forked from K1-Klipper/cartographer-klipper
-
Notifications
You must be signed in to change notification settings - Fork 0
/
arg_fit_new.py
161 lines (159 loc) · 7.03 KB
/
arg_fit_new.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
# 打开文件
from scipy.optimize import curve_fit
import numpy as np
import matplotlib.pyplot as plt
class TempModel:
def __init__(self, a_a, a_b, b_a, b_b, fmin, fmin_temp):
self.a_a=a_a
self.a_b=a_b
self.b_a=b_a
self.b_b=b_b
self.fmin = fmin
self.fmin_temp = fmin_temp
def compensate(self, freq, temp_source, temp_target):
if self.a_a == None or self.a_b == None or self.b_a == None or self.b_b == None:
return freq
if(self.a_a==self.a_b==0):
param_b=param_linear(freq-model.fmin,self.b_a,self.b_b)
return freq-param_b*temp_source+param_b*temp_target
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
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
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
if(B**2-4*A*C<0):
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
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
ax=(np.sqrt(B**2-4*A*C)-B)/2/A
param_a=param_linear(ax,self.a_a,self.a_b)
param_b=param_linear(ax,self.b_a,self.b_b)
return param_a*(temp_target+param_b/2/param_a)**2+ax+model.fmin
#print(-param_linear(ax,self.b_a,self.b_b)/2/param_linear(ax,self.a_a,self.a_b))
#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
#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
def line_fit(x,a,b,c):
return a*x**2+b*x+c
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-10)
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-10)
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-10)
return linear_params
def data_process(path):
freq=[]
temp=[]
with open(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=line.split(',')
try:
freq.append(float(data[3]))
temp.append(float(data[5]))
except:pass
dv=int(len(temp)/1000)
if dv>1:
freq=np.array(freq[::dv])
temp=np.array(temp[::dv])
plt.plot(temp[20:],freq[20:])
#linear_params=area_find(temp[20:],freq[20:])
param_bounds=([0,-np.inf,-np.inf],[100,np.inf,np.inf])
linear_params, params_covariance = curve_fit(line_fit, temp[20:],freq[20:],bounds=param_bounds,maxfev=100000,ftol=1e-10,xtol=1e-10)
plt.plot(temp[20:],line_fit(temp[20:],linear_params[0],linear_params[1],linear_params[2]))
try:
plt.title("Range:"+str(int(np.max(freq[20:])-np.min(freq[20:]))))
except:
pass
axis=-1*linear_params[1]/2/linear_params[0]
if(axis>120):
linear_params1, params_covariance = curve_fit(param_linear, temp[20:],freq[20:],maxfev=100000,ftol=1e-10,xtol=1e-10)
axis=120
return [0,linear_params1[0],param_linear(axis,linear_params1[0],linear_params1[1])]
linear_params[2]=line_fit(axis,linear_params[0],linear_params[1],linear_params[2])
return linear_params
def param_linear(x,a,b):
return a*x+b
while(1):
plt.figure(figsize=(25, 15))
paths=['./data1','./data2','./data3','./data4']
a=[]
b=[]
freqs=[]
num=241
#threshold=int(input('threshold set(recommend start from 1000):\n请输入阈值设置(默认推荐1000):\n'))
try:
for path in paths:
plt.subplot(num)
num+=1
temp=data_process(path)
a.append(temp[0])
b.append(temp[1])
freqs.append(temp[2])
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(None,None,None,None,2943053,23.33)
param_bounds_a=([0,-np.inf],[np.inf,np.inf])
if(np.sum(np.array(a)==0)==4):
model.a_a=0
model.a_b=0
linear_params1, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,b,maxfev=100000,ftol=1e-10,xtol=1e-10)
else:
linear_params, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,a,bounds=param_bounds_a,maxfev=100000,ftol=1e-10,xtol=1e-10)
model.a_a=linear_params[0]
model.a_b=linear_params[1]
param_bounds_b=([-np.inf,-np.inf],[0,np.inf])
linear_params1, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,b,bounds=param_bounds_b,maxfev=100000,ftol=1e-10,xtol=1e-10)
model.b_a=linear_params1[0]
model.b_b=linear_params1[1]
for path in paths:
plt.subplot(num)
num+=1
freq=[]
temp=[]
with open(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=line.split(',')
try:
freq.append(float(data[3]))
temp.append(float(data[5]))
except:pass
dv=int(len(temp)/10000)
if dv>1:
freq=np.array(freq[::dv])
temp=np.array(temp[::dv])
temp=temp[200:]
freq=freq[200:]
result0=[]
for i in range(len(temp)):
result0.append(model.compensate(freq[i],temp[i],50))
plt.plot(temp,result0)
try:
plt.title("Range:"+str(int(np.max(result0)-np.min(result0))))
except:
pass
plt.savefig('fit_output.png')
print('fit result:')
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))
break