Updated documentation, descriptions for v1.0 release.

scripts/NCEP NARR Data analysis/ncep_atmosphere_analysis.py

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+#!/usr/bin/env Python3
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib import cm
+from matplotlib.colors import LogNorm
+from AstroAtmosphere import *
+from scipy import interpolate
+import json
+
+
+'''
+@ Author:		Joost van den Born 
+@ Contact:		born@astron.nl
+@ Description:	This script uses the NCEP NARR data to model a real structured atmosphere 
+				from data with a resolution of 2.5 km per data point. We interpolate this
+				data to a higher resolution.
+				Then a full integration over this atmosphere is done and compared to the 
+				values for the refraction and dispersion if we had only taken the atmospheric
+				conditions directly at the observatory.
+'''
+
+
+# Load a variable from a json text file.
+def loadVariableFromTXT(filename):
+	variableFile = open(filename, 'r')
+	variable = json.load(variableFile)
+	variableFile.close()
+	return np.asarray(variable)
+
+
+plt.rc('font', family='serif')
+plt.rc('axes', labelsize='large')
+
+# h, _, _, _, Temp, Press, Dens, vSound, _,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
+
+dist 	= loadVariableFromTXT('export_dist.txt')
+heights = loadVariableFromTXT('export_heights.txt')
+press 	= loadVariableFromTXT('export_press_atmosphere.txt')
+temp 	= loadVariableFromTXT('export_temp_atmosphere.txt')
+print("Loading complete!")
+
+# Conditions at Cerro Armazones
+T   = temp[0,0]	# k
+P   = press[0,0]# Pa
+H   = 0.0
+xc  = 314   	# ppm
+lat = -24.5983 	# degrees
+h0   = 0  		# m
+l1  = 1.49 		# micron
+l2  = 1.78 		# micron
+zenith = 60 	# degrees
+
+# Calculate the index of refraction
+at  = Observatory()
+n1  = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
+n2  = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
+rho = at.rho(p=P, T=T, RH=H, xc=xc)
+
+RHlist = np.asarray([H]*len(press[:,0]))
+xclist = np.asarray([xc]*len(press[:,0]))
+Dens = at.rho(p=press[:,0], T=temp[:,0], RH=RHlist, xc=xclist)
+
+refr = refraction(lat, h0) 
+refr.setReducedHeight(P, rho)
+disp = dispersion(lat, h0)
+disp.setReducedHeight(P, rho)
+
+
+# We must create a cube of weather data:
+# We assume a flat atmosphere for now, so that h does not need to be adjusted
+# x_max = 200 km, gives zenith_max = ~66.7 degrees
+# Every 20 km in horizontal direction a new perturbation and interpolation
+# for every 500 m.
+x_max = dist[-1] # km
+
+Temp_cube_n = []
+Press_cube_n = []
+
+for x in range(len(dist)):
+	Temp_cube_n.append(temp[:,0])
+	Press_cube_n.append(press[:,0])
+
+Temp_cube_n = np.asarray(Temp_cube_n).T
+Press_cube_n = np.asarray(Press_cube_n).T
+
+
+
+fig = plt.figure()
+ax1 = fig.add_subplot(211)
+im = ax1.pcolormesh(dist,heights, temp, cmap=cm.coolwarm)
+fig.colorbar(im, ax=ax1, label='Temperature (K)')
+ax1.set_aspect(1)
+ax1.set_ylabel('Height (km)')
+
+ax2 = fig.add_subplot(212)
+im2 = ax2.pcolormesh(dist, heights, press, cmap=cm.coolwarm, norm=LogNorm())
+ax2.set_aspect(1)
+ax2.set_xlabel('Horizontal distance (km)')
+ax2.set_ylabel('Height (km)')
+fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
+plt.tight_layout()
+plt.show(block=False)
+
+# Now for a given zenith angle we need to find the temp and press along the line of sight
+f_Temp_2d  		= interpolate.interp2d(dist, heights, temp, kind='linear')
+f_Press_2d 		= interpolate.interp2d(dist, heights, press, kind='linear')
+f_Temp_2d_n  	= interpolate.interp2d(dist, heights, Temp_cube_n, kind='linear')
+f_Press_2d_n 	= interpolate.interp2d(dist, heights, Press_cube_n, kind='linear')
+print('Interpolation complete!')
+
+
+
+zenithRange = np.linspace(30,65,10)
+refrDiff, dispDiff = [], []
+
+for zenith in zenithRange:
+	z = np.deg2rad(zenith)
+	y_los = heights
+	x_los = np.linspace(0, heights.max()*np.tan(z), len(heights))
+
+	Temp_los, Press_los = [], []
+	Temp_los_n, Press_los_n = [], []
+	for i in range(len(heights)):
+		 Temp_los.append(f_Temp_2d(x_los[i], y_los[i])[0])
+		 Press_los.append(f_Press_2d(x_los[i], y_los[i])[0])
+		 Temp_los_n.append(f_Temp_2d_n(x_los[i], y_los[i])[0])
+		 Press_los_n.append(f_Press_2d_n(x_los[i], y_los[i])[0])
+
+	Temp_los  	= np.asarray(Temp_los)
+	Press_los 	= np.asarray(Press_los)
+	Temp_los_n 	= np.asarray(Temp_los_n)
+	Press_los_n = np.asarray(Press_los_n)
+	rho_los   	= at.rho(p=Press_los, T=Temp_los, RH=H, xc=xc)
+	rho_los_n 	= at.rho(p=Press_los_n, T=Temp_los_n, RH=H, xc=xc)
+
+	refrInt = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3600*1000
+	refrInt_pert = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3600*1000
+	refrDiff.append(refrInt_pert - refrInt)
+
+	dispInt = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3.6e6
+	dispInt_pert = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3.6e6
+	dispDiff.append(dispInt_pert - dispInt)
+
+
+
+
+
+fig3 = plt.figure(figsize=(9,7))
+ax1 = fig3.add_subplot(211)
+ax2 = fig3.add_subplot(212)
+ax1.tick_params(axis ='both', direction='in', which='both', labelsize=16) 
+ax2.tick_params(axis ='both', direction='in', which='both', labelsize=16) 
+ax1.grid(True, alpha=0.3)#, which='major')
+ax2.grid(True, alpha=0.3)#, which='major')
+
+
+ax1.plot(zenithRange, np.log10(np.abs(refrDiff)), 'k-', linewidth=2)
+ax2.plot(zenithRange, np.log10(np.abs(dispDiff)), 'k-', linewidth=2)
+ax1.set_xlabel('Zenith (degrees)', fontsize=18)
+ax2.set_xlabel('Zenith (degrees)', fontsize=18)
+ax1.set_ylabel('$\log_{10}($|$R - R_{o}$|$)$ (mas)', fontsize=18)
+ax2.set_ylabel('$\log_{10}($|$\Delta R - \Delta R_{o}$|$)$ (mas)', fontsize=18)
+plt.tight_layout()
+plt.show()
+
+
+