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create_dataset.py
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create_dataset.py
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import numpy as np
from pycbc import waveform, detector
from tqdm import tqdm
import csv
import h5py
import pandas as pd
from scipy import signal
import scipy.io.wavfile as s
next_val=0
#------------------------------------------------------------------
# TRAINING DATASETS PREPRATION ---
#------------------------------------------------------------------
class dataprep_train:
def __init__():
None
#------------------------------------------------------------------
# SIMULATED SIGNALS+TRANSIENT NOISE
#------------------------------------------------------------------
def simulated_signals(noise):
apx = ['TaylorT1','TaylorT2','EOBNRv2','SEOBNRv1','SEOBNRv2']
with open('gdrive/My Drive/GW data/labels.csv', 'a', newline='') as file:
for a in tqdm(range(len(apx))):
check=np.zeros(noise.shape[1])
k=0
for m1 in range(5,16,5):
for m2 in range(5,16,5):
for d in [50, 250, 450]:
for fu in [60,120]:
if (m1+m2+d+fl) not in check:
check[k]=m1+m2+d+fl
hp,hc = waveform.get_td_waveform(approximant=apx[a],
mass1=m1,mass2=m2,
delta_t=1.0/4096,
f_lower=50, f_final=fu,
distance=d)
strain = detector.Detector('H1').project_wave(hp, hc, 0, 0, 1.75)
if len(strain)<=noise.shape[1]:
signal_gw[next_val:next_val+noise.shape[0],:]=np.copy(noise)
pos=np.random.randint(0,noise.shape[1]-len(strain))
signal_gw[next_val:next_val+noise.shape[0],pos:pos+len(strain)]+=strain
writer = csv.writer(file)
for i in range(noise.shape[0]):
col=np.zeros(23)
# Store 1 in those columns corresponding to injected values
if m1 % 5 == .. :
col[..] = 1
..
..
writer.writerow(col)
k=k+1
#--------update next_val ----------#
next_val+=(noise.shape[0])
#------------------------------------------------------------------
# ECHOES +TRANSIENT NOISE
#------------------------------------------------------------------
def echoes(noise):
global next_val,signal_gw
signal_gw[next_val:next_val+noise.shape[0],:]=np.copy(noise)
with open('gdrive/My Drive/GW data/labels_5.csv', 'a', newline='') as file:
for loop in tqdm(range(10)):
t=np.linspace(0,.3,np.random.randint(noise.shape[1]))
y1,y2=np.zeros(len(t)),np.zeros(len(t))
i=0
for j in range(8):
for f in [250,280]:
for i in range(len(t)):
aa=t[i]-0.0295-j*0.0295
y1[i]+=1.5*10e-21*(-1)**j*(1.5*10e-21*.5/(3+j))*np.exp(-(aa**2)/(2*.006**2))*np.cos(2*np.pi*f*aa)
pos=np.random.randint(0,noise.shape[1]-len(t))
signal_gw[next_val:next_val+noise.shape[0]-1,pos:pos+len(t)]+=y1
next_val+=(noise.shape[0])
writer = csv.writer(file)
for i in range(noise.shape[0]):
col=np.zeros(23)
# Store 1 in those columns corresponding to injected values
if m1 % 5 == .. :
col[..] = 1
..
..
writer.writerow(col)
r=.3
for j in range(8):
for f in [250,280]:
for i in range(len(t)):
aa=t[i]-0.0295-j*0.0295-(j*(j+1)/2)*r*0.0295
y2[i]+=1.5*10e-21*(-1)**j*(1.5*10e-21*.5/(3+j))*np.exp(-(aa**2)/(2*.006**2))*np.cos(2*np.pi*f*aa)
pos=np.random.randint(0,noise.shape[1]-len(t))
signal_gw[next_val:next_val+noise.shape[0]-1,pos:pos+len(t)]+=y2
next_val+=(noise.shape[0])
writer = csv.writer(file)
for i in range(noise.shape[0]):
col=np.zeros(23)
# Store 1 in those columns corresponding to injected value
..
..
writer.writerow(col)
#------------------------------------------------------------------
# GLITCHES+TRANSIENT NOISE
#------------------------------------------------------------------
def glitches(noise):
global next_val,signal_gw
signal_gw[next_val:next_val+noise.shape[0],:]=np.copy(noise)
with open('gdrive/My Drive/GW data/labels_5.csv', 'a', newline='') as file:
for i in tqdm(['...']): # LIST OF FILES
loc='gdrive/My Drive/GW data/Glitches/'+i+'.wav'
rate,data=s.read(loc)
..
..
#-------filter signal using scipy -------------------
..
..
#------------ Inject signal----------------
..
..
#-------------update next_val------------------
..
..
#--------------write to csv-------------------
#------------------------------------------------------------------------------
# CCSNe+Transient Noise -------------
#------------------------------------------------------------------------------
def ccsne(noise):
global next_val,signal_gw
signal_gw[next_val:next_val+noise.shape[0]]=np.copy(noise)
with open('gdrive/My Drive/GW data/labels_5.csv', 'a', newline='') as file:
val=['..'] # .dat Files
for aak in tqdm(val):
loc='gdrive/My Drive/GW data/CCSNe/'+aak
x, y = np.loadtxt(loc,unpack=True, usecols=[0,1])
for r in [10,30]:
for theta in [30,60]:
y = 1/8*np.sqrt(15/np.pi)*y/r*(np.sin(theta))**2
new_arr=np.zeros(noise.shape[1]-500)
j=0
for i in range(0,len(y),2):
new_arr[j]=y[i]
j+=1
pos=np.random.randint(0,noise.shape[1]-len(new_arr))
signal_gw[next_val:next_val+noise.shape[0],pos:pos+len(new_arr)]+=new_arr
writer = csv.writer(file)
for i in range(noise.shape[0]):
col=np.zeros(23)
# Store 1 in those columns corresponding to injected value
..
