diff --git a/Student.zip b/Student.zip new file mode 100644 index 0000000000000000000000000000000000000000..744420663f66deebf3ffb9cd8164cde19d611090 Binary files /dev/null and b/Student.zip differ diff --git a/lab-cabds/InjectionDetection/.keep b/lab-cabds/InjectionDetection/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-cabds/InjectionDetection/SQL_detection.py b/lab-cabds/InjectionDetection/SQL_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..daafd3984e4c35304216c732ed2e491f27a86dc8 --- /dev/null +++ b/lab-cabds/InjectionDetection/SQL_detection.py @@ -0,0 +1,428 @@ +# -*- coding: utf-8 -*- + +import re +import time +import math +import sys +import json +import os +import base64 +import datetime +import urllib +import config +# import MySQLdb +# import interface_sftp as ftp + +keywords = ['union', 'truncate', 'xp_cmdshell', 'load_file', 'outfile', 'dumpfile', 'exec', 'having', 'between', 'waitfor', 'delay', '@variable', '@version', '@servername', 'substring', 'varchar', 'print', 'sleep', + 'select', 'insert', 'delete', 'where', 'update', 'count', 'group', 'order', 'drop', 'table', 'master', 'version', 'true', 'false', + 'or', 'by', 'and', 'into', 'from', 'like', 'net', 'as', 'set'] + +bgKw = ['union', 'truncate', 'xp_cmdshell', 'load_file', 'outfile', 'dumpfile', 'exec', 'having', 'between', 'waitfor', 'delay', '@variable', '@version', '@servername', 'substring', 'varchar', 'print', 'sleep', + 'select', 'insert', 'delete', 'where', 'update', 'count', 'group', 'order', 'drop', 'table', 'master', 'char', 'version', 'true', 'false', ] +smKw = ['or', 'by', 'and', 'into', 'like', 'from', 'net', 'set', 'as'] + +valueMap = { + # big weight + 'union': 100, + 'truncate': 100, + 'xp_cmdshell': 100, + 'load_file': 100, + 'outfile': 100, + 'dumpfile': 100, + 'exec': 100, + 'having': 100, + 'between': 100, + 'waitfor': 100, + 'delay': 100, + '@variable': 100, + '@version': 100, + '@servername': 100, + 'substring': 100, + 'varchar': 10, + 'print': 10, + 'sleep': 10, + # middle weight + 'select': 10, + 'insert': 10, + 'delete': 10, + 'where': 10, + 'update': 10, + 'count': 10, + 'group': 10, + 'order': 10, + 'drop': 10, + 'table': 10, + 'master': 10, + 'version': 10, + 'true': 10, + 'false': 10, + # small weight + 'or': 1, + 'by': 1, + 'and': 1, + 'from': 1, + 'into': 1, + 'like': 1, + 'net': 1, + 'char': 1, + 'set': 1, + 'as': 1, +} + +specialChar = ['+', '-', '*', '/', '=', '<', '>', + '!', '^', '#', "'", '"', '(', ')', '@', ','] + +SQL_F_MLP_P = 'SQL_Features_MLP_Pos.txt' +SQL_F_MLP_N = 'SQL_Features_MLP_Neg.txt' +SQL_F_LSTM_P = 'SQL_Features_LSTM_Pos.txt' +SQL_F_LSTM_N = 'SQL_Features_LSTM_Neg.txt' + + +def parseFeatures(requests): + ts = time.time() + + # files open area + f = open('SQL_paramstr.txt', 'a') + f_SQL_F_LSTM_N = open(SQL_F_LSTM_N, 'a') + f_SQL_F_MLP_N = open(SQL_F_MLP_N, 'a') + # end + + features = [] + featuresVec = [] + + difSrcUrl = set([]) + + for request in requests: + url = request['url'] + srcIp = request['srcIp'] + + with open('temp.txt', 'a') as f: + f.write(url + '\n') + + url = urllib.unquote(url) + url = urllib.unquote(url) + url = url.replace('+', ' ') + + domain = request['domain'] + if '@' in domain: + print domain + + # print '----' + # print url + + # get url of different srcIp + if srcIp not in difSrcUrl: + difSrcUrl.add(url) + else: + continue + + if len(url.split('?')) <= 1: + continue + + paramStr = url.split('?')[1] + if len(paramStr.split('&')) <= 0: + continue + + paramVals = '' + keywordVal = 0 + spaceNum = 0 + specialCharNum = 0 + otherCharNum = 0 + + malword = [] + malchar = [] + words = [] + + for param in paramStr.split('&'): + if len(param.split('=')) <= 1: + continue + paramVal = param.split('=')[1] + + if len(paramVal) == 0 or len(paramVal) > 100: + continue + + paramVals += paramVal + + if len(paramVal.split(' ')) > 1: + spaceNum = len(paramVal.split(' ')) - 1 + for val in paramVal.split(' '): + if val != '': + words.append(val.strip()) + else: + words.append(paramVal.strip()) + + for char in specialChar: + if char in paramVal: + specialCharNum += 1 + malchar.append(char) + + for word in words: + for keyword in bgKw: + if keyword.lower() in word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + for keyword in smKw: + if keyword.lower() == word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + + # print words + + if len(paramVals) > 0: + + spaceNum = round(float(spaceNum) / len(paramVals), 3) + + specialCharNum = round( + float(specialCharNum) / len(paramVals), 3) + + otherCharNum = round(1 - spaceNum - specialCharNum, 3) + features.append({'keywordVal': keywordVal, + 'spaceNum': spaceNum, + 'specialCharNum': specialCharNum, + 'otherCharNum': otherCharNum}) + + if len(paramVals) <= 100: + # for LSTM + ascList = [] + for i in paramVals: + ascList.append(ord(i)) + + for i in range(100 - len(paramVals)): + ascList.append(0) + + json.dump(ascList, f_SQL_F_LSTM_N) + f_SQL_F_LSTM_N.write('\n') + + # end of a request + + # end of requests + print len(difSrcUrl) + + for feature in features: + keywordVal = feature['keywordVal'] + spaceNum = feature['spaceNum'] + specialCharNum = feature['specialCharNum'] + otherCharNum = feature['otherCharNum'] + + featuresVec = [keywordVal, spaceNum, specialCharNum, otherCharNum] + + json.dump(featuresVec, f_SQL_F_MLP_N) + f_SQL_F_MLP_N.write('\n') + + te = time.time() + print '---- feature time: ', te - ts + + # files close area + f.close() + f_SQL_F_MLP_N.close() + f_SQL_F_LSTM_N.close() + # end + + +def parseMalFeatures(requests): + ts = time.time() + + # files open area + f = open('SQL_paramstr.txt', 'a') + f_SQL_F_MLP_P = open(SQL_F_MLP_P, 'a') + f_SQL_F_LSTM_P = open(SQL_F_LSTM_P, 'a') + # end + + features = [] + featuresVec = [] + count = 0 + + print len(requests) + for request in requests: + count += 1 + # print count + + paramVal = request['paramVal'] + # print paramVal + + # for ascii + ascList = [] + + paramVal = urllib.unquote(paramVal) + paramVal = urllib.unquote(paramVal) + paramVal = paramVal.replace('+', ' ') + # print '----' + # print paramVal + + if len(paramVal) == 0 or len(paramVal) > 100: + continue + + if len(paramVal) > 100: + # maxVal = len(paramVal) + continue + + keywordVal = 0 + spaceNum = 0 + specialCharNum = 0 + otherCharNum = 0 + + malword = [] + malchar = [] + words = [] + + if len(paramVal.split(' ')) > 0: + spaceNum = len(paramVal.split(' ')) - 1 + for val in paramVal.split(' '): + if val != '': + words.append(val.strip()) + else: + words.append(paramVal.strip()) + + # print words + for char in specialChar: + if char in paramVal: + specialCharNum += 1 + malchar.append(char) + + for word in words: + for keyword in bgKw: + if keyword.lower() in word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + for keyword in smKw: + if keyword.lower() == word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + + paramVals = paramVal + if len(paramVals) > 0: + spaceNum = round(float(spaceNum) / len(paramVals), 3) + + specialCharNum = round( + float(specialCharNum) / len(paramVals), 3) + + otherCharNum = round(1 - spaceNum - specialCharNum, 3) + features.append({'keywordVal': keywordVal, + 'spaceNum': spaceNum, + 'specialCharNum': specialCharNum, + 'otherCharNum': otherCharNum}) + + if len(paramVals) <= 100: + # for LSTM + ascList = [] + for i in paramVals: + ascList.append(ord(i)) + + for i in range(100 - len(paramVals)): + ascList.append(0) + + json.dump(ascList, f_SQL_F_LSTM_P) + f_SQL_F_LSTM_P.write('\n') + + # end of a request + + # end of requests + + for feature in features: + keywordVal = feature['keywordVal'] + spaceNum = feature['spaceNum'] + specialCharNum = feature['specialCharNum'] + otherCharNum = feature['otherCharNum'] + featuresVec = [keywordVal, spaceNum, specialCharNum, otherCharNum] + + json.dump(featuresVec, f_SQL_F_MLP_P) + f_SQL_F_MLP_P.write('\n') + + te = time.time() + print '---- mal feature time: ', te - ts + + # files close area + f.close() + f_SQL_F_MLP_P.close() + f_SQL_F_LSTM_P.close() + # end + + +def readPayloads(): + requests = [] + with open('SQL_Injection_Payloads.txt', 'r') as f: + for line in f.readlines(): + requests.append({'paramVal': line.split('\r\n')[0]}) + parseMalFeatures(requests) + +# main + + +def main_idc(): + files = os.listdir('data/20171120230000/') + paths = [] + for file in files: + paths.append('data/20171120230000/' + file) + paths = sorted(paths)[0:1] + + info = ['timeStamp', 'srcIp', 'destIp', 'domain', 'url', 'userAgent'] + result = [] + srcIpList = [] + destIpList = [] + features = {} + + read_file_start = time.time() + ts_start = 0 + ts_end = 0 + + for path in paths: + with open(path, 'r') as f: + # read files + for line in f.readlines(): + words = [] + raw = re.split('\|', line) + # timestamp + words.append(int(raw[11][:-1])) + # srcIP + words.append(raw[2]) + # destIP + words.append(raw[3]) + # domain + words.append(base64.decodestring(raw[6])) + # url + words.append(base64.decodestring(raw[7])) + # user agent + words.append(base64.decodestring(raw[9])) + ua = base64.decodestring(raw[9]) + # print ua + dic = dict(zip(info, words)) + result.append(dic) + + print "complete read file:", path + + read_file_end = time.time() + + print '---- read time: ', read_file_end - read_file_start + print 'total http requests:', len(result) + + parseFeatures(result) + +# ------------- + + +def initailize(): + all_time_start = time.time() + + dirlist = [] + hourcount = 0 + + # global var + global datetime + + # try: + # ftp.get_file() + # except Exception,e: + # print Exception,":",e + + os.system('rm SQL_Features_*') + + try: + main_idc() + readPayloads() + except Exception, e: + print Exception, ":", e + + all_time_end = time.time() + print '---- all time: ', all_time_end - all_time_start + +initailize() diff --git a/lab-cabds/InjectionDetection/config.py b/lab-cabds/InjectionDetection/config.py new file mode 100644 index 0000000000000000000000000000000000000000..837b7188204f1de18456b9db2f2a81c7eb8c79f5 --- /dev/null +++ b/lab-cabds/InjectionDetection/config.py @@ -0,0 +1,35 @@ + # -*- coding: utf-8 -*- +import time +# for web_crawbler.py +date = time.strftime('%Y-%m-%d', time.localtime()) # for example: 2017-01-01 + +# botnet + +# zues +BOTNET_URLBL_ZEUS = 'https://zeustracker.abuse.ch/blocklist.php?download=compromised' +BOTNET_DOMBL_ZEUS = 'https://zeustracker.abuse.ch/blocklist.php?download=domainblocklist' +BOTNET_IPBL_ZEUS = 'https://zeustracker.abuse.ch/blocklist.php?download=ipblocklist' + +# osint +BOTNET_DOMBL_OSINT = 'http://osint.bambenekconsulting.com/feeds/c2-dommasterlist.txt' +BOTNET_IPBL_OSINT = 'http://osint.bambenekconsulting.com/feeds/c2-ipmasterlist.txt' + +# ransomware +BOTNET_DOMBL_RW = 'https://ransomwaretracker.abuse.ch/downloads/RW_DOMBL.txt' +BOTNET_URLBL_RW = 'https://ransomwaretracker.abuse.ch/downloads/RW_URLBL.txt' +BOTNET_IPBL_RW = 'https://ransomwaretracker.abuse.ch/downloads/RW_IPBL.txt' + +# feodo +BOTNET_IPBL_FEODO = 'https://feodotracker.abuse.ch/blocklist/?download=ipblocklist' + +F_BOTNET_URLBL = 'BOTNET_URLBL.txt' +F_BOTNET_DOMBL = 'BOTNET_DOMBL.txt' +F_BOTNET_IPBL = 'BOTNET_IPBL.txt' + +# for phishing +phishing_url = 'http://webscan.360.cn/url' +filename_phishing = './list/phishing/phishing.txt' +filename_botnet = './list/botnet/botnet.txt' + +phishing_dir = './list/phishing/' +botnet_dir = './list/botnet/' \ No newline at end of file diff --git a/lab-cabds/InjectionDetection/get_data.py b/lab-cabds/InjectionDetection/get_data.py new file mode 100644 index 0000000000000000000000000000000000000000..17426d604610f16516d9be4713fe5ea367c478e6 --- /dev/null +++ b/lab-cabds/InjectionDetection/get_data.py @@ -0,0 +1,566 @@ +# -*- coding: utf-8 -*- +import os +import time +import base64 +import json +import urllib +import codecs +import cPickle as pickle +import torch +import torch.nn as nn +import torchvision.datasets as dsets +import torchvision.transforms as transforms +from torch.autograd import Variable +import numpy + +# parameters +MODEL_PATH = '/home/user/tp/sql/model/' +PICKLE_PATH = '/home/user/tp/sql/pickle/' +FEATURE_PATH = '/home/user/tp/sql/feature/' + +# SQL_MODEL_MLP = MODEL_PATH + 'SQL_MLP_model_02.pkl' +SQL_MODEL_MLP = MODEL_PATH + 'SQL_MLP_model_good_01.pkl' +SQL_MODEL_LSTM = MODEL_PATH + 'SQL_LSTM_model.pkl' +SQL_PICKLE = PICKLE_PATH + 'predict.pickle' +SQL_FEATURE = FEATURE_PATH + 'SQL_Features.txt' + + +keywords = ['union', 'truncate', 'xp_cmdshell', 'load_file', 'outfile', 'dumpfile', 'exec', 'having', 'between', 'waitfor', 'delay', '@variable', '@version', '@servername', 'substring', 'varchar', 'print', 'sleep', + 'select', 'insert', 'delete', 'where', 'update', 'count', 'group', 'order', 'drop', 'table', 'master', 'version', 'true', 'false', + 'or', 'by', 'and', 'into', 'from', 'like', 'net', 'as', 'set'] + +bgKw = ['union', 'truncate', 'xp_cmdshell', 'load_file', 'outfile', 'dumpfile', 'exec', 'having', 'between', 'waitfor', 'delay', '@variable', '@version', '@servername', 'substring', 'varchar', 'print', 'sleep', + 'select', 'insert', 'delete', 'where', 'update', 'count', 'group', 'order', 'drop', 'table', 'master', 'char', 'version', 'true', 'false', ] +smKw = ['or', 'by', 'and', 'into', 'like', 'from', 'net', 'set', 'as'] + +valueMap = { + # big weight + 'union': 100, + 'truncate': 100, + 'xp_cmdshell': 100, + 'load_file': 100, + 'outfile': 100, + 'dumpfile': 100, + 'exec': 100, + 'having': 100, + 'between': 100, + 'waitfor': 100, + 'delay': 100, + '@variable': 100, + '@version': 100, + '@servername': 100, + 'substring': 100, + 'varchar': 10, + 'print': 10, + 'sleep': 10, + # middle weight + 'select': 10, + 'insert': 10, + 'delete': 10, + 'where': 10, + 'update': 10, + 'count': 10, + 'group': 10, + 'order': 10, + 'drop': 10, + 'table': 10, + 'master': 10, + 'version': 10, + 'true': 10, + 'false': 10, + # small weight + 'or': 1, + 'by': 1, + 'and': 1, + 'from': 1, + 'into': 1, + 'like': 1, + 'net': 1, + 'char': 1, + 'set': 1, + 'as': 1, +} + +specialChar = ['+', '-', '*', '/', '<', '>', '=', + '!', '^', '#', "'", '"', '(', ')', '@', ','] + + +def getData(timeStr): + ts = time.time() + files = os.listdir('/home/user/tp/sql/data/metadata/' + timeStr) + paths = [] + for file in files: + paths.append('/home/user/tp/sql/data/metadata/' + timeStr + '/' + file) + paths = sorted(paths) + + info = ['timeStamp', 'srcIp', 'destIp', 'domain', 'url', 'userAgent'] + result = [] + + for path in paths[0:1]: + s = time.time() + with open(path, 'r') as f: + for line in f.readlines(): + words = [] + raw = line.split('|') + # timestamp + words.append(int(raw[11][:-1])) + # srcIP + words.append(raw[2]) + # destIP + words.append(raw[3]) + # domain + words.append(base64.decodestring(raw[6])) + # url + words.append(base64.decodestring(raw[7])) + # userAgent + words.append(base64.decodestring(raw[9])) + ua = base64.decodestring(raw[9]) + # print ua + dic = dict(zip(info, words)) + result.append(dic) + + e = time.time() + print path + print round((e - s), 2) + + te = time.time() + print '---- getData: ', te - ts + print 'total http requests:', len(result) + return result + + +def getFlowsBySrc(requests): + ts = time.time() + srcs = set([]) + flows = {} + for request in requests: + src = request['srcIp'] + if src not in srcs: + srcs.add(src) + flows[src] = [] + flows[src].append(request) + else: + flows[src].append(request) + te = time.time() + print '---- getFlowsBySrc: ', te - ts + + global srcCount, reqCount + srcCount += len(flows) + reqCount += len(requests) + + return flows + + +def parseFeatures(flows): + ts = time.time() + + f_paramStr = open('/home/user/tp/sql/data/SQL_paramstr.txt', 'a') + f_SQL_Features = open(SQL_FEATURE, 'a') + + features = [] + global featuresVec, featuresVecAsc, params + featuresVec = [] + params = [] + featuresVecAsc = [] + + for src in flows: + details = flows[src] + detail_len = len(details) + + for j in range(detail_len): + detail = details[j] + url = detail['url'] + srcIp = detail['srcIp'] + destIp = detail['destIp'] + timestamp = detail['timeStamp'] + + url = urllib.unquote(url) + url = urllib.unquote(url) + url = url.replace('+', ' ') + + # without parameters + if len(url.split('?')) <= 1: + continue + paramStr = url.split('?')[1] + if len(paramStr.split('&')) <= 0: + continue + + paramVals = '' + + keywordVal = 0 + spaceNum = 0 + specialCharNum = 0 + otherCharNum = 0 + + malword = [] + malchar = [] + words = [] + + for param in paramStr.split('&'): + # without values of parameters + if len(param.split('=')) <= 1: + continue + paramVal = param.split('=')[1] + if paramVal == '' or len(paramVal) > 100: + continue + + paramVals += ' ' + paramVal + + if len(paramVal.split('%20')) > 1: + spaceNum += len(paramVal.split('%20')) - 1 + for val in paramVal.split('%20'): + words.append(val) + elif len(paramVal.split(' ')) > 1: + spaceNum += len(paramVal.split(' ')) - 1 + for val in paramVal.split(' '): + words.append(val) + elif len(paramVal.split('+')) > 1: + spaceNum = len(paramVal.split('+')) - 1 + for val in paramVal.split('+'): + words.append(val) + else: + words.append(paramVal) + + for char in specialChar: + if char in paramVal: + specialCharNum += 1 + malchar.append(char) + + for word in words: + for keyword in bgKw: + if keyword.lower() in word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + for keyword in smKw: + if keyword.lower() == word.lower(): + keywordVal += valueMap[keyword] + malword.append(keyword) + + if len(paramVals) > 0: + spaceNum = round(float(spaceNum) / len(paramVals), 3) + + specialCharNum = round( + float(specialCharNum) / len(paramVals), 3) + + otherCharNum = round(1 - spaceNum - specialCharNum, 3) + features.append({'keywordVal': keywordVal, + 'spaceNum': spaceNum, + 'specialCharNum': specialCharNum, + 'otherCharNum': otherCharNum}) + + params.append( + {'srcIp': srcIp, 'destIp': destIp, 'paramVals': paramVals}) + + if len(paramVals) <= 100: + # for LSTM + ascList = [] + for i in paramVals: + ascList.append(ord(i)) + + for i in range(100 - len(paramVals)): + ascList.append(0) + + featuresVecAsc.append(ascList) + + for i in range(len(features)): + feature = features[i] + keywordVal = feature['keywordVal'] + spaceNum = feature['spaceNum'] + specialCharNum = feature['specialCharNum'] + otherCharNum = feature['otherCharNum'] + + featuresVec.append( + [keywordVal, spaceNum, specialCharNum, otherCharNum]) + + # files close area + f_paramStr.close() + f_SQL_Features.close() + # end + + te = time.time() + + print '---- parseFeatures: ', te - ts + + +def normalize(arg, max_arg, min_arg): + return round(float(arg - min_arg) / (max_arg - min_arg), 3) + + +def compare(arg, max_arg, min_arg): + if arg > max_arg: + max_arg = arg + if arg < min_arg: + min_arg = arg + return [max_arg, min_arg] + + +def loadFeatures(): + ts = time.time() + + # read array + global featuresVec + tlist = featuresVec + + # global featuresVecAsc + # tlist = featuresVecAsc + + # read file + # tlist = [] + # filename = SQL_FEATURE + # file = codecs.open(filename, 'r', 'utf-8') + # for line in file: + # fsi = line.find('[') + # fei = line.find(']') + # if fsi != -1 and fei != -1: # find the features + # feas = line[fsi + 1:fei] + # feas = feas.split(',') + # feas = [float(it.strip()) for it in feas] + # tlist.append(feas) + # file.close() + + print 'requests to be predicted:', len(tlist) + + with open(SQL_PICKLE, 'wb') as f: + pickle.dump(tlist, f) + + te = time.time() + print '---- loadFeatures: ', te - ts + + +class FeatureDataset(object): + + def __init__(self, istrain=True): + with open(SQL_PICKLE, 'rb') as f: + self.prelist = pickle.load(f) + testcnt = int(len(self.prelist)) + self.ys = [1] * len(self.prelist) + + def __getitem__(self, index): + # You should build this function to return one data for given index + return (numpy.asarray(self.prelist[index], dtype=numpy.float32), self.ys[index]) + + def __len__(self): + return len(self.prelist) + + +class MLP(nn.Module): + + def __init__(self, input_size, hidden_size, num_classes): + super(MLP, self).__init__() + self.fc1 = nn.Linear(input_size, hidden_size) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(hidden_size, num_classes) + + def forward(self, x): + out = self.fc1(x) + out = self.relu(out) + out = self.fc2(out) + return out + + +class LSTM(nn.Module): + + def __init__(self, input_size, hidden_size, num_classes, num_layers): + super(LSTM, self).__init__() + self.rnn = nn.LSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + ) + + self.out = nn.Linear(hidden_size, num_classes) + + def forward(self, x): + r_out, (h_n, h_c) = self.rnn(x, None) + out = self.out(r_out[:, -1, :]) + return out + + +def loadMLP(): + input_size = 4 + hidden_size = 8 + num_classes = 2 + + net = MLP(input_size, hidden_size, num_classes) + # net.cuda() + paras = torch.load(SQL_MODEL_MLP) + + for k, v in net.state_dict().items(): + param = paras[k] + v.copy_(param) + + return net + + +def loadLSTM(): + input_size = 1 + hidden_size = 200 + num_layers = 1 + num_classes = 2 + + net = LSTM(input_size, hidden_size, num_classes, num_layers) + # net.cuda() + paras = torch.load(SQL_MODEL_LSTM) + + for k, v in net.state_dict().items(): + param = paras[k] + v.copy_(param) + + return net + + +def predictMLP(): + ts = time.time() + batch_size = 1000 + test_dataset = FeatureDataset() + test_loader = torch.utils.data.DataLoader(dataset=test_dataset, + batch_size=batch_size, + shuffle=False) + net = loadMLP() + global params + count = 0 + countmal = 0 + batch = 0 + difSrc = set([]) + difDest = set([]) + + print '----------------------------' + for images, fakeys in test_loader: + images = Variable(images) + outputs = net(images) + _, predicted = torch.max(outputs.data, 1) + + predicts = predicted.cpu() + + for i in range(len(predicted)): + if predicts[i][0] == 1: + count += 1 + index = batch * batch_size + i + paramVal = params[index]['paramVals'] + srcIp = params[index]['srcIp'] + destIp = params[index]['destIp'] + words = [] + keywordVal = 0 + + if len(paramVal.split('%20')) > 1: + for val in paramVal.split('%20'): + words.append(val) + elif len(paramVal.split(' ')) > 1: + for val in paramVal.split(' '): + words.append(val) + elif len(paramVal.split('+')) > 1: + spaceNum = len(paramVal.split('+')) - 1 + for val in paramVal.split('+'): + words.append(val) + else: + words.append(paramVal) + + for word in words: + for keyword in bgKw: + if keyword.lower() in word.lower(): + keywordVal += valueMap[keyword] + for keyword in smKw: + if keyword.lower() == word.lower(): + keywordVal += valueMap[keyword] + + if keywordVal > 10: + countmal += 1 + if srcIp not in difSrc: + difSrc.add(srcIp) + if destIp not in difDest: + difDest.add(destIp) + with open('/home/user/tp/sql/data/SQL_Predict_Mal.txt', 'a') as f: + f.write(paramVal + '\n') + + batch += 1 + # print '---- predict sql:', count + print '---- total sql requests:', countmal + print '---- sql srcIp', len(difSrc) + print '---- sql destIp', len(difDest) + + te = time.time() + + print '---- predictMLP:', te - ts + return {'countmal': countmal, 'srcIp': len(difSrc), 'destIp': len(difDest)} + + +def predictLSTM(): + input_size = 1 + time_step = 100 + batch_size = 1000 + test_dataset = FeatureDataset() + test_loader = torch.utils.data.DataLoader(dataset=test_dataset, + batch_size=batch_size, + shuffle=False) + net = loadLSTM() + global params + count = 0 + batch = 0 + print '0 - normal' + print '1 - mal' + print '----------------------------' + for x, y in test_loader: + b_x = Variable(x.view(-1, time_step, input_size)) + outputs = net(b_x) + _, predicted = torch.max(outputs.data, 1) + + predicts = predicted.cpu() + for i in range(len(predicted)): + if predicts[i][0] == 1: + count += 1 + index = batch * batch_size + i + + batch += 1 + print '---- total sql requests:', count + + +def main(timeStr): + ts = time.time() + global srcCount, reqCount + srcCount = 0 + reqCount = 0 + result = getData(timeStr) + flows = getFlowsBySrc(result) + parseFeatures(flows) + loadFeatures() + + ret = predictMLP() + # predictLSTM() + + te = time.time() + + print '#### total time: ', te - ts + print '#### total requests: ', reqCount + print '#### total source IP: ', srcCount + + analysis['totalTime'] += round(te - ts, 3) + analysis['totalReqs'] += reqCount + analysis['totalSrcIp'] += srcCount + analysis['totalMalReqs'] += ret['countmal'] + analysis['totalMalSrcIp'] += ret['srcIp'] + analysis['totalMalDestIp'] += ret['destIp'] + + +def initialize(): + os.system('rm /home/user/tp/sql/data/SQL_Predict_Mal.txt') + global analysis + analysis = { + 'totalTime': 0, + 'totalReqs': 0, + 'totalSrcIp': 0, + 'totalMalReqs': 0, + 'totalMalSrcIp': 0, + 'totalMalDestIp': 0 + } + + dataList = [ + '20171120200000', + ] + for data in dataList: + main(data) + + print analysis + + +initialize() diff --git a/lab-iisec/AI3603 lab/lab1/A_star.mp4 b/lab-iisec/AI3603 lab/lab1/A_star.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..d6eb0607d20d169fe63a932b577d1a337b2bbd1b Binary files /dev/null and b/lab-iisec/AI3603 lab/lab1/A_star.mp4 differ diff --git a/lab-iisec/AI3603 lab/lab1/A_star.py b/lab-iisec/AI3603 lab/lab1/A_star.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb038d0efcf600b60f0ed702732aeb91df2acf2 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab1/A_star.py @@ -0,0 +1,166 @@ +import DR20API +import numpy as np + + + +### START CODE HERE ### +# This code block is optional. You can define your utility function and class in this block if necessary. + +# 引入优先级队列，便于open_list的判断 +from queue import PriorityQueue as PQ + +# 由于模型误差，每次运行结束后最终坐标与目标坐标位置有偏差，故设定mis来记录每次运行结束后实际位置 +# 初始值设定为[16，17] +mis = [16,17] + + +### END CODE HERE ### + +def A_star(current_map, current_pos, goal_pos): + """ + Given current map of the world, current position of the robot and the position of the goal, + plan a path from current position to the goal using A* algorithm. + + Arguments: + current_map -- A 120*120 array indicating current map, where 0 indicating traversable and 1 indicating obstacles. + current_pos -- A 2D vector indicating the current position of the robot. + goal_pos -- A 2D vector indicating the position of the goal. + + Return: + path -- A N*2 array representing the planned path by A* algorithm. + """ + + ### START CODE HERE ### + path = [] + + # 每次规划路径时将current_pos更新为记录的实际坐标 + global mis + + current_pos[0] = mis[0] + current_pos[1] = mis[1] + + # 初始化open_list + pq = PQ() + + # 计算当前坐标的f、g、h值，并将f值作为队列排序的标准 + # 其中h为当前节点和目标点的曼哈顿距离，g为cost，每两个相邻的坐标间cost=10 + h = abs(goal_pos[0]-current_pos[0])+abs(goal_pos[1]-current_pos[1]) + # 将当前坐标加入open_list，以元组的形式，包括其坐标、g值、h值、父节点坐标 + pq.put((h,list(current_pos),0,h,[])) + + close_list = [] + + while(True): + # 从open_list中取出f值最小的节点坐标并加入close_list + node = pq.get() + father = list(node[1]) + path.append(father) + close_list.append(node) + + # 找出当前节点的相邻节点并分别计算其g值和h值，如果其不在close_list中并且是可遍历的，则将其加入open_list队列中 + r = [father[0]+1,father[1]] + l = [father[0]-1,father[1]] + u = [father[0],father[1]+1] + d = [father[0],father[1]-1] + + + if(r not in path and not current_map[r[0],r[1]]): + g = node[2]+10 + h = abs(r[0]-goal_pos[0])+abs(r[1]-goal_pos[1]) + if(not any(all(r) in item for item in pq.queue)): + pq.put((g+h,r,g,h,father)) + + if(l not in path and not current_map[l[0],l[1]]): + g = node[2]+10 + h = abs(l[0]-goal_pos[0])+abs(l[1]-goal_pos[1]) + if(not any(all(l) in item for item in pq.queue)): + pq.put((g+h,l,g,h,father)) + + if(u not in path and not current_map[u[0],u[1]]): + g = node[2]+10 + h = abs(u[0]-goal_pos[0])+abs(u[1]-goal_pos[1]) + if(not any(all(u) in item for item in pq.queue)): + pq.put((g+h,u,g,h,father)) + + if(d not in path and not current_map[d[0],d[1]]): + g = node[2]+10 + h = abs(d[0]-goal_pos[0])+abs(d[1]-goal_pos[1]) + if(not any(all(d) in item for item in pq.queue)): + pq.put((g+h,d,g,h,father)) + + # 设置每次搜索步长为10步(也可以不设定，直接搜索到最终路径，每增加一步搜索时间就翻倍) + if(g>100): + break + + # 如果当前出队节点并符合结束要求，就停止搜索 + if(reach_goal(father,goal_pos)): + break + # if(node[3]<=5): + # break + + # 根据搜索到的最终节点和记录的父节点回溯，找到规划的最优路径 + b = path[-1] + final_path = [] + + while(True): + index = path.index(b) + b = close_list[index][-1] + final_path.append(b) + if(all(b == current_pos)): + break + + final_path.reverse() + mis = final_path[-1] + print(final_path) + + ### END CODE HERE ### + return final_path + +def reach_goal(current_pos, goal_pos): + """ + Given current position of the robot, + check whether the robot has reached the goal. + + Arguments: + current_pos -- A 2D vector indicating the current position of the robot. + goal_pos -- A 2D vector indicating the position of the goal. + + Return: + is_reached -- A bool variable indicating whether the robot has reached the goal, where True indicating reached. + """ + + ### START CODE HERE ### + + # 若当前节点在目标点范围内，则搜索完成 + if((abs(current_pos[0]-goal_pos[0])<5 and abs(current_pos[1]-goal_pos[1])<5) or (abs(mis[0]-goal_pos[0])<=5 and abs(mis[1]-goal_pos[1])<=5)): + is_reached = True + else: + is_reached =False + + + ### END CODE HERE ### + return is_reached + +if __name__ == '__main__': + # Define goal position of the exploration, shown as the gray block in the scene. + goal_pos = [100, 100] + controller = DR20API.Controller() + + # Initialize the position of the robot and the map of the world. + current_pos = controller.get_robot_pos() + # current_pos[0]+=1 + current_map = controller.update_map() + + # Plan-Move-Perceive-Update-Replan loop until the robot reaches the goal. + while not reach_goal(current_pos, goal_pos): + # Plan a path based on current map from current position of the robot to the goal. + path = A_star(current_map, current_pos, goal_pos) + # Move the robot along the path to a certain distance. + controller.move_robot(path) + # Get current position of the robot. + current_pos = controller.get_robot_pos() + # Update the map based on the current information of laser scanner and get the updated map. + current_map = controller.update_map() + + # Stop the simulation. + controller.stop_simulation() \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab1/Improved A_star.mp4 b/lab-iisec/AI3603 lab/lab1/Improved A_star.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..5f82b3d725c5309bd0dfaa1ea7bdf52774f169ff Binary files /dev/null and b/lab-iisec/AI3603 lab/lab1/Improved A_star.mp4 differ diff --git a/lab-iisec/AI3603 lab/lab1/Improved_A_star.py b/lab-iisec/AI3603 lab/lab1/Improved_A_star.py new file mode 100644 index 0000000000000000000000000000000000000000..a8fa21b3cfe3f8648da634193c2088004856a68d --- /dev/null +++ b/lab-iisec/AI3603 lab/lab1/Improved_A_star.py @@ -0,0 +1,232 @@ +import DR20API +import numpy as np + +### START CODE HERE ### +# This code block is optional. You can define your utility function and class in this block if necessary. + +# 与A_star算法基本相同，模块也大致相似，只是加入了八个方向的移动，并且在加入open_list时考虑到当前节点到下一个节点的cost，如果小于则将其父节点重新设置 +from queue import PriorityQueue as PQ + +mis = [16,17] + +### END CODE HERE ### + +def Improved_A_star(current_map, current_pos, goal_pos): + """ + Given current map of the world, current position of the robot and the position of the goal, + plan a path from current position to the goal using improved A* algorithm. + + Arguments: + current_map -- A 120*120 array indicating current map, where 0 indicating traversable and 1 indicating obstacles. + current_pos -- A 2D vector indicating the current position of the robot. + goal_pos -- A 2D vector indicating the position of the goal. + + Return: + path -- A N*2 array representing the planned path by improved A* algorithm. + """ + + ### START CODE HERE ### + path1 = [] + + global mis + + current_pos[0] = mis[0] + current_pos[1] = mis[1] + + pq = PQ() + + h = abs(goal_pos[0]-current_pos[0])+abs(goal_pos[1]-current_pos[1]) + pq.put((h,list(current_pos),0,h,())) + + close_list = [] + + while(True): + node = pq.get() + + + father = list(node[1]) + + path1.append(father) + close_list.append(node) + + + r = [father[0]+1,father[1]] + l = [father[0]-1,father[1]] + u = [father[0],father[1]+1] + d = [father[0],father[1]-1] + ru = [father[0]+1,father[1]+1] + lu = [father[0]-1,father[1]+1] + rd = [father[0]+1,father[1]-1] + ld = [father[0]-1,father[1]-1] + + r_in = False + l_in = False + u_in = False + u_in = False + d_in = False + ru_in = False + lu_in = False + rd_in = False + ld_in = False + + for item in pq.queue: + if(all(r) in item): + r_in = True + r_node = item + if(all(l) in item): + l_in = True + l_node = item + if(all(d) in item): + d_in = True + d_node = item + if(all(u) in item): + u_in = True + u_node = item + if(all(ru) in item): + ru_in = True + ru_node = item + if(all(rd) in item): + rd_in = True + rd_node = item + if(all(ld) in item): + ld_in = True + ld_node = item + if(all(lu) in item): + lu_in = True + lu_node = item + + if(r not in path1 and not current_map[r[0],r[1]]): + g = node[2]+10 + h = abs(r[0]-goal_pos[0])+abs(r[1]-goal_pos[1]) + if(not r_in): + pq.put((g+h,r,g,h,father)) + elif(g < r_node[2]): + pq.put((g+h,r,g,h,father)) + + if(l not in path1 and not current_map[l[0],l[1]]): + g = node[2]+10 + h = abs(l[0]-goal_pos[0])+abs(l[1]-goal_pos[1]) + if(not l_in): + pq.put((g+h,l,g,h,father)) + elif(g < l_node[2]): + pq.put((g+h,l,g,h,father)) + + if(u not in path1 and not current_map[u[0],u[1]]): + g = node[2]+10 + h = abs(u[0]-goal_pos[0])+abs(u[1]-goal_pos[1]) + if(not u_in): + pq.put((g+h,u,g,h,father)) + elif(g < u_node[2]): + pq.put((g+h,u,g,h,father)) + + if(d not in path1 and not current_map[d[0],d[1]]): + g = node[2]+10 + h = abs(d[0]-goal_pos[0])+abs(d[1]-goal_pos[1]) + if(not d_in): + pq.put((g+h,d,g,h,father)) + elif(g < d_node[2]): + pq.put((g+h,d,g,h,father)) + + if(ru not in path1 and not current_map[ru[0],ru[1]]): + g = node[2]+14 + h = abs(ru[0]-goal_pos[0])+abs(ru[1]-goal_pos[1]) + if(not ru_in): + pq.put((g+h,ru,g,h,father)) + elif(g < ru_node[2]): + pq.put((g+h,ru,g,h,father)) + + if(rd not in path1 and not current_map[rd[0],rd[1]]): + g = node[2]+14 + h = abs(rd[0]-goal_pos[0])+abs(rd[1]-goal_pos[1]) + if(not rd_in): + pq.put((g+h,rd,g,h,father)) + elif(g < rd_node[2]): + pq.put((g+h,rd,g,h,father)) + + if(lu not in path1 and not current_map[lu[0],lu[1]]): + g = node[2]+14 + h = abs(lu[0]-goal_pos[0])+abs(lu[1]-goal_pos[1]) + if(not lu_in): + pq.put((g+h,lu,g,h,father)) + elif(g < lu_node[2]): + pq.put((g+h,lu,g,h,father)) + + if(ld not in path1 and not current_map[ld[0],ld[1]]): + g = node[2]+14 + h = abs(ld[0]-goal_pos[0])+abs(ld[1]-goal_pos[1]) + if(not ld_in): + pq.put((g+h,ld,g,h,father)) + elif(g < ld_node[2]): + pq.put((g+h,ld,g,h,father)) + + if(g>=70): + break + + if(reach_goal(father,goal_pos)): + break + # if(node[3]<=5): + # break + + b = path1[-1] + path = [] + + while(True): + index = path1.index(b) + b = close_list[index][-1] + path.append(b) + if(all(b == current_pos)): + break + + path.reverse() + mis = path[-1] + print(path) + + + ### END CODE HERE ### + return path + +def reach_goal(current_pos, goal_pos): + """ + Given current position of the robot, + check whether the robot has reached the goal. + + Arguments: + current_pos -- A 2D vector indicating the current position of the robot. + goal_pos -- A 2D vector indicating the position of the goal. + + Return: + is_reached -- A bool variable indicating whether the robot has reached the goal, where True indicating reached. + """ + + ### START CODE HERE ### + if((abs(current_pos[0]-goal_pos[0])<5 and abs(current_pos[1]-goal_pos[1])<5) or (abs(mis[0]-goal_pos[0])<=5 and abs(mis[1]-goal_pos[1])<=5)): + is_reached = True + else: + is_reached =False + + + ### END CODE HERE ### + return is_reached + +if __name__ == '__main__': + # Define goal position of the exploration, shown as the gray block in the scene. + goal_pos = [100, 100] + controller = DR20API.Controller() + + # Initialize the position of the robot and the map of the world. + current_pos = controller.get_robot_pos() + current_map = controller.update_map() + + # Plan-Move-Perceive-Update-Replan loop until the robot reaches the goal. + while not reach_goal(current_pos, goal_pos): + # Plan a path based on current map from current position of the robot to the goal. + path = Improved_A_star(current_map, current_pos, goal_pos) + # Move the robot along the path to a certain distance. + controller.move_robot(path) + # Get current position of the robot. + current_pos = controller.get_robot_pos() + # Update the map based on the current information of laser scanner and get the updated map. + current_map = controller.update_map() + + # Stop the simulation. + controller.stop_simulation() \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab2/agent.py b/lab-iisec/AI3603 lab/lab2/agent.py new file mode 100644 index 0000000000000000000000000000000000000000..a93e1f344ff31f6ab2115cf8c76bd073e912886e --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/agent.py @@ -0,0 +1,122 @@ +# -*- coding:utf-8 -*- +import math, os, time, sys +import numpy as np +import gym +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + +""" +Instruction: +Currently, the following agents are `random` policy. +You need to implement the Q-learning agent, Sarsa agent and Dyna-Q agent in this file. +""" + +# ------------------------------------------------------------------------------------------- # + +"""TODO: Implement your Sarsa agent here""" +class SarsaAgent(object): + ##### START CODING HERE ##### + def __init__(self, all_actions): + """initialize the agent. Maybe more function inputs are needed.""" + self.all_actions = all_actions + self.epsilon = 1.0 + + def choose_action(self, observation): + """choose action with epsilon-greedy algorithm.""" + action = np.random.choice(self.all_actions) + return action + + def learn(self): + """learn from experience""" + time.sleep(0.5) + print("[INFO] The learning process complete. (ﾉ｀⊿´)ﾉ") + return True + + def your_function(self, params): + """You can add other functions as you wish.""" + do_something = True + return None + + ##### END CODING HERE ##### + +# ------------------------------------------------------------------------------------------- # + +"""TODO: Implement your Q-Learning agent here""" +class QLearningAgent(object): + ##### START CODING HERE ##### + def __init__(self, all_actions): + """initialize the agent. Maybe more function inputs are needed.""" + self.all_actions = all_actions + self.epsilon = 1.0 + + def choose_action(self, observation): + """choose action with epsilon-greedy algorithm.""" + action = np.random.choice(self.all_actions) + return action + + def learn(self): + """learn from experience""" + time.sleep(0.5) + print("[INFO] The learning process complete. (ﾉ｀⊿´)ﾉ") + return True + + def your_function(self, params): + """You can add other functions as you wish.""" + do_something = True + return None + + ##### END CODING HERE ##### + +# ------------------------------------------------------------------------------------------- # + +"""TODO: Implement your Dyna-Q agent here""" +class DynaQAgent(object): + ##### START CODING HERE ##### + def __init__(self, all_actions): + """initialize the agent. Maybe more function inputs are needed.""" + self.all_actions = all_actions + self.epsilon = 1.0 + + def choose_action(self, observation): + """choose action with epsilon-greedy algorithm.""" + action = np.random.choice(self.all_actions) + return action + + def learn(self): + """learn from experience""" + time.sleep(0.5) + print("[INFO] The learning process complete. (ﾉ｀⊿´)ﾉ") + return True + + def your_function(self, params): + """You can add other functions as you wish.""" + do_something = True + return None + + ##### END CODING HERE ##### + +# ------------------------------------------------------------------------------------------- # + +"""TODO: (optional) Implement RL agent(s) with other exploration methods you have found""" +##### START CODING HERE ##### +class RLAgentWithOtherExploration(object): + """initialize the agent""" + def __init__(self, all_actions): + self.all_actions = all_actions + self.epsilon = 1.0 + + def choose_action(self, observation): + """choose action with other exploration algorithms.""" + action = np.random.choice(self.all_actions) + return action + + def learn(self): + """learn from experience""" + time.sleep(0.5) + print("[INFO] The learning process complete. (ﾉ｀⊿´)ﾉ") + return True +##### END CODING HERE ##### + +# ------------------------------------------------------------------------------------------- # \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab2/cliff_walk_qlearning.py b/lab-iisec/AI3603 lab/lab2/cliff_walk_qlearning.py new file mode 100644 index 0000000000000000000000000000000000000000..37cb696289c74c4a8c4869977de788f03dbce478 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/cliff_walk_qlearning.py @@ -0,0 +1,61 @@ +# -*- coding:utf-8 -*- +# Train Q-Learning in cliff-walking environment +import math, os, time, sys +import numpy as np +import random +import gym +from gym_gridworld import CliffWalk +from agent import QLearningAgent +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + +# construct the environment +env = CliffWalk() +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + +##### START CODING HERE ##### + +# construct the intelligent agent. +agent = QLearningAgent(all_actions) + +# start training +for episode in range(1000): + # record the reward in an episode + episode_reward = 0 + # reset env + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + # choose an action + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + # update the episode reward + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + # agent learns from experience + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +##### START CODING HERE ##### + + diff --git a/lab-iisec/AI3603 lab/lab2/cliff_walk_sarsa.py b/lab-iisec/AI3603 lab/lab2/cliff_walk_sarsa.py new file mode 100644 index 0000000000000000000000000000000000000000..4cdb7de4d31471967276afce9e28858eb2e89b2d --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/cliff_walk_sarsa.py @@ -0,0 +1,62 @@ +# -*- coding:utf-8 -*- +# Train Sarsa in cliff-walking environment +import math, os, time, sys +import numpy as np +import random +import gym +from gym_gridworld import CliffWalk +from agent import SarsaAgent +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + + +# construct the environment +env = CliffWalk() +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + +####### START CODING HERE ####### + +# construct the intelligent agent. +agent = SarsaAgent(all_actions) + +# start training +for episode in range(1000): + # record the reward in an episode + episode_reward = 0 + # reset env + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + # choose an action + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + # update the episode reward + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + # agent learns from experience + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +####### START CODING HERE ####### + + diff --git a/lab-iisec/AI3603 lab/lab2/gym_gridworld.py b/lab-iisec/AI3603 lab/lab2/gym_gridworld.py new file mode 100644 index 0000000000000000000000000000000000000000..fbda3edf7a17a9c8e8405562c7f42d0f197aa4d7 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_gridworld.py @@ -0,0 +1,399 @@ +# -*- coding:utf-8 -*- +# gridworld(cliff-walking) environment FOR AI 3603 +# --------- 作业环境，请勿修改！-------------- + +import math, os, time, sys +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np + + +class Grid(object): + def __init__(self, x:int = None, + y:int = None, + type:int = 0, + reward:int = 0.0, + value:float = 0.0): # value, for possible future usage + self.x = x # coordinate x + self.y = y + self.type = type # Type (0:empty；1:obstacle or boundary) + self.reward = reward # instant reward for an agent entering this grid cell + self.value = value # the value of this grid cell, for future usage + self.name = None # name of this grid. + self._update_name() + + def _update_name(self): + self.name = "X{0}-Y{1}".format(self.x, self.y) + + def __str__(self): + return "name:{4}, x:{0}, y:{1}, type:{2}, value{3}".format(self.x, + self.y, + self.type, + self.reward, + self.value, + self.name + ) + +class GridMatrix(object): + '''格子矩阵，通过不同的设置，模拟不同的格子世界环境 + ''' + def __init__(self, n_width:int, # defines the number of cells horizontally + n_height:int, # vertically + default_type: int = 0, # default cell type + default_reward: float = 0.0, # default instant reward + default_value: float = 0.0 # default value + ): + self.grids = None + self.n_height = n_height + self.n_width = n_width + self.len = n_width * n_height + self.default_reward = default_reward + self.default_value = default_value + self.default_type = default_type + self.reset() + + def reset(self): + self.grids = [] + for x in range(self.n_height): + for y in range(self.n_width): + self.grids.append(Grid(x, + y, + self.default_type, + self.default_reward, + self.default_value)) + + def get_grid(self, x, y=None): + '''get a grid information + args: represented by x,y or just a tuple type of x + return: grid object + ''' + xx, yy = None, None + if isinstance(x, int): + xx, yy = x, y + elif isinstance(x, tuple): + xx, yy = x[0], x[1] + assert(xx>=0 and yy>=0 and xx < self.n_width and yy < self.n_height),\ + "coordinates should be in reasonable range" + index = yy * self.n_width + xx + return self.grids[index] + + def set_reward(self, x, y, reward): + """ + 设定每个格子的reward. + """ + grid = self.get_grid(x,y) + if grid is not None: + grid.reward = reward + else: + raise("grid doesn't exist") + + def set_value(self, x, y, value): + grid = self.get_grid(x,y) + if grid is not None: + grid.value = value + else: + raise("grid doesn't exist") + + def set_type(self, x, y, type): + """ + 设定env中格子的样式。 + """ + grid = self.get_grid(x,y) + if grid is not None: + grid.type = type + else: + raise("grid doesn't exist") + + def get_reward(self, x, y): + grid = self.get_grid(x, y) + if grid is None: + return None + return grid.reward + + def get_value(self, x, y): + grid = self.get_grid(x, y) + if grid is None: + return None + return grid.value + + def get_type(self, x, y): + grid = self.get_grid(x, y) + if grid is None: + return None + return grid.type + + +class GridWorldEnv(gym.Env): + '''格子世界环境，可以模拟各种不同的格子世界 + ''' + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 30 + } + + def __init__(self, n_width:int=10, + n_height:int = 7, + u_size = 40, + default_reward:float = 0, + default_type = 0, + windy=False): + self.u_size = u_size # size for each cell (pixels) + self.n_width = n_width # width of the env calculated by number of cells. + self.n_height = n_height # height... + self.width = u_size * n_width # scenario width (pixels) + self.height = u_size * n_height # height + self.default_reward = default_reward + self.default_type = default_type + self._adjust_size() + + self.grids = GridMatrix(n_width = self.n_width, + n_height = self.n_height, + default_reward = self.default_reward, + default_type = self.default_type, + default_value = 0.0) + self.reward = 0 # for rendering + self.action = None # for rendering + self.windy = windy # whether this is a windy environment + + # 0,1,2,3,4 represent left, right, up, down, -, five moves. + self.action_space = spaces.Discrete(4) + # 观察空间由low和high决定 + self.observation_space = spaces.Discrete(self.n_height * self.n_width) + # 坐标原点为左下角，这个pyglet是一致的, left-bottom corner is the position of (0,0) + # 通过设置起始点、终止点以及特殊奖励和类型的格子可以构建各种不同类型的格子世界环境 + self.ends = [(7,3)] # 终止格子坐标，可以有多个, goal cells position list + self.start = (0,3) # 起始格子坐标，只有一个, start cell position, only one start position + self.types = [] # 特殊种类的格子在此设置。[(3,2,1)]表示(3,2)处值为1. + # special type of cells, (x,y,z) represents in position(x,y) the cell type is z + self.rewards= [] # 特殊奖励的格子在此设置，终止格子奖励0, special reward for a cell + self.refresh_setting() + self.viewer = None # 图形接口对象 + self.seed() # 产生一个随机子 + self.reset() + + def _adjust_size(self): + ''' + 调整场景尺寸适合最大宽度、高度不超过800 + ''' + pass + + + def seed(self, seed=None): + # 产生一个随机化时需要的种子，同时返回一个np_random对象，支持后续的随机化生成操作 + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def step(self, action): + assert self.action_space.contains(action), \ + "%r (%s) invalid" % (action, type(action)) + + self.action = action # action for rendering + old_x, old_y = self._state_to_xy(self.state) + new_x, new_y = old_x, old_y + + # wind effect: + # 有风效果，其数字表示个体离开(而不是进入)该格子时朝向别的方向会被吹偏离的格子数 + # this effect is just used for the windy env in David Silver's youtube video. + if self.windy: + if new_x in [3, 4, 5, 8]: + new_y += 1 + elif new_x in [6, 7]: + new_y += 2 + + if action == 0: new_x -= 1 # left + elif action == 1: new_x += 1 # right + elif action == 2: new_y += 1 # up + elif action == 3: new_y -= 1 # down + + elif action == 4: new_x,new_y = new_x-1,new_y-1 + elif action == 5: new_x,new_y = new_x+1,new_y-1 + elif action == 6: new_x,new_y = new_x+1,new_y-1 + elif action == 7: new_x,new_y = new_x+1,new_y+1 + # boundary effect + if new_x < 0: new_x = 0 + if new_x >= self.n_width: new_x = self.n_width-1 + if new_y < 0: new_y = 0 + if new_y >= self.n_height: new_y = self.n_height-1 + + # wall effect, obstacles or boundary. + # 类型为1的格子为障碍格子，不可进入 + if self.grids.get_type(new_x,new_y) == 1: + new_x, new_y = old_x, old_y + + self.reward = self.grids.get_reward(new_x, new_y) + + done = self._is_end_state(new_x, new_y) + self.state = self._xy_to_state(new_x, new_y) + # 提供格子世界所有的信息在info内 + info = {"x":new_x,"y":new_y} + return self.state, self.reward, done, info + + # 将状态变为横纵坐标, set status into an one-axis coordinate value + def _state_to_xy(self, s): + x = s % self.n_width + y = int((s - x) / self.n_width) + return x,y + + def _xy_to_state(self, x, y = None): + if isinstance(x, int): + assert(isinstance(y, int)), "incomplete Position info" + return x + self.n_width * y + elif isinstance(x, tuple): + return x[0] + self.n_width * x[1] + return -1 # 未知状态, unknow status + + def refresh_setting(self): + ''' + 用户在使用该类创建格子世界后可能会修改格子世界某些格子类型或奖励值 + 的设置，修改设置后通过调用该方法使得设置生效。 + ''' + for x,y,r in self.rewards: + self.grids.set_reward(x,y,r) + for x,y,t in self.types: + self.grids.set_type(x,y,t) + + def reset(self): + self.state = self._xy_to_state(self.start) + return self.state + + # 判断是否是终止状态 + def _is_end_state(self, x, y=None): + if y is not None: + xx, yy = x, y + elif isinstance(x, int): + xx, yy = self._state_to_xy(x) + else: + assert(isinstance(x, tuple)),"incomplete coordinate values" + xx ,yy = x[0], x[1] + for end in self.ends: + if xx == end[0] and yy == end[1]: + return True + return False + + def close(self): + """ + 关闭渲染，程序退出前必须执行一次，否则gym会报错。 + """ + self.render(close=True) + + # 图形化界面, Graphic UI + def render(self, mode='human', close=False): + if close: + if self.viewer is not None: + self.viewer.close() + self.viewer = None + return + zero = (0,0) + u_size = self.u_size + m = 2 # gaps between two cells + + # 如果还没有设定屏幕对象，则初始化整个屏幕具备的元素。 + if self.viewer is None: + from gym.envs.classic_control import rendering + self.viewer = rendering.Viewer(self.width, self.height) + + # 在Viewer里绘制一个几何图像的步骤如下： + # the following steps just tells how to render an shape in the environment. + # 1. 建立该对象需要的数据本身 + # 2. 使用rendering提供的方法返回一个geom对象 + # 3. 对geom对象进行一些对象颜色、线宽、线型、变换属性的设置（有些对象提供一些个 + # 性化的方法来设置属性，具体请参考继承自这些Geom的对象），这其中有一个重要的 + # 属性就是变换属性， + # 该属性负责对对象在屏幕中的位置、渲染、缩放进行渲染。如果某对象 + # 在呈现时可能发生上述变化，则应建立关于该对象的变换属性。该属性是一个 + # Transform对象，而一个Transform对象，包括translate、rotate和scale + # 三个属性，每个属性都由以np.array对象描述的矩阵决定。 + # 4. 将新建立的geom对象添加至viewer的绘制对象列表里，如果在屏幕上只出现一次， + # 将其加入到add_onegeom(）列表中，如果需要多次渲染，则将其加入add_geom() + # 5. 在渲染整个viewer之前，对有需要的geom的参数进行修改，修改主要基于该对象 + # 的Transform对象 + # 6. 调用Viewer的render()方法进行绘制 + ''' 绘制水平竖直格子线，由于设置了格子之间的间隙，可不用此段代码 + for i in range(self.n_width+1): + line = rendering.Line(start = (i*u_size, 0), + end =(i*u_size, u_size*self.n_height)) + line.set_color(0.5,0,0) + self.viewer.add_geom(line) + for i in range(self.n_height): + line = rendering.Line(start = (0, i*u_size), + end = (u_size*self.n_width, i*u_size)) + line.set_color(0,0,1) + self.viewer.add_geom(line) + ''' + + # 绘制格子, draw cells + for x in range(self.n_width): + for y in range(self.n_height): + v = [(x*u_size+m, y*u_size+m), + ((x+1)*u_size-m, y*u_size+m), + ((x+1)*u_size-m, (y+1)*u_size-m), + (x*u_size+m, (y+1)*u_size-m)] + + rect = rendering.FilledPolygon(v) + r = self.grids.get_reward(x,y)/10 + if r < -5: + rect.set_color(0.3, 0.3, 0.3) + elif r == -0.1: + rect.set_color(0.9, 0.9, 0.9) + elif r < 0: + print(r) + rect.set_color(0.9-r, 0.9 + r, 0.9 + r) + elif r > 0: + rect.set_color(0.3, 0.5 + r, 0.3) + else: + rect.set_color(0.9,0.9,0.9) + self.viewer.add_geom(rect) + # 绘制边框, draw frameworks + v_outline = [(x*u_size+m, y*u_size+m), + ((x+1)*u_size-m, y*u_size+m), + ((x+1)*u_size-m, (y+1)*u_size-m), + (x*u_size+m, (y+1)*u_size-m)] + outline = rendering.make_polygon(v_outline, False) + outline.set_linewidth(3) + + # if self._is_end_state(x,y): + # # 给终点方格添加金黄色边框, give end state cell a yellow outline. + # outline.set_color(0.9,0.9,0) + # self.viewer.add_geom(outline) + if self.start[0] == x and self.start[1] == y: + outline.set_color(0.5, 0.5, 0.8) + rect.set_color(0.5,0.5,0.8) + self.viewer.add_geom(outline) + if self.grids.get_type(x,y) == 1: # 障碍格子用深灰色表示, obstacle cells are with gray color + rect.set_color(0.3,0.3,0.3) + else: + pass + # 绘制个体, draw agent + self.agent = rendering.make_circle(u_size/4, 30, True) + self.agent.set_color(1.0, 1.0, 0.0) + self.viewer.add_geom(self.agent) + self.agent_trans = rendering.Transform() + self.agent.add_attr(self.agent_trans) + + # 更新个体位置 update position of an agent + x, y = self._state_to_xy(self.state) + self.agent_trans.set_translation((x+0.5)*u_size, (y+0.5)*u_size) + + return self.viewer.render(return_rgb_array = mode=='rgb_array') + +def CliffWalk(): + ''' + 悬崖行走格子世界环境 + 用于学习Q-leaning和Sarsa在冒险策略上的区别。 + ''' + env = GridWorldEnv(n_width=12, + n_height = 4, + u_size = 60, + default_reward = -1, + default_type = 0, + windy=False) + env.action_space = spaces.Discrete(4) # left or right + env.start = (0,0) + env.ends = [(11,0)] + env.rewards.append((11,0,10)) + for i in range(10): + env.rewards.append((i+1,0,-100)) + env.ends.append((i+1,0)) + env.refresh_setting() + return env diff --git a/lab-iisec/AI3603 lab/lab2/gym_recoder_instruction.py b/lab-iisec/AI3603 lab/lab2/gym_recoder_instruction.py new file mode 100644 index 0000000000000000000000000000000000000000..7a31b7415dab290be9ae0edd4144232424b134c7 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_recoder_instruction.py @@ -0,0 +1,46 @@ +# -*- coding:utf-8 -*- +# gym录像机教程，仅供参考，不需提交该文件。 + +import math, os, time, sys +import numpy as np +import random +import gym +from gym.wrappers import Monitor +from gym_gridworld import CliffWalk +import gym_sokoban +from agent import * + +""" +1. This is an example to record video with gym library. +2. You can choose other video record tools as you wish. +3. You DON'T NEED to upload this file in your assignment. +""" + +# record sokoban environment +# the video file will be saved at the ./video folder. +env = Monitor(gym.make('Sokoban-hw2-v0'), './video', force=True) + +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + +agent = QLearningAgent(all_actions) + +s = env.reset() +env.render() +for iter in range(200): + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + agent.learn() + s = s_ + if isdone: + break + +# the recorder will stop when calling `env.close()` function +# the video file .mp4 will be saved at the ./video folder +env.close() + diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/__init__.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f78f0eb6f0cd306373b523bd5219d23c857f7dd1 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/__init__.py @@ -0,0 +1,19 @@ +import logging +import pkg_resources +import json +from gym.envs.registration import register + +logger = logging.getLogger(__name__) + +resource_package = __name__ +env_json = pkg_resources.resource_filename(resource_package, '/'.join(('envs', 'available_envs.json'))) + +with open(env_json) as f: + + envs = json.load(f) + + for env in envs: + register( + id=env["id"], + entry_point=env["entry_point"] + ) diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/__init__.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c389dbc615bfcde8febf9773c1fe8a5751e3c9a4 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/__init__.py @@ -0,0 +1,5 @@ +from gym_sokoban.envs.sokoban_env import SokobanEnv, ACTION_LOOKUP, CHANGE_COORDINATES +from gym_sokoban.envs import room_utils +from gym_sokoban.envs.sokoban_env_variations import * + + diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/available_envs.json b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/available_envs.json new file mode 100644 index 0000000000000000000000000000000000000000..2892ceba6c140b7d4bb29f69b08c6a264d4ee7f3 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/available_envs.json @@ -0,0 +1,14 @@ +[ + { + "id": "Sokoban-small-v0", + "entry_point": "gym_sokoban.envs:SokobanEnv_Small0" + }, + { + "id": "Sokoban-small-v1", + "entry_point": "gym_sokoban.envs:SokobanEnv_Small1" + }, + { + "id": "Sokoban-hw2-v0", + "entry_point": "gym_sokoban.envs:SokobanEnv_Small2" + } +] \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/env_save/envHW.npy b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/env_save/envHW.npy new file mode 100644 index 0000000000000000000000000000000000000000..444e8291eca8c118c18c86ac7b1b61f825411f54 Binary files /dev/null and b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/env_save/envHW.npy differ diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/render_utils.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/render_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e02bf7c83323e20195bc3be726e2f2cb9f92ad1a --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/render_utils.py @@ -0,0 +1,343 @@ +import numpy as np +import pkg_resources +import imageio + + +def room_to_rgb(room, room_structure=None): + """ + Creates an RGB image of the room. + :param room: + :param room_structure: + :return: + """ + resource_package = __name__ + + room = np.array(room) + if not room_structure is None: + # Change the ID of a player on a target + room[(room == 5) & (room_structure == 2)] = 6 + + # Load images, representing the corresponding situation + box_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'box.png'))) + box = imageio.imread(box_filename) + + box_on_target_filename = pkg_resources.resource_filename(resource_package, + '/'.join(('surface', 'box_on_target.png'))) + box_on_target = imageio.imread(box_on_target_filename) + + box_target_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'box_target.png'))) + box_target = imageio.imread(box_target_filename) + + floor_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'floor.png'))) + floor = imageio.imread(floor_filename) + + player_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'player.png'))) + player = imageio.imread(player_filename) + + player_on_target_filename = pkg_resources.resource_filename(resource_package, + '/'.join(('surface', 'player_on_target.png'))) + player_on_target = imageio.imread(player_on_target_filename) + + wall_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'wall.png'))) + wall = imageio.imread(wall_filename) + + surfaces = [wall, floor, box_target, box_on_target, box, player, player_on_target] + + # Assemble the new rgb_room, with all loaded images + room_rgb = np.zeros(shape=(room.shape[0] * 16, room.shape[1] * 16, 3), dtype=np.uint8) + for i in range(room.shape[0]): + x_i = i * 16 + + for j in range(room.shape[1]): + y_j = j * 16 + surfaces_id = room[i, j] + + room_rgb[x_i:(x_i + 16), y_j:(y_j + 16), :] = surfaces[surfaces_id] + + return room_rgb + + +def room_to_tiny_world_rgb(room, room_structure=None, scale=1): + + room = np.array(room) + if not room_structure is None: + # Change the ID of a player on a target + room[(room == 5) & (room_structure == 2)] = 6 + + wall = [0, 0, 0] + floor = [243, 248, 238] + box_target = [254, 126, 125] + box_on_target = [254, 95, 56] + box = [142, 121, 56] + player = [160, 212, 56] + player_on_target = [219, 212, 56] + + surfaces = [wall, floor, box_target, box_on_target, box, player, player_on_target] + + # Assemble the new rgb_room, with all loaded images + room_small_rgb = np.zeros(shape=(room.shape[0]*scale, room.shape[1]*scale, 3), dtype=np.uint8) + for i in range(room.shape[0]): + x_i = i * scale + for j in range(room.shape[1]): + y_j = j * scale + surfaces_id = int(room[i, j]) + room_small_rgb[x_i:(x_i+scale), y_j:(y_j+scale), :] = np.array(surfaces[surfaces_id]) + + return room_small_rgb + + +def room_to_rgb_FT(room, box_mapping, room_structure=None): + """ + Creates an RGB image of the room. + :param room: + :param room_structure: + :return: + """ + resource_package = __name__ + + room = np.array(room) + if not room_structure is None: + # Change the ID of a player on a target + room[(room == 5) & (room_structure == 2)] = 6 + + # Load images, representing the corresponding situation + box_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'box.png'))) + box = imageio.imread(box_filename) + + box_on_target_filename = pkg_resources.resource_filename(resource_package, + '/'.join(('surface', 'box_on_target.png'))) + box_on_target = imageio.imread(box_on_target_filename) + + box_target_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'box_target.png'))) + box_target = imageio.imread(box_target_filename) + + floor_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'floor.png'))) + floor = imageio.imread(floor_filename) + + player_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'player.png'))) + player = imageio.imread(player_filename) + + player_on_target_filename = pkg_resources.resource_filename(resource_package, + '/'.join(('surface', 'player_on_target.png'))) + player_on_target = imageio.imread(player_on_target_filename) + + wall_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'wall.png'))) + wall = imageio.imread(wall_filename) + + surfaces = [wall, floor, box_target, box_on_target, box, player, player_on_target] + + # Assemble the new rgb_room, with all loaded images + room_rgb = np.zeros(shape=(room.shape[0] * 16, room.shape[1] * 16, 3), dtype=np.uint8) + for i in range(room.shape[0]): + x_i = i * 16 + + for j in range(room.shape[1]): + y_j = j * 16 + + surfaces_id = room[i, j] + surface = surfaces[surfaces_id] + if 1 < surfaces_id < 5: + try: + surface = get_proper_box_surface(surfaces_id, box_mapping, i, j) + except: + pass + room_rgb[x_i:(x_i + 16), y_j:(y_j + 16), :] = surface + + return room_rgb + + +def get_proper_box_surface(surfaces_id, box_mapping, i, j): + # not used, kept for documentation + # names = ["wall", "floor", "box_target", "box_on_target", "box", "player", "player_on_target"] + + box_id = 0 + situation = '' + + if surfaces_id == 2: + situation = '_target' + box_id = list(box_mapping.keys()).index((i, j)) + elif surfaces_id == 3: + box_id = list(box_mapping.values()).index((i, j)) + box_key = list(box_mapping.keys())[box_id] + if box_key == (i, j): + situation = '_on_target' + else: + situation = '_on_wrong_target' + pass + elif surfaces_id == 4: + box_id = list(box_mapping.values()).index((i, j)) + + surface_name = 'box{}{}.png'.format(box_id, situation) + resource_package = __name__ + filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'multibox', surface_name))) + surface = imageio.imread(filename) + + return surface + + +def room_to_tiny_world_rgb_FT(room, box_mapping, room_structure=None, scale=1): + room = np.array(room) + if not room_structure is None: + # Change the ID of a player on a target + room[(room == 5) & (room_structure == 2)] = 6 + + wall = [0, 0, 0] + floor = [243, 248, 238] + box_target = [254, 126, 125] + box_on_target = [254, 95, 56] + box = [142, 121, 56] + player = [160, 212, 56] + player_on_target = [219, 212, 56] + + surfaces = [wall, floor, box_target, box_on_target, box, player, player_on_target] + + # Assemble the new rgb_room, with all loaded images + room_small_rgb = np.zeros(shape=(room.shape[0] * scale, room.shape[1] * scale, 3), dtype=np.uint8) + for i in range(room.shape[0]): + x_i = i * scale + for j in range(room.shape[1]): + y_j = j * scale + + surfaces_id = int(room[i, j]) + surface = np.array(surfaces[surfaces_id]) + if 1 < surfaces_id < 5: + try: + surface = get_proper_tiny_box_surface(surfaces_id, box_mapping, i, j) + except: + pass + room_small_rgb[x_i:(x_i + scale), y_j:(y_j + scale), :] = surface + + return room_small_rgb + + +def get_proper_tiny_box_surface(surfaces_id, box_mapping, i, j): + + box_id = 0 + situation = 'box' + + if surfaces_id == 2: + situation = 'target' + box_id = list(box_mapping.keys()).index((i, j)) + elif surfaces_id == 3: + box_id = list(box_mapping.values()).index((i, j)) + box_key = list(box_mapping.keys())[box_id] + if box_key == (i, j): + situation = 'on_target' + else: + situation = 'on_wrong_target' + pass + elif surfaces_id == 4: + box_id = list(box_mapping.values()).index((i, j)) + + surface = [255, 255, 255] + if box_id == 0: + if situation == 'target': + surface = [111, 127, 232] + elif situation == 'on_target': + surface = [6, 33, 130] + elif situation == 'on_wrong_target': + surface = [69, 81, 122] + else: + # Just the box + surface = [11, 60, 237] + + elif box_id == 1: + if situation == 'target': + surface = [195, 127, 232] + elif situation == 'on_target': + surface = [96, 5, 145] + elif situation == 'on_wrong_target': + surface = [96, 63, 114] + else: + surface = [145, 17, 214] + + elif box_id == 2: + if situation == 'target': + surface = [221, 113, 167] + elif situation == 'on_target': + surface = [140, 5, 72] + elif situation == 'on_wrong_target': + surface = [109, 60, 71] + else: + surface = [239, 0, 55] + + elif box_id == 3: + if situation == 'target': + surface = [247, 193, 145] + elif situation == 'on_target': + surface = [132, 64, 3] + elif situation == 'on_wrong_target': + surface = [94, 68, 46] + else: + surface = [239, 111, 0] + + return surface + + +def color_player_two(room_rgb, position, room_structure): + resource_package = __name__ + + player_filename = pkg_resources.resource_filename(resource_package, '/'.join(('surface', 'multiplayer', 'player1.png'))) + player = imageio.imread(player_filename) + + player_on_target_filename = pkg_resources.resource_filename(resource_package, + '/'.join(('surface', 'multiplayer', 'player1_on_target.png'))) + player_on_target = imageio.imread(player_on_target_filename) + + x_i = position[0] * 16 + y_j = position[1] * 16 + + if room_structure[position[0], position[1]] == 2: + room_rgb[x_i:(x_i + 16), y_j:(y_j + 16), :] = player_on_target + + else: + room_rgb[x_i:(x_i + 16), y_j:(y_j + 16), :] = player + + return room_rgb + + +def color_tiny_player_two(room_rgb, position, room_structure, scale = 4): + + x_i = position[0] * scale + y_j = position[1] * scale + + if room_structure[position[0], position[1]] == 2: + room_rgb[x_i:(x_i + scale), y_j:(y_j + scale), :] = [195, 127, 232] + + else: + room_rgb[x_i:(x_i + scale), y_j:(y_j + scale), :] = [96, 5, 145] + + return room_rgb + + +TYPE_LOOKUP = { + 0: 'wall', + 1: 'empty space', + 2: 'box target', + 3: 'box on target', + 4: 'box not on target', + 5: 'player' +} + +ACTION_LOOKUP = { + 0: 'push up', + 1: 'push down', + 2: 'push left', + 3: 'push right', + 4: 'move up', + 5: 'move down', + 6: 'move left', + 7: 'move right', +} + +# Moves are mapped to coordinate changes as follows +# 0: Move up +# 1: Move down +# 2: Move left +# 3: Move right +CHANGE_COORDINATES = { + 0: (-1, 0), + 1: (1, 0), + 2: (0, -1), + 3: (0, 1) +} diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/room_utils.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/room_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1d33685764367d2831bc79da47270fdd5cf41bd8 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/room_utils.py @@ -0,0 +1,352 @@ +import random +import numpy as np +import marshal + + +def generate_room(dim=(13, 13), p_change_directions=0.35, num_steps=25, num_boxes=3, tries=4, second_player=False): + """ + Generates a Sokoban room, represented by an integer matrix. The elements are encoded as follows: + wall = 0 + empty space = 1 + box target = 2 + box not on target = 3 + box on target = 4 + player = 5 + + :param dim: + :param p_change_directions: + :param num_steps: + :return: Numpy 2d Array + """ + room_state = np.zeros(shape=dim) + room_structure = np.zeros(shape=dim) + + # Some times rooms with a score == 0 are the only possibility. + # In these case, we try another model. + for t in range(tries): + room = room_topology_generation(dim, p_change_directions, num_steps) + room = place_boxes_and_player(room, num_boxes=num_boxes, second_player=second_player) + + # Room fixed represents all not movable parts of the room + room_structure = np.copy(room) + room_structure[room_structure == 5] = 1 + + # Room structure represents the current state of the room including movable parts + room_state = room.copy() + room_state[room_state == 2] = 4 + + room_state, score, box_mapping = reverse_playing(room_state, room_structure) + room_state[room_state == 3] = 4 + + if score > 0: + break + + if score == 0: + raise RuntimeWarning('Generated Model with score == 0') + + return room_structure, room_state, box_mapping + + +def room_topology_generation(dim=(10, 10), p_change_directions=0.35, num_steps=15): + """ + Generate a room topology, which consits of empty floors and walls. + + :param dim: + :param p_change_directions: + :param num_steps: + :return: + """ + dim_x, dim_y = dim + + # The ones in the mask represent all fields which will be set to floors + # during the random walk. The centered one will be placed over the current + # position of the walk. + masks = [ + [ + [0, 0, 0], + [1, 1, 1], + [0, 0, 0] + ], + [ + [0, 1, 0], + [0, 1, 0], + [0, 1, 0] + ], + [ + [0, 0, 0], + [1, 1, 0], + [0, 1, 0] + ], + [ + [0, 0, 0], + [1, 1, 0], + [1, 1, 0] + ], + [ + [0, 0, 0], + [0, 1, 1], + [0, 1, 0] + ] + ] + + # Possible directions during the walk + directions = [(1, 0), (0, 1), (-1, 0), (0, -1)] + direction = random.sample(directions, 1)[0] + + # Starting position of random walk + position = np.array([ + random.randint(1, dim_x - 1), + random.randint(1, dim_y - 1)] + ) + + level = np.zeros(dim, dtype=int) + + for s in range(num_steps): + + # Change direction randomly + if random.random() < p_change_directions: + direction = random.sample(directions, 1)[0] + + # Update position + position = position + direction + position[0] = max(min(position[0], dim_x - 2), 1) + position[1] = max(min(position[1], dim_y - 2), 1) + + # Apply mask + mask = random.sample(masks, 1)[0] + mask_start = position - 1 + level[mask_start[0]:mask_start[0] + 3, mask_start[1]:mask_start[1] + 3] += mask + + level[level > 0] = 1 + level[:, [0, dim_y - 1]] = 0 + level[[0, dim_x - 1], :] = 0 + + return level + + +def place_boxes_and_player(room, num_boxes, second_player): + """ + Places the player and the boxes into the floors in a room. + + :param room: + :param num_boxes: + :return: + """ + # Get all available positions + possible_positions = np.where(room == 1) + num_possible_positions = possible_positions[0].shape[0] + num_players = 2 if second_player else 1 + + if num_possible_positions <= num_boxes + num_players: + raise RuntimeError('Not enough free spots (#{}) to place {} player and {} boxes.'.format( + num_possible_positions, + num_players, + num_boxes) + ) + + # Place player(s) + ind = np.random.randint(num_possible_positions) + player_position = possible_positions[0][ind], possible_positions[1][ind] + room[player_position] = 5 + + if second_player: + ind = np.random.randint(num_possible_positions) + player_position = possible_positions[0][ind], possible_positions[1][ind] + room[player_position] = 5 + + # Place boxes + for n in range(num_boxes): + possible_positions = np.where(room == 1) + num_possible_positions = possible_positions[0].shape[0] + + ind = np.random.randint(num_possible_positions) + box_position = possible_positions[0][ind], possible_positions[1][ind] + room[box_position] = 2 + + return room + + +# Global variables used for reverse playing. +explored_states = set() +num_boxes = 0 +best_room_score = -1 +best_room = None +best_box_mapping = None + + +def reverse_playing(room_state, room_structure, search_depth=100): + """ + This function plays Sokoban reverse in a way, such that the player can + move and pull boxes. + It ensures a solvable level with all boxes not being placed on a box target. + :param room_state: + :param room_structure: + :param search_depth: + :return: 2d array + """ + global explored_states, num_boxes, best_room_score, best_room, best_box_mapping + + # Box_Mapping is used to calculate the box displacement for every box + box_mapping = {} + box_locations = np.where(room_structure == 2) + num_boxes = len(box_locations[0]) + for l in range(num_boxes): + box = (box_locations[0][l], box_locations[1][l]) + box_mapping[box] = box + + # explored_states globally stores the best room state and score found during search + explored_states = set() + best_room_score = -1 + best_box_mapping = box_mapping + depth_first_search(room_state, room_structure, box_mapping, box_swaps=0, last_pull=(-1, -1), ttl=300) + + return best_room, best_room_score, best_box_mapping + + +def depth_first_search(room_state, room_structure, box_mapping, box_swaps=0, last_pull=(-1, -1), ttl=300): + """ + Searches through all possible states of the room. + This is a recursive function, which stops if the tll is reduced to 0 or + over 1.000.000 states have been explored. + :param room_state: + :param room_structure: + :param box_mapping: + :param box_swaps: + :param last_pull: + :param ttl: + :return: + """ + global explored_states, num_boxes, best_room_score, best_room, best_box_mapping + + ttl -= 1 + if ttl <= 0 or len(explored_states) >= 300000: + return + + state_tohash = marshal.dumps(room_state) + + # Only search this state, if it not yet has been explored + if not (state_tohash in explored_states): + + # Add current state and its score to explored states + room_score = box_swaps * box_displacement_score(box_mapping) + if np.where(room_state == 2)[0].shape[0] != num_boxes: + room_score = 0 + + if room_score > best_room_score: + best_room = room_state + best_room_score = room_score + best_box_mapping = box_mapping + + explored_states.add(state_tohash) + + for action in ACTION_LOOKUP.keys(): + # The state and box mapping need to be copied to ensure + # every action start from a similar state. + room_state_next = room_state.copy() + box_mapping_next = box_mapping.copy() + + room_state_next, box_mapping_next, last_pull_next = \ + reverse_move(room_state_next, room_structure, box_mapping_next, last_pull, action) + + box_swaps_next = box_swaps + if last_pull_next != last_pull: + box_swaps_next += 1 + + depth_first_search(room_state_next, room_structure, + box_mapping_next, box_swaps_next, + last_pull, ttl) + + +def reverse_move(room_state, room_structure, box_mapping, last_pull, action): + """ + Perform reverse action. Where all actions in the range [0, 3] correspond to + push actions and the ones greater 3 are simmple move actions. + :param room_state: + :param room_structure: + :param box_mapping: + :param last_pull: + :param action: + :return: + """ + player_position = np.where(room_state == 5) + player_position = np.array([player_position[0][0], player_position[1][0]]) + + change = CHANGE_COORDINATES[action % 4] + next_position = player_position + change + + # Check if next position is an empty floor or an empty box target + if room_state[next_position[0], next_position[1]] in [1, 2]: + + # Move player, independent of pull or move action. + room_state[player_position[0], player_position[1]] = room_structure[player_position[0], player_position[1]] + room_state[next_position[0], next_position[1]] = 5 + + # In addition try to pull a box if the action is a pull action + if action < 4: + possible_box_location = change[0] * -1, change[1] * -1 + possible_box_location += player_position + + if room_state[possible_box_location[0], possible_box_location[1]] in [3, 4]: + # Perform pull of the adjacent box + room_state[player_position[0], player_position[1]] = 3 + room_state[possible_box_location[0], possible_box_location[1]] = room_structure[ + possible_box_location[0], possible_box_location[1]] + + # Update the box mapping + for k in box_mapping.keys(): + if box_mapping[k] == (possible_box_location[0], possible_box_location[1]): + box_mapping[k] = (player_position[0], player_position[1]) + last_pull = k + + return room_state, box_mapping, last_pull + + +def box_displacement_score(box_mapping): + """ + Calculates the sum of all Manhattan distances, between the boxes + and their origin box targets. + :param box_mapping: + :return: + """ + score = 0 + + for box_target in box_mapping.keys(): + box_location = np.array(box_mapping[box_target]) + box_target = np.array(box_target) + dist = np.sum(np.abs(box_location - box_target)) + score += dist + + return score + + +TYPE_LOOKUP = { + 0: 'wall', + 1: 'empty space', + 2: 'box target', + 3: 'box on target', + 4: 'box not on target', + 5: 'player' +} + +ACTION_LOOKUP = { + 0: 'push up', + 1: 'push down', + 2: 'push left', + 3: 'push right', + 4: 'move up', + 5: 'move down', + 6: 'move left', + 7: 'move right', +} + +# Moves are mapped to coordinate changes as follows +# 0: Move up +# 1: Move down +# 2: Move left +# 3: Move right +CHANGE_COORDINATES = { + 0: (-1, 0), + 1: (1, 0), + 2: (0, -1), + 3: (0, 1) +} diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env.py new file mode 100644 index 0000000000000000000000000000000000000000..d9eb640779e6fe66160e63cf3b3c3649335a4d79 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env.py @@ -0,0 +1,317 @@ +# -*- coding:utf-8 -*- +# sokoban env for AI 3603 class homework +import os +import gym +from gym.utils import seeding +from gym.spaces.discrete import Discrete +from gym.spaces import Box +from .room_utils import generate_room +from .render_utils import room_to_rgb, room_to_tiny_world_rgb +import numpy as np + + +class SokobanEnv(gym.Env): + metadata = { + 'render.modes': ['human', 'rgb_array', 'tiny_human', 'tiny_rgb_array', 'raw'] + } + + def __init__(self, + dim_room=(10, 10), + max_steps=120, + num_boxes=4, + num_gen_steps=None, + reset=False): + + # General Configuration + self.dim_room = dim_room + if num_gen_steps == None: + self.num_gen_steps = int(1.7 * (dim_room[0] + dim_room[1])) + else: + self.num_gen_steps = num_gen_steps + + self.num_boxes = num_boxes + self.boxes_on_target = 0 + + # Penalties and Rewards + self.penalty_for_step = -0.1 + self.penalty_box_off_target = -1 + self.reward_box_on_target = 1 + self.reward_finished = 10 + self.reward_last = 0 + + # Other Settings + self.viewer = None + self.max_steps = max_steps + self.action_space = Discrete(len(ACTION_LOOKUP)) + screen_height, screen_width = (dim_room[0] * 16, dim_room[1] * 16) + self.observation_space = Box(low=0, high=255, shape=(screen_height, screen_width, 3), dtype=np.uint8) + + if reset: + # Initialize Room + _ = self.reset() + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def step(self, action, observation_mode='HW2'): + assert action in ACTION_LOOKUP + assert observation_mode in ['rgb_array', 'tiny_rgb_array', 'raw', 'HW2'] + + self.num_env_steps += 1 + + self.new_box_position = None + self.old_box_position = None + + moved_player, moved_box = self._push(action) + + self._calc_reward() + + done = self._check_if_done() + + # Convert the observation to RGB frame + observation = self.render(mode=observation_mode) + + info = { + "action.name": ACTION_LOOKUP[action], + "action.moved_player": moved_player, + "action.moved_box": moved_box, + } + if done: + info["maxsteps_used"] = self._check_if_maxsteps() + info["all_boxes_on_target"] = self._check_if_all_boxes_on_target() + + return observation, self.reward_last, done, info + + def _push(self, action): + """ + Perform a push, if a box is adjacent in the right direction. + If no box, can be pushed, try to move. + :param action: + :return: Boolean, indicating a change of the room's state + """ + change = CHANGE_COORDINATES[(action - 0) % 4] + new_position = self.player_position + change + current_position = self.player_position.copy() + + # No push, if the push would get the box out of the room's grid + new_box_position = new_position + change + if new_box_position[0] >= self.room_state.shape[0] \ + or new_box_position[1] >= self.room_state.shape[1]: + return False, False + + + can_push_box = self.room_state[new_position[0], new_position[1]] in [3, 4] + can_push_box &= self.room_state[new_box_position[0], new_box_position[1]] in [1, 2] + if can_push_box: + + self.new_box_position = tuple(new_box_position) + self.old_box_position = tuple(new_position) + + # Move Player + self.player_position = new_position + self.room_state[(new_position[0], new_position[1])] = 5 + self.room_state[current_position[0], current_position[1]] = \ + self.room_fixed[current_position[0], current_position[1]] + + # Move Box + box_type = 4 + if self.room_fixed[new_box_position[0], new_box_position[1]] == 2: + box_type = 3 + self.room_state[new_box_position[0], new_box_position[1]] = box_type + return True, True + + # Try to move if no box to push, available + else: + return self._move(action), False + + def _move(self, action): + """ + Moves the player to the next field, if it is not occupied. + :param action: + :return: Boolean, indicating a change of the room's state + """ + change = CHANGE_COORDINATES[(action - 0) % 4] + new_position = self.player_position + change + current_position = self.player_position.copy() + + # Move player if the field in the moving direction is either + # an empty field or an empty box target. + if self.room_state[new_position[0], new_position[1]] in [1, 2]: + self.player_position = new_position + self.room_state[(new_position[0], new_position[1])] = 5 + self.room_state[current_position[0], current_position[1]] = \ + self.room_fixed[current_position[0], current_position[1]] + + return True + + return False + + def _calc_reward(self): + """ + Calculate Reward Based on + :return: + """ + # Every step a small penalty is given, This ensures + # that short solutions have a higher reward. + self.reward_last = self.penalty_for_step + + # count boxes off or on the target + empty_targets = self.room_state == 2 + player_on_target = (self.room_fixed == 2) & (self.room_state == 5) + total_targets = empty_targets | player_on_target + + current_boxes_on_target = self.num_boxes - \ + np.where(total_targets)[0].shape[0] + + # Add the reward if a box is pushed on the target and give a + # penalty if a box is pushed off the target. + if current_boxes_on_target > self.boxes_on_target: + self.reward_last += self.reward_box_on_target + elif current_boxes_on_target < self.boxes_on_target: + self.reward_last += self.penalty_box_off_target + + game_won = self._check_if_all_boxes_on_target() + if game_won: + self.reward_last += self.reward_finished + + self.boxes_on_target = current_boxes_on_target + + def _check_if_done(self): + # Check if the game is over either through reaching the maximum number + # of available steps or by pushing all boxes on the targets. + return self._check_if_all_boxes_on_target() or self._check_if_maxsteps() + + def _check_if_all_boxes_on_target(self): + empty_targets = self.room_state == 2 + player_hiding_target = (self.room_fixed == 2) & (self.room_state == 5) + are_all_boxes_on_targets = np.where(empty_targets | player_hiding_target)[0].shape[0] == 0 + return are_all_boxes_on_targets + + def _check_if_maxsteps(self): + """Check if the current steps used is up to the maximum""" + return (self.max_steps <= self.num_env_steps) + # return (self.max_steps == self.num_env_steps) + + def reset(self, load_env_from_file=True, filename='envHW.npy', second_player=False, render_mode='HW2'): + """reset the environment by generating a random room OR loading a room env from file""" + try: + if load_env_from_file: + # load from file + self.load_env(filename=filename) + else: + # generate a room randomly + self.room_fixed, self.room_state, self.box_mapping = generate_room( + dim=self.dim_room, + num_steps=self.num_gen_steps, + num_boxes=self.num_boxes, + second_player=second_player + ) + except (RuntimeError, RuntimeWarning) as e: + print("[SOKOBAN] Runtime Error/Warning: {}".format(e)) + print("[SOKOBAN] Retrying . . .") + return self.reset(second_player=second_player, render_mode=render_mode) + self.player_position = np.argwhere(self.room_state == 5)[0] + self.num_env_steps = 0 + self.reward_last = 0 + self.boxes_on_target = 0 + + starting_observation = self.render(render_mode) + return starting_observation + + def save_env(self, filename='env.npy'): + """save the environment settings to a file, can be called after env.reset() function""" + curr_path = os.path.split(os.path.realpath(__file__))[0] + file_path = os.path.join(curr_path, 'env_save', filename) + save_file = np.array([self.room_fixed, self.room_state, self.box_mapping], dtype=np.object) + np.save(file_path, save_file) + print("[INFO] Save current environment done.") + return None + + def load_env(self, filename='envHW.npy'): + try: + curr_path = os.path.split(os.path.realpath(__file__))[0] + file_path = os.path.join(curr_path, 'env_save', filename) + load_file = np.load(file_path, allow_pickle=True) + self.room_fixed, self.room_state, self.box_mapping = load_file.tolist() + except Exception as e: + print("[ERROR] load environment errors: {}".format(e)) + input("input anything to retry...") + return self.load_env(filename=filename) + return None + + def render(self, mode='human', close=None, scale=1): + assert mode in RENDERING_MODES + + img = self.get_image(mode, scale) + + if 'rgb_array' in mode: + return img + + elif 'human' in mode: + from gym.envs.classic_control import rendering + if self.viewer is None: + self.viewer = rendering.SimpleImageViewer() + self.viewer.imshow(img) + return self.viewer.isopen + + elif 'raw' in mode: + arr_walls = (self.room_fixed == 0).view(np.int8) + arr_goals = (self.room_fixed == 2).view(np.int8) + arr_boxes = ((self.room_state == 4) + (self.room_state == 3)).view(np.int8) + arr_player = (self.room_state == 5).view(np.int8) + + return arr_walls, arr_goals, arr_boxes, arr_player + elif 'HW2' in mode: + pos_player = np.argwhere(self.room_state == 5)[0] + pos_boxes = np.argwhere((self.room_state == 4) | (self.room_state == 3)) + observation = np.concatenate([pos_player.flatten(), pos_boxes.flatten()]) + return observation + + else: + super(SokobanEnv, self).render(mode=mode) # just raise an exception + + def get_image(self, mode, scale=1): + + # if mode.startswith('tiny_'): + if mode.startswith('tiny_'): + img = room_to_tiny_world_rgb(self.room_state, self.room_fixed, scale=scale) + else: + img = room_to_rgb(self.room_state, self.room_fixed) + + return img + + def close(self): + if self.viewer is not None: + self.viewer.close() + + def set_maxsteps(self, num_steps): + self.max_steps = num_steps + + def get_action_lookup(self): + return ACTION_LOOKUP + + def get_action_meanings(self): + return ACTION_LOOKUP + + +ACTION_LOOKUP = { + 0: 'push up', + 1: 'push down', + 2: 'push left', + 3: 'push right' +} + +# Moves are mapped to coordinate changes as follows +# 0: Move up +# 1: Move down +# 2: Move left +# 3: Move right +CHANGE_COORDINATES = { + 0: (-1, 0), + 1: (1, 0), + 2: (0, -1), + 3: (0, 1) +} + +RENDERING_MODES = ['rgb_array', 'human', 'tiny_rgb_array', 'tiny_human', 'raw', 'HW2'] diff --git a/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env_variations.py b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env_variations.py new file mode 100644 index 0000000000000000000000000000000000000000..3525af6386080020a9cda7b619088552c153173f --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/gym_sokoban/envs/sokoban_env_variations.py @@ -0,0 +1,36 @@ +from .sokoban_env import SokobanEnv + +class SokobanEnv_Small0(SokobanEnv): + metadata = { + 'render.modes': ['human', 'rgb_array', 'tiny_human', 'tiny_rgb_array'], + } + + def __init__(self, **kwargs): + kwargs['dim_room'] = kwargs.get('dim_room', (7, 7)) + kwargs['max_steps'] = kwargs.get('max_steps', 200) + kwargs['num_boxes'] = kwargs.get('num_boxes', 2) + super(SokobanEnv_Small0, self).__init__(**kwargs) + + +class SokobanEnv_Small1(SokobanEnv): + metadata = { + 'render.modes': ['human', 'rgb_array', 'tiny_human', 'tiny_rgb_array'], + } + + def __init__(self, **kwargs): + kwargs['dim_room'] = kwargs.get('dim_room', (7, 7)) + kwargs['max_steps'] = kwargs.get('max_steps', 200) + kwargs['num_boxes'] = kwargs.get('num_boxes', 3) + super(SokobanEnv_Small1, self).__init__(**kwargs) + +class SokobanEnv_Small2(SokobanEnv): + """FOR HW2 AI3603 CLASS""" + metadata = { + 'render.modes': ['human', 'rgb_array', 'tiny_human', 'tiny_rgb_array'], + } + + def __init__(self, **kwargs): + kwargs['dim_room'] = kwargs.get('dim_room', (7, 7)) + kwargs['max_steps'] = kwargs.get('max_steps', 100) # 100 + kwargs['num_boxes'] = kwargs.get('num_boxes', 2) + super(SokobanEnv_Small2, self).__init__(**kwargs) diff --git a/lab-iisec/AI3603 lab/lab2/sokoban_dynaq.py b/lab-iisec/AI3603 lab/lab2/sokoban_dynaq.py new file mode 100644 index 0000000000000000000000000000000000000000..8737bea15d20c98ffa043eef644e13885ad24e39 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/sokoban_dynaq.py @@ -0,0 +1,63 @@ +# -*- coding:utf-8 -*- +# Train Dyna-Q in Sokoban environment +import math, os, time, sys +import pdb +import numpy as np +import random, gym +from gym.wrappers import Monitor +from agent import DynaQAgent +import gym_sokoban +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + + +# construct the environment +env = gym.make('Sokoban-hw2-v0') +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + + +####### START CODING HERE ####### + +# construct the intelligent agent. +agent = DynaQAgent(all_actions) + +# start training +for episode in range(1000): + episode_reward = 0 + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +####### START CODING HERE ####### + + + + + diff --git a/lab-iisec/AI3603 lab/lab2/sokoban_new_exploration.py b/lab-iisec/AI3603 lab/lab2/sokoban_new_exploration.py new file mode 100644 index 0000000000000000000000000000000000000000..0e6fa4d250b168a5af51cf21a50d5b0e1ad888a7 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/sokoban_new_exploration.py @@ -0,0 +1,63 @@ +# -*- coding:utf-8 -*- +# Train any RL agent in Sokoban environment with new exploration method. + +import math, os, time, sys +import numpy as np +import random, gym +from gym.wrappers import Monitor +from agent import RLAgentWithOtherExploration +import gym_sokoban +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + + +# construct the environment +env = gym.make('Sokoban-hw2-v0') +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + + +####### START CODING HERE ####### + +# construct the intelligent agent. +agent = RLAgentWithOtherExploration(all_actions) + +# start training +for episode in range(1000): + episode_reward = 0 + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +####### START CODING HERE ####### + + + + + diff --git a/lab-iisec/AI3603 lab/lab2/sokoban_qlearning.py b/lab-iisec/AI3603 lab/lab2/sokoban_qlearning.py new file mode 100644 index 0000000000000000000000000000000000000000..585380c439cba90f055ed085c77ed75c15682872 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/sokoban_qlearning.py @@ -0,0 +1,63 @@ +# -*- coding:utf-8 -*- +# Train Q-Learning in Sokoban environment +import math, os, time, sys +import pdb +import numpy as np +import random, gym +from gym.wrappers import Monitor +from agent import QLearningAgent +import gym_sokoban +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + + +# construct the environment +env = gym.make('Sokoban-hw2-v0') +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + + +####### START CODING HERE ####### + +# construct the intelligent agent. +agent = QLearningAgent(all_actions) + +# start training +for episode in range(1000): + episode_reward = 0 + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +####### START CODING HERE ####### + + + + + diff --git a/lab-iisec/AI3603 lab/lab2/sokoban_sarsa.py b/lab-iisec/AI3603 lab/lab2/sokoban_sarsa.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ac6e8f7876382c1e2d06f1a8c56a5625f3c91f --- /dev/null +++ b/lab-iisec/AI3603 lab/lab2/sokoban_sarsa.py @@ -0,0 +1,63 @@ +# -*- coding:utf-8 -*- +# Train Sarsa in Sokoban environment +import math, os, time, sys +import pdb +import numpy as np +import random, gym +from gym.wrappers import Monitor +from agent import SarsaAgent +import gym_sokoban +##### START CODING HERE ##### +# This code block is optional. You can import other libraries or define your utility functions if necessary. + +##### END CODING HERE ##### + + +# construct the environment +env = gym.make('Sokoban-hw2-v0') +# get the size of action space +num_actions = env.action_space.n +all_actions = np.arange(num_actions) +# set random seed and make the result reproducible +RANDOM_SEED = 0 +env.