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In [1]:



    


%matplotlib inline
import matplotlib.pyplot as plt



    











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import numpy as np



    











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import pandas as pd



    











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from sqlalchemy import create_engine



    











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# Uncomment these lines and change directories to write to hdf instead

# dbname = '/Users/rbiswas/data/LSST/OpSimData/kraken_1042_sqlite.db'#enigma_1189_sqlite.db'
# engine = create_engine('sqlite:///' + dbname)
# Summary = pd.read_sql_table('Summary', engine, index_col='obsHistID')
# Summary.to_hdf('/Users/rbiswas/data/LSST/OpSimData/kraken_1042.hdf', 'table')



    











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df = pd.read_hdf('/Users/rbiswas/data/LSST/OpSimData/kraken_1042.hdf', 'table')



    











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df = df.query('fieldID==1427 and propID == 152')



    











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print(df.expMJD.min())
print((df.expMJD.max() + df.expMJD.min()) / 2.)



    


















    






59580.125716
61401.1227295

















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df.propID.hist()



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x15184d290>










    
























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import OpSimSummary.summarize_opsim as so



    











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ds  = so.SummaryOpsim(summarydf=df)



    











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(df.expMJD.max() + df.expMJD.min()) / 2.



    


















    
Out[12]:








61401.122729499999

If I forget dithers and just look at how many observations per field:

  • Look at the number of unique nights with different bands: For the full survey this number of visits is 1680, see figure in Input 13
  • For half of the survey, the same quantity is 824, see figure 1nput 14
  • For a year the number is ~150. See figure on input 15 




In [13]:



    


full_survey = ds.cadence_plot(fieldID=1427, mjd_center=61404, mjd_range=[-1825, 1825], 
                              observedOnly=False, colorbar=True);
plt.close()



    











In [14]:



    


full_survey[0]



    


















    
Out[14]:
























In [15]:



    


half_survey = ds.cadence_plot(fieldID=1427, mjd_center=61404, mjd_range=[-1825, 1], 
                              observedOnly=False, colorbar=True);



    


















    
























In [16]:



    


second_year = ds.cadence_plot(fieldID=1427, mjd_center=60200, mjd_range=[-150, 150], 
                              observedOnly=False, colorbar=True);



    


















    
























In [17]:



    


secondYearObs = ds.cadence_plot(fieldID=1427, mjd_center=60300, mjd_range=[-0, 30], observedOnly=False)
plt.close()
secondYearObs[0]



    


















    
Out[17]:

List of obsHistIDs with unique nights

Proposal :

  1. We want to select all the unique night and band combinations in the 10 years of Kraken. As we have seen above this is 1680 visits.
  2. We will give the phosim instance catalogs and seds for running phosim on the cluster, and we should run these in order of expMJD or night. If we run out of compute time at a 1000, then we will have a little more than half of the survey
  3. When we get more (phoSim) simulation time, we can finish the 10 years. If we have more time than than, I suggest we fill in other observations in the 2nd year as shown in the above figure around 60300. This is another 500 observations. After than we can fill in the rest of the second year and then other years. The priority for the the second year (and could have been elsewhere) is because I can make sure that there a few supernovae that are at high redshift and 'visible' at that time. I need to know this ahead of time, and that is why I putting this out now.




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df['obsID'] = df.index.values



    











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uniqueObs = df.groupby(['night', 'filter'])



    











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aa = uniqueObs['airmass'].agg({'myInds': lambda x: x.idxmin()}).myInds.astype(int).values



    











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ourOpSim = df.ix[aa]

How much does it help our airmass distribution by choosing the lowest airmass of the available ones





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axs = df.hist(by='filter', column='airmass', histtype='step', lw=2, alpha=1, color='k', normed=True);
axs = df.ix[aa].hist(by='filter', column='airmass', histtype='step', lw=2, alpha=1, color='r', ax=axs, normed=True)



    


















    
























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df.obsID.unique().size, df.obsID.size



    


















    
Out[23]:








(23673, 23673)

















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ourOpSim.head()



    


















    
Out[24]:










  
    

      
      sessionID
      propID
      fieldID
      fieldRA
      fieldDec
      filter
      expDate
      expMJD
      night
      visitTime
      ...
      darkBright
      rawSeeing
      wind
      humidity
      slewDist
      slewTime
      fiveSigmaDepth
      ditheredRA
      ditheredDec
      obsID
    

    

      obsHistID
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      220
      1042
      152
      1427
      0.925184
      -0.4789
      g
      11699
      59580.135415
      0
      34
      ...
      84.048697
      0.869226
      0
      0
      0.000000
      120
      24.357859
      0.907976
      -0.505351
      220
    

    

      230
      1042
      152
      1427
      0.925184
      -0.4789
      i
      12177
      59580.140948
      0
      34
      ...
      84.905894
      0.791241
      0
      0
      0.000000
      120
      23.529519
      0.907976
      -0.505351
      230
    

    

      200
      1042
      152
      1427
      0.925184
      -0.4789
      r
      10861
      59580.125716
      0
      34
      ...
      82.698160
      0.744120
      0
      0
      0.837387
      140
      24.234817
      0.907976
      -0.505351
      200
    

    

      276
      1042
      152
      1427
      0.925184
      -0.4789
      y
      14069
      59580.162846
      0
      34
      ...
      89.102576
      1.132846
      0
      0
      0.000000
      120
      21.187902
      0.907976
      -0.505351
      276
    

    

      250
      1042
      152
      1427
      0.925184
      -0.4789
      z
      13015
      59580.150647
      0
      34
      ...
      86.600272
      0.928894
      0
      0
      0.000000
      120
      22.841986
      0.907976
      -0.505351
      250
    

  



5 rows × 47 columns

Our culled opsim that we shall try out first is now 'ourOpSim' . We can write this our to a csv file, or a database. We can also view the list of obsHistIDs





In [25]:



    


ourOpSim.obsID.values



    


















    
Out[25]:








array([    220,     230,     200, ..., 2442742, 2442799, 2442775])

















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ourOpSim.obsID.to_csv('FirstSet_obsHistIDs.csv')



    











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ourOpSim.to_csv('SelectedKrakenVisits.csv')



    











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xx = ourOpSim.groupby(['night', 'filter']).aggregate('count')



    











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assert(all(xx.max() == 1))

Scratch

Find the obsHistIDs corresponding to the lowest airmass of visits





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dff = uniqueObs['airmass'].agg({'myInds': lambda x: x.idxmin()})
aa = dff.myInds.astype(int).values
aa.sort()



    











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l = []
for key in keys:
    l.append(uniqueObs.get_group(key).airmass.idxmin())



    











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Content source: rbiswas4/Twinkles

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