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.TH pkstat 1 "14 June 2016" "" ""
.SH NAME
pkstat \- calculate basic statistics from raster dataset
.SH SYNOPSIS
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\fBpkstat\fR \kx
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'in \n(.iu+\nxu
[\fIoptions\fR]
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'hy
.SH DESCRIPTION
\fBpkstat\fR is utility to calculate basic statistics from
a raster dataset.
.SH OPTIONS
.TP 
\*(T<\fB\-i\fR\*(T> \fIfilename\fR, \*(T<\fB\-\-input\fR\*(T> \fIfilename\fR
name of the input raster dataset
.TP 
\*(T<\fB\-b\fR\*(T> \fIband\fR, \*(T<\fB\-\-band\fR\*(T> \fIband\fR
band(s) on which to calculate statistics
.TP 
\*(T<\fB\-f\fR\*(T>, \*(T<\fB\-\-filename\fR\*(T>
Shows image filename
.TP 
\*(T<\fB\-stats\fR\*(T>, \*(T<\fB\-\-statistics\fR\*(T>
Shows basic statistics
(calculate in memory)
(min,max, mean and stdDev of the raster datasets)
.TP 
\*(T<\fB\-fstats\fR\*(T>, \*(T<\fB\-\-fstatistics\fR\*(T>
Shows basic statistics using GDAL computeStatistics
(min,max, mean and stdDev of the raster datasets)
.TP 
\*(T<\fB\-ulx\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-ulx\fR\*(T> \fIvalue\fR
Upper left x value bounding box
.TP 
\*(T<\fB\-uly\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-uly\fR\*(T> \fIvalue\fR
Upper left y value bounding box
.TP 
\*(T<\fB\-lrx\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-lrx\fR\*(T> \fIvalue\fR
Lower right x value bounding box
.TP 
\*(T<\fB\-lry\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-lry\fR\*(T> \fIvalue\fR
Lower right y value bounding box
.TP 
\*(T<\fB\-nodata\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-nodata\fR\*(T> \fIvalue\fR
Set nodata value(s)
.TP 
\*(T<\fB\-down\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-down\fR\*(T> \fIvalue\fR
Down sampling factor (for raster sample datasets only).
Can be used to create grid points.
.TP 
\*(T<\fB\-rnd\fR\*(T> \fInumber\fR, \*(T<\fB\-\-rnd\fR\*(T> \fInumber\fR
generate random numbers
.TP 
\*(T<\fB\-mean\fR\*(T>, \*(T<\fB\-\-mean\fR\*(T>
calculate mean
.TP 
\*(T<\fB\-median\fR\*(T>, \*(T<\fB\-\-median\fR\*(T>
calculate median
.TP 
\*(T<\fB\-var\fR\*(T>, \*(T<\fB\-\-var\fR\*(T>
calculate variance
.TP 
\*(T<\fB\-skew\fR\*(T>, \*(T<\fB\-\-skewness\fR\*(T>
calculate skewness
.TP 
\*(T<\fB\-kurt\fR\*(T>, \*(T<\fB\-\-kurtosis\fR\*(T>
calculate kurtosis
.TP 
\*(T<\fB\-stdev\fR\*(T>, \*(T<\fB\-\-stdev\fR\*(T>
calculate standard deviation
.TP 
\*(T<\fB\-sum\fR\*(T>, \*(T<\fB\-\-sum\fR\*(T>
calculate sum of column
.TP 
\*(T<\fB\-mm\fR\*(T>, \*(T<\fB\-\-minmax\fR\*(T>
calculate minimum and maximum value
.TP 
\*(T<\fB\-min\fR\*(T>, \*(T<\fB\-\-min\fR\*(T>
calculate minimum value
.TP 
\*(T<\fB\-max\fR\*(T>, \*(T<\fB\-\-max\fR\*(T>
calculate maximum value
.TP 
\*(T<\fB\-src_min\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-src_min\fR\*(T> \fIvalue\fR
start reading source from this minimum value
.TP 
\*(T<\fB\-src_max\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-src_max\fR\*(T> \fIvalue\fR
stop reading source from this maximum value
.TP 
\*(T<\fB\-hist\fR\*(T>, \*(T<\fB\-\-hist\fR\*(T>
calculate histogram
.TP 
\*(T<\fB\-hist2d\fR\*(T>, \*(T<\fB\-\-hist2d\fR\*(T>
.TP 
\*(T<\fB\-nbin\fR\*(T> \fIvalue\fR, \*(T<\fB\-\-nbin\fR\*(T> \fIvalue\fR
number of bins to calculate histogram
.TP 
\*(T<\fB\-rel\fR\*(T>, \*(T<\fB\-\-relative\fR\*(T>
use percentiles for histogram to calculate histogram
.TP 
\*(T<\fB\-kde\fR\*(T>, \*(T<\fB\-\-kde\fR\*(T>
Use Kernel density estimation when producing histogram.
The standard deviation is estimated based on Silverman's rule of thumb.
.TP 
\*(T<\fB\-cor\fR\*(T>, \*(T<\fB\-\-correlation\fR\*(T>
calculate Pearson produc-moment correlation coefficient between
two raster datasets (defined by
\*(T<\fB\-c\fR\*(T> \fIcol1\fR
\*(T<\fB\-c\fR\*(T> \fIcol2\fR)
.TP 
\*(T<\fB\-rmse\fR\*(T>, \*(T<\fB\-\-rmse\fR\*(T>
calculate root mean square error between two raster datasets
.TP 
\*(T<\fB\-reg\fR\*(T>, \*(T<\fB\-\-regression\fR\*(T>
calculate linear regression between two raster datasets and get
correlation coefficient
.TP 
\*(T<\fB\-regerr\fR\*(T>, \*(T<\fB\-\-regerr\fR\*(T>
calculate linear regression between two raster datasets and get
root mean square error
.TP 
\*(T<\fB\-preg\fR\*(T>, \*(T<\fB\-\-preg\fR\*(T>
calculate perpendicular regression between two raster datasets and
get correlation coefficient
.TP 
\*(T<\fB\-pregerr\fR\*(T>, \*(T<\fB\-\-pregerr\fR\*(T>
calculate perpendicular regression between two raster datasets and
get root mean square error
.TP 
\*(T<\fB\-v\fR\*(T> \fIlevel\fR, \*(T<\fB\-\-verbose\fR\*(T> \fIlevel\fR
verbose mode when positive