python - Method for finding max. point on linear plot -


this in astronomy, think question elementary - i'm not experienced, apologise.

i plotting relationship between colour of star-forming galaxy (y axis) redshift (x axis). plot line rises around 0 maybe 9, decays again -2. peak (~9 colour) around 4 in terms of redshift, , want find peak more exactly. redshift given quite confusing function, , can't figure out how differentiate or else that.

  • could maybe differentiate complicated redshift (z) function? if so, how?

  • if not, how estimate peak graphically/numerically?

sorry basic question , thank in advance. code below.

import numpy np import matplotlib.pyplot plt import igm import scipy.integrate integrate  sf = np.load('starforming.npy')  lam = sf[0]  sed = sf[1]  filters = ['f435w','f606w','f814w','f105w','f125w','f140w','f160w'] filters_wl = {'f435w':0.435,'f606w':0.606,'f814w':0.814,'f105w':1.05,'f125w':1.25,'f140w':1.40,'f160w':1.60} # filter dictionary give wavelengths of filters in microns  ft = {} # dictionary  f in filters:  data = np.loadtxt(f+'.txt').t  ft[f]= data      fluxes = {}  f in filters: fluxes[f] = [] # make empty list each   redshifts = np.arange(0.0,10.0,0.1) # redshifts going 0 10  z in redshifts:      lamz = lam * (1. + z)     obssed = sed * igm.madau(lamz, z)      f in filters:          newt = np.interp(lamz,ft[f][0],ft[f][1]) # each filter, refer          bb_flux = integrate.trapz((1./lamz)*obssed*newt,x=lamz)/integrate.trapz((1./lamz)*newt,x=lamz)          # 1st bit integrates, 2nd bit divides area under filter normalise filter          # loops on z, z creates new sed, redshift wl grid              fluxes[f].append(bb_flux)   f in filters: fluxes[f] = np.array(fluxes[f])  colour = -2.5*np.log10(fluxes['f435w']/fluxes['f606w'])  plt.plot(redshifts,colour) plt.xlabel('redshift') plt.ylabel('colour') plt.show

i not have high enough reputation comment, may solve problem, guess answer. store y-coordinates in list, use max(list) function find max. if want ordered pair, store coordinates (y,x) tuples , use max(list)

lst = [(3,2), (4,1), (1, 200)] max(lst)

yields (4,1)


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