# -*- coding: utf-8 -*-

from datetime import datetime
print(datetime.now())


path2de_csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_med.csv"
path2de_csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_small.csv"
#path2de_csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_mini.csv"
path2de_csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/de_tickets.csv"

path2en_csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/en_tickets.csv"


#idee roh-corpus (nur whitespace weg) speichern -> pregeprocesster corpus -> damit arbeiten


path_csv_split = path2de_csv.split("/")
print(path_csv_split[len(path_csv_split) - 1])
path_csv_split = path2en_csv.split("/")
print(path_csv_split[len(path_csv_split) - 1])


import time
start = time.time()

import logging
from nltk.corpus import stopwords
import csv
import functools
import re
import xml.etree.ElementTree as ET
import spacy
import textacy
from scipy import *
import sys
csv.field_size_limit(sys.maxsize)




# ssh madonna "nohup /usr/bin/python3 -u /home/jannis.grundmann/PycharmProjects/topicModelingTickets/preprocessing.py &> /home/jannis.grundmann/PycharmProjects/topicModelingTickets/printout_preprocessing.log &"



# todo configuration file ?
"""
config_ini = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/config.ini"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)
"""



# config logging
logfile = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/topicModelTickets.log"
logging.basicConfig(filename=logfile, level=logging.INFO)
# logging.basicConfig(filename=config.get("filepath","logfile"), level=logging.INFO)




# THESAURUS

# thesauruspath = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/openthesaurus.csv"
# thesauruspath = config.get("filepath","thesauruspath")
# THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))
lexicalentries = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/lexicalentries.xml"
synsets = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/synsets.xml"

# SPELLCHECKING
path2words = '/home/jannis.grundmann/PycharmProjects/topicModelingTickets/deu_news_2015_1M-sentences.txt'



DE_PARSER = spacy.load("de")
EN_PARSER = spacy.load("en")




"""
de_stop_words= set(
    list(__import__("spacy." + DE_PARSER.lang, globals(), locals(), ['object']).STOP_WORDS) +
    list(textacy.fileio.read_file_lines("stopwords-de.txt"))
)


LEMMAS = list(textacy.fileio.read_file_lines(filepath="lemmatization-de.txt"))

VORNAMEN = list(textacy.fileio.read_file_lines("vornamen.txt"))
"""

de_stop_words = list(map(textacy.preprocess.normalize_whitespace, textacy.fileio.read_file_lines(
    "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/de_stop_words.txt"))) + list(set(stopwords.words('german')))

en_stop_words= set(list(__import__("spacy." + EN_PARSER.lang, globals(), locals(), ['object']).STOP_WORDS)+ list(set(stopwords.words('english'))))




LEMMAS = list(textacy.fileio.read_file_lines("/home/jannis.grundmann/PycharmProjects/topicModelingTickets/lemmas.txt"))

VORNAMEN = list(map(textacy.preprocess.normalize_whitespace, textacy.fileio.read_file_lines(
    "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/firstnames.txt")))

NOUNS = list(textacy.fileio.read_file_lines("/home/jannis.grundmann/PycharmProjects/topicModelingTickets/nomen2.txt")) + list(textacy.fileio.read_file_lines("/home/jannis.grundmann/PycharmProjects/topicModelingTickets/nomen.txt"))
NOUNS = list(map(textacy.preprocess.normalize_whitespace, NOUNS))


"""
print(de_stop_words[10:30])
print(LEMMAS[10:30])
print(VORNAMEN[10:30])
print(NOUNS[10:30])
"""


mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
topLVLFinder = re.compile(r'\.[a-z]{2,3}(\.[a-z]{2,3})?', re.IGNORECASE)
specialFinder = re.compile(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./>?]', re.IGNORECASE)
hardSFinder = re.compile(r'[ß]', re.IGNORECASE)


import pickle

def save_obj(obj, path):
    with open(path + '.pkl', 'wb') as f:
        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)

