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Copy pathoneD_correction_test.py
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oneD_correction_test.py
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#!/bin/python -u
import numpy
import math
import unittest
import matplotlib.pyplot as plt
import Globals.configPaths
import Optimization.DistanceFunction.DistanceFunctionOptimization
from Optimization.DistanceFunction import OptimizationMaker
import Writers.VTKMeshWriter
import Geometry.FrechetDistance
import Geometry.ProjectionDistance
from Geometry.Curve import Curve
from Geometry.Curve import Curve2DPol
from Geometry.Curve import Curve1DPol
from Geometry.Curve import LogSpiral
from Geometry.Curve import Circle
from Geometry.Curve import Segment
import plotly.graph_objects as go
from Discretization.Meshers import CurveMesher,SurfaceMesher
from Writers.NumpyMeshWriter import NumpyMeshWriter
from Globals.configPython import *
import polynomial
import quadratures
def convergence_IO(nR, ne, value, pX, pU, title):
print("____________________________________________________________________\n")
print("----------------------- POLYNOMIAL DEGREES: X ",pX," T ",pU," ----------------")
print("____________________________________________________________________\n")
print("\n \t ",title,"\n\n")
print ("N\t E(x,t)\t ORDER sqrt(E) ORDER")
print("------------------------------------------------------------------------\n")
for r in range(nR + 1):
ne1 = pow(2, r) * ne
if r == 0:
print (ne1,"\t%1.3e"%value[r]," |","%1.3e"%numpy.sqrt(value[r]))
else:
a = numpy.log10( value[r-1] / value[r]) / numpy.log10(2.0)
b = numpy.log10(numpy.sqrt(value[r-1])/ numpy.sqrt(value[r])) / numpy.log10(2.0)
print (ne1,"\t%1.3e"%value[r]," %1.2f"%a, " | %1.3e"%numpy.sqrt(value[r])," %1.2f" %b)
print("____________________________________________________________________\n")
def newton_root (guess, eL, eR, polyX, polyT, f):
tol = 1.e-15
itMAX = 20
r = 1.0 #0.5 * (eR - eL)
#eM = 0.5 * (eR + eL)
zp = guess
zn = zp
t = numpy.zeros([1,1])
for it in range(itMAX):
x = polyX.evaluate(-1.0, 1.0, zn)
aux = polyT.evaluate(-1.0, 1.0, zn)
t[0,0] = aux[0]
ft = float(f.tangent(t))
val = x[3] - ft * r * aux[3]
if (abs (val) < tol):
zp = zn
break
dft = float(f.hessian(t))
dval = x[6] - (dft * r * r * aux[3] * aux[3] + ft * aux[6])
zp = zn
if (abs (dval) < tol):
print(" NULL DERIVATIVE ", dval)
break
zn = zp - val / dval
if (abs(zn - zp) < tol):
break
if (it == itMAX): print ("NEWTON didn't converge")
return zp
class TestDistanceFunctionOptimization(unittest.TestCase):
@staticmethod
def getGeometry1D(c, a, b):
if c == 0 or c == 10: return Curve1DPol.Curve1DCos (a, b)
elif c == 1: return Curve1DPol.Curve1DPol1(a, b)
elif c == 2: return Curve1DPol.Curve1DPol2(a, b)
elif c == 4: return Curve1DPol.Curve1DPol4(a, b)
elif c == 5: return Curve1DPol.Curve1DPol5(a, b)
elif c == 6: return Curve1DPol.Curve1Dexp (a, b)
elif c == 7: return Curve1DPol.Curve1DSine(a, b)
elif c == 8: return Curve1DPol.Curve1DCosh(a, b)
