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from __future__ import division
##############################################################################
#
# Copyright (c) 2003-2018 by The University of Queensland
# http://www.uq.edu.au
#
# Primary Business: Queensland, Australia
# Licensed under the Apache License, version 2.0
# http://www.apache.org/licenses/LICENSE-2.0
#
# Development until 2012 by Earth Systems Science Computational Center (ESSCC)
# Development 2012-2013 by School of Earth Sciences
# Development from 2014 by Centre for Geoscience Computing (GeoComp)
#
##############################################################################
__copyright__="""Copyright (c) 2003-2018 by The University of Queensland
http://www.uq.edu.au
Primary Business: Queensland, Australia"""
__license__="""Licensed under the Apache License, version 2.0
http://www.apache.org/licenses/LICENSE-2.0"""
__url__="https://launchpad.net/escript-finley"
# This example demonstrates both interpolation and saving data in CSV format
from esys.escript import saveDataCSV, sup, interpolateTable
import numpy
try:
from esys.finley import Rectangle
HAVE_FINLEY = True
except ImportError:
HAVE_FINLEY = False
if not HAVE_FINLEY:
print("Finley module not available")
else:
n=4 #Change this to whatever you like
r=Rectangle(n,n)
x=r.getX()
x0=x[0]
x1=x[1] #we'll use this later
toobig=100 #An exception will be thrown if interpolation produces a value larger than this
#First one dimensional interpolation
#In this example we will interpolate a sine curve
#The values we take from the domain will range from 0 to 1 (inclusive)
sine_table=[0, 0.70710678118654746, 1, 0.70710678118654746, 0, -0.70710678118654746, -1, -0.70710678118654746, 0]
numslices=len(sine_table)-1
minval=0
maxval=1
step=sup(maxval-minval)/numslices #The width of the gap between entries in the table
result=interpolateTable(sine_table, x0, minval, step, toobig)
#Now we save the input and output for comparison
saveDataCSV("1d.csv", inp=x0, out=result)
#Now 2D interpolation
#This time the sine curve will be at full height along the x (ie x0) axis.
#Its amplitude will decrease to a flat line along x1=1.1
#Interpolate works with numpy arrays so we'll use them
st=numpy.array(sine_table)
table=[st, 0.5*st, 0*st ] #Note that this table is 2D
#The y dimension should be the outer the dimension of the table
#Note that the order of tuples for the 2nd and 3rd param is (x,y)
result2=interpolateTable(table, x, (minval,0), (0.55, step), toobig)
saveDataCSV("2d.csv",inp0=x0, inp2=x1, out=result2)
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