def load_obj(path ):
    with open(path + '.pkl', 'rb') as f:
        return pickle.load(f)
def printlog(string, level="INFO"):
    """log and prints"""
    print(string)
    if level == "INFO":
        logging.info(string)
    elif level == "DEBUG":
        logging.debug(string)
    elif level == "WARNING":
        logging.warning(string)

printlog("Load functions")


def compose(*functions):
    def compose2(f, g):
        return lambda x: f(g(x))

    return functools.reduce(compose2, functions, lambda x: x)


def get_calling_function():
    """finds the calling function in many decent cases.
    https://stackoverflow.com/questions/39078467/python-how-to-get-the-calling-function-not-just-its-name
    """
    fr = sys._getframe(1)  # inspect.stack()[1][0]
    co = fr.f_code
    for get in (
            lambda: fr.f_globals[co.co_name],
            lambda: getattr(fr.f_locals['self'], co.co_name),
            lambda: getattr(fr.f_locals['cls'], co.co_name),
            lambda: fr.f_back.f_locals[co.co_name],  # nested
            lambda: fr.f_back.f_locals['func'],  # decorators
            lambda: fr.f_back.f_locals['meth'],
            lambda: fr.f_back.f_locals['f'],
    ):
        try:
            func = get()
        except (KeyError, AttributeError):
            pass
        else:
            if func.__code__ == co:
                return func
    raise AttributeError("func not found")


def printRandomDoc(textacyCorpus):
    import random
    print()

    printlog("len(textacyCorpus) = %i" % len(textacyCorpus))
    randIndex = int((len(textacyCorpus) - 1) * random.random())
    printlog("Index: {0} ; Text: {1} ; Metadata: {2}\n".format(randIndex, textacyCorpus[randIndex].text,
                                                               textacyCorpus[randIndex].metadata))

    print()


def csv_to_contentStream(path2csv: str, content_collumn_name: str):
    """
    :param path2csv: string
    :param content_collumn_name: string
    :return: string-generator
    """
    stream = textacy.fileio.read_csv(path2csv, delimiter=";")  # ,encoding='utf8')
    content_collumn = 0  # standardvalue

    for i, lst in enumerate(stream):
        if i == 0:
            # look for desired column
            for j, col in enumerate(lst):
                if col == content_collumn_name:
                    content_collumn = j
        else:
            yield lst[content_collumn]


def csv_to_metaStream(path2csv: str, metalist: [str]):
    """
    :param path2csv: string
    :param metalist: list of strings
    :return: dict-generator
    """
    stream = textacy.fileio.read_csv(path2csv, delimiter=";")  # ,encoding='utf8')

    content_collumn = 0  # standardvalue
    metaindices = []
    metadata_temp = {}
    for i, lst in enumerate(stream):
        if i == 0:
            for j, col in enumerate(lst):  # geht bestimmt effizienter... egal, weil passiert nur einmal
                for key in metalist:
                    if key == col:
                        metaindices.append(j)
            metadata_temp = dict(
                zip(metalist, metaindices))  # zB {'Subject' : 1, 'categoryName' : 3, 'Solution' : 10}

        else:
            metadata = metadata_temp.copy()
            for key, value in metadata.items():
                metadata[key] = lst[value]
            yield metadata


def save_corpus(corpus, corpus_path, corpus_name, parser=DE_PARSER):

    # save stringstore
    stringstore_path = corpus_path + corpus_name + '_strings.json'
    with open(stringstore_path, "w") as file:
        parser.vocab.strings.dump(file)

    # save content
    contentpath = corpus_path + corpus_name + "_content.bin"
    textacy.fileio.write_spacy_docs((doc.spacy_doc for doc in corpus), contentpath)

    # save meta
    metapath = corpus_path + corpus_name + "_meta.json"
    textacy.fileio.write_json_lines((doc.metadata for doc in corpus), metapath)



