问题
有许多待拟合的曲线，需批量拟合。
解决
写一个类
1 # -*- coding: utf-8 -*- 2 """ 3 @author: kurrrr 4 """ 5 6 import numpy as np 7 import matplotlib.pyplot as plt 8 import struct 9 from scipy.optimize import curve_fit 10 11 t_pre = 26 # global 12 p = 0.6 # global 13 q = 1.2 # global 14 15 16 class Signal(object): 17 _t = [x/250.0 for x in range(0, 20000)] 18 19 def __init__(self): 20 self._u = [] 21 22 @property 23 def u(self): 24 return self._u 25 26 @u.setter 27 def u(self, u): 28 self._u = u 29 30 @property 31 def t(self): 32 return self._t 33 34 def draw(self): 35 plt.scatter(self._t, self._u, marker='.', label="original data") 36 plt.show() 37 38 def get_baseline(self): 39 return np.mean(self._u[0:250]) # 250 points to get baseline 40 41 def get_max(self): 42 return np.max(self._u) 43 44 def get_max_t(self): 45 return np.argmax(self._u)/250.0 46 47 48 ''' 49 t_pre : preamplifier tau 50 b: number of fast photons 51 c: number of slow photons 52 p: tau of fast photons 53 q: tau of slow photons 54 v: x offset 55 w: y offset 56 ''' 57 58 59 def pre_func_1(x, w): 60 return w 61 62 63 def pre_func_2(x, b, c, p, q, v, w): 64 global t_pre 65 return b*t_pre/(t_pre-p) * 66 (np.exp(-(x-v)/t_pre)-np.exp(-(x-v)/p)) 67 + c*t_pre/(t_pre-q) * 68 (np.exp(-(x-v)/t_pre)-np.exp(-(x-v)/q)) + w 69 70 71 def pre_func(x, b, c, p, q, v, w): 72 return np.piecewise(x, [x < v, x >= v], [lambda x: pre_func_1(x, w), 73 lambda x: pre_func_2(x, b, c, p, q, v, w)]) 74 75 76 def main(): 77 global t_pre 78 global p 79 global q 80 81 data_file = open('run0035.bin', 'rb') 82 signal = Signal() 83 84 fast = [] 85 slow = [] 86 u_temp = [] 87 for n in range(10000): 88 u_temp.clear() 89 for _ in range(0, 20000): 90 data_temp = data_file.read(2) 91 data_dec, = struct.unpack('h', data_temp) 92 u_temp.append(-data_dec) 93 signal.u = u_temp 94 95 u_baseline = signal.get_baseline() 96 u_max = signal.get_max() 97 t_max = signal.get_max_t() 98 b = (u_baseline - u_max) / 2.0 99 c = (u_baseline - u_max) / 2.0 100 v = t_max 101 w = u_max 102 103 fit_par = [b, c, p, q, v, w] 104 popt, pcov = curve_fit(pre_func, signal.t, 105 signal.u, fit_par, maxfev=50000) 106 107 if popt[2] < popt[3]: 108 fast.append(popt[2]) 109 slow.append(popt[3]) 110 else: 111 fast.append(popt[3]) 112 slow.append(popt[2]) 113 114 fast_slow = list(zip(fast, slow)) 115 for xx in fast_slow: 116 if xx[0] > 100 or xx[1] >100: 117 fast_slow.remove(xx) 118 119 fast_new = [] 120 slow_new = [] 121 for xx in fast_slow: 122 fast_new.append(xx[0]) 123 slow_new.append(xx[1]) 124 plt.scatter(fast_new, slow_new, marker='.') 125 plt.show() 126 127 data_file.close() 128 129 130 if __name__ == '__main__': 131 main()
- 第 114 行，通过 zip() 函数将两个 list 关联，做成一个新的列表，其元素是元组。