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Fit_LC_non_detection.py
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Fit_LC_non_detection.py
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import matplotlib, psycopg2
matplotlib.use('Agg')
import numpy as np
import astropy
import sncosmo
import matplotlib.pyplot as plt
from mpi4py import MPI
flat_cols=['#1abc9c','#2ecc71','#3498db','#9b59b6','#34495e','#f39c12','#d35400','#c0392b','#7f8c8d']
l=np.genfromtxt('PTFNAME_Coord_List_Ia.dat', usecols=(0,), delimiter=',', dtype=None)
source=sncosmo.get_source('salt2',version='2.4')
model=sncosmo.Model(source=source)
bpass=np.loadtxt('PTF48R.dat')
wavelength=bpass[:,0]
transmission=bpass[:,1]
band=sncosmo.Bandpass(wavelength,transmission, name='ptf48r')
sncosmo.registry.register(band, force=True)
def Check_Dates(hml, peakd):
low=False
high=False
#print dates
dates=hml[:,0].astype(float)[hml[:,0].astype(float)>0]
dates_trim=dates[(dates>(peakd-20.)) & (dates<(peakd+50.))]
dates_trim=np.sort(dates_trim)
#print dates_trim
#print 'sn_arr', sn_arr[:,0]
#print 'All diff:', np.diff(sn_arr[:,0])
check_low=dates_trim[(dates_trim<peakd)]
#print check_low
#print 'Differences:', np.diff(check_low)
if len(np.diff(check_low)[np.diff(check_low)>0.5])>=1:
low=True
# print 'Check', np.diff(check_low)[np.diff(check_low)>0.5]
# print 'Great Success on low'
check_high=dates_trim[(dates_trim>peakd)] #CHECK THIS!!!!
#print check_high
#print 'Differences:', np.diff(check_high)
if len(np.diff(check_high)[np.diff(check_high)>0.5])>=1:
high=True
# print 'Check', np.diff(check_high)[np.diff(check_high)>0.5]
# print 'Great Success on high'
if low and high == True:
#print 'YES!'
return True
else:
#print 'NO!'
return False
N_MODELS_TOTAL = len(l) # total number of models to run
nproc = MPI.COMM_WORLD.Get_size() # number of processes
my_rank = MPI.COMM_WORLD.Get_rank() # The number/rank of this process
my_node = MPI.Get_processor_name() # Node where this MPI process runs
# total number of models to run
n_models = N_MODELS_TOTAL / nproc # number of models for each thread
remainder = N_MODELS_TOTAL - ( n_models * nproc ) # the remainder. e.g. your number of models may
# little trick to spread remainder out among threads
# if say you had 19 total models, and 4 threads
# then n_models = 4, and you have 3 remainder
# this little loop would distribute these three
if remainder < my_rank + 1:
my_extra = 0
extra_below = remainder
else:
my_extra = 1
extra_below = my_rank
# where to start and end your loops for each thread
my_nmin = (my_rank * n_models) + extra_below
my_nmax = my_nmin + n_models + my_extra
# total number you actually do
ndo = my_nmax - my_nmin
##Connecting to the Database
conn = psycopg2.connect(host='srv01050.soton.ac.uk', user='frohmaier', password='rates', database='frohmaier')
cur = conn.cursor()
for i in range( my_nmin, my_nmax):
try:
hml=np.load(str(l[i])+'_LC.npy')
hml_dat=astropy.table.Table(data=hml, names=('time','flux', 'flux_err','zp','filter','zpsys'), dtype=('float','float','float','float','str','str'))
print 'Doing:', l[i]
cur.execute("SELECT redshift, obsdate, phase from specinfo where ptfname=%s;",(str(l[i]),))
zed=cur.fetchone()
if len(zed)==0:
print 'Bad Query'
break
#print zed[0]
model.set(z=zed[0])
res, fitted_model=sncosmo.mcmc_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, nburn=100, nsamples=5000)
#res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, verbose=True)
#res, fitted_model=sncosmo.nest_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)},)
pdate=res.parameters[1]
pass_4cut=Check_Dates(hml, pdate)
#print l[i], pass_4cut
fig=sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color=np.random.choice(flat_cols), figtext=str(l[i])+'\n'+str(pass_4cut), xfigsize=10, pulls=False)
plt.axvline(-20., color='black', linestyle='--')
plt.axvline(+50., color='black', linestyle='--')
plt.savefig('LC_Fixed/'+str(l[i])+'.png', dpi=150, bbox_inches='tight')
plt.close()
print '### Parameters ###'
print str(l[i]), float(zed[0]), float(0), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c'])
print 'chi2', sncosmo.chisq(hml_dat, fitted_model)
print 'ndof', len(hml_dat)-4. #len(data)-len(vparam_names)
print 'red_chi2', sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)
print 'absolute magnitue', fitted_model.source_peakabsmag('bessellb','ab')
# print 'chi2', res.chisq
# print 'res.ndof', res.ndof
# print 'red_chisq', res.chisq/res.ndof
#cur.execute("INSERT INTO sncosmo_fits (ptfname, redshift, redshift_err, t0, t0_err, x0, x0_err, x1, x1_err, c, c_err, ra, dec, pass_cut, redchi2, abs_mag) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);",(str(l[i]), float(zed[0]), float(0), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c']), float(hml[:,8][0]), float(hml[:,9][0]), pass_4cut,float(sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)), float(fitted_model.source_peakabsmag('bessellb','ab')),))
#conn.commit()
print 'Done:', l[i]
except ValueError:
print 'Value Error'