How to make scipy.interpolate give an extrapolated result beyond the input range?
Question:
I’m trying to port a program which uses a hand-rolled interpolator (developed by a mathematician colleage) over to use the interpolators provided by scipy. I’d like to use or wrap the scipy interpolator so that it has as close as possible behavior to the old interpolator.
A key difference between the two functions is that in our original interpolator – if the input value is above or below the input range, our original interpolator will extrapolate the result. If you try this with the scipy interpolator it raises a ValueError. Consider this program as an example:
import numpy as np
from scipy import interpolate
x = np.arange(0,10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y)
print f(9)
print f(11) # Causes ValueError, because it's greater than max(x)
Is there a sensible way to make it so that instead of crashing, the final line will simply do a linear extrapolate, continuing the gradients defined by the first and last two points to infinity.
Note, that in the real software I’m not actually using the exp function – that’s here for illustration only!
Answers:
1. Constant extrapolation
You can use interp function from scipy, it extrapolates left and right values as constant beyond the range:
>>> from scipy import interp, arange, exp
>>> x = arange(0,10)
>>> y = exp(-x/3.0)
>>> interp([9,10], x, y)
array([ 0.04978707, 0.04978707])
2. Linear (or other custom) extrapolation
You can write a wrapper around an interpolation function which takes care of linear extrapolation. For example:
from scipy.interpolate import interp1d
from scipy import arange, array, exp
def extrap1d(interpolator):
xs = interpolator.x
ys = interpolator.y
def pointwise(x):
if x < xs[0]:
return ys[0]+(x-xs[0])*(ys[1]-ys[0])/(xs[1]-xs[0])
elif x > xs[-1]:
return ys[-1]+(x-xs[-1])*(ys[-1]-ys[-2])/(xs[-1]-xs[-2])
else:
return interpolator(x)
def ufunclike(xs):
return array(list(map(pointwise, array(xs))))
return ufunclike
extrap1d takes an interpolation function and returns a function which can also extrapolate. And you can use it like this:
x = arange(0,10)
y = exp(-x/3.0)
f_i = interp1d(x, y)
f_x = extrap1d(f_i)
print f_x([9,10])
Output:
[ 0.04978707 0.03009069]
I’m afraid that there is no easy to do this in Scipy to my knowledge. You can, as I’m fairly sure that you are aware, turn off the bounds errors and fill all function values beyond the range with a constant, but that doesn’t really help. See this question on the mailing list for some more ideas. Maybe you could use some kind of piecewise function, but that seems like a major pain.
I did it by adding a point to my initial arrays. In this way I avoid defining self-made functions, and the linear extrapolation (in the example below: right extrapolation) looks ok.
import numpy as np
from scipy import interp as itp
xnew = np.linspace(0,1,51)
x1=xold[-2]
x2=xold[-1]
y1=yold[-2]
y2=yold[-1]
right_val=y1+(xnew[-1]-x1)*(y2-y1)/(x2-x1)
x=np.append(xold,xnew[-1])
y=np.append(yold,right_val)
f = itp(xnew,x,y)
Here’s an alternative method that uses only the numpy package. It takes advantage of numpy’s array functions, so may be faster when interpolating/extrapolating large arrays:
import numpy as np
def extrap(x, xp, yp):
"""np.interp function with linear extrapolation"""
y = np.interp(x, xp, yp)
y = np.where(x<xp[0], yp[0]+(x-xp[0])*(yp[0]-yp[1])/(xp[0]-xp[1]), y)
y = np.where(x>xp[-1], yp[-1]+(x-xp[-1])*(yp[-1]-yp[-2])/(xp[-1]-xp[-2]), y)
return y
x = np.arange(0,10)
y = np.exp(-x/3.0)
xtest = np.array((8.5,9.5))
print np.exp(-xtest/3.0)
print np.interp(xtest, x, y)
print extrap(xtest, x, y)
Edit: Mark Mikofski’s suggested modification of the “extrap” function:
def extrap(x, xp, yp):
"""np.interp function with linear extrapolation"""
y = np.interp(x, xp, yp)
y[x < xp[0]] = yp[0] + (x[x<xp[0]]-xp[0]) * (yp[0]-yp[1]) / (xp[0]-xp[1])
y[x > xp[-1]]= yp[-1] + (x[x>xp[-1]]-xp[-1])*(yp[-1]-yp[-2])/(xp[-1]-xp[-2])
return y
You can take a look at InterpolatedUnivariateSpline
Here an example using it:
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
# given values
xi = np.array([0.2, 0.5, 0.7, 0.9])
yi = np.array([0.3, -0.1, 0.2, 0.1])
# positions to inter/extrapolate
x = np.linspace(0, 1, 50)
