Type: Package
Title: 3D Reconstruction of Archaeological Excavations
Version: 0.3.0
Maintainer: Clemens Schmid <clemens@nevrome.de>
Description: A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
Date: 2017-02-15
License: GPL-2
LazyData: TRUE
RoxygenNote: 6.0.1
URL: https://github.com/ISAAKiel/recexcavAAR
Imports: Rcpp (≥ 0.12.7)
Suggests: devtools (≥ 1.12.0), dplyr (≥ 0.5.0), knitr (≥ 1.15.1), magrittr (≥ 1.5), rgl (≥ 0.96.0), rmarkdown (≥ 1.0), roxygen2 (≥ 5.0.1), testthat (≥ 1.0.2)
VignetteBuilder: knitr
Depends: R (≥ 3.3.2), kriging (≥ 1.1)
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2017-02-25 21:29:06 UTC; clemens
Author: Clemens Schmid [cre, cph, aut], Benjamin Serbe [aut]
Repository: CRAN
Date/Publication: 2017-02-25 23:55:29

KT_data: Niveau measurements from the fictional trench of a excavation KT

Description

A dataset containing coordinates of niveau measurements of a fictional excavation KT with 4 spits.

Format

A data frame with 304 rows and 4 variables:

See Also

Other KT_data: KT_squarecorners, KT_vessel

KT_data: Corner points of a 1m*1m raster within the trench of a fictional excavation KT

Description

A dataset containing horizontal coordinates of corner points of a 1m*1m raster within the rectangular trench (corner points of squares).

Format

A data frame with 63 rows and 2 variables:

See Also

Other KT_data: KT_spits, KT_vessel

KT_data: Information about individual sherds of a reconstructed vessel from the trench of a fictional excavation KT

Description

A dataset containing spatial and contextual information for individual sherds of a single vessel. Some sherds were documented in the field with single find measurements. For the others only spit and square attribution is possible.

Format

A data frame with 7 rows and 7 variables:

See Also

Other KT_data: KT_spits, KT_squarecorners

Tool for transforming local metric coordinates

Description

This function transforms local metric coordinates to absolute coordinates of referenced systems by use of a two dimensional four parameter Helmert transformation. This function does not cover the transformation of three dimensional points or transformation between two different datums.

Usage

cootrans(pair_matrix, pm_column, data_matrix, dm_column, checking = FALSE,
  checkplot = TRUE)

Arguments

pair_matrix

data.frame or matrix with pairs of local and corresponding absolute coordinates (Minimum two!)

pm_column

vector with numerical index of the columns in order: local x-value, local y-value, absolute x-value, absolute y-value

data_matrix

data.frame with local x- and y-values which schould be transformed.

dm_column

vector with numerical index of the columns in order: local x-value, local y-value.

checking

boolean switch to turn on the checking ability. Default: FALSE. If TRUE showes combined coordinate plots with indexed points and alters return of function.

checkplot

boolean switch to turn off the checking plot. Default: TRUE. Only matters if checking == TRUE.

Value

Original data.frame with additional columns containing the absolute x- and y-coordinates. In case of 'checking = TRUE' returns pair_matrix data.frame with additional columns of scale and rotation arc in degrees.

Examples

coord_data <- data.frame(
 loc_x = c(1,3,1,3),
 loc_y = c(1,1,3,3),
 abs_x = c(107.1,107,104.9,105),
 abs_y = c(105.1,107,105.1,106.9)
)

data_table <- data.frame(
 x = c(1.5,1.2,1.6,2),
 y = c(1,5,2.1,2),
 type = c("flint","flint","pottery","bone")
)

new_frame <- cootrans(coord_data, c(1,2,3,4), data_table, c(1,2))

check_data <- cootrans(coord_data, c(1,2,3,4), data_table, c(1,2), checking = TRUE)

Draws a circular point cloud (3D)

Description

Draws a 2D circle on x- and y-plane around a center point in 3D space.

Usage

draw_circle(centerx, centery, centerz, radius, resolution = 30L)

Arguments

centerx

x axis value of circle center point

centery

y axis value of circle center point

centerz

z axis value of circle center point

radius

circle radius

resolution

amount of circle points (default = 30)

Value

data.frame with the spatial coordinates of the resulting points

Examples

draw_circle(
  centerx = 4,
  centery = 5,
  centerz = 1,
  radius = 3,
  resolution = 20
)

circ <- draw_circle(1,2,3,2)

plot(circ$x, circ$y)

Draws a spherical point cloud (3D)

Description

Draws a sphere around a center point in 3D space.