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + + +####### START CODING HERE ####### + +# construct the intelligent agent. +agent = SarsaAgent(all_actions) + +# start training +for episode in range(1000): + episode_reward = 0 + s = env.reset() + # render env. You can comment all render() to turn off the GUI to accelerate training. + env.render() + # agent interacts with the environment + for iter in range(500): + a = agent.choose_action(s) + s_, r, isdone, info = env.step(a) + env.render() + episode_reward += r + print(f"{s} {a} {s_} {r} {isdone}") + agent.learn() + s = s_ + if isdone: + time.sleep(0.5) + break + + print('episode:', episode, 'episode_reward:', episode_reward, 'epsilon:', agent.epsilon) +print('\ntraining over\n') + +# close the render window after training. +env.close() + +####### START CODING HERE ####### + + + + + diff --git a/lab-iisec/AI3603 lab/lab3/2-MCL_local.py b/lab-iisec/AI3603 lab/lab3/2-MCL_local.py new file mode 100644 index 0000000000000000000000000000000000000000..76cc7d79fe9fbe9d5e72524c9f4d43ecfd9d4ebb --- /dev/null +++ b/lab-iisec/AI3603 lab/lab3/2-MCL_local.py @@ -0,0 +1,522 @@ +from numpy.core.defchararray import index +from numpy.matrixlib import defmatrix +import DR20API +import numpy as np +import matplotlib.pyplot as plt +import math +from math import sin, cos, pi +import time + +### START CODE HERE ### +# You can tune the hyper-parameter, and define your utility function or class in this block if necessary. + +# Particle filter parameter +NP = 400 # Number of Particle +NTh = NP / 2.0 # Number of particle for re-sampling + +# Set the random seed to ensure the repeatability. +seed=1 +np.random.seed(seed) + +# Estimation parameter of PF, you may use them in the PF algorithm. You can use the recommended values as follows. +Q = np.diag([0.15]) ** 2 # range error +R = np.diag([0.1, np.deg2rad(10)]) ** 2 # input error + +### END CODE HERE ### + +# Parameter of LiDAR +scanningAngle = 180 +pts=5 + +# Simulation parameter +Q_sim = np.diag([0.05]) ** 2 # add noise to lidar readings +R_sim = np.diag([0.03, np.deg2rad(3)]) ** 2 # add noise to control command + +v=0.5 #linear velocity +w=0.25 #angular velocity + +DT = 0.1 # time tick [s] +SIM_TIME = 200.0 # simulation time [s] + + +entropys = np.zeros(NP) + +class Room(object): + """ + Generate the map. + """ + + def map_range_x(self, start, stop, number, y): + return [[start + (stop - start) * i / number, y] for i in range(number + 1)] + + def map_range_y(self, start, stop, number, x): + return [[x, start + (stop - start) * i / number] for i in range(number + 1)] + + def map_square(self, top_left, bottom_right, points): + tl_x, tl_y = top_left + br_x, br_y = bottom_right + res = self.map_range_y(tl_y, br_y, points, tl_x) + res += self.map_range_y(tl_y, br_y, points, br_x) + res += self.map_range_x(tl_x, br_x, points, tl_y) + res += self.map_range_x(tl_x, br_x, points, br_y) + return res + + def make_room(self): + walls = self.map_square((0.0,0.0), (5.0,5.0), 30) + table1 = self.map_square((2.0,2.0), (3,3.5), 10) + table2 = self.map_square((2.0,1.0), (2.5,1.5), 5) + table3 = self.map_square((3.0,1.0), (3.5,1.5), 5) + table4 = self.map_square((1.0,2.0), (1.5,2.5), 5) + table5 = self.map_square((4.0,4.0), (4.5,4.5), 5) + return walls + table1 + table2 + table3 + table4 + table5 + + +def motion_model(x, u): + """ + Given the current state and control input, return the state at next time. + + Arguments: + x -- A 3*1 matrix indicating the state of robots or particles. Data format: [[x] [y] [yaw]] + u -- A 2*1 matrix indicating the control input. Data format: [[v] [w]] + + Return: + x -- A 3*1 matrix indicating the state at next time. Data format: [[x] [y] [yaw]] + """ + + F = np.array([[1.0, 0, 0], + [0, 1.0, 0], + [0, 0, 1.0]]) + + B = np.array([[DT * math.cos(x[2, 0]), 0], + [DT * math.sin(x[2, 0]), 0], + [0.0, DT],]) + x = F.dot(x) + B.dot(u) + return x + +def calc_input(v,w): + """ + Adding noise to the input control commands. + + Argument: + v -- linear velocity + w -- angular velocity + + Return: + ud -- A 2*1 matrix indicating the noisy input. + """ + + v = v + np.random.randn() * R_sim[0, 0] ** 0.5 + w = w + np.random.randn() * R_sim[1, 1] ** 0.5 + ud = np.array([[v, w]]).T + return ud + +def generate_particles(pos): + """ + Generate NP particles, each particle contains three state quantities and weight. + If you set the Nearby_flag as True, then the particles will be sprinkled around the real robot. + Otherwise, the particles will be scattered uniformly in the map. + In this file, Nearby_flag is set to True to solve a local localization problem. + + Arguments: + pos -- The robot current position. + + Return: + px -- A 3*NP matrix, each column represents the status of a particle. + pw -- A 1*NP matrix, each column represents the weight value of correspoding particle. + """ + Nearby_flag = True + + if Nearby_flag: + x = pos[0] - 1 + 2 * np.random.random(size=(1,NP)) + y = pos[1] - 1 + 2 * np.random.random(size=(1,NP)) + yaw = 2 * math.pi * np.random.random(size=(1,NP)) + else: + x = 5 * np.random.random(size=(1,NP)) + y = 5 * np.random.random(size=(1,NP)) + yaw = 2 * math.pi * np.random.random(size=(1,NP)) + + px = np.zeros((3, NP)) # Particle store + pw = np.zeros((1, NP)) + 1.0 / NP # Particle weight + px[0],px[1],px[2],=x,y,yaw + return px,pw + +def judge(x,y,angle): + X = [0,2,3,1,4,1.5,2.5,3.5,4.5,5] + Y = [0,2,1,4,1.5,2.5,3.5,4.5,5] + while(angle>=2*pi): + angle -= 2*pi + while(angle<0): + angle += 2*pi + k = math.tan(angle) + points = [] + dis = [] + for i in X: + if(angle>=pi/2 and angle<=pi*3/2): + if(i<=x): + y_new = k*(i-x)+y + if(i==0 or i==5): + if(y_new>=0 and y_new<=5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2.5 or i==3.5): + if(y_new>=1 and y_new<=1.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if(y_new>=2 and y_new<=2.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2 or i==3): + if((y_new>=1 and y_new<=1.5) or (y_new>=2 and y_new<=3.5)): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(y_new>=4 and y_new<=4.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + else: + if(i>x): + y_new = k*(i-x)+y + if(i==0 or i==5): + if(y_new>=0 and y_new<=5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2.5 or i==3.5): + if(y_new>=1 and y_new<=1.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if(y_new>=2 and y_new<=2.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2 or i==3): + if((y_new>=1 and y_new<=1.5) or (y_new>=2 and y_new<=3.5)): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(y_new>=4 and y_new<=4.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + for i in Y: + if(angle>=0 and angle<=pi): + if(i>=y): + x_new = (i-y)/k+x + if(i==0 or i==5): + if(x_new>=0 and x_new<=5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if((x_new>=2 and x_new<=2.5)or(x_new>=3 and x_new<=3.5)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2): + if((x_new>=1 and x_new<=1.5)or (x_new>=2 and x_new<=3)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2.5): + if(x_new>=1 and x_new<=1.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==3.5): + if(x_new>=2 and x_new<=3): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(x_new>=4 and x_new<=4.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + else: + if(i=0 and x_new<=5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if((x_new>=2 and x_new<=2.5)or(x_new>=3 and x_new<=3.5)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2): + if((x_new>=1 and x_new<=1.5)or (x_new>=2 and x_new<=3)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2.5): + if(x_new>=1 and x_new<=1.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==3.5): + if(x_new>=2 and x_new<=3): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(x_new>=4 and x_new<=4.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + if(dis): + d = min(dis) + point = points[dis.index(d)] + else: + d = 10 + point = [10,10] + + return d,point +def pf_localization(px,pw,data,u): + """ + Localization with Particle filter. In this function, you need to: + + (1) Prediction step: Each particle predicts its new location based on the actuation command given. + (2) Update step: Update the weight of each particle. That is, particles consistent with sensor readings will have higher weight. + (3) Resample step: Generate a set of new particles, with most of them generated around the previous particles with more weight. + You need to decide when to resample the particles and how to resample the particles. + + Argument: + px -- A 3*NP matrix, each column represents the status of a particle. + pw -- A 1*NP matrix, each column represents the weight value of correspoding particle. + data -- A List contains the output of the LiDAR sensor. It is represented by a distance value in counterclockwise order. + u -- A 2*1 matrix indicating the control input. Data format: [[v] [w]] + + Return: + x_est -- A 3*1 matrix, indicating the estimated state after particle filtering. + px -- A 3*NP matrix. The predicted state of the next time. + pw -- A 1*NP matrix. The updated weight of each particle. + """ + t1 = time.time() + ### START CODE HERE ### + for ip in range(NP): + # Prediction step: Predict with random input sampling + x = np.array([[px[0][ip]],[px[1][ip]],[px[2][ip]]]) + x_new = motion_model(x,u) + px[0][ip] = x_new[0][0] + px[1][ip] = x_new[1][0] + px[2][ip] = x_new[2][0] + + d1,p1 = judge(px[0][ip],px[1][ip],px[2][ip]+pi/2) + d2,p2 = judge(px[0][ip],px[1][ip],px[2][ip]+pi/4) + d3,p3 = judge(px[0][ip],px[1][ip],px[2][ip]) + d4,p4 = judge(px[0][ip],px[1][ip],px[2][ip]-pi/4) + d5,p5 = judge(px[0][ip],px[1][ip],px[2][ip]-pi/2) + + + # print(px[0][ip],px[1][ip],px[2][ip]) + # print(d1,d2,d3,d4,d5) + # print(p1,p2,p3,p4,p5) + # plt.cla() + # # for stopping simulation with the esc key. + # plt.gcf().canvas.mpl_connect( + # 'key_release_event', + # lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + # x, y = zip(*room) + # plt.scatter(x, y) + # plt.plot(np.array(h_x_true[0, :]).flatten(), + # np.array(h_x_true[1, :]).flatten(), "-b") + # plt.scatter(px[0][ip],px[1][ip]) + # plt.scatter(p1[0],p1[1],c='#000000')#black + # plt.scatter(p2[0],p2[1],c='#A52A2A')#brown + # plt.scatter(p3[0],p3[1],c='#A9A9A9')#darkgray + # plt.scatter(p4[0],p4[1],c='#008000')#green + # plt.scatter(p5[0],p5[1],c='#FFB6C1')#lightpink + # # plt.scatter() + # plt.show() + # time.sleep(10) + + entropy = (data[0]-d5- np.random.randn()*Q[0])**2 + (data[1]-d4- np.random.randn()*Q[0])**2 + (data[2]- d3-np.random.randn()*Q[0])**2 + (data[3]- d2-np.random.randn()*Q[0])**2 + (data[4]- d1-np.random.randn()*Q[0])**2 + pw[0,ip] = np.exp(1 / (entropy+0.01)) + # if(px[0][ip]<=0 or px[0][ip]>=5 or px[1][ip]<=0 or px[1][ip]>=5): + # pw[0,ip] = 0 + + pw = pw/pw.sum() + + + x_est = px.dot(pw.T) + + # Resample step: Resample the particles. + t = 0 + for ip in range(len(px[0])): + if pw[0][ip] > 0.01: + t+=1 + if t < 5: + px, pw = re_sampling(px,pw) + ### END CODE HERE ### + print("The time used for each iteration:",time.time()-t1," s") + return x_est,px, pw + +def re_sampling(px, pw): + """ + Robot generates a set of new particles, with most of them generated around the previous particles with more weight. + + Argument: + px -- The state of all particles befor resampling. + pw -- The weight of all particles befor resampling. + + Return: + px -- The state of all particles after resampling. + pw -- The weight of all particles after resampling. + """ + ### START CODE HERE ### + p = np.zeros([50,50]) + for i in range(len(pw[0])): + x = int(px[0][i]*10) + y = int(px[1][i]*10) + if(x>=0 and x<50 and y>=0 and y<50): + p[x][y] += pw[0][i] + + p = p/p.sum() + + px = np.zeros([3, NP]) # Particle store + pw = np.zeros([1, NP]) + 1.0 / NP # Particle weight + for ip in range(NP): + pro = np.random.rand() + for i in range(2500): + x1 = int(i // 50) + y1 = int(i % 50) + pro = pro-p[x1][y1] + if pro < 0: + px[0][ip] = x1/10 + np.random.rand()/10 + px[1][ip] = y1 / 10 + np.random.rand() / 10 + px[2][ip] = 2*pi*np.random.rand() + break + ### END CODE HERE ### + return px, pw + +if __name__ == '__main__': + plt.figure(figsize=(8,8)) + # Build the room map points. + r = Room() + room = r.make_room() + # Initialize the controller of robot DR20. + controller = DR20API.Controller() + data = controller.get_lidar(Q_sim) + pos = controller.get_robot_pos() + ori = controller.get_robot_ori() + # Generate particles, and the data format is [x y yaw]',[weight]. + px, pw = generate_particles(pos) + # Use the weighted average to calculate the estimated state.(You can use other way, such as clustering.) + x_est = np.reshape(px.dot(pw.T),(3,1)) + # Initialize the data history. + h_x_est = x_est + h_x_true=np.array([[pos[0]],[pos[1]],[ori]]) + tic = 0.0 + point_x = [] + point_y = [] + t=0 + # Start simulation. + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + x, y = zip(*room) + plt.scatter(x, y) + plt.plot(np.array(h_x_true[0, :]).flatten(), + np.array(h_x_true[1, :]).flatten(), "-b") + + # Plot the particles + plt.scatter(px[0,:],px[1,:],color = 'g',s=150*pw[0,:]) + plt.plot(point_x,point_y,color='orange') + print(h_x_est[0][-1]) + + d1,point1 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/2) + d2,point2 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/4) + d3,point3 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]) + d4,point4 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/4) + d5,point5 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/2) + + print(point1) + + plt.plot(list([h_x_true[0][-1],point1[0]]),list([h_x_true[1][-1],point1[1]]),color='red') + plt.plot([h_x_true[0][-1],point2[0]],[h_x_true[1][-1],point2[1]],color='red') + plt.plot([h_x_true[0][-1],point3[0]],[h_x_true[1][-1],point3[1]],color='red') + plt.plot([h_x_true[0][-1],point4[0]],[h_x_true[1][-1],point4[1]],color='red') + plt.plot([h_x_true[0][-1],point5[0]],[h_x_true[1][-1],point5[1]],color='red') + + + plt.axis("equal") + plt.grid(True) + plt.xlim(-0.5,5.5) + plt.ylim(-0.5,5.5) + plt.pause(0.01) + time.sleep(10) + while SIM_TIME >= tic: + tic += DT + u = calc_input(v,w) + # Move the robot + controller.move_robot_vw(u[0, 0],u[1, 0]) + # Locate the robot + x_est, px, pw = pf_localization(px, pw, data, u) + # print(px) + point_x.append(x_est[0][0]) + point_y.append(x_est[1][0]) + # store data history. + h_x_est = np.hstack((h_x_est, x_est)) + h_x_true = np.hstack((h_x_true, np.array([[pos[0]],[pos[1]],[ori]]))) + + # Get the LiDAR sensor data and real state. + data = controller.get_lidar(Q_sim) + pos = controller.get_robot_pos() + ori = controller.get_robot_ori() + ### START CODE HERE ### + # Visualization + if True: + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + x, y = zip(*room) + plt.scatter(x, y) + plt.plot(np.array(h_x_true[0, :]).flatten(), + np.array(h_x_true[1, :]).flatten(), "-b") + + # Plot the particles + plt.scatter(px[0,:],px[1,:],color = 'g',s=150*pw[0,:]) + + plt.plot(point_x,point_y,color='orange') + print(h_x_est[0][-1]) + + d1,point1 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/2) + d2,point2 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/4) + d3,point3 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]) + d4,point4 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/4) + d5,point5 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/2) + + print(point1) + + plt.plot(list([h_x_true[0][-1],point1[0]]),list([h_x_true[1][-1],point1[1]]),color='red') + plt.plot([h_x_true[0][-1],point2[0]],[h_x_true[1][-1],point2[1]],color='red') + plt.plot([h_x_true[0][-1],point3[0]],[h_x_true[1][-1],point3[1]],color='red') + plt.plot([h_x_true[0][-1],point4[0]],[h_x_true[1][-1],point4[1]],color='red') + plt.plot([h_x_true[0][-1],point5[0]],[h_x_true[1][-1],point5[1]],color='red') + + + plt.axis("equal") + plt.grid(True) + plt.xlim(-0.5,5.5) + plt.ylim(-0.5,5.5) + plt.pause(0.01) + # if(t==0): + # t+=1 + # time.sleep(10) + ### END CODE HERE ### + controller.stop_simulation() \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab3/3-MCL_global.py b/lab-iisec/AI3603 lab/lab3/3-MCL_global.py new file mode 100644 index 0000000000000000000000000000000000000000..c69e637f3d6c4e871f31af5e2e3c39aad8473101 --- /dev/null +++ b/lab-iisec/AI3603 lab/lab3/3-MCL_global.py @@ -0,0 +1,522 @@ +from numpy.core.defchararray import index +from numpy.matrixlib import defmatrix +import DR20API +import numpy as np +import matplotlib.pyplot as plt +import math +from math import sin, cos, pi +import time + +### START CODE HERE ### +# You can tune the hyper-parameter, and define your utility function or class in this block if necessary. + +# Particle filter parameter +NP = 400 # Number of Particle +NTh = NP / 2.0 # Number of particle for re-sampling + +# Set the random seed to ensure the repeatability. +seed=1 +np.random.seed(seed) + +# Estimation parameter of PF, you may use them in the PF algorithm. You can use the recommended values as follows. +Q = np.diag([0.15]) ** 2 # range error +R = np.diag([0.1, np.deg2rad(10)]) ** 2 # input error + +### END CODE HERE ### + +# Parameter of LiDAR +scanningAngle = 180 +pts=5 + +# Simulation parameter +Q_sim = np.diag([0.05]) ** 2 # add noise to lidar readings +R_sim = np.diag([0.03, np.deg2rad(3)]) ** 2 # add noise to control command + +v=0.5 #linear velocity +w=0.25 #angular velocity + +DT = 0.1 # time tick [s] +SIM_TIME = 200.0 # simulation time [s] + + +entropys = np.zeros(NP) + +class Room(object): + """ + Generate the map. + """ + + def map_range_x(self, start, stop, number, y): + return [[start + (stop - start) * i / number, y] for i in range(number + 1)] + + def map_range_y(self, start, stop, number, x): + return [[x, start + (stop - start) * i / number] for i in range(number + 1)] + + def map_square(self, top_left, bottom_right, points): + tl_x, tl_y = top_left + br_x, br_y = bottom_right + res = self.map_range_y(tl_y, br_y, points, tl_x) + res += self.map_range_y(tl_y, br_y, points, br_x) + res += self.map_range_x(tl_x, br_x, points, tl_y) + res += self.map_range_x(tl_x, br_x, points, br_y) + return res + + def make_room(self): + walls = self.map_square((0.0,0.0), (5.0,5.0), 30) + table1 = self.map_square((2.0,2.0), (3,3.5), 10) + table2 = self.map_square((2.0,1.0), (2.5,1.5), 5) + table3 = self.map_square((3.0,1.0), (3.5,1.5), 5) + table4 = self.map_square((1.0,2.0), (1.5,2.5), 5) + table5 = self.map_square((4.0,4.0), (4.5,4.5), 5) + return walls + table1 + table2 + table3 + table4 + table5 + + +def motion_model(x, u): + """ + Given the current state and control input, return the state at next time. + + Arguments: + x -- A 3*1 matrix indicating the state of robots or particles. Data format: [[x] [y] [yaw]] + u -- A 2*1 matrix indicating the control input. Data format: [[v] [w]] + + Return: + x -- A 3*1 matrix indicating the state at next time. Data format: [[x] [y] [yaw]] + """ + + F = np.array([[1.0, 0, 0], + [0, 1.0, 0], + [0, 0, 1.0]]) + + B = np.array([[DT * math.cos(x[2, 0]), 0], + [DT * math.sin(x[2, 0]), 0], + [0.0, DT],]) + x = F.dot(x) + B.dot(u) + return x + +def calc_input(v,w): + """ + Adding noise to the input control commands. + + Argument: + v -- linear velocity + w -- angular velocity + + Return: + ud -- A 2*1 matrix indicating the noisy input. + """ + + v = v + np.random.randn() * R_sim[0, 0] ** 0.5 + w = w + np.random.randn() * R_sim[1, 1] ** 0.5 + ud = np.array([[v, w]]).T + return ud + +def generate_particles(pos): + """ + Generate NP particles, each particle contains three state quantities and weight. + If you set the Nearby_flag as True, then the particles will be sprinkled around the real robot. + Otherwise, the particles will be scattered uniformly in the map. + In this file, Nearby_flag is set to True to solve a local localization problem. + + Arguments: + pos -- The robot current position. + + Return: + px -- A 3*NP matrix, each column represents the status of a particle. + pw -- A 1*NP matrix, each column represents the weight value of correspoding particle. + """ + Nearby_flag = False + + if Nearby_flag: + x = pos[0] - 1 + 2 * np.random.random(size=(1,NP)) + y = pos[1] - 1 + 2 * np.random.random(size=(1,NP)) + yaw = 2 * math.pi * np.random.random(size=(1,NP)) + else: + x = 5 * np.random.random(size=(1,NP)) + y = 5 * np.random.random(size=(1,NP)) + yaw = 2 * math.pi * np.random.random(size=(1,NP)) + + px = np.zeros((3, NP)) # Particle store + pw = np.zeros((1, NP)) + 1.0 / NP # Particle weight + px[0],px[1],px[2],=x,y,yaw + return px,pw + +def judge(x,y,angle): + X = [0,2,3,1,4,1.5,2.5,3.5,4.5,5] + Y = [0,2,1,4,1.5,2.5,3.5,4.5,5] + while(angle>=2*pi): + angle -= 2*pi + while(angle<0): + angle += 2*pi + k = math.tan(angle) + points = [] + dis = [] + for i in X: + if(angle>=pi/2 and angle<=pi*3/2): + if(i<=x): + y_new = k*(i-x)+y + if(i==0 or i==5): + if(y_new>=0 and y_new<=5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2.5 or i==3.5): + if(y_new>=1 and y_new<=1.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if(y_new>=2 and y_new<=2.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2 or i==3): + if((y_new>=1 and y_new<=1.5) or (y_new>=2 and y_new<=3.5)): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(y_new>=4 and y_new<=4.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + else: + if(i>x): + y_new = k*(i-x)+y + if(i==0 or i==5): + if(y_new>=0 and y_new<=5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2.5 or i==3.5): + if(y_new>=1 and y_new<=1.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if(y_new>=2 and y_new<=2.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==2 or i==3): + if((y_new>=1 and y_new<=1.5) or (y_new>=2 and y_new<=3.5)): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(y_new>=4 and y_new<=4.5): + points.append([i,y_new]) + d = math.sqrt((i-x)**2+(y_new-y)**2) + dis.append(d) + for i in Y: + if(angle>=0 and angle<=pi): + if(i>=y): + x_new = (i-y)/k+x + if(i==0 or i==5): + if(x_new>=0 and x_new<=5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if((x_new>=2 and x_new<=2.5)or(x_new>=3 and x_new<=3.5)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2): + if((x_new>=1 and x_new<=1.5)or (x_new>=2 and x_new<=3)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2.5): + if(x_new>=1 and x_new<=1.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==3.5): + if(x_new>=2 and x_new<=3): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(x_new>=4 and x_new<=4.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + else: + if(i=0 and x_new<=5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==1 or i==1.5): + if((x_new>=2 and x_new<=2.5)or(x_new>=3 and x_new<=3.5)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2): + if((x_new>=1 and x_new<=1.5)or (x_new>=2 and x_new<=3)): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==2.5): + if(x_new>=1 and x_new<=1.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==3.5): + if(x_new>=2 and x_new<=3): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + elif(i==4 or i==4.5): + if(x_new>=4 and x_new<=4.5): + points.append([x_new,i]) + d = math.sqrt((x_new-x)**2+(i-y)**2) + dis.append(d) + if(dis): + d = min(dis) + point = points[dis.index(d)] + else: + d = 10 + point = [10,10] + + return d,point +def pf_localization(px,pw,data,u): + """ + Localization with Particle filter. In this function, you need to: + + (1) Prediction step: Each particle predicts its new location based on the actuation command given. + (2) Update step: Update the weight of each particle. That is, particles consistent with sensor readings will have higher weight. + (3) Resample step: Generate a set of new particles, with most of them generated around the previous particles with more weight. + You need to decide when to resample the particles and how to resample the particles. + + Argument: + px -- A 3*NP matrix, each column represents the status of a particle. + pw -- A 1*NP matrix, each column represents the weight value of correspoding particle. + data -- A List contains the output of the LiDAR sensor. It is represented by a distance value in counterclockwise order. + u -- A 2*1 matrix indicating the control input. Data format: [[v] [w]] + + Return: + x_est -- A 3*1 matrix, indicating the estimated state after particle filtering. + px -- A 3*NP matrix. The predicted state of the next time. + pw -- A 1*NP matrix. The updated weight of each particle. + """ + t1 = time.time() + ### START CODE HERE ### + for ip in range(NP): + # Prediction step: Predict with random input sampling + x = np.array([[px[0][ip]],[px[1][ip]],[px[2][ip]]]) + x_new = motion_model(x,u) + px[0][ip] = x_new[0][0] + px[1][ip] = x_new[1][0] + px[2][ip] = x_new[2][0] + + d1,p1 = judge(px[0][ip],px[1][ip],px[2][ip]+pi/2) + d2,p2 = judge(px[0][ip],px[1][ip],px[2][ip]+pi/4) + d3,p3 = judge(px[0][ip],px[1][ip],px[2][ip]) + d4,p4 = judge(px[0][ip],px[1][ip],px[2][ip]-pi/4) + d5,p5 = judge(px[0][ip],px[1][ip],px[2][ip]-pi/2) + + + # print(px[0][ip],px[1][ip],px[2][ip]) + # print(d1,d2,d3,d4,d5) + # print(p1,p2,p3,p4,p5) + # plt.cla() + # # for stopping simulation with the esc key. + # plt.gcf().canvas.mpl_connect( + # 'key_release_event', + # lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + # x, y = zip(*room) + # plt.scatter(x, y) + # plt.plot(np.array(h_x_true[0, :]).flatten(), + # np.array(h_x_true[1, :]).flatten(), "-b") + # plt.scatter(px[0][ip],px[1][ip]) + # plt.scatter(p1[0],p1[1],c='#000000')#black + # plt.scatter(p2[0],p2[1],c='#A52A2A')#brown + # plt.scatter(p3[0],p3[1],c='#A9A9A9')#darkgray + # plt.scatter(p4[0],p4[1],c='#008000')#green + # plt.scatter(p5[0],p5[1],c='#FFB6C1')#lightpink + # # plt.scatter() + # plt.show() + # time.sleep(10) + + entropy = (data[0]-d5- np.random.randn()*Q[0])**2 + (data[1]-d4- np.random.randn()*Q[0])**2 + (data[2]- d3-np.random.randn()*Q[0])**2 + (data[3]- d2-np.random.randn()*Q[0])**2 + (data[4]- d1-np.random.randn()*Q[0])**2 + pw[0,ip] = np.exp(1 / (entropy+0.01)) + # if(px[0][ip]<=0 or px[0][ip]>=5 or px[1][ip]<=0 or px[1][ip]>=5): + # pw[0,ip] = 0 + + pw = pw/pw.sum() + + + x_est = px.dot(pw.T) + + # Resample step: Resample the particles. + t = 0 + for ip in range(len(px[0])): + if pw[0][ip] > 0.01: + t+=1 + if t < 5: + px, pw = re_sampling(px,pw) + ### END CODE HERE ### + print("The time used for each iteration:",time.time()-t1," s") + return x_est,px, pw + +def re_sampling(px, pw): + """ + Robot generates a set of new particles, with most of them generated around the previous particles with more weight. + + Argument: + px -- The state of all particles befor resampling. + pw -- The weight of all particles befor resampling. + + Return: + px -- The state of all particles after resampling. + pw -- The weight of all particles after resampling. + """ + ### START CODE HERE ### + p = np.zeros([50,50]) + for i in range(len(pw[0])): + x = int(px[0][i]*10) + y = int(px[1][i]*10) + if(x>=0 and x<50 and y>=0 and y<50): + p[x][y] += pw[0][i] + + p = p/p.sum() + + px = np.zeros([3, NP]) # Particle store + pw = np.zeros([1, NP]) + 1.0 / NP # Particle weight + for ip in range(NP): + pro = np.random.rand() + for i in range(2500): + x1 = int(i // 50) + y1 = int(i % 50) + pro = pro-p[x1][y1] + if pro < 0: + px[0][ip] = x1/10 + np.random.rand()/10 + px[1][ip] = y1 / 10 + np.random.rand() / 10 + px[2][ip] = 2*pi*np.random.rand() + break + ### END CODE HERE ### + return px, pw + +if __name__ == '__main__': + plt.figure(figsize=(8,8)) + # Build the room map points. + r = Room() + room = r.make_room() + # Initialize the controller of robot DR20. + controller = DR20API.Controller() + data = controller.get_lidar(Q_sim) + pos = controller.get_robot_pos() + ori = controller.get_robot_ori() + # Generate particles, and the data format is [x y yaw]',[weight]. + px, pw = generate_particles(pos) + # Use the weighted average to calculate the estimated state.(You can use other way, such as clustering.) + x_est = np.reshape(px.dot(pw.T),(3,1)) + # Initialize the data history. + h_x_est = x_est + h_x_true=np.array([[pos[0]],[pos[1]],[ori]]) + tic = 0.0 + point_x = [] + point_y = [] + t=0 + # Start simulation. + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + x, y = zip(*room) + plt.scatter(x, y) + plt.plot(np.array(h_x_true[0, :]).flatten(), + np.array(h_x_true[1, :]).flatten(), "-b") + + # Plot the particles + plt.scatter(px[0,:],px[1,:],color = 'g',s=150*pw[0,:]) + plt.plot(point_x,point_y,color='orange') + print(h_x_est[0][-1]) + + d1,point1 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/2) + d2,point2 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/4) + d3,point3 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]) + d4,point4 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/4) + d5,point5 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/2) + + print(point1) + + plt.plot(list([h_x_true[0][-1],point1[0]]),list([h_x_true[1][-1],point1[1]]),color='red') + plt.plot([h_x_true[0][-1],point2[0]],[h_x_true[1][-1],point2[1]],color='red') + plt.plot([h_x_true[0][-1],point3[0]],[h_x_true[1][-1],point3[1]],color='red') + plt.plot([h_x_true[0][-1],point4[0]],[h_x_true[1][-1],point4[1]],color='red') + plt.plot([h_x_true[0][-1],point5[0]],[h_x_true[1][-1],point5[1]],color='red') + + + plt.axis("equal") + plt.grid(True) + plt.xlim(-0.5,5.5) + plt.ylim(-0.5,5.5) + plt.pause(0.01) + time.sleep(10) + while SIM_TIME >= tic: + tic += DT + u = calc_input(v,w) + # Move the robot + controller.move_robot_vw(u[0, 0],u[1, 0]) + # Locate the robot + x_est, px, pw = pf_localization(px, pw, data, u) + # print(px) + point_x.append(x_est[0][0]) + point_y.append(x_est[1][0]) + # store data history. + h_x_est = np.hstack((h_x_est, x_est)) + h_x_true = np.hstack((h_x_true, np.array([[pos[0]],[pos[1]],[ori]]))) + + # Get the LiDAR sensor data and real state. + data = controller.get_lidar(Q_sim) + pos = controller.get_robot_pos() + ori = controller.get_robot_ori() + ### START CODE HERE ### + # Visualization + if True: + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [controller.stop_simulation() if event.key == 'escape' else None]) + x, y = zip(*room) + plt.scatter(x, y) + plt.plot(np.array(h_x_true[0, :]).flatten(), + np.array(h_x_true[1, :]).flatten(), "-b") + + # Plot the particles + plt.scatter(px[0,:],px[1,:],color = 'g',s=150*pw[0,:]) + + plt.plot(point_x,point_y,color='orange') + print(h_x_est[0][-1]) + + d1,point1 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/2) + d2,point2 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]-pi/4) + d3,point3 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]) + d4,point4 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/4) + d5,point5 = judge(h_x_true[0][-1],h_x_true[1][-1],h_x_true[2][-1]+pi/2) + + print(point1) + + plt.plot(list([h_x_true[0][-1],point1[0]]),list([h_x_true[1][-1],point1[1]]),color='red') + plt.plot([h_x_true[0][-1],point2[0]],[h_x_true[1][-1],point2[1]],color='red') + plt.plot([h_x_true[0][-1],point3[0]],[h_x_true[1][-1],point3[1]],color='red') + plt.plot([h_x_true[0][-1],point4[0]],[h_x_true[1][-1],point4[1]],color='red') + plt.plot([h_x_true[0][-1],point5[0]],[h_x_true[1][-1],point5[1]],color='red') + + + plt.axis("equal") + plt.grid(True) + plt.xlim(-0.5,5.5) + plt.ylim(-0.5,5.5) + plt.pause(0.01) + # if(t==0): + # t+=1 + # time.sleep(10) + ### END CODE HERE ### + controller.stop_simulation() \ No newline at end of file diff --git a/lab-iisec/AI3603 lab/lab3/MCL_global.mp4 b/lab-iisec/AI3603 lab/lab3/MCL_global.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..4bb56837ee856baa4032a53ce31724ae00663a5a Binary files /dev/null and b/lab-iisec/AI3603 lab/lab3/MCL_global.mp4 differ diff --git a/lab-iisec/AI3603 lab/lab3/MCL_local .mp4 b/lab-iisec/AI3603 lab/lab3/MCL_local .mp4 new file mode 100644 index 0000000000000000000000000000000000000000..425aecae4e41b345c208be3a4b165317ae713bab Binary files /dev/null and b/lab-iisec/AI3603 lab/lab3/MCL_local .mp4 differ diff --git a/lab-iisec/Cityscapes-anotation/Cityscapes_ann_convert.py b/lab-iisec/Cityscapes-anotation/Cityscapes_ann_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..2bba38e7dc53ddf38323f77f04a390228d55ab78 --- /dev/null +++ b/lab-iisec/Cityscapes-anotation/Cityscapes_ann_convert.py @@ -0,0 +1,78 @@ +import json +import os +from pathlib import Path +import re +from tqdm import tqdm +from functools import reduce + + +def convert_annotation(image_id, paths): + global label_map + + def find_box(points): # 该函数用来找出xmin, xmax, ymin ,ymax 即bbox包围框 + _x, _y = [float(pot[0]) for pot in points], [float(pot[1]) for pot in points] + return min(_x), max(_x), min(_y), max(_y) + + def convert(size, bbox): # 转为中心坐标 + # size: (原图宽， 原图长) + center_x, center_y = (bbox[0] + bbox[1]) / 2.0 - 1, (bbox[2] + bbox[3]) / 2.0 - 1 + center_w, center_h = bbox[1] - bbox[0], bbox[3] - bbox[2] + return center_x / size[0], center_y / size[1], center_w / size[0], center_h / size[1] + + final_img_path, final_label_path, final_output_path = paths + label_json_url = final_label_path / f'{image_id}_gtFine_polygons.json' + # 输出到 ：final_output_path / f'{image_id}_gtFine_polygons.json' + + load_dict = json.load(open(label_json_url, 'r')) # 图像的实例 + datas = [] + for obj in load_dict['objects']: # load_dict['objects'] -> 目标的几何框体 + obj_label = obj['label'] # 目标的类型 + if obj_label in ['out of roi', 'ego vehicle']: # 直接跳过这两种类型 注意测试集里只有这两种类型 跳过的话测试集合里将为空的标签 + continue + + if obj_label not in label_map.keys(): # 记录目标类型转为int值 + label_map[obj_label] = len(label_map.keys()) # 标签从0开始 + + x, y, w, h = convert((load_dict['imgWidth'], load_dict['imgHeight']), find_box(obj['polygon'])) # 归一化为中心点 + + # yolo 标准格式：img.jpg -> img.txt + # 内容的类别 归一化后的中心点x坐标 归一化后的中心点y坐标 归一化后的目标框宽度w 归一化后的目标况高度h + datas.append(f'{label_map[obj_label]} {x} {y} {w} {h}\n') + + with open(final_output_path / f'{image_id}_leftImg8bit.txt', 'w') as label_f: # 写出标签文件 + label_f.writelines(datas) + # 下面两行是用于labelImg测试用的 + # with open(label_output_dir / f'{image_id}_leftImg8bit.txt', 'w') as label_f: # 写出标签文件 + # label_f.writelines(datas) + + +if __name__ == '__main__': + root_dir = Path(__file__).parent + image_dir = root_dir / 'leftImg8bit' + label_dir = root_dir / 'getfine' + label_output_dir = root_dir / 'labels' + label_map = {} + for _t_ in tqdm(os.listdir(image_dir)): # _t_ as ['train', 'test' 'val;] + type_files = [] + for cities_name in os.listdir(image_dir / _t_): + _final_img_path = image_dir / _t_ / cities_name # root_dir / leftImg8bit / test / berlin + _final_label_path = label_dir / _t_ / cities_name # root_dir / getfine / test / berlin + _final_output_path = label_output_dir / _t_ / cities_name # root_dir / txt_labels / test / berlin + if not os.path.exists(_final_output_path): # 构建保存label输出目录 + os.makedirs(_final_output_path) + + # berlin_000000_000019_leftImg8bit.png -> berlin_000000_000019_gtFine_polygons.json + image_ids = list(map(lambda s: re.sub(r'_leftImg8bit\.png', '', s), os.listdir(_final_img_path))) + # print(names[:0]) -> berlin_000000_000019 + + for img_id in image_ids: + convert_annotation(img_id, [_final_img_path, _final_label_path, _final_output_path]) + + type_files.append([f'{str(_final_img_path / _img)}\n' for _img in os.listdir(_final_img_path)]) + + with open(root_dir / f'yolo_{_t_}.txt', 'w') as f: # 记录训练样本等的具体地址 + f.writelines(reduce(lambda a, b: a + b, type_files)) + + with open(label_output_dir / 'classes.txt', 'w') as f: # 写出类别总表 + for k, v in label_map.items(): + f.write(f'{k}\n') \ No newline at end of file diff --git a/lab-iisec/Cityscapes-anotation/labels/classes.txt b/lab-iisec/Cityscapes-anotation/labels/classes.txt new file mode 100644 index 0000000000000000000000000000000000000000..4a4a13adf5d22f89b1407fa00cb122559ca5bc3e --- /dev/null +++ b/lab-iisec/Cityscapes-anotation/labels/classes.txt @@ -0,0 +1,32 @@ +sky +road +ground +building +parking +car +pole +terrain +vegetation +static +sidewalk +cargroup +person +license plate +truck +dynamic +traffic light +rider +bicycle +fence +traffic sign +wall +motorcycle +bus +persongroup +guard rail +bicyclegroup +bridge +tunnel +train +polegroup +truckgroup diff --git a/lab-iisec/Cityscapes-anotation/labels/train/ulm/ulm_000002_000019_leftImg8bit.txt b/lab-iisec/Cityscapes-anotation/labels/train/ulm/ulm_000002_000019_leftImg8bit.txt new file mode 100644 index 0000000000000000000000000000000000000000..d535e99f9754705d744c988d1da95c553feddf2f --- /dev/null +++ b/lab-iisec/Cityscapes-anotation/labels/train/ulm/ulm_000002_000019_leftImg8bit.txt @@ -0,0 +1,51 @@ +0 0.4091796875 0.1484375 0.2294921875 0.298828125 +3 0.24365234375 0.49755859375 0.48828125 0.9970703125 +1 0.498291015625 0.70458984375 0.99951171875 0.5849609375 +10 0.814208984375 0.62255859375 0.36767578125 0.3857421875 +2 0.873291015625 0.5439453125 0.24951171875 0.09375 +6 0.311767578125 0.33837890625 0.00439453125 0.1552734375 +8 0.203125 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k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S0 = -344 + C2: - 16 k0 + 8 k21 + 8 k23 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k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S7 = 8 + C9: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S8 = 8 + C10: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S9 = 8 + C11: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S10 = 40 + C12: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S11 = 8 + C13: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S12 = 8 + C14: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S13 = 8 + C15: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S14 = 40 + C16: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S15 = 8 + C17: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S16 = 8 + C18: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S17 = 8 + C19: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S18 = 8 + C20: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S19 = -56 + C21: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S20 = 8 + C22: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S21 = 8 + C23: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S22 = 8 + C24: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S23 = -56 + C25: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S24 = 8 + C26: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S25 = -56 + C27: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S26 = 8 + C28: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S27 = 8 + C29: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S28 = 8 + C30: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S29 = -56 + C31: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S30 = 8 + C32: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S31 = 8 + C33: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S32 = 8 + C34: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S33 = 8 + C35: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S34 = 8 + C36: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S35 = -24 + C37: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S36 = 8 + C38: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S37 = -56 + C39: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S38 = -24 + C40: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S39 = 8 + C41: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S40 = 8 + C42: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S41 = 8 + C43: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S42 = -24 + C44: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S43 = 8 + C45: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S44 = -24 + C46: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S45 = -24 + C47: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S46 = -24 + C48: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S47 = 8 + C49: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S48 = 8 + C50: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S49 = -24 + C51: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S50 = 8 + C52: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S51 = -24 + C53: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S52 = -24 + C54: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S53 = 8 + C55: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S54 = -56 + C56: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S55 = -24 + C57: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S56 = 8 + C58: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S57 = 8 + C59: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S58 = -24 + C60: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S59 = -24 + C61: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S60 = 8 + C62: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S61 = 8 + C63: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S62 = 8 + C64: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S63 = 8 + C65: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S64 = 8 + C66: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S65 = 8 + C67: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S66 = 8 + C68: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S67 = 8 + C69: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S68 = 8 + C70: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S69 = -56 + C71: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S70 = -24 + C72: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S71 = -24 + C73: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S72 = 8 + C74: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S73 = -24 + C75: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S74 = -24 + C76: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S75 = 8 + C77: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S76 = -24 + C78: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S77 = 8 + C79: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S78 = -24 + C80: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S79 = 8 + C81: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S80 = 8 + C82: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S81 = -24 + C83: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S82 = 8 + C84: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S83 = 8 + C85: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S84 = -24 + C86: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S85 = 8 + C87: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S86 = 8 + C88: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S87 = 8 + C89: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S88 = 8 + C90: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S89 = -24 + C91: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S90 = -24 + C92: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S91 = -24 + C93: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S92 = -56 + C94: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S93 = -24 + C95: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S94 = 8 + C96: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S95 = 8 + C97: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S96 = 40 + C98: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S97 = 8 + C99: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S98 = -24 + C100: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S99 = 8 + C101: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S100 = -24 + C102: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S101 = 8 + C103: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S102 = -24 + C104: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S103 = 8 + C105: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S104 = -24 + C106: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S105 = 8 + C107: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S106 = 40 + C108: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S107 = -56 + C109: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S108 = -24 + C110: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S109 = 8 + C111: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S110 = -24 + C112: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S111 = -56 + C113: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S112 = 8 + C114: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S113 = -56 + C115: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S114 = -24 + C116: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S115 = 8 + C117: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S116 = 8 + C118: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S117 = 8 + C119: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S118 = -24 + C120: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S119 = 8 + C121: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S120 = -24 + C122: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S121 = 8 + C123: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S122 = 8 + C124: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S123 = 8 + C125: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S124 = -24 + C126: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S125 = 8 + C127: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S126 = 8 + C128: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S127 = -56 + C129: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S128 = 40 + C130: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S129 = 8 + C131: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S130 = 8 + C132: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S131 = -24 + C133: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S132 = -24 + C134: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S133 = 8 + C135: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S134 = 8 + C136: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S135 = -24 + C137: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S136 = 8 + C138: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S137 = -24 + C139: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S138 = -24 + C140: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S139 = -56 + C141: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S140 = 8 + C142: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S141 = -24 + C143: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S142 = 40 + C144: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S143 = -56 + C145: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S144 = 8 + C146: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S145 = 8 + C147: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S146 = -24 + C148: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S147 = 8 + C149: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S148 = -56 + C150: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S149 = 8 + C151: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S150 = -24 + C152: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S151 = 8 + C153: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S152 = -24 + C154: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S153 = 8 + C155: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S154 = 8 + C156: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S155 = 8 + C157: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S156 = -24 + C158: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S157 = 8 + C159: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S158 = -56 + C160: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S159 = 8 + C161: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 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k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S161 = 8 + C163: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S162 = -24 + C164: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 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4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S165 = -56 + C167: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 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k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S167 = 8 + C169: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S168 = -24 + C170: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S169 = 8 + C171: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S170 = -24 + C172: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S171 = 8 + C173: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S172 = 8 + C174: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S173 = -24 + C175: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S174 = -24 + C176: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S175 = 8 + C177: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S176 = 8 + C178: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S177 = -24 + C179: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S178 = 8 + C180: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S179 = 8 + C181: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S180 = -24 + C182: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S181 = 8 + C183: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S182 = 8 + C184: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S183 = 8 + C185: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S184 = -56 + C186: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S185 = -24 + C187: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S186 = -24 + C188: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S187 = -24 + C189: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S188 = 8 + C190: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S189 = -24 + C191: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S190 = 8 + C192: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S191 = 8 + C193: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S192 = 8 + C194: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S193 = 8 + C195: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S194 = -24 + C196: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S195 = 8 + C197: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S196 = 8 + C198: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S197 = -56 + C199: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S198 = 8 + C200: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S199 = -24 + C201: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S200 = -24 + C202: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S201 = -24 + C203: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S202 = -24 + C204: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S203 = 8 + C205: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S204 = 8 + C206: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S205 = 8 + C207: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S206 = -24 + C208: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S207 = 8 + C209: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S208 = 8 + C210: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S209 = -24 + C211: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S210 = -56 + C212: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S211 = -24 + C213: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S212 = -24 + C214: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S213 = 8 + C215: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S214 = 8 + C216: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S215 = -24 + C217: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S216 = 8 + C218: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S217 = 8 + C219: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S218 = -24 + C220: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S219 = -24 + C221: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S220 = 8 + C222: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S221 = 8 + C223: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S222 = 8 + C224: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S223 = 8 + C225: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S224 = 40 + C226: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S225 = 8 + C227: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S226 = -24 + C228: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S227 = -24 + C229: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S228 = 40 + C230: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S229 = 8 + C231: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S230 = -24 + C232: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S231 = -24 + C233: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 + 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 - 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S232 = -24 + C234: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 - 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 + 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S233 = -24 + C235: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 + 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 - 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 + 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S234 = -24 + C236: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 - 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 + 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 - 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S235 = 8 + C237: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 + 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 - 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S236 = -24 + C238: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 - 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 + 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S237 = -24 + C239: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 + 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 - 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 + 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S238 = -24 + C240: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 - 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 + 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 - 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S239 = 8 + C241: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 + 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 - 4 k56 - 4 k57 - 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 + 4 k72 + 4 k73 + 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 - 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 - 8 k154 - 4 k156 - 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 + 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S240 = 8 + C242: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 - 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 - 4 k56 + 4 k57 - 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 + 4 k72 - 4 k73 - 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 - 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 - 8 k154 - 4 k156 + 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 + 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S241 = -56 + C243: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 + 4 k40 + 4 k41 - 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 - 4 k56 - 4 k57 + 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 + 4 k72 + 4 k73 - 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 - 4 k88 - 4 k89 + 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 + 8 k154 - 4 k156 - 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 + 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 - 8 k200 + 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S242 = 8 + C244: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 + 4 k40 - 4 k41 + 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 - 4 k56 + 4 k57 + 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 + 4 k72 - 4 k73 + 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 - 4 k88 + 4 k89 + 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 + 8 k154 - 4 k156 + 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 + 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 - 8 k200 - 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S243 = 8 + C245: - 16 k0 - 8 k21 - 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 + 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 - 4 k56 - 4 k57 - 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 + 4 k72 + 4 k73 + 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 - 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 - 4 k152 - 4 k153 - 8 k154 + 4 k156 + 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 + 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 - 4 k248 - 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S244 = -56 + C246: - 16 k0 + 8 k21 + 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 - 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 - 4 k56 + 4 k57 - 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 + 4 k72 - 4 k73 - 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 - 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 - 4 k152 + 4 k153 - 8 k154 + 4 k156 - 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 + 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 - 4 k248 + 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S245 = 8 + C247: - 16 k0 - 8 k21 + 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 + 4 k40 + 4 k41 - 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 - 4 k56 - 4 k57 + 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 + 4 k72 + 4 k73 - 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 - 4 k88 - 4 k89 + 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 + 4 k136 + 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 - 4 k152 - 4 k153 + 8 k154 + 4 k156 + 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 + 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 - 8 k200 + 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 + 4 k232 + 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 - 4 k248 - 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S246 = 8 + C248: - 16 k0 + 8 k21 - 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 + 4 k40 - 4 k41 + 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 - 4 k56 + 4 k57 + 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 + 4 k72 - 4 k73 + 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 - 4 k88 + 4 k89 + 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 + 4 k136 - 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 - 4 k152 + 4 k153 + 8 k154 + 4 k156 - 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 + 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 - 8 k200 - 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 + 4 k232 - 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 - 4 k248 + 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S247 = 8 + C249: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 + 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 - 8 k43 - 8 k45 - 4 k46 - 4 k47 - 4 k50 - 4 k51 - 4 k52 - 4 k53 - 8 k54 + 4 k56 + 4 k57 + 8 k58 + 4 k62 + 4 k63 + 4 k66 + 4 k67 + 4 k68 + 4 k69 + 8 k71 - 4 k72 - 4 k73 - 8 k75 - 4 k78 - 4 k79 - 4 k82 - 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 + 8 k90 + 8 k92 + 4 k94 + 4 k95 - 8 k99 - 8 k102 + 8 k105 + 8 k108 + 8 k115 + 8 k116 - 8 k121 - 8 k126 + 4 k130 + 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 - 8 k142 - 8 k143 - 8 k145 - 4 k146 - 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 + 8 k154 + 4 k156 + 4 k157 - 8 k162 - 4 k164 - 4 k165 - 4 k166 - 4 k167 + 8 k171 + 4 k172 + 4 k173 + 4 k174 + 4 k175 + 8 k177 + 4 k180 + 4 k181 + 4 k182 + 4 k183 - 8 k184 - 4 k188 - 4 k189 - 4 k190 - 4 k191 - 4 k196 - 4 k197 - 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 + 4 k205 + 4 k206 + 4 k207 + 8 k209 + 8 k210 + 4 k212 + 4 k213 + 4 k214 + 4 k215 - 4 k220 - 4 k221 - 4 k222 - 4 k223 + 4 k226 + 4 k227 + 8 k228 + 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 - 4 k242 - 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 + 8 k250 + 4 k252 + 4 k253 - 128 S248 = 8 + C250: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 - 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 + 8 k43 + 8 k45 - 4 k46 + 4 k47 - 4 k50 + 4 k51 - 4 k52 + 4 k53 - 8 k54 + 4 k56 - 4 k57 + 8 k58 + 4 k62 - 4 k63 + 4 k66 - 4 k67 + 4 k68 - 4 k69 - 8 k71 - 4 k72 + 4 k73 + 8 k75 - 4 k78 + 4 k79 - 4 k82 + 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 + 8 k90 + 8 k92 + 4 k94 - 4 k95 + 8 k99 - 8 k102 - 8 k105 + 8 k108 - 8 k115 + 8 k116 + 8 k121 - 8 k126 + 4 k130 - 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 - 8 k142 + 8 k143 + 8 k145 - 4 k146 + 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 + 8 k154 + 4 k156 - 4 k157 - 8 k162 - 4 k164 + 4 k165 - 4 k166 + 4 k167 - 8 k171 + 4 k172 - 4 k173 + 4 k174 - 4 k175 - 8 k177 + 4 k180 - 4 k181 + 4 k182 - 4 k183 - 8 k184 - 4 k188 + 4 k189 - 4 k190 + 4 k191 - 4 k196 + 4 k197 - 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 - 4 k205 + 4 k206 - 4 k207 - 8 k209 + 8 k210 + 4 k212 - 4 k213 + 4 k214 - 4 k215 - 4 k220 + 4 k221 - 4 k222 + 4 k223 + 4 k226 - 4 k227 + 8 k228 - 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 - 4 k242 + 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 + 8 k250 + 4 k252 - 4 k253 - 128 S249 = 8 + C251: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 - 4 k35 + 4 k36 + 4 k37 - 4 k40 - 4 k41 + 8 k43 - 8 k45 + 4 k46 + 4 k47 + 4 k50 + 4 k51 - 4 k52 - 4 k53 + 8 k54 + 4 k56 + 4 k57 - 8 k58 - 4 k62 - 4 k63 - 4 k66 - 4 k67 + 4 k68 + 4 k69 - 8 k71 - 4 k72 - 4 k73 + 8 k75 + 4 k78 + 4 k79 + 4 k82 + 4 k83 - 4 k84 - 4 k85 + 4 k88 + 4 k89 - 8 k90 + 8 k92 - 4 k94 - 4 k95 + 8 k99 + 8 k102 + 8 k105 + 8 k108 - 8 k115 + 8 k116 - 8 k121 + 8 k126 - 4 k130 - 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 - 4 k140 - 4 k141 + 8 k142 + 8 k143 - 8 k145 + 4 k146 + 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 - 8 k154 + 4 k156 + 4 k157 + 8 k162 - 4 k164 - 4 k165 + 4 k166 + 4 k167 - 8 k171 + 4 k172 + 4 k173 - 4 k174 - 4 k175 + 8 k177 + 4 k180 + 4 k181 - 4 k182 - 4 k183 - 8 k184 - 4 k188 - 4 k189 + 4 k190 + 4 k191 - 4 k196 - 4 k197 + 4 k198 + 4 k199 + 8 k200 - 8 k203 + 4 k204 + 4 k205 - 4 k206 - 4 k207 + 8 k209 - 8 k210 + 4 k212 + 4 k213 - 4 k214 - 4 k215 - 4 k220 - 4 k221 + 4 k222 + 4 k223 - 4 k226 - 4 k227 + 8 k228 + 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 - 4 k236 - 4 k237 - 8 k241 + 4 k242 + 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 - 8 k250 + 4 k252 + 4 k253 - 128 S250 = 8 + C252: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 + 4 k35 + 4 k36 - 4 k37 - 4 k40 + 4 k41 - 8 k43 + 8 k45 + 4 k46 - 4 k47 + 4 k50 - 4 k51 - 4 k52 + 4 k53 + 8 k54 + 4 k56 - 4 k57 - 8 k58 - 4 k62 + 4 k63 - 4 k66 + 4 k67 + 4 k68 - 4 k69 + 8 k71 - 4 k72 + 4 k73 - 8 k75 + 4 k78 - 4 k79 + 4 k82 - 4 k83 - 4 k84 + 4 k85 + 4 k88 - 4 k89 - 8 k90 + 8 k92 - 4 k94 + 4 k95 - 8 k99 + 8 k102 - 8 k105 + 8 k108 + 8 k115 + 8 k116 + 8 k121 + 8 k126 - 4 k130 + 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 - 4 k140 + 4 k141 + 8 k142 - 8 k143 + 8 k145 + 4 k146 - 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 - 8 k154 + 4 k156 - 4 k157 + 8 k162 - 4 k164 + 4 k165 + 4 k166 - 4 k167 + 8 k171 + 4 k172 - 4 k173 - 4 k174 + 4 k175 - 8 k177 + 4 k180 - 4 k181 - 4 k182 + 4 k183 - 8 k184 - 4 k188 + 4 k189 + 4 k190 - 4 k191 - 4 k196 + 4 k197 + 4 k198 - 4 k199 + 8 k200 + 8 k203 + 4 k204 - 4 k205 - 4 k206 + 4 k207 - 8 k209 - 8 k210 + 4 k212 - 4 k213 - 4 k214 + 4 k215 - 4 k220 + 4 k221 + 4 k222 - 4 k223 - 4 k226 + 4 k227 + 8 k228 - 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 - 4 k236 + 4 k237 + 8 k241 + 4 k242 - 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 - 8 k250 + 4 k252 - 4 k253 - 128 S251 = 8 + C253: - 16 k0 - 8 k21 - 8 k23 + 8 k29 + 8 k31 + 4 k34 + 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 - 8 k43 + 8 k45 + 4 k46 + 4 k47 - 4 k50 - 4 k51 + 4 k52 + 4 k53 + 8 k54 + 4 k56 + 4 k57 + 8 k58 - 4 k62 - 4 k63 + 4 k66 + 4 k67 - 4 k68 - 4 k69 - 8 k71 - 4 k72 - 4 k73 - 8 k75 + 4 k78 + 4 k79 - 4 k82 - 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 + 8 k90 - 8 k92 - 4 k94 - 4 k95 - 8 k99 + 8 k102 + 8 k105 - 8 k108 + 8 k115 - 8 k116 - 8 k121 + 8 k126 + 4 k130 + 4 k131 - 4 k134 - 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 + 8 k142 + 8 k143 - 8 k145 - 4 k146 - 4 k147 + 4 k150 + 4 k151 + 4 k152 + 4 k153 + 8 k154 - 4 k156 - 4 k157 - 8 k162 + 4 k164 + 4 k165 + 4 k166 + 4 k167 + 8 k171 - 4 k172 - 4 k173 - 4 k174 - 4 k175 + 8 k177 - 4 k180 - 4 k181 - 4 k182 - 4 k183 - 8 k184 + 4 k188 + 4 k189 + 4 k190 + 4 k191 + 4 k196 + 4 k197 + 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 - 4 k205 - 4 k206 - 4 k207 + 8 k209 + 8 k210 - 4 k212 - 4 k213 - 4 k214 - 4 k215 + 4 k220 + 4 k221 + 4 k222 + 4 k223 + 4 k226 + 4 k227 - 8 k228 - 8 k229 - 4 k230 - 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 - 4 k242 - 4 k243 + 4 k246 + 4 k247 + 4 k248 + 4 k249 + 8 k250 - 4 k252 - 4 k253 - 128 S252 = 8 + C254: - 16 k0 + 8 k21 + 8 k23 - 8 k29 - 8 k31 + 4 k34 - 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 + 8 k43 - 8 k45 + 4 k46 - 4 k47 - 4 k50 + 4 k51 + 4 k52 - 4 k53 + 8 k54 + 4 k56 - 4 k57 + 8 k58 - 4 k62 + 4 k63 + 4 k66 - 4 k67 - 4 k68 + 4 k69 + 8 k71 - 4 k72 + 4 k73 + 8 k75 + 4 k78 - 4 k79 - 4 k82 + 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 + 8 k90 - 8 k92 - 4 k94 + 4 k95 + 8 k99 + 8 k102 - 8 k105 - 8 k108 - 8 k115 - 8 k116 + 8 k121 + 8 k126 + 4 k130 - 4 k131 - 4 k134 + 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 + 8 k142 - 8 k143 + 8 k145 - 4 k146 + 4 k147 + 4 k150 - 4 k151 + 4 k152 - 4 k153 + 8 k154 - 4 k156 + 4 k157 - 8 k162 + 4 k164 - 4 k165 + 4 k166 - 4 k167 - 8 k171 - 4 k172 + 4 k173 - 4 k174 + 4 k175 - 8 k177 - 4 k180 + 4 k181 - 4 k182 + 4 k183 - 8 k184 + 4 k188 - 4 k189 + 4 k190 - 4 k191 + 4 k196 - 4 k197 + 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 + 4 k205 - 4 k206 + 4 k207 - 8 k209 + 8 k210 - 4 k212 + 4 k213 - 4 k214 + 4 k215 + 4 k220 - 4 k221 + 4 k222 - 4 k223 + 4 k226 - 4 k227 - 8 k228 + 8 k229 - 4 k230 + 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 - 4 k242 + 4 k243 + 4 k246 - 4 k247 + 4 k248 - 4 k249 + 8 k250 - 4 k252 + 4 k253 - 128 S253 = 8 + C255: - 16 k0 - 8 k21 + 8 k23 + 8 k29 - 8 k31 - 4 k34 - 4 k35 - 4 k36 - 4 k37 - 4 k40 - 4 k41 + 8 k43 + 8 k45 - 4 k46 - 4 k47 + 4 k50 + 4 k51 + 4 k52 + 4 k53 - 8 k54 + 4 k56 + 4 k57 - 8 k58 + 4 k62 + 4 k63 - 4 k66 - 4 k67 - 4 k68 - 4 k69 + 8 k71 - 4 k72 - 4 k73 + 8 k75 - 4 k78 - 4 k79 + 4 k82 + 4 k83 + 4 k84 + 4 k85 + 4 k88 + 4 k89 - 8 k90 - 8 k92 + 4 k94 + 4 k95 + 8 k99 - 8 k102 + 8 k105 - 8 k108 - 8 k115 - 8 k116 - 8 k121 - 8 k126 - 4 k130 - 4 k131 + 4 k134 + 4 k135 - 4 k136 - 4 k137 + 4 k140 + 4 k141 - 8 k142 - 8 k143 - 8 k145 + 4 k146 + 4 k147 - 4 k150 - 4 k151 + 4 k152 + 4 k153 - 8 k154 - 4 k156 - 4 k157 + 8 k162 + 4 k164 + 4 k165 - 4 k166 - 4 k167 - 8 k171 - 4 k172 - 4 k173 + 4 k174 + 4 k175 + 8 k177 - 4 k180 - 4 k181 + 4 k182 + 4 k183 - 8 k184 + 4 k188 + 4 k189 - 4 k190 - 4 k191 + 4 k196 + 4 k197 - 4 k198 - 4 k199 + 8 k200 - 8 k203 - 4 k204 - 4 k205 + 4 k206 + 4 k207 + 8 k209 - 8 k210 - 4 k212 - 4 k213 + 4 k214 + 4 k215 + 4 k220 + 4 k221 - 4 k222 - 4 k223 - 4 k226 - 4 k227 - 8 k228 - 8 k229 + 4 k230 + 4 k231 - 4 k232 - 4 k233 + 4 k236 + 4 k237 - 8 k241 + 4 k242 + 4 k243 - 4 k246 - 4 k247 + 4 k248 + 4 k249 - 8 k250 - 4 k252 - 4 k253 - 128 S254 = -56 + C256: - 16 k0 + 8 k21 - 8 k23 - 8 k29 + 8 k31 - 4 k34 + 4 k35 - 4 k36 + 4 k37 - 4 k40 + 4 k41 - 8 k43 - 8 k45 - 4 k46 + 4 k47 + 4 k50 - 4 k51 + 4 k52 - 4 k53 - 8 k54 + 4 k56 - 4 k57 - 8 k58 + 4 k62 - 4 k63 - 4 k66 + 4 k67 - 4 k68 + 4 k69 - 8 k71 - 4 k72 + 4 k73 - 8 k75 - 4 k78 + 4 k79 + 4 k82 - 4 k83 + 4 k84 - 4 k85 + 4 k88 - 4 k89 - 8 k90 - 8 k92 + 4 k94 - 4 k95 - 8 k99 - 8 k102 - 8 k105 - 8 k108 + 8 k115 - 8 k116 + 8 k121 - 8 k126 - 4 k130 + 4 k131 + 4 k134 - 4 k135 - 4 k136 + 4 k137 + 4 k140 - 4 k141 - 8 k142 + 8 k143 + 8 k145 + 4 k146 - 4 k147 - 4 k150 + 4 k151 + 4 k152 - 4 k153 - 8 k154 - 4 k156 + 4 k157 + 8 k162 + 4 k164 - 4 k165 - 4 k166 + 4 k167 + 8 k171 - 4 k172 + 4 k173 + 4 k174 - 4 k175 - 8 k177 - 4 k180 + 4 k181 + 4 k182 - 4 k183 - 8 k184 + 4 k188 - 4 k189 - 4 k190 + 4 k191 + 4 k196 - 4 k197 - 4 k198 + 4 k199 + 8 k200 + 8 k203 - 4 k204 + 4 k205 + 4 k206 - 4 k207 - 8 k209 - 8 k210 - 4 k212 + 4 k213 + 4 k214 - 4 k215 + 4 k220 - 4 k221 - 4 k222 + 4 k223 - 4 k226 + 4 k227 - 8 k228 + 8 k229 + 4 k230 - 4 k231 - 4 k232 + 4 k233 + 4 k236 - 4 k237 + 8 k241 + 4 k242 - 4 k243 - 4 k246 + 4 k247 + 4 k248 - 4 k249 - 8 k250 - 4 k252 + 4 k253 - 128 S255 = -56 + C257: S0 + S1 + S2 + S3 + S4 + S5 + S6 + S7 + S8 + S9 + S10 + S11 + S12 + S13 + S14 + S15 = 1 + C258: S16 + S17 + S18 + S19 + S20 + S21 + S22 + S23 + S24 + S25 + S26 + S27 + S28 + S29 + S30 + S31 = 1 + C259: S32 + S33 + S34 + S35 + S36 + S37 + S38 + S39 + S40 + S41 + S42 + S43 + S44 + S45 + S46 + S47 = 1 + C260: S48 + S49 + S50 + S51 + S52 + S53 + S54 + S55 + S56 + S57 + S58 + S59 + S60 + S61 + S62 + S63 = 1 + C261: S64 + S65 + S66 + S67 + S68 + S69 + S70 + S71 + S72 + S73 + S74 + S75 + S76 + S77 + S78 + S79 = 1 + C262: S80 + S81 + S82 + S83 + S84 + S85 + S86 + S87 + S88 + S89 + S90 + S91 + S92 + S93 + S94 + S95 = 1 + C263: S96 + S97 + S98 + S99 + S100 + S101 + S102 + S103 + S104 + S105 + S106 + S107 + S108 + S109 + S110 + S111 = 1 + C264: S112 + S113 + S114 + S115 + S116 + S117 + S118 + S119 + S120 + S121 + S122 + S123 + S124 + S125 + S126 + S127 = 1 + C265: S128 + S129 + S130 + S131 + S132 + S133 + S134 + S135 + S136 + S137 + S138 + S139 + S140 + S141 + S142 + S143 = 1 + C266: S144 + S145 + S146 + S147 + S148 + S149 + S150 + S151 + S152 + S153 + S154 + S155 + S156 + S157 + S158 + S159 = 1 + C267: S160 + S161 + S162 + S163 + S164 + S165 + S166 + S167 + S168 + S169 + S170 + S171 + S172 + S173 + S174 + S175 = 1 + C268: S176 + S177 + S178 + S179 + S180 + S181 + S182 + S183 + S184 + S185 + S186 + S187 + S188 + S189 + S190 + S191 = 1 + C269: S192 + S193 + S194 + S195 + S196 + S197 + S198 + S199 + S200 + S201 + S202 + S203 + S204 + S205 + S206 + S207 = 1 + C270: S208 + S209 + S210 + S211 + S212 + S213 + S214 + S215 + S216 + S217 + S218 + S219 + S220 + S221 + S222 + S223 = 1 + C271: S224 + S225 + S226 + S227 + S228 + S229 + S230 + S231 + S232 + S233 + S234 + S235 + S236 + S237 + S238 + S239 = 1 + C272: S240 + S241 + S242 + S243 + S244 + S245 + S246 + S247 + S248 + S249 + S250 + S251 + S252 + S253 + S254 + S255 = 1 + C273: S0 + S16 + S32 + S48 + S64 + S80 + S96 + S112 + S128 + S144 + S160 + S176 + S192 + S208 + S224 + S240 = 1 + C274: S1 + S17 + S33 + S49 + S65 + S81 + S97 + S113 + S129 + S145 + S161 + S177 + S193 + S209 + S225 + S241 = 1 + C275: S2 + S18 + S34 + S50 + S66 + S82 + S98 + S114 + S130 + S146 + S162 + S178 + S194 + S210 + S226 + S242 = 1 + C276: S3 + S19 + S35 + S51 + S67 + S83 + S99 + S115 + S131 + S147 + S163 + S179 + S195 + S211 + S227 + S243 = 1 + C277: S4 + S20 + S36 + S52 + S68 + S84 + S100 + S116 + S132 + S148 + S164 + S180 + S196 + S212 + S228 + S244 = 1 + C278: S5 + S21 + S37 + S53 + S69 + S85 + S101 + S117 + S133 + S149 + S165 + S181 + S197 + S213 + S229 + S245 = 1 + C279: S6 + S22 + S38 + S54 + S70 + S86 + S102 + S118 + S134 + S150 + S166 + S182 + S198 + S214 + S230 + S246 = 1 + C280: S7 + S23 + S39 + S55 + S71 + S87 + S103 + S119 + S135 + S151 + S167 + S183 + S199 + S215 + S231 + S247 = 1 + C281: S8 + S24 + S40 + S56 + S72 + S88 + S104 + S120 + S136 + S152 + S168 + S184 + S200 + S216 + S232 + S248 = 1 + C282: S9 + S25 + S41 + S57 + S73 + S89 + S105 + S121 + S137 + S153 + S169 + S185 + S201 + S217 + S233 + S249 = 1 + C283: S10 + S26 + S42 + S58 + S74 + S90 + S106 + S122 + S138 + S154 + S170 + S186 + S202 + S218 + S234 + S250 = 1 + C284: S11 + S27 + S43 + S59 + S75 + S91 + S107 + S123 + S139 + S155 + S171 + S187 + S203 + S219 + S235 + S251 = 1 + C285: S12 + S28 + S44 + S60 + S76 + S92 + S108 + S124 + S140 + S156 + S172 + S188 + S204 + S220 + S236 + S252 = 1 + C286: S13 + S29 + S45 + S61 + S77 + S93 + S109 + S125 + S141 + S157 + S173 + S189 + S205 + S221 + S237 + S253 = 1 + C287: S14 + S30 + S46 + S62 + S78 + S94 + S110 + S126 + S142 + S158 + S174 + S190 + S206 + S222 + S238 + S254 = 1 + C288: S15 + S31 + S47 + S63 + S79 + S95 + S111 + S127 + S143 + S159 + S175 + S191 + S207 + S223 + S239 + S255 = 1 +Bounds +Binaries + k0 k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13 k14 k15 k16 k17 k18 k19 k20 k21 k22 k23 k24 k25 k26 k27 k28 k29 k30 k31 k32 k33 k34 k35 k36 k37 k38 k39 k40 k41 k42 k43 k44 k45 k46 k47 k48 k49 k50 k51 k52 k53 k54 k55 k56 k57 k58 k59 k60 k61 k62 k63 k64 k65 k66 k67 k68 k69 k70 k71 k72 k73 k74 k75 k76 k77 k78 k79 k80 k81 k82 k83 k84 k85 k86 k87 k88 k89 k90 k91 k92 k93 k94 k95 k96 k97 k98 k99 k100 k101 k102 k103 k104 k105 k106 k107 k108 k109 k110 k111 k112 k113 k114 k115 k116 k117 k118 k119 k120 k121 k122 k123 k124 k125 k126 k127 k128 k129 k130 k131 k132 k133 k134 k135 k136 k137 k138 k139 k140 k141 k142 k143 k144 k145 k146 k147 k148 k149 k150 k151 k152 k153 k154 k155 k156 k157 k158 k159 k160 k161 k162 k163 k164 k165 k166 k167 k168 k169 k170 k171 k172 k173 k174 k175 k176 k177 k178 k179 k180 k181 k182 k183 k184 k185 k186 k187 k188 k189 k190 k191 k192 k193 k194 k195 k196 k197 k198 k199 k200 k201 k202 k203 k204 k205 k206 k207 k208 k209 k210 k211 k212 k213 k214 k215 k216 k217 k218 k219 k220 k221 k222 k223 k224 k225 k226 k227 k228 k229 k230 k231 k232 k233 k234 k235 k236 k237 k238 k239 k240 k241 k242 k243 k244 k245 k246 k247 k248 k249 k250 k251 k252 k253 k254 k255 S0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58 S59 S60 S61 S62 S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92 S93 S94 S95 S96 S97 S98 S99 S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115 S116 S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131 S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145 S146 S147 S148 S149 S150 S151 S152 S153 S154 S155 S156 S157 S158 S159 S160 S161 S162 S163 S164 S165 S166 S167 S168 S169 S170 S171 S172 S173 S174 S175 S176 S177 S178 S179 S180 S181 S182 S183 S184 S185 S186 S187 S188 S189 S190 S191 S192 S193 S194 S195 S196 S197 S198 S199 S200 S201 S202 S203 S204 S205 S206 S207 S208 S209 S210 S211 S212 S213 S214 S215 S216 S217 S218 S219 S220 S221 S222 S223 S224 S225 S226 S227 S228 S229 S230 S231 S232 S233 S234 S235 S236 S237 S238 S239 S240 S241 S242 S243 S244 S245 S246 S247 S248 S249 S250 S251 S252 S253 S254 S255 +End \ No newline at end of file diff --git a/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj new file mode 100644 index 0000000000000000000000000000000000000000..bfda30f000ce152c4a0bf7bb677d18d74f4959c0 --- /dev/null +++ b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj @@ -0,0 +1,144 @@ + + + + + Debug + Win32 + + + Release + Win32 + + + Debug + x64 + + + Release + x64 + + + + 16.0 + Win32Proj + {7f02e7ef-9686-4ec4-9490-6c7f809d3271} + FromDDT2Sbox + 10.0 + + + + Application + true + v143 + Unicode + + + Application + false + v143 + true + Unicode + + + Application + true + v143 + Unicode + + + Application + false + v143 + true + Unicode + + + + + + + + + + + + + + + + + + + + + + Level3 + true + WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + + + Console + true + + + + + Level3 + true + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + + + Console + true + true + true + + + + + Level3 + true + _DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + D:\gurobi951\win64\include + + + Console + true + D:\gurobi951\win64\lib + gurobi95.lib;gurobi_c++mdd2019.lib;$(CoreLibraryDependencies);%(AdditionalDependencies) + + + + + Level3 + true + true + true + NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + + + Console + true + true + true + + + + + true + + + true + false + + + + + + \ No newline at end of file diff --git a/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.filters b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.filters new file mode 100644 index 0000000000000000000000000000000000000000..717313c6fed17eb6ea6eeb7b77b31fbb76bba453 --- /dev/null +++ b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.filters @@ -0,0 +1,25 @@ + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + cpp;c;cc;cxx;c++;cppm;ixx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;h++;hm;inl;inc;ipp;xsd + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + + + 源文件 + + + 源文件 + + + \ No newline at end of file diff --git a/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.user b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.user new file mode 100644 index 0000000000000000000000000000000000000000..0f14913f3c72094bb7b1e695e153ade04b17d5b0 --- /dev/null +++ b/lab-iisec/DDT2Sbox/FromDDT2Sbox.vcxproj.user @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/lab-iisec/DDT2Sbox/code_test.cpp b/lab-iisec/DDT2Sbox/code_test.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c263d1afd7c38be6a89f3e058ab217c4616f6fec --- /dev/null +++ b/lab-iisec/DDT2Sbox/code_test.cpp @@ -0,0 +1,124 @@ +#include "D:\ZHJN\code\ZJN_function.h" + +const int n = 2; +const int sbox_size = 4; //2^n +const int DDT_size = 16; //(2^n)^2 +const int LAT_size = 16; //(2^n)^2 + +int sbox[sbox_size] = { 2,0,3,1 }; +//int sbox[sbox_size] = { 0xC,0x5,0x6,0xB,0x9,0x0,0xA,0xD,0x3,0xE,0xF,0x8,0x4,0x7,0x1,0x2 }; +int DDT[DDT_size] = {}; +int LAT_Square[LAT_size] = { 0 }; +int LAT_Abs[LAT_size] = { 0 }; + +struct spectrum { //ֵṹ壬(a-b*ki),kiȡ0/1aΪconstantbΪcoefficient + int constant; + int coefficient; +}; + +void dec2bin_var(int a, int b[LAT_size]) +{ + for (int i = LAT_size - 1; i >= 0; i--){ + b[i] = a % 2; a = a / 2; + } +} + +int InnerProduct(int a, int b) +{ + int output = 0; + int aa[LAT_size] = { 0 }; + int bb[LAT_size] = { 0 }; + dec2bin_var(a, aa); + dec2bin_var(b, bb); + + for (int i = 0; i < LAT_size; i++){ + int k = aa[i] * bb[i]; + output = output ^ k; + } + return output; +} + +void DDT_gen(int sbox[], int DDT[], int n) { //nSУsbox[2^n]DDT[(2^n)^2] + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + int xor_in = i ^ j; + int xor_out = sbox[i] ^ sbox[j]; + DDT[sbox_size * xor_in + xor_out] += 1; + } + } +} + +void FourierHadamardTransform(int f[], int size) { //fΪֵ,sizeΪҪ任״СsizeҪ任ֵsizeFourier任ֵ + int scale = 1; + + while (scale < size) { + for (int i = 0; i < size; i = i + 2 * scale) { + for (int j = i; j < i + scale; j++) { + int x = f[j]; + int y = f[j + scale]; + f[j] = x + y; + f[j + scale] = x - y; + } + } + scale *= 2; + } +} + +int main() { + DDT_gen(sbox, DDT, n); //SУDDT + cout << "DDT:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << DDT[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl; + + //DDT׽fourier-hadamard任LAT׵ƽ + //_hat(a,b)=4((a,b))^2 + memcpy(LAT_Square, DDT, (DDT_size) * sizeof(int)); + FourierHadamardTransform(LAT_Square, LAT_size); + for (int i = 0; i < LAT_size; i++) { + LAT_Square[i] = LAT_Square[i] / 4; + } + cout << "LAT_Square:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << LAT_Square[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl; + + //LAT׵ƽõLAT׵ľֵ + memcpy(LAT_Abs, LAT_Square, (LAT_size) * sizeof(int)); + for (int i = 0; i < LAT_size; i++) { + LAT_Abs[i] = sqrt(LAT_Abs[i]); + } + cout << "LAT_Abs:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << LAT_Abs[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl; + + spectrum LAT[LAT_size]; + for (int i = 0; i < LAT_size; i++) { + LAT[i].constant = LAT_Abs[i]; + LAT[i].coefficient = LAT_Abs[i] * 2; + } + + for (int i = 0; i < LAT_size; i++) { + int count = 0; + for (int j = 0; j < LAT_size; j++) { + if (LAT_Abs[j] != 0) { + int index = pow(-1, InnerProduct(i, j)); + count += index * LAT[j].constant; + if (index == 1) { + cout <<" - " << LAT[j].coefficient << " " << "k" << j << " "; + } + if (index == -1) { + cout <<" + " << LAT[j].coefficient << " " << "k" << j << " "; + } + } + } + cout << " - " << pow(2, 2 * n - 1) << " S" << i << " = " << (-1) * count << endl; + } +} diff --git a/lab-iisec/DDT2Sbox/main.cpp b/lab-iisec/DDT2Sbox/main.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b7f51429883e8f81fbac84685cd1397b3cc926a7 --- /dev/null +++ b/lab-iisec/DDT2Sbox/main.cpp @@ -0,0 +1,251 @@ +#include "D:\ZHJN\code\ZJN_function.h" +#include "gurobi_c++.h" + +const int n = 4; +const int sbox_size = 16; //2^n +const int DDT_size = 256; //(2^n)^2 +const int LAT_size = 256; //(2^n)^2 + +//int sbox[sbox_size] = { 2,0,3,1 }; +int sbox[sbox_size] = { 0xC,0x5,0x6,0xB,0x9,0x0,0xA,0xD,0x3,0xE,0xF,0x8,0x4,0x7,0x1,0x2 }; +int DDT[DDT_size] = { 0 }; +//int DDT[DDT_size] = { +// 16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, +// 0,0,2,2,0,2,0,0,2,2,0,2,2,0,0,2, +// 0,2,0,2,0,2,2,0,0,0,2,0,2,2,2,0, +// 0,2,0,2,2,0,2,2,0,0,2,2,0,0,0,2, +// 0,0,2,0,0,2,2,2,0,2,2,0,2,0,2,0, +// 0,2,2,0,2,0,0,0,0,2,2,2,2,2,0,0, +// 0,2,2,0,0,0,2,2,2,0,0,2,2,0,2,0, +// 0,0,2,2,2,2,2,0,2,0,0,0,2,0,0,2, +// 0,2,0,2,0,0,2,2,0,2,0,2,0,2,0,2, +// 0,0,2,2,2,0,2,0,2,2,2,0,2,0,0,0, +// 0,0,2,2,0,2,0,2,2,0,2,0,0,2,2,0, +// 0,2,0,0,2,0,0,0,2,2,2,2,0,2,0,2, +// 0,0,4,0,0,0,0,2,2,2,2,0,0,2,2,2, +// 0,2,0,2,2,0,0,2,0,0,2,2,0,2,2,0, +// 0,0,2,2,0,0,2,2,2,2,0,0,2,2,0,0, +// 0,2,2,0,0,2,0,2,2,0,4,0,2,0,0,0 }; +int LAT_Square[LAT_size] = { 0 }; +int LAT_Abs[LAT_size] = { 0 }; + +int constrain_count = 1; + +struct spectrum { //ֵṹ壬(a-b*ki),kiȡ0/1aΪconstantbΪcoefficient + int constant; + int coefficient; +}; + +void dec2bin_var(int a, int b[20]) +{ + for (int i = 20 - 1; i >= 0; i--) { + b[i] = a % 2; a = a / 2; + } +} + +int InnerProduct(int a, int b) +{ + int output = 0; + int aa[20] = { 0 }; + int bb[20] = { 0 }; + dec2bin_var(a, aa); + dec2bin_var(b, bb); + + for (int i = 0; i < 20; i++) { + int k = aa[i] * bb[i]; + output = output ^ k; + } + return output; +} + +void DDT_gen(int sbox[], int DDT[], int n) { //nSУsbox[2^n]DDT[(2^n)^2] + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + int xor_in = i ^ j; + int xor_out = sbox[i] ^ sbox[j]; + DDT[sbox_size * xor_in + xor_out] += 1; + } + } +} + +void FourierHadamardTransform(int f[], int size) { //fΪֵ,sizeΪҪ任״СsizeҪ任ֵsizeFourier任ֵ + int scale = 1; + + while (scale < size) { + for (int i = 0; i < size; i = i + 2 * scale) { + for (int j = i; j < i + scale; j++) { + int x = f[j]; + int y = f[j + scale]; + f[j] = x + y; + f[j + scale] = x - y; + } + } + scale *= 2; + } +} + +void LPfile_Start() { + ofstream file("DDT2Sbox.lp", ios::trunc); + file.close(); + + ofstream write("DDT2Sbox.lp", ios::app); + if (!write.is_open()) + { + cout << "OPEN FAILED1!" << endl; + } + write << "Minimize" << endl << " "; + write << "S0" << endl; + write.close(); +} + +void LPFile_Constrains_LATToSbox() { + ofstream write("DDT2Sbox.lp", ios::app); + if (!write.is_open()) { + cout << "OPEN FAILED2!" << endl; + } + write << "Subject To" << endl; + + //һԼ_hat(a,b)=2^(2n-1)(a,b) (a,b)ΪSѡS(a)=bʱΪ1Ϊ0 + //(a,b)ΪʽS(16*a+b) + + spectrum LAT[LAT_size]; + for (int i = 0; i < LAT_size; i++) { + LAT[i].constant = LAT_Abs[i]; + LAT[i].coefficient = LAT_Abs[i] * 2; + } + + for (int i = 0; i < LAT_size; i++) { + write << " C" << constrain_count << ":"; + int count = 0; + for (int j = 0; j < LAT_size; j++) { + if (LAT_Abs[j] != 0) { + int index = pow(-1, InnerProduct(i, j)); + count += index * LAT[j].constant; + if (index == 1) { + write << " - " << LAT[j].coefficient << " " << "k" << j; + } + if (index == -1) { + write << " + " << LAT[j].coefficient << " " << "k" << j; + } + } + } + write << " - " << pow(2, 2 * n - 1) << " S" << i << " = " << (-1) * count << endl; + constrain_count++; + } + + //ڶԼƽԼ + for (int i = 0; i < sbox_size; i++) { + write << " C" << constrain_count << ": "; + for (int j = 0; j < sbox_size; j++) { + write << "S" << i * sbox_size + j; + if (j != sbox_size - 1) + write << " + "; + } + write << " = 1" << endl; + constrain_count++; + } + for (int i = 0; i < sbox_size; i++) { + write << " C" << constrain_count << ": "; + for (int j = 0; j < sbox_size; j++) { + write << "S" << j * sbox_size + i; + if (j != sbox_size - 1) + write << " + "; + } + write << " = 1" << endl; + constrain_count++; + } + + write.close(); +} + +void LPFile_VariableType() { + ofstream write("DDT2Sbox.lp", ios::app); + if (!write.is_open()) { + cout << "OPEN FAILED3!" << endl; + } + + write << "Bounds" << endl; + write << "Binaries" << endl << " "; + for (int i = 0; i < LAT_size; i++) { + write << "k" << i << " "; + } + for (int i = 0; i < LAT_size; i++) { + write << "S" << i << " "; + } + write << endl << "End"; + write.close(); +} + +int gurobi_solve() +{ + try + { + GRBEnv env = GRBEnv(); + //GRBModel m = GRBModel(env, "10_round_present_find_active_sbox.lp"); + GRBModel m = GRBModel(env, "DDT2Sbox.lp"); + + m.optimize(); + m.write("output.sol"); + int status = m.get(GRB_IntAttr_Status); + if (status == 3) + { + cout << "ģͲ" << endl; + } + else cout << "ģͿɽ" << endl; + cout << "status" << status << endl; + + } + catch (GRBException e) + { + cout << "Error code = " << e.getErrorCode() << endl; + cout << e.getMessage() << endl; + } + catch (...) + { + cout << "Exception during optimization" << endl; + } + return 0; +} + +int main() { + DDT_gen(sbox, DDT, n); //SУDDT + /*cout << "DDT:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << DDT[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl;*/ + + //DDT׽fourier-hadamard任LAT׵ƽ + //_hat(a,b)=4((a,b))^2 + memcpy(LAT_Square, DDT, (DDT_size) * sizeof(int)); + FourierHadamardTransform(LAT_Square, LAT_size); + for (int i = 0; i < LAT_size; i++) { + LAT_Square[i] = LAT_Square[i] / 4; + } + /*cout << "LAT_Square:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << LAT_Square[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl;*/ + + //LAT׵ƽõLAT׵ľֵ + memcpy(LAT_Abs, LAT_Square, (LAT_size) * sizeof(int)); + for (int i = 0; i < LAT_size; i++) { + LAT_Abs[i] = sqrt(LAT_Abs[i]); + } + /*cout << "LAT_Abs:" << endl; + for (int i = 0; i < sbox_size; i++) { + for (int j = 0; j < sbox_size; j++) { + cout << setw(2) << LAT_Abs[sbox_size * i + j] << " "; + }cout << endl; + }cout << endl;*/ + + + LPfile_Start(); + LPFile_Constrains_LATToSbox(); + LPFile_VariableType(); + + gurobi_solve(); +} \ No newline at end of file diff --git a/lab-iisec/DDT2Sbox/output.sol b/lab-iisec/DDT2Sbox/output.sol new file mode 100644 index 0000000000000000000000000000000000000000..1b4a65112544aac8d102785e0c95226103e734b4 --- /dev/null +++ b/lab-iisec/DDT2Sbox/output.sol @@ -0,0 +1,513 @@ +# Objective value = 0 +S0 0 +k0 0 +k21 1 +k23 0 +k29 1 +k31 1 +k34 0 +k35 0 +k36 1 +k37 1 +k40 1 +k41 0 +k43 0 +k45 0 +k46 0 +k47 1 +k50 1 +k51 1 +k52 1 +k53 0 +k54 1 +k56 1 +k57 0 +k58 0 +k62 1 +k63 1 +k66 1 +k67 0 +k68 1 +k69 1 +k71 1 +k72 0 +k73 0 +k75 1 +k78 0 +k79 1 +k82 0 +k83 1 +k84 0 +k85 1 +k88 0 +k89 0 +k90 0 +k92 1 +k94 0 +k95 0 +k99 0 +k102 1 +k105 1 +k108 1 +k115 0 +k116 0 +k121 0 +k126 1 +k130 1 +k131 0 +k134 1 +k135 0 +k136 1 +k137 0 +k140 0 +k141 1 +k142 0 +k143 0 +k145 1 +k146 1 +k147 1 +k150 1 +k151 0 +k152 0 +k153 0 +k154 1 +k156 1 +k157 0 +k162 0 +k164 0 +k165 0 +k166 1 +k167 0 +k171 1 +k172 0 +k173 0 +k174 0 +k175 1 +k177 1 +k180 0 +k181 0 +k182 0 +k183 1 +k184 1 +k188 1 +k189 1 +k190 0 +k191 1 +k196 1 +k197 1 +k198 0 +k199 0 +k200 1 +k203 1 +k204 1 +k205 0 +k206 1 +k207 0 +k209 0 +k210 1 +k212 0 +k213 0 +k214 0 +k215 0 +k220 1 +k221 0 +k222 0 +k223 1 +k226 1 +k227 1 +k228 0 +k229 1 +k230 1 +k231 1 +k232 1 +k233 1 +k236 0 +k237 0 +k241 1 +k242 1 +k243 0 +k246 0 +k247 0 +k248 0 +k249 1 +k250 0 +k252 0 +k253 0 +S1 0 +S2 0 +S3 0 +S4 0 +S5 0 +S6 0 +S7 0 +S8 0 +S9 0 +S10 0 +S11 1 +S12 0 +S13 0 +S14 0 +S15 0 +S16 0 +S17 0 +S18 0 +S19 0 +S20 0 +S21 0 +S22 0 +S23 0 +S24 0 +S25 0 +S26 0 +S27 0 +S28 1 +S29 0 +S30 0 +S31 0 +S32 0 +S33 0 +S34 0 +S35 0 +S36 0 +S37 0 +S38 1 +S39 0 +S40 0 +S41 0 +S42 0 +S43 0 +S44 0 +S45 0 +S46 0 +S47 0 +S48 0 +S49 0 +S50 0 +S51 0 +S52 0 +S53 0 +S54 0 +S55 0 +S56 0 +S57 0 +S58 0 +S59 0 +S60 0 +S61 0 +S62 0 +S63 1 +S64 0 +S65 0 +S66 0 +S67 0 +S68 0 +S69 0 +S70 0 +S71 0 +S72 0 +S73 0 +S74 0 +S75 0 +S76 0 +S77 1 +S78 0 +S79 0 +S80 1 +S81 0 +S82 0 +S83 0 +S84 0 +S85 0 +S86 0 +S87 0 +S88 0 +S89 0 +S90 0 +S91 0 +S92 0 +S93 0 +S94 0 +S95 0 +S96 0 +S97 0 +S98 0 +S99 1 +S100 0 +S101 0 +S102 0 +S103 0 +S104 0 +S105 0 +S106 0 +S107 0 +S108 0 +S109 0 +S110 0 +S111 0 +S112 0 +S113 0 +S114 0 +S115 0 +S116 0 +S117 0 +S118 0 +S119 0 +S120 0 +S121 0 +S122 1 +S123 0 +S124 0 +S125 0 +S126 0 +S127 0 +S128 0 +S129 0 +S130 0 +S131 0 +S132 1 +S133 0 +S134 0 +S135 0 +S136 0 +S137 0 +S138 0 +S139 0 +S140 0 +S141 0 +S142 0 +S143 0 +S144 0 +S145 0 +S146 0 +S147 0 +S148 0 +S149 0 +S150 0 +S151 1 +S152 0 +S153 0 +S154 0 +S155 0 +S156 0 +S157 0 +S158 0 +S159 0 +S160 0 +S161 1 +S162 0 +S163 0 +S164 0 +S165 0 +S166 0 +S167 0 +S168 0 +S169 0 +S170 0 +S171 0 +S172 0 +S173 0 +S174 0 +S175 0 +S176 0 +S177 0 +S178 1 +S179 0 +S180 0 +S181 0 +S182 0 +S183 0 +S184 0 +S185 0 +S186 0 +S187 0 +S188 0 +S189 0 +S190 0 +S191 0 +S192 0 +S193 0 +S194 0 +S195 0 +S196 0 +S197 0 +S198 0 +S199 0 +S200 0 +S201 0 +S202 0 +S203 0 +S204 0 +S205 0 +S206 1 +S207 0 +S208 0 +S209 0 +S210 0 +S211 0 +S212 0 +S213 0 +S214 0 +S215 0 +S216 0 +S217 1 +S218 0 +S219 0 +S220 0 +S221 0 +S222 0 +S223 0 +S224 0 +S225 0 +S226 0 +S227 0 +S228 0 +S229 0 +S230 0 +S231 0 +S232 1 +S233 0 +S234 0 +S235 0 +S236 0 +S237 0 +S238 0 +S239 0 +S240 0 +S241 0 +S242 0 +S243 0 +S244 0 +S245 1 +S246 0 +S247 0 +S248 0 +S249 0 +S250 0 +S251 0 +S252 0 +S253 0 +S254 0 +S255 0 +k1 0 +k2 0 +k3 0 +k4 0 +k5 0 +k6 0 +k7 0 +k8 0 +k9 0 +k10 0 +k11 0 +k12 0 +k13 0 +k14 0 +k15 0 +k16 0 +k17 0 +k18 0 +k19 0 +k20 0 +k22 0 +k24 0 +k25 0 +k26 0 +k27 0 +k28 0 +k30 0 +k32 0 +k33 0 +k38 0 +k39 0 +k42 0 +k44 0 +k48 0 +k49 0 +k55 0 +k59 0 +k60 0 +k61 0 +k64 0 +k65 0 +k70 0 +k74 0 +k76 0 +k77 0 +k80 0 +k81 0 +k86 0 +k87 0 +k91 0 +k93 0 +k96 0 +k97 0 +k98 0 +k100 0 +k101 0 +k103 0 +k104 0 +k106 0 +k107 0 +k109 0 +k110 0 +k111 0 +k112 0 +k113 0 +k114 0 +k117 0 +k118 0 +k119 0 +k120 0 +k122 0 +k123 0 +k124 0 +k125 0 +k127 0 +k128 0 +k129 0 +k132 0 +k133 0 +k138 0 +k139 0 +k144 0 +k148 0 +k149 0 +k155 0 +k158 0 +k159 0 +k160 0 +k161 0 +k163 0 +k168 0 +k169 0 +k170 0 +k176 0 +k178 0 +k179 0 +k185 0 +k186 0 +k187 0 +k192 0 +k193 0 +k194 0 +k195 0 +k201 0 +k202 0 +k208 0 +k211 0 +k216 0 +k217 0 +k218 0 +k219 0 +k224 0 +k225 0 +k234 0 +k235 0 +k238 0 +k239 0 +k240 0 +k244 0 +k245 0 +k251 0 +k254 0 +k255 0 diff --git a/lab-iisec/DDT2Sbox/output1.sol b/lab-iisec/DDT2Sbox/output1.sol new file mode 100644 index 0000000000000000000000000000000000000000..b3a9fdf7144f5a70bc044b929eafe7956c1cc83f --- /dev/null +++ b/lab-iisec/DDT2Sbox/output1.sol @@ -0,0 +1,34 @@ +# Objective value = 4 +S0 1 +S1 0 +S2 0 +S3 0 +S4 0 +S5 0 +S6 1 +S7 0 +S8 0 +S9 1 +S10 0 +S11 0 +S12 0 +S13 0 +S14 0 +S15 1 +0 0 +k0 0 +k6 0 +k9 0 +k15 0 +k1 0 +k2 0 +k3 0 +k4 0 +k5 0 +k7 0 +k8 0 +k10 0 +k11 0 +k12 0 +k13 0 +k14 0 diff --git a/lab-iisec/DDT2Sbox/test.lp b/lab-iisec/DDT2Sbox/test.lp new file mode 100644 index 0000000000000000000000000000000000000000..a433d08e041318f7212bb0fff9487b684cfdf2ea --- /dev/null +++ b/lab-iisec/DDT2Sbox/test.lp @@ -0,0 +1,31 @@ +Minimize + S0 + S1 + S2 + S3 + S4 + S5 + S6 + S7 + S8 + S9 + S10 + S11 + S12 + S13 + S14 + S15 + 0 +Subject To + C1: -4 k0 - 4 k6 - 4 k9 - 4 k15 - 8 S0 = -8 + C2: -4 k0 - 4 k6 + 4 k9 + 4 k15 - 8 S1 = 0 + C3: -4 k0 + 4 k6 - 4 k9 + 4 k15 - 8 S2 = 0 + C4: -4 k0 + 4 k6 + 4 k9 - 4 k15 - 8 S3 = 0 + C5: -4 k0 + 4 k6 - 4 k9 + 4 k15 - 8 S4 = 0 + C6: -4 k0 + 4 k6 + 4 k9 - 4 k15 - 8 S5 = 0 + C7: -4 k0 - 4 k6 - 4 k9 - 4 k15 - 8 S6 = -8 + C8: -4 k0 - 4 k6 + 4 k9 + 4 k15 - 8 S7 = 0 + C9: -4 k0 - 4 k6 + 4 k9 + 4 k15 - 8 S8 = 0 + C10: -4 k0 - 4 k6 - 4 k9 - 4 k15 - 8 S9 = -8 + C11: -4 k0 + 4 k6 + 4 k9 - 4 k15 - 8 S10 = 0 + C12: -4 k0 + 4 k6 - 4 k9 + 4 k15 - 8 S11 = 0 + C13: -4 k0 + 4 k6 + 4 k9 - 4 k15 - 8 S12 = 0 + C14: -4 k0 + 4 k6 - 4 k9 + 4 k15 - 8 S13 = 0 + C15: -4 k0 - 4 k6 + 4 k9 + 4 k15 - 8 S14 = 0 + C16: -4 k0 - 4 k6 - 4 k9 - 4 k15 - 8 S15 = -8 + C17: S0 + S1 + S2 + S3 = 1 + C18: S4 + S5 + S6 + S7 = 1 + C19: S8 + S9 + S10 + S11 = 1 + C20: S12 + S13 + S14 + S15 = 1 + C21: S0 + S4 + S8 + S12 = 1 + C22: S1 + S5 + S9 + S13 = 1 + C23: S2 + S6 + S10 + S14 = 1 + C24: S3 + S7 + S11 + S15 = 1 +Bounds +Binaries + k0 k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13 k14 k15 S0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 +End \ No newline at end of file diff --git "a/lab-iisec/DDT2Sbox/\350\257\264\346\230\216.md" "b/lab-iisec/DDT2Sbox/\350\257\264\346\230\216.md" new file mode 100644 index 0000000000000000000000000000000000000000..e00ab6b0a5fe8e6489926c26746077479c83a748 --- /dev/null +++ "b/lab-iisec/DDT2Sbox/\350\257\264\346\230\216.md" @@ -0,0 +1,6 @@ +DDT恢复S盒 + +1.DDT求LAT^2 +2.LAT^2开方设系数-1或1,求S盒 + +4bitS盒可求，6bit以上约束过大 \ No newline at end of file diff --git a/lab-iisec/IS407 final project/communicate.py b/lab-iisec/IS407 final project/communicate.py new file mode 100644 index 0000000000000000000000000000000000000000..8acd14dc8e3d9332a48bb2223863c66e3e382101 --- /dev/null +++ b/lab-iisec/IS407 final project/communicate.py @@ -0,0 +1,79 @@ +import hmac +from des import my_desctr +from rsa import my_rsa +from hmac import my_hmac, my_md5 +import random + +# 客户端1和客户端2分别生成各自的rsa公私钥密码 +n1, e1, d1 = my_rsa.get_n_e_d() +n2, e2, d2 = my_rsa.get_n_e_d() +# 作业报告中使用： +# n1, e1, d1 = 465846462803805331432241, 65537, 431172934176621077768273 +# n2, e2, d2 = 658957379509805858776759, 65537, 126830468057981231153825 +print('客户端1: 公钥: ({0}, {1}), 私钥: ({0}, {2})'.format(n1, e1, d1)) +print('客户端2: 公钥: ({0}, {1}), 私钥: ({0}, {2})'.format(n2, e2, d2)) +print() + +print('客户端1随机选择对称加密的key和iv') +key = '{:56b}'.format(random.randint(0, 2**56-1)).replace(' ', '0') +iv = '{:64b}'.format(random.randint(0, 2**64-1)).replace(' ', '0') +# 作业报告中使用： +# key = '10001111111011010111111010110111010100001010110011011000' +# iv = '0001111101011110001101101001001100000100101010000101110001111110' +print('key: ', key, '(2进制)') +print('iv: ', iv, '(2进制)') +print() +print('客户端1将对称加密des的key和iv用客户端2的rsa公钥加密后发送给客户端2') +print('同时, 客户端1用自己的rsa私钥加密key||iv的16位MD5作为数字签名, 再用客户端2的rsa公钥加密后发送给客户端2') +c1 = my_rsa.fast_mod(int(key,2), e2, n2) +c2 = my_rsa.fast_mod(int(iv,2), e2, n2) +ds = my_rsa.fast_mod(my_rsa.fast_mod(int(my_md5.md5(key+iv)[8:24], 16), d1, n1), e2, n2) +print('全部密文包含：') +print('加密后的key: ', c1, '(10进制)') +print('加密后的iv: ', c2, '(10进制)') +print('加密后的数字签名: ', ds, '(10进制)') +print() + +print('客户端2接受到全部密文, 用自己的rsa私钥解密, 并用客户端1的rsa公钥解密数字签名部分') +print('客户端2用得到的明文部分计算消息摘要, 验证这2个消息摘要是否相同') +m1 = my_rsa.fast_mod(c1, d2, n2) +m1 = '{:56b}'.format(m1).replace(' ', '0') +m2 = my_rsa.fast_mod(c2, d2, n2) +m2 = '{:64b}'.format(m2).replace(' ', '0') +h = my_md5.md5(m1+m2)[8:24] +deds = my_rsa.fast_mod(my_rsa.fast_mod(ds, d2, n2), e1, n1) +print('解密后的key: ', m1, '(2进制)') +print('解密后的iv: ', m2, '(2进制)') +print('两个摘要相同: ', deds == int(h, 16)) +print() +# print(m1==key) +# print(m2==iv) + +with open('msg/plain.txt', 'r', encoding='utf-8') as f: + input_ = f.read() +print('客户端1利用上述key和iv, 用des-ctr模式加密明文, 并利用key和密文计算HMAC') +print('明文: ', input_) +en = my_desctr.encrypt(key, iv, input_) +print('密文: ', en, '(16进制)') +with open('msg/encrypt.txt', 'w', encoding='utf-8') as f: + f.write(en) +hmac_ = my_hmac.hmac(key, en, my_md5.md5) +print('HMAC: ', hmac_, '(16进制)') +with open('msg/hmac.txt', 'w', encoding='utf-8') as f: + f.write(hmac_) + +with open('msg/hmac.txt', 'r', encoding='utf-8') as f: + hmac2 = f.read() +with open('msg/encrypt.txt', 'r', encoding='utf-8') as f: + input_ = f.read() +flag = my_hmac.hmac(key, input_, my_md5.md5)==hmac2 +print('客户端2接收到密文和HMAC, 利用key和密文计算HMAC并比较与接收到的是否相同') +print('比较结果：', flag) +if flag: + de = my_desctr.decrypt(key, iv, input_) + print('若相同, 解密得到明文：', de) + + with open('msg/decrypt.txt', 'w', encoding='utf-8') as f: + f.write(de) + + \ No newline at end of file diff --git a/lab-iisec/IS407 final project/des/__init__.py b/lab-iisec/IS407 final project/des/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-iisec/IS407 final project/des/des.py b/lab-iisec/IS407 final project/des/des.py new file mode 100644 index 0000000000000000000000000000000000000000..1e5e3fc155af407681584dc984614a5a091c75d7 --- /dev/null +++ b/lab-iisec/IS407 final project/des/des.py @@ -0,0 +1,44 @@ +import des.func as f + + +# 省略了初始代换 +def des(text, key, flag): + sub_keys = f.sub_keys(key) # 生成16个子密钥 + if flag == 1: + sub_keys.reverse() + L, R = text[:32], text[32:] #分为左右2部分 + for net in range(16): # 16轮迭代 + temp = R + temp = f.expand(temp) + temp = f.xor(temp, sub_keys[net]) + temp = f.s_box(temp) + temp = f.p_box(temp) + temp = f.xor(temp, L) + L, R = R, temp + return R + L + + +def des_input_plain(input_): + input_ = f.str2bin(input_) + n = len(input_) // 64 + mod = len(input_) % 64 + if mod != 0: + n = n + 1 + input_ = f.zero_fill(input_, mod, 64, 'r') + return input_, n + + +def des_input_cipher(input_): + input_ = f.hex2bin(input_) + n = len(input_) // 64 + return input_, n + + +def des_output_cipher(cipher_str): + # print("加密后16进制密文：", c.bin2hex(cipher_str)) + return f.bin2hex(cipher_str) + + +def des_output_plain(plain_str): + # print("解密后明文：", c.bin2str(plain_str)) + return f.bin2str(plain_str) diff --git a/lab-iisec/IS407 final project/des/func.py b/lab-iisec/IS407 final project/des/func.py new file mode 100644 index 0000000000000000000000000000000000000000..8ccfae2941f20ac192e0c51d8c7ccf4026cf5014 --- /dev/null +++ b/lab-iisec/IS407 final project/des/func.py @@ -0,0 +1,167 @@ +# E、S、P盒 +key_table_l_1 = [49, 42, 35, 28, 21, 14, 7, + 0, 50, 43, 36, 29, 22, 15, + 8, 1, 51, 44, 37, 30, 23, + 16, 9, 2, 52, 45, 38, 31] + +key_table_r_1 = [55, 48, 41, 34, 27, 20, 13, + 6, 54, 47, 40, 33, 26, 19, + 12, 5, 53, 46, 39, 32, 25, + 18, 11, 4, 24, 17, 10, 3] + +key_table_l_2 = [13, 16, 10, 23, 0, 4, 2, 27, 14, 5, 20, 9, + 22, 18, 11, 3, 25, 7, 15, 6, 26, 19, 12, 1] + +key_table_r_2 = [12, 23, 2, 8, 18, 26, 1, 11, 22, 16, 4, 19, + 15, 20, 10, 27, 5, 24, 17, 13, 21, 7, 0, 3] + +expand_table = [31, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7, 8, + 7, 8, 9, 10, 11, 12, 11, 12, 13, 14, 15, 16, + 15, 16, 17, 18, 19, 20, 19, 20, 21, 22, 23, 24, + 23, 24, 25, 26, 27, 28, 27, 28, 29, 30, 31, 0] + +s_boxes_table_1 = [14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7, + 0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8, + 4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0, + 15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13] + +s_boxes_table_2 = [15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10, + 3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5, + 0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15, + 13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9] + +s_boxes_table_3 = [10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8, + 13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1, + 13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7, + 1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12] + +s_boxes_table_4 = [7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15, + 13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9, + 10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4, + 3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14] + +s_boxes_table_5 = [2, 12, 4, 1, 7, 10, 11, 6, 8, 5, 3, 15, 13, 0, 14, 9, + 14, 11, 2, 12, 4, 7, 13, 1, 5, 0, 15, 10, 3, 9, 8, 6, + 4, 2, 1, 11, 10, 13, 7, 8, 15, 9, 12, 5, 6, 3, 0, 14, + 11, 8, 12, 7, 1, 14, 2, 13, 6, 15, 0, 9, 10, 4, 5, 3] + +s_boxes_table_6 = [12, 1, 10, 15, 9, 2, 6, 8, 0, 13, 3, 4, 14, 7, 5, 11, + 10, 15, 4, 2, 7, 12, 9, 5, 6, 1, 13, 14, 0, 11, 3, 8, + 9, 14, 15, 5, 2, 8, 12, 3, 7, 0, 4, 10, 1, 13, 11, 6, + 4, 3, 2, 12, 9, 5, 15, 10, 11, 14, 1, 7, 6, 0, 8, 13 ] + +s_boxes_table_7 = [4, 11, 2, 14, 15, 0, 8, 13, 3, 12, 9, 7, 5, 10, 6, 1, + 13, 0, 11, 7, 4, 9, 1, 10, 14, 3, 5, 12, 2, 15, 8, 6, + 1, 4, 11, 13, 12, 3, 7, 14, 10, 15, 6, 8, 0, 5, 9, 2, + 6, 11, 13, 8, 1, 4, 10, 7, 9, 5, 0, 15, 14, 2, 3, 12] + +s_boxes_table_8 = [13, 2, 8, 4, 6, 15, 11, 1, 10, 9, 3, 14, 5, 0, 12, 7, + 1, 15, 13, 8, 10, 3, 7, 4, 12, 5, 6, 11, 0, 14, 9, 2, + 7, 11, 4, 1, 9, 12, 14, 2, 0, 6, 10, 13, 15, 3, 5, 8, + 2, 1, 14, 7, 4, 10, 8, 13, 15, 12, 9, 0, 3, 5, 6, 11] + +s_boxes_table = [s_boxes_table_1, s_boxes_table_2, s_boxes_table_3, s_boxes_table_4, + s_boxes_table_5, s_boxes_table_6, s_boxes_table_7, s_boxes_table_8] + +p_box_table = [15, 6, 19, 20, 28, 11, 27, 16, 0, 14, 22, 25, 4, 17, 30, 9, + 1, 7, 23, 13, 31, 26, 2, 8, 18, 12, 29, 5, 21, 10, 3, 24] + +# 左移函数 +def shift(key, step): # key: 56bit + return key[step:] + key[:step] + +#补0函数 +def zero_fill(str_, num1, num2, pos): + if num1 < num2: + for _ in range(num2 - num1): + if pos == 'l': + str_ = '0' + str_ + if pos == 'r': + str_ = str_ + '0' + return str_ + +# 子密钥生成，产生16轮的48位子秘钥 +def sub_keys(key): # key: 56bit + key_list = [] + key_l = [key[key_table_l_1[_]] for _ in range(28)] + key_r = [key[key_table_r_1[_]] for _ in range(28)] + for i in range(16): + if i == 0 or i == 1 or i == 8 or i == 15: + step = 1 + else: + step = 2 + key_l = shift(key_l, step) + key_r = shift(key_r, step) + key_l_ = [key_l[key_table_l_2[_]] for _ in range(24)] + key_r_ = [key_l[key_table_r_2[_]] for _ in range(24)] + key_list.append(key_l_ + key_r_) + return key_list + +# 按位异或 +def xor(x, y): + return [str(int(x[_]) ^ int(y[_])) for _ in range(len(x))] + +# E盒扩展置换 +def expand(r_): + return [r_[expand_table[_]] for _ in range(48)] + +# S盒非线性代换 +def s_box(expand): # expand_ 48bit + s_boxes = [] + for i in range(8): + row = 2 * int(expand[i * 6]) + int(expand[i * 6 + 5]) + col = 8 * int(expand[i * 6 + 1]) + 4 * int(expand[i * 6 + 2]) + 2 * int(expand[i * 6 + 3]) + int(expand[i * 6 + 4]) + t_ = s_boxes_table[i][row * 16 + col] + s_ = bin(s_boxes_table[i][t_])[2:] + s_ = zero_fill(s_, len(s_), 4, 'l') + s_boxes.extend(s_) + return s_boxes # 32bit + +# P盒线性置换 +def p_box(s_boxes_): # 32bit + return [s_boxes_[p_box_table[_]] for _ in range(32)] + + +# 字符转2进制 +def str2bin(s): + res = [] + for c in s: + tem = bin(ord(c)).replace('b', '') + if len(tem) < 8: + tem = "0" + tem + res.append(tem) + return ''.join(res) + + +# 2进制转字符 +def bin2str(s): + res = [] + for i in range(int(len(s) / 8)): + b = s[8 * i: 8 * (i + 1)] + res.append(chr(int(b, 2))) + while res[-1] == '\x00': # 去除NUL + res.pop() + return ''.join(res) + + +# 2进制转16进制 +def bin2hex(s): + res = [] + for i in range(int(len(s) / 4)): + b = s[4 * i: 4 * (i + 1)] + res.append(hex(int(b, 2)).replace('0x', '')) + return ''.join(res).upper() + + +# 16进制转2进制 +def hex2bin(s): + res = [] + for c in s: + tem = bin(int(c, 16)).replace('0b', '') + while True: + if len(tem) < 4: + tem = "0" + tem + else: + break + res.append(tem) + return ''.join(res) \ No newline at end of file diff --git a/lab-iisec/IS407 final project/des/my_desctr.py b/lab-iisec/IS407 final project/des/my_desctr.py new file mode 100644 index 0000000000000000000000000000000000000000..e003f9ad868e7075598929744c5d1aa849a5e9a0 --- /dev/null +++ b/lab-iisec/IS407 final project/des/my_desctr.py @@ -0,0 +1,38 @@ +# des_ctr +import des.des as d +import des.func as f + + +# 加密 +def encrypt(key, iv, input_): + # 本次实验中为方便将文本中的换行回车等转为空格 + plain, n = d.des_input_plain(input_.replace('\n', ' ').replace('\r', ' ').replace('\s', ' ').replace('\t', ' ')) + ctr = iv + cipher_str = '' + for i in range(n): + plaintext = plain[i * 64: (i + 1) * 64] # 分组 + flag = 0 + t = d.des(ctr, key, flag) + cipher_str = cipher_str + ''.join(f.xor(plaintext, t)) + ctr = bin(int(ctr, 2) + 1)[2:] #计数器+1 + ctr = f.zero_fill(ctr, len(ctr), 64, 'l') + cipher_hex = d.des_output_cipher(cipher_str) + # print() + return cipher_hex + + +# 解密 +def decrypt(key, iv, input_): + cipher, n = d.des_input_cipher(input_) + ctr = iv + plain_str = '' + for i in range(n): + ciphertext = cipher[i * 64: (i + 1) * 64] + flag = 0 + t = d.des(ctr, key, flag) + plain_str = plain_str + ''.join(f.xor(ciphertext, t)) + ctr = bin(int(ctr, 2) + 1)[2:] + ctr = f.zero_fill(ctr, len(ctr), 64, 'l') + plain_str = d.des_output_plain(plain_str) + # print() + return plain_str diff --git a/lab-iisec/IS407 final project/des/test.py b/lab-iisec/IS407 final project/des/test.py new file mode 100644 index 0000000000000000000000000000000000000000..06e1cf499555c1dd1516e9b850c02ea3323ea58c --- /dev/null +++ b/lab-iisec/IS407 final project/des/test.py @@ -0,0 +1,17 @@ +import my_desctr + +if __name__ == '__main__': + key = '01010101010101010101010101010101010101010101010101010101' # 56bit + iv = '1010101010101010101010101010101010101010101010101010101010101010' # 64bit + + with open('message/plain.txt', 'r', encoding='utf-8') as f: + input_ = f.read() + en = my_desctr.encrypt(key, iv, input_) + with open('message/encrypt.txt', 'w', encoding='utf-8') as f: + f.write(en) + + with open('message/encrypt.txt', 'r', encoding='utf-8') as f: + input_ = f.read() + de = my_desctr.decrypt(key, iv, input_) + with open('message/decrypt.txt', 'w', encoding='utf-8') as f: + f.write(de) diff --git a/lab-iisec/IS407 final project/hmac/__init__.py b/lab-iisec/IS407 final project/hmac/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-iisec/IS407 final project/hmac/my_hmac.py b/lab-iisec/IS407 final project/hmac/my_hmac.py new file mode 100644 index 0000000000000000000000000000000000000000..cf43ae2c0edc59606206e1606ebce1df753e65f4 --- /dev/null +++ b/lab-iisec/IS407 final project/hmac/my_hmac.py @@ -0,0 +1,26 @@ +def hmac(key, message, hash_function): + + block_size = 64 + opad = bytearray() + ipad = bytearray() + h = int(key, 2) + rev = [] + while h != 0x0: + rev.append(chr(h & 0xFF)) + h = h >> 8 + rev.reverse() + key = bytes(''.join(rev), encoding='iso-8859-1') + + if len(key) > block_size: + key = bytearray(hash_function(key)) + t = len(key) + while block_size > t: + t += 1 + key += b"\x00" + + for i in range(block_size): + ipad.append(0x36 ^ key[i]) + opad.append(0x5c ^ key[i]) + return hash_function(str(bytes(opad)) + hash_function(str(bytes(ipad)) + message)) + + \ No newline at end of file diff --git a/lab-iisec/IS407 final project/hmac/my_md5.py b/lab-iisec/IS407 final project/hmac/my_md5.py new file mode 100644 index 0000000000000000000000000000000000000000..5b733e628c7462b9a988beb70a8b86188e0deefe --- /dev/null +++ b/lab-iisec/IS407 final project/hmac/my_md5.py @@ -0,0 +1,86 @@ +# 常数表 4294967296*abs(sin(i)) i = 1~64 +T = [0xd76aa478, 0xe8c7b756, 0x242070db, 0xc1bdceee, + 0xf57c0faf, 0x4787c62a, 0xa8304613, 0xfd469501, + 0x698098d8, 0x8b44f7af, 0xffff5bb1, 0x895cd7be, + 0x6b901122, 0xfd987193, 0xa679438e, 0x49b40821, + 0xf61e2562, 0xc040b340, 0x265e5a51, 0xe9b6c7aa, + 0xd62f105d, 0x02441453, 0xd8a1e681, 0xe7d3fbc8, + 0x21e1cde6, 0xc33707d6, 0xf4d50d87, 0x455a14ed, + 0xa9e3e905, 0xfcefa3f8, 0x676f02d9, 0x8d2a4c8a, + 0xfffa3942, 0x8771f681, 0x6d9d6122, 0xfde5380c, + 0xa4beea44, 0x4bdecfa9, 0xf6bb4b60, 0xbebfbc70, + 0x289b7ec6, 0xeaa127fa, 0xd4ef3085, 0x04881d05, + 0xd9d4d039, 0xe6db99e5, 0x1fa27cf8, 0xc4ac5665, + 0xf4292244, 0x432aff97, 0xab9423a7, 0xfc93a039, + 0x655b59c3, 0x8f0ccc92, 0xffeff47d, 0x85845dd1, + 0x6fa87e4f, 0xfe2ce6e0, 0xa3014314, 0x4e0811a1, + 0xf7537e82, 0xbd3af235, 0x2ad7d2bb, 0xeb86d391, ] +# 循环左移位数 +S = [7, 12, 17, 22, 7, 12, 17, 22, + 7, 12, 17, 22, 7, 12, 17, 22, + 5, 9, 14, 20, 5, 9, 14, 20, + 5, 9, 14, 20, 5, 9, 14, 20, + 4, 11, 16, 23, 4, 11, 16, 23, + 4, 11, 16, 23, 4, 11, 16, 23, + 6, 10, 15, 21, 6, 10, 15, 21, + 6, 10, 15, 21, 6, 10, 15, 21, ] + +# 数据填充 +def fill(input_): + seq = list(bytes(input_, 'utf-8')) + length = len(seq) + seq += [0]*(((length+8)//64+1)*64-length) + seq[length] = 0b10000000 + mul_4bytes = [] + for i in range(len(seq)//4): + seq[i*4+3], seq[i*4+2], seq[i*4+1], seq[i *4] = tuple(seq[i*4:(i+1)*4]) + mul_4bytes.append(int("".join(["{:08b}".format(j) for j in seq[i*4:(i+1)*4]]), 2)) + mul_4bytes[-2], mul_4bytes[-1] = int("{:064b}".format(length*8)[32:], 2), int("{:064b}".format(length*8)[:32], 2) + return mul_4bytes + +# 循环左移 +def shift(x, n): + return ((x << n) | (x >> (32-n))) + + +# 非线性函数 +def F(X, Y, Z): return (X & Y) | ((~X) & Z) +def G(X, Y, Z): return (X & Z) | (Y & (~Z)) +def H(X, Y, Z): return X ^ Y ^ Z +def I(X, Y, Z): return Y ^ (X | (~Z)) +def cal(a, b, c, d, func, m, s, t): + return (b+shift((a + func(b, c, d) + int(m) + t) % 0x100000000, s)) % 0x100000000 + +# 转16进制，大端转小端 +def final_format(a): + md5 = '' + for i in a: + x = '{:08x}'.format(i) + md5 += x[6:]+x[4:6]+x[2:4]+x[:2] + return md5.upper() + + +def md5(input_): + # 初始化 + A = 0X67452301 + B = 0XEFCDAB89 + C = 0X98BADCFE + D = 0X10325476 + # 本次实验中为方便将文本中的换行回车等转为空格 + text_int4 = fill(input_.replace('\n', ' ').replace('\r', ' ').replace('\s', ' ').replace('\t', ' ')) + for i in range(len(text_int4)//16): + a, b, c, d = A, B, C, D + M = [text_int4[i*16+j] for j in range(16)] + for j in range(64): + if j < 16: + A, B, C, D = D, cal(A, B, C, D, F, M[j], S[j], T[j]), B, C + elif j < 32: + A, B, C, D = D, cal(A, B, C, D, G, M[(j*5+1) % 16], S[j], T[j]), B, C + elif j < 48: + A, B, C, D = D, cal(A, B, C, D, H, M[((j*3)+5) % 16], S[j], T[j]), B, C + else: + A, B, C, D = D, cal(A, B, C, D, I, M[j*7 % 16], S[j], T[j]), B, C + A, B, C, D = (A+a) % 0x100000000, (B+b) % 0x100000000, (C +c) % 0x100000000, (D+d) % 0x100000000 + md5 = final_format([A, B, C, D]) + return md5 + diff --git a/lab-iisec/IS407 final project/hmac/test.py b/lab-iisec/IS407 final project/hmac/test.py new file mode 100644 index 0000000000000000000000000000000000000000..96155d833e5cfab12f62a41b2bd9c694ff8d2d14 --- /dev/null +++ b/lab-iisec/IS407 final project/hmac/test.py @@ -0,0 +1,19 @@ +import my_md5 +import my_hmac + + + +if __name__ == '__main__': + key = '01010101010101010101010101010101010101010101010101010101' + with open('message/text.txt', 'r', encoding='utf-8') as f: + input_ = f.read() + md5_ = my_md5.md5(input_) + print(md5_) + hmac = my_hmac.hmac(key, input_, my_md5.md5) + print(hmac) + with open('message/md5.txt', 'w', encoding='utf-8') as f: + f.write(md5_) + with open('message/hmac.txt', 'w', encoding='utf-8') as f: + f.write(hmac) + + diff --git a/lab-iisec/IS407 final project/msg/decrypt.txt b/lab-iisec/IS407 final project/msg/decrypt.txt new file mode 100644 index 0000000000000000000000000000000000000000..60f71348b679713c16ef8087f2dbff56d81e5a4f --- /dev/null +++ b/lab-iisec/IS407 final project/msg/decrypt.txt @@ -0,0 +1 @@ +I love cryptology! 