CODEWITHSUNDEEP
pythonmatplotlib
Kevin Powell
Kevin Powell

Reputation: 601

How to draw cubic spline in matplotlib

I want to connect the following points using smooth line, say cubic spline

points = [(3.28,0.00),(4.00,0.50),(4.40,1.0),(4.60,1.52),(5.00,2.5),(5.00,3.34),(4.70,3.8)]
points = points + [(4.50,3.96),(4.20,4.0),(3.70,3.90),(3.00,3.5),(2.00,2.9)]

and finally get orange line like this (this one is created using a vector plotting language Asymptote)

cubic spline produced in Asymptote

I'm wondering how to do it in matplotlib in a simple way. I already had a look at similar question, e.g. Generating smooth line graph using matplotlib, but direct use of that method produces figure like this enter image description here

which is of course not what I want.

Upvotes: 6

Views: 16303

Answers (2)

Scott
Scott

Reputation: 6389

This is pretty much following the circle example here.

import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

def annotate_points(ax, A, B):
    for xy in zip(A, B):
        ax.annotate('(%s, %s)' % xy, xy=xy, textcoords='offset points')

points = [(3.28,0.00),(4.00,0.50),(4.40,1.0),(4.60,1.52),(5.00,2.5),(5.00,3.34),(4.70,3.8)]
points = points + [(4.50,3.96),(4.20,4.0),(3.70,3.90),(3.00,3.5),(2.00,2.9)]
x, y = zip(*points)

fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(x, y, color='black')
annotate_points(ax, x, y)

tck,u = interpolate.splprep([x, y], s=0)
unew = np.arange(0, 1.01, 0.01)
out = interpolate.splev(unew, tck)

plt.plot(x, y, 'orange', out[0], out[1])
plt.legend(['connect the dots', 'cubic spline'])

plt.show()

enter image description here

Upvotes: 1

tom10
tom10

Reputation: 69182

You need to take a parametric approach, like this:

enter image description here

import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

points = [(3.28,0.00),(4.00,0.50),(4.40,1.0),(4.60,1.52),(5.00,2.5),(5.00,3.34),(4.70,3.8)]
points = points + [(4.50,3.96),(4.20,4.0),(3.70,3.90),(3.00,3.5),(2.00,2.9)]
data = np.array(points)

tck,u = interpolate.splprep(data.transpose(), s=0)
unew = np.arange(0, 1.01, 0.01)
out = interpolate.splev(unew, tck)

plt.figure()
plt.plot(out[0], out[1], color='orange')
plt.plot(data[:,0], data[:,1], 'ob')
plt.show()

This is basically just reworked from the last example in the section here.

Upvotes: 5

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