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In [1]:

    
%load_ext autoreload
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import sncosmo



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import os
from copy import deepcopy



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import gedankenLSST
import numpy as np
import pandas as pd



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from LSSTmetrics import PerSNMetric



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import LSSTmetrics



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LSSTmetrics.__file__









    Out[6]:





'/Users/rbiswas/.local/lib/python2.7/site-packages/LSSTmetrics/__init__.pyc'



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LSSTmetrics.__version__









    Out[7]:





'0.0.4dev'



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from lsst.sims.photUtils import BandpassDict

Some setup



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lsst_bp = BandpassDict.loadTotalBandpassesFromFiles()



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# sncosmo Bandpasses required for fitting
throughputsdir = os.getenv('THROUGHPUTS_DIR')

from astropy.units import Unit
bandPassList = ['u', 'g', 'r', 'i', 'z', 'y']
banddir = os.path.join(os.getenv('THROUGHPUTS_DIR'), 'baseline')

for band in bandPassList:

    # setup sncosmo bandpasses
    bandfname = banddir + "/total_" + band + '.dat'


    # register the LSST bands to the SNCosmo registry
    # Not needed for LSST, but useful to compare independent codes
    # Usually the next two lines can be merged,
    # but there is an astropy bug currently which affects only OSX.
    numpyband = np.loadtxt(bandfname)
    print band
    sncosmoband = sncosmo.Bandpass(wave=numpyband[:, 0],
                                   trans=numpyband[:, 1],
                                   wave_unit=Unit('nm'),
                                   name=band)
    sncosmo.registry.register(sncosmoband, force=True)









    



u
g
r
i
z
y

Example of SVD Cadence



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SVDCadence = deepcopy(gedankenLSST.LSSTReq)



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# This is a dictionary with the following keys
SVDCadence.keys()









    Out[23]:





['Duration', 'bF', 'meanNumVisits', 'medianSVD']



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SVDCadence['Duration']









    Out[24]:





3650.0



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SVDCadence['bF']









    Out[25]:





u    1
g    1
r    1
i    1
z    1
y    1
dtype: float64



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SVDCadence['meanNumVisits']









    Out[26]:





u     56
g     80
r    184
i    184
z    160
y    160
dtype: float64



In [27]:

    
SVDCadence['medianSVD']









    Out[27]:





u    23.9
g    25.0
r    24.7
i    24.0
z    23.3
y    22.1
dtype: float64



In [29]:

    
#Now create a set of observations at a particular ra, dec within a a time window
SVD_obs = gedankenLSST.GSN_Obs(mjd_center=49570., 
                                    lsstrequirements=SVDCadence,
                                    ra=58., dec=-27.,
                                    timeWindow=[-30., 50.])



In [36]:

    
SN_SVD = PerSNMetric(summarydf=SVD_obs.summary,t0=49570., raCol='ra', decCol='dec', lsst_bp=lsst_bp)



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# What are the properties of the SN?



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_ = SN_SVD.lcplot()









    



Hello
(19, Index([u'expMJD', u'filter', u'ra', u'dec', u'fiveSigmaDepth', u'fieldID',
       u'night'],
      dtype='object'))



In [32]:

    
SN_SVD.SN.SNstate









    Out[32]:





{'MWE(B-V)': 0.053107433021068573,
 'ModelSource': 'salt2-extended',
 '_dec': -27.0,
 '_ra': 58.0,
 'c': 0.0,
 'hostebv': 0.0,
 'hostr_v': 3.1000000000000001,
 'mwebv': 0.0,
 'mwr_v': 3.1000000000000001,
 't0': 49570.0,
 'x0': 1.0068661711630977e-05,
 'x1': 0.0,
 'z': 0.5}



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# Change the redshift
SN_SVD.redshiftSN(1.0)



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SN_SVD.SN.SNstate









    Out[41]:





{'MWE(B-V)': 0.053107433021068573,
 'ModelSource': 'salt2-extended',
 '_dec': -27.0,
 '_ra': 58.0,
 'c': 0.0,
 'hostebv': 0.0,
 'hostr_v': 3.1000000000000001,
 'mwebv': 0.0,
 'mwr_v': 3.1000000000000001,
 't0': 49570.0,
 'x0': 1.838420175046384e-06,
 'x1': 0.0,
 'z': 1.0}



In [42]:

    
SN_SVD.lcplot()









    



/Users/rbiswas/.local/lib/python2.7/site-packages/LSSTmetrics/metrics.py:163: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df['SNR'] = df['flux'] / df['fluxerr']
/Users/rbiswas/.local/lib/python2.7/site-packages/LSSTmetrics/metrics.py:165: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df['DetectionEfficiency'] = df.apply(self.func, axis=1)






    



Hello
(19, Index([u'expMJD', u'filter', u'ra', u'dec', u'fiveSigmaDepth', u'fieldID',
       u'night'],
      dtype='object'))






    



/Users/rbiswas/.local/lib/python2.7/site-packages/LSSTmetrics/metrics.py:166: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df.sort_values('SNR', ascending=False, inplace=True)






    Out[42]:



In [50]:

    
snDaily.qualityMetric()









    



Hello
(19, Index([u'expMJD', u'filter', u'ra', u'dec', u'fiveSigmaDepth', u'fieldID',
       u'night'],
      dtype='object'))






    Out[50]:





0.24831543189185717

Example of Daily Cadence

Here we set up an example where the SN are measured at a daily cadence



In [43]:

    
dailyCadence = deepcopy(gedankenLSST.LSSTReq)



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# This is a dictionary with the following keys
dailyCadence.keys()









    Out[44]:





