https://github.com/telegraphic/leda_analysis_2016
Tip revision: 36a9b3e7db06932189694bf2ae316f7467358448 authored by Danny Price on 29 April 2018, 12:40:16 UTC
Updated to add aaply_vna keyword
Updated to add aaply_vna keyword
Tip revision: 36a9b3e
08_plot_fg.py
#!/usr/bin/env python
"""
# 08_plot_fg.py
Plot the calibration coefficients from Bowman et al (2012), F and G.
These coefficients are used when applying VNA calibration.
"""
import matplotlib as mpl
import seaborn as sns
import tables as tb
from leda_cal.leda_cal import *
from leda_cal.skymodel import *
from leda_cal.dpflgr import *
from lmfit import minimize, Parameters, report_fit
sns.set_style('ticks')
sns.set_context("paper",font_scale=1.5)
def quicklook():
balun_loss = hkl.load('cal_data/balun_loss.hkl')
vna_cal = hkl.load('cal_data/vna_calibration.hkl')
ant_ids = ['a252x']#, 'a254x', 'a255x']
for ant_id in ant_ids:
ra = vna_cal[ant_id]["ra"]
rl = vna_cal[ant_id]["rl"]
f = vna_cal["f"]
F = compute_F(ra, rl)
G = compute_G(rl)
plt.subplot(3,1,1)
plt.plot(f, (1-mag2(ra)), c='#002147', label='$H_{\\rm{ant}}$')
plt.yticks([0.5, 0.6, 0.7, 0.8])
plt.ylim(0.5, 0.8)
plt.xticks([40, 45, 50, 55, 60, 65, 70, 75, 80, 85],
["", "", "", "", "", "", "", "", "", ""])
plt.subplot(3, 1, 3)
plt.plot(f, G, c='#002147', label='$H_{\\rm{lna}}$')
plt.yticks([0.997, 0.998, 0.999, 1.000])
plt.ylim(0.997, 1.000)
plt.subplot(3, 1, 2)
plt.plot(f, mag2(F), c='#002147', label='$|F|^2$')
plt.xticks([40, 45, 50, 55, 60, 65, 70, 75, 80, 85],
["", "", "", "", "", "", "", "", "", ""])
for ii in (1,2,3):
plt.subplot(3,1,ii)
plt.xlim(40, 85)
plt.rcParams["legend.fontsize"] = 12
plt.legend(loc=4, frameon=False)
#plt.legend(["$|F|^2$"], frameon=False, loc=4)
plt.xlabel("Frequency [MHz]")
plt.tight_layout()
#plt.legend(["$G$"], frameon=False, loc=4)
#plt.text(40, 0.999, "$G$")
plt.show()
if __name__ == "__main__":
quicklook()