def load_light_select_dataset(input_path, file_name, is_mix=False): string_name = file_name.split('_') data_type = string_name[0] pattern = string_name[1] # path = 'C:\\git\\data_stream\\lightcurve_benchmark\\{}'.format(data_type) path = '{}{}'.format(input_path, data_type) if is_mix: data = data_stream(path=path, type=data_type, pattern=pattern, mix_path=file_name[4:]) data.load_mixdata_fromfile() else: L = int(string_name[2][1:]) I = int(string_name[3][1:]) data = data_stream(path=path, type=data_type, pattern=pattern, len=L, interval=I) data.load_data_fromfile() return data
def load_light_dataset(input_path, data_type): data_list = {} LS = [1, 5] IS = [5, 10, 30, 60] patterns = ["sq"] for pattern in patterns: for L in LS: for I in IS: title = "{}_{}_L{}_I{}".format(data_type, pattern, L, I) path = '{}{}'.format(input_path, data_type) # path = 'C:\\git\\data_stream\\lightcurve_benchmark\\{}'.format(data_type) data = data_stream(path=path, type=type, pattern=pattern, len=L, interval=I) data.load_data_fromfile() data_list[title] = data return data_list
LS = [5] IS = [10, 30, 60] # IS = [5] process_var = 1 process_mean = 0 dic = {} for type in types: for pattern in patterns: for L in LS: for I in IS: # data = data_stream(path='C:\\Users\\karnk\\git\\data_stream\\dataset', type=type, pattern=pattern, len=L, # interval=I) data = data_stream( path= 'D:\\git_project\\data stream\\lightcurve_benchmark\\pca', type=type, pattern=pattern, len=L, interval=I) data.load_data_fromfile() key_dict = "{}_L{}_I_{}".format(pattern, L, I) dic[key_dict] = data choice = "" while choice != "exit": # try: pattern_in = input("Input pattern {} : ".format(patterns)) # L_in = input("Input Len {} : ".format(LS)) I_in = input("Input Interval {} : ".format(IS)) input_format = "{}_L{}_I_{}".format(pattern_in, LS[0], I_in) element = dic.get(input_format)
IS = [10, 30, 60] bin_periods = [4, 6] # first loop for bin_period in bin_periods: for data_type in data_types: for type in types: for pattern in patterns: for L in LS: for I in IS: transfounds = [] data = data_stream( path= 'C:\\git\\data_stream\\lightcurve_benchmark\\{}' .format(data_type), type=type, # path='D:\\git_project\\data stream\\lightcurve_benchmark\\{}'.format(data_type), type=type, pattern=pattern, len=L, interval=I) # path='C:\\lightcurve_benchmark\\lightcurve_benchmark\\{}'.format(data_type), type=type, # pattern=pattern, len=L, # interval=I) data.load_data_fromfile() for k in K_start: data = cal_k_tran(data=data, input_k=k, input_bin=bin_period) true_alerts, false_alerts, tran_found = data.count_TF_tranfound( ) transfounds.append([k, tran_found])
types = ["Ligthcurve"] # types = ["gaia"] patterns = ["sq"] L = 1 IS = [5,10,30,60] bin_periods = [3,4,10,15,20,23,25,30] # first loop for type in types: for data_type in data_types: for pattern in patterns: for I in IS: # path = 'D:\\git_project\\data stream\\lightcurve_benchmark\\{}'.format(data_type) path = 'C:\\git\\data_stream\\lightcurve_benchmark\\{}'.format(data_type) # path = 'C:\\git\\data_stream\\gaia\\{}'.format(data_type) data = data_stream(path=path, type=type,pattern=pattern, len=L,interval=I) data.load_data_fromfile() for bin_period in bin_periods: file_format_name = '{}_{}_bin{}'.format(type, data_type, bin_period) for k in ks: data_sk, result_text = cal_sk_bin(data=data, input_bin=bin_period,input_k=k, file_format="{}_k{}".format(file_format_name,k), L=L, I=I) print("{}_k{} cheb sk".format(file_format_name,k)) head, rows = data_sk.result_for_ROC(data_type, Algo='SK',K=k, Bin=bin_period, cheb_size=Cheb_win) ut.list_to_csv(rows=rows, csv_name="ks_bin//roc_bin.csv", is_append=True) head, rows = data_sk.result_for_ROC(data_type, Algo='SK', K=k, Bin=bin_period,is_normalization=True,cheb_size=Cheb_win) ut.list_to_csv(rows=rows, csv_name="ks_bin//roc_nor.csv", is_append=True) # # # ut.list_to_txt(rows=result_text, csv_name="ks_bin//{}".format(txt_file_name),