252 lines
8.8 KiB
Python
252 lines
8.8 KiB
Python
import pdb
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import re
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"""
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Find make lists of #, *, @ words
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If only 1 hash word and nothing else, then it is token related ( tokencreation or tokentransfer )
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If @ is present, then we know it is smart contract related
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@ (#)pre: - participation , deposit
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@ * (#)pre: - one time event creation
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@ * (# #)post: - token swap creation
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@ - trigger
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Check for 1 @ only
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Check for 1 # only
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Check for @ (#)pre:
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Check for @ * (#)pre:
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Check for @ * (# #)post:
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special_character_frequency = {
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'precolon': {
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'#':0,
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'*':0,
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'@':0,
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':':0
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}
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for word in allList:
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if word.endswith('#'):
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special_character_frequency['#'] = special_character_frequency['#'] + 1
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elif word.endswith('*'):
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special_character_frequency['*'] = special_character_frequency['*'] + 1
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elif word.endswith('@'):
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special_character_frequency['@'] = special_character_frequency['@'] + 1
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elif word.endswith(':'):
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special_character_frequency[':'] = special_character_frequency[':'] + 1
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"""
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'''def className(rawstring):
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# Create a list that contains @ , # , * and : ; in actual order of occurence with their words. Only : is allowed to exist without a word in front of it.
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# Check for 1 @ only followed by :, and the class is trigger
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# Check for 1 # only, then the class is tokensystem
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# Check for @ in the first position, * in the second position, # in the third position and : in the fourth position, then class is one time event creation
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# Check for @ in the first position, * in the second position and : in the third position, then hash is in 4th position, then hash in 5th position | Token swap creation
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allList = findrules(rawstring,['#','*','@',':'])
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pattern_list1 = ['rmt@','rmt*',':',"rmt#","rmt#"]
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pattern_list2 = ['rmt#',':',"rmt@"]
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pattern_list3 = ['rmt#']
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pattern_list4 = ["rmt@","one-time-event*","floAddress$",':',"rupee#","bioscope#"]
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patternmatch = find_first_classification(pattern_list4, search_patterns)
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print(f"Patternmatch is {patternmatch}")
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rawstring = "test rmt# rmt@ rmt* : rmt# rmt# test"
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#className(rawstring) '''
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# Variable configurations
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search_patterns = {
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'tokensystem-C':{
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1:['#']
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},
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'smart-contract-creation-C':{
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1:['@','*','#','$',':'],
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2:['@','*','#','$',':','#']
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},
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'smart-contract-participation-deposit-C':{
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1:['#','@',':'],
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2:['#','@','$',':']
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},
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'userchoice-trigger':{
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1:['@']
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},
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'smart-contract-participation-ote-ce-C':{
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1:['#','@'],
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2:['#','@','$']
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},
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'smart-contract-creation-ce-tokenswap':{
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1:['@','*','$',':','#','#']
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}
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}
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conflict_matrix = {
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'tokensystem-C':{
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# Check for send, check for create, if both are there noise, else conflict resolved
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'tokentransfer',
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'tokencreation'
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},
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'smart-contract-creation-C':{
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# Check contract-conditions for userchoice, if present then userchoice contract, else time based contract
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'creation-one-time-event-userchoice',
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'creation-one-time-event-timebased'
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},
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'smart-contract-participation-deposit-C':{
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# Check *-word, its either one-time-event or a continuos-event
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'participation-one-time-event-userchoice',
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'deposit-continuos-event-tokenswap'
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},
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'smart-contract-participation-ote-ce-C':{
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# Check *-word, its either one-time-event or a continuos-event
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'participation-one-time-event-timebased',
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'participation-continuos-event-tokenswap'
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}
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}
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# Find some value or return as noise
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def apply_rule1(*argv):
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a = argv[0](*argv[1:])
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if a is False:
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return None
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else:
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return a
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def extract_specialcharacter_words(rawstring, special_characters):
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wordList = []
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for word in rawstring.split(' '):
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if (len(word) != 1 or word==":") and word[-1] in special_characters:
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wordList.append(word)
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return wordList
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def find_first_classification(parsed_word_list, search_patterns):
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for first_classification in search_patterns.keys():
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counter = 0
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for key in search_patterns[first_classification].keys():
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if checkSearchPattern(parsed_word_list, search_patterns[first_classification][key]):
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return {'categorization':f"{first_classification}",'key':f"{key}",'pattern':search_patterns[first_classification][key], 'wordlist':parsed_word_list}
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return {'categorization':"noise"}
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def sort_specialcharacter_wordlist(inputlist):
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weight_values = {
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'@': 5,
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'*': 4,
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'#': 3,
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'$': 2
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}
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weightlist = []
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for word in inputlist:
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if word.endswith("@"):
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weightlist.append(5)
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elif word.endswith("*"):
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weightlist.append(4)
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elif word.endswith("#"):
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weightlist.append(4)
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elif word.endswith("$"):
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weightlist.append(4)
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def classify_rawstring(rawstring):
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specialcharacter_wordlist = extract_specialcharacter_words(rawstring,['@','*','$','#',':'])
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first_classification = find_first_classification(specialcharacter_wordlist, search_patterns)
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return first_classification
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def checkSearchPattern(parsed_list, searchpattern):
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if len(parsed_list)!=len(searchpattern):
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return False
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else:
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for idx,val in enumerate(parsed_list):
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if not parsed_list[idx].endswith(searchpattern[idx]):
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return False
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return True
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def extractAmount_rule(text):
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base_units = {'thousand': 10 ** 3, 'million': 10 ** 6, 'billion': 10 ** 9, 'trillion': 10 ** 12}
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textList = text.split(' ')
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counter = 0
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value = None
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for idx, word in enumerate(textList):
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try:
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result = float(word)
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if textList[idx + 1] in base_units:
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value = result * base_units[textList[idx + 1]]
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counter = counter + 1
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else:
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value = result
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counter = counter + 1
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except:
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for unit in base_units:
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result = word.split(unit)
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if len(result) == 2 and result[1] == '' and result[0] != '':
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try:
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value = float(result[0]) * base_units[unit]
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counter = counter + 1
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except:
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continue
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if counter == 1:
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return value
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else:
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return None
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def selectCateogry(rawstring, wordlist, category1, category2):
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def text_preprocessing(text):
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# strip white spaces at the beginning and end
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processed_text = text.strip()
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# remove tab spaces
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processed_text = re.sub('\t', ' ', processed_text)
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# remove new lines/line changes
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processed_text = re.sub('\n', ' ', processed_text)
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# remove extra whitespaces in between
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processed_text = ' '.join(processed_text.split())
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processed_text = re.sub(' +', ' ', processed_text)
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# make everything lowercase
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processed_text = processed_text.lower()
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return processed_text
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text_list = [
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"create 500 million rmt#",
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"transfer 200 rmt#",
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"Create Smart Contract with the name India-elections-2019@ of the type one-time-event* using the asset rmt# at the address F7osBpjDDV1mSSnMNrLudEQQ3cwDJ2dPR1$ with contract-conditions: (1) contractAmount=0.001rmt (2) userChoices=Narendra Modi wins| Narendra Modi loses (3) expiryTime= Wed May 22 2019 21:00:00 GMT+0530",
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"send 0.001 rmt# to india-elections-2019@ to FLO address F7osBpjDDV1mSSnMNrLudEQQ3cwDJ2dPR1 with the userchoice:'narendra modi wins'",
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"india-elections-2019@ winning-choice:'narendra modi wins'",
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"Create Smart Contract with the name India-elections-2019@ of the type one-time-event* using the asset rmt# at the address F7osBpjDDV1mSSnMNrLudEQQ3cwDJ2dPR1$ with contract-conditions: (1) contractAmount=0.001rmt (2) expiryTime= Wed May 22 2019 21:00:00 GMT+0530",
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"send 0.001 rmt# to india-elections-2019@ to FLO address F7osBpjDDV1mSSnMNrLudEQQ3cwDJ2dPR1",
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"Create Smart Contract with the name swap-rupee-bioscope@ of the type continuous-event* at the address oRRCHWouTpMSPuL6yZRwFCuh87ZhuHoL78$ with contract-conditions : (1) subtype = tokenswap (2) accepting_token = rupee# (3) selling_token = bioscope# (4) price = '15' (5) priceType = ‘predetermined’ (6) direction = oneway",
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"Deposit 15 bioscope# to swap-rupee-bioscope@ its FLO address being oRRCHWouTpMSPuL6yZRwFCuh87ZhuHoL78$ with deposit-conditions: (1) expiryTime= Wed Nov 17 2021 21:00:00 GMT+0530 ",
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"Send 15 rupee# to swap-rupee-article@ its FLO address being FJXw6QGVVaZVvqpyF422Aj4FWQ6jm8p2dL$"
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]
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text_list1 = [
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"create 5 million rmt#"
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]
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for text in text_list1:
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text = text_preprocessing(text)
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first_classification = classify_rawstring(text)
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if first_classification['categorization'] == 'tokensystem-C':
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amount = apply_rule1(extractAmount_rule,text)
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operation = apply_rule1()
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