519030910108 Zhou Zikun \ No newline at end of file diff --git a/lab-iisec/IS407 final project/msg/encrypt.txt b/lab-iisec/IS407 final project/msg/encrypt.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4da1d41e706d4accafbaa5b82a1eeef69b8d9cb --- /dev/null +++ b/lab-iisec/IS407 final project/msg/encrypt.txt @@ -0,0 +1 @@ +BD53AE702D4E4F628BB8AA771C3A2ADFA9E258FA3E84D58037CD111A1F59020C009DD663860A949A3493CBDC7A532EC2 \ No newline at end of file diff --git a/lab-iisec/IS407 final project/msg/hmac.txt b/lab-iisec/IS407 final project/msg/hmac.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8224456e13de5a2ffa3d81631becdcbab8235c1 --- /dev/null +++ b/lab-iisec/IS407 final project/msg/hmac.txt @@ -0,0 +1 @@ +70047402FF3ED82AB63B3872368A8E0D \ No newline at end of file diff --git a/lab-iisec/IS407 final project/msg/plain.txt b/lab-iisec/IS407 final project/msg/plain.txt new file mode 100644 index 0000000000000000000000000000000000000000..60f71348b679713c16ef8087f2dbff56d81e5a4f --- /dev/null +++ b/lab-iisec/IS407 final project/msg/plain.txt @@ -0,0 +1 @@ +I love cryptology! 519030910108 Zhou Zikun \ No newline at end of file diff --git a/lab-iisec/IS407 final project/rsa/__init__.py b/lab-iisec/IS407 final project/rsa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-iisec/IS407 final project/rsa/get_prime_number.py b/lab-iisec/IS407 final project/rsa/get_prime_number.py new file mode 100644 index 0000000000000000000000000000000000000000..cbac1a0c5c8b4811b1f44c726bf29850d1c23919 --- /dev/null +++ b/lab-iisec/IS407 final project/rsa/get_prime_number.py @@ -0,0 +1,53 @@ +import random + +# 得到大素数 +def miller_rabin(num): + max_ = 10 #检验次数 + s = num - 1 + t = 0 + while s % 2 == 0: + s = s // 2 + t += 1 + + b = [] + while len(b) < max_: + b.append(random.randrange(2, num - 1)) + b = list(set(b)) + # print(b) + for idx in range(max_): + a = random.randrange(2, num - 1) + v = pow(a, s, num) + if v != 1: + i = 0 + while v != (num - 1): + if i == t - 1: + # print('false') + return False + else: + i = i + 1 + v = (v ** 2) % num + # print('true') + return True + + +def is_prime(num): + if num < 2: + return False + # small_primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997] + # if num in small_primes: + # return True + # for prime in small_primes: + # if num % prime == 0: + # return False + return miller_rabin(num) + + +def get_prime_num(key_size=40): + while True: + num = random.randrange(2**(key_size-1)+1, 2**key_size) + if num % 2 ==0: + continue + # print(num) + if is_prime(num): + return num + \ No newline at end of file diff --git a/lab-iisec/IS407 final project/rsa/my_rsa.py b/lab-iisec/IS407 final project/rsa/my_rsa.py new file mode 100644 index 0000000000000000000000000000000000000000..6d12e1d9252f4413b027ec55c9b17f5a6ca66c2d --- /dev/null +++ b/lab-iisec/IS407 final project/rsa/my_rsa.py @@ -0,0 +1,45 @@ +import rsa.get_prime_number as g + + +def get_n_e_d(key_size=40): + p = g.get_prime_num(key_size) + q = g.get_prime_num(key_size) + while q == p: + q = g.get_prime_num(key_size) + + # print(p,q) + + n = p * q + phi_n = (p-1) * (q-1) + # print(n, phi_n) + + e = 65537 + + # 扩展欧几里得算法 + r, s, t = e, 1, 0 + r1, s1, t1 = phi_n, 0, 1 + while r1 != 0: + qq = r//r1 + t1, t2, t3 = r-qq*r1, s-qq*s1, t-qq*t1 + r, s, t = r1, s1, t1 + r1, s1, t1 = t1, t2, t3 + + d = s % phi_n + # print(d) + # print(e*d % phi_n) + return n, e, d + + +# 快速幂 +def fast_mod(x, n, m): + a = 1 + b = x + while True: + t = n + if n % 2 == 1 : + a = a * b % m + b = b * b % m + n = n//2 + if t < 1 : + return a + diff --git a/lab-iisec/IS407 final project/rsa/test.py b/lab-iisec/IS407 final project/rsa/test.py new file mode 100644 index 0000000000000000000000000000000000000000..6f2f7962aa0e9f1d14479cf69ef2e4cb766346d8 --- /dev/null +++ b/lab-iisec/IS407 final project/rsa/test.py @@ -0,0 +1,13 @@ +import my_rsa + +if __name__ == '__main__': + m = 0b1010101010101010101010101010101010101010101010101010101 + n, e, d = my_rsa.get_n_e_d() + c = my_rsa.fast_mod(m, e, n) + print(c) + print(bin(c)) + de_m = my_rsa.fast_mod(c, d, n) + print(de_m) + print(bin(de_m)) + print(de_m == m) + \ No newline at end of file diff --git a/lab-iisec/LoCCS-gossip-cryptompk/.gitignore b/lab-iisec/LoCCS-gossip-cryptompk/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c1376e47577723cbee14b69923962eb44e214e78 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/.gitignore @@ -0,0 +1,8 @@ +build/* +log.txt +*.tar.gz +cfg.* +.tags +peda* +.gdb_history +*.o diff --git a/lab-iisec/LoCCS-gossip-cryptompk/CMakeLists.txt b/lab-iisec/LoCCS-gossip-cryptompk/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..26c406738452fc53c1cdf890b9a21df303aa444f --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/CMakeLists.txt @@ -0,0 +1,20 @@ +cmake_minimum_required(VERSION 3.1) + +find_package(LLVM REQUIRED CONFIG) +add_definitions(${LLVM_DEFINITIONS}) +include_directories(${LLVM_INCLUDE_DIRS}) +link_directories(${LLVM_LIBRARY_DIRS}) + +message(${LLVM_LIBRARY_DIRS}) + +file(GLOB SAA_SRCS "src/*.cpp") +include_directories("include/") + +add_library(SAAPass MODULE ${SAA_SRCS}) +target_compile_features(SAAPass PRIVATE cxx_range_for cxx_auto_type) + +set_target_properties(SAAPass PROPERTIES + COMPILE_FLAGS "-O2 -g -fno-rtti -Wall" +) + +add_subdirectory(tools) # For utilities diff --git a/lab-iisec/LoCCS-gossip-cryptompk/README.md b/lab-iisec/LoCCS-gossip-cryptompk/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b5d7a1dfac78261c57478973909b8aba052e1f77 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/README.md @@ -0,0 +1,55 @@ +## Build + +### build CryptoMPK +``` +mkdir build && cd build +cmake .. && make +export CRYPTOMPK=$(realpath .) # env used by testcases +``` + +### build clang +build LLVM-7.0 +``` +mkdir files + +cd files +wget http://releases.llvm.org/7.0.0/llvm-7.0.0.src.tar.xz +wget http://releases.llvm.org/7.0.0/cfe-7.0.0.src.tar.xz +wget http://releases.llvm.org/7.0.0/compiler-rt-7.0.0.src.tar.xz +wget http://releases.llvm.org/7.0.0/libcxx-7.0.0.src.tar.xz +wget http://releases.llvm.org/7.0.0/libcxxabi-7.0.0.src.tar.xz +wget http://releases.llvm.org/7.0.0/clang-tools-extra-7.0.0.src.tar.xz +cd .. + +tar xf files/llvm-7.0.0.src.tar.xz +tar xf files/cfe-7.0.0.src.tar.xz +tar xf files/compiler-rt-7.0.0.src.tar.xz +tar xf files/libcxx-7.0.0.src.tar.xz +tar xf files/libcxxabi-7.0.0.src.tar.xz +tar xf files/clang-tools-extra-7.0.0.src.tar.xz + +mv ./llvm-7.0.0.src llvm +mv ./cfe-7.0.0.src llvm/tools/clang +mv ./libcxx-7.0.0.src/ llvm/projects/libcxx +mv ./compiler-rt-7.0.0.src llvm/projects/compiler-rt +mv ./libcxxabi-7.0.0.src/ llvm/projects/libcxxabi +mv ./clang-tools-extra-7.0.0.src/ llvm/tools/clang/tools/extra + +export LLVM_DIR=$PWD/llvm + +cd llvm && mkdir build && cd build && cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Debug $LLVM_DIR && make -j32 +``` + +runtime/runtime/clang7.patch is patch file for clang-7.0.0 to add custom directives used by CryptoMPK. + +Set it as the default clang used by the system. +``` +export PATH=$LLVM_DIR/build/bin:$PATH +``` + +### build jemalloc +``` +cd runtime/runtime/jemalloc/ +tar xf 5.2.0_mpk.tar.gz && cd jemalloc-5.2.0 +bash ../../jemalloc_build.sh +``` diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/AliasAnalysisVisitor.h b/lab-iisec/LoCCS-gossip-cryptompk/include/AliasAnalysisVisitor.h new file mode 100644 index 0000000000000000000000000000000000000000..f5a629ce9b4a0e0b1ac0baf5911c652927b1239a --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/AliasAnalysisVisitor.h @@ -0,0 +1,35 @@ +#ifndef ALIASVISITOR_H +#define ALIASVISITOR_H + +#include "VisitorCallback.h" +#include "AliasTaintCtx.h" + + +struct AliasAnalysisVisitor: public VisitorCallback { + AliasAnalysisVisitor(AliasTaintContext* &ctx, Module &m); + + virtual void visitAllocaInst(AllocaInst &I); + virtual void visitCastInst(CastInst &I); + + virtual void visitBinaryOperator(BinaryOperator &I); + virtual void visitPHINode(PHINode &I); + virtual void visitSelectInst(SelectInst &I); + + virtual void visitGetElementPtrInst(GetElementPtrInst &I); + virtual void visitLoadInst(LoadInst &I); + virtual void visitStoreInst(StoreInst &I); + virtual void visitMemTransferInst(MemTransferInst &I); + + virtual void visitReturnInst(ReturnInst &I); + virtual bool visitCallInst(CallInst &I, Function *targetFunction); + + virtual void setupChildContext(CallInst &I, AliasTaintContext *parentContext); + virtual void stitchChildContext(CallInst &I, AliasTaintContext *childContext); + +private: + std::set designated_mallocobj; + + void visitLibFunctions(CallInst &I, Function *targetFunction); +}; + +#endif // ALIASVISITOR_H \ No newline at end of file diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/AliasTaintCtx.h b/lab-iisec/LoCCS-gossip-cryptompk/include/AliasTaintCtx.h new file mode 100644 index 0000000000000000000000000000000000000000..83927def4df5c0d1920097cc664fef97dabff612 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/AliasTaintCtx.h @@ -0,0 +1,255 @@ +#ifndef ALIASTAINTCTX_H +#define ALIASTAINTCTX_H + +#include +#include +#include +#include +#include "ContextBase.h" +#include "ModObject.h" + + +struct AliasObject; +typedef int FieldIdTy; + + +struct PointsTo { + FieldIdTy dstoff; + AliasObject *target; + Instruction *propagator; + + PointsTo(AliasObject *obj, FieldIdTy off, Instruction *inst) + : dstoff(off), target(obj), propagator(inst) { } + + bool operator<(const PointsTo &rhs) const { + if (target == rhs.target) + return dstoff < rhs.dstoff; + return target < rhs.target; + } +}; + + +struct FieldObject { + bool ignoresink; + std::set pointsto; + int taintflag, sinktaint; + Instruction *tainter, *sinktainter; + + FieldObject(): ignoresink(false), taintflag(0), sinktaint(0), tainter(nullptr), sinktainter(nullptr) { } + + void addPointsTo(AliasObject *obj, FieldIdTy off, Instruction *inst) { + pointsto.insert(PointsTo(obj, off, inst)); + } + + void mergePointsTo(FieldObject *src, Instruction *inst) { + for (auto &item: src->pointsto) { + auto tmp = item; + tmp.propagator = inst; + pointsto.insert(tmp); + } + } + + void flowTaint(FieldObject *src, Instruction *inst) { + if (src->sinktaint) { + sinktaint = src->sinktaint; + sinktainter = inst; + } + if ((!src->sinktaint || ignoresink) && src->taintflag) { +#ifndef ONLY_MASTERKEY + taintflag = src->taintflag; + tainter = inst; +#endif + } + // if (src->ignoresink && src->taintflag) { + // dbgs() << "HHHHHHHHHHHHHHHHHHHHHHHHHHHHHH\n"; + // } + } + + void setTaint(Instruction *inst) { + taintflag = 1; + tainter = inst; + } + + void setSinkTaint(Instruction *inst) { + sinktaint = 1; + taintflag = 0; + tainter = nullptr; + sinktainter = inst; + } +}; + + +struct RegObject: public FieldObject { + Value *represented; + + RegObject(Value* obj): represented(obj) { } +}; + + +inline bool hasPointsTo(FieldObject *reg) { + return reg && reg->pointsto.size(); +} + + +inline bool hasTaint(FieldObject *reg) { + return reg && (reg->taintflag || reg->sinktaint); +} + + +struct AliasObject { + std::map fieldmap; + Value *represented; + bool fake, tainted, sink; + Instruction *tainter; + + AliasObject(Value* obj) + : represented(obj), fake(false), tainted(false), sink(false), tainter(nullptr) { } + + FieldObject* findFieldObj(FieldIdTy fid) { + auto it = fieldmap.find(fid); + if (it != fieldmap.end()) + return &(it->second); + return nullptr; + } + + FieldObject* getFieldObj(FieldIdTy fid) { + return &(fieldmap[fid]); + } + + void updateTaintByField(FieldIdTy fid, FieldObject* fobj) { + if ((fobj->sinktaint && !fobj->ignoresink)) { + sink = true; + tainted = false; + } + // if (fobj->taintflag && sink) { + // DEBUG_LOADSTOR(dbgs() << "hello" << "\n"); + // } + if (fobj->taintflag && !sink && !tainted) { + tainted = true; + tainter = fobj->tainter; + } + } + + bool isstackobj() { + if (represented && dyn_cast(represented)) + return true; + else + return false; + } +}; + + +inline bool checkPointsToTaint(FieldObject *reg, bool ignorestack=false) { + for (auto &pt: reg->pointsto) + if ((!ignorestack || !pt.target->isstackobj()) && pt.target->tainted) + return true; + return false; +} + +inline bool checkPointsToSink(FieldObject *reg, bool ignorestack=false) { + for (auto &pt: reg->pointsto) + if ((!ignorestack || !pt.target->isstackobj()) && pt.target->sink) + return true; + return false; +} + +struct ObjectMap { + std::map > regmap; + std::map > memmap; + + std::pair createRegMemPair(Value *val) { + auto regs = getOrCreateObject(regmap, val); + auto mems = getOrCreateObject(memmap, val); + if (regs.second || mems.second) { + auto inst = static_cast(val); + regs.first->addPointsTo(mems.first, 0, inst); + } + return std::make_pair(regs.first, mems.first); + } + + RegObject* getRegObj(Value *val) { + return getOrCreateObject(regmap, val).first; + } + + RegObject* findRegObj(Value *val) { + return getNoCreateObject(regmap, val); + } + + AliasObject* findMemObj(Value *val) { + return getNoCreateObject(memmap, val); + } + +private: + template + T* getNoCreateObject(Map &map, Value *val) { + auto it = map.find(val); + if (it != map.end()) + return it->second.get(); + return nullptr; + } + + template + std::pair getOrCreateObject(Map &map, Value *val) { + auto ins = map.emplace(val, std::unique_ptr(nullptr)); + auto &uptr = ins.first->second; + if (ins.second) uptr.reset(new T(val)); + return std::make_pair(uptr.get(), ins.second); + } +}; + + +struct AliasTaintContext: public ContextBase { + static ObjectMap globalobjects; + ObjectMap localobjects; + std::set retval; + FuncMod funcmod; + bool isdirector; + + // MemObj management + + std::pair + createRegMemPair(Value *val, bool fake = false) { + auto ret = localobjects.createRegMemPair(val); + // later create may overwrite previous fake flag + ret.second->fake = fake; + return ret; + } + + // RegObj management + + RegObject* getDestReg(Value *val) { + auto newval = val->stripPointerCasts(); + if (newval != val) return getDestReg(newval); + // no new globalobjects will be created + if (isa(val)) + return globalobjects.findRegObj(val); + return localobjects.getRegObj(val); + } + + RegObject* findOpReg(Value *val) { + auto newval = val->stripPointerCasts(); + if (newval != val) return findOpReg(newval); + if (isa(val)) + return globalobjects.findRegObj(val); + auto ret = localobjects.findRegObj(val); + // create missing pointees on last round of loop + if (!ret && !inside_loop && val->getType()->isPointerTy() + && !isa(val)) { + DEBUG_LOADSTOR(dbgs() << "findOpReg failed: " << *val << "\n"); + ret = createRegMemPair(val, true).first; + } + return ret; + } + + // interfaces + + static void setupGlobals(Module &m); + + AliasTaintContext(Instruction *inst, Function *func) + : ContextBase(inst, func), isdirector(false) { } + + void getFuncPtrTargets(Value *fp, std::vector &ret); +}; + + +#endif // ALIASTAINTCTX_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/ContextBase.h b/lab-iisec/LoCCS-gossip-cryptompk/include/ContextBase.h new file mode 100644 index 0000000000000000000000000000000000000000..e536a30c42456df163ea64d58c3ff80dd7e98d5e --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/ContextBase.h @@ -0,0 +1,91 @@ +#ifndef CONTEXTBASE_H +#define CONTEXTBASE_H + +#include "llvm_basics.h" +#include +#include +#include + + +struct timerwrapper { + typedef std::chrono::steady_clock sysclock; + sysclock::time_point t1; + + void start() { + t1 = sysclock::now(); + } + + double get() { + using namespace std::chrono; + sysclock::time_point t2 = sysclock::now(); + return duration_cast>(t2 - t1).count(); + } +}; + + +template +struct ContextBase { + std::vector children; + CtxClass *parent, *self; + + Instruction *inst; + Function *func; + bool inside_loop, lastloopiter; + int loopidx; + + double totaltimer, childtimer; + timerwrapper timer; + + // timer management + + void init() { + childtimer = 0; + timer.start(); + } + + void consume_childctx(CtxClass *rhs) { + childtimer += rhs->totaltimer; + } + + std::pair get_timer() { + totaltimer = timer.get(); + return std::make_pair(totaltimer, totaltimer - childtimer); + } + + // context navigation + + std::pair getOrCreateChildCtx(Instruction *inst, Function *func) { + for (auto ctxptr: children) { + if (ctxptr->inst == inst && ctxptr->func == func) { + return std::make_pair(ctxptr, false); + } + } + auto ret = new CtxClass(inst, func); + ret->parent = self; + children.push_back(ret); + return std::make_pair(ret, true); + } + + bool checkRecursive(Instruction &I) { + for (auto ctx = self; ctx; ctx = ctx->parent) { + if (&I == ctx->inst) { + return true; + } + } + return false; + } + + // interfaces + + ContextBase(Instruction *inst, Function *func) + : parent(nullptr), inst(inst), func(func), loopidx(0) { + self = static_cast(this); + } + + void getFuncPtrTargets(Value *fp, std::vector &ret) { + assert(false); + } +}; + + +#endif // CONTEXTBASE_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/GlobalVisitor.h b/lab-iisec/LoCCS-gossip-cryptompk/include/GlobalVisitor.h new file mode 100644 index 0000000000000000000000000000000000000000..a3e53a08d5947a455b201b66dd53fd7fca5c2151 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/GlobalVisitor.h @@ -0,0 +1,998 @@ +#ifndef GLOBALVISITOR_H +#define GLOBALVISITOR_H + +#include "llvm_basics.h" +#include +#include +#include +#include +#include "VisitorCallback.h" +#include "ContextBase.h" +#include "Utils.h" + +struct Rules { + static bool checkBlacklist(Function *func) { + if (Globals::SkipFuncs.count(func->getName()) != 0) + // func->getName().equals("CRYPTO_lock") + // || func->getName().equals("ERR_put_error") + // || func->getName().equals("RAND_bytes") + // || func->getName().equals("RAND_pseudo_bytes") + // || func->getName().equals("RAND_add") + // || func->getName().equals("ASN1_item_ex_i2d") + // || func->getName().equals("ASN1_item_ex_d2i") + // || func->getName().equals("ERR_load_ENGINE_strings") + // || func->getName().equals("ERR_load_CRYPTO_strings") + // || func->getName().equals("ERR_load_OBJ_strings") + // || func->getName().equals("ERR_load_BN_strings") + // || func->getName().equals("ERR_load_EC_strings") + // || func->getName().equals("ERR_load_RSA_strings") + // || func->getName().equals("ERR_load_DSA_strings") + // || func->getName().equals("ERR_load_ECDSA_strings") + // || func->getName().equals("ERR_load_DH_strings") + // || func->getName().equals("ERR_load_ECDH_strings") + // || func->getName().equals("ERR_load_DSO_strings") + // || func->getName().equals("ERR_load_ENGINE_strings") + // || func->getName().equals("ERR_load_BUF_strings") + // || func->getName().equals("ERR_load_BIO_strings") + // || func->getName().equals("ERR_load_RAND_strings") + // || func->getName().equals("ERR_get_implementation") + // || func->getName().equals("ERR_set_implementation") + // || func->getName().equals("ERR_load_ERR_strings") + // || func->getName().equals("ERR_load_strings") + // || func->getName().equals("ERR_unload_strings") + // || func->getName().equals("ERR_free_strings") + // || func->getName().equals("ERR_put_error") + // || func->getName().equals("ERR_get_state") + // || func->getName().equals("ERR_clear_error") + // || func->getName().equals("ERR_get_error") + // || func->getName().equals("ERR_get_error_line") + // || func->getName().equals("ERR_get_error_line_data") + // || func->getName().equals("ERR_peek_error") + // || func->getName().equals("ERR_peek_error_line") + // || func->getName().equals("ERR_peek_error_line_data") + // || func->getName().equals("ERR_peek_last_error") + // || func->getName().equals("ERR_peek_last_error_line") + // || func->getName().equals("ERR_peek_last_error_line_data") + // || func->getName().equals("ERR_error_string_n") + // || func->getName().equals("ERR_lib_error_string") + // || func->getName().equals("ERR_func_error_string") + // || func->getName().equals("ERR_reason_error_string") + // || func->getName().equals("ERR_error_string") + // || func->getName().equals("ERR_get_string_table") + // || func->getName().equals("ERR_get_err_state_table") + // || func->getName().equals("ERR_release_err_state_table") + // || func->getName().equals("ERR_remove_thread_state") + // || func->getName().equals("ERR_remove_state") + // || func->getName().equals("ERR_STATE_free") + // || func->getName().equals("ERR_get_next_error_library") + // || func->getName().equals("ERR_set_error_data") + // || func->getName().equals("ERR_add_error_data") + // || func->getName().equals("ERR_add_error_vdata") + // || func->getName().equals("ERR_set_mark") + // || func->getName().equals("ERR_pop_to_mark") + // || func->getName().equals("ERR_load_crypto_strings") + // || func->getName().equals("ERR_print_errors_cb") + // || func->getName().equals("ERR_print_errors_fp") + // || func->getName().equals("ERR_print_errors") + // || func->getName().equals("ERR_load_EVP_strings") + // || func->getName().equals("ERR_load_ASN1_strings") + // || func->getName().equals("ERR_load_PEM_strings") + // || func->getName().equals("ERR_load_X509_strings") + // || func->getName().equals("ERR_load_X509V3_strings") + // || func->getName().equals("ERR_load_CONF_strings") + // || func->getName().equals("ERR_load_PKCS7_strings") + // || func->getName().equals("ERR_load_PKCS12_strings") + // || func->getName().equals("ERR_load_COMP_strings") + // || func->getName().equals("ERR_load_OCSP_strings") + // || func->getName().equals("ERR_load_UI_strings") + // || func->getName().equals("ERR_load_CMS_strings") + // || func->getName().equals("ERR_load_TS_strings") + // || func->getName().equals("ENGINE_finish") + // || func->getName().equals("lh_new") + // || func->getName().equals("lh_strhash") + // || func->getName().equals("lh_free") + // || func->getName().equals("lh_insert") + // || func->getName().equals("lh_delete") + // || func->getName().equals("lh_retrieve") + // || func->getName().equals("lh_doall") + // || func->getName().equals("lh_doall_arg") + // || func->getName().equals("lh_num_items") + // || func->getName().equals("lh_stats") + // || func->getName().equals("lh_stats_bio") + // || func->getName().equals("lh_node_stats") + // || func->getName().equals("lh_node_stats_bio") + // || func->getName().equals("lh_node_usage_stats") + // || func->getName().equals("lh_node_usage_stats_bio") + // || func->getName().equals("EC_EX_DATA_clear_free_all_data") + // || func->getName().equals("EC_EX_DATA_clear_free_data") + // || func->getName().equals("EC_EX_DATA_free_all_data") + // || func->getName().equals("EC_EX_DATA_free_data") + // || func->getName().equals("EC_EX_DATA_get_data") + // || func->getName().equals("EC_EX_DATA_set_data") + // || func->getName().equals("EC_GF2m_simple_method") + // || func->getName().equals("EC_GFp_mont_method") + // || func->getName().equals("EC_GFp_nist_method") + // || func->getName().equals("EC_GFp_simple_method") + // || func->getName().equals("EC_GROUP_check") + // || func->getName().equals("EC_GROUP_check_discriminant") + // || func->getName().equals("EC_GROUP_clear_free") + // || func->getName().equals("EC_GROUP_cmp") + // || func->getName().equals("EC_GROUP_copy") + // || func->getName().equals("EC_GROUP_dup") + // || func->getName().equals("EC_GROUP_free") + // || func->getName().equals("EC_GROUP_get0_generator") + // || func->getName().equals("EC_GROUP_get0_seed") + // || func->getName().equals("EC_GROUP_get_asn1_flag") + // || func->getName().equals("EC_GROUP_get_basis_type") + // || func->getName().equals("EC_GROUP_get_cofactor") + // || func->getName().equals("EC_GROUP_get_curve_GF2m") + // || func->getName().equals("EC_GROUP_get_curve_GFp") + // || func->getName().equals("EC_GROUP_get_curve_name") + // || func->getName().equals("EC_GROUP_get_degree") + // || func->getName().equals("EC_GROUP_get_mont_data") + // || func->getName().equals("EC_GROUP_get_order") + // || func->getName().equals("EC_GROUP_get_pentanomial_basis") + // || func->getName().equals("EC_GROUP_get_point_conversion_form") + // || func->getName().equals("EC_GROUP_get_seed_len") + // || func->getName().equals("EC_GROUP_get_trinomial_basis") + // || func->getName().equals("EC_GROUP_have_precompute_mult") + // || func->getName().equals("EC_GROUP_method_of") + // || func->getName().equals("EC_GROUP_new") + // || func->getName().equals("EC_GROUP_new_by_curve_name") + // || func->getName().equals("EC_GROUP_new_curve_GF2m") + // || func->getName().equals("EC_GROUP_new_curve_GFp") + // || func->getName().equals("EC_GROUP_precompute_mult") + // || func->getName().equals("EC_GROUP_set_asn1_flag") + // || func->getName().equals("EC_GROUP_set_curve_GF2m") + // || func->getName().equals("EC_GROUP_set_curve_GFp") + // || func->getName().equals("EC_GROUP_set_curve_name") + // || func->getName().equals("EC_GROUP_set_generator") + // || func->getName().equals("EC_GROUP_set_point_conversion_form") + // || func->getName().equals("EC_GROUP_set_seed") + // || func->getName().equals("EC_KEY_check_key") + // || func->getName().equals("EC_KEY_clear_flags") + // || func->getName().equals("EC_KEY_copy") + // || func->getName().equals("EC_KEY_dup") + // || func->getName().equals("EC_KEY_free") + // || func->getName().equals("EC_KEY_generate_key") + // || func->getName().equals("EC_KEY_get0_group") + // || func->getName().equals("EC_KEY_get0_private_key") + // || func->getName().equals("EC_KEY_get0_public_key") + // || func->getName().equals("EC_KEY_get_conv_form") + // || func->getName().equals("EC_KEY_get_enc_flags") + // || func->getName().equals("EC_KEY_get_flags") + // || func->getName().equals("EC_KEY_get_key_method_data") + // || func->getName().equals("EC_KEY_insert_key_method_data") + // || func->getName().equals("EC_KEY_new") + // || func->getName().equals("EC_KEY_new_by_curve_name") + // || func->getName().equals("EC_KEY_precompute_mult") + // || func->getName().equals("EC_KEY_print") + // || func->getName().equals("EC_KEY_print_fp") + // || func->getName().equals("EC_KEY_set_asn1_flag") + // || func->getName().equals("EC_KEY_set_conv_form") + // || func->getName().equals("EC_KEY_set_enc_flags") + // || func->getName().equals("EC_KEY_set_flags") + // || func->getName().equals("EC_KEY_set_group") + // || func->getName().equals("EC_KEY_set_private_key") + // || func->getName().equals("EC_KEY_set_public_key") + // || func->getName().equals("EC_KEY_set_public_key_affine_coordinates") + // || func->getName().equals("EC_KEY_up_ref") + // || func->getName().equals("EC_METHOD_get_field_type") + // || func->getName().equals("EC_POINT_add") + // || func->getName().equals("EC_POINT_bn2point") + // || func->getName().equals("EC_POINT_clear_free") + // || func->getName().equals("EC_POINT_cmp") + // || func->getName().equals("EC_POINT_copy") + // || func->getName().equals("EC_POINT_dbl") + // || func->getName().equals("EC_POINT_dup") + // || func->getName().equals("EC_POINT_free") + // || func->getName().equals("EC_POINT_get_Jprojective_coordinates_GFp") + // || func->getName().equals("EC_POINT_get_affine_coordinates_GF2m") + // || func->getName().equals("EC_POINT_get_affine_coordinates_GFp") + // || func->getName().equals("EC_POINT_hex2point") + // || func->getName().equals("EC_POINT_invert") + // || func->getName().equals("EC_POINT_is_at_infinity") + // || func->getName().equals("EC_POINT_is_on_curve") + // || func->getName().equals("EC_POINT_make_affine") + // || func->getName().equals("EC_POINT_method_of") + // || func->getName().equals("EC_POINT_mul") + // || func->getName().equals("EC_POINT_new") + // || func->getName().equals("EC_POINT_oct2point") + // || func->getName().equals("EC_POINT_point2bn") + // || func->getName().equals("EC_POINT_point2hex") + // || func->getName().equals("EC_POINT_point2oct") + // || func->getName().equals("EC_POINT_set_Jprojective_coordinates_GFp") + // || func->getName().equals("EC_POINT_set_affine_coordinates_GF2m") + // || func->getName().equals("EC_POINT_set_affine_coordinates_GFp") + // || func->getName().equals("EC_POINT_set_compressed_coordinates_GF2m") + // || func->getName().equals("EC_POINT_set_compressed_coordinates_GFp") + // || func->getName().equals("EC_POINT_set_to_infinity") + // || func->getName().equals("EC_POINTs_make_affine") + // || func->getName().equals("EC_POINTs_mul") + // || func->getName().equals("EC_PRIVATEKEY_free") + // || func->getName().equals("EC_PRIVATEKEY_new") + // || func->getName().equals("EC_curve_nid2nist") + // || func->getName().equals("EC_curve_nist2nid") + // || func->getName().equals("EC_get_builtin_curves") + // || func->getName().equals("ec_GF2m_have_precompute_mult") + // || func->getName().equals("ec_GF2m_montgomery_point_multiply") + // || func->getName().equals("ec_GF2m_precompute_mult") + // || func->getName().equals("ec_GF2m_simple_add") + // || func->getName().equals("ec_GF2m_simple_cmp") + // || func->getName().equals("ec_GF2m_simple_dbl") + // || func->getName().equals("ec_GF2m_simple_field_div") + // || func->getName().equals("ec_GF2m_simple_field_mul") + // || func->getName().equals("ec_GF2m_simple_field_sqr") + // || func->getName().equals("ec_GF2m_simple_group_check_discriminant") + // || func->getName().equals("ec_GF2m_simple_group_clear_finish") + // || func->getName().equals("ec_GF2m_simple_group_copy") + // || func->getName().equals("ec_GF2m_simple_group_finish") + // || func->getName().equals("ec_GF2m_simple_group_get_curve") + // || func->getName().equals("ec_GF2m_simple_group_get_degree") + // || func->getName().equals("ec_GF2m_simple_group_init") + // || func->getName().equals("ec_GF2m_simple_group_set_curve") + // || func->getName().equals("ec_GF2m_simple_invert") + // || func->getName().equals("ec_GF2m_simple_is_at_infinity") + // || func->getName().equals("ec_GF2m_simple_is_on_curve") + // || func->getName().equals("ec_GF2m_simple_make_affine") + // || func->getName().equals("ec_GF2m_simple_mul") + // || func->getName().equals("ec_GF2m_simple_oct2point") + // || func->getName().equals("ec_GF2m_simple_point2oct") + // || func->getName().equals("ec_GF2m_simple_point_clear_finish") + // || func->getName().equals("ec_GF2m_simple_point_copy") + // || func->getName().equals("ec_GF2m_simple_point_finish") + // || func->getName().equals("ec_GF2m_simple_point_get_affine_coordinates") + // || func->getName().equals("ec_GF2m_simple_point_init") + // || func->getName().equals("ec_GF2m_simple_point_set_affine_coordinates") + // || func->getName().equals("ec_GF2m_simple_point_set_to_infinity") + // || func->getName().equals("ec_GF2m_simple_points_make_affine") + // || func->getName().equals("ec_GF2m_simple_set_compressed_coordinates") + // || func->getName().equals("ec_GFp_mont_field_decode") + // || func->getName().equals("ec_GFp_mont_field_encode") + // || func->getName().equals("ec_GFp_mont_field_mul") + // || func->getName().equals("ec_GFp_mont_field_set_to_one") + // || func->getName().equals("ec_GFp_mont_field_sqr") + // || func->getName().equals("ec_GFp_mont_group_clear_finish") + // || func->getName().equals("ec_GFp_mont_group_copy") + // || func->getName().equals("ec_GFp_mont_group_finish") + // || func->getName().equals("ec_GFp_mont_group_init") + // || func->getName().equals("ec_GFp_mont_group_set_curve") + // || func->getName().equals("ec_GFp_nist_field_mul") + // || func->getName().equals("ec_GFp_nist_field_sqr") + // || func->getName().equals("ec_GFp_nist_group_copy") + // || func->getName().equals("ec_GFp_nist_group_set_curve") + // || func->getName().equals("ec_GFp_simple_add") + // || func->getName().equals("ec_GFp_simple_cmp") + // || func->getName().equals("ec_GFp_simple_dbl") + // || func->getName().equals("ec_GFp_simple_field_mul") + // || func->getName().equals("ec_GFp_simple_field_sqr") + // || func->getName().equals("ec_GFp_simple_get_Jprojective_coordinates_GFp") + // || func->getName().equals("ec_GFp_simple_group_check_discriminant") + // || func->getName().equals("ec_GFp_simple_group_clear_finish") + // || func->getName().equals("ec_GFp_simple_group_copy") + // || func->getName().equals("ec_GFp_simple_group_finish") + // || func->getName().equals("ec_GFp_simple_group_get_curve") + // || func->getName().equals("ec_GFp_simple_group_get_degree") + // || func->getName().equals("ec_GFp_simple_group_init") + // || func->getName().equals("ec_GFp_simple_group_set_curve") + // || func->getName().equals("ec_GFp_simple_invert") + // || func->getName().equals("ec_GFp_simple_is_at_infinity") + // || func->getName().equals("ec_GFp_simple_is_on_curve") + // || func->getName().equals("ec_GFp_simple_make_affine") + // || func->getName().equals("ec_GFp_simple_oct2point") + // || func->getName().equals("ec_GFp_simple_point2oct") + // || func->getName().equals("ec_GFp_simple_point_clear_finish") + // || func->getName().equals("ec_GFp_simple_point_copy") + // || func->getName().equals("ec_GFp_simple_point_finish") + // || func->getName().equals("ec_GFp_simple_point_get_affine_coordinates") + // || func->getName().equals("ec_GFp_simple_point_init") + // || func->getName().equals("ec_GFp_simple_point_set_affine_coordinates") + // || func->getName().equals("ec_GFp_simple_point_set_to_infinity") + // || func->getName().equals("ec_GFp_simple_points_make_affine") + // || func->getName().equals("ec_GFp_simple_set_Jprojective_coordinates_GFp") + // || func->getName().equals("ec_GFp_simple_set_compressed_coordinates") + // || func->getName().equals("ec_asn1_group2pkparameters") + // || func->getName().equals("ec_asn1_pkparameters2group") + // || func->getName().equals("ec_bits") + // || func->getName().equals("ec_cmp_parameters") + // || func->getName().equals("ec_copy_parameters") + // || func->getName().equals("ec_missing_parameters") + // || func->getName().equals("ec_pkey_ctrl") + // || func->getName().equals("ec_pre_comp_clear_free") + // || func->getName().equals("ec_pre_comp_dup") + // || func->getName().equals("ec_pre_comp_free") + // || func->getName().equals("ec_precompute_mont_data") + // || func->getName().equals("ec_wNAF_have_precompute_mult") + // || func->getName().equals("ec_wNAF_mul") + // || func->getName().equals("ec_wNAF_precompute_mult") + + + // || func->getName().equals("_ASN1_ENUMERATED_set") + // || func->getName().equals("_i2d_ASN1_SET") + // || func->getName().equals("_i2a_ASN1_OBJECT") + // || func->getName().equals("_ASN1_template_new") + // || func->getName().equals("_d2i_ASN1_SET") + // || func->getName().equals("_ASN1_item_pack") + // || func->getName().equals("_ASN1_item_unpack") + // || func->getName().equals("_ASN1_OBJECT_new") + // || func->getName().equals("_ASN1_d2i_bio") + // || func->getName().equals("_asn1_enc_save") + // || func->getName().equals("_ASN1_ENUMERATED_to_BN") + // || func->getName().equals("_s2i_ASN1_INTEGER") + // || func->getName().equals("_ASN1_primitive_free") + // || func->getName().equals("_PEM_ASN1_write_bio") + // || func->getName().equals("_ASN1_GENERALIZEDTIME_adj") + // || func->getName().equals("_asn1_ex_i2c") + // || func->getName().equals("_EVP_PKEY_asn1_find_str") + // || func->getName().equals("_asn1_template_print_ctx") + // || func->getName().equals("_ASN1_UTCTIME_print") + // || func->getName().equals("_a2d_ASN1_OBJECT") + // || func->getName().equals("_i2s_ASN1_ENUMERATED") + // || func->getName().equals("_EVP_PKEY_asn1_add0") + // || func->getName().equals("_ASN1_UTCTIME_adj") + // || func->getName().equals("_PEM_ASN1_write") + // || func->getName().equals("_ASN1_TYPE_cmp") + // || func->getName().equals("_s2i_ASN1_OCTET_STRING") + // || func->getName().equals("_SMIME_write_ASN1") + // || func->getName().equals("_ENGINE_pkey_asn1_find_str") + // || func->getName().equals("_ASN1_item_dup") + // || func->getName().equals("_d2i_ASN1_OBJECT") + // || func->getName().equals("_ASN1_item_verify") + // || func->getName().equals("_ENGINE_get_pkey_asn1_meth") + // || func->getName().equals("_i2s_ASN1_INTEGER") + // || func->getName().equals("_ASN1_INTEGER_to_BN") + // || func->getName().equals("_ASN1_GENERALIZEDTIME_print") + // || func->getName().equals("_ASN1_item_d2i_fp") + // || func->getName().equals("_ASN1_TIME_diff") + // || func->getName().equals("_ASN1_TYPE_set_octetstring") + // || func->getName().equals("_ASN1_INTEGER_set") + // || func->getName().equals("_d2i_ASN1_UINTEGER") + // || func->getName().equals("_TS_ASN1_INTEGER_print_bio") + // || func->getName().equals("_SMIME_read_ASN1") + // || func->getName().equals("_ASN1_TIME_adj") + // || func->getName().equals("_ASN1_TYPE_set_int_octetstring") + // || func->getName().equals("_ASN1_dup") + // || func->getName().equals("_ASN1_STRING_set_by_NID") + // || func->getName().equals("_ASN1_item_ex_i2d") + // || func->getName().equals("_ASN1_STRING_print") + // || func->getName().equals("_ASN1_mbstring_ncopy") + // || func->getName().equals("_c2i_ASN1_BIT_STRING") + // || func->getName().equals("_asn1_utctime_to_tm") + // || func->getName().equals("_ASN1_get_object") + // || func->getName().equals("_EVP_PKEY_asn1_find") + // || func->getName().equals("_i2c_ASN1_BIT_STRING") + // || func->getName().equals("_ASN1_item_i2d_bio") + // || func->getName().equals("_BN_to_ASN1_INTEGER") + // || func->getName().equals("_ASN1_i2d_bio") + // || func->getName().equals("_ASN1_generate_v3") + // || func->getName().equals("_EVP_PKEY_asn1_free") + // || func->getName().equals("_PEM_ASN1_read") + // || func->getName().equals("_i2d_ASN1_bio_stream") + // || func->getName().equals("_asn1_do_adb") + // || func->getName().equals("_ASN1_d2i_fp") + // || func->getName().equals("_ASN1_bn_print") + // || func->getName().equals("_ASN1_item_sign_ctx") + // || func->getName().equals("_ASN1_OBJECT_free") + // || func->getName().equals("_asn1_generalizedtime_to_tm") + // || func->getName().equals("_ASN1_item_ex_d2i") + // || func->getName().equals("_RSA_sign_ASN1_OCTET_STRING") + // || func->getName().equals("_ASN1_TYPE_get_int_octetstring") + // || func->getName().equals("_ASN1_TIME_to_generalizedtime") + // || func->getName().equals("_EVP_PKEY_asn1_free_10881338769681343115") + // || func->getName().equals("_d2i_ASN1_BOOLEAN") + // || func->getName().equals("_ASN1_TYPE_set1") + // || func->getName().equals("_asn1_item_combine_free") + // || func->getName().equals("_cms_env_asn1_ctrl") + // || func->getName().equals("_ASN1_item_d2i_bio") + // || func->getName().equals("_asn1_ex_c2i") + // || func->getName().equals("_ASN1_template_free") + // || func->getName().equals("_ASN1_primitive_new") + // || func->getName().equals("_ASN1_STRING_dup") + // || func->getName().equals("_ERR_load_ASN1_strings") + // || func->getName().equals("_ASN1_i2d_fp") + // || func->getName().equals("_c2i_ASN1_OBJECT") + // || func->getName().equals("_i2c_ASN1_INTEGER") + // || func->getName().equals("_ASN1_BIT_STRING_set_bit") + // || func->getName().equals("_PEM_ASN1_read_bio") + // || func->getName().equals("_EVP_CIPHER_param_to_asn1") + // || func->getName().equals("_ASN1_TYPE_get_octetstring") + // || func->getName().equals("_ASN1_STRING_set") + // || func->getName().equals("_ASN1_item_i2d_fp") + // || func->getName().equals("_c2i_ASN1_INTEGER") + // || func->getName().equals("_ASN1_STRING_type_new") + // || func->getName().equals("_ASN1_STRING_clear_free") + // || func->getName().equals("_i2a_ASN1_STRING") + // || func->getName().equals("_i2a_ASN1_INTEGER") + // || func->getName().equals("engine_pkey_asn1_meths_free") + // || func->getName().equals("EVP_PKEY_asn1_free") + // || func->getName().equals("ASN1_item_free") + // || func->getName().equals("asn1_item_combine_free") + // || func->getName().equals("ASN1_template_free") + // || func->getName().equals("ASN1_primitive_free") + // || func->getName().equals("asn1_get_choice_selector") + // || func->getName().equals("asn1_get_field_ptr") + // || func->getName().equals("asn1_do_lock") + // || func->getName().equals("asn1_enc_free") + // || func->getName().equals("asn1_do_adb") + // || func->getName().equals("ASN1_INTEGER_get") + // || func->getName().equals("ASN1_OBJECT_free") + // || func->getName().equals("ASN1_STRING_free") + // || func->getName().equals("ASN1_TYPE_get") + // || func->getName().equals("ASN1_item_i2d") + // || func->getName().equals("asn1_item_flags_i2d") + // || func->getName().equals("ASN1_item_ex_i2d") + // || func->getName().equals("asn1_template_ex_i2d") + // || func->getName().equals("asn1_i2d_ex_primitive") + // || func->getName().equals("asn1_enc_restore") + // || func->getName().equals("ASN1_object_size") + // || func->getName().equals("ASN1_put_object") + // || func->getName().equals("ASN1_put_eoc") + // || func->getName().equals("asn1_ex_i2c") + // || func->getName().equals("i2c_ASN1_BIT_STRING") + // || func->getName().equals("i2c_ASN1_INTEGER") + // || func->getName().equals("ASN1_item_d2i") + // || func->getName().equals("ASN1_item_ex_d2i") + // || func->getName().equals("asn1_template_ex_d2i") + // || func->getName().equals("asn1_d2i_ex_primitive") + // || func->getName().equals("asn1_check_tlen") + // || func->getName().equals("ASN1_tag2bit") + // || func->getName().equals("asn1_set_choice_selector") + // || func->getName().equals("ASN1_item_ex_new") + // || func->getName().equals("ASN1_item_ex_free") + // || func->getName().equals("asn1_enc_save") + // || func->getName().equals("asn1_item_ex_combine_new") + // || func->getName().equals("ASN1_template_new") + // || func->getName().equals("ASN1_primitive_new") + // || func->getName().equals("asn1_enc_init") + // || func->getName().equals("ASN1_STRING_type_new") + // || func->getName().equals("asn1_template_clear") + // || func->getName().equals("ASN1_get_object") + // || func->getName().equals("asn1_collect") + // || func->getName().equals("asn1_ex_c2i") + // || func->getName().equals("ASN1_TYPE_new") + // || func->getName().equals("ASN1_TYPE_set") + // || func->getName().equals("c2i_ASN1_OBJECT") + // || func->getName().equals("c2i_ASN1_BIT_STRING") + // || func->getName().equals("c2i_ASN1_INTEGER") + // || func->getName().equals("ASN1_STRING_set") + // || func->getName().equals("ASN1_TYPE_free") + // || func->getName().equals("ASN1_OBJECT_new") + // || func->getName().equals("ASN1_item_new") + // || func->getName().equals("asn1_template_noexp_d2i") + // || func->getName().equals("BN_to_ASN1_INTEGER") + // || func->getName().equals("i2d_ASN1_INTEGER") + // || func->getName().equals("ASN1_INTEGER_free") + // || func->getName().equals("ASN1_STRING_set0") + // || func->getName().equals("EVP_CIPHER_param_to_asn1") + // || func->getName().equals("ASN1_STRING_length") + // || func->getName().equals("ASN1_STRING_data") + // || func->getName().equals("ASN1_STRING_new") + // || func->getName().equals("EVP_CIPHER_set_asn1_iv") + // || func->getName().equals("ASN1_TYPE_set_octetstring") + // || func->getName().equals("EVP_CIPHER_asn1_to_param") + // || func->getName().equals("EVP_CIPHER_get_asn1_iv") + // || func->getName().equals("ASN1_TYPE_get_octetstring") + // || func->getName().equals("d2i_ASN1_INTEGER") + // || func->getName().equals("ASN1_INTEGER_to_BN") + // || func->getName().equals("EVP_PKEY_asn1_find_str") + // || func->getName().equals("EVP_PKEY_asn1_find") + // || func->getName().equals("ENGINE_get_pkey_asn1_meth_engine") + // || func->getName().equals("ENGINE_get_pkey_asn1_meth") + // || func->getName().equals("ENGINE_get_pkey_asn1_meths") + // || func->getName().equals("ENGINE_pkey_asn1_find_str") + // || func->getName().equals("EVP_PKEY_asn1_get_count") + // || func->getName().equals("EVP_PKEY_asn1_get0") + // || func->getName().equals("ASN1_bn_print") + // || func->getName().equals("ASN1_BIT_STRING_free") + // || func->getName().equals("ASN1_STRING_clear_free") + // || func->getName().equals("ASN1_INTEGER_set") + // || func->getName().equals("ASN1_OCTET_STRING_new") + // || func->getName().equals("ASN1_OCTET_STRING_set") + // || func->getName().equals("ASN1_OCTET_STRING_free") + // || func->getName().equals("ec_asn1_group2pkparameters") + // || func->getName().equals("EC_GROUP_get_asn1_flag") + // || func->getName().equals("ASN1_BIT_STRING_new") + // || func->getName().equals("ASN1_BIT_STRING_set") + // || func->getName().equals("ASN1_INTEGER_new") + // || func->getName().equals("ASN1_NULL_new") + // || func->getName().equals("ec_asn1_pkparameters2group") + // || func->getName().equals("EC_GROUP_set_asn1_flag") + // || func->getName().equals("d2i_ASN1_SEQUENCE_ANY") + // || func->getName().equals("d2i_ASN1_UINTEGER") + // || func->getName().equals("ASN1_STRING_dup") + // || func->getName().equals("ASN1_STRING_copy") + // || func->getName().equals("ASN1_item_pack") + // || func->getName().equals("ASN1_OCTET_STRING_dup") + // || func->getName().equals("RSA_sign_ASN1_OCTET_STRING") + // || func->getName().equals("i2d_ASN1_OCTET_STRING") + // || func->getName().equals("i2a_ASN1_OBJECT") + // || func->getName().equals("i2a_ASN1_INTEGER") + // || func->getName().equals("i2t_ASN1_OBJECT") + // || func->getName().equals("RSA_verify_ASN1_OCTET_STRING") + // || func->getName().equals("d2i_ASN1_OCTET_STRING") + // || func->getName().equals("rc2_set_asn1_type_and_iv") + // || func->getName().equals("rc2_get_asn1_type_and_iv") + // || func->getName().equals("ASN1_TYPE_get_int_octetstring") + // || func->getName().equals("asn1_GetSequence") + // || func->getName().equals("ASN1_const_check_infinite_end") + // || func->getName().equals("ASN1_TYPE_set_int_octetstring") + // || func->getName().equals("i2d_ASN1_bytes") + // || func->getName().equals("i2d_ASN1_BIT_STRING") + // || func->getName().equals("ASN1_tag2str") + // || func->getName().equals("i2d_ASN1_TYPE") + // || func->getName().equals("ASN1_STRING_to_UTF8") + // || func->getName().equals("ASN1_mbstring_copy") + // || func->getName().equals("ASN1_mbstring_ncopy") + // || func->getName().equals("ASN1_STRING_cmp") + // || func->getName().equals("ASN1_ENUMERATED_get") + // || func->getName().equals("ASN1_ENUMERATED_free") + // || func->getName().equals("ASN1_GENERALIZEDTIME_new") + // || func->getName().equals("ASN1_GENERALIZEDTIME_adj") + // || func->getName().equals("ASN1_GENERALIZEDTIME_set_string") + // || func->getName().equals("ASN1_GENERALIZEDTIME_print") + // || func->getName().equals("ASN1_GENERALIZEDTIME_free") + // || func->getName().equals("ASN1_GENERALIZEDTIME_check") + // || func->getName().equals("asn1_generalizedtime_to_tm") + // || func->getName().equals("ASN1_BIT_STRING_get_bit") + // || func->getName().equals("ASN1_generate_v3") + // || func->getName().equals("asn1_cb") + // || func->getName().equals("d2i_ASN1_TYPE") + // || func->getName().equals("s2i_ASN1_INTEGER") + // || func->getName().equals("ASN1_TIME_check") + // || func->getName().equals("ASN1_BIT_STRING_set_bit") + // || func->getName().equals("ASN1_UTCTIME_check") + // || func->getName().equals("asn1_utctime_to_tm") + // || func->getName().equals("i2d_ASN1_SET_ANY") + // || func->getName().equals("i2d_ASN1_SEQUENCE_ANY") + // || func->getName().equals("ASN1_item_dup") + // || func->getName().equals("ASN1_STRING_set_by_NID") + // || func->getName().equals("ASN1_PRINTABLE_type") + // || func->getName().equals("ASN1_STRING_TABLE_get") + // || func->getName().equals("a2d_ASN1_OBJECT") + // || func->getName().equals("d2i_ASN1_OBJECT") + // || func->getName().equals("i2s_ASN1_INTEGER") + // || func->getName().equals("s2i_asn1_int") + // || func->getName().equals("ASN1_STRING_print") + // || func->getName().equals("i2a_ASN1_STRING") + // || func->getName().equals("i2s_ASN1_ENUMERATED_TABLE") + // || func->getName().equals("i2s_ASN1_ENUMERATED") + // || func->getName().equals("ASN1_ENUMERATED_to_BN") + // || func->getName().equals("i2v_ASN1_BIT_STRING") + // || func->getName().equals("v2i_ASN1_BIT_STRING") + // || func->getName().equals("i2s_ASN1_OCTET_STRING") + // || func->getName().equals("s2i_ASN1_OCTET_STRING") + // || func->getName().equals("i2s_ASN1_IA5STRING") + // || func->getName().equals("s2i_ASN1_IA5STRING") + // || func->getName().equals("ASN1_item_digest") + // || func->getName().equals("ASN1_item_verify") + // || func->getName().equals("ASN1_INTEGER_cmp") + // || func->getName().equals("asn1_bio_write") + // || func->getName().equals("asn1_bio_read") + // || func->getName().equals("asn1_bio_puts") + // || func->getName().equals("asn1_bio_gets") + // || func->getName().equals("asn1_bio_ctrl") + // || func->getName().equals("asn1_bio_new") + // || func->getName().equals("asn1_bio_free") + // || func->getName().equals("asn1_bio_callback_ctrl") + // || func->getName().equals("ASN1_UTCTIME_adj") + // || func->getName().equals("ASN1_TIME_adj") + // || func->getName().equals("ASN1_item_d2i_bio") + // || func->getName().equals("asn1_d2i_read_bio") + // || func->getName().equals("ASN1_OCTET_STRING_cmp") + // || func->getName().equals("PEM_ASN1_read_bio") + // || func->getName().equals("ASN1_TIME_free") + // || func->getName().equals("ASN1_TYPE_set1") + // || func->getName().equals("cms_env_asn1_ctrl") + // || func->getName().equals("ASN1_OBJECT_create") + // || func->getName().equals("EC_KEY_set_asn1_flag") + // || func->getName().equals("ENGINE_set_default_pkey_asn1_meths") + // || func->getName().equals("engine_unregister_all_pkey_asn1_meths") + // || func->getName().equals("ENGINE_register_pkey_asn1_meths") + // || func->getName().equals("ENGINE_unregister_pkey_asn1_meths") + // || func->getName().equals("ENGINE_register_all_pkey_asn1_meths") + // || func->getName().equals("ENGINE_set_pkey_asn1_meths") + // || func->getName().equals("ENGINE_get_pkey_asn1_meth_str") + // || func->getName().equals("ERR_load_ASN1_strings") + // || func->getName().equals("ASN1_add_oid_module") + // || func->getName().equals("i2d_ASN1_OBJECT") + // || func->getName().equals("ASN1_BIT_STRING_check") + // || func->getName().equals("ASN1_UTCTIME_set_string") + // || func->getName().equals("ASN1_UTCTIME_set") + // || func->getName().equals("ASN1_UTCTIME_cmp_time_t") + // || func->getName().equals("ASN1_GENERALIZEDTIME_set") + // || func->getName().equals("d2i_ASN1_TIME") + // || func->getName().equals("i2d_ASN1_TIME") + // || func->getName().equals("ASN1_TIME_new") + // || func->getName().equals("ASN1_TIME_set") + // || func->getName().equals("ASN1_TIME_to_generalizedtime") + // || func->getName().equals("ASN1_TIME_set_string") + // || func->getName().equals("ASN1_TIME_diff") + // || func->getName().equals("ASN1_INTEGER_dup") + // || func->getName().equals("ASN1_UNIVERSALSTRING_to_string") + // || func->getName().equals("ASN1_TYPE_cmp") + // || func->getName().equals("i2d_ASN1_SET") + // || func->getName().equals("d2i_ASN1_SET") + // || func->getName().equals("asn1_add_error") + // || func->getName().equals("ASN1_dup") + // || func->getName().equals("ASN1_d2i_fp") + // || func->getName().equals("ASN1_d2i_bio") + // || func->getName().equals("ASN1_item_d2i_fp") + // || func->getName().equals("ASN1_i2d_fp") + // || func->getName().equals("ASN1_i2d_bio") + // || func->getName().equals("ASN1_item_i2d_fp") + // || func->getName().equals("ASN1_item_i2d_bio") + // || func->getName().equals("ASN1_ENUMERATED_set") + // || func->getName().equals("BN_to_ASN1_ENUMERATED") + // || func->getName().equals("ASN1_sign") + // || func->getName().equals("ASN1_item_sign") + // || func->getName().equals("ASN1_item_sign_ctx") + // || func->getName().equals("ASN1_digest") + // || func->getName().equals("ASN1_verify") + // || func->getName().equals("ASN1_STRING_print_ex") + // || func->getName().equals("ASN1_STRING_print_ex_fp") + // || func->getName().equals("ASN1_UTF8STRING_free") + // || func->getName().equals("ASN1_UTF8STRING_new") + // || func->getName().equals("ASN1_parse_dump") + // || func->getName().equals("asn1_parse2") + // || func->getName().equals("d2i_ASN1_BOOLEAN") + // || func->getName().equals("d2i_ASN1_ENUMERATED") + // || func->getName().equals("ASN1_TIME_print") + // || func->getName().equals("ASN1_UTCTIME_print") + // || func->getName().equals("ASN1_BIT_STRING_name_print") + // || func->getName().equals("ASN1_BIT_STRING_set_asc") + // || func->getName().equals("ASN1_BIT_STRING_num_asc") + // || func->getName().equals("ASN1_item_ndef_i2d") + // || func->getName().equals("ASN1_template_i2d") + // || func->getName().equals("ASN1_template_d2i") + // || func->getName().equals("i2d_ASN1_ENUMERATED") + // || func->getName().equals("ASN1_ENUMERATED_new") + // || func->getName().equals("d2i_ASN1_BIT_STRING") + // || func->getName().equals("d2i_ASN1_NULL") + // || func->getName().equals("i2d_ASN1_NULL") + // || func->getName().equals("ASN1_NULL_free") + // || func->getName().equals("d2i_ASN1_UTF8STRING") + // || func->getName().equals("i2d_ASN1_UTF8STRING") + // || func->getName().equals("d2i_ASN1_PRINTABLESTRING") + // || func->getName().equals("i2d_ASN1_PRINTABLESTRING") + // || func->getName().equals("ASN1_PRINTABLESTRING_new") + // || func->getName().equals("ASN1_PRINTABLESTRING_free") + // || func->getName().equals("d2i_ASN1_T61STRING") + // || func->getName().equals("i2d_ASN1_T61STRING") + // || func->getName().equals("ASN1_T61STRING_new") + // || func->getName().equals("ASN1_T61STRING_free") + // || func->getName().equals("d2i_ASN1_IA5STRING") + // || func->getName().equals("i2d_ASN1_IA5STRING") + // || func->getName().equals("ASN1_IA5STRING_new") + // || func->getName().equals("ASN1_IA5STRING_free") + // || func->getName().equals("d2i_ASN1_GENERALSTRING") + // || func->getName().equals("i2d_ASN1_GENERALSTRING") + // || func->getName().equals("ASN1_GENERALSTRING_new") + // || func->getName().equals("ASN1_GENERALSTRING_free") + // || func->getName().equals("d2i_ASN1_UTCTIME") + // || func->getName().equals("i2d_ASN1_UTCTIME") + // || func->getName().equals("ASN1_UTCTIME_new") + // || func->getName().equals("ASN1_UTCTIME_free") + // || func->getName().equals("d2i_ASN1_GENERALIZEDTIME") + // || func->getName().equals("i2d_ASN1_GENERALIZEDTIME") + // || func->getName().equals("d2i_ASN1_VISIBLESTRING") + // || func->getName().equals("i2d_ASN1_VISIBLESTRING") + // || func->getName().equals("ASN1_VISIBLESTRING_new") + // || func->getName().equals("ASN1_VISIBLESTRING_free") + // || func->getName().equals("d2i_ASN1_UNIVERSALSTRING") + // || func->getName().equals("i2d_ASN1_UNIVERSALSTRING") + // || func->getName().equals("ASN1_UNIVERSALSTRING_new") + // || func->getName().equals("ASN1_UNIVERSALSTRING_free") + // || func->getName().equals("d2i_ASN1_BMPSTRING") + // || func->getName().equals("i2d_ASN1_BMPSTRING") + // || func->getName().equals("ASN1_BMPSTRING_new") + // || func->getName().equals("ASN1_BMPSTRING_free") + // || func->getName().equals("d2i_ASN1_PRINTABLE") + // || func->getName().equals("i2d_ASN1_PRINTABLE") + // || func->getName().equals("ASN1_PRINTABLE_new") + // || func->getName().equals("ASN1_PRINTABLE_free") + // || func->getName().equals("d2i_ASN1_SET_ANY") + // || func->getName().equals("ASN1_PCTX_new") + // || func->getName().equals("ASN1_PCTX_free") + // || func->getName().equals("ASN1_PCTX_get_flags") + // || func->getName().equals("ASN1_PCTX_set_flags") + // || func->getName().equals("ASN1_PCTX_get_nm_flags") + // || func->getName().equals("ASN1_PCTX_set_nm_flags") + // || func->getName().equals("ASN1_PCTX_get_cert_flags") + // || func->getName().equals("ASN1_PCTX_set_cert_flags") + // || func->getName().equals("ASN1_PCTX_get_oid_flags") + // || func->getName().equals("ASN1_PCTX_set_oid_flags") + // || func->getName().equals("ASN1_PCTX_get_str_flags") + // || func->getName().equals("ASN1_PCTX_set_str_flags") + // || func->getName().equals("ASN1_item_print") + // || func->getName().equals("asn1_item_print_ctx") + // || func->getName().equals("asn1_print_fsname") + // || func->getName().equals("asn1_template_print_ctx") + // || func->getName().equals("EVP_PKEY_asn1_add0") + // || func->getName().equals("EVP_PKEY_asn1_add_alias") + // || func->getName().equals("EVP_PKEY_asn1_new") + // || func->getName().equals("EVP_PKEY_asn1_get0_info") + // || func->getName().equals("EVP_PKEY_get0_asn1") + // || func->getName().equals("EVP_PKEY_asn1_copy") + // || func->getName().equals("EVP_PKEY_asn1_set_public") + // || func->getName().equals("EVP_PKEY_asn1_set_private") + // || func->getName().equals("EVP_PKEY_asn1_set_param") + // || func->getName().equals("EVP_PKEY_asn1_set_free") + // || func->getName().equals("EVP_PKEY_asn1_set_ctrl") + // || func->getName().equals("EVP_PKEY_asn1_set_item") + // || func->getName().equals("a2i_ASN1_INTEGER") + // || func->getName().equals("a2i_ASN1_STRING") + // || func->getName().equals("i2a_ASN1_ENUMERATED") + // || func->getName().equals("a2i_ASN1_ENUMERATED") + // || func->getName().equals("asn1_const_Finish") + // || func->getName().equals("i2d_ASN1_BOOLEAN") + // || func->getName().equals("BIO_f_asn1") + // || func->getName().equals("BIO_asn1_set_prefix") + // || func->getName().equals("BIO_asn1_get_prefix") + // || func->getName().equals("BIO_asn1_set_suffix") + // || func->getName().equals("BIO_asn1_get_suffix") + // || func->getName().equals("i2d_ASN1_bio_stream") + // || func->getName().equals("PEM_write_bio_ASN1_stream") + // || func->getName().equals("B64_write_ASN1") + // || func->getName().equals("SMIME_write_ASN1") + // || func->getName().equals("SMIME_read_ASN1") + // || func->getName().equals("b64_read_asn1") + // || func->getName().equals("ASN1_generate_nconf") + // || func->getName().equals("ASN1_parse") + // || func->getName().equals("ASN1_check_infinite_end") + // || func->getName().equals("asn1_Finish") + // || func->getName().equals("ASN1_STRING_length_set") + // || func->getName().equals("ASN1_STRING_type") + // || func->getName().equals("d2i_ASN1_type_bytes") + // || func->getName().equals("d2i_ASN1_bytes") + // || func->getName().equals("int_d2i_ASN1_bytes") + // || func->getName().equals("ASN1_STRING_set_default_mask") + // || func->getName().equals("ASN1_STRING_get_default_mask") + // || func->getName().equals("ASN1_STRING_set_default_mask_asc") + // || func->getName().equals("ASN1_STRING_TABLE_add") + // || func->getName().equals("ASN1_STRING_TABLE_cleanup") + // || func->getName().equals("ASN1_seq_unpack") + // || func->getName().equals("ASN1_seq_pack") + // || func->getName().equals("ASN1_unpack_string") + // || func->getName().equals("ASN1_pack_string") + // || func->getName().equals("ASN1_item_unpack") + // || func->getName().equals("PEM_ASN1_write_bio") + // || func->getName().equals("PEM_ASN1_read") + // || func->getName().equals("PEM_ASN1_write") + // || func->getName().equals("TS_ASN1_INTEGER_print_bio") + return true; + else + return false; + } +}; + + +template +class GlobalVisitor: public InstVisitor > { + static_assert( + std::is_base_of, CtxClass>::value, + "Type CtxClass should be derived from ContextBase"); + + public: + typedef VisitorCallback CallbackBase; + Module &mod; + Function &entry; + CtxClass *currCtx; + + private: + typedef InstVisitor > VisitorBase; + std::vector > allCallbacks; + std::vector > contexts; + VisitorBase *super; + + + public: + GlobalVisitor(Module &mod, Function &entry) + : mod(mod), entry(entry), currCtx(nullptr) { + super = static_cast(this); + contexts.push_back(std::unique_ptr( + new CtxClass(nullptr, &entry) )); + } + + + template + T* addCallback() { + static_assert( + std::is_base_of::value, + "Type T should be derived from VisitorCallback"); + auto ret = new T(currCtx, mod); + allCallbacks.push_back( + std::unique_ptr(ret)); + return ret; + } + + + void clearCallbacks() { + allCallbacks.clear(); + } + + + void analyze() { + currCtx = contexts[0].get(); + if (Globals::IsLib) currCtx->isdirector = true; + analyze(entry); + } + + + private: + CtxClass* pushContext(Instruction &inst, Function *func) { + auto tmp = currCtx->getOrCreateChildCtx(&inst, func); + if (tmp.second) contexts.push_back( + std::unique_ptr( tmp.first )); + currCtx = tmp.first; + return currCtx->parent; + } + + + void analyze(Function &func) { + DEBUG_CTXTIME(dbgs() << "Enter Function: " << func.getName() << "\n"); + currCtx->init(); + + int scc_cnt = 0; + std::vector > traversalOrder; + getSCCTraversalOrder(func, traversalOrder); + + for (auto &currSCC: traversalOrder) { + if (currSCC.size() > 1) { + scc_cnt++; + unsigned num_to_analyze = getNumTimesToAnalyze(currSCC); + DEBUG_GVISITOR(dbgs() << "Enter SCC. Loop = " << num_to_analyze+1 << "\n"); + this->currCtx->inside_loop = true; + + for(unsigned i = 0; i < num_to_analyze; i++) { + this->visitSCC(currSCC); + } + this->currCtx->lastloopiter = true; + this->currCtx->loopidx = scc_cnt; + } else + this->currCtx->lastloopiter = false; + + this->currCtx->inside_loop = false; + this->visitSCC(currSCC); + + if (currSCC.size() > 1) { + this->currCtx->lastloopiter = false; + DEBUG_GVISITOR(dbgs() << "Exit SCC.\n"); + } + } + + auto totalself = currCtx->get_timer(); + DEBUG_CTXTIME(dbgs() << "Exit Function: " << func.getName() + << " total = " << totalself.first + << " self = " << totalself.second << "\n"); + } + + + CtxClass* popContext() { + auto child = currCtx; + currCtx = child->parent; + currCtx->consume_childctx(child); + return child; + } + + + void visitSCC(std::vector &currSCC) { + for (auto currBB: currSCC) { + super->visit(currBB); + } + } + + + public: + /// called by InstVisitor::visit(BasicBlock&) + void visitBasicBlock(BasicBlock &BB) { + DEBUG_GVISITOR(dbgs() << "Visit Basic Block: " << BB.getName() << "\n"); + } + + + /// called before each Instruction is handled + void visit(Instruction &I) { + for (auto &currCallback: allCallbacks) + if (currCallback->enabled) + currCallback->visit(I); + super->visit(I); + } + + + /// called if Instruction is not handled + void visitInstruction(Instruction &I) { + errs() << I << "\n"; + // assert(false); + } + + +#define DEFINE_VISIT_FUNC(TYPE) \ + void visit##TYPE(TYPE &I) { \ + for (auto &currCallback: allCallbacks) \ + if (currCallback->enabled) \ + currCallback->visit##TYPE(I); \ + } +#include "Instruction.def" +#undef DEFINE_VISIT_FUNC + + + void visitCallInst(CallInst &I) { + Function *currFunc = I.getCalledFunction(); + if(currCtx->inside_loop && !(currFunc && currFunc->isDeclaration())) { + errs() << "Function inside loop, will be analyzed at last iteration\n"; + return; + } + + if(currFunc) { + this->processCalledFunction(I, currFunc); + } + else if (I.isInlineAsm()) { + return; + // assert(false); + } + else { + Value *calledValue = I.getCalledValue(); + std::vector targets; + currCtx->getFuncPtrTargets(calledValue, targets); + if (targets.size()) { + for (auto func: targets) { + if (func->arg_size() == I.getNumArgOperands()) + this->processCalledFunction(I, func); + else + DEBUG_CALLINST(dbgs() << "Number of arguments unmatch: " << I << "\n"); + } + } else { + DEBUG_CALLINST(dbgs() << "No targets found: " << I << "\n"); + // benign case found in libsodium: _misuse_handler + // assert(false); + } + } + } + + void processCalledFunction(CallInst &I, Function *currFunc) { + std::vector disabledcallbacks; + bool divein = false; + for (auto &currCallback: allCallbacks) { + if (currCallback->enabled) { + if (currCallback->visitCallInst(I, currFunc)) { + divein = true; + } else { + disabledcallbacks.push_back(currCallback.get()); + } + } + } + + if (Rules::checkBlacklist(currFunc)) { + DEBUG_CALLINST(dbgs() << "Function in Black list: " << currFunc->getName() << "\n"); + return; + } + if (currCtx->checkRecursive(I)) { + DEBUG_CALLINST(dbgs() << "Recursive found: " << I << "\n"); + return; + } + + if (divein) { + assert(!currFunc->isDeclaration()); + for (auto cb: disabledcallbacks) { + cb->enabled = false; + } + auto parentCtx = pushContext(I, currFunc); + for (auto &currCallback: allCallbacks) { + if (currCallback->enabled) { + currCallback->setupChildContext(I, parentCtx); + } + } + analyze(*currFunc); + auto childCtx = popContext(); + for (auto &currCallback: allCallbacks) { + if (currCallback->enabled) { + currCallback->stitchChildContext(I, childCtx); + } + } + for (auto cb: disabledcallbacks) { + cb->enabled = true; + } + } + } +}; + +#endif // GLOBALVISITOR_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/Instruction.def b/lab-iisec/LoCCS-gossip-cryptompk/include/Instruction.def new file mode 100644 index 0000000000000000000000000000000000000000..2983bea97226a9a9a59d10e661f320ebbdeaed82 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/Instruction.def @@ -0,0 +1,29 @@ +// Control Flow + +DEFINE_VISIT_FUNC( UnreachableInst ) +DEFINE_VISIT_FUNC( BranchInst ) +DEFINE_VISIT_FUNC( SwitchInst ) +DEFINE_VISIT_FUNC( ReturnInst ) + +// Value Flow + +DEFINE_VISIT_FUNC( BinaryOperator ) +DEFINE_VISIT_FUNC( SelectInst ) +DEFINE_VISIT_FUNC( PHINode ) +DEFINE_VISIT_FUNC( CmpInst ) + +// Pointer + +DEFINE_VISIT_FUNC( AllocaInst ) +DEFINE_VISIT_FUNC( StoreInst ) +DEFINE_VISIT_FUNC( LoadInst ) + +// Cast + +DEFINE_VISIT_FUNC( GetElementPtrInst ) +DEFINE_VISIT_FUNC( CastInst ) + +// Memory Intrinsics + +DEFINE_VISIT_FUNC( MemTransferInst ) +DEFINE_VISIT_FUNC( MemSetInst ) \ No newline at end of file diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/ModObject.h b/lab-iisec/LoCCS-gossip-cryptompk/include/ModObject.h new file mode 100644 index 0000000000000000000000000000000000000000..ca06b4e59734c94f430921f8bf628b411436117d --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/ModObject.h @@ -0,0 +1,126 @@ +#ifndef MODOBJECT_H +#define MODOBJECT_H +#include "llvm_basics.h" +#include +#include "Utils.h" + + +enum struct InstModType { + MPKWrap, + AllocaInst, + MemFunc, + FuncPtr, + FuncDirect +}; + + +struct InstMod { + + struct CallTarget { + Function *func; + InstModType type; + size_t hash; + + CallTarget() { } + + CallTarget(Function *f, InstModType t, size_t h) + : func(f), type(t), hash(h) { } + }; + + bool inloop; + int loopidx; + Instruction* inst; + InstModType type; + bool tainted, ignorepriv; + std::map calltargets; + + InstMod() + : inst(nullptr), tainted(false), ignorepriv(false) { } + + size_t calcHash() { + size_t hash = 0; + boost::hash_combine(hash, Globals::ValueUidMap[inst]); + boost::hash_combine(hash, type); + + std::map tmpMap; + for (auto &pair: calltargets) { + tmpMap[Globals::ValueUidMap[pair.first]] = pair.second; + } + + for (auto &pair: tmpMap) { + auto &target = pair.second; + boost::hash_combine(hash, Globals::ValueUidMap[target.func]); + boost::hash_combine(hash, target.type); + boost::hash_combine(hash, target.hash); + } + return hash; + } +}; + +struct FuncMod { + std::map map; + + std::vector returnlist; + bool anytainted, isdirector = false, calledbydirector = false; + int cnt_total, cnt_tainted; + + FuncMod(): anytainted(false), cnt_total(0), cnt_tainted(0) { } + + size_t calcHash() { + size_t hash = 0; + std::map tmpMap; + for (auto &pair: map) { + tmpMap[Globals::ValueUidMap[pair.first]] = pair.second; + } + for (auto &pair: tmpMap) { + auto &instmod = pair.second; + if (instmod.tainted) + boost::hash_combine(hash, instmod.calcHash()); + } + return hash; + } + + InstMod* getInstMod(Instruction &I, InstModType type, bool inloop=false, int loopidx=-1) { + auto inst = &I; + auto it = map.find(inst); + if (it == map.end()) { + auto &temp = map[inst]; + temp.inst = inst; + temp.type = type; + temp.inloop = inloop; + temp.loopidx = loopidx; + return &temp; + } + return &(it->second); + } + + InstMod* getInstMod(Instruction &I) { + auto inst = &I; + auto it = map.find(inst); + if (it == map.end()) return nullptr; + return &(it->second); + } + + void setTaint(InstMod *instmod) { + instmod->tainted = true; + anytainted = true; + } + + void addCallTarget(InstMod *instmod, Function* func, size_t ctx) { + instmod->calltargets[func] = + InstMod::CallTarget(func, InstModType::FuncDirect, ctx); + setTaint(instmod); + } + + void addLibFuncCall(InstMod *instmod, Function *func, InstModType type) { + if (instmod->type != InstModType::FuncPtr) { + setTaint(instmod); + } else { + instmod->calltargets[func] = + InstMod::CallTarget(func, type, 0); + setTaint(instmod); + } + } +}; + +#endif //MODOBJECT_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/ModifyVisitor.h b/lab-iisec/LoCCS-gossip-cryptompk/include/ModifyVisitor.h new file mode 100644 index 0000000000000000000000000000000000000000..f39600bf190e127d08d3463a06ab54933ed867de --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/ModifyVisitor.h @@ -0,0 +1,112 @@ +#ifndef MODIFYVISITOR_H +#define MODIFYVISITOR_H + +#include +#include +#include "AliasTaintCtx.h" +#include "Utils.h" +#include "VisitorCallback.h" + +struct ModifiedFunction : public FuncMod { + Function *func; + size_t ctxhash; + bool isentry, callerprotect, need_callerprotect; + Function *newfunc; + std::unique_ptr vmap; + + ModifiedFunction() : isentry(false), callerprotect(false), newfunc(nullptr) {} + + template + T *resolve_inst(T *val) { + if (!vmap) return val; + auto tmp = (*vmap)[val]; + if (!tmp) return val; + auto ret = dyn_cast(tmp); + assert(ret); + return ret; + } +}; + +struct ModifiedFunctionList { + std::map, ModifiedFunction> map; + std::vector list; + + ModifiedFunction *tryinsert(AliasTaintContext *ctx) { + ModifiedFunction tmp; + tmp.func = ctx->func; + tmp.map = std::move(ctx->funcmod.map); + tmp.returnlist = std::move(ctx->funcmod.returnlist); + tmp.ctxhash = tmp.calcHash(); + tmp.calledbydirector = ctx->funcmod.calledbydirector; + tmp.isdirector = ctx->funcmod.isdirector; + + auto key = std::make_pair(tmp.func, tmp.ctxhash); + auto ins = map.emplace(key, std::move(tmp)); + auto modfunc = &(ins.first->second); + if (ins.second) + list.push_back(modfunc); + else { + modfunc->calledbydirector |= ctx->funcmod.calledbydirector; + modfunc->isdirector |= ctx->funcmod.isdirector; + } + return modfunc; + } + + void libexports() { + std::set newDirFuncs; + for (auto &DirFunc : Globals::DirFuncs) { + for (auto modfunc : list) { + if (modfunc->func == DirFunc) { + newDirFuncs.insert(modfunc->newfunc); + } + } + } + std::set exports; + for (auto modfunc : list) { + if (modfunc->calledbydirector && !modfunc->isdirector) exports.insert(modfunc); + } + for (auto modfunc : exports) { + auto target = modfunc->newfunc; + auto wrapper_name = (modfunc->func->getName() + Globals::ExportLabel).str(); + auto origname = target->getName(); + std::set calls; + for (auto user : target->users()) { + CallSite CS(user); + CallInst *callinst = cast(CS.getInstruction()); + calls.insert(callinst); + } + for (auto callinst : calls) + funcwrap(target, wrapper_name, callinst, modfunc->need_callerprotect); + DEBUG_MODIFY(dbgs() << formatv("wrap {0} with {1}\n", origname, wrapper_name)); + (*Globals::ApisReport) << wrapper_name << "\n"; + } + } +}; + +struct ModifyCallbackVisitor : public VisitorCallback { + static ModifiedFunctionList newfunctions; + static std::set analyzed_functions; + + ModifyCallbackVisitor(AliasTaintContext *&ctx, Module &m) : VisitorCallback(ctx, m) {} + + virtual void visitAllocaInst(AllocaInst &I); + virtual void visitLoadInst(LoadInst &I); + virtual void visitStoreInst(StoreInst &I); + virtual void visitMemTransferInst(MemTransferInst &I); + virtual void visitMemSetInst(MemSetInst &I); + virtual bool visitCallInst(CallInst &I, Function *func); + virtual void visitReturnInst(ReturnInst &I); + virtual void setupChildContext(CallInst &I, AliasTaintContext *child); + virtual void stitchChildContext(CallInst &I, AliasTaintContext *child); + + void prestat(); + void poststat(); + void run_modify(); + +private: + FuncMod *funcmod() { return &(currCtx->funcmod); } + + void visitLibFunction(CallInst &I, Function *func, InstMod *instmod); +}; + +#endif // MODIFYVISITOR_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/TaintAnalysisVisitor.h b/lab-iisec/LoCCS-gossip-cryptompk/include/TaintAnalysisVisitor.h new file mode 100644 index 0000000000000000000000000000000000000000..3d1203fa7912340c4d189183918add4e3be9d72f --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/TaintAnalysisVisitor.h @@ -0,0 +1,31 @@ +#ifndef TAINTVISITOR_H +#define TAINTVISITOR_H + +#include "VisitorCallback.h" +#include "AliasTaintCtx.h" + + +struct TaintAnalysisVisitor: public VisitorCallback { + TaintAnalysisVisitor(AliasTaintContext* &ctx, Module &m) + : VisitorCallback(ctx, m) { } + + virtual void visitCastInst(CastInst &I); + + virtual void visitBinaryOperator(BinaryOperator &I); + virtual void visitPHINode(PHINode &I); + virtual void visitSelectInst(SelectInst &I); + + virtual void visitGetElementPtrInst(GetElementPtrInst &I); + virtual void visitLoadInst(LoadInst &I); + virtual void visitStoreInst(StoreInst &I); + virtual void visitMemTransferInst(MemTransferInst &I); + + virtual void visitReturnInst(ReturnInst &I); + virtual void visitLibFunctions(CallInst &I, Function *func); + virtual bool visitCallInst(CallInst &I, Function *targetFunction); + + virtual void setupChildContext(CallInst &I, AliasTaintContext *parentContext); + virtual void stitchChildContext(CallInst &I, AliasTaintContext *childContext); +}; + +#endif // TAINTVISITOR_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/Utils.h b/lab-iisec/LoCCS-gossip-cryptompk/include/Utils.h new file mode 100644 index 0000000000000000000000000000000000000000..f658b4cc3f6cb80c903bc8d7e4d3e8c79aa7013f --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/Utils.h @@ -0,0 +1,162 @@ +#ifndef UTILS_H +#define UTILS_H +#include "llvm/IRReader/IRReader.h" +#include "llvm/Support/SourceMgr.h" +#include "llvm/IR/DebugInfoMetadata.h" +#include "llvm_basics.h" +#include + +struct Globals { + static std::map ValueUidMap; + static std::set DirFuncs; + static std::string ExportLabel; + static raw_fd_ostream *ApisReport; + static raw_fd_ostream *TaintReport; + static double Threshold; + static bool IsLib; + static std::set Hotspots; + static std::set SkipFuncs; +}; + +void initValueUid(Module &M, std::map &valueUidMap); + +void getSCCTraversalOrder(Function &currF, std::vector > &bbTraversalList); + + +size_t getNumTimesToAnalyze(std::vector &currSCC); + + +// Parser utilities + + +inline std::pair extractConstantInt(Value *val) { + auto ci = dyn_cast(val); + if (ci) return std::make_pair(ci->getZExtValue(), true); + return std::make_pair(0, false); +} + + +size_t getGEPOffset(GetElementPtrInst &I, const DataLayout &dl); + + +size_t getGEPOffset(ConstantExpr *cexpr, const DataLayout &dl, LLVMContext &ctx); + + +struct InitializerWalker { + struct GlobalPointsTo { + GlobalObject *target; + size_t srcoff, dstoff; + + GlobalPointsTo(GlobalObject *t, size_t s, size_t d) + : target(t), srcoff(s), dstoff(d) { } + }; + + std::vector results; + const DataLayout &layout; + LLVMContext &ctx; + + InitializerWalker(const DataLayout &dl, LLVMContext &c) + : layout(dl), ctx(c) { } + + void scan(Constant* init) { + results.clear(); + scan(init, 0); + } + + void scan(Constant* init, size_t off); + + void handleNonAgg(Constant* init, size_t off); +}; + + +// Transform utilities + + +void splitConstExpr(Module &M); + + +void insertWRPKRU(Module &M, Instruction *I, int perm_val, Value *dep=nullptr); + + +CallInst* replaceAllocaWithMPKMalloc(Module &M, AllocaInst *I); + + +CallInst* insertMPKFree(Module &M, Value *target, Instruction *insertbefore); +CallInst* insertMemset(Module &M, Value *target, Instruction *insertbefore); + +void replaceRuntime(Module &M); + +struct ExpandFuncPtr { + std::vector args; + CallInst *originst; + Value *funcptr; + FunctionType *fptype; + Function *F; + Module *M; + + BasicBlock *curblock, *tailblock; + std::vector retvals; + + ExpandFuncPtr(CallInst *inst); + + void splitBlock(); + + CallInst* addTarget(Function *target); + + CallInst* addFallback(); + + PHINode* addPHINode(); +}; + +Function *funcwrap(Function *target, std::string wrapper_name, CallInst *callinst, bool); + +struct DbgInfo { + static std::map DbgUidValueMap; + static Module *DbgM; + + static void load(std::string& dbgbc) { + SMDiagnostic Err; + + LLVMContext *LLVMCtx = new LLVMContext(); + DbgM = parseIRFile(dbgbc, Err, *LLVMCtx).release(); + std::size_t cnt = 0xdeadbeef00000000; + + splitConstExpr(*DbgM); + for (auto &F : *DbgM) { + DbgUidValueMap[cnt] = &F; + cnt++; + for (auto &BB : F) { + DbgUidValueMap[cnt] = &BB; + cnt++; + for (auto &II : BB) { + if (isa(II)) continue; + DbgUidValueMap[cnt] = &II; + cnt++; + } + } + } + } + + static int getSrcLine(Instruction *I) { + auto DI = dyn_cast(DbgUidValueMap[Globals::ValueUidMap[I]]); + const DebugLoc &currDC = DI->getDebugLoc(); + if (currDC) { + return currDC.getLine(); + } + return -1; + } + + static std::string getSrcFileName(Instruction *I) { + auto DI = dyn_cast(DbgUidValueMap[Globals::ValueUidMap[I]]); + const DebugLoc &currDC = DI->getDebugLoc(); + if (currDC) { + auto *Scope = cast(currDC->getScope()); + return (Scope->getDirectory()+"/"+Scope->getFilename()).str(); + } + return std::string(""); + } + +}; + + +#endif // UTILS_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/VisitorCallback.h b/lab-iisec/LoCCS-gossip-cryptompk/include/VisitorCallback.h new file mode 100644 index 0000000000000000000000000000000000000000..638daab261d81e698f29d9796aa097d818583f44 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/VisitorCallback.h @@ -0,0 +1,33 @@ +#ifndef VISITORCALLBACK_H +#define VISITORCALLBACK_H + +#include "llvm_basics.h" + + +template +struct VisitorCallback { + CtxClass* &currCtx; + Module &mod; + bool enabled; + + VisitorCallback(CtxClass* &ctx, Module &m) + : currCtx(ctx), mod(m), enabled(true) { } + + virtual ~VisitorCallback() { } + + virtual void visit(Instruction &I) { } + +#define DEFINE_VISIT_FUNC(TYPE) \ + virtual void visit##TYPE(TYPE &I) { } +#include "Instruction.def" +#undef DEFINE_VISIT_FUNC + + virtual bool visitCallInst(CallInst &I, Function *targetFunction) { + return !targetFunction->isDeclaration(); + } + + virtual void setupChildContext(CallInst &I, CtxClass *parentContext) { } + virtual void stitchChildContext(CallInst &I, CtxClass *childContext) { } +}; + +#endif // VISITORCALLBACK_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/llvm_basics.h b/lab-iisec/LoCCS-gossip-cryptompk/include/llvm_basics.h new file mode 100644 index 0000000000000000000000000000000000000000..ffa3ea83d175f530b7f04dc9684c6898d0ab690c --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/llvm_basics.h @@ -0,0 +1,18 @@ +#ifndef LLVM_BASICS_H +#define LLVM_BASICS_H +#include +#include +#include +#include +#include +using namespace llvm; + +#define DEBUG_PASSENTRY(msg) DEBUG_WITH_TYPE("entry", msg) +#define DEBUG_GVISITOR(msg) DEBUG_WITH_TYPE("gvisitor", msg) +#define DEBUG_CTXTIME(msg) DEBUG_WITH_TYPE("ctxtime", msg) +#define DEBUG_GLOBOBJ(msg) DEBUG_WITH_TYPE("globobj", msg) +#define DEBUG_CALLINST(msg) DEBUG_WITH_TYPE("callinst", msg) +#define DEBUG_LOADSTOR(msg) DEBUG_WITH_TYPE("loadstor", msg) +#define DEBUG_MODIFY(msg) DEBUG_WITH_TYPE("modify", msg) + +#endif // LLVM_BASICS_H diff --git a/lab-iisec/LoCCS-gossip-cryptompk/include/marcos.h b/lab-iisec/LoCCS-gossip-cryptompk/include/marcos.h new file mode 100644 index 0000000000000000000000000000000000000000..8aa8e637268cbac264e3bc8f0d2246cc74549bd9 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/include/marcos.h @@ -0,0 +1,8 @@ +#ifndef MARCOS_H +#define MARCOS_H + +// #define MEMMANAGER_OFF +// #define WRPKRU_OFF +// #define ONLY_MASTERKEY + +#endif diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/.gitignore b/lab-iisec/LoCCS-gossip-cryptompk/runtime/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..04a316303497e08715f7596a418415b4bab5d92f --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/.gitignore @@ -0,0 +1 @@ +jemalloc/* \ No newline at end of file diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/clang.patch b/lab-iisec/LoCCS-gossip-cryptompk/runtime/clang.patch new file mode 100644 index 0000000000000000000000000000000000000000..13006897e7a73e34d1a684449d9e6cd4d817bb29 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/clang.patch @@ -0,0 +1,373 @@ +diff --git a/include/clang/Parse/Parser.h b/include/clang/Parse/Parser.h +index 59adfdcc7d..69bf88a371 100644 +--- a/include/clang/Parse/Parser.h ++++ b/include/clang/Parse/Parser.h +@@ -191,9 +191,6 @@ class Parser : public CodeCompletionHandler { + std::unique_ptr STDCUnknownHandler; + std::unique_ptr AttributePragmaHandler; + +- std::unique_ptr TaintHelperHandler; +- std::unique_ptr SinkHelperHandler; +- + std::unique_ptr CommentSemaHandler; + + /// Whether the '>' token acts as an operator or not. This will be +diff --git a/lib/Parse/ParsePragma.cpp b/lib/Parse/ParsePragma.cpp +index cd61a582d7..07f48e0779 100644 +--- a/lib/Parse/ParsePragma.cpp ++++ b/lib/Parse/ParsePragma.cpp +@@ -21,9 +21,6 @@ + #include "clang/Sema/LoopHint.h" + #include "clang/Sema/Scope.h" + #include "llvm/ADT/StringSwitch.h" +- +-#include +- + using namespace clang; + + namespace { +@@ -252,20 +249,6 @@ struct PragmaAttributeHandler : public PragmaHandler { + + } // end namespace + +-/// "\#pragma tainter taint(cond)" +-struct PragmaTaintHelperHandler : public PragmaHandler { +- PragmaTaintHelperHandler() +- : PragmaHandler("taint") {} +- void HandlePragma(Preprocessor &PP, PragmaIntroducerKind Introducer, +- Token &FirstToken) override; +-}; +-struct PragmaSinkHelperHandler : public PragmaHandler { +- PragmaSinkHelperHandler() +- : PragmaHandler("sinktaint") {} +- void HandlePragma(Preprocessor &PP, PragmaIntroducerKind Introducer, +- Token &FirstToken) override; +-}; +- + void Parser::initializePragmaHandlers() { + AlignHandler.reset(new PragmaAlignHandler()); + PP.AddPragmaHandler(AlignHandler.get()); +@@ -357,11 +340,6 @@ void Parser::initializePragmaHandlers() { + PP.AddPragmaHandler("clang", CUDAForceHostDeviceHandler.get()); + } + +- TaintHelperHandler.reset(new PragmaTaintHelperHandler()); +- PP.AddPragmaHandler("tainter", TaintHelperHandler.get()); +- SinkHelperHandler.reset(new PragmaSinkHelperHandler()); +- PP.AddPragmaHandler("tainter", SinkHelperHandler.get()); +- + OptimizeHandler.reset(new PragmaOptimizeHandler(Actions)); + PP.AddPragmaHandler("clang", OptimizeHandler.get()); + +@@ -2319,6 +2297,7 @@ void PragmaMSVtorDisp::HandlePragma(Preprocessor &PP, + } + } + ++ + uint64_t Value = 0; + if (Action & Sema::PSK_Push || Action & Sema::PSK_Set) { + const IdentifierInfo *II = Tok.getIdentifierInfo(); +@@ -2366,303 +2345,6 @@ void PragmaMSVtorDisp::HandlePragma(Preprocessor &PP, + PP.EnterToken(AnnotTok); + } + +-/////////////////////////////// +-// +-// Taint helper directives +-// +- +-// name of the call inserted in place of the triton assert directive +-// It must be provided at link time +-static char const TaintHelperFuncName[] = "__taint_fdf0f8a65855a52bbe69cd2075f89027"; +- +-// push the declaration of __taint_fdf0f8a65855a52bbe69cd2075f89027(char const*, ...) into the stream +-// it is added before each call +-// FIXME: when in C++ mode, add the "C" of extern "C" +-static void PushTaintHelperDecl(Preprocessor& PP, SmallVectorImpl& TokenList, Token const& FirstToken) { +- Token FuncNameTok; +- FuncNameTok.startToken(); +- FuncNameTok.setKind(tok::identifier); +- FuncNameTok.setIdentifierInfo(PP.getIdentifierInfo(TaintHelperFuncName)); +- +- Token ExternTok; +- ExternTok.startToken(); +- ExternTok.setKind(tok::kw_extern); +- +- Token VoidTok; +- VoidTok.startToken(); +- VoidTok.setKind(tok::kw_void); +- +- Token CharTok; +- CharTok.startToken(); +- CharTok.setKind(tok::kw_char); +- +- Token ConstTok; +- ConstTok.startToken(); +- ConstTok.setKind(tok::kw_const); +- +- Token StarTok; +- StarTok.startToken(); +- StarTok.setKind(tok::star); +- +- Token CommaTok; +- CommaTok.startToken(); +- CommaTok.setKind(tok::comma); +- +- Token LParTok; +- LParTok.startToken(); +- LParTok.setKind(tok::l_paren); +- +- Token EllipsisTok; +- EllipsisTok.startToken(); +- EllipsisTok.setKind(tok::ellipsis); +- +- Token RParTok; +- RParTok.startToken(); +- RParTok.setKind(tok::r_paren); +- +- Token SemiTok; +- SemiTok.startToken(); +- SemiTok.setKind(tok::semi); +- +- TokenList.push_back(ExternTok); +- TokenList.push_back(VoidTok); +- TokenList.push_back(FuncNameTok); +- TokenList.push_back(LParTok); +- TokenList.push_back(CharTok); +- TokenList.push_back(ConstTok); +- TokenList.push_back(StarTok); +- TokenList.push_back(CommaTok); +- TokenList.push_back(EllipsisTok); +- TokenList.push_back(RParTok); +- TokenList.push_back(SemiTok); +- +-} +- +-// Handle #pragma tainter taint(cond), where cond can make reference to identifiers +-// constants and address of identifiers. +-// +-// This works by inserting new tokens into the preprocessor stream, turning +-// +-// #pragma tainter taint(&a != &b && b > (c - 1) || c ^ 4) +-// +-// into +-// +-// extern __taint_fdf0f8a65855a52bbe69cd2075f89027(char const* fmt, ...); +-// __taint_fdf0f8a65855a52bbe69cd2075f89027("%0 != %1 && %2 > (%3 - 1) || %3 ^ 4", &a, &b, b, c) +-// +-void PragmaTaintHelperHandler::HandlePragma(Preprocessor &PP, +- PragmaIntroducerKind Introducer, +- Token &FirstToken) { +- SmallVector TokenList; +- // an identifier is either a variable name, or its address (2 tokens) +- // so use a vector of very small token vector to represent them +- SmallVector, 8> IdentifierTokenList; +- std::map Identifiers; +- Token Tok; +- PP.Lex(Tok); +- +- // first add the extern declarator for extern __taint_fdf0f8a65855a52bbe69cd2075f89027(char const* fmt, ...); +- PushTaintHelperDecl(PP, TokenList, FirstToken); +- +- if (Tok.isNot(tok::l_paren)) { +- PP.Diag(Tok.getLocation(), diag::err_function_is_not_record) +- << PP.getSpelling(Tok) ; +- return; +- } +- +- Token FuncNameTok; +- FuncNameTok.startToken(); +- FuncNameTok.setKind(tok::identifier); +- FuncNameTok.setIdentifierInfo(PP.getIdentifierInfo(TaintHelperFuncName)); +- +- Token LParTok; +- LParTok.startToken(); +- LParTok.setKind(tok::l_paren); +- +- Token CommaTok; +- CommaTok.startToken(); +- CommaTok.setKind(tok::comma); +- +- Token RParTok; +- RParTok.startToken(); +- RParTok.setKind(tok::r_paren); +- +- Token SemiTok; +- SemiTok.startToken(); +- SemiTok.setKind(tok::semi); +- +- Token FmtTok; +- FmtTok.startToken(); +- FmtTok.setKind(tok::string_literal); +- auto sdirective = std::string("\"AAAAAAAAAAAAAAAAAAAA\""); +- FmtTok.setLiteralData(strdup(sdirective.c_str())); +- FmtTok.setLength(sdirective.size()); +- +- TokenList.push_back(FuncNameTok); +- TokenList.push_back(LParTok); +- TokenList.push_back(FmtTok); +- TokenList.push_back(CommaTok); +- +- PP.Lex(Tok); +- while(Tok.isNot(tok::eod)) { +- TokenList.push_back(Tok); +- //llvm::dbgs() << PP.getSpelling(Tok) << "\n"; +- PP.Lex(Tok); +- } +- //llvm::dbgs() << "aaaaaaaaaaaaaaaaaa" << "\n"; +- +- TokenList.push_back(SemiTok); +- +- // lazily fix locations +- for(Token& Tok : TokenList) +- Tok.setLocation(FirstToken.getLocation()); +- +- // finally prepare memory, insert tokens in memory and feed the stream +- auto TokenArray = llvm::make_unique(TokenList.size()); +- std::copy(TokenList.begin(), TokenList.end(), TokenArray.get()); +- +- PP.EnterTokenStream(std::move(TokenArray), TokenList.size(), +- /*DisableMacroExpansion=*/false); +-} +- +-static char const SinkHelperFuncName[] = "__sinktaint_fdf0f8a65855a52bbe69cd2075f89027"; +-static void PushSinkHelperDecl(Preprocessor& PP, SmallVectorImpl& TokenList, Token const& FirstToken) { +- Token FuncNameTok; +- FuncNameTok.startToken(); +- FuncNameTok.setKind(tok::identifier); +- FuncNameTok.setIdentifierInfo(PP.getIdentifierInfo(SinkHelperFuncName)); +- +- Token ExternTok; +- ExternTok.startToken(); +- ExternTok.setKind(tok::kw_extern); +- +- Token VoidTok; +- VoidTok.startToken(); +- VoidTok.setKind(tok::kw_void); +- +- Token CharTok; +- CharTok.startToken(); +- CharTok.setKind(tok::kw_char); +- +- Token ConstTok; +- ConstTok.startToken(); +- ConstTok.setKind(tok::kw_const); +- +- Token StarTok; +- StarTok.startToken(); +- StarTok.setKind(tok::star); +- +- Token CommaTok; +- CommaTok.startToken(); +- CommaTok.setKind(tok::comma); +- +- Token LParTok; +- LParTok.startToken(); +- LParTok.setKind(tok::l_paren); +- +- Token EllipsisTok; +- EllipsisTok.startToken(); +- EllipsisTok.setKind(tok::ellipsis); +- +- Token RParTok; +- RParTok.startToken(); +- RParTok.setKind(tok::r_paren); +- +- Token SemiTok; +- SemiTok.startToken(); +- SemiTok.setKind(tok::semi); +- +- TokenList.push_back(ExternTok); +- TokenList.push_back(VoidTok); +- TokenList.push_back(FuncNameTok); +- TokenList.push_back(LParTok); +- TokenList.push_back(CharTok); +- TokenList.push_back(ConstTok); +- TokenList.push_back(StarTok); +- TokenList.push_back(CommaTok); +- TokenList.push_back(EllipsisTok); +- TokenList.push_back(RParTok); +- TokenList.push_back(SemiTok); +- +-} +- +- +-void PragmaSinkHelperHandler::HandlePragma(Preprocessor &PP, +- PragmaIntroducerKind Introducer, +- Token &FirstToken) { +- SmallVector TokenList; +- // an identifier is either a variable name, or its address (2 tokens) +- // so use a vector of very small token vector to represent them +- SmallVector, 8> IdentifierTokenList; +- std::map Identifiers; +- Token Tok; +- PP.Lex(Tok); +- +- // first add the extern declarator for extern __taint_fdf0f8a65855a52bbe69cd2075f89027(char const* fmt, ...); +- PushSinkHelperDecl(PP, TokenList, FirstToken); +- +- if (Tok.isNot(tok::l_paren)) { +- PP.Diag(Tok.getLocation(), diag::err_function_is_not_record) +- << PP.getSpelling(Tok) ; +- return; +- } +- +- Token FuncNameTok; +- FuncNameTok.startToken(); +- FuncNameTok.setKind(tok::identifier); +- FuncNameTok.setIdentifierInfo(PP.getIdentifierInfo(SinkHelperFuncName)); +- +- Token LParTok; +- LParTok.startToken(); +- LParTok.setKind(tok::l_paren); +- +- Token CommaTok; +- CommaTok.startToken(); +- CommaTok.setKind(tok::comma); +- +- Token RParTok; +- RParTok.startToken(); +- RParTok.setKind(tok::r_paren); +- +- Token SemiTok; +- SemiTok.startToken(); +- SemiTok.setKind(tok::semi); +- +- Token FmtTok; +- FmtTok.startToken(); +- FmtTok.setKind(tok::string_literal); +- auto sdirective = std::string("\"AAAAAAAAAAAAAAAAAAAA\""); +- FmtTok.setLiteralData(strdup(sdirective.c_str())); +- FmtTok.setLength(sdirective.size()); +- +- TokenList.push_back(FuncNameTok); +- TokenList.push_back(LParTok); +- TokenList.push_back(FmtTok); +- TokenList.push_back(CommaTok); +- PP.Lex(Tok); +- while(Tok.isNot(tok::eod)) { +- TokenList.push_back(Tok); +- //llvm::dbgs() << PP.getSpelling(Tok) << "\n"; +- PP.Lex(Tok); +- } +- //llvm::dbgs() << "aaaaaaaaaaaaaaaaaa" << "\n"; +- +- TokenList.push_back(SemiTok); +- +- // lazily fix locations +- for(Token& Tok : TokenList) +- Tok.setLocation(FirstToken.getLocation()); +- +- // finally prepare memory, insert tokens in memory and feed the stream +- auto TokenArray = llvm::make_unique(TokenList.size()); +- std::copy(TokenList.begin(), TokenList.end(), TokenArray.get()); +- +- PP.EnterTokenStream(std::move(TokenArray), TokenList.size(), +- /*DisableMacroExpansion=*/false); +-} +- +- +- + /// Handle all MS pragmas. Simply forwards the tokens after inserting + /// an annotation token. + void PragmaMSPragma::HandlePragma(Preprocessor &PP, diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc520.patch b/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc520.patch new file mode 100644 index 0000000000000000000000000000000000000000..214df467fd295a60f93857c4631109978fcf960f --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc520.patch @@ -0,0 +1,63 @@ +diff --git a/src/jemalloc.c b/src/jemalloc.c +index 3942c82b..c8afa9c4 100644 +--- a/src/jemalloc.c ++++ b/src/jemalloc.c +@@ -215,8 +215,14 @@ malloc_init_a0(void) { + return false; + } + ++#define _GNU_SOURCE ++#include ++JEMALLOC_EXPORT int mpk_pkey = -1; ++ + JEMALLOC_ALWAYS_INLINE bool + malloc_init(void) { ++ mpk_pkey = pkey_alloc(0, 0); ++ printf("pkey : %d\n", mpk_pkey); + if (unlikely(!malloc_initialized()) && malloc_init_hard()) { + return true; + } +diff --git a/src/pages.c b/src/pages.c +index bee99915..13de27a0 100644 +--- a/src/pages.c ++++ b/src/pages.c +@@ -50,6 +50,10 @@ static void os_pages_unmap(void *addr, size_t size); + + /******************************************************************************/ + ++#define _GNU_SOURCE ++#include ++JEMALLOC_EXPORT int mpk_pkey; ++ + static void * + os_pages_map(void *addr, size_t size, size_t alignment, bool *commit) { + assert(ALIGNMENT_ADDR2BASE(addr, os_page) == addr); +@@ -77,6 +81,9 @@ os_pages_map(void *addr, size_t size, size_t alignment, bool *commit) { + int prot = *commit ? PAGES_PROT_COMMIT : PAGES_PROT_DECOMMIT; + + ret = mmap(addr, size, prot, mmap_flags, -1, 0); ++ pkey_mprotect(ret, size, prot, mpk_pkey); ++ printf("os_pages_map %lx %lx\n", ret, size); ++ + } + assert(ret != NULL); + +@@ -202,6 +209,7 @@ pages_map(void *addr, size_t size, size_t alignment, bool *commit) { + } + + void *ret = mmap(addr, size, prot, flags, -1, 0); ++ printf("pages_map %lx\n", addr); + if (ret == MAP_FAILED) { + ret = NULL; + } +@@ -260,8 +268,8 @@ pages_commit_impl(void *addr, size_t size, bool commit) { + #else + { + int prot = commit ? PAGES_PROT_COMMIT : PAGES_PROT_DECOMMIT; +- void *result = mmap(addr, size, prot, mmap_flags | MAP_FIXED, +- -1, 0); ++ void *result = mmap(addr, size, prot, mmap_flags | MAP_FIXED, -1, 0); ++ printf("pages_commit_impl %lx\n", addr); + if (result == MAP_FAILED) { + return true; + } diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc_build.sh b/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc_build.sh new file mode 100644 index 0000000000000000000000000000000000000000..3c02eecd837e4413756570b706b9c560d501a3c4 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/jemalloc_build.sh @@ -0,0 +1,3 @@ +./autogen.sh --with-jemalloc-prefix=mpk_ --prefix=$(realpath ..) +make +make install \ No newline at end of file diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/runtime.c b/lab-iisec/LoCCS-gossip-cryptompk/runtime/runtime.c new file mode 100644 index 0000000000000000000000000000000000000000..cd87058560f29525825e6230c8b5c214ea0a1077 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/runtime.c @@ -0,0 +1,62 @@ +#include +#include +#include + +void *mpk_malloc(size_t size); +void mpk_free(void *ptr); +void *mpk_realloc(void *ptr, size_t size); +int mpk_check_pages(void *ptr); +size_t mpk_usable_size(void *ptr); + +void *u_malloc(size_t size) { + void *ptr = malloc(size); + return ptr; +} + +void *m_malloc(size_t size) { + void *ptr = mpk_malloc(size); + return ptr; +} + +void m_free(void *ptr) { + if (mpk_check_pages(ptr) != -1) { + // printf("check_pages %p, %x\n", ptr, mpk_check_pages(ptr)); + mpk_free(ptr); + } + else { + // printf("check_pages %p, %x\n", ptr, mpk_check_pages(ptr)); + free(ptr); + } +} + +void *m_realloc(void *ptr, size_t size) { + if (mpk_check_pages(ptr) != -1) + return mpk_realloc(ptr, size); + else + return realloc(ptr, size); +} + + +void __taint_fdf0f8a65855a52bbe69cd2075f89027(const char *fmt, ...) { } +void __sinktaint_fdf0f8a65855a52bbe69cd2075f89027(const char *fmt, ...) { } + +#include +#include +void *__taint_replace_fdf0f8a65855a52bbe69cd2075f89027(void *ptr) { + pkey_set(1, 0); + size_t size = 0; + if (mpk_check_pages(ptr) != -1) { + size = mpk_malloc_usable_size(ptr); + } else { + size = malloc_usable_size(ptr); + } + void *new = m_malloc(size); + memcpy(new, ptr, size); + memset(ptr, 0, size); + m_free(ptr); + pkey_set(1, 3); + + return new; +} + + diff --git a/lab-iisec/LoCCS-gossip-cryptompk/runtime/test_jemalloc.c b/lab-iisec/LoCCS-gossip-cryptompk/runtime/test_jemalloc.c new file mode 100644 index 0000000000000000000000000000000000000000..4f9dd83e948eafa12195cdc7c2ccf58c235c1595 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/runtime/test_jemalloc.c @@ -0,0 +1,37 @@ +// clang -O0 test_jemalloc.c -L. -ljemalloc +// LD_PRELOAD=./libjemalloc.so ./a.out + +#define _GNU_SOURCE +#include +#include +#include + +void *mpk_malloc(size_t size); +size_t mpk_malloc_usable_size(void *ptr); +void *mpk_realloc(void *ptr, size_t size); +void mpk_free(void *ptr); +int mpk_check_paegs(void *ptr); + +int main(int argc, char const* argv[]) +{ + pkey_set(1, 0); + void *p1, *p2; + size_t s1, s2; + + for (int i = 0; i < 0x1000; i++) { + p1 = mpk_malloc(0x1000); + s1 = mpk_check_pages(p1); + p2 = malloc(0x1000); + s2 = mpk_check_pages(p2); + printf("%p\n", p1); + printf("%x\n", s1); + printf("%p\n", p2); + printf("%x\n", s2); + if (!