import math import numpy import copy from sans.pr.invertor import Invertor class Num_terms(): """ """ def __init__(self, invertor): """ """ self.invertor = invertor self.nterm_min = 10 self.nterm_max = len(self.invertor.x) if self.nterm_max > 50: self.nterm_max = 50 self.isquit_func = None self.osc_list = [] self.err_list = [] self.alpha_list = [] self.mess_list = [] self.dataset = [] def is_odd(self, n): """ """ return bool(n % 2) def sort_osc(self): """ """ #import copy osc = copy.deepcopy(self.dataset) lis = [] for i in range(len(osc)): osc.sort() re = osc.pop(0) lis.append(re) return lis def median_osc(self): """ """ osc = self.sort_osc() dv = len(osc) med = float(dv) / 2.0 odd = self.is_odd(dv) medi = 0 for i in range(dv): if odd == True: medi = osc[int(med)] else: medi = osc[int(med) - 1] return medi def get0_out(self): """ """ inver = self.invertor self.osc_list = [] self.err_list = [] self.alpha_list = [] for k in range(self.nterm_min, self.nterm_max, 1): if self.isquit_func != None: self.isquit_func() best_alpha, message, _ = inver.estimate_alpha(k) inver.alpha = best_alpha inver.out, inver.cov = inver.lstsq(k) osc = inver.oscillations(inver.out) err = inver.get_pos_err(inver.out, inver.cov) if osc > 10.0: break self.osc_list.append(osc) self.err_list.append(err) self.alpha_list.append(inver.alpha) self.mess_list.append(message) new_osc1 = [] new_osc2 = [] new_osc3 = [] flag9 = False flag8 = False flag7 = False for i in range(len(self.err_list)): if self.err_list[i] <= 1.0 and self.err_list[i] >= 0.9: new_osc1.append(self.osc_list[i]) flag9 = True if self.err_list[i] < 0.9 and self.err_list[i] >= 0.8: new_osc2.append(self.osc_list[i]) flag8 = True if self.err_list[i] < 0.8 and self.err_list[i] >= 0.7: new_osc3.append(self.osc_list[i]) flag7 = True if flag9 == True: self.dataset = new_osc1 elif flag8 == True: self.dataset = new_osc2 else: self.dataset = new_osc3 return self.dataset def ls_osc(self): """ """ # Generate data ls_osc = self.get0_out() med = self.median_osc() #TODO: check 1 ls_osc = self.dataset ls = [] for i in range(len(ls_osc)): if int(med) == int(ls_osc[i]): ls.append(ls_osc[i]) return ls def compare_err(self): """ """ ls = self.ls_osc() #print "ls", ls nt_ls = [] for i in range(len(ls)): r = ls[i] n = self.osc_list.index(r) + 10 #er = self.err_list[n] #nt = self.osc_list.index(r) + 10 nt_ls.append(n) #print "nt list", nt_ls return nt_ls def num_terms(self, isquit_func=None): """ """ try: self.isquit_func = isquit_func nts = self.compare_err() div = len(nts) tem = float(div)/2.0 odd = self.is_odd(div) if odd == True: nt = nts[int(tem)] else: nt = nts[int(tem) - 1] return nt, self.alpha_list[nt - 10], self.mess_list[nt-10] except: return self.nterm_min, self.alpha_list[10], self.mess_list[10] #For testing def load(path): # Read the data from the data file data_x = numpy.zeros(0) data_y = numpy.zeros(0) data_err = numpy.zeros(0) scale = None min_err = 0.0 if not path == None: input_f = open(path,'r') buff = input_f.read() lines = buff.split('\n') for line in lines: try: toks = line.split() test_x = float(toks[0]) test_y = float(toks[1]) if len(toks) > 2: err = float(toks[2]) else: if scale == None: scale = 0.05 * math.sqrt(test_y) #scale = 0.05/math.sqrt(y) min_err = 0.01 * y err = scale * math.sqrt(test_y) + min_err #err = 0 data_x = numpy.append(data_x, test_x) data_y = numpy.append(data_y, test_y) data_err = numpy.append(data_err, err) except: pass return data_x, data_y, data_err if __name__ == "__main__": i = Invertor() x, y, erro = load("test/Cyl_A_D102.txt") i.d_max = 102.0 i.nfunc = 10 #i.q_max = 0.4 #i.q_min = 0.07 i.x = x i.y = y i.err = erro #i.out, i.cov = i.lstsq(10) # Testing estimator est = Num_terms(i) print est.num_terms()