/usr/share/doc/libplplot11/examples/perl/x21.pl is in libplplot-dev 5.9.9-2ubuntu2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 | #! /usr/bin/env perl
#
# Demo x21 for the PLplot PDL binding
#
# Grid data demo
#
# Copyright (C) 2004 Rafael Laboissiere
#
# This file is part of PLplot.
#
# PLplot is free software; you can redistribute it and/or modify
# it under the terms of the GNU Library General Public License as published
# by the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# PLplot is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General Public License
# along with PLplot; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
# SYNC: x21c.c 1.12
use PDL;
use PDL::Graphics::PLplot;
use Math::Trig qw [pi];
use Getopt::Long qw [:config pass_through];
use POSIX qw [clock];
sub cmap1_init {
my $i = pdl ([0.0, # left boundary
1.0]); # right boundary
my $h = pdl ([240, # blue -> green -> yellow ->
0]); # -> red
my $l = pdl ([0.6, 0.6]);
my $s = pdl ([0.8, 0.8]);
plscmap1n (256);
plscmap1l (0, $i, $h, $l, $s, pdl ([]));
}
my ($xm, $ym, $xM, $yM);
my $randn = 0;
my $rosen = 0;
sub main {
my $clev;
# Options data structure definition. */
my $pts = 500;
my $xp = 25;
my $yp = 20;
my $nl = 16;
my $knn_order = 20;
my $threshold = 1.001;
my $wmin = -1e3;
GetOptions ("npts=i" => \$pts,
"randn" => \$randn,
"rosen" => \$rosen,
"nx=i" => \$xp,
"ny=i" => \$yp,
"nlevel=i" => \$nl,
"knn_order=i" => \$knn_order,
"threshold=f" => \$threshold,
"help" => \$help);
if ($help) {
print (<<EOT);
$0 options:
--npts points Specify number of random points to generate [500]
--randn Normal instead of uniform sampling -- the effective
number of points will be smaller than the specified.
--rosen Generate points from the Rosenbrock function.
--nx points Specify grid x dimension [25]
--ny points Specify grid y dimension [20]
--nlevel Specify number of contour levels [15]
--knn_order order Specify the number of neighbors [20]
--threshold float Specify what a thin triangle is [1. < [1.001] < 2.]
EOT
push (@ARGV, "-h");
}
unshift (@ARGV, $0);
@title = ("Cubic Spline Approximation",
"Delaunay Linear Interpolation",
"Natural Neighbors Interpolation",
"KNN Inv. Distance Weighted",
"3NN Linear Interpolation",
"4NN Around Inv. Dist. Weighted");
@opt = (0., 0., 0., 0., 0., 0.);
$xm = $ym = -0.2;
$xM = $yM = 0.6;
plParseOpts (\@ARGV, PL_PARSE_PARTIAL);
$opt[2] = $wmin;
$opt[3] = $knn_order;
$opt[4] = $threshold;
# Initialize plplot
plinit ();
plseed (5489);
my ($x, $y, $z) = create_data ($pts); # the sampled data
my $zmin = min ($z);
my $zmax = max ($z);
my ($xg, $yg) = create_grid ($xp, $yp); # grid the data at
# the output grided data
plcol0 (1);
plenv ($xm, $xM, $ym, $yM, 2, 0);
plcol0 (15);
pllab ("X", "Y", "The original data sampling");
plcol0 (2);
plpoin ($x, $y, 5);
pladv (0);
plssub (3, 2);
for (my $k = 0; $k < 2; $k++) {
pladv (0);
for (my $alg = 1; $alg < 7; $alg++) {
$zg = plgriddata ($x, $y, $z, $xg, $yg, $alg, $opt [$alg - 1]);
