LIGO Chirp data extraction

In [1]:

    

import numpy as np
import librosa
import matplotlib.pyplot as plt
%matplotlib inline


    

    




Vendor:  Continuum Analytics, Inc.
Package: mkl
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In [2]:

    

from IPython.display import Audio


    

In [3]:

    

y, sr = librosa.load('LIGO Chirp-1080p.flac', sr=None, mono=False)


    

In [4]:

    

y.shape, sr


    

    
Out[4]:





((2, 575488), 48000)

In [5]:

    

plt.figure(figsize=(12, 4))
plt.subplot(1,2,1)
librosa.display.waveplot(y, sr=sr, alpha=0.25, label='Full signal')
plt.legend(loc='best')
plt.tight_layout()


    

In [6]:

    

# It looks like there's some leading zeros that we should get rid of

# Find the first non-zero
idx = np.argmax(np.abs(y).sum(axis=0) > 0) - 1


    

In [7]:

    

# Check that y.shape[1] - idx  is a multiple of 4
print(idx, (y.shape[1] - idx) % 4)


    

    




1992 0

In [8]:

    

# Trim off the leading silence
y = y[:, idx:]


    

In [9]:

    

# The whole signal is played four times
n = y.shape[1] // 4


    

In [10]:

    

plt.figure(figsize=(12, 4))
plt.subplot(1,2,1)
librosa.display.waveplot(y[:, :n], sr=sr, alpha=0.25, label='Native frequency')
plt.legend(loc='best')
plt.subplot(1,2,2)
librosa.display.waveplot(y[:, n:2*n], sr=sr, alpha=0.25, label='High frequency')
plt.legend(loc='best')
plt.tight_layout()


    

In [11]:

    

Audio(data=y[:, :n], rate=sr)


    

    
Out[11]:





                
              

In [12]:

    

Audio(data=y[:, n:2*n], rate=sr)


    

    
Out[12]:





                
              

In [13]:

    

# And write it out to disk
librosa.output.write_wav('ligo_chirps_raw.wav', y[:, :n], sr, norm=False)