def snobj():
import snpy
snobj = snpy.get_sn('SN2006ax.txt')
return snobj
def test_sn_inequality(snobj):
from snpy import get_sn
sn2 = get_sn('SN2006ax.txt')
sn2.B.MJD[0] += 0.1
assert sn2 != snobj
def test_loadTxt(self):
s = snpy.get_sn(os.path.join(self.data, 'SN2006ax.txt'))
self.assertEqual(s.name, "SN2006ax")
def test_save_load(snobj):
import snpy
snobj.save('temp.snpy')
t = snpy.get_sn('temp.snpy')
assert snobj == t
def test_object_equality(snobj):
from snpy import get_sn
sn2 = get_sn('SN2006ax.txt')
assert sn2 == snobj
def snobj():
import snpy
snobj = snpy.get_sn('SN2006ax.txt')
for f in snobj.data:
snobj.data[f].template(method='spline2')
return snobj
def snobj():
import snpy
return snpy.get_sn('SN2006ax.txt')
def test_sn_inequality(snobj):
from snpy import get_sn
sn2 = get_sn('SN2006ax.txt')
sn2.B.MJD[0] += 0.1
assert sn2 != snobj
def test_loadTxt(self):
s = snpy.get_sn(os.path.join(self.data, "SN2006ax.txt"))
self.assertEqual(s.name, "SN2006ax")
def test_save_load(snobj):
import snpy
snobj.save('temp.snpy')
t = snpy.get_sn('temp.snpy')
assert snobj == t
def test_object_equality(snobj):
from snpy import get_sn
sn2 = get_sn('SN2006ax.txt')
assert sn2 == snobj
'J_K': J-band for Swope at LCO
'H_K': H-band for Swope at LCO
'K_K': K-band for Swope at LCO
stritzinger
'Us': Kron-Cousins U filter based on Stritzinger et al. 2005
'Bs': Kron-Cousins B filter based on Stritzinger et al. 2005
'Vs': Kron-Cousins B filter based on Stritzinger et al. 2005
'Rs': Kron-Cousins R filter based on Stritzinger et al. 2005
'Is': Kron-Cousins I filter based on Stritzinger et al. 2005
'''
datpath = '/data1/SN2019ein/work/lc_v2/'
datpath = '/data7/cschoi/sngal/NGC3367/phot/cut/fitting/'
# open new terminal and type 'snpy' starting with new environmnet, to avoid sql connetion error
s = get_sn('SN2018kp_snpy.txt', sql=None)
# 2nd try
model = 'EBV_model'
s.choose_model(model, stype='dm15')
s.restbands['BANDI'] = 'Bi'
s.restbands['VANDI'] = 'Vi'
s.restbands['RANDI'] = 'Ri'
s.restbands['IANDI'] = 'Ii'
s.restbands['WFCAMJ'] = 'WFCAMJ'
s.restbands['WFCAMH'] = 'WFCAMH'
s.restbands['WFCAMK'] = 'WFCAMK'
#s.fit( mangle=True, kcorr=True, k_stretch=True) # 20201009 -> success!
#DM = 33.003 +/- 0.012 +/- 0.121 (sys)
#!/usr/bin/env python
import matplotlib
matplotlib.use('Agg')
from snpy import get_sn
from snpy.utils import fit1dcurve
from snpy.version import __version__
# Test the loading of the objects, interpolating the light-curves, and
# computing k-corrections.
s = get_sn('SN2006ax.txt')
for f in s.data:
s.data[f].template()
s.kcorr()
s.save('SN2006ax_kcorr.snpy')
s.save('old_saves/SN2006ax_kcorr-%s.snpy' % __version__)
methods = fit1dcurve.functions.keys()
for method in methods:
s.B.template(method=method)
s.B.plot()
from os import listdir from os.path import isfile, join ########## Customer set ################################################## figfolder = "/Users/yanxiaomeng/Dropbox/project/snoopy/pipeline/Figs/ThirdReleaseData/004kcorr/" # Figure output path mypath = "/Users/yanxiaomeng/Dropbox/project/snoopy/pipeline/Data/ThirdReleaseData/FormateBVRI/" # Read in path ksfolder = "/Users/yanxiaomeng/Dropbox/project/snoopy/pipeline/Data/ThirdReleaseData/kCorrected/" # File output path ########################################################################### onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))] f = 17 print f filepath = mypath + onlyfiles[f] s = snpy.get_sn(filepath) s.restbands['B'] = 'Bs' s.restbands['r'] = 'Rs' s.restbands['i'] = 'Is' s.restbands['V0'] = 'V' s.restbands['V'] = 'V' s.fit() figpath = figfolder + onlyfiles[f] + "lc.png" s.plot() matplotlib.pyplot.savefig(figpath) # for f in range(0, len(onlyfiles)): # f = 17 # try: # print f # filepath = mypath + onlyfiles[f]
def snobj():
import snpy
s = snpy.get_sn('SN2006ax.txt')
s.choose_model('max_model', stype='st')
s.fit(['B', 'V'])
return s
def snobj():
import snpy
snobj = snpy.get_sn("SN2006ax.txt")
return snobj
def snobj():
import snpy
snobj = snpy.get_sn('SN2006ax.txt')
for f in snobj.data:
snobj.data[f].template(method='spline2')
return snobj
def snobj():
import snpy
s = snpy.get_sn('SN2006ax.txt')
s.choose_model('max_model', stype='st')
s.fit(['B','V'])
return s
