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12_skymodel_compare.py
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12_skymodel_compare.py
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#!/usr/bin/env python
"""
# 12_skymodel_compare.py
Compare calibrated spectra against skymodel
"""
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.useful import *
from leda_cal.dpflgr import *
from leda_cal.git import get_repo_fingerprint
sns.set_style('ticks')
sns.set_context("paper",font_scale=1.5)
def quicklook(filename, ant='252A', lfsm=False, emp=False, n_poly=7):
h5 = tb.open_file(filename)
T_ant = apply_calibration(h5)
f_leda = T_ant['f']
lst = T_ant['lst']
print T_ant.keys()
ant_ids = [ant,]
print("Plotting %s..." % ant_ids[0])
fig, ax = plt.subplots(figsize=(8, 6))
mid = closest(lst, 11)
print T_ant['lst'][mid]
sl = 20
T_flagged = rfi_flag(T_ant[ant_ids[0]], freqs=f_leda)
sl = 250
plt.subplot2grid((3, 1), (0, 0), rowspan=2)
plt.plot(f_leda, T_flagged[mid-sl:mid+sl].mean(axis=0), c='#009933')
import hickle as hkl
gsm = hkl.load("cal_data/gsm-spec-lst11.hkl")
plt.plot(gsm["f"], gsm["T_ew"], c='#333333', ls='dashed')
if lfsm and emp:
smdl = SkyModelLFSMEmp
smlbl = 'LFSM+Emp'
elif lfsm and not emp:
smdl = SkyModelLFSM
smlbl = 'LFSM'
elif not lfsm and emp:
smdl = SkyModelGSMEmp
smlbl = 'GSM+Emp'
else:
smdl = SkyModelGSM
smlbl = 'GSM'
s = smdl(pol='y' if ant_ids[0][-1] == 'A' else 'x')
asm = s.generate_tsky(lst[mid-sl:mid+sl], f_leda*1e6).mean(axis=0)
plt.plot(f_leda, asm)
d = T_flagged[mid-sl:mid+sl].mean(axis=0)
f_t, d_t = trim(f_leda, d, 40, 80)
if ant_ids[0][-1] == 'A':
T_hsm = np.interp(f_t, gsm["f"], gsm["T_ew"])
else:
T_hsm = np.interp(f_t, gsm["f"], gsm["T_ns"])
T_asm = np.interp(f_t, f_leda, asm)
scale_offset = np.mean(T_hsm / d_t)
scale_offset_asm = np.mean(T_asm / d_t)
print scale_offset, scale_offset_asm
#plt.plot(f_)
plt.xlim(40, 80)
plt.ylim(500, 8000)
plt.minorticks_on()
plt.ylabel("Temperature [K]")
plt.legend([ant_ids[0], "GSM (.hkl)", smlbl])
plt.subplot2grid((3, 1), (2, 0), rowspan=1)
plt.plot(rebin(f_t, 8), rebin(d_t / T_hsm, 8), c='#333333', linestyle='--')
plt.plot(rebin(f_t, 8), rebin(scale_offset * d_t / T_hsm, 8), c='#333333', linestyle='--')
plt.plot(rebin(f_t, 8), rebin(d_t / T_asm, 8), c='#333333')
plt.plot(rebin(f_t, 8), rebin(scale_offset_asm * d_t / T_asm, 8), c='#333333')
plt.xlabel("Frequency [MHz]")
#plt.yticks([0.85, 0.87, 0.89, 0.91, 0.93])
# plt.ylim(0.85, 0.93)
plt.ylabel("data / model")
plt.tight_layout()
plt.minorticks_on()
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.savefig("figures/skymodel-compare.pdf")
plt.show()
resid = d_t - T_hsm
resid -= fit_poly(f_t, resid, n_poly)
resid_asm = d_t - T_asm
resid_asm -= fit_poly(f_t, resid_asm, n_poly)
plt.plot(f_t, resid, linestyle='--')
plt.plot(f_t, resid_asm)
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.show()
if __name__ == "__main__":
import optparse, sys
usage = '%prog [opts] filename_of_hdf5_observation'
o = optparse.OptionParser()
o.set_usage(usage)
o.set_description(__doc__)
o.add_option('--ant', dest='ant', action='store', default='252A',
help='Name of the antenna to plot. Default: 252A')
o.add_option('--lfsm', dest='lfsm', action='store_true', default=False,
help='Use the LFSM instead of the GSM')
o.add_option('--empirical', dest='emp', action='store_true', default=False,
help='Apply an empirical corretion to the dipole gain pattern model')
o.add_option('--n_poly', dest='n_poly', action='store', default=7, type="int",
help='Order of the polynomial to fit to the residuals. Default: 7')
opts, args = o.parse_args(sys.argv[1:])
if len(args) != 1:
o.print_help()
exit(1)
else:
filename = args[0]
quicklook(filename, ant=opts.ant, lfsm=opts.lfsm, emp=opts.emp, n_poly=opts.n_poly)