elif c == 9: return Curve1DPol.Curve1DSinh(a, b)
@staticmethod
def getMeshDistances(mesh, parametrization, functionName, tol, gp, fixU = False):
disparityDistanceComputer=Geometry.FrechetDistance.FrechetDistance(
mesh,parametrization,
functionName)
if fixU:
oldParametricMask = mesh.theParametricNodesMask.copy()
mesh.theParametricNodesMask[:] = True
disparityDistanceComputer.theFTolRel=tol
disparityDistanceComputer.theXTolRel=tol
disparityValue,normalError=disparityDistanceComputer.run()
projectorDistance = Geometry.ProjectionDistance.ProjectionDistance(
mesh,parametrization,gp)
projectorValue = projectorDistance.run()
if fixU:
mesh.theParametricNodesMask = oldParametricMask
return disparityValue, projectorValue, normalError
@staticmethod
def testDistanceFunction(pX, pU, ne, nR, curve, I, showPlots):
relocateX = False
fixU = False
callFix = True
method = 'Newton'
tolDistanceCalculation = 1.e-7
tol = 1.e-7
disparity_e = numpy.zeros([2,nR + 1])
disparity_XA = numpy.zeros([2,nR + 1])
disparity_DXAT = numpy.zeros([2,nR + 1])
gp = 50
objectiveFunctionName = "Intrinsic"
frechetFunctionName = "Intrinsic"
parametrization = TestDistanceFunctionOptimization.getGeometry1D(curve, I[0], I[1])
figcount = 1
ea = numpy.zeros(4)
dea = numpy.zeros(2)
pltInfo = ' pX = '+str(pX)+' pT = '+str(pU)
gpx, uw = quadratures.qType(pX + 1, quadratures.eLGL)
gpu, pw = quadratures.qType(pU + 1, quadratures.eLGL)
for ref in range(nR + 1):
h = (parametrization.theT1 - parametrization.theT0) / (pow (2, ref) * ne)
optimizer = Optimization.DistanceFunction.DistanceFunctionOptimization.DistanceFunctionOptimization(
parametrization,
h,pX,pU,
objectiveFunctionName,
tol,
initialP = pX,
method = method,
relocateX = relocateX,
fixU = fixU
)
meshO, meshI = optimizer.run()
newMasterElementX = meshO.theMasterElementMakerX.createMasterElement(pX, gp-1)
newMasterElementU = meshO.theMasterElementMakerU.createMasterElement(pU, gp-1)
oldMasterElementX = meshI.theMasterElementX
meshO.theMasterElementX = newMasterElementX
meshO.theMasterElementU = newMasterElementU
meshI.theMasterElementX = newMasterElementX
meshI.theMasterElementU = newMasterElementU
w = meshI.theMasterElementX.theGaussWeights
z = meshI.theMasterElementX.theGaussPoints
n = meshI.theNOfElements
eBD = numpy.zeros(n + 1)
for i in range(n+1):
eBD[i] = parametrization.theT0 + h * i
x = numpy.empty((2,n, gp,1))
err = numpy.empty((2,n, gp,1))
polyE = numpy.empty((2,n, gp,1))
molyE = numpy.empty((2,n, gp,1))
t = numpy.empty((2,n, gp,1))
myt = numpy.empty((2,n, gp,1))
dt = numpy.empty((2,n, gp,1,1))
eO = numpy.empty((2,n, gp,1))
eI = numpy.empty((2,n, gp,1))
zx = numpy.empty(( n, gp, 1))
zp = numpy.empty(( n, pX + 1))
zu = numpy.empty(( n, pU + 1))
myalpha = numpy.empty((2,n, gp,1))
alpha = numpy.empty((2,n, gp,1))
aPF = numpy.empty((n, gp,1))
dx = numpy.empty((2,n, gp,1,1))
dadt = numpy.empty((2,n, gp,1))
for type in range(2):
if type == 0: mesh = meshO
else: mesh = meshI
disf,proje,norm = TestDistanceFunctionOptimization.getMeshDistances(
