In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import pandas as pd
import seaborn as sns
sns.set_context("talk")


    

In [3]:

    

bhb=pd.read_csv('../LISA/newhalodcoparameters.dat',delim_whitespace=True,header=None)


    

In [4]:

    

bhb.columns=['M1','M2','Period','Dist','theta_s','phi_s','theta_l','phi_l','phi_o','gamma','e','bhb']


    

In [5]:

    

bhb.describe().ix[[3,7]]


    

    
Out[5]:






  

    

      

      
M1
      
M2
      
Period
      
Dist
      
theta_s
      
phi_s
      
theta_l
      
phi_l
      
phi_o
      
gamma
      
e
    
  
  

    

      
min
      
1.212225
      
1.159066
      
4.402512e+03
      
7.272752e+02
      
0.010957
      
0.000032
      
0.013222
      
0.000194
      
0.000051
      
0.000010
      
0.002313
    
    

      
max
      
25.964150
      
32.364620
      
7.739648e+17
      
1.074319e+07
      
3.134960
      
6.282903
      
3.130007
      
6.282926
      
6.283092
      
6.283091
      
0.980000
    
  

In [ ]:

    




    

In [ ]:

    




    

In [16]:

    

pop=pd.read_csv('/Users/domi/Desktop/LISA/population.dat',delim_whitespace=True,header=None)


    

In [17]:

    

pop.columns=['h0','e','f']


    

In [18]:

    

pop.describe()


    

    
Out[18]:






  

    

      

      
h0
      
e
      
f
    
  
  

    

      
count
      
1.000000e+02
      
100.000000
      
1.000000e+02
    
    

      
mean
      
7.466758e-25
      
0.412210
      
4.369515e-07
    
    

      
std
      
2.433641e-24
      
0.322285
      
1.804891e-06
    
    

      
min
      
3.957510e-30
      
0.015024
      
4.002134e-13
    
    

      
25%
      
1.440662e-26
      
0.071599
      
8.568407e-11
    
    

      
50%
      
7.505745e-26
      
0.388203
      
1.060388e-09
    
    

      
75%
      
3.344292e-25
      
0.696951
      
4.201545e-08
    
    

      
max
      
1.637405e-23
      
0.971493
      
1.395937e-05
    
  

In [19]:

    

ax = subplot(111)
# Axis scale must be set prior to declaring the Formatter
# If it is not the Formatter will use the default log labels for ticks.
ax.set_xscale('log')
ax.set_yscale('log')

scatter(pop.f,pop.h0)


    

    
Out[19]:





<matplotlib.collections.PathCollection at 0x1165a33c8>






    

In [27]:

    

spec=pd.read_csv('../LISA/spectrum.dat',delim_whitespace=True,header=None)


    

In [28]:

    

tim=pd.read_csv('../LISA/time.dat',delim_whitespace=True,header=None)


    

In [29]:

    

spec.describe().ix[['min','max']]


    

    
Out[29]:






  

    

      

      
0
      
1
      
2
      
3
    
  
  

    

      
min
      
0.000000
      
-2.652008e-16
      
-2.210016e-16
      
-1.262110e-16
    
    

      
max
      
0.003323
      
1.604174e-16
      
1.957390e-16
      
1.699157e-16
    
  

In [37]:

    

tim.describe()#.ix[['min','max']]


    

    
Out[37]:






  

    

      

      
0
      
1
      
2
      
3
    
  
  

    

      
count
      
1.048576e+06
      
1.048576e+06
      
1.048576e+06
      
1.048576e+06
    
    

      
mean
      
7.889530e+07
      
-2.693103e-24
      
6.610641e-23
      
-3.273015e-23
    
    

      
std
      
4.555029e+07
      
1.974668e-21
      
1.977796e-21
      
1.334110e-21
    
    

      
min
      
0.000000e+00
      
-8.199107e-21
      
-8.224560e-21
      
-5.798551e-21
    
    

      
25%
      
3.944765e+07
      
-1.342998e-21
      
-1.268234e-21
      
-9.175249e-22
    
    

      
50%
      
7.889530e+07
      
-2.321263e-24
      
6.437028e-23
      
-2.339624e-23
    
    

      
75%
      
1.183429e+08
      
1.332689e-21
      
1.397425e-21
      
8.496073e-22
    
    

      
max
      
1.577906e+08
      
8.582353e-21
      
8.741668e-21
      
5.524214e-21
    
  

In [46]:

    

scatter(tim[0],((tim[1]*10**20)),alpha=0.2)


    

    
Out[46]:





<matplotlib.collections.PathCollection at 0x12296aef0>






    

In [25]:

    

spec2=pd.read_csv('/Users/domi/Desktop/LISA/mp/spectrum.dat',delim_whitespace=True,header=None)


    

In [40]:

    

scatter(log10(spec2[0]),log10(abs(spec2[1])),alpha=0.2)


    

    




/Users/domi/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':






    
Out[40]:





<matplotlib.collections.PathCollection at 0x119c8f470>






    

In [44]:

    

scatter(log10(spec2[0]),log10((spec2[1])),alpha=0.2)


    

    




/Users/domi/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':
/Users/domi/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in log10
  if __name__ == '__main__':






    
Out[44]:





<matplotlib.collections.PathCollection at 0x10ec77160>






    

In [41]:

    

scatter(log10(spec[0]),log10(abs(spec[1])),alpha=0.2)


    

    




/Users/domi/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':






    
Out[41]:





<matplotlib.collections.PathCollection at 0x11a41f6d8>






    

In [14]:

    

lisa=pd.read_csv('../data/LISA20pmrtHzStrain.dat',sep='\t',comment='#',names=['f_gw','strain'])


    

In [15]:

    

lisa.head(5)


    

    
Out[15]:






  

    

      

      
f_gw
      
strain
    
  
  

    

      
0
      
9.765035e-08
      
1.465146e-15
    
    

      
1
      
9.992561e-08
      
1.399184e-15
    
    

      
2
      
1.022538e-07
      
1.336193e-15
    
    

      
3
      
1.046364e-07
      
1.276037e-15
    
    

      
4
      
1.070744e-07
      
1.218588e-15
    
  

In [18]:

    

loglog(spec[0],spec[1],alpha=0.2,ls='None',marker='o',ms=5,mec='k',mew=0.2,label='BBH inspirals')
xlabel('Binary Frequency')
ylabel('Strain Amplitude')
loglog(lisa.f_gw,lisa.strain,label='LISA Standard Configurations')
xlim([1e-9,1e-2])
legend()

# savefig('../Fig/signal_specture.jpeg',dpi=200,bbox_inches='tight')


    

    
Out[18]:





<matplotlib.legend.Legend at 0x111751eb8>






    

In [ ]:

    




    

In [55]:

    

sns.pairplot(spec,x_vars=[0],y_vars=[1,2,3],kind='scatter')


    

    
Out[55]:





<seaborn.axisgrid.PairGrid at 0x123af2b10>






    

In [56]:

    

sns.pairplot(tim,x_vars=[0],y_vars=[1,2,3],kind='scatter')


    

    
Out[56]:





<seaborn.axisgrid.PairGrid at 0x14906ba10>






    

In [ ]:

    




    

In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [17]:

    

random.choice(["ita",'sea'])


    

    
Out[17]:





'ita'

In [ ]: