In [1]:
import os
import numpy as np
import pandas as pd
import time
In [2]:
import opsimsummary as oss
In [3]:
import analyzeSN as ans
In [4]:
%matplotlib inline
import matplotlib.pyplot as plt
In [5]:
import snsims
import healpy as hp
In [6]:
import snsims
import healpy as hp
In [7]:
oss.__VERSION__
Out[7]:
'0.8.3'
In [8]:
print snsims.__file__
/Users/rbiswas/.local/lib/python2.7/site-packages/snsims/__init__.pyc
In [9]:
print (snsims.__VERSION__)
1.0.2
In [10]:
opsimOut = oss.OpSimOutput.fromOpSimHDF('/Users/rbiswas/data/LSST/OpSimData/minion_1016.hdf',
subset='combined')
read in proposal
read in proposal
combined {u'conf/survey/northeclipticspur-18c.conf': 55, u'conf/survey/galacticplaneprop.conf': 52, 'ddf': 56, 'wfd': 54, u'conf/survey/southcelestialpole-18.conf': 53}
propIDs [56, 54] <type 'list'> <type 'numpy.int64'>
summarydf cols Index([ u'obsHistID', u'sessionID', u'propID',
u'fieldID', u'fieldRA', u'fieldDec',
u'filter', u'expDate', u'expMJD',
u'night', u'visitTime', u'visitExpTime',
u'finRank', u'FWHMeff', u'FWHMgeom',
u'transparency', u'airmass', u'vSkyBright',
u'filtSkyBrightness', u'rotSkyPos', u'rotTelPos',
u'lst', u'altitude', u'azimuth',
u'dist2Moon', u'solarElong', u'moonRA',
u'moonDec', u'moonAlt', u'moonAZ',
u'moonPhase', u'sunAlt', u'sunAz',
u'phaseAngle', u'rScatter', u'mieScatter',
u'moonIllum', u'moonBright', u'darkBright',
u'rawSeeing', u'wind', u'humidity',
u'slewDist', u'slewTime', u'fiveSigmaDepth',
u'ditheredRA', u'ditheredDec'],
dtype='object')
squery_str propID == @propIDs
Num entries 2448282
/usr/local/miniconda/lib/python2.7/site-packages/pandas/util/decorators.py:91: 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
return func(*args, **kwargs)
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/indexing.py:476: 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
self.obj[item] = s
In [11]:
NSIDE = 256
In [12]:
hptile = oss.HealpixTiles(nside=256,
preComputedMap='/Users/rbiswas/data/LSST/OpSimData/healpixelized_MINION_1016_256.db')
In [13]:
!rm simTiles.hdf
tstart = time.time()
tiles = [0]
for tileID in tiles:
simTile = snsims.SimulationTile(snsims.GMM_SALT2Params,
NSIDE=NSIDE,
tileID=tileID,
hpOpSim='/Users/rbiswas/data/LSST/OpSimData/healpixelized_MINION_1016_256.db',
rate=snsims.PowerLawRates,
allPointings=opsimOut,
)
simTile.writeTile(fileName='simTiles.hdf')
tend = time.time()
another 50 0
/Users/rbiswas/soft/LSST/DarwinX86/sims_catUtils/2.3.3.2.sims+7/python/lsst/sims/catUtils/supernovae/snObject.py:735: RuntimeWarning: divide by zero encountered in log10
return -2.5 * np.log10(fluxinMaggies)
/Users/rbiswas/soft/LSST/DarwinX86/sims_photUtils/2.3.3.sims/python/lsst/sims/photUtils/SignalToNoise.py:356: RuntimeWarning: divide by zero encountered in double_scalars
fluxRatio = m5Flux/sourceFlux
/Users/rbiswas/soft/LSST/DarwinX86/sims_catUtils/2.3.3.2.sims+7/python/lsst/sims/catUtils/supernovae/snObject.py:779: RuntimeWarning: invalid value encountered in double_scalars
return fluxinMaggies / SNR
another 50 50
another 50 100
/usr/local/miniconda/lib/python2.7/site-packages/tables/path.py:100: NaturalNameWarning: object name is not a valid Python identifier: '0'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though
NaturalNameWarning)
In [14]:
simTile.minPeakTime
Out[14]:
59812.421125000001
In [15]:
simTile.snParams().t0.hist(histtype='step', bins=20)
plt.axvline(59800., ls='--', color='k', lw=2)
plt.axvline(59800. + 3650., ls='--', color='k', lw=2)
Out[15]:
<matplotlib.lines.Line2D at 0x1447d75d0>
In [16]:
!du -h simTiles.hdf
556K simTiles.hdf
In [17]:
df = pd.read_hdf('simTiles.hdf')
In [18]:
df.snid.unique()
Out[18]:
array([ 0, 1, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43,
44, 45, 46, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
98, 99, 100, 101, 102, 103, 104, 105, 107, 108, 110, 111, 112,
113, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124])
In [19]:
print('Writing the {0} tiles took {1} minutes'.format(len(tiles), (tend -tstart) / 60. ))
Writing the 1 tiles took 0.502310482661 minutes
In [21]:
import line_profiler
In [ ]:
%lprun
In [16]:
import multiprocessing
In [17]:
from joblib import Parallel, delayed
In [19]:
help(snsims.SimulationTile)
Help on class SimulationTile in module snsims.simulations:
class SimulationTile(snsims.universe.Universe)
| Method resolution order:
| SimulationTile
| snsims.universe.Universe
| __builtin__.object
|
| Methods defined here:
|
| SN(self, snid, timeRange='model')
|
| __init__(self, paramDist, rate, NSIDE, tileID, hpOpSim, allPointings=None, timeRange=None, angularUnits='radians')
|
| lc(self, snid)
|
| modelFlux(self, snid, time, bandpassobject)
|
| snParams(self)
|
| writeSN(self, snid, fileName, timeRange='model')
| Write light curve of SN to disc
|
| Parameters
| ----------
| snid : int/string
| SN id of SN
| fileName : string, mandatory
|
| timeRange : string, optional, defaults to model
| time range over which the light curve is written to disk
|
| writeSNParams(self, paramFileName)
|
| writeTile(self, fileName, timeRange='model', paramFileName=None)
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| getSNCosmoParamDict(odict, SNCosmoModel)
|
| staticModelFlux(sn, time, bandpassobject)
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| maxPeakTime
|
| minPeakTime
|
| randomState
|
| snParamTable
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __abstractmethods__ = frozenset([])
|
| ----------------------------------------------------------------------
| Data descriptors inherited from snsims.universe.Universe:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
In [20]:
simTile = snsims.SimulationTile(snsims.SimpleSALTDist, NSIDE=256, tileID=0,
hpOpSim='/Users/rbiswas/data/LSST/OpSimData/healpixelized_MINION_1016_256.db',
rate=snsims.PowerLawRates,
allPointings=opsimOut)
In [35]:
fields = np.array(list(len(simTile.lc(i).lightCurve.fieldID.unique()) for i in range(125)))
In [38]:
fields.sort()
In [39]:
fields
Out[39]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 5, 5, 5, 5, 5, 5, 5, 5, 5])
In [40]:
np.bincount(fields)
Out[40]:
array([10, 3, 18, 49, 36, 9])
In [41]:
simTile.writeSNParams('simTiles_0.hdf')
In [42]:
simTile.writeSN(0, 'simTile_SN_0.hdf')
In [43]:
def func(i):
tileID = i
simTile = snsims.SimulationTile(snsims.SimpleSALTDist,
NSIDE=NSIDE,
tileID=tileID,
hpOpSim='/Users/rbiswas/data/LSST/OpSimData/healpixelized_MINION_1016_256.db',
rate=snsims.PowerLawRates,
allPointings=opsimOut)
simTile.writeTile(fileName='simTiles_new_{}.hdf'.format(i))
In [44]:
func(0)
another 50 0
another 50 50
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In [31]:
!ls -ltr simTiles_0.hdf
-rw-r--r-- 1 rbiswas staff 1310257 Aug 30 16:41 simTiles_0.hdf
In [45]:
df = pd.read_hdf('simTiles_new_0.hdf')
In [50]:
df["SNR"] = df['flux']/df['fluxerr']
In [69]:
t = df.groupby(['snid', 'band']).agg(dict(SNR=max))
In [83]:
t.i
In [ ]:
t
In [75]:
t.pivot('band', 'SNR')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-75-31bd02014d29> in <module>()
----> 1 t.pivot('band', 'SNR')
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/frame.pyc in pivot(self, index, columns, values)
3944 """
3945 from pandas.core.reshape import pivot
-> 3946 return pivot(self, index=index, columns=columns, values=values)
3947
3948 def stack(self, level=-1, dropna=True):
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/reshape.pyc in pivot(self, index, columns, values)
325 cols = [columns] if index is None else [index, columns]
326 append = index is None
--> 327 indexed = self.set_index(cols, append=append)
328 return indexed.unstack(columns)
329 else:
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
2915 names.append(None)
2916 else:
-> 2917 level = frame[col]._values
2918 names.append(col)
2919 if drop:
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
2057 return self._getitem_multilevel(key)
2058 else:
-> 2059 return self._getitem_column(key)
2060
2061 def _getitem_column(self, key):
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
2064 # get column
2065 if self.columns.is_unique:
-> 2066 return self._get_item_cache(key)
2067
2068 # duplicate columns & possible reduce dimensionality
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
1384 res = cache.get(item)
1385 if res is None:
-> 1386 values = self._data.get(item)
1387 res = self._box_item_values(item, values)
1388 cache[item] = res
/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item, fastpath)
3539
3540 if not isnull(item):
-> 3541 loc = self.items.get_loc(item)
3542 else:
3543 indexer = np.arange(len(self.items))[isnull(self.items)]
/usr/local/miniconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in get_loc(self, key, method, tolerance)
2134 return self._engine.get_loc(key)
2135 except KeyError:
-> 2136 return self._engine.get_loc(self._maybe_cast_indexer(key))
2137
2138 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4443)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4289)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13733)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13687)()
KeyError: 'band'
In [55]:
snids = df.query('SNR > 5.').snid.unique()
In [47]:
paramsdf = pd.read_hdf('simTiles_new_0_params.hdf')
In [59]:
paramsdf.ix[snids].query('z > 0.5')
Out[59]:
M
Mabs
c
mB
t0
x0
x1
z
ra
dec
snid
10
0.214044
-19.111911
-0.092163
23.228410
62009.996781
0.000008
0.684987
0.502823
45.128527
0.170843
11
0.225921
-19.011316
-0.106985
23.471313
61967.459509
0.000006
1.000109
0.531056
45.026977
0.171316
17
-0.211272
-19.412374
0.056236
23.120935
60706.635391
0.000009
0.315359
0.541464
45.005850
0.087968
18
-0.155829
-19.661403
0.037353
22.947547
61976.675191
0.000010
0.350387
0.557353
45.088926
0.193214
19
-0.104034
-19.308738
0.020568
23.392860
62047.773284
0.000007
0.358654
0.577413
44.977330
0.254360
20
-0.264504
-19.614197
0.097013
23.189614
60661.417978
0.000008
-0.364698
0.600331
44.937488
0.127818
24
-0.072787
-19.358389
-0.007065
23.558529
62745.361451
0.000006
0.863365
0.626692
45.047671
0.220437
28
-0.415238
-19.544647
0.159488
23.471915
61754.101931
0.000006
-0.777752
0.650821
45.003032
0.093773
29
0.092671
-19.157934
-0.033086
24.001516
62820.403558
0.000004
0.101992
0.686966
44.901991
0.160760
32
0.309103
-18.790335
-0.096685
24.305679
62035.560629
0.000003
-0.050117
0.670689
44.913945
0.217471
33
-0.519466
-19.781466
0.111048
23.396248
60486.848323
0.000007
1.552513
0.691721
44.921682
0.102157
38
-0.208805
-19.530959
0.034342
23.843823
61039.270390
0.000005
0.917927
0.745079
45.088880
0.200996
39
-0.084526
-19.577288
0.042684
23.742009
60238.766701
0.000005
-0.450014
0.729670
45.051890
0.068181
40
0.159811
-19.333199
-0.057886
24.032186
61391.398872
0.000004
0.199542
0.742448
45.101556
0.107298
43
-0.074489
-19.433374
0.068278
24.184010
60930.303322
0.000003
-1.271853
0.816212
45.001406
0.077265
48
-0.714701
-20.139616
0.165124
23.619857
63113.791471
0.000006
1.783745
0.860880
44.951090
0.170655
In [27]:
import analyzeSN as ans
In [28]:
import sncosmo
In [29]:
sncosmo.plot_lc(simTile.lc(0).snCosmoLC(), color='k')
Out[29]:
In [22]:
df.head()
Out[22]:
mjd
band
ModelFlux
fieldID
fieldID
flux
fluxerr
zp
zpsys
obsHistID
523872
60350.029723
z
9.796973e-10
2614
2614
8.177362e-10
3.418413e-10
0.0
ab
998790
61056.105779
g
2.377164e-12
2626
2626
-5.335908e-11
4.100303e-11
0.0
ab
999363
61057.044635
z
3.582656e-11
2614
2614
-7.531402e-10
3.491041e-10
0.0
ab
1007432
61068.045236
z
9.339480e-10
2626
2626
5.730262e-10
4.039501e-10
0.0
ab
1007482
61068.069193
z
9.366196e-10
2626
2626
7.269686e-10
3.192418e-10
0.0
ab
In [ ]:
df.
In [15]:
%load_ext line_profiler
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-34be74c73d73> in <module>()
----> 1 get_ipython().magic(u'load_ext line_profiler')
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in magic(self, arg_s)
2156 magic_name, _, magic_arg_s = arg_s.partition(' ')
2157 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2158 return self.run_line_magic(magic_name, magic_arg_s)
2159
2160 #-------------------------------------------------------------------------
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_line_magic(self, magic_name, line)
2077 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2078 with self.builtin_trap:
-> 2079 result = fn(*args,**kwargs)
2080 return result
2081
<decorator-gen-62> in load_ext(self, module_str)
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/magics/extension.pyc in load_ext(self, module_str)
35 if not module_str:
36 raise UsageError('Missing module name.')
---> 37 res = self.shell.extension_manager.load_extension(module_str)
38
39 if res == 'already loaded':
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/extensions.pyc in load_extension(self, module_str)
83 __import__(module_str)
84 mod = sys.modules[module_str]
---> 85 if self._call_load_ipython_extension(mod):
86 self.loaded.add(module_str)
87 else:
/usr/local/miniconda/lib/python2.7/site-packages/IPython/core/extensions.pyc in _call_load_ipython_extension(self, mod)
130 def _call_load_ipython_extension(self, mod):
131 if hasattr(mod, 'load_ipython_extension'):
--> 132 mod.load_ipython_extension(self.shell)
133 return True
134
/usr/local/miniconda/lib/python2.7/site-packages/line_profiler.pyc in load_ipython_extension(ip)
370 """ API for IPython to recognize this module as an IPython extension.
371 """
--> 372 ip.define_magic('lprun', magic_lprun)
373
374
AttributeError: 'ZMQInteractiveShell' object has no attribute 'define_magic'
In [16]:
%prun func(0)
another 50 0
another 50 50
another 50 100
another 50 150
another 50 200
another 50 250
In [15]:
from math import sqrt
In [16]:
Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in range(10))
Out[16]:
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
In [12]:
!rm simTiles*.hdf
tstart = time.time()
Parallel(n_jobs=1)(delayed(func)(i) for i in range(2))
tend = time.time()
print(tend - tstart)/60.
rm: simTiles*.hdf: No such file or directory
--------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-12-846911a9e1ea> in <module>()
1 get_ipython().system(u'rm simTiles*.hdf')
2 tstart = time.time()
----> 3 Parallel(n_jobs=1)(delayed(func)(i) for i in range(2))
4 tend = time.time()
5 print(tend - tstart)/60.
NameError: name 'Parallel' is not defined
In [15]:
rm: simTiles.hdf: No such file or directory
In [19]:
simTile = snsims.SimulationTile(snsims.SimpleSALTDist,
NSIDE=NSIDE,
tileID=2,
hpOpSim='/Users/rbiswas/data/LSST/OpSimData/healpixelized_MINION_1016_256.db',
rate=snsims.PowerLawRates,
allPointings=opsimOut)
In [20]:
simTile.snParamTable.head()
Out[20]:
M
Mabs
c
mB
t0
x0
x1
z
ra
dec
snid
2097152
0.442292
-18.992923
-0.150325
20.455115
62941.247556
0.000104
0.270252
0.157302
44.780113
0.283009
2097153
0.157796
-19.293411
-0.021235
20.903510
60526.898643
0.000069
-0.828357
0.214700
44.710646
0.328082
2097154
0.286477
-19.222541
-0.042347
22.184690
61892.986509
0.000021
-1.395514
0.349570
44.726804
0.236579
2097155
-0.167665
-19.404277
0.041154
21.991132
60345.984615
0.000025
0.349466
0.347941
44.836194
0.389179
2097156
0.170920
-18.761429
-0.030319
23.026965
62084.079594
0.000010
-0.688337
0.406018
44.733796
0.314037
In [21]:
lc = simTile.lc(2097153)
In [17]:
lc.lightCurve
Out[17]:
mjd
band
ModelFlux
fieldID
flux
fluxerr
zp
zpsys
fieldID
obsHistID
621994
60506.406015
i
5.601077e-11
2736
1.372912e-10
1.084989e-10
0.0
ab
2736
622019
60506.417278
i
5.638012e-11
2736
1.833578e-10
1.028751e-10
0.0
ab
2736
622044
60506.429944
y
3.000870e-11
2736
9.087386e-10
6.588306e-10
0.0
ab
2736
622061
60506.437600
y
3.013523e-11
2736
-2.171769e-10
6.457955e-10
0.0
ab
2736
628038
60512.444334
z
8.802395e-10
2626
1.250376e-09
3.177229e-10
0.0
ab
2626
628915
60514.377041
z
1.297934e-09
2626
1.464532e-09
2.748773e-10
0.0
ab
2626
628943
60514.389750
z
1.300871e-09
2626
1.131854e-09
2.575214e-10
0.0
ab
2626
628952
60514.393784
z
1.301803e-09
2736
1.453811e-09
2.630324e-10
0.0
ab
2736
628995
60514.413775
z
1.306430e-09
2736
1.524704e-09
2.407971e-10
0.0
ab
2736
630781
60517.430686
y
1.460063e-09
2736
2.483061e-10
6.481520e-10
0.0
ab
2736
630799
60517.438779
y
1.461503e-09
2614
1.829821e-09
6.302288e-10
0.0
ab
2614
636046
60528.434356
z
2.609672e-09
2626
2.683530e-09
3.286021e-10
0.0
ab
2626
641155
60534.354213
i
3.367197e-09
2736
3.414369e-09
7.261345e-11
0.0
ab
2736
641161
60534.356891
i
3.366780e-09
2614
3.410095e-09
7.100003e-11
0.0
ab
2614
641187
60534.368772
i
3.364929e-09
2736
3.220699e-09
6.984022e-11
0.0
ab
2736
641193
60534.371464
i
3.364509e-09
2614
3.280501e-09
6.830980e-11
0.0
ab
2614
642187
60535.372902
g
2.177272e-09
2614
2.177357e-09
4.236079e-11
0.0
ab
2614
642229
60535.392386
g
2.173879e-09
2614
2.185775e-09
4.083520e-11
0.0
ab
2614
642276
60535.414186
g
2.170080e-09
2736
2.220494e-09
4.082698e-11
0.0
ab
2736
643578
60537.343191
i
2.812950e-09
2736
2.588054e-09
8.499557e-11
0.0
ab
2736
643585
60537.346316
i
2.812376e-09
2626
2.782164e-09
7.814968e-11
0.0
ab
2626
643611
60537.358566
i
2.810132e-09
2736
2.697204e-09
8.822524e-11
0.0
ab
2736
643618
60537.361708
i
2.809558e-09
2626
2.735741e-09
8.107848e-11
0.0
ab
2626
645522
60539.316099
y
1.377208e-09
2736
2.009640e-09
5.412098e-10
0.0
ab
2736
645524
60539.316994
y
1.377164e-09
2626
1.911722e-09
5.174248e-10
0.0
ab
2626
646725
60540.399926
y
1.328579e-09
2736
1.636107e-09
3.910953e-10
0.0
ab
2736
648745
60542.408324
z
1.368396e-09
2736
1.659703e-09
2.524729e-10
0.0
ab
2736
657385
60554.280828
u
5.636396e-11
2626
1.003580e-10
1.295333e-10
0.0
ab
2626
658547
60555.375018
g
3.295957e-10
2736
3.197823e-10
2.721421e-11
0.0
ab
2736
658591
60555.395147
g
3.291393e-10
2736
3.200164e-10
2.751574e-11
0.0
ab
2736
659285
60556.275361
i
1.623233e-09
2736
1.617443e-09
9.135564e-11
0.0
ab
2736
659290
60556.277594
i
1.623092e-09
2626
1.696994e-09
8.623040e-11
0.0
ab
2626
659316
60556.289681
i
1.622326e-09
2626
1.689124e-09
8.253051e-11
0.0
ab
2626
659317
60556.290130
i
1.622298e-09
2736
1.637013e-09
8.606299e-11
0.0
ab
2736
660329
60557.302915
u
4.342131e-11
2626
-2.166780e-11
8.236143e-11
0.0
ab
2626
660330
60557.303364
u
4.342075e-11
2736
-2.698117e-11
8.681143e-11
0.0
ab
2736
660331
60557.303813
u
4.342019e-11
2626
3.351244e-11
8.210341e-11
0.0
ab
2626
660939
60558.275788
r
9.686349e-10
2626
9.210048e-10
4.871916e-11
0.0
ab
2626
660972
60558.290791
r
9.678491e-10
2626
9.517038e-10
4.635168e-11
0.0
ab
2626
662682
60560.354097
g
2.399943e-10
2626
2.298640e-10
3.748808e-11
0.0
ab
2626
662716
60560.369887
g
2.397896e-10
2626
2.196869e-10
3.767767e-11
0.0
ab
2626
664658
60567.267185
y
8.551441e-10
2614
5.368729e-10
3.434901e-10
0.0
ab
2614
664665
60567.270376
y
8.549837e-10
2614
8.913691e-10
3.406230e-10
0.0
ab
2614
665521
60568.236914
y
8.067555e-10
2614
1.539628e-09
4.122912e-10
0.0
ab
2614
665522
60568.237363
y
8.067332e-10
2736
6.877024e-10
4.160118e-10
0.0
ab
2736
667242
60570.303837
y
7.082060e-10
2614
4.023596e-10
3.810670e-10
0.0
ab
2614
667250
60570.307448
y
7.080429e-10
2626
3.589980e-10
3.778725e-10
0.0
ab
2626
667251
60570.307897
y
7.080226e-10
2736
8.857057e-10
3.866232e-10
0.0
ab
2736
667252
60570.308346
y
7.080023e-10
2614
3.576280e-10
3.794257e-10
0.0
ab
2614
667259
60570.311594
y
7.078556e-10
2736
8.883488e-10
3.856185e-10
0.0
ab
2736
669031
60572.270533
z
7.199811e-10
2626
3.843424e-10
3.498957e-10
0.0
ab
2626
669032
60572.270982
z
7.199714e-10
2736
1.486321e-09
3.583201e-10
0.0
ab
2736
669072
60572.290996
z
7.195369e-10
2626
1.003501e-09
3.360383e-10
0.0
ab
2626
669078
60572.294066
z
7.194703e-10
2736
1.111539e-09
3.414114e-10
0.0
ab
2736
674205
60581.222981
r
4.217026e-10
2614
3.927521e-10
5.236957e-11
0.0
ab
2614
674231
60581.234687
r
4.216016e-10
2614
3.175100e-10
5.024017e-11
0.0
ab
2614
674356
60581.292177
r
4.211064e-10
2614
4.742435e-10
4.515711e-11
0.0
ab
2614
674398
60581.311951
r
4.209364e-10
2614
4.435851e-10
4.502873e-11
0.0
ab
2614
675126
60582.229937
u
3.260823e-11
2614
-1.666570e-10
1.150924e-10
0.0
ab
2614
In [17]:
lc.coaddedLC(coaddTimes=1.)
