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In [1]:



    


#import necessary python libraries
import json
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit, minimize
import pandas as pd
import math
from JSON_to_DF import JSON_to_DataFrame
from Lightcurve_class import *
import celerite
import pickle
%matplotlib notebook



    











In [2]:



    


SN = deserialize('../../../OSC_data/pickled_data/SN2011fe.pickle')
SN.Lightcurves['B_10'].Kapernka_fit_plot()



    


















    
Out[2]:








9.2363405212793914

Supernovae SN2011fe





In [2]:



    


#create new supernovae object and load meta and Lightcurve data
SN2011fe = Supernovae('../../../OSC_data/JSON_data/SN2011fe.json')
SN2011fe.load_LightCurves()
SN2011fe.meta_data()
SN2011fe.serialize()



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/pandas/core/generic.py:1299: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['band', 'source', 'telescope']]

  return pytables.to_hdf(path_or_buf, key, self, **kwargs)

















In [4]:



    


#Create Kernels for Gaussian Process

#Real term parameter initialization
a = 1e-5
c = 1
#Matern term parameter initialization
sig = 1e-5
rho = 100

#Bounds on parameters 
bounds = dict(log_a = (-15,15), log_c = (-15,15))
bounds = dict(log_sigma = (-15, 15), log_rho = (-15, 15))

#Create Kernels
Real_Kernel = celerite.terms.RealTerm(log_a = np.log(a), log_c = np.log(c), bounds=bounds)
Matern_Kernel = celerite.terms.Matern32Term(log_sigma = np.log(sig), log_rho = np.log(rho))



    











In [7]:



    


#Matern_Kernel fit
plt.figure()
SN2011fe.Lightcurves['V_10'].Gaussian_process(Matern_Kernel, plot=True)



    


















    




















    
















    
Out[7]:








{'GP': 1.2447961035641013}

















In [25]:



    


plt.figure()
SN2011fe.Lightcurves['V_10'].Gaussian_process(Real_Kernel, plot=True)



    


















    




















    
















    
Out[25]:








1.4071382456682968

















In [26]:



    


plt.figure()
SN2011fe.Lightcurves['V_10'].Kapernka_fit_plot(plot=True)



    


















    




















    
















    
Out[26]:








8.4052962691613136

















In [60]:



    


plt.figure()
SN2011fe.Lightcurves['V_10'].polynomial_fit_plot(6, plot=True)



    


















    




















    
















    
Out[60]:








80.376483492032946

















In [61]:



    


plt.figure()
SN2011fe.Lightcurves['V_10'].polynomial_fit_plot(8, plot=True)



    


















    




















    
















    
Out[61]:








59.358258660495324

















In [63]:



    


plt.figure()
SN2011fe.Lightcurves['V_10'].Bazin_fit_plot(plot=True)



    


















    




















    
















    
Out[63]:








8.2476021464714364

















In [64]:



    


SN2011fe.Lightcurves['V_10'].Rchi2



    


















    
Out[64]:








{'Bazin': 8.2476021464714364,
 'GP': 1.4071382456682968,
 'Kapernka': 8.4052962691613136,
 'polynomial_6': 80.376483492032946,
 'polynomial_8': 59.358258660495324}

Supernovae SN2008es





In [66]:



    


#create new supernovae object and load meta and Lightcurve data
SN2008es = Supernovae('../../../OSC_data/JSON_data/SN2008es.json')
SN2008es.load_LightCurves()
SN2008es.meta_data()
SN2008es.serialize()



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/pandas/core/generic.py:1299: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['band', 'source', 'telescope']]

  return pytables.to_hdf(path_or_buf, key, self, **kwargs)

















In [ ]:



    


SN2008es = deserialize("../../../OSC_data/pickled_data/SN2008es.pickle")



    











In [68]:



    


#Matern_Kernel fit
plt.figure()
SN2008es.Lightcurves['B_2'].Gaussian_process(Matern_Kernel, plot=True)



    


















    




















    
















    
Out[68]:








10.412150799183538

















In [69]:



    


plt.figure()
SN2008es.Lightcurves['B_2'].Gaussian_process(Real_Kernel, plot=True)



    


















    




















    
















    
Out[69]:








8.1946270825979912

















In [71]:



    


plt.figure()
SN2008es.Lightcurves['B_2'].Kapernka_fit_plot(plot=True)



    


















    




















    
















    
Out[71]:








1.7368095546096145

















In [72]:



    


plt.figure()
SN2008es.Lightcurves['B_2'].polynomial_fit_plot(6, plot=True)



    


















    




















    
















    
Out[72]:








1604.2742933956702

















In [73]:



    


plt.figure()
SN2008es.Lightcurves['B_2'].polynomial_fit_plot(8, plot=True)