Usage

draw_sphere(centerx, centery, centerz, radius, phires = 10L, thetares = 10L)

Arguments

centerx

x axis value of sphere center point

centery

y axis value of sphere center point

centerz

z axis value of sphere center point

radius

sphere radius

phires

phi resolution (default = 10)

thetares

theta resolution (default = 10)

Value

data.frame with the spatial coordinates of the resulting points

Examples

sphere <- draw_sphere(
  centerx = 4,
  centery = 5,
  centerz = 1,
  radius = 3,
  phires = 20,
  thetares = 20
)

#library(rgl)
#plot3d(sphere)

Fills hexahedrons with a regular point raster (3D)

Description

A hexahedron is a three dimensional shape that is defined by 6 faces and 8 corner points. fillhexa allows to fill such a shape with a regular point raster.

Usage

fillhexa(hex, res)

Arguments

hex

dataframe with three columns and eight rows to define a hexahedron by its corner point coordinates x, y and z

res

numeric value > 0 and <= 1 for the resolution of the point raster

Details

See https://stackoverflow.com/questions/36115215/filling-a-3d-body-with-a-systematic-point-raster for a description of the function and how it was developed.

Value

data.frame with the spatial coordinates of the resulting points of the grid

Examples

hexatestdf <- data.frame(
  x = c(0,1,0,4,5,5,5,5),
  y = c(1,1,4,4,1,1,4,4),
  z = c(4,8,4,9,4,8,4,6)
)

cx = fillhexa(hexatestdf, 0.1)

#library(rgl)
#plot3d(
# cx[,1], cx[,2], cx[,3],
# type = "p",
# xlab = "x", ylab = "y", zlab = "z"
#)

Apply kriging {kriging} to a list of data.frames

Description

kriging {kriging} is a simple and highly optimized ordinary kriging algorithm to plot geographical data. This interface to the method allows to not just apply it to one data.frame but to a list of data.frames. The result is reduced to the data.frame with the predicted values. For a more detailed output kriging {kriging} has to be called for the individual input data.frames.

Usage

kriglist(plist, x = 1, y = 2, z = 3, rdup = TRUE, ...)

Arguments

plist

List of data.frames with point coordinates

x

index of data.frame column with x-axis spatial points. Defaults to 1

y

index of data.frame column with y-axis spatial points. Defaults to 2

z

index of data.frame column with z-axis spatial points. Defaults to 3

rdup

switch to activate removal of double values for single horizontal positions in the input data.frames. Defaults to TRUE

...

Arguments to be passed to method kriging {kriging}

Value

list with data.frames which contains the predicted values along with the coordinate covariates

Examples

df1 <- data.frame(
 x = rnorm(50),
 y = rnorm(50),
 z = rnorm(50) - 5
)

df2 <- data.frame(
 x = rnorm(50),
 y = rnorm(50),
 z = rnorm(50) + 5
)

lpoints <- list(df1, df2)

surfacelist <- kriglist(lpoints, lags = 3, model = "spherical")

Check if a point is within a polygon (2D)

Description

pnp is able to determine if a point is within a polygon in 2D space. The polygon is described by its corner points. The points must be in a correct drawing order.

Based on this solution: Copyright (c) 1970-2003, Wm. Randolph Franklin http://wrf.ecse.rpi.edu/pmwiki/pmwiki.php/Main/Software#toc24

Usage

pnp(vertx, verty, testx, testy)

Arguments

vertx

vector of x axis values of polygon corner points

verty

vector of y axis values of polygon corner points

testx

x axis value of point of interest

testy

y axis value of point of interest

Details

For discussion see: http://stackoverflow.com/questions/217578/how-can-i-determine-whether-a-2d-point-is-within-a-polygon/2922778#2922778

Value

boolean value - TRUE, if the point is within the polygon. Otherwise FALSE.