# - CSA can generate NaNs (only interpolates?!).
# - DTLI and NNI can generate NaNs for points outside the convex hull
# of the data points.
# - NNLI can generate NaNs if a sufficiently thick triangle is not found
#
# PLplot should be NaN/Inf aware, but changing it now is quite a job...
# so, instead of not plotting the NaN regions, a weighted average over
# the neighbors is done.
if ($alg == GRID_CSA || $alg == GRID_DTLI
|| $alg == GRID_NNLI || $alg == GRID_NNI) {
for (my $i = 0; $i < $xp; $i++) {
for (my $j = 0; $j < $yp; $j++) {
if (not isfinite ($zg->slice ("$i,$j"))) {
# average (IDW) over the 8 neighbors
$zg->slice ("$i,$j") .= 0;
my $dist = 0;
for (my $ii = $i - 1; $ii <= $i + 1 && $ii < $xp; $ii++) {
for (my $jj = $j - 1; $jj <= $j + 1 && $jj < $yp; $jj++) {
my $zgij = $zg->slice ("$ii,$jj");
if ($ii >= 0 && $jj >= 0 && isfinite ($zgij)) {
$d = (abs ($ii - $i) + abs ($jj - $j)) == 1 ? 1. : 1.4142;
$zg->slice ("$i,$j") .= $zg->slice ("$i,$j")
+ $zgij / ($d * $d);
$dist += $d;
}
}
}
if ($dist != 0.) {
$zg->slice ("$i,$j") .= $zg->slice ("$i,$j") / $dist;
} else {
$zg->slice ("$i,$j") .= $zmin;
}
}
}
}
}
my $lzM = max ($zg);
my $lzm = min ($zg);
$lzm = min pdl ([$lzm, $zmin]);
$lzM = max pdl ([$lzM, $zmax]);
$lzm = $lzm - 0.01;
$lzM = $lzM + 0.01;
plcol0 (1);
pladv ($alg);
if ($k == 0) {
$clev = $lzm + ($lzM - $lzm) * sequence ($nl)/ ($nl - 1);
plenv0 ($xm, $xM, $ym, $yM, 2, 0);
plcol0 (15);
pllab ("X", "Y", $title [$alg - 1]);
plshades ($zg, $xm, $xM, $ym, $yM,
$clev, 1, 0, 1, 1, 0, 0, 0);
plcol0 (2);
} else {
$clev = $lzm + ($lzM - $lzm) * sequence ($nl)/ ($nl - 1);
cmap1_init ();
plvpor (0.0, 1.0, 0.0, 0.9);
plwind (-1.1, 0.75, -0.65, 1.20);
#
# For the comparition to be fair, all plots should have the
# same z values, but to get the max/min of the data generated
# by all algorithms would imply two passes. Keep it simple.
#
# plw3d(1., 1., 1., xm, xM, ym, yM, zmin, zmax, 30, -60);
#
plw3d (1., 1., 1., $xm, $xM, $ym, $yM, $lzm, $lzM, 30.0, -40.0);
plbox3 (0.0, 0, 0.0, 0, 0.5, 0,
"bntu", "X", "bntu", "Y", "bcdfntu", "Z");
plcol0 (15);
pllab ("", "", $title [$alg - 1]);
plot3dc ($xg, $yg, $zg, DRAW_LINEXY | MAG_COLOR | BASE_CONT, $clev);
}
}
}
plend ();
}
sub create_grid {
my ($px, $py) = @_;
my ($x, $y);
$x = $xm + ($xM - $xm) * sequence ($px) / ($px - 1);
$y = $ym + ($yM - $ym) * sequence ($py) / ($py - 1);
return ($x, $y);
}
sub create_data {
my $pts = shift;
my ($x, $y, $z);
# generate a vector of pseudo-random numbers 2*$pts long
my $t = zeroes(2*$pts);
plrandd($t); # use the PLplot standard random number generator for consistency with other demos
# Use every other random number for the x vect and y vect.
# This is done in this funny way to make the results identical
# to the c version, which calls plrandd once for x, then once for y in a loop.
my $xt = ($xM-$xm) * $t->mslice([0,(2*$pts)-2,2]);
my $yt = ($yM-$ym) * $t->mslice([1,(2*$pts)-1,2]);
if (not $randn) {
$x = $xt + $xm;
$y = $yt + $ym;
} else { # std=1, meaning that many points are outside the plot range
$x = sqrt(-2 * log($xt)) * cos(2 * pi * $yt) + $xm;
$y = sqrt(-2 * log($xt)) * sin(2 * pi * $yt) + $ym;
}
if (not $rosen) {
my $r = sqrt ($x ** 2 + $y ** 2);
$z = exp (- $r ** 2) * cos (2 * pi * $r);
} else {
$z = log ((1 - $x) ** 2) + 100 * ($y - $x ** 2) ** 2;
}
return ($x, $y, $z);
}
main ();
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