Normalisation library

The ML methods require the data they operate on all to be normalised to a common range of around +-1. This library contains functions for converting each variable used to this range (from its units in 20CR2c) and back again.

# Functions for normalising (and unnormalising) weather data
#  to a range around 0-1

# Each function takes a numpy array as argument and returns a 
#  scaled copy of the array. So pass the .data component
#  of an iris Cube.

import numpy

def normalise_insolation(p):
   res=numpy.copy(p)
   res /= 25
   return res

def unnormalise_insolation(p):
   res=numpy.copy(p)
   res *= 25
   return res

def normalise_t2m(p):
   res=numpy.copy(p)
   res -= 280
   res /= 50
   return res

def unnormalise_t2m(p):
   res=numpy.copy(p)
   res *= 50
   res += 280
   return res

def normalise_wind(p):
   res=numpy.copy(p)
   res /= 12
   return res

def unnormalise_wind(p):
   res=numpy.copy(p)
   res *= 12
   return res

def normalise_prmsl(p):
   res=numpy.copy(p)
   res -= 101325
   res /= 3000
   return res

def unnormalise_prmsl(p):
   res=numpy.copy(p)
   res *= 3000
   res += 101325
   return res