/
07_fit_alpha.py
executable file
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/
07_fit_alpha.py
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#!/usr/bin/env python
"""
# 07_fit_alpha.py
Fit sky power law (frequency^alpha) to the data over 10 MHz chunks,
starting at 40-50 MHz and ending at 70-80 MHz, then plot alpha as
a function of frequency.
NOTE: Also see 10_fit_alpha2.py
"""
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 leda_cal.useful import fit_poly, closest, trim, fourier_fit, poly_fit, rebin
from lmfit import minimize, Parameters, report_fit
sns.set_style('ticks')
sns.set_context("paper",font_scale=1.5)
def residual(params, model, x, data, errs=1.0):
model_vals = model(params, x)
return (data - model_vals) / errs
def model(params, x):
amp = params['amp'].value
alpha = params['alpha'].value
model = amp*np.power(x, alpha) #+ off #+ a_s * np.sin(theta * x) #+ a_c * np.cos(theta * x)
#print mo
return model
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, thr_f=0.2, thr_t=0.2, rho=1.5,
bp_window_f=16, bp_window_t=16,
max_frac_f=0.5, max_frac_t=0.5)
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.show()
if __name__ == "__main__":
import sys
try:
filename = sys.argv[1]
except:
print "USAGE: ./quicklook.py filename_of_hdf5_observation"
exit()
quicklook(filename)