Out[17]:
band
mjd
flux
zpsys
zp
fluxerr
SNR
discreteTime
0
i
60506.411646
1.603245e-10
ab
0.0
7.475844e-11
2.144567
0
y
60506.433772
3.457809e-10
ab
0.0
4.612780e-10
0.749615
6
z
60512.444334
1.250376e-09
ab
0.0
3.177229e-10
3.935428
7
z
60514.386858
1.350066e-09
ab
0.0
1.531379e-10
8.816016
8
z
60514.413775
1.524704e-09
ab
0.0
2.407971e-10
6.331905
11
y
60517.434732
1.039063e-09
ab
0.0
4.520203e-10
2.298710
22
z
60528.434356
2.683530e-09
ab
0.0
3.286021e-10
8.166505
27
i
60534.362835
3.331416e-09
ab
0.0
3.522925e-11
94.563917
28
g
60535.382644
2.181566e-09
ab
0.0
2.941917e-11
74.154570
29
g
60535.414186
2.220494e-09
ab
0.0
4.082698e-11
54.387894
30
i
60537.352445
2.700791e-09
ab
0.0
4.160005e-11
64.922772
32
y
60539.316547
1.960681e-09
ab
0.0
3.743783e-10
5.237165
33
y
60540.399926
1.636107e-09
ab
0.0
3.910953e-10
4.183398
36
z
60542.408324
1.659703e-09
ab
0.0
2.524729e-10
6.573789
47
u
60554.280828
1.003580e-10
ab
0.0
1.295333e-10
0.774766
48
g
60555.385082
3.198994e-10
ab
0.0
1.935025e-11
16.532052
49
i
60556.283191
1.660143e-09
ab
0.0
4.330102e-11
38.339587
50
u
60557.303364
-5.045509e-12
ab
0.0
4.837423e-11
-0.104302
51
r
60558.283290
9.363543e-10
ab
0.0
3.362304e-11
27.848591
53
g
60560.361992
2.247754e-10
ab
0.0
2.657519e-11
8.458093
60
y
60567.268781
7.141210e-10
ab
0.0
2.418726e-10
2.952467
61
y
60568.237139
1.113665e-09
ab
0.0
2.928523e-10
3.802822
63
y
60570.307824
5.786080e-10
ab
0.0
1.708968e-10
3.385716
65
z
60572.281644
9.964257e-10
ab
0.0
1.732599e-10
5.751047
74
r
60581.265449
4.070227e-10
ab
0.0
2.415238e-11
16.852283
75
u
60582.229937
-1.666570e-10
ab
0.0
1.150924e-10
-1.448027
In [20]:
lc.snCosmoLC()
Out[20]:
<Table length=59>
time band ModelFlux fieldID [2] flux fluxerr zp zpsys float64 unicode1 float64 int64 float64 float64 float64 str2 60506.406015 i 5.60107665011e-11 2736 .. 2736 1.37291155254e-10 1.08498863144e-10 0.0 ab 60506.417278 i 5.63801241752e-11 2736 .. 2736 1.83357830157e-10 1.0287512778e-10 0.0 ab 60506.429944 y 3.00086952725e-11 2736 .. 2736 9.08738607278e-10 6.58830642806e-10 0.0 ab 60506.4376 y 3.01352306532e-11 2736 .. 2736 -2.1717688328e-10 6.45795504698e-10 0.0 ab 60512.444334 z 8.80239490846e-10 2626 .. 2626 1.2503758159e-09 3.17722947303e-10 0.0 ab 60514.377041 z 1.29793370422e-09 2626 .. 2626 1.4645323852e-09 2.74877337943e-10 0.0 ab 60514.38975 z 1.30087068345e-09 2626 .. 2626 1.13185446439e-09 2.57521390501e-10 0.0 ab 60514.393784 z 1.30180347521e-09 2736 .. 2736 1.45381093515e-09 2.63032388594e-10 0.0 ab 60514.413775 z 1.30643000276e-09 2736 .. 2736 1.52470433984e-09 2.40797112262e-10 0.0 ab 60517.430686 y 1.46006259282e-09 2736 .. 2736 2.48306084765e-10 6.48151991345e-10 0.0 ab ... ... ... ... ... ... ... ... 60570.311594 y 7.07855598428e-10 2736 .. 2736 8.88348793243e-10 3.85618489289e-10 0.0 ab 60572.270533 z 7.19981125653e-10 2626 .. 2626 3.84342398749e-10 3.49895686308e-10 0.0 ab 60572.270982 z 7.19971367389e-10 2736 .. 2736 1.48632075239e-09 3.58320064414e-10 0.0 ab 60572.290996 z 7.19536890897e-10 2626 .. 2626 1.00350068045e-09 3.36038342878e-10 0.0 ab 60572.294066 z 7.19470330943e-10 2736 .. 2736 1.11153879131e-09 3.41411362652e-10 0.0 ab 60581.222981 r 4.21702567619e-10 2614 .. 2614 3.9275213547e-10 5.23695739644e-11 0.0 ab 60581.234687 r 4.21601595873e-10 2614 .. 2614 3.17510005132e-10 5.02401730024e-11 0.0 ab 60581.292177 r 4.21106435071e-10 2614 .. 2614 4.74243530027e-10 4.51571098365e-11 0.0 ab 60581.311951 r 4.20936401116e-10 2614 .. 2614 4.43585139482e-10 4.50287258716e-11 0.0 ab 60582.229937 u 3.2608226913e-11 2614 .. 2614 -1.66656983109e-10 1.15092443562e-10 0.0 ab
In [21]:
import analyzeSN as ans
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-21-c6c683938637> in <module>()
----> 1 import analyzeSN as ans
/Users/rbiswas/.local/lib/python2.7/site-packages/analyzeSN/__init__.py in <module>()
3 from .version import __version__
4 from . import filters
----> 5 from . import snanaSims
6 from .analyzelcFits import *
7 from .cov_utils import *
/Users/rbiswas/.local/lib/python2.7/site-packages/analyzeSN/snanaSims.py in <module>()
7 import numpy as np
8 from astropy.io import fits
----> 9 import fitsio
10 from astropy.utils import lazyproperty
11 from astropy.table import Table, Column
ImportError: No module named fitsio
In [14]:
simTile.writeTile(fileName='SimTile-2.hdf')
another 50 2097152
/Users/rbiswas/.local/lib/python2.7/site-packages/snsims/simulations.py:101: 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['snid'] = snid
/Users/rbiswas/.local/lib/python2.7/site-packages/snsims/simulations.py:113: 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['flux'] = fluxes
/Users/rbiswas/.local/lib/python2.7/site-packages/snsims/simulations.py:114: 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['fluxerrs'] = fluxerrs
/Users/rbiswas/.local/lib/python2.7/site-packages/snsims/simulations.py:128: 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['filter'] = df['filter'].astype(str)
another 50 2097202
another 50 2097252
another 50 2097302
another 50 2097352
another 50 2097402
In [18]:
tend = time.time()
In [19]:
print(tend - tstart)
125.169522047
In [20]:
len(simTile.snParamTable)
Out[20]:
282
In [19]:
df = pd.read_hdf('simTiles.hdf')
/usr/local/software/lib/python2.7/site-packages/tables/group.py:1213: UserWarning: problems loading leaf ``/tile_0/table``::
HDF5 error back trace
File "H5Dio.c", line 173, in H5Dread
can't read data
File "H5Dio.c", line 551, in H5D__read
can't read data
File "H5Dchunk.c", line 1875, in H5D__chunk_read
unable to read raw data chunk
File "H5Dchunk.c", line 2905, in H5D__chunk_lock
data pipeline read failed
File "H5Z.c", line 1372, in H5Z_pipeline
filter returned failure during read
File "H5Zdeflate.c", line 125, in H5Z_filter_deflate
inflate() failed
End of HDF5 error back trace
Problems reading the array data.
The leaf will become an ``UnImplemented`` node.
% (self._g_join(childname), exc))
/usr/local/software/lib/python2.7/site-packages/tables/group.py:1213: UserWarning: problems loading leaf ``/tile_1/table``::
HDF5 error back trace
File "H5Dio.c", line 173, in H5Dread
can't read data
File "H5Dio.c", line 551, in H5D__read
can't read data
File "H5Dchunk.c", line 1875, in H5D__chunk_read
unable to read raw data chunk
File "H5Dchunk.c", line 2905, in H5D__chunk_lock
data pipeline read failed
File "H5Z.c", line 1372, in H5Z_pipeline
filter returned failure during read
File "H5Zdeflate.c", line 125, in H5Z_filter_deflate
inflate() failed
End of HDF5 error back trace
Problems reading the array data.
The leaf will become an ``UnImplemented`` node.
% (self._g_join(childname), exc))
/usr/local/software/lib/python2.7/site-packages/tables/group.py:1213: UserWarning: problems loading leaf ``/tile_2/table``::
HDF5 error back trace
File "H5Dio.c", line 173, in H5Dread
can't read data
File "H5Dio.c", line 551, in H5D__read
can't read data
File "H5Dchunk.c", line 1875, in H5D__chunk_read
unable to read raw data chunk
File "H5Dchunk.c", line 2905, in H5D__chunk_lock
data pipeline read failed
File "H5Z.c", line 1372, in H5Z_pipeline
filter returned failure during read
File "H5Zdeflate.c", line 125, in H5Z_filter_deflate
inflate() failed
End of HDF5 error back trace
Problems reading the array data.
The leaf will become an ``UnImplemented`` node.
% (self._g_join(childname), exc))
/usr/local/software/lib/python2.7/site-packages/tables/group.py:1213: UserWarning: problems loading leaf ``/tile_3/table``::
HDF5 error back trace
File "H5Dio.c", line 173, in H5Dread
can't read data
File "H5Dio.c", line 551, in H5D__read
can't read data
File "H5Dchunk.c", line 1875, in H5D__chunk_read
unable to read raw data chunk
File "H5Dchunk.c", line 2905, in H5D__chunk_lock
data pipeline read failed
File "H5Z.c", line 1372, in H5Z_pipeline
filter returned failure during read
File "H5Zdeflate.c", line 125, in H5Z_filter_deflate
inflate() failed
End of HDF5 error back trace
Problems reading the array data.
The leaf will become an ``UnImplemented`` node.
% (self._g_join(childname), exc))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-d39d6bd02462> in <module>()
----> 1 df = pd.read_hdf('simTiles.hdf')
/usr/local/software/lib/python2.7/site-packages/pandas/io/pytables.pyc in read_hdf(path_or_buf, key, **kwargs)
325 keys = store.keys()
326 if len(keys) != 1:
--> 327 raise ValueError('key must be provided when HDF file contains '
328 'multiple datasets.')
329 key = keys[0]
ValueError: key must be provided when HDF file contains multiple datasets.