# spline order: 1 linear, 2 quadratic, 3 cubic ...
order = 1
# do inter/extrapolation
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
# example showing the interpolation for linear, quadratic and cubic interpolation
plt.figure()
plt.plot(xi, yi)
for order in range(1, 4):
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
plt.plot(x, y)
plt.show()
What about scipy.interpolate.splrep (with degree 1 and no smoothing):
>> tck = scipy.interpolate.splrep([1, 2, 3, 4, 5], [1, 4, 9, 16, 25], k=1, s=0)
>> scipy.interpolate.splev(6, tck)
34.0
It seems to do what you want, since 34 = 25 + (25 – 16).
It may be faster to use boolean indexing with large datasets, since the algorithm checks if every point is in outside the interval, whereas boolean indexing allows an easier and faster comparison.
For example:
# Necessary modules
import numpy as np
from scipy.interpolate import interp1d
# Original data
x = np.arange(0,10)
y = np.exp(-x/3.0)
# Interpolator class
f = interp1d(x, y)
# Output range (quite large)
xo = np.arange(0, 10, 0.001)
# Boolean indexing approach
# Generate an empty output array for "y" values
yo = np.empty_like(xo)
# Values lower than the minimum "x" are extrapolated at the same time
low = xo < f.x[0]
yo[low] = f.y[0] + (xo[low]-f.x[0])*(f.y[1]-f.y[0])/(f.x[1]-f.x[0])
# Values higher than the maximum "x" are extrapolated at same time
high = xo > f.x[-1]
yo[high] = f.y[-1] + (xo[high]-f.x[-1])*(f.y[-1]-f.y[-2])/(f.x[-1]-f.x[-2])
# Values inside the interpolation range are interpolated directly
inside = np.logical_and(xo >= f.x[0], xo <= f.x[-1])
yo[inside] = f(xo[inside])
In my case, with a data set of 300000 points, this means an speed up from 25.8 to 0.094 seconds, this is more than 250 times faster.
The below code gives you the simple extrapolation module. k is the value to which the data set y has to be extrapolated based on the data set x. The numpy module is required.
def extrapol(k,x,y):
xm=np.mean(x);
ym=np.mean(y);
sumnr=0;
sumdr=0;
length=len(x);
for i in range(0,length):
sumnr=sumnr+((x[i]-xm)*(y[i]-ym));
sumdr=sumdr+((x[i]-xm)*(x[i]-xm));
m=sumnr/sumdr;
c=ym-(m*xm);
return((m*k)+c)
Standard interpolate + linear extrapolate:
def interpola(v, x, y):
if v <= x[0]:
return y[0]+(y[1]-y[0])/(x[1]-x[0])*(v-x[0])
elif v >= x[-1]:
return y[-2]+(y[-1]-y[-2])/(x[-1]-x[-2])*(v-x[-2])
else:
f = interp1d(x, y, kind='cubic')
return f(v)
As of SciPy version 0.17.0, there is a new option for scipy.interpolate.interp1d that allows extrapolation. Simply set fill_value=’extrapolate’ in the call. Modifying your code in this way gives:
import numpy as np
from scipy import interpolate
x = np.arange(0,10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y, fill_value='extrapolate')
print f(9)
print f(11)
and the output is:
0.0497870683679
0.010394302658
I don’t have enough reputation to comment, but in case somebody is looking for an extrapolation wrapper for a linear 2d-interpolation with scipy, I have adapted the answer that was given here for the 1d interpolation.