    


















    




















    
















    
Out[73]:








55828.308501091487

















In [74]:



    


SN2008es.Lightcurves['B_2'].Rchi2



    


















    
Out[74]:








{'GP': 8.1946270825979912,
 'Kapernka': 1.7368095546096145,
 'polynomial_6': 1604.2742933956702,
 'polynomial_8': 55828.308501091487}

Supernovae SN2009ip





In [75]:



    


#create new supernovae object and load meta and Lightcurve data
#create new supernovae object and load meta and Lightcurve data
SN2009ip = Supernovae('../../../OSC_data/JSON_data/SN2009ip.json')
SN2009ip.load_LightCurves()
SN2009ip.meta_data()
SN2009ip.serialize()



SN2009ip = deserialize("../../../OSC_data/pickled_data/SN2009ip.pickle")



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/pandas/core/generic.py:1299: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['band', 'source', 'telescope']]

  return pytables.to_hdf(path_or_buf, key, self, **kwargs)

















In [76]:



    


SN2009ip = deserialize("../../../OSC_data/pickled_data/SN2009ip.pickle")



    











In [78]:



    


plt.figure()
SN2009ip.Lightcurves['B_7'].Gaussian_process(Matern_Kernel, plot=True)



    


















    




















    
















    
Out[78]:








12.957286132717321

















In [79]:



    


plt.figure()
SN2009ip.Lightcurves['B_7'].Gaussian_process(Real_Kernel, plot=True)



    


















    




















    
















    
Out[79]:








15.914480686894366

















In [80]:



    


plt.figure()
SN2009ip.Lightcurves['B_7'].polynomial_fit_plot(6, plot=True)



    


















    




















    
















    
Out[80]:








350.50405090098383

















In [81]:



    


plt.figure()
SN2009ip.Lightcurves['B_7'].polynomial_fit_plot(8, plot=True)



    


















    




















    
















    
Out[81]:








168.30276544907997

















In [82]:



    


SN2009ip.Lightcurves['B_7'].Rchi2



    


















    
Out[82]:








{'GP': 15.914480686894366,
 'polynomial_6': 350.50405090098383,
 'polynomial_8': 168.30276544907997}

Supernovae SN1999em





In [2]:



    


#create new supernovae object and load meta and Lightcurve data
SN1999em = Supernovae('../../../OSC_data/JSON_data/SN1999em.json')
SN1999em.load_LightCurves()
SN1999em.meta_data()
SN1999em.serialize()



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/pandas/core/generic.py:1299: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['band', 'source', 'telescope']]

  return pytables.to_hdf(path_or_buf, key, self, **kwargs)

















In [3]:



    


SN1999em = deserialize("../../../OSC_data/pickled_data/SN1999em.pickle")



    











In [86]:



    


plt.figure()
SN1999em.Lightcurves['B_1'].Gaussian_process(Matern_Kernel, plot=True)



    


















    




















    
















    
Out[86]:








147.03106460370762

















In [87]:



    


plt.figure()
SN1999em.Lightcurves['B_1'].Gaussian_process(Real_Kernel, plot=True)



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)










    




















    
















    
Out[87]:








35.155403947089951

















In [88]:



    


plt.figure()
SN1999em.Lightcurves['B_1'].Kapernka_fit_plot(plot = True)



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)










    




















    
















    
Out[88]:








25.955431298979633

















In [89]:



    


plt.figure()
SN1999em.Lightcurves['B_1'].polynomial_fit_plot(6, plot=True)



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)










    




















    
















    
Out[89]:








12958.730072279166

















In [90]:



    


plt.figure()
SN1999em.Lightcurves['B_1'].polynomial_fit_plot(8, plot=True)



    


















    






/Users/ethanmarx/anaconda/envs/NUREU17/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)










    




















    
















    
Out[90]:








15987.022116534446

















In [91]:



    


SN1999em.Lightcurves['B_1'].Rchi2



    


















    
Out[91]:








{'GP': 35.155403947089951,
 'Kapernka': 25.955431298979633,
 'polynomial_6': 12958.730072279166,
 'polynomial_8': 15987.022116534446}

Code Errors





In [3]:



    


SN2016fzm  = deserialize('../../../OSC_data/pickled_data/SN2017uq.pickle')



    











In [26]:



    






    


















    
Out[26]:








array([  2.88084571e-08,   2.85259423e-08,   2.73652865e-08,
         2.66097013e-08,   2.78048144e-08,   2.75372140e-08,
         2.77792170e-08,   2.73451305e-08,   2.46286156e-09,
         1.99177373e-09])

















In [ ]:

Content source: knowledgewithin/NUREU17

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