In [23]:
df.fieldID.unique()
Out[23]:
array([2736, 2626, 2614, 2498, 2742])
In [38]:
df['SNR'] = df.flux / df.fluxerrs
In [51]:
totalCounts, edges = np.histogram(simTile.snParamTable.ix[df.query('SNR > 0').snid.unique()].z, bins=np.arange(0., 1.4, 0.1))
fiveSigmaCounts, edges = np.histogram(simTile.snParamTable.ix[df.query('SNR > 5').snid.unique()].z, bins=np.arange(0., 1.4, 0.1))
In [65]:
fig, ax = plt.subplots()
ax.plot(0.5 * (edges[: -1] + edges[1:] ), fiveSigmaCounts / totalCounts.astype(np.float), 'o')
Out[65]:
[<matplotlib.lines.Line2D at 0x106ec59d0>]
In [56]:
#/ np.float(totalCounts)
Out[56]:
array([0, 0, 1, 0, 4, 3, 6, 7, 2, 0, 0, 0, 0])
In [34]:
df.snid.unique().size * 200 /10000
Out[34]:
5
In [29]:
df.expMJD.max() - df.expMJD.min()
Out[29]:
3388.7746629999965
In [347]:
class SimulationTile(snsims.Universe):
def __init__(self, paramDist, rate, NSIDE, tileID, hpOpSim, allPointings=None, timeRange=None):
self._randomState = None
self.Tiling = snsims.HealpixTiles(nside=NSIDE, preComputedMap=hpOpSim)
self.tileID = tileID
self.fieldArea = self.Tiling.area(tileID)
self.zdist = rate(rng=self.randomState, fieldArea=self.fieldArea)
self.zsamples = self.zdist.zSamples
self.numSN = len(self.zsamples)
self.positions = self.Tiling.positions(self.tileID, self.numSN, rng=self.randomState)
self._snParamTable = None
self.columns = ('expMJD', 'filter', 'fieldID', 'fiveSigmaDepth')
self.tilePointings = self.Tiling.pointingSequenceForTile(self.tileID,
allPointings=allPointings, columns=self.columns)
self._timeRange = timeRange
self.bandPasses = BandpassDict.loadTotalBandpassesFromFiles()
@property
def minPeakTime(self):
if self._timeRange is None:
minTime = self.tilePointings.expMJD.min()
else:
minTime = self._timeRange[0]
return minTime
@property
def maxPeakTime(self):
if self._timeRange is None:
maxTime = self.tilePointings.expMJD.max()
else:
maxTime = self._timeRange[1]
return maxTime
@property
def snParamTable(self):
if self._snParamTable is None:
self.snParams()
return self._snParamTable
@property
def randomState(self):
if self._randomState is None:
self._randomState = np.random.RandomState(self.tileID)
return self._randomState
def snParams(self):
zsamples = self.zdist.zSamples
numSN = len(zsamples)
positions = self.Tiling.positions(self.tileID, numSN, rng=self.randomState)
ra = self.positions[0]
dec = - self.positions[1] + 45.0
# Why do we need numSN
sp = snsims.SimpleSALTDist(numSN=numSN, rng=self.randomState, zSamples=self.zsamples).paramSamples
sp['ra'] = self.positions[0]
sp['dec'] = self.positions[1]
sp['snid'] = np.left_shift(self.tileID, 20) + np.arange(numSN)
sp.set_index('snid', inplace=True)
self._snParamTable = sp
if self.minPeakTime is None or self.maxPeakTime is None:
pass
else:
sp['t0'] = self.minPeakTime + (self.maxPeakTime - self.minPeakTime) * sp['t0']
return sp
@staticmethod
def getSNCosmoParamDict(odict, SNCosmoModel):
mydict = dict()
param_names = SNCosmoModel.param_names
for param in odict.index.values:
if param in param_names:
mydict[param] = odict[param]
return mydict
def SN(self, snid, timeRange='model'):
mySNParams = self.snParamTable.ix[snid]
sn = SNObject(ra=mySNParams.ra, dec=mySNParams.dec)
#print mySNParams
sncosmo_params = self.getSNCosmoParamDict(mySNParams, sn)
#print(sncosmo_params)
sn.set(**sncosmo_params)
z = sn.get('z')
t0 = sn.get('t0')
lcMinTime = t0 - 20. * (1.0 + z)
lcMaxTime = t0 + 50. * (1.0 + z )
df = self.tilePointings.query('expMJD < @lcMaxTime and expMJD > @lcMinTime')
df['snid'] = snid
fluxes = []
fluxerrs = []
for rows in df.iterrows():
row = rows[1]
# print(row['expMJD'], row['filter'], row['fiveSigmaDepth'])
bp = self.bandPasses[row['filter']]
flux = sn.catsimBandFlux(bandpassobject=bp, time=row['expMJD'])
fluxerr = sn.catsimBandFluxError(time=row['expMJD'], bandpassobject=bp, fluxinMaggies=flux,
m5=row['fiveSigmaDepth'])
fluxes.append(flux)
fluxerrs.append(fluxerr)
df['flux'] = fluxes
df['fluxerrs'] = fluxerrs
return sn, df
def writeTile(self, fileName, timeRange='model'):
count = 0
for snid in self.snParamTable.index.values:
self.writeSN(snid, fileName, timeRange=timeRange)
if count % 50 == 0:
print('another 50', snid)
count += 1
def writeSN(self, snid, fileName, timeRange='model'):
sn, df = self.SN(snid, timeRange)
df['filter'] = df['filter'].astype(str)
with pd.get_store(fileName) as store:
# with pd.HDFStore(fileName, mode='a') as hdf:
# hdf.append(df, format='table')
store.append('tile_{}'.format(self.tileID), df)
#df.to_hdf(fileName, key='tile_{}'.format(self.tileID), mode='a', format='t')
In [10]:
hpOpSim = oss.HealPixelizedOpSim.fromOpSimDB(opsimdb, NSIDE=256)
reading from database sqlite:////Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/enigma_1189_micro.db
SELECT * FROM Summary WHERE PROPID in (366, 364)
In [11]:
hpTiles = snsims.HealpixTiles(nside=16, healpixelizedOpSim=hpOpSim)
In [12]:
hpTiles.positions(2, 100, rng=np.random.RandomState(2))
Out[12]:
(array([ 40.78438026, 40.53065749, 42.29979016, 42.23088482,
42.04748867, 42.23080733, 41.25210975, 41.58830994,
41.08216056, 43.02952082, 41.00179235, 41.70250909,
42.22058337, 40.72819844, 44.64701905, 42.87129852,
42.16330051, 40.95422055, 42.60908388, 41.49905649,
43.52293982, 42.23783771, 40.91914043, 39.77683503,
40.93875362, 42.7592876 , 41.65064611, 43.23235613,
41.48024909, 43.36557202, 41.2016626 , 40.19841896,
42.4880581 , 41.60520257, 42.00338112, 42.67501915,
41.15691278, 40.75339679, 40.76006139, 40.25751167,
42.9898204 , 40.24773312, 42.2821802 , 42.45909195,
44.23997366, 41.13242615, 41.25275956, 43.02907919,
42.94733378, 44.4284698 , 43.54080231, 42.62398676,
41.5606913 , 41.55608179, 40.58244776, 41.74328026,
41.97500502, 40.45460385, 40.6112282 , 42.84786018,
41.22512085, 42.88364646, 41.56590067, 40.73226624,
43.63989235, 44.22580378, 42.83873827, 41.71281488,
44.51692818, 40.41824931, 40.41955535, 41.50131486,
42.01389966, 40.74762892, 41.92743264, 41.77187084,
41.9394813 , 41.69498235, 42.1997047 , 41.03555032,
40.19057939, 42.22770515, 40.45789122, 42.9645104 ,
41.48243098, 40.36067236, 42.73880247, 40.39744532,
40.87729158, 41.77184442, 41.16654972, 42.52291993,
43.29058206, 41.82808798, 42.26931004, 41.35374365,
41.34913149, 41.3317311 , 41.18135242, 42.39383796]),
array([ 4.4096664 , 4.00725996, 4.4357117 , 4.37198771, 6.54176591,
5.00399039, 4.35886545, 3.5044309 , 5.9492576 , 5.32659848,
5.19739233, 5.22583866, 4.70478257, 4.55512869, 4.74476064,
6.1584255 , 5.47488482, 5.22586614, 2.98710767, 5.69103113,
4.18730713, 3.1651741 , 4.54765189, 5.06979222, 5.5726826 ,
5.29119858, 5.83224165, 3.3800472 , 4.74287666, 6.11292237,
4.4860077 , 4.29806211, 4.79475989, 5.47316263, 5.7072372 ,
4.57630729, 4.50610545, 4.23821891, 4.38102088, 5.31237264,
3.72910639, 5.30424308, 6.2792739 , 5.30399341, 4.13876184,
5.21017983, 3.64020274, 4.11360259, 5.61593269, 4.806868 ,
5.10266995, 5.36804467, 6.392753 , 6.14374318, 5.21651255,
6.41536414, 4.08006604, 5.12945509, 5.07898161, 4.48507476,
5.24476701, 6.46372351, 5.18235169, 5.24699723, 5.24180306,
5.0097361 , 6.49608147, 4.93577215, 4.68939408, 4.82959745,
3.94540011, 4.74275572, 3.65773946, 4.94980261, 4.16203122,
3.15219073, 5.85678645, 4.41315414, 5.75406385, 3.93854688,
5.43097248, 2.72424967, 5.29883225, 5.60890194, 5.23494823,
5.31722165, 4.39154549, 4.83996934, 6.05647494, 6.57848526,
5.59074902, 5.88628965, 3.33679719, 4.5346994 , 4.55214996,
4.44712924, 5.58984985, 3.73379447, 3.74017409, 4.19443021]))
In [11]:
mapvalues = np.ones(hp.nside2npix(256)) * hp.UNSEEN
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-11-5ede3f5a91ed> in <module>()
----> 1 mapvalues = np.ones(hp.nside2npix(256)) * hp.UNSEEN
NameError: name 'hp' is not defined
In [12]:
mapvalues[100] = 100
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-12-9e3413cde9e4> in <module>()
----> 1 mapvalues[100] = 100
NameError: name 'mapvalues' is not defined
In [158]:
r, t = hp.pix2ang(nside=256, ipix=100, nest=True)
In [159]:
np.degrees(r)
Out[159]:
87.761314039157497
In [160]:
np.degrees(t)
Out[160]:
46.054687499999993
In [161]:
hp.mollview(mapvalues)
hp.projscatter(r, t)
hp.projscatter(theta, phi)
Out[161]:
<matplotlib.collections.PathCollection at 0x1145064d0>
In [84]:
phi, theta = hpTiles.positions(100, 100)
phi = np.radians(phi)
theta = np.radians(theta)
In [ ]:
hp
In [34]:
hpTiles.area(tileID=23)/ 41252.96
Out[34]:
1.271565793719964e-06
In [35]:
hpTiles.positions()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-a02077a73e27> in <module>()
----> 1 hpTiles.positions()
TypeError: positions() takes at least 3 arguments (1 given)
In [16]:
import healpy as hp
In [18]:
zdist.zSamples
Out[18]:
array([ 0.00860442, 0.00400548, 0.01708304, ..., 0.95772653,
0.91247313, 0.98569087])
In [19]:
zdist.numSN()
Out[19]:
array([ 443, 3220, 9038, 17877, 29362, 43063, 59049, 76234,
95144, 115460])
In [20]:
zdist.zbinEdges
Out[20]:
array([ 1.00000000e-04, 1.00100000e-01, 2.00100000e-01,
3.00100000e-01, 4.00100000e-01, 5.00100000e-01,
6.00100000e-01, 7.00100000e-01, 8.00100000e-01,
9.00100000e-01, 1.00010000e+00])
In [ ]:
In [22]:
zdist.zSampleSize()
Out[22]:
array([ 447.65671761, 3288.5656771 , 9134.03591601,
17916.69513074, 29401.70134609, 43256.16515423,
59105.79756238, 76575.27375973, 95313.48794103,
115006.94175052])
In [25]:
from future.builtins import range
In [26]:
sd = snsims.SimpleSALTDist(rng=np.random.RandomState(43), numSN=3, zSamples=[0.03, 0.2, 0.5])
In [27]:
sd.numSN
Out[27]:
3
In [28]:
sd = snsims.SimpleSALTDist(3, zSamples=[0.2, 0.5, 0.7], rng=np.random.RandomState(2))
In [29]:
sd.paramSamples
Out[29]:
M
Mabs
c
mB
t0
x0
x1
z
0
0.681387
-18.507682
-0.195380
21.516975
0.435995
0.000039
-0.617223
0.2
1
0.663392
-18.850143
-0.158688
23.475538
0.025926
0.000006
-1.501007
0.5
2
0.239084
-19.227525
-0.107446
23.981698
0.549662
0.000004
0.893590
0.7
In [106]:
hp.nside2npix(256)
Out[106]:
786432
In [112]:
np.binary_repr(hp.nside2npix(256))
Out[112]:
'11000000000000000000'
In [113]:
np.binary_repr(1000)
Out[113]:
'1111101000'
In [116]:
np.binary_repr(x)
Out[116]:
'111110100000000000110000000000'
In [115]:
x = hp.nside2npix(16) + np.left_shift(1000, 20)
In [105]:
hp.nside2npix(256)
Out[105]:
824633720832
In [103]:
np.log2(hp.nside2npix(256))
Out[103]:
19.584962500721158
In [12]:
from lsst.sims.catUtils.supernovae import SNObject
In [32]:
NSIDE=256
tileSim = snsims.HealpixTiles(nside=NSIDE, healpixelizedOpSim=hpOpSim)
In [33]:
tileSim.positions(2, 5, rng=np.random.RandomState(2))
Out[33]:
(array([ 44.79113638, 44.84988784, 44.79078878, 44.83928145, 44.89679089]),
array([ 0.3861089 , 0.23676652, 0.40196668, 0.29139572, 0.30716451]))
In [ ]:
SNObject(ra = sn)
In [ ]:
from lsst.sims.photUtils import BandpassDict
In [348]:
NSIDE=256
In [349]:
opsimout = oss.OpSimOutput.fromOpSimDB(opsimdb)
reading from database sqlite:////Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/enigma_1189_micro.db
SELECT * FROM Summary WHERE PROPID in (366, 364)
In [350]:
!rm simulatedTile-2.hdf
rm: simulatedTile-2.hdf: No such file or directory
In [351]:
import time
tstart = time.time()
In [353]:
ss = simTile.snParamTable
In [354]:
ss.tail()
Out[354]:
M
Mabs
c
mB
t0
x0
x1
z
ra
dec
snid
2097429
0.084355
-19.003140
-0.012830
25.989778
49587.534368
6.373471e-07
-0.400639
1.364771
44.720561
0.292130
2097430
-0.084751
-19.226817
0.084354
25.706809
49575.245355
8.304062e-07
-1.637463
1.334876
44.813034
0.315281
2097431
0.250335
-19.460537
-0.040465
25.591896
49578.543855
9.217440e-07
-1.120686
1.395461
44.869924
0.273011
2097432
-0.377360
-19.669900
0.090366
25.282814
49590.864107
1.216124e-06
0.850998
1.344427
44.810630
0.182443
2097433
0.495625
-19.064607
-0.125411
25.923843
49576.042725
6.780369e-07
-0.925769
1.362495
44.687604
0.285268
In [355]:
simTile.writeTile(fileName='simulatedTile-2.hdf')
('another 50', 2097152)
('another 50', 2097202)
('another 50', 2097252)
('another 50', 2097302)
('another 50', 2097352)
('another 50', 2097402)
In [ ]:
# simTile.writeSN(snid=2097152, fileName='simulatedTile-2.hdf')
In [356]:
pd.read_hdf('simulatedTile-2.hdf')
Out[356]:
expMJD
filter
fieldID
fiveSigmaDepth
snid
flux
fluxerrs
obsHistID
172349
49571.063219
g
2437
24.459918
2097152
1.728647e-11
3.252719e-11
172350
49571.063666
g
2453
24.513292
2097152
1.729167e-11
3.095152e-11
172351
49571.064115
g
2557
24.445066
2097152
1.729689e-11
3.297966e-11
172352
49571.064563
g
2679
24.430031
2097152
1.730210e-11
3.344406e-11
175206
49573.964751
z
2707
22.206644
2097152
2.743777e-10
2.613294e-10
175207
49573.965200
z
2593
22.222713
2097152
2.744513e-10
2.574825e-10
175213
49573.967886
z
2579
22.222465
2097152
2.748916e-10
2.575418e-10
175214
49573.968334
z
2703
22.177684
2097152
2.749650e-10
2.684084e-10
175215
49573.968797
z
2687
22.176571
2097152
2.750410e-10
2.686843e-10
175216
49573.969247
z
2575
22.190010
2097152
2.751148e-10
2.653725e-10
175217
49573.969696
z
2461
22.204532
2097152
2.751885e-10
2.618398e-10
175241
49573.980553
z
2579
22.213298
2097152
2.769726e-10
2.597313e-10
175242
49573.981018
z
2593
22.215790
2097152
2.770492e-10
2.591347e-10
175243
49573.981466
z
2707
22.201214
2097152
2.771229e-10
2.626441e-10
175244
49573.981929
z
2703
22.146786
2097152
2.771992e-10
2.761735e-10
175245
49573.982390
z
2687
22.142589
2097152
2.772751e-10
2.772453e-10
175246
49573.982843
z
2575
22.154760
2097152
2.773497e-10
2.741488e-10
175247
49573.983292
z
2461
22.170884
2097152
2.774236e-10
2.700997e-10
175285
49574.002604
i
2541
23.964494
2097152
4.003477e-10
5.224931e-11
175286
49574.003051
i
2534
23.927611
2097152
4.004334e-10
5.401082e-11
175287
49574.003500
i
2651
23.915709
2097152
4.005194e-10
5.459274e-11
175288
49574.003946
i
2665
23.948188
2097152
4.006049e-10
5.302096e-11
175289
49574.004395
i
2773
23.899535
2097152
4.006909e-10
5.539429e-11
175290
49574.004841
i
2763
23.859204
2097152
4.007764e-10
5.744673e-11
175315
49574.016625
i
2541
24.110986
2097152
4.030384e-10
4.584434e-11
175316
49574.017072
i
2534
24.068772
2097152
4.031244e-10
4.760048e-11
175317
49574.017521
i
2651
24.058710
2097152
4.032107e-10
4.802989e-11
175318
49574.017967
i
2665
24.096084
2097152
4.032964e-10
4.645675e-11
175319
49574.018416
i
2773
24.044255
2097152
4.033828e-10
4.865421e-11
175320
49574.018863
i
2763
23.997830
2097152
4.034687e-10
5.071767e-11
...