def extrap2d(interpolator):
xs = interpolator.x
ys = interpolator.y
zs = interpolator.z
zs = np.reshape(zs, (-1, len(xs)))
def pointwise(x, y):
if x < xs[0] or y < ys[0]:
x1_index = np.argmin(np.abs(xs - x))
x2_index = x1_index + 1
y1_index = np.argmin(np.abs(ys - y))
y2_index = y1_index + 1
x1 = xs[x1_index]
x2 = xs[x2_index]
y1 = ys[y1_index]
y2 = ys[y2_index]
z11 = zs[x1_index, y1_index]
z12 = zs[x1_index, y2_index]
z21 = zs[x2_index, y1_index]
z22 = zs[x2_index, y2_index]
return (z11 * (x2 - x) * (y2 - y) +
z21 * (x - x1) * (y2 - y) +
z12 * (x2 - x) * (y - y1) +
z22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
elif x > xs[-1] or y > ys[-1]:
x1_index = np.argmin(np.abs(xs - x))
x2_index = x1_index - 1
y1_index = np.argmin(np.abs(ys - y))
y2_index = y1_index - 1
x1 = xs[x1_index]
x2 = xs[x2_index]
y1 = ys[y1_index]
y2 = ys[y2_index]
z11 = zs[x1_index, y1_index]
z12 = zs[x1_index, y2_index]
z21 = zs[x2_index, y1_index]
z22 = zs[x2_index, y2_index]#
return (z11 * (x2 - x) * (y2 - y) +
z21 * (x - x1) * (y2 - y) +
z12 * (x2 - x) * (y - y1) +
z22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
else:
return interpolator(x, y)
def ufunclike(xs, ys):
if isinstance(xs, int) or isinstance(ys, int) or isinstance(xs, np.int32) or isinstance(ys, np.int32):
res_array = pointwise(xs, ys)
else:
res_array = np.zeros((len(xs), len(ys)))
for x_c in range(len(xs)):
res_array[x_c, :] = np.array([pointwise(xs[x_c], ys[y_c]) for y_c in range(len(ys))]).T
return res_array
return ufunclike
I haven’t commented a lot and I am aware, that the code isn’t super clean. If anybody sees any errors, please let me know. In my current use-case it is working without a problem 🙂
I’m trying to port a program which uses a hand-rolled interpolator (developed by a mathematician colleage) over to use the interpolators provided by scipy. I’d like to use or wrap the scipy interpolator so that it has as close as possible behavior to the old interpolator.
A key difference between the two functions is that in our original interpolator – if the input value is above or below the input range, our original interpolator will extrapolate the result. If you try this with the scipy interpolator it raises a ValueError. Consider this program as an example:
import numpy as np
from scipy import interpolate
x = np.arange(0,10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y)
print f(9)
print f(11) # Causes ValueError, because it's greater than max(x)
Is there a sensible way to make it so that instead of crashing, the final line will simply do a linear extrapolate, continuing the gradients defined by the first and last two points to infinity.
Note, that in the real software I’m not actually using the exp function – that’s here for illustration only!
1. Constant extrapolation
You can use interp function from scipy, it extrapolates left and right values as constant beyond the range:
>>> from scipy import interp, arange, exp
>>> x = arange(0,10)
>>> y = exp(-x/3.0)
>>> interp([9,10], x, y)
array([ 0.04978707, 0.04978707])
2. Linear (or other custom) extrapolation
You can write a wrapper around an interpolation function which takes care of linear extrapolation. For example:
from scipy.interpolate import interp1d
from scipy import arange, array, exp
def extrap1d(interpolator):
xs = interpolator.x
ys = interpolator.y
def pointwise(x):
if x < xs[0]:
return ys[0]+(x-xs[0])*(ys[1]-ys[0])/(xs[1]-xs[0])
elif x > xs[-1]:
return ys[-1]+(x-xs[-1])*(ys[-1]-ys[-2])/(xs[-1]-xs[-2])
else:
return interpolator(x)
def ufunclike(xs):
return array(list(map(pointwise, array(xs))))
return ufunclike
extrap1d takes an interpolation function and returns a function which can also extrapolate. And you can use it like this:
x = arange(0,10)
y = exp(-x/3.0)
f_i = interp1d(x, y)
f_x = extrap1d(f_i)
print f_x([9,10])
Output:
[ 0.04978707 0.03009069]
I’m afraid that there is no easy to do this in Scipy to my knowledge. You can, as I’m fairly sure that you are aware, turn off the bounds errors and fill all function values beyond the range with a constant, but that doesn’t really help. See this question on the mailing list for some more ideas. Maybe you could use some kind of piecewise function, but that seems like a major pain.
