add argument fit

arg_fit.py

@@ -0,0 +1,131 @@
+# 打开文件
+from scipy.optimize import curve_fit
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+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
+    threshold=250
+    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
+plt.figure(figsize=(25, 15))
+paths=['./data1','./data2','./data3','./data4']
+datas=[]
+num=241
+try:
+    for path in paths:
+        plt.subplot(num)
+        num+=1
+        datas.append(data_process(path))
+except:
+    print("check if the data is prepared!")
+#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=1000,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]))

requirements.txt

@@ -1,2 +1,4 @@
 # klipper python virtual environment requirements for IDM
 numpy>=1.16.6
+pandas>=1.4.2
+scipy>=1.10.0