rcosmo (version 1.1.2)

HPDataFrame: HPDataFrame class

Description

HPDataFrames are a type of data.frame designed to carry data located on the unit sphere. Each row of a HPDataFrame is associated with a HEALPix pixel index. The HPDataFrame also holds an attribute called nside which stores the HEALPix Nside parameter (i.e., the resolution of the HEALPix grid that is being used). Unlike CMBDataFrame, HPDataFrames may have repeated pixel indices. They are made this way so that multiple data points falling within a given pixel can be stored in different rows of any given HPDataFrame.

Usage

HPDataFrame(..., nside, ordering = "nested", auto.spix = FALSE, spix,
  assumedUniquePix = FALSE, delete.duplicates = FALSE,
  save.dots = FALSE, save.duplicate.indices = FALSE)

Arguments

...

Data. Can be named vectors or a data.frame. May include columns (x,y,z) or (theta, phi) representing Cartesian or spherical coordinates of points on the unit sphere.

nside

Integer number \(2^k\), the nside parameter, i.e, resolution. If nside is unspecified, then the an attempt is made to use columns x,y and z from the provided data, as Cartesian coordinates, to calculate an nside that is sufficient to ensure all points belong to unique pixels.

ordering

The HEALPix ordering scheme ("ring" or "nested").

auto.spix

Boolean. If TRUE then spix will be found from the coordinates provided in the data. That is, each row of data will be assigned the pixel index of its closest HEALPix pixel center. There must be columns x,y,z for cartesian or theta, phi for spherical colatitude and longitude respectively. If auto.spix = FALSE then nside must be specified.

spix

A vector of HEALPix pixel indices indicating the pixel locations of the data. Note that spix is ignored if auto.spix = TRUE.

assumedUniquePix

A boolean. Sets the assumedUniquePix attribute of the HPDataFrame. This attribute indicates whether or not the rows of a HPDataFrame can be assumed to belong to unique pixels.

delete.duplicates

Boolean. If TRUE then rows corresponding to duplicate pixel indices will be dropped from the returned HPDataFrame, and assumedUniquePix will be set to TRUE.

save.dots

A logical. If TRUE then the dot product of each observation with the nearest child HEALPix pixel center will be stored as a column called "distance" in the returned HPDataFrame, provided that auto.spix = TRUE. Note that a 'child' pixel is any one of the four pixels contained in the current pixel, in the nested scheme, at the next highest resolution. See children.

save.duplicate.indices

A logical. If TRUE and delete.duplicates is also TRUE, then the row indices of duplicated pixels will be retained as an attribute called "duplicates". Note that row index refers to the row position of the duplicated pixel in the original HPDataFrame, and not the actual pixel index itself.

Details

HPDataFrame with auto.spix = TRUE can be used to transform any spherical data (not necessarily CMB) to the Healpix representation, see Example 3 below.

Examples

# NOT RUN {
##Example 1.

hp1 <- HPDataFrame(I=rnorm(5), nside = 1, spix = c(1,1,2,2,3))
pix(hp1)
coords(hp1, new.coords = "cartesian")
class(hp1)
assumedUniquePix(hp1)

##Example 2.

# Where nside is not specified
sky <- CMBDataFrame(nside = 32, coords = "cartesian", ordering = "nested")
sky.s <- CMBDataFrame(sky, sample.size = 100)
hpdf <- HPDataFrame(sky.s, auto.spix = TRUE)
class(hpdf)
assumedUniquePix(hpdf)


# ## Example 3.
# ## Create a HPDataFrame with NON-UNIQUE pixel indices
#
# ## With earth data.
# ## Download World Cities Database from
# ## https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.4.zip
# ## unpack the file worldcities.csv
#
# worldcities <- read.csv("worldcities.csv")
#
# ## Prepare a data frame with cities' coordinates
# sph <- geo2sph(data.frame(lon = pi/180*worldcities$lng,
#                           lat = pi/180*worldcities$lat))
# df <- data.frame(phi = sph$phi,
#                  theta = sph$theta,
#                  I = rep(1,nrow(sph)))
#
# ## Create and plot the corresponding HPDataFrame with
# ## pixel indices that are not necessarily unique
# ## by choosing your desired resolution (nside)
# hp <- HPDataFrame(usdf, auto.spix = TRUE, nside = 1024)
# plot(hp, size = 3, col = "darkgreen", back.col = "white")
# ## Add some pixels to visualise the sphere
# plot(CMBDataFrame(nside = 64), add = TRUE, col = "gray")

# ## Example 4.
# ## Create a HPDataFrame with UNIQUE pixel indices.
#
# ## With earth data.
# ## Download World Cities Database from
# ## https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.4.zip
# ## unpack the file worldcities.csv
#
# worldcities <- read.csv("worldcities.csv")
# uscities <- worldcities[worldcities$country == "United States",]
#
# ## Prepare a data frame with cities' coordinates
# sph <- geo2sph(data.frame(lon = pi/180*uscities$lng,
#                           lat = pi/180*uscities$lat))
# usdf <- data.frame(phi = sph$phi,
#                    theta = sph$theta,
#                    I = rep(1,nrow(sph)))
#
# ## Select k cities with unique coordinates. The
# ## coordinates must be unique otherwise the
# ## automatically chosen separating nside
# ## will be infinite.
# k <- 1000
# usdf <- usdf[sample(nrow(usdf), k), ]
# plot(usdf$phi, usdf$theta)
# usdf[duplicated(usdf), ]
# usdf <- usdf[!duplicated(usdf), ]
# usdf[duplicated(usdf), ]
# usdf <- coords(usdf, new.coords = "cartesian")
#
# ## Create and plot the corresponding HPDataFrame . To make
# ## sure the pixels are unique, do not select a resolution
# ## resolution (nside), since it will be chosen automatically.
# ushp <- HPDataFrame(usdf, auto.spix = TRUE)
# nside(ushp)
# assumedUniquePix(ushp)
# plot(ushp, size = 3, col = "darkgreen", back.col = "white")
# ## Add some pixels to visualise the sphere
# plot(CMBDataFrame(nside = 64), add = TRUE, col = "gray")

# }