..
writer.writerow(col)
#------------------------------------------------------------------------------
# MIXED SIGNALS BBH/BNS/GW-Echoes ------------
#------------------------------------------------------------------------------
def mixed_signals_BHBNSB(noise):
global next_val,signal_gw
with open('gdrive/My Drive/GW data/labels_5.csv', 'a', newline='') as file:
for aab in ['TaylorT1', 'EOBNRv2']:
check=np.zeros(noise.shape[1])
k=0
for m1 in tqdm(range(5,16,5)):
for m2 in range(5,16,5):
for d in [50, 250, 450]:
for fu in [60,120]:
if (m1+m2+d+fl) not in check:
check[k]=m1+m2+d+fl
hp,hc = get_td_waveform(approximant=aab,
mass1=m1,mass2=m2,
delta_t=1.0/4096,
f_lower=50, f_final=fu,
distance=d)
strain = detector.Detector('H1').project_wave(hp, hc, 0, 0, 1.75)
if len(strain)<=noise.shape[1]:
t=np.linspace(0,.3,np.random.randint(noise.shape[1]))
y2=np.zeros(len(t))
r=.3
for j in range(3,8):
for i in range(len(t)):
aa=t[i]-0.0295-j*0.0295-(j*(j+1)/2)*r*0.0295
y2[i]+=1.5*10e-21*(-1)**j*(1.5*10e-21*.5/(3+j))*np.exp(-(aa**2)/(2*.006**2))*np.cos(2*np.pi*250*aa)
## REPEAT FOR CIE ##
for i in tqdm(['...']): # LIST OF FILES
loc='gdrive/My Drive/GW data/Glitches/'+i+'.wav'
rate,data=s.read(loc)
..
..
#-------filter signal using scipy -------------------
..
..
#------------ Inject signal----------------
..
..
#-------------update next_val------------------
..
..
#--------------write to csv-------------------
#------------------------------------------------------------------------------
# MIXED SIGNALS CCSNe -------------
#------------------------------------------------------------------------------
def mixed_signals_CCSNe(noise):
global next_val,signal_gw
with open('gdrive/My Drive/GW data/labels_5.csv', 'a', newline='') as file:
val=['..'] # .dat Files
for aak in tqdm(val):
loc='gdrive/My Drive/GW data/CCSNe/'+aak
x, y = np.loadtxt(loc,unpack=True, usecols=[0,1])
for r in [10,30]:
for theta in [30,60]:
y = 1/8*np.sqrt(15/np.pi)*y/r*(np.sin(theta))**2
new_arr=np.zeros(noise.shape[1]-500)
j=0
for i in range(0,len(y),2):
new_arr[j]=y[i]
j+=1
for i in tqdm(['...']): # LIST OF FILES
loc='gdrive/My Drive/GW data/Glitches/'+i+'.wav'
rate,data=s.read(loc)
..
..
#-------filter signal using scipy -------------------
..
..
#------------ Inject signal----------------
..
..
#-------------update next_val------------------
..
..
#--------------write to csv-------------------
#----------------------------------------------------------------------------------
# PIPELINES ---
#----------------------------------------------------------------------------------
def train_pipeline(noise):
val=dataprep_train
with open('gdrive/My Drive/GW data/labels.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow([.., .., .., .., .., #------ADD 23 labels---------])
print('\nSimulated GW .......')
val.simulated_signals(noise)
print('\nSimulated GW training set 100%')
print('data size :'+ str(next_val)+'\n')
print('\nEchoes...')
val.echoes(noise)
print('data size :'+ str(next_val)+'\n')
print('\nEchoes 100%')
print('\nCCSNE...')
val.ccsne(noise)
print('data size :'+ str(next_val)+'\n')
print('\nCCSNE 100%')
print('\nGlitches...')
val.glitches(noise)
print('data size :'+ str(next_val)+'\n')
print('\nGlitches 100%')
print('\nMixed training BHBNSB ...' )
val.mixed_signals_BHBNSB(noise)
print('\nMixed set BHBNSB 100%')
print('data size :'+ str(next_val)+'\n')
print('\nMixed training CCSNe...' )
val.mixed_signals_CCSNe(noise)
print('\nMixed set CCSNe 100%')
print('data size :'+ str(next_val)+'\n')
hf = h5py.File('gdrive/My Drive/GW data/data.h5', 'w')
hf.create_dataset('data', data=signal_gw)
hf.close()
#--------------------------------------------------------
# MAIN -----
#--------------------------------------------------------
if __name__ == '__main__':
hf= h5py.File('gdrive/My Drive/GW data/noise_data.hdf5', 'r')
group_key = list(hf.keys())
strain=hf['strain']['Strain'].value
ts = hf['strain']['Strain'].attrs['Xspacing']
metaKeys = hf['meta'].keys()
meta = hf['meta']
gpsStart = meta['GPSstart'].value
duration = meta['Duration'].value
gpsEnd = gpsStart + duration
time = np.arange(gpsStart, gpsEnd, ts)
# --------------- define noise segment------------------- #
print('\n\nPreparing data..... ')
train_pipeline(noise)
print('\nPreparing data........100%\n\n')
#------------------normalize data points ------------------ #
..
..
#------------------reshape array into nxnx3 dims ------------------ #
..
..