...
...
...
...
...
...
...
175285
49574.002604
i
2541
23.964494
2097433
3.640540e-11
5.136191e-11
175286
49574.003051
i
2534
23.927611
2097433
3.640482e-11
5.315264e-11
175287
49574.003500
i
2651
23.915709
2097433
3.640423e-11
5.374365e-11
175288
49574.003946
i
2665
23.948188
2097433
3.640365e-11
5.214607e-11
175289
49574.004395
i
2773
23.899535
2097433
3.640306e-11
5.455728e-11
175290
49574.004841
i
2763
23.859204
2097433
3.640248e-11
5.663986e-11
175315
49574.016625
i
2541
24.110986
2097433
3.638706e-11
4.482271e-11
175316
49574.017072
i
2534
24.068772
2097433
3.638648e-11
4.661712e-11
175317
49574.017521
i
2651
24.058710
2097433
3.638589e-11
4.705527e-11
175318
49574.017967
i
2665
24.096084
2097433
3.638530e-11
4.544816e-11
175319
49574.018416
i
2773
24.044255
2097433
3.638472e-11
4.769188e-11
175320
49574.018863
i
2763
23.997830
2097433
3.638413e-11
4.979502e-11
190873
49592.970260
z
2651
22.126297
2097433
3.713486e-11
2.812913e-10
190874
49592.970707
z
2665
22.150118
2097433
3.713422e-11
2.751684e-10
190875
49592.971153
z
2667
22.170356
2097433
3.713358e-11
2.700710e-10
190876
49592.971598
z
2679
22.187656
2097433
3.713294e-11
2.657883e-10
190877
49592.972050
z
2557
22.194730
2097433
3.713230e-11
2.640567e-10
190878
49592.972496
z
2551
22.174127
2097433
3.713166e-11
2.691317e-10
190879
49592.972943
z
2541
22.149981
2097433
3.713102e-11
2.752032e-10
190880
49592.973390
z
2534
22.121744
2097433
3.713039e-11
2.824769e-10
190883
49592.974732
z
2431
22.225853
2097433
3.712847e-11
2.565708e-10
190884
49592.975179
z
2437
22.245469
2097433
3.712783e-11
2.519617e-10
190913
49592.988285
z
2551
22.038242
2097433
3.710912e-11
3.051276e-10
190914
49592.988732
z
2557
22.062423
2097433
3.710848e-11
2.983881e-10
190915
49592.989180
z
2667
22.029090
2097433
3.710784e-11
3.077178e-10
190916
49592.989627
z
2679
23.123455
2097433
3.710720e-11
1.117833e-10
190919
49592.990991
z
2437
23.136405
2097433
3.710525e-11
1.104486e-10
190920
49592.991439
z
2431
23.089909
2097433
3.710461e-11
1.153158e-10
190986
49593.024216
g
2707
24.425194
2097433
2.656800e-13
3.355393e-11
190987
49593.024665
g
2593
24.436324
2097433
2.656502e-13
3.320757e-11
13536 rows × 7 columns
In [276]:
sn, lc = simTile.SN(2097152)
In [357]:
tend = time.time()
In [358]:
tend - tstart
Out[358]:
60.5964150428772
In [279]:
len(simTile.snParamTable)
Out[279]:
282
In [359]:
data = pd.read_hdf('simulatedTile-2.hdf', 'tile_2')
In [361]:
len(data.snid.unique())
Out[361]:
282
In [362]:
len(data)
Out[362]:
13536
In [171]:
glc
Out[171]:
expMJD
filter
fieldID
fiveSigmaDepth
snid
flux
fluxerrs
obsHistID
172349
49571.063219
g
2437
24.459918
2097152
1.728647e-11
3.252719e-11
172350
49571.063666
g
2453
24.513292
2097152
1.729167e-11
3.095152e-11
172351
49571.064115
g
2557
24.445066
2097152
1.729689e-11
3.297966e-11
172352
49571.064563
g
2679
24.430031
2097152
1.730210e-11
3.344406e-11
190986
49593.024216
g
2707
24.425194
2097152
5.252354e-09
4.441515e-11
190987
49593.024665
g
2593
24.436324
2097152
5.252289e-09
4.415396e-11
In [177]:
glc.fluxerrs
Out[177]:
obsHistID
172349 3.252719e-11
172350 3.095152e-11
172351 3.297966e-11
172352 3.344406e-11
190986 4.441515e-11
190987 4.415396e-11
Name: fluxerrs, dtype: float64
In [181]:
fig, ax = plt.subplots()
glc = lc.query('filter == "i"')
print(len(glc))
ax.errorbar(glc.expMJD, glc.flux, yerr=glc.fluxerrs.values, fmt='o')
12
Out[181]:
<Container object of 3 artists>
In [142]:
mySN = SNObject(ra=44.78, dec=0.28)
In [153]:
simTile.snParamTable.ix[2097152]
Out[153]:
array(['M', 'Mabs', 'c', 'mB', 't0', 'x0', 'x1', 'z', 'ra', 'dec'], dtype=object)
In [150]:
for param in simTile.snParamTable.ix[2097152]:
print param
0.442291547071
-18.9929233772
-0.150324608986
20.4551153582
49591.2215108
0.000103922920062
0.270252046785
0.157302415606
44.7801132604
0.283009138837
In [149]:
mySN.param_names
Out[149]:
['z', 't0', 'x0', 'x1', 'c', 'hostebv', 'hostr_v', 'mwebv', 'mwr_v']
In [147]:
simTile.getSNCosmoParamDict(simTile.snParamTable.ix[2097152], mySN)
Out[147]:
{}
In [116]:
ht = snsims.HealpixTiles(nside=256,
preComputedMap='/Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/healpixels_micro.db')
In [33]:
subset = ht.pointingSequenceForTile(allPointings=opsimOut, columns=('expMJD', 'filter', 'fiveSigmaDepth'), tileID=2)
In [39]:
subset.head()
Out[39]:
expMJD
filter
fiveSigmaDepth
obsHistID
172349
49571.063219
g
24.459918
172350
49571.063666
g
24.513292
172351
49571.064115
g
24.445066
172352
49571.064563
g
24.430031
175206
49573.964751
z
22.206644
In [46]:
row = next(subset.iterrows())
In [56]:
for rows in subset.head().iterrows():
row = rows[1]
print(row['expMJD'], row['filter'], row['fiveSigmaDepth'])
(49571.063219, u'g', 24.459918)
(49571.063666, u'g', 24.513292)
(49571.064115, u'g', 24.445066)
(49571.064563, u'g', 24.430031)
(49573.964751, u'z', 22.206644)
In [52]:
row[1]['expMJD']
Out[52]:
49571.063219
In [18]:
opsimdb
Out[18]:
'/Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/enigma_1189_micro.db'
In [ ]:
hpopSim = oss.HealPixelizedOpSim
In [77]:
from lsst.sims.photUtils import BandpassDict
In [76]:
opsimOut
Out[76]:
<opsimsummary.opsim_out.OpSimOutput at 0x104f942d0>
In [119]:
ss.head()
Out[119]:
M
Mabs
c
mB
t0
x0
x1
z
ra
dec
snid
2097152
-0.172234
-19.193721
0.073487
16.966703
49583.315239
0.002592
-0.531945
0.037492
40.874748
3.657655
2097153
0.056920
-19.187167
-0.023738
18.271408
49589.279658
0.000778
0.160154
0.066762
41.139171
4.183124
2097154
0.402092
-18.867247
-0.087259
17.328732
49576.979866
0.001861
-1.164548
0.038095
43.627595
4.123643
2097155
-0.099114
-19.539191
0.025303
12.497307
49582.614289
0.158649
0.178738
0.005747
44.264664
4.600741
2097156
-0.009494
-19.393026
0.026732
15.923752
49588.676135
0.006780
-0.676756
0.025644
44.037583
4.858994
In [102]:
from lsst.sims.catUtils.supernovae import SNObject
In [123]:
sn, df = simTile.SN(2097152)
In [128]:
sn.SNstate
Out[128]:
{'MWE(B-V)': 0.08050362765789032,
'ModelSource': 'salt2-extended',
'_dec': 0.0049394412859329953,
'_ra': 0.78156041581027214,
'c': 0.0,
'hostebv': 0.0,
'hostr_v': 3.1000000000000001,
'mwebv': 0.0,
'mwr_v': 3.1000000000000001,
't0': 0.0,
'x0': 1.0,
'x1': 0.0,
'z': 0.0}
In [104]:
simTile.tilePointings.head()
Out[104]:
expMJD
filter
fieldID
fiveSigmaDepth
obsHistID
172349
49571.063219
g
2437
24.459918
172350
49571.063666
g
2453
24.513292
172351
49571.064115
g
2557
24.445066
172352
49571.064563
g
2679
24.430031
175206
49573.964751
z
2707
22.206644
In [ ]:
simTile.tilePointings
In [28]:
opsimout.summary.ix[pointings].fieldID.unique()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-28-68eb6a52f9ff> in <module>()
----> 1 opsimout.summary.ix[pointings].fieldID.unique()
NameError: name 'pointings' is not defined
In [69]:
opsimout.summary.ix[pointings][['fieldRA', 'fieldDec', 'fieldID', 'fiveSigmaDepth', 'expMJD', 'filter']]
Out[69]:
fieldRA
fieldDec
fieldID
fiveSigmaDepth
expMJD
filter
obsHistID
187059
0.792429
0.034090
2736
21.341876
49585.394674
y
187073
0.792429
0.034090
2736
21.393171
49585.401121
y
194374
0.792429
0.034090
2736
23.583906
49596.316600
i
194421
0.792429
0.034090
2736
23.552233
49596.338125
i
195238
0.792429
0.034090
2736
24.757496
49597.273542
g
195240
0.766063
-0.007846
2626
24.823001
49597.274437
g
195308
0.792429
0.034090
2736
25.102092
49597.307155
g
195309
0.766063
-0.007846
2626
25.147792
49597.307604
g
196190
0.766063
-0.007846
2626
24.379822
49598.283632
g
196191
0.792429
0.034090
2736
24.324589
49598.284081
g
196234
0.766063
-0.007846
2626
24.447786
49598.308991
g
196236
0.792429
0.034090
2736
24.582029
49598.313019
g
196401
0.737881
0.037368
2742
21.772138
49598.416210
z
196402
0.766063
-0.007846
2626
21.796638
49598.416659
z
196403
0.792429
0.034090
2736
21.784335
49598.417109
z
200849
0.792429
0.034090
2736
21.474850
49609.404495
y
201724
0.792429
0.034090
2736
22.377414
49610.395337
z
201725
0.737881
0.037368
2742
22.362867
49610.395786
z
201731
0.766063
-0.007846
2626
22.381839
49610.398466
z
201754
0.766063
-0.007846
2626
22.452820
49610.408864
z
203413
0.792429
0.034090
2736
21.646022
49612.302750
y
203421
0.792429
0.034090
2736
21.648387
49612.306372
y
203439
0.792429
0.034090
2736
21.522201
49612.314538
y
203455
0.792429
0.034090
2736
21.487602
49612.321829
y
203480
0.792429
0.034090
2736
21.780742
49612.335812
y
204360
0.792429
0.034090
2736
21.746881
49613.323212
y
204375
0.792429
0.034090
2736
21.723283
49613.330322
y
205282
0.792429
0.034090
2736
21.305452
49614.321199
y
205298
0.792429
0.034090
2736
21.330164
49614.328452
y
206016
0.792429
0.034090
2736
22.140939
49615.243678
z
...