I did it by adding a point to my initial arrays. In this way I avoid defining self-made functions, and the linear extrapolation (in the example below: right extrapolation) looks ok.
import numpy as np
from scipy import interp as itp
xnew = np.linspace(0,1,51)
x1=xold[-2]
x2=xold[-1]
y1=yold[-2]
y2=yold[-1]
right_val=y1+(xnew[-1]-x1)*(y2-y1)/(x2-x1)
x=np.append(xold,xnew[-1])
y=np.append(yold,right_val)
f = itp(xnew,x,y)
Here’s an alternative method that uses only the numpy package. It takes advantage of numpy’s array functions, so may be faster when interpolating/extrapolating large arrays:
import numpy as np
def extrap(x, xp, yp):
"""np.interp function with linear extrapolation"""
y = np.interp(x, xp, yp)
y = np.where(x<xp[0], yp[0]+(x-xp[0])*(yp[0]-yp[1])/(xp[0]-xp[1]), y)
y = np.where(x>xp[-1], yp[-1]+(x-xp[-1])*(yp[-1]-yp[-2])/(xp[-1]-xp[-2]), y)
return y
x = np.arange(0,10)
y = np.exp(-x/3.0)
xtest = np.array((8.5,9.5))
print np.exp(-xtest/3.0)
print np.interp(xtest, x, y)
print extrap(xtest, x, y)
Edit: Mark Mikofski’s suggested modification of the “extrap” function:
def extrap(x, xp, yp):
"""np.interp function with linear extrapolation"""
y = np.interp(x, xp, yp)
y[x < xp[0]] = yp[0] + (x[x<xp[0]]-xp[0]) * (yp[0]-yp[1]) / (xp[0]-xp[1])
y[x > xp[-1]]= yp[-1] + (x[x>xp[-1]]-xp[-1])*(yp[-1]-yp[-2])/(xp[-1]-xp[-2])
return y
You can take a look at InterpolatedUnivariateSpline
Here an example using it:
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
# given values
xi = np.array([0.2, 0.5, 0.7, 0.9])
yi = np.array([0.3, -0.1, 0.2, 0.1])
# positions to inter/extrapolate
x = np.linspace(0, 1, 50)
# spline order: 1 linear, 2 quadratic, 3 cubic ...
order = 1
# do inter/extrapolation
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
# example showing the interpolation for linear, quadratic and cubic interpolation
plt.figure()
plt.plot(xi, yi)
for order in range(1, 4):
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
plt.plot(x, y)
plt.show()
What about scipy.interpolate.splrep (with degree 1 and no smoothing):
>> tck = scipy.interpolate.splrep([1, 2, 3, 4, 5], [1, 4, 9, 16, 25], k=1, s=0)
>> scipy.interpolate.splev(6, tck)
34.0
It seems to do what you want, since 34 = 25 + (25 – 16).
It may be faster to use boolean indexing with large datasets, since the algorithm checks if every point is in outside the interval, whereas boolean indexing allows an easier and faster comparison.
For example:
# Necessary modules
import numpy as np
from scipy.interpolate import interp1d
# Original data
x = np.arange(0,10)
y = np.exp(-x/3.0)
# Interpolator class
f = interp1d(x, y)
# Output range (quite large)
xo = np.arange(0, 10, 0.001)
# Boolean indexing approach
# Generate an empty output array for "y" values
yo = np.empty_like(xo)
# Values lower than the minimum "x" are extrapolated at the same time
low = xo < f.x[0]
yo[low] = f.y[0] + (xo[low]-f.x[0])*(f.y[1]-f.y[0])/(f.x[1]-f.x[0])
# Values higher than the maximum "x" are extrapolated at same time
high = xo > f.x[-1]
yo[high] = f.y[-1] + (xo[high]-f.x[-1])*(f.y[-1]-f.y[-2])/(f.x[-1]-f.x[-2])
# Values inside the interpolation range are interpolated directly
inside = np.logical_and(xo >= f.x[0], xo <= f.x[-1])
yo[inside] = f(xo[inside])
In my case, with a data set of 300000 points, this means an speed up from 25.8 to 0.094 seconds, this is more than 250 times faster.
