python应用 曲线拟合03

问题

---

有许多待拟合的曲线，需批量拟合。

解决

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写一个类

 # -*- coding: utf-8 -*-
 """
 @author: kurrrr
 """

 import numpy as np
 import matplotlib.pyplot as plt
 import struct
 from scipy.optimize import curve_fit

 t_pre = 26  # global
 p = 0.6  # global
 q = 1.2  # global

 class Signal(object):
     _t = [x/250.0 for x in range(0, 20000)]

     def __init__(self):
         self._u = []

     @property
     def u(self):
         return self._u

     @u.setter
     def u(self, u):
         self._u = u

     @property
     def t(self):
         return self._t

     def draw(self):
         plt.scatter(self._t, self._u, marker='.', label="original data")
         plt.show()

     def get_baseline(self):
         return np.mean(self._u[0:250])  # 250 points to get baseline

     def get_max(self):
         return np.max(self._u)

     def get_max_t(self):
         return np.argmax(self._u)/250.0

 '''
 t_pre :     preamplifier tau
 b:          number of fast photons
 c:          number of slow photons
 p:          tau of fast photons
 q:          tau of slow photons
 v:          x offset
 w:          y offset
 '''

 def pre_func_1(x, w):
     return w

 def pre_func_2(x, b, c, p, q, v, w):
     global t_pre
     return b*t_pre/(t_pre-p) * \
             (np.exp(-(x-v)/t_pre)-np.exp(-(x-v)/p)) \
             + c*t_pre/(t_pre-q) * \
             (np.exp(-(x-v)/t_pre)-np.exp(-(x-v)/q)) + w

 def pre_func(x, b, c, p, q, v, w):
     return np.piecewise(x, [x < v, x >= v], [lambda x: pre_func_1(x, w),
                         lambda x: pre_func_2(x, b, c, p, q, v, w)])

 def main():
     global t_pre
     global p
     global q

     data_file = open('run0035.bin', 'rb')
     signal = Signal()

     fast = []
     slow = []
     u_temp = []
     for n in range(10000):
         u_temp.clear()
         for _ in range(0, 20000):
             data_temp = data_file.read(2)
             data_dec, = struct.unpack('h', data_temp)
             u_temp.append(-data_dec)
         signal.u = u_temp

         u_baseline = signal.get_baseline()
         u_max = signal.get_max()
         t_max = signal.get_max_t()
         b = (u_baseline - u_max) / 2.0
         c = (u_baseline - u_max) / 2.0
         v = t_max
         w = u_max

         fit_par = [b, c, p, q, v, w]
         popt, pcov = curve_fit(pre_func, signal.t,
                                signal.u, fit_par, maxfev=50000)

         if popt[2] < popt[3]:
             fast.append(popt[2])
             slow.append(popt[3])
         else:
             fast.append(popt[3])
             slow.append(popt[2])

     fast_slow = list(zip(fast, slow))
     for xx in fast_slow:
         if xx[0] > 100 or xx[1] >100:
             fast_slow.remove(xx)

     fast_new = []
     slow_new = []
     for xx in fast_slow:
         fast_new.append(xx[0])
         slow_new.append(xx[1])
     plt.scatter(fast_new, slow_new, marker='.')
     plt.show()

     data_file.close()

 if __name__ == '__main__':
     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。