Delete training_script.ipynb

training_script.ipynb

-%% Cell type:code id: tags:
-
-``` python
-import pylab
-import pycbc
-from pycbc.waveform import get_td_waveform
-from pycbc.waveform import get_fd_waveform
-from pycbc.waveform import fd_approximants
-import pycbc.noise
-import pycbc.psd
-from pycbc.types import TimeSeries
-from pycbc import distributions
-import numpy as np
-for i in range(10):
-    mass1_distribution = distributions.uniform_log.UniformLog10(mass1=(5, 95))
-    mass1_value=mass1_distribution.rvs(size=1)
-    mass2_distribution = distributions.uniform_log.UniformLog10(mass2=(5, 95))
-    mass2_value=mass2_distribution.rvs(size=1)
-    print(mass1_value['mass1'])
-    print(mass2_value['mass2'])
-    hp, hc = get_td_waveform(approximant='SEOBNRv4',
-                                     mass1=mass1_value['mass1'],
-                                     mass2=mass2_value['mass2'],
-                                     delta_t=1.0/4096,
-                                     f_lower=40)
-    for j in range(10):
-        seed_value=np.random.randint(1, 200)
-        flow = 30.0
-        delta_f = 1.0 / 16
-        flen = int(2048 / delta_f) + 1
-        psd = pycbc.psd.aLIGOZeroDetHighPower(flen, delta_f, flow)
-        delta_t = 1.0 / 4096
-        tsamples = int(5 / delta_t)
-        ts = pycbc.noise.noise_from_psd(tsamples, delta_t, psd, seed=seed_value)
-        tlen = 5 / delta_t
-        hp.resize(tlen)
-        ts.resize(tlen)
-        hp_new = TimeSeries(hp.data.data, dtype=hp.dtype,delta_t=delta_t, epoch=0)
-        final = hp_new + ts
-        
-        np.savetxt(str(i) + str(j) + '.txt',np.transpose([final.sample_times,final])) 
-    
-```
-
-%% Cell type:code id: tags:
-
-``` python
-import sys
-!{sys.executable} -m pip install pycbc ligo-common --no-cache-dir
-```
-
-%% Cell type:code id: tags:
-
-``` python
-cd
-```
-
-%% Cell type:code id: tags:
-
-``` python
-cd OneDrive
-```
-
-%% Cell type:code id: tags:
-
-``` python
-
-```
-
-%% Cell type:code id: tags:
-
-``` python
-cd Documents
-```
-
-%% Cell type:code id: tags:
-
-``` python
-cd aei_research
-```
-
-%% Cell type:code id: tags:
-
-``` python
-from pycbc import distributions
-mass1_distribution = distributions.uniform_log.UniformLog10(mass1=(5, 95))
-mass1_value=mass1_distribution.rvs(size=1)
-print(mass1_value['mass1'])
-```
-
-%% Cell type:code id: tags:
-
-``` python
-mass1_distribution = distributions.uniform_log.UniformLog10(mass1=(5, 95))
-mass1_value=mass1_distribution.rvs(size=1)
-mass2_distribution = distributions.uniform_log.UniformLog10(mass2=(5, 95))
-mass2_value=mass2_distribution.rvs(size=1)
-print(mass1_value['mass1'])
-print(mass2_value['mass2'])
-```
-
-%% Cell type:code id: tags:
-
-``` python
-
-```