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_loadTxt(self): s = snpy.get_sn(os.path.join(self.data, "SN2006ax.txt")) self.assertEqual(s.name, "SN2006ax")
'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 s = snpy.get_sn('SN2006ax.txt') s.choose_model('max_model', stype='st') s.fit(['B','V']) return s