.

v1_multifailure.py

@@ -0,0 +1,348 @@
+# Csar Fdez, UdL, 2025
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import keras
+import os.path
+import pickle
+from keras import layers
+from optparse import OptionParser
+
+
+parser = OptionParser()
+parser.add_option("-t", "--train", dest="train", help="Trains the models (false)", default=False, action="store_true")
+
+(options, args) = parser.parse_args()
+
+
+# data files arrays. Index:
+# 0.  No failure
+# 1.  Blocked evaporator
+# 2.   Full Blocked condenser
+# 3.   Partial Blocked condenser
+# 4   Fan condenser not working
+# 5.  Open door
+
+
+NumberOfFailures=5
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+datafiles=[]
+for i in range(NumberOfFailures+1):
+    datafiles.append([])
+
+# Next set of ddata corresponds to Freezer, SP=-26
+datafiles[0]=['2024-08-07_5_','2024-08-08_5_'] 
+datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
+datafiles[2]=['2024-12-18_5_','2024-12-19_5_'] 
+datafiles[3]=['2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_','2024-12-25_5_','2024-12-26_5_'] 
+datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_'] 
+#datafiles[4]=[] 
+
+# Features suggested by Xavier
+features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
+features=['r1 s1','r1 s2','r1 s3','r1 s4','r1 s5','r1 s6','r1 s7','r1 s8','r1 s9','r1 s10','r2 s1','r2 s2','r2 s3','r2 s4','r2 s5','r2 s6','r2 s7','r2 s8','r2 s9','pa1 apiii','tc s1','tc s2','tc s3']
+NumFeatures=len(features)
+
+df_list=[]
+for i in range(NumberOfFailures+1):
+    df_list.append([])
+
+for i in range(NumberOfFailures+1):
+    dftemp=[]
+    for f in datafiles[i]:
+        print("                 ", f)
+        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
+        df1 = pd.read_csv('./data/'+f+'.csv')
+        dftemp.append(df1)
+    df_list[i]=pd.concat(dftemp)
+
+
+# subsampled to 5'  =  30 * 10"
+# We consider smaples every 5' because in production, we will only have data at this frequency
+subsamplingrate=30
+
+dataframe=[]
+for i in range(NumberOfFailures+1):
+    dataframe.append([])
+
+for i in range(NumberOfFailures+1):
+    datalength=df_list[i].shape[0]
+    dataframe[i]=df_list[i].iloc[range(0,datalength,subsamplingrate)][features]
+    dataframe[i].reset_index(inplace=True,drop=True)
+    dataframe[i].dropna(inplace=True)
+
+
+# Train data is first 2/3 of data
+# Test data is: last 1/3 of data 
+dataTrain=[]
+dataTest=[]
+for i in range(NumberOfFailures+1):
+    dataTrain.append(dataframe[i].values[0:int(dataframe[i].shape[0]*2/3),:])
+    dataTest.append(dataframe[i].values[int(dataframe[i].shape[0]*2/3):,:])
+
+
+def normalize2(train,test):
+    # merges train and test
+    means=[]
+    stdevs=[]
+    for i in range(NumFeatures):
+        means.append(train[:,i].mean())
+        stdevs.append(train[:,i].std())
+    return( (train-means)/stdevs, (test-means)/stdevs )
+
+dataTrainNorm=[]
+dataTestNorm=[]
+for i in range(NumberOfFailures+1):
+    dataTrainNorm.append([])
+    dataTestNorm.append([])
+
+for i in range(NumberOfFailures+1):
+    (dataTrainNorm[i],dataTestNorm[i])=normalize2(dataTrain[i],dataTest[i])
+
+def plotData():    
+    fig, axes = plt.subplots(
+        nrows=NumberOfFailures+1, ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumberOfFailures+1):
+        axes[i][0].plot(np.concatenate((dataTrainNorm[i][:,0],dataTestNorm[i][:,0])),label="Fail "+str(i)+",  feature 0")
+        axes[i][1].plot(np.concatenate((dataTrainNorm[i][:,1],dataTestNorm[i][:,1])),label="Fail "+str(i)+",  feature 1")
+    #axes[1].legend()
+    #axes[0].set_ylabel(features[0])
+    #axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData()
+
+
+TIME_STEPS = 12
+def create_sequences(values, time_steps=TIME_STEPS):
+    output = []
+    for i in range(len(values) - time_steps + 1):
+        output.append(values[i : (i + time_steps)])
+    return np.stack(output)
+
+x_train=[]
+for i in range(NumberOfFailures+1):
+    x_train.append(create_sequences(dataTrainNorm[i]))
+
+
+model=[]
+modelckpt_callback =[]
+es_callback =[]
+path_checkpoint=[]
+for i in range(NumberOfFailures+1):
+    model.append([])
+    model[i] = keras.Sequential(
+        [
+            layers.Input(shape=(x_train[i].shape[1], x_train[i].shape[2])),
+            layers.Conv1D(
+                filters=64,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=0.2),
+            layers.Conv1D(
+                filters=32,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(
+                filters=32,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=0.2),
+            layers.Conv1DTranspose(
+                filters=64,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(filters=x_train[i].shape[2], kernel_size=7, padding="same"),
+        ]
+    )
+    model[i].compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
+    model[i].summary()
+    path_checkpoint.append("model_v1_"+str(i)+"._checkpoint.weights.h5")
+    es_callback.append(keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=15))
+    modelckpt_callback.append(keras.callbacks.ModelCheckpoint( monitor="val_loss", filepath=path_checkpoint[i], verbose=1, save_weights_only=True, save_best_only=True,))
+
+
+if options.train:
+    history=[]
+    for i in range(NumberOfFailures+1):
+        history.append(model[i].fit( x_train[i], x_train[i], epochs=400, batch_size=128, validation_split=0.3, callbacks=[  es_callback[i], modelckpt_callback[i]      ],))
+
+    fig, axes = plt.subplots(
+        nrows=int(np.ceil((NumberOfFailures+1)/2)), ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(int(np.ceil((NumberOfFailures+1)/2))):
+        for j in range(2):
+            r=2*i+j
+            if r < NumberOfFailures+1:
+                axes[i][j].plot(history[r].history["loss"], label="Training Loss")
+                axes[i][j].plot(history[r].history["val_loss"], label="Val Loss")
+                axes[i][j].legend()
+    plt.show()
+else:
+    for i in range(NumberOfFailures+1):
+        model[i].load_weights(path_checkpoint[i])
+
+
+x_train_pred=[]
+train_mae_loss=[]
+threshold=[]
+for i in range(NumberOfFailures+1):
+    x_train_pred.append(model[i].predict(x_train[i]))
+    train_mae_loss.append(np.mean(np.abs(x_train_pred[i] - x_train[i]), axis=1))
+    threshold.append(np.max(train_mae_loss[i],axis=0))
+
+print("Threshold : ",threshold)
+for i in range(NumberOfFailures+1):
+    threshold[i]=threshold[i]*1.7
+# Threshold is enlarged because, otherwise, for subsamples at 5' have many false positives
+
+
+#  1st scenario. Detect only anomaly.  Later, we will classiffy it
+# Test data=  testnormal + testfail1 + testtail2 + testfail3 + testfail4 + testnormal
+d=np.vstack((dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4],dataTestNorm[0]))
+
+x_test = create_sequences(d)
+x_test_pred = model[0].predict(x_test)
+test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+
+
+# Define ranges for plotting in different colors
+testRanges=[]
+r=dataTestNorm[0].shape[0]
+testRanges.append([0,r])
+for i in range(1,NumberOfFailures+1):
+    rnext=r+dataTestNorm[i].shape[0]
+    testRanges.append([r,rnext] )
+    r=rnext
+testRanges.append([r, x_test.shape[0]  ])
+
+
+def AtLeastOneTrue(x):
+    for i in range(NumFeatures):
+        if x[i]:
+            return True
+    return False
+
+anomalies = test_mae_loss > threshold[0]
+anomalous_data_indices = []
+for i in range(anomalies.shape[0]):
+    if AtLeastOneTrue(anomalies[i]):
+    #if anomalies[i][0] or anomalies[i][1] or anomalies[i][2] or anomalies[i][3]:
+        anomalous_data_indices.append(i)
+
+#print(anomalous_data_indices)
+
+
+# Let's plot only a couple of features
+def plotData2():    
+    fig, axes = plt.subplots(
+        nrows=2, ncols=1, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    axes[0].plot(range(len(x_train[0])),x_train[0][:,0,0],label="normal")
+    axes[0].plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,0],label="abnormal")
+    axes[0].plot(len(x_train[0])+np.array(anomalous_data_indices),x_test[anomalous_data_indices,0,0],color='red',marker='.',linewidth=0,label="abnormal detection")
+    axes[0].legend()
+    axes[1].plot(range(len(x_train[0])),x_train[0][:,0,1],label="normal")
+    axes[1].plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,1],label="abnormal")
+    axes[1].plot(len(x_train[0])+np.array(anomalous_data_indices),x_test[anomalous_data_indices,0,1],color='red',marker='.',linewidth=0,label="abnormal detection")
+    axes[1].legend()
+    axes[0].set_ylabel(features[0])
+    axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData2()
+
+
+#   2nd scenario. Go over anomalies and classify it by less error
+'''   
+#This code works, but too slow
+anomalous_data_type=[]
+for i in anomalous_data_indices:
+    error=[]
+    for m in range(1,NumberOfFailures+1):
+        error.append(np.mean(np.mean(np.abs(model[m].predict(x_test[i:i+1,:,:])-x_test[i:i+1,:,:]),axis=1)))
+    anomalous_data_type.append(np.argmin(error)+1)
+'''
+
+anomalous_data_type=[]
+x_test_predict=[]
+for m in range(NumberOfFailures+1):
+    x_test_predict.append(model[m].predict(x_test))
+
+
+for i in anomalous_data_indices:
+    error=[]
+    for m in range(1,NumberOfFailures+1):
+        error.append(np.mean(np.mean(np.abs(x_test_predict[m][i:i+1,:,:]-x_test[i:i+1,:,:]),axis=1)))
+    anomalous_data_type.append(np.argmin(error)+1)
+
+
+# For plotting purposes
+
+
+anomalous_data_indices_by_failure=[]
+for i in range(NumberOfFailures+1):
+    anomalous_data_indices_by_failure.append([])
+
+for i in range(len(anomalous_data_indices)):
+    print(i," ",anomalous_data_type[i])
+    anomalous_data_indices_by_failure[anomalous_data_type[i]].append(anomalous_data_indices[i])  
+
+
+colorline=['violet','lightcoral','cyan','lime','grey']
+colordot=['darkviolet','red','blue','green','black']
+
+featuresToPlot=['r1 s1','r1 s3','r1 s5','r2 s3','r2 s4','pa1 apiii','tc s1','tc s2','tc s3']
+indexesToPlot=[]
+for i in featuresToPlot:
+    indexesToPlot.append(features.index(i))
+
+def plotData3():
+    NumFeaturesToPlot=len(indexesToPlot)
+    fig, axes = plt.subplots(
+        nrows=NumFeaturesToPlot, ncols=1, figsize=(25, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumFeaturesToPlot):
+        init=0
+        end=len(x_train[0])
+        axes[i].plot(range(init,end),x_train[0][:,0,indexesToPlot[i]],label="normal train")
+        #axes.plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,0],label="abnormal")
+        init=end
+        end+=testRanges[0][1]
+        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]],label="normal test")
+        init=end
+        end+=(testRanges[1][1]-testRanges[1][0])
+        for j in range(1,NumberOfFailures+1):
+            axes[i].plot(range(init,end),x_test[testRanges[j][0]:testRanges[j][1],0,indexesToPlot[i]],label="fail type "+str(j), color=colorline[j-1])
+            init=end
+            end+=(testRanges[j+1][1]-testRanges[j+1][0])
+
+            axes[i].plot(len(x_train[0])+np.array(anomalous_data_indices_by_failure[j]),x_test[anomalous_data_indices_by_failure[j],0,indexesToPlot[i]],color=colordot[j-1],marker='.',linewidth=0,label="abnormal detection type "+str(j))
+
+        init=end-(testRanges[NumberOfFailures+1][1]-testRanges[NumberOfFailures+1][0])
+        end=init+(testRanges[0][1]-testRanges[0][0])
+        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]],color='orange')
+
+        if i==0:
+            axes[i].legend(bbox_to_anchor=(1, 0.5))
+        axes[i].set_ylabel(features[indexesToPlot[i]])
+        axes[i].grid()
+    plt.show()
+
+
+plotData3()
+

v1_multifailure_importance_analysis.py

@@ -0,0 +1,348 @@
+# Csar Fdez, UdL, 2025
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import keras
+import os.path
+import pickle
+from keras import layers
+from optparse import OptionParser
+
+
+parser = OptionParser()
+parser.add_option("-t", "--train", dest="train", help="Trains the models (false)", default=False, action="store_true")
+
+(options, args) = parser.parse_args()
+
+
+# data files arrays. Index:
+# 0.  No failure
+# 1.  Blocked evaporator
+# 2.   Full Blocked condenser
+# 3.   Partial Blocked condenser
+# 4   Fan condenser not working
+# 5.  Open door
+
+
+NumberOfFailures=5
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+datafiles=[]
+for i in range(NumberOfFailures+1):
+    datafiles.append([])
+
+# Next set of ddata corresponds to Freezer, SP=-26
+datafiles[0]=['2024-08-07_5_','2024-08-08_5_'] 
+datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
+datafiles[2]=['2024-12-18_5_','2024-12-19_5_'] 
+datafiles[3]=['2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_','2024-12-25_5_','2024-12-26_5_'] 
+datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_'] 
+#datafiles[4]=[] 
+
+# Features suggested by Xavier
+features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
+features=['r1 s1','r1 s2','r1 s3','r1 s4','r1 s5','r1 s6','r1 s7','r1 s8','r1 s9','r1 s10','r2 s1','r2 s2','r2 s3','r2 s4','r2 s5','r2 s6','r2 s7','r2 s8','r2 s9','pa1 apiii','tc s1','tc s2','tc s3']
+NumFeatures=len(features)
+
+df_list=[]
+for i in range(NumberOfFailures+1):
+    df_list.append([])
+
+for i in range(NumberOfFailures+1):
+    dftemp=[]
+    for f in datafiles[i]:
+        print("                 ", f)
+        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
+        df1 = pd.read_csv('./data/'+f+'.csv')
+        dftemp.append(df1)
+    df_list[i]=pd.concat(dftemp)
+
+
+# subsampled to 5'  =  30 * 10"
+# We consider smaples every 5' because in production, we will only have data at this frequency
+subsamplingrate=30
+
+dataframe=[]
+for i in range(NumberOfFailures+1):
+    dataframe.append([])
+
+for i in range(NumberOfFailures+1):
+    datalength=df_list[i].shape[0]
+    dataframe[i]=df_list[i].iloc[range(0,datalength,subsamplingrate)][features]
+    dataframe[i].reset_index(inplace=True,drop=True)
+    dataframe[i].dropna(inplace=True)
+
+
+# Train data is first 2/3 of data
+# Test data is: last 1/3 of data 
+dataTrain=[]
+dataTest=[]
+for i in range(NumberOfFailures+1):
+    dataTrain.append(dataframe[i].values[0:int(dataframe[i].shape[0]*2/3),:])
+    dataTest.append(dataframe[i].values[int(dataframe[i].shape[0]*2/3):,:])
+
+
+def normalize2(train,test):
+    # merges train and test
+    means=[]
+    stdevs=[]
+    for i in range(NumFeatures):
+        means.append(train[:,i].mean())
+        stdevs.append(train[:,i].std())
+    return( (train-means)/stdevs, (test-means)/stdevs )
+
+dataTrainNorm=[]
+dataTestNorm=[]
+for i in range(NumberOfFailures+1):
+    dataTrainNorm.append([])
+    dataTestNorm.append([])
+
+for i in range(NumberOfFailures+1):
+    (dataTrainNorm[i],dataTestNorm[i])=normalize2(dataTrain[i],dataTest[i])
+
+def plotData():    
+    fig, axes = plt.subplots(
+        nrows=NumberOfFailures+1, ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumberOfFailures+1):
+        axes[i][0].plot(np.concatenate((dataTrainNorm[i][:,0],dataTestNorm[i][:,0])),label="Fail "+str(i)+",  feature 0")
+        axes[i][1].plot(np.concatenate((dataTrainNorm[i][:,1],dataTestNorm[i][:,1])),label="Fail "+str(i)+",  feature 1")
+    #axes[1].legend()
+    #axes[0].set_ylabel(features[0])
+    #axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData()
+
+
+TIME_STEPS = 12
+def create_sequences(values, time_steps=TIME_STEPS):
+    output = []
+    for i in range(len(values) - time_steps + 1):
+        output.append(values[i : (i + time_steps)])
+    return np.stack(output)
+
+x_train=[]
+for i in range(NumberOfFailures+1):
+    x_train.append(create_sequences(dataTrainNorm[i]))
+
+
+model=[]
+modelckpt_callback =[]
+es_callback =[]
+path_checkpoint=[]
+for i in range(NumberOfFailures+1):
+    model.append([])
+    model[i] = keras.Sequential(
+        [
+            layers.Input(shape=(x_train[i].shape[1], x_train[i].shape[2])),
+            layers.Conv1D(
+                filters=64,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=0.2),
+            layers.Conv1D(
+                filters=32,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(
+                filters=32,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=0.2),
+            layers.Conv1DTranspose(
+                filters=64,
+                kernel_size=7,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(filters=x_train[i].shape[2], kernel_size=7, padding="same"),
+        ]
+    )
+    model[i].compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
+    model[i].summary()
+    path_checkpoint.append("model_v1_"+str(i)+"._checkpoint.weights.h5")
+    es_callback.append(keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=15))
+    modelckpt_callback.append(keras.callbacks.ModelCheckpoint( monitor="val_loss", filepath=path_checkpoint[i], verbose=1, save_weights_only=True, save_best_only=True,))
+
+
+if options.train:
+    history=[]
+    for i in range(NumberOfFailures+1):
+        history.append(model[i].fit( x_train[i], x_train[i], epochs=400, batch_size=128, validation_split=0.3, callbacks=[  es_callback[i], modelckpt_callback[i]      ],))
+
+    fig, axes = plt.subplots(
+        nrows=int(np.ceil((NumberOfFailures+1)/2)), ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(int(np.ceil((NumberOfFailures+1)/2))):
+        for j in range(2):
+            r=2*i+j
+            if r < NumberOfFailures+1:
+                axes[i][j].plot(history[r].history["loss"], label="Training Loss")
+                axes[i][j].plot(history[r].history["val_loss"], label="Val Loss")
+                axes[i][j].legend()
+    plt.show()
+else:
+    for i in range(NumberOfFailures+1):
+        model[i].load_weights(path_checkpoint[i])
+
+
+x_train_pred=[]
+train_mae_loss=[]
+threshold=[]
+for i in range(NumberOfFailures+1):
+    x_train_pred.append(model[i].predict(x_train[i]))
+    train_mae_loss.append(np.mean(np.abs(x_train_pred[i] - x_train[i]), axis=1))
+    threshold.append(np.max(train_mae_loss[i],axis=0))
+
+print("Threshold : ",threshold)
+for i in range(NumberOfFailures+1):
+    threshold[i]=threshold[i]*1.7
+# Threshold is enlarged because, otherwise, for subsamples at 5' have many false positives
+
+
+#  1st scenario. Detect only anomaly.  Later, we will classiffy it
+# Test data=  testnormal + testfail1 + testtail2 + testfail3 + testfail4 + testnormal
+d=np.vstack((dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4],dataTestNorm[0]))
+
+x_test = create_sequences(d)
+x_test_pred = model[0].predict(x_test)
+test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+
+
+# Define ranges for plotting in different colors
+testRanges=[]
+r=dataTestNorm[0].shape[0]
+testRanges.append([0,r])
+for i in range(1,NumberOfFailures+1):
+    rnext=r+dataTestNorm[i].shape[0]
+    testRanges.append([r,rnext] )
+    r=rnext
+testRanges.append([r, x_test.shape[0]  ])
+
+
+def AtLeastOneTrue(x):
+    for i in range(NumFeatures):
+        if x[i]:
+            return True
+    return False
+
+anomalies = test_mae_loss > threshold[0]
+anomalous_data_indices = []
+for i in range(anomalies.shape[0]):
+    if AtLeastOneTrue(anomalies[i]):
+    #if anomalies[i][0] or anomalies[i][1] or anomalies[i][2] or anomalies[i][3]:
+        anomalous_data_indices.append(i)
+
+#print(anomalous_data_indices)
+
+
+# Let's plot only a couple of features
+def plotData2():    
+    fig, axes = plt.subplots(
