def fit_lc(self, taxis, band): data = simlc().reals(taxis, band)[0] mod = self.model res, fitted_model = sncosmo.fit_lc(data, mod, ['t0']) return res, fitted_model
def fit_lc(self, taxis, band):
data = simlc().reals(taxis, band)[0]
mod = self.model
res, fitted_model = sncosmo.fit_lc(data, mod, ['t0'])
return res, fitted_model
def is_discover(self, band, z, sys, ep):
"""
For a given
"""
fcosm = self.obs_filt(band)[0]
mod = simlc().set_params(band, z, peakmag=-19.1)
mag_arr=mod.bandmag(fcosm, sys, ep)
filt_arr = np.array(self.filters)
limmag = np.array(self.limits)[filt_arr == fcosm]
disc_arr = mag_arr[mag_arr < limmag]
if len(disc_arr) > 0:
print "SN is discovered by LSST"
return disc_arr
else:
print "No Observation above the threshold"
return 0
def is_discover(self, band, z, sys, ep):
"""
For a given
"""
fcosm = self.obs_filt(band)[0]
mod = simlc().set_params(band, z, peakmag=-19.1)
mag_arr = mod.bandmag(fcosm, sys, ep)
filt_arr = np.array(self.filters)
limmag = np.array(self.limits)[filt_arr == fcosm]
disc_arr = mag_arr[mag_arr < limmag]
if len(disc_arr) > 0:
print "SN is discovered by LSST"
return disc_arr
else:
print "No Observation above the threshold"
return 0
def obs_filt(self, band ,z):
"""
For a given instrument (in this case NIRCam), test which one has the greatest overlap with the redshifted rest-frame filter (i or Y in most cases)
Input: rest frame filter, redshift of observation
Output: Filter with greatest overlap, overlap value
"""
#use the SNCosmo function for extracting the bandpass
b = sncosmo.get_bandpass(band)
#obtain the wavelength and transmission values as python readable arrays
wv = b.wave
trans = b.trans
#redshifted wavelength for the rest frame filter
wv_red = wv*(1+z)
#integrate the total flux in the region of the redshifted filter
tran_int = simps(trans, wv_red)
#define array for filling the filters that have any wavelength overlap
overlap_array = []
print "Checking the filter list", self.filters
for i in self.filters:
#extract the bandpass for LSST
bp = simlc().create_LSST_bandpass(i)
wv_obs= bp.wave
tran_obs = bp.trans
if wv_red[0] > wv_obs[-1]:
print "The filter being tested is", i
print "The redshifted filter is very very red"
elif wv_red[-1] < wv_obs[0]:
print "The filter being tested is", i
print "The redshifted filter is not red enough"
else:
print "There is some wavelength overlap with filter", i
overlap_array.append(i)
print "The LSST filters which overlap with the redshifted filter are: ", overlap_array
overlap_percent=[]
for j in overlap_array:
bp = simlc().create_LSST_bandpass(i)
trans_thresh = max(bp.trans)/1e1
wv_obs = bp.wave[bp.trans > trans_thresh]
cond = (wv_red > wv_obs[0] ) & (wv_red < wv_obs[-1])
overlap_int=simps(trans[cond], wv_red[cond])
overlap_percent.append([j, overlap_int*100/tran_int])
#store the overlap percentage
overlap_percent=np.array(overlap_percent)
print "The percentages of the overlap are", overlap_percent
wave_eff_arr =[]
eff_wave_rf = b.wave_eff
eff_wave_obs = eff_wave_rf *(1+z)
for k in overlap_percent:
if len(np.unique(overlap_percent[:,1])) < len(overlap_percent):
bp = simlc().create_LSST_bandpass(k[0])
wave_eff_arr.append([k[0], abs(bp.wave_eff-eff_wave_obs)])
print "The difference between the effective wavelength for the LSST filters and the redshifted rest frame filter is:", wave_eff_arr
#deal with unique and non-unique cases separately.
