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