# -*- coding: utf-8 -*-
import string

import numpy as np
import pandas as pd
from tqdm import tqdm


def get_character_dict():
    return dict((char, idx) for (idx, char) in
                enumerate(string.ascii_lowercase + string.punctuation))


def get_user_chunks(dataFrame, windowSize=10, overlapping=False,
                    maxLengthInSeconds=300):
    maxMilliSeconds = maxLengthInSeconds * 1000
    outDomainLists = []
    outDFFrames = []
    if overlapping == False:
        numBlocks = int(np.ceil(float(len(dataFrame)) / float(windowSize)))
        userIDs = np.arange(len(dataFrame))
        for blockID in np.arange(numBlocks):
            curIDs = userIDs[(blockID * windowSize):((blockID + 1) * windowSize)]
            # print(curIDs)
            useData = dataFrame.iloc[curIDs]
            curDomains = useData['domain']
            if maxLengthInSeconds != -1:
                curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
                underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
                if len(underTimeOutIDs) != len(curIDs):
                    curIDs = curIDs[underTimeOutIDs]
                    useData = dataFrame.iloc[curIDs]
                    curDomains = useData['domain']
            outDomainLists.append(list(curDomains))
            outDFFrames.append(useData)
    else:
        numBlocks = len(dataFrame) + 1 - windowSize
        userIDs = np.arange(len(dataFrame))
        for blockID in np.arange(numBlocks):
            curIDs = userIDs[blockID:blockID + windowSize]
            useData = dataFrame.iloc[curIDs]
            curDomains = useData['domain']
            if maxLengthInSeconds != -1:
                curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
                underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
                if len(underTimeOutIDs) != len(curIDs):
                    curIDs = curIDs[underTimeOutIDs]
                    useData = dataFrame.iloc[curIDs]
                    curDomains = useData['domain']
            outDomainLists.append(list(curDomains))
            outDFFrames.append(useData)
    return (outDomainLists, outDFFrames)


def get_domain_features(domain, vocab, max_length=40):
    curFeature = np.zeros([max_length, ])
    for j in range(np.min([len(domain), max_length])):
        curCharacter = domain[-j]
        if curCharacter in vocab:
            curFeature[j] = vocab[curCharacter]
    return curFeature


def get_flow_features(flow):
    keys = ['duration', 'bytes_down', 'bytes_up']
    features = np.zeros([len(keys), ])
    for i, key in enumerate(keys):
        # TODO: does it still works after exceptions occur -- default: zero!
        # i wonder whether something brokes
        # if there are exceptions regarding to inconsistent feature length
        try:
            features[i] = np.log1p(flow[key]).astype(float)
        except:
            pass
    return features


def get_cisco_features(curDataLine, urlSIPDict):
    numCiscoFeatures = 30
    try:
        ciscoFeatures = urlSIPDict[str(curDataLine['domain']) + str(curDataLine['server_ip'])]
        # log transform
        ciscoFeatures = np.log1p(ciscoFeatures).astype(float)
        return ciscoFeatures.ravel()
    except:
        return np.zeros([numCiscoFeatures, ]).ravel()


def create_dataset_from_flows(user_flow_df, char_dict, maxLen, windowSize=10, use_cisco_features=False):
    domainLists = []
    dfLists = []
    print("get chunks from user data frames")
    for i, user_flow in enumerate(get_flow_per_user(user_flow_df)):
        (domainListsTmp, dfListsTmp) = get_user_chunks(user_flow, windowSize=windowSize,
                                                       overlapping=True, maxLengthInSeconds=-1)
        domainLists += domainListsTmp
        dfLists += dfListsTmp
        # TODO: remove later
        if i >= 10:
            break

    print("create training dataset")
    return create_dataset_from_lists(
        domains=domainLists, dfs=dfLists, vocab=char_dict,
        maxLen=maxLen,
        use_cisco_features=use_cisco_features, urlSIPDIct=dict(),
        window_size=windowSize)


def create_dataset_from_lists(domains, dfs, vocab, maxLen,
                              use_cisco_features=False, urlSIPDIct=dict(),
                              window_size=10):
    # TODO: check for hits vs vth consistency
    if 'hits' in dfs[0].keys():
        hitName = 'hits'
    elif 'virusTotalHits' in dfs[0].keys():
        hitName = 'virusTotalHits'
    numFlowFeatures = 3
    numCiscoFeatures = 30
    numFeatures = numFlowFeatures
    if use_cisco_features:
        numFeatures += numCiscoFeatures
    Xs = []
    hits = []
    names = []
    servers = []
    trusted_hits = []
    for i in range(window_size):
        Xs.append(np.zeros([len(domains), maxLen]))
        Xs.append(np.zeros([len(domains), numFeatures]))

    for i in tqdm(np.arange(len(domains)), miniters=10):
        ctr = 0
        for j in range(np.min([window_size, len(domains[i])])):
            Xs[ctr][i, :] = get_domain_features(domains[i][j], vocab, maxLen)
            ctr += 1
            if use_cisco_features:
                Xs[ctr][i, 0:numFlowFeatures] = get_flow_features(dfs[i].iloc[j])
                Xs[ctr][i, numFlowFeatures:] = get_cisco_features(dfs[i].iloc[j], urlSIPDIct)
            else:
                Xs[ctr][i, :] = get_flow_features(dfs[i].iloc[j])
            ctr += 1

        hits.append(np.max(dfs[i][hitName]))
        names.append(np.unique(dfs[i]['user_hash']))
        servers.append(np.max(dfs[i]['serverLabel']))
        trusted_hits.append(np.max(dfs[i]['trustedHits']))
    return Xs, np.array(hits), np.array(names), np.array(servers), np.array(trusted_hits)


def discretize_label(values, threshold):
    maxVal = np.max(values)
    if maxVal >= threshold:
        return 1.0
    elif maxVal == -1:
        return -1.0
    elif 0 < maxVal < threshold:
        return -2.0
    else:
        return 0.0


def get_user_flow_data():
    df = pd.read_csv("data/rk_data.csv.gz")
    df.drop("Unnamed: 0", 1, inplace=True)
    df.set_index(keys=['user_hash'], drop=False, inplace=True)
    return df


def get_flow_per_user(df):
    users = df['user_hash'].unique().tolist()
    for user in users:
        yield df.loc[df.user_hash == user]