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In [3]:



    


import sncosmo



    











In [4]:



    


from analyzeSN import SNANASims



    











In [5]:



    


# read in simulation
snana_eg = SNANASims.fromSNANAfileroot(snanafileroot='LSST_Ia',
                                       location='/Users/rbiswas/data/LSST/SNANA_data/MINION_1016_10YR_DDF_v2/',
                                       coerce_inds2int=False)



    











In [6]:



    


snana_eg.headData.head()



    


















    
Out[6]:










  
    

      
      IAUC
      FAKE
      RA
      DECL
      PIXSIZE
      NXPIX
      NYPIX
      SNTYPE
      NOBS
      PTROBS_MIN
      ...
      SIM_EXPOSURE_r
      SIM_EXPOSURE_i
      SIM_EXPOSURE_z
      SIM_EXPOSURE_Y
      SIM_GALFRAC_u
      SIM_GALFRAC_g
      SIM_GALFRAC_r
      SIM_GALFRAC_i
      SIM_GALFRAC_z
      SIM_GALFRAC_Y
    

    

      SNID
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      13
      NULL
      2
      0.000000
      -45.524532
      0.2
      -9
      -9
      1
      71
      1
      ...
      1.0
      1.0
      1.0
      1.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
    

    

      31
      NULL
      2
      150.362350
      2.836485
      0.2
      -9
      -9
      1
      90
      73
      ...
      1.0
      1.0
      1.0
      1.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
    

    

      42
      NULL
      2
      34.393394
      -5.090329
      0.2
      -9
      -9
      1
      80
      164
      ...
      1.0
      1.0
      1.0
      1.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
    

    

      61
      NULL
      2
      349.386444
      -63.321003
      0.2
      -9
      -9
      1
      68
      245
      ...
      1.0
      1.0
      1.0
      1.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
    

    

      89
      NULL
      2
      150.362350
      2.836485
      0.2
      -9
      -9
      1
      76
      314
      ...
      1.0
      1.0
      1.0
      1.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
      -9.0
    

  



5 rows × 78 columns



















In [7]:



    


snids = snana_eg.headData.index.values



    











In [8]:



    


lc = snana_eg.get_SNANA_photometry(snid='31')



    











In [9]:



    


max(lc.lightCurve['flux']/lc.lightCurve['fluxerr'] )



    


















    
Out[9]:








4.1012692

















In [10]:



    


from analyzeSN import ResChar



    











In [11]:



    


def inferParams(snanaSims, model, infer_method, i, minsnr=3.):
    """
    infer the parameters for the ith supernova in the simulation
    """
    snid = snanaSims.headData.index.values[i]
    z = snanaSims.headData.ix[snid, 'REDSHIFT_FINAL']
    lcinstance = snanaSims.get_SNANA_photometry(snid=snid)
    model.set(z=z)
    print(z)
    resfit = infer_method(lcinstance.snCosmoLC(), model, vparam_names=['t0', 'x0', 'x1', 'c'],
                          modelcov=True, minsnr=minsnr)
    reschar = ResChar.fromSNCosmoRes(resfit)
    return reschar



    











In [12]:



    


dust = sncosmo.CCM89Dust()
model = sncosmo.Model(source='salt2-extended', effects=[dust, dust],
                     effect_names=['host', 'mw'], effect_frames=['rest', 'obs'])



    











In [13]:



    


#example
r = inferParams(snana_eg, model, sncosmo.fit_lc, 0)



    


















    






1.16624

















In [14]:



    


r.parameters



    


















    
Out[14]:








z          1.166243e+00
t0         6.096514e+04
x0         4.229721e-07
x1        -4.430033e+00
c         -3.031123e-01
hostebv    0.000000e+00
hostr_v    3.100000e+00
mwebv      0.000000e+00
mwr_v      3.100000e+00
dtype: float64

















In [15]:



    


r.covariance



    


















    
Out[15]:










  
    

      
      t0
      x0
      x1
      c
    

  
  
    

      t0
      6.221200e-02
      -9.897254e-10
      1.119111e-02
      -2.054025e-03
    

    

      x0
      -9.897254e-10
      7.092694e-15
      -7.058787e-11
      1.042686e-08
    

    

      x1
      1.119111e-02
      -7.058787e-11
      9.346116e-03
      3.397151e-04
    

    

      c
      -2.054025e-03
      1.042686e-08
      3.397151e-04
      3.238695e-02
    

  




















In [ ]:



    


r = inferParams(snana_eg, model, sncosmo.fit_lc, 1)



    


















    






0.699517

















In [1]:



    


r.parameters



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-a8f64bc37db3> in <module>()
----> 1 r.parameters

NameError: name 'r' is not defined
















In [ ]:



    


r.covariance



    











In [ ]:



    


r = inferParams(snana_eg, model, sncosmo.fit_lc, 0, minsnr=5.)

PSEUDO CODE (SERIAL)

  • read in simulation
  • create model
  • iterate over number of SN:
    • try :
      • call inferParams : r = inferParams(...)
      • write r.params, r.covariance (after packing to 10 or 15 independent numbers), r.samples (if exists, desired)
    • except : failures

PSEUDO CODE (Parallel ?)

  • read in simulation and create chunks

  • Distribute chunks to nodes

  • On each node
    • iterate over number of SN (more SN than nodes :) ):
    • create model
      • try :
        • call inferParams : r = inferParams(...)
        • write r.params, r.covariance (after packing to 10 or 15 independent numbers), r.samples (if exists, desired)
      • except : failures




In [ ]:



    


def inferParams(snanaSims, model, infer_method, i, minsnr=3.):
    """
    infer the parameters for the ith supernova in the simulation
    """
    snid = snanaSims.headData.index.values[i]
    z = snanaSims.headData.ix[snid, 'REDSHIFT_FINAL']
    lcinstance = snanaSims.get_SNANA_photometry(snid=snid)
    model.set(z=z)
    print(z)
    resfit = infer_method(lcinstance.snCosmoLC(), model, vparam_names=['t0', 'x0', 'x1', 'c'],
                          modelcov=True, minsnr=minsnr)
    reschar = ResChar.fromSNCosmoRes(resfit)
    return reschar

snana_eg = SNANASims.fromSNANAfileroot(snanafileroot='LSST_Ia',
                                       location='/Users/rbiswas/data/LSST/SNANA_data/MINION_1016_10YR_DDF_v2/',
                                       coerce_inds2int=False)

Content source: rbiswas4/photozSN

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