def quicklook(filename):
n = 1
fs, fe = 40, 83
h5 = tb.open_file(filename)
T_ant = apply_calibration(h5)
f_leda = T_ant['f']
lst_leda = T_ant["lst"]
print T_ant.keys()
ant_ids = ['252A']#, '254A', '255A', '254B', '255B']
print("Plotting...")
T0_all = []
alpha_all = []
lst_trials = np.arange(1, 23, 0.5)
for ll in lst_trials:
mid = closest(lst_leda, ll)
sl = 100
for ant_id in ant_ids:
T_flagged = T_ant[ant_id][mid-sl:mid+sl]
print T_ant[ant_id].shape
T_flagged = rfi_flag(T_flagged, freqs=f_leda)
#T_flagged = np.ma.array(T_flagged)
d = T_flagged.mean(axis=0)
d_std = T_flagged.std(axis=0)
print d.shape, f_leda.shape
print ant_id, mid, sl
f_t, d_t = trim(f_leda, d, fs, fe)
f_t, d_std_t = trim(f_leda, d_std, fs, fe)
f_t = np.ma.array(f_t)
f_t.mask = d_t.mask
params = Parameters()
params.add('a0', value=1e6)
params.add('b', value=-2.49)
fc = f_t.compressed()
dc = d_t.compressed()
dc_errs = d_std_t.compressed()
out = minimize(residual, params, args=(model, fc, dc, dc_errs))
print report_fit(out)
alpha_all.append(out.params['b'].value)
T0_all.append(out.params['a0'].value)
print "B: ", out.params['b'].value
# print out.values
#p = fit_poly(f_leda, d, n=n, print_fit=True)
p = model(out.params, fc)
print p.shape
plt.figure("ANTZ")
plt.plot(fc, dc, label=ant_id)
#plt.plot(fc, p, label='fit')
plt.xlim(fs, fe)
plt.minorticks_on()
plt.ylim(500, 7000)
plt.xlabel("Frequency [MHz]")
plt.ylabel("Temperature [K]")
plt.legend(frameon=False)
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.show()
alpha = np.array(alpha_all)
print "mean std max min"
print np.mean(alpha), np.std(alpha), np.max(alpha), np.min(alpha)
plt.plot(lst_trials, alpha, c='#333333')
plt.xlabel("Sidereal time [hr]")
plt.ylabel("Spectral index $\\alpha$")
plt.minorticks_on()
plt.ylim(-2.4, -2.27)
plt.twinx()
plt.plot(lst_trials, T0_all, c='#cc0000', ls='dashed')
plt.ylabel("$T_{70}$ [K]", color='#cc0000')
plt.tight_layout()
plt.minorticks_on()
plt.xlim(0, 24)
plt.ylim(1500, 3300)
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.savefig("figures/spectral-index.pdf")
plt.show()
def quicklook(filename):
n = 1
fs, fe = 40, 80
h5 = tb.open_file(filename)
T_ant = apply_calibration(h5)
f_leda = T_ant['f']
print T_ant.keys()
ant_ids = ['252A', '254A', '255A']
pol_id = 'y'
print("Plotting...")
fig, ax = plt.subplots(figsize=(8, 6))
mid = T_ant["252A"].shape[0] / 2
print T_ant['lst'][mid]
sl = 250
f0 = np.arange(40, 70)
f1 = f0 + 10
f_trials = zip(f0, f1)
f_trials.append((40, 80))
alpha_out = []
for fs, fe in f_trials:
T_flagged = T_ant[ant_ids[0]][mid - sl:mid + sl]
T_flagged = rfi_flag(T_flagged, freqs=f_leda)
d = np.median(T_flagged, axis=0)
d_errs = np.std(T_flagged, axis=0)
f_trim, d = trim(f_leda, d, fs, fe)
f_trim = np.ma.array(f_trim)
f_trim.mask = d.mask
f_trim2, d_errs = trim(f_leda, d_errs, fs, fe)
d_errs = np.ma.array(d_errs)
d_errs.mask = d.mask
params = Parameters()
params.add('amp', value=1e8, min=1e4, max=1e10)
params.add('alpha', value=-2.5, min=-4, max=-1)
fc = f_trim.compressed()
dc = d.compressed()
dc_errs = d_errs.compressed()
print dc.shape, dc_errs.shape
out = minimize(residual, params, args=(model, fc, dc, dc_errs))
print report_fit(out)
# print out.values
#p = fit_poly(f_leda, d, n=n, print_fit=True)
p = model(out.params, fc)
alpha_out.append(out.params['alpha'].value)
"""
plt.plot(fc, dc, label=ant_ids[0])
plt.plot(fc, p, label='fit')
plt.xlim(fs, fe)
plt.minorticks_on()
plt.ylim(500, 7000)
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
plt.xlabel("Frequency [MHz]")
plt.ylabel("Temperature [K]")
plt.legend(frameon=False)
plt.show()
"""
print "HERE", f0.shape, len(alpha_out)
plt.plot(f0, alpha_out[:-1])
plt.figure()
res = dc - p
res_1p = dc - poly_fit(fc, dc, 1, log=True)
#res_2p = dc - poly_fit(fc, dc, 2, log=True)
res_3p = dc - poly_fit(fc, dc, 3, log=True)
#res_4p = dc - poly_fit(fc, dc, 4, log=True)
res_5p = dc - poly_fit(fc, dc, 5, log=True)
res_7p = dc - poly_fit(fc, dc, 7, log=True)
#res_3p3f = res_3p - fourier_fit(res_3p, 0, 3)
#plt.plot(fc, res)
plt.plot(rebin(fc, 8), rebin(res_1p, 8))
plt.plot(rebin(fc, 8), rebin(res_3p, 8))
plt.plot(rebin(fc, 8), rebin(res_5p, 8))
plt.plot(rebin(fc, 8), rebin(res_7p, 8))
#plt.plot(rebin(fc, 8), rebin(res_5p, 8))
print np.std(rebin(res_1p, 8))
print np.std(rebin(res_3p, 8))
print np.std(rebin(res_5p, 8))
print np.std(rebin(res_7p, 8))
#print np.std(rebin(res_5p, 8))
#plt.plot(rebin(fc, 8), rebin(res_3p3f, 8))
plt.text(0.005,
0.005,
get_repo_fingerprint(),
transform=fig.transFigure,
size=8)
plt.show()