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update arg_fit.py.

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Signed-off-by: Sgr A* VMT <1611902585@qq.com>
This commit is contained in:
Sgr A* VMT
2024-06-24 03:11:13 +00:00
committed by Gitee
parent cc369f13f2
commit 59b0f9853e
1 changed files with 3 additions and 14 deletions
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17
arg_fit.py
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@@ -1,4 +1,3 @@
# 打开文件
from scipy.optimize import curve_fit from scipy.optimize import curve_fit
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@@ -24,9 +23,6 @@ class TempModel:
param_a=param_linear(ax,self.a_a,self.a_b) param_a=param_linear(ax,self.a_a,self.a_b)
param_b=param_linear(ax,self.b_a,self.b_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 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): def line_fit(x,a,b,c):
return a*x**2+b*x+c return a*x**2+b*x+c
def line0(x,a,c): def line0(x,a,c):
@@ -61,9 +57,7 @@ def data_process(path):
freq=[] freq=[]
temp=[] temp=[]
with open(path, 'r') as file: with open(path, 'r') as file:
# 逐行读取文件内容
lines = file.readlines() lines = file.readlines()
# 遍历每行内容
for line in lines: for line in lines:
data=line.split(',') data=line.split(',')
try: try:
@@ -75,7 +69,6 @@ def data_process(path):
freq=np.array(freq[::dv]) freq=np.array(freq[::dv])
temp=np.array(temp[::dv]) temp=np.array(temp[::dv])
plt.plot(temp[20:],freq[20:]) plt.plot(temp[20:],freq[20:])
#linear_params=area_find(temp[20:],freq[20:])
param_bounds=([0,-np.inf,-np.inf],[np.inf,np.inf,np.inf]) param_bounds=([0,-np.inf,-np.inf],[np.inf,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) linear_params, params_covariance = curve_fit(line_fit, temp[20:],freq[20:],bounds=param_bounds,maxfev=100000,ftol=1e-10,xtol=1e-10)
@@ -100,12 +93,11 @@ def param_linear(x,a,b):
while(1): while(1):
plt.figure(figsize=(25, 15)) plt.figure(figsize=(25, 15))
paths=['./data1','./data2','./data3','./data4'] paths=['./data1','./data2','./data3']
a=[] a=[]
b=[] b=[]
freqs=[] freqs=[]
num=241 num=231
#threshold=int(input('threshold set(recommend start from 1000):\n请输入阈值设置(默认推荐1000):\n'))
try: try:
for path in paths: for path in paths:
plt.subplot(num) plt.subplot(num)
@@ -115,9 +107,8 @@ while(1):
b.append(temp[1]) b.append(temp[1])
freqs.append(temp[2]) freqs.append(temp[2])
except: 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") 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?")
break break
#反向求值
model=TempModel(None,None,None,None,2943053.8415908813,23.33) model=TempModel(None,None,None,None,2943053.8415908813,23.33)
linear_params, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,a,maxfev=100000,ftol=1e-10,xtol=1e-10) linear_params, params_covariance = curve_fit(param_linear, np.array(freqs)-model.fmin,a,maxfev=100000,ftol=1e-10,xtol=1e-10)
model.a_a=linear_params[0] model.a_a=linear_params[0]
@@ -131,9 +122,7 @@ while(1):
freq=[] freq=[]
temp=[] temp=[]
with open(path, 'r') as file: with open(path, 'r') as file:
# 逐行读取文件内容
lines = file.readlines() lines = file.readlines()
# 遍历每行内容
for line in lines: for line in lines:
data=line.split(',') data=line.split(',')
try: try: