pr.add_argument("--search_length", "-t",type=float,default=32.0)
pr.add_argument("--significance", "-f",type=float,default=3)
pr.add_argument("--method", "-m",default='std')
args = pr.parse_args()
data_path = args.lcdatadir
file_name = args.filename
if data_path[-1]!='/': data_path = data_path+'/'
unit1_data_band1 = fits.open(data_path+file_name)
unit1_time_band1, unit1_rate_band1, unit1_r_er_band1 = mf.data_extractor(unit1_data_band1)
#~ print unit1_time_band1[-1]-unit1_time_band1[0]
gap_start = mf.gap_detector(unit1_time_band1,10)
gap_end = gap_start+1
#~ print gap_start
list_of_peak_indices = []
list_of_peak_times = []
# Search criteria
f = args.significance
T = args.search_length
shot_sep = args.shot_sep
pr.add_argument("--fitted_text", "-f", default="")
pr.add_argument("--append_text", "-a", default="")
pr.add_argument("--boundary_dist", "-b", default=0.1, type=float)
pr.add_argument("--par_ratio", "-r", default=0.3, type=float)
args = pr.parse_args()
data_path = args.lcdatadir
file_name1 = args.filename1
if data_path[-1] != '/': data_path = data_path + '/'
fitted_text = args.fitted_text
unit1_data_total = fits.open(data_path + file_name1)
unit1_time_total, unit1_rate_total, unit1_r_er_total = mf.data_extractor(
unit1_data_total)
unit1_peak_features = np.loadtxt(args.append_text + '_unit1_fitted_vals_' +
fitted_text + '.txt')
unit2_peak_features = np.loadtxt(args.append_text + '_unit2_fitted_vals_' +
fitted_text + '.txt')
peak_time_from_file = np.loadtxt('{0}_peak_time_list_{1}.txt'.format(
args.append_text, args.peak_file_text))
peak_index = np.loadtxt(args.append_text + '_index_list_' +
args.peak_file_text + ".txt",
dtype=int)
bounds = np.array([[1, 1e-3, -100], [20000, 100, -1e-3]])
min_bound = bounds[0]
max_bound = bounds[1]
#~ good_fits_index = np.where ()
import numpy as np import matplotlib.pyplot as plt from astropy.io import fits import my_funcs as mf data_path = "../lc_data/" unit1_data_band1 = fits.open(data_path+"laxpc_lc_0p05_unit1_3.0_5.0keV.lc") unit1_data_band2 = fits.open(data_path+"laxpc_lc_0p05_unit1_5.0_10.0keV.lc") unit2_data_band1 = fits.open(data_path+"laxpc_lc_0p05_unit2_3.0_5.0keV.lc") unit2_data_band2 = fits.open(data_path+"laxpc_lc_0p05_unit2_5.0_10.0keV.lc") unit1_time_band1, unit1_rate_band1, unit1_r_er_band1 = mf.data_extractor(unit1_data_band1) unit2_time_band1, unit2_rate_band1, unit2_r_er_band1 = mf.data_extractor(unit2_data_band1) unit1_time_band2, unit1_rate_band2, unit1_r_er_band2 = mf.data_extractor(unit1_data_band2) unit2_time_band2, unit2_rate_band2, unit2_r_er_band2 = mf.data_extractor(unit2_data_band2) gap_start = mf.gap_detector(unit1_time_band1,10) gap_end = gap_start+1 f_list = np.arange(1,11,1) number_peaks = np.zeros(len(f_list)) print f_list #~ stat_flag = False