import numpy as np
import pypolint
from matplotlib.pyplot import *
xa = np.linspace(0, 1, 30)
ya = np.sin(xa + np.square(xa))
xx = np.linspace(-0.5, 1.5, 600)
yy = np.sin(xx + np.square(xx))
n = 5
[y1, dy1] = pypolint.nearestpolint(xa, ya, n, xx)
n = 8
[y2, dy2] = pypolint.nearestpolint(xa, ya, n, xx)
n = 12
[y3, dy3] = pypolint.nearestpolint(xa, ya, n, xx)
figure(1)
plot(xa, ya, 'g*', label='Table Values')
plot(xx, y1, '-', label='4th order interpolation')
plot(xx, y2, 'r-', label='7th order interpolation')
plot(xx, y3, 'k-', label='11th order interpolation')
xlabel('x')
ylabel('Predicted y values')
legend(loc=3)
figure(2)
gca().set_yscale('log')
plot(xx, abs(y1 - yy), 'b-', label='4th order interpolation')
plot(xx, abs(y2 - yy), 'r-', label='7th order interpolation')
import numpy as np
import pypolint
from matplotlib.pyplot import *
xa = np.linspace(0, 1, 5)
ya = np.sin(xa + np.square(xa))
n = 5
xx = np.linspace(-0.5, 1.5, 200)
yy = np.sin(xx + np.square(xx))
[y, dy] = pypolint.nearestpolint(xa, ya, n, xx)
figure(1)
plot(xa, ya, 'k*', label='Theoretical Values')
plot(xx, y, '-', label='4th order interpolation')
xlabel('x')
ylabel('Predicted y values')
legend(loc=3)
figure(2)
gca().set_yscale('log')
plot(xx, abs(y - yy), '-')
xlabel('x')
ylabel('Error (measured against theoretical value)')
show()
ya=np.zeros_like(xa)
for i in range(1,6):
j=2*i+1
ya=ya+np.sin(j*xa)/j
return ya
pi=np.pi
xa=np.linspace(-pi,pi,101)
ya=fx(xa)
xx=np.linspace(0,2*pi,999)
yy=fx(xx)
maxdy=np.zeros(18)
for n in range(3,21):
print n
[y,dy]=pypolint.nearestpolint(xa,ya,n,xx)
maxdy[n-3]=max(abs(y-yy))
print maxdy
# figure(0)
# plot(xx[0:500],yy[0:500],'g-')
# plot(xx[0:500],y[0:500],'-')
# figure(1)
# plot(xx,yy,'g-')
# plot(xx,y,'-')
# figure(2)
# gca().set_yscale('log')
# plot(xx,abs(y-yy))
import numpy as np
import pypolint
from matplotlib.pyplot import *
xa=np.linspace(0,1,30)
xb=np.linspace(0,1,5)
ya=np.sin(xa+np.square(xa))
yb=np.sin(xb+np.square(xb))
n=5
xx=np.linspace(-0.5,6.5,1000)
yy=np.sin(xx+np.square(xx))
[y,dy]=pypolint.nearestpolint(xa,ya,n,xx)
[y1,dy1]=pypolint.nearestpolint(xb,yb,n,xx)
figure(1)
plot(xa,ya,'k*',label='Table Values')
plot(xx,y1,'r-',label='5 sample interpolation')
plot(xx,y,'-',label='30 sample interpolation')
xlabel('x')
ylabel('Predicted y values')
legend(loc=3)
figure(2)
gca().set_yscale('log')
plot(xx,abs(y1-yy),'r-',label='5 samples')
plot(xx,abs(y-yy),'-',label='30 samples')
xlabel('x')
ylabel('Error (measured against theoretical value)')
legend(loc=3)
show()
import numpy as np
import pypolint
from matplotlib.pyplot import *
xa=np.linspace(0,1,30)
ya=np.sin(xa+np.square(xa))
xx=np.linspace(-0.5,1.5,600)
yy=np.sin(xx+np.square(xx))
n=5
[y1,dy1]=pypolint.nearestpolint(xa,ya,n,xx)
n=8
[y2,dy2]=pypolint.nearestpolint(xa,ya,n,xx)
n=12
[y3,dy3]=pypolint.nearestpolint(xa,ya,n,xx)
figure(1)
plot(xa,ya,'g*',label='Table Values')
plot(xx,y1,'-',label='4th order interpolation')
plot(xx,y2,'r-',label='7th order interpolation')
plot(xx,y3,'k-',label='11th order interpolation')
xlabel('x')
ylabel('Predicted y values')
legend(loc=3)
figure(2)
gca().set_yscale('log')
import numpy as np
import pypolint
from matplotlib.pyplot import *
xa = np.linspace(0, 1, 30)
xb = np.linspace(0, 1, 5)
ya = np.sin(xa + np.square(xa))
yb = np.sin(xb + np.square(xb))
n = 5
xx = np.linspace(-0.5, 6.5, 1000)
yy = np.sin(xx + np.square(xx))
[y, dy] = pypolint.nearestpolint(xa, ya, n, xx)
[y1, dy1] = pypolint.nearestpolint(xb, yb, n, xx)
figure(1)
plot(xa, ya, 'k*', label='Table Values')
plot(xx, y1, 'r-', label='5 sample interpolation')
plot(xx, y, '-', label='30 sample interpolation')
xlabel('x')
ylabel('Predicted y values')
legend(loc=3)
figure(2)
gca().set_yscale('log')
plot(xx, abs(y1 - yy), 'r-', label='5 samples')
plot(xx, abs(y - yy), '-', label='30 samples')
xlabel('x')
ylabel('Error (measured against theoretical value)')
legend(loc=3)
show()
