linear interpolation — make grid

By : aloha

I want to interpolate between different models. To make things easier, my data is shown below:

enter image description here

I have 10 different simulations (which I will call z). For each z I have an array x and an array y (where for a given z, len(x)=len(y)). For example:

for z=1: x.shape=(1200,) and y.shape=(1200,)

for z=2: x.shape=(1250,) and y.shape=(1250,)

for z=3: x.shape=(1236,) and y.shape=(1236,)

and so on ...

I want to interpolate so that for a given z and x, I get y. For example, for z=2.5 and x=10**9, the code outputs y. I am assuming that:

y = a*x + b*z + c where of course I don't know a, b, and c.

My question is that how do I store the data in a grid? I am confused since for a different z the size of x and y differs. How is it possible to build a grid?

By : aloha


It seems that in your problem the curves y(x) are well behaving, so you could probably just interpolate y(x) for the given values of z first and then interpolate between the obtained y-values afterwards.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

import random

# Generate some data
generate = lambda x, z: 1./(x+1.)+(z*x/75.+z/25.)

def f(z):
    #create an array of values between zero and 100 of random length
    x = np.linspace(0,10., num=random.randint(42,145))
    #generate corresponding y values
    y = generate(x, z)
    return np.array([x,y])

Z = [1, 2, 3, 3.6476, 4, 5.1]
A = [f(z) for z in Z]
#now A contains the dataset of [x,y] pairs for each z value

# Interpolation
def do_interpolation(x,z):
    #assume Z being sorted in ascending order
    #look for indizes of z values closest to given z
    ig = np.searchsorted(Z, z)
    il = ig-1
    #interpolate y(x) for those z values
    yg = np.interp(x, A[ig][0,:], A[ig][1,:])
    yl = np.interp(x, A[il][0,:], A[il][1,:])
    #linearly interpolate between yg and yl  
    return yl + (yg-yl)*float(z-Z[il])/(Z[ig] - Z[il])  

# do_interpolation(x,z) will now provide the interpolated data
print do_interpolation( np.linspace(0, 10), 2.5) 

# Plotting, use Slider to change the value of z. 
for i in range(len(Z)):
    ax.plot(A[i][0,:] , A[i][1,:], label="{z}".format(z=Z[i]) )

l, = ax.plot(np.linspace(0, 10) , do_interpolation( np.linspace(0, 10), 2.5), label="{z}".format(z="interpol"), linewidth=2., color="k" )

axn1 = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg='#e4e4e4')
sn1 = Slider(axn1, 'z', Z[0], Z[-1], valinit=2.5)
def update(val):
    l.set_data(np.linspace(0, 10), do_interpolation( np.linspace(0, 10), val))


try something like this.

You need the variable accessible in both functions so it needs to be declared outside those functions and then defined inside the first one and use clearInterval() to cancel an existing interval

var timer;
$('#name').mouseenter(function() {
   timer = setInterval(mosaic, 150);
}).mouseleave(function() {

This video can help you solving your question :)
By: admin