Using numpy.interp
I am able to compute the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.
Is it a similar function to return me the logarithmic interpolation?
In the past, I've just wrapped the normal interpolation to do it in log-space, i.e.
def log_interp(zz, xx, yy):
logz = np.log10(zz)
logx = np.log10(xx)
logy = np.log10(yy)
return np.power(10.0, np.interp(logz, logx, logy))
Personally, I much prefer the scipy interpolation functions (as @mylesgallagher mentions), for example:
import scipy as sp
import scipy.interpolate
def log_interp1d(xx, yy, kind='linear'):
logx = np.log10(xx)
logy = np.log10(yy)
lin_interp = sp.interpolate.interp1d(logx, logy, kind=kind)
log_interp = lambda zz: np.power(10.0, lin_interp(np.log10(zz)))
return log_interp
Then you can just call this as a function on an arbitrary value.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With