mesh,parametrization,frechetFunctionName,
tolDistanceCalculation, gp-1)
disparity_e[type, ref] = disf * disf * 0.5
#create interpolating t
for i in range(n):
if type == 0:
zx[i] = 0.5 * ( (eBD[i + 1] - eBD[i]) * z + eBD[i + 1] + eBD[i] )
aPF[i] = parametrization.value(zx[i])
zp[i] = 0.5 * ( (eBD[i + 1] - eBD[i]) * gpx + eBD[i + 1] + eBD[i] )
zu[i] = 0.5 * ( (eBD[i + 1] - eBD[i]) * gpu + eBD[i + 1] + eBD[i] )
x[type,i] = mesh.getXElement(i)
dx[type,i] = mesh.getDNXElement(i)
t[type,i] = mesh.getUElement(i)
alpha[type,i] = parametrization.value(t[type,i])
# reconstruct x,t lagrange to take 2nd derivatives
idEX = mesh.theElementsX[i,:]
idEU = mesh.theElementsU[i,:]
x_nodes = mesh.theNodes[idEX,:]
t_nodes = mesh.theParametricNodes[idEU,:]
IY = numpy.zeros([pX +1,3])
IT = numpy.zeros([pU +1,3])
IY[:,0] = x_nodes[:,0]
IT[:,0] = t_nodes[:,0]
x_lagr = polynomial.polynomial(pX, 1)
x_lagr.interpolate(gpx,IY)
t_lagr = polynomial.polynomial(pU, 1)
t_lagr.interpolate(gpu,IT)
# Approximate error function by peaks
if (type == 1): pE = max(pX, pU) + 1
else: pE = pX + pU
zl, wl = quadratures.qType(pE, quadratures.eLGL)
IX = numpy.zeros( pE + 1 )
IY = numpy.zeros([pE + 1,3])
IX[ 0] = zl[ 0]
IX[ -1] = zl[-1]
dummy = numpy.empty((1,1))
for k in range(1, pE):
IX[k] = newton_root(0.5 * (zl[k] + zl[k -1]), eBD[i], eBD[i + 1], x_lagr, t_lagr, parametrization)
for k in range(pE + 1):
xt = x_lagr.evaluate(-1.0,1.0, IX[k])
tt = t_lagr.evaluate(-1.0,1.0, IX[k])
dummy[0,0] = tt[0]
IY[k,0] = xt[0] - float(parametrization.value(dummy))
e_lagr = polynomial.polynomial(pE, 1)
e_lagr.interpolate(IX,IY)
e_leg = polynomial.polynomial(pE, 0)
ef = x[type,i,:,0] - alpha[type,i,:,0]
uvals = e_leg.l2_legPro(pE, ef , z, w)
plt.plot(z,uvals)
plt.show()
for k in range(gp): print(uvals[k],x[type,i,k])
da = parametrization.tangent(t[type,i])
dt[type,i] = numpy.einsum('li,klj->kij',
mesh.theParametricNodes[mesh.theElementsU[i, :], :],
mesh.theMasterElementU.theShapeFunctionsDerivatives)
sumXA = 0.0
sumDXAT = 0.0
for j in range (gp):
dadt[type,i,j,0] = da[j,0] * dt[type,i,j,0]
aux = e_lagr.evaluate(z[j,0])
polyE[type,i,j] = aux[0]
aux = e_leg.evaluate(z[j,0])
molyE[type,i,j] = aux[0]
err[type,i,j] = x[type,i,j,0] - alpha[type,i,j,0]
dxa = abs(err[type,i,j]) #* (alpha[type,i,j,0] - x[type,i,j,0])
ddxat = abs(dx[type,i,j,0] - dadt[type,i,j,0]) #* (dadt[type,i,j,0] - dx[type,i,j,0])
ea[type] = max ( ea[type], dxa)
dea[type] = max (dea[type], ddxat)
ea[type + 2] = max ( ea[type + 2], abs(x[type,i,j,0] - aPF[i,j,0]))
derx = abs(dx[type,i,j]) * w[j]
sumXA += dxa * derx
sumDXAT += ddxat * derx
disparity_XA [type,ref] += sumXA * 0.5
disparity_DXAT[type,ref] += sumDXAT * 0.5
if ref != 0: continue
leg_alpha = 'alpha'
leg_x = 'x'
leg_t = 't(xi)'
leg_xi = 'xi(t)'
leg_comp = 'alpha(t(xi))'
xAXIS = numpy.zeros(n + 1)
yAXIS = numpy.zeros(n + 1)
ypAXIS = numpy.zeros(n * (pX + 1))
yuAXIS = numpy.zeros(n * (pU + 1))
toAXIS = numpy.zeros(n + 1)
tiAXIS = numpy.zeros(n + 1)
xoEP = numpy.zeros(n + 1)