(s1 >= 0 && s1 < 0x100 && s2 == -1)) { + puts("failed"); + break; + } + } + + return 0; +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/AliasAnalysisVisitor.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/AliasAnalysisVisitor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..84d3c391aa828f59f4340579cccbc3176565de00 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/AliasAnalysisVisitor.cpp @@ -0,0 +1,281 @@ +#include "AliasAnalysisVisitor.h" +#include "Utils.h" + + +AliasAnalysisVisitor::AliasAnalysisVisitor(AliasTaintContext* &ctx, Module &m) + : VisitorCallback(ctx, m) { + AliasTaintContext::setupGlobals(m); +} + + +void AliasAnalysisVisitor::visitAllocaInst(AllocaInst &I) { + DEBUG_LOADSTOR(dbgs() << "CreateRegMemPair: " << I << "\n"); + currCtx->createRegMemPair(&I); +} + + +void AliasAnalysisVisitor::visitCastInst(CastInst &I) { + // TODO: Sanitize + // some kinds of casts should not be allowed + // e.g. struct to array + auto op = I.getOpcode(); + if (op != Instruction::PtrToInt && op != Instruction::IntToPtr) + return; + auto src = I.getOperand(0); + auto srcreg = currCtx->findOpReg(src); + if (hasPointsTo(srcreg)) { + DEBUG_LOADSTOR(dbgs() << "Cast with points-to: " << I << "\n"); + auto dstreg = currCtx->getDestReg(&I); + dstreg->mergePointsTo(srcreg, &I); + } +} + + +void AliasAnalysisVisitor::visitBinaryOperator(BinaryOperator &I) { + if (I.getOpcode() == Instruction::Mul) return; + auto lhs = I.getOperand(0); + auto rhs = I.getOperand(1); + auto lreg = currCtx->findOpReg(lhs); + auto rreg = currCtx->findOpReg(rhs); + if (hasPointsTo(lreg)) { + if (hasPointsTo(rreg)) { + DEBUG_LOADSTOR(dbgs() << "BinOp with double aliases: " + << I << "\n"); + // possibly calculating the offset between two pointers + // ignore + } + auto dstreg = currCtx->getDestReg(&I); + dstreg->mergePointsTo(lreg, &I); + } + if (hasPointsTo(rreg)) { + auto dstreg = currCtx->getDestReg(&I); + dstreg->mergePointsTo(rreg, &I); + } +} + + +void AliasAnalysisVisitor::visitPHINode(PHINode &I) { + RegObject *dst = nullptr; + for (auto &use: I.incoming_values()) { + auto reg = currCtx->findOpReg(use.get()); + if (hasPointsTo(reg)) { + if (!dst) dst = currCtx->getDestReg(&I); + dst->mergePointsTo(reg, &I); + } + } +} + + +void AliasAnalysisVisitor::visitSelectInst(SelectInst &I) { + auto lhs = I.getTrueValue(); + auto rhs = I.getFalseValue(); + auto lreg = currCtx->findOpReg(lhs); + auto rreg = currCtx->findOpReg(rhs); + if (hasPointsTo(lreg) || hasPointsTo(rreg)) { + auto dstreg = currCtx->getDestReg(&I); + if (hasPointsTo(lreg)) dstreg->mergePointsTo(lreg, &I); + if (hasPointsTo(rreg)) dstreg->mergePointsTo(rreg, &I); + } +} + + +void AliasAnalysisVisitor::visitGetElementPtrInst(GetElementPtrInst &I) { + if (I.hasAllZeroIndices()) return; + unsigned offset = getGEPOffset(I, mod.getDataLayout()); + auto val = I.getPointerOperand(); + auto srcreg = currCtx->findOpReg(val); + if (hasPointsTo(srcreg)) { + auto dstreg = currCtx->getDestReg(&I); + for (auto &pt: srcreg->pointsto) { + //DEBUG_LOADSTOR(dbgs() << "Gep Offset: " << pt.dstoff+offset << " "<< I << "\n"); + dstreg->addPointsTo(pt.target, pt.dstoff+offset, &I); + } + } +} + + +void AliasAnalysisVisitor::visitLoadInst(LoadInst &I) { + auto src = I.getPointerOperand(); + auto srcreg = currCtx->findOpReg(src); + if (!hasPointsTo(srcreg)) return; + // whether 2nd-level points-to should be ensured + bool ensure2Lpt = I.getType()->isPointerTy() + && (isa(src) || !currCtx->inside_loop); + RegObject *dstreg = nullptr; + AliasObject *newobj = nullptr; + for (auto &pt: srcreg->pointsto) { + // load 1st-level points-to + auto fieldobj = pt.target->getFieldObj(pt.dstoff); + if (hasPointsTo(fieldobj)) { + // copy 2nd-level points-to + if (!dstreg) dstreg = currCtx->getDestReg(&I); + dstreg->mergePointsTo(fieldobj, &I); + } else if (ensure2Lpt && !designated_mallocobj.size()) { + // create new 2nd-level points-to + DEBUG_LOADSTOR(dbgs() << "Load pointer failed: " << I << "\n"); + if (!newobj) newobj = currCtx->createRegMemPair(&I, true).second; + fieldobj->addPointsTo(newobj, 0, nullptr); + } + } +} + + +void AliasAnalysisVisitor::visitStoreInst(StoreInst &I) { + // where to store + auto dstptr = I.getPointerOperand(); + auto dstreg = currCtx->findOpReg(dstptr); + if (!hasPointsTo(dstreg)) return; + // what to store + auto srcval = I.getValueOperand(); + auto srcreg = currCtx->findOpReg(srcval); + if (!hasPointsTo(srcreg)) return; + // action + for (auto &pt: dstreg->pointsto) { + auto fieldobj = pt.target->getFieldObj(pt.dstoff); + fieldobj->mergePointsTo(srcreg, &I); + } +} + + +void AliasAnalysisVisitor::visitMemTransferInst(MemTransferInst &I) { + auto len = I.getOperand(2); + auto src = I.getOperand(1); + auto dst = I.getOperand(0); + auto srcreg = currCtx->findOpReg(src); + auto dstreg = currCtx->findOpReg(dst); + if (!hasPointsTo(srcreg) || !hasPointsTo(dstreg)) return; + // collect fields to transfer + std::vector > tmp; + auto lenint = dyn_cast(len); + auto length = lenint ? lenint->getZExtValue() : 1; + for (auto &pt: srcreg->pointsto) { + auto &fmap = pt.target->fieldmap; + auto baseoff = pt.dstoff; + auto end = fmap.lower_bound(baseoff + length); + for (auto it = fmap.lower_bound(baseoff); it != end; ++it) { + auto offset = it->first - baseoff; + auto field = &(it->second); + if (hasPointsTo(field)) + tmp.push_back(std::make_pair(offset, field)); + } + } + // transfer collected fields + if (tmp.size()) { + errs() << I << "\n"; + for (auto &pt: dstreg->pointsto) { + for (auto &fp: tmp) { + auto offset = pt.dstoff + fp.first; + auto field = pt.target->getFieldObj(offset); + field->mergePointsTo(fp.second, &I); + } + } + } +} + + +void AliasAnalysisVisitor::visitReturnInst(ReturnInst &I) { + auto retval = I.getReturnValue(); + if (!retval) return; + currCtx->retval.insert(retval); +} + + +void AliasAnalysisVisitor::visitLibFunctions(CallInst &I, Function *func) { + // TODO: realloc should create new object if 1st arg has no pointsto + static const char* copy1starg[] = { + "realloc", + "strcpy", "strncpy", + "strcat", "strncat", + "strchr", "strrchr", + }; + DEBUG_CALLINST(dbgs() << "Function with no definition: " + << func->getName() << "\n"); + for (auto sample: copy1starg) { + if (func->getName().equals(sample)) { + // retval must alias with 1st arg + auto arg = I.getArgOperand(0); + auto argreg = currCtx->findOpReg(arg); + if (hasPointsTo(argreg)) { + auto dstreg = currCtx->getDestReg(&I); + dstreg->mergePointsTo(argreg, &I); + } + return; + } + } + if (func->getName().equals("malloc") && designated_mallocobj.size()) { + auto dstreg = currCtx->getDestReg(&I); + for (auto reg: designated_mallocobj) { + dstreg->mergePointsTo(reg, &I); + for (auto &pt: dstreg->pointsto) { + auto field = pt.target->findFieldObj(pt.dstoff); + if (field) field->ignoresink = true; + } + } + return; + } + // default + if (I.getType()->isPointerTy()) { + DEBUG_LOADSTOR(dbgs() << "CreateRegMemPair: " << I << "\n"); + currCtx->createRegMemPair(&I); + } +} + + +bool AliasAnalysisVisitor::visitCallInst(CallInst &I, Function *targetFunction) { + if (targetFunction->isDeclaration()) { + visitLibFunctions(I, targetFunction); + return false; + } + return true; +} + + +void AliasAnalysisVisitor::setupChildContext(CallInst &I, AliasTaintContext *parentContext) { + for (auto &arg: currCtx->func->args()) { + auto operand = I.getArgOperand(arg.getArgNo()); + auto srcreg = parentContext->findOpReg(operand); + // DEBUG_LOADSTOR(dbgs() << "setupChildContext: " << arg.getArgNo() << " " << hasPointsTo(srcreg) << "\n"); + if (hasPointsTo(srcreg)) { + auto dstreg = currCtx->getDestReg(&arg); + dstreg->mergePointsTo(srcreg, nullptr); + } + } + auto func2enter = currCtx->func; + if (func2enter->getName().equals("BN_POOL_get") + || func2enter->getName().equals("bn_expand_internal")) { + auto val = func2enter->arg_begin(); + auto reg = currCtx->findOpReg(val); + for (auto &pt: reg->pointsto) { + auto field = pt.target->findFieldObj(pt.dstoff); + if (hasPointsTo(field)) { + designated_mallocobj.insert(field); + } + } + } +} + + +void AliasAnalysisVisitor::stitchChildContext(CallInst &I, AliasTaintContext *childContext) { + auto func2exit = childContext->func; + bool is_pool_malloc = + func2exit->getName().equals("BN_POOL_get") || + func2exit->getName().equals("bn_expand_internal"); + RegObject *dstreg = nullptr; + for (auto val: childContext->retval) { + auto srcreg = childContext->findOpReg(val); + if (hasPointsTo(srcreg)) { + if (!dstreg) dstreg = currCtx->getDestReg(&I); + // dstreg->mergePointsTo(srcreg, &I); + for (auto &item: srcreg->pointsto) { + if (!is_pool_malloc || !item.target->fake) { + auto tmp = item; + tmp.propagator = &I; + dstreg->pointsto.insert(tmp); + } + } + } + } + if (is_pool_malloc) { + designated_mallocobj.clear(); + } +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/AliasTaintCtx.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/AliasTaintCtx.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b839a3376f5675e5b0a15b3bb9cc4bd2b164f562 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/AliasTaintCtx.cpp @@ -0,0 +1,52 @@ +#include "AliasTaintCtx.h" +#include "Utils.h" + + +ObjectMap AliasTaintContext::globalobjects; + + +void AliasTaintContext::setupGlobals(Module &m) { + for (Function &func: m) { + globalobjects.createRegMemPair(&func); + } + for (GlobalVariable &var: m.getGlobalList()) { + globalobjects.createRegMemPair(&var); + } + + InitializerWalker walker(m.getDataLayout(), m.getContext()); + for (GlobalVariable &var: m.getGlobalList()) { + if (var.hasInitializer()) { + auto init = var.getInitializer(); + walker.scan(init); + if (!walker.results.size()) continue; + + DEBUG_GLOBOBJ(dbgs() << var.getName() << "\n"); + auto srcobj = globalobjects.findMemObj(&var); + for (auto &item: walker.results) { + auto dstobj = globalobjects.findMemObj(item.target); + auto field = srcobj->getFieldObj(item.srcoff); + field->addPointsTo(dstobj, item.dstoff, nullptr); + DEBUG_GLOBOBJ(dbgs() + << "\t" << item.srcoff << "\t" << item.dstoff + << "\t" << item.target->getName() << "\n"); + } + } + } +} + + +void AliasTaintContext::getFuncPtrTargets(Value *fp, std::vector &ret) { + auto fpReg = findOpReg(fp); + if (!hasPointsTo(fpReg)) return; + for (auto &pt: fpReg->pointsto) { + auto dst = pt.target; + if (auto func = dyn_cast(dst->represented)) + ret.push_back(func); + } + if (ret.size()) { + DEBUG_CALLINST(dbgs() << "Targets found for: " << *fp << "\n"); + for (auto func: ret) { + DEBUG_CALLINST(dbgs() << "\t" << func->getName() << "\n"); + } + } +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/ModifyVisitor.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/ModifyVisitor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b0001e5e505b8c17a1943cd6569f01d01e6ae6a0 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/ModifyVisitor.cpp @@ -0,0 +1,587 @@ +#include "ModifyVisitor.h" +#include "GlobalVisitor.h" +#include "marcos.h" + +#define LOOPFACTOR 10 +#define CALLFACTOR 30 + +static bool ignorestack = false; + +ModifiedFunctionList ModifyCallbackVisitor::newfunctions; +std::set ModifyCallbackVisitor::analyzed_functions; + +void ModifyCallbackVisitor::visitAllocaInst(AllocaInst &I) { + if (currCtx->inside_loop) return; + if (!I.isStaticAlloca() || !I.getAllocatedType()->isAggregateType()) return; + auto inst = + funcmod()->getInstMod(I, InstModType::AllocaInst, currCtx->lastloopiter, currCtx->loopidx); + + inst->ignorepriv = true; + + auto reg = currCtx->getDestReg(&I); + if (checkPointsToTaint(reg) && (I.getParent() == &(currCtx->func->getEntryBlock()))) { + funcmod()->setTaint(inst); + } +} + +void ModifyCallbackVisitor::visitLoadInst(LoadInst &I) { + if (currCtx->inside_loop) return; + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + + auto src = I.getPointerOperand(); + auto srcreg = currCtx->findOpReg(src); + if (checkPointsToTaint(srcreg, ignorestack)) { + funcmod()->setTaint(inst); + // int lineno = DbgInfo::getSrcLine(&I); + // if (lineno >= 0) { + // std::string filename = DbgInfo::getSrcFileName(&I); + // DEBUG_MODIFY(dbgs() << formatv("KeyTaint: {0},{1},{2}\n", filename, lineno, + // *srcreg->represented)); + // } else { + // DEBUG_MODIFY(dbgs() << "KeyTaint: Cannot get lineno\n"); + // } + } + // if (checkPointsToSink(srcreg, ignorestack)) { + // int lineno = DbgInfo::getSrcLine(&I); + // if (lineno >= 0) { + // std::string filename = DbgInfo::getSrcFileName(&I); + // DEBUG_MODIFY(dbgs() << formatv("SinkTaint: {0},{1},{2}\n", filename, lineno, + // *srcreg->represented)); + // } else { + // DEBUG_MODIFY(dbgs() << "SinkTaint: Cannot get lineno\n"); + // } + // } +} + +void ModifyCallbackVisitor::visitStoreInst(StoreInst &I) { + if (currCtx->inside_loop) return; + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + + auto dst = I.getPointerOperand(); + auto dstreg = currCtx->findOpReg(dst); + if (checkPointsToTaint(dstreg, ignorestack)) { + funcmod()->setTaint(inst); + // int lineno = DbgInfo::getSrcLine(&I); + // if (lineno >= 0) { + // std::string filename = DbgInfo::getSrcFileName(&I); + // DEBUG_MODIFY(dbgs() << formatv("KeyTaint: {0},{1},{2}\n", filename, lineno, + // *dstreg->represented)); + // } else { + // DEBUG_MODIFY(dbgs() << "KeyTaint: Cannot get lineno\n"); + // } + } + // if (checkPointsToSink(dstreg, ignorestack)) { + // int lineno = DbgInfo::getSrcLine(&I); + // if (lineno >= 0) { + // std::string filename = DbgInfo::getSrcFileName(&I); + // DEBUG_MODIFY(dbgs() << formatv("SinkTaint: {0},{1},{2}\n", filename, lineno, + // *dstreg->represented)); + // } else { + // DEBUG_MODIFY(dbgs() << "SinkTaint: Cannot get lineno\n"); + // } + // } +} + +void ModifyCallbackVisitor::visitMemTransferInst(MemTransferInst &I) { + if (currCtx->inside_loop) return; + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + + auto src = I.getOperand(1); + auto dst = I.getOperand(0); + auto srcreg = currCtx->findOpReg(src); + auto dstreg = currCtx->findOpReg(dst); + if (checkPointsToTaint(srcreg, ignorestack) || checkPointsToTaint(dstreg, ignorestack)) { + funcmod()->setTaint(inst); + } +} + +void ModifyCallbackVisitor::visitMemSetInst(MemSetInst &I) { + if (currCtx->inside_loop) return; + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + + auto dst = I.getOperand(0); + auto dstreg = currCtx->findOpReg(dst); + if (checkPointsToTaint(dstreg, ignorestack)) { + funcmod()->setTaint(inst); + } +} + +bool ModifyCallbackVisitor::visitCallInst(CallInst &I, Function *func) { + if (currCtx->inside_loop) return false; + + // if (func->getName() == "bn_expand_internal") { + // auto reg = currCtx->getDestReg(&I); + // if (checkPointsToTaint(reg)) { + // dbgs() << "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n"; + // } + // } + + if (Rules::checkBlacklist(func)) { + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + visitLibFunction(I, func, inst); + return false; + } + if (currCtx->checkRecursive(I)) { + auto inst = + funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, currCtx->loopidx); + funcmod()->setTaint(inst); + return false; + } + if (I.getCalledFunction()) { + if (func->isDeclaration()) { + visitLibFunction(I, func, nullptr); + } else + funcmod()->getInstMod(I, InstModType::FuncDirect, currCtx->lastloopiter, + currCtx->loopidx); + } else { + auto inst = + funcmod()->getInstMod(I, InstModType::FuncPtr, currCtx->lastloopiter, currCtx->loopidx); + if (func->isDeclaration()) { + visitLibFunction(I, func, inst); + } + } + return !func->isDeclaration(); +} + +void ModifyCallbackVisitor::setupChildContext(CallInst &I, AliasTaintContext *child) { + Function *F = currCtx->func; + if (Globals::DirFuncs.size() && (Globals::DirFuncs.find(F) != Globals::DirFuncs.end())) + currCtx->isdirector = true; +} + +void ModifyCallbackVisitor::stitchChildContext(CallInst &I, AliasTaintContext *child) { + analyzed_functions.insert(child->func); + if (!child->funcmod.anytainted) return; + child->funcmod.calledbydirector = currCtx->isdirector; + child->funcmod.isdirector = child->isdirector; + auto funcobj = newfunctions.tryinsert(child); + auto instmod = funcmod()->getInstMod(I); + funcmod()->addCallTarget(instmod, child->func, funcobj->ctxhash); +} + +void ModifyCallbackVisitor::visitLibFunction(CallInst &I, Function *func, InstMod *instmod) { + if (func->getName().equals("malloc") || func->getName().equals("realloc")) { + if (!instmod) + instmod = funcmod()->getInstMod(I, InstModType::MemFunc, currCtx->lastloopiter, + currCtx->loopidx); + auto reg = currCtx->getDestReg(&I); + if (checkPointsToTaint(reg)) { + funcmod()->addLibFuncCall(instmod, func, InstModType::MemFunc); + } + return; + } + if (func->getName().equals("free") || func->getName().equals("realloc")) { + if (!instmod) + instmod = funcmod()->getInstMod(I, InstModType::MemFunc, currCtx->lastloopiter, + currCtx->loopidx); + // currCtx->findOpReg(I.getArgOperand(0)); + funcmod()->addLibFuncCall(instmod, func, InstModType::MemFunc); + return; + } + if (func->getName().equals("pam_authenticate")) { + if (!instmod) + instmod = funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, + currCtx->loopidx); + funcmod()->addLibFuncCall(instmod, func, InstModType::MPKWrap); + } + for (auto &use : I.arg_operands()) { + auto reg = currCtx->findOpReg(use.get()); + if (reg && checkPointsToTaint(reg, ignorestack)) { + if (!instmod) + instmod = funcmod()->getInstMod(I, InstModType::MPKWrap, currCtx->lastloopiter, + currCtx->loopidx); + funcmod()->addLibFuncCall(instmod, func, InstModType::MPKWrap); + return; + } + } +} + +void ModifyCallbackVisitor::visitReturnInst(ReturnInst &I) { + if (currCtx->inside_loop) return; + funcmod()->returnlist.push_back(&I); +} + +struct FunctionModifyRunner { + ModifyCallbackVisitor *vis; + ModifiedFunction *funcmod; + static std::set stats_tainted_insts; + + FunctionModifyRunner(ModifyCallbackVisitor *v, ModifiedFunction *f) : vis(v), funcmod(f) {} + + template + void foreach_tainted_instmod(Func func) { + for (auto &pair : funcmod->map) { + auto instmod = &(pair.second); + if (instmod->tainted) func(instmod); + } + } + + template + void foreach_privsensitive_instmod(Func func) { + for (auto &pair : funcmod->map) { + auto instmod = &(pair.second); + if (!instmod->ignorepriv) func(instmod); + } + } + + template + void foreach_instmod(InstModType ty, Func func) { + for (auto &pair : funcmod->map) { + auto instmod = &(pair.second); + if (instmod->type == ty) func(instmod); + } + } + + void copyNew() { + if (!funcmod->isentry) { + funcmod->vmap.reset(new ValueToValueMapTy()); + funcmod->newfunc = CloneFunction(funcmod->func, *(funcmod->vmap)); + auto newname = funcmod->func->getName().str() + "_" + std::to_string(funcmod->ctxhash); + funcmod->newfunc->setName(newname); + } + } + + void expandFuncPtr() { + std::vector funcptrs; + foreach_tainted_instmod([&](InstMod *instmod) { + if (instmod->type == InstModType::FuncPtr) { + instmod->ignorepriv = true; + funcptrs.push_back(instmod); + } + }); + for (auto instmod1 : funcptrs) { + auto newinst = funcmod->resolve_inst(instmod1->inst); + auto callinst = dyn_cast(newinst); + ExpandFuncPtr expander(callinst); + expander.splitBlock(); + for (auto &pair : instmod1->calltargets) { + auto &target = pair.second; + auto newcall = expander.addTarget(target.func); + auto instmod2 = + funcmod->getInstMod(*newcall, target.type, instmod1->inloop, instmod1->loopidx); + if (target.type == InstModType::FuncDirect) + funcmod->addCallTarget(instmod2, target.func, target.hash); + else + funcmod->setTaint(instmod2); + } + auto newcall = expander.addFallback(); + funcmod->getInstMod(*newcall, InstModType::FuncPtr, instmod1->inloop, + instmod1->loopidx); + if (!expander.addPHINode()) { + callinst->eraseFromParent(); + } + } + } + + void substitueCallTarget() { + foreach_instmod(InstModType::FuncDirect, [&](InstMod *instmod) { + if (instmod->tainted) { + auto newinst = funcmod->resolve_inst(instmod->inst); + auto callinst = dyn_cast(newinst); + auto &target = instmod->calltargets.begin()->second; + auto &targetfuncmod = + vis->newfunctions.map[std::make_pair(target.func, target.hash)]; + callinst->setCalledFunction(targetfuncmod.newfunc); + instmod->tainted = targetfuncmod.callerprotect; + } else { + instmod->ignorepriv = true; + } + }); + } + + void replaceAllocs() { + foreach_tainted_instmod([&](InstMod *instmod) { + if (instmod->type == InstModType::MemFunc) { + auto newinst = funcmod->resolve_inst(instmod->inst); + auto callinst = dyn_cast(newinst); + Function *calledfunc = callinst->getCalledFunction(); + if (calledfunc->getName().equals("free") || calledfunc->getName().equals("realloc")) + return; + else if (calledfunc->getName().equals("malloc")) { +#ifndef MEMMANAGER_OFF + auto functype = calledfunc->getFunctionType(); + auto funcname = std::string("m_") + calledfunc->getName().str(); + auto newfunc = vis->mod.getOrInsertFunction(funcname, functype); + callinst->setCalledFunction(dyn_cast(newfunc.getCallee())); +#endif + } else { +#ifndef MEMMANAGER_OFF + auto functype = calledfunc->getFunctionType(); + auto funcname = std::string("mpk_") + calledfunc->getName().str(); + auto newfunc = vis->mod.getOrInsertFunction(funcname, functype); + callinst->setCalledFunction(dyn_cast(newfunc.getCallee())); +#endif + } + } else if (instmod->type == InstModType::AllocaInst) { +#ifndef STACKMEMSET +#ifndef MEMMANAGER_OFF + auto newinst = funcmod->resolve_inst(instmod->inst); + auto allocainst = dyn_cast(newinst); + auto newmalloc = replaceAllocaWithMPKMalloc(vis->mod, allocainst); + auto mallocmod = funcmod->getInstMod(*newmalloc, InstModType::MPKWrap, false); + funcmod->setTaint(mallocmod); + for (auto returninst : funcmod->returnlist) { + auto newreturn = funcmod->resolve_inst(returninst); + auto newfree = insertMPKFree(vis->mod, newmalloc, newreturn); + auto freemod = funcmod->getInstMod(*newfree, InstModType::MPKWrap, false); + funcmod->setTaint(freemod); + } +#endif +#else +#ifndef MEMMANAGER_OFF + DEBUG_MODIFY(dbgs() << "testtest\n"); + auto newinst = funcmod->resolve_inst(instmod->inst); + auto allocainst = dyn_cast(newinst); + for (auto returninst : funcmod->returnlist) { + auto newreturn = funcmod->resolve_inst(returninst); + insertMemset(vis->mod, allocainst, newreturn); + } +#endif +#endif + } + }); + } + + void countTaint() { + size_t cnt_total = 0, cnt_tainted = 0; + std::map> loop_map; + foreach_privsensitive_instmod([&](InstMod *instmod) { + auto score_pair = std::make_pair(0, 0); + if (!funcmod->isentry && !funcmod->isdirector && + (dyn_cast(instmod->inst) || dyn_cast(instmod->inst) || + dyn_cast(instmod->inst))) + stats_tainted_insts.insert(instmod->inst); + if (instmod->type == InstModType::FuncDirect) { + auto &target = instmod->calltargets.begin()->second; + auto &targetfuncmod = + vis->newfunctions.map[std::make_pair(target.func, target.hash)]; + double child_score = (double)targetfuncmod.cnt_tainted / targetfuncmod.cnt_total; + score_pair.first = CALLFACTOR; + score_pair.second = CALLFACTOR * child_score; + } else { + score_pair.first = 1; + if (instmod->tainted) score_pair.second = 1; + } + if (instmod->inloop) { + score_pair.first *= LOOPFACTOR; + score_pair.second *= LOOPFACTOR; + auto ins = loop_map.emplace(instmod->loopidx, score_pair); + // DEBUG_MODIFY(dbgs() << formatv("\t\tloop{0} taint: {1}\n", + // instmod->loopidx, score_pair.second)); + if (!ins.second) { + auto &exist_pair = ins.first->second; + exist_pair.first += score_pair.first; + // if (instmod->tainted) + exist_pair.second += score_pair.second; + } + } + cnt_total += score_pair.first; + cnt_tainted += score_pair.second; + }); + funcmod->cnt_total = cnt_total; + funcmod->cnt_tainted = cnt_tainted; + double score = (double)funcmod->cnt_tainted / funcmod->cnt_total; + if (funcmod->newfunc) + DEBUG_MODIFY(dbgs() << formatv("\tfunction score: {0:F}\n", score * 100)); + if (score >= Globals::Threshold) { + funcmod->need_callerprotect = true; + return; + } + // check loop score + for (auto &pair : loop_map) { + auto &score_pair = pair.second; + double score = (double)score_pair.second / score_pair.first; + if (funcmod->newfunc) + DEBUG_MODIFY(dbgs() << formatv("\t\tloop{0} score: {1:F} taint: {2}\n", pair.first, + score * 100, score_pair.second)); + if (score >= Globals::Threshold) { + funcmod->need_callerprotect = true; + return; + } + } + funcmod->need_callerprotect = false; + + for (auto &name : Globals::Hotspots) { + if (funcmod->func->getName() == name) { + funcmod->need_callerprotect = true; + DEBUG_MODIFY(dbgs() << "Hotspot : " << funcmod->newfunc->getName()); + } + } + // if (funcmod->func->getName() == "xShiftSubst" || + // funcmod->func->getName() == "xrijndaelEncrypt" || + // funcmod->func->getName() == "xrijndaelDecrypt") { + // funcmod->need_callerprotect = false; + // } + } + + void insertWrpkruInst() { + if (funcmod->newfunc) { + DEBUG_MODIFY(dbgs() << formatv("\tcalledbydirector: {0}\n", funcmod->calledbydirector)); + DEBUG_MODIFY(dbgs() << formatv("\tisdirector: {0}\n", funcmod->isdirector)); + DEBUG_MODIFY( + dbgs() << formatv("\tneed_callerprotect {0}\n", funcmod->need_callerprotect)); + } + if (funcmod->isentry || funcmod->isdirector || !funcmod->need_callerprotect) { + if (funcmod->newfunc) DEBUG_MODIFY(dbgs() << "\t*Fine-grained protected\n"); + foreach_privsensitive_instmod([&](InstMod *instmod) { + if (instmod->tainted) { + auto newinst = funcmod->resolve_inst(instmod->inst); + insertWRPKRU(vis->mod, newinst, 0); + insertWRPKRU(vis->mod, newinst->getNextNode(), 3, newinst); + } + }); + } else { + if (funcmod->newfunc) DEBUG_MODIFY(dbgs() << "\t*Caller protected\n"); + funcmod->callerprotect = true; + foreach_privsensitive_instmod([&](InstMod *instmod) { + // func calls must be excluded + if (!instmod->tainted && instmod->type == InstModType::FuncDirect) { + auto newinst = funcmod->resolve_inst(instmod->inst); + // insertWRPKRU(vis->mod, newinst, 3); + insertWRPKRU(vis->mod, newinst->getNextNode(), 0); + } + }); + } + } + + void reportTaint() { + foreach_tainted_instmod([&](InstMod *instmod) { + if (instmod->type == InstModType::MPKWrap || instmod->type == InstModType::MemFunc) { + // DEBUG_MODIFY(dbgs() << "Report " << *instmod->inst << "\n"); + int lineno = DbgInfo::getSrcLine(instmod->inst); + if (lineno >= 0) { + std::string filename = DbgInfo::getSrcFileName(instmod->inst); + (*Globals::TaintReport) << formatv("{0},{1}\n", filename, lineno); + } else { + DEBUG_MODIFY(dbgs() << "Cannot get lineno\n"); + } + } + }); + } +}; + +std::set FunctionModifyRunner::stats_tainted_insts; + +void ModifyCallbackVisitor::prestat() { + int cnt_memop = 0; + for (auto &F : mod) { + for (auto &BB : F) { + for (auto &I : BB) { + if (Globals::DirFuncs.size() && + (Globals::DirFuncs.find(&F) != Globals::DirFuncs.end())) + continue; + if (dyn_cast(&I) || dyn_cast(&I) || + dyn_cast(&I)) + cnt_memop++; + dbgs() << I.getFunction()->getName() << " "; + dbgs() << I << "\n"; + } + } + } + dbgs() << "\n** Number of all memop insts: " << cnt_memop << "\n"; +} + +void ModifyCallbackVisitor::poststat() { + std::map cntmap; + std::map cntmap2; + + dbgs() << "\n** Traversed functions\n"; + for (auto func : analyzed_functions) { + dbgs() << func->getName() << "\n"; + } + + dbgs() << "\n** All replicated functions\n"; + for (auto &pair : newfunctions.map) { + auto func = pair.first.first; + // auto hash = pair.first.second; + auto &funcmod = pair.second; + if (!funcmod.isentry && !funcmod.isdirector) dbgs() << funcmod.newfunc->getName() << "\n"; + if (!funcmod.isentry && !funcmod.isdirector) { + if (cntmap.find(func) == cntmap.end()) + cntmap[func] = 1; + else + cntmap[func]++; + } + if (!funcmod.isentry && !funcmod.isdirector) { + for (auto &pair : funcmod.map) { + auto instmod = &(pair.second); + if (instmod->tainted) { + if (instmod->type == InstModType::MPKWrap || + instmod->type == InstModType::MemFunc) { + if (cntmap2.find(func) == cntmap2.end()) + cntmap2[func] = 1; + else + cntmap2[func]++; + break; + } + } + } + } + } + + dbgs() << "\n** Number of replica for each function\n"; + for (auto &pair : cntmap) { + auto func = pair.first; + auto cnt = pair.second; + dbgs() << func->getName() << " " << cnt << "\n"; + } + + dbgs() << "\n** Functions replicated for MPKWrap MemFunc\n"; + for (auto &pair : cntmap2) { + auto func = pair.first; + auto cnt = pair.second; + dbgs() << func->getName() << " " << cnt; + for (auto &pair : newfunctions.map) { + auto func_iter = pair.first.first; + auto &funcmod = pair.second; + if (func != func_iter) continue; + for (auto &pair : funcmod.map) { + auto instmod = &(pair.second); + if (!instmod->tainted) continue; + if (instmod->type == InstModType::MPKWrap || + instmod->type == InstModType::MemFunc) { + dbgs() << " " << funcmod.newfunc->getName(); + break; + } + } + } + dbgs() << "\n"; + } + + int cnt_sens_memop = FunctionModifyRunner::stats_tainted_insts.size(); + dbgs() << "\n** Number of all wrapped memop insts: " << cnt_sens_memop << "\n"; + for (auto inst : FunctionModifyRunner::stats_tainted_insts) { + dbgs() << inst->getFunction()->getName() << " "; + dbgs() << *inst << "\n"; + } +} + +void ModifyCallbackVisitor::run_modify() { + prestat(); + // stitch root context + DEBUG_MODIFY(dbgs() << "Run_modify " + << "\n"); + DEBUG_MODIFY(dbgs() << "Threshold : " << Globals::Threshold << "\n"); + if (!currCtx->funcmod.anytainted) return; + auto funcobj = newfunctions.tryinsert(currCtx); + funcobj->isentry = true; + // process every new function + for (auto funcobj : newfunctions.list) { + FunctionModifyRunner runner(this, funcobj); + runner.reportTaint(); + runner.copyNew(); + runner.expandFuncPtr(); + runner.substitueCallTarget(); + runner.replaceAllocs(); + if (funcobj->newfunc) DEBUG_MODIFY(dbgs() << funcobj->newfunc->getName() << "\n"); + runner.countTaint(); + runner.insertWrpkruInst(); + } + if (Globals::IsLib) newfunctions.libexports(); + poststat(); +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/TaintAnalysisVisitor.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/TaintAnalysisVisitor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..beefbb30717b875f82320f1fd739cacd87998a85 --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/TaintAnalysisVisitor.cpp @@ -0,0 +1,251 @@ +#include "TaintAnalysisVisitor.h" +#include "Utils.h" + +void TaintAnalysisVisitor::visitCastInst(CastInst &I) { + if (I.stripPointerCasts() != &I) return; + auto src = I.getOperand(0); + auto srcreg = currCtx->findOpReg(src); + if (hasTaint(srcreg)) { + auto dstreg = currCtx->getDestReg(&I); + dstreg->flowTaint(srcreg, &I); + } +} + +void TaintAnalysisVisitor::visitBinaryOperator(BinaryOperator &I) { + auto lhs = I.getOperand(0); + auto rhs = I.getOperand(1); + auto lreg = currCtx->findOpReg(lhs); + auto rreg = currCtx->findOpReg(rhs); + if (hasTaint(lreg) || hasTaint(rreg)) { + auto dstreg = currCtx->getDestReg(&I); + if (hasTaint(lreg)) dstreg->flowTaint(lreg, &I); + if (hasTaint(rreg)) dstreg->flowTaint(rreg, &I); + } +} + +void TaintAnalysisVisitor::visitPHINode(PHINode &I) { + RegObject *dst = nullptr; + for (auto &use : I.incoming_values()) { + auto reg = currCtx->findOpReg(use.get()); + if (hasTaint(reg)) { + if (!dst) dst = currCtx->getDestReg(&I); + dst->flowTaint(reg, &I); + } + } +} + +void TaintAnalysisVisitor::visitSelectInst(SelectInst &I) { + auto lhs = I.getTrueValue(); + auto rhs = I.getFalseValue(); + auto lreg = currCtx->findOpReg(lhs); + auto rreg = currCtx->findOpReg(rhs); + if (hasTaint(lreg) || hasTaint(rreg)) { + auto dstreg = currCtx->getDestReg(&I); + if (hasTaint(lreg)) dstreg->flowTaint(lreg, &I); + if (hasTaint(rreg)) dstreg->flowTaint(rreg, &I); + } +} + +void TaintAnalysisVisitor::visitGetElementPtrInst(GetElementPtrInst &I) { + if (I.stripPointerCasts() != &I) return; + auto val = I.getPointerOperand(); + auto srcreg = currCtx->findOpReg(val); + if (hasTaint(srcreg)) { + auto dstreg = currCtx->getDestReg(&I); + dstreg->flowTaint(srcreg, &I); + } +} + +void TaintAnalysisVisitor::visitLoadInst(LoadInst &I) { + auto src = I.getPointerOperand(); + auto srcreg = currCtx->findOpReg(src); + if (hasPointsTo(srcreg)) { + RegObject *dstreg = nullptr; + for (auto &pt : srcreg->pointsto) { + auto fieldobj = pt.target->findFieldObj(pt.dstoff); + if (hasTaint(fieldobj)) { + if (!dstreg) dstreg = currCtx->getDestReg(&I); + dstreg->flowTaint(fieldobj, &I); + } + } + } +} + +void TaintAnalysisVisitor::visitStoreInst(StoreInst &I) { + // where to store + auto dstptr = I.getPointerOperand(); + auto dstreg = currCtx->findOpReg(dstptr); + if (!hasPointsTo(dstreg)) return; + // what to store + auto srcval = I.getValueOperand(); + auto srcreg = currCtx->findOpReg(srcval); + if (!hasTaint(srcreg)) return; + // action + for (auto &pt : dstreg->pointsto) { + auto fieldobj = pt.target->getFieldObj(pt.dstoff); + fieldobj->flowTaint(srcreg, &I); + pt.target->updateTaintByField(pt.dstoff, fieldobj); + } +} + +void TaintAnalysisVisitor::visitMemTransferInst(MemTransferInst &I) { + auto len = I.getOperand(2); + auto src = I.getOperand(1); + auto dst = I.getOperand(0); + auto srcreg = currCtx->findOpReg(src); + auto dstreg = currCtx->findOpReg(dst); + if (!hasPointsTo(srcreg) || !hasPointsTo(dstreg)) return; + // collect fields to transfer + std::vector> tmp; + auto lenint = dyn_cast(len); + auto length = lenint ? lenint->getZExtValue() : 1; + for (auto &pt : srcreg->pointsto) { + auto &fmap = pt.target->fieldmap; + auto baseoff = pt.dstoff; + auto end = fmap.lower_bound(baseoff + length); + for (auto it = fmap.lower_bound(baseoff); it != end; ++it) { + auto offset = it->first - baseoff; + auto field = &(it->second); + if (hasTaint(field)) tmp.push_back(std::make_pair(offset, field)); + } + } + // transfer collected fields + if (tmp.size()) { + errs() << I << "\n"; + for (auto &pt : dstreg->pointsto) { + for (auto &fp : tmp) { + auto offset = pt.dstoff + fp.first; + auto field = pt.target->getFieldObj(offset); + field->flowTaint(fp.second, &I); + pt.target->updateTaintByField(offset, field); + } + } + } +} + +void TaintAnalysisVisitor::visitReturnInst(ReturnInst &I) { + auto retval = I.getReturnValue(); + if (retval) currCtx->retval.insert(retval); +} + +void TaintAnalysisVisitor::visitLibFunctions(CallInst &I, Function *func) { + static const char* copy1to0ret[] = { + "strcpy", "strncpy", + "strcat", "strncat", + }; + static const char* copy0toret[] = { + "realloc", + "strchr", "strrchr", + }; + + auto srcpos = -1; + + for (auto sample: copy1to0ret) { + if (func->getName().equals(sample)) { + srcpos = 1; + } + } + for (auto sample: copy0toret) { + if (func->getName().equals(sample)) { + srcpos = 0; + } + } + + if (srcpos == -1) return; + + // fetch src + auto srcreg = currCtx->findOpReg(I.getOperand(srcpos)); + if (!hasPointsTo(srcreg)) return; + + std::vector tmp; + for (auto &pt : srcreg->pointsto) { + auto fieldobj = pt.target->getFieldObj(0); + if (hasTaint(fieldobj)) + tmp.push_back(fieldobj); + } + + if (tmp.size() == 0) return; + + auto dstreg = currCtx->findOpReg(&I); + // transfer collected fields + if (hasPointsTo(dstreg)) { + for (auto &pt : dstreg->pointsto) { + for (auto &fp : tmp) { + auto field = pt.target->getFieldObj(0); + field->flowTaint(fp, &I); + pt.target->updateTaintByField(0, field); + } + } + } + + if (srcpos == 1) { + dstreg = currCtx->findOpReg(I.getOperand(0)); + if (hasPointsTo(dstreg)) { + for (auto &pt : dstreg->pointsto) { + for (auto &fp : tmp) { + auto field = pt.target->getFieldObj(0); + field->flowTaint(fp, &I); + pt.target->updateTaintByField(0, field); + } + } + } + } +} + +bool TaintAnalysisVisitor::visitCallInst(CallInst &I, Function *targetFunction) { + if (targetFunction->isDeclaration()) { + if (targetFunction->getName().equals("__taint_fdf0f8a65855a52bbe69cd2075f89027")) { + auto val = I.getArgOperand(1); + auto reg = currCtx->findOpReg(val); + for (auto &pt : reg->pointsto) { + auto field = pt.target->getFieldObj(pt.dstoff); + field->setTaint(&I); + pt.target->updateTaintByField(pt.dstoff, field); + } + } + else if (targetFunction->getName().equals("__sinktaint_fdf0f8a65855a52bbe69cd2075f89027")) { + auto val = I.getArgOperand(1); + auto reg = currCtx->findOpReg(val); + for (auto &pt : reg->pointsto) { + auto field = pt.target->getFieldObj(pt.dstoff); + field->setSinkTaint(&I); + pt.target->updateTaintByField(pt.dstoff, field); + } + } + else { + visitLibFunctions(I, targetFunction); + } + return false; + } + return true; +} + +void TaintAnalysisVisitor::setupChildContext(CallInst &I, AliasTaintContext *parentContext) { + for (auto &arg : currCtx->func->args()) { + auto operand = I.getArgOperand(arg.getArgNo()); + auto srcreg = parentContext->findOpReg(operand); + if (hasTaint(srcreg)) { + auto dstreg = currCtx->getDestReg(&arg); + dstreg->flowTaint(srcreg, nullptr); + } + } +} + +void TaintAnalysisVisitor::stitchChildContext(CallInst &I, AliasTaintContext *childContext) { + RegObject *dstreg = nullptr; + for (auto val : childContext->retval) { + auto srcreg = childContext->findOpReg(val); + if (hasTaint(srcreg)) { + if (!dstreg) dstreg = currCtx->getDestReg(&I); + dstreg->flowTaint(srcreg, &I); + } + } + for (auto &mem : childContext->localobjects.memmap) { + auto memobj = mem.second.get(); + if (memobj->tainted) { + dbgs() << "Tainted: " << *(memobj->represented) << "\t" << *(memobj->tainter) << "\n"; + } + } + dbgs() << "RegObjects: " << childContext->localobjects.memmap.size() << "\n"; + dbgs() << "AliasObjects: " << childContext->localobjects.regmap.size() << "\n"; +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/Utils.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/Utils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..063468cc416d5c7552fc10259ef2bf6ecbeea4ae --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/Utils.cpp @@ -0,0 +1,479 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "Utils.h" +#include "marcos.h" + +// SCC utilities + +void getSCCTraversalOrder( + Function &currF, + std::vector > &bbTraversalList) { + + Function *F = &currF; + for (auto I = scc_begin(F), IE = scc_end(F); I != IE; ++I) { + const std::vector &constvec = *I; + std::vector currSCC(constvec.rbegin(), constvec.rend()); + bbTraversalList.push_back(std::move(currSCC)); + } + std::reverse(bbTraversalList.begin(), bbTraversalList.end()); +} + + +size_t getNumTimesToAnalyze(std::vector &currSCC) { + /*** + * get number of times all the loop basicblocks should be analyzed. + * This is same as the longest use-def chain in the provided + * strongly connected component. + * + * Why is this needed? + * This is needed because we want to ensure that all the + * information inside the loops have been processed. + */ + + std::set globalvisited; + size_t numToAnalyze = 1; + + for (auto currBBMain: currSCC) { + // if never visited: BFS on currBBMain + if (!globalvisited.count(currBBMain)) { + std::queue queue; + std::set localvisited; + queue.push(currBBMain); + localvisited.insert(currBBMain); + + while (!queue.empty()) { + auto currBB = queue.front(); queue.pop(); + + for (auto &currIns: *currBB) { + for(auto &use: currIns.operands()) { + if (auto useIns = dyn_cast(use.get())) { + auto useBB = useIns->getParent(); + auto it = std::find( + currSCC.begin(), currSCC.end(), useBB); + // if useBB in currSCC + if (it != currSCC.end()) { + // if useBB not in localvisited + if (localvisited.insert(useBB).second) + queue.push(useBB); + } + } + } + } + } + + numToAnalyze = std::max(numToAnalyze, localvisited.size()); + globalvisited.insert(localvisited.begin(), localvisited.end()); + } + } + + return numToAnalyze; +} + + +// Parser utilities + + +static size_t getGEPOffset(Type* &currtype, User &I, const DataLayout &dl) { + size_t offset = 0; + for (auto it = I.op_begin() + 2; it != I.op_end(); ++it) { + if (currtype->isArrayTy()) { + currtype = currtype->getArrayElementType(); + } else if (auto stype = dyn_cast(currtype)) { + auto idxint = extractConstantInt(it->get()).first; + auto sl = dl.getStructLayout(stype); + offset += sl->getElementOffset(idxint); + currtype = stype->getElementType(idxint); + } else { + assert(false); + } + } + return offset; +} + + +static size_t getGEPOffset(Type* &currtype, size_t rawoff, const DataLayout &dl) { + size_t offset = 0; + while (rawoff) { + if (currtype->isArrayTy()) { + currtype = currtype->getArrayElementType(); + auto elemsize = dl.getTypeAllocSize(currtype); + rawoff %= elemsize; + } else if (auto stype = dyn_cast(currtype)) { + auto sl = dl.getStructLayout(stype); + auto elem = sl->getElementContainingOffset(rawoff); + auto base = sl->getElementOffset(elem); + currtype = stype->getElementType(elem); + offset += base; + rawoff -= base; + } else { + assert(false); + } + } + return offset; +} + + +size_t getGEPOffset(GetElementPtrInst &I, const DataLayout &dl) { + auto currtype = I.getSourceElementType(); + auto offset = getGEPOffset(currtype, I, dl); + assert(currtype == I.getResultElementType()); + return offset; +} + + +size_t getGEPOffset(ConstantExpr *cexpr, const DataLayout &dl, LLVMContext &ctx) { + auto target = cexpr->getOperand(0); + auto stripped = target->stripPointerCasts(); + auto currtype = stripped->getType()->getPointerElementType(); + if (target == stripped) { + // normal GEP + auto dstoff = getGEPOffset(currtype, *cexpr, dl); + assert(currtype == cexpr->getType()->getPointerElementType()); + return dstoff; + } + // i8 GEP: (gep (cast to i8*) 0, rawoff) + assert(cexpr->getNumOperands() == 2 + && target->getType() == Type::getInt8PtrTy(ctx)); + auto rawoff = extractConstantInt(cexpr->getOperand(1)).first; + auto offset = getGEPOffset(currtype, rawoff, dl); + DEBUG_GLOBOBJ(dbgs() << "i8 GEP: " << rawoff << " -> " + << offset << " " << stripped->getName() << "\n"); + return offset; +} + + +void InitializerWalker::scan(Constant* init, size_t off) { + assert(!isa(init)); + if (auto sobj = dyn_cast(init)) { + auto slayout = layout.getStructLayout(sobj->getType()); + Constant *curr = sobj->getAggregateElement(0U); + for (unsigned i = 0; curr; curr = sobj->getAggregateElement(++i)) { + scan(curr, off + slayout->getElementOffset(i)); + } + } else if (isa(init)) { + Constant *curr = init->getAggregateElement(0U); + for (unsigned i = 0; curr; curr = init->getAggregateElement(++i)) { + scan(curr, off); + } + } else { + handleNonAgg(init, off); + } +} + + +void InitializerWalker::handleNonAgg(Constant* init, size_t srcoff) { + assert(!isa(init)); + if (isa(init)) return; + // simple case: globalobject + auto stripped = init->stripPointerCasts(); + if (auto obj = dyn_cast(stripped)) { + results.push_back(GlobalPointsTo(obj, srcoff, 0)); + return; + } + // then maybe gep? + auto cexpr = dyn_cast(stripped); + if (cexpr->getOpcode() == Instruction::GetElementPtr) { + auto target = cexpr->getOperand(0)->stripPointerCasts(); + if (auto obj = dyn_cast(target)) { + auto dstoff = getGEPOffset(cexpr, layout, ctx); + results.push_back(GlobalPointsTo(obj, srcoff, dstoff)); + return; + } + } + // then ??? + errs() << *cexpr << "\n"; + assert(false); +} + + +// Transform utilities + + +static void splitConstExpr(Instruction *inst) { + size_t idx = 0; + if (auto phi = dyn_cast(inst)) { + for (auto &use: phi->incoming_values()) { + if (auto expr = dyn_cast(use.get())) { + auto newinst = expr->getAsInstruction(); + auto block = phi->getIncomingBlock(use); + newinst->insertBefore(block->getTerminator()); + phi->setIncomingValue(idx, newinst); + splitConstExpr(newinst); + } + idx++; + } + } else { + for (auto &use: inst->operands()) { + if (auto expr = dyn_cast(use.get())) { + auto newinst = expr->getAsInstruction(); + newinst->insertBefore(inst); + inst->setOperand(idx, newinst); + splitConstExpr(newinst); + } + idx++; + } + } +} + + +void splitConstExpr(Module &M) { + // care: will this invalidate iterators? + for (Function &F: M) + for (BasicBlock &bbl: F) + for (Instruction &inst: bbl) + splitConstExpr(&inst); +} + + +void initValueUid(Module &M, std::map &valueUidMap) { + std::size_t cnt = 0xdeadbeef00000000; + for (auto &F : M) { + valueUidMap[&F] = cnt; + // dbgs() << &F << "\n"; + cnt++; + for (auto &BB : F) { + valueUidMap[&BB] = cnt; + // dbgs() << " " << &BB << "\n"; + cnt++; + for (auto &II : BB) { + valueUidMap[&II] = cnt; + // dbgs() << " " << &II << " "; + cnt++; + } + } + } +} + + +void insertWRPKRU(Module &M, Instruction *I, int perm_val, Value *dep) { + // 0 : ALLOW + // 1 : DISABLE_READ + // 3 : DISABLE_ACCESS + assert(perm_val == 0 || perm_val == 1 || perm_val == 3); + + // get Value + int pkey = 1; + LLVMContext &ctx = M.getContext(); + Type *voidty = Type::getVoidTy(ctx); + Type *int32ty = Type::getInt32Ty(ctx); + Value *zero = ConstantInt::get(int32ty, 0); + Value *perm = ConstantInt::get(int32ty, perm_val << (2 * pkey)); + + // build IR + IRBuilder<> builder(I); + if (dep && (!dep->getType()->isVoidTy() || isa(dep))) { + // explict dependence bwtween wrpkru and sandboxed inst + auto SI = dyn_cast(dep); + if (SI) dep = SI->getPointerOperand(); + // dbgs() << *dep->getType() << "\n"; + // dbgs() << *dep << "\n"; + if (dep->getType()->isPointerTy()) + dep = builder.CreatePtrToInt(dep, int32ty); + else + dep = builder.CreateIntCast(dep, int32ty, true); + // auto asmtype = TypeBuilder::get(ctx); + auto asmtype = FunctionType::get(voidty, {int32ty, int32ty, int32ty, int32ty}, false); + auto asminst = InlineAsm::get(asmtype, "wrpkru", "{ax},{cx},{dx},{bx}", true); +#ifndef WRPKRU_OFF + builder.CreateCall(asminst, SmallVector{perm, zero, zero, dep}); +#endif + } else { + // auto asmtype = TypeBuilder::get(ctx); + auto asmtype = FunctionType::get(voidty, {int32ty, int32ty, int32ty}, false); + auto asminst = InlineAsm::get(asmtype, "wrpkru", "{ax},{cx},{dx}", true); +#ifndef WRPKRU_OFF + builder.CreateCall(asminst, SmallVector{perm, zero, zero}); +#endif + } +} + + +CallInst* replaceAllocaWithMPKMalloc(Module &M, AllocaInst *I) { + // get function + auto &ctx = M.getContext(); + auto int8ptrty = Type::getInt8PtrTy(ctx); + auto int64ty = Type::getInt64Ty(ctx); + // auto malloctype = TypeBuilder::get(ctx); + auto malloctype = FunctionType::get(int8ptrty, {int64ty}, false); + auto mallocfunc = M.getOrInsertFunction("m_malloc", malloctype); + // get size + auto &dl = M.getDataLayout(); + auto allocasize = dl.getTypeAllocSize(I->getAllocatedType()); + auto argsize = ConstantInt::get(int64ty, allocasize); + // create callinst + Value* args[] = { argsize }; + auto callinst = CallInst::Create(mallocfunc, args, "", I); + // create bitcast + auto castinst = CastInst::CreatePointerCast(callinst, I->getType()); + ReplaceInstWithInst(I, castinst); + return callinst; +} + + +CallInst* insertMPKFree(Module &M, Value *target, Instruction *insertbefore) { + auto &ctx = M.getContext(); + auto int8ptrty = Type::getInt8PtrTy(ctx); + auto voidty = Type::getVoidTy(ctx); + // auto freetype = TypeBuilder::get(M.getContext()); + auto freetype = FunctionType::get(voidty, {int8ptrty}, false); + // auto freefunc = M.getOrInsertFunction("mpk_free", freetype); + auto freefunc = M.getOrInsertFunction("m_free", freetype); + Value* args[] = { target }; + return CallInst::Create(freefunc, args, "", insertbefore); +} + + +CallInst* insertMemset(Module &M, Value *target, Instruction *insertbefore) { + auto I = dyn_cast(target); + if (!I) return nullptr; + auto &dl = M.getDataLayout(); + auto allocasize = dl.getTypeAllocSize(I->getAllocatedType()); + Type *Int8Ty = Type::getInt8Ty(M.getContext()); + auto IntZero = ConstantInt::get(Int8Ty, 0); + IRBuilder<> builder(insertbefore); + CallInst *Set0CI = builder.CreateMemSet( + I, IntZero, allocasize, MaybeAlign(I->getAlignment())); + return Set0CI; +} + + +ExpandFuncPtr::ExpandFuncPtr(CallInst *inst) { + originst = inst; + funcptr = inst->getCalledValue(); + fptype = inst->getFunctionType(); + F = inst->getFunction(); + M = inst->getModule(); + for (auto &use: inst->arg_operands()) { + args.push_back(use.get()); + } +} + + +void ExpandFuncPtr::splitBlock() { + auto origblock = originst->getParent(); + tailblock = SplitBlock(origblock, originst); + curblock = BasicBlock::Create(M->getContext(), "funcptr", F); + auto firstbr = dyn_cast(origblock->getTerminator()); + firstbr->setSuccessor(0, curblock); +} + + +CallInst* ExpandFuncPtr::addTarget(Function *target) { + // create cond block + IRBuilder<> condbuilder(curblock); + Value *fp2 = funcptr; + if (fp2->getType() != target->getType()) + fp2 = condbuilder.CreateBitCast(fp2, target->getType()); + auto cond = condbuilder.CreateICmpEQ(fp2, target); + auto nextcur = BasicBlock::Create(M->getContext(), "funcptr", F); + auto callblock = BasicBlock::Create(M->getContext(), "funcptr.call", F); + condbuilder.CreateCondBr(cond, callblock, nextcur); + // create call block + IRBuilder<> callbuilder(callblock); + std::vector args2(args); + auto functype = target->getFunctionType(); + for (size_t i = 0; i < args2.size(); i++) { + auto dsttype = functype->getParamType(i); + if (args2[i]->getType() != dsttype) + args2[i] = callbuilder.CreateBitCast(args2[i], dsttype); + } + auto callinst = callbuilder.CreateCall(functype, target, args2); + Instruction *ret = callinst; + if (ret->getType() != fptype->getReturnType()) + ret = dyn_cast( + callbuilder.CreateBitCast(ret, fptype->getReturnType())); + assert(ret); + retvals.push_back(ret); + callbuilder.CreateBr(tailblock); + // update curblock + curblock = nextcur; + return callinst; +} + + +CallInst* ExpandFuncPtr::addFallback() { + IRBuilder<> lastbuilder(curblock); + auto ret = lastbuilder.CreateCall(fptype, funcptr, args); + lastbuilder.CreateBr(tailblock); + retvals.push_back(ret); + return ret; +} + + +PHINode* ExpandFuncPtr::addPHINode() { + auto returnty = originst->getType(); + if (returnty->isVoidTy()) return nullptr; + auto phi = PHINode::Create(returnty, retvals.size()); + for (auto val: retvals) { + phi->addIncoming(val, val->getParent()); + } + ReplaceInstWithInst(originst, phi); + return phi; +} + +void replaceRuntime(Module &M) { +#ifndef MEMMANAGER_OFF + auto &ctx = M.getContext(); + auto voidty = Type::getVoidTy(ctx); + auto int8ptrty = Type::getInt8PtrTy(ctx); + auto int64ty = Type::getInt64Ty(ctx); + + auto freefunc = M.getFunction("free"); + // auto freetype = TypeBuilder::get(M.getContext()); + auto freetype = FunctionType::get(voidty, {int8ptrty}, false); + auto mfreefunc = M.getOrInsertFunction("m_free", freetype); + if (freefunc) + freefunc->replaceAllUsesWith(mfreefunc.getCallee()); + + // auto mallocfunc = M.getFunction("malloc"); + // auto malloctype = TypeBuilder::get(M.getContext()); + // auto umallocfunc = M.getOrInsertFunction("u_malloc", malloctype); + // if (mallocfunc) + // mallocfunc->replaceAllUsesWith(umallocfunc); + + auto reallocfunc = M.getFunction("realloc"); + // auto realloctype = TypeBuilder::get(M.getContext()); + auto realloctype = FunctionType::get(int8ptrty, {int8ptrty, int64ty}, false); + auto mreallocfunc = M.getOrInsertFunction("m_realloc", realloctype); + if (reallocfunc) + reallocfunc->replaceAllUsesWith(mreallocfunc.getCallee()); +#endif +} + + +Function *funcwrap(Function *target, std::string wrapper_name, CallInst *callinst, bool callerprotect) { + auto M = target->getParent(); + auto wrapper = M->getFunction(wrapper_name); + if (!wrapper) { + wrapper = cast(M->getOrInsertFunction(wrapper_name, target->getFunctionType()).getCallee()); + auto entryBB = BasicBlock::Create(wrapper->getContext(), "entryBB", wrapper); + IRBuilder<> IRBentryBB(entryBB); + std::vector args; + for (auto& arg: wrapper->args()) { + args.push_back(&arg); + } + auto functype = target->getFunctionType(); + IRBuilder<> builder(entryBB); + auto newcall = builder.CreateCall(functype, target, args); + if (functype->getReturnType()->isVoidTy()) + builder.CreateRet(nullptr); + else + builder.CreateRet(newcall); + if (callerprotect) { + insertWRPKRU(*M, newcall, 0); + insertWRPKRU(*M, newcall->getNextNode(), 3); + } + } + callinst->setCalledFunction(wrapper); + return wrapper; +} diff --git a/lab-iisec/LoCCS-gossip-cryptompk/src/passentry.cpp b/lab-iisec/LoCCS-gossip-cryptompk/src/passentry.cpp new file mode 100644 index 0000000000000000000000000000000000000000..983d384c528d294699b03340a9d8dc0e8db45b8a --- /dev/null +++ b/lab-iisec/LoCCS-gossip-cryptompk/src/passentry.cpp @@ -0,0 +1,151 @@ +#include "llvm_basics.h" +#include +#include +#include +#include +#include "llvm/Support/raw_ostream.h" +#include "llvm/Support/SourceMgr.h" +#include +#include "ContextBase.h" +#include "GlobalVisitor.h" +#include "AliasAnalysisVisitor.h" +#include "TaintAnalysisVisitor.h" +#include "ModifyVisitor.h" +#include "Utils.h" + + +static cl::opt CheckFunctionName("toCheckFunction", + cl::desc("Function which is to be considered as entry point " + "into the program"), + cl::value_desc("full name of the function"), cl::init("")); + +static cl::opt CreateLib("createLib", + cl::desc("Director functions"), + cl::value_desc("file of function's full name"), cl::init("")); + +static cl::opt ExportLabel("exportLabel", + cl::desc("suffix of exported functions"), + cl::value_desc("suffix of exported functions"), cl::init("")); + +static cl::opt Threshold("threshold", + cl::desc("Threshold for full-protection"), + cl::value_desc("double"), cl::init("")); + +static cl::opt ApisReportFile("apisReport", + cl::desc("Report of API names exported"), + cl::value_desc("file to output API names"), cl::init("")); + +static cl::opt TaintReportFile("taintReport", + cl::desc("Report of taints"), + cl::value_desc("file name"), cl::init(""), cl::Required); + +static cl::opt DbgBc("debugbc", + cl::desc("Debug version of bitcode"), + cl::value_desc("llvm bitcode"), cl::init(""), cl::Required); + +static cl::opt HotspotsFile("hotspots", + cl::desc("Hotspot functions"), + cl::value_desc("text"), cl::init("")); + +static cl::opt SkipFile("skipfuncs", + cl::desc("Functions to skip"), + cl::value_desc("text"), cl::init("")); + +bool Globals::IsLib = false; +double Globals::Threshold = 0.5; +std::map Globals::ValueUidMap; +std::set Globals::DirFuncs; +std::set Globals::Hotspots; +std::set Globals::SkipFuncs; +std::string Globals::ExportLabel; +std::map DbgInfo::DbgUidValueMap; +raw_fd_ostream *Globals::ApisReport = nullptr; +raw_fd_ostream *Globals::TaintReport = nullptr; +Module *DbgInfo::DbgM = nullptr; + +struct SAAPass: public ModulePass { + static char ID; + + SAAPass(): ModulePass(ID) {} + + ~SAAPass() {} + + static void init(Module &m) { + splitConstExpr(m); + initValueUid(m, Globals::ValueUidMap); + DbgInfo::load(DbgBc); + Globals::ExportLabel = std::move(ExportLabel); + if (CreateLib != "") { + std::ifstream ifile(CreateLib); + std::string line; + while (std::getline(ifile, line)) { + Globals::DirFuncs.insert(m.getFunction(line)); + } + ifile.close(); + Globals::IsLib = true; + + static std::error_code EC; + static raw_fd_ostream Output(ApisReportFile, EC, sys::fs::OF_Append); + Globals::ApisReport = &Output; + } + if (HotspotsFile != "") { + std::ifstream ifile(HotspotsFile); + std::string line; + while (std::getline(ifile, line)) { + Globals::Hotspots.insert(line); + } + ifile.close(); + } + if (SkipFile != "") { + std::ifstream ifile(SkipFile); + std::string line; + while (std::getline(ifile, line)) { + Globals::SkipFuncs.insert(line); + } + ifile.close(); + } + static std::error_code EC; + static raw_fd_ostream Output(TaintReportFile, EC, sys::fs::OF_Append); + Globals::TaintReport = &Output; + if (Threshold != "") + Globals::Threshold = atof(Threshold.c_str()); + } + + bool runOnModule(Module &m) override { + init(m); + for (Function &func: m) { + if (func.getName().str() == CheckFunctionName) { + errs() << "Entry Point Found!\n"; + start_analyze(m, func); + break; + } + } + replaceRuntime(m); + //if (Globals::IsLib) { + // Globals::ApisReport->close(); + //} + //Globals::TaintReport->close(); + return true; + } + + void getAnalysisUsage(AnalysisUsage &AU) const override { + AU.setPreservesAll(); + AU.addRequired(); + } + + void start_analyze(Module &m, Function &entry) { + GlobalVisitor visitor(m, entry); + visitor.addCallback(); + visitor.addCallback(); + visitor.analyze(); + visitor.clearCallbacks(); + DEBUG_PASSENTRY(dbgs() << "ModifyVisitor analyze\n"); + auto modifyvisitor = visitor.addCallback(); + visitor.analyze(); + modifyvisitor->run_modify(); + } +}; + + +char SAAPass::ID = 0; +static RegisterPass x("dr_checker", "Soundy Program Rewriter"); diff --git a/lab-iisec/Multivariate Statistics/1-Characterizing data/1-4.R b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-4.R new file mode 100644 index 0000000000000000000000000000000000000000..d3de9785643f9a683501ddbf0ccadc03c7f34425 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-4.R @@ -0,0 +1,40 @@ +penguins_ = penguins[complete.cases(penguins),] +y1 = penguins_["bill_length_mm"] +y2 = penguins_["bill_depth_mm"] +y3 = penguins_["flipper_length_mm"] +y4 = penguins_["body_mass_g"] +pairs(penguins[,3:6],main="Scatterplot Matrix") + +round(colMeans(penguins_[,3:6]),2) +S = round(cov(penguins_[,3:6]),2) +R = round(cor(penguins_[,3:6]),2) + +det(S) +sum(diag(S)) + +Ds = -diag(sqrt(diag(S))) +round(Ds%*%R%*%Ds,2) +S +chol(S) +crossprod(chol(S),chol(S)) + +eS = eigen(S) +Srdiag = diag(sqrt(eS$values)) +Qs = eS$vectors +Sroot = Qs%*%Srdiag%*%t(Qs) +Sroot +eR = eigen(R) +Rrdiag = diag(sqrt(eR$values)) +Qr = eR$vectors +Rroot = Qr%*%Rrdiag%*%t(Qr) +Rroot +det(S) +det(R) + +install.packages("scatterplot3d") +library(scatterplot3d) +y1num=as.numeric(unlist(y1)) +y2num=as.numeric(unlist(y2)) +y3num=as.numeric(unlist(y3)) +scatterplot3d(x = y1num, y = y2num, z = y3num, xlab="y1",ylab="y2",zlab="y3", grid = TRUE) +#画出来的点没颜色。。 \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/1-Characterizing data/1-5.R b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-5.R new file mode 100644 index 0000000000000000000000000000000000000000..7a671f0432cb073593138ad687b07c3c85668423 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-5.R @@ -0,0 +1,36 @@ +A=matrix(c(1,1,-1,1),nrow=2,ncol=2) +B=matrix(c(1,1,1,1,1,-2),nrow=2, ncol=3) +d=matrix(c(4,-1,0.5,-0.5,0,-1,3,1,-1,0,0.5,1,6,1,-1,-0.5,-1,1,4,0,0,0,-1,0,2),nrow=5,ncol=5) +u=matrix(c(2,4,-1,3,0),nrow=5,ncol=1) + +C=matrix(c(1,-1,2,1,2,-3,1,-3,4,1,3,0,1,-2,0),nrow=3,ncol=5) +EZ=C%*%u +COVZ=C%*%d%*%t(C) + +PZ=cov2cor(COVZ) #cov化为cor + +det(COVZ) +sum(diag(COVZ)) + +##谱分解 +C1=eigen(COVZ)$vectors +C1%*%diag(eigen(COVZ)$values)%*%t(C1) +##Cholesky分解 +chol(COVZ) +crossprod(chol(COVZ),chol(COVZ)) +##平方根矩阵 +eC = eigen(COVZ) +Crdiag = diag(sqrt(eC$values)) +QC = eC$vectors +Croot = QC%*%Crdiag%*%t(QC) +Croot +##对PZ相同操作 +P1=eigen(PZ)$vectors +P1%*%diag(eigen(PZ)$values)%*%t(P1) +chol(PZ) +crossprod(chol(PZ),chol(PZ)) +eP = eigen(PZ) +Prdiag = diag(sqrt(eP$values)) +QP = eP$vectors +Proot = QP%*%Prdiag%*%t(QP) +Proot \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/1-Characterizing data/1-6.R b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-6.R new file mode 100644 index 0000000000000000000000000000000000000000..5dbbc70c268e5cebfaa2b07c66462664074a269b --- /dev/null +++ b/lab-iisec/Multivariate Statistics/1-Characterizing data/1-6.R @@ -0,0 +1,7 @@ +data = read.csv("D:/pentathlon.csv") + +round(colMeans(data[,2:9]),2) +S = round(cov(data[,2:9]),2) +R = round(cor(data[,2:9]),2) + +pairs(data[,2:9]) \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-5.R b/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-5.R new file mode 100644 index 0000000000000000000000000000000000000000..f907909d6a4dd79d14f8216ba9bd2db6338b2e53 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-5.R @@ -0,0 +1,40 @@ +df = read.table("D:/company.txt") + +#(1) +qqnorm(df[,1],main="Q-Q plot for x1"); qqline(df[,1]) +qqnorm(df[,2],main="Q-Q plot for x2"); qqline(df[,2]) +qqnorm(df[,3],main="Q-Q plot for x3"); qqline(df[,3]) + +#(2) +shapiro.test(df[,1]) +shapiro.test(df[,2]) +shapiro.test(df[,3]) + +install.packages("nortest") +library(nortest) + +ks.test(df[,1],"pnorm",mean(df[,1]),sqrt(var(df[,1]))) +ks.test(df[,2],"pnorm",mean(df[,2]),sqrt(var(df[,2]))) +ks.test(df[,3],"pnorm",mean(df[,3]),sqrt(var(df[,3]))) + +cvm.test(df[,1]) +cvm.test(df[,2]) +cvm.test(df[,3]) + +ad.test(df[,1]) +ad.test(df[,2]) +ad.test(df[,3]) + +#(3) +pairs(df) + +y=df +cm=colMeans(y) +S=cov(y) +d=apply(y,1,function(y) t(y-cm)%*%solve(S)%*%(y-cm)) +plot(qc<-qchisq((1:nrow(y)-1/2) / nrow(y), df=3), sd <-sort(d), + xlab=expression(paste(chi[3]^2, "Quantile")), + ylab="Ordered distances",xlim=range(qc)*c(1, 1.1)) +oups=which(rank(abs(qc-sd), ties="random") > nrow(y)-3) +text(qc[oups],sd[oups]-1.5,names(oups)) +abline(a=0,b=1) \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-6.R b/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-6.R new file mode 100644 index 0000000000000000000000000000000000000000..83c9cfc2bb9576bfefabcde8115dc4f2dde80d8c --- /dev/null +++ b/lab-iisec/Multivariate Statistics/2-Multivariate Normal Distribution/2-6.R @@ -0,0 +1,41 @@ +download.file( + url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", + destfile = "wine.data" +) +wine=read.csv("wine.data", header = FALSE) +colnames(wine)=c("Type","Alcohol", "Malic","Ash", "Alcalinity", + "Magnesium", "Phenols", "Flavanoids", + "Nonflavanoids","Proanthocyanins","Color", + "Hue","Dilution","Proline") + +#(1) +wine1=wine[,2:3] +mean.vect=apply(wine1, 2, mean) #不加括号不输出！！！ +cov.matrix=cov(wine1) +n=dim(wine1)[1] +p=dim(wine1)[2] +mu.0=c(13,2) +(T.sq=n*t(mean.vect-mu.0)%*%solve(cov.matrix)%*%(mean.vect-mu.0)) +alpha=0.1 +(cut.off=(n-1)*p/(n-p)*qf(1-alpha,p,n-p)) +(p.value=1-pf(T.sq*(n-p)/(n-1)/p,p,n-p)) + +#(2) +install.packages("dplyr") +library(dplyr) +type1 = wine %>% + filter(Type==1) %>% + select(Alcohol,Malic) +type2 = wine %>% + filter(Type==2) %>% + select(Alcohol,Malic) + +mu.1=colMeans(type1) +mu.2=colMeans(type2) +n1=dim(type1)[1] +n2=dim(type2)[1] +p=dim(type1)[2] +s=((n1-1)*cov(type1)+(n2-1)*cov(type2))/(n1+n2-2) +(T_sq=n1*n2*t(mu.1-mu.2)%*%solve(s)%*%(mu.1-mu.2)/(n1+n2)) +alpha=0.1 +(T=qchisq(1-(alpha/2),df=p)) diff --git a/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-5.R b/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-5.R new file mode 100644 index 0000000000000000000000000000000000000000..8de0023e98533a7960f655b91bf385551ad2bdfd --- /dev/null +++ b/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-5.R @@ -0,0 +1,63 @@ +install.packages("palmerpenguins") +library(palmerpenguins) +install.packages("dplyr") +library(dplyr) + +penguins_ = penguins[complete.cases(penguins),] + +penguins_=penguins_ %>% + select(species,bill_length_mm,bill_depth_mm,flipper_length_mm) + +Ad = penguins_ %>% + filter(species=="Adelie") %>% + select(bill_length_mm,bill_depth_mm,flipper_length_mm) +Ge = penguins_ %>% + filter(species=="Gentoo") %>% + select(bill_length_mm,bill_depth_mm,flipper_length_mm) +Ch = penguins_ %>% + filter(species=="Chinstrap") %>% + select(bill_length_mm,bill_depth_mm,flipper_length_mm) + +#“样本方差” +n1=dim(Ad)[1] +a1=(n1-1)*cov(Ad) +n2=dim(Ge)[1] +a2=(n2-1)*cov(Ge) +n3=dim(Ch)[1] +a3=(n3-1)*cov(Ch) +a=a1+a2+a3 + +n=n1+n2+n3 +t=(n-1)*cov(penguins_[2:4]) +spl=a/(n-3) + +#（1）p=3,k=3 +l1=(det(cov(Ad))/det(spl))^((n1-1)/2) +l2=(det(cov(Ge))/det(spl))^((n2-1)/2) +l3=(det(cov(Ch))/det(spl))^((n3-1)/2) +lambda=l1*l2*l3 +M=-2*log(lambda) +g=3*4*2/2 +d=(2*9+9-1)*(1/(n1-1)+1/(n2-1)+1/(n3-1)-1/(n-3)) +(1-d)*M +qchisq(1-0.05,df=g) + +#（2） +bar1=colMeans(Ad) +sse1=matrix(c(0,0,0,0,0,0,0,0,0),nrow=3,ncol=3) +for(i in 1:n1) +{ + sse1=sse1+as.matrix(t(Ad[i,]-bar1))%*%as.matrix((Ad[i,]-bar1)) +} +sse1 #发现和a1是一样的 + +lambda_=det(a)/det(t) +-log(lambda_) +qchisq(1-0.05,df=3*2)/(n-1-3) + +#（3） +bar2=colMeans(Ge) +spll=(a1+a2)/(n1+n2-2) +T2=n1*n2*t(bar2-bar1)%*%solve(spll)%*%(bar2-bar1)/(n1+n2) +(n1+n2-4)*T2/((n1+n2-2)*3) +qf(1-0.05,3,n1+n2-4) \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-6.R b/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-6.R new file mode 100644 index 0000000000000000000000000000000000000000..316c541ed2432ab6fa2838a50881d4a7d3a4a957 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/3-Hypothesis test/3-6.R @@ -0,0 +1,26 @@ +install.packages("car") +library(car) + +sweat=read.table("D:\\Multivariate Statistics\\sweat.dat",header = FALSE) +sweat=sweat[,1:2] + +#(b) +n=dim(sweat)[1] +p=dim(sweat)[2] +alpha=0.05 +(cut.off.f = qf(1-alpha/2,p,n-p)) +(mean.vect = colMeans(sweat)) + +dataEllipse(sweat[,1],sweat[,2],levels=0.95,col="black",xlim=c(0,15),ylim=c(10,80), + xlab="sweat1",ylab="sweat2") + +#(c) +S=cov(sweat) +eigen(S) +2*sqrt(cut.off.f*eigen(S)$values) #椭圆长短轴 + +#(d) +sweat1=sweat[,1] +sweat2=sweat[,2] +t.test(sweat1) +t.test(sweat2) \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/4-Multivariate Regression.Rmd b/lab-iisec/Multivariate Statistics/4-Multivariate Regression.Rmd new file mode 100644 index 0000000000000000000000000000000000000000..738d735e8f931253b82f7fc8674a6f3e3af9f02e --- /dev/null +++ b/lab-iisec/Multivariate Statistics/4-Multivariate Regression.Rmd @@ -0,0 +1,242 @@ +--- +title: "hw4" +output: + html_document: default + pdf_document: default + word_document: default +date: "2022-11-13" +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +![](images/1-01.jpg) + +![](images/2-01.jpg) + +# ![](images/3.jpg)![](images/4.jpg)4. + +## (a)多因子线性回归 + +下面是一部分数据 + +```{r} +fiber = read.table("D:\\Multivariate Statistics\\fiber.dat",header = FALSE) +colnames(fiber) = c("y1","y2","y3","y4","x1","x2","x3","x4") +fiber[1:10,] +``` + +### $Y_1$ + +1. 运用逐步挑选法，以AIC为衡量指标，进行变量选择。 + +```{r} +lm.fiber1 = lm(y1~x1+x2+x3+x4, data = fiber)#第三问 +summary(lm.fiber1)#第三问 +lm.step1 = step(lm.fiber1,direction = "both") +summary(lm.step1) +``` + +合适的线性回归模型为$y_1=-3.12x_1+0.10x_2+0.05x_3+85.08x_4-74.23$ + +2. 残差为 0.7569，可见比全局的好。其中x4影响最大。 + +3. 进行总体假设检验，$H_0:\beta_1=\cdots=\beta_4=0$ + +```{r} +lm.fiber1 = lm(y1~x1+x2+x3+x4, data = fiber) +summary(lm.fiber1) +``` + +F统计量的值为42.18，p值很小，表明拒绝原假设，即这4个变量中有与$y_1$有线性关系的。 + +4. 已知一个新预测， + +根据公式：$Ey_0$ 的置信区间为 + +$x_0^\top \hat{\beta} \pm t_{\alpha/2}(n-q-1)\sqrt{s^2[x_0^\top (X^\top X)^{-1}x_0]}$ + +$y_0$ 的预测区间为 + +$x_0^\top \hat{\beta} \pm t_{\alpha/2}(n-q-1)\sqrt{s^2[1+x_0^\top (X^\top X)^{-1}x_0]}$ + +```{r} +new=data.frame(x1=0.330,x2=45.500,x3=20.375,x4=1.010) +predict(lm.step1, newdata=new, interval="confidence") +predict(lm.step1, newdata=new, interval="prediction") +``` + +得到了95%平均值置信区间为[14.02,18.21]，95%预测区间为[12.45,19.77] + +### $Y_2$ + +同样地 + +```{r} +lm.fiber = lm(y2~x1+x2+x3+x4, data = fiber) +summary(lm.fiber) +lm.step = step(lm.fiber,direction = "both") +summary(lm.step) +predict(lm.step, newdata=new, interval="confidence") +predict(lm.step, newdata=new, interval="prediction") +``` + +1. 合适的线性回归模型为$y_2=-0.78x_1+0.01x_3+29.73x_4-24.69$ + +2. 这个模型原来的残差与adjusted残差为 0.7855和 0.7704 ，变量选择后变为0.7797和0.7683，有改进。 + + $x_4$ 的影响力较大。 + +3. 全局假设检验 $H_0:\beta_1=\cdots=\beta_4=0$ ，F统计量为52.18，所得p值\< 2.2e-16，在99%的显著性水平上拒绝原假设，这意味着至少有一个变量与$y_2$是线性相关的。 + +4. 在给定一组新观测时，得到的95% 平均值置信区间为[4.77, 5.73]，95% 预测区间为[4.41, 6.09] + +### $Y_3$ + +```{r} +lm.fiber = lm(y3~x1+x2+x3+x4, data = fiber) +summary(lm.fiber) +lm.step = step(lm.fiber,direction = "both") +summary(lm.step) +predict(lm.step, newdata=new, interval="confidence") +predict(lm.step, newdata=new, interval="prediction") +``` + +1. 合适的线性回归模型为$y_3=-1.49x_1+0.05x_2+0.03x_3+45.80x_4-45.76$ + +2. 这个模型的原来的残差与adjusted残差为0.8171和0.8043 ，变量选择后仍为0.8171和0.8043 。 + + $x_4$ 的影响力较大。 + +3. 全局假设检验 $H_0:\beta_1=\cdots=\beta_4=0$ ，F统计量为63.66，所得p值\< 2.2e-16，在99%的显著性水平上拒绝原假设，这意味着至少有一个变量与$y_3$是线性相关的。 + +4. 在给定一组新观测时，得到的95% 平均值置信区间为[1.75, 3.56]，95% 预测区间为[1.08, 4.24] + +### $Y_4$ + +```{r} +lm.fiber = lm(y4~x1+x2+x3+x4, data = fiber) +summary(lm.fiber) +lm.step = step(lm.fiber,direction = "both") +summary(lm.step) +predict(lm.step, newdata=new, interval="confidence") +predict(lm.step, newdata=new, interval="prediction") +``` + +1. 合适的线性回归模型为$y_4=0.02x_2+0.01x_3+15.77x_4-17.00$ + +2. 这个模型原来的残差与adjusted残差为0.7633和0.7466，变量选择后变成 0.7569和0.7444 ，有改进。 + + $x_4$ 的影响力较大。 + +3. 全局假设检验 $H_0:\beta_1=\cdots=\beta_4=0$ ，F统计量为45.94，所得p值\< 2.2e-16，在99%的显著性水平上拒绝原假设，这意味着至少有一个变量与$y_4$是线性相关的。 + +4. 在给定一组新观测时，得到的95% 平均值置信区间为[-0.21, 0.60]，95% 预测区间为[-0.62, 1.00] + +## (b)多对多线性回归 + +```{r} +#install.packages("carData") +library(car) +``` + +1. 线性回归模型为$Y=XB+\epsilon$（这是大写的Epsilon），B和$\Sigma$ 具体如下。 + +```{r} +mod.fiber = lm(cbind(y1,y2,y3,y4)~cbind(x1,x2,x3,x4), data = fiber) +B.hat = mod.fiber$coeff +B.hat +``` + +```{r} +summary(Manova(mod.fiber)) +names(summary(Manova(mod.fiber))) +E=summary(Manova(mod.fiber))$SSPE +Se=E/(62-4-1) +Se +``` + +2. 整体假设检验 + +```{r} +summary(Manova(mod.fiber))$multivariate.test +``` + +可见p值都很小，拒绝原假设，即去掉第一行的B不为0。 + +3. LOF 关于$x_3,x_4$, $H_0：B_d=0$ + +```{r} +mod = lm(cbind(y1,y2,y3,y4)~cbind(x1,x2), data = fiber) +anova(mod,mod.fiber,test="Wilks") +``` + +可见p值很小，拒绝原假设，所以$x_3,x_4$ 的系数是显著的。 + +4. 给定一个观测，可得如下95%平均值区间和预测区间。 + +```{r} + +X=as.matrix(cbind(rep(1,62),fiber[,5:8])) +h=solve(t(X)%*%X) + +fct = function(B,S,x0,n,p,q,alpha,j){ +betaj=B[,j] +sjj=S[j,j] +e = t(betaj)%*%x0 +a = sqrt((n-q-1)*p/(n-q-p)*qf(1-alpha,p,n-q-p)) +b = sqrt(sjj*(t(x0)%*% h %*% x0)) +c = sqrt(sjj*(1 + t(x0)%*% h %*% x0)) +conf1=e-a*b +conf2=e+a*b +pre1=e-a*c +pre2=e+a*c +print(c(conf1,conf2,pre1,pre2)) +} +x0=matrix(c(1,0.330,45.500,20.375,1.010),nrow=5,ncol = 1) +for (j in 1:4){ + fct(B.hat,Se,x0,62,4,4,0.05,j) +} + +``` + +## (c) + +```{r} +modc = lm(cbind(y1,y2)~cbind(x1,x2,x3,x4), data = fiber) + +library(ggplot2) + +el=function(s1,s2,c){ + a=s1*c + b=s2*c + x=seq(from =-a, to=a, length.out=400) + points=data.frame(x1 =c(-x,x),x2= NA) + points$x2[1:400]=sqrt(((a*b)^2-(b*x)^2)/a^2) + points$x2[401:800]=-sqrt(((a*b)^2-(b*x)^2)/a^2) + return(points) +} +ell=function(mu,s,c){ + lambda=diag(eigen(s)$values) + P=eigen(s)$vectors + Y=el(s1=sqrt(lambda[1,1]),s2=sqrt(lambda[2,2]),c=c) + X=t(P%*%t(Y)+mu) + X=as.data.frame(X) + colnames(X)=c('x1','x2') + return(X) +} + +n=62;p=2;q=4 +BHAT=modc$coefficients +Se1=Manova(modc)$SSPE/(n-q-1) +bary=as.vector(t(x0)%*%BHAT) +var1=as.numeric(t(x0)%*%solve(t(X)%*%X)%*%x0)*Se1 +var2=as.numeric(1+t(x0)%*%solve(t(X)%*%X)%*%x0)*Se1 + +cut.off.f=(n-q-1)*p/(n-q-p)*qf(1-0.05,p,n-q-p) +CON=ell(mu=bary,s=var1,c=sqrt(cut.off.f)) +PRE=ell(mu=bary,s=var2,c=sqrt(cut.off.f)) +ggplot()+geom_path(data=CON,mapping = aes(x=x1,y=x2),color="blue")+ + geom_path(data=PRE,mapping = aes(x=x1,y=x2),color="black") +``` diff --git a/lab-iisec/Multivariate Statistics/5-Discrimination and Classification.Rmd b/lab-iisec/Multivariate Statistics/5-Discrimination and Classification.Rmd new file mode 100644 index 0000000000000000000000000000000000000000..2714173377a0981318e11bb6e4c8349ef887c889 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/5-Discrimination and Classification.Rmd @@ -0,0 +1,198 @@ +--- +--- +--- + +------------------------------------------------------------------------ + +title: "hw5" output: html_document date: "2022-11-25" --- + +# 5. + +```{r} +library(palmerpenguins) +library(dplyr) + +penguins_ = penguins[complete.cases(penguins),] +penguins_=penguins_ %>% + dplyr::select(species,bill_length_mm,bill_depth_mm) + +Ad = penguins_ %>% + filter(species=="Adelie") %>% + dplyr::select(bill_length_mm,bill_depth_mm) +Ch = penguins_ %>% + filter(species=="Chinstrap") %>% + dplyr::select(bill_length_mm,bill_depth_mm) +bi = penguins_[penguins_$species=="Adelie" | penguins_$species== "Chinstrap",] +``` + +## (a). + +这两个组别的样本均值和协方差如下： + +```{r} +admu=colMeans(Ad) +chmu=colMeans(Ch) +adcov=cov(Ad) +chcov=cov(Ch) + +admu;chmu;adcov;chcov +``` + +## (b). LDA + +```{r} +library(MASS) +bi$species = as.factor(bi$species) +ld=lda(bi$species ~ bi$bill_length_mm + bi$bill_depth_mm) +ld +``` + +可见判别函数为Z = 0.393length - 0.439depth + +```{r} +library(ggplot2) + +spl=(145*adcov+67*chcov)/212 +G=solve(spl)%*%(admu-chmu) +z1=t(G)%*%admu +z2=t(G)%*%chmu +G1=t(c(1,0))%*%G +G2=t(c(0,1))%*%G + +{plot(bi$bill_length_mm,bi$bill_depth_mm,col=c("blue","red")[unclass(bi$species)],xlab="bill_length_mm",ylab="bill_depth_mm") +abline(a=(z1+z2)/(2*G2), b=-G1/G2)} +``` + +## (c). Bayes + +```{r} +ld2 = lda(bi$species ~ bi$bill_length_mm + bi$bill_depth_mm, prior=c(151/219,68/219,0)) +ld2 +``` + +k可见判别函数为Z = 0.393length - 0.439depth，与LDA结果一样；过程里有先验概率，也是一样的。这几只值为NA的企鹅对于最终的判别函数几乎没有影响。 + +## (d) + +```{r} +Ge = penguins_ %>% + filter(species=="Gentoo") %>% + dplyr::select(bill_length_mm,bill_depth_mm) +gemu = colMeans(Ge) +gecov = cov(Ge) +mu = colMeans(penguins_[,2:3]) + +m1 = (admu - mu)%*%t(admu - mu) +m2 = (chmu - mu)%*%t(chmu - mu) +m3 = (gemu - mu)%*%t(gemu - mu) +B = m1+m2+m3 + +W = 145*adcov+67*chcov+118*gecov + +H = solve(W)%*%B +eigen(H) + +0.0576958/(0.0576958+0.00839015) +``` + +从结果可以看出，使用第一个特征值就可以解释超过85%。 + +LDA为 Z1 = 0.367length - 0.930depth, Z2 = 0.313length + 0.950depth + +从下面判别空间的散点图可见，判别函数良好。 + +```{r} +ggplot(penguins_, mapping=aes(x=bill_length_mm, y=bill_depth_mm, shape=species, color=species)) + geom_point(size=2) + theme_bw() +``` + +```{r} +ggplot(penguins_, mapping=aes(x=0.367*bill_length_mm-0.93*bill_depth_mm, y=0.313*bill_length_mm+0.95*bill_depth_mm, shape=species, color=species)) + geom_point(size=2) + theme_bw() +``` + +## (e). Bayes + +```{r} +penguins_=penguins_ %>% + dplyr::select(species,bill_length_mm,bill_depth_mm) + +species = as.factor(penguins_$species) +bill_length_mm = penguins_$bill_length_mm +bill_depth_mm = penguins_$bill_depth_mm +ld2 = lda(species ~ bill_length_mm + bill_depth_mm,prior=c(151/342,68/342,123/342)) +ld2 +``` + +## (f). 有新观测 + +```{r} +new = data.frame(cbind(bill_length_mm=45, bill_depth_mm=15)) +e1=eigen(H)$vectors%*%t(t(c(1,0))) +(t(e1)%*%(t(new)-t(t(admu))))^2 +(t(e1)%*%(t(new)-t(t(gemu))))^2 +(t(e1)%*%(t(new)-t(t(chmu))))^2 +``` + +按LDA，分成Adelie类 + +```{r} +predict(ld2,new)$class +``` + +按Bayes，分成Gentoo类 + +# 6. + +## (a). + +两组数据的均值，协方差等如下： + +```{r} +financial=read.table("D:\\Multivariate Statistics\\financial.dat",header = FALSE) +colnames(financial)=c("x1","x2","x3","x4","type") + +bank = financial %>% + filter(type==0) %>% + dplyr::select(1:4,) +nonbank = financial %>% + filter(type==1) %>% + dplyr::select(1:4,) + +mu.b = colMeans(bank) +mu.n = colMeans(nonbank) +cov.b = cov(bank) +cov.n = cov(nonbank) +spl = (20*cov.b+24*cov.n)/(21+25-2) +mu.b;cov.b;mu.n;cov.n;spl +``` + +## (b). LDA + +```{r} +type = as.factor(financial$type) +levels(type) = c("0","1") +ld=lda(type ~ financial$x1+financial$x2+financial$x3+financial$x4) +ld +``` + +得到判别方程为 Z = 0.66x1 + 4.39x2 + 0.89x3 - 1.18x4 + +对现有观测分类： + +```{r} +Z = predict(ld) +predG = Z$class +result = cbind(type, predG, Z$x) +colnames(result) = c("Truegroup", "Predicted", "TransformedData") +result +``` + +## (c). + +```{r} +k = t(mu.b-mu.n)%*%solve(spl) +m = 0.5*k%*%(mu.b+mu.n) +n = log(2*(0.95/0.05)) +k;m;n +``` + +当k%\*%y-m大于等于n时，算为破产类；反之，没破产。 diff --git a/lab-iisec/Multivariate Statistics/6-PCA and CCA.Rmd b/lab-iisec/Multivariate Statistics/6-PCA and CCA.Rmd new file mode 100644 index 0000000000000000000000000000000000000000..1c7af0840438f66ee91acf2fd7ddfd5532527595 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/6-PCA and CCA.Rmd @@ -0,0 +1,131 @@ +--- +title: "hw6" +output: html_document +date: "2022-12-09" +--- + +# 3. PCA + +## (a). + +```{r} +csv = read.csv("D:\\Multivariate Statistics\\soccer-standings.csv", header = T) +csv. = csv[, 5:10] +cov = round(cov(csv.), 3) +cov +``` + +```{r} +PCA = princomp(csv.) +summary(PCA, loadings=T) +``` + +```{r} +screeplot(PCA, type = "lines") +``` + +由上图可见，选取前两个主成分。 + +```{r} +scores = round(PCA$scores, 3) +scores +``` + +```{r} +pairs(scores, col=csv[,2]) +``` + +```{r} +plot(scores[,1],scores[,2]) +``` + +## (b). + +PC1 = 0.152W +0.000D - 0.143L + 0.502G - 0.286GA +0.788GD + +解释：从各变量的系数以及各球队得分可以看出，如果赢的比赛次数越多，净胜球数越多，那么得分越高，意味着球队踢得好。相反，如果输掉比赛的次数和失球的个数越多，得分就会越低。比如，失球次数过多，以至于净胜球个数为负数，那么分数自然就低，会是负数，表示踢得差。 + +PC2 = 0.000W + 0.000D +0.000L +0.635G + 0.762GA - 0.127GD + +解释：进球与失球前面的系数为正，净胜球前面的系数为负，其余系数均为0，衡量的是球队进球水平。 + +## (c). + +```{r} +cor = round(cor(csv.), 3) +cor +``` + +```{r} +PCAr = princomp(csv., cor=T) +summary(PCAr, loadings=T) +``` + +```{r} +screeplot(PCAr, type = "lines") +``` + +由上图可见，选取前4个主成分。 + +```{r} +scoresr = round(PCAr$scores, 3) +scoresr +``` + +```{r} +pairs(scoresr, col=csv[,2]) +``` + +## (d). + +这个数据集选基于R的PCA更好。一方面选取的4个主成分能够解释部分，比基于S选取的两个主成分，可以解释的比例与方面更多；另一方面，初始变量分别与比赛场数和进球数有关，变化尺度不同，基于R的PCA相当于先进行了标准化，更加科学。 + +# 4. CCA + +## (a). + +```{r} +csv.std = scale(csv0) +ca = cancor(csv.std[,2:],csv.std[,c(4,5,3)]) #####第二个顺序不对！！！ +ca$cor; ca$xcoef; ca$ycoef +``` + +可见1st典型相关系数是0.986， + +1st典型相关变量为 U1 = 0.259W + 0.121D，V1 = 0.020G - 0.001GA - 0.212L + +## (b). + +```{r} +U1 = 0.259*csv.std[,1] +0.121 *csv.std[,2] +V1 = 0.020*csv.std[,4] - 0.001*csv.std[,5] - 0.212*csv.std[,3] + +plot(U1,V1) +``` + +从图中可以看出，U1和V1近似于线性相关，这是因为CCA的目标是最大化相关性，并且求解得1st典型相关系数0.986。 + +## (C). + +由(a)可见2nd典型相关系数是0.564， + +2nd典型相关变量为 U2 = -0.000W + 0.229D，V2 = -0.350G + 0.204GA - 0.429L + +```{r} +U2 = 0.229 *csv.std[,2] +V2 = -0.35*csv.std[,4] + 0.204*csv.std[,5] - 0.429*csv.std[,3] +plot(U2,V2) +``` + +可见，可以按照U2取值对点进行分类。 + +```{r} +library(ggplot2) +plot2=ggplot(csv.)+ + geom_point(aes(x=U1,y=V1),color="red")+ + geom_point(aes(x=U2,y=V2),color="blue")+ + xlab("U")+ylab("V") +plot2 +``` + +可见，CCA的两组典型相关变量衡量了球队的不同方面，1st是能力，2nd是分类。 diff --git a/lab-iisec/Multivariate Statistics/company.txt b/lab-iisec/Multivariate Statistics/company.