from pycbc.frame import read_frame
from pycbc.filter import highpass_fir, matched_filter
from pycbc.waveform import get_fd_waveform 
from pycbc.psd import welch, interpolate
import urllib

# Read data and remove low frequency content
fname = 'H-H1_LOSC_4_V2-1126259446-32.gwf'
url = "https://losc.ligo.org/s/events/GW150914/" + fname
urllib.urlretrieve(url, filename=fname)
h1 = read_frame('H-H1_LOSC_4_V2-1126259446-32.gwf', 'H1:LOSC-STRAIN')
h1 = highpass_fir(h1, 15, 8)

# Calculate the noise spectrum
psd = interpolate(welch(h1), 1.0 / 32)

# Generate a template to filter with
hp, hc = get_fd_waveform(approximant="IMRPhenomD", mass1=40, mass2=32, 
                         f_lower=20, delta_f=1.0/32)
hp.resize(len(h1) / 2 + 1)

# Calculate the complex (two-phase SNR)
snr = matched_filter(hp, h1, psd=psd, low_frequency_cutoff=20.0)

# Remove regions corrupted by filter wraparound 
snr = snr[len(snr) / 4: len(snr) * 3 / 4]

import pylab
pylab.plot(snr.sample_times, abs(snr))
pylab.ylabel('signal-to-noise')
pylab.xlabel('GPS Time (s)')
pylab.show()