# Atmospheric transmission curve for Plateau de Bure Interferometer

_{0}=e

^{-τ }and plot it against the frequency.

! use the new atm, the 2009 version

! test that it works with “exam atm%version”

! after the command below

set atm new

!

! set observatory, the available names are

! BURE (or PDBI), PICOVELETA (or VELETA) ALMA, APEX,

! ATF, CSO, FCRAO, JCMT, KITTPEAK, KOSMA, SEST, SMA,

! SMT EFFELSBERG, VLA LASILLA, MAUNAKEA, PARANAL (or VLT),

! IOTA, PTI, GI2T

! if not satisfied, use coordinates as

! OBS [longitude latitude altitude [radius]]

! radius is the sun avoidance zone in degrees, (def 30)

obs pdbi !<—- change site here!

!

! which airmass to calculate for 1 = zenith

let airmass 1

! ground temperature in Kelvin

let temperature 273

!

! create variable freq

! and the incremental change in frequency

define real freq

let freq 0.1

define real incr

let incr 0.1

!

! the amount of water vapor in mm

let water 0.3 ! <—- change water vapor here!

!

! the name of the output file

sic out pdbi_atm_0_3.dat

!

for n 0 to 16000

let freq_sig = freq

let freq_ima = freq

atm

! write to file

say ‘freq’ ‘tau_tot’

! add incr to the frequency and

! restart the loop

let freq = freq+incr

next

sic out

Just run this with

astro -nw @script_name.astro

After this you are left with the file pdbi_atm_0_3.dat in this case. Run this script for different amount of water vapor, say 0.3, 0.8 and 1.5. Then you jump into Python and do the following in the same directory as your files

# general imports

from scipy import exp, loadtxt

import matplotlib.pyplot as pl

# various settings

pl.ion()

pl.rcParams[‘font.size’] = 15

# calculate the transmission in percent

transm_percent = lambda tau : exp(-1*tau)*100

# load the data columns

data0_3 = loadtxt(‘pdbi_atm_0_3.dat’).transpose()

data0_8 = loadtxt(‘pdbi_atm_0_8.dat’).transpose()

data1_5 = loadtxt(‘pdbi_atm_1_5.dat’).transpose()

# initialize the figure

pl.figure(1,(11,6))

# plot the graphs

pl.plot(data0_3[0], transm_percent(data0_3[1]))

pl.xlabel(‘Frequency [GHz]’)

pl.ylabel(‘Transmission (%)’)

pl.title(‘Atmospheric Transmission at PdBI’)

What you should end up with is the following