include pchipinterpolator docs reference

doc/sphinx/source/n3fit/methodology.md

@@ -185,7 +185,7 @@ Up to NNPDF4.0 the input to the neural network consisted of an input node `(x)`,
 
 To solve this problem there is the possibility to apply a different feature scaling to the input by adding a `interpolation_points: [number of points]` flag to the `n3fit` runcard. By adding this flag the `(x,log(x))` scaling is replaced by a scaling in such a way that all input `x` values are evenly distributed on the domain `[-1,1]`, and the input node is no longer split in two. 
 
-Of course this scaling is discrete while the pdfs must be continuous. Therefore a monotonically increasing cubic spline is used to interpolate after the scaling has been applied. However, this way the neural network will be agnostic to the existence of this interpolation function meaning it can no longer learn the true underlying law. To fix this, the interpolation function has to be probed as well. This is done by only using `[number of points]` set by the `interpolation_points` flag to define the interpolation function after the scaling has been applied. Using this methodology the points used in the interpolation are again evenly distributed. 
+Of course this scaling is discrete while the pdfs must be continuous. Therefore a monotonically increasing cubic spline is used to interpolate after the scaling has been applied. To this end the [PchipInterpolator](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.PchipInterpolator.html) function from the scipy library is used. However, this way the neural network will be agnostic to the existence of this interpolation function meaning it can no longer learn the true underlying law. To fix this, the interpolation function has to be probed as well. This is done by only using `[number of points]` set by the `interpolation_points` flag to define the interpolation function after the scaling has been applied. Using this methodology the points used in the interpolation are again evenly distributed. 
 
 
 ``` image:: figures/feature_scaling.png