In [1]:

    

import json
import pickle
import os, sys
import numpy as np
import matplotlib.pyplot as plt


    

In [2]:

    

path_to_SNe = '../gen_lightcurves/gp_smoothed/'
SNe_lightcurves = os.listdir(path_to_SNe)

average_time_span_Ia = []
average_time_span_nonIa = []
average_flux_Ia = []
average_flux_nonIa = []

for i, lcurve in enumerate(SNe_lightcurves):
    lightcurve_path = path_to_SNe + lcurve
    #print(lightcurve_path)
    with open(path_to_SNe + lcurve) as f:
        lcurve_data = json.load(f)
        
    filt_set = set(['g','r','i'])
    if not filt_set.issubset(set(lcurve_data.keys()):
        continue
    mag_start = lcurve_data['g']['mjd'][0]
    mag_end = lcurve_data['g']['mjd'][-1]
    mag = np.array(lcurve_data['g']['mag'])
    avg_mag = mag.mean()
    sntype = lcurve_data['g']['type']
    
    if sntype == 'Ia':    
        average_time_span_Ia.append(mag_end - mag_start)
        average_flux_Ia.append(avg_mag)
    else:
        average_time_span_nonIa.append(mag_end - mag_start)
        average_flux_nonIa.append(avg_mag)
        
    
    #if i>20:
    #    break
print(len(average_time_span_Ia))
print(len(average_time_span_nonIa))


    

    




---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-2-3dc8a3f68162> in <module>()
     15     if 'g' and 'r' and 'i' not in lcurve_data.keys():
     16         continue
---> 17     mag_start = lcurve_data['g']['mjd'][0]
     18     mag_end = lcurve_data['g']['mjd'][-1]
     19     mag = np.array(lcurve_data['g']['mag'])

KeyError: 'g'

In [9]:

    

from scipy.stats import gaussian_kde

def plot_density(time_spans, start, end, figname, cov_factor=0.125, ylabel='Density', xlabel='X'):
    fig = plt.figure(figsize=(6,6))
    ax = fig.add_subplot(111)
    ax.set_xlabel(xlabel, {'fontsize': 'large'})
    ax.set_ylabel(ylabel, {'fontsize': 'large'})
    for i, time_span in enumerate(time_spans):
        density = gaussian_kde(time_span)
        xs = np.linspace(start, end, 500)
        density.covariance_factor = lambda: cov_factor
        density._compute_covariance()

        if i==0:
            label='Ia'
            color='black'
        elif i==1:
            label='Non-Ia'
            color='red'
        ax.plot(xs,density(xs), label=label,color=color)
    ax.spines['top'].set_color('none')
    ax.spines['right'].set_color('none')
    ax.legend()
    #plt.grid()
    fig.savefig(figname)
    plt.show()

plot_density([average_time_span_Ia, average_time_span_nonIa], 0, 130, figname='density_time_span_OSC.pdf', xlabel='Time Span (days)')
plot_density([average_flux_Ia, average_flux_nonIa], 10, 25, figname='density_magnitude_OSC.pdf', cov_factor=0.1, xlabel='Average Magnitude (g band)')


    

In [97]:

    

path_to_SNe = '../DES_Pipeline/gen_lightcurves/des_sn.p'

with open(path_to_SNe, 'rb') as f:
    SNe_lightcurves=  pickle.load(f)

average_time_span_Ia = []
average_time_span_nonIa = []
average_flux_Ia = []
average_flux_nonIa = []

for i, lcurve in enumerate(SNe_lightcurves):
    lcurve_data = SNe_lightcurves[lcurve]
        
    if 'g' not in lcurve_data.keys():
        continue
    mag_start = lcurve_data['g']['mjd'][0]
    mag_end = lcurve_data['g']['mjd'][-1]
    mag = np.array(lcurve_data['g']['mag'])
    avg_mag = mag.mean()
    sntype = lcurve_data['g']['type']
    
    if sntype == 1:    
        average_time_span_Ia.append(mag_end - mag_start)
        average_flux_Ia.append(avg_mag)
    else:
        average_time_span_nonIa.append(mag_end - mag_start)
        average_flux_nonIa.append(avg_mag)
        
    
    #if i>20:
    #    break
print(len(average_time_span_Ia))
print(len(average_time_span_nonIa))


    

    




4878
12206

In [98]:

    

plot_density([average_time_span_Ia, average_time_span_nonIa], 0, 200, figname='density_time_span_DES.pdf', xlabel='Time Span (days)')
plot_density([average_flux_Ia, average_flux_nonIa], -5, 100, figname='density_flux_DES.pdf', cov_factor=0.01, xlabel='Average Flux (g band)')


    

In [ ]: