# 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
# Care with 'tc s3' because on datafiles[0] is always nulll
# Seems to be incoropored in new tests

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']

#features=['r2 s2', 'tc s1','r1 s10','r1 s6','r2 s8']

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()

NumFilters=64
KernelSize=7
DropOut=0.2
ThresholdFactor=2
TIME_STEPS = 48
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=NumFilters,
                kernel_size=KernelSize,
                padding="same",
                strides=2,
                activation="relu",
            ),
            layers.Dropout(rate=DropOut),
            layers.Conv1D(
                filters=int(NumFilters/2),
                kernel_size=KernelSize,
                padding="same",
                strides=2,
                activation="relu",
            ),
            layers.Conv1DTranspose(
                filters=int(NumFilters/2),
                kernel_size=KernelSize,
                padding="same",
                strides=2,
                activation="relu",
            ),
            layers.Dropout(rate=DropOut),
            layers.Conv1DTranspose(
                filters=NumFilters,
                kernel_size=KernelSize,
                padding="same",
                strides=2,
                activation="relu",
            ),
            layers.Conv1DTranspose(filters=x_train[i].shape[2], kernel_size=KernelSize, 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]*ThresholdFactor

# 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]+TIME_STEPS  ])


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 some features

colorline=['violet','lightcoral','cyan','lime','grey']
colordot=['darkviolet','red','blue','green','black']

featuresToPlot=['r1 s1','r1 s2','r1 s3','pa1 apiii']
#featuresToPlot=features

indexesToPlot=[]
for i in featuresToPlot:
    indexesToPlot.append(features.index(i))

def plotData2():
    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")
        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])
        # Shift TIME_STEPS because detection is performed at the end of time serie
        trail=np.hstack((x_test[:,0,indexesToPlot[i]], x_test[-1:,1:TIME_STEPS,indexesToPlot[i]].reshape(TIME_STEPS-1)))
        axes[i].plot(len(x_train[0])+np.array(anomalous_data_indices)+TIME_STEPS,trail[np.array(anomalous_data_indices)+TIME_STEPS],color='grey',marker='.',linewidth=0,label="abnormal detection" )
        
        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()


def anomalyMetric(testList):  # first of list is non failure data
    # FP, TP: false/true positive
    # TN, FN: true/false negative
    # Sensitivity: probab failure detection if data is fail: TP/(TP+FN)
    # Specificity: true negative ratio given  data is OK: TN/(TN+FP)

    x_test = create_sequences(testList[0])
    x_test_pred = model[0].predict(x_test)
    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
    anomalies = test_mae_loss > threshold[0]
    count=0
    for i in range(anomalies.shape[0]):
        if AtLeastOneTrue(anomalies[i]):
            count+=1
    FP=count
    TN=anomalies.shape[0]-count
    count=0
    TP=np.zeros((NumberOfFailures))
    FN=np.zeros((NumberOfFailures))
    for i in range(1,len(testList)):
        x_test = create_sequences(testList[i])
        x_test_pred = model[0].predict(x_test)
        test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
        anomalies = test_mae_loss > threshold[0]
        count=0
        for j in range(anomalies.shape[0]):
            if AtLeastOneTrue(anomalies[j]):
                count+=1
        TP[i-1] = count
        FN[i-1] = anomalies.shape[0]-count
    Sensitivity=TP.sum()/(TP.sum()+FN.sum())
    Specifity=TN/(TN+FP)
    print("Sensitivity: ",Sensitivity)
    print("Specifity: ",Specifity)
    print("FP: ",FP)
    return Sensitivity+Specifity



anomalyMetric([dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]])
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)


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])  

MaxIndex=0
for i in range(1,NumberOfFailures+1):
    MaxIndex=max(MaxIndex,max(anomalous_data_indices_by_failure[i]))

# Enlarge x_test to plot failure points
XTest=np.vstack((x_test[:,0,:],x_test[-1,1:TIME_STEPS,:]))

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")
        init=end
        end+=testRanges[0][1]
        axes[i].plot(range(init,end),XTest[testRanges[0][0]:testRanges[0][1],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),XTest[testRanges[j][0]:testRanges[j][1],indexesToPlot[i]],label="fail type "+str(j), color=colorline[j-1])
            init=end
            end+=(testRanges[j+1][1]-testRanges[j+1][0])
            ##   MODIFY here as in PRevious Plot
            axes[i].plot(len(x_train[0])+np.array(anomalous_data_indices_by_failure[j])+TIME_STEPS,XTest[np.array(anomalous_data_indices_by_failure[j])+TIME_STEPS,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),XTest[testRanges[0][0]:testRanges[0][1],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()



anomalyMetric([dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]])
plotData3()

#  A new anomalyMEtric for multiclass must be defined
    # look at anomalies detected on first stage
    # l0ok at classification of second stage 
    # then: determine if: correct classified  or  bad classified/unclassified