The below code gives you the simple extrapolation module. k is the value to which the data set y has to be extrapolated based on the data set x. The numpy module is required.
def extrapol(k,x,y):
xm=np.mean(x);
ym=np.mean(y);
sumnr=0;
sumdr=0;
length=len(x);
for i in range(0,length):
sumnr=sumnr+((x[i]-xm)*(y[i]-ym));
sumdr=sumdr+((x[i]-xm)*(x[i]-xm));
m=sumnr/sumdr;
c=ym-(m*xm);
return((m*k)+c)
Standard interpolate + linear extrapolate:
def interpola(v, x, y):
if v <= x[0]:
return y[0]+(y[1]-y[0])/(x[1]-x[0])*(v-x[0])
elif v >= x[-1]:
return y[-2]+(y[-1]-y[-2])/(x[-1]-x[-2])*(v-x[-2])
else:
f = interp1d(x, y, kind='cubic')
return f(v)
As of SciPy version 0.17.0, there is a new option for scipy.interpolate.interp1d that allows extrapolation. Simply set fill_value=’extrapolate’ in the call. Modifying your code in this way gives:
import numpy as np
from scipy import interpolate
x = np.arange(0,10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y, fill_value='extrapolate')
print f(9)
print f(11)
and the output is:
0.0497870683679
0.010394302658
I don’t have enough reputation to comment, but in case somebody is looking for an extrapolation wrapper for a linear 2d-interpolation with scipy, I have adapted the answer that was given here for the 1d interpolation.
def extrap2d(interpolator):
xs = interpolator.x
ys = interpolator.y
zs = interpolator.z
zs = np.reshape(zs, (-1, len(xs)))
def pointwise(x, y):
if x < xs[0] or y < ys[0]:
x1_index = np.argmin(np.abs(xs - x))
x2_index = x1_index + 1
y1_index = np.argmin(np.abs(ys - y))
y2_index = y1_index + 1
x1 = xs[x1_index]
x2 = xs[x2_index]
y1 = ys[y1_index]
y2 = ys[y2_index]
z11 = zs[x1_index, y1_index]
z12 = zs[x1_index, y2_index]
z21 = zs[x2_index, y1_index]
z22 = zs[x2_index, y2_index]
return (z11 * (x2 - x) * (y2 - y) +
z21 * (x - x1) * (y2 - y) +
z12 * (x2 - x) * (y - y1) +
z22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
elif x > xs[-1] or y > ys[-1]:
x1_index = np.argmin(np.abs(xs - x))
x2_index = x1_index - 1
y1_index = np.argmin(np.abs(ys - y))
y2_index = y1_index - 1
x1 = xs[x1_index]
x2 = xs[x2_index]
y1 = ys[y1_index]
y2 = ys[y2_index]
z11 = zs[x1_index, y1_index]
z12 = zs[x1_index, y2_index]
z21 = zs[x2_index, y1_index]
z22 = zs[x2_index, y2_index]#
return (z11 * (x2 - x) * (y2 - y) +
z21 * (x - x1) * (y2 - y) +
z12 * (x2 - x) * (y - y1) +
z22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
else:
return interpolator(x, y)
def ufunclike(xs, ys):
if isinstance(xs, int) or isinstance(ys, int) or isinstance(xs, np.int32) or isinstance(ys, np.int32):
res_array = pointwise(xs, ys)
else:
res_array = np.zeros((len(xs), len(ys)))
for x_c in range(len(xs)):
res_array[x_c, :] = np.array([pointwise(xs[x_c], ys[y_c]) for y_c in range(len(ys))]).T
return res_array
return ufunclike
I haven’t commented a lot and I am aware, that the code isn’t super clean. If anybody sees any errors, please let me know. In my current use-case it is working without a problem 🙂