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee5775698b98413a30822996775b8cf808653a04 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/company.txt @@ -0,0 +1,11 @@ + 108.28 17.05 1484.10 + 152.36 16.59 750.33 + 95.04 10.91 766.42 + 65.45 14.14 1110.46 + 62.97 9.52 1031.29 + 263.99 25.33 195.26 + 265.19 18.54 193.83 + 285.06 15.73 191.11 + 92.01 8.10 1175.16 + 165.68 11.13 211.15 + diff --git a/lab-iisec/Multivariate Statistics/fiber.dat b/lab-iisec/Multivariate Statistics/fiber.dat new file mode 100644 index 0000000000000000000000000000000000000000..04a2b72b3be9fcad8aa869d0905a4a5ae1ce18dd --- /dev/null +++ b/lab-iisec/Multivariate Statistics/fiber.dat @@ -0,0 +1,63 @@ +21.312 7.039 5.326 0.932 -0.030 35.239 36.991 1.057 +21.206 6.979 5.237 0.871 0.015 35.713 36.851 1.064 +20.709 6.779 5.060 0.742 0.025 39.220 30.586 1.053 +19.542 6.601 4.479 0.513 0.030 39.756 21.072 1.050 +20.449 6.795 4.912 0.577 -0.070 32.991 36.570 1.049 +20.841 6.919 5.108 0.784 -0.050 31.140 38.115 1.052 +19.060 6.447 4.246 0.358 -0.247 28.375 41.364 1.044 +18.597 6.261 4.032 0.215 -0.099 32.580 36.430 1.038 +19.346 6.572 4.358 0.432 -0.242 23.889 49.080 1.042 +18.720 6.455 4.072 0.372 -0.188 28.027 39.243 1.042 +18.587 6.295 4.068 0.239 -0.099 33.128 32.802 1.052 +19.813 6.775 4.604 0.637 -0.232 26.492 40.939 1.042 +19.989 6.737 4.686 0.779 -0.045 32.169 32.524 1.045 +19.116 6.512 4.299 0.588 0.055 35.103 31.139 1.042 +18.769 6.335 4.089 0.470 0.070 40.893 21.473 1.049 +18.708 6.271 3.978 0.457 -0.015 32.649 31.554 1.038 +19.323 6.550 4.404 0.588 -0.109 27.749 38.538 1.036 +17.433 5.948 3.486 0.104 0.000 36.187 25.927 1.022 +19.195 6.213 4.300 0.405 -0.193 34.491 25.519 1.047 +19.436 6.387 4.404 0.519 -0.090 31.827 29.209 1.050 +20.136 6.725 4.723 0.652 -0.154 29.622 32.385 1.057 +16.740 6.168 3.201 0.104 -0.154 35.917 29.346 1.033 +18.589 6.531 3.989 0.336 -0.149 30.658 35.730 1.033 +19.422 6.615 4.382 0.432 -0.271 29.415 33.775 1.033 +24.420 7.874 6.999 1.730 0.243 51.638 15.922 1.099 +25.288 8.034 7.406 1.873 0.340 58.686 9.159 1.101 +26.119 8.222 7.771 1.946 0.080 49.025 27.700 1.097 +23.113 7.288 6.329 1.513 0.131 46.266 23.893 1.076 +25.209 7.955 7.296 1.792 0.136 50.333 17.888 1.095 +25.444 8.045 7.477 1.847 0.176 44.218 26.880 1.090 +23.699 7.593 6.609 1.482 0.151 43.887 33.775 1.082 +24.303 7.775 6.861 1.583 0.207 48.894 23.219 1.081 +24.793 8.123 7.202 1.703 -0.015 40.158 42.074 1.066 +23.438 7.650 6.457 1.477 0.126 54.559 11.293 1.089 +24.197 7.794 6.833 1.583 0.131 49.025 17.494 1.088 +24.741 7.996 7.152 1.728 0.070 49.287 23.354 1.092 +24.170 7.766 6.846 1.615 0.156 45.673 29.622 1.070 +24.174 7.877 6.826 1.692 0.055 45.475 21.072 1.076 +25.052 8.287 7.332 1.773 -0.015 42.958 33.636 1.085 +23.846 7.639 6.615 1.560 0.090 48.632 13.977 1.070 +24.822 8.041 7.129 1.721 0.015 49.025 18.284 1.073 +25.200 8.270 7.356 1.785 0.010 43.821 27.290 1.087 +23.695 7.460 6.567 1.543 0.131 46.530 18.284 1.069 +24.941 7.929 7.286 1.703 0.000 46.398 18.416 1.075 +25.007 8.081 7.287 1.787 -0.099 44.946 24.164 1.078 +21.183 7.156 5.388 0.924 -0.188 51.898 19.209 1.064 +21.875 7.336 5.762 1.068 -0.173 48.436 26.880 1.065 +22.095 7.447 5.790 1.182 -0.227 47.254 29.346 1.066 +25.166 7.913 7.211 1.813 0.314 56.627 2.925 1.118 +24.560 7.854 7.020 1.701 0.217 53.458 0.511 1.122 +22.007 8.259 7.322 1.169 0.381 60.993 0.000 1.118 +21.115 7.913 6.557 0.928 0.397 58.429 1.147 1.129 +26.194 8.454 7.816 2.145 0.289 56.755 0.407 1.113 +25.674 8.208 7.534 2.046 0.202 56.111 0.407 1.104 +25.930 8.100 7.669 2.037 0.273 53.847 2.023 1.111 +21.390 7.475 5.294 0.875 0.558 63.035 -0.391 1.113 +18.441 6.652 3.946 0.140 -0.672 3.448 76.878 1.020 +16.441 6.315 2.997 -0.400 -0.605 2.845 84.554 1.008 +16.294 6.572 3.017 -0.478 -0.694 1.515 81.988 0.998 +20.289 7.719 4.866 0.239 -0.559 2.054 8.786 1.081 +17.163 7.086 3.396 -0.236 -0.415 3.018 5.855 1.033 +20.289 7.437 4.859 0.470 -0.324 17.639 28.934 1.070 + diff --git a/lab-iisec/Multivariate Statistics/financial.DAT b/lab-iisec/Multivariate Statistics/financial.DAT new file mode 100644 index 0000000000000000000000000000000000000000..28f919108a5c95cb598b31b637a309285e8901dc --- /dev/null +++ b/lab-iisec/Multivariate Statistics/financial.DAT @@ -0,0 +1,46 @@ +-0.45 -0.41 1.09 0.45 0 +-0.56 -0.31 1.51 0.16 0 +0.06 0.02 1.01 0.40 0 +-0.07 -0.09 1.45 0.26 0 +-0.10 -0.09 1.56 0.67 0 +-0.14 -0.07 0.71 0.28 0 +0.04 0.01 1.50 0.71 0 +-0.07 -0.06 1.37 0.40 0 +0.07 -0.01 1.37 0.34 0 +-0.14 -0.14 1.42 0.43 0 +-0.23 -0.30 0.33 0.18 0 +0.07 0.02 1.31 0.25 0 +0.01 0.00 2.15 0.70 0 +-0.28 -0.23 1.19 0.66 0 +0.15 0.05 1.88 0.27 0 +0.37 0.11 1.99 0.38 0 +-0.08 -0.08 1.51 0.42 0 +0.05 0.03 1.68 0.95 0 +0.01 0.00 1.26 0.60 0 +0.12 0.11 1.14 0.17 0 +-0.28 -0.27 1.27 0.51 0 +0.51 0.10 2.49 0.54 1 +0.08 0.02 2.01 0.53 1 +0.38 0.11 3.27 0.35 1 +0.19 0.05 2.25 0.33 1 +0.32 0.07 4.24 0.63 1 +0.31 0.05 4.45 0.69 1 +0.12 0.05 2.52 0.69 1 +-0.02 0.02 2.05 0.35 1 +0.22 0.08 2.35 0.40 1 +0.17 0.07 1.80 0.52 1 +0.15 0.05 2.17 0.55 1 +-0.10 -0.01 2.50 0.58 1 +0.14 -0.03 0.46 0.26 1 +0.14 0.07 2.61 0.52 1 +0.15 0.06 2.23 0.56 1 +0.16 0.05 2.31 0.20 1 +0.29 0.06 1.84 0.38 1 +0.54 0.11 2.33 0.48 1 +-0.33 -0.09 3.01 0.47 1 +0.48 0.09 1.24 0.18 1 +0.56 0.11 4.29 0.44 1 +0.20 0.08 1.99 0.30 1 +0.47 0.14 2.92 0.45 1 +0.17 0.04 2.45 0.14 1 +0.58 0.04 5.06 0.13 1 diff --git a/lab-iisec/Multivariate Statistics/pentathlon.csv b/lab-iisec/Multivariate Statistics/pentathlon.csv new file mode 100644 index 0000000000000000000000000000000000000000..993b86f3519e099e8dc99a54e588851f44fc00fe --- /dev/null +++ b/lab-iisec/Multivariate Statistics/pentathlon.csv @@ -0,0 +1,26 @@ +,hurdles,highjump,shot,run200m,longjump,javelin,run800m,score +Joyner-Kersee (USA),12.69,1.86,15.8,22.56,7.27,45.66,128.51,7291 +John (GDR),12.85,1.8,16.23,23.65,6.71,42.56,126.12,6897 +Behmer (GDR),13.2,1.83,14.2,23.1,6.68,44.54,124.2,6858 +Sablovskaite (URS),13.61,1.8,15.23,23.92,6.25,42.78,132.24,6540 +Choubenkova (URS),13.51,1.74,14.76,23.93,6.32,47.46,127.9,6540 +Schulz (GDR),13.75,1.83,13.5,24.65,6.33,42.82,125.79,6411 +Fleming (AUS),13.38,1.8,12.88,23.59,6.37,40.28,132.54,6351 +Greiner (USA),13.55,1.8,14.13,24.48,6.47,38,133.65,6297 +Lajbnerova (CZE),13.63,1.83,14.28,24.86,6.11,42.2,136.05,6252 +Bouraga (URS),13.25,1.77,12.62,23.59,6.28,39.06,134.74,6252 +Wijnsma (HOL),13.75,1.86,13.01,25.03,6.34,37.86,131.49,6205 +Dimitrova (BUL),13.24,1.8,12.88,23.59,6.37,40.28,132.54,6171 +Scheider (SWI),13.85,1.86,11.58,24.87,6.05,47.5,134.93,6137 +Braun (FRG),13.71,1.83,13.16,24.78,6.12,44.58,142.82,6109 +Ruotsalainen (FIN),13.79,1.8,12.32,24.61,6.08,45.44,137.06,6101 +Yuping (CHN),13.93,1.86,14.21,25,6.4,38.6,146.67,6087 +Hagger (GB),13.47,1.8,12.75,25.47,6.34,35.76,138.48,5975 +Brown (USA),14.07,1.83,12.69,24.83,6.13,44.34,146.43,5972 +Mulliner (GB),14.39,1.71,12.68,24.92,6.1,37.76,138.02,5746 +Hautenauve (BEL),14.04,1.77,11.81,25.61,5.99,35.68,133.9,5734 +Kytola (FIN),14.31,1.77,11.66,25.69,5.75,39.48,133.35,5686 +Geremias (BRA),14.23,1.71,12.95,25.5,5.5,39.64,144.02,5508 +Hui-Ing (TAI),14.85,1.68,10,25.23,5.47,39.14,137.3,5290 +Jeong-Mi (KOR),14.53,1.71,10.83,26.61,5.5,39.26,139.17,5289 +Launa (PNG),16.42,1.5,11.78,26.16,4.88,46.38,163.43,4566 \ No newline at end of file diff --git a/lab-iisec/Multivariate Statistics/soccer-standings.csv b/lab-iisec/Multivariate Statistics/soccer-standings.csv new file mode 100644 index 0000000000000000000000000000000000000000..2e6adba5ddabf6070e3d2feec261624c4d5a129e --- /dev/null +++ b/lab-iisec/Multivariate Statistics/soccer-standings.csv @@ -0,0 +1,21 @@ +Team,Rank,P,M,W,D,L,G,GA,GD +Arsenal,1,37,14,12,1,1,33,11,22 +Manchester City,2,32,14,10,2,2,40,14,26 +Newcastle United,3,30,15,8,6,1,29,11,18 +Tottenham Hotspur,4,29,15,9,2,4,31,21,10 +Manchester United,5,26,14,8,2,4,20,20,0 +Liverpool,6,22,14,6,4,4,28,17,11 +Brighton & Hove Albion,7,21,14,6,3,5,23,19,4 +Chelsea,8,21,14,6,3,5,17,17,0 +Fulham,9,19,15,5,4,6,24,26,-2 +Brentford,10,19,15,4,7,4,23,25,-2 +Crystal Palace,11,19,14,5,4,5,15,18,-3 +Aston Villa,12,18,15,5,3,7,16,22,-6 +Leicester City,13,17,15,5,2,8,25,25,0 +AFC Bournemouth,14,16,15,4,4,7,18,32,-14 +Leeds United,15,15,14,4,3,7,22,26,-4 +West Ham United,16,14,15,4,2,9,12,17,-5 +Everton,17,14,15,3,5,7,11,17,-6 +Nottingham Forest,18,13,15,3,4,8,11,30,-19 +Southampton,19,12,15,3,3,9,13,27,-14 +Wolverhampton,20,10,15,2,4,9,8,24,-16 diff --git a/lab-iisec/Multivariate Statistics/sweat.dat b/lab-iisec/Multivariate Statistics/sweat.dat new file mode 100644 index 0000000000000000000000000000000000000000..676b02627ea201a39d3a769396515482dd9b0063 --- /dev/null +++ b/lab-iisec/Multivariate Statistics/sweat.dat @@ -0,0 +1,20 @@ + 3.7 48.5 9.3 + 5.7 65.1 8.0 + 3.8 47.2 10.9 + 3.2 53.2 12.0 + 3.1 55.5 9.7 + 4.6 36.1 7.9 + 2.4 24.8 14.0 + 7.2 33.1 7.6 + 6.7 47.4 8.5 + 5.4 54.1 11.3 + 3.9 36.9 12.7 + 4.5 58.8 12.3 + 3.5 27.8 9.8 + 4.5 40.2 8.4 + 1.5 13.5 10.1 + 8.5 56.4 7.1 + 4.5 71.6 8.2 + 6.5 52.8 10.9 + 4.1 44.1 11.2 + 5.5 40.9 9.4 diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/Readme.md b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/Readme.md new file mode 100644 index 0000000000000000000000000000000000000000..db8cf92f951da87786de35b402566e0652d867b9 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/Readme.md @@ -0,0 +1,123 @@ +# 共识协议的简单应用 +[TOC] + +## 前言 +区块链是目前最热门的技术之一，许多新的概念如Web3, 元宇宙, DeFi等都建立在区块链的基础之上。而区块链离不开共识协议, 共识协议为区块链提供了底层安全性保证。区块链是一个分布式账本，共识协议的作用是在所有的账本副本上保持相同的内容，保证区块链的不可篡改性，同时不同的共识协议也决定了整体区块链的性能。在目前的区块链共识协议中，可以基本分为以下种类（共识节点指的是参与共识协议的节点）： + * 公链共识协议（共识节点数量未知）： + * PoW: 工作量证明算法, 如Bitcoin[1]，Eth1.0[2] + * PoS: 权益证明机制，如Dfinity[3], Algorand[4], Eth2.0[5] + * 其他: PoX, DPoS等等 + * 联盟链共识协议（共识节点数量已知）： + * PBFT[6]: 基于leader的半同步拜占庭容错协议，经典的分布式共识协议 + * HotStuff[7]: 基于leader的半同步拜占庭协议，具有线性的通信复杂度以及可响应性，被Facebook(Meta)的区块链项目[Diem](https://github.com/diem/diem)所采用的 + * Honeybadger BFT[8]: 第一个异步拜占庭共识协议 + * 其他：Dumbo[9], Tusk[10]等等 + +上面针对共识协议仅进行了基本的分类，但是目前区块链共识协议发展十分迅速，并且呈现出多样化的趋势，对于其他的共识协议不再赘述。 +## 实验目标 +了解共识的运行过程，并动手实现一个共识协议。通过协议的运行的保证所有参与节点以相同的顺序对所有的请求进行提交。 + + + + +## 实验准备 +### 实验系统：linux +* 由于测试会在linux系统下进行，可以直接在linux环境下开发，Windows可使用[WSL2](https://learn.microsoft.com/zh-cn/windows/wsl/install)或者虚拟机软件（Vmware，Virtualbox）创建虚拟机环境，也可以在Windows下进行开发，保证最终的提交版本能在linux下编译运行即可。 +### Go语言 +#### Go 简介 +* Go（又称golang）是Google开发的一种静态强类型、编译型、并发型，并具有垃圾回收功能的编程语言，由于实验环境是在go环境下开发，因此需要预先对go的语法规则有个基本的了解。 +#### Go 环境配置 +* [GO安装教程](https://go.dev/doc/install) +* [GO modules项目依赖管理](https://go.dev/blog/using-go-modules) + * `go env -w GO111MODULE=on` +* GO更换国内代理 + * `go env -w GOPROXY="https://goproxy.io"` +* [GO VSCode插件](https://code.visualstudio.com/docs/languages/go) +![](https://notes.sjtu.edu.cn/uploads/upload_5c114a4e051e5436d469b6712bce1909.png) +#### Go 学习 +* [GO之旅](https://tour.go-zh.org/welcome/1) +* [GO文档](https://go.dev/doc/) + + +## 代码实现 +### 基本定义 +* 节点（node）：参与共识协议过程的一个进程 +* 提议（propose）：节点提议一组交易内容，参与共识协议的过程，确定是否提交，如在实际区块链系统中，会选取一组客户发起的交易，在本实验不考虑实际的含义，选择随机的内容进行提议。 +* 提交（commit）：对于共识确定的提议进行“记账”，成为链上不可篡改的内容。 +### 实现目标 +* 节点的每个"位置"（Sequence Number）代表一次共识的结果，通过实现的共识协议，保证以下性质： + * 正确节点一个位置最多只有一个区块代表共识的结果 + ![](https://notes.sjtu.edu.cn/uploads/upload_385c05274ecf7ab689bfd0cbed1f1080.png) + + * 正确节点的一个位置可以没有区块 + ![](https://notes.sjtu.edu.cn/uploads/upload_b4d15629eb1cb331c2e104a5e1d153d3.png) + + * 所有正确节点相同的位置必须有保证相同的区块（如果没有区块，所有正确节点都没有） + +### 代码模板 +* 代码模板地址：[Github](https://github.com/Waynezee/SimpleConsensus)，[Gitee](https://gitee.com/xiangzhew/SimpleConsensus) + * `mylogger`: 实现简单的日志记录功能 + * `myrpc`: 实现简单的rpc功能，节点间通过rpc进行相互通信 + * `core`: **待实现**的共识协议 + * `config`: 保存节点的配置文件 + * `log`: 保存节点的日志信息 + * `cmd`: 保存可执行文件 + * `scripts`: 测试和运行脚本 +* 测试流程： + 1. `git clone https://github.com/Waynezee/SimpleConsensus` + 2. `cd SimpleConsensus/ && mkdir log` + 3. `cd cmd/ && make` + 4. `cd ../scripts/ && chmod +x *.sh` + 5. `./run_nodes.sh [test time (seconds)]` + 6. 等待运行结束 + 7. `python3 check.py 7` +* 当编写你的代码的时候需要注意： + * 需要保证`cmd/node.go`的输入参数形式与原始的一致 + * 保证`core/block.go`中`getBlock()`和`commitBlock()`函数不变，并在合适的时候进行调用 + * `getBlock()`: 生成一个区块，同时会将相关信息记录到日志中 + * `commitBlock()` : 当协议确定性的完成共识后进行调用，同时会将相关信息记录到日志中 + * 主要需要需要实现的部分在`core/core.go` + * 代码模板中其他的地方都可以在不影响测试的条件下进行修改。 +* 调试： + * 分布式程序一般debug的模式是通过日志进行分析(测试中也是通过日志来检测协议的正确性)，可以在关键步骤进行日志打印，逐渐缩小范围定位出问题的代码。 + * 在本地环境下，可使用[Vscode Debug](https://code.visualstudio.com/docs/editor/debugging#_multitarget-debugging)调试多个进程 +## 评价标准 + +测试会在7节点规模下进行测试，通过运行共识协议一段时间之后对各个节点的日志进行分析，对以下指标进行检测: +* 一定的性能：在一定时间内完成提交的数量（>= 10 ops/s） +* 安全性（Safety）：节点之间提交的提议顺序是否一致 + + +测试将分为三个场景下进行： +> crash指停机，一般认为节点不会重新启动 +* （正常场景）所有节点都正常工作，测试如下： + * `cd ../scripts/ && ./normal_test.sh` +* （crash场景）随机一个节点停机(进程被中止)： + * 如 `cd ../scripts/ && ./crash1_test.sh 0` +* （crash场景）随机两个节点停机 + * 如 `cd ../scripts/ && ./crash2_test.sh 0 1` + +## 学习路线 +* 学习GO +* 选择一个共识协议（PoW, [Paxos](https://www.microsoft.com/en-us/research/publication/2016/12/paxos-simple-Copy.pdf), [Raft](https://raft.github.io/)等)，只需要关注与实验相关的部分即可。 + * [一个可能的实现思路](https://notes.sjtu.edu.cn/s/cf7nS_HGl) +* 了解代码模板和基本的测试模式 +* 着手实现 + +## 讨论 +> 如果发现代码模板有问题，可以随时指出，我会尽快修复 +* [Github issue](https://github.com/Waynezee/SimpleConsensus/issues) +* 邮箱: xiangzhe_wang@163.com +* WX: wxz18568738360 + +## 参考文献 +[1] Nakamoto S. Bitcoin: A peer-to-peer electronic cash system[J]. Decentralized business review, 2008: 21260. +[2] Ethash: https://ethereum.org/en/developers/docs/consensus-mechanisms/pow/mining-algorithms/ethash/ +[3] Hanke T, Movahedi M, Williams D. Dfinity technology overview series, consensus system[J]. arXiv preprint arXiv:1805.04548, 2018. +[4] Gilad Y, Hemo R, Micali S, et al. Algorand: Scaling byzantine agreements for cryptocurrencies[C]//Proceedings of the 26th symposium on operating systems principles. 2017: 51-68. +[5] Buterin V, Hernandez D, Kamphefner T, et al. Combining GHOST and casper[J]. arXiv preprint arXiv:2003.03052, 2020. +[6] Castro M, Liskov B. Practical byzantine fault tolerance[C]//OsDI. 1999, 99(1999): 173-186. +[7] Yin M, Malkhi D, Reiter M K, et al. HotStuff: BFT consensus with linearity and responsiveness[C]//Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. 2019: 347-356. +[8] Miller A, Xia Y, Croman K, et al. The honey badger of BFT protocols[C]//Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016: 31-42. +[9] Guo B, Lu Z, Tang Q, et al. Dumbo: Faster asynchronous bft protocols[C]//Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 2020: 803-818. +[10] Danezis G, Kokoris-Kogias L, Sonnino A, et al. Narwhal and tusk: a dag-based mempool and efficient bft consensus[C]//Proceedings of the Seventeenth European Conference on Computer Systems. 2022: 34-50. diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/Makefile b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..e7b60e2b86ca240a3550bb88d25154d43ac4a765 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/Makefile @@ -0,0 +1,5 @@ +all: node.go + go build -o node node.go + +clean: + rm node \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/node.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/node.go new file mode 100644 index 0000000000000000000000000000000000000000..adf1f07cc69bfe7a47ab26f4e19362d667c4ee31 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/cmd/node.go @@ -0,0 +1,23 @@ +package main + +import ( + "flag" + "fmt" + "time" + + "nis3607/core" +) + +func main() { + //default: 7 nodes + id := flag.Int("i", 0, "[node id]") + testTime := flag.Int("t", 30, "[test time]") + flag.Parse() + config := core.GetConfig(*id) + c := core.InitConsensus(config) + //start to run node for testTime s + go c.Run() + + time.Sleep(time.Duration(*testTime) * time.Second) + fmt.Printf("Node %v finished test\n", *id) +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node0.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node0.json new file mode 100644 index 0000000000000000000000000000000000000000..e0b76aef699c4a98c633ceb90dfbacb0e8a090e6 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node0.json @@ -0,0 +1,15 @@ +{ + "id": 0, + "n": 7, + "port": 10000, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node1.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node1.json new file mode 100644 index 0000000000000000000000000000000000000000..2429ccd6a7671d1c62ef74fd82582e58316f7aed --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node1.json @@ -0,0 +1,15 @@ +{ + "id": 1, + "n": 7, + "port": 10010, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node2.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node2.json new file mode 100644 index 0000000000000000000000000000000000000000..e5882760c170de4b39a5db9d4cdf5547054e2f3d --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node2.json @@ -0,0 +1,15 @@ +{ + "id": 2, + "n": 7, + "port": 10020, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node3.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node3.json new file mode 100644 index 0000000000000000000000000000000000000000..cea661e2a17ab49d6375d6789e9fa94a1266fc9a --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node3.json @@ -0,0 +1,15 @@ +{ + "id": 3, + "n": 7, + "port": 10030, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node4.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node4.json new file mode 100644 index 0000000000000000000000000000000000000000..95e6ebf5db07a656fe123c925ef931107fda0f19 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node4.json @@ -0,0 +1,15 @@ +{ + "id": 4, + "n": 7, + "port": 10040, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node5.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node5.json new file mode 100644 index 0000000000000000000000000000000000000000..cc1605f72626f2eaebe5c9a3d30dad1a36df44fe --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node5.json @@ -0,0 +1,15 @@ +{ + "id": 5, + "n": 7, + "port": 10050, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node6.json b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node6.json new file mode 100644 index 0000000000000000000000000000000000000000..a5be5b783a0032683e9cdbdb293523a26de17ba7 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/config/node6.json @@ -0,0 +1,15 @@ +{ + "id": 6, + "n": 7, + "port": 10060, + "blocksize": 1000, + "committee": [ + 10000, + 10010, + 10020, + 10030, + 10040, + 10050, + 10060 + ] +} \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/blockchain.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/blockchain.go new file mode 100644 index 0000000000000000000000000000000000000000..0d4f05b5946eca7eb91b66c4f61c774d6f70ceb1 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/blockchain.go @@ -0,0 +1,85 @@ +package core + +import ( + "math/rand" + "nis3607/mylogger" + "sync" + "time" +) + +type Block struct { + Seq uint64 + Data []byte +} + +type BlockChain struct { + Id uint8 + BlockSize uint64 + Blocks []*Block + BlocksMap map[string]*Block + KeysMap map[*Block]string + logger *mylogger.MyLogger + mu sync.Mutex +} + +func InitBlockChain(id uint8, blocksize uint64) *BlockChain { + blocks := make([]*Block, 1024) + blocksMap := make(map[string]*Block) + keysMap := make(map[*Block]string) + //Generate gensis block + blockChain := &BlockChain{ + Id: id, + BlockSize: blocksize, + Blocks: blocks, + BlocksMap: blocksMap, + KeysMap: keysMap, + logger: mylogger.InitLogger("blockchain", id), + mu: sync.Mutex{}, + } + return blockChain +} + +func Block2Hash(block *Block) []byte { + hash, _ := ComputeHash(block.Data) + return hash +} + +func Hash2Key(hash []byte) string { + var key []byte + for i := 0; i < 20; i++ { + key = append(key, uint8(97)+uint8(hash[i]%(26))) + } + return string(key) +} +func Block2Key(block *Block) string { + return Hash2Key(Block2Hash(block)) +} + +func (bc *BlockChain) AddBlockToChain(block *Block) { + bc.mu.Lock() + defer bc.mu.Unlock() + bc.Blocks = append(bc.Blocks, block) + bc.KeysMap[block] = Block2Key(block) + bc.BlocksMap[Block2Key(block)] = block +} + +// Generate a Block: max rate is 20 blocks/s +func (bc *BlockChain) getBlock(seq uint64) *Block { + //slow down + time.Sleep(time.Duration(50) * time.Millisecond) + data := make([]byte, bc.BlockSize) + for i := uint64(0); i < bc.BlockSize; i++ { + data[i] = byte(rand.Intn(256)) + } + block := &Block{ + Seq: seq, + Data: data, + } + bc.logger.DPrintf("generate Block[%v] in seq %v at %v", Block2Key(block), block.Seq, time.Now().UnixNano()) + return block +} + +func (bc *BlockChain) commitBlock(block *Block) { + bc.AddBlockToChain(block) + bc.logger.DPrintf("commit Block[%v] in seq %v at %v", Block2Key(block), block.Seq, time.Now().UnixNano()) +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/core.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/core.go new file mode 100644 index 0000000000000000000000000000000000000000..f10895aa71ba1482a825b6dd7ba79e32a6983543 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/core.go @@ -0,0 +1,116 @@ +package core + +import ( + "log" + "math/rand" + "net" + "net/http" + "net/rpc" + "strconv" + "time" + + "nis3607/mylogger" + "nis3607/myrpc" +) + +type Consensus struct { + id uint8 + n uint8 + port uint64 + seq uint64 + //BlockChain + blockChain *BlockChain + //logger + logger *mylogger.MyLogger + //rpc network + peers []*myrpc.ClientEnd + + //message channel exapmle + msgChan chan *myrpc.ConsensusMsg +} + +func InitConsensus(config *Configuration) *Consensus { + rand.Seed(time.Now().UnixNano()) + c := &Consensus{ + id: config.Id, + n: config.N, + port: config.Port, + seq: 0, + blockChain: InitBlockChain(config.Id, config.BlockSize), + logger: mylogger.InitLogger("node", config.Id), + peers: make([]*myrpc.ClientEnd, 0), + + msgChan: make(chan *myrpc.ConsensusMsg, 1024), + } + for _, peer := range config.Committee { + clientEnd := &myrpc.ClientEnd{Port: uint64(peer)} + c.peers = append(c.peers, clientEnd) + } + go c.serve() + + return c +} + +func (c *Consensus) serve() { + rpc.Register(c) + rpc.HandleHTTP() + l, e := net.Listen("tcp", ":"+strconv.Itoa(int(c.port))) + if e != nil { + log.Fatal("listen error:", e) + } + go http.Serve(l, nil) +} + +func (c *Consensus) OnReceiveMessage(args *myrpc.ConsensusMsg, reply *myrpc.ConsensusMsgReply) error { + + c.logger.DPrintf("Invoke RpcExample: receive message from %v at %v", args.From, time.Now().Nanosecond()) + c.msgChan <- args + return nil +} + +func (c *Consensus) broadcastMessage(msg *myrpc.ConsensusMsg) { + reply := &myrpc.ConsensusMsgReply{} + for id := range c.peers { + c.peers[id].Call("Consensus.OnReceiveMessage", msg, reply) + } +} + +func (c *Consensus) handleMsgExample(msg *myrpc.ConsensusMsg) { + block := &Block{ + Seq: msg.Seq, + Data: msg.Data, + } + c.blockChain.commitBlock(block) +} + +func (c *Consensus) proposeLoop() { + for { + if c.id == 0 { + block := c.blockChain.getBlock(c.seq) + msg := &myrpc.ConsensusMsg{ + From: c.id, + Seq: block.Seq, + Data: block.Data, + } + c.broadcastMessage(msg) + c.seq++ + } + } +} + +func (c *Consensus) Run() { + // wait for other node to start + time.Sleep(time.Duration(1) * time.Second) + //init rpc client + for id := range c.peers { + c.peers[id].Connect() + } + + go c.proposeLoop() + //handle received message + for { + + msg := <-c.msgChan + c.handleMsgExample(msg) + } +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/utils.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/utils.go new file mode 100644 index 0000000000000000000000000000000000000000..7c51c9a47bbdc351d35199e14e978467a156c472 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/core/utils.go @@ -0,0 +1,41 @@ +package core + +import ( + "crypto/sha256" + "encoding/json" + "fmt" + "io/ioutil" + "os" +) + +// ComputeHash computes hash of the given raw message +func ComputeHash(raw []byte) ([]byte, error) { + hash := sha256.New() + hash.Write(raw) + return hash.Sum(nil), nil +} + +type Configuration struct { + Id uint8 `json:"id"` + N uint8 `json:"n"` + Port uint64 `json:"port"` + Committee []uint64 `json:"committee"` + BlockSize uint64 `json:"blocksize"` +} + +func GetConfig(id int) *Configuration { + configFile := fmt.Sprintf("../config/node%d.json", id) + jsonFile, err := os.Open(configFile) + if err != nil { + panic(fmt.Sprint("os.Open: ", err)) + } + defer jsonFile.Close() + + data, err := ioutil.ReadAll(jsonFile) + if err != nil { + panic(fmt.Sprint("ioutil.ReadAll: ", err)) + } + var config Configuration + json.Unmarshal([]byte(data), &config) + return &config +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/mylogger/logger.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/mylogger/logger.go new file mode 100644 index 0000000000000000000000000000000000000000..b2e87e7d78b770a224021d97820d00f1cbc87df7 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/mylogger/logger.go @@ -0,0 +1,35 @@ +package mylogger + +import ( + "fmt" + "log" + "os" + "strconv" +) + +type MyLogger struct { + Debug bool + logger *log.Logger +} + +func InitLogger(name string, id uint8) *MyLogger { + logfile := "../log/" + name + strconv.Itoa(int(id)) + ".log" + f, err := os.OpenFile(logfile, os.O_CREATE|os.O_RDWR|os.O_TRUNC, 0777) + if err != nil { + fmt.Println("create log file failed:", err.Error()) + return nil + } + logger := log.New(f, "[Consesus log] ", log.LstdFlags|log.Lshortfile|log.LUTC|log.Lmicroseconds) + mylogger := &MyLogger{ + Debug: true, + logger: logger, + } + return mylogger +} + +func (l *MyLogger) DPrintf(format string, a ...interface{}) (err error) { + if l.Debug { + l.logger.Printf(format, a...) + } + return nil +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/message.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/message.go new file mode 100644 index 0000000000000000000000000000000000000000..afeb90ddd9ca29df785bda1aa77728724bfe8991 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/message.go @@ -0,0 +1,32 @@ +package myrpc + +// MessageType example +// type MessageType uint8 + +// const ( +// PREPREPARE MessageType = iota +// PREPARE +// COMMIT +// ) + +// func (mt MessageType) String() string { +// switch mt { +// case PREPREPARE: +// return "PREPREPARE" +// case PREPARE: +// return "PREPARE" +// case COMMIT: +// return "COMMIT" +// } +// return "UNKNOW MESSAGETYPE" +// } + +type ConsensusMsg struct { + // Type MessageType + From uint8 + Seq uint64 + Data []byte +} + +type ConsensusMsgReply struct { +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/myrpc.go b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/myrpc.go new file mode 100644 index 0000000000000000000000000000000000000000..5fcfd6f954ae4f273acc5ec2bb91979950eba08c --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/myrpc/myrpc.go @@ -0,0 +1,42 @@ +package myrpc + +import ( + "fmt" + "net/rpc" + "strconv" +) + +type ClientEnd struct { + Port uint64 + rpcClient *rpc.Client +} + +func (e *ClientEnd) Connect() { + c, err := rpc.DialHTTP("tcp", "127.0.0.1:"+strconv.Itoa(int(e.Port))) + if err == nil { + e.rpcClient = c + } + // log.Fatal("dialing:", err) + +} + +func (e *ClientEnd) Call(svcMeth string, args interface{}, reply interface{}) bool { + // c, err := rpc.DialHTTP("tcp", "127.0.0.1:"+strconv.Itoa(int(e.Port))) + // if err != nil { + // log.Fatal("dialing:", err) + // } + // defer c.Close() + if e.rpcClient == nil { + return false + } + + err := e.rpcClient.Call(svcMeth, args, reply) + if err == nil { + return true + } + + //exception handling: may reconnect or retransmit + fmt.Println(err) + return false + +} diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/check.py b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/check.py new file mode 100644 index 0000000000000000000000000000000000000000..673c467b4624b9b9f70b8bfb5abb43cdc306940c --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/check.py @@ -0,0 +1,109 @@ +import sys +import re + +class node_info: + def __init__(self, id): + self.id = id + self.proposals = [] + self.commits = {} + self.maxSeq = 0 + self.read_from_log(id) + + def read_from_log(self, id): + filename = '../log/blockchain' + str(id) + '.log' + propose_pattern = re.compile("generate Block\[(.*?)\] in seq (\d+) at (\d+)\n") + commit_pattern = re.compile("commit Block\[(.*?)\] in seq (\d+) at (\d+)\n") + with open(filename, "r") as f: + lines = f.readlines() + for line in lines: + m1 = propose_pattern.search(line) + m2 = commit_pattern.search(line) + if m1 != None: + self.proposals.append(m1.group(1)) + if m2 != None: + if int(m2.group(2)) not in self.commits.keys(): + self.maxSeq = max(self.maxSeq, int(m2.group(2))) + self.commits[int(m2.group(2))] = m2.group(1) + else: + if self.commits[int(m2.group(2))] != m2.group(1): + print(f"node {id} has two or more different commits at position {m2.group(2)}") + sys.exit(-1) + + print(f"node {id} commits {len(self.commits.keys())} times") + + +def check_safety(nodes): + n = len(nodes) + for i in range(n): + for j in range(n): + if i < j: + commits1 = nodes[i].commits + commits2 = nodes[j].commits + k = 0 + for k in range(min(nodes[i].maxSeq, nodes[j].maxSeq)): + block1 = None + if k in commits1.keys(): + block1 = commits1[k] + block2 = None + if k in commits2.keys(): + block2 = commits2[k] + if block1 != block2: + print(f"node {i} has different commit with node {j} at position {k}, node {i} is {block1} while node {j} is {block2}") + return False + return True + + +def check_validity(nodes): + all_proposals = [] + all_commits = [] + for node in nodes: + all_proposals += node.proposals + all_commits += node.commits.values() + for commit in all_commits: + if commit not in all_proposals: + print(f"commit {commit} has never been proposed!") + return False + return True + + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: python3 check.py [node number] [crash node...] ") + print("\t node number: total node number \n") + print("\t crash node: 0-2 crash nodes\n") + sys.exit() + n = 0 + try: + n = int(sys.argv[1]) + crash_nodes = [] + if len(sys.argv) >= 3: + crash_nodes = [int(x) for x in sys.argv[2:]] + except e: + print("The parameter must be an integer") + sys.exit() + print("----------begin to check------------") + nodes = [] + nodes_for_safety = [] + + for i in range(n): + node = node_info(i) + nodes.append(node) + + for i in range(n): + if i not in crash_nodes: + nodes_for_safety.append(nodes[i]) + + + print("---------- check validity ------------") + if check_validity(nodes): + print("pass") + else: + print("not pass") + + print("---------- check safety --------------") + if check_safety(nodes_for_safety): + print("pass") + else: + print("not pass") + print("---------- end -----------------------") \ No newline at end of file diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash1_test.sh b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash1_test.sh new file mode 100644 index 0000000000000000000000000000000000000000..6b1a8c3273d55874a92003d05c0977f2a40db926 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash1_test.sh @@ -0,0 +1,18 @@ +#!/bin/bash +go build -o ../cmd/node ../cmd/node.go + +tmux new -d -s node0 "../cmd/node -i 0 -t 20" +tmux new -d -s node1 "../cmd/node -i 1 -t 20" +tmux new -d -s node2 "../cmd/node -i 2 -t 20" +tmux new -d -s node3 "../cmd/node -i 3 -t 20" +tmux new -d -s node4 "../cmd/node -i 4 -t 20" +tmux new -d -s node5 "../cmd/node -i 5 -t 20" +tmux new -d -s node6 "../cmd/node -i 6 -t 20" + +sleep 10 + +tmux kill-window -t node$1 + +sleep 15 + +python3 check.py 7 $1 diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash2_test.sh b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash2_test.sh new file mode 100644 index 0000000000000000000000000000000000000000..87b5742eab24217590c164312c0e7da22d212947 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/crash2_test.sh @@ -0,0 +1,20 @@ +#!/bin/bash +go build -o ../cmd/node ../cmd/node.go + +tmux new -d -s node0 "../cmd/node -i 0 -t 30" +tmux new -d -s node1 "../cmd/node -i 1 -t 30" +tmux new -d -s node2 "../cmd/node -i 2 -t 30" +tmux new -d -s node3 "../cmd/node -i 3 -t 30" +tmux new -d -s node4 "../cmd/node -i 4 -t 30" +tmux new -d -s node5 "../cmd/node -i 5 -t 30" +tmux new -d -s node6 "../cmd/node -i 6 -t 30" + +sleep 10 +tmux kill-window -t node$1 + +sleep 5 +tmux kill-window -t node$2 + +sleep 25 + +python3 check.py 7 $1 $2 diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/normal_test.sh b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/normal_test.sh new file mode 100644 index 0000000000000000000000000000000000000000..67d68febd23b6d2829fd36f6593221c065c9028f --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/normal_test.sh @@ -0,0 +1,14 @@ +#!/bin/bash +go build -o ../cmd/node ../cmd/node.go + +tmux new -d -s node0 "../cmd/node -i 0 -t 30" +tmux new -d -s node1 "../cmd/node -i 1 -t 30" +tmux new -d -s node2 "../cmd/node -i 2 -t 30" +tmux new -d -s node3 "../cmd/node -i 3 -t 30" +tmux new -d -s node4 "../cmd/node -i 4 -t 30" +tmux new -d -s node5 "../cmd/node -i 5 -t 30" +tmux new -d -s node6 "../cmd/node -i 6 -t 30" + +sleep 35 + +python3 check.py 7 diff --git a/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/run_nodes.sh b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/run_nodes.sh new file mode 100644 index 0000000000000000000000000000000000000000..8a71a201147e6a3ae684c18fcafc36c9921af7e9 --- /dev/null +++ b/lab-iisec/NIS3607_lab/lab1-SimpleConsensus/scripts/run_nodes.sh @@ -0,0 +1,9 @@ +go build -o ../cmd/node ../cmd/node.go + +../cmd/node -i 0 -t $1 & +../cmd/node -i 1 -t $1 & +../cmd/node -i 2 -t $1 & +../cmd/node -i 3 -t $1 & +../cmd/node -i 4 -t $1 & +../cmd/node -i 5 -t $1 & +../cmd/node -i 6 -t $1 & diff --git a/lab-iisec/Scientific Computation/1-Interpolation and Approximation.ipynb b/lab-iisec/Scientific Computation/1-Interpolation and Approximation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..eaf7e1928179ac8df34d50e1a60456ab70776f30 --- /dev/null +++ b/lab-iisec/Scientific Computation/1-Interpolation and Approximation.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 插值与逼近\n", + "\n", + "## ex1:Runge函数的插值与逼近\n", + "\n", + "\n", + "对 Runge 函数：\n", + "$$\n", + "R(x) = \\frac{1}{1+x^2},\\quad x\\in[-5,5]\n", + "$$\n", + "利用下列条件做插值、逼近并与 $R(x)$ 的图像进行比较\n", + "1. 用等距节点 $x_i = -5 + i \\; (i = 0, 1, \\cdots, 10)$, 绘出它的 10 次 Newton 插值多项式的图像;\n", + "2. 用节点 $x_i = 5 \\cos \\left ( \\frac{2i+1}{42} \\pi \\right ) \\; (i = 0, 1, \\cdots, 20)$, 绘出它的 20 次 Lagrange 插值多项式的图像;\n", + "3. 用等距节点 $x_i = -5 + i \\; (i = 0, 1, \\cdots, 10)$, 绘出它的分段线性插值函数的图像." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Solutions\n", + "\n", + "根据牛顿差商公式：\n", + "$$\n", + "P(x) = f[x_1] + f[x_1 x_2](x-x_1) + \\cdots + f[x_1 x_2 \\cdots x_n](x-x_1)\\cdots(x-x_n)\n", + "$$\n", + "这里\n", + "$\n", + "f[x_k \\cdots x_{k+i}] = \\frac{f[x_{k+1} \\cdots x_{k+i}] - f[x_k \\dots x_{k+i-1}]}{x_{k+i} - x_k}\n", + "$，\n", + "所以可知\n", + "$$\n", + "f[x_1 \\cdots x_k] = \\sum_{i=1}^k \\frac{f(x_i)}{\\prod_{j=1,i\\neq j}^k {(x_i - x_j)}}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 定义Runge函数\n", + "\n", + "def R(x):\n", + " return 1.0 / (1.0 + x**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# 定义牛顿插值\n", + "\n", + "def newton(grids, x):\n", + " \n", + " grids_x , grids_y = grids\n", + " \n", + " if len(grids_x) != len(grids_x):\n", + " print(\"ERROR\")\n", + " \n", + " else:\n", + " ntsum = 0.0\n", + " for i in range(len(grids_x)):\n", + " func = 1.0\n", + " for j in range(i):\n", + " func *= x - grids_x[j]\n", + " \n", + " coef = 0.0\n", + " for j in range(i+1):\n", + " g = 1.0\n", + " for k in range(i+1):\n", + " if k != j :\n", + " d = grids_x[j] - grids_x[k]\n", + " if abs(d) > 1.0e-8:\n", + " g *= d\n", + " else:\n", + " print(\"ERROR\")\n", + " \n", + " coef += grids_y[j] / g\n", + " \n", + " ntsum += coef * func\n", + " \n", + " return ntsum" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 画图\n", + "\n", + "x = np.linspace(-5, 5, 11)\n", + "xx = np.linspace(-5, 5, 101)\n", + "y = newton(grids = [x, R(x)], x = xx)\n", + "plt.style.use(\"seaborn-dark\")\n", + "plt.plot(x, R(x),color = \"r\", linewidth = 0.0, marker = \"x\", label = r\"grids\")\n", + "plt.plot(xx, R(xx), label = r\"R(x)\")\n", + "plt.plot(xx, y, linestyle = 'dashed', label = r\"$P_{10}(x)$\")\n", + "plt.xlabel(r\"$x$\")\n", + "plt.ylabel(r\"$y$\")\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Solutions\n", + "\n", + "根据拉格朗日插值多项式：\n", + "$$\n", + "P_n (x)=y_0 L_0(x) + \\cdots + y_n L_n (x)\n", + "$$\n", + "这里$L_k(x) = \\prod_{j \\neq k} \\frac{x - x_j}{x_k - x_j}$" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def lagrange(grids, x):\n", + " \n", + " grids_x,grids_y = grids\n", + " if len(grids_x) != len(grids_y):\n", + " print(\"ERROR\")\n", + " \n", + " else:\n", + " length = len(grids_x)\n", + " lasum = 0.0\n", + " for k in range(length):\n", + " func = 1.0\n", + " for j in range(length):\n", + " if j != k:\n", + " d = grids_x[k] - grids_x[j]\n", + " if abs(d) > 1.0e-8:\n", + " func *= (x - grids_x[j]) / d\n", + " else:\n", + " print(\"ERROR\")\n", + " \n", + " lasum += grids_y[k] * func\n", + " \n", + " return lasum" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = np.linspace(0, 20, 21)\n", + "x= 5.0 * np.cos((2.0 * index + 1.0) * np.pi / 42.0)\n", + "xx = np.linspace(-5, 5, 101)\n", + "y = lagrange(grids = [x, R(x)], x= xx)\n", + "plt.plot(x, R(x), color = \"r\", linewidth = 0.0, marker = \"x\", label = \"grids\")\n", + "plt.plot(xx, R(xx), label = \"R(x)\")\n", + "plt.plot(xx, y, linestyle = 'dashed', label = \"$L_{20}(x)$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Solutions\n", + "\n", + "分段线性插值" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def linear(grids, x):\n", + " \n", + " grids_x,grids_y = grids\n", + " if len(grids_x) != len(grids_y):\n", + " print(\"ERROR\")\n", + " \n", + " else:\n", + " order = np.argsort(grids_x)\n", + " grids_x,grids_y = grids_x[order],grids_y[order]\n", + " \n", + " lisum = 0.0\n", + " func = 1.0\n", + " for i in range(len(grids_x)-1):\n", + " d = grids_x[i+1] - grids_x[i]\n", + " if abs(d) > 1.0e-8:\n", + " func = (grids_y[i+1] - grids_y[i]) / d * (x - grids_x[i]) + grids_y[i]\n", + " shixin = lambda x: 1.0 if (grids_x[i] <= x < grids_x[i+1]) else 0.0\n", + " cond = np.array(list(map(shixin, x)))\n", + " lisum += func * cond\n", + " else:\n", + " print(\"ERROR\")\n", + " \n", + " return lisum\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-5, 5, 11)\n", + "xx = np.linspace(-5, 5, 101)\n", + "y = linear(grids = [x, R(x)], x = xx)\n", + "plt.plot(x, R(x), color = \"r\", linewidth = 0.0, marker = \"x\", label = \"grids\")\n", + "plt.plot(xx, y, linestyle = \"dashed\", label = \"$L_{10}(x)$\")\n", + "plt.plot(xx, R(xx), label = \"R(x)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ex2:三角插值与FFT\n", + "\n", + "1. 用FFT求解线性方程 $Ax = b \\, (A \\in \\mathbb{R}^{N \\times N}, b \\in \\mathbb{R}^N)$ ，其中$A$是 如下的循环矩阵，$b$是如下向量：\n", + "$$\n", + "A = \\begin{equation*}\n", + "\\left( \\begin{array}{cccc}\n", + " 3 & -1 & \\cdots & -1 \\\\\n", + " -1 & 3 & & \\vdots \\\\\n", + " \\vdots & \\ddots & \\ddots & \\vdots \\\\\n", + " -1 & \\cdots & -1 & 3\n", + "\\end{array} \\right),\n", + "\\end{equation*}\n", + "$$\n", + "\n", + "$$\n", + "b = [1, 1, \\cdots, 1]^T\n", + "$$\n", + "\n", + "2. 用FST求解一维Poisson方程:\n", + "$$\n", + "\\begin{equation*}\n", + "\\left\\{\n", + " \\begin{array}{ll}\n", + " - u^{\"} (x) = f(x), \\; x \\in (0, \\pi)\\, \\\\\n", + " u(0) = u(\\pi) = 0\n", + " \\end{array}\n", + " \\right.\n", + "\\end{equation*}\n", + "$$\n", + "画出解的图像，并与解析解 $u_{\\text{ex}}(x) = \\sin(x)$比较" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Solutions\n", + "矩阵 $A$ 的特征值和相对应的特征向量为： \n", + "$$\n", + "\\lambda_k = 3 - 2 \\cos( \\frac{2 \\pi}{N} k) = 1 + 4 \\sin^2 ( \\frac{\\pi}{N} k) , \\; v^j_k = e^{- \\mathrm{i} \\frac{2 \\pi}{N} k * j}, \\; k,j = 0, 1, \\dots, N-1.\n", + "$$\n", + "\n", + "考虑Fourier 矩阵和DFT，即：\n", + "$$\n", + "({\\bf F})_{jk}= \\frac{1}{\\sqrt{N}} e^{-2\\pi i (j-1)(k-1)/N}, \\; j,k = 1,\\cdots,N,\n", + "$$\n", + "$$\n", + "\\mathcal{F}[u] = {\\bf F} u, \\; u \\in \\mathbb{R}^N.\n", + "$$ \n", + "\n", + "这里易知${\\bf F} = {\\bf F}^T $，F是酉矩阵，且有下式\n", + "\n", + "$$\n", + "{\\bf F} A {\\bf F}^H = \\text{diag}(\\lambda_0, \\cdots, \\lambda_{N-1}) := \\Lambda\n", + "$$\n", + "故\n", + "$$\n", + "{\\bf F} b = {\\bf F} A x = \\Lambda {\\bf F} x\n", + "$$\n", + "=> 实际步骤:\n", + "##### - Step 1: 解 $y = \\Lambda^{-1} {\\bf F} b = \\Lambda^{-1} \\mathcal{F}[b]$\n", + "##### - Step 2: 解 $x = F^H y = \\mathcal{F}[y]$ " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy \n", + "from scipy import fft" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "循环矩阵A: [[ 3. -1. 0. ... 0. 0. -1.]\n", + " [-1. 3. -1. ... 0. 0. 0.]\n", + " [ 0. -1. 3. ... 0. 0. 0.]\n", + " ...\n", + " [ 0. 0. 0. ... 3. -1. 0.]\n", + " [ 0. 0. 0. ... -1. 3. -1.]\n", + " [-1. 0. 0. ... 0. -1. 3.]]\n", + "FFT SOLUTION: [1.+0.j 1.+0.j 1.+0.j ... 1.+0.j 1.+0.j 1.+0.j]\n", + "x ERROR: 4.440892098500626e-16\n", + "b ERROR: 0.0\n" + ] + } + ], + "source": [ + "N = 2**10\n", + "A = np.diag(3.0 * np.ones(N)) + np.diag(-np.ones(N-1), -1) + np.diag(-np.ones(N-1), 1)\n", + "A[0, -1] = A[-1, 0] = -1 #由于是循环矩阵\n", + "print(\"循环矩阵A:\", A)\n", + "\n", + "b = np.ones(N)\n", + "F_b = fft.fft(b) / N**0.5\n", + "\n", + "eigenval = np.array([4.0 * np.sin(k * np.pi / N)**2 +1 for k in range(N)])\n", + "y = F_b / eigenval\n", + "\n", + "xhat = fft.fft(y) / N**0.5\n", + "print(\"FFT SOLUTION:\", xhat)\n", + "\n", + "x = np.dot(np.linalg.inv(A), b)\n", + "print(\"x ERROR:\", max(np.abs(x - xhat)))\n", + "\n", + "bhat = np.dot(A, xhat)\n", + "print(\"b ERROR:\", max(np.abs(b - bhat)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Solutions\n", + "步骤:\n", + "\n", + "Step1.离散定义域$[0, \\pi]$——网格化:\n", + "$$\n", + "x_i = ih, i = 0, 1, \\cdots, N+1, \\; h = \\frac{\\pi}{N+1}.\n", + "$$\n", + "\n", + "Step2. 离散微分方程:\n", + "$$\n", + "- u^{\"} (x_i) = f(x_i), \\; i = 1, \\cdots, N, \\; \\text{and} \\; u(x_0) = u(x_{N+1}) = 0.\n", + "$$\n", + "\n", + "Step3. Taylor展开，用插值:\n", + "$$\n", + "\\frac{g_{i+1} - 2 g_i + g_{i-1}}{h^2} = g_i^{\"} + O(h^2),here: g_i = g(x_i)\n", + "$$\n", + "\n", + "\n", + "Step 4: 用差分方程近似微分方程，这里求出的是近似解:\n", + "$$\n", + "-\\frac{g_{i+1} - 2 g_i + g_{i-1}}{h^2} = f_i,here: f_i = f(x_i)\n", + "$$\n", + "\n", + "=>写成矩阵形式\n", + "$$\n", + "\\bf{ A} \\cdot \\bf{ g} = \\bf{ f},\n", + "$$\n", + "这里 $\\bf{ g} = (g_1,\\dots,g_{N})^T$, $\\bf{ f} = (f_1,\\dots,f_{N})^T$, and the matrix $\\bf{ A}$ is tridiagonal, with entries（三对角矩阵，这里要求用…）:\n", + "$$\n", + "\\begin{equation}\n", + "a_{i,j} = \\left \\lbrace \\begin{array}{cr} -\\frac{1}{h^2}& |i-j|=1, \\\\\n", + "\\frac{2}{h^2} & i=j, \\\\\n", + "0& \\text{otherwise}. \\end{array}\n", + "\\right .\n", + "\\end{equation}\n", + "$$\n", + "求A的特征值特征向量 \n", + "$$\n", + "\\lambda_k = \\frac{2 - 2\\cos \\left ( \\frac{k \\pi}{N+1} \\right )}{h^2}, \\; v^j_k = \\sin \\left ( \\frac{k j \\pi}{N+1} \\right ), \\; k,j = 1, \\dots, N.\n", + "$$\n", + "\n", + "考虑DST（离散正弦变换）\n", + "$$\n", + "({\\bf S})_{jk}= \\sqrt{\\frac{2}{N+1}} \\sin \\left ( \\frac{k j \\pi}{N+1} \\right ), \\; j,k = 1,\\cdots,N,\n", + "$$\n", + "$$\n", + "\\mathcal{S}[u] = {\\bf S} u, \\; u \\in \\mathbb{R}^N.\n", + "$$ \n", + "同上面Fourier变换的矩阵，正弦变换矩阵也有类似性质：${\\bf S} = {\\bf S}^T$，正交矩阵，且\n", + "$$\n", + "{\\bf S} A {\\bf S} = \\Lambda := \\text{diag}(\\lambda_1, \\cdots, \\lambda_{N}).\n", + "$$\n", + "\n", + "故\n", + "$$\n", + "{\\bf S} \\bf{ f} = {\\bf S} A \\bf{ g} = \\Lambda {\\bf S} \\bf{ g}\n", + "$$\n", + "\n", + "CONCLUDE:\n", + "\n", + "first solve $\\bf{ y} = \\Lambda^{-1} {\\bf S} \\bf{ g} = \\Lambda^{-1} \\mathcal{S}[\\bf{ g}]$\n", + "\n", + "second solve $\\bf{ u} = S y = \\mathcal{S}[\\bf{ y}]$ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "注：python: help(fft.dst) -> type1\n", + "$$\n", + "y_k = 2 \\sum_{n=0}^{N-1} x_n \\sin\\left(\\frac{\\pi(k+1)(n+1)}{N+1}\\right)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "三对角矩阵: [[ 212901.13712846 -106450.56856423 0. ... 0.\n", + " 0. 0. ]\n", + " [-106450.56856423 212901.13712846 -106450.56856423 ... 0.\n", + " 0. 0. ]\n", + " [ 0. -106450.56856423 212901.13712846 ... 0.\n", + " 0. 0. ]\n", + " ...\n", + " [ 0. 0. 0. ... 212901.13712846\n", + " -106450.56856423 0. ]\n", + " [ 0. 0. 0. ... -106450.56856423\n", + " 212901.13712846 -106450.56856423]\n", + " [ 0. 0. 0. ... 0.\n", + " -106450.56856423 212901.13712846]]\n", + "u Error: 7.828420611755149e-07\n" + ] + } + ], + "source": [ + "N = 2**10\n", + "h = np.pi / (N + 1)\n", + "A = (np.diag(2.0*np.ones(N)) + np.diag(-np.ones(N-1), -1) + np.diag(-np.ones(N-1), 1)) / h**2\n", + "print(\"三对角矩阵:\", A)\n", + "\n", + "func = lambda x: np.sin(x)\n", + "g_x = func(x)\n", + "S_g = fft.dst(g_x, type = 1) \n", + "\n", + "eigenval = np.array([2.0 - 2.0*np.cos(k*np.pi/(N+1)) for k in range(1, N+1)]) / h**2\n", + "y = S_g / eigenval \n", + "\n", + "u_hat = fft.idst(y, type = 1) \n", + "u_ex = func(x)\n", + "\n", + "print(\"u Error:\", max(np.abs(u_ex - u_hat)))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, u_ex, color = \"r\", linewidth = 1.0, linestyle = 'dashed', label = r\"$u(x)$\")\n", + "plt.plot(x, u_hat, color = \"b\",marker = \"x\", markevery = 64, linewidth = 0.0, label = r\"$\\hat{u}(x) \\; (N = 2^{10})$\")\n", + "plt.xlabel(r\"$x$\")\n", + "plt.ylabel(r\"$u$\")\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lab-iisec/Scientific Computation/2-Numerical Solution of ODE.ipynb b/lab-iisec/Scientific Computation/2-Numerical Solution of ODE.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..91fc5b8d16514556b8873fdd0a09c0c00991e1d3 --- /dev/null +++ b/lab-iisec/Scientific Computation/2-Numerical Solution of ODE.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 常微分方程数值解\n", + "\n", + "## ex1：\n", + "给定初值问题：\n", + "$$\n", + "\\left\\{\n", + "\\begin{aligned}\n", + "&y'=\\frac{1}{x^2} - \\frac{y}{x}, 1\\leq x \\leq 2,\\\\\n", + "&y(1) = 1\n", + "\\end{aligned}\n", + "\\right.\n", + "$$\n", + "用下面数值方法编程求解;并与解析解的图像进行比较，画出收敛阶图像（取h=0.2,0.1,0.05,0.02,0.01,0.001)\n", + "\n", + "1. 向前Euler\n", + "\n", + "2. 改进的Euler\n", + "\n", + "3. RK-4\n", + "\n", + "4. BDF-2\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solutions\n", + "\n", + "下面列出各方法的公式：\n", + "\n", + "（1）向前欧拉:\n", + "$$\n", + "y_{n+1} - y_{n} = hf(x_n, y_n)\n", + "$$\n", + "\n", + "（2）改进欧拉： \n", + "$$\n", + "y_{n+1} = y_{n} +h[f(x_n,y_n) + f(x_{n+1}, y_n + hf(x_n, y_n))]/2\n", + "$$\n", + "\n", + "（3）RK4\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\begin{cases}\n", + "y_{n+1} = y_n +h(K_1 + 2K_2 + 2K_3 +K_4)/6 \\\\\n", + "K_1 = f(x_n, y_n) \\\\\n", + "K_2 = f(x_n + h/2, y_n + hK_1/2) \\\\\n", + "K_3 = f(x_n + h/2, y_n + hK_2/2) \\\\\n", + "K_4 = f(x_n + h, y_n + hK_3/2) \\\\\n", + "\\end{cases}\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "（4）BDF2\n", + "$$\n", + "y_{n+2} = 4y_{n+1}/3 - y_n/3 + 2hf(t_{n+2}, y_{n+2})/3\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import math \n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class ODE(object):\n", + " def __init__(self):\n", + " self.dt = dt\n", + " self.init = init\n", + " self.T = T\n", + " \n", + " def ForwardEuler(self):\n", + " t = np.arange(1, self.T + self.dt, self.dt)\n", + " nt = t.shape[0]\n", + " y = np.zeros(nt)\n", + " \n", + " y[0] = self.init\n", + " \n", + " for n in range(nt-1):\n", + " y[n+1] = y[n] + self.f(t[n], y[n])*self.dt\n", + " \n", + " return t, y \n", + " \n", + " def ModifiedEuler(self):\n", + " t = np.arange(1, self.T + self.dt, self.dt)\n", + " nt = t.shape[0]\n", + " y = np.zeros(nt)\n", + " \n", + " y[0] = init\n", + " \n", + " for n in range(nt-1):\n", + " y[n+1] = y[n] + self.dt * (self.f(t[n], y[n]) + self.f(t[n+1], y[n] + self.dt * self.f(t[n], y[n]))) / 2\n", + " \n", + " return t,y\n", + " \n", + " def RK4(self):\n", + " t = np.arange(1, self.T + self.dt, self.dt)\n", + " nt = t.shape[0]\n", + " y = np.zeros(nt)\n", + " \n", + " y[0] = self.init\n", + " \n", + " for n in range(nt-1):\n", + " k1 = self.f(t[n], y[n])\n", + " k2 = self.f(t[n] + 0.5 * self.dt, y[n] + 0.5 * self.dt * k1)\n", + " k3 = self.f(t[n] + 0.5 * self.dt, y[n] + 0.5 * self.dt * k2)\n", + " k4 = self.f(t[n+1] + self.dt, y[n] + self.dt * k3)\n", + " y[n+1] = y[n] + self.dt * (k1 + 2.0*k2 + 2.0*k3 + k4) / 6.0\n", + " \n", + " return t,y\n", + " \n", + " def BDF2(self):\n", + " t = np.arange(1, self.T + self.dt, self.dt)\n", + " nt = t.shape[0]\n", + " y = np.zeros(nt)\n", + " \n", + " Iter = 100 #Iter!!!\n", + " y[0] = init\n", + " y[1] = y[0] + self.dt * self.f(t[0], y[0]) #初始值有两个！！！\n", + " \n", + " for n in range(nt-2):\n", + " for j in range(Iter):\n", + " y[n+2] = 4.0 * y[n+1] / 3.0 - y[n] / 3.0 + 2.0 * self.dt * self.f(t[n+2], y[n+2]) / 3.0\n", + " \n", + " return t, y\n", + " \n", + " def f(self, t, y):\n", + " return 1/t**2 - y/t\n", + " \n", + " def ex_sol(self, x): #解析解！\n", + " return (1.0 + np.log(x)) / x\n", + " \n", + " def plot(self, scheme):\n", + " plt.figure(figsize = (8,8))\n", + " t = np.linspace(self.init, self.T, 100)\n", + " y_ex = self.ex_sol(t)\n", + " \n", + " if scheme == \"ForwardEuler\":\n", + " t_num, y_num = self.ForwardEuler()\n", + " elif scheme == \"ModifiedEuler\":\n", + " t_num, y_num = self.ModifiedEuler()\n", + " elif scheme == \"RK4\":\n", + " t_num, y_num = self.RK4()\n", + " elif scheme == \"BDF2\":\n", + " t_num, y_num = self.BDF2()\n", + " else:\n", + " print(\"ERROR\")\n", + " \n", + " plt.plot(t_num, y_num, 'x', t, y_ex)\n", + " plt.grid()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "只需替换下面两个代码框里的BDF2，就可以得到其他格式。\n", + "\n", + "发现BDF2的结果相对来说较好。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "h = 0.05\n", + "init, T = 1.0, 2.0 #要写在一起，不然运行不了！\n", + "dt = h\n", + "\n", + "solver = ODE()\n", + "solver.plot(scheme = \"BDF2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "init, T = 1.0, 2.0 \n", + "h_list = [0.2, 0.1, 0.05, 0.02, 0.01, 0.001]\n", + "err_list = []\n", + "\n", + "for h in h_list:\n", + " dt = h\n", + " solver = ODE()\n", + " t_num, y_num = solver.BDF2() #这边都用solver写\n", + " \n", + " err = abs(solver.ex_sol(t_num[-1]) - y_num[-1])\n", + " err_list.append(err)\n", + " \n", + "h2_list = [h**2 for h in h_list]\n", + "plt.loglog(h_list, err_list, \"rx\", h_list, h2_list, \"-.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ex2：\n", + "任选一种R-K求解如下Lorenz方程：\n", + "$$\n", + "\\begin{equation}\n", + " \\left\\{\n", + " \\begin{aligned}\n", + " & \\frac{\\mathbf{d} x}{\\mathbf{d} t} = 10 (y - x), \\\\\n", + " & \\frac{\\mathbf{d} y}{\\mathbf{d} t} = 28x - y - xz , \\\\\n", + " & \\frac{\\mathbf{d} z}{\\mathbf{d} t} = xy - \\frac{8}{3} z.\n", + " \\end{aligned}\n", + " \\right.\n", + "\\end{equation}\n", + "$$\n", + "取几组不同的初值（如坐标原点等），观察解的特点" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib as mpl\n", + "mpl.rcParams['legend.fontsize'] = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "def F(a, b, c, x, y, z):\n", + " return a*(y - x), x*(b - z) - y, x*y - c*z" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "def lorenz_solver(F, init, T, dt):\n", + " a = 10\n", + " b = 28\n", + " c = 8.0 / 3.0\n", + " \n", + " t = np.arange(0, T + dt, dt)\n", + " nt = t.shape[0]\n", + " x = np.zeros(nt)\n", + " x[0] = init[0]\n", + " y = np.zeros(nt)\n", + " y[0] = init[1]\n", + " z = np.zeros(nt)\n", + " z[0] = init[-1]\n", + " \n", + " for n in range(nt-1):\n", + " k1, l1, m1 = F(a, b, c, x[n], y[n], z[n])\n", + " k2, l2, m2 = F(a, b, c, x[n] + 0.5 * dt * k1, y[n] + 0.5 * dt * l1, z[n] + 0.5 * dt * m1)\n", + " k3, l3, m3 = F(a, b, c, x[n] + 0.5 * dt * k2, y[n] + 0.5 * dt * l2, z[n] + 0.5 * dt * m2)\n", + " k4, l4, m4 = F(a, b, c, x[n] + dt * k3, y[n] + dt * l3, z[n] + dt * m3) \n", + " \n", + " x[n+1] = x[n] + (k1 + 2.0 * k2 + 2.0 * k3 + k4) * dt / 6.0\n", + " y[n+1] = y[n] + (l1 + 2.0 * l2 + 2.0 * l3 + l4) * dt / 6.0\n", + " z[n+1] = z[n] + (m1 + 2.0 * m2 + 2.0 * m3 + m4) * dt / 6.0\n", + " \n", + " return t, x, y, z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "下面观察可发现，解有蝴蝶效应" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 12))\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "h = 0.001\n", + "t_num, x_num, y_num, z_num = lorenz_solver(F = F, init = [1.0, 1.0, 1.0], T = 5.0, dt = h)\n", + "\n", + "ax.plot(x_num, y_num, z_num, label='parametric curve')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 12))\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "h = 0.01\n", + "t_num, x_num, y_num, z_num = lorenz_solver(F = F, init = [1.0, 1.0, 1.0], T = 10.0, dt = h)\n", + "\n", + "ax.plot(x_num, y_num, z_num, label='parametric curve')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 12))\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "h = 0.001\n", + "t_num, x_num, y_num, z_num = lorenz_solver(F = F, init = [1.0, 1.0, 1.0], T = 20.0, dt = h)\n", + "\n", + "ax.plot(x_num, y_num, z_num, label='parametric curve')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lab-iisec/Scientific Computation/3-Random Algorithms and Optimization.ipynb b/lab-iisec/Scientific Computation/3-Random Algorithms and Optimization.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bfeb4a881f82ed1a02a6a1d25f91e80a9b5d0860 --- /dev/null +++ b/lab-iisec/Scientific Computation/3-Random Algorithms and Optimization.ipynb @@ -0,0 +1,696 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 随机算法、优化\n", + "\n", + "## ex1 \n", + "\n", + "编写 Monte Carlo 数值积分程序计算如下d维积分 \n", + "$$\n", + "\\mathcal{I} = \\int_\\Omega e^{-|{x}|^2} \\mathrm{d} {x} = \\left (\\int_0^1 e^{-|{x}|^2} \\mathrm{d} {x} \\right )^d, \\; \\Omega = [0, 1]^d\n", + "$$\n", + "\n", + "1. 利用d=1情形验证 Monte Carlo 方法为半阶收敛（与精确解比较）并与复合梯形法比较\n", + "\n", + "2. 在d=2情形与中矩形公式比较。\n", + "\n", + "3. 测试d=10情形，画出收敛阶。\n", + "\n", + "注：代码参考上课文件https://github.com/mazhengcn/scientific-computing-with-python/blob/main/others/mc_integration.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.integrate as integrate" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def integration(f, quads):\n", + " points, weights = quads\n", + " if isinstance(weights, np.ndarray):\n", + " assert points.shape[0] == weights.shape[0]\n", + " \n", + " integration = np.sum(weights * f(points))\n", + " return integration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " x_norm = np.linalg.norm(x, ord=2, axis=-1, keepdims=True)\n", + " return np.exp(-x_norm**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. d=1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7369941595051059" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 2**10\n", + "rng = np.random.default_rng(0)\n", + "rpoints = rng.uniform(0.0, 1.0, size=(N, 1)) #注意这里的size，不然会报错data type not understood。相应下面也改一下\n", + "weights = np.ones((N, 1)) / N\n", + "MC1 = integration(f, (rpoints, weights)) #这里(),[]都可以\n", + "MC1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7468240742251904" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#复合梯形法\n", + "N = 2**10\n", + "points = np.linspace(0.0, 1.0, N).reshape((-1, 1))\n", + "weights = np.ones((N, 1)) / (N-1)\n", + "weights[0, 0] = weights[-1, 0] = 0.5 / (N-1)\n", + "I1 = integration(f, (points, weights))\n", + "I1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#验证为半阶收敛\n", + "mc1_err = []\n", + "ns = []\n", + "I1 = integrate.quad(lambda x: np.exp(-x**2), 0, 1)[0]\n", + "\n", + "for n in np.linspace(100, 10000, 100):\n", + " int_n = int(n)\n", + " ns.append(int_n)\n", + " rpoints = rng.uniform(0.0, 1.0, size=(int_n, 1)) #intn!!\n", + " weights = np.ones((int_n, 1)) / int_n\n", + " mc1 = integration(f, (rpoints, weights))\n", + " mc1_err.append(np.abs(mc1 - I1))\n", + " \n", + "plt.loglog(ns, mc1_err, ns, 1.0/np.sqrt(np.asarray(ns)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. d=2" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5565640170002342" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 2**10\n", + "rng = np.random.default_rng(0)\n", + "rpoints = rng.uniform(0.0, 1.0, size=(N, 2))\n", + "weights = np.ones((N, 1)) / N ##这里不是（N，2），是（N，1）。下面n=10也是\n", + "mc2 = integration(f, (rpoints, weights))\n", + "mc2" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5577463290199819" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 中矩形公式\n", + "def f2(z):\n", + " x = z[0]\n", + " y = z[1] \n", + " return np.exp(-x**2-y**2)\n", + "\n", + "nx = ny = 2**10\n", + "h = 1.0 / nx\n", + "x = np.arange(0.0 + 0.5*h, 1.0, h) # arange碰不到右端点\n", + "y = np.arange(0.0 + 0.5*h, 1.0, h) \n", + "xy = np.asarray(np.meshgrid(x,y)) #(2,N=nx*ny)\n", + "points = xy.reshape(2,-1)\n", + "weights = h**2\n", + "\n", + "I2 = integration(f2, (points, weights))\n", + "I2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. d=10" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05397077078684821" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 2**25 #这里N改大了\n", + "rng = np.random.default_rng(0)\n", + "rpoints = rng.uniform(0.0, 1.0, size=(N, 10))\n", + "weights = np.ones((N, 1)) / N\n", + "mc10 = integration(f, (rpoints, weights))\n", + "mc10" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05397385432900757" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "I10 = I1**10\n", + "I10" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PArA8CNZ0wnitf6HPHcDw2LY1jsX8wavukB5+YO+UHeXWldvYcaSOM8cM4JdLipkwJDvWzRIiMlwuqNxq2RTekgByR3sngOxhsW1rHEmogG+SHr5/bU4XK97bxx9f30ltYytfPn0UPzhvHP0zZJkG0cu5XHDkk/bgX/42NNUYr0kC8EiogC89/OCcaGjhrjd28Y93y8lMSeb7547ly7NHYZdlGkRf4XLCka0BEsAYSwKY06cSQEIFfJP08IOz80gdt67cxttlRxk7KJNfLilm3tiBsW6WENHncvrcAbwTIAHMheyhsW1rBEnA7+W01rxRWsltL26jvLqBcycO5qYLJ1KQlxHrpgkRO74JYO870OxOAAOK2hPAqDm9KgEkVMCXkk7PNbc5efSdvdyzahctThdXzi3khrPHyjINQoCRAA5/bLkDWOc/ARTMhawhsW1rCBIq4Jukh99zlbVN/OHVHfx7cwUDs1L4n/PHc+n0EbJMgxBWnSaAsd7LQSdQApCA30d9uP8Ev1q5lS37TnDKiBxuXjKJGaNkmQYh/HI54fBHPgmg1ngtgRJAQgV8KemEl9aa5z44yO0vl3KktpnPnTqMH392AkNz0mLdNCHimzUB7CmBfevbE0DeuPbgP2ouZA2ObVstEirgm6SHH14nm9u4/61P+dva3diU4toFY7h6/mhZpkGIYDnbOt4BtNQZr3kSgHsMIHNQzJopAV947D/WwO0vl/LSx4cZ3i+Nn184kc9OHiLLNAjRXZ4E4F4Kony9JQGM9y4BRTEBSMAXHaz/tJpbVm5l++E6Zo/O5ZeLJ1E8TJZpEKLHnG1w+EPLHYCfBFA4zygBZUbuWZmECvhSw4+eNqeLJzfu54+v7aCmsZWls/L5wXnjGJCZEuumCZH4zASwx30HsG89tNQbrw2c4D0GEMYEkFAB3yQ9/OipaWjlrlU7eXx9ORkOG987dxxfPUOWaRAirJxtcOjD9hKQ3wTgfhAshAQgAV8EZdeROm59YRslu44yZmAGv1wyibPGyTINQkSEJwGsdSeAd9sTwFeegaJzenRaCfgiaFpr3txeya9f2Mbe6gbOmTCImxYXUyjLNAgRWc7W9juA6V+H9NwenUYCvui25jYnf1+3l7+sKqO5zck35hRy/dlFZKfaY900IUQnEirgy6BtfKmqa+YPr27n35srGJDh4Efnj+eyGSOxyTINQsSlhAr4Junhx5ePKk5wy8ptbC4/zuTh2dy8ZBKnFfTsllMIETmBAr5MwRBBO2VEP56+5gzuvvxUqutb+ML96/nOii0cPNEY66YJIYIgAV90i1KKi08dzqobz+I754zl1a2HOfuPa7j7jV00tjhj3TwhRCck4IseSXck84PzxrHqxrM4Z+Jg/vzGTs7901u88NFB4rlM2FeV7Kqirqk11s0QMSYBX4RkRP907ls2nX8tn01Omp3r/7mFL/3tXT45UBPrpgm37Ydr+erD77HswQ2caGiJdXNEDEU14CulMpRSm5VSi6P5uSLyTh89gJU3zOX2S6ZQVlXPknvf5qfPfMTR+uZYN63P++SAsZzv1oM1LH1wA9XyO+mzggr4SqlHlFKVSqlPfI4vUkrtUEqVKaV+EsSpfgw81ZOGivhnS1IsnZXP6h8u4Mo5hfx7UwUL/7CGh0p209LminXz+qzSQ7Wk2pN4+IrT2F1Vz9IH36WqLvSgv2ZHJT/894dhaKGIlmB7+I8Bi6wHlFI24D7gs0AxsFQpVayUmqKUesHna5BS6lxgG3AkjO0XcSgnzc4vFhfzyvfmM6OgP7e9WMqiu9eyekdlrJvWJ5UeqmX84CwWjh/Eo1ecxv5jjVz+wHqO1DYFfY7nPjjA4Rrv9z9YspunN1fImE0CCSrga63XAsd8Ds8CyrTWu7XWLcCTwMVa64+11ot9viqBhcBsYBlwtVJKxg96uaJBmTz2jVk8esVpoOEbj27kG4++x6dV9bFuWp+htab0UC0ThhjLXp9ZlMffr5zF4ZomvvS39UGV3Cprm/jukx9w+8ulnmM1Da28u9sICa3OjgH/qw9v4KGS3WG6ChEuoQTd4cB+y/cV7mN+aa1/rrX+HvBP4EGttd97fKXUcqXUJqXUpqqqqhCaJ+LFwgmDeOV787npwols2nuc8/+8ltte2EZNo8waibTKumaON7QycWiW59iswlwev2oWe6sbeHpzRZfneH/fcQBe/viwJ0G8ueMITpcR6FucHf9X3rT3OBv3+vYRRayFEvD9PVff5b2d1voxrfULnbz+AHAL8L7D4QiheSKeOJKT+Oa80az+0QK+MHMED7+zh7PvXMOK9/Z5AocIv22HjAHbiUO9N7aZMSqXcYMzeafsaJfn2LLvBLYkRYvTxVObjD7eq5+0V2Z9x2da2lw0tjo5UiuDw/EmlIBfAYy0fD8COBhacwxa65Va6+U5OTnhOJ2II3mZKdx+ySmsvH4uYwZm8tNnPmbJPW+zYXd1rJvWK5W6A/6EoR13MptTlMfGvcdobuv8gbkt+04wZXgOZ4wewD837KOhpY23dlaR7jD2Qm716eGbd26V3RgjENERSsDfCIxVShUqpRzA5cDz4WiUUmqJUuqBmhqZy91bTR6ew7++NZt7lk7jREMLX3rgXa775/sckGUawmr7oTqG90sjJ63jCqdzxuTR1Ori/fITAX++1eniowMnmJbfj6/MHkXF8UZ+82Ipja1Ozp04GOjYw691P+BVWdeMKwx3byW7qiirDN+4T01jK69v65tzR4KdlrkCWA+MV0pVKKWu0lq3AdcDrwKlwFNa663haJT08PsGpRRLpg5j1Y0L+N65Y1lVeoSz71zDn1/fKcs0hEnpoVqv+r3V6aNzsSUp1n0auKyz/VAdTa0upuf35zOTBjMwK4UnNuwjKzWZ+e6NcZrb/Pfw21yaY2F40OvGpz7kf9d8GvJ5TN9ZsYWrH9/UYdaRyeXSnqTV2wQ7S2ep1nqo1tqutR6htX7YffwlrfU4rfUYrfVvwtUo6eH3LWnuLRVX3biAz0wawt2rdnH2H9fw/IeyTEMomlqd7D56skP93pSVaueUETm83Ukdf8t+Y8B2Wn4/7LYkLj/NqOKePWEQGV2UdIBuTf30x+XSVJ9s6XEAdrl0h5LVriPGpuJtLv/Phty1ahen/Oo1jp/sfU8lx+XUSOnh903D+6Vxz9JpPPWtM8jNcPCdFVv4wv3r+bhCEr+pOwmwrLIep0sHDPgAc4vy+KiiJmBAfb/8OIOyUhjeLw2AZafnk5eZwqXTR+BINsJHh5KOJeBXBjlw++JHh1j24LsdkkddUxtOl+7xOkA/feZjxt/0itexFvc00kCTBVZ+aAxFnuiFs8jiMuBLD79vm1WYy/PXz+V3l05hb/VJLrrvbX789EdheTo0EVUcb+DRd/aw7MF3Gf+LV1gT5ANs5gydCUP8l3QAzhyTh9Ol2bDb/xTKLfuN+r1SxqS8oTlpbLrpXOaPG9ge8EPs4Wut+cuqXaz7tJq3dnhPxTZLQvXNbWzZd5yahvZzv7/vOFvcU0YD+Zd7VpE1kZh/DvT0t9nzt6net8FPXAZ86eELW5LiS6fl8+YPF3D1vNE8s6WChXeu4YG1n/b6ZRq01mw9WMNdb+zkgrtLmPu71dyychtVdc2kO2z8Y315UOcpPVRLmt3GqAGB9yKePqofqfYkv9Mzq+ubKa9uYFp+f78/a7cZ4aPVt4bfYA34XSfpzeXH2eEus/znfe/nAo6dNH6+prGVz/91HV/823rPa5f8dR2f/+u6Ls8P0GAZE2pzB3zfsQeT030HYLP1voCfHOsG+GPZ4jDWTRExlp1q52cXTOTy00Zy24ul/Pal7ax4bz83XTiRsycM8vQ8E12r08XGPcd4bdsRXt92hAMnGlEKZo7qz88umMB5xUMozMvgjpe382DJbqrqmhmYldLpOUsP1TJ+SFanW1GmJNs4rSDX78Dtln3G7J3pAQK+2cNv9tPDT7PbSHfYOFLXdQ//n+/tIyslmcVTh/L05gqOn2yhf4bxDM6xk0byMAdYzcRgpbXu8t9BQ0ubZ6ZSq7uUEyjgm68nh2ELz8q6JvIyUkiKk+1ApYcvEsLogZk8csVpPPqN01AKrvr7Jr7+6EbKKjsGgERSWdfED576gJm3vcGyhzaw4r19TByaze8vPYWNPz+Xf19zJsvnj6Ewz+ilXzZjOE6X5rkPDnR6Xq012w/XdVq/N80pymPnkfoO8+a37D9OcpJiynD//x86bP5r+DWNreSk2RmUnRrUXPwdh+uYWdCfC6YMpdVptNtkDpxal2/YdaSOKx/b6Pm+OojB1ZPN7T38rko6Zm0/1PkCh2uamPWbVfz5jZ2hnSiM4rKHL0QgC8cPYm5RHo+vL+euN3ay6K4SvnZGAd89Zyw56R3nmsezAyca+cpDGzhU08iFU4bxmUmDmTc2j3RH4P8tiwZlMXVkP57eXMFVcwsD9mwP1zZxwmdJhUDmjMkDYN2n1XxuWvvqKFv2nWDi0GzS3LNxfAUatDUD/uDslKBKOieb28gcmEn/dKNXbx2g9RfMr3h0o9fzGrurTpKcpFBK+X3eAODtXVWkOWwM75fmCeTNbU5uf7kUl0vz8wuLPe81E4LueuGATlW6727W7Kjixs+MD+lc4RKXPXwhOmO3JXHV3ELW/HABXzxtJI+u28PCP67hiQ3lCbNMw56jJ/nC/67jaH0zT3zzdP74xamcP2lIp8HedNmMEWw/XMfWg7UB31MaYEkFf4qHZZOTZveq4ztdmg/dA7aBmD1835k1tU3ugJ+VGtSgbX2zk8wUG1mpye6fb/O8dtzPPH7fh/P2HK3n1FtfZ+otr3V4r1lJ+dXKbcy5402v11raXPztrd08WLLHa0KA+W8olH9KL318iIvufafT9+w/1hDwWYBIicuAL7N0RDAGZKbw289P4YUb5lI0KJOfP/sJi+95m/WfxvcyDaWHavnC/etpbnPx5PLZzBiV262fX3LKUBy2pA4DnN6fYZRFOpuhY7IlKc4YPYB3yo6itUZrzTPvV3Cyxdl5wA/Yw28j293DP1rf7BkkBaPU9D9Pf8g6S3I52dxGhiOZ7FSjd27t4R8LolxjbvAS6NqsrE/+WmcXnfabNzx/9gR8lw5qLn6r08X836/2KrNd+8T7nj8HGl6Y9/vVzL59VZfnD6e4DPhSwxfdMWlYDv9aPpu/fnk6tY2tLH3wXa59YjP7jzXEumkdbNl3nMsfeJfkJMW/vnUGk4Z1/994v3QH5xUP5rkPDgasQ5ceqmVkbhpZqcGVueaMzeNgTRMb9hxj+T8286OnP2LK8BzOcS+f4I85S8d3WmatpYbv0t5lmfrmNp7aVMF9a8oAI7g2tjrJSElu7+E3tvfwgwn4b5QGXibBt+RVYkk0vn93bU4Xf11T5hnM/fPrO5n269c77BD2yYEazvrD6vY1g+qa2Xesge8++YH/NgDryo4y7dbXqG9u8/ueaInLgC9EdymluGDKUFbdeBY3njeO1durOPdPb/HH13bQ0BLb/8lM6z+t5isPbSAnzc6/rzmDokGZPT7XpTOGc+xkS8BNZaxr4AdjzpgBAFz+wLuU7KriZxdM4Nlrz/T0uv3puoafCnjPxTcD+LpPq6msbfL8bjJTkkm2JZHusHk9BHbsZAv2TqZHLpo0hEOdlEV859Jb5/n7ztJ5ZssBfv/KDq/vARpbvZ/U/fPrOymvbuC9PcazC0ct5SDrlFQPpfjDazs43tDKjsOxnWQgAV/0Kql2GzecM5Y3f3gWiyYP4Z43yzj7zrd47oMDMV2mYfX2Sq549D2G9Uvj39ecwcjc9JDON3/sQPIyU/iPn/Xsm1qd7OlkSQV/CvMymJbfj3lj83j1e/NZPn8MybbOw0OKnwev2pwu6pvbPIO24D0X/2i9EfC1hpUfHfLMnslIMXr32al2r5LO8YYWz1O+mSnJniRjmjoycMkJ2mv4pl2WWV2++zHct7rM7znsPn8PZg45drKZuqZWrwQ19dbXeHXrYe/30z7FFTTHTraw9+jJTtsdKXEZ8KWGL0I1NCeNuy+fxtPXnMHArBS+++QHXHb/ej6qCLwyZKS88NFBrn58E+MGZ/Gvb7W/fk8AABpuSURBVJ3h6fmGItmWxOenDePN7ZUdSg47j9Th0lAcxAwdk1KKZ6+dwz+uOr3TB7Ws7H6mZZoDrtlpyZ328DMcNp774ICnxJGRYswEykpN9irpnGxuY2iOEfBz0uyk+gT8/l3MzPKd/16yq72k4/v3Vl7tvwS4/PFN3PHyds/3Zpnox//5mLm/W91hcPdb/9jcaZvOv2stC+5c0+H4/mMNPLulgn9u2BexcmRcBnyp4YtwmVmQy3PXzeH3l51CeXUDF937Dj/694eeKXOR9tTG/XxnxRam5ffjiatPJzcjfJv6XDpjBG0uzfMfem9D0Z0ZOqGwJSlsScprlo7Za85JszMgw4EjOclraWPzydnzJw3xWsMn0+zhp9mpa27vMTe0OBnkvlPol24n1e49RbRfeud/n50tz1xdH9ziaB9W1HD/W+2rdVpTSE1jK64u7hytOUdrAi4Rcun/ruP7//qQnz37MfN+vzqotnVXXAZ8IcIpKUnxxZkjWf3Ds/jWWaP57wcHOPvOt7j/rU+73PwjFI+8vYf/+c9HzCnK4/ErT++0Ht4TE4ZkM3l4dodtCksP1ZHhsDGyf2hlo2A4bElePXxrwE+2JTG3KI/Xtx3xlNPMks449+yhA8eNKZbtJZ32Hr7WxoDuoKz2gJ9i714Pv6mTZTj8TfkMRodZN11UCt/f135X+a+N+wO+rzIKa0VJwBd9RlaqnZ9+diKvff8sZo8ewB0vb+czf17rFZDCQWvNPat2cesL21g0aQgPfX1mwIeXQnXZ9BFsPVjr6dWDsWja+CFZUXmc325TAQM+wPmTBnPgRKPnmYFjJ1tId9gYmmOUe/YfN0oXZg8/K9Xu6fU3t7nQGvpnOLAlKXdJJ/gefqvT1elzGat39GzPbOWzu2tXPXyrfwfYQzhaNX0J+KLPKczL4KGvz+TxK2dhtyVx9eOb+Noj73nWSQ+F1po7Xt7OH1/fySXTh3PvsmmkJEcm2ANcdOpw7DblGbzVWrP9UG3EyzkmR7LNs9wwtC+NbAb8cycOJknBa+6BzOr6ZgZkOjylrQp3D9/cLjE7LZk69ziAuQlOut1G/3QHeZkpHUo6nc3gaWoN7u7N3MglGE2tTpJ8omaoz/pVHG/oMBMIYFsnD9b1VFwGfBm0FdEwf9xAXv7uPG5eUsyH+0+w6O4SfvX8Vk708Fbf5dLc9N9P+Nva3Xx19ijuvGxqlzNdQpWb4eDsCYP47wcHaHW6OFjTRG1TW9QCfkpy4JIOGA/HzSzI5dWtxlz56pMt5GakeJZRMAcnvXr4ja1orWlwB8E0h40HvzaD6xYWeWYGTR3Zj+2/XtTpwnCBFkfz5Qjyd7Rl33Em/OIV1vjcGYR6d/iL/37i9/jJCEwnjsuAL4O2IlrstiS+MaeQNT9ayNJZI3l8/V4W3rmGf7xb7vWEaFfanC5+8NQHPLFhH99eMIZbL54UtRUSL50+gqP1LazdWUXpQXPANvgZOqGw25TXtEwz4Gdb1rT5TPFgdhypY+/Rkxw72cKADIdnNcyONXw7bS5NU6vL08NPcyQzLb8/g7NTPT38NHsSqXZbpwE/2GW0HcnB/Z42lxtr7zf4bL+5vItZOV1ZvaOKR9/Z0+F4JP75xGXAFyLacjMc3Pa5Kbz4nXmMH5LFL/5rLNOwrpPt/0zNbU6ufeJ9/vvBQX50/nh+vGhCVJdtXjhhEAMyHDy9uYLth42AP74bD12FwpGc5LUefm1jKynJSV6ll7PcJZPN5ceprjcCfq67h1/hXgbaWtIBYz0eT8C3nCvVPWhrlsmSfesrFr5r/PzhslOYlt+PWy6a5HXcd559IMHeMfTEHr81/PD/G5KAL4TFxKHZrLh6Nvd/ZTr1zW0se2gD1/wj8DINDS1tfPPvm3ht2xFuuWgS1y2M/h4OdlsSF506jFWllazfXc2oAemeEkmkOZKTOvTwfVesHD0wk1R7ElsP1nLsZAu5mQ7SHDZPOSjDkexJkFmW9XTMuna6ZcDbDPRmace3h28tr5gB31zeedHkITx77Ry+fmaB9zUEGfCDHRPoCX8dhEj08GV5ZCF8KKVYNHkoC8YP4uG393Dvm2W8uaOSq+cVcu2CIk/5oaaxlSsf28iWfce58wtTuWzGiJi1+bIZI3j0nb28U1bNoklDova5dj/TMn0Dvi1JMWFINhv2VNPidDHAXc7JzXBwqKbJ89AVGNMyjfO0eZZdsN4tmNMyU+xmD987KrY6NY5kxV1v7PQE/GsXjOH8SUMClth8n94NJKIB38+xpAjcJUoPX4gAUu02rltYxOofLmDxlKHct/pTFt65hmfer+BofTPLHnyXjypOcO+y6TEN9mAsIGeujDkhSvV7cM/D9+nhZ/tZk37SsGzP1MwBGca8enPgNsNyN2L28K0lHWsP3wz+Zg/fN4ib+9He9cYu7lttPCzlSE7qdDwl2ID/YEnHOnu4bNjTcU/hhA74SqkFSqkSpdT9SqkF0fpcIUI1JCeVP33pVP7z7TMZmpPKD576kLm/e5Oyynoe+NpMLpgyNNZNBPAknWjN0AF3SaeLHj7gtSpobqYR6PtnGO+zlp9y3DX8uqa2ACWdJM/ngp8eflvHGTNd1eiDLelEW4sz/HcUQV2pUuoRpVSlUuoTn+OLlFI7lFJlSqmfdHEaDdQDqUDghbyFiFMzRvXn2WvncOcXpjJ5WA6PXzmLheMHxbpZHktn5fPDz4zzDJJGg78nbf0H/PYkZJZ0PD18y6Yv5tPItY2tntkw3oO2ndfwfZdqBv8B3/p3FOygbbRFYi+fYGv4jwH3Ao+bB5RSNuA+4DyMAL5RKfU8YANu9/n5K4ESrfVbSqnBwJ+AL4fWdCGiLylJcdmMETEv4fiTkZLM9WePjepnOpKTvGbD1DW1eerwVuZm6k6XZkBm1yWduqY2z0NV1qeUUz2Dtv5r+E2tzg7r5/ibdnn/V2Yw8ZeveK4hHkVinldQAV9rvVYpVeBzeBZQprXeDaCUehK4WGt9O7C4k9MdB1K631QhRLzxnaXT2OIkzc82jal2G2MGZrDzSH17Dz/DDPje0y7tNkVtU6unZ+9/Wqb/Hr6/Rcf89eCtSSReA34kFvMOZZbOcMC6ElAFcHqgNyulLgHOB/ph3C0Eet9yYDlAfn5+CM0TQkSataTjdGlanC6vAG01eVgOh040ecoyue6Fz6w9fKWU52lb8/zWp5XNQG/O1gnmeYeuSjZxW9KJQE0nlIDv7286YAu11s8Az3R1Uq31A0qpQ8ASh8MxI4T2CSEizG4p6ZjTFlPt/gPo984dx0WnDvN8b/bwfZ8ZyE5Ndpd0kjqcq72GH/z6RF0O2gbRwx+SncrhIDZkD6dI9PBDSW0VwEjL9yOAgwHe2y2ytIIQicFhS/I8gdpkWfvGn/wB6SywDHL7G7QFY1kGc1pmus9rZsDvThmmq1k4Dp8F2PzN4OxsCYdIicQGbaEE/I3AWKVUoVLKAVwOPB+ORsniaUIkBuviaeY0St8ljAPJ9VPDB2PXq7qmNhpanR2Sh6ek042Ab+9irRzf5HHP0ukd3tPJCg4RE4ktOYOdlrkCWA+MV0pVKKWu0lq3AdcDrwKlwFNa663haJT08IVIDHabtaRj/Dc1yLX/B2enYktSDPLZ8jHbXcNvbGnrMB7gOy0z2DZ2xmHz/gx/vXnfzdCjwR6BweRgZ+ksDXD8JeClsLYIo4cPLCkqiv66JEKI4DmSk3BpY7VQTw0/yEA1MCuFV747j8I87z10zR5+o78evs/iacHoetDWO5j7TvWEjk/0RsPMUf3Dfs64HJ6WHr4QicEsh7RYAn53dvcaOzirw54B2e5drxpanF5P2UJ7GSjQ1ob+4nKXNXyfBGXzs6lKtHv4t148KSIrrsZlwJcavhCJwew9t7bp9hp+gGmZwcpOs9PQ4qSuqa3DuSYMyea56+YwqzDX78/6++zOdsWCjgnBXw8/2oO2XzujICLnjcuALz18IRKD2Ttudjo9NfxA8/CDleV+UreytqlDDx+M3a4C9X59a/tKBRGsfV729/5ILGQWC3EZ8KWHL0RiSHH3jlvaXJYefmhhxbOeTlPHQduudNzzNqnbpRF/wT0W0zIjIS4DvvTwhUgM5pTHVqe2PHgVeknHNDQnrVs/6/vZPVkJ0+/a9BLwhRB9nTmlsaXNFbaAn2VZfM26ymYwpo3s5/V9chf1e3/83RH4q+snorgM+FLSESIxeGbpWAJ+qDV8s6QDMGl48AH/hrOL+O0lU7zKL53teevh83yT3ydtgygLjczt3t1ILMRlwJeSjhCJwZwB0+J00djifvAqjD38IT4PZXXm9MIBpNptXsG5J+ui+YvtweSNp685k5e+M6/7HxhFcRnwhRCJwauH3+bEYUsKeYDTWsPvzoCr+bmDsttXX1c9WlW+Z4O2OWl2irtZgoo2CfhCiB5LsTx41djiDHmGDkCWe/XM4m5u1WgG5RVXz2b0QOPp3Z7kHr89fMvBB782k5sunNjpe+JVXAZ8qeELkRjaH7wyavihlnPAmBHz3HVzWLF8drd+zgz4I3PT+ersUUDn6/pcc9YYpo7s12EZ4q6mZY4ZmMHcsXkd3pMA8T4+A77U8IVIDL5LK3RnWYXOTB3Zz+/euJ3xGqx1JyJ/D26ZfvLZCTx33RyvY3dffqrfIpB1XCBQmUl6+EKIXs3h8+BVsEsjR4J16qS5xn26vev1If09nevLOg9f4X9sIBEezpKAL4ToMbOkY/TwXUEvjRwJ1h62OR0zmDuOGT6rUvoN5pZzJ0JPPhAJ+EKIHktJ9u3hxy6keJd03D38IAK+UooLTxnq+bO/eG49dwLHewn4Qoies07LbA5jDb8nrEHZ3HbRd4vErii6LukQ4D2JIC4DvszSESIxeGbpOGNfw7cG/MYW46nfYHr4gNfTtv5KOtZ4r1Rk9puNhrgM+DJLR4jE4L20giumPXzroG1DNwO+dkd8pbqeh5+kFNlp3btzADjVss5PRoz+nuIy4AshEkNyklHzbjF7+GF48KqnrGUXsx2Dg1yaweyxK/zX8Ae4d9oCIyEMzUnj5iXF3WrfnKIB7Z/XrZ8Mn+6nKSGEcFNKYbcleebhh+PBq56y9vC/MnsUyUmKpbPyu3UOpfzPwhmS0544zJLPKSO6V4EIVAa6/yszuOb/NnfrXD0lAV8IEZIUW5JntcxYBnxroLbbkvhqN7YJ1F41/I5G9E9vf72HA7aBevXnFQ/u2Ql7IGoBXymVBPwayAY2aa3/Hq3PFkJEjiM5iaZWJ61OHfLSyKEIZc16Tw2fjgE9J81OtmUFz54GfFccjPQGVXBTSj2ilKpUSn3ic3yRUmqHUqpMKfWTLk5zMTAcaAUqetZcIUS8sduSqG1sA0Lf3jAUoexK5anhK/Dt4w/IdHgdap/F073Ps8b7/FzLHUO3zhKaYH87jwGLrAeUUjbgPuCzQDGwVClVrJSaopR6wedrEDAeWK+1/gHw7fBdghAilhzJSdQ2tQKhb34SivDsSqU6rLDpu5RCj3v4LiPiX3FmASNzA5eIzNVCIyGoM2ut1yqlCnwOzwLKtNa7AZRSTwIXa61vBxb7nkMpVQG0uL919rTBQoj44khOorbRCPgpMQz4oaxlYy22+FscTXn18P39VPCf0T/dwcETjQHfF8mHukK5/xoO7Ld8X+E+FsgzwPlKqXuAtYHepJRarpTapJTaVFVVFULzhBDRYLclUdtklHRi2cMPKeBbSjr+zmI91tO1dMwafoc7iCg+thvKvYO/VgZMeVrrBuCqrk6qtX4AeABg5syZsR/lEEJ0ypGcRGVtExD69oahCGbf2cACD9qCd1AO9mOe+ObplFc38LNnPzY+wR3NQhlrCFUoPfwKYKTl+xHAwdCaY5ClFYRIHCm2JGoaY1fD/7+rTufiU4eFJZAqpbrswQe7beKcojyWnd7+HIDW7U/zxkooPfyNwFilVCFwALgcWBaWVgkhEoY9WdHmHpCMxSyduWPz/O5A1R2D3E/kZvoZMPVdQVP18BLNckUsl1cOdlrmCmA9MF4pVaGUukpr3QZcD7wKlAJPaa23hqNRspaOEInD3AQFYlvSCcUvLizmT1+cyuzRuf5LOgH+3B2BavjRFOwsnaUBjr8EvBTWFmGUdIAlRUVF4T61ECLMHMmJH/DTHDYumT4CCGKWTg976J4afrz38KNNevhCJA67pYcfy9Uyw8V/OFYd/tTdB2cXTR4CGLX9WInLtXSkhy9E4vDq4cdwx6tw8RfHrZ3ynvbQTyvIZe8dF/asUWESl78d6eELkTism4D3hh6+9um6K3xq+GGoyFwwZWjA1yI5Lz8uA74QInFYSzqx3PEqXPyVarqq679541ldntf6/s9N6+wZ1ciRko4QIiTmLB1HclJMHyqKpM56+NPz+zF6YGa3z/n7S0+heFh2aA3rprjs4UtJR4jEYdbwY7msQjj57+G3/7mnNXzfB7a+eNpIJg+PboyLy4AvhEgcZkknlksjh5O/deuVn1k6iSguf0OytIIQiaPX9fB9vvfd2Nys53d3WmYsl1QwxWXAl5KOEInDnKWTqA9d+RrRP42Zo/pz68WT/L7uG7ejudplqOJy0FYIkTjaSzq9I+DbbUk8/e0z2XG4znPMu4ffs/PGQ1qIyx6+ECJxOJJ7Vw3fH+8dr+IhdPdMXP6GpIYvROIwp2X2lhq+SVuq+WaMHzUgPcC7uxZsoojXHa8iRmr4QiQOey+r4ftSqLgYcA2HuAz4QojE0Vt7+Fb+Nj0ZPySLzJRkvnfu2CDPEZwbzg7ufD0hg7ZCiJCYs3RiuYF5JBQMyGD0wAx+uaTYbw8/K9XOJ7ec3+F4yf8s9GwI012RXlxNAr4QIiT2XtrDT7XbePPGBQDsOlLX+ZstRub6r/PHQ1koLks6MmgrROLoE7N04iBYh0Nc/oZk0FaIxNHbnrT1LzwbpMdaXAZ8IUTisNuMQNZbZ+mA9PCFEAKwLK3QCzY/CaSXxHsZtBVChCY71Y5SMCDDEeumREykyzFjBmbwxZkjI/oZIAFfCBGiQdmpvHDDXMYPzop1UyKmpxuXB2uVezZQpEUt4Cul5gFfdn9msdb6zGh9thAisiYN690TLPpUDV8p9YhSqlIp9YnP8UVKqR1KqTKl1E86O4fWukRrfQ3wAvD3njdZCCFETwTbw38MuBd43DyglLIB9wHnARXARqXU84ANuN3n56/UWle6/7wM+GYIbRZCCNEDQQV8rfVapVSBz+FZQJnWejeAUupJ4GKt9e3AYn/nUUrlAzVa69oet1gIIUSPhDItcziw3/J9hftYZ64CHu3sDUqp5UqpTUqpTVVVVSE0TwghhFUog7b+hjE6HcPWWt/c1Um11g8opQ4BSxwOx4yeNk4IIYS3UHr4FYB14ugI4GBozTHI0gpCCBF+oQT8jcBYpVShUsoBXA48H45GyeJpQggRfsFOy1wBrAfGK6UqlFJXaa3bgOuBV4FS4Cmt9dZwNEp6+EIIEX7BztJZGuD4S8BLYW0RRg8fWFJUVBTuUwshRJ8Vl4unSQ9fCCHCLy4DvtTwhRAi/OIy4EsPXwghwi8uA7708IUQ8SRSq2RGW1wGfOnhCyHiUaKvmhmXAV8IIUT4ScAXQogg9aS0M3l4dvgb0kNxueOVzMMXQsSTUEo5K66eTVVdc/gaE4K47OFLDV8IEU+G5qQxeXg2t18ypds/m5VqZ/TAzAi0qvvisocvhBDxxJGcxAs3zIt1M0IWlz18IYQQ4ReXAV/m4QshRPjFZcCXGr4QQoRfXAZ8IYQQ4ScBXwgh+ggJ+EII0UfEZcCXQVshhAi/uAz4MmgrhBDhp3Qcr/uplKoCyjt5Sw4Q7G1AV+/t7HV/r/kes37f2Wt5wNEg2huMaFx/d6/d93u5doNce3jEy7X7Hou3ax+ltR7Y4ajWOmG/gAfC9d7OXvf3mu8x6/ddvLYpka6/u9fe2d+FXLtce2+59i6uN+bXHugrLks63bAyjO/t7HV/r/keWxnka+EUjevv7rX7fi/XHn5y7eF5byjX7nss3q7dr7gu6fRGSqlNWuuZsW5HLMi1y7X3NfF27Ynew09ED8S6ATEk1943ybXHCenhCyFEHyE9fCGE6CMk4AshRB8hAV8IIfoICfgxpJSaqJS6Xyn1tFLq27FuT7QppTKUUpuVUotj3ZZoU0otUEqVuH//C2LdnmhSSiUppX6jlLpHKfX1WLcnmpRS89y/84eUUuui/fkS8MNMKfWIUqpSKfWJz/FFSqkdSqkypdRPALTWpVrra4AvAnEzdaununPtbj8GnopuKyOnm9evgXogFaiIdlvDrZvXfjEwHGilj1271rrE/f/8C8Dfo97YcD0FJl+ep+HmA9OBTyzHbMCnwGjAAXwIFLtfuwhYByyLddujee3AucDlwBXA4li3PQbXn+R+fTDwRKzbHuVr/wnwLfd7no5126N57ZbXnwKyo91W6eGHmdZ6LXDM5/AsoExrvVtr3QI8idHLQWv9vNb6TODL0W1p+HXz2hcCs4FlwNVKqYT/t9id69dau9yvHwdSotjMiOjm774C47oBnNFrZWR09/95pVQ+UKO1ro1uSyE52h/YRw0H9lu+rwBOd9duL8H4H/6lGLQrGvxeu9b6egCl1BXAUUsA7G0C/e4vAc4H+gH3xqJhUeD32oG7gXuUUvOAtbFoWBQEunaAq4BHo94iJOBHi/JzTGut1wBrotuUqPN77Z4/aP1Y9JoSE4F+988Az0S7MVEW6NobMIJebxbw373W+uYot8Uj4W+jE0QFMNLy/QjgYIzaEm19+dqhb1+/XHu7uLh2CfjRsREYq5QqVEo5MAYrn49xm6KlL1879O3rl2uPs2uXgB9mSqkVwHpgvFKqQil1lda6DbgeeBUoBZ7SWm+NZTsjoS9fO/Tt65drT4xrl8XThBCij5AevhBC9BES8IUQoo+QgC+EEH2EBHwhhOgjJOALIUQfIQFfCCH6CAn4QgjRR0jAF0KIPkICvhBC9BH/DzVAZTA+zjuxAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 收敛阶\n", + "mc10_err = []\n", + "ns = []\n", + "\n", + "for n in np.linspace(2**8, 2**24, 2**8): #这里linspace括号里和上面N一样改一下\n", + " int_n = int(n)\n", + " ns.append(int_n)\n", + " rpoints = rng.uniform(0.0, 1.0, size=(int_n, 10)) #intn什么意思？？？？？？？？？？？？\n", + " weights = np.ones((int_n, 1)) / int_n\n", + " mc10 = integration(f, (rpoints, weights))\n", + " mc10_err.append(np.abs(mc10 - I10))\n", + " \n", + "plt.loglog(ns, mc10_err, ns, 1.0/np.sqrt(np.asarray(ns)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ex2\n", + "编写程序，求解对于 Runge 函数\n", + "$$\n", + "R(x) = \\frac{1}{1+x^2}, \\quad x\\in[-5,5]\n", + "$$\n", + "的回归问题：\n", + "$$\n", + "\\min_{\\theta} L(\\theta) = \\frac{1}{2N} \\sum_{i=0}^{N-1} (f_\\theta(x_i)- y_i)^2\n", + "$$\n", + "其中\n", + "$$\n", + "y_i = R(x_i)\n", + "$$\n", + "\n", + "1. 对于模型函数 $f_\\theta(x) = wx + b$,N=100，$\\{x_i\\}_{i=0}^{99}$为均匀分布随机取点，编写梯度下降法与牛顿法求解，画出拟合函数与原函数比较。\n", + "\n", + "2. 编写程序对于 n 阶多项式为模型函数，使用梯度下降法求解。对于n=3, 4, 5,N=100，$ \\{x_i\\}_{i=0}^{99}$为等距节点，画出拟合后的解与原函数比较。\n", + "\n", + "注：代码参考上课文件https://github.com/mazhengcn/scientific-computing-with-python/blob/main/others/fitting.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. 1阶" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "def linear(w: float, b: float, x: np.ndarray):\n", + " return w * x + b\n", + "\n", + "def grad_w(x):\n", + " return x\n", + "\n", + "def grad_b(x):\n", + " return 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "def gd_step(params, t, x, y):\n", + " wk, bk = params\n", + " w = wk - t * np.mean((linear(wk, bk, x) - y) * grad_w(x))\n", + " b = bk - t * np.mean((linear(wk, bk, x) - y) * grad_b(x))\n", + " return w, b" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "def newton_step(params, t, x, y):\n", + " wk, bk = params\n", + " gw = np.mean((linear(wk, bk, x) - y) * grad_w(x))\n", + " gb = np.mean((linear(wk, bk, x) - y) * grad_b(x))\n", + "\n", + " H = np.zeros((2, 2))\n", + " H[0, 0] = np.mean(grad_w(x) * grad_w(x))\n", + " H[0, 1] = np.mean(grad_w(x) * grad_b(x))\n", + " H[1, 0] = np.mean(grad_b(x) * grad_w(x))\n", + " H[1, 1] = np.mean(grad_b(x) * grad_b(x))\n", + " \n", + " dwb = np.stack([gw, gb]).dot(np.linalg.inv(H))\n", + " \n", + " return wk - t * dwb[0], bk - t * dwb[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-5, 5, 100)\n", + "\n", + "def R(x):\n", + " return 1.0 / (1.0 + x**2)\n", + "\n", + "plt.plot(x, R(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "def fit(x, y, init_params, init_t, eps=1e-4, max_steps=100000000): \n", + " params, t = init_params, init_t \n", + " for step in range(max_steps):\n", + " new_params = newton_step(params, t, x, y) #和上面一样的，就这里改一下\n", + " diff = np.asarray(new_params) - np.asarray(params) \n", + " if np.sqrt(np.sum(diff**2)) < eps:\n", + " return new_params, step\n", + " else:\n", + " params = new_params\n", + " \n", + " return new_params, step" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(w_m, b_m), num_steps = fit(x, y, [1.0, 1.0], 0.2)\n", + "\n", + "plt.plot(x, linear(w_m, b_m, x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. n阶" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "def linear(params, x): #params:np.ndarray x:np.ndarray\n", + " return params[0] * x + params[1]\n", + "\n", + "def quadratic(params, x):\n", + " return params[0] * x**2 + params[1] * x + params[2]\n", + "\n", + "def grad_linear(x): \n", + " grad = np.stack([x, np.ones_like(x)], axis=-1)\n", + " return grad\n", + "\n", + "def grad_quad(x): \n", + " grad = np.stack([x**2, x, np.ones_like(x)], axis=-1)\n", + " return grad\n", + "\n", + "def gd_step(params: np.ndarray, t: float, x: np.ndarray, y: np.ndarray):\n", + " params -= t * np.mean((quadratic(params, x) - y)[..., None] * grad_quad(x), axis=0)\n", + " return params" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a n-th order polynomial function\n", + "def create_poly_fn(n):\n", + " def poly_fn(params, x):\n", + " assert params.shape[0] == n + 1, f\"Number of params should equal {n + 1}\"\n", + " value, power = 0.0, 1.0\n", + " for i in range(n + 1):\n", + " value += params[n-i] * power\n", + " power *= x\n", + " return value\n", + "\n", + " return poly_fn\n", + "\n", + "def create_grad_poly_fn(n):\n", + " def grad_poly_fn(x):\n", + " stack_list = [x**(n-i) for i in range(n)] + [np.ones_like(x)]\n", + " grad = np.stack(stack_list, axis=-1)\n", + " return grad\n", + "\n", + " return grad_poly_fn" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "def fit(x, y, init_params, init_t, eps=1e-4, max_steps=100000):\n", + " params, t = init_params, init_t\n", + " \n", + " for step in range(max_steps):\n", + " old_params = params.copy() #先单独保存旧的，否则会覆盖掉!!!!!\n", + " params = gd_step(params, t, x, y)\n", + " if np.sqrt(np.sum((params - old_params)**2)) < eps: #for这里是数组\n", + " return params, step\n", + "\n", + " return params, step" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "n = 3\n", + "poly_fn = create_poly_fn(n)\n", + "grad_poly_fn = create_grad_poly_fn(n)\n", + "\n", + "def gd_step(params, t, x, y):\n", + " params -= t * np.mean((poly_fn(params, x) - y)[..., None] * grad_poly_fn(x), axis=0)\n", + " return params" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "def fit(x, y, init_params, init_t, eps=1e-4, max_steps=1000000):\n", + " params, t = init_params, init_t\n", + " \n", + " for step in range(max_steps):\n", + " old_params = params.copy()\n", + " params = gd_step(params, t, x, y)\n", + " if np.sqrt(np.sum((params - old_params)**2)) < eps:\n", + " return params, step\n", + "\n", + " return params, step" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-5, 5, 100)\n", + "y = R(x)\n", + "plt.plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "params_min, num_steps = fit(x, y, np.array([-0.1, 0.0, 1.0, 0.0]), 0.1)\n", + "\n", + "plt.plot(x, poly_fn(params_min, x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "n=3调整参数可画出图像。\n", + "n=4，n=5的情形只需更改上面n=3，并增加一个参数即可。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lab-iisec/Stochastic Process/T221.m b/lab-iisec/Stochastic Process/T221.m new file mode 100644 index 0000000000000000000000000000000000000000..4ea3b3e52fb1ac961dd53d9c9416676eb15d4695 --- /dev/null +++ b/lab-iisec/Stochastic Process/T221.m @@ -0,0 +1,25 @@ +x1 = zeros(1,100); +x2 = zeros(1,100); +x3 = zeros(1,100); +B = randn(1,100)*0.1; +T = (0:1:99)/100; + +%生成三条轨道 +for k=1:100-1 + x1(1,k+1) = x1(1,k)+0.3*(0.5-x1(1,k))*0.01+0.6*(B(1,k+1)-B(1,k)); +end +for k=1:100-1 + x2(1,k+1) = x2(1,k)+0.6*(0.5-x2(1,k))*0.01+0.8*(B(1,k+1)-B(1,k)); +end +for k=1:100-1 + x3(1,k+1) = x3(1,k)+0.9*(0.5-x3(1,k))*0.01+0.5*(B(1,k+1)-B(1,k)); +end +hold on; + +%画图 +pl1 = plot(T, x1, 'b'); +pl2 = plot(T, x2, 'r'); +pl3 = plot(T, x3, 'k'); +xlabel('t'); +ylabel('X(t)'); +title('X的多条轨道'); \ No newline at end of file diff --git a/lab-iisec/Stochastic Process/T222.m b/lab-iisec/Stochastic Process/T222.m new file mode 100644 index 0000000000000000000000000000000000000000..f70fa09584b72cacf29fc57d4cbc492bad15d55c --- /dev/null +++ b/lab-iisec/Stochastic Process/T222.m @@ -0,0 +1,27 @@ +%x0 +x1 = zeros(1,100); +x2 = zeros(1,100); x2(1,1) = 0.1; +x3 = zeros(1,100); x3(1,1) = 0.2; +B = randn(1,100)*0.1; +T = (0:1:99)/100; + +%生成三条轨道 +for k=1:100-1 + x1(1,k+1) = x1(1,k)+1*(0.1-x1(1,k))*0.01+0.1*(B(1,k+1)-B(1,k)); +end +for k=1:100-1 + x2(1,k+1) = x2(1,k)+1*(0.1-x2(1,k))*0.01+0.1*(B(1,k+1)-B(1,k)); +end +for k=1:100-1 + x3(1,k+1) = x3(1,k)+1*(0.1-x3(1,k))*0.01+0.1*(B(1,k+1)-B(1,k)); +end +hold on; + +%画图 +pl1 = plot(T, x1, 'b'); +pl2 = plot(T, x2, 'r'); +pl3 = plot(T, x3, 'k'); +xlabel('t'); +ylabel('X(t)'); +title('初始值x0对轨道的影响'); +legend('x0=0','x0=0.1','x0=0.2') \ No newline at end of file diff --git a/lab-iisec/Stochastic Process/T23.m b/lab-iisec/Stochastic Process/T23.m new file mode 100644 index 0000000000000000000000000000000000000000..c3d3e28a1958d503e294e823db55160db555d122 --- /dev/null +++ b/lab-iisec/Stochastic Process/T23.m @@ -0,0 +1,13 @@ +n = 1000000; +T = rand(1,n); +T = sort(T); +x1 = zeros(1,n); +B = randn(1,n); +for k = 1:n-1 + x1(1,k+1) = x1(1,k)+0.2*(0.5-x1(1,k))*sqrt(T(1,k+1)-T(1,k))+0.6*(B(1,k+1)-B(1,k))*sqrt(T(1,k+1)-T(1,k)); +end +%EX1 +a = sum(x1)/n +%DX1 +b = sum(x1.^2)/n; +c = b-a^2 \ No newline at end of file diff --git a/lab-iisec/Stochastic Process/T31.m b/lab-iisec/Stochastic Process/T31.m new file mode 100644 index 0000000000000000000000000000000000000000..5fb325eaa3264ce3d95e67d121cdb14ac62180e4 --- /dev/null +++ b/lab-iisec/Stochastic Process/T31.m @@ -0,0 +1,17 @@ +x = zeros(1,100); +B = randn(1,100)*0.1; +T = (0:1:99)/100; +s = zeros(1,100); +W = rand(1,100)*0.1; +for k = 1:100-1 + x(1,k+1) = x(1,k)+0.2*(0.5-x(1,k))*0.01+0.1*(B(1,k+1)-B(1,k)); +end +for j = 1:100-1 + s(1,j+1)=s(1,j)+0.1*(x(1,j)-s(1,j))*0.01+0.1*(B(1,j+1)-B(1,j))+0.5*(W(1,j+1)-W(1,j)); +end +hold on; +plot(T,x,'b'); +plot(T,s,'r'); +xlabel('T'); +ylabel('T/S'); +title('(X,S)的多条轨道'); \ No newline at end of file diff --git a/lab-iisec/Stochastic Process/T321.m b/lab-iisec/Stochastic Process/T321.m new file mode 100644 index 0000000000000000000000000000000000000000..c755882c9d3adb8fd997a639360ef9980a1cd66f --- /dev/null +++ b/lab-iisec/Stochastic Process/T321.m @@ -0,0 +1,33 @@ +%theta +x = zeros(1,100); +B = randn(1,100)*0.1; +T = (0:1:99)/100; +s = zeros(1,100); +s1 = zeros(1,100);s1(1,1)=0.1; +s2 = zeros(1,100);s2(1,1)=0.2; +W = rand(1,100)*0.1; +%X +for k = 1:100-1 + x(1,k+1) = x(1,k)+0.2*(0.5-x(1,k))*0.01+0.1*(B(1,k+1)-B(1,k)); +end +%S +for j = 1:100-1 + s(1,j+1)=s(1,j)+0.1*(x(1,j)-s(1,j))*0.01+0.1*(B(1,j+1)-B(1,j))+0.1*(W(1,j+1)-W(1,j)); +end +%S1 +for j = 1:100-1 + s1(1,j+1)=s1(1,j)+1*(x(1,j)-s1(1,j))*0.01+0.1*(B(1,j+1)-B(1,j))+0.1*(W(1,j+1)-W(1,j)); +end +%S2 +for j = 1:100-1 + s2(1,j+1)=s2(1,j)+10*(x(1,j)-s2(1,j))*0.01+0.1*(B(1,j+1)-B(1,j))+0.1*(W(1,j+1)-W(1,j)); +end +hold on; +plot(T,x,'--'); +plot(T,s,'b'); +plot(T,s1,'r'); +plot(T,s2,'k'); +xlabel('T'); +ylabel('T/S'); +title('s0对(S)轨道的影响'); +legend('X','S:s0=0','S:s0=0.1','S:s0=0.2'); \ No newline at end of file diff --git a/lab-iisec/Stochastic Process/T33.m b/lab-iisec/Stochastic Process/T33.m new file mode 100644 index 0000000000000000000000000000000000000000..5dccea832ae1e08c01698bf2f75c6fe963640112 --- /dev/null +++ b/lab-iisec/Stochastic Process/T33.m @@ -0,0 +1,18 @@ +n = 1000000; +T = rand(1,n); +T = sort(T); +x = zeros(1,n); +B = randn(1,n); +for k = 1:n-1 + x(1,k+1) = x(1,k)+0.2*(0.5-x(1,k))*sqrt(T(1,k+1)-T(1,k))+0.6*(B(1,k+1)-B(1,k))*sqrt(T(1,k+1)-T(1,k)); +end +s1 = zeros(1,n); +W = rand(1,n); +for l = 1:n-1 + s1(1,l+1)=s1(1,l)+0.1*(x(1,l)-s1(1,l))*sqrt(T(1,k+1)-T(1,k))+0.1*(B(1,k+1)-B(1,k))*sqrt(T(1,k+1)-T(1,k)); +end +%ES1 +a = sum(s1/n) +%DS1 +b = sum(s1.^2)/n; +c = b-a^2 \ No newline at end of file diff --git a/lab-iisec/python-jpeg/decoder.py b/lab-iisec/python-jpeg/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..db2026f70ce0b2f1db668e45c6325f83c7bf2c08 --- /dev/null +++ b/lab-iisec/python-jpeg/decoder.py @@ -0,0 +1,187 @@ +import argparse +import math +import numpy as np +from utils import * +from scipy import fftpack +from PIL import Image + + +class JPEGFileReader: + TABLE_SIZE_BITS = 16 + BLOCKS_COUNT_BITS = 32 + + DC_CODE_LENGTH_BITS = 4 + CATEGORY_BITS = 4 + + AC_CODE_LENGTH_BITS = 8 + RUN_LENGTH_BITS = 4 + SIZE_BITS = 4 + + def __init__(self, filepath): + self.__file = open(filepath, 'r') + + def read_int(self, size): + if size == 0: + return 0 + + # the most significant bit indicates the sign of the number + bin_num = self.__read_str(size) + if bin_num[0] == '1': + return self.__int2(bin_num) + else: + return self.__int2(binstr_flip(bin_num)) * -1 + + def read_dc_table(self): + table = dict() + + table_size = self.__read_uint(self.TABLE_SIZE_BITS) + for _ in range(table_size): + category = self.__read_uint(self.CATEGORY_BITS) + code_length = self.__read_uint(self.DC_CODE_LENGTH_BITS) + code = self.__read_str(code_length) + table[code] = category + return table + + def read_ac_table(self): + table = dict() + + table_size = self.__read_uint(self.TABLE_SIZE_BITS) + for _ in range(table_size): + run_length = self.__read_uint(self.RUN_LENGTH_BITS) + size = self.__read_uint(self.SIZE_BITS) + code_length = self.__read_uint(self.AC_CODE_LENGTH_BITS) + code = self.__read_str(code_length) + table[code] = (run_length, size) + return table + + def read_blocks_count(self): + return self.__read_uint(self.BLOCKS_COUNT_BITS) + + def read_huffman_code(self, table): + prefix = '' + # TODO: break the loop if __read_char is not returing new char + while prefix not in table: + prefix += self.__read_char() + return table[prefix] + + def __read_uint(self, size): + if size <= 0: + raise ValueError("size of unsigned int should be greater than 0") + return self.__int2(self.__read_str(size)) + + def __read_str(self, length): + return self.__file.read(length) + + def __read_char(self): + return self.__read_str(1) + + def __int2(self, bin_num): + return int(bin_num, 2) + + +def read_image_file(filepath): + reader = JPEGFileReader(filepath) + + tables = dict() + for table_name in ['dc_y', 'ac_y', 'dc_c', 'ac_c']: + if 'dc' in table_name: + tables[table_name] = reader.read_dc_table() + else: + tables[table_name] = reader.read_ac_table() + + blocks_count = reader.read_blocks_count() + + dc = np.empty((blocks_count, 3), dtype=np.int32) + ac = np.empty((blocks_count, 63, 3), dtype=np.int32) + + for block_index in range(blocks_count): + for component in range(3): + dc_table = tables['dc_y'] if component == 0 else tables['dc_c'] + ac_table = tables['ac_y'] if component == 0 else tables['ac_c'] + + category = reader.read_huffman_code(dc_table) + dc[block_index, component] = reader.read_int(category) + + cells_count = 0 + + # TODO: try to make reading AC coefficients better + while cells_count < 63: + run_length, size = reader.read_huffman_code(ac_table) + + if (run_length, size) == (0, 0): + while cells_count < 63: + ac[block_index, cells_count, component] = 0 + cells_count += 1 + else: + for i in range(run_length): + ac[block_index, cells_count, component] = 0 + cells_count += 1 + if size == 0: + ac[block_index, cells_count, component] = 0 + else: + value = reader.read_int(size) + ac[block_index, cells_count, component] = value + cells_count += 1 + + return dc, ac, tables, blocks_count + + +def zigzag_to_block(zigzag): + # assuming that the width and the height of the block are equal + rows = cols = int(math.sqrt(len(zigzag))) + + if rows * cols != len(zigzag): + raise ValueError("length of zigzag should be a perfect square") + + block = np.empty((rows, cols), np.int32) + + for i, point in enumerate(zigzag_points(rows, cols)): + block[point] = zigzag[i] + + return block + + +def dequantize(block, component): + q = load_quantization_table(component) + return block * q + + +def idct_2d(image): + return fftpack.idct(fftpack.idct(image.T, norm='ortho').T, norm='ortho') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("input", help="path to the input image") + args = parser.parse_args() + + dc, ac, tables, blocks_count = read_image_file(args.input) + + # assuming that the block is a 8x8 square + block_side = 8 + + # assuming that the image height and width are equal + image_side = int(math.sqrt(blocks_count)) * block_side + + blocks_per_line = image_side // block_side + + npmat = np.empty((image_side, image_side, 3), dtype=np.uint8) + + for block_index in range(blocks_count): + i = block_index // blocks_per_line * block_side + j = block_index % blocks_per_line * block_side + + for c in range(3): + zigzag = [dc[block_index, c]] + list(ac[block_index, :, c]) + quant_matrix = zigzag_to_block(zigzag) + dct_matrix = dequantize(quant_matrix, 'lum' if c == 0 else 'chrom') + block = idct_2d(dct_matrix) + npmat[i:i+8, j:j+8, c] = block + 128 + + image = Image.fromarray(npmat, 'YCbCr') + image = image.convert('RGB') + image.show() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/lab-iisec/python-jpeg/encoder.py b/lab-iisec/python-jpeg/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..89b6b9fabf9e5b6ee9e1ec24c85292dfe2bf51d7 --- /dev/null +++ b/lab-iisec/python-jpeg/encoder.py @@ -0,0 +1,170 @@ +import argparse +import os +import math +import numpy as np +from utils import * +from scipy import fftpack +from PIL import Image +from huffman import HuffmanTree + + +def quantize(block, component): + q = load_quantization_table(component) + return (block / q).round().astype(np.int32) + + +def block_to_zigzag(block): + return np.array([block[point] for point in zigzag_points(*block.shape)]) + + +def dct_2d(image): + return fftpack.dct(fftpack.dct(image.T, norm='ortho').T, norm='ortho') + + +def run_length_encode(arr): + # determine where the sequence is ending prematurely + last_nonzero = -1 + for i, elem in enumerate(arr): + if elem != 0: + last_nonzero = i + + # each symbol is a (RUNLENGTH, SIZE) tuple + symbols = [] + + # values are binary representations of array elements using SIZE bits + values = [] + + run_length = 0 + + for i, elem in enumerate(arr): + if i > last_nonzero: + symbols.append((0, 0)) + values.append(int_to_binstr(0)) + break + elif elem == 0 and run_length < 15: + run_length += 1 + else: + size = bits_required(elem) + symbols.append((run_length, size)) + values.append(int_to_binstr(elem)) + run_length = 0 + return symbols, values + + +def write_to_file(filepath, dc, ac, blocks_count, tables): + try: + f = open(filepath, 'w') + except FileNotFoundError as e: + raise FileNotFoundError( + "No such directory: {}".format( + os.path.dirname(filepath))) from e + + for table_name in ['dc_y', 'ac_y', 'dc_c', 'ac_c']: + + # 16 bits for 'table_size' + f.write(uint_to_binstr(len(tables[table_name]), 16)) + + for key, value in tables[table_name].items(): + if table_name in {'dc_y', 'dc_c'}: + # 4 bits for the 'category' + # 4 bits for 'code_length' + # 'code_length' bits for 'huffman_code' + f.write(uint_to_binstr(key, 4)) + f.write(uint_to_binstr(len(value), 4)) + f.write(value) + else: + # 4 bits for 'run_length' + # 4 bits for 'size' + # 8 bits for 'code_length' + # 'code_length' bits for 'huffman_code' + f.write(uint_to_binstr(key[0], 4)) + f.write(uint_to_binstr(key[1], 4)) + f.write(uint_to_binstr(len(value), 8)) + f.write(value) + + # 32 bits for 'blocks_count' + f.write(uint_to_binstr(blocks_count, 32)) + + for b in range(blocks_count): + for c in range(3): + category = bits_required(dc[b, c]) + symbols, values = run_length_encode(ac[b, :, c]) + + dc_table = tables['dc_y'] if c == 0 else tables['dc_c'] + ac_table = tables['ac_y'] if c == 0 else tables['ac_c'] + + f.write(dc_table[category]) + f.write(int_to_binstr(dc[b, c])) + + for i in range(len(symbols)): + f.write(ac_table[tuple(symbols[i])]) + f.write(values[i]) + f.close() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("input", help="path to the input image") + parser.add_argument("output", help="path to the output image") + args = parser.parse_args() + + input_file = args.input + output_file = args.output + + image = Image.open(input_file) + ycbcr = image.convert('YCbCr') + + npmat = np.array(ycbcr, dtype=np.uint8) + + rows, cols = npmat.shape[0], npmat.shape[1] + + # block size: 8x8 + if rows % 8 == cols % 8 == 0: + blocks_count = rows // 8 * cols // 8 + else: + raise ValueError(("the width and height of the image " + "should both be mutiples of 8")) + + # dc is the top-left cell of the block, ac are all the other cells + dc = np.empty((blocks_count, 3), dtype=np.int32) + ac = np.empty((blocks_count, 63, 3), dtype=np.int32) + + for i in range(0, rows, 8): + for j in range(0, cols, 8): + try: + block_index += 1 + except NameError: + block_index = 0 + + for k in range(3): + # split 8x8 block and center the data range on zero + # [0, 255] --> [-128, 127] + block = npmat[i:i+8, j:j+8, k] - 128 + + dct_matrix = dct_2d(block) + quant_matrix = quantize(dct_matrix, + 'lum' if k == 0 else 'chrom') + zz = block_to_zigzag(quant_matrix) + + dc[block_index, k] = zz[0] + ac[block_index, :, k] = zz[1:] + + H_DC_Y = HuffmanTree(np.vectorize(bits_required)(dc[:, 0])) + H_DC_C = HuffmanTree(np.vectorize(bits_required)(dc[:, 1:].flat)) + H_AC_Y = HuffmanTree( + flatten(run_length_encode(ac[i, :, 0])[0] + for i in range(blocks_count))) + H_AC_C = HuffmanTree( + flatten(run_length_encode(ac[i, :, j])[0] + for i in range(blocks_count) for j in [1, 2])) + + tables = {'dc_y': H_DC_Y.value_to_bitstring_table(), + 'ac_y': H_AC_Y.value_to_bitstring_table(), + 'dc_c': H_DC_C.value_to_bitstring_table(), + 'ac_c': H_AC_C.value_to_bitstring_table()} + + write_to_file(output_file, dc, ac, blocks_count, tables) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/lab-iisec/python-jpeg/huffman.py b/lab-iisec/python-jpeg/huffman.py new file mode 100644 index 0000000000000000000000000000000000000000..6e67179fb6e5a585360fc277a93cfba71947bd67 --- /dev/null +++ b/lab-iisec/python-jpeg/huffman.py @@ -0,0 +1,87 @@ +from queue import PriorityQueue + + +class HuffmanTree: + + class __Node: + def __init__(self, value, freq, left_child, right_child): + self.value = value + self.freq = freq + self.left_child = left_child + self.right_child = right_child + + @classmethod + def init_leaf(self, value, freq): + return self(value, freq, None, None) + + @classmethod + def init_node(self, left_child, right_child): + freq = left_child.freq + right_child.freq + return self(None, freq, left_child, right_child) + + def is_leaf(self): + return self.value is not None + + def __eq__(self, other): + stup = self.value, self.freq, self.left_child, self.right_child + otup = other.value, other.freq, other.left_child, other.right_child + return stup == otup + + def __nq__(self, other): + return not (self == other) + + def __lt__(self, other): + return self.freq < other.freq + + def __le__(self, other): + return self.freq < other.freq or self.freq == other.freq + + def __gt__(self, other): + return not (self <= other) + + def __ge__(self, other): + return not (self < other) + + def __init__(self, arr): + q = PriorityQueue() + + # calculate frequencies and insert them into a priority queue + for val, freq in self.__calc_freq(arr).items(): + q.put(self.__Node.init_leaf(val, freq)) + + while q.qsize() >= 2: + u = q.get() + v = q.get() + + q.put(self.__Node.init_node(u, v)) + + self.__root = q.get() + + # dictionaries to store huffman table + self.__value_to_bitstring = dict() + + def value_to_bitstring_table(self): + if len(self.__value_to_bitstring.keys()) == 0: + self.__create_huffman_table() + return self.__value_to_bitstring + + def __create_huffman_table(self): + def tree_traverse(current_node, bitstring=''): + if current_node is None: + return + if current_node.is_leaf(): + self.__value_to_bitstring[current_node.value] = bitstring + return + tree_traverse(current_node.left_child, bitstring + '0') + tree_traverse(current_node.right_child, bitstring + '1') + + tree_traverse(self.__root) + + def __calc_freq(self, arr): + freq_dict = dict() + for elem in arr: + if elem in freq_dict: + freq_dict[elem] += 1 + else: + freq_dict[elem] = 1 + return freq_dict \ No newline at end of file diff --git a/lab-iisec/python-jpeg/utils.py b/lab-iisec/python-jpeg/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5eb054ce93c810498720378ffac5c405f4175250 --- /dev/null +++ b/lab-iisec/python-jpeg/utils.py @@ -0,0 +1,109 @@ +import numpy as np + + +def load_quantization_table(component): + if component == 'lum': + q = np.array([[2, 2, 2, 2, 3, 4, 5, 6], + [2, 2, 2, 2, 3, 4, 5, 6], + [2, 2, 2, 2, 4, 5, 7, 9], + [2, 2, 2, 4, 5, 7, 9, 12], + [3, 3, 4, 5, 8, 10, 12, 12], + [4, 4, 5, 7, 10, 12, 12, 12], + [5, 5, 7, 9, 12, 12, 12, 12], + [6, 6, 9, 12, 12, 12, 12, 12]]) + elif component == 'chrom': + q = np.array([[3, 3, 5, 9, 13, 15, 15, 15], + [3, 4, 6, 11, 14, 12, 12, 12], + [5, 6, 9, 14, 12, 12, 12, 12], + [9, 11, 14, 12, 12, 12, 12, 12], + [13, 14, 12, 12, 12, 12, 12, 12], + [15, 12, 12, 12, 12, 12, 12, 12], + [15, 12, 12, 12, 12, 12, 12, 12], + [15, 12, 12, 12, 12, 12, 12, 12]]) + else: + raise ValueError(( + "component should be either 'lum' or 'chrom', " + "but '{comp}' was found").format(comp=component)) + + return q + + +def zigzag_points(rows, cols): + # constants for directions + UP, DOWN, RIGHT, LEFT, UP_RIGHT, DOWN_LEFT = range(6) + + # move the point in different directions + def move(direction, point): + return { + UP: lambda point: (point[0] - 1, point[1]), + DOWN: lambda point: (point[0] + 1, point[1]), + LEFT: lambda point: (point[0], point[1] - 1), + RIGHT: lambda point: (point[0], point[1] + 1), + UP_RIGHT: lambda point: move(UP, move(RIGHT, point)), + DOWN_LEFT: lambda point: move(DOWN, move(LEFT, point)) + }[direction](point) + + # return true if point is inside the block bounds + def inbounds(point): + return 0 <= point[0] < rows and 0 <= point[1] < cols + + # start in the top-left cell + point = (0, 0) + + # True when moving up-right, False when moving down-left + move_up = True + + for i in range(rows * cols): + yield point + if move_up: + if inbounds(move(UP_RIGHT, point)): + point = move(UP_RIGHT, point) + else: + move_up = False + if inbounds(move(RIGHT, point)): + point = move(RIGHT, point) + else: + point = move(DOWN, point) + else: + if inbounds(move(DOWN_LEFT, point)): + point = move(DOWN_LEFT, point) + else: + move_up = True + if inbounds(move(DOWN, point)): + point = move(DOWN, point) + else: + point = move(RIGHT, point) + + +def bits_required(n): + n = abs(n) + result = 0 + while n > 0: + n >>= 1 + result += 1 + return result + + +def binstr_flip(binstr): + # check if binstr is a binary string + if not set(binstr).issubset('01'): + raise ValueError("binstr should have only '0's and '1's") + return ''.join(map(lambda c: '0' if c == '1' else '1', binstr)) + + +def uint_to_binstr(number, size): + return bin(number)[2:][-size:].zfill(size) + + +def int_to_binstr(n): + if n == 0: + return '' + + binstr = bin(abs(n))[2:] + + # change every 0 to 1 and vice verse when n is negative + return binstr if n > 0 else binstr_flip(binstr) + + +def flatten(lst): + return [item for sublist in lst for item in sublist] \ No newline at end of file diff --git a/lab-isic/Student.zip b/lab-isic/Student.zip new file mode 100644 index 0000000000000000000000000000000000000000..744420663f66deebf3ffb9cd8164cde19d611090 Binary files /dev/null and b/lab-isic/Student.zip differ diff --git a/lab-miots/Brain_like/.keep b/lab-miots/Brain_like/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/Brain_like/eeg_name_lstm_36-72 b/lab-miots/Brain_like/eeg_name_lstm_36-72 new file mode 100644 index 0000000000000000000000000000000000000000..b7f133e4c668e7566be98ca59e42da11b4d541d3 Binary files /dev/null and b/lab-miots/Brain_like/eeg_name_lstm_36-72 differ diff --git a/lab-miots/Brain_like/egg_features_lstm_36-72 b/lab-miots/Brain_like/egg_features_lstm_36-72 new file mode 100644 index 0000000000000000000000000000000000000000..59081b38e5c3f54741196f7b3e4c874936352287 Binary files /dev/null and b/lab-miots/Brain_like/egg_features_lstm_36-72 differ diff --git a/lab-miots/Brain_like/fc_layer.ipynb b/lab-miots/Brain_like/fc_layer.