if len(wave_eff_arr) > 0:
print "In case of similar overlapping values, effective wavelengths were used to decide which filter to use"
wave_eff_arr = np.array(wave_eff_arr)
return wave_eff_arr[wave_eff_arr[:,1].astype('float32') == min(wave_eff_arr[:,1].astype('float32'))]
else:
print "The values for the overlap were all unique"
return overlap_percent[overlap_percent[:,1].astype('float32')==max(overlap_percent[:,1].astype('float32')) ][0]
def is_discover(self, band, z, sys, ep):
"""
For a given
"""
fcosm = self.obs_filt(band)[0]
mod = simlc().set_params(band, z, peakmag=-19.1)
mag_arr=mod.bandmag(fcosm, sys, ep)
filt_arr = np.array(self.filters)
limmag = np.array(self.limits)[filt_arr == fcosm]
disc_arr = mag_arr[mag_arr < limmag]
if len(disc_arr) > 0:
print "SN is discovered by LSST"
return disc_arr
else:
print "No Observation above the threshold"
return 0
def z_dist_lsst(self):
time = 1000
area= 10
return list(sncosmo.zdist(0, 1.2, time=time, area=area))
def z_disc_lsst(self, band, z, sys,ep):
"""
the redshift distribution of the SNe actually discovered by LSST
"""
expected_z = self.z_dist_lsst
obs_z_arr=[]
for i in expected_z:
disc_arr =self.is_discover(band,z,sys,ep)
if len(disc_arr) > 1:
obs_z_arr.append(i)
return np.array(obs_z_arr)
def obs_filt(self, band, z):
"""
For a given instrument (in this case NIRCam), test which one has the greatest overlap with the redshifted rest-frame filter (i or Y in most cases)
Input: rest frame filter, redshift of observation
Output: Filter with greatest overlap, overlap value
"""
#use the SNCosmo function for extracting the bandpass
b = sncosmo.get_bandpass(band)
#obtain the wavelength and transmission values as python readable arrays
wv = b.wave
trans = b.trans
#redshifted wavelength for the rest frame filter
wv_red = wv * (1 + z)
#integrate the total flux in the region of the redshifted filter
tran_int = simps(trans, wv_red)
#define array for filling the filters that have any wavelength overlap
overlap_array = []
print "Checking the filter list", self.filters
for i in self.filters:
#extract the bandpass for LSST
bp = simlc().create_LSST_bandpass(i)
wv_obs = bp.wave
tran_obs = bp.trans
if wv_red[0] > wv_obs[-1]:
print "The filter being tested is", i
print "The redshifted filter is very very red"
elif wv_red[-1] < wv_obs[0]:
print "The filter being tested is", i
print "The redshifted filter is not red enough"
else:
print "There is some wavelength overlap with filter", i
overlap_array.append(i)
print "The LSST filters which overlap with the redshifted filter are: ", overlap_array
overlap_percent = []
for j in overlap_array:
bp = simlc().create_LSST_bandpass(i)
trans_thresh = max(bp.trans) / 1e1
wv_obs = bp.wave[bp.trans > trans_thresh]
cond = (wv_red > wv_obs[0]) & (wv_red < wv_obs[-1])
overlap_int = simps(trans[cond], wv_red[cond])
overlap_percent.append([j, overlap_int * 100 / tran_int])
#store the overlap percentage
overlap_percent = np.array(overlap_percent)
print "The percentages of the overlap are", overlap_percent
wave_eff_arr = []
eff_wave_rf = b.wave_eff
eff_wave_obs = eff_wave_rf * (1 + z)
for k in overlap_percent:
if len(np.unique(overlap_percent[:, 1])) < len(overlap_percent):
bp = simlc().create_LSST_bandpass(k[0])
wave_eff_arr.append([k[0], abs(bp.wave_eff - eff_wave_obs)])
print "The difference between the effective wavelength for the LSST filters and the redshifted rest frame filter is:", wave_eff_arr
#deal with unique and non-unique cases separately.
if len(wave_eff_arr) > 0:
print "In case of similar overlapping values, effective wavelengths were used to decide which filter to use"
wave_eff_arr = np.array(wave_eff_arr)
return wave_eff_arr[wave_eff_arr[:, 1].astype('float32') == min(
wave_eff_arr[:, 1].astype('float32'))]
else:
print "The values for the overlap were all unique"
return overlap_percent[overlap_percent[:, 1].astype(
'float32') == max(overlap_percent[:, 1].astype('float32'))][0]
def is_discover(self, band, z, sys, ep):
"""
For a given
"""
fcosm = self.obs_filt(band)[0]
mod = simlc().set_params(band, z, peakmag=-19.1)
mag_arr = mod.bandmag(fcosm, sys, ep)
filt_arr = np.array(self.filters)
limmag = np.array(self.limits)[filt_arr == fcosm]
disc_arr = mag_arr[mag_arr < limmag]
if len(disc_arr) > 0:
print "SN is discovered by LSST"
return disc_arr
else:
print "No Observation above the threshold"
return 0
def z_dist_lsst(self):
time = 1000
area = 10
return list(sncosmo.zdist(0, 1.2, time=time, area=area))
def z_disc_lsst(self, band, z, sys, ep):
"""
the redshift distribution of the SNe actually discovered by LSST
"""
expected_z = self.z_dist_lsst
obs_z_arr = []
for i in expected_z:
disc_arr = self.is_discover(band, z, sys, ep)
if len(disc_arr) > 1:
obs_z_arr.append(i)
return np.array(obs_z_arr)