...
...
...
...
...
...
234881
0.792429
0.034090
2736
24.859172
49658.134057
g
235681
0.766063
-0.007846
2626
24.749668
49659.153899
r
235724
0.766063
-0.007846
2626
24.870040
49659.174081
r
235779
0.792429
0.034090
2736
24.810938
49659.200348
r
235805
0.792429
0.034090
2736
25.003929
49659.213078
r
236380
0.792429
0.034090
2736
24.672432
49660.114418
r
236383
0.766063
-0.007846
2626
24.723603
49660.115758
r
236434
0.792429
0.034090
2736
24.834556
49660.139330
r
236435
0.766063
-0.007846
2626
24.873750
49660.139779
r
239398
0.820751
-0.011770
2614
22.203249
49671.156527
z
239435
0.820751
-0.011770
2614
22.180317
49671.173450
z
243014
0.766063
-0.007846
2626
21.972250
49676.046270
z
243050
0.766063
-0.007846
2626
22.135084
49676.062418
z
244790
0.766063
-0.007846
2626
23.350230
49678.204497
i
248059
0.766063
-0.007846
2626
21.468952
49683.026795
y
251634
0.820751
-0.011770
2614
22.055549
49688.019271
z
251666
0.820751
-0.011770
2614
22.080761
49688.033547
z
251678
0.820751
-0.011770
2614
21.987923
49688.038907
z
253031
0.820751
-0.011770
2614
22.087834
49690.020969
z
253033
0.792429
0.034090
2736
22.082767
49690.021864
z
253068
0.820751
-0.011770
2614
22.107926
49690.037511
z
253070
0.792429
0.034090
2736
22.043207
49690.038406
z
253742
0.792429
0.034090
2736
22.222208
49691.024274
z
253776
0.792429
0.034090
2736
22.464604
49691.039675
z
254454
0.820751
-0.011770
2614
22.092272
49692.023526
z
254492
0.820751
-0.011770
2614
22.121867
49692.040515
z
255888
0.792429
0.034090
2736
21.167007
49694.023357
y
255893
0.766063
-0.007846
2626
21.201085
49694.025588
y
266076
0.820751
-0.011770
2614
23.492887
49708.173359
i
266478
0.766063
-0.007846
2626
21.619843
49709.030331
y
103 rows × 6 columns
In [59]:
pointings = simTile.Tiling.pointingSequenceForTile(2, allPointings=None)
In [39]:
ss.head()
Out[39]:
M
Mabs
c
mB
t0
x0
x1
z
ra
dec
snid
2097152
-0.172234
-19.193721
0.073487
16.966703
0.557888
0.002592
-0.531945
0.037492
40.874748
3.657655
2097153
0.056920
-19.187167
-0.023738
18.271408
0.829474
0.000778
0.160154
0.066762
41.139171
4.183124
2097154
0.402092
-18.867247
-0.087259
17.328732
0.269411
0.001861
-1.164548
0.038095
43.627595
4.123643
2097155
-0.099114
-19.539191
0.025303
12.497307
0.525970
0.158649
0.178738
0.005747
44.264664
4.600741
2097156
-0.009494
-19.393026
0.026732
15.923752
0.801993
0.006780
-0.676756
0.025644
44.037583
4.858994
In [40]:
snObj = SNObject(ra=ss.ix[2097152].ra, dec=ss.ix[2097152].dec)
In [42]:
snObj.set(ss.ix[209])
<SNObject at 0x111090550>
source:
class : SALT2Source
name : 'salt2-extended'
version : 1.0
phases : [-20, .., 50] days
wavelengths: [300, .., 18000] Angstroms
effect (name='host' frame='rest'):
class : OD94Dust
wavelength range: [909.09, 33333.3] Angstroms
effect (name='mw' frame='obs'):
class : OD94Dust
wavelength range: [909.09, 33333.3] Angstroms
parameters:
z = 0.0
t0 = 0.0
x0 = 1.0
x1 = 0.0
c = 0.0
hostebv = 0.0
hostr_v = 3.1000000000000001
mwebv = 0.0
mwr_v = 3.1000000000000001
In [53]:
mydict = dict()
for key in ss.ix[2097152].index.values:
if key in snObj.param_names:
mydict[key] = ss.ix[2097152][key]
In [52]:
ss.ix[2097152].index.values
Out[52]:
array(['M', 'Mabs', 'c', 'mB', 't0', 'x0', 'x1', 'z', 'ra', 'dec'], dtype=object)
In [54]:
mydict
Out[54]:
{'c': 0.073486538283977915,
't0': 0.55788769050580311,
'x0': 0.0025923933113910268,
'x1': -0.53194513206142791,
'z': 0.037492301877033854}
In [55]:
snObj.set(**mydict)
In [ ]:
In [42]:
ss.z.hist(histtype='step')
Out[42]:
<matplotlib.axes._subplots.AxesSubplot at 0x1129cef10>
In [43]:
plt.hexbin(ss.ra, ss.dec)
Out[43]:
<matplotlib.collections.PolyCollection at 0x112fdd8d0>
In [31]:
ss.dec.hist(histtype='step')
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x111ae09d0>
In [128]:
ss.ra.ix[0]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-128-ccc42765b558> in <module>()
----> 1 ss.ra.ix[0]
/usr/local/software/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
2670 if name in self._info_axis:
2671 return self[name]
-> 2672 return object.__getattribute__(self, name)
2673
2674 def __setattr__(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'ra'
In [119]:
sp.paramSamples
Out[119]:
M
Mabs
c
mB
t0
x0
x1
z
0
-0.277888
-19.383193
0.080845
16.777230
0.192640
3.078120e-03
0.218513
0.037492
1
-0.351036
-19.781365
0.105987
17.677211
0.917166
1.343638e-03
0.165804
0.066762
2
0.247270
-19.015754
-0.099019
17.180225
0.015636
2.120422e-03
0.578635
0.038095
3
-0.190211
-19.750369
0.123533
12.286130
0.042574
1.939202e-01
-1.797110
0.005747
4
0.158791
-19.220540
-0.067292
16.096238
0.245332
5.758305e-03
0.477328
0.025644
5
-0.288393
-19.472536
0.057160
17.727202
0.782295
1.279627e-03
0.990104
0.059560
6
0.006882
-19.441654
0.035016
16.104269
0.047289
5.748751e-03
-1.062122
0.028439
7
0.320966
-19.137254
-0.086276
18.490193
0.223354
6.370129e-04
-0.455084
0.071904
8
-0.437145
-19.731452
0.222073
18.340214
0.333372
7.346587e-04
-2.365134
0.087302
9
0.359724
-18.642377
-0.090541
19.072094
0.734712
3.730201e-04
-0.685680
0.074700
10
0.067852
-19.232065
0.009348
19.932374
0.397554
1.690771e-04
-0.883674
0.139570
11
-0.500698
-19.712495
0.157190
19.424226
0.782596
2.687579e-04
0.064733
0.137941
12
-0.226028
-19.465764
0.030242
19.458233
0.266482
2.596374e-04
1.191536
0.126018
13
0.247713
-19.136416
-0.086172
19.514480
0.556925
2.473878e-04
0.207892
0.112127
14
-0.047285
-19.216028
0.066580
19.661530
0.774130
2.173767e-04
-1.470689
0.123547
15
-0.311997
-19.640943
0.095256
18.282531
0.147097
7.696191e-04
0.117202
0.081846
16
-0.300649
-19.710175
0.129031
18.444582
0.853044
6.652030e-04
-0.950074
0.090512
17
0.215339
-19.091920
-0.161749
19.443190
0.257458
2.611869e-04
2.659582
0.106685
18
-0.136113
-19.408426
-0.001707
18.857084
0.112410
4.515182e-04
1.286130
0.094964
19
-0.605268
-20.070655
0.144772
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...
...
...
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69772
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69802 rows × 8 columns
In [115]:
simTile.numSN
Out[115]:
69802
In [96]:
simTile.snParams
Out[96]:
<bound method SimulationTile.snParams of <__main__.SimulationTile object at 0x11592a750>>
In [91]:
simTile.zsamples.max()
Out[91]:
1.3999920837791637
In [93]:
plt.hist(simTile.zsamples, bins=20, histtype='step')
Out[93]:
(array([ 13., 74., 205., 373., 637., 988., 1379., 1747.,
2221., 2605., 3178., 3761., 4372., 5025., 5603., 6290.,
6771., 7634., 8157., 8769.]),
array([ 0.00574665, 0.07545893, 0.1451712 , 0.21488347, 0.28459574,
0.35430801, 0.42402028, 0.49373255, 0.56344483, 0.6331571 ,
0.70286937, 0.77258164, 0.84229391, 0.91200618, 0.98171846,
1.05143073, 1.121143 , 1.19085527, 1.26056754, 1.33027981,
1.39999208]),
<a list of 1 Patch objects>)
In [75]:
mapvals = np.ones(hp.nside2npix(NSIDE))* hp.UNSEEN
mapvals[2] =1
hp.mollview(mapvals, nest=True)
hp.projscatter(np.radians(simTile.positions[1][:50]), np.radians(simTile.positions[0][:50]), **dict(s=0.01))
Out[75]:
<matplotlib.collections.PathCollection at 0x115b9ff10>
In [7]:
class TestUniverse(snsims.Universe()):
pass
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-69f60c306fa5> in <module>()
----> 1 class TestUniverse(snsims.Universe()):
2 pass
TypeError: Can't instantiate abstract class Universe with abstract methods SN, randomState, snParams
In [ ]:
zdist = snsims.PowerLawRates(rng=np.random.)