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..26d8d5a1cd063783a0150061a47a32b36999f826 --- /dev/null +++ b/lab-miots/Brain_like/fc_layer.ipynb @@ -0,0 +1,52113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pickle\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[[0.8566824197769165, 0.9512288570404053, 0.9180577993392944, 1.0063488483428955, 0.9180569648742676, 0.8452045917510986, 0.9085195660591125, 1.1550922393798828, 0.9036865830421448, 0.9490708708763123, 0.8708645105361938, 0.8004925847053528, 0.9457182884216309, 0.8714073896408081, 0.8780463933944702, 0.9441290497779846, 0.90299391746521, 1.3808794021606445, 0.8918903470039368, 0.7936988472938538, 0.8401678800582886, 0.9847278594970703, 0.8737267851829529, 0.8871181607246399, 0.9096394181251526, 0.9206547737121582, 0.9063596129417419, 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0.9267653226852417]]\n" + ] + } + ], + "source": [ + "# 查看图片特征\n", + "img_f = open('../resnet_classify/img_features_resnet18','rb') # 以二进制读模式（rb）打开pkl文件\n", + "img_feature = pickle.load(img_f) # 读取存储的pickle文件\n", + "print(type(img_feature)) # 查看数据类型\n", + "# print(np.array(img_feature).shape)\n", + "# 读取图片特征 字典中前十个键值对\n", + "# for i, k in enumerate(img_feature): \n", + "# if i in range(0, 10):\n", + "# print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# 查看图片名称标签\n", + "img_n = open('../resnet_classify/img_names_resnet18','rb') # 以二进制读模式（rb）打开pkl文件\n", + "img_name = pickle.load(img_n) # 读取存储的pickle文件\n", + "print(type(img_name)) # 查看数据类型\n", + "# print(np.array(img_name_1).shape)\n", + "# for i, k in enumerate(img_name): # 读取字典中前十个键值对\n", + "# if i in range(0, 10):\n", + "# print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(8640, 1, 124)\n" + ] + } + ], + "source": [ + "# 查看脑电特征\n", + "eeg_f = open('./egg_features_lstm_36-72','rb') # 以二进制读模式（rb）打开pkl文件\n", + "eeg_feature = pickle.load(eeg_f) # 读取存储的pickle文件\n", + "print(type(eeg_feature)) # 查看数据类型\n", + "print(np.array(eeg_feature,dtype=object).shape)\n", + "# for i, k in enumerate(eeg_feature): # 读取字典中前十个键值对\n", + "# if i in range(0, 10):\n", + "# print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# 查看脑电对应的图片名称\n", + "eeg_n = open('./eeg_name_lstm_36-72','rb') # 以二进制读模式（rb）打开pkl文件\n", + "eeg_name = pickle.load(eeg_n) # 读取存储的pickle文件\n", + "print(type(eeg_name)) # 查看数据类型\n", + "# print(np.array(eeg_name,dtype=object).shape)\n", + "# for i, k in enumerate(eeg_name): # 读取字典中前十个键值对\n", + "# if i in range(0, 10):\n", + "# print(k[0], len(k[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name_1: dfi-1-005-70.png\n", + "name_2: dfi-1-005-70.png\n", + "name_1: dfi-1-010-100.png\n", + "name_2: dfi-1-010-100.png\n", + "name_1: dfi-1-014-50.png\n", + "name_2: dfi-1-014-50.png\n", + "name_1: dfi-1-022-70.png\n", + "name_2: dfi-1-022-70.png\n", + "name_1: dfi-1-002-0.png\n", + "name_2: dfi-1-002-0.png\n", + "name_1: dfi-1-012-60.png\n", + "name_2: dfi-1-012-60.png\n", + "name_1: dfi-1-014-60.png\n", + "name_2: dfi-1-014-60.png\n", + "name_1: dfi-1-023-60.png\n", + "name_2: dfi-1-023-60.png\n", + "name_1: dfi-1-021-70.png\n", + "name_2: dfi-1-021-70.png\n", + "name_1: dfi-1-007-80.png\n", + "name_2: dfi-1-007-80.png\n", + "name_1: dfi-1-005-65.png\n", + "name_2: dfi-1-005-65.png\n", + "name_1: dfi-1-022-50.png\n", + "name_2: dfi-1-022-50.png\n", 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sim-2-086-neg.png\n", + "name_1: sim-2-024-neg.png\n", + "name_2: sim-2-024-neg.png\n", + "name_1: sim-2-082-neg.png\n", + "name_2: sim-2-082-neg.png\n", + "name_1: sim-2-043-pos.png\n", + "name_2: sim-2-043-pos.png\n", + "name_1: sim-2-031-pos.png\n", + "name_2: sim-2-031-pos.png\n", + "name_1: sim-2-080-neg.png\n", + "name_2: sim-2-080-neg.png\n", + "name_1: sim-2-082-pos.png\n", + "name_2: sim-2-082-pos.png\n", + "name_1: sim-2-003-pos.png\n", + "name_2: sim-2-003-pos.png\n", + "name_1: sim-2-067-neg.png\n", + "name_2: sim-2-067-neg.png\n", + "name_1: sim-2-023-pos.png\n", + "name_2: sim-2-023-pos.png\n", + "name_1: sim-2-093-neg.png\n", + "name_2: sim-2-093-neg.png\n", + "name_1: sim-2-076-pos.png\n", + "name_2: sim-2-076-pos.png\n", + "name_1: sim-2-014-neg.png\n", + "name_2: sim-2-014-neg.png\n", + "name_1: sim-2-029-pos.png\n", + "name_2: sim-2-029-pos.png\n", + "name_1: sim-2-060-neg.png\n", + "name_2: sim-2-060-neg.png\n", + "name_1: sim-2-071-pos.png\n", + "name_2: 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sim-1-048-pos.png\n", + "name_1: sim-1-096-neg.png\n", + "name_2: sim-1-096-neg.png\n", + "name_1: sim-1-027-neg.png\n", + "name_2: sim-1-027-neg.png\n", + "name_1: sim-1-074-pos.png\n", + "name_2: sim-1-074-pos.png\n", + "name_1: sim-1-011-neg.png\n", + "name_2: sim-1-011-neg.png\n", + "name_1: sim-1-083-pos.png\n", + "name_2: sim-1-083-pos.png\n", + "name_1: sim-1-040-pos.png\n", + "name_2: sim-1-040-pos.png\n", + "name_1: sim-1-002-neg.png\n", + "name_2: sim-1-002-neg.png\n", + "name_1: sim-1-082-neg.png\n", + "name_2: sim-1-082-neg.png\n", + "name_1: sim-1-063-pos.png\n", + "name_2: sim-1-063-pos.png\n", + "name_1: sim-1-044-neg.png\n", + "name_2: sim-1-044-neg.png\n", + "name_1: sim-1-061-neg.png\n", + "name_2: sim-1-061-neg.png\n", + "name_1: sim-1-029-neg.png\n", + "name_2: sim-1-029-neg.png\n", + "name_1: sim-1-018-pos.png\n", + "name_2: sim-1-018-pos.png\n", + "name_1: sim-1-052-pos.png\n", + "name_2: sim-1-052-pos.png\n", + "name_1: sim-1-048-neg.png\n", + "name_2: sim-1-048-neg.png\n", + "name_1: sim-1-065-pos.png\n", + "name_2: sim-1-065-pos.png\n", + "name_1: sim-1-051-pos.png\n", + "name_2: sim-1-051-pos.png\n", + "name_1: sim-1-077-neg.png\n", + "name_2: sim-1-077-neg.png\n", + "name_1: sim-1-005-neg.png\n", + "name_2: sim-1-005-neg.png\n", + "name_1: sim-1-022-neg.png\n", + "name_2: sim-1-022-neg.png\n", + "name_1: sim-1-085-pos.png\n", + "name_2: sim-1-085-pos.png\n", + "name_1: sim-1-096-pos.png\n", + "name_2: sim-1-096-pos.png\n", + "name_1: con-1-031-neg.png\n", + "name_2: con-1-031-neg.png\n", + "name_1: con-1-077-pos.png\n", + "name_2: con-1-077-pos.png\n", + "name_1: con-1-048-neg.png\n", + "name_2: con-1-048-neg.png\n", + "name_1: con-1-092-pos.png\n", + "name_2: con-1-092-pos.png\n", + "name_1: con-1-032-neg.png\n", + "name_2: con-1-032-neg.png\n", + "name_1: con-1-032-pos.png\n", + "name_2: con-1-032-pos.png\n", + "name_1: con-1-098-neg.png\n", + "name_2: con-1-098-neg.png\n", + "name_1: con-1-078-pos.png\n", + "name_2: con-1-078-pos.png\n", + "name_1: con-1-086-neg.png\n", + "name_2: con-1-086-neg.png\n", + "name_1: con-1-014-neg.png\n", + "name_2: con-1-014-neg.png\n", + "name_1: con-1-096-neg.png\n", + "name_2: con-1-096-neg.png\n", + "name_1: con-1-036-neg.png\n", + "name_2: con-1-036-neg.png\n", + "name_1: con-1-012-neg.png\n", + "name_2: con-1-012-neg.png\n", + "name_1: con-1-071-neg.png\n", + "name_2: con-1-071-neg.png\n", + "name_1: con-1-081-pos.png\n", + "name_2: con-1-081-pos.png\n", + "name_1: con-1-078-neg.png\n", + "name_2: con-1-078-neg.png\n", + "name_1: con-1-001-pos.png\n", + "name_2: con-1-001-pos.png\n", + "name_1: con-1-005-neg.png\n", + "name_2: con-1-005-neg.png\n", + "name_1: con-1-044-neg.png\n", + "name_2: con-1-044-neg.png\n", + "name_1: con-1-070-neg.png\n", + "name_2: con-1-070-neg.png\n", + "name_1: con-1-069-neg.png\n", + "name_2: con-1-069-neg.png\n", + "name_1: con-1-001-neg.png\n", + "name_2: con-1-001-neg.png\n", + "name_1: con-1-015-neg.png\n", + "name_2: con-1-015-neg.png\n", + "name_1: con-1-073-pos.png\n", + "name_2: con-1-073-pos.png\n", + "name_1: con-1-016-neg.png\n", + "name_2: con-1-016-neg.png\n", + "name_1: con-1-097-neg.png\n", + "name_2: con-1-097-neg.png\n", + "name_1: con-1-006-neg.png\n", + "name_2: con-1-006-neg.png\n", + "name_1: con-1-057-neg.png\n", + "name_2: con-1-057-neg.png\n", + "name_1: con-1-041-neg.png\n", + "name_2: con-1-041-neg.png\n", + "name_1: con-1-024-neg.png\n", + "name_2: con-1-024-neg.png\n", + "name_1: con-1-025-neg.png\n", + "name_2: con-1-025-neg.png\n", + "name_1: con-1-067-neg.png\n", + "name_2: con-1-067-neg.png\n", + "name_1: con-1-037-neg.png\n", + "name_2: con-1-037-neg.png\n", + "name_1: con-1-059-neg.png\n", + "name_2: con-1-059-neg.png\n", + "name_1: con-1-033-neg.png\n", + "name_2: con-1-033-neg.png\n", + "name_1: con-1-085-pos.png\n", + "name_2: con-1-085-pos.png\n", + "name_1: con-1-024-pos.png\n", + "name_2: con-1-024-pos.png\n", + "name_1: con-1-045-neg.png\n", + "name_2: con-1-045-neg.png\n", + "name_1: con-1-020-pos.png\n", + "name_2: con-1-020-pos.png\n", + "name_1: con-1-060-neg.png\n", + "name_2: con-1-060-neg.png\n", + "name_1: con-1-066-neg.png\n", + "name_2: con-1-066-neg.png\n", + "name_1: con-1-080-pos.png\n", + "name_2: con-1-080-pos.png\n", + "name_1: con-1-068-neg.png\n", + "name_2: con-1-068-neg.png\n", + "name_1: con-1-083-neg.png\n", + "name_2: con-1-083-neg.png\n", + "name_1: con-1-088-neg.png\n", + "name_2: con-1-088-neg.png\n", + "name_1: con-1-085-neg.png\n", + "name_2: con-1-085-neg.png\n", + "name_1: con-1-058-neg.png\n", + "name_2: con-1-058-neg.png\n", + "name_1: con-1-054-neg.png\n", + "name_2: con-1-054-neg.png\n", + "name_1: con-1-094-pos.png\n", + "name_2: con-1-094-pos.png\n", + "name_1: con-1-002-neg.png\n", + "name_2: con-1-002-neg.png\n", + "name_1: con-1-025-pos.png\n", + "name_2: con-1-025-pos.png\n", + "name_1: con-1-023-neg.png\n", + "name_2: con-1-023-neg.png\n", + "name_1: con-1-018-neg.png\n", + "name_2: con-1-018-neg.png\n", + "name_1: con-1-089-neg.png\n", + "name_2: con-1-089-neg.png\n", + "name_1: con-1-077-neg.png\n", + "name_2: con-1-077-neg.png\n", + "name_1: con-1-035-pos.png\n", + "name_2: con-1-035-pos.png\n", + "name_1: con-1-093-neg.png\n", + "name_2: con-1-093-neg.png\n", + "name_1: con-1-095-pos.png\n", + "name_2: con-1-095-pos.png\n", + "name_1: con-1-063-pos.png\n", + "name_2: con-1-063-pos.png\n", + "name_1: con-1-080-neg.png\n", + "name_2: con-1-080-neg.png\n" + ] + } + ], + "source": [ + "# 获取图片名称相对应的脑电特征\n", + "# img_name_1与img_feature一一对应\n", + "# eeg_name与eeg_feature一一对应\n", + "eeg_features = []\n", + "for i, k in enumerate(img_name):\n", + " name_1 = str(k)\n", + " print(\"name_1: \", name_1)\n", + " for j, v in enumerate(eeg_name):\n", + " name_2 = str(v[0]).strip()\n", + " if name_1 == name_2:\n", + " print(\"name_2: \", name_2)\n", + " eeg_features.append(eeg_feature[j])\n", + " break\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8640, 1, 124)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(eeg_features).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25920, 512)\n", + "(8640, 124)\n" + ] + } + ], + "source": [ + "# 三维转二维\n", + "img_feature = np.array(img_feature).reshape(25920, -1)\n", + "print(img_feature.shape)\n", + "eeg_features = np.array(eeg_features).reshape(25920, -1)\n", + "print(eeg_features.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 获取特征和标签值\n", + "train = img_feature # 获取前13组特征\n", + "target = eeg_features # 获取标签\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(train)\n", + "train = scaler.transform(train)\n", + "target = np.array(target) # 将y_data转换成数组\n", + "x_train, x_test, y_train, y_test = train_test_split(train, target, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "在线性模型下，R2 决定系数-拟合优度为 0.31152110273912476\n", + "在岭回归的模型下，R2 决定系数-拟合优度为： 0.3115263953906277\n", + "在决策树回归的模型下，R2 决定系数-拟合优度为 0.9988193145345462\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = all_algorithme(x_train, x_test, y_train, y_test)\n", + "a.sk_LinearRegression()\n", + "a.sk_ridge()\n", + "# a.sk_PolynomialFeatures()\n", + "a.sk_DecisionTreeRegressor()\n", + "# a.sk_byes_network()\n", + "# a.tf_dnn()\n", + "\n", + "a.get_plot()\n", + "# a.get_r2_score()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab-miots/Brain_like/regression_show.ipynb b/lab-miots/Brain_like/regression_show.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7009eb9c7822ec37641e5404a24859e405e23d11 --- /dev/null +++ b/lab-miots/Brain_like/regression_show.ipynb @@ -0,0 +1,603 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "载入数据" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "img_feature.shape: (25920, 1, 512)\n", + "img_name.shape: (25920,)\n", + "eeg_feature.shape: (8640, 1, 124)\n", + "eeg_name.shape: (8640, 1)\n" + ] + } + ], + "source": [ + "# 查看图片特征\n", + "img_f = open('../resnet_classify/img_features_resnet18','rb') # 以二进制读模式（rb）打开pkl文件\n", + "img_feature = pickle.load(img_f) # 读取存储的pickle文件\n", + "print(\"img_feature.shape: \", np.array(img_feature).shape) \n", + "# 查看图片名称标签\n", + "img_n = open('../resnet_classify/img_names_resnet18','rb') # 以二进制读模式（rb）打开pkl文件\n", + "img_name = pickle.load(img_n) # 读取存储的pickle文件\n", + "print(\"img_name.shape: \", np.array(img_name).shape) \n", + "# 查看脑电特征\n", + "eeg_f = open('./egg_features_lstm_36-72','rb') # 以二进制读模式（rb）打开pkl文件\n", + "eeg_feature = pickle.load(eeg_f) # 读取存储的pickle文件\n", + "print(\"eeg_feature.shape: \", np.array(eeg_feature).shape) \n", + "# 查看脑电对应的图片名称\n", + "eeg_n = open('./eeg_name_lstm_36-72','rb') # 以二进制读模式（rb）打开pkl文件\n", + "eeg_name = pickle.load(eeg_n) # 读取存储的pickle文件\n", + "print(\"eeg_name.shape: \", np.array(eeg_name).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 获取图片名称相对应的脑电特征\n", + "# img_name与img_feature一一对应\n", + "# eeg_name与eeg_feature一一对应\n", + "from typing import Dict, Tuple, List\n", + "eeg_features = []\n", + "name2eeg: Dict[str, List[int]] = {}\n", + "for j, v in enumerate(eeg_name):\n", + " name_2 = str(v[0]).strip()\n", + " name2eeg.setdefault(name_2, []).append(j)\n", + "for i, k in enumerate(img_name):\n", + " name_1 = str(k)\n", + " # print(\"name_1: \", name_1)\n", + " eeg_features.append(np.mean(\n", + " [eeg_feature[j] for j in name2eeg[name_1]], axis=0))\n", + " # break" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25920, 512)\n", + "(25920, 124)\n" + ] + } + ], + "source": [ + "# 三维转二维\n", + "img_feature = np.array(img_feature).reshape(25920, -1)\n", + "print(img_feature.shape)\n", + "eeg_features = np.array(eeg_features).reshape(25920, -1)\n", + "print(eeg_features.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# 获取特征和标签值\n", + "train = img_feature # 获取前13组特征\n", + "target = eeg_features # 获取标签\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(train)\n", + "train = scaler.transform(train)\n", + "target = np.array(target) # 将y_data转换成数组\n", + "x_train, x_test, y_train, y_test = train_test_split(train, target, test_size=0.3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "评价指标" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "余弦相似性通过计算两个向量的余弦角来测量两个向量之间的相似性，范围是[-1, 1]；\n", + "当两个向量夹角为0°时，即两个向量重合时，相似度为1；当夹角为180°时，即两个向量方向相反时，相似度为-1；\n", + "余弦相似度更加注重两个向量在方向上的差异，而非距离或长度上" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# 计算余弦相似度\n", + "from scipy import spatial\n", + "def cos_sim(data1, data2):\n", + " error = []\n", + " for i, v in enumerate(data1):\n", + " v_pre = data2[i]\n", + " cos_sim_1 = 1 - spatial.distance.cosine(v_pre, v)\n", + " error.append(cos_sim_1)\n", + " error = np.mean(np.array(error))\n", + " return error" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# 计算模长和余弦相似度\n", + "def cos_modulus(data1, data2):\n", + " error = []\n", + " for i, v in enumerate(data1):\n", + " v_pre = data2[i]\n", + " length1 = np.linalg.norm(v)\n", + " length2 = np.linalg.norm(v_pre)\n", + " if length1 > length2:\n", + " length = length2/length1\n", + " else:\n", + " length = length1/length2\n", + " cos_sim_1 = 1 - spatial.distance.cosine(v_pre, v)\n", + " error.append(0.5*cos_sim_1 + 0.5*length)\n", + " error = np.mean(np.array(error))\n", + " return error\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "神经网络回归预测" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitting model right now\n", + "MSE: \n", + "Train ERROR = 0.02131998967413884\n", + "Test ERROR = 0.021999006957577873\n", + "cos_sim: \n", + "Train ERROR = 0.7552309600699754\n", + "Test ERROR = 0.7491095186357238\n", + "cos_modulus: \n", + "Train ERROR = 0.7568632519518593\n", + "Test ERROR = 0.7510554681336787\n" + ] + } + ], + "source": [ + "from sklearn.neural_network import MLPRegressor\n", + "fit1 = MLPRegressor(\n", + " hidden_layer_sizes=(500,200,100,50), activation='relu',solver='adam',\n", + " alpha=0.01,max_iter=200)\n", + "print (\"fitting model right now\")\n", + "fit1.fit(x_train,y_train)\n", + "pred1_train = fit1.predict(x_train)\n", + "pred1_test = fit1.predict(x_test)\n", + "# 计算训练集 MSE\n", + "from sklearn.metrics import mean_squared_error\n", + "mse_1 = mean_squared_error(pred1_train,y_train)\n", + "mse_2 = mean_squared_error(pred1_test,y_test)\n", + "print (\"MSE: \")\n", + "print (\"Train ERROR = \", mse_1)\n", + "print (\"Test ERROR = \", mse_2)\n", + "# 计算余弦相似度\n", + "train_error1 = cos_sim(y_train, pred1_train)\n", + "test_error1 = cos_sim(y_test, pred1_test)\n", + "print (\"cos_sim: \")\n", + "print (\"Train ERROR = \", train_error1)\n", + "print (\"Test ERROR = \", test_error1)\n", + "# 计算余弦相似度和模长\n", + "train_error2 = cos_modulus(y_train, pred1_train)\n", + "test_error2 = cos_modulus(y_test, pred1_test)\n", + "print (\"cos_modulus: \")\n", + "print (\"Train ERROR = \", train_error2)\n", + "print (\"Test ERROR = \", test_error2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "线性回归预测" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitting model right now\n", + "Train ERROR = 0.02962839399758851\n", + "Test ERROR = 0.03171007824216303\n", + "Train ERROR = 0.6908955501040763\n", + "Test ERROR = 0.6619484364584785\n", + "Train ERROR = 0.6970460748446584\n", + "Test ERROR = 0.6843503638318849\n" + ] + } + ], + "source": [ + "from sklearn import linear_model\n", + "fit2 = linear_model.LinearRegression()\n", + "print (\"fitting model right now\")\n", + "fit2.fit(x_train,y_train)\n", + "pred2_train = fit2.predict(x_train)\n", + "pred2_test = fit2.predict(x_test)\n", + "\n", + "# 计算训练集 MSE\n", + "from sklearn.metrics import mean_squared_error\n", + "mse_1 = mean_squared_error(pred2_train,y_train)\n", + "mse_2 = mean_squared_error(pred2_test,y_test)\n", + "print (\"Train ERROR = \", mse_1)\n", + "print (\"Test ERROR = \", mse_2)\n", + "# 计算余弦相似度\n", + "train_error1 = cos_sim(y_train, pred2_train)\n", + "test_error1 = cos_sim(y_test, pred2_test)\n", + "print (\"Train ERROR = \", train_error1)\n", + "print (\"Test ERROR = \", test_error1)\n", + "# 计算余弦相似度和模长\n", + "train_error2 = cos_modulus(y_train, pred2_train)\n", + "test_error2 = cos_modulus(y_test, pred2_test)\n", + "print (\"Train ERROR = \", train_error2)\n", + "print (\"Test ERROR = \", test_error2)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "自定义神经网络:\n", + "训练神经网络模型一共四层，第一层为输入变量，后三层为Dense " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "567/567 [==============================] - 3s 3ms/step - loss: 3.8378\n", + "Epoch 2/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.4002\n", + "Epoch 3/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0604\n", + "Epoch 4/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0421\n", + "Epoch 5/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 6/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 7/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 8/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 9/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 10/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 11/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 12/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 13/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 14/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0416\n", + "Epoch 15/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 16/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 17/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 18/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 19/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 20/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 21/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 22/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 23/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0415\n", + "Epoch 24/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 25/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 26/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 27/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 28/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 29/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 30/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 31/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 32/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 33/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 34/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 35/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 36/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0414\n", + "Epoch 37/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 38/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 39/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 40/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 41/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 42/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 43/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 44/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 45/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 46/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 47/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 48/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 49/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 50/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 51/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 52/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 53/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0413\n", + "Epoch 54/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 55/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 56/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 57/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 58/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 59/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 60/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 61/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 62/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 63/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 64/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 65/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 66/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 67/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0412\n", + "Epoch 68/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 69/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 70/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 71/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 72/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 73/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 74/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 75/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 76/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 77/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 78/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 79/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 80/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 81/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 82/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 83/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 84/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 85/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 86/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 87/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 88/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0411\n", + "Epoch 89/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 90/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 91/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 92/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 93/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 94/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 95/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 96/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 97/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 98/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 99/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n", + "Epoch 100/100\n", + "567/567 [==============================] - 2s 3ms/step - loss: 0.0410\n" + ] + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras import regularizers\n", + "\n", + "# 定义神经网络模型\n", + "model = Sequential()\n", + "model.add(Dense(500, activation='relu', input_shape=(512,), kernel_regularizer=regularizers.l2(0.01)))\n", + "model.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n", + "model.add(Dense(124, activation='linear'))\n", + "# 误差记录\n", + "optimizer = Adam(learning_rate = 0.0001)\n", + "model.compile(optimizer=optimizer, loss = 'mse')\n", + "\n", + "# 训练模型\n", + "history = model.fit(x_train, y_train, epochs=100, batch_size=32)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred3_train = model.predict(x_train)\n", + "pred3_test = model.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train ERROR = 0.040665876128245224\n", + "Test ERROR = 0.04046680603178016\n", + "Train ERROR = 0.47711056602289326\n", + "Test ERROR = 0.47615225270381306\n", + "Train ERROR = 0.47762725243112397\n", + "Test ERROR = 0.47752538429735514\n" + ] + } + ], + "source": [ + "# 计算训练集 MSE\n", + "mse_1 = mean_squared_error(np.array(pred3_train),y_train)\n", + "mse_2 = mean_squared_error(np.array(pred3_test),y_test)\n", + "print (\"Train ERROR = \", mse_1)\n", + "print (\"Test ERROR = \", mse_2)\n", + "# 计算余弦相似度\n", + "train_error1 = cos_sim(y_train, np.array(pred3_train))\n", + "test_error1 = cos_sim(y_test, np.array(pred3_test))\n", + "print (\"Train ERROR = \", train_error1)\n", + "print (\"Test ERROR = \", test_error1)\n", + "# 计算余弦相似度和模长\n", + "train_error2 = cos_modulus(y_train, np.array(pred3_train))\n", + "test_error2 = cos_modulus(y_test, np.array(pred3_test))\n", + "print (\"Train ERROR = \", train_error2)\n", + "print (\"Test ERROR = \", test_error2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab-miots/Brain_like/resnet18_ft.ipynb b/lab-miots/Brain_like/resnet18_ft.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..52e6a6a112713ea90c725d59a261420ff3e4d1c9 --- /dev/null +++ b/lab-miots/Brain_like/resnet18_ft.ipynb @@ -0,0 +1,52369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "from PIL import Image\n", + "\n", + "import pickle\n", + "import gzip\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import scipy.io as sio\n", + "from torch.utils.data.dataset import Dataset\n", + "from torch.utils.data import DataLoader\n", + "import torchvision.transforms as transforms\n", + "import torch.optim as optim\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.utils import shuffle\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "hello_pytorch_DIR = os.path.abspath(os.getcwd()+os.path.sep+\"..\"+os.path.sep+\"..\")\n", + "sys.path.append(hello_pytorch_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "use device :cpu\n" + ] + } + ], + "source": [ + "import torchvision.models as models\n", + "import torchvision\n", + "BASEDIR = os.path.dirname(os.getcwd())\n", + "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "device = torch.device(\"cpu\")\n", + "print(\"use device :{}\".format(device))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# 参数设置\n", + "MAX_EPOCH = 25\n", + "BATCH_SIZE = 16\n", + "LR = 0.001\n", + "log_interval = 10\n", + "val_interval = 1\n", + "classes = 2\n", + "start_epoch = -1\n", + "lr_decay_step = 7" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================ step 1/5 数据 ============================\n", + "f = pd.read_csv('./csvdata/image_label.csv')\n", + "df_f = pd.DataFrame(f)\n", + "def load_data (path):\n", + " x = []\n", + " y = []\n", + " z = []\n", + " listing = os.listdir(path)\n", + " for i in range(len(df_f)):\n", + " document = df_f[i:i+1]\n", + " df_name = document['name'][i]\n", + " df_label = document['label'][i]\n", + " y.append(int(df_label))\n", + " z.append(str(df_name))\n", + " for file in listing:\n", + " if str(df_name) == str(file):\n", + " img = Image.open(path + '/' + file)\n", + " img = img.resize((512,512))\n", + " img = img.convert('RGB')\n", + " img = np.array(img)\n", + " x.append(img) #将值填入x\n", + " print('loading : '+ df_name)#打印信息\n", + " break\n", + " \n", + " x_ = np.array(x)\n", + " y_ = np.array(y)\n", + " z_ = np.array(z)\n", + " return x_, y_, z_\n", + " # y_=np.array(y_)\n", + " # y_=y_.astype(np.int64)#转换数据类型为64位整形\n", + " # return x_,y_#返回输入特征x，标签y_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading : dfi-1-005-70.png\n", + "loading : dfi-1-010-100.png\n", + "loading : dfi-1-014-50.png\n", + "loading : dfi-1-022-70.png\n", + "loading : dfi-1-002-0.png\n", + "loading : dfi-1-012-60.png\n", + "loading : dfi-1-014-60.png\n", + "loading : dfi-1-023-60.png\n", + "loading : dfi-1-021-70.png\n", + "loading : dfi-1-007-80.png\n", + "loading : dfi-1-005-65.png\n", + "loading : dfi-1-022-50.png\n", + "loading : dfi-1-023-100.png\n", + "loading : dfi-1-001-60.png\n", + "loading : dfi-1-001-65.png\n", + "loading : dfi-1-024-60.png\n", + "loading : dfi-1-018-60.png\n", + "loading : dfi-1-014-65.png\n", + "loading : dfi-1-008-80.png\n", + "loading : dfi-1-011-100.png\n", + "loading : dfi-1-022-65.png\n", + "loading : dfi-1-001-70.png\n", + "loading : dfi-1-015-65.png\n", + "loading : dfi-1-006-80.png\n", + 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con-1-083-neg.png\n", + "loading : con-1-088-neg.png\n", + "loading : con-1-085-neg.png\n", + "loading : con-1-058-neg.png\n", + "loading : con-1-054-neg.png\n", + "loading : con-1-094-pos.png\n", + "loading : con-1-002-neg.png\n", + "loading : con-1-025-pos.png\n", + "loading : con-1-023-neg.png\n", + "loading : con-1-018-neg.png\n", + "loading : con-1-089-neg.png\n", + "loading : con-1-077-neg.png\n", + "loading : con-1-035-pos.png\n", + "loading : con-1-093-neg.png\n", + "loading : con-1-095-pos.png\n", + "loading : con-1-063-pos.png\n", + "loading : con-1-080-neg.png\n" + ] + } + ], + "source": [ + "data,label,name = load_data(\"./images\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25920, 512, 512, 3)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "img names saved!!!\n" + ] + } + ], + "source": [ + "pickle.dump(name, open('./img_names_resnet18', 'wb'))\n", + "print(\"img names saved!!!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (20812, 512, 512, 3)\n", + "20812 train samples\n", + "5204 test samples\n" + ] + } + ], + "source": [ + "#划分80%为训练集和20%为测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=4)\n", + "\n", + "# 训练集数据维度的调整：N H W C\n", + "X_train = np.reshape(X_train,(X_train.shape[0],512,512,3))\n", + "# 测试集数据维度的调整：N H W C\n", + "X_test = np.reshape(X_test,(X_test.shape[0],512,512,3))\n", + "#类型转换\n", + "# X_train = X_train.astype('float32')\n", + "# X_test = X_test.astype('float32')\n", + "\n", + "print('X_train shape:', X_train.shape)\n", + "print(X_train.shape[0], 'train samples')\n", + "print(X_test.shape[0], 'test samples')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = torch.as_tensor(X_train)\n", + "X_test = torch.as_tensor(X_test)\n", + "y_train = torch.as_tensor(y_train)\n", + "y_test = torch.as_tensor(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================ step 2/5 模型 ============================\n", + "class resnet18_ft(nn.Module):\n", + " def __init__(self, num_classes=2):\n", + " super().__init__()\n", + " net = models.resnet18(pretrained=True)\n", + " net.fc= nn.Sequential() # 将分类层（fc）置空\n", + " self.features = net\n", + " self.fc= nn.Sequential( # 定义一个卷积网络结构\n", + " nn.Linear(512*1*1, 512),\n", + " nn.ReLU(True),\n", + " nn.Dropout(),\n", + " nn.Linear(512, 128),\n", + " nn.ReLU(True),\n", + " nn.Dropout(),\n", + " nn.Linear(128, num_classes),\n", + " )\n", + " \n", + " def forward(self, x):\n", + " x1 = self.features(x)\n", + " x2 = x1.view(-1, 512)\n", + " x2 = self.fc(x2)\n", + " return x1" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以下代码用于设置损失函数、优化器，并训练模型分类" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================ step 3/5 损失函数 ============================\n", + "criterion = nn.CrossEntropyLoss() # 选择损失函数" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================ step 4/5 优化器 ============================\n", + "# 法2 : conv 小学习率\n", + "# flag = 0\n", + "model = resnet18_ft()\n", + "model = model.to(device)\n", + "optimizer = optim.SGD(model.train().parameters(), lr=LR, momentum=0.9) # 选择优化器\n", + "\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step, gamma=0.1) # 设置学习率下降策略" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training:Epoch[000/025] Iteration[010/20812] Loss: 0.6885 Acc:50.00%\n", + "Training:Epoch[000/025] Iteration[020/20812] Loss: 0.7183 Acc:55.00%\n", + "Training:Epoch[000/025] Iteration[030/20812] Loss: 0.7012 Acc:50.00%\n", + "Training:Epoch[000/025] Iteration[040/20812] Loss: 0.6899 Acc:50.00%\n", + "Training:Epoch[000/025] Iteration[050/20812] Loss: 0.7652 Acc:54.00%\n", + "Training:Epoch[000/025] Iteration[060/20812] Loss: 0.5320 Acc:58.33%\n", + "Training:Epoch[000/025] Iteration[070/20812] Loss: 0.6608 Acc:60.00%\n", + "Training:Epoch[000/025] Iteration[080/20812] Loss: 0.7980 Acc:60.00%\n", + "Training:Epoch[000/025] Iteration[090/20812] Loss: 0.7890 Acc:57.78%\n", + "Training:Epoch[000/025] Iteration[100/20812] Loss: 0.9142 Acc:54.00%\n", + "Training:Epoch[000/025] Iteration[110/20812] Loss: 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\u001b[39m=\u001b[39m criterion(outputs, labels)\n\u001b[1;32m 28\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n", + "File \u001b[0;32m~/miniconda3/envs/tf2/lib/python3.8/site-packages/torch/optim/optimizer.py:192\u001b[0m, in \u001b[0;36mOptimizer.zero_grad\u001b[0;34m(self, set_to_none)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 191\u001b[0m p\u001b[39m.\u001b[39mgrad\u001b[39m.\u001b[39mrequires_grad_(\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m--> 192\u001b[0m p\u001b[39m.\u001b[39;49mgrad\u001b[39m.\u001b[39;49mzero_()\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# ============================ step 5/5 训练 ============================\n", + "train_curve = list()\n", + "valid_curve = list()\n", + "\n", + "for epoch in range(start_epoch + 1, MAX_EPOCH):\n", + " loss_mean = 0.\n", + " correct = 0.\n", + " total = 0.\n", + "\n", + " model.train()\n", + " for i, data in enumerate(X_train):\n", + " # forward\n", + " if hasattr(torch.cuda, 'empty_cache'):\n", + " torch.cuda.empty_cache()\n", + " inputs = data\n", + " inputs = inputs.transpose(0,2)\n", + " inputs = torch.unsqueeze(inputs, dim=0)\n", + " labels = y_train[i]\n", + " labels = torch.unsqueeze(labels, dim=0)\n", + " inputs = inputs.float()\n", + " labels = labels.long()\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " outputs = model(inputs)\n", + "\n", + " # backward\n", + " optimizer.zero_grad()\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + "\n", + " # update weights\n", + " optimizer.step()\n", + "\n", + " # 统计分类情况\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).squeeze().cpu().sum().numpy()\n", + "\n", + " # 打印训练信息\n", + " loss_mean += loss.item()\n", + " train_curve.append(loss.item())\n", + " if (i+1) % log_interval == 0:\n", + " loss_mean = loss_mean / log_interval\n", + " print(\"Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}\".format(\n", + " epoch, MAX_EPOCH, i+1, len(X_train), loss_mean, correct / total))\n", + " loss_mean = 0.\n", + "\n", + " # if flag_m1:\n", + " # print(\"epoch:{} conv1.weights[0, 0, ...] :\\n {}\".format(epoch, resnet18_ft.conv1.weight[0, 0, ...]))\n", + "\n", + " scheduler.step() # 更新学习率\n", + "\n", + " # validate the model\n", + " if (epoch+1) % val_interval == 0:\n", + " correct_val = 0.\n", + " total_val = 0.\n", + " loss_val = 0.\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for j, data in enumerate(X_test):\n", + " inputs = data\n", + " inputs = inputs.transpose(0,2)\n", + " inputs = torch.unsqueeze(inputs, dim=0)\n", + " labels = y_test[j]\n", + " labels = torch.unsqueeze(labels, dim=0)\n", + " inputs = inputs.float()\n", + " labels = labels.long()\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " \n", + " if hasattr(torch.cuda, 'empty_cache'):\n", + " torch.cuda.empty_cache()\n", + "\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total_val += labels.size(0)\n", + " correct_val += (predicted == labels).squeeze().cpu().sum().numpy()\n", + "\n", + " loss_val += loss.item()\n", + "\n", + " loss_val_mean = loss_val/len(X_test)\n", + " valid_curve.append(loss_val_mean)\n", + " print(\"Valid:\\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}\".format(\n", + " epoch, MAX_EPOCH, j+1, len(X_test), loss_val_mean, correct_val / total_val))\n", + " model.train()\n", + "# train_x = range(len(train_curve))\n", + "# train_y = train_curve\n", + "\n", + "# train_iters = len(X_train)\n", + "# valid_x = np.arange(1, len(valid_curve)+1) * train_iters*val_interval # 由于valid中记录的是epochloss，需要对记录点进行转换到iterations\n", + "# valid_y = valid_curve\n", + "\n", + "# plt.plot(train_x, train_y, label='Train')\n", + "# plt.plot(valid_x, valid_y, label='Valid')\n", + "\n", + "# plt.legend(loc='upper right')\n", + "# plt.ylabel('loss value')\n", + "# plt.xlabel('Iteration')\n", + "# plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以下代码用于提取每层的特征并可视化" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================ 提取图片特征 ============================\n", + "features = []\n", + "data_f = data\n", + "data_f = np.reshape(data_f,(data_f.shape[0],512,512,3))\n", + "data_f = torch.as_tensor(data_f)\n", + "# label_f = torch.as_tensor(label)\n", + "\n", + "model.train()\n", + "for i, img_data in enumerate(data_f):\n", + " # forward\n", + " if hasattr(torch.cuda, 'empty_cache'):\n", + " torch.cuda.empty_cache()\n", + " inputs = img_data\n", + " inputs = inputs.transpose(0,2)\n", + " inputs = torch.unsqueeze(inputs, dim=0)\n", + " # labels = y_train[i]\n", + " # labels = torch.unsqueeze(labels, dim=0)\n", + " inputs = inputs.float()\n", + " # labels = labels.long()\n", + " inputs = inputs.to(device)\n", + " \n", + " outputs = model(inputs)\n", + " features.append(outputs.tolist())\n", + "\n", + " model.train()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "img features saved!!!\n" + ] + } + ], + "source": [ + "pickle.dump(features, open('./img_features_resnet18', 'wb'))\n", + "print(\"img features saved!!!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab-miots/Conditional_Diffusion/main. b/lab-miots/Conditional_Diffusion/main. new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/DeepCrawler/test.py b/lab-miots/DeepCrawler/test.py new file mode 100644 index 0000000000000000000000000000000000000000..361b8a6e4502508f3d41c001525f8d6b4eb4cc9d --- /dev/null +++ b/lab-miots/DeepCrawler/test.py @@ -0,0 +1,257 @@ +from selenium import webdriver +from selenium.webdriver.common.action_chains import ActionChains +from selenium.webdriver.common.by import By +import matplotlib.pyplot as plt +import time +import os +from skimage.metrics import structural_similarity as compare_ssim +#~ import skimage as ssim +import imutils +import cv2 +import numpy as np +from tqdm import tqdm + + +def init(): + driver = webdriver.Firefox() + driver.maximize_window() + return driver +# driver.save_screenshot('test_screenshot.png') +# img = cv.imread('test_screenshot.png') + +# driver.get_screenshot_as_base64() + +def move_your_mouse(driver,x,y): + action = ActionChains(driver) + action.reset_actions() + action.move_by_offset(x,y).click().perform() + +def save(url,datas): + path = mkdir(url) + num = 0 + for data in datas: + with open(path+"data.txt",'a') as f: + num += 1 + + f.write(url) + f.write('\n') + f.write(data[0]) + f.write('\n') + f.write("images\\image%d.jpg"%num) + f.write('\n') + f.write(str(data[-1])) + f.write('\n\n') + with open(path+"images\\image%d.jpg"%num,'wb') as i: + i.write(data[1]) + + +def found(datas,x,y): + css = 10 + for data in datas: + p = data[-1] + if x>=p[0]['x']-css and y>=p[0]['y']-css and x<=p[0]['x']+p[1]['width']+css and y<=p[0]['y']+p[1]['height']+css: + return True + return False + + +def mkdir(url): + path = os.getcwd() + '\\data\\%s\\images\\'%url.replace(':','').replace('/','').replace('|','').replace('*','').replace('>','').replace('<','') + folder = os.path.exists(path) + if not folder: #判断是否存在文件夹如果不存在则创建为文件夹 + os.makedirs(path) #makedirs 创建文件时如果路径不存在会创建这个路径 + return os.getcwd() + '\\data\\%s\\'%url.replace(':','').replace('/','').replace('|','').replace('*','').replace('>','').replace('<','') + + +def move_your_mouse(driver,x,y): + action = ActionChains(driver) + action.reset_actions() + action.move_by_offset(x,y).click().perform() +# action.move_by_offset(x,y).click().perform() +# action.move_by_offset(x,y).perform() +# action.move_to_element(button).perform() + +def scaning(driver,url): + driver.get(url) + if len(driver.find_element(By.TAG_NAME,'body').get_attribute('outerHTML'))<2000: + raise Exception('Maybe the page 404.',url) + # height = driver.execute_script('return document.body.clientHeight') + # width = driver.execute_script('return document.body.clientWidth') + height = 707 + width = 1700 + + datas = [] + url = driver.current_url + elements = get_position_data(driver) + with tqdm(total=len(elements), desc='%s'%url, leave=True, ncols=100, unit='click', unit_scale=True) as pbar: + for e in elements: + x,y = e[-2] + if x>1700 or y>707: + continue + if not found(datas,x,y): + try: + if work(driver,url,x,y) != 'Nothing happened.': + data = e + datas.append(data ) + except Exception as e: + print(e) + pbar.update(1) + + return datas + +def work(driver,url,x,y): + + # driver.save_screenshot('tmp1.png') + imgA = driver.get_screenshot_as_png() + # dom1 = driver.find_element_by_tag_name('body').get_attribute('outerHTML') + move_your_mouse(driver,x,y) + # time.sleep(0.5) + # driver.save_screenshot('tmp2.png') + imgB = driver.get_screenshot_as_png() + # dom2 = driver.find_element_by_tag_name('body').get_attribute('outerHTML') + if driver.current_url != url: + driver.get(url) + time.sleep(0.5) + return 'Page switch!' + + if len(driver.window_handles)>1: + while len(driver.window_handles)>1: + driver.switch_to.window(driver.window_handles[1]) + driver.close() + driver.switch_to.window(driver.window_handles[0]) + return "A new window is created!" + + # if dom1!=dom2: + if pictureCompare(imgA,imgB): + # print('Something is changed!') + driver.refresh() + time.sleep(0.5) + return 'Something is changed!' + return 'Nothing happened.' +# work(driver,x,y) + + +def get_position_data(driver): + elements = [] + datas = [] + elements += driver.find_elements(By.TAG_NAME,'div') + elements += driver.find_elements(By.TAG_NAME,'button') + elements += driver.find_elements(By.TAG_NAME,'input') + elements += driver.find_elements(By.TAG_NAME,'a') + elements += driver.find_elements(By.TAG_NAME,'form') + elements += driver.find_elements(By.TAG_NAME,'span') + elements += driver.find_elements(By.TAG_NAME,'img') + # print(len(elements)) + for e in elements: + try: + if e.is_displayed(): + s = e.size['height']*e.size['width'] + if s > 10 and s < 300000: + p = [e.location['x']+e.size['width']//2,e.location['y']+e.size['height']//2] + datas.append([e.get_attribute('outerHTML'),e.screenshot_as_png,p,[e.location,e.size]]) + + else: + continue + except Exception as e: + print('get_position_data',e) + + return datas +# datas = [get_position_data(driver,x,y)] + + + +def pictureCompare(imageA='tmp1.png',imageB='tmp2.png'): + if type(imageA) == str: + imageA = cv2.imread(imageA) + imageB = cv2.imread(imageB) + else: + imageA = cv2.imdecode(np.frombuffer(imageA, np.uint8), cv2.IMREAD_COLOR) + imageB = cv2.imdecode(np.frombuffer(imageB, np.uint8), cv2.IMREAD_COLOR) + thresh = 200 + grayA = cv2.cvtColor(imageA,cv2.COLOR_BGR2GRAY) + grayB = cv2.cvtColor(imageB,cv2.COLOR_BGR2GRAY) + binaryA = cv2.threshold(grayA, thresh, 255, cv2.THRESH_BINARY)[1] + binaryB = cv2.threshold(grayB, thresh, 255, cv2.THRESH_BINARY)[1] + (score,diff) = compare_ssim(binaryA,binaryB,full = True) + diff = (diff *255).astype("uint8") + # print("SSIM:{}".format(score)) + + thresh = cv2.threshold(diff,0,255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] + cnts,_ = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) + # cnts = cnts[0] if imutils.is_cv2() else cnts[1] + if len(cnts) > 20: + return False + s = 0 + for c in cnts: + (x,y,w,h) = cv2.boundingRect(c) + s += w*h + if s > 2000 and s < 600000: + return True + return False +# pictureCompare() + + + +with open('fofa10000.csv','r') as f: + urls = [i.split(',')[0] for i in f.readlines()[1:]] +for i in range(len(urls)): + if not urls[i].startswith('http'): + urls[i] = 'http://'+urls[i] + +urls = urls[:100] +print(urls) + + + +import threading +import queue + +class myThread(threading.Thread): + def __init__(self, name ,q) -> None: + threading.Thread.__init__(self) + self.name = name + self.q = q + def run(self): + print("Starting " + self.name) + driver = init() + while True: + try: + crawl(self.name, self.q,driver) + except Exception as e: + print(self.name,'crawl over.') + break + + + print("Exiting " + self.name) + +def crawl(threadNmae, q, driver): + + url = q.get(timeout=2) + try: + datas = scaning(driver,url) + save(url,datas) + except Exception as e: + print(threadNmae, "Error: ",url,' ' ,e) + + + +# urls = [url for i in range(4)] +# 填充队列 +workQueue = queue.Queue(len(urls)) +for url in urls: + workQueue.put(url) + +threads = [] +for i in range(1,5): + # 创建4个新线程 + thread = myThread("Thread-" + str(i), q=workQueue) + # 开启新线程 + thread.start() + # 添加新线程到线程列表 + threads.append(thread) + +# 等待所有线程完成 +for thread in threads: + thread.join() + + +print("Exiting Main Thread") diff --git a/lab-miots/EEG_preprocessing/.keep b/lab-miots/EEG_preprocessing/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/EEG_preprocessing/ERP_preprocessing.py b/lab-miots/EEG_preprocessing/ERP_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..4d585fccb6f3221c1a7df7e08a2ac6e56d0d19c0 --- /dev/null +++ b/lab-miots/EEG_preprocessing/ERP_preprocessing.py @@ -0,0 +1,447 @@ +# 制成数据集 +# 输入：预处理后，未分段的多个被试的脑电数据 +# 输出：脑电数据集，可被其他数据需求直接使用 +# 要求：自动化的脚本 +import os +import glob +import h5py +import time +import torch +import numpy as np +import pandas as pd +import scipy.io as sio +import torch.nn as nn +import torch.optim as optim +# from torch.utils.data.dataset import Dataset +from torch.utils.data import DataLoader +from ERP_classifier.test_classifier import Test_Model,train_Test_Model +from ERP_classifier.utils import EEGdatasDataset +from ERP_classifier.classifier import lstm_classifier +from ERP_classifier.utils import EarlyStopping +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +def load_single_ERP_data(data_path): + x = sio.loadmat(data_path) + datas = x['ERPs'] + # labels=x['labels'] + labels=x['labels'][0] + #labels = labels.reshape(24,1) + return datas,labels + +def load_certain_subjects_data(data_dir='EEG_preprocessing/Generated_ERPs',subjects=[1,2,3]): + all_datas = [] + all_labels = [] + file_cnt=0 + for i in subjects: + file_name='S'+str(i)+'.mat' + file_path_temp = os.path.join(data_dir, file_name) + datas, labels = load_single_ERP_data(file_path_temp) + file_cnt+=1 + print('processing{},{}'.format(file_name, file_cnt)) + if file_cnt == 1: + all_datas = datas + all_labels = labels + else: + all_datas = np.concatenate((all_datas, datas), axis=0) + all_labels = np.concatenate((all_labels, labels), axis=0) + return all_datas, all_labels + +def Train_Large_Model(model,f_datas,subjects,save_path,each=3,n_epoch=20, learning_rate=1e-3,step_size=15,gamma=0.9): + criterion = nn.NLLLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) + + print('Start training!') + all_acc=[] + all_val_acc=[] + all_loss=[] + all_val_loss=[] + #N*16*600 + sub_cnts=len(subjects) + batch_size=32 + patience=20 + early_stopping = EarlyStopping(save_path=save_path,patience=patience, verbose=True) + + for epoch in range(n_epoch): + validation_loss, validation_evaluation, validation_cnts =0.0,[],0 + model.to(device).train() + running_loss = 0.0 + evaluation = [] + _start_time = time.time() + cnts=0 + for f in f_datas: + train_data=f['train_data'][:] + test_data=f['test_data'][:] + train_label=f['train_label'][:] + test_label=f['test_label'][:] + train_data = EEGdatasDataset(label=train_label, data=train_data) + test_data = EEGdatasDataset(label=test_label, data=test_data) + + trainloader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + testloader = DataLoader(test_data, batch_size=batch_size) + model.train() + for i, data in enumerate(trainloader): + # get the inputs; data is a list of [inputs, labels] + features, labels= data + features=features.to(device) + labels=labels.to(device) + optimizer.zero_grad() + outputs = model(features) + predicted = outputs.argmax(1) + evaluation.append((predicted == labels).tolist()) + + loss = criterion(outputs, labels.squeeze()) + loss.backward() + # torch.nn.utils.clip_grad_norm(model.parameters(), 5) + optimizer.step() + # print('index:%d,%.5f'%(i,loss.item())) + running_loss += loss.item() + + cnts=cnts+i+1 + + model.eval() + for i, data in enumerate(testloader): + features, labels = data + features = features.to(device) + labels = labels.to(device) + outputs = model(features) + predicted = outputs.argmax(1) + validation_evaluation.append((predicted == labels).tolist()) + loss = criterion(outputs, labels.squeeze()) + validation_loss += loss.item() + validation_cnts +=i+1 + + running_loss = running_loss / (cnts + 1) + validation_loss=validation_loss/validation_cnts + # print(running_loss) + + evaluation = [item for sublist in evaluation for item in sublist] + running_acc = sum(np.array(evaluation,dtype=object)) / len(evaluation) + + validation_evaluation = [item for sublist in validation_evaluation for item in sublist] + validation_acc = sum(validation_evaluation)/ len(validation_evaluation) + if epoch>5: + early_stopping(validation_acc, model) + _end_time = time.time() + scheduler.step() + + print('Epoch %d/%3d\n time: %.2fs\t loss: %.4f\tAccuracy : %.4f\t \tval-loss: %.4f\tval-Accuracy : %.4f\tLearning Rate : %.5f' % + (epoch + 1, n_epoch,_end_time-_start_time, running_loss, running_acc, validation_loss, validation_acc,optimizer.state_dict()['param_groups'][0]['lr'])) + all_loss.append(running_loss) + all_acc.append(running_acc) + all_val_acc.append(validation_acc) + all_val_loss.append(validation_loss) + # if epoch%10==9: + # torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + # if running_acc>=0.89 and validation_acc>=0.89: + # break + if early_stopping.early_stop: + print("Early stopping") + break + +def train_large_all(subjects=[0,1,2,3,4,5,6,7],save_path = 'EEG_preprocessing/ERP_classifier/Trained_classifier/LSTM.ckpt', data_dir='EEG_preprocessing/Generated_ERPs',is_reload=False,n_epoch=20,batch_size=32): + sub_cnts=len(subjects) + f_data = [] + if sub_cnts > 3: + temp = [] + for i, subject in enumerate(subjects): + temp.append(subject) + if i % 3 == 2: + datas, labels = load_certain_subjects_data(data_dir,subjects=temp) + # N*124*32 + datas=datas.swapaxes(1,2) + print(datas.shape) + length = len(datas) + index = [i for i in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + + train_data = datas[:int(5 * length//6)] + test_data = datas[int(1 * length//6):] + train_label = labels[:int(5 * length//6)] + test_label = labels[int(1 * length//6):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # N*124*32 + # train_label = torch.from_numpy(train_label).type(torch.LongTensor) + # test_label = torch.from_numpy(test_label).type(torch.LongTensor) + train_label = torch.from_numpy(train_label) + test_label = torch.from_numpy(test_label) + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i // 3), 'w') + f['train_data'] = train_data + f['test_data'] = test_data + f['train_label'] = train_label + f['test_label'] = test_label + f.close() + + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i // 3), 'r') + f_data.append(f) + print('Data save in ' + 'loader_temp_' + str(i // 3) + 'ready!') + temp = [] + if i % 3 != 2: + datas, labels = load_certain_subjects_data(data_dir, subjects=temp) + # N*124*32 + datas=datas.swapaxes(1,2) + print(datas.shape) + + length = len(datas) + index = [i for i in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + + train_data = datas[:int(5 * length // 6)] + test_data = datas[int(1 * length // 6):] + train_label = labels[:int(5 * length // 6)] + test_label = labels[int(1 * length // 6):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # train_data = train_data.unsqueeze(1) + # test_data = test_data.unsqueeze(1) + # N*1*124*32 + train_label = torch.from_numpy(train_label).type(torch.LongTensor) + test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i // 3), 'w') + f['train_data'] = train_data + f['test_data'] = test_data + f['train_label'] = train_label + f['test_label'] = test_label + f.close() + + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i // 3), 'r') + f_data.append(f) + print('Data save in ' + 'loader_temp_' + str(i // 3) + 'ready!') + + else: + datas, labels = load_certain_subjects_data(data_dir, subjects=subjects) + # N*124*32 + datas = datas.swapaxes(1, 2) + print(datas.shape) + + length = len(datas) + index = [i for i in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + + train_data = datas[:int(5 * length // 6)] + test_data = datas[int(1 * length // 6):] + train_label = labels[:int(5 * length // 6)] + test_label = labels[int(1 * length // 6):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # train_data = train_data.unsqueeze(1) + # test_data = test_data.unsqueeze(1) + # N*1*124*32 + train_label = torch.from_numpy(train_label).type(torch.LongTensor) + test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp', 'w') + f['train_data'] = train_data + f['test_data'] = test_data + f['train_label'] = train_label + f['test_label'] = test_label + f.close() + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp', 'r') + f_data.append(f) + print('Data save in loader_temp ready!') + + model=lstm_classifier(input_size=64, classifier_size=2) + if is_reload: + reload_path=save_path + if os.path.isfile(reload_path): + model.load_state_dict(torch.load(reload_path)) + print('model reload done!') + + model = model.to(device) + Train_Large_Model(model.train(),f_datas=f_data,save_path=save_path,subjects=subjects,learning_rate=1e-3,step_size=15,n_epoch=n_epoch) + +# 处理预处理后的.mat数据 +def preprocessing(file_name, file_label, no): + x = sio.loadmat(file_name) + chanlocs = x['chanlocs'] # chanlocs中记录的是64个电极的名称和位置坐标等信息 + print('chanlocs.shape:', chanlocs.shape) + event = x['event'] # event数据中记录着marker数据 + print('event.shape:', event.shape) + data = x['data'] # data数据中记录着57个电极的脑电数据，采样率1000Hz + print('data.shape:', data.shape) + + # 查看maker数据 + event0 = event[0] + df = pd.DataFrame(event0) + df.iloc[0,0][0][0] + df.to_csv("EEG_preprocessing/csvdata/event_tmp.csv", index=None) + + with open("EEG_preprocessing/csvdata/event_tmp.csv",'r') as f: + content = f.read() + content = content.replace('[','') + content = content.replace(']', '') + content = content.replace("'", '') + with open("EEG_preprocessing/csvdata/event_data.csv",'w') as f: + f.write(content) + + # 获取所有S150的latency，即图片出现时刻 + event_csv = pd.read_csv('EEG_preprocessing/csvdata/event_data.csv') + df = pd.DataFrame(event_csv) + trial_time = [] # 将所有S150的latency存储在trial_time数组中 + for i in range(len(df)): + document = df[i:i+1] + df_type = document["type"][i] + if df_type == "S150": + trial_time.append(document["latency"][i]) + print('trial_time的数量:', len(trial_time)) # 一共720张图片 + + import pickle + import gzip + import numpy as np + # 保存trial_time数组至trial_time中 + with gzip.open("EEG_preprocessing/csvdata/trial_time.gz", 'w', compresslevel=1) as w: + pickle.dump(trial_time, w) + + # 查看图片标签数据 + y = sio.loadmat(file_label) + LN2022 = y['LN2022'][0] + images = LN2022["Image"][0] + print('images的数量', images.shape) # 图片数量720，与脑电数据中的S150maker相同 + + # 获取每张图片的名称 + names = [name[0][0] for name in images["name"]] + image = np.array(names) + image = pd.DataFrame(image) + image.to_csv("EEG_preprocessing/csvdata/image_name.csv", index=None,header=None) + file1 = pd.read_csv('EEG_preprocessing/csvdata/image_name.csv',names=['name']) + df_img = pd.DataFrame(file1) + + # 创建标签， + category_level = [] # 6大类 + exemplar_level = [] # 子类别 + label = [] # 正反例 + for i in range(len(df_img)): + document = df_img[i:i+1] + df_name = document['name'][i] + category_level.append(df_name[0:3]) + exemplar_level.append(df_name[0:5]) + label.append(df_name[10:13]) + # 保存标签 + df_img["category_level"] = category_level + df_img["exemplar_level"] = exemplar_level + df_img["label"] = label + df_img.to_csv('EEG_preprocessing/csvdata/image_label.csv') + + # 处理脑电数据 + with gzip.open("EEG_preprocessing/csvdata/trial_time.gz", 'r') as r: + trial_time = pickle.load(r) + # 分段，以图片出现时刻开始，每张图片的时间设为800 + eeg = np.zeros((64,800,720)) # channel*trial_length*trial_num + for i in range(len(trial_time)): + start = int(trial_time[i]) + sub_data = data[:, start:start+800] + eeg[:,:,i] = sub_data + print('eeg.shape:', eeg.shape) + with gzip.open("EEG_preprocessing/csvdata/eeg.gz", 'w', compresslevel=1) as w: + pickle.dump(eeg, w) + + # 在利用LSTM对数据进行分类之前，对数据预处理 + with gzip.open("EEG_preprocessing/csvdata/eeg.gz", 'r') as r: + read_data = pickle.load(r) + datas = read_data.swapaxes(0,2) + datas = datas.swapaxes(1,2) + # 构建标签 + file2 = pd.read_csv('EEG_preprocessing/csvdata/image_label.csv') + df_img = pd.DataFrame(file2) + e_labels = df_img['name'] + c_labels = df_img['label'] + + # 对图片编号，同一张图片编号相同 + elabels = {} + count = 0 + for i in range(len(e_labels)): + key = e_labels[i] + if key in elabels: + index = elabels[key] + key_t = key + '_' + str(i*10) + elabels[key_t] = index + count = count + 1 + else: + i_t = i-count + elabels[key] = i_t + e_labels = elabels.values() + e_labels = np.array(list(e_labels)) + + # 正反例改为数字 + for i in range(len(c_labels)): + key = c_labels.iloc[i] + if key == 'pos': + c_labels.iloc[i] = 1 + else: + c_labels.iloc[i] = 0 + + trials_index=np.zeros([240,3]).astype(int)# 第i行第j列表示第i张图片第j次trial在datas中位置 + note_index=np.zeros([240,]).astype(int)# 辅助来生成trials_index矩阵，第i个表示当前第i张图已经统计到了第几个trial + for index,label in enumerate(e_labels): + if note_index[label]==3: # 每张图只有3个trials + continue + trials_index[label,(note_index[label])]=index + note_index[label]+=1 + # 对每张图开始进行求ERP + ERPs=[] + labels=[] + for i in range(240): + all_trials_eeg_data=datas[trials_index[i]]# 单张图片的3个trials + ERPS_temp = np.zeros([1, 64, 800]) + labels_temp=np.asarray([c_labels[trials_index[i][0]]]) + for j in range(1):# 计算1个ERP + each_3_data=all_trials_eeg_data[3*j:3*(j+1)]# 3个3个截取 + ERPS_temp[j]=np.mean(each_3_data,axis=0) + if i==0: + ERPs=ERPS_temp + labels=labels_temp + else: + ERPs=np.concatenate((ERPs,ERPS_temp),axis=0) + labels=np.concatenate((labels,labels_temp),axis=0) + print('ERPs.shape:', ERPs.shape) + print('labels.shape:', labels.shape) + + # 保存数据 + name = 'ERP_'+str(no+36) + '.mat' # 带后缀的文件名 + save_path = 'EEG_preprocessing/ERP_data/' + name + sio.savemat(save_path, {"ERPs":ERPs, 'labels': labels}) + print("(total)ERPs and Labels Saved in ", save_path) + + # 对每个人的ERP分为10组，划分测试集 + xyERPs = np.zeros((24,64,800)) + xylabels = np.zeros((24,)) + ERPs = np.array(ERPs) + labels = np.array(labels) + save_path = 'EEG_preprocessing/Generated_ERPs/' + str(no+36) + if not os.path.exists(save_path): + os.mkdir(save_path) + for i in range(10): + xyERPs = ERPs[i*24:(i+1)*24,: ,: ] + xylabels = labels[i*24:(i+1)*24] + name = 'S' + str(i) + '.mat' # 带后缀的文件名 + sio.savemat(save_path + '/' + name, {"ERPs":xyERPs, 'labels': xylabels}) + print("ERPs and Labels Saved in ", save_path + '/' + name) + +if __name__=='__main__': + # loadpath = 'EEG_preprocessing/data/' + # file_list = glob.glob(loadpath + '*_filt_cut_ic_remove_ic.mat') + for i in range(1, 36): + # print(file_list[i]) + # dirname, filename = os.path.split(file_list[i]) + # filename = filename.split('_',1)[0] + # file_label = glob.glob(loadpath + filename+ '.mat') + # print(file_label[0]) + # preprocessing(file_list[i], file_label[0], i) + train_large_all(subjects=[0,1,2,3,4,5,6,7,8,9],save_path = 'EEG_preprocessing/ERP_classifier/Trained_classifier/LSTM.ckpt', + data_dir='EEG_preprocessing/Generated_ERPs/'+str(i), is_reload=False,n_epoch=20) + \ No newline at end of file diff --git a/lab-miots/ERP_classifier/.keep b/lab-miots/ERP_classifier/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/ERP_classifier/LSTM_classifier.py b/lab-miots/ERP_classifier/LSTM_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..f67178195e553b8dbf13740a6a3a08554a69356a --- /dev/null +++ b/lab-miots/ERP_classifier/LSTM_classifier.py @@ -0,0 +1,352 @@ +import pandas as pd +import torch.nn as nn +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +import numpy as np +import matplotlib.pyplot as plt +import scipy.io as sio +import time +import os +from test_classifier import Test_Model,train_Test_Model +from utils import EEGdatasDataset +from classifier import lstm_classifier +import h5py +from utils import EarlyStopping +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +def load_single_ERP_data(data_path): + x = sio.loadmat(data_path) + datas = x['ERPs'] + labels=x['labels'][0] + #labels = labels.reshape(24,1) + return datas,labels + +def load_certain_subjects_data(data_dir='EEG_preprocessing/ERP_data',subjects=[1,2,3]): + all_datas = [] + all_labels = [] + file_cnt=0 + for i in subjects: + file_name='ERP_'+str(i)+'.mat' + file_path_temp = os.path.join(data_dir, file_name) + datas, labels = load_single_ERP_data(file_path_temp) + file_cnt+=1 + print('processing{},{}'.format(file_name, file_cnt)) + if file_cnt == 1: + all_datas = datas + all_labels = labels + else: + all_datas = np.concatenate((all_datas, datas), axis=0) + all_labels = np.concatenate((all_labels, labels), axis=0) + return all_datas, all_labels + +def my_test_m_data(model,subjects=[1,2,3], data_dir='EEG_preprocessing/ERP_data'): + all_mat_file = os.walk(data_dir) + file_cnt = 0 + accuracies = [] + file_names = [] + choice = '' + + for i in range(10): + if i not in subjects: + file_name = 'ERP_'+str(i) + '.mat' + file_cnt += 1 + _start_time = time.time() + file_path_temp = os.path.join(data_dir, file_name) + datas, labels = load_single_ERP_data(file_path_temp) # datas(N*600*60) + datas=datas.swapaxes(1,2) + batch_size =32 + test_data = torch.from_numpy(datas).type(torch.FloatTensor) + test_label = torch.from_numpy(labels).type(torch.LongTensor) + + test_datas = EEGdatasDataset(label=test_label, data=test_data) + testloader = DataLoader(test_datas, batch_size=batch_size) + loss, accuracy = Test_Model(model, testloader) + accuracies.append(accuracy) + file_names.append(file_name) + _end_time = time.time() + print("time: %.2fs\n %d:\t filename: %s\t test loss:%.2f\t accuracy:%.2f\t " % ( + _end_time - _start_time, i, file_name, loss, accuracy)) + + accuracies = np.asarray(accuracies) + plt.plot(accuracies) + plt.title('accuracy') + plt.ylabel('accuracy') + plt.xlabel('file') + print(choice + 'test data mean accuracy is: ', accuracies.mean()) + plt.show() + plt.close() + +def Train_Large_Model(model,f_datas,subjects,save_path,each=3,n_epoch=20, learning_rate=1e-3,step_size=15,gamma=0.9): + criterion = nn.NLLLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) + + print('Start training!') + all_acc=[] + all_val_acc=[] + all_loss=[] + all_val_loss=[] + #N*16*600 + sub_cnts=len(subjects) + batch_size=32 + patience=20 + early_stopping = EarlyStopping(save_path=save_path,patience=patience, verbose=True) # 关于 EarlyStopping 的代码可先看博客后面的内容 + + for epoch in range(n_epoch): + validation_loss, validation_evaluation, validation_cnts =0.0,[],0 + model.to(device).train() + running_loss = 0.0 + evaluation = [] + _start_time = time.time() + cnts=0 + for f in f_datas: + train_data=f['train_data'][:] + test_data=f['test_data'][:] + train_label=f['train_label'][:] + test_label=f['test_label'][:] + train_data = EEGdatasDataset(label=train_label, data=train_data) + test_data = EEGdatasDataset(label=test_label, data=test_data) + + trainloader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + testloader = DataLoader(test_data, batch_size=batch_size) + model.train() + for i, data in enumerate(trainloader): + # get the inputs; data is a list of [inputs, labels] + features, labels= data + features=features.to(device) + labels=labels.to(device) + optimizer.zero_grad() + outputs = model(features) + predicted = outputs.argmax(1) + evaluation.append((predicted == labels).tolist()) + + loss = criterion(outputs, labels.squeeze()) + loss.backward() + # torch.nn.utils.clip_grad_norm(model.parameters(), 5) + optimizer.step() + # print('index:%d,%.5f'%(i,loss.item())) + running_loss += loss.item() + + cnts=cnts+i+1 + + model.eval() + for i, data in enumerate(testloader): + features, labels = data + features = features.to(device) + labels = labels.to(device) + outputs = model(features) + predicted = outputs.argmax(1) + validation_evaluation.append((predicted == labels).tolist()) + loss = criterion(outputs, labels.squeeze()) + validation_loss += loss.item() + validation_cnts +=i+1 + + running_loss = running_loss / (cnts + 1) + validation_loss=validation_loss/validation_cnts + # print(running_loss) + + evaluation = [item for sublist in evaluation for item in sublist] + running_acc = sum(np.array(evaluation,dtype=object)) / len(evaluation) + + validation_evaluation = [item for sublist in validation_evaluation for item in sublist] + validation_acc = sum(validation_evaluation)/ len(validation_evaluation) + if epoch>5: + early_stopping(validation_acc, model) + _end_time = time.time() + scheduler.step() + + print('Epoch %d/%3d\n time: %.2fs\t loss: %.4f\tAccuracy : %.4f\t \tval-loss: %.4f\tval-Accuracy : %.4f\tLearning Rate : %.5f' % + (epoch + 1, n_epoch,_end_time-_start_time, running_loss, running_acc, validation_loss, validation_acc,optimizer.state_dict()['param_groups'][0]['lr'])) + all_loss.append(running_loss) + all_acc.append(running_acc) + all_val_acc.append(validation_acc) + all_val_loss.append(validation_loss) + # if epoch%10==9: + # torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + # if running_acc>=0.90 and validation_acc>=0.90: + # break + if early_stopping.early_stop: + print("Early stopping") + break + + +def train_large_all(subjects=[0,1,2,3,4,5,6,7],save_path = 'EEG_preprocessing/ERP_classifier/Trained_classifier/LSTM.ckpt',is_reload=False,n_epoch=20,batch_size=32): + f_data = [] + for i, subject in enumerate(subjects): + temp = [] + temp.append(subject) + datas, labels = load_certain_subjects_data(data_dir='EEG_preprocessing/ERP_data',subjects=temp) + # N*124*32 + datas=datas.swapaxes(1,2) + print(datas.shape) + length = len(datas) + index = [j for j in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + + train_data = datas[:int(5 * length//6)] + test_data = datas[int(1 * length//6):] + train_label = labels[:int(5 * length//6)] + test_label = labels[int(1 * length//6):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # N*124*32 + # train_label = torch.from_numpy(train_label).type(torch.LongTensor) + # test_label = torch.from_numpy(test_label).type(torch.LongTensor) + train_label = torch.from_numpy(train_label) + test_label = torch.from_numpy(test_label) + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i), 'w') + # f['train_data'] = train_data + # f['test_data'] = test_data + # f['train_label'] = train_label + # f['test_label'] = test_label + d1=f.create_dataset('train_data', data=train_data, chunks=True, compression="gzip") + d2=f.create_dataset('test_data', data=test_data, chunks=True, compression="gzip") + d3=f.create_dataset('train_label', data=train_label, chunks=True, compression="gzip") + d4=f.create_dataset('test_label', data=test_label, chunks=True, compression="gzip") + f.close() + + f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_' + str(i), 'r') + f_data.append(f) + print('Data save in ' + 'loader_temp_' + str(i) + 'ready!') + # if sub_cnts > 5: + # temp = [] + # for i, subject in enumerate(subjects): + # temp.append(subject) + # if i % 5 == 4: + # datas, labels = load_certain_subjects_data(data_dir='EEG_preprocessing/Generated_ERPs/'+str(j+1),subjects=temp) + # # N*124*32 + # datas=datas.swapaxes(1,2) + # print(datas.shape) + # length = len(datas) + # index = [i for i in range(length)] + # np.random.shuffle(index) + # datas = datas[index] + # labels = labels[index] + + # train_data = datas[:int(3 * length//5)] + # test_data = datas[int(2 * length//5):] + # train_label = labels[:int(3 * length//5)] + # test_label = labels[int(2 * length//5):] + + # train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + # test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # # N*124*32 + # # train_label = torch.from_numpy(train_label).type(torch.LongTensor) + # # test_label = torch.from_numpy(test_label).type(torch.LongTensor) + # train_label = torch.from_numpy(train_label) + # test_label = torch.from_numpy(test_label) + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_'+str(j+1)+'_'+str(i // 5), 'w') + # f['train_data'] = train_data + # f['test_data'] = test_data + # f['train_label'] = train_label + # f['test_label'] = test_label + # f.close() + + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_'+str(j+1)+'_'+str(i // 5), 'r') + # f_data.append(f) + # print('Data save in ' + 'loader_temp_'+str(j+1)+'_'+str(i // 5)+ 'ready!') + # temp = [] + # if i % 5 != 4: + # datas, labels = load_certain_subjects_data(data_dir='EEG_preprocessing/Generated_ERPs/'+str(j+1), subjects=temp) + # # N*124*32 + # datas=datas.swapaxes(1,2) + # print(datas.shape) + + # length = len(datas) + # index = [i for i in range(length)] + # np.random.shuffle(index) + # datas = datas[index] + # labels = labels[index] + + # # train_data = datas[:int(5 * length // 6)] + # # test_data = datas[int(1 * length // 6):] + # # train_label = labels[:int(5 * length // 6)] + # # test_label = labels[int(1 * length // 6):] + # train_data = datas[:int(3 * length//5)] + # test_data = datas[int(2 * length//5):] + # train_label = labels[:int(3 * length//5)] + # test_label = labels[int(2 * length//5):] + + # train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + # test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # # train_data = train_data.unsqueeze(1) + # # test_data = test_data.unsqueeze(1) + # # N*1*124*32 + # train_label = torch.from_numpy(train_label).type(torch.LongTensor) + # test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_'+str(j+1)+'_'+str(i // 5), 'w') + # f['train_data'] = train_data + # f['test_data'] = test_data + # f['train_label'] = train_label + # f['test_label'] = test_label + # f.close() + + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp_'+str(j+1)+'_'+str(i // 5), 'r') + # f_data.append(f) + # print('Data save in ' + 'loader_temp_'+str(j+1)+'_'+str(i // 5)+ 'ready!') + + # else: + # datas, labels = load_certain_subjects_data(data_dir='EEG_preprocessing/Generated_ERPs/'+str(j+1), subjects=subjects) + # # N*124*32 + # datas = datas.swapaxes(1, 2) + # print(datas.shape) + + # length = len(datas) + # index = [i for i in range(length)] + # np.random.shuffle(index) + # datas = datas[index] + # labels = labels[index] + + # # train_data = datas[:int(5 * length // 6)] + # # test_data = datas[int(1 * length // 6):] + # # train_label = labels[:int(5 * length // 6)] + # # test_label = labels[int(1 * length // 6):] + # train_data = datas[:int(3 * length//5)] + # test_data = datas[int(2 * length//5):] + # train_label = labels[:int(3 * length//5)] + # test_label = labels[int(2 * length//5):] + + # train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + # test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + # # train_data = train_data.unsqueeze(1) + # # test_data = test_data.unsqueeze(1) + # # N*1*124*32 + # train_label = torch.from_numpy(train_label).type(torch.LongTensor) + # test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp', 'w') + # f['train_data'] = train_data + # f['test_data'] = test_data + # f['train_label'] = train_label + # f['test_label'] = test_label + # f.close() + # f = h5py.File('EEG_preprocessing/ERP_classifier/loader_temp/loader_temp', 'r') + # f_data.append(f) + # print('Data save in loader_temp ready!') + + model=lstm_classifier(input_size=64, classifier_size=2) + if is_reload: + reload_path=save_path + if os.path.isfile(reload_path): + model.load_state_dict(torch.load(reload_path)) + print('model reload done!') + + model = model.to(device) + Train_Large_Model(model.train(),f_datas=f_data,save_path=save_path,subjects=subjects,learning_rate=1e-3,step_size=15,n_epoch=n_epoch) + +if __name__=='__main__': + train_large_all(subjects=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30, + 31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60, + 61,62,63,64,65,66,67,68,69,70,71,72], + save_path = 'EEG_preprocessing/ERP_classifier/Trained_classifier/LSTM.ckpt',is_reload=False,n_epoch=30) + + diff --git a/lab-miots/ERP_classifier/Trained_classifier/EEGNet.ckpt b/lab-miots/ERP_classifier/Trained_classifier/EEGNet.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..6671f8f042f41c8520e7286a999d4a8db125aff3 Binary files /dev/null and b/lab-miots/ERP_classifier/Trained_classifier/EEGNet.ckpt differ diff --git a/lab-miots/ERP_classifier/Trained_classifier/EEGNet_temp.ckpt b/lab-miots/ERP_classifier/Trained_classifier/EEGNet_temp.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..260f8a9cfb33cbff12d1a6672a3bdd9bbf719e98 Binary files /dev/null and b/lab-miots/ERP_classifier/Trained_classifier/EEGNet_temp.ckpt differ diff --git a/lab-miots/ERP_classifier/Trained_classifier/LSTM.ckpt b/lab-miots/ERP_classifier/Trained_classifier/LSTM.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..b44e704c89868cd1cc3e885b7dcbd3d29c095d51 Binary files /dev/null and b/lab-miots/ERP_classifier/Trained_classifier/LSTM.ckpt differ diff --git a/lab-miots/ERP_classifier/Trained_classifier/LSTM_CNN.ckpt b/lab-miots/ERP_classifier/Trained_classifier/LSTM_CNN.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..d54062b8750ceafe74ae6ac4e2d237b0f7fafb90 Binary files /dev/null and b/lab-miots/ERP_classifier/Trained_classifier/LSTM_CNN.ckpt differ diff --git a/lab-miots/ERP_classifier/classifier.py b/lab-miots/ERP_classifier/classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..0caa174d657e928a562654820b7a6dd7f39a9dbe --- /dev/null +++ b/lab-miots/ERP_classifier/classifier.py @@ -0,0 +1,325 @@ +import torch.nn as nn +import torch +import os +import torch.nn.functional as F +import torch.nn.init as init +from torch.autograd import Variable + +class Res_Block(nn.Module): + def __init__(self, Nin=512, out=512, ksize=5, stride=1): + super(Res_Block, self).__init__() + self.conv1 = nn.Conv1d(Nin, out, ksize, stride,padding=2) + self.elu = nn.ELU() + self.bn = nn.BatchNorm1d(out) + + def forward(self, input): + x = input + x = self.conv1(x) + x = self.bn(x) + x = self.elu(x) + mid=x + x = self.conv1(x) + x = self.bn(x) + + x = x + mid + output = self.elu(x) + return output + + +class Conv_Res(nn.Module): + def __init__(self): + super(Conv_Res, self).__init__() + self.Lconv=nn.Sequential( + nn.Conv1d(in_channels=124,out_channels=512,kernel_size=5), + nn.BatchNorm1d(512), + nn.ELU()) + self.Lres1=Res_Block() + self.Lres2=Res_Block() + self.fc1=nn.Sequential( + nn.Linear(in_features=14336,out_features=500), + nn.Dropout(0.5), + nn.ReLU() + ) + self.fc2 = nn.Sequential( + nn.Linear(in_features=500, out_features=100), + nn.Dropout(0.5), + nn.ReLU() + ) + self.fc3 =nn.Linear(in_features=100, out_features=6) + def forward(self,x): + size_1=x.shape[0] + outs=self.Lconv(x) + outs=self.Lres1(outs) + outs=self.Lres2(outs) + outs=outs.reshape(size_1,-1) + outs=self.fc1(outs) + outs=self.fc2(outs) + outs=self.fc3(outs) + return F.softmax(outs,dim=1) + + +class CNN_LSTM(nn.Module): + ''' + define model used CNN+LSTM + ''' + def __init__(self): + super(CNN_LSTM, self).__init__() + self.conv1 = nn.Conv1d(in_channels=124, out_channels=64, kernel_size=5, stride=1, bias=False) + self.con2 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3) + self.con3 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, stride=1) + self.batch_normalization = nn.BatchNorm1d(64) + self.relu = nn.ReLU() + self.leaky_relu=nn.LeakyReLU() + self.dropout = nn.Dropout(0.5) + self.lstm1 = nn.LSTM(input_size=64, hidden_size=64, dropout=0.5, num_layers=2,batch_first=True) + self.lstm2 = nn.LSTM(input_size=64, hidden_size=32, num_layers=1,batch_first=True) + self.dense = nn.Linear(32,6) + + + def initmodel(self): + '''Init self parameters.''' + for m in self.modules(): + if isinstance(m, nn.Conv1d): + init.xavier_normal_(m.weight,gain=3) + # init.normal_(m.weight, mean=3, std=1) + # init.xavier_normal_(m.weight) + # if m.bias != 0: + # init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm1d): + init.constant_(m.weight, 3) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + # init.normal_(m.weight, std=1e-3) + # init.normal_(m.weight, mean=3, std=1) + init.xavier_normal_(m.weight,gain=3) + elif isinstance(m, nn.LSTM): + if m.num_layers==2: + init.orthogonal_(m.weight_ih_l0) + init.orthogonal_(m.weight_hh_l0) + + init.orthogonal_(m.weight_ih_l1) + init.orthogonal_(m.weight_hh_l1) + + init.constant_(m.bias_ih_l0, 0) + init.constant_(m.bias_hh_l0, 0) + + init.constant_(m.bias_ih_l1, 0) + init.constant_(m.bias_hh_l1, 0) + else: + init.orthogonal_(m.weight_ih_l0) + init.orthogonal_(m.weight_hh_l0) + init.constant_(m.bias_ih_l0, 0) + init.constant_(m.bias_hh_l0, 0) + + def forward(self, input): + # input shape N*60*307, (N,) + batch_size = input.shape[0] + out = self.conv1(input) # [N, 64, 293] + #print(out.shape) + out = self.relu(out) # [N, 64, 293] + + # out = stratified_norm(out, identity) + + out = self.con2(out) # [N, 64, 291] + #print(out.shape) + out = self.leaky_relu(out) + # out=stratified_norm(out,identity) + + out = self.con3(out) # [N, 64, 145] + #print(out.shape) + out = self.batch_normalization(out) # [N, 64, 145] + # + out = self.dropout(out) # [N, 64, 145] + # + out = self.con2(out) # [N, 64, 143] + #print(out.shape) + out = self.con3(out) # [N, 64, 71] + #print(out.shape) + out = self.batch_normalization(out) # [N, 64, 71] + # 71是time_len + out = out.permute(0, 2, 1) # [N,71,64] + out, _ = self.lstm1(out) ##[N, 71,64] + #print(out.shape) + out = self.lstm2(out)[0][:, -1, :] # [N,32]只取最后的out + #print(out.shape) + out = self.dropout(out) + out = out.reshape(batch_size, -1) + # out=self.dense1(out) + # out=self.dense2(out) + # out=self.dense3(out) + out = self.dense(out) # [N,3] + return F.log_softmax(out, dim=1) + + +# #X.shape - (#samples, 1, #timepoints, #channels) +# class EEGNet(nn.Module): +# def __init__(self,C,T): +# super(EEGNet, self).__init__() +# self.T = T +# self.C=C +# +# # Layer 1 +# self.conv1 = nn.Conv1d(1, 16, (1, self.C), padding=0) +# self.batchnorm1 = nn.BatchNorm2d(16, False) +# +# # Layer 2 +# self.padding1 = nn.ZeroPad2d((16, 17, 0, 1)) +# self.conv2 = nn.Conv2d(1, 4, (2, 32)) +# self.batchnorm2 = nn.BatchNorm2d(4, False) +# self.pooling2 = nn.MaxPool2d(2, 4) +# +# # Layer 3 +# self.padding2 = nn.ZeroPad2d((2, 1, 4, 3)) +# self.conv3 = nn.Conv2d(4, 4, (8, 4)) +# self.batchnorm3 = nn.BatchNorm2d(4, False) +# self.pooling3 = nn.MaxPool2d((2, 4)) +# +# # FC Layer +# # NOTE: This dimension will depend on the number of timestamps per sample in your data. +# # I have 120 timepoints. +# self.fc1 = nn.Linear(4 * 2 * 7, 1) +# +# def forward(self, x): +# # Layer 1 +# +# x = F.elu(self.conv1(x)) +# +# x = self.batchnorm1(x) +# x = F.dropout(x, 0.25) +# x = x.permute(0, 3, 1, 2) +# +# +# # Layer 2 +# x = self.padding1(x) +# x = F.elu(self.conv2(x)) +# x = self.batchnorm2(x) +# x = F.dropout(x, 0.25) +# x = self.pooling2(x) +# +# # Layer 3 +# x = self.padding2(x) +# x = F.elu(self.conv3(x)) +# x = self.batchnorm3(x) +# x = F.dropout(x, 0.25) +# x = self.pooling3(x) +# +# # FC Layer +# x = x.view(-1, 4 * 2 * 7) +# x = F.log_softmax(self.fc1(x),dim=1) +# return x + + +#X.shape - (#samples, 1, #timepoints, #channels) +class EEGNet(nn.Module): + def __init__(self,C,T): + super(EEGNet, self).__init__() + self.T = T + self.C=C + + # Layer 1 + #input 1*C*T + self.conv1 = nn.Conv1d(1, 16, (self.C,1), padding=0) + self.batchnorm1 = nn.BatchNorm2d(16, False) + + # Layer 2 + self.padding1 = nn.ZeroPad2d((16, 15, 0, 1)) + self.conv2 = nn.Conv2d(1, 4, (2, 32)) + self.batchnorm2 = nn.BatchNorm2d(4, False) + self.pooling2 = nn.MaxPool2d(kernel_size=(2, 4)) + + # Layer 3 + self.padding2 = nn.ZeroPad2d((2, 1, 4, 3)) + self.conv3 = nn.Conv2d(4, 4, (8, 4)) + self.batchnorm3 = nn.BatchNorm2d(4, False) + self.pooling3 = nn.MaxPool2d((2, 4)) + + # FC Layer + # NOTE: This dimension will depend on the number of timestamps per sample in your data. + # I have 120 timepoints. + self.fc1 = nn.Linear(4 * 4 * self.T//16,6) + + def forward(self, x): + # Layer 1 + + x = F.elu(self.conv1(x)) + x = self.batchnorm1(x) + x = F.dropout(x, 0.25) + x = x.permute(0, 2, 1, 3) + + + + + # Layer 2 + x = self.padding1(x) + + x = F.elu(self.conv2(x)) + + x = self.batchnorm2(x) + x = self.pooling2(x) + + x = F.dropout(x, 0.25) + # + # print(x.shape) + # # Layer 3 + + x = self.padding2(x) + x = F.elu(self.conv3(x)) + + x = self.batchnorm3(x) + x = self.pooling3(x) + x = F.dropout(x, 0.25) + # print(x.shape) + # + # # FC Layer + x = x.view(-1, 4 * 4 * self.T//16) + x = F.log_softmax(self.fc1(x),dim=1) + return x + + +class lstm_classifier(nn.Module): + + def __init__(self, input_size=124, lstm_size=124, lstm_layers=1, lstm_output_size=124, classifier_size=6): + # Call parent + super().__init__() + # Define parameters + self.input_size = input_size + self.lstm_size = lstm_size + self.lstm_layers = lstm_layers + self.output_size = lstm_output_size + + # Define internal modules + self.lstm = nn.LSTM(input_size, lstm_size, num_layers=lstm_layers, batch_first=True) + self.output = nn.Linear(lstm_size, lstm_output_size) + self.classifier = nn.Linear(lstm_output_size, classifier_size) + + def forward(self, x): + # Prepare LSTM initiale state + batch_size = x.size(0) + lstm_init = (torch.zeros(self.lstm_layers, batch_size, self.lstm_size), + torch.zeros(self.lstm_layers, batch_size, self.lstm_size),) + if x.is_cuda: lstm_init = (lstm_init[0].cuda(), lstm_init[1].cuda()) + + # Forward LSTM and get final state + x = self.lstm(x, lstm_init)[0][:, -1,:] + # x = self.lstm(x)[0][:, -1, :] + # Forward output + x = F.relu(self.output(x)) + x = self.classifier((x)) + x = F.log_softmax(x,dim=1) + return x + + +if __name__=='__main__': + # model=Res_Block() + # model=Conv_Res() + # model=EEGNet(C=124,T=32) + model=lstm_classifier() + x=torch.randn((32,32,124)) + # x = torch.randn((100, 1,120, 64)) + # x = torch.randn((10, 512, 32)) + y=model(x) + print(y.shape) + + # model=Latent_CNN_LSTM() + # x=torch.randn((10,310,300)) + # model(x) diff --git a/lab-miots/ERP_classifier/test_classifier.py b/lab-miots/ERP_classifier/test_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..25ecca5ff6893bac3d4b47334610111125315a1a --- /dev/null +++ b/lab-miots/ERP_classifier/test_classifier.py @@ -0,0 +1,107 @@ +import torch +from torch.utils.data import DataLoader +from utils import load_single_data,load_single_ERP_data +import numpy as np +import matplotlib.pyplot as plt +import time +import os +from utils import EEGdatasDataset +import torch.nn as nn + +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +def train_Test_Model(net,Testloader,criterion=nn.CrossEntropyLoss()): + ''' + test model + :param net: model we test + :param Testloader: testloader + :param criterion: + :return: loss and accuracy + ''' + running_loss = 0.0 + evaluation = [] + for i, data in enumerate(Testloader, 0): + features, labels= data + features=features.to(device) + labels=labels.to(device) + outputs = net(features) + _, predicted = torch.max(outputs.data, 1) + evaluation.append((predicted == labels).tolist()) + loss = criterion(outputs, labels) + running_loss += loss.item() + cnts=i+1 + return running_loss, evaluation,cnts + +def Test_Model(net,Testloader,criterion=nn.CrossEntropyLoss()): + ''' + test model + :param net: model we test + :param Testloader: testloader + :param criterion: + :return: loss and accuracy + ''' + running_loss = 0.0 + evaluation = [] + for i, data in enumerate(Testloader, 0): + features, labels= data + features=features.to(device) + labels=labels.to(device) + outputs = net(features) + _, predicted = torch.max(outputs.data, 1) + evaluation.append((predicted == labels).tolist()) + loss = criterion(outputs, labels) + running_loss += loss.item() + running_loss = running_loss / (i + 1) + evaluation = [item for sublist in evaluation for item in sublist] + running_acc = sum(evaluation) / len(evaluation) + + return running_loss, running_acc + + + +def my_test_m_data(model,subjects=[1,2,3], + data_dir='E:\\browser_download\\图片脑电数据集\\stanford_数据集_10subs_72exm\\'): + ''' + + :param model: + :param m: data files' number we used + :param data_dir: + :return: show the result(different data files' loss,accuracies) + ''' + + all_mat_file = os.walk(data_dir) + file_cnt = 0 + accuracies = [] + file_names = [] + choice = '' + + for i in range(1,11): + if i not in subjects: + file_name = 'S'+str(i) + '.mat' + file_cnt += 1 + _start_time = time.time() + file_path_temp = os.path.join(data_dir, file_name) + datas, labels = load_single_ERP_data(file_path_temp) # datas(N*600*60) + batch_size =32 + test_data = torch.from_numpy(datas).type(torch.FloatTensor) + test_label = torch.from_numpy(labels).type(torch.LongTensor) + + test_data = test_data.unsqueeze(1) + + test_datas = EEGdatasDataset(label=test_label, data=test_data) + testloader = DataLoader(test_datas, batch_size=batch_size) + loss, accuracy = Test_Model(model, testloader) + accuracies.append(accuracy) + file_names.append(file_name) + _end_time = time.time() + print("time: %.2fs\n %d:\t filename: %s\t test loss:%.2f\t accuracy:%.2f\t " % ( + _end_time - _start_time, i, file_name, loss, accuracy)) + + accuracies = np.asarray(accuracies) + plt.plot(accuracies) + plt.title('accuracy') + plt.ylabel('accuracy') + plt.xlabel('file') + print(choice + ' test data mean accuracy is: ', accuracies.mean()) + plt.show() + plt.close() diff --git a/lab-miots/ERP_classifier/train_classifier.py b/lab-miots/ERP_classifier/train_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..4cff7ae35d5918e81d7d7b0128eab8a1fa369c32 --- /dev/null +++ b/lab-miots/ERP_classifier/train_classifier.py @@ -0,0 +1,348 @@ +'''预训练LSTM Classifier''' +import torch.nn as nn +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +import numpy as np +import matplotlib.pyplot as plt +import time +import os +from test_classifier import Test_Model,my_test_m_data,train_Test_Model +from utils import EEGdatasDataset,load_certain_subjects_data +from classifier import CNN_LSTM,Conv_Res,EEGNet +import h5py +from utils import EarlyStopping +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' +device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') +#用来测试出去encoder之后只看classifier +def Train_Model(model, trainloader, testloader, save_path,each=3,subjects=[10,11,12],n_epoch=20, learning_rate=0.001,step_size=15,gamma=0.9): + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) + print('Start training!') + all_acc=[] + all_val_acc=[] + all_loss=[] + all_val_loss=[] + #N*16*600 + + + for epoch in range(n_epoch): + model.to(device).train() + running_loss = 0.0 + evaluation = [] + _start_time=time.time() + for i, data in enumerate(trainloader, 0): + # get the inputs; data is a list of [inputs, labels] + features, labels= data + features=features.to(device) + labels=labels.to(device) + optimizer.zero_grad() + outputs = model(features) + _, predicted = torch.max(outputs.data, 1) + evaluation.append((predicted == labels).tolist()) + + loss = criterion(outputs, labels) + loss.backward() + # torch.nn.utils.clip_grad_norm(model.parameters(), 5) + optimizer.step() + # print('index:%d,%.5f'%(i,loss.item())) + running_loss += loss.item() + + scheduler.step() + _end_time=time.time() + running_loss = running_loss / (i + 1) + # print(running_loss) + evaluation = [item for sublist in evaluation for item in sublist] + running_acc = sum(evaluation) / len(evaluation) + validation_loss, validation_acc = Test_Model(model.eval(),testloader) + + print('Epoch %d/%3d\n time: %.2fs\t loss: %.4f\tAccuracy : %.4f\t \tval-loss: %.4f\tval-Accuracy : %.4f\tLearning Rate : %.5f' % + (epoch + 1, n_epoch,_end_time-_start_time, running_loss, running_acc, validation_loss, validation_acc,optimizer.state_dict()['param_groups'][0]['lr'])) + all_loss.append(running_loss) + all_acc.append(running_acc) + all_val_acc.append(validation_acc) + all_val_loss.append(validation_loss) + if epoch%10==9: + torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + print('Model saved in {}'.format(save_path)) + if running_acc>=0.89 and validation_acc>=0.89: + break + + + # if epoch % 6 == 5: + # torch.save(model.state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + print('Model saved in {}'.format(save_path)) + # plt.plot(all_acc) + # plt.plot(all_val_acc) + # plt.title('model accuracy') + # plt.ylabel('accuracy') + # plt.xlabel('epoch') + # plt.legend(['train', 'test'], loc='upper left') + # # plt.savefig('./all_accuracy.png') + # # plt.close() + # plt.show() + # # summarize history for loss + # plt.plot(all_loss) + # plt.plot(all_val_loss) + # plt.title('model loss') + # plt.ylabel('loss') + # plt.xlabel('epoch') + # plt.legend(['train', 'test'], loc='upper left') + # plt.show() + # plt.close() + my_test_m_data(model.to(device).eval(),subjects=subjects,each=each,is_session=True) + my_test_m_data(model.to(device).eval(),subjects=subjects, each=each, is_subject=True) + # + + +def train_all(subjects=[1, 2, 3], each=1, n_epoch=50): + sub_cnts = len(subjects) + save_path = './Pretrained_classifier/Con_Res' + str(sub_cnts) + '_' + str(each) + '.ckpt' + # datas,labels=load_all_LSTM_Generated_data() + # N*600*60 + + datas, labels = load_certain_subjects_data(subjects=subjects, each=each) + print(datas.shape) + datas = datas.swapaxes(1, 2) + # N*310*300 + length = len(datas) + index = [i for i in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + batch_size = 5 * sub_cnts * each * 2 + + train_data = datas[:int(0.8 * length)] + test_data = datas[int(.8 * length):] + train_label = labels[:int(0.8 * length)] + test_label = labels[int(.8 * length):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + + + + train_label = torch.from_numpy(train_label).type(torch.LongTensor) + test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + f = h5py.File('loader_temp', 'w') + f['train_data'] = train_data + f['test_data'] = test_data + f['train_label'] = train_label + f['test_label'] = test_label + f.close() + print('Data ready!') + + train_data = EEGdatasDataset(label=train_label, data=train_data) + test_data = EEGdatasDataset(label=test_label, data=test_data) + + trainloader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + testloader = DataLoader(test_data, batch_size=batch_size) + + # f=h5py.File('loader_temp','w') + # f['trainloader']=trainloader + # f['testloader']=testloader + # f.close() + print('Data ready!') + + # model = CNN_LSTM() + # model.initmodel() + model=Conv_Res() + if os.path.isfile(save_path): + model.load_state_dict(torch.load(save_path)) + print('model reload done!') + # + model = model.to(device) + + Train_Model(model.train(), trainloader, testloader, save_path, subjects=subjects, each=each, learning_rate=1e-3, + step_size=5, n_epoch=n_epoch) + + + +def Train_Large_Model(model,train_loader,test_loader,subjects,save_path,each=3,n_epoch=20, learning_rate=0.001,step_size=15,gamma=0.9): + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) + print('Start training!') + all_acc=[] + all_val_acc=[] + all_loss=[] + all_val_loss=[] + #N*16*600 + patience=20 + early_stopping = EarlyStopping(save_path=save_path,patience=patience, verbose=True) # 关于 EarlyStopping 的代码可先看博客后面的内容 + + for epoch in range(n_epoch): + validation_loss, validation_evaluation, validation_cnts =0.0,[],0 + model.to(device).train() + running_loss = 0.0 + evaluation = [] + _start_time = time.time() + cnts=0 + for i, data in enumerate(train_loader, 0): + # get the inputs; data is a list of [inputs, labels] + features, labels= data + features=features.to(device) + labels=labels.to(device) + optimizer.zero_grad() + outputs = model(features) + _, predicted = torch.max(outputs.data, 1) + evaluation.append((predicted == labels).tolist()) + # print(outputs) + # print(labels) + loss = criterion(outputs, labels) + loss.backward() + # torch.nn.utils.clip_grad_norm(model.parameters(), 5) + optimizer.step() + # print('index:%d,%.5f'%(i,loss.item())) + running_loss += loss.item() + + cnts=cnts+i+1 + + model.eval() + for i, data in enumerate(test_loader, 0): + features, labels = data + features = features.to(device) + labels = labels.to(device) + outputs = model(features) + _, predicted = torch.max(outputs.data, 1) + validation_evaluation.append((predicted == labels).tolist()) + loss = criterion(outputs, labels) + validation_loss += loss.item() + validation_cnts +=i+1 + model.to(device).train() + + running_loss = running_loss / (cnts + 1) + validation_loss=validation_loss/validation_cnts + # print(running_loss) + + + evaluation = [item for sublist in evaluation for item in sublist] + running_acc = sum(evaluation) / len(evaluation) + + validation_evaluation = [item for sublist in validation_evaluation for item in sublist] + validation_acc = sum(validation_evaluation) / len(validation_evaluation) + # if epoch>5: + # early_stopping(validation_acc, model) + _end_time = time.time() + scheduler.step() + + + print('Epoch %d/%3d\n time: %.2fs\t loss: %.4f\tAccuracy : %.4f\t \tval-loss: %.4f\tval-Accuracy : %.4f\tLearning Rate : %.5f' % + (epoch + 1, n_epoch,_end_time-_start_time, running_loss, running_acc, validation_loss, validation_acc,optimizer.state_dict()['param_groups'][0]['lr'])) + all_loss.append(running_loss) + all_acc.append(running_acc) + all_val_acc.append(validation_acc) + all_val_loss.append(validation_loss) + # if epoch%10==9: + # torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + if running_acc>=0.89 and validation_acc>=0.89: + break + + # if early_stopping.early_stop: + # print("Early stopping") + # break + + # if epoch % 6 == 5: + # torch.save(model.state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + # torch.save(model.to(torch.device('cpu')).state_dict(), save_path) + # print('Model saved in {}'.format(save_path)) + + # plt.plot(all_acc) + # plt.plot(all_val_acc) + # plt.title('model accuracy') + # plt.ylabel('accuracy') + # plt.xlabel('epoch') + # plt.legend(['train', 'test'], loc='upper left') + # # plt.savefig('./all_accuracy.png') + # # plt.close() + # plt.show() + # # summarize history for loss + # plt.plot(all_loss) + # plt.plot(all_val_loss) + # plt.title('model loss') + # plt.ylabel('loss') + # plt.xlabel('epoch') + # plt.legend(['train', 'test'], loc='upper left') + # plt.show() + # plt.close() + + model=EEGNet(C=124,T=32) + # model.initmodel() + reload_path=save_path + # reload_path='./Pretrained_classifier/LSTM_CNN_12_1' + '.ckpt' + if os.path.isfile(reload_path): + model.load_state_dict(torch.load(reload_path)) + print('model reload done!') + + # model = model.to(device) + + my_test_m_data(model.to(device).eval(),subjects=subjects) + + + + +def train_large_all(subjects=[1],n_epoch=50,batch_size=32): + sub_cnts=len(subjects) + # save_path='./Pretrained_classifier/LSTM_CNN_'+str(sub_cnts)+'_'+str(each)+'.ckpt' + save_path = './Trained_classifier/EEGNet' + '.ckpt' + datas,labels=load_certain_subjects_data(subjects=subjects) + length = len(datas) + index = [i for i in range(length)] + np.random.shuffle(index) + datas = datas[index] + labels = labels[index] + train_data = datas[:int(0.8 * length)] + test_data = datas[int(.8 * length):] + train_label = labels[:int(0.8 * length)] + test_label = labels[int(.8 * length):] + + train_data = torch.from_numpy(train_data).type(torch.FloatTensor) + test_data = torch.from_numpy(test_data).type(torch.FloatTensor) + + + train_label = torch.from_numpy(train_label).type(torch.LongTensor) + test_label = torch.from_numpy(test_label).type(torch.LongTensor) + + train_data = train_data.unsqueeze(1) + test_data = test_data.unsqueeze(1) + + train_data = EEGdatasDataset(label=train_label, data=train_data) + test_data = EEGdatasDataset(label=test_label, data=test_data) + trainloader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + testloader = DataLoader(test_data, batch_size=batch_size) + + # model=CNN_LSTM() + # model.initmodel() + # model=Conv_Res() + model=EEGNet(C=124,T=32) + reload_path=save_path + # reload_path='./Pretrained_classifier/LSTM_CNN_12_1' + '.ckpt' + if os.path.isfile(reload_path): + model.load_state_dict(torch.load(reload_path)) + print('model reload done!') + + model = model.to(device) + # + + + Train_Large_Model(model.train(),train_loader=trainloader,test_loader=testloader,save_path=save_path,subjects=subjects,learning_rate=1e-3,step_size=5,n_epoch=n_epoch) + +if __name__=='__main__': + train_large_all(subjects=[1,2,3],n_epoch=100) + # model=CNN_LSTM() + # model.initmodel() + # save_path = './Pretrained_classifier/LSTM_CNN_1_12_each_1.ckpt' + # if os.path.isfile(save_path): + # model.load_state_dict(torch.load(save_path)) + # print('model reload done!') + # + # model = model.to(device) + # my_test_m_data(model.eval(), subjects=[1,2,3,4,5,6,7,8,9,10,11,12,13,14], each=1, is_session=True) + # my_test_m_data(model.to(device).eval(), subjects=[1,2,3,4,5,6,7,8,9,10,11,12,13,14], each=1, is_subject=True) + diff --git a/lab-miots/ERP_classifier/utils.py b/lab-miots/ERP_classifier/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..98f3999ca38d1c9ed7a6dfa2dd1698776cb617c4 --- /dev/null +++ b/lab-miots/ERP_classifier/utils.py @@ -0,0 +1,178 @@ +import glob +import numpy as np +import os +import scipy.io as sio +import torch +from torch.utils.data.dataset import Dataset +import torch.nn as nn +import random +import torch.nn.init as init + + +class EarlyStopping: + """Early stops the training if validation loss doesn't improve after a given patience.""" + def __init__(self,save_path,patience=7, verbose=False, delta=0.005): + """ + Args: + patience (int): How long to wait after last time validation loss improved. + Default: 7 + verbose (bool): If True, prints a message for each validation loss improvement. + Default: False + delta (float): Minimum change in the monitored quantity to qualify as an improvement. + Default: 0 + """ + self.save_path=save_path + self.patience = patience + self.verbose = verbose + self.counter = 0 + self.best_score = None + self.early_stop = False + self.val_acc_max = np.Inf + self.delta = delta + + def __call__(self, val_acc, model): + + score = val_acc + + if self.best_score is None: + self.best_score = score + self.save_checkpoint(val_acc, model) + elif score < self.best_score + self.delta: + self.counter += 1 + print(f'EarlyStopping counter: {self.counter} out of {self.patience}') + if self.counter >= self.patience: + self.early_stop = True + else: + self.best_score = score + self.save_checkpoint(val_acc, model) + self.counter = 0 + + def save_checkpoint(self, val_acc, model): + '''Saves model when validation loss decrease.''' + if self.verbose: + print(f'Validation acc increased ({self.val_acc_max:.6f} --> {val_acc:.6f}). Saving model in {self.save_path:s}') + torch.save(model.state_dict(), self.save_path) # 这里会存储迄今最优模型的参数 + self.val_acc_max = val_acc + + +def set_seed(seed): + torch.manual_seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + random.seed(seed) + + +class EEGdatasDataset(Dataset): + """EEGLearn Images Dataset from EEG.""" + + def __init__(self,data,label): + self.label = label + self.datas = data + def __len__(self): + return len(self.label) + + def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + data = self.datas[idx] + label = self.label[idx] + sample = (data, label) + return sample + +def load_single_data(data_path): + x = sio.loadmat(data_path) + datas = x['DataEEG'] + labels=x['categoryLabels'][0]-1 + datas=datas.swapaxes(0,2) + datas = datas.swapaxes(1, 2) + # labels = labels.reshape(labels.shape[1], -1).squeeze() + return datas,labels + +def load_single_ERP_data(data_path): + x = sio.loadmat(data_path) + datas = x['ERPs'] + labels = x['labels'] + labels = labels.reshape(24,1) + return datas,labels + +def load_certain_subjects_data(data_dir='Generated_ERPs\\',subjects=[1,2,3]): + all_datas = [] + all_labels = [] + file_cnt=0 + for i in subjects: + file_name='S'+str(i)+'.mat' + file_path_temp = os.path.join(data_dir, file_name) + datas, labels = load_single_ERP_data(file_path_temp) + file_cnt+=1 + print('processing{},{}'.format(file_name, file_cnt)) + if file_cnt == 1: + all_datas = datas + all_labels = labels + else: + all_datas = np.concatenate((all_datas, datas), axis=0) + all_labels = np.concatenate((all_labels, labels), axis=0) + return all_datas, all_labels + +def Generate_Single_Sub_ERPs(sub, file_name): + ''' + 获取事件相关电位,共5188，72images每张图72个trials，把12个trials平均一下 + 则每个测试者，每张图有6个ERP + Returns: + ''' + x = sio.loadmat(file_name) + datas = x['DataEEG'] + datas=datas.swapaxes(0,2) + datas = datas.swapaxes(1, 2) + print(datas.shape) + # 构建标签 + num = datas.shape[0] + exemplar = [[1] * num] * 1 + x['exemplarLabels'] = np.array(exemplar) + cue = int(str(file_name)[-7]) + category = [[cue] * num] * 1 + x['categoryLabels'] = np.array(category) +# print(x['exemplarLabels']) + e_labels = x['exemplarLabels'][0]-1 + c_labels = x['categoryLabels'][0]-1 + trials_index=np.zeros([36,36]).astype(int)#第i行第j列表示第i张图片第j次trial在datas中位置 + note_index=np.zeros([36,]).astype(int)#辅助来生成trials_index矩阵，第i个表示当前第i张图已经统计到了第几个trial + + for index,label in enumerate(e_labels): + if note_index[label]==36: #每张图只要72个trials + continue + trials_index[label,(note_index[label])]=index + note_index[label]+=1 + + ERPs=[] #对每张图开始进行求ERP + labels=[] + for i in range(36): + all_trials_eeg_data=datas[trials_index[i]]#单张图片的72个trials + ERPS_temp = np.zeros([6, 30, 2050]) + labels_temp=np.asarray([c_labels[trials_index[i][0]]]*6) + for j in range(6):#计算6个ERP + each_6_data=all_trials_eeg_data[6*j:6*(j+1)]#12个12个截取 + ERPS_temp[j]=np.mean(each_6_data,axis=0) + if i==0: + ERPs=ERPS_temp + labels=labels_temp + else: + ERPs=np.concatenate((ERPs,ERPS_temp),axis=0) + labels=np.concatenate((labels,labels_temp),axis=0) + + print(ERPs.shape) + print(labels.shape) + # name = os.path.basename(file_name) # 带后缀的文件名 + name = 'S'+str(sub) + '.mat' # 带后缀的文件名 + save_path = 'Generated_ERPs/' + name + sio.savemat(save_path, {"ERPs":ERPs, 'labels': labels}) + + print("ERPs and Labels Saved in ", save_path) + +def Generate_all_Sub_ERPs(file_dir= '..