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')