#############  filter tokens

def keepPOS(pos_list):
    return lambda tok: tok.pos_ in pos_list


def keepNouns(noun_list=NOUNS):
    return lambda tok: tok.lower_ in noun_list


def removePOS(pos_list):
    return lambda tok: tok.pos_ not in pos_list


def removeWords(words, keep=None):
    if hasattr(keep, '__iter__'):
        for k in keep:
            try:
                words.remove(k)
            except ValueError:
                pass

    return lambda tok: tok.lower_ not in words


def keepENT(ent_list):
    return lambda tok: tok.ent_type_ in ent_list


def removeENT(ent_list):
    return lambda tok: tok.ent_type_ not in ent_list


def remove_words_containing_Numbers():
    return lambda tok: not bool(re.search('\d', tok.lower_))


"""
def remove_words_containing_topLVL():
    return lambda tok: not bool(re.search(regex_topLvl, tok.lower_))


def remove_words_containing_specialCharacters():
    return lambda tok: not bool(re.search(regex_specialChars, tok.lower_))
"""


def remove_long_words():
    return lambda tok: not len(tok.lower_) < 2


def remove_short_words():
    return lambda tok: not len(tok.lower_) > 35


def remove_first_names():
    return lambda tok: tok.lower_ not in [name.lower() for name in VORNAMEN]


############# strings

def replaceRockDots():
    return lambda string: re.sub(r'[ß]', "ss",
                                 (re.sub(r'[ö]', "oe", (re.sub(r'[ü]', "ue", (re.sub(r'[ä]', "ae", string.lower())))))))

def remove_addresses(string):
    pass  # todo


"""   
def stringcleaning(stringstream, funclist):
    for string in stringstream:
        for f in funclist:

            string = f(string)
        yield string

def cut_after(word="gruss"):
    return lambda string: string.rpartition(word)[0] if word in string else string

def seperate_words_on_regex(regex=regex_specialChars):
    return lambda string: " ".join(re.compile(regex).split(string))



def remove_words_containing_topLVL():
    return lambda string: " ".join([w.lower() for w in string.split() if not re.search(regex_topLvl, w) ])


def replaceSpecialChars(replace_with=" "):
    return lambda string: re.sub(regex_specialChars, replace_with, string.lower())


def replaceNumbers(replace_with="NUMBER"):
    return lambda string : textacy.preprocess.replace_numbers(string.lower(), replace_with=replace_with)


def replacePhonenumbers(replace_with="PHONENUMBER"):
    return lambda string: textacy.preprocess.replace_phone_numbers(string.lower(), replace_with=replace_with)


def replaceSharpS(replace_with="ss"):
    return lambda string: re.sub(r'[ß]',replace_with,string.lower())

def fixUnicode():
    return lambda string: textacy.preprocess.fix_bad_unicode(string.lower(), normalization=u'NFC')
"""

"""
def lemmatizeWord(word,filepath=LEMMAS):
    for line in list(textacy.fileio.read_file_lines(filepath=filepath)):
        if word.lower() == line.split()[1].strip().lower():
            return line.split()[0].strip().lower()
    return word.lower() # falls nix gefunden wurde


def create_lemma_dicts(lemmalist=LEMMAS):
    w_dict = {}
    lem_dict = {}

    for i, line in enumerate(lemmalist):
        try:
            lem_word_pair = line.split()

            if len(lem_word_pair) != 2:
                print(line)

            lemma = lem_word_pair[0].strip().lower()

            word = lem_word_pair[1].strip().lower()
        except:
            print(line)

        if lemma not in lem_dict:
            lem_dict[lemma] = i

        if word not in w_dict:
            w_dict[word] = lem_dict[lemma]

    l_dict = {v: k for k, v in lem_dict.items()}  # switch key/values

    return l_dict,w_dict

lemma_dict,word_dict = create_lemma_dicts()



def lemmatizeWord(word,l_dict=lemma_dict,w_dict=word_dict):
    #mehrmals machen
    for i in range(3):
        try:
            word = l_dict[w_dict[word.lower()]] if word.lower() in w_dict else word.lower()
        except:
            print(word)
    return word

def lemmatize():
    return lambda doc: " ".join([lemmatizeWord(tok.lower_) for tok in doc])

def lemmatize():
    return lambda string: " ".join([lemmatizeWord(s.lower()) for s in string.split()])

