In [1]:

    

import numpy as np
import wisps
import matplotlib.pyplot as plt
import pandas as pd

from tqdm import tqdm
%matplotlib inline


    

    




Warning: spectrum object has a flux vector of zero length - maybe empty?

Warning: normalize is attempting to divide by nan; ignoring

Warning: spectrum object has a flux vector of zero length - maybe empty?

Warning: normalize is attempting to divide by nan; ignoring

In [2]:

    

import wisps.simulations as wispsim


    

In [3]:

    

brf=wispsim.simulate_spts(recompute=False, name="baraffe2003")


    

In [4]:

    

brf.keys()


    

    
Out[4]:





dict_keys(['sing_evol', 'sing_spt', 'prim_evol', 'prim_spt', 'sec_evol', 'sec_spt', 'binary_spt'])

In [5]:

    

m0=(brf['sing_evol'])['mass']
m1=(brf['prim_evol'])['mass']
m2=(brf['sec_evol'])['mass']


    

In [6]:

    

fig, ax=plt.subplots(ncols=3, figsize=(10, 4))
h=ax[0].hist(m1.value, bins='auto', histtype='step', normed=False)
h=ax[1].hist(m2.value, bins='auto', histtype='step', normed=False)
h=ax[2].hist(m1.value/m2.value, bins='auto',  histtype='step', normed=False)

ax[1].set_yscale('log')
ax[0].set_yscale('log')

ax[0].set_xlabel(r'$M_1$', fontsize=18)
ax[1].set_xlabel(r'$M_2$', fontsize=18)
ax[2].set_xlabel(r'$M_1/M_2$', fontsize=18)
plt.tight_layout()

for a in ax:
    a.minorticks_on()


    

In [19]:

    

plt.scatter(m1.value, m2.value, s=5.)
plt.plot(m1.value, m1.value,  c='b')


    

    
Out[19]:





[<matplotlib.lines.Line2D at 0x1c73abc6a0>]






    

In [7]:

    

def my_limit(spts):
    idx=np.logical_and((spts>= 17), (spts <=40))
    return spts[idx]


    

In [8]:

    

#custom_histogram(teffs_bar, teffgrid, 150)*normteff_bar
spgrid=np.arange(17, 40, 5)


    

In [9]:

    

import seaborn as sns
#flatui =['#001f3f', '#0074D9', '#7FDBFF', '#39CCCC', '#3D9970', '#2ECC40', '#01FF70', '#FFDC00']
sns.set_palette(sns.color_palette("cubehelix", 18))


    

In [10]:

    

def get_values(model, b):
    b=round(b, 1)
    
    syst=wispsim.make_systems(model_name=model, bfraction=b)
    
    sps=(syst['system_spts'])
    
    vls=wisps.custom_histogram(sps, spgrid, binsize=5)
    
    l0s=np.nansum(wisps.custom_histogram(sps, [36., 40.], binsize=1))
    
    return vls/(l0s)


    

In [11]:

    

spgrid


    

    
Out[11]:





array([17, 22, 27, 32, 37])

In [12]:

    

binfr=np.arange(0.1, 0.9,0.1)

fig, ax=plt.subplots(figsize=(14, 6), ncols=3, sharey=True, sharex=True)

for b in binfr:
    b=round(b, 1)
    ax[0].step(spgrid, get_values('saumon2008', b), label=r'$\epsilon$ = {}'.format(b), where='mid')
    ax[1].step(spgrid, get_values('baraffe2003', b), label=r'$\epsilon$ = {}'.format(b), where='mid')
    ax[2].step(spgrid, get_values('marley2019', b), label=r'$\epsilon$ = {}'.format(b), where='mid')
    


for a in ax:
    a.minorticks_on()
    a.set_xlabel('SpT', fontsize=18)
    a.set_ylabel(r'N/N$_{[L0-L1]}$', fontsize=18)
    a.set_xticks(spgrid)
    a.set_xticklabels(['M7-L0', 'L0-L5', 'L5-T0', 'T0-T5', 'T5-Y0'])
    
    
ax[2].legend(fontsize=15)
ax[0].set_title('Model= S03', fontsize=18)
ax[1].set_title('Model= B03', fontsize=18)
ax[2].set_title('Model= Sonora', fontsize=18)

plt.tight_layout()

plt.savefig(wisps.OUTPUT_FIGURES+'/binary_effects.pdf', bbox_inches='tight')


    

In [ ]: