add main

arg_fit.py

@@ -89,43 +89,48 @@ def data_process(path):
     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
-threshold=int(input('threshold set(recommend start from 250):\n请输入阈值设置(默认推荐250):\n'))
-try:
+def main():
+    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")
+        return
+    #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
-        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")
-#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]))
+        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]))
+
+if __name__== "__main__" :
+    main()