DE_SPELLCHECKER = enchant.Dict("de_DE")
EN_SPELLCHECKER = enchant.Dict("en_US")

def autocorrectWord(word,spellchecker=DE_SPELLCHECKER):

    try:
        return spellchecker.suggest(word)[0] if not spellchecker.check(word) else word
    except:
        return word



def autocorrect():
    return lambda string: " ".join([autocorrectWord(s.lower()) for s in string.split()])
"""


def create_lemma_dicts(lemmalist=LEMMAS):
    w_dict = {}
    lem_dict = {}

    for i, line in enumerate(lemmalist):
        try:
            lem_word_pair = line.split()

            if len(lem_word_pair) != 2:
                print(line)

            lemma = lem_word_pair[0].strip().lower()

            word = lem_word_pair[1].strip().lower()
        except:
            print(line)

        if lemma not in lem_dict:
            lem_dict[lemma] = i

        if word not in w_dict:
            w_dict[word] = lem_dict[lemma]

    l_dict = {v: k for k, v in lem_dict.items()}  # switch key/values

    return l_dict, w_dict


lemma_dict, word_dict = create_lemma_dicts()

def lemmatizeWord(word, l_dict=lemma_dict, w_dict=word_dict, n=3):
    # mehrmals machen
    for i in range(n):
        try:
            word = l_dict[w_dict[word.lower()]] if word.lower() in w_dict else word.lower()
        except:
            print(word)
    return word


def build_thesaurus(path2lexicalentries, path2synsets):
    lextree = ET.parse(path2lexicalentries, ET.XMLParser(encoding="utf-8"))
    syntree = ET.parse(path2synsets, ET.XMLParser(encoding="utf-8"))

    lexroot = lextree.getroot()
    synroot = syntree.getroot()

    thesaurus = []

    for r in synroot:
        for element in r:

            if element.tag == "Synset":
                sysnet = []
                attrib = element.attrib
                id = attrib["id"]

                for ro in lexroot:
                    for elem in ro:
                        if elem.tag == "LexicalEntry":
                            subs_dicts = [subentry.attrib for subentry in elem]
                            # <class 'list'>: [{'partOfSpeech': 'n', 'writtenForm': 'Kernspaltung'}, {'synset': 'de-1-n', 'id': 'w1_1-n'}]

                            dic = {k: v for x in subs_dicts for k, v in x.items()}  # to one dict
                            if "synset" in dic.keys():
                                if dic["synset"] == id:
                                    string = (dic["writtenForm"])

                                    # replaceRockDots
                                    string = re.sub(r'[ß]', "ss", string)
                                    string = re.sub(r'[ö]', "oe", string)
                                    string = re.sub(r'[ü]', "ue", string)
                                    string = re.sub(r'[ä]', "ae", string)

                                    # alle punkte raus
                                    string = re.sub(r'[.]', "", string)

                                    # alles in klammern raus
                                    string = re.sub(r"\((.*)\)", " ", string)

                                    # längeres leerzeichen normalisieren
                                    string = textacy.preprocess.normalize_whitespace(string)

                                    sysnet.append(string.lower().strip())

                                    # nach anzhal der wörter in den strings sortieren
                                    sysnet.sort(key=lambda x: len(x.split()))
                if len(sysnet) != 0:
                    # todo warum sind manche leer?
                    thesaurus.append(sysnet)
    return thesaurus

printlog("Build Thesaurus")
THESAURUS = []
THESAURUS = build_thesaurus(path2lexicalentries=lexicalentries, path2synsets=synsets)


def getFirstSynonym(word, thesaurus=THESAURUS):
    if not isinstance(word, str):
        return str(word)

    word = word.lower()