+        nrows=2, ncols=1, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    axes[0].plot(range(len(x_train[0])),x_train[0][:,0,0],label="normal")
+    axes[0].plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,0],label="abnormal")
+    axes[0].plot(len(x_train[0])+np.array(anomalous_data_indices),x_test[anomalous_data_indices,0,0],color='red',marker='.',linewidth=0,label="abnormal detection")
+    axes[0].legend()
+    axes[1].plot(range(len(x_train[0])),x_train[0][:,0,1],label="normal")
+    axes[1].plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,1],label="abnormal")
+    axes[1].plot(len(x_train[0])+np.array(anomalous_data_indices),x_test[anomalous_data_indices,0,1],color='red',marker='.',linewidth=0,label="abnormal detection")
+    axes[1].legend()
+    axes[0].set_ylabel(features[0])
+    axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData2()
+
+
+#   2nd scenario. Go over anomalies and classify it by less error
+'''   
+#This code works, but too slow
+anomalous_data_type=[]
+for i in anomalous_data_indices:
+    error=[]
+    for m in range(1,NumberOfFailures+1):
+        error.append(np.mean(np.mean(np.abs(model[m].predict(x_test[i:i+1,:,:])-x_test[i:i+1,:,:]),axis=1)))
+    anomalous_data_type.append(np.argmin(error)+1)
+'''
+
+anomalous_data_type=[]
+x_test_predict=[]
+for m in range(NumberOfFailures+1):
+    x_test_predict.append(model[m].predict(x_test))
+
+
+for i in anomalous_data_indices:
+    error=[]
+    for m in range(1,NumberOfFailures+1):
+        error.append(np.mean(np.mean(np.abs(x_test_predict[m][i:i+1,:,:]-x_test[i:i+1,:,:]),axis=1)))
+    anomalous_data_type.append(np.argmin(error)+1)
+
+
+# For plotting purposes
+
+
+anomalous_data_indices_by_failure=[]
+for i in range(NumberOfFailures+1):
+    anomalous_data_indices_by_failure.append([])
+
+for i in range(len(anomalous_data_indices)):
+    print(i," ",anomalous_data_type[i])
+    anomalous_data_indices_by_failure[anomalous_data_type[i]].append(anomalous_data_indices[i])  
+
+
+colorline=['violet','lightcoral','cyan','lime','grey']
+colordot=['darkviolet','red','blue','green','black']
+
+featuresToPlot=['r1 s1','r1 s3','r1 s5','r2 s3','r2 s4','pa1 apiii','tc s1','tc s2','tc s3']
+indexesToPlot=[]
+for i in featuresToPlot:
+    indexesToPlot.append(features.index(i))
+
+def plotData3():
+    NumFeaturesToPlot=len(indexesToPlot)
+    fig, axes = plt.subplots(
+        nrows=NumFeaturesToPlot, ncols=1, figsize=(25, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumFeaturesToPlot):
+        init=0
+        end=len(x_train[0])
+        axes[i].plot(range(init,end),x_train[0][:,0,indexesToPlot[i]],label="normal train")
+        #axes.plot(range(len(x_train[0]),len(x_train[0])+len(x_test)),x_test[:,0,0],label="abnormal")
+        init=end
+        end+=testRanges[0][1]
+        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]],label="normal test")
+        init=end
+        end+=(testRanges[1][1]-testRanges[1][0])
+        for j in range(1,NumberOfFailures+1):
+            axes[i].plot(range(init,end),x_test[testRanges[j][0]:testRanges[j][1],0,indexesToPlot[i]],label="fail type "+str(j), color=colorline[j-1])
+            init=end
+            end+=(testRanges[j+1][1]-testRanges[j+1][0])
+
+            axes[i].plot(len(x_train[0])+np.array(anomalous_data_indices_by_failure[j]),x_test[anomalous_data_indices_by_failure[j],0,indexesToPlot[i]],color=colordot[j-1],marker='.',linewidth=0,label="abnormal detection type "+str(j))
+
+        init=end-(testRanges[NumberOfFailures+1][1]-testRanges[NumberOfFailures+1][0])
+        end=init+(testRanges[0][1]-testRanges[0][0])
+        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]],color='orange')
+
+        if i==0:
+            axes[i].legend(bbox_to_anchor=(1, 0.5))
+        axes[i].set_ylabel(features[indexesToPlot[i]])
+        axes[i].grid()
+    plt.show()
+
+
+plotData3()
+