Gravitational Wave Data Analytics Quickview¶

This IPython notebook provides a quick look at short segments of data from the Gravitational Wave Open Science Center

Set the GPS time (t0) and detector in the first cell
Click "Run All" in the cell menu at the top
Your plots will appear below

SET PARAMETERS: Detector and GPS Time¶

In [1]:

# -- Set a GPS time:
t0 = 1126259462.4    # -- GW150914

#-- Choose detector as H1, L1, or V1
detector = 'H1'

We could also try some of these examples times in the H1 detector:

t0 = 1126259462.4    # -- GW150914
t0 = 1187008882.4    # -- GW170817
t0 = 933200215       # -- Loud hardware injection
t0 = 1132401286.33   # -- Koi Fish Glitch

Importing Packages¶

In [2]:

import requests, os
import matplotlib.pyplot as plt
%config InlineBackend.figure_format = 'retina'

try:
    from gwpy.timeseries import TimeSeries
except:
    ! pip install -q "gwpy==3.0.7"
    ! pip install -q "matplotlib==3.5.3"
    ! pip install -q "astropy==6.0.0"
    from gwpy.timeseries import TimeSeries    

Querying and Downloading Data¶

The gwosc package provides an interface to querying the open data releases hosted on Gravitational Wave Open Science Center. Documentations and info of the gwosc package can be found in the following links

In [3]:

from gwosc.locate import get_urls
url = get_urls(detector, t0, t0)[-1]

print('Downloading: ' , url)
fn = os.path.basename(url)
with open(fn,'wb') as strainfile:                 
    straindata = requests.get(url)
    strainfile.write(straindata.content)

Downloading:  http://gwosc.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126257415-4096.hdf5

Plotting the Raw Time-Series Data¶

In [4]:

# -- Read strain data
strain = TimeSeries.read(fn,format='hdf5.gwosc')
center = int(t0)
strain = strain.crop(center-16, center+16)
fig1 = strain.plot()
plt.show()

Plotting the ASD¶

In [5]:

# -- Plot ASD
fig2 = strain.asd(fftlength=8).plot()
plt.xlim(10,2000)
plt.ylim(1e-24, 1e-19)
plt.show()

Whitening and Band-Passing the Data¶

In [7]:

# -- Whiten and bandpass data
white_data = strain.whiten()
bp_data = white_data.bandpass(30, 400)
fig3 = bp_data.plot()
plt.xlim(t0-0.2, t0+0.1)
plt.show()

Plotting a q-transform of the Data¶

In [8]:

dt = 1  #-- Set width of q-transform plot, in seconds
hq = strain.q_transform(outseg=(t0-dt, t0+dt))
fig4 = hq.plot()
ax = fig4.gca()
ax.grid(False)
ax.set_yscale('log')
try: fig4.colorbar(label="Normalized energy")
except: pass
plt.show()