def quicklook(filename):
h5 = tb.open_file(filename)
T_ant = apply_calibration(h5)
f_leda = T_ant['f']
ant_ids = ['252A', '254A', '255A']
pol_id = 'y'
print("Plotting...")
fig, ax = plt.subplots(figsize=(8, 6))
mid = T_ant["252A"].shape[0] / 2
sl = 250
d0 = T_ant[ant_ids[0]][mid - sl:mid + sl]
d1 = T_ant[ant_ids[1]][mid - sl:mid + sl]
d2 = T_ant[ant_ids[2]][mid - sl:mid + sl]
n_chans = 42
d0 = rfi_flag(d0,
thr_f=0.15,
thr_t=0.15,
rho=1.5,
bp_window_f=16,
bp_window_t=16,
max_frac_f=0.9,
max_frac_t=0.9)
d0 = useful.rebin(d0, 1, n_chans)
f_leda = useful.rebin(f_leda, n_chans)
d0_s = np.std(d0, axis=0) / np.sqrt(n_chans)**2
d0 = np.median(d0, axis=0)
#d1 = rfi_flag(d1, thr_f=0.15, thr_t=0.15, rho=1.5,
# bp_window_f=16, bp_window_t=16,
# max_frac_f=0.9, max_frac_t=0.9)
#d1 = np.median(d1, axis=0)
#
#d2 = rfi_flag(d2, thr_f=0.15, thr_t=0.15, rho=1.5,
# bp_window_f=16, bp_window_t=16,
# max_frac_f=0.9, max_frac_t=0.9)
#d2 = np.median(d2, axis=0)
#plt.imshow(d0, cmap='viridis', aspect='auto')
#plt.colorbar()
#plt.show()
#exit()
return f_leda, d0, d0_s
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()