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# Copyright (C) 2007, 2008 Andrew Ross
# Copyright (C) 2007-2016 Alan W. Irwin
# Grid data demo.
#
# 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
#
from numpy import *
import time
pts = 500
xp = 25
yp = 20
nl = 16
knn_order = 20
threshold = 1.001
wmin = -1e3
randn = 0
rosen = 0
def cmap1_init(w):
i = [0, 1]
h = [240, 0]
l = [0.6, 0.6]
s = [0.8, 0.8]
w.plscmap1n(256)
w.plscmap1l(0, i, h, l, s)
# main
#
#
def main(w):
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 = -0.2
ym = -0.2
xM = 0.6
yM = 0.6
opt[2] = wmin
opt[3] = knn_order
opt[4] = threshold
cmap1_init(w)
w.plseed(5489)
# Create the sampled data
# For consistency across languages use w.plrandd to create the
# pseudo-random data that are required.
xt = zeros(pts)
yt = zeros(pts)
for i in range(pts) :
xt[i] = (xM-xm)*w.plrandd()
yt[i] = (yM-ym)*w.plrandd()
if randn == 0 :
x = xt + xm
y = yt + ym
else:
x = sqrt(-2.*log(xt))*cos(2.*pi*yt) + xm
y = sqrt(-2.*log(xt))*sin(2.*pi*yt) + ym
if rosen == 0:
r = sqrt( x*x + y*y)
z = exp(-r*r)*cos(2*pi*r)
else:
z = log(pow(1.-x,2) + 100.*pow(y-pow(x,2),2))
zmin = min(z)
zmax = max(z)
# Create regular grid
xg = xm + (xM-xm)*arange(xp)/(xp-1.)
yg = ym + (yM-ym)*arange(yp)/(yp-1.)
xx = zeros(1)
yy = zeros(1)
w.plcol0(1)
w.plenv(xm,xM,ym,yM,2,0)
w.plcol0(15)
w.pllab('X','Y','The original data sampling')
for i in range(pts):
w.plcol1( ( z[i] - zmin )/( zmax - zmin ) )
xx[0] = x[i]
yy[0] = y[i]
w.plstring( xx, yy, '#(727)' )
w.pladv(0)
w.plssub(3,2)
for k in range(2):
w.pladv(0)
for alg in range(1,7):
zg = w.plgriddata(x, y, z, xg, yg, alg, opt[alg-1])
# for i in range(xp):
# for j in range(yp):
# print("zg[%d,%d] = %g" % (i, j, zg[i][j]))
if alg == w.GRID_CSA or alg == w.GRID_DTLI or alg == w.GRID_NNLI or alg == w.GRID_NNI:
for i in range(xp):
for j in range(yp):
# Average (IDW) over the 8 neighbours for Nan
if isnan(zg[i][j]):
zg[i][j] = 0.0
dist = 0.0
for ii in range(maximum(i-1,0),minimum(i+2,xp)):
for jj in range(maximum(j-1,0),minimum(j+2,yp)):
if (not isnan(zg[ii][jj])):
d = abs(ii-i) + abs(jj-j)
if (d != 1.0) :
d = 1.4142
zg[i][j] += zg[ii][jj]/(d*d)
dist += d
if dist != 0.0 :
zg[i][j] /= dist
else:
zg[i][j] = zmin
lzM = max(zg.flat)
lzm = min(zg.flat)
lzm = minimum(lzm,zmin)
lzM = maximum(lzM,zmax)
lzm = lzm - 0.01
lzM = lzM + 0.01
w.plcol0(1)
w.pladv(alg)
if (k == 0):
clev = lzm + (lzM-lzm)*arange(nl)/(nl-1)
w.plenv0(xm,xM,ym,yM,2,0)
w.plcol0(15)
w.pllab('X','Y',title[alg-1])
w.plshades(zg, xm, xM, ym, yM, clev, 1., 1, None, None)
w.plcol0(2)
else:
clev = lzm + (lzM-lzm)*arange(nl)/(nl-1)
w.plvpor(0.0, 1.0, 0.0, 0.9)
w.plwind(-1.1, 0.75, -0.65, 1.20)
w.plw3d(1.0, 1.0, 1.0, xm, xM, ym, yM, lzm, lzM, 30.0, -40.0)
w.plbox3('bntu', 'X', 0.0, 0,'bntu', 'Y', 0.0, 0,'bcdfntu', 'Z', 0.5, 0)
w.plcol0(15)
w.pllab('','',title[alg-1])
w.plot3dc(xg, yg, zg, w.DRAW_LINEXY | w.MAG_COLOR | w.BASE_CONT, clev)
# Restore defaults
# cmap1 default color palette.
w.plspal1("cmap1_default.pal",1)
w.plssub(1, 1)
w.pleop()
# Must be done independently because otherwise this changes output files
# and destroys agreement with C examples.
#w.plcol0(1)
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