    # durch den thesaurrus iterieren
    for syn_block in thesaurus:  # syn_block ist eine liste mit Synonymen

        for syn in syn_block:
            syn = syn.lower()
            if re.match(r'\A[\w-]+\Z', syn):  # falls syn einzelwort ist todo phrasen auch normalisieren
                if word == syn:
                    return syn_block[0]

    return str(word)  # zur Not das ursrpüngliche Wort zurückgeben


##########################    Spellchecking        ##########################################
# http://norvig.com/spell-correct.html
# http://wortschatz.uni-leipzig.de/en/download

import re
from collections import Counter


def words(text): return re.findall(r'\w+', text.lower())

printlog("Build Wordlist for Spellchecking")
WORDS = {}
WORDS = Counter(words(open(path2words).read()))

def P(word, N=sum(WORDS.values())):
    "Probability of `word`."
    return WORDS[word] / N


def correction(word):
    "Most probable spelling correction for word."
    return max(candidates(word), key=P)


def candidates(word):
    "Generate possible spelling corrections for word."
    return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])


def known(words):
    "The subset of `words` that appear in the dictionary of WORDS."
    return set(w for w in words if w in WORDS)


def edits1(word):
    "All edits that are one edit away from `word`."
    letters = 'abcdefghijklmnopqrstuvwxyz'
    splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
    deletes = [L + R[1:] for L, R in splits if R]
    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
    replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
    inserts = [L + c + R for L, R in splits for c in letters]
    return set(deletes + transposes + replaces + inserts)


def edits2(word):
    "All edits that are two edits away from `word`."
    return (e2 for e1 in edits1(word) for e2 in edits1(e1))


"""
DE_SPELLCHECKER = enchant.Dict("de_DE")
EN_SPELLCHECKER = enchant.Dict("en_US")

def autocorrectWord(word, spellchecker=DE_SPELLCHECKER):
    try:
        return spellchecker.suggest(word)[0] if not spellchecker.check(word) else word
    except:
        return word
"""


def autocorrectWord(word):
    try:
        return correction(word)
    except:
        return word


##################################################################################################



############# stringcleaning

def stringcleaning(stringstream):
    regex_specialChars = r'[`\-=~!#@,.$%^&*()_+\[\]{};\'\\:"|</>?]'
    regex_topLvl = r'\.[a-z]{2,3}(\.[a-z]{2,3})?'

    for string in stringstream:
        string = string.lower()

        # fixUnicode
        string = textacy.preprocess.fix_bad_unicode(string.lower(), normalization=u'NFC')

        # remove_words_containing_topLVL
        string = " ".join([w.lower() for w in string.split() if not re.search(regex_topLvl, w)])

        # replaceRockDots
        string = re.sub(r'[ß]', "ss", string)
        string = re.sub(r'[ö]', "oe", string)
        string = re.sub(r'[ü]', "ue", string)
        string = re.sub(r'[ä]', "ae", string)

        # seperate_words_on_regex:
        string = " ".join(re.compile(regex_specialChars).split(string))

        # cut_after
        word = "gruss"
        string = string.rpartition(word)[0] if word in string else string

        # lemmatize
        string = " ".join([lemmatizeWord(word) for word in string.split()])

        # synonyme normalisieren    #idee vor oder nach lemmatize?
        string = " ".join([getFirstSynonym(word) for word in string.split()])

        # autocorrect
        string = " ".join([autocorrectWord(word) for word in string.split()])

        yield string


def processContentstream(textstream, token_filterlist=None, parser=DE_PARSER):
    """
    :param textstream: string-gen
    :param funclist: [func]
    :param parser: spacy-parser
    :return: string-gen
    """

    """
    filter_tokens=[
    #removeENT(["PERSON"]),
    #idee addressen enfernen #bisher mit cut_after("gruss") --> postal.parser
    #idee rechtschreibkorrektur --> PyEnchant
    #idee thesaurus --> WordNet, eigener

    remove_words_containing_Numbers(),

    removePOS(["PUNCT","SPACE","NUM"]),

    removeWords(de_stop_words+custom_words),

    remove_long_words(),
    remove_short_words(),
    remove_first_names(),

    keepPOS(["NOUN"]),

    ]
    """

    # pre_parse
    textstream = stringcleaning(textstream)

    pipe = parser.pipe(textstream)

    tokens = []
    for doc in pipe:

        tokens = [tok for tok in doc]

