genFitness.py~

import numpy as np
uvWeight=0.5
irWeight=0.3
def fitInvSSE(model):
    return 1/np.sum(model.subSpec.y**2)
def fitInvSSEW(model):
    fitness=1/np.sum(model.subSpec[:6500.].y**2)
    w=model['w']
    wFactor=np.exp(-(abs(w-0.5)**5)/0.00005)
    fitness*=wFactor
    return fitness
def fitInvSSEW2(model):
    uvRange=4000-model.subSpec.x.min()
    opticalRange=6500-4000
    irRange=model.subSpec.x.max()-6500
    uv=np.sum(model.subSpec[:4000.].y**2)
    optical=np.sum(model.subSpec[4000.:6300.].y**2)
    ir=np.sum(model.subSpec[6300.:].y**2)
    w=model['w']
    wFactor=np.exp(-(abs(w-0.5)**5)/0.00005)
    fitness=(1/(1*optical))
    return fitness

def fitInvSSEW3(model):
    w=model['w']
    wFactor=wFilter(w)
    stretch=30
    sFilter=specFilter(model.subSpec.x,6300,4000,uvWeight,irWeight,stretch=30)
    squareSpec=model.subSpec.y**2
    sFitness=1/np.sum(squareSpec*sFilter)
    return sFitness*w*wFactor


def fitInvSSEW4(model):
    w=model['w']
    wFactor=wFilter(w)
    stretch=30
    sFilter=specFilter(model.subSpec.x,6300,4000,uvWeight,irWeight,stretch=30)
    squareSpec=model.subSpec.y**2
    sFitness=1/np.sum(squareSpec*sFilter)
    return sFitness*w*wFactor
def wFilter(w):
    stretch=0.01
    upLimit=0.7
    lowLimit=0.4
    filter=(1/(np.exp((w-upLimit)/stretch)+1))*(1/(np.exp((-w+lowLimit)/stretch)+1))
    return filter

def diracFermiDown(x,loc,stretch,before,after):
    return ((before-after)/(np.exp((x-loc)/stretch)+1))+after
    
def diracFermiUp(x,loc,stretch,before,after):
    return ((before-after)/(np.exp((-x+loc)/stretch)+1))+after
    
def specFilter(x,loc1,loc2,amp1,amp2,stretch):
    return diracFermiDown(x,loc1,stretch,1,amp1)*diracFermiUp(x,loc2,stretch,1,amp2)
    
    
fitFunc=fitInvSSEW3