\\EEG_Classification\\EGGdata\\'): + file_list = glob.glob(file_dir+'*.mat') # 得到所有的mat链接 + for i in range(len(file_list)): + Generate_Single_Sub_ERPs(i+1, file_list[i]) + + +if __name__=='__main__': + Generate_all_Sub_ERPs() diff --git a/lab-miots/darknet-market/.gitignore b/lab-miots/darknet-market/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..04c75d5371768f68d740902b0f6812e48f5cb307 --- /dev/null +++ b/lab-miots/darknet-market/.gitignore @@ -0,0 +1,135 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +.idea + +logs + +test/data diff --git a/lab-miots/darknet-market/LICENSE b/lab-miots/darknet-market/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d7992ce2ec7c8c9030cccf2baa98b3d37ce9f148 --- /dev/null +++ b/lab-miots/darknet-market/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 QQQQing + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/lab-miots/darknet-market/README.md b/lab-miots/darknet-market/README.md new file mode 100644 index 0000000000000000000000000000000000000000..61e07fc19886a5c3ed4337569a789fd22344ef6b --- /dev/null +++ b/lab-miots/darknet-market/README.md @@ -0,0 +1,73 @@ +###### 快速运行 +* 需要设定用到的redis以及mysql数据库，在配置文件conf/application.yml的相应位置修改。 +* 需要按照market给出的样例市场配置其相关信息，包括主页url等， +以及xpath——用于解析相应的页面，主要流程就是先从主页url解析分类 +分类信息也可以直接指定url，可以通过一个list指定），然后从每个分类解析物品的url， +当然，可能会需要翻页，现有的分页逻辑如果不满足要求可以修改代码；最后解析物品的信息，可以自己添加字段 +* 最后运行python bootstrap.py即可 +* 页面存储按需存储，可以存到本地文件系统，也可以存到es中 + +###### 具体安装步骤 +Centos7环境 +1 安装mysql-server： +sudo rpm -ivh https://dev.mysql.com/get/mysql57-community-release-el7-11.noarch.rpm +sudo yum -y install mysql-community-server +sudo systemctl start mysqld +sudo systemctl enable mysqld +sudo systemctl status mysqld +cat /var/log/mysqld.log | grep -i 'temporary password' +mysql_secure_installation +root密码可设置为Zft@5012 +设置数据库编码为utf8 +sudo vim /etc/my.cnf增加 +[client] +default-character-set=utf8 + +[mysql] +default-character-set=utf8 + +[mysqld] +collation-server = utf8_unicode_ci +init-connect='SET NAMES utf8' +character-set-server = utf8 +重启Mysql即可 +sudo systemctl restart mysqld + +2 安装Redis +sudo yum install epel-release yum-utils +sudo yum install http://rpms.remirepo.net/enterprise/remi-release-7.rpm +sudo yum-config-manager --enable remi +sudo yum install redis +sudo systemctl start redis +sudo systemctl enable redis +sudo systemctl status redis +设置密码 +redis-cli +redis 127.0.0.1:6379> config set requirepass Zft@5012 +auth Zft@5012 + config get requirepass + 测试 redis 127.0.0.1:6379> config set requirepass Zft@5012 + + 3 配置conf/application.yml + database: + rdb: + type: mysql + database: market + connection: + host: 127.0.0.1 + port: 3306 + user: root + password: Zft@5012 + charset: utf8mb4 + max_connections: 20 + redis: + host: 127.0.0.1 + port: 6379 + password: Zft@5012 + +4 python3环境安装 +anaconda + +5 pip install -r requirements.txt + +6 python3 bootstrap.py diff --git a/lab-miots/darknet-market/bootstrap.py b/lab-miots/darknet-market/bootstrap.py new file mode 100644 index 0000000000000000000000000000000000000000..313dfcdb14fe328f8ae17b426ba76f9326603ffa --- /dev/null +++ b/lab-miots/darknet-market/bootstrap.py @@ -0,0 +1,17 @@ +from sites.chang_an_crawler import ChangAnMarket +from sites.customize import UniversalMarket +from const import CONFIG +from core.dispatcher import dispatcher + +if __name__ == '__main__': + markets = CONFIG['markets'] + x = {} + for market, config in markets.items(): + if config.pop('crawl'): + # dispatcher.register(market, UniversalMarket(name=market, **config)) + if market == "长安不夜城": + crawler = ChangAnMarket() + crawler.dispatch() + else: + dis = UniversalMarket(name=market, **config) + dis.dispatch() diff --git a/lab-miots/darknet-market/components.py b/lab-miots/darknet-market/components.py new file mode 100644 index 0000000000000000000000000000000000000000..8ebe15173ea0531eac3dc34899f65e06b4bfc19d --- /dev/null +++ b/lab-miots/darknet-market/components.py @@ -0,0 +1,42 @@ +import time + +from db.redis import RedisClient +from db.models import Forum, MarketItem, DisplayPoster, DisplayPostings, DisplayPostcomments +from const import CONFIG, RDB +from helper.logger import logger +from helper.scheduler import scheduler + +logger.info('[Components] Start ...') +initialing_start = time.time() + +''' +Relational Database +''' +RDB_CONFIG = CONFIG['database']['rdb'] +logger.info('[Components.RDB] Connecting ...') +logger.info(f'[Components.RDB] Connection arguments : {RDB_CONFIG["connection"]}') +RDB.init(RDB_CONFIG['database'], **RDB_CONFIG['connection']) +RDB.connect() +# RDB = RelationalDB(RDB) + +logger.info(f'[Components.RDB] Creating tables ...') +RDB.create_tables([Forum, MarketItem, DisplayPoster, DisplayPostings, DisplayPostcomments]) + +''' +Redis +''' +REDIS_CONF = CONFIG['database']['redis'] +logger.info('[Components.Redis] Connecting ...') +logger.info(f'[Components.Redis] Connection arguments : {REDIS_CONF}') +REDIS = RedisClient(**REDIS_CONF) +REDIS.ping() + +''' +定时任务 +''' +logger.info(f'[Scheduler] Starting') +SCHEDULER = scheduler +# 每月一号执行创建index +SCHEDULER.print_jobs() + +logger.info(f'[Components] Finished! Cost {time.time() - initialing_start:.4f} seconds') diff --git a/lab-miots/darknet-market/conf/application.yml b/lab-miots/darknet-market/conf/application.yml new file mode 100644 index 0000000000000000000000000000000000000000..c03b07dc9ee10a2e523fc3a84f397d6c38d9de9a --- /dev/null +++ b/lab-miots/darknet-market/conf/application.yml @@ -0,0 +1,47 @@ +configure: + active: [markets,forum] + +database: + rdb: + type: mysql + database: dark + connection: + host: 127.0.0.1 + port: 13306 + user: root + password: mysql + charset: utf8mb4 + max_connections: 20 + redis: + host: 127.0.0.1 + port: 16379 + password: redis + +storage: + path: ./data + +proxy: + # {'http': 'socks5h://127.0.0.1:9050', 'https': 'socks5h://127.0.0.1:9050'} + tor: + target: [http, https] + protocol: socks5h + host: 172.20.48.1 + port: 9050 + +logging: + formatter: "[%(asctime)s] %(filename)s:%(lineno)d %(process)d.%(thread)d [%(levelname)s] %(message)s" + level: info + path: ./logs/ + history: ./logs/_his + prefix: darknet-market + rotate: + # day/hour/month + type: day + maxsize: 5M + +retry: + times: 3 + interval: 0.5 + +ntp: + server: cn.pool.ntp.org diff --git a/lab-miots/darknet-market/conf/forum.yml b/lab-miots/darknet-market/conf/forum.yml new file mode 100644 index 0000000000000000000000000000000000000000..85ef3fa81e72007b7eaba5e6325aa879b479317a --- /dev/null +++ b/lab-miots/darknet-market/conf/forum.yml @@ -0,0 +1,133 @@ +forum: + Crypt BB: + crawl: false + home_url: http://cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion/ + root_url: http://cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion/ + cookie: loginattempts=2; mybb[lastactive]=1676964350; mybb[lastvisit]=1676963369; mybbuser=63821_0FghfrITZgOkutGdLgJHBttesN68HUS1bdE1J8W5H8v0A3M79x; PHPSESSID=d1m5psje9un9umobsmucohrgs5; sid=956286b4459eeb72a807e03c2a6be432 + xpath: + section_url: [ + "/html/body/div[1]/div[2]/div/div[6]/div[2]/div[1]/table/tbody/tr[position()>1]/td[2]/strong/a/@href" + ] + thread_url: [ + "/html/body/div[1]/div[2]/div/table[2]/tr[position()>3]/td[3]/div/span/span/a/@href", + ] + thread_turn_page: + specialized: false + max_page: /html/body/div/div[2]/div/div[3]/div/a[last()-1]/@href + params: + page: "[page]" + page_pattern: 'page=\d+' + post_turn_page: + specialized: false + max_page: /html/body/div[1]/div[2]/div/div[3]/div/a[last()-1]/@href + params: + page: "[page]" + page_pattern: 'page=\d+' + thread_category: /html/body/div[1]/div[2]/div/table[2]/tr[1]/td/div[2]/strong/text() + thread_info: + relative: /html/body/div[1]/div[2]/div/table[2]/tr[@class='inline_row'] + author: ["./td[3]/div/div/a/span/strong/text()","./td[3]/div/div/a/text()", "./td[3]/div/div/a/span/text()"] + author_url: ./td[3]/div/div/a/@href + thread_topic: ./td[3]/div/span/span/a/text() + thread_url: ./td[3]/div/span/span[contains(@class,'subject')]/a/@href + replies: ./td[4]/text() + views: ./td[5]/text() + post_time: ./td[6]/span/text() + post: + relative: /html/body/div[1]/div[2]/div/table/tr[2]/td/div/div # should have more general way like class or id?? + author_url: ./div[1]/div[2]/strong/span/a/@href # relative path... + author: ["./div[1]/div[2]/strong/span/a/span/strong/text()","./div[1]/div[2]/strong/span/a/span/text()"] + post_time: ./div[2]/div[1]/span/text() + post_url: ./div[2]/div[1]/div/strong/a/@href + content: ./div[2]/div[2] #content should be rich text + floor: ./div[2]/div[1]/div/strong/a/text() + + The Hub: + crawl: false + home_url: http://thehubmcwyzwijjoqvdtpmu36npcueypjbgnvbqz4jliwjmmnpfkzkqd.onion/ + root_url: http://thehubmcwyzwijjoqvdtpmu36npcueypjbgnvbqz4jliwjmmnpfkzkqd.onion/ + cookie: PHPSESSID=2f5fe02n3d5ku8jqce1fc71alm; SMFCookie474=a%3A4%3A%7Bi%3A0%3Bs%3A6%3A%22230562%22%3Bi%3A1%3Bs%3A40%3A%229259dd8c5ac203bbf28facd859e49a715c59483a%22%3Bi%3A2%3Bi%3A1868147862%3Bi%3A3%3Bi%3A0%3B%7D + xpath: + section_url: [ + "/html/body/div/div/div/div[2]/div/div/div[3]/table/tbody/tr/td[2]/a/@href", + "/html/body/div/div/div/div[2]/div/div/div[3]/table/tbody/tr/td/strong/a[1]/@href" + ] + thread_url: [ + "/html/body/div/div/div/div[2]/div/div/div[4]/table/tbody/tr/td[3]/div/strong/span/a/@href", + "/html/body/div/div/div/div[2]/div/div/div[4]/table/tbody/tr/td[3]/div/span/a/@href" + ] + thread_turn_page: + page_turner: sites.page_turner.the_hub + specialized: true + max_page: /html/body/div/div/div/div[2]/div/div/div[@class='pagesection']/div[1]/a[last()]/text() + max_page_url: /html/body/div/div/div/div[2]/div/div/div[@class='pagesection']/div[1]/a[last()]/@href + post_turn_page: + specialized: true + page_turner: sites.page_turner.the_hub + max_page: /html/body/div/div/div/div[2]/div/div/div[2]/div[3]/a[@class='navPages'][last()]/text() + max_page_url: /html/body/div/div/div/div[2]/div/div/div[2]/div[3]/a[@class='navPages'][last()]/@href + thread_category: /html/body/div/div/div/div[2]/div/div/div[1]/ul/li[3]/a/span/text() + thread_info: + relative: /html/body/div/div/div/div[2]/div/div/div[@id='messageindex']/table/tbody/tr + author: [ + "./td[3]/div/p/a/text()", + ] + author_url: ./td[3]/div/p/a/@href + thread_topic: "./td[3]/div/span/a/text() | ./td[3]/div/strong/span/a/text()" + thread_url: "./td[3]/div/span/a/@href | ./td[3]/div/strong/span/a/@href" + replies: ./td[4]/text() + views: ./td[4]/text() + post_time: ./td[5]/text() + post: + relative: /html/body/div/div/div/div[2]/div/div/div[3]/form/div/div + author_url: ./div[1]/h4/a/@href + author: ["./div[1]/h4/a/text()"] + post_time: ./div[2]/div[1]/div/div[2]/text() + post_url: ./div[2]/div[1]/div/h5/a/@href + content: ./div[2]/div[2]/div + floor: ./div[2]/div[1]/div/div[2]/strong/text() + + Libre: + crawl: true + home_url: http://libreeunomyly6ot7kspglmbd5cvlkogib6rozy43r2glatc6rmwauqd.onion/discover + root_url: http://libreeunomyly6ot7kspglmbd5cvlkogib6rozy43r2glatc6rmwauqd.onion + cookie: anonguard=FvbA903l4s7Vq7cC_PxSkjByGKxmpIcY; libre=j2Oo0UYi3cFBfP6CALwSVTgW1Vli5ciG + xpath: + section_url: [ + "/html/body/div/div/div[3]/div/div/div[1]/a/@href", + ] + thread_url: [ + "/html/body/div/div/div[5]/div/div[2]/div[1]/a/@href", + ] + thread_turn_page: + specialized: false + max_page: /html/body/div/div/div[6]/a[@class='link'][last()]/@href + params: + page: "[page]" + page_pattern: 'page=(\d+)' + post_turn_page: + specialized: false + max_page: + thread_category: /html/body/div/div/div[1]/div[2]/h1/text() + thread_info: + relative: /html/body/div/div/div[5]/div + author: [ + 'substring-after(./div[2]/p/a/text(),"/u/")' + ] + author_url: ./div[2]/p/a/@href + thread_topic: ./div[2]/div[1]/a/text() + thread_url: ./div[2]/div[1]/a/@href + replies: ./div[3]/div[3]/p/text() + views: ./div[3]/div[2]/p/text() + post_time: ./div[2]/p/text() # use relative get one! + post: + relative: "/html/body/div/div/div[div[contains(@class,'content-p')]] | /html/body/div/div/div[2]/div/div[div[contains(@class,'content-c')]] " + author_url: "./div[1]/div[1]/div[1]/a/@href | ./" + author: [ + 'substring-after(./div[1]/div[contains(@class,"info-p")]/div[1]/a/text(),"/u/")','substring-after(./div[1]/p/a/text(),"/u/")' + ] + post_time: "./div[1]/div[contains(@class,'info-p')]/div[1]/span[1]/text() | ./div[1]/p/text()" + post_url: "./div[1]/div[contains(@class,'info-p')]/div[2]/a/@href | ./@id" # if empty just use current page url + content: "./div[contains(@class,'content-p')] | ./div[contains(@class,'content-c')]" + floor: "" # if empty use cnt instead + diff --git a/lab-miots/darknet-market/conf/markets.yml b/lab-miots/darknet-market/conf/markets.yml new file mode 100644 index 0000000000000000000000000000000000000000..13464b78e3ef63a14209e91af23a1a370ae78b0d --- /dev/null +++ b/lab-miots/darknet-market/conf/markets.yml @@ -0,0 +1,148 @@ +markets: + CYPHER MARKETPLACE: + crawl: false + home_url: http://6c5qaeiibh6ggmobsrv6vuilgb5uzjejpt2n3inoz2kv2sgzocymdvyd.onion/home + root_url: http://6c5qaeiibh6ggmobsrv6vuilgb5uzjejpt2n3inoz2kv2sgzocymdvyd.onion + xpath: + category_url: /html/body/div[2]/div/div/div[1]/div/div/div[1]/div[2]/ul/li/ul/li/a/@href + #'/html/body/div[2]/div/div/div[1]/div/div/div[1]/div[2]/ul/li[1]/ul/li[1]/a' + #'/html/body/div[2]/div/div/div[1]/div/div/div[1]/div[2]/ul/li[4]/ul/li[2]/a' + item_url: /html/body/div[2]/div/div/div[1]/div[2]/div[3]/div/div/div/div[2]/a/@href + item_turn_page: + max_page: /html/body/div[2]/div/div/div[2]/div/nav/ul/li[last()-1]/a/@href + #'/html/body/div[2]/div/div/div[2]/div/nav/ul/li[11]/a' + params: + page: '[page]' + page_pattern: 'page=\d+' + item: + name: /html/body/div[2]/div/div/div[2]/div[2]/div/div[1]/div/div[1]/div/div[2]/text() + category: /html/body/div[2]/div/div/div[2]/div[2]/div/div[1]/div/div[3]/div/div[2]/text() + description: /html/body/div[2]/div/div/div[2]/div[2]/div/div[4]/div/div[2]/p/text() + price: /html/body/div[2]/div/div/div[2]/div[2]/div/div[2]/div/div[2]/strong/text() + price_pattern: null + currency: /html/body/div[2]/div/div/div[2]/div[2]/div/div[1]/div/div[5]/div/div[2]/div/text() + vendor: /html/body/div[2]/div/div/div[2]/div[1]/div/div/div[2]/div[1]/div/div[2]/text() + cookie: cypher_marketplacemarket=eyJpdiI6IlhxYTNieklQMlwvbWJvdWM1ZUVlTWlnPT0iLCJ2YWx1ZSI6IlNsSmszM1ViVW9selpFNHVaUzNqNVFoTlVrenBVbjE3Vk1uNFp4dFNNU29TeGMwanMwK0ZCYThFNW1paUh4OGEiLCJtYWMiOiJkMzI0ZTEzY2EzMmMxZGNiMjcwNzY5NWRkNTcyYTU1NmEwN2EwOTZlMjcyNjU3MDc3MGM1NzMxMDZlNWEyOTllIn0%3D; dcap=9B7EBE185494691101DD3A66C348C8B54CA06303567F9081666CF456B4A8F565EF35B2842538ABEE8D8D9AFE0B5E9E97641ABAF38E457DE15C315F5F98076C825C8C23A63D86A5E8CA1CE9371CC302BF2DBDBD4E469FB0A87CBFBE86350A8AA313668F448DF570A5B9708E91A1DDCC1F72B5E262300651BDAB6FCB39B428BC08; XSRF-TOKEN=eyJpdiI6ImNRdjBZWmR4dXozSHE2VGx2ZGJSbmc9PSIsInZhbHVlIjoiUytcL0xpN2t3Wlo1S0EyeUZOTTZOUVZIdnYraXRoYTZpelwvZGlNWmFRa21wVmZGeWxVQjh4bUI3RkZvVmhPN0NnIiwibWFjIjoiZTQ3Y2EzYmYwZTRmZWU0OGViMTAxMDAxMWE5NTU3M2EzY2ZlNDc0MTAyNjA4YjhjMWE0MTY3Y2Y2NzZlNTQ2ZiJ9 + parallel: + pool: + category: 1 + item: 4 + VICE CITY: + crawl: false + home_url: http://vice2e3gr3pmaikukidllstulxvkb7a247gkguihzvyk3gqwdpolqead.onion/ + root_url: http://vice2e3gr3pmaikukidllstulxvkb7a247gkguihzvyk3gqwdpolqead.onion/ + xpath: + category_url: //div[@class="menu"]/div[@class="menu_box"]/nav/div[@class="menu-item"]/a/@href + item_url: //div[@class="contentmenu"]/*/div[@class="wLf"]/*/div[@class="wLfName"]/a/@href + item_turn_page: + max_page: //div[@class="contentmenu"]/div[@class="pagination"]/a[last()]/@href + params: + pg: "[page]" + page_pattern: 'pg=\d+' + item: + name: /html/body/div[2]/div/div/div[3]/div/div/section[1]/div/div[2]/span[1]/text() + category: /html/body/div[2]/div/div/div[3]/div/div/section[1]/div/div[2]/span[2]/text() + description: /html/body/div[2]/div/div/div[3]/div/div/section[1]/div/p/text() + price: //div[@class="listing_right"]/div/*/pre/span[2]/text() + price_pattern: '[\d|\.|,]+' + currency: BTC + cookie: PHPSESSID=02q7bkkhbt4lbqkpukqs7u0hg4; dcap=E877CC888BB4A1347C443357FAA66A038FFDBD7033AED2FD2A7BDFE5BED03D1696C2AC76CD092324BC9E8E75BBD9CF13 + parallel: + pool: + category: 1 + item: 4 + HEINEKEN EXPRESS: + crawl: false + home_url: http://heinekexxo77vjgmasoq3xqxqwndq74iq7grpqw3bfvdpb6vcmf2kcqd.onion/home + root_url: http://heinekexxo77vjgmasoq3xqxqwndq74iq7grpqw3bfvdpb6vcmf2kcqd.onion + xpath: + # category_url: //*[@id="vp"]/a/@href + category_url: + [ + "http://heinekexxo77vjgmasoq3xqxqwndq74iq7grpqw3bfvdpb6vcmf2kcqd.onion/home", + ] + item_url: //div[@class="card card-search-block"]/div/a/@href + item: + name: /html/body/div/div[3]/div/div[1]/h4/text() + category: /html/body/div/div[3]/div/div[2]/table/tbody/tr/td/table/tbody/tr[4]/td/a[1]/text() + description: /html/body/div/div[3]/div/div[2]/table/tbody/tr/td/table/tbody/tr[8]/td/text() + price: /html/body/div/div[3]/div/div[2]/table/tbody/tr/td/table/tbody/tr[11]/td/table/tbody/tr[1]/td[2]/text() + price_pattern: '[\d|\.|,]+' + currency: EUR + parallel: + pool: + category: 1 + item: 1 + NEMESIS MARKET: + crawl: false + home_url: http://nemesis55zi33t4ddaqhpgygsenwwugyqf5innwy7uslo7pvxyr34kyd.onion/market + root_url: http://nemesis55zi33t4ddaqhpgygsenwwugyqf5innwy7uslo7pvxyr34kyd.onion + xpath: + category_url: /html/body/div/nav[2]/div/div/ul/li/ul/li/a/@href + item_url: /html/body/div/div[1]/div/div/div/div/div[1]/div[2]/a/@href + item_turn_page: + max_page: //ul[@class="pagination"]/li[last()-1]/a/@href + params: + page: '[page]' + page_pattern: 'page=\d+' + item: + name: /html/body/div/div[1]/div/div[1]/div[2]/div/div[1]/div[2]/a[1]/text() + category: /html/body/div/div[1]/div/div[1]/div[2]/div/div[1]/div[1]/a[2]/text() + description: /html/body/div/div[1]/div/div[2]/div[2]/div[1]/text() + price: /html/body/div/div[1]/div/div[1]/div[2]/div/div[2]/form[1]/div[1]/text() + price_pattern: '[\d|\.|,]+' + currency: USD + cookie: nemesis_market_sessions=eyJpdiI6IlJucnJBSjZ2azNFTkpOTXhNN0hwL3c9PSIsInZhbHVlIjoiK1VFYVQvaDFkWUdLV0pwWGk3V3dJWTZVc2JVSWZQUTRteFdiam02WG9ZQkNGOE1aZUg5NGN3V3loTHViZGE0clJFNXp6bk5KZkJ4QWxka1dZOFJwOWFBRkRTQVRMdnZwdkl6QngvMThRaXo4amFjT2NrOTYySmhSblZ3L1ByeVoiLCJtYWMiOiI0NmE2NjJiYTBmYzE4ZWVmMjkzMjE0ODg5MjBiY2IwNTUyNWM2NzRmMWEwYjM0ODAwNjZlODM1ODIxMDU0MDk0IiwidGFnIjoiIn0%3D; dcap=eyJpdiI6Im5IbjBwSDR5TnkyelU1UHJKT2hDbEE9PSIsInZhbHVlIjoiN2ZkL1FhOG9RQ0N5cHo2OUZsR0ljVmR2R1NBRUFZTHQrWW5NT0kyUjNrZ2NEK0lYSncxVnBGOCtuTG1hcnJFcnZjeUxpcWp2K1VnQUxwVnNwYzh2aWZZZzVLSDc0OUlPaTFuM3lRaC92dHRGeWZqL3hZdTM0WUNrNTVzL2Q0WmkiLCJtYWMiOiI0NWZjYzQ4ZGRjMmJhY2UxOTlkYzgzMjlhYTc0NzYzNjJhYTUxNzM4MzMzMTViYzBhZDkxOWZhMjA4NGY4NTc1IiwidGFnIjoiIn0%3D + parallel: + pool: + category: 1 + item: 4 + ASAP MARKET: + crawl: true + home_url: http://asap4u7rq4tyakf5gdahmj2c77blwc4noxnsppp5lzlhk7x34x2e22yd.onion/home + root_url: http://asap4u7rq4tyakf5gdahmj2c77blwc4noxnsppp5lzlhk7x34x2e22yd.onion + xpath: + category_url: "/html/body/div/div[3]/nav/section/section[2]/ul/li/section/label/a/@href | /html/body/div/div[3]/nav/section/section[2]/ul/li/a/@href" + item_url: /html/body/div/div[3]/div/div[3]/div/form/div/div/div[2]/div/span[1]/a/@href + item_turn_page: + max_page: /html/body/div/div[3]/div/div[4]/ul/li[last()]/a/@href + params: + page: '[page]' + page_pattern: 'page=\d+' + item: + name: /html/body/div/div[3]/div/div[1]/h4/text() + category: /html/body/div/div[3]/div/div[2]/table/tbody/tr/td/table/tbody/tr[th[label[contains(text(),'Category')]]]/td/a[1]/text() + description: /html/body/div/div[3]/div/div[2]/section/div[1]/text() + price: /html/body/div/div[3]/div/div[2]/table/tbody/tr/td/table/tbody/tr[1]/td/text() + price_pattern: '[\d|\.|,]+' + currency: USD + cookie: dcap=988CA7867664613895FD8450AC968BDBAC1B8C086C4FA6590C6EACD3AEBE2F9926F76FFA10234EA99213A1F05C2B896E1218DE803616946532CF0AE07C0C338BD696B4AD8B0A3837803DD86011E5C727D24039A053B6A524442EA935BDD103A674C1099B12A581959680E2B71EBD6AB9 + parallel: + pool: + category: 1 + item: 4 + 长安不夜城: + crawl: false + 天狗: + crawl: false + home_url: http://222666ak6peq6gldcs7gufbnhthekxsewtc3mgdu6r643lwp6gcgb5id.onion/ + root_url: http://222666ak6peq6gldcs7gufbnhthekxsewtc3mgdu6r643lwp6gcgb5id.onion/ + xpath: + category_url: /html/body/div[3]/div[2]/div/div[1]/div[1]/nav/ul/li/a/@href + item_url: /html/body/div[3]/div[2]/div/div[2]/div[position()>1]/div/div/div/a[1]/@href + item_turn_page: + max_page: /html/body/div[3]/div[2]/div/div[2]/nav/ul/li[last()-1]/a/@href + params: + page: "[page]" + page_pattern: 'page=\d+' + item: + name: /html/body/div[2]/div[2]/div/div[2]/h2/text() + category: /html/body/div[2]/div[2]/nav/ol/li[2]/a/text() + description: + price: /html/body/div[2]/div[2]/div/div[2]/div/div/form/table/tbody/tr[3]/td[2]/ul/li/strong/text() + price_pattern: null #check in source code (optional xpath + vendor: /html/body/div[2]/div[2]/div/div[3]/div[2]/div[2]/div[1]/a/span/text() + cookie: _session=eyJpdiI6IllObHJpdDVYWXJOM0k4VkhwVU4yRWc9PSIsInZhbHVlIjoiNU9BWWlnakd0SkQzcWdxdWhTZEtUaXhUS1RwYXE2RFRUbFZYUEpmK0d1R3hPakhoNnZrNHIzajI0UTlURm5BSSIsIm1hYyI6IjhhMjkxMzQ1MmY4NjlmZTI5MWQ0NjU5NjVjYTZlYTYxMWRlNThlNDE3N2JiZWUxZTRhMmZmOWZmNzQxNGU4NmQifQ%3D%3D; XSRF-TOKEN=eyJpdiI6ImFFVStDbFVycXcxT1NMa3QyNWdEdWc9PSIsInZhbHVlIjoibnhiWmp0bnBSTEp3bG9yaWRyQ29qSDZNN2pwNzlKTDh0ZHp6TjFsaUNFYWQ2Q01yZWcwMUV2c2xXSFVka25hWiIsIm1hYyI6IjU1NjZiMjEzODEwZDVjNzc1YmY0OGNkOTcyZDgzZTVhZjYzNmJmNjUyMGJhYWQxZjg3ODVhNTliMGU0NjY4MjQifQ%3D%3D; PHPSESSID=p3p1pgitdh0hmqb7sq5fenetcp + + + diff --git a/lab-miots/darknet-market/const.py b/lab-miots/darknet-market/const.py new file mode 100644 index 0000000000000000000000000000000000000000..04e5a13d2900db562dbd1617266cda2bd60a379f --- /dev/null +++ b/lab-miots/darknet-market/const.py @@ -0,0 +1,36 @@ +import peewee +import typing +import os +from playhouse.pool import PooledMySQLDatabase, PooledPostgresqlDatabase, PooledSqliteDatabase +from tzlocal import get_localzone +import pytz + +from helper.config_loader import ConfigLoader + +CONFIG = ConfigLoader().config + +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) + +RDB_CONFIG = CONFIG['database']['rdb'] +if RDB_CONFIG['type'] == 'mysql': + RDB: peewee.Database = PooledMySQLDatabase(None) +elif RDB_CONFIG['type'] in ['sqlite', 'sqlite3']: + RDB: peewee.Database = PooledSqliteDatabase(None) +del RDB_CONFIG + +TOR_PROXY = {} +TOR_PROXY_CONF = CONFIG['proxy']['tor'] +for target in TOR_PROXY_CONF['target']: + TOR_PROXY.update({target: f"{TOR_PROXY_CONF['protocol']}://{TOR_PROXY_CONF['host']}:{TOR_PROXY_CONF['port']}"}) + +T = typing.TypeVar("T") + +CURRENCY_MAP = { + '€': 'EUR' +} + +TIME_ZONE = get_localzone() +TZ = pytz.timezone('Asia/Shanghai') + +STORAGE_PATH = CONFIG['storage']['path'] +os.makedirs(STORAGE_PATH, exist_ok=True) diff --git a/lab-miots/darknet-market/core/__init__.py b/lab-miots/darknet-market/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/darknet-market/core/dispatcher.py b/lab-miots/darknet-market/core/dispatcher.py new file mode 100644 index 0000000000000000000000000000000000000000..19d5479c5ca3212d84c22e2fb17a851bb5d1b8d8 --- /dev/null +++ b/lab-miots/darknet-market/core/dispatcher.py @@ -0,0 +1,22 @@ +from datetime import datetime, timedelta +import random + +from components import SCHEDULER + + +class Dispatcher: + + def __init__(self): + self.jobs = dict() + self.registered = set() + + def register(self, name, site=None, entry_point=None, immediate=True, days=0, hours=6): + _random_ts = immediate or random.randint(1, (days * 24 + 6) * 60) + start_date = str(datetime.now() + timedelta(minutes=_random_ts)) + SCHEDULER.add_job(entry_point or site.dispatch, trigger='interval', + hours=hours, days=days, start_date=start_date) + self.jobs[name] = (site, entry_point) + SCHEDULER.print_jobs() + + +dispatcher = Dispatcher() diff --git a/lab-miots/darknet-market/db/__init__.py b/lab-miots/darknet-market/db/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/darknet-market/db/models.py b/lab-miots/darknet-market/db/models.py new file mode 100644 index 0000000000000000000000000000000000000000..e26019325ac42b71179d7c63cceae87daa30684b --- /dev/null +++ b/lab-miots/darknet-market/db/models.py @@ -0,0 +1,91 @@ +import peewee +from const import RDB +import datetime + + +def table_function(model_class: peewee.Model) -> str: + name: str = model_class.__name__ + if name.endswith('Model'): + name = name[:-5] + return peewee.make_snake_case(name) + + +class BaseModel(peewee.Model): + """ + peewee BaseMode + reference: http://docs.peewee-orm.com/en/latest/peewee/models.html + """ + + # Basic metadata for data models. + class Meta: + database = RDB + table_function = table_function + +class LongTextField(peewee.TextField): + field_type = 'LONGTEXT' +class Forum(BaseModel): + name = peewee.CharField(null=False, primary_key=True, help_text="forum name") + hosts = peewee.TextField(null=True, help_text="hosts for the forum", default="") + alive = peewee.BooleanField(null=False, default=True, help_text="whether the forum is alive or not") + update_time = peewee.DateTimeField(null=False, default=datetime.datetime.now) + +class DisplayPoster(BaseModel): + id = peewee.PrimaryKeyField(help_text="id") + name = peewee.CharField(help_text="user who post") + forum = peewee.CharField(help_text="forum to which the post belongs") + # url = peewee.TextField(help_text="url of the user profile") + # url_hash = peewee.CharField(null=False, unique=True) + intelligence = LongTextField(help_text="forum intelligence info") + +class ForumPost(BaseModel): + id = peewee.PrimaryKeyField(help_text="id") + user = peewee.CharField(help_text="user who post") + section = peewee.TextField(help_text="section of the post") + title = peewee.TextField(help_text="thread topic of the post") + content = peewee.TextField(help_text="rich text content of the post") + post_time = peewee.DateTimeField(null=False, help_text="date time of the post ") + url = peewee.TextField(help_text="url of the post") + forum = peewee.TextField(help_text="forum to which the post belongs") + url_hash = peewee.CharField(null=False, unique=True) + +class DisplayPostings(BaseModel): + id = peewee.PrimaryKeyField(help_text="id") + poster_id = peewee.ForeignKeyField(DisplayPoster, to_field='id') + title = peewee.CharField(help_text="thread topic of the post") + category = peewee.CharField(help_text = "category of posting") + forum = peewee.CharField(help_text="forum to which the post belongs") + url = peewee.TextField(help_text="url of the post") + url_hash = peewee.CharField(null=False, unique=True) + post_time = peewee.CharField(help_text="date time of the post ") + replies = peewee.CharField(help_text="reply number of thread") + views = peewee.CharField(help_text="views number of thread") + create_time = peewee.DateTimeField(help_text="data entry creation time") + update_time = peewee.DateTimeField(help_text="data entry update time") + +class DisplayPostcomments(BaseModel): + id = peewee.PrimaryKeyField(help_text="id") + poster_id = peewee.ForeignKeyField(DisplayPoster, to_field='id') + post_id_id = peewee.ForeignKeyField(DisplayPostings, to_field='id') + content = peewee.TextField(help_text="rich text content of the post") + forum = peewee.CharField(help_text="forum to which the post belongs") + url = peewee.TextField(help_text="url of the post") + url_hash = peewee.CharField(null=False, unique=True) + post_time = peewee.CharField(help_text="date time of the post") + floor = peewee.CharField(help_text="floor number of the comment") + create_time = peewee.DateTimeField(null=False, help_text="datetime of the record") + update_time = peewee.DateTimeField(null=False, default=datetime.datetime.now, help_text="datetime of the record") + + +class MarketItem(BaseModel): + id = peewee.PrimaryKeyField(help_text="id") + seller = peewee.CharField(help_text="seller of the item") + name = peewee.CharField(null=False, default="", help_text="name of the item") + description = peewee.TextField(help_text="description of the item") + price = peewee.CharField(max_length=10, help_text="price of the item") + currency = peewee.CharField(max_length=10, help_text="currency type") + category = peewee.CharField(help_text="category of the item") + market = peewee.CharField(null=False, help_text="market to which the item belongs") + url = peewee.TextField(help_text="url of the item") + url_hash = peewee.CharField(null=False, unique=True) + create_time = peewee.DateTimeField(null=False, help_text="datetime of the record") + update_time = peewee.DateTimeField(null=False, default=datetime.datetime.now, help_text="datetime of the record") diff --git a/lab-miots/darknet-market/db/rdb.py b/lab-miots/darknet-market/db/rdb.py new file mode 100644 index 0000000000000000000000000000000000000000..4947385725f0bb0c34fb3d6c6f651d9635c36eff --- /dev/null +++ b/lab-miots/darknet-market/db/rdb.py @@ -0,0 +1,35 @@ +import peewee +import typing +import types +import time +from helper.utils import format_args +from const import CONFIG, T +from helper.logger import logger +from db.models import Forum, MarketItem + + +class RelationalDB: + + def __init__(self, db: peewee.Database): + self.db: peewee.Database = db + self.retry: dict = CONFIG['retry'] + + def operate(self, method: typing.Callable[..., T], *args, **kwargs): + retry = 0 + is_success = False + value = None + arguments = format_args(*args, **kwargs) + method_name = typing.cast(types.MethodType, method).__self__.__class__.__name__ + + while not is_success and retry < self.retry['times']: + try: + value = method(*args, **kwargs) + is_success = True + except Exception as e: + logger.error(f"execute {method_name} failed with arguments = {arguments}, error : {e}") + retry += 1 + time.sleep(self.retry['interval']) + return value + + def create_tables(self): + self.operate(self.db.create_tables, [MarketItem, Forum]) diff --git a/lab-miots/darknet-market/db/redis.py b/lab-miots/darknet-market/db/redis.py new file mode 100644 index 0000000000000000000000000000000000000000..2c69f6772a70dae48247fa3b9ce53a7eb6b5b842 --- /dev/null +++ b/lab-miots/darknet-market/db/redis.py @@ -0,0 +1,75 @@ +import redis +from multiprocessing import Lock +import time + +from helper.logger import logger +from helper.utils import format_args + + +class RedisClient: + + def __init__(self, host, port, password=None, retry=None): + if retry is None: + retry = {} + + self.retry_times = retry.setdefault('times', 0) + self.retry_interval = retry.setdefault('interval', 1) + + self._pool = dict() + self.host = host + self.port = port + self.password = password + self.lock = Lock() + + def _get_client(self, db) -> redis.StrictRedis: + if db in self._pool: + return self._pool[db] + else: + self.lock.acquire() + connection_pool = redis.ConnectionPool( + host=self.host, + port=self.port, + decode_responses=True, + db=db, + password=self.password + ) + client = redis.StrictRedis(connection_pool=connection_pool) + self._pool[db] = client + return self._pool[db] + + def ping(self): + client = self._get_client(0) + client.ping() + + def set(self, key, value, db=0): + client = self._get_client(db) + client.set(key, value) + + def execute(self, command: str, db=0, *args, **kwargs): + client = self._get_client(db) + retry = 0 + method = getattr(client, command) + while True: + try: + value = method(*args, **kwargs) + return value + except Exception as error: + _arg_msg = format_args(*args, **kwargs) + logger.error(f'[REDIS] Execute {command}({_arg_msg}) failed. Reason: {error}') + if self.retry_times > retry: + retry += 1 + time.sleep(self.retry_interval) + else: + return None + + def get(self, key, db=0): + client = self._get_client(db) + return client.get(key) + + def add_set(self, key, members, db=0): + client = self._get_client(db) + client.sadd(key, members) + + def is_member_of_set(self, key, member, db=0): + client = self._get_client(db) + return client.sismember(key, member) diff --git a/lab-miots/darknet-market/docker-compose.yaml b/lab-miots/darknet-market/docker-compose.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d41b18147390464864e0d9dab9bc9b2af16fc519 --- /dev/null +++ b/lab-miots/darknet-market/docker-compose.yaml @@ -0,0 +1,23 @@ +version: "1" +volumes: + mysql_data: + +services: + redis: + image: redis:7 + ports: + - 16379:6379 + command: redis-server --requirepass redis + environment: + - REDIS_PASSWORD=redis + + mysql: + image: mysql:5.7 + ports: + - 13306:3306 + environment: + MYSQL_ROOT_PASSWORD: mysql + MYSQL_DATABASE: dark + volumes: + - "mysql_data:/var/lib/mysql:rw" + - ./mycustom.cnf:/etc/mysql/conf.d/custom.cnf diff --git a/lab-miots/darknet-market/helper/__init__.py b/lab-miots/darknet-market/helper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/darknet-market/helper/config_loader.py b/lab-miots/darknet-market/helper/config_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..3f1cf58ff1e46dd971f131b09c1128ada54355ba --- /dev/null +++ b/lab-miots/darknet-market/helper/config_loader.py @@ -0,0 +1,23 @@ +import yaml +import os + +root_path = os.path.dirname(os.path.dirname(__file__)) + + +class ConfigLoader: + def __init__(self): + self.config_dir = os.path.join(root_path, 'conf') + self.config = dict() + self.load() + + def load(self): + self.config = self.load_config('application.yml') + if 'configure' in self.config and 'active' in self.config['configure']: + for file in self.config['configure']['active']: + self.config.update(self.load_config(f"{file}.yml")) + + def load_config(self, config_name): + config_path = os.path.join(self.config_dir, config_name) + if not os.path.exists(config_path): + return {} + return yaml.load(open(config_path, 'r').read(), Loader=yaml.FullLoader) diff --git a/lab-miots/darknet-market/helper/logger.py b/lab-miots/darknet-market/helper/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..6c519d4e993415e2a1d4288b2ed7155b28dbccc4 --- /dev/null +++ b/lab-miots/darknet-market/helper/logger.py @@ -0,0 +1,146 @@ +import logging +from logging.handlers import WatchedFileHandler +import time +from threading import Thread +from const import ROOT_DIR, CONFIG +import datetime +import shutil +import os + + +class LogLevelFilter(logging.Filter): + + def __init__(self, name='', level=logging.INFO): + super(LogLevelFilter, self).__init__(name) + self.level = level + + def filter(self, record): + return record.levelno == self.level + + +class Log: + def __init__(self, formatter, path, prefix, rotate, history, level): + + self.logger = logging.getLogger() + self.lock = False + + self._formatter = logging.Formatter(formatter) + self._path = os.path.join(ROOT_DIR, path) + self._history = os.path.join(ROOT_DIR, history) + self._prefix = prefix + self._rotate_type = rotate['type'] + self._maxsize = self.calc_size(rotate['maxsize']) + + self._handler_files = { + 'info': (os.path.join(self._path, f"{prefix}-info.log"), logging.INFO), + 'warning': (os.path.join(self._path, f"{prefix}-warning.log"), logging.WARNING), + 'error': (os.path.join(self._path, f"{prefix}-error.log"), logging.ERROR), + 'debug': (os.path.join(self._path, f"{prefix}-debug.log"), logging.DEBUG), + 'all': (os.path.join(self._path, f"{prefix}-all.log"), logging.DEBUG) + } + self.logger.setLevel(self._handler_files[level][1]) + + self._clean() + self._rotate() + self._set_handler() + + def _set_handler(self): + os.makedirs(self._path, exist_ok=True) + os.makedirs(self._history, exist_ok=True) + for name, (file, level) in self._handler_files.items(): + if name == 'all': + handler = logging.StreamHandler() + handler.setLevel(level) + handler.setFormatter(self._formatter) + self.logger.addHandler(handler) + handler = WatchedFileHandler(file, 'w') + handler.setLevel(level) + handler.setFormatter(self._formatter) + if name != 'all': + handler.addFilter(LogLevelFilter(level=level)) + self.logger.addHandler(handler) + + def _rotate(self): + Thread(target=self.__rotate).start() + + def __rotate(self): + last_rotate = None + curr = time.time() + _, last_rotate = self._check_rotate(last_rotate) + time.sleep(10 - int(curr) % 10 - (curr - int(curr))) + while True: + need_rotate, last_rotate = self._check_rotate(last_rotate) + if need_rotate: + for _type, (file, _) in self._handler_files.items(): + history_file = os.path.join(self._history, self._format_rotate(_type)) + self._do_rotate(file, history_file) + else: + # check if log file reached the maxsize + for _type, (file, _) in self._handler_files.items(): + if os.path.getsize(file) >= self._maxsize: + history_file = os.path.join(self._history, self._format_rotate(_type)) + self._do_rotate(file, history_file) + curr = time.time() + time.sleep(10 - int(curr) % 10 - (curr - int(curr))) + + def _check_rotate(self, rotate): + curr_datetime = datetime.datetime.now() + if self._rotate_type == 'hour': + curr_rotate = curr_datetime.hour + elif self._rotate_type == 'month': + curr_rotate = curr_datetime.month + else: + curr_rotate = curr_datetime.day + if curr_rotate == rotate: + return False, curr_rotate + else: + return True, curr_rotate + + def _format_rotate(self, _type, dt: datetime.datetime = datetime.datetime.now()): + if self._rotate_type == 'hour': + name = f"{self._prefix}-{_type}-{dt.year}-" \ + f"{dt.month}-{dt.day}_{dt.hour}" + elif self._rotate_type == 'month': + name = f"{self._prefix}-{_type}-{dt.year}-{dt.month}" + else: + name = f"{self._prefix}-{_type}-{dt.year}-{dt.month}-{dt.day}" + history_files = set(os.listdir(self._history)) + if f"{name}.log" not in history_files: + return f"{name}.log" + + cnt = 1 + name_ = f"{name}.{cnt}" + while f"{name_}.log" in history_files: + cnt += 1 + name_ = f"{name}.{cnt}" + return f"{name_}.log" + + @staticmethod + def calc_size(size: str): + kb = 1024 + mb = 1024 * kb + gb = 1024 * mb + if size.endswith("MB") or size.endswith("mb"): + return int(size[:-2]) * mb + elif size.endswith("GB") or size.endswith("GB"): + return int(size[:-2]) * gb + elif size.endswith("KB") or size.endswith("kb"): + return int(size[:-2]) * kb + else: + return 50 * mb + + def _clean(self): + for _type, (file, _) in self._handler_files.items(): + if not os.path.exists(file): + continue + last_modified = datetime.datetime.fromtimestamp(os.stat(file).st_mtime) + history_file = os.path.join(self._history, self._format_rotate(_type, last_modified)) + self._do_rotate(file, history_file) + + @staticmethod + def _do_rotate(src, dst): + shutil.copy(src, dst) + os.popen(f"echo '' > {src}") + + +logger = Log(**CONFIG['logging']).logger diff --git a/lab-miots/darknet-market/helper/scheduler.py b/lab-miots/darknet-market/helper/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..f20fac854be6c3f695c0608fdb1f4f7d0fd62e70 --- /dev/null +++ b/lab-miots/darknet-market/helper/scheduler.py @@ -0,0 +1,5 @@ +from apscheduler.schedulers.background import BackgroundScheduler + + +scheduler = BackgroundScheduler() +scheduler.start() diff --git a/lab-miots/darknet-market/helper/utils.py b/lab-miots/darknet-market/helper/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2f3d4d34cd0a14d1d900b34338d60532213b4262 --- /dev/null +++ b/lab-miots/darknet-market/helper/utils.py @@ -0,0 +1,55 @@ +from hashlib import md5 + + +def format_args(*args, **kwargs) -> str: + _args = ', '.join(map(repr, args)) + _kwargs = ', '.join(f'{k}={v!r}' for k, v in kwargs.items()) + if _kwargs: + if _args: + _args += ', ' + _args += _kwargs + return _args + + +def cookie2str(cookie: dict): + cookies = "" + for key, value in cookie.items(): + cookies += f'{key}={value}; ' + if cookies.endswith("; "): + cookies = cookies[:-2] + return cookies + + +def calc_hash_s(string: str, type_=md5): + m = type_() + m.update(string.encode('utf8')) + return m.hexdigest() + + +def calc_hash_b(byte: bytes, type_=md5): + m = type_() + m.update(byte) + return m.hexdigest() + + +def get_one_element(html, xpath: str or int): + if type(xpath) == int: + return xpath + if not xpath: + return None + if not xpath.startswith("/"): + return xpath + elements = html.xpath(xpath) + if len(elements) == 0: + return None + else: + return elements[0].strip() + + +def get_elements(html, xpath: str or list): + if not xpath: + return None + if type(xpath) == str: + return html.xpath(xpath) + elif type(xpath) == list: + return xpath diff --git a/lab-miots/darknet-market/mycustom.cnf b/lab-miots/darknet-market/mycustom.cnf new file mode 100644 index 0000000000000000000000000000000000000000..29016ba9dbf3b7dcebe94242dffba97e27fcc9db --- /dev/null +++ b/lab-miots/darknet-market/mycustom.cnf @@ -0,0 +1,10 @@ +[client] +default-character-set=utf8 + +[mysql] +default-character-set=utf8 + +[mysqld] +collation-server = utf8_unicode_ci +init-connect='SET NAMES utf8' +character-set-server = utf8 \ No newline at end of file diff --git a/lab-miots/darknet-market/mysql-apt-config_0.8.13-1_all.deb b/lab-miots/darknet-market/mysql-apt-config_0.8.13-1_all.deb new file mode 100644 index 0000000000000000000000000000000000000000..a061d2d19af00bc5dacb6886fffc542ca5b281b0 Binary files /dev/null and b/lab-miots/darknet-market/mysql-apt-config_0.8.13-1_all.deb differ diff --git a/lab-miots/darknet-market/proxy.bash b/lab-miots/darknet-market/proxy.bash new file mode 100755 index 0000000000000000000000000000000000000000..ea240e49f722515a8c4c34e1eb518ced7c526f21 --- /dev/null +++ b/lab-miots/darknet-market/proxy.bash @@ -0,0 +1,15 @@ +#!/usr/bin/zsh +echo "zft13917331612" +/usr/bin/expect < typing.List[str]: + pass + + def item_page_parser(self, page: BeautifulSoup): + pass + + def main_page_parser(self) -> typing.List[str]: + pass + + def dispatch(self): + pass diff --git a/lab-miots/darknet-market/sites/chang_an_crawler.py b/lab-miots/darknet-market/sites/chang_an_crawler.py new file mode 100644 index 0000000000000000000000000000000000000000..213bb792269f22dec03c8067cbca7ee5fcddac50 --- /dev/null +++ b/lab-miots/darknet-market/sites/chang_an_crawler.py @@ -0,0 +1,167 @@ +import datetime +from copy import copy + +from helper.logger import logger +from helper.utils import calc_hash_s +from sites.base import BaseMarket +from typing import List +from db.models import MarketItem +from urllib.parse import urlencode + +authorization = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHBpcmUiOjE2Nzc4MzMwNDYsImhpZCI6IjY2MDY3MzU0MSIsImxldmVsIjowfQ.L7bwojHFFhL-qXAsUCwMsX2W7UymAvnay6ubIQXSHM8" + +class ChangAnMarket(BaseMarket): + def __init__(self): + super().__init__("长安不夜城") + self.homeUrl = "http://cabyceogpsji73sske5nvo45mdrkbz4m3qd3iommf3zaaa6izg3j2cqd.onion/" + self.set_authorization(authorization) + self.categoryList = ["数据资源","服务业务","虚拟物品","私人专拍","卡料CVV","影视音像","其他类别","技术技能","实体物品","可信交易"] + # cid 范围 1-9 page 范围 未知。 + self.params = { + "cid":"[category]", + "page_num":"[page]", + "":"", + } + + def testConnection(self): + ## get the item first and then see how we can interact with it. + params = { + "cid": 1, + "page_num": 1, + "page_size": 10, + "order": "", + "order_by": "" + } + """ + response: { + data:{ + goods: [ + { + 'cid':1, + '':1 + } + ] + } + } + """ + resp = self.get(self.homeUrl + "api/category/goods", params=params) + res_data:dict = resp.json() + res_data = res_data.setdefault("data",None) + res_data:List[dict] = res_data.setdefault("goods") + if res_data is not None: + # iterate response data type ... + for good in res_data: + #first try to print the good out. + print(good) + name = good.setdefault('name',None) + price = good.setdefault('price',None) + # category + description = good.setdefault('intro',None) + seller = good.setdefault('owner',None).setdefault('name',None) # maybe it is none + market = self.name + create_time = good.setdefault('ctime',None) + update_time = datetime.datetime.now() + id = good.setdefault('id',None) # form url + good_id_params = { + "id":id + } + good_id_str = urlencode(good_id_params) + url = self.homeUrl + "api/goods/details?" + good_id_str + + item = { + 'name': name, + 'price':price, + 'currency':"", + 'category':"", + 'description':description, + 'seller':seller, + 'market':market, + 'create_time':create_time, + 'update_time':update_time, + 'url':url, + 'url_hash':calc_hash_s(url) # for get rid of repeated data + } + MarketItem.insert(item).on_conflict(preserve = [MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + + print(item) + + print(res_data) + + + def getConfig(self): + pass + #get category and so on... + + def dispatch(self): + # should retrieve config like category first... + params = { + "cid": 1, + "page_num": 1, + "page_size": 10, + "order": "", + "order_by": "" + } + resp = self.get(self.homeUrl + "api/category/goods", params=params) + + for cur_category_index in range(len(self.categoryList)): + cur_category = self.categoryList[cur_category_index] + cur_params = copy(params) + cur_params["cid"] = cur_category_index+ 1 + # only check whether it is empty or not + haveMoreItem = True + cur_page = 1; + category_item_sum = 0 + while haveMoreItem: + cur_params["page_num"] = cur_page + resp = self.get(self.homeUrl + "api/category/goods", params=cur_params) + res_data: dict = resp.json() + res_data = res_data.setdefault("data", None) + res_data: List[dict] = res_data.setdefault("goods") + if res_data is not None: + # iterate response data type ... + haveMoreItem = False + if len(res_data)>0: + haveMoreItem=True + for good in res_data: + # first try to print the good out. + name = good.setdefault('name', None) + price = good.setdefault('price', None) + # category + description = good.setdefault('intro', None) + seller = good.setdefault('owner', None).setdefault('name', None) # maybe it is none + market = self.name + create_time = good.setdefault('ctime', None) + update_time = datetime.datetime.now() + id = good.setdefault('id', None) # form url + good_id_params = { + "id": id + } + good_id_str = urlencode(good_id_params) + url = self.homeUrl + "api/goods/details?" + good_id_str + item = { + 'name': name, + 'price': price, + 'currency': "", + 'category': cur_category, + 'description': description, + 'seller': seller, + 'market': market, + 'create_time': create_time, + 'update_time': update_time, + 'url': url, + 'url_hash': calc_hash_s(url) # for get rid of repeated data + } + MarketItem.insert(item).on_conflict( + preserve=[MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + logger.info(f'[{self.name}] Get {len(res_data)} Item URLs from [{self.homeUrl+"api/category/goods"}] params[{cur_params}]') + cur_page+=1 + category_item_sum+= len(res_data) + logger.info(f'[{self.name}] Get {category_item_sum} Item URLs from [{cur_category}]') + #print(resp.text) + + + + + diff --git a/lab-miots/darknet-market/sites/customize.py b/lab-miots/darknet-market/sites/customize.py new file mode 100644 index 0000000000000000000000000000000000000000..d2d569b240b024f7aed94d7ec623ced9864db462 --- /dev/null +++ b/lab-miots/darknet-market/sites/customize.py @@ -0,0 +1,197 @@ +import datetime +import re +import time +import typing +from concurrent.futures import ThreadPoolExecutor +from copy import copy +from traceback import format_exc + +import peewee +from lxml import html + +from components import REDIS +from components import logger +from db.models import MarketItem +from helper.utils import calc_hash_s, get_one_element, get_elements +from sites.base import BaseMarket + +etree = html.etree + +class UniversalMarket(BaseMarket): + + def __init__(self, name, home_url, xpath, root_url, cookie=None, parallel=None, *args, **kwargs): + super().__init__(name) + self.item_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=4) + self.category_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=1) + self.home_url = home_url + self.root_url = root_url + self.xpath: dict = xpath + self.set_cookie(cookie) + self.items = [] + self._pool_setting = parallel + self.init_pool() + + self.ERROR_ITEM_URL = f'{self.name.replace(" ", "_")}_item_error' + self.TODO_ITEM_URL = f'{self.name.replace(" ", "_")}_item_todo' + self.FINISHED_ITEM_URL = f'{self.name.replace(" ", "_")}_item_finished' + + def _fetch_one_item(self): + return REDIS.execute("spop", 0, self.TODO_ITEM_URL) + + def _store_item_todo(self, url): + if not REDIS.execute("sismember", 0, self.TODO_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.ERROR_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.FINISHED_ITEM_URL, url): + REDIS.execute("sadd", 0, self.TODO_ITEM_URL, url) + + def _store_item_error(self, url): + REDIS.execute("sadd", 0, self.ERROR_ITEM_URL, url) + + def _store_item_finished(self, url): + REDIS.execute("sadd", 0, self.FINISHED_ITEM_URL, url) + + def init_pool(self): + try: + if self._pool_setting: + self.item_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['item']) + self.category_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['category']) + except Exception as e: + logger.error(f'[{self.name}] Create Executor Pool Failed, Error : {e}.' + f'Now initialize with Thread Pool with [max_worker=4]') + + def dispatch(self): + """ + dispatch the market + main page -> category urls -> item urls -> items + """ + + # get category urls from main page + category_urls: typing.List[str] = self.main_page_parser() + + # get item urls from category page + + for category_url in category_urls: + self.category_executor.submit(self.category_page_parser, category_url) + + while True: + item_url = self._fetch_one_item() + if item_url: + self.item_executor.submit(self.item_page_parser, item_url) + else: + time.sleep(5) + + def save_items(self): + to_save = self.items.copy() + self.items.clear() + for batch in peewee.chunked(to_save, 10): + MarketItem.insert_many(batch).on_conflict( + preserve=[MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + + def main_page_parser(self) -> typing.List[str]: + categories: typing.List[str] = [] + resp = self.get(self.home_url) + html = etree.HTML(resp.text) + for href in get_elements(html, self.xpath['category_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + categories.append(href) + logger.info(f'[{self.name}] Get {len(categories)} Category URLs from [{self.home_url}]') + return list(set(categories)) + + def category_page_parser(self, url: str): + + def parse_page(href_str): + if self.xpath['item_turn_page'].setdefault('page_pattern', None): + regex_result = re.search(self.xpath['item_turn_page']['page_pattern'], href_str) + if regex_result: + href_str = regex_result.group() + return int(re.search(r'\d+', href_str).group()) + + resp = self.get(url) + html = etree.HTML(resp.text) + pages = set() + max_page = 1 + # need turn page + if self.xpath.setdefault('item_turn_page', None) and self.xpath['item_turn_page'].setdefault('max_page', None) \ + and self.xpath['item_turn_page'].setdefault('params', None): + try: + max_page_str = str(get_one_element(html, self.xpath['item_turn_page']['max_page'])) + max_page = parse_page(max_page_str) + except IndexError: + logger.error( + f'[{self.name}] No element matches [{self.xpath["item_turn_page"]["max_page"]}] on [{url}]') + except Exception as e: + logger.error(f'[{self.name}] Failed to parse max page on [{url}] ' + f'with xpath [{self.xpath["item_turn_page"]["max_page"]}]. ' + f'Error : {e}') + logger.error(format_exc()) + + # first page + cnt = 0 + for href in get_elements(html, self.xpath['item_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + pages.add(href) + cnt += 1 + self._store_item_todo(href) + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{url}] page[1]') + + # next pages + if max_page > 1: + params = copy(self.xpath['item_turn_page']['params']) + to_modify = None + for key, value in params.items(): + if value == '[page]': + to_modify = key + for page in range(0, max_page + 1): # should start from 2 btw... + cnt = 0 + params[to_modify] = page + resp = self.get(url, params=params) + html = etree.HTML(resp.text) + for href in html.xpath(self.xpath['item_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + self._store_item_todo(href) + pages.add(href) + cnt += 1 + if cnt == 0: + break + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{url}] params[{params}]') + + def item_page_parser(self, url: str): + try: + resp = self.get(url) + if not resp or resp.status_code != 200: + return + html = etree.HTML(resp.text) + price: str = " ".join(get_elements(html, self.xpath['item']['price'])).strip() + if self.xpath['item'].setdefault('price_pattern', None) and price: + regex_result = re.search(self.xpath['item']['price_pattern'], price) + if regex_result: + price = regex_result.group() + name = get_one_element(html, self.xpath['item']['name']) + category = get_one_element(html, self.xpath['item']['category']) + description = "\n".join(html.xpath(self.xpath['item']['description'])).strip() + currency = get_one_element(html, self.xpath['item']['currency']) + item = { + 'name': name, + 'price': price, + 'currency': currency, + 'category': category, + 'description': description, + 'seller': self.name, + 'market': self.name, + 'create_time': datetime.datetime.now(), + 'update_time': datetime.datetime.now(), + 'url': url, + 'url_hash': calc_hash_s(url), + } + MarketItem.insert(item).on_conflict( + preserve=[MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + self._store_item_finished(url) + except Exception as e: + logger.error(f"[{self.name}] Failed to parse item on [{url}]. Error : {e}") + logger.error(format_exc()) + self._store_item_error(url) diff --git a/lab-miots/darknet-market/sites/customize_bak_bak.py b/lab-miots/darknet-market/sites/customize_bak_bak.py new file mode 100644 index 0000000000000000000000000000000000000000..9adcec9ccd68582cdef8b429b7339799a3fc43ef --- /dev/null +++ b/lab-miots/darknet-market/sites/customize_bak_bak.py @@ -0,0 +1,247 @@ +import datetime +import re +import time +import typing +from concurrent.futures import ThreadPoolExecutor +from copy import copy +from traceback import format_exc + +import peewee +from lxml import html + +from components import REDIS +from helper.logger import logger +from db.models import MarketItem +from helper.utils import calc_hash_s, get_one_element, get_elements +from sites.base import BaseMarket + +etree=html.etree + +class UniversalMarket(BaseMarket): + + def __init__(self, name, home_url, xpath, root_url, cookie=None, parallel=None, *args, **kwargs): + super().__init__(name) + self.item_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=4) + self.category_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=1) + self.home_url = home_url + self.root_url = root_url + self.xpath: dict = xpath + self.set_cookie(cookie) + self.items = [] + self._pool_setting = parallel + self.init_pool() + + self.ERROR_ITEM_URL = f'{self.name.replace(" ", "_")}_item_error' + self.TODO_ITEM_URL = f'{self.name.replace(" ", "_")}_item_todo' + self.FINISHED_ITEM_URL = f'{self.name.replace(" ", "_")}_item_finished' + #print('init universalMarket') + + def _fetch_one_item(self): + return REDIS.execute("spop", 0, self.TODO_ITEM_URL) + + def _store_item_todo(self, url): + if not REDIS.execute("sismember", 0, self.TODO_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.ERROR_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.FINISHED_ITEM_URL, url): + REDIS.execute("sadd", 0, self.TODO_ITEM_URL, url) + + def _store_item_error(self, url): + REDIS.execute("sadd", 0, self.ERROR_ITEM_URL, url) + + def _store_item_finished(self, url): + REDIS.execute("sadd", 0, self.FINISHED_ITEM_URL, url) + + def init_pool(self): + try: + if self._pool_setting: + self.item_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['item']) + self.category_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['category']) + except Exception as e: + logger.error(f'[{self.name}] Create Executor Pool Failed, Error : {e}.' + f'Now initialize with Thread Pool with [max_worker=4]') + + def dispatch(self): + """ + dispatch the market + main page -> category urls -> item urls -> items + """ + + # get category urls from main page + category_urls: typing.List[str] = self.main_page_parser() + #print("dispatch category") + + # get item urls from category page + for category_url in category_urls: + self.category_executor.submit(self.category_page_parser, category_url) + #print("dispatch get categories") + + while True: + # wait for item url to come ... + item_url = self._fetch_one_item() + if item_url: + self.item_executor.submit(self.item_page_parser, item_url) + else: + time.sleep(5) + #print("dispatch item") + + def save_items(self): + to_save = self.items.copy() + self.items.clear() + for batch in peewee.chunked(to_save, 10): + MarketItem.insert_many(batch).on_conflict( + preserve=[MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + + def main_page_parser(self) -> typing.List[str]: + categories: typing.List[str] = [] + resp = self.get(self.home_url) + html = etree.HTML(resp.text) + print('html'+resp.text) + logger.info(f'[category_url]: {self.xpath["category_url"]}') + for href in get_elements(html, self.xpath['category_url']): + logger.info(f'[href]: {href}') + if not href.startswith('http'): + href = f'{self.root_url}{href}' + categories.append(href) + logger.info(f'[{self.name}] Get {len(categories)} Category URLs from [{self.home_url}]') + return list(set(categories)) + + def category_page_parser(self, url: str): + + def get_pages_url(url:str,num): + try: + pages_url=[] + for i in range(2,num+1): + urls=url+'?page='+str(i) #turn page logic hard code??? + pages_url.append(urls) + except IndexError: + logger.error( + f'[{self.name}] No element matches [{self.xpath["item_turn_page"]["max_page"]}] on [{url}]') + return pages_url + + def parse_page(href_str): + + if self.xpath['item_turn_page'].setdefault('page_pattern', None): + regex_result = re.search(self.xpath['item_turn_page']['page_pattern'], href_str) + if regex_result: + href_str = regex_result.group() + if re.search(r'page=\d+', href_str): + page_int=re.search(r'page=\d+', href_str).group() + page_num=page_int[5::] + # print('page_num: '+str(page_num)) + return int(page_num) + return 1 + + resp = self.get(url) + html = etree.HTML(resp.text) + pages = set() + max_page = 1 + # need turn page + if self.xpath.setdefault('item_turn_page', None) and self.xpath['item_turn_page'].setdefault('max_page', None) \ + and self.xpath['item_turn_page'].setdefault('params', None): + try: + max_page_str = str(get_one_element(html, self.xpath['item_turn_page']['max_page'])) + #print('max_page_str: '+max_page_str) + max_page=parse_page(max_page_str) + urls=get_pages_url(url,max_page) + for u in urls: + resp2=self.get(u) + html2=etree.HTML(resp2.text) + cnt = 0 + for href in get_elements(html2, self.xpath['item_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + pages.add(href) + cnt += 1 + self._store_item_todo(href) + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{u}]') + print("max_page: "+str(max_page)) + + except IndexError: + logger.error( + f'[{self.name}] No element matches [{self.xpath["item_turn_page"]["max_page"]}] on [{url}]') + except Exception as e: + logger.error(f'[{self.name}] Failed to parse max page on [{url}] ' + f'with xpath [{self.xpath["item_turn_page"]["max_page"]}]. ' + f'Error : {e}') + logger.error(format_exc()) + # first page + cnt = 0 + for href in get_elements(html, self.xpath['item_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + pages.add(href) + cnt += 1 + self._store_item_todo(href) + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{url}] page[1]') + + + # next pages + if max_page > 1: + params = copy(self.xpath['item_turn_page']['params']) + to_modify = None + for key, value in params.items(): + if value == '[page]': + to_modify = key + for page in range(2, max_page + 1): + cnt = 0 + params[to_modify] = page + print('params: '+params) + resp = self.get(url, params=params) + print('resp: '+resp.url) + html = etree.HTML(resp.text) + for href in html.xpath(self.xpath['item_url']): + if not href.startswith('http'): + href = f'{self.root_url}{href}' + self._store_item_todo(href) + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{url}] page[{page}]') + pages.add(href) + cnt += 1 + print('href: '+pages) + if cnt == 0: + break + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{url}] params[{params}]') + + + + + def item_page_parser(self, url: str): + try: + resp = self.get(url) + if not resp or resp.status_code != 200: + return + html = etree.HTML(resp.text) + price: str = " ".join(get_elements(html, self.xpath['item']['price'])).strip() + if self.xpath['item'].setdefault('price_pattern', None) and price: + regex_result = re.search(self.xpath['item']['price_pattern'], price) + if regex_result: + price = regex_result.group() + name = get_one_element(html, self.xpath['item']['name']) + category = get_one_element(html, self.xpath['item']['category']) + description = "\n".join(html.xpath(self.xpath['item']['description'])).strip() + #description = get_one_element(html, self.xpath['item']['description']) + currency = get_one_element(html, self.xpath['item']['currency']) + + vendor=get_one_element(html, self.xpath['item']['vendor']) + + item = { + 'name': name, + 'price': price, + 'currency': currency, + 'category': category, + 'seller': vendor, + 'description': description, + 'market': self.name, + 'create_time': datetime.datetime.now(), + 'update_time': datetime.datetime.now(), + 'url': url, + 'url_hash': calc_hash_s(url), + } + MarketItem.insert(item).on_conflict( + preserve=[MarketItem.currency, MarketItem.name, MarketItem.price, + MarketItem.description, MarketItem.update_time]).execute() + self._store_item_finished(url) + except Exception as e: + logger.error(f"[{self.name}] Failed to parse item on [{url}]. Error : {e}") + logger.error(format_exc()) + self._store_item_error(url) \ No newline at end of file diff --git a/lab-miots/darknet-market/sites/forum.py b/lab-miots/darknet-market/sites/forum.py new file mode 100644 index 0000000000000000000000000000000000000000..72a094c46acf17e2c1fd4452d41591773f821202 --- /dev/null +++ b/lab-miots/darknet-market/sites/forum.py @@ -0,0 +1,458 @@ +import datetime +import os.path +import importlib +import time +from concurrent.futures import ThreadPoolExecutor +from copy import copy + +import peewee +from lxml import html +from lxml.html import tostring + +from bs4 import BeautifulSoup + +from traceback import format_exc + +from components import REDIS +from components import logger +from const import ROOT_DIR +from db.models import DisplayPoster, DisplayPostings, DisplayPostcomments +from helper.utils import get_elements, get_one_element, calc_hash_s +from sites.base import BaseForum +from typing import List +import re +from components import REDIS +from sites.page_turner.page_turner import section_iterate + + +## used to store things in section page +class ThreadForParse(): + def __init__(self,thread_url,thread_topic,replies=None,views=None,post_time=None): + self.thread_url = thread_url + self.thread_topic = thread_topic + self.replies = replies + self.views = views + self.post_time = post_time + + + +etree = html.etree +# PROCEDURE: +# MAIN PAGE -> SECTION PAGE (turn page) -> THREAD PAGE (turn page) -> POST (minimal unit) +class UniversalForum(BaseForum): + ## None only for testing, should remove none in release mode. + def __init__(self,name:str,home_url:str,xpath=None,root_url=None,cookie=None,parallel=None, *args, **kwargs): + super().__init__(name) + # user thread section(category) post reply + self.post_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=4) + self.section_executor: ThreadPoolExecutor = ThreadPoolExecutor(max_workers=1) + self.home_url = home_url + self.root_url = root_url + self.xpath: dict = xpath + # section xpath should support a list grab section from a list... + self.set_cookie(cookie) + self.testPhase = False + #TODO: turn internal ram storage into redis storage. handle multi thread. + self.thread_item_list:List[str] = [] + self.ERROR_ITEM_URL = f'{self.name.replace(" ", "_")}_item_error' + self.TODO_ITEM_URL = f'{self.name.replace(" ", "_")}_item_todo' + self.FINISHED_ITEM_URL = f'{self.name.replace(" ", "_")}_item_finished' + self.items = { + self.TODO_ITEM_URL: [], + self.ERROR_ITEM_URL: [], + self.FINISHED_ITEM_URL:[] + } + self._pool_setting = parallel + self.init_pool() + pass + + def _fetch_one_item(self): + return REDIS.execute("spop", 0, self.TODO_ITEM_URL) + + def _store_item_todo(self, url): + if not REDIS.execute("sismember", 0, self.TODO_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.ERROR_ITEM_URL, url) \ + and not REDIS.execute("sismember", 0, self.FINISHED_ITEM_URL, url): + REDIS.execute("sadd", 0, self.TODO_ITEM_URL, url) + + def _store_item_error(self, url): + REDIS.execute("sadd", 0, self.ERROR_ITEM_URL, url) + + def _store_item_finished(self, url): + REDIS.execute("sadd", 0, self.FINISHED_ITEM_URL, url) + + def store_item_todo(self,url): + self.items[self.TODO_ITEM_URL].append(url) + + def fetch_one_item(self)->str: + return self.items[self.TODO_ITEM_URL].pop() + def store_item_error(self,url): + self.items[self.ERROR_ITEM_URL].append(url) + def store_item_finish(self,url): + self.items[self.FINISHED_ITEM_URL].append(url) + + def init_pool(self): + try: + if self._pool_setting: + self.post_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['thread']) + self.section_executor = ThreadPoolExecutor(max_workers=self._pool_setting['pool']['section']) + except Exception as e: + logger.error(f'[{self.name}] Create Executor Pool Failed, Error : {e}.' + f'Now initialize with Thread Pool with [max_worker=4]') + + def dispatch(self): + section_urls = self.main_page_parser() + for section in section_urls: + if not section.startswith('http'): + section_url = self.join_url(self.root_url,section) + else: + section_url = section + self.section_executor.submit(self.section_page_parser, section_url) + # post_url = self._fetch_one_item() + # if post_url: + # self.thread_page_parser(post_url) + # else: + # print('no post url') + while True: + post_url = self._fetch_one_item() + if post_url: + self.post_executor.submit(self.thread_page_parser, post_url) + else: + time.sleep(5) + pass + + def testPhaseDispatch(self): + section_urls = self.main_page_parser() + cnt =1 + self.testPhase = True + for section in section_urls: + if len(self.items[self.TODO_ITEM_URL]) > 0: + break + if cnt >0: + cnt-=1 + continue + if not section.startswith('http'): + section_url = self.join_url(self.root_url,section) + else: + section_url = section + try: + self.section_page_parser(section_url) + except Exception as e: + logger.error(f'error when requesting {section_url}, err:{e}') + + # use referer header for user behaviour tracking... + # section parser... + # thread parser... + logger.info(f"request section{section} success") + cnt =0 + # post_url = self._fetch_one_item() + # if post_url: + # self.thread_page_parser(post_url) + # else: + # print('no post url') + for thread in self.items[self.TODO_ITEM_URL]: + self.thread_page_parser(thread) + cnt +=1 + #self.write_to_file(f"test/page/{section}.html",resp.text) + + + def main_page_parser(self)->List[str]: + #should return the list of the page + try: + resp = self.get(self.home_url) + except Exception as e: + logger.error(f'error when requesting {self.home_url}, err:{e}') + return [] + main_html = etree.HTML(resp.text) + sections = self.elements_getter(main_html,self.xpath['section_url']) + return sections + """" + should support multiple way of turning page?? + a. able to know max page, (guess turn page base on url???) + b. able to + 1. request section url + 2. + + """ + + def section_page_parser(self,section_home_url:str): + #inside section, parse different thread also handle turn page...how? + # forums and page are both using params... php turn page, can i use both params and url?? + resp = self.get(section_home_url) + html = etree.HTML(resp.text) + pages = self.thread_page_turner_invoker(html,section_home_url) + # print(pages) + cnt = 0 + thread_eles = get_elements(html,self.xpath['thread_info']['relative']) + category = get_one_element(html,self.xpath['thread_category']) + ## start thread parsing + for ele in thread_eles: + #author = self.relative_getter(ele,self.xpath['thread_info']['author']) + try: + self.thread_item_parse_store(ele,category) + cnt+=1 + except Exception as e: + logger.error(f'error when parse thread, err:{e}') + #if author is not None: + #cnt+=1 + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{section_home_url}]') + for page in pages: + + if not page.startswith('http'): + page = f"{section_home_url}{page}" + resp = self.get(page) + cnt = 0 + next_html = etree.HTML(resp.text) + page_threads = get_elements(next_html, self.xpath['thread_info']['relative']) + for tag in page_threads: + try: + self.thread_item_parse_store(tag, category) + cnt += 1 + except Exception as e: + logger.error(f'error when parse thread , err:{e}') + + logger.info(f'[{self.name}] Get {cnt} Item URLs from [{page}] ') + ''' + each category handler entrypoint + inside should invoke three function: + 1. handle first page parsing, determine whether need to turn page + 2. invoke turn page function, a hot-load function, can be invoke base on self name + 3. parser, for every entry in each page, we need a parser to parse the data. + 4. + ''' + def thread_page_turner_invoker(self, html_page,home_url): + if self.xpath.setdefault('thread_turn_page',None) and self.xpath['thread_turn_page'].setdefault('specialized',None): + #use dedicated page_turner + if self.xpath['thread_turn_page'].setdefault('page_turner', None) is None: + raise ValueError("thread turn page::page_turner should not be null!") + page_turner = importlib.import_module(self.xpath['thread_turn_page']['page_turner']).section_iterate + return page_turner(html_page,self.xpath['thread_turn_page'],home_url) + else: + return section_iterate(html_page,self.xpath['thread_turn_page'],home_url) + pass + + + ## return count of thread item store in + def thread_item_parse_store(self, thread_row:BeautifulSoup, section:str)->int: + ## first check author + + try: + author = self.relative_multi_getter(thread_row, self.xpath['thread_info']['author']) + #author_url = self.relative_getter(thread_row, self.xpath['thread_info']['author_url']) + # if author_url is None or author is None: + # return 0 + if author is None: + return 0 + poster_id = -1 + poster_item = { + "name":author, + "forum":self.name, + "intelligence":'{"email":[],"cryptocurrency":[],"key":[]}', + } + try: + poster_id = DisplayPoster.select(DisplayPoster.id).where(DisplayPoster.name == poster_item["name"],DisplayPoster.forum==poster_item["forum"]).get() + except Exception as e: + poster_id = DisplayPoster.insert(poster_item).execute() + href = self.relative_getter(thread_row, self.xpath['thread_info']['thread_url']) + if not href.startswith('http'): + href = f'{self.root_url}{href}' + if self.testPhase: + self.store_item_todo(href) + else: + self._store_item_todo(href) + + title = self.relative_getter(thread_row, self.xpath['thread_info']['thread_topic']) + replies = self.relative_getter(thread_row, self.xpath['thread_info']['replies']) + views = self.relative_getter(thread_row, self.xpath['thread_info']['views']) + post_time = self.relative_getter(thread_row, self.xpath['thread_info']['post_time']) + posting_id = DisplayPostings.select(peewee.fn.MAX(DisplayPostings.id)).scalar() + thread_item = { + "id":posting_id+1, + "poster_id":poster_id, + "title":title, + "category":section, + "forum":self.name, + "url":href, + "url_hash":calc_hash_s(href), + "replies":replies, + "views":views, + "post_time":post_time, + "create_time": datetime.datetime.now(), + "update_time":datetime.datetime.now(), + } + DisplayPostings.insert(thread_item).on_conflict(preserve=[DisplayPostings.post_time, DisplayPostings.title, DisplayPostings.update_time,DisplayPostings.views,DisplayPostings.replies]).execute() + return 1 + except Exception as e: + raise ValueError(f'error when parse thread item {href}, err:{e}') + + + def max_parser(self,href_str,regex_str): + regex_result = re.search(regex_str,href_str) + if regex_result: + href_str = regex_result.group() + return int(re.search(r'\d+',href_str).group()) + + def relative_getter(self, target, xpath:str): + res = get_elements(target, xpath) + if res is None: + return None + if len(res)>0: + if isinstance(res[0],str): + val = "" + for i in res: + if isinstance(i,str): + val+=i.strip() + return val + return res[0] + else: + return None + def relative_multi_getter(self,target,xpaths): + res = [] + for xpath in xpaths: + tmps = get_elements(target,xpath) + if isinstance(tmps,str) and tmps != "": + res.append(tmps) + continue + for tmp in tmps: + res.append(tmp) + if len(res) > 0: + return res[0] + else: + return None + + def elements_getter(self, target:BeautifulSoup, xpaths:List[str]): + result = [] + for xpath in xpaths: + new_list = get_elements(target, xpath) + if new_list is not None: + for item in new_list: + result.append(item) + return result + + def thread_page_parser(self,thread_url:str): + # get page + ## get id in database + try: + posting_id = -1 + posting_id = DisplayPostings.select(DisplayPostings.id).where(DisplayPostings.url_hash == calc_hash_s(thread_url)).get() + resp = self.get(thread_url) + html = etree.HTML(resp.text) + # decide whether to turn page + max_page = 1 + pages = self.post_page_turner_invoker(html,thread_url) + posts = get_elements(html, self.xpath['post']['relative']) + cnt = 0 + for post in posts: + self.post_parser(post, posting_id) + cnt += 1 + logger.info(f"{cnt} items scraped from thread {thread_url}") + for page in pages: + cnt = 0 + if not page.startswith('http'): + page = f'{self.root_url}{page}' + next_resp = self.get(page) + html = etree.HTML(next_resp.text) + posts = get_elements(html, self.xpath['post']['relative']) + for post in posts: + self.post_parser(post, posting_id) + cnt += 1 + logger.info(f"{cnt} items scraped from thread {page}") + self._store_item_finished(thread_url) + except Exception as e: + print("posting not found, exit") + print(e) + self._store_item_error(thread_url) + + + + def post_page_turner_invoker(self,html_page,home_url): + if self.xpath.setdefault('post_turn_page',None) and self.xpath['post_turn_page'].setdefault('specialized',None): + if self.xpath['post_turn_page'].setdefault('page_turner',None) is None: + raise ValueError("thread turn page::page_turner should not be null!") + page_turner = importlib.import_module(self.xpath['thread_turn_page']['page_turner']).thread_iterate + return page_turner(html_page,self.xpath['post_turn_page'],home_url) + else: + return section_iterate(html_page,self.xpath['post_turn_page'],home_url) + def next_page_params(self,orig_params:dict,page)->dict: + new_params = copy(orig_params) + to_modify= "" + for key,value in new_params.items(): + if value == '[page]': + to_modify = key + if to_modify=="": + logger.error("params not found!") + new_params[to_modify] = page + return new_params + + + def post_parser(self,html:BeautifulSoup,posting_id): + # author_url = self.relative_getter(html,self.xpath['post']['author_url']) + author = self.relative_multi_getter(html,self.xpath['post']['author']) + if author is None: + return + #author_url_hash = calc_hash_s(author_url) + poster_id = -1 + poster_item = { + "name": author, + "forum":self.name, + "intelligence":'{"email":[],"cryptocurrency":[],"key":[]}' + # "url": author_url, + # "url_hash": author_url_hash + } + try: + poster_id = DisplayPoster.select(DisplayPoster.id).where(DisplayPoster.name == poster_item["name"],DisplayPoster.forum==poster_item["forum"]).get() + except Exception as e: + poster_id = DisplayPoster.insert(poster_item).execute() + try: + post_time =self.relative_getter(html,self.xpath['post']['post_time']) or "" + content = self.relative_getter(html,self.xpath['post']['content']) + url = self.relative_getter(html,self.xpath['post']['post_url']) or "" + floor = self.relative_getter(html,self.xpath['post']['floor']) or "" + + if not url.startswith('http'): + url = f'{self.root_url}{url}' + comment_id = DisplayPostcomments.select(peewee.fn.Max(DisplayPostcomments.id)).scalar() + item = { + 'id':comment_id+1, + 'poster_id':poster_id, + 'content': tostring(content), + 'post_time':post_time, + 'forum':self.name, + 'url': url, + 'url_hash': calc_hash_s(url), + 'floor':floor, + 'post_id_id':posting_id, + 'create_time':datetime.datetime.now(), + 'update_time':datetime.datetime.now() + } + DisplayPostcomments.insert(item).on_conflict( + preserve = [DisplayPostcomments.content,DisplayPostcomments.update_time,DisplayPostcomments.create_time] + ).execute() + except Exception as e: + raise ValueError(f"error when parsing post, err:{e}") + + + + def retrieveOnePage(self,url,fileName): + try: + resp = self.get(url) + code = resp.status_code + except Exception as e: + logger.error(f'error when requesting {self.home_url}, err:{e}') + return + logger.info(f'request success') + self.write_to_file(fileName,resp.text) + print(resp.text) + + def main_parse(self, page: BeautifulSoup): + pass + + def join_url(self,root:str,path:str)->str: + return f'{root}{path}' + def write_to_file(self,filepath:str,content:str): + root_dir = ROOT_DIR + new_path = os.path.join(root_dir,filepath) + with open(new_path,'w+') as f: + f.write(content) + + diff --git a/lab-miots/darknet-market/sites/page_turner/__init__.py b/lab-miots/darknet-market/sites/page_turner/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/darknet-market/sites/page_turner/page_turner.py b/lab-miots/darknet-market/sites/page_turner/page_turner.py new file mode 100644 index 0000000000000000000000000000000000000000..03d2f72075542667ff7b09c67a05de2b190ce1ca --- /dev/null +++ b/lab-miots/darknet-market/sites/page_turner/page_turner.py @@ -0,0 +1,69 @@ + + +import re + +from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse +from copy import copy + +from helper.utils import get_one_element + +def update_query_param(url, param_dict): + """ + Updates a query parameter in the given URL. + + Arguments: + url -- the input URL to modify + param_dict -- a dictionary of query parameters to update in the URL + + Returns: + a new URL string with the updated query parameter + """ + # Parse the input URL + parsed_url = urlparse(url) + + # Extract the existing query parameters + query_params = dict(parse_qsl(parsed_url.query)) + + # Update the specified query parameter(s) + query_params.update(param_dict) + + # Encode the updated query parameters as a query string + encoded_params = urlencode(query_params) + + # Construct the new URL with the updated query string + new_url = urlunparse((parsed_url.scheme, parsed_url.netloc, parsed_url.path, parsed_url.params, encoded_params, parsed_url.fragment)) + + return new_url + +# get max page url, and then use params to get next page url +# need to pass in original url right? +def section_iterate(html_page,xpath,home_url)->[]: + urls = [] + try: + max_page_str = str(get_one_element(html_page,xpath['max_page'])) + except Exception as e: + return urls + href_str = copy(max_page_str) + try: + if xpath.setdefault('page_pattern',None): + regex_result = re.search(xpath['page_pattern'],max_page_str) + if regex_result: + max_page_str = regex_result.group() + max_page = int(re.search(r'\d+',max_page_str).group()) + else: + return urls + except Exception as e: + return urls + + params = copy(xpath['params']) + to_modify = None + for key, value in params.items(): + if value == '[page]': + to_modify = key + for i in range(1,max_page+1): + params[to_modify] = i + urls.append(update_query_param(href_str,params)) + return urls + + + diff --git a/lab-miots/darknet-market/sites/page_turner/the_hub.py b/lab-miots/darknet-market/sites/page_turner/the_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..b32f2b385ee8595fa36e31e3cb4e1b2293d1f9fc --- /dev/null +++ b/lab-miots/darknet-market/sites/page_turner/the_hub.py @@ -0,0 +1,47 @@ +''' + given a category/page beautifulsoup object, it return an iterator, where can be used to + determine have more page or not, how many page still exist... +''' +import re + +from helper.utils import get_one_element + +# for advance usage should return iterator instead of list to dynamically generate next page url. +def section_iterate(html_page,xpath,home_url=None)->[]: + urls = [] + try: + max_page = int(get_one_element(html_page,xpath['max_page'])) + except Exception as e: + return urls + url = get_one_element(html_page,xpath['max_page_url']) + match = re.search(r"board=(\d+)",url) + if match: + board_value = match.group(1) + else: + raise ValueError("Invalid base URL") + for i in range(1,max_page+1): + page_number = i*20 + tmp = f"{url.split('?')[0]}?board={board_value}.{page_number}" + urls.append(tmp) + + #need max_page and max page url + + return urls + +def thread_iterate(html_page,xpath,home_url=None): + urls = [] + try: + max_page = int(get_one_element(html_page,xpath['max_page'])) + except Exception as e: + return urls + url = get_one_element(html_page,xpath['max_page_url']) + match = re.search(r"topic=(\d+)",url) + if match: + topic_value = match.group(1) + else: + raise ValueError("Invalid base URL") + for i in range(1,max_page+1): + page_number = i*20 + tmp= f"{url.split('?')[0]}?topic={topic_value}.{page_number}" + urls.append(tmp) + return urls diff --git a/lab-miots/darknet-market/sites/proxy/__init__.py b/lab-miots/darknet-market/sites/proxy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-miots/darknet-market/sites/proxy/tor.py b/lab-miots/darknet-market/sites/proxy/tor.py new file mode 100644 index 0000000000000000000000000000000000000000..083fd0dc863fb84be5c7418230bc3ae0fffc320f --- /dev/null +++ b/lab-miots/darknet-market/sites/proxy/tor.py @@ -0,0 +1,38 @@ +import requests +from requests.adapters import HTTPAdapter + +from const import TOR_PROXY +from helper.logger import logger +from helper.utils import cookie2str + + +class TorProxy: + + def __init__(self): + self.session = requests.session() + self.session.proxies = TOR_PROXY + self.session.headers["User-Agent"]="Mozilla/5.0 (Windows NT 10.0; rv:102.0) Gecko/20100101 Firefox/102.0" + self.session.mount('http://', HTTPAdapter(max_retries=3)) + self.session.mount('https://', HTTPAdapter(max_retries=3)) + + def set_cookie(self, cookie: dict or str): + if not cookie: + return + if type(cookie) == dict: + self.session.headers['cookie'] = cookie2str(cookie) + elif type(cookie) == str: + self.session.headers['cookie'] = cookie + + def set_referer(self,referer:str): + self.session.headers['Referer'] =referer + def set_authorization(self, token:str): + self.session.headers['Authorization'] = "Bearer " + token + + def get(self, url, **kwargs): + try: + resp = self.session.get(url, timeout=60, **kwargs) + # 可以在这里加下页面存储 + except requests.exceptions.ConnectTimeout as e: + logger.warning(f"Query {url} timeout, error : {e}") + return None + return resp diff --git a/lab-miots/darknet-market/sql2sql.py b/lab-miots/darknet-market/sql2sql.py new file mode 100644 index 0000000000000000000000000000000000000000..f32b5e79b89a7cfac69e28b16f23545627d3ec46 --- /dev/null +++ b/lab-miots/darknet-market/sql2sql.py @@ -0,0 +1,84 @@ +import pymysql +import json +db=pymysql.connect(host='127.0.0.1',port=3306,user='root',password='zft13917331612@SJTU',database='darkweb') +cursor=db.cursor(cursor=pymysql.cursors.DictCursor) + +data=[] +with open('/root/sql2sql2','r',encoding='utf-8') as f: + result = json.load(f) + for i in result: + item = {} + item['url'] = i.get('url') + item['title'] = i.get('name') + item['price'] = i.get('price') + item['seller'] = i.get('seller') + item['category'] = i.get('category') + #item['ships_from'] = i.get('source'); + #item['ship_to'] = i.get('shipto'); + item['url_hash'] = i.get('category') + item['create_time'] = i.get('create_time') + item['update_time'] = i.get('update_time') + item['currency'] = i.get('currency') + item['discription'] =i.get('description') + id=i.get('market') + if(id=="INCOGNITO MARKETPLACE"): + item['market_id']=18 + elif(id=="ARCHETYP MARKET"): + item['market_id'] = 25 + elif (id == "MGM GRAND"): + item['market_id'] = 28 + elif (id == "NEMESIS MARKET"): + item['market_id'] = 26 + elif (id == "CYPHER MARKETPLACE"): + item['market_id'] = 27 + data.append(item) +for i in data: + num = 'select * from display_marketseller where name=' +str(i['seller']) + if cursor.execute(num): + result = cursor.fetchone() + i['vendor_id']=result['id'] + else: + newseller="insert into display_marketseller (name,market_id) values ("+str(i['seller'])+","+i['market_id']+")" + cursor.execute(newseller) + num = 'select * from display_marketseller where name=' + str(i['seller']) + cursor.execute(num) + result = cursor.fetchone() + i['vendor_id'] = result['id'] + insertsql='insert into display_item(vendor_id,name,discription,price,price,category,market_id,url,url_hash,create_time,update_time) values' + insertsql=insertsql +"('%d','%s','%s','%s','%s','%s','%d','%s','%s','%s','%s');\r\n" %(i['vendor_id'],i['name'],i['discription'],i['price'],i['category'],i['market_id'],i['url'],i['url_hash'],i['create_time'],i['update_time']) + cursor.execute(insertsql) + +#db2=pymysql.connect(host='202.120.1.158:10105',port=3306,user='root',password='zft13917331612@SJTU',database='darkweb') +#cursor=db.cursor(cursor=pymysql.cursors.DictCursor) + +#sql="select * from display_marketitem where id =100" +#cursor.execute(sql) +#result = cursor.fetchall() +#for item in result: + #print(item) + ''' +data_list=[] +for i in range(1,24311): + sql00='select * from market_item where id='+str(i) + cursor.execute(sql00) + results = cursor.fetchall() + data={} + for row in results: + data['name']=row["name"] + data['seller']=row['seller'] + data['id'] = row['id'] + data['description'] = row['description'] + data['price'] = row['price'] + data['currency'] = row['currency'] + data['category'] = row['category'] + data['market'] = row['market'] + data['url'] = row['url'] + data['url_hash'] = row['url_hash'] + data['create_time'] = row['create_time'] + data['update_time'] = row['update_time'] + data_list.append(data) + #print(data) +result_file = open('item.json', 'w', encoding='utf-8') +result_file.write(json.dumps(data_list, indent=4, ensure_ascii=False, default=str)) +result_file.close() +''' \ No newline at end of file diff --git a/lab-miots/darknet-market/test/app.py b/lab-miots/darknet-market/test/app.py new file mode 100644 index 0000000000000000000000000000000000000000..e669ff71d1556cd999edaa14c316a897fbb0ad9d --- /dev/null +++ b/lab-miots/darknet-market/test/app.py @@ -0,0 +1,18 @@ +from sites.forum import UniversalForum + +from const import CONFIG + +print(CONFIG) +forums:dict = CONFIG['forum'] +print(f'[forum]: {forums}') +for forum,config in forums.items(): + print(config) + if config.pop('crawl'): + newForum = UniversalForum(name=forum,**config) + newForum.testPhaseDispatch() + print(f'[new forum]: {newForum}') + #newForum.retrieveOnePage("http://cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion/showthread.php?tid=2028","test/page/thread/thread_turn_page_example.html") + + + +# RUN FORUM TO GET MAIN PAGE diff --git a/lab-miots/darknet-market/test/chang_an_tester.py b/lab-miots/darknet-market/test/chang_an_tester.py new file mode 100644 index 0000000000000000000000000000000000000000..11b9807cc11348b45a0a85e5053ff8923be92e94 --- /dev/null +++ b/lab-miots/darknet-market/test/chang_an_tester.py @@ -0,0 +1,8 @@ +from sites.chang_an_crawler import ChangAnMarket +from const import CONFIG + +markets = CONFIG['markets'] +print(markets) + +crawler = ChangAnMarket() +crawler.testConnection() \ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/forumdisplay.php?fid=19.html b/lab-miots/darknet-market/test/page/forumdisplay.php?fid=19.html new file mode 100644 index 0000000000000000000000000000000000000000..9251b8752ed78b855c3d5de70241aa02879c098a --- /dev/null +++ b/lab-miots/darknet-market/test/page/forumdisplay.php?fid=19.html @@ -0,0 +1 @@ + CryptBB - Password Required
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Please enter the password below:

