AtmosphericModel/scripts/compare_refractivity_models.py

#!/usr/bin/env Python3
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import host_subplot
from matplotlib.colors import LogNorm
from tabulate import tabulate
import AstroAtmosphere


'''
@ Author:		Joost van den Born 
@ Contact:		born@astron.nl
@ Description:	A comparison of various atmospheric refraction models.
				Also these are used to calculate the difference in 
				atmospheric dispersion, given a set of conditions.
				This is a reproduction of Figure 1 of Spàno (2014).
'''


class atmosphericModel:

	def __init__(self, T, P, H, CO2, l1, l2):
		self.T 		= T
		self.P 		= P
		self.H 		= H
		self.CO2	= CO2
		self.lrange = np.linspace(l1, l2, 100)


	def BarrellAndSears(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.BarrellAndSears(l, T=self.T, p=self.P)-1)


	def Ciddor(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.Ciddor(l, CO2=self.CO2)-1)


	def Edlen1953(self, l=None):
		if not l:
			l = self.lrange
		return(AstroAtmosphere.Edlen1953(l)-1)


	def Edlen1966(self, l=None):
		if not l:
			l = self.lrange
		return(AstroAtmosphere.Edlen1966(l)-1)	


	def Owens(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.Owens(l, T=self.T, p=self.P, H=self.H)-1)


	def BonschPotulski(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.BonschPotulski(l, T=self.T, p=self.P, CO2=self.CO2)-1)

	def BirchDowns(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.BirchDowns(l)-1)

	def PeckReeder(self, l=None):
		if not l:
			l = self.lrange
		return (AstroAtmosphere.PeckReeder(l)-1)

	def slalib(self, l=None):
		if not l:
			l = self.lrange
		return(AstroAtmosphere.slalib(l, T=self.T, p=self.P, H=self.H)-1)


if __name__ == '__main__':

	# Calculation of the refractivity of the different models
	# multiplication by 1e8 to make the differentials more
	# pronounced.
	at = atmosphericModel(T=288.15, P=101325, H=0, CO2=300, l1=0.3, l2=2.5)
	BSdata 				= 1e8 * at.BarrellAndSears()
	CiddorData 			= 1e8 * at.Ciddor()
	Edlen1953Data 		= 1e8 * at.Edlen1953()
	Edlen1966Data 		= 1e8 * at.Edlen1966()
	OwensData 			= 1e8 * at.Owens()
	BonschPotulskiData 	= 1e8 * at.BonschPotulski()
	BirchDownsData 		= 1e8 * at.BirchDowns()
	PeckReederData 		= 1e8 * at.PeckReeder()
	slalibData 			= 1e8 * at.slalib()
	
	# Plotting the results
	fig = plt.figure(figsize=(9,5))
	ax2 = fig.add_subplot(111)
	ax2.grid(True, alpha=0.3)
	ax2.plot(at.lrange, BSdata - CiddorData, 'b-', label='Barrell & Sears (1939)')
	ax2.plot(at.lrange, CiddorData - CiddorData, 'k-', label='Ciddor (1996)')
	ax2.plot(at.lrange, Edlen1953Data - CiddorData, 'r:', label='Edlen (1953)')
	ax2.plot(at.lrange, Edlen1966Data - CiddorData, 'g--', label='Edlen (1966)')
	ax2.plot(at.lrange, OwensData - CiddorData, 'm--', label='Owens (1967)')
	ax2.plot(at.lrange, BonschPotulskiData - CiddorData, 'g-.', label='Bonsch & Potulski (1998)')
	ax2.plot(at.lrange, BirchDownsData - CiddorData, 'r-.', label='Birch & Downs (1993, 1994)')
	ax2.plot(at.lrange, PeckReederData - CiddorData, 'b--', label='Peck & Reeder (1972)')
	ax2.plot(at.lrange, slalibData - CiddorData, 'y-', label='ZEMAX / SLALIB')
	box = ax2.get_position()
	ax2.set_position([box.x0, box.y0, box.width, box.height])
	ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
	ax2.set_xlabel('Wavelength ($\mu m$)')
	ax2.set_ylabel('$\Delta$n $\\times$ $10^{8}$')
	ax2.set_ylim([-3,3])
	plt.tight_layout()
	plt.show()


	
	# Let's compare the impact on the atmospheric dispersion.
	# For the dispersion we must compare the delta for two lambda values
	methodList = 	[	at.BarrellAndSears,
						at.Ciddor,
						at.Edlen1953,
						at.Edlen1966,
						at.Owens,
						at.BonschPotulski,
						at.BirchDowns,
						at.PeckReeder,
						at.slalib
					]
	

	filters = 	[	[0.800, 0.934], # I-band
					[1.150, 1.345], # J-band
					[1.490, 1.780], # H-band
					[1.970, 2.320]	# K-band
				]
	filterDispersion = []
	
	# Iterate over the four MICADO broad band filters
	for f in filters:
		# Wavelengths of this particular filter
		l1 = f[0]
		l2 = f[1]

		dispersionList = []
		for m in methodList:
			d1 = m(l=l1)
			d2 = m(l=l2)
			deltan = d1 - d2
			
			# We assume, for sake of simplicity, a plane parallel atmosphere
			# and calculate the dispersion in milli arcseconds.
			dispersion = deltan * np.tan(np.deg2rad(45)) * 206265 * 1000 
			dispersionList.append(dispersion)

		# We compare the atmospheric dispersion relative to the atmospheric
		# dispersion as calculated using the Ciddor model.
		dispersionList = np.asarray(dispersionList) - dispersionList[1]
		filterDispersion.append(dispersionList)

	# Print the results in a table.
	methodStringList = ['Barrell & Sears (1939)',
						'Ciddor (1996)',
						'Edlen (1953)',
						'Edlen (1966)',
						'Bonsch & Potulski (1998)',
						'Birch & Downs (1993, 1994)',
						'Peck & Reeder (1972)',
						'slalib']
	tableData = []
	for i in range(len(methodStringList)):
		tableData.append([methodStringList[i], filterDispersion[0][i], filterDispersion[1][i], filterDispersion[2][i], filterDispersion[3][i]])
	print(tabulate(tableData, headers=['Model', 'I-band\n(0.8 - 0.934)', 'J-band\n(1.15 - 1.345)', 'H-band\n(1.49 - 1.78)', 'K-band\n(1.97 - 2.32)']))