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\section{Requirements}  We want the interpolation to: to  \begin{enumerate}  \item{{\b \item{{\bf  Capture the diversity in the SN sample:} Using a non parametric model allows me to capture peculiar a behavior. I fit the lightcurves with Gaussian Processes using the \tt{george} \tt{Python} {\tt george} {\tt Python}  module.} \item{{\b \item{{\bf  Capture the early time variability, and the late time smoothness:} Since variability is expected to be larger at early times, I fit the time in logarithm space.}  \item{{\b \item{{\bf  Fit the observed datapoints:} this is taken care by a $\chi^2$ minimization.} \item{{\b \item{{\bf  Maintain the smoothness requirement, while the uncertainties may be underestimated or overestimated, and fill gaps with simple extrapolations: linear or nearly linear, so as to not to over-interpret the data:} The smoothness requirement can be enforced by minimizing the second derivative of the fit.} \end{enumerate}  I choose a square exponential kernel (\tt{ExpSquaredKernel} in \ttt{george}) $$k(d)=θ_1^2 \exp(\frac{−d^2}{θ_2^2})$$. I choose the hypermarameters $θ_1^2$ and $θ_2^2$.