xiEP = numpy.zeros(n + 1)
aoEP = numpy.zeros(n + 1)
aiEP = numpy.zeros(n + 1)
aEP = numpy.zeros(n + 1)
for i in range(n):
xAXIS[i] = zx[i][0]
toAXIS[i] = t[0][i][0][0]
tiAXIS[i] = t[1][i][0][0]
xoEP[i] = x[0][i][0][0]
xiEP[i] = x[1][i][0][0]
aoEP[i] = alpha[0][i][0][0]
aiEP[i] = alpha[1][i][0][0]
aEP[i] = aPF[i][0][0]
xAXIS[n] = zx[n-1][-1]
toAXIS[n] = t[0][n-1][-1][0]
tiAXIS[n] = t[1][n-1][-1][0]
xoEP[n] = x[0][n-1][-1][0]
xiEP[n] = x[1][n-1][-1][0]
aoEP[n] = alpha[0][n-1][-1][0]
aiEP[n] = alpha[1][n-1][-1][0]
aEP[n] = aPF[n-1][-1][0]
zx = zx.flatten()
zp = zp.flatten()
zu = zu.flatten()
alphaO = alpha[0].flatten()
alphaI = alpha[1].flatten()
eO = err[0].flatten()
eI = err[1].flatten()
ePolO = polyE[0].flatten()
ePolI = polyE[1].flatten()
eMolO = molyE[0].flatten()
eMolI = molyE[1].flatten()
xO = x[0].flatten()
xI = x[1].flatten()
tO = t[0].flatten()
tI = t[1].flatten()
dtO = dt[0].flatten()
dtI = dt[1].flatten()
dxO = dx[0].flatten()
dxI = dx[1].flatten()
dadtO = dadt[0].flatten()
dadtI = dadt[1].flatten()
aPF = aPF.flatten()
fig = plt.figure(figcount)
figcount += 1
plt.suptitle(' Curves ' + pltInfo)
plt.subplot(2,3,1)
plt.title(' Opti Solution')
plt.plot(zx, xO, c = 'b', linestyle='-.')
plt.xlabel('z')
plt.subplot(2,3,2)
plt.title('Target s')
plt.plot(zx, alphaO, c = 'r', linestyle=':', label = 'alpha(t)')
plt.plot(zx, aPF , c = 'orange', linestyle='-', label = 'alpha(z)')
plt.xlabel('z')
plt.legend()
plt.subplot(2,3,3)
plt.title(' Overlap')
plt.plot(zx, aPF , c = 'orange', linestyle='-', label = 'alpha(z)')
plt.plot(zx, xO, c = 'b', linestyle='-.', label = 'x(z)')
plt.plot(zx, alphaO, c = 'r', linestyle=':', label = 'alpha(t )')
plt.scatter(xAXIS, xoEP, c = 'b', s = 5)
plt.scatter(xAXIS, aoEP, c = 'r', s = 5)
plt.xlabel('z')
plt.legend()
plt.subplot(2,3,4)
plt.title(' Interpol Solution')
plt.plot(zx, xI, c = 'b', linestyle='-.')
#plt.plot(zx, xO, c = 'c', linestyle='-')
plt.subplot(2,3,5)
plt.title('Target Curves')
plt.plot(zx, alphaI, c = 'r', linestyle=':', label = 'alpha(t )')
plt.plot(zx, aPF , c = 'orange', linestyle='-', label = 'alpha(z)')
plt.legend()
plt.xlabel('t')
plt.subplot(2,3,6)
plt.title(' Overlap')
plt.plot(zx, aPF , c = 'orange', linestyle='-', label = 'alpha(z)')
plt.plot(zx, xI, c = 'b', linestyle='-.', label = 'x(z)')
plt.plot(zx, alphaI, c = 'r', linestyle=':', label = 'alpha(t )')
# plt.plot(zx, xO, c = 'c', linestyle='--')
plt.scatter(xAXIS, xiEP, c = 'b', s = 5)
plt.scatter(xAXIS, aiEP, c = 'r', s = 5)
plt.xlabel('z')
errO = eO / ea[0]
errI = eI / ea[1]
errOO = (xO - aPF) / ea[2]
errII = (xI - aPF) / ea[3]
fig = plt.figure(figcount)
figcount += 1
plt.suptitle('Error from z ' + pltInfo)
plt.subplot(2,3,1)
plt.title('Optimized x')
plt.plot(zx, xO - aPF, c = 'b' , linestyle='-.', label='x(z) - alpha(z)')
plt.scatter(xAXIS, xoEP - aEP, c = 'g', s = 8)
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.xlabel('z')
plt.legend()
plt.subplot(2,3,2)
plt.title('Optimized x and t')
plt.plot(zx,eO, c = 'c' , linestyle='-', label='x(z) - alpha(t)')
