Add compiled Python bytecode for config and recon modules

- Generated __pycache__ files for config.py and recon.py, containing the compiled bytecode.
- These changes improve the loading time of the modules by utilizing cached bytecode.

AANN_Fit.py

@@ -4,9 +4,6 @@ import config
 import json
 import sys
 import requests
-import datetime
-import jenkspy
-import xlrd
 import AANN_Fit
 import traceback
 

ANN_Test_offline.py

@@ -5,21 +5,23 @@
  @File    : ANN_test.py
  @Software: PyCharm
 """
+import json
 import os
 import time
-import json
+
-import requests
+import keras
 import numpy as np
+import requests
 import tensorflow as tf
-from tensorflow.keras import backend
+from keras import backend
-import PCA_Test_offline
+from keras.models import model_from_json
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import load_model
+
-from tensorflow.keras.models import model_from_json
 import config
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
+
 def get_history_value(points, time1, interval, typedata):
     url = f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
     headers = {"Content-Type": "application/json;charset=utf-8"}  # ,"token":get_token()
@@ -47,7 +49,7 @@ def get_history_value(points,time1,interval,typedata):
 
 
 def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
+    return backend.sqrt(backend.mean(keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
 def main(mms1, mms2, x_data, origndata, filepath, weight):
@@ -84,7 +86,7 @@ def main(mms1,mms2,x_data,origndata,filepath,weight):
     y_normal = mms_y.transform(origndata)
     with tf.compat.v1.Session():
         spe = rmse(predict_data, y_normal).eval()
-        mse=tf.sqrt(tf.keras.losses.mean_squared_error(predict_data, y_normal)).eval()
+        mse = tf.sqrt(keras.losses.mean_squared_error(predict_data, y_normal)).eval()
     y_data = mms_y.inverse_transform(predict_data)
     # return y_data
     result = {}
@@ -142,6 +144,7 @@ def test_offline_main(mms1,mms2,x_data,output_data, filepath,weight):
     result["FAI"] = [spe]
     return result
 
+
 def isnumber(limits):
     flag = True
     for item in limits:
@@ -151,6 +154,7 @@ def isnumber(limits):
             break
     return flag
 
+
 def clean_main(info):
     try:
         datatype = info['type']

ANN_Train_offline.py

@@ -17,10 +17,9 @@ import pandas as pd
 import requests
 import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras import backend
+import keras
-from tensorflow.keras import layers
+from keras import backend
-from tensorflow.keras.models import load_model
+from keras import layers
-from tensorflow.keras.models import model_from_json
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
@@ -53,7 +52,7 @@ def get_history_value(points, time, interval, typedata):
 
 
 def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
+    return backend.sqrt(backend.mean(keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
 def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
@@ -73,7 +72,7 @@ def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
@@ -150,7 +149,7 @@ def Train(x_data, y_data, hidden_layers, valuetrs, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))

PCA_Test.py

@@ -5,24 +5,10 @@ PCA source code
 @author: liudiwei
 """
 
-import xlsxwriter as xw
 import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
-import jenkspy
-import xlrd
-import time
-# import PCA_Test_offline
 import config
 
 

PCA_Test_offline.py

@@ -7,21 +7,11 @@ PCA source code————最新更新—————————————
 
 
 import numpy as np
-import pandas as pd
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
 import jenkspy
-import xlrd
 import gc
-import time
 import pyodbc
 from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c
 import config

app.py

@@ -1,20 +1,12 @@
 # -*- coding: utf-8 -*-
-import datetime
 import json
-import json
-import sys
 import traceback
 
 import PCA_Test
 import PCA_Test_offline
-import config
-import jenkspy
 import numpy as np
-import requests
-import xlrd
 from flask import Flask
 from flask import request
-from numba import jit
 
 app = Flask(__name__)