In [8]:
np.pi * 4 * np.degrees(1.)**2
Out[8]:
41252.961249419277
In [9]:
1.0/ 12.0/ 256**2
Out[9]:
1.2715657552083333e-06
In [10]:
rng = np.random.RandomState()
In [11]:
hpTiles.nside
Out[11]:
256
In [16]:
p, t = hpTiles.positions(23, 20, rng)
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3930011607, 1740622828, 1298160148, 105980734, 1716290753,
917932258, 3119607871, 3662331243, 3694958268], dtype=uint32), 300, 0, 0.0)
('MT19937', array([1036876355, 1873300696, 2779425791, 2339800103, 3723880741,
1327008191, 2106492371, 3203408818, 701761016, 3043702971,
1871833405, 3219103356, 856212731, 3351154237, 39152719,
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1932672900, 170628749, 1248007132, 2905198590, 4248539190,
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2204020674, 3780306391, 1749224308, 721743895, 3460509447,
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3735656167, 1328971956, 713585087, 324734679, 3738765443,
620548421, 3179430819, 2441777770, 4011838506, 3804062450,
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4138091625, 2908820844, 2825308699, 1570922876, 2570830376,
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917932258, 3119607871, 3662331243, 3694958268], dtype=uint32), 304, 0, 0.0)
In [17]:
p
Out[17]:
array([ 0.80556144, 0.80335385, 0.80176178, 0.80259193, 0.80474502,
0.80468303, 0.80351648, 0.80483751, 0.8012384 , 0.80291049,
0.80464681, 0.80217262, 0.80574301, 0.80291342, 0.80305263,
0.80643479, 0.80504134, 0.80201016, 0.80307029, 0.80482529])
In [18]:
t
Out[18]:
array([ 1.54689008, 1.54549507, 1.54708207, 1.54709994, 1.54654693,
1.54778268, 1.5484891 , 1.54630112, 1.54711004, 1.54748579,
1.54632425, 1.54851335, 1.54735947, 1.54695191, 1.54871257,
1.5472008 , 1.54719835, 1.54673222, 1.54856364, 1.54577162])
In [12]:
hp.pix2ang(256, 23, nest=True)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-12-1d4e4c44d8ee> in <module>()
----> 1 hp.pix2ang(256, 23, nest=True)
NameError: name 'hp' is not defined
In [13]:
hpTiles._angularSamples(phi_c, theta_c, 0.13, 20, 23, rng)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-13-33313583f32b> in <module>()
----> 1 hpTiles._angularSamples(phi_c, theta_c, 0.13, 20, 23, rng)
NameError: name 'phi_c' is not defined
In [ ]:
phi,theta = hpTiles.samplePatchOnSphere(phi_c, theta_c, 0.13, 20, rng, degrees=False)
In [ ]:
phi
In [14]:
phi_c
Out[14]:
46.054687499999993
In [18]:
hp.ang2pix(256, theta, phi, nest=True)
Out[18]:
array([23, 23, 23, 23, 21, 23, 23, 23, 21, 23, 23, 22, 23, 22, 29, 21, 23,
23, 29, 23])
In [14]:
import healpy as hp
In [10]:
theta_c, phi_c = np.degrees(hp.pix2ang(nside=256, ipix=23, nest=True))
In [9]:
theta_c
Out[9]:
88.657007193856458
In [35]:
np.sqrt(hp.nside2pixarea(256, degrees=True) / np.pi)
Out[35]:
0.1292177098644825
In [14]:
import healpy as hp
In [17]:
hp.nside2pixarea(256, degrees=True)
Out[17]:
0.052455852825697924
In [7]:
hpTiles.pointingSequenceForTile(tileID=23, allPointings=None)
Out[7]:
array([187059, 187073, 194374, 194421, 195238, 195308, 196191, 196236,
196403, 200849, 201724, 203414, 203422, 203440, 203458, 203477,
204360, 204362, 204375, 204377, 204429, 205282, 205284, 205296,
205298, 205394, 206016, 206017, 206062, 206063, 206977, 210862,
210918, 211231, 211279, 211537, 212812, 212813, 214180, 214181,
214218, 214219, 214247, 214248, 215735, 215778, 215913, 218394,
220581, 220620, 221239, 221281, 225425, 230524, 230575, 232649,
232689, 233394, 233397, 233432, 233435, 234057, 234073, 234074,
234831, 234881, 235734, 235761, 235777, 235779, 235805, 235806,
236380, 236434, 239401, 239434, 246728, 246746, 247531, 247571,
251003, 251634, 251666, 251675, 251678, 253031, 253033, 253068,
253070, 253741, 253742, 253775, 253776, 254454, 254492, 255888,
266076, 266477, 266489, 266490, 266497, 269956])
In [12]:
import healpy as hp
In [31]:
from lsst.sims.utils import samplePatchOnSphere
In [ ]:
samplePatchOnSphere()
In [48]:
def samplePatchOnSphere(phi, theta, delta, size):
"""
Uniformly distributes samples on a patch on a sphere between phi \pm delta,
and theta \pm delta on a sphere. Uniform distribution implies that the
number of points in a patch of sphere is proportional to the area of the
patch. Here, the coordinate system is the usual
spherical coordinate system but with the azimuthal angle theta going from
90 degrees at the North Pole, to -90 degrees at the South Pole, through
0. at the equator.
This function is not equipped to handle wrap-around the ranges of theta
phi and therefore does not work at the poles.
Parameters
----------
phi: float, mandatory, degrees
center of the spherical patch in ra with range
theta: float, mandatory, degrees
delta: float, mandatory, degrees
size: int, mandatory
number of samples
seed : int, optional, defaults to 1
random Seed used for generating values
Returns
-------
tuple of (phivals, thetavals) where phivals and thetavals are arrays of
size size in degrees.
"""
u = rng.uniform(size=size)
v = rng.uniform(size=size)
phi = np.radians(phi)
theta = np.radians(theta)
delta = np.radians(delta)
phivals = 2. * delta * u + (phi - delta)
phivals = np.where ( phivals >= 0., phivals, phivals + 2. * np.pi)
# use conventions in spherical coordinates
theta = np.pi/2.0 - theta
thetamax = theta + delta
thetamin = theta - delta
if thetamax > np.pi or thetamin < 0. :
raise ValueError('Function not implemented to cover wrap around poles')
# Cumulative Density Function is cos(thetamin) - cos(theta) / cos(thetamin) - cos(thetamax)
a = np.cos(thetamin) - np.cos(thetamax)
thetavals = np.arccos(-v * a + np.cos(thetamin))
# Get back to -pi/2 to pi/2 range of decs
thetavals = np.pi/2.0 - thetavals
return np.degrees(phivals) , np.degrees(thetavals)
In [30]:
class CoordSamples(snsims.PositionSamples, snsims.HealpixTiles):
def __init__(self, nside, hpOpSim, rng):
self.nside = nside
super(self.__class__, self).__init__(nside=nside, healpixelizedOpSim=hpOpSim)
self._rng = rng
@property
def randomState(self):
if self._rng is None:
raise ValueError('self._rng should not be None')
return self._rng
def positions(self, tileID, numSamples):
res_phi =
ang = hp.pix2ang(nside=self.nside, ipix=tileID, nest=True)
radius = np.degrees(np.sqrt(hp.nside2pixarea / np.pi))
phi_c, theta_c = np.degrees(ang[::-1])
phi, theta = super(self.__class__).samplePatchOnSphere(phi_c, theta_c, delta=radius, size=numSamples)
return ang
In [51]:
ang[::-1]
Out[51]:
(0.80380593285207591, 1.5473566804970937)
In [27]:
rng = np.random.RandomState(23)
In [28]:
cs = CoordSamples(nside=256, hpOpSim=hpOpSim, rng=rng)
In [32]:
ang = cs.positions(23, 20)
In [34]:
area = hp.nside2pixarea(256)
side = np.sqrt(area / np.pi)
In [46]:
res = samplePatchOnSphere(phi=ang[1], theta=ang[0], delta=side, size=100)
In [47]:
hp.ang2pix(256, theta=res[1], phi=res[0], nest=True)
Out[47]:
array([23, 23, 29, 23, 29, 23, 22, 23, 23, 23, 23, 23, 29, 23, 22, 23, 23,
23, 23, 22, 21, 21, 22, 23, 66, 23, 23, 29, 22, 23, 23, 23, 23, 23,
23, 23, 23, 23, 29, 23, 23, 23, 22, 21, 22, 23, 21, 23, 23, 22, 22,
66, 23, 23, 23, 29, 23, 23, 23, 23, 22, 23, 23, 23, 22, 23, 23, 23,
23, 23, 23, 29, 23, 23, 23, 23, 23, 23, 21, 23, 23, 23, 23, 23, 23,
66, 23, 23, 23, 23, 23, 21, 23, 23, 23, 29, 21, 23, 23, 23])
In [8]:
snsims.SimulatedTile(tileID=23)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-2b02aba4cd2c> in <module>()
----> 1 snsims.SimulatedTile(tileID=23)
TypeError: __new__() got an unexpected keyword argument 'tileID'
In [9]:
class SimTile(snsims.SimulatedTile):
pass
In [11]:
simulatedTile = SimTile(tileID=23)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-ddd1b068ea54> in <module>()
----> 1 simulatedTile = SimTile(tileID=23)
TypeError: __new__() got an unexpected keyword argument 'tileID'
In [12]:
from future.utils import with_metaclass
In [15]:
import abc
In [ ]:
In [27]:
class TestDist(with_metaclass(abc.ABCMeta, snsims.Universe)):
@property
def randomState(self):
if self._rng is None:
self._rng = np.randomState(self.tileID)
return self._rng
In [23]:
class ConcreteImplementation(TestDist):
def __init__(self, tileID, NSIDE=1):
hpOpSim = oss.HealPixelizedOpSim.fromOpSimDB(NSIDE=NSIDE, opSimDBpath=opsimdb)
self.hpTile = snsims.HealpixTiles(nside=NSIDE, healpixelizedOpSim=hpOpSim)
self.tileID = tileID
self._rng = None
@property
def randomState(self):
if self._rng is None:
self._rng = np.randomState(self.tileID)
return self._rng
@property
def snParams(self):
In [24]:
imp = ConcreteImplementation()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-ca202b7be05e> in <module>()
----> 1 imp = ConcreteImplementation()
TypeError: Can't instantiate abstract class ConcreteImplementation with abstract methods SN, randomState, snParams
In [28]:
import numpy as np
In [35]:
rng = np.random.RandomState(1)
In [36]:
rng.poisson(lam=np.array([3405.3, 34., 21.2]))
Out[36]:
array([3391, 27, 16])
In [37]:
state =rng.get_state()
In [38]:
state
Out[38]:
('MT19937', array([2629073562, 2983301384, 681580311, 4033622241, 792772838,
3306961981, 92883131, 1785085746, 3364128315, 2402025379,
3224868746, 1145213362, 3784365245, 1948434636, 2667646161,
2598854474, 921967201, 1345782310, 4019597455, 2906395199,
1349669984, 2676817993, 4201769589, 2002781766, 3540177092,
4224925813, 3661313599, 1709930435, 1812273278, 2973452884,
1592291796, 3452239013, 3588672187, 3228651068, 3191454495,
3286135343, 2640545275, 3096953148, 3746505897, 2292163827,
2164382601, 1581410039, 2413832827, 2536571847, 179684232,
1638923698, 3155158821, 1454330362, 4050607484, 607322300,
2216566078, 597866774, 1036426282, 732815996, 3131865469,
2440339870, 2814550949, 1479383443, 2449469876, 3810238677,
2923086221, 437801529, 2891199990, 1886893516, 3898673786,
376204646, 1392372379, 4123661669, 1140754642, 3539167101,
2386309702, 3740957436, 4033654965, 1720449988, 3434980330,
4213508374, 3576835843, 2818865106, 1653162115, 2935588114,
3870616539, 102614847, 1476834675, 1220770796, 1233652508,
2385138085, 1300608482, 2753953039, 262993567, 4009374062,
2143978386, 1613109469, 3072671496, 1223816410, 4088822114,
3382188205, 2250056281, 1926821318, 3806317775, 2882166470,
94227745, 2877123406, 1030225246, 1555072155, 95009460,
1855512191, 2840453856, 2478087736, 836340051, 1566383306,
2625414289, 3519123538, 840509954, 2373484829, 676528503,
2783662465, 3557034492, 2566048980, 2347785709, 3566819907,
1311855742, 198269976, 2693520819, 2127070362, 961491174,
3932714317, 837664826, 4277891831, 3535515583, 2831416447,
3505045078, 3763313683, 367436315, 3614057572, 3780746374,
2693039652, 2297021184, 2224934154, 698822522, 2718629137,
1175446314, 2603507610, 2067589016, 2280810156, 2037033584,
3956938481, 1112874779, 3264939860, 2054107185, 2354026721,
1958640221, 2844284824, 3775753525, 2462549847, 3562644229,
3683686884, 3714884555, 2266356233, 2808583945, 980888698,
1137581788, 2771236582, 1975939317, 1605707990, 614167064,
767063856, 4227905160, 3590303986, 2932373212, 2230415839,
127157074, 2328724316, 3356372094, 3215726425, 282321962,
4226412442, 106823192, 3925701436, 610765913, 281952627,
1832011890, 2670621135, 1012800992, 1489632964, 1371755819,
2529629289, 607643288, 3941535311, 3202770816, 284461833,
3696778854, 325625733, 2671656400, 1391137252, 1240723705,
3132941411, 314202987, 1301784708, 2575857120, 313287791,
3569720512, 981744121, 2986286440, 1051168756, 3881027887,
1088809168, 3421075971, 2655923113, 2577977181, 3968201444,
1406585576, 4001025594, 1854233928, 3825832114, 954907921,