['Duration', 'bF', 'meanNumVisits', 'medianSVD']



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dailyCadence['Duration'] # Says the survey is 10 years









    Out[13]:





3650.0



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dailyCadence['meanNumVisits'] # Gives the number of visits during the duration, (mean is over LSST fields)









    Out[14]:





u     56
g     80
r    184
i    184
z    160
y    160
dtype: float64



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dailyCadence['bF'] # bumpFactors increase the cadence in filters by these factors









    Out[15]:





u    1
g    1
r    1
i    1
z    1
y    1
dtype: float64



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dailyCadence['medianSVD'] # median single visit 5 sigma depth in LSST









    Out[16]:





u    23.9
g    25.0
r    24.7
i    24.0
z    23.3
y    22.1
dtype: float64



In [17]:

    
# Now, we will make the cadence a 'daily cadence' in each band
dailyCadence['meanNumVisits'] = pd.Series(np.repeat(3650.,6), index=['u','g','r','i','z','y'], copy=True)



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dailyCadence['meanNumVisits']









    Out[18]:





u    3650
g    3650
r    3650
i    3650
z    3650
y    3650
dtype: float64



In [45]:

    
#Now create a set of observations at a particular ra, dec within a a time window
observations = gedankenLSST.GSN_Obs(mjd_center=49570., 
                                    lsstrequirements=dailyCadence,
                                    ra=58., dec=-27.,
                                    timeWindow=[-30., 50.])



In [46]:

    
# This is a pandas.DataFrame which should look like columns of OpSim
observations.summary.head()









    Out[46]:






  
    
      
      expMJD
      filter
      ra
      dec
      fiveSigmaDepth
      fieldID
      night
    
  
  
    
      0
      49540.000000
      g
      58
      -27
      25
      1
      49540
    
    
      1
      49566.666667
      g
      58
      -27
      25
      1
      49566
    
    
      2
      49593.333333
      g
      58
      -27
      25
      1
      49593
    
    
      3
      49540.000000
      i
      58
      -27
      24
      1
      49540
    
    
      4
      49556.000000
      i
      58
      -27
      24
      1
      49556



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snDaily = PerSNMetric(summarydf=observations.summary,t0=49570., raCol='ra', decCol='dec', lsst_bp=lsst_bp)



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snDaily.SN.SNstate









    Out[48]:





{'MWE(B-V)': 0.053107433021068573,
 'ModelSource': 'salt2-extended',
 '_dec': -27.0,
 '_ra': 58.0,
 'c': 0.0,
 'hostebv': 0.0,
 'hostr_v': 3.1000000000000001,
 'mwebv': 0.0,
 'mwr_v': 3.1000000000000001,
 't0': 49570.0,
 'x0': 1.0068661711630977e-05,
 'x1': 0.0,
 'z': 0.5}



In [49]:

    
snDaily.lcplot()









    



Hello
(19, Index([u'expMJD', u'filter', u'ra', u'dec', u'fiveSigmaDepth', u'fieldID',
       u'night'],
      dtype='object'))






    Out[49]:



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snDaily.redshiftSN(1.0)



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snDaily.SN.SNstate



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snDaily.lcplot()



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from lsst.sims.catUtils.supernovae import SNObject



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snobj = SNObject.fromSNState(snstate)



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snstate



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snobj.SNstate



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# Now we can look at a light curve (no scatter)
snDaily.lightcurve.head()



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# We can coadd the light curve in nights if necessary
snDaily.coaddedLightCurve.head()



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# We can get plots with scatter, and a seed. This can also have a nightly coadd 
snDaily.lcplot(scattered=True, seed=0)



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# the same light curves in a format where we can easily fit
snDaily.SNCosmoLC()

An LSST Cadence



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lsstCadence = deepcopy(gedankenLSST.LSSTReq)



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lsst_obs = gedankenLSST.GSN_Obs(mjd_center=49570., 
                                    lsstrequirements=lsstCadence,
                                    ra=58., dec=-27.,
                                    timeWindow=[-30., 50.])



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lsstCadence



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snLSST = PerSNMetric(summarydf=lsst_obs.summary,t0=49570., raCol='ra', decCol='dec', lsst_bp=lsst_bp)



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snLSST.lcplot()



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snLSST.SNCosmoLC()

Using BumpFactor



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thriceLsstCadence = deepcopy(gedankenLSST.LSSTReq)



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thriceLsstCadence['bF'] = 3



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lsst_Denseobs = gedankenLSST.GSN_Obs(mjd_center=49570., 
                                    lsstrequirements=thriceLsstCadence,
                                    ra=58., dec=-27.,
                                    timeWindow=[-30., 50.])



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snLSSTdense = PerSNMetric(summarydf=lsst_Denseobs.summary,t0=49570., raCol='ra', decCol='dec', lsst_bp=lsst_bp)



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snLSSTdense.lcplot(scattered=True)

Scratch



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df = sn.summary



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df['filter'].unique()



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df.columns



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df[df['filter'] == 'u'].hist('night',bins=80)



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s = gedankenLSST.SNObs(summarydf=df, t0=49530, lsst_bp=lsst_bp)



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np.degrees(0)



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s.radeg



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l = s.lightcurve



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type(l)



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l.query('SNR > 5.').night



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import sncosmo



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dir(sn)



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In [ ]:

	expMJD	filter	ra	dec	fiveSigmaDepth	fieldID	night
0	49540.000000	g	58	-27	25	1	49540
1	49566.666667	g	58	-27	25	1	49566
2	49593.333333	g	58	-27	25	1	49593
3	49540.000000	i	58	-27	24	1	49540
4	49556.000000	i	58	-27	24	1	49556