Source code for sas.sascalc.pr.num_term

from __future__ import print_function, division

import math
import numpy as np
import copy
import sys
import logging
from sas.sascalc.pr.invertor import Invertor

logger = logging.getLogger(__name__)

[docs]class NTermEstimator(object): """ """
[docs] 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 = []
[docs] def is_odd(self, n): """ """ return bool(n % 2)
[docs] 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
[docs] def median_osc(self): """ """ osc = self.sort_osc() dv = len(osc) med = 0.5*dv odd = self.is_odd(dv) medi = 0 for i in range(dv): if odd: medi = osc[int(med)] else: medi = osc[int(med) - 1] return medi
[docs] 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 is not 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 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]) if flag9: self.dataset = new_osc1 elif flag8: self.dataset = new_osc2 else: self.dataset = new_osc3 return self.dataset
[docs] def ls_osc(self): """ """ # Generate data 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
[docs] def compare_err(self): """ """ ls = self.ls_osc() nt_ls = [] for i in range(len(ls)): r = ls[i] n = self.osc_list.index(r) + 10 nt_ls.append(n) return nt_ls
[docs] def num_terms(self, isquit_func=None): """ """ try: self.isquit_func = isquit_func nts = self.compare_err() div = len(nts) tem = 0.5*div if self.is_odd(div): nt = nts[int(tem)] else: nt = nts[int(tem) - 1] return nt, self.alpha_list[nt - 10], self.mess_list[nt - 10] except: #TODO: check the logic above and make sure it doesn't # rely on the try-except. return self.nterm_min, self.invertor.alpha, ''
#For testing
[docs]def load(path): # Read the data from the data file data_x = [] data_y = [] data_err = [] scale = None min_err = 0.0 if path is not 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 is 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.append(test_x) data_y.append(test_y) data_err.append(err) except Exception as exc: logger.error(exc) data_x = np.reshape(data_x, (len(data_x),)) data_y = np.reshape(data_y, (len(data_y),)) data_err = np.reshape(data_err, (len(data_err),)) return data_x, data_y, data_err
if __name__ == "__main__": invert = Invertor() x, y, erro = load("test/Cyl_A_D102.txt") invert.d_max = 102.0 invert.nfunc = 10 invert.x = x invert.y = y invert.err = erro # Testing estimator est = NTermEstimator(invert) print(est.num_terms())