3109212504, 1706316388, 1550715292, 2934259476, 1892992132,
4050317728, 4110743408, 359920673, 3542060425, 597068400,
843058885, 3799566677, 2064802063, 860032883, 2732673041,
3457529378, 3660165513, 1125914118, 4212233956, 3757086488,
2632116865, 462233764, 161423763, 141910303, 2265109737,
1983126147, 2231395445, 2147383559, 611750005, 2194081082,
1699618892, 3841025952, 1294478167, 936120505, 4102008957,
4268194620, 1994019922, 1187332833, 3953675561, 1917283619,
896867387, 1634068959, 3950876764, 485558549, 45383166,
2795959904, 1717914918, 3449856475, 3796449494, 3318166191,
1007512487, 480498390, 1730605673, 3972660041, 2287008439,
539639323, 1569683418, 3795864463, 453065119, 2449404120,
265049099, 3514892628, 2665563904, 2534523080, 1969344934,
4294466702, 1478959417, 1858551310, 3029422620, 4121720519,
2933843154, 3209557296, 3111170404, 4264711934, 2448282623,
1388503581, 1999436060, 818418468, 3283991819, 2356924521,
2684567658, 1424193429, 1187340812, 3847120877, 1987859863,
1879502046, 3422594099, 1419478013, 4148487199, 2538837125,
3694851522, 636350498, 1097832595, 3779331880, 351970715,
2534774459, 3389311029, 3762283879, 3742425828, 3882821767,
2683353, 1981273229, 4068324016, 539226467, 2411256222,
1780609115, 2059099269, 2889497980, 1123848930, 540086248,
3467353606, 3362203896, 4058078927, 93044840, 426751932,
824266620, 3444590461, 2122918776, 3339845861, 4233923286,
3733051177, 2957657929, 3110908772, 2551930098, 1294126893,
2213336821, 2124571119, 49780221, 2901327722, 2493306969,
2545470627, 102527300, 3876142393, 4097726412, 3510695954,
660912408, 2033930425, 1601509561, 969180562, 635252598,
177954239, 3054207519, 4122051269, 2787463443, 1664731394,
2907371963, 1484884283, 1560546623, 2902374922, 2395942225,
451352804, 3346805556, 3459298550, 3482428591, 10753957,
4101820340, 3306178891, 1122941824, 355877597, 1683964498,
238724805, 3926649337, 3197046734, 4277634633, 2288745211,
4202067531, 526022968, 4017453944, 1499184106, 2677441952,
2227353703, 1995296581, 1690255681, 2920887680, 605849491,
3072795503, 746910746, 2709796449, 1225135658, 657841564,
4070363626, 144842260, 3718575695, 3159187032, 88291794,
3129049475, 410962657, 1728726693, 2397606939, 4126386549,
751549633, 4226219908, 1549973222, 3060733996, 3741110422,
2530947598, 2627897488, 2317706652, 1170828427, 2671701715,
1153351468, 3762293788, 4093330405, 1641962571, 699324101,
3173743570, 1798831929, 3467616712, 4198420524, 2448981354,
499920867, 968642107, 2140815539, 4193124145, 1639223168,
284638153, 396985542, 3543438633, 237854258, 3938010494,
24441053, 2947436871, 1273496002, 719279415, 3574242559,
1040109604, 3849196601, 3250223302, 3411729501, 3031943234,
2932285520, 2932420675, 2011314805, 2480850074, 3207806491,
462404995, 3279042455, 4270524229, 1064389665, 1894847490,
721365878, 357178131, 2827490451, 2604438657, 242514037,
678802395, 1322770750, 2747624534, 4246466163, 4188936761,
1207204018, 980275996, 841637218, 1468131552, 4102349079,
586888764, 3105466755, 1628818384, 2991889790, 2801191520,
2114916962, 1124291831, 3242113092, 1082871720, 3625937786,
2796251125, 1651820702, 3427511545, 2035120316, 1024058911,
4209506140, 2527167744, 213886228, 2514956543, 2450260579,
327684603, 3444379103, 3884997363, 2844468873, 2261078634,
3926825101, 439487268, 3789435080, 1212963762, 4259079565,
2772611204, 2534236055, 2430244594, 916922266, 903950702,
3381351589, 2268543712, 3616954837, 1273083041, 1682465785,
1342921678, 2593265787, 3033724173, 2988544460, 1824668777,
1214999983, 257453352, 4187931679, 3523379959, 1481153225,
4290295859, 220376185, 4136013972, 511679284, 3510589272,
1404047266, 3712771231, 2125374725, 32037606, 3601685135,
3433623522, 841138647, 1610171318, 3920699442, 1084892922,
3146108732, 3672652561, 1148331655, 2473777375, 3039860130,
1170979324, 575756423, 1389255297, 339011744, 2351938991,
4050094885, 2773634239, 3715040333, 3920910597, 562407139,
29887881, 1623822350, 358193390, 1822261341, 1243290919,
755541153, 153529770, 994467513, 1339524978, 3174283928,
2782204324, 182251010, 1833692038, 3477775846, 2820237500,
2165639585, 4089211432, 4010345846, 1238643345, 3710224584,
2251039304, 1196168985, 3165387311, 3920626153, 1796963839,
2112227260, 3358845388, 593715887, 3046897033, 2968478428,
3846781604, 3565923316, 2452128692, 68338106, 1427007580,
2192917968, 75235680, 2134869635, 1857807303, 3745016485,
2601327385, 35512535, 559792668, 2093376088, 939608650,
2259549051, 2605376692, 2058599240, 2980581379, 3160415220,
2739135905, 254886981, 1652380747, 371107437, 1123937393,
4185309254, 864314942, 2739416220, 2185572068, 1163546293,
3491702910, 996401156, 1198755052, 2898003956, 745796080,
4127642404, 4237523457, 1274635091, 3144139009, 1600421663,
4226154574, 538248802, 373236455, 116925273], dtype=uint32), 16, 0, 0.0)
In [ ]:
rng.uniform()
In [40]:
zbin_edges = np.array([0., 1.0, 2.0, 3.0])
In [41]:
zbin_edges
Out[41]:
array([ 0., 1., 2., 3.])
In [75]:
x = zbin_edges[:-1]
In [76]:
y = zbin_edges[1:]
In [77]:
y
Out[77]:
array([ 1., 2., 3.])
In [78]:
zip(x , y)
Out[78]:
[(0.0, 1.0), (1.0, 2.0), (2.0, 3.0)]
In [79]:
zz = (34, 23, 56)
In [52]:
map(len, list(rng.uniform(low=xx, high=yy, size=zzz) for (xx, yy, zzz) in zip(x,y, zz)))
Out[52]:
[34, 23, 56]
In [80]:
arr = list(rng.uniform(low=xx, high=yy, size=zzz).tolist() for (xx, yy, zzz) in zip(x,y, zz))
In [81]:
list(_x for lst in arr for _x in lst)
Out[81]:
[0.022271511532804156,
0.8211218982189646,
0.18846839966641848,
0.6857568991725472,
0.35520938933964374,
0.9742126278950913,
0.12277212944941995,
0.18756989261723955,
0.8894424754644789,
0.5834063146082279,
0.3092463846587796,
0.0007643854781880233,
0.22759773832225305,
0.1454036341712357,
0.2034338243700906,
0.8963225568040046,
0.8730943782696405,
0.7002894376334498,
0.47049767615220905,
0.8271360023322883,
0.49582639708521237,
0.36348868328490525,
0.27560310276085875,
0.9247408135383717,
0.38698923425629217,
0.49288087343132025,
0.2544381023144221,
0.8849665188380633,
0.38326345680528673,
0.29757471531387525,
0.7175949621947544,
0.19086994317230743,
0.11819262934703845,
0.8369977822135122,
1.7441359213695353,
1.5905205476471171,
1.2428915749648706,
1.623247145187584,
1.638355320741173,
1.3275694441123251,
1.0674893430675423,
1.8805385975643267,
1.4599567310742585,
1.76387271806643,
1.2538886209427398,
1.5913871822675167,
1.4287951603487978,
1.3821006425491396,
1.1267133838469228,
1.2442666632089758,
1.0793415829583188,
1.3028583047175786,
1.7417048002315554,
1.6150157249710488,
1.6724110989339258,
1.0083830795250317,
1.67837697225132,
2.5031806905428873,
2.230908450484179,
2.1748965089819223,
2.1849829655508297,
2.8442600050750984,
2.9825897893050852,
2.9137396248416314,
2.3473694203728126,
2.780224579691364,
2.5464545907014307,
2.816442397555938,
2.4837269511889057,
2.589951298299404,
2.7872634475242615,
2.1002346431031325,
2.537007116840541,
2.3659833466696543,
2.6043068436688723,
2.9588025156370006,
2.3928229449969622,
2.3929729979416163,
2.7906369935895987,
2.6450783990854263,
2.3938840078553767,
2.6889550482302513,
2.404001609054379,
2.136113154248122,
2.4506432686721347,
2.3344193926972725,
2.217968930061168,
2.92577480163644,
2.6896393990795575,
2.598820890623422,
2.746657472123005,
2.5419710617714415,
2.7005330726195025,
2.930559819249845,
2.862616694505222,
2.167441857506521,
2.2631390239238742,
2.0716102794418045,
2.978672805158347,
2.2363840986603756,
2.660598355928599,
2.0896306989274622,
2.2107709420870187,
2.5028243835184045,
2.104953668329928,
2.3888734026947245,
2.537987720139681,
2.3392041848850997,
2.5559002111265756,
2.536768321942464,
2.3906104987369057,
2.696270083101103,
2.678482453997924]
In [66]:
mylist = []
for lst in arr:
for x in lst:
mylist.append(x)
In [67]:
len(mylist)
Out[67]:
113
In [68]:
mylist
Out[68]:
[0.5898236481098892,
0.03606831456985993,
0.20018211641098693,
0.7883011896679689,
0.012096760015357866,
0.3033456717085691,
0.021375961088070716,
0.9974849927283295,
0.5820299763434618,
0.29337627848605663,
0.9289494618931157,
0.5071194235482045,
0.4546922275524069,
0.5878715062961865,
0.2641376442105696,
0.3052882993474333,
0.37165457672418245,
0.24444799937161088,
0.5845472661166298,
0.6958461339323264,
0.07194722016400112,
0.9710833196254638,
0.7530706558694549,
0.8061631629534083,
0.7516404040812905,
0.0800609772712173,
0.48169628302117706,
0.44567295640165605,
0.6724726505249299,
0.4487372588243219,
0.7043133983839096,
0.6816453376026229,
0.6971485287527562,
0.6186007425615364,
1.1509807332796458,
1.760802456305756,
1.7810458221939571,
1.9041029859138048,
1.233768692604106,
1.1766788930723413,
1.3910991231067624,
1.3205646539951603,
1.815476929005626,
1.6135258996684316,
1.7600017523497489,
1.4276650996969642,
1.0994405629155892,
1.1150350120502464,
1.3734071317371952,
1.1938477675095072,
1.8207464986229382,
1.5996360084237167,
1.688845259018665,
1.4911099613042962,
1.0881736812421812,
1.2350776366861704,
1.461602080891622,
2.198591503053926,
2.0254762357788203,
2.7293976982561525,
2.724916747219119,
2.3303917463617028,
2.8434941260636553,
2.4284424759510737,
2.8646861447635343,
2.6570654246228895,
2.58103565741809,
2.200727488035091,
2.529546610478395,
2.8944324184275723,
2.304993975835216,
2.8709456091610535,
2.908745272858388,
2.3298445626818824,
2.683074366336564,
2.8992209495659687,
2.0756968817583035,
2.8785878632047703,
2.1908393632684806,
2.8497689100179304,
2.667118788699204,
2.344339195450054,
2.151375569992799,
2.6353709614884533,
2.8477880437147416,
2.8218493747640103,
2.6282395971306576,
2.956301509755232,
2.5899231270757697,
2.197832784647919,
2.429330722616492,
2.336745998701167,
2.991945011907281,
2.3802364961172646,
2.992700873664818,
2.5188042507334143,
2.1722797014771253,
2.07457407125148,
2.370305693773888,
2.1234188598147297,
2.6345187162918418,
2.413919845729409,
2.99091563252545,
2.929955623653318,
2.14923603230975,
2.3948822625368003,
2.4615797109623863,
2.560948755121945,
2.7803635232892683,
2.4871939090734236,
2.419993552010241,
2.2619997047193356,
2.9108061390867586]
In [ ]:
list(x for x in )
In [65]:
np.shape(arr)
Out[65]:
(3,)
In [61]:
np.shape(arr)
Out[61]:
(3,)
In [82]:
class coordSamples(snsims.HealpixTiles):
pass
In [18]:
from astropy.cosmology import Planck15
In [24]:
Planck15.comoving_volume(z=1.4) - Planck15.comoving_volume(z=1.3)
Out[24]:
$4.4903336 \times 10^{10} \; \mathrm{Mpc^{3}}$
In [25]:
Planck15.comoving_volume(z=0.1) - Planck15.comoving_volume(z=0.01)
Out[25]:
$3.3763831 \times 10^{8} \; \mathrm{Mpc^{3}}$
In [13]:
from lsst.sims.catUtils.supernovae import SNObject
In [15]:
snobj = SNObject(ra=54., dec=-47.)
In [ ]:
snobj.set(**dict(x0=1.4e-6, x1-))
In [70]:
t = ('expMJD', 'filter', 'fiveSigmaDepth')
In [74]:
list(t)
Out[74]:
['expMJD', 'filter', 'fiveSigmaDepth']
In [ ]:
In [31]:
import healpy as hp
In [32]:
hp.ud_grade(np.arange(hp.nside2npix(16)), nside_out=256)
Out[32]:
array([ 0, 1, 2, ..., 3069, 3070, 3071])
In [29]:
8189/60/60
Out[29]:
2
In [36]:
8202/ 60.
Out[36]:
136.7
In [20]:
189216030.0 /365/60/60 /24
Out[20]:
6.000000951293759
In [21]:
94608030.0 /365/60/60 /24
Out[21]:
3.0000009512937598
In [22]:
189216060.0 / 365/60/60 /24
Out[22]:
6.0000019025875195
In [23]:
283824060.0 / 365/60/60 /24
Out[23]:
9.00000190258752
In [24]:
94608000.0/ 365/60/60 /24
Out[24]:
3.0
In [ ]:
l = [3, 4, 5]
In [ ]:
Content source: rbiswas4/SNsims
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