def main():
for i in list_of_index:
# The number of observations
nobs = Give_Func.give_me_nobs(i,model,n)
print(nobs)
# The Down sampled array
d_array = Give_Func.Give_Me_Down_Sample(nobs)
# It returns this : No. observations, 'n' number of periods, and system's best ave
l_curves = Give_Func.Give_Me_Light_Curves_Down_Sampled(i,nobs,model,n,d_array)
print(l_curves)
print('The main loop works')
def main_no():
# This is NOT down-sampled
for i in list_of_index:
# Gets all the information of the rr_lyrae
t,mag,dmag,metadata,nobs = rr_info(i)
# Returns
nobs,period_n,period_true = Give_Func.give_me_n_best_periods(i,model,n,t,mag,dmag,
metadata,nobs,threshold)
# The n best periods' power as <type 'numpy.ndarray'> of <type 'numpy.float64'>
power = model.score(period_n)
print('power:',power)
# Comparing power with the threshold
for j,significance in enumerate(power):
if significance > threshold_pow:
if period_n[j] < 1 :
print('fit template')
print('signififnace',significance)
print('period',period_n[j])
def main():
for i in list_of_index:
# It returns this : No. of observations, 'n' number of periods, and the system's best ave
l_curves = Give_Func.give_me_n_best_periods(i,model,n)
print(l_curves)
print('The main loop works')
