137 lines
5.3 KiB
Python
137 lines
5.3 KiB
Python
# 打开文件
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from scipy.optimize import curve_fit
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import warnings
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warnings.filterwarnings("ignore")
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class TempModel:
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def __init__(self, amfg, tcc, tcfl, tctl, fmin, fmin_temp):
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self.amfg = amfg
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self.tcc = tcc
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self.tcfl = tcfl
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self.tctl = tctl
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self.fmin = fmin
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self.fmin_temp = fmin_temp
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def _tcf(self, f, df, dt, tctl):
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tctl = self.tctl if tctl is None else tctl
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tc = self.tcc + self.tcfl * df + tctl * df * df
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return f + self.amfg * tc * dt * f
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def compensate(self, freq, temp_source, temp_target, tctl=None):
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dt = temp_target - temp_source
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dfmin = self.fmin * self.amfg * self.tcc * \
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(temp_source - self.fmin_temp)
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df = freq - (self.fmin + dfmin)
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if dt < 0.:
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f2 = self._tcf(freq, df, dt, tctl)
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dfmin2 = self.fmin * self.amfg * self.tcc * \
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(temp_target - self.fmin_temp)
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df2 = f2 - (self.fmin + dfmin2)
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f3 = self._tcf(f2, df2, -dt, tctl)
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ferror = freq - f3
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freq = freq + ferror
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df = freq - (self.fmin + dfmin)
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return self._tcf(freq, df, dt, tctl)
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def line_fit(x,a,b,c):
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return a*x**2+b*x+c
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def fit(data,tcc,tcfl,tctl):
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result=[]
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model.tcc=tcc
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model.tcfl=tcfl
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model.tctl-tctl
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for j in range(len(datas)):
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for i in range(len(data)):
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result.append(model.compensate(datas[j][3000],35,data[i]))
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return result
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def area_find(temp,freq):
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middle=int(len(temp)/100/2)*100
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i=j=100
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i_flag=True
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j_flag=True
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for c in range(100):
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if(i_flag):
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i=i+100
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if middle-i>=0:
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linear_params, params_covariance = curve_fit(line_fit, temp[middle-i:middle+j],freq[middle-i:middle+j],maxfev=100000,ftol=1e-10,xtol=1e-20)
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minus=line_fit(temp[middle-i:middle+j],linear_params[0],linear_params[1],linear_params[2])-freq[middle-i:middle+j]
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if np.sum(np.square(minus))/len(minus)>threshold:
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i=i-100
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i_flag=False
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if(j_flag):
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j=j+100
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if middle+j<=len(freq):
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linear_params, params_covariance = curve_fit(line_fit, temp[middle-i:middle+j],freq[middle-i:middle+j],maxfev=100000,ftol=1e-10,xtol=1e-20)
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minus=line_fit(temp[middle-i:middle+j],linear_params[0],linear_params[1],linear_params[2])-freq[middle-i:middle+j]
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if np.sum(np.square(minus))/len(minus)>threshold:
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j=j-100
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j_flag=False
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linear_params, params_covariance = curve_fit(line_fit, temp[middle-i:middle+j],freq[middle-i:middle+j],maxfev=100000,ftol=1e-10,xtol=1e-20)
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return linear_params
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def data_process(path):
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data=[]
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file_path = path # 替换为你的文件路径
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with open(file_path, 'r') as file:
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# 逐行读取文件内容
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lines = file.readlines()
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# 遍历每行内容
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for line in lines:
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data.append(line.split(','))
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file.close()
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full_data=pd.DataFrame(data[1:-1],columns=data[0])
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temp=np.array(full_data['temp']).astype(np.float32)
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freq=np.array(full_data['freq']).astype(np.float32)
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freq=freq[::100]
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temp=temp[::100]
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plt.plot(temp[10:],freq[10:])
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linear_params=area_find(temp,freq)
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plt.plot(temp,line_fit(temp,linear_params[0],linear_params[1],linear_params[2]))
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data0=line_fit(np.arange(5,80,0.01),linear_params[0],linear_params[1],linear_params[2])
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return data0
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def main():
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plt.figure(figsize=(25, 15))
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paths=['./data1','./data2','./data3','./data4']
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datas=[]
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num=241
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threshold=int(input('threshold set(recommend start from 250):\n请输入阈值设置(默认推荐250):\n'))
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try:
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for path in paths:
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plt.subplot(num)
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num+=1
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datas.append(data_process(path))
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except:
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print("please make sure you have move the 4 data file to IDM folder\n请确认你有把4个文件拷到IDM文件夹内")
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return
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#反向求值
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model=TempModel(1,-2.1429828e-05,-1.8980091e-10,3.6738370e-16,2943053.84,20.33)
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p0=[-2.1429828e-05,-1.8980091e-10,3.6738370e-16]
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params, params_covariance = curve_fit(fit,np.arange(5,80,0.01),np.hstack(datas),p0=p0,maxfev=1000000,ftol=1e-10,xtol=1e-10)
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for path in paths:
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plt.subplot(num)
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num+=1
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data=[]
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file_path = path # 替换为你的文件路径
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with open(file_path, 'r') as file:
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# 逐行读取文件内容
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lines = file.readlines()
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# 遍历每行内容
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for line in lines:
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data.append(line.split(','))
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file.close()
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full_data=pd.DataFrame(data[1:-1],columns=data[0])
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temp=np.array(full_data['temp']).astype(np.float32)
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freq=np.array(full_data['freq']).astype(np.float32)
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freq=freq[::100]
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temp=temp[::100]
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result0=[]
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for i in range(len(temp)):
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result0.append(model.compensate(freq[i],temp[i],20.66))
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plt.plot(temp[10:],result0[10:])
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plt.savefig('fit.png')
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print('fit result:')
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print('tc_tcc:'+str(params[0])+'\ntc_tcfl:'+str(params[1])+'\ntc_tctl:'+str(params[2]))
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if __name__== "__main__" :
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threshold＝250
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main() |