        # in_parse
        if token_filterlist is not None:
            tokens = filterTokens(tokens, token_filterlist)

        yield " ".join([tok.lower_ for tok in tokens])
        # yield " ".join(list(set([tok.lower_ for tok in tokens])))


def processDictstream(dictstream, funcdict, parser=DE_PARSER):
    """

    :param dictstream: dict-gen
    :param funcdict:
                    clean_in_meta = {
                        "Solution":funclist,
                        ...
                    }

    :param parser: spacy-parser
    :return: dict-gen
    """
    for dic in dictstream:
        result = {}
        for key, value in dic.items():

            if key in funcdict:

                doc = parser(value)
                tokens = [tok for tok in doc]
                funclist = funcdict[key]

                tokens = filterTokens(tokens, funclist)

                result[key] = " ".join([tok.lower_ for tok in tokens])


            else:
                result[key] = value
        yield result


def filterTokens(tokens, funclist):
    # in:tokenlist, funclist
    # out: tokenlist
    for f in funclist:
        tokens = list(filter(f, tokens))

    return tokens


custom_words = ["geehrt", "dame", "herr", "hilfe", "problem", "lauten", "bedanken", "voraus",
                "hallo", "gerne", "freundlich", "fragen", "fehler", "bitten", "ehre", "lieb", "helfen",
                "versuchen", "unbestimmt", "woche", "tadelos", "klappen", "mittlerweile", "bekommen",
                "erreichbar", "gruss", "auffahren", "vorgang", "hinweis", "institut", "universitaet",
                "name", "gruss", "id", "erfolg", "mail","folge",
                "nummer", "team", "fakultaet", "email", "absender", "tu", "versenden", "vorname", "message",
                "service", "strasse", "prozess", "portal", "raum", "personal", "moeglichkeit", "fremd", "wende",
                "rueckfrage", "stehen", "verfuegung",
                "funktionieren", "kollege", "pruefen", "hoffen"
                ]

filter_tokens = [
    # removeENT(["PERSON"]),
    # idee addressen enfernen #bisher mit cut_after("gruss") --> postal.parser

    keepNouns(),

    remove_words_containing_Numbers(),

    removePOS(["PUNCT", "SPACE", "NUM"]),

    removeWords(de_stop_words + custom_words),

    remove_long_words(),
    remove_short_words(),
    remove_first_names()


]

metaliste = [
    "Subject",
    "categoryName",
    "Solution"
]

clean_in_meta = {
    "Solution": [removePOS(["SPACE"])],
    "Subject": [removePOS(["SPACE", "PUNCT"])],
    "categoryName": [removePOS(["SPACE", "PUNCT"])]
}

"""
pipe=[

    ##String

    fixUnicode(),
    replaceHardS(),
    resolveAbbrivations(),

    remove_words_containing_topLVL(),

    replaceSpecialChars(" "), (mit Leerzeichen erstzen, dadruch werden Terme wie 8203;verfügung getrennt

    remove_words_containing_Numbers(),



    ##spacyParse

    removeENT("PERSON"),
    keepPOS(["NOUN"]),

    #ODER

    lemmatize(),
    removeWords(de_stop_words + config.get("preprocessing","custom_words").split(",")),


    # evtl.
    spellCorrection(),
    keepUniqeTokens(),

]




"""

de_corpus = textacy.Corpus(DE_PARSER)
en_corpus = textacy.Corpus(EN_PARSER)



## add files to textacy-corpus,
printlog("Add texts to textacy-corpus")
de_corpus.add_texts(
    processContentstream(csv_to_contentStream(path2de_csv, "Description"), token_filterlist=filter_tokens),
    processDictstream(csv_to_metaStream(path2de_csv, metaliste), clean_in_meta)
)



# leere docs aus corpus kicken
de_corpus.remove(lambda doc: len(doc) == 0)

for i in range(20):
    printRandomDoc(de_corpus)


#save corpus
corpus_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpus/"
corpus_name = "de_corpus"

save_corpus(corpus=de_corpus,corpus_path=corpus_path,corpus_name=corpus_name)


#todo das selbe mit en_corpus



end = time.time()
printlog("Time Elapsed Preprocessing:{0} min".format((end - start) / 60))