object of class data.frame with 51303 rows and 7 columns.
Details
The mean position of the group of sunspots is obtained by a weighted average of the positions of the single sunspots by the whole spot area of the single spots. The areas are corrected to account for foreshortening.
The (\theta, \phi) angles are such their associated Cartesian
coordinates are:
(\cos(\phi) \cos(\theta), \cos(\phi) \sin(\theta), \sin(\phi)),
with (0, 0, 1) denoting the north pole.
The DPD data has different states of completeness and quality control. The longest span of "final complete data" (no missing observation days and the data has undergone a systematic quality control) is from 2005 to 2015.
The data has been preprocessed using the following pipeline:
Retrieve data from the GPR and DPD sunspot catalogs.
Omit observations with
NAs in the sunspot positions.Filter for sunspot groups.
Relabel the NOAA identifier for the sunspot group for records before 1974, prefixing the "GPR" string. Otherwise, very different groups of sunspots from the two catalogs may share the same identifier.
Keep only the first row of each NOAA instance, the first-ever observation of each sunspot group.
The script performing the preprocessing is available at
sunspots.R
Source
http://fenyi.solarobs.epss.hun-ren.hu
References
Baranyi, T., Győri, L., Ludmány, A. (2016) On-line tools for solar data compiled at the Debrecen observatory and their extensions with the Greenwich sunspot data. Solar Physics, 291(9–10):3081–3102. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11207-016-0930-1")}
Győri, L., Ludmány, A., Baranyi, T. (2019) Comparative analysis of Debrecen sunspot catalogues. Monthly Notices of the Royal Astronomical Society, 465(2):1259–1273. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/mnras/stw2667")}
See Also
test_rotasym.
Examples
# Load births data of the 23rd cycle
data("sunspots_births")
sp_bir_23 <- subset(sunspots_births, cycle == 23)
# Transform to Cartesian coordinates
sp_bir_23$X_bir <-
cbind(cos(sp_bir_23$phi) * cos(sp_bir_23$theta),
cos(sp_bir_23$phi) * sin(sp_bir_23$theta),
sin(sp_bir_23$phi))
# Plot births
n_cols <- 100
cuts <- cut(x = sp_bir_23$date, include.lowest = TRUE,
breaks = quantile(sp_bir_23$date,
probs = seq(0, 1, l = n_cols + 1)))
scatterplot3d::scatterplot3d(sp_bir_23$X_bir, xlim = c(-1, 1),
ylim = c(-1, 1), zlim = c(-1, 1),
color = viridisLite::viridis(n_cols)[cuts],
pch = 16, cex.symbols = 0.5, xlab = "",
ylab = "", zlab = "", angle = 30)
# Spörer's law: sunspots at the beginning of the solar cycle (dark blue
# color) tend to appear at higher latitudes, gradually decreasing to the
# equator as the solar cycle advances (yellow color)
# Estimation of the density of the cosines
V <- cosines(X = sp_bir_23$X_bir, theta = c(0, 0, 1))
h <- bw.SJ(x = V, method = "dpi")
plot(kde <- density(x = V, bw = h, n = 2^13, from = -1, to = 1), col = 1,
xlim = c(-1, 1), ylim = c(0, 3), axes = FALSE, main = "",
xlab = "Cosines (latitude angles)", lwd = 2)
at <- seq(-1, 1, by = 0.25)
axis(2); axis(1, at = at)
axis(1, at = at, line = 1, tick = FALSE,
labels = paste0("(", 90 - round(acos(at) / pi * 180, 1), "º)"))
rug(V)
legend("topright", legend = c("Full cycle", "Initial 25% cycle",
"Final 25% cycle"),
lwd = 2, col = c(1, viridisLite::viridis(12)[c(3, 8)]))
# Density for the observations within the initial 25% of the cycle
part1 <- sp_bir_23$date < quantile(sp_bir_23$date, 0.25)
V1 <- cosines(X = sp_bir_23$X[part1, ], theta = c(0, 0, 1))
h1 <- bw.SJ(x = V1, method = "dpi")
lines(kde1 <- density(x = V1, bw = h1, n = 2^13, from = -1, to = 1),
col = viridisLite::viridis(12)[3], lwd = 2)
# Density for the observations within the final 25% of the cycle
part2 <- sp_bir_23$date > quantile(sp_bir_23$date, 0.75)
V2 <- cosines(X = sp_bir_23$X[part2, ], theta = c(0, 0, 1))
h2 <- bw.SJ(x = V2, method = "dpi")
lines(kde2 <- density(x = V2, bw = h2, n = 2^13, from = -1, to = 1),
col = viridisLite::viridis(12)[8], lwd = 2)
# Computation the level set of a kernel density estimator that contains
# at least 1 - alpha of the probability (kde stands for an object
# containing the output of density(x = data))
kde_level_set <- function(kde, data, alpha) {
# Estimate c from alpha
c <- quantile(approx(x = kde$x, y = kde$y, xout = data)$y, probs = alpha)
# Begin and end index for the potentially many intervals in the level sets
kde_larger_c <- kde$y >= c
run_length_kde <- rle(kde_larger_c)
begin <- which(diff(kde_larger_c) > 0) + 1
end <- begin + run_length_kde$lengths[run_length_kde$values] - 1
# Return the [a_i, b_i], i = 1, ..., K in the K rows
return(cbind(kde$x[begin], kde$x[end]))
}
# Level set containing the 90% of the probability, in latitude angles
90 - acos(kde_level_set(kde = kde, data = V, alpha = 0.10)) / pi * 180
# Modes (in cosines and latitude angles)
modes <- c(kde$x[kde$x < 0][which.max(kde$y[kde$x < 0])],
kde$x[kde$x > 0][which.max(kde$y[kde$x > 0])])
90 - acos(modes) / pi * 180
# Load deaths data of the 23rd cycle
data("sunspots_deaths")
sp_dea_23 <- subset(sunspots_deaths, cycle == 23)
# Transform to Cartesian coordinates
sp_dea_23$X_dea <-
cbind(cos(sp_dea_23$phi) * cos(sp_dea_23$theta),
cos(sp_dea_23$phi) * sin(sp_dea_23$theta),
sin(sp_dea_23$phi))
# Match births and deaths, and exclude single-day observations
sp_23 <- dplyr::full_join(sp_bir_23, sp_dea_23, by = "NOAA",
suffix = c("_bir", "_dea"))
sp_23 <- sp_23[sp_23$date_bir != sp_23$date_dea, ]
# Plot births and deaths associated to the 23rd cycle, and color according
# to movement direction of sunspots
sc <- scatterplot3d::scatterplot3d(sp_23$X_bir, xlim = c(-1, 1),
ylim = c(-1, 1), zlim = c(-1, 1),
color = 1, cex.symbols = 0.1,
pch = 16, xlab = "", ylab = "", zlab = "",
angle = 30)
sc$points3d(sp_23$X_dea, col = 1, cex = 0.1)
for (i in seq_len(nrow(sp_23))) {
sc$points3d(rbind(sp_23$X_bir[i, ], sp_23$X_dea[i, ]),
col = ifelse((sp_23$theta_bir[i] - sp_23$theta_dea[i]) %%
(2 * pi) < pi, 2, 3), type = "l")
}
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