plt.plot(zx,ePolO, c = 'r' , linestyle=':', label='error poly')
plt.plot(zx,eMolO, c = 'orange' , linestyle='-.', label='error modal')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(zp,ypAXIS, c = 'r', s = 6, label='z- pX')
plt.scatter(zu,yuAXIS, c = 'orange', s = 6, label='z- pT')
plt.scatter(xAXIS, xoEP - aoEP , c = 'g', s = 15)
plt.xlabel('z')
plt.legend()
ePolO[:] /= ea[0]
eMolO[:] /= ea[0]
plt.subplot(2,3,3)
plt.title('Normalized Curves ')
plt.plot(zx, errOO, c = 'b' , linestyle='-.', label='alpha(z)')
plt.plot(zx, errO, c = 'c' , linestyle='-', label='alpha(t)')
plt.plot(zx,ePolO, c = 'r' , linestyle=':', label='error poly')
plt.plot(zx,eMolO, c = 'orange' , linestyle='-.', label='error modal')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(zp,ypAXIS, c = 'r', s = 6, label='z- pX')
plt.scatter(zu,yuAXIS, c = 'orange', s = 6, label='z- pT')
plt.scatter(xAXIS, xoEP - aoEP , c = 'g', s = 15)
plt.subplot(2,3,4)
plt.title('Interpol x')
plt.plot(zx,xI - aPF, c = 'b' , linestyle='-.', label='x(z) - alpha(z)')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(zp,ypAXIS, c = 'r', s = 6, label='z- pX')
plt.scatter(zu,yuAXIS, c = 'orange', s = 6, label='z- pT')
plt.scatter(xAXIS, xiEP - aEP , c = 'g', s = 15)
plt.xlabel('z')
plt.legend()
plt.subplot(2,3,5)
plt.title('Interpol x opti t')
plt.plot(zx,eI, c = 'c' , linestyle='-', label='x(z) - alpha(t)')
plt.plot(zx,ePolI, c = 'r' , linestyle=':', label='error poly')
plt.plot(zx,eMolI, c = 'orange' , linestyle='-.', label='error modal')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(zp,ypAXIS, c = 'r', s = 6, label='z- pX')
plt.scatter(zu,yuAXIS, c = 'orange', s = 6, label='z- pT')
plt.scatter(xAXIS, xiEP - aiEP , c = 'g', s = 15)
plt.xlabel('z')
plt.legend()
ePolI[:] /= ea[1]
eMolI[:] /= ea[1]
plt.subplot(2,3,6)
plt.title('Normalized Curves ')
plt.plot(zx,errII, c = 'b' , linestyle='-.', label='alpha(z)')
plt.plot(zx,errI, c = 'c' , linestyle='-', label='alpha(t)')
plt.plot(zx,ePolI, c = 'r' , linestyle=':', label='error poly')
plt.plot(zx,eMolI, c = 'orange' , linestyle='-.', label='error modal')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(zp,ypAXIS, c = 'r', s = 6, label='z- pX')
plt.scatter(zu,yuAXIS, c = 'orange', s = 6, label='z- pT')
plt.scatter(xAXIS, xiEP - aiEP , c = 'g', s = 15)
plt.xlabel('z')
plt.legend()
fig = plt.figure(figcount)
figcount += 1
plt.suptitle('Error Function approximation from z ' + pltInfo)
ePolI[:] *= ea[1]
eMolI[:] *= ea[1]
ePolO[:] *= ea[0]
eMolO[:] *= ea[0]
plt.subplot(2,2,1)
plt.title('Optimized x Lagrange ')
plt.plot(zx,eO - ePolO, c = 'b' , linestyle='-')
plt.subplot(2,2,2)
plt.title('Optimized x Modal ')
plt.plot(zx,eO - eMolO, c = 'r' , linestyle=':')
plt.xlabel('z')
plt.subplot(2,2,3)
plt.title('Interpol x Lagrange')
plt.plot(zx,eI - ePolI, c = 'b' , linestyle='-')
plt.subplot(2,2,4)
plt.title('Interpol x Modal')
plt.plot(zx,eI - eMolI, c = 'r' , linestyle=':')
plt.xlabel('z')
plt.legend()
'''
errO = (dxO - dadtO) / dea[0]