See Also

Other pnpfuncs: pnpmulti

Examples

df <- data.frame(
  x = c(1,1,2,2),
  y = c(1,2,1,2)
)

pnp(df$x, df$y, 1.5, 1.5)
pnp(df$x, df$y, 2.5, 2.5)

# caution: false-negatives in edge-cases:
pnp(df$x, df$y, 2, 1.5)

Check if multiple points are within a polygon (2D)

Description

pnpmulti works as pnp but for multiple points.

Usage

pnpmulti(vertx, verty, testx, testy)

Arguments

vertx

vector of x axis values of polygon corner points

verty

vector of y axis values of polygon corner points

testx

vector of x axis values of points of interest

testy

vector of y axis values of points of interest

Value

vector with boolean values - TRUE, if the respective point is within the polygon. Otherwise FALSE.

See Also

Other pnpfuncs: pnp

Examples

polydf <- data.frame(
  x = c(1,1,2,2),
  y = c(1,2,1,2)
)

testdf <- data.frame(
  x = c(1.5, 2.5),
  y = c(1.5, 2.5)
)

pnpmulti(polydf$x, polydf$y, testdf$x, testdf$y)

Multiple point position decision in relation to a set of stacked surfaces (3D)

Description

posdec has the purpose to make a decision about the position of individual points in relation to a set of stacked surfaces in 3D space. The decision is made by comparing the mean z axis value of the four horizontally closest points of a surface to the z axis value of the point in question.

Usage

posdec(crdf, maplist)

Arguments

crdf

data.frame with the spatial coordinates of the points of interest. Must contain three columns with the x axis values, y axis values and z axis values of the points in the order x, y, z

maplist

list of data.frames which contain the points that make up the surfaces. The individual data.frames must have the same structure as crdf

Value

data.frame with the spatial coordinates of the points of interest and the respective position information

See Also

Other posdecfuncs: posdeclist

Examples

df1 <- data.frame(
  x = rnorm(50),
  y = rnorm(50),
  z = rnorm(50) - 5
)

df2 <- data.frame(
  x = rnorm(50),
  y = rnorm(50),
  z = rnorm(50) + 5
)

lpoints <- list(df1, df2)

maps <- kriglist(lpoints, lags = 3, model = "spherical")

finds <- data.frame(
  x = c(0, 1, 0.5, 0.7),
  y = c(0.5, 0, 1, 0.7),
  z = c(-10, 10, 0, 2)
)

posdec(finds, maps)

Multiple point position decision in relation to a set of stacked surfaces (3D) for multiple data.frames in a list

Description

posdeclist works as posdec but not just for a single data.frame with individual points but for a list of data.frames

Usage

posdeclist(crdflist, maplist)

Arguments

crdflist

list of data.frames with the spatial coordinates of the points of interest (for details see posdec)

maplist

list of data.frames which contain the points that make up the surfaces

Value

list of data.frames with the spatial coordinates of the points of interest and the respective position information

See Also

Other posdecfuncs: posdec

Examples

df1 <- data.frame(
  x = rnorm(50),
  y = rnorm(50),
  z = rnorm(50) - 5
)

df2 <- data.frame(
  x = rnorm(50),
  y = rnorm(50),
  z = rnorm(50) + 5
)

lpoints <- list(df1, df2)

maps <- kriglist(lpoints, lags = 3, model = "spherical")

hexadf1 <- data.frame(
  x = c(0, 1, 0, 4, 5, 5, 5, 5),
  y = c(1, 1, 4, 4, 1, 1, 4, 4),
  z = c(1, 5, 1, 6, 1, 5, 1, 3)
)

hexadf2 <- data.frame(
  x = c(0, 1, 0, 4, 5, 5, 5, 5),
  y = c(1, 1, 4, 4, 1, 1, 4, 4),
  z = c(-1, -5, -1, -6, -1, -5, -1, -3)
)

cx1 <- fillhexa(hexadf1, 0.1)
cx2 <- fillhexa(hexadf2, 0.1)

cubelist <- list(cx1, cx2)

posdeclist(cubelist, maps)

Scales a point cloud (3D)

Description

Scales a 3D point cloud on every axis.

