149 lines
6.4 KiB
Python
149 lines
6.4 KiB
Python
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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class TempModel:
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def __init__(self, a_a, a_b, b_a, b_b, fmin, fmin_temp):
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self.a_a=a_a
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self.a_b=a_b
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self.b_a=b_a
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self.b_b=b_b
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self.fmin = fmin
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self.fmin_temp = fmin_temp
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def compensate(self, freq, temp_source, temp_target):
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if self.a_a == None or self.a_b == None or self.b_a == None or self.b_b == None:
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return freq
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A=4*(temp_source*self.a_a)**2+4*temp_source*self.a_a*self.b_a+self.b_a**2+4*self.a_a
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B=8*temp_source**2*self.a_a*self.a_b+4*temp_source*(self.a_a*self.b_b+self.a_b*self.b_a)+2*self.b_a*self.b_b+4*self.a_b-4*(freq-model.fmin)*self.a_a
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C=4*(temp_source*self.a_b)**2+4*temp_source*self.a_b*self.b_b+self.b_b**2-4*(freq-model.fmin)*self.a_b
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if(B**2-4*A*C<0):
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param_c=freq-param_linear(freq-model.fmin,self.a_a,self.a_b)*temp_source**2-param_linear(freq-model.fmin,self.b_a,self.b_b)*temp_source
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return param_linear(freq-model.fmin,self.a_a,self.a_b)*temp_target**2+param_linear(freq-model.fmin,self.b_a,self.b_b)*temp_target+param_c
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ax=(np.sqrt(B**2-4*A*C)-B)/2/A
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param_a=param_linear(ax,self.a_a,self.a_b)
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param_b=param_linear(ax,self.b_a,self.b_b)
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return param_a*(temp_target+param_b/2/param_a)**2+ax+model.fmin
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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 line0(x,a,c):
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return a*x**2+c
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def line120(x,a,c):
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return a*x**2-240*a*x+c
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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-10)
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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-10)
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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-10)
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return linear_params
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def data_process(path):
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freq=[]
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temp=[]
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with open(path, 'r') as file:
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lines = file.readlines()
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for line in lines:
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data=line.split(',')
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try:
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freq.append(float(data[3]))
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temp.append(float(data[5]))
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except:pass
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dv=int(len(temp)/1000)
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if dv>1:
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freq=np.array(freq[::dv])
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temp=np.array(temp[::dv])
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plt.plot(temp[20:],freq[20:])
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param_bounds=([0,-np.inf,-np.inf],[np.inf,np.inf,np.inf])
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linear_params, params_covariance = curve_fit(line_fit, temp[20:],freq[20:],bounds=param_bounds,maxfev=100000,ftol=1e-10,xtol=1e-10)
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try:
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plt.title("Range:"+str(int(np.max(freq[20:])-np.min(freq[20:]))))
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except:
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pass
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axis=-1*linear_params[1]/2/linear_params[0]
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if(axis>120):
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linear_params1, params_covariance = curve_fit(line120, temp[20:],freq[20:],bounds=([0,-np.inf],[np.inf,np.inf]),maxfev=100000,ftol=1e-10,xtol=1e-10)
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plt.plot(temp[20:],line120(temp[20:],linear_params1[0],linear_params1[1]))
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return [linear_params1[0],-240*linear_params1[0],line120(120,linear_params1[0],linear_params1[1])]
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elif(axis<0):
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linear_params1, params_covariance = curve_fit(line0, temp[20:],freq[20:],bounds=([0,-np.inf],[np.inf,np.inf]),maxfev=100000,ftol=1e-10,xtol=1e-10)
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plt.plot(temp[20:],line0(temp[20:],linear_params1[0],linear_params1[1]))
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return [linear_params1[0],0,line0(0,linear_params1[0],linear_params1[1])]
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plt.plot(temp[20:],line_fit(temp[20:],linear_params[0],linear_params[1],linear_params[2]))
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linear_params[2]=line_fit(axis,linear_params[0],linear_params[1],linear_params[2])
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return linear_params
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def param_linear(x,a,b):
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return a*x+b
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while(1):
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plt.figure(figsize=(25, 15))
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paths=['./data1','./data2','./data3']
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a=[]
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b=[]
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freqs=[]
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num=231
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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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temp=data_process(path)
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a.append(temp[0])
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b.append(temp[1])
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freqs.append(temp[2])
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except:
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print("please make sure you have move the 3 data file to cartographer-klipper folder\n if the files have been moved, are you running this from the cartographer-klipper folder?")
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break
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model=TempModel(None,None,None,None,2943053.8415908813,23.33)
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linear_params, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,a,maxfev=100000,ftol=1e-10,xtol=1e-10)
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model.a_a=linear_params[0]
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model.a_b=linear_params[1]
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linear_params1, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,b,maxfev=100000,ftol=1e-10,xtol=1e-10)
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model.b_a=linear_params1[0]
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model.b_b=linear_params1[1]
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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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freq=[]
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temp=[]
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with open(path, 'r') as file:
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lines = file.readlines()
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for line in lines:
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data=line.split(',')
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try:
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freq.append(float(data[3]))
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temp.append(float(data[5]))
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except:pass
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dv=int(len(temp)/10000)
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if dv>1:
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freq=np.array(freq[::dv])
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temp=np.array(temp[::dv])
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temp=temp[200:]
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freq=freq[200:]
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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],50))
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plt.plot(temp,result0)
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try:
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plt.title("Range:"+str(int(np.max(result0)-np.min(result0))))
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except:
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pass
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plt.savefig('fit_output.png')
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print('fit result:')
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print('tc_a_a:'+str(model.a_a)+'\ntc_a_b:'+str(model.a_b)+'\ntc_b_a:'+str(model.b_a)+'\ntc_b_b:'+str(model.b_b))
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break |