errI = (dxI - dadtI) / dea[1]
fig = plt.figure(figcount)
figcount += 1
plt.suptitle(' 1st-derivative Curves ' + pltInfo)
plt.subplot(2,3,1)
plt.title(' Opti Solution')
plt.plot(zx, dadtO, c = 'c', linestyle='-',label='alphaO')
plt.plot(zx, dxO, c = 'b', linestyle='-.',label='xO')
plt.xlabel('xi')
plt.legend()
plt.subplot(2,3,2)
plt.title('Error ')
plt.plot(zx,dxO - dadtO, c = 'b' , linestyle='-')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(xAXIS, yAXIS, c = 'g', s = 15)
plt.xlabel('z')
plt.subplot(2,3,3)
plt.title('Normalized Error ')
plt.plot(zx,errO, c = 'b' , linestyle='-')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(xAXIS, yAXIS, c = 'g', s = 15)
plt.xlabel('z')
plt.subplot(2,3,4)
plt.title('Interpolation Solution')
plt.plot(zx, dadtI, c = 'c', linestyle='-',label='alphaO')
plt.plot(zx, dxI, c = 'b', linestyle='-.',label='xO')
plt.xlabel('z')
plt.legend()
plt.subplot(2,3,5)
plt.title('Error ')
plt.plot(zx,dxI - dadtI, c = 'b' , linestyle='-')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(xAXIS, yAXIS, c = 'g', s = 15)
plt.xlabel('z')
plt.subplot(2,3,6)
plt.title('Normalized Error ')
plt.plot(zx,errI, c = 'b' , linestyle='-')
plt.plot(xAXIS,yAXIS, c = 'g', linewidth = 0.25, linestyle = ':')
plt.scatter(xAXIS, yAXIS, c = 'g', s = 15)
plt.xlabel('z')
fig = plt.figure(figcount)
figcount += 1
plt.suptitle('Approx t --> alpha ^-1 ' + pltInfo)
plt.subplot(2,1,1)
plt.plot(zx, tO,color = 'b', label='Opti')
plt.plot(zx, tI, color = 'c', label='Interp')
plt.xlabel('xi')
plt.legend()
plt.subplot(2,1,2)
plt.plot(zx, dtO,color = 'b', label='Opti')
plt.plot(zx, dtI, color = 'c', label='Interp')
plt.xlabel('z')
plt.legend()
'''
for type in range(2):
if (type == 0): print(' ******** OPTIMIZED MESH ***********')
else: print(' ******** INTERPOLATING MESH ***********')
convergence_IO(nR, ne, disparity_e[type] , pX, pU, 'ELOI DISPARITY: || x_p (xi) - alpha (xi)||_sigma')
convergence_IO(nR, ne, disparity_XA[type] , pX, pU, 'MY DISPARITY: || x_p (xi) - alpha (xi)||_sigme')
convergence_IO(nR, ne, disparity_DXAT[type], pX, pU, '1st-der DISPARITY: || dx_p (xi) - dalpha o dt (xi)||_sigma')
if (showPlots == True): plt.show()
if __name__ == '__main__':
argc = len(sys.argv)
if argc != 7:
print (" I NEED DEGREEX + degree T + INITIAL ELEMENTS + REFINEMENTS + CURVE TYPE + SHOW MESH")
print(sys.argv)
quit(1)
degX = int(sys.argv[1]) # number of elements
degT = int(sys.argv[2]) # number of elements
elmts = int(sys.argv[3]) # number of elements
refine = int(sys.argv[4]) # number of elements
curve = int(sys.argv[5]) # number of elements
showPlots = int(sys.argv[6]) # number of elements
if (curve == 0):
I = [0, numpy.pi]
print(" SOLVING COS(x) x in [0, pi]")
elif (curve == 10):
I = [0, 2.0 * numpy.pi]
print(" SOLVING COS(x) x in [0, 2pi]")
elif (curve == 5):
I = [1, 2]
print(" SOLVING a poly deg 5 ")
elif curve == 8: I = [0, 1]
else: I = [0,1]
TestDistanceFunctionOptimization.testDistanceFunction(degX, degT, elmts, refine, curve, I, showPlots)