Usage

rescale(x, y, z, scalex = 1, scaley = 1, scalez = 1)

Arguments

x

vector of x axis values of scale point cloud

y

vector of y axis values of scale point cloud

z

vector of z axis values of scale point cloud

scalex

scaling factor on x axis (default = 1)

scaley

scaling factor on y axis (default = 1)

scalez

scaling factor on z axis (default = 1)

Value

data.frame with the spatial coordinates of the resulting points

Examples

s <- draw_sphere(1,1,1,3)

#library(rgl)
#plot3d(s)

s2 <- rescale(s$x, s$y, s$z, scalex = 4, scalez = 5)

#library(rgl)
#plot3d(s2)

Rotate a point cloud around a pivot point (3D)

Description

Rotate a point cloud around a defined pivot point by defined angles. The default rotation angle around each axis is zero and the default pivot point is the center point of the point cloud (defined by mean())

Usage

rotate(x, y, z, degrx = 0, degry = 0, degrz = 0, pivotx = NA_real_,
  pivoty = NA_real_, pivotz = NA_real_)

Arguments

x

vector of x axis values of rotation point cloud

y

vector of y axis values of rotation point cloud

z

vector of z axis values of rotation point cloud

degrx

rotation angle around x axis in degree (default = 0)

degry

rotation angle around y axis in degree (default = 0)

degrz

rotation angle around z axis in degree (default = 0)

pivotx

x axis value of pivot point (default = mean(x))

pivoty

y axis value of pivot point (default = mean(y))

pivotz

z axis value of pivot point (default = mean(z))

Value

data.frame with the spatial coordinates of the resulting points

Examples

circ <- draw_circle(0,0,0,5)

#library(rgl)
#plot3d(
#  circ,
#  xlim = c(-6,6),
#  ylim = c(-6,6),
#  zlim = c(-6,6)
#)

rotcirc <- rotate(circ$x, circ$y, circ$z, degrx = 45)

#plot3d(
#  rotcirc,
#  xlim = c(-6,6),
#  ylim = c(-6,6),
#  zlim = c(-6,6)
#)

Transformation of numeric matrices from wide to long format

Description

spatiallong transforms a set of two independent variables in vectors and a dependent variable in a wide matrix to a long matrix that combines the information. The result is exported as a data.frame.

Usage

spatiallong(x, y, z)

Arguments

x

vector of first independent variable. e.g. vector with x axis spatial points

y

vector of second independent variable. e.g. vector with y axis spatial points

z

matrix of dependent variable. e.g. matrix with z axis spatial points

Value

data.frame with three columns x, y and z

See Also

Other transfuncs: spatialwide

Examples

x <- c(1, 1, 1, 2, 2, 2, 3, 3, 4)
y <- c(1, 2, 3, 1, 2, 3, 1, 2, 3)
z <- c(3, 4, 2, 3, NA, 5, 6, 3, 1)

sw <- spatialwide(x, y, z, digits = 3)

spatiallong(sw$x, sw$y, sw$z)

Transformation of numeric matrices from long to wide format

Description

Transforms a set of two independent and one dependent variables in vectors from a long to a wide format and exports this result as a list

Usage

spatialwide(x, y, z, digits)

Arguments

x

vector of first independent variable. e.g. vector with x-axis spatial points

y

vector of second independent variable. e.g. vector with y-axis spatial points

z

vector of dependent variable. e.g. vector with z-axis spatial points

digits

integer indicating the number of decimal places to be used for rounding the dependent variables x and y.

Value

List with three elements:

$x: vector with ascendingly sorted, unique values of the first independent variable x

$y: vector with ascendingly sorted, unique values of the second independent variable y

$z: matrix with the values of z for the defined combinations of x (columns) and y (rows)

See Also

Other transfuncs: spatiallong

Examples

x <- c(1, 1, 1, 2, 2, 2, 3, 3, 4)
y <- c(1, 2, 3, 1, 2, 3, 1, 2, 3)
z <- c(3, 4, 2, 3, NA, 5, 6, 3, 1)

spatialwide(x, y, z, digits = 3)

Center determination for hexahedrons

Description

A hexahedron is a three dimensional shape that is defined by 6 faces and 8 corner points. spitcenter determines a center point for an input hexahedron by calculating the mean of the maximal extent on all three axis.

