CODEWITHSUNDEEP
pythonnumpymatplotlibscipy
gammapoint
gammapoint

Reputation: 1153

Solving for zeroes in interpolated data in numpy/matplotlib

I have some data over a 2D range that I am interested in analyzing. These data were originally in lists x,y, and z where z[i] was the value for the point located at (x[i],y[i]). I then interpolated this data onto a regular grid using 

x=np.array(x)
y=np.array(y)
z=np.array(z)
xi=np.linspace(minx,maxx,100)
yi=np.linspace(miny,maxy,100)
zi=griddata(x,y,z,xi,yi)

I then plotted the xi,yi,zi data using 

plt.contour(xi,yi,zi)
plt.pcolormesh(xi,yi,zi,cmap=plt.get_cmap('PRGn'),norm=plt.Normalize(-10,10),vmin=-10,vmax=10)

This produced this plot:contour plot

In this plot you can see the S-like curve where the values are equal to zero (aside: the data doesn't vary as rapidly as shown in the colorbar -- that's simply a result of me normalizing the data to -10-10 when it actually extends far beyond that range; I did this to make the zero-valued region show up better -- maybe there's a better way of doing this too...).

The scattered dots are simply the points at which I have original data (yes, in this case my data was already on a regular grid). What I'm curious about is whether there is a good way for me to extract the values for which the curve is zero and obtain x,y pairs that, if plotted as a line, would trace that zero-region in the colormesh. I could interpolate to a really fine grid and then just brute force search for the values which are closest to zero. But is there a more automatic way of doing this, or a more automatic way of plotting this "zero-line"?

And a secondary question: I am using griddata correctly, right? I have these simple 1D arrays although elsewhere people use various meshgrids, loading texts, etc., before calling griddata.

Upvotes: 1

Views: 736

Answers (1)

HYRY
HYRY

Reputation: 97291

Here is a full example:

import numpy as np
import matplotlib.pyplot as plt
y, x = np.ogrid[-1.5:1.5:200j, -1.5:1.5:200j]
f = (x**2 + y**2)**4 - (x**2 - y**2)**2

plt.figure(figsize=(9,4))
plt.subplot(121)
extent = [np.min(x), np.max(x), np.min(y), np.max(y)]
cs = plt.contour(f, extent=extent, levels=[0.1], 
     colors=["b", "r"], linestyles=["solid", "dashed"], linewidths=[2, 2])

plt.subplot(122)
# get the points on the lines
for c in cs.collections:
    data = c.get_paths()[0].vertices
    plt.plot(data[:,0], data[:,1], 
        color=c.get_color()[0],  linewidth=c.get_linewidth()[0])

plt.show()

here is the output:

enter image description here

Upvotes: 1

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