Usage

spitcenter(hex)

Arguments

hex

dataframe with three columns and eight rows to define a hexahedron by its corner point coordinates x, y and z

Value

vector with the spatial coordinates of the center point of the input hexahedron

See Also

Other centerdetfuncs: spitcenternatlist, spitcenternat

Examples

hexatestdf <- data.frame(
  x = c(0,1,0,4,5,5,5,5),
  y = c(1,1,4,4,1,1,4,4),
  z = c(4,8,4,9,4,8,4,6)
)

center <- spitcenter(hexatestdf)

#library(rgl)
#plot3d(
# hexatestdf$x, hexatestdf$y, hexatestdf$z,
# type = "p",
# xlab = "x", ylab = "y", zlab = "z"
#)
#plot3d(
#  center[1], center[2], center[3],
#  type = "p",
#  col = "red",
#  add = TRUE
#)

Center determination for rectangles whose tops and bottoms are defined by irregular surfaces (3D)

Description

spitcenternat first of all calculates the horizontal center of an input rectangle. Then it determines the vertical positions of the center points in relation to a surface stack.

Usage

spitcenternat(hex, maplist)

Arguments

hex

data.frame with the 2D corners of the rectangle defined by four points

maplist

list of data.frames which contain the points that make up the surfaces

Value

data.frame with the spatial coordinates of the center points

See Also

Other centerdetfuncs: spitcenternatlist, spitcenter

Examples

df1 <- data.frame(
  x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
  y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
  z = c(0.9+0.05*rnorm(6), 0.9+0.05*rnorm(14), 1.3+0.05*rnorm(14), 1.2+0.05*rnorm(6))
)

df2 <- data.frame(
    x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
    y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
    z = c(0.6+0.05*rnorm(6), 0.6+0.05*rnorm(14), 1.0+0.05*rnorm(14), 0.9+0.05*rnorm(6))
)

df3 <- data.frame(
    x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
    y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
    z = c(0.3+0.05*rnorm(6), 0.3+0.05*rnorm(14), 0.7+0.05*rnorm(14), 0.6+0.05*rnorm(6))
)

lpoints <- list(df1, df2, df3)

maps <- kriglist(lpoints, lags = 3, model = "spherical")

hexatestdf <- data.frame(
    x = c(1, 1, 1, 1, 2, 2, 2, 2),
    y = c(0, 1, 0, 1, 0, 1, 0, 1)
)

spitcenternat(hexatestdf, maps)

Center determination for rectangles whose tops and bottoms are defined by irregular surfaces (3D) for multiple data.frames in a list

Description

spitcenternatlist works as spitcenternat but not just for a single data.frame but for a list of data.frames

Usage

spitcenternatlist(hexlist, maplist)

Arguments

hexlist

list of data.frames with the 2D corners of the rectangles

maplist

list of data.frames which contain the points that make up the surfaces

Value

list of data.frames with the spatial coordinates of the center points

See Also

Other centerdetfuncs: spitcenternat, spitcenter

Examples

df1 <- data.frame(
x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
  y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
  z = c(0.9+0.05*rnorm(6), 0.9+0.05*rnorm(14), 1.3+0.05*rnorm(14), 1.2+0.05*rnorm(6))
)

df2 <- data.frame(
    x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
    y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
    z = c(0.6+0.05*rnorm(6), 0.6+0.05*rnorm(14), 1.0+0.05*rnorm(14), 0.9+0.05*rnorm(6))
)

df3 <- data.frame(
    x = c(rep(0, 6), seq(0.2, 2.8, 0.2), seq(0.2, 2.8, 0.2), rep(3,6)),
    y = c(seq(0, 1, 0.2), rep(0, 14), rep(1, 14), seq(0, 1, 0.2)),
    z = c(0.3+0.05*rnorm(6), 0.3+0.05*rnorm(14), 0.7+0.05*rnorm(14), 0.6+0.05*rnorm(6))
)

lpoints <- list(df1, df2, df3)

maps <- kriglist(lpoints, lags = 3, model = "spherical")

hexatestdf1 <- data.frame(
  x = c(1, 1, 1, 1, 2, 2, 2, 2),
  y = c(0, 1, 0, 1, 0, 1, 0, 1)
)

hexatestdf2 <- data.frame(
  x = c(0, 0, 0, 0, 1, 1, 1, 1),
  y = c(0, 1, 0, 1, 0, 1, 0, 1)
)

hexs <- list(hexatestdf1, hexatestdf2)

spitcenternatlist(hexs, maps)