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Cyberjagu •
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#1
Yesterday, 02:46 PM
I have heard many members are saying that the inbox is full , whoever is facing this problem please follow this

First go to the User CP > Inbox

check mark the pm's you want to delete
Below that you will have the option to delete < so do it
You just have to do one last step
Select on the trash can in the user CP itself , Here you will show the pm's that you have just deleted from the inbox.
Now check mark again and delete

Your inbox must be empty now

Code:
User CP > Inbox > delete > Trash can > delete again
Cyberjagu@jabber.calyxinstitute.org [OTR]
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yany3 •
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#2
Yesterday, 02:59 PM
(Yesterday, 02:46 PM)Cyberjagu Wrote: I have heard many members are saying that the inbox is full , whoever is facing this problem please follow this

First go to the User CP > Inbox

check mark the pm's you want to delete
Below that you will have the option to delete < so do it
You just have to do one last step
Select on the trash can in the user CP itself , Here you will show the pm's that you have just deleted from the inbox.
Now check mark again and delete

Your inbox must be empty now

Code:
User CP > Inbox > delete > Trash can > delete again

had this problem recently. "Delete all" function could be handy
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Cyberjagu •
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#3
Yesterday, 03:04 PM
(Yesterday, 02:59 PM)yany3 Wrote: had this problem recently. "Delete all" function could be handy

yes we are going to bring it in this update
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_1.html b/lab-miots/darknet-market/test/page/thread/thread_1.html new file mode 100644 index 0000000000000000000000000000000000000000..eadf902176669ba2e93f275fe06d10e4dc593d01 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_1.html @@ -0,0 +1,28 @@ + Instructions on setting up CryptBB XMPP
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Instructions on setting up CryptBB XMPP
Hyperion •
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#1
05-09-2021, 04:30 PM
Too many posts about issues, here's how to set up CryptBB's XMPP account on Pidgin client via Tor.

Steps:
  1. Go to CryptBB XMPP account creation page and create your account: http://cryptbbsfmzv6dq4ec2iv6ravrw5ohgmk...lqd.onion/
  2. Oepn up Pidgin client;
  3. Accounts → Manage Accounts → Add...;
  4. Basic tab:
    1. Protocol → XMPP;
    2. Username → the username you chose;
    3. Domain → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    4. Password → your chosen passowrd;
    5. Select the Remember Password checkbox (optional);
    6. You can select a Local Alias too - a name which is displayed in chat instead of a long account name with ID (optional);
  5. Advanced tab:
    1. Connection security → Use encryption if available
    2. Select the Allow plaintext auth over unencrypted streams checkbox;
    3. Conncet port → 5222
    4. Connect server → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    5. Deselect Show Custom Smileys checkbox (it will ruin all PGP messages);
  6. Proxy tab:
    1. Proxy type → Tor/Privacy (SOCKS5);
    2. Host → 127.0.0.1
    3. Port → 9050
  7. Click Save;
  8. Accounts → Enable Account → select your newly created account.
That's it, you have now created an account on CryptBB's XMPP server, and added/enabled it on your Pidgin client running through Tor.
You can test it out, here's my XMPP:
Hyperion@cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
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V0RTec •
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#2
10-15-2021, 07:54 PM
(05-09-2021, 04:30 PM)Hyperion Wrote: Too many posts about issues, here's how to set up CryptBB's XMPP account on Pidgin client via Tor.

Steps:
  1. Go to CryptBB XMPP account creation page and create your account: http://cryptbbsfmzv6dq4ec2iv6ravrw5ohgmk...lqd.onion/
  2. Oepn up Pidgin client;
  3. Accounts → Manage Accounts → Add...;
  4. Basic tab:
    1. Protocol → XMPP;
    2. Username → the username you chose;
    3. Domain → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    4. Password → your chosen passowrd;
    5. Select the Remember Password checkbox (optional);
    6. You can select a Local Alias too - a name which is displayed in chat instead of a long account name with ID (optional);
  5. Advanced tab:
    1. Connection security → Use encryption if available
    2. Select the Allow plaintext auth over unencrypted streams checkbox;
    3. Conncet port → 5222
    4. Connect server → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    5. Deselect Show Custom Smileys checkbox (it will ruin all PGP messages);
  6. Proxy tab:
    1. Proxy type → Tor/Privacy (SOCKS5);
    2. Host → 127.0.0.1
    3. Port → 9050
  7. Click Save;
  8. Accounts → Enable Account → select your newly created account.
That's it, you have now created an account on CryptBB's XMPP server, and added/enabled it on your Pidgin client running through Tor.
You can test it out, here's my XMPP:
Hyperion@cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion

Thank you for writing this! Everyone here should join. I'm just responding so this thread does not vanish between all the other old threads.
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Cyberjagu •
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#3
10-19-2021, 05:38 PM
added to Important Threads .
Cyberjagu@jabber.calyxinstitute.org [OTR]
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l33tchopstick •
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#4
02-18-2022, 03:40 PM
(05-09-2021, 04:30 PM)Hyperion Wrote: Too many posts about issues, here's how to set up CryptBB's XMPP account on Pidgin client via Tor.

Steps:
  1. Go to CryptBB XMPP account creation page and create your account: http://cryptbbsfmzv6dq4ec2iv6ravrw5ohgmk...lqd.onion/
  2. Oepn up Pidgin client;
  3. Accounts → Manage Accounts → Add...;
  4. Basic tab:
    1. Protocol → XMPP;
    2. Username → the username you chose;
    3. Domain → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    4. Password → your chosen passowrd;
    5. Select the Remember Password checkbox (optional);
    6. You can select a Local Alias too - a name which is displayed in chat instead of a long account name with ID (optional);
  5. Advanced tab:
    1. Connection security → Use encryption if available
    2. Select the Allow plaintext auth over unencrypted streams checkbox;
    3. Conncet port → 5222
    4. Connect server → cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion
    5. Deselect Show Custom Smileys checkbox (it will ruin all PGP messages);
  6. Proxy tab:
    1. Proxy type → Tor/Privacy (SOCKS5);
    2. Host → 127.0.0.1
    3. Port → 9050
  7. Click Save;
  8. Accounts → Enable Account → select your newly created account.
That's it, you have now created an account on CryptBB's XMPP server, and added/enabled it on your Pidgin client running through Tor.
You can test it out, here's my XMPP:
Hyperion@cryptbbsfmzv6dq4ec2iv6ravrw5ohgmklfagqhtvkgiaknvte5fylqd.onion

Port could be 9150 for some people, if anyone is having trouble connecting.
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smallgoodjon •
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#5
05-20-2022, 06:57 PM
I can't seem to connect.
It keeps telling me my username and password are incorrect. But I am pretty sure they aren't.
Is there a way to delete the account and remake it, or reset password etc?
Or maybe I am just doing something else wrong(all tho it seems pretty straight forward and simple?)
(05-20-2022, 06:57 PM)smallgoodjon Wrote: I can't seem to connect.
It keeps telling me my username and password are incorrect. But I am pretty sure they aren't.
Is there a way to delete the account and remake it, or reset password etc?
Or maybe I am just doing something else wrong(all tho it seems pretty straight forward and simple?)

Yes. As a test, I made a new account 'goodsmalljon' (vs my OG of 'smallgoodjon'). I made sure to keep password in clipboard between making and logging in. Still unable to login
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b3nd3r •
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#6
05-23-2022, 02:14 PM
XMPP registration not working for me either.

(14:13:40) connection: Connection error on 0x5f48ec125540 (reason: 2 description: Not Authorized)

Web says "Username Already Used!" when trying same name again.

Ping @cyberjagu
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Cyberjagu •
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#7
05-23-2022, 04:40 PM
(05-23-2022, 02:14 PM)b3nd3r Wrote: XMPP registration not working for me either.

(14:13:40) connection: Connection error on 0x5f48ec125540 (reason: 2 description: Not Authorized)

Web says "Username Already Used!" when trying same name again.

Ping @cyberjagu

we will check it soon
Cyberjagu@jabber.calyxinstitute.org [OTR]
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Big-Crunch7 •
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#8
05-23-2022, 11:48 PM
(05-23-2022, 02:14 PM)b3nd3r Wrote: XMPP registration not working for me either.

(14:13:40) connection: Connection error on 0x5f48ec125540 (reason: 2 description: Not Authorized)

Web says "Username Already Used!" when trying same name again.

Ping @cyberjagu
look like your username already used by other member or something
in some case try to change the username
can't say about @smallgoodjon
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b3nd3r •
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#9
05-24-2022, 11:38 AM
(05-23-2022, 11:48 PM)Big-Crunch7 Wrote: look like your username already used by other member or something
in some case try to change the username
can't say about @smallgoodjon

Yea I get that.. What I meant by that the frontend seems to be registering correctly, but for some reason the XMPP server is not working.
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mr_frog •
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#10
06-14-2022, 02:42 AM
I am getting auth. errors, tried it with different sets of usernames/passwords.
It's been like that since I have tried setting it up. Even tried different client software as well.. Nothing.
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_2.html b/lab-miots/darknet-market/test/page/thread/thread_2.html new file mode 100644 index 0000000000000000000000000000000000000000..52c270208c64511137c77e8b7c289990c0759cb4 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_2.html @@ -0,0 +1,28 @@ + Membership Applications
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Founding Fathers
#1
09-01-2020, 11:24 AM
Membership Applications
We require all new users to complete a basic challenge before applying.

For seller applications, see the Seller Application sticky.

Before you start, please ensure you are visiting:
CRYPTBB2GEZHOHKU.ONION
CRYPTBBTG65GIBADEEO2AWE3J7S6EVG7EKLSEREHQR4W4E2BIS5TEBID.ONION

Or any mirror listed on DarkIndex

If this is not the URL in your browser then you are on a phishing site. Log out and make a new account. Your password has likely been compromised.

READ THIS BEFORE APPLYING
The application process is long. If you don't have time to spend completing it, then you won't have time to spend contributing as a member. It's important you realize this before you start.

WE ONLY HAVE CHALLENGES FOR A LIMITED NUMBER OF SKILLS
  • If you don't have one of these skills, then you must get access using Member Vouch.
  • This works by contributing to public forums and helping people out.
  • If a member recognizes your skills, they can give you their vouch token.
  • This will bypass the initial challenge and allow you to post an application.

OTHERWISE, YOU MUST COMPLETE A BASIC CHALLENGE

CLICK HERE TO BEGIN THE CHALLENGE
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_3.html b/lab-miots/darknet-market/test/page/thread/thread_3.html new file mode 100644 index 0000000000000000000000000000000000000000..5a13fe7ec1a9b359026d335ba99c1c016a3e5aa9 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_3.html @@ -0,0 +1,28 @@ + Sellers Application
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Power •
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#1
01-18-2019, 06:06 PM
Sellers Application
We require all new users offering a product/service to post an application detailing basic information about their offering.

Before posting an application, please ensure you are visiting:
http://cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion

If this is not the URL in your browser then you are on a phishing site. Log out and make a new account. Your password has likely been compromised.

Please note the following:
  • Applications can only be posted once.
  • DO NOT apply offering hacking/programming services, instead apply as a member.
  • DO NOT apply if you are not already an established seller on TOR.
  • You must receive a vouch from a member to post an application.
  • You may send @cyberjagu a PM describing your service for a vouch.
  • Applications should be posted here.
  • You are required to answer any follow-up questions.
  • Proof of service is proof that you are capable of providing the product/service you are offering.
  • If your application has not received a response, you can reply to the application to show that you are still interested.
  • Do not contact any member of staff directly regarding the status of your application.

Please use the official application template found below.

Code:
[b][+][/b] Why do you want to use the CryptBB market?
[b][+][/b] What products and/or services will you be offering to the forum?
[b][+][/b] Where did you hear about us?
[b][+][/b] What are your skills (include proof of service)?
[b][+][/b] Have you sold your product on any other market?
[b][+][/b] Additional information (skills, interests, products, services):

If you are interested in joining the forum as a member, please read this.
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Power •
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#1
11-27-2017, 06:06 PM
Introduction Format
We require all new users to post an introduction thread detailing basic information about their interests and skills.

Before posting an application, please ensure you are visiting:
cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion

If this is not the URL in your browser then you are on a phishing site. Log out and make a new account. Your password has likely been compromised.

READ THIS BEFORE APPLYING
Applications often take days, even sometimes weeks to finish. It can take a few days for an admin to review your application and accept you. It can take a few hours to complete a challenge to get access. You might fail the challenge. The point is: If you're not willing to spend a few hours of your time doing the introduction process then you probably won't spend much time making meaningful contributions to our forum if we accept you. Moreover, we don't want to waste our time on applicants who intend on leaving after a few days.

WE ARE CURRENTLY ONLY SEARCHING FOR MEMBERS WITH AT LEAST ONE OF THE FOLLOWING SKILLS:
  • Programming
  • Web Application Hacking
  • Penetration Testing
  • Binary Exploitation
  • Malware Development
  • Reverse Engineering
You will be issued a challenge to prove your skill(s), it is recommended to take some time to browse the rest of the forum which is available to you before making an application.

We are interested in members with all skills, if your skill is not above then do not apply. We can not issue you a challenge. Instead, post useful information in the Newbie forum and eventually you will be recognized by other members or admins who will decide whether to make you a full member or not. Alternatively, you can ask questions and learn the above skills in order to apply.

Please note the following:
  • Applications can only be posted once.
  • Applications should be posted here.
  • You are required to answer any follow-up questions.
  • Proof of knowledge is proof that you are able to do what you claim. These include scripts, explanations, methodology, etc.
  • If your application has not received a response, you can reply to the application to show that you are still interested.
  • Do not contact any member of staff directly regarding the status of your application.

Please use the official application template found below.

READ THIS BEFORE POSTING
Make use of [code] tags where appropriate.

Code:
[b][+][/b] Why do you want to join CryptBB?
[b][+][/b] How can you contribute to CryptBB?
[b][+][/b] Which topics are you interested in?
[b][+][/b] Do you plan on selling any products/services?
[b][+][/b] Where did you hear about us?
[b][+][/b] What are your skills (include proof of knowledge)?
[b][+][/b] What languages can you code (include proof of knowledge)?
[b][+][/b] Do you have experience with malware development, web exploiting or anything else?
[b][+][/b] Have you been a member of any other notable websites (include proof)?
[b][+][/b] Additional proof of knowledge (scripts, methodologies, tutorials, etc):

If you are interested in joining the forum to sell a product, please read this.
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gjiouklna1213 •
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#1
02-16-2023, 06:40 PM
Hi there,

I suddenly lost access to my account. While it's easy to just create a new one, I don't want to go through the process of completing the challenges again. Is there any way I can regain access to my account? The best I can do is to give the answer to my challenge again.
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Cyberjagu •
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#2
02-16-2023, 07:24 PM
(02-16-2023, 06:40 PM)gjiouklna1213 Wrote: Hi there,

I suddenly lost access to my account. While it's easy to just create a new one, I don't want to go through the process of completing the challenges again. Is there any way I can regain access to my account? The best I can do is to give the answer to my challenge again.

What was your account name? And did you add pgp?
Cyberjagu@jabber.calyxinstitute.org [OTR]
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gjiouklna1213 •
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#3
02-17-2023, 01:01 AM
(02-16-2023, 07:24 PM)Cyberjagu Wrote:
(02-16-2023, 06:40 PM)gjiouklna1213 Wrote: Hi there,

I suddenly lost access to my account. While it's easy to just create a new one, I don't want to go through the process of completing the challenges again. Is there any way I can regain access to my account? The best I can do is to give the answer to my challenge again.

What was your account name? And did you add pgp?

Will send you a PM.
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_6.html b/lab-miots/darknet-market/test/page/thread/thread_6.html new file mode 100644 index 0000000000000000000000000000000000000000..f33e84fbb62f0fb91574096fcd899a2968c5c090 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_6.html @@ -0,0 +1,28 @@ + Can I change my name?
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K@lypso •
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#1
02-09-2023, 08:52 AM
Like the title says, can I change my username on CryptBB? I don't see any option for it, so I'm guessing a moderator would have to do it or something. Any help is appreciated.
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Cyberjagu •
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#2
02-09-2023, 10:51 AM
(02-09-2023, 08:52 AM)K@lypso Wrote: Like the title says, can I change my username on CryptBB? I don't see any option for it, so I'm guessing a moderator would have to do it or something. Any help is appreciated.

No you can't change it
Cyberjagu@jabber.calyxinstitute.org [OTR]
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fungalinfection •
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#1
02-03-2023, 10:52 PM
Good day. I am unable to login and it appears that all of my posts have been wiped. Leading me to believe my account has just been banned, I am unable to find any apparent reason as to why :p. I was under the impression I was making some decent contributions to this forum's activity, and just recently passed the entry challenges.

Hopefully a simple mistake, if not, it would be nice to know the violation I committed leading to such actions.

I'd like to add that this is quite upsetting to me, given the time commitment I have put into being present here, all being wiped out in an instant ;(.


Peace
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#2
02-03-2023, 11:01 PM
(02-03-2023, 10:52 PM)fungalinfection Wrote: Good day. I am unable to login and it appears that all of my posts have been wiped. Leading me to believe my account has just been banned, I am unable to find any apparent reason as to why :p. I was under the impression I was making some decent contributions to this forum's activity, and just recently passed the entry challenges.

Hopefully a simple mistake, if not, it would be nice to know the violation I committed leading to such actions.


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- kurwa

This is completely on me, my apologies. Working on a fix, please hold on.
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#3
02-03-2023, 11:03 PM
(02-03-2023, 11:01 PM)Hyperion Wrote:
(02-03-2023, 10:52 PM)fungalinfection Wrote: Good day. I am unable to login and it appears that all of my posts have been wiped. Leading me to believe my account has just been banned, I am unable to find any apparent reason as to why :p. I was under the impression I was making some decent contributions to this forum's activity, and just recently passed the entry challenges.

Hopefully a simple mistake, if not, it would be nice to know the violation I committed leading to such actions.


Peace
- kurwa

This is completely on me, my apologies. Working on a fix, please hold on.

Haha ok it's no problem. I'm just glad to hear I was not shadow banned by some tyrannical forum admins or something of the like. Take your time and thanks for the quick response.
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#4
02-04-2023, 08:23 PM
(02-03-2023, 11:03 PM)fungalinfection Wrote:
(02-03-2023, 11:01 PM)Hyperion Wrote:
(02-03-2023, 10:52 PM)fungalinfection Wrote: Good day. I am unable to login and it appears that all of my posts have been wiped. Leading me to believe my account has just been banned, I am unable to find any apparent reason as to why :p. I was under the impression I was making some decent contributions to this forum's activity, and just recently passed the entry challenges.

Hopefully a simple mistake, if not, it would be nice to know the violation I committed leading to such actions.


Peace
- kurwa

This is completely on me, my apologies. Working on a fix, please hold on.

Haha ok it's no problem. I'm just glad to hear I was not shadow banned by some tyrannical forum admins or something of the like. Take your time and thanks for the quick response.
contact me via jabber i will sort something for you
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#5
02-04-2023, 09:28 PM
(02-04-2023, 08:23 PM)Cyberjagu Wrote:
(02-03-2023, 11:03 PM)fungalinfection Wrote:
(02-03-2023, 11:01 PM)Hyperion Wrote:
(02-03-2023, 10:52 PM)fungalinfection Wrote: Good day. I am unable to login and it appears that all of my posts have been wiped. Leading me to believe my account has just been banned, I am unable to find any apparent reason as to why :p. I was under the impression I was making some decent contributions to this forum's activity, and just recently passed the entry challenges.

Hopefully a simple mistake, if not, it would be nice to know the violation I committed leading to such actions.


Peace
- kurwa

This is completely on me, my apologies. Working on a fix, please hold on.

Haha ok it's no problem. I'm just glad to hear I was not shadow banned by some tyrannical forum admins or something of the like. Take your time and thanks for the quick response.
contact me via jabber i will sort something for you

Done. I understand mistakes happen so I'm not going to request much other than my username back and a verified account so I don't have to go through the entry challenges again.
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#6
02-05-2023, 12:21 AM
I only just noticed the lack of emojis because I wanted to throw a hundred of laughing ones into this thread... and a hundred more at myself for not noticing sooner.

Having been a staff member of other forums I remember the headaches that went with bans but I also remember that accidents do happen and it was very humorous to me in that sick everyone loves to watch a trainwreck sort of way but nicer because I am also glad that it was an accident and everything is getting sorted for you.
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#7
02-05-2023, 01:16 AM
(02-05-2023, 12:21 AM)TheAngryDwarf Wrote: I only just noticed the lack of emojis because I wanted to throw a hundred of laughing ones into this thread... and a hundred more at myself for not noticing sooner.

Having been a staff member of other forums I remember the headaches that went with bans but I also remember that accidents do happen and it was very humorous to me in that sick everyone loves to watch a trainwreck sort of way but nicer because I am also glad that it was an accident and everything is getting sorted for you.

Haha. Kurwa and their emojis will be back soon enough ;)
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_8.html b/lab-miots/darknet-market/test/page/thread/thread_8.html new file mode 100644 index 0000000000000000000000000000000000000000..557842deaa7769dae73f8dcf4e82bf9fde8adc07 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_8.html @@ -0,0 +1,28 @@ + Confirmation of programming challenge?
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CryptBB › General › Support › Confirmation of programming challenge?

Confirmation of programming challenge?
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#1
01-02-2023, 10:15 AM
Just wondering, after completion of the programming challenge, will I be instantly notified if I completed the challenge correctly? Or is it manually checked?
I'm fairly certain I'd done it right on the 3rd attempt, but I also sent the sliced truncated code + the full md5 hash of the final 50 iterations as another answer to the challenge... will I be given the introduction forum password as long as one of my answers were correct?

First time playing with md5 and I did go slightly cross eyed but I think I got it right!
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#2
01-02-2023, 10:26 AM
(01-02-2023, 10:15 AM)4go10 Wrote: Just wondering, after completion of the programming challenge, will I be instantly notified if I completed the challenge correctly? Or is it manually checked?
I'm fairly certain I'd done it right on the 3rd attempt, but I also sent the sliced truncated code + the full md5 hash of the final 50 iterations as another answer to the challenge... will I be given the introduction forum password as long as one of my answers were correct?

First time playing with md5 and I did go slightly cross eyed but I think I got it right!

If your answer is correct you will get Introduction sub password and proof token on same page
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#3
01-02-2023, 10:36 AM
(01-02-2023, 10:26 AM)Cyberjagu Wrote:
(01-02-2023, 10:15 AM)4go10 Wrote: Just wondering, after completion of the programming challenge, will I be instantly notified if I completed the challenge correctly? Or is it manually checked?
I'm fairly certain I'd done it right on the 3rd attempt, but I also sent the sliced truncated code + the full md5 hash of the final 50 iterations as another answer to the challenge... will I be given the introduction forum password as long as one of my answers were correct?

First time playing with md5 and I did go slightly cross eyed but I think I got it right!

If your answer is correct you will get Introduction sub password and proof token on same page

Instantly after submitting?
So if it just reloads to the challenges page, I've done it wrong?
(01-02-2023, 10:36 AM)4go10 Wrote:
(01-02-2023, 10:26 AM)Cyberjagu Wrote:
(01-02-2023, 10:15 AM)4go10 Wrote: Just wondering, after completion of the programming challenge, will I be instantly notified if I completed the challenge correctly? Or is it manually checked?
I'm fairly certain I'd done it right on the 3rd attempt, but I also sent the sliced truncated code + the full md5 hash of the final 50 iterations as another answer to the challenge... will I be given the introduction forum password as long as one of my answers were correct?

First time playing with md5 and I did go slightly cross eyed but I think I got it right!

If your answer is correct you will get Introduction sub password and proof token on same page

Instantly after submitting?
So if it just reloads to the challenges page, I've done it wrong?

Never mind! I realized my mistake and now have the password and proof token, thank you!

What an interesting challenge that was
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page/thread/thread_turn_page_example.html b/lab-miots/darknet-market/test/page/thread/thread_turn_page_example.html new file mode 100644 index 0000000000000000000000000000000000000000..0db26c84bc5d37314b3e63ed94cb9e2850071ae5 --- /dev/null +++ b/lab-miots/darknet-market/test/page/thread/thread_turn_page_example.html @@ -0,0 +1,28 @@ + Advise about how to start hacking
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Advise about how to start hacking
Century Child •
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#1
08-15-2020, 04:04 PM
How to start hacking?

Why this?
I read a lot of posts in the newbie section about this topic and the answers are always the same ("Depends on what you want"), so I decided to create a thread for this answering all the questions.

Like I said: It depends on your goal!

So I will talk about a few topics here and how to start. I am not an expert of all hacking topics XD, so every member is welcome to edit and improve this thread!

Kali
Some tutorials require tools. You don't need to install them manually. There's a linux distribution called "Kali" with about 600 tools preinstalled. Here's how to install it

But it's also very blown. Later you should go with a debian and install the tools you need manually to keep it clean.

Testing Environment
You need a testing system to test and to attack. How to setup a testing environment


Index
A. Malware development
B. Web applications
C. Binary exploitation
D. Database hacking
E. Reverse engineering
F. (Offline) cracking / bruteforce
G. Network hacking

A. Malware development

There are different kinds of malware:
  • Ransomware: Ransomware encrypts all your personal files and wants a ransom to release them. Popular are Wannacry or Petya. The most Ransomwares are based on the same technologies. They are written in C / C++ to run on every (Windows) machine and they use AES and RSA encryption algorithms for security.
  • Keylogger: It is a small program also written in C / C++ to run on every (Windows) machine to keep the keylogs and get e.g. login data.
  • Cryptostealer: It is a small program which replaces Cryptoadresses on the victims PC with your own adress.
  • I will add more (members, feel free do edit)

So you definitely have to learn C / C++. You can do it with a book (https://www.amazon.com/s?k=C+%2F+C%2B%2B&ref=nb_sb_noss) or internet sites (just google). It is also a good idea to have a goal (like developing a keylogger) and google as much as you have what you want. The second point is encryption. Encryption algorithms are very important for malware (not just for ransomware). The best algorithms are AES (https://en.wikipedia.org/wiki/Advanced_E...n_Standard) and RSA(https://en.wikipedia.org/wiki/RSA_(cryptosystem)). AES is symmetrical and RSA asymmetrical (You will know what it is after reading the Wikipedia pages).

A lot of malwares have a server, where specific data is sent to (like ransomware keys or keylog files), so you also need to know some languages for server stuff. It is possible to do it in C / C++, but I would not recommend it. Better languages are python or php (but be careful with php and write very secure code, it is in security criticism especially for beginners).

Later you have to look at obfuscating to bypass AV's.

B. Web applications

The most important point of web application hacking is OPSEC (http://cryptbb2gezhohku.onion/showthread...ight=OPSEC). You need to know how to protect yourself. No, a VPN is not enough!

The most webpages are based on these technologies and they also cause easy, but very dangerous security issues.

So, learn HTML & CSS, JavaScript, SQL and php could also be helful. It is also useful to learn python for scanning pages.

I recommend not to start with tools (e.g. nmap, wpscan for wordpress, sqlmap). Learn some languages and you can have a look at them later.


C. Binary exploitation

D. Database hacking

E. Reverse engineering

F. (Offline) cracking / bruteforce

For (offline) cracking / bruteforce exists basically one way to go. In the most cases you have a hash or a file you want to crack. You don't have to learn any programming languages for this, there are great tools. Hashcat(https://hashcat.net/hashcat/) is very popular and powerful, it uses multi-threading and the GPU. For faster cracking you need a faster GPU with a higher hashrate. Use NVIDIA GPU's, because of the compatibility with different tools. Great GPU's for cracking are the GTX 1080 or GTX 980 ti.

Before start cracking you should have a basic understanding of hashes, so read this: https://en.wikipedia.org/wiki/Cryptograp...h_function.

Then you can install hashcat. e.g. on ubuntu:

Code:
sudo apt install hashcat

After that type hashcat in console:

Code:
root@ubuntu:~$ hashcat

The output explains the usage, you can also see it here: https://hashcat.net/wiki/doku.php?id=hashcat. If you don't know how to work with the output, it might be the best way to google for hashcat tutorials and get into the tool this way.

Happy cracking Smile

G. Network hacking

First let's talk about programming languages. One of the most important languages (maybe the best) for network hacking is Python. A lot of Network tools are written in Python and there is for beginner network hacking no way around learning it. Python has a lot of network libraries and you don't have to code everything on your own. Python is a very popular language and there are so many free courses. Just google for "learning python". When you've learned the basics you should focus on these libraries: requests, urllib, urllib2 ,socket, twisted and asyncore. For getting into these libraries you can try programming a small chat program between 2 computers via sockets, but still learn the basics first.

Here are some projects for beginners:
  • Cracking Wifi: Have a look at aircrack-ng (https://github.com/aircrack-ng/aircrack-ng). It's a tool to crack wifi passwords via bruteforce (also have a look at F. (Offline) cracking / bruteforce). Follow this official tutorial to crack WPA / WPA2: https://www.aircrack-ng.org/doku.php?id=cracking_wpa
  • Man in the middle: This attack is kind of ruined, because of encrypted traffic, but it's still useful to know it. Read about it: https://en.wikipedia.org/wiki/Man-in-the-middle_attack.
  • nmap: nmap (https://nmap.org/) is a great network scanning tool and indispensable. You can e.g. scan ports of a server or just list all devices in your current network. But it's still just a scanning tool.
  • Jammer / Fake AP: A jammer is something you can write on your own with just a little knowledge of python. This is a tutorial what it is and how to code one: https://www.shellvoide.com/python/how-to...am-python/ . But just jamming isn't very useful, maybe for fun, but you need a sence. Fluxion (https://github.com/FluxionNetwork/fluxion) is a tool to get the wifi password of wifis with a lot of users. It's a jammer, but after starting it, fluxion creates another wifi with the same name. At e.g. a big motel some users will try your fake wifi, because youre jamming the real wifi, and you get the real password Smile
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#2
08-15-2020, 05:53 PM
can I ask you why did you write to be careful with php? what is the security criticism with it?
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#3
08-15-2020, 10:10 PM
(08-15-2020, 05:53 PM)Err0r Wrote: can I ask you why did you write to be careful with php? what is the security criticism with it?

php has some security issues. Today you can use php for normal illegal stuff, but you shouldn't use them for extremely illegal stuff. Or your php code is very safe, then it's no problem, but a lot of beginners don't care about it.
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#4
08-16-2020, 12:51 AM
Thanks for the information much appreciated.
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the Red
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#5
08-16-2020, 11:58 AM
Please also make a detailed guide about getting started with binary exploitation...

Learning python + assembly be enough for this???

And I am still confused will python be enough for malware development??
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#6
08-16-2020, 02:44 PM
(08-16-2020, 11:58 AM)the Red Wrote: Please also make a detailed guide about getting started with binary exploitation...

Learning python + assembly be enough for this???

And I am still confused will python be enough for malware development??

No python is never enough for malware. You should learn C / C++, python only for backend stuff.

I am not an expert in binary exploitation, it would be better if any member could edit the post and add something about it.
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the Red
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08-16-2020, 02:56 PM
(08-16-2020, 02:44 PM)Century Child Wrote:
(08-16-2020, 11:58 AM)the Red Wrote: Please also make a detailed guide about getting started with binary exploitation...

Learning python + assembly be enough for this???

And I am still confused will python be enough for malware development??

No python is never enough for malware. You should learn C / C++, python only for backend stuff.

I am not an expert in binary exploitation, it would be better if any member could edit the post and add something about it.

Thank you Century Child, for sparing some time in clarifying the doubt of a newbie
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#8
08-16-2020, 04:59 PM
(08-16-2020, 11:58 AM)the Red Wrote: Please also make a detailed guide about getting started with binary exploitation...

Learning python + assembly be enough for this???

And I am still confused will python be enough for malware development??

Well barebones C(++) isn't even enough imho. You should study assembly, memory and how a computer works if you want to be a good malware dev. Learn about process hollowing.
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#9
08-16-2020, 05:21 PM
(08-15-2020, 10:10 PM)Century Child Wrote: php has some security issues. Today you can use php for normal illegal stuff, but you shouldn't use them for extremely illegal stuff. Or your php code is very safe, then it's no problem, but a lot of beginners don't care about it.

thank you, but can you be more specific? what are the security issues it has?
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#10
08-16-2020, 08:31 PM
(08-16-2020, 05:21 PM)Err0r Wrote:
(08-15-2020, 10:10 PM)Century Child Wrote: php has some security issues. Today you can use php for normal illegal stuff, but you shouldn't use them for extremely illegal stuff. Or your php code is very safe, then it's no problem, but a lot of beginners don't care about it.

thank you, but can you be more specific? what are the security issues it has?

A good example are SQL Injections. It's easier to have those kind of issues in php than in other languages.
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\ No newline at end of file diff --git a/lab-miots/darknet-market/test/page_parser.py b/lab-miots/darknet-market/test/page_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..5dcc0f72b8c755a1e1de6d1fed3883d0c4826338 --- /dev/null +++ b/lab-miots/darknet-market/test/page_parser.py @@ -0,0 +1,18 @@ +import os + +import lxml + +from const import ROOT_DIR + +etree = lxml.html.etree +class XPath_Tester: + def __init__(self,filePath): + with open(filePath,"r") as f: + self.text = f.read() + print(len(self.text)) + self.html = etree.HTML(self.text) + + +tester = XPath_Tester(os.path.join(ROOT_DIR,"test/page/main.html")) + + diff --git a/lab-miots/darknet-market/test/parse_bench.ipynb b/lab-miots/darknet-market/test/parse_bench.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e1b75b13df64b16383b2a8639f25073959cc9d00 --- /dev/null +++ b/lab-miots/darknet-market/test/parse_bench.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'const'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[1], line 5\u001B[0m\n\u001B[1;32m 3\u001B[0m \u001B[39mimport\u001B[39;00m \u001B[39mos\u001B[39;00m\n\u001B[1;32m 4\u001B[0m \u001B[39mfrom\u001B[39;00m \u001B[39mlxml\u001B[39;00m \u001B[39mimport\u001B[39;00m html\n\u001B[0;32m----> 5\u001B[0m \u001B[39mfrom\u001B[39;00m \u001B[39mconst\u001B[39;00m \u001B[39mimport\u001B[39;00m ROOT_DIR\n\u001B[1;32m 6\u001B[0m etree \u001B[39m=\u001B[39m html\u001B[39m.\u001B[39metree\n\u001B[1;32m 7\u001B[0m \u001B[39mfrom\u001B[39;00m \u001B[39mhelper\u001B[39;00m\u001B[39m.\u001B[39;00m\u001B[39mutils\u001B[39;00m \u001B[39mimport\u001B[39;00m get_elements,get_one_element\n", + "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'const'" + ] + } + ], + "source": [ + "from lxml.html import tostring\n", + "from bs4 import BeautifulSoup\n", + "import os\n", + "from lxml import html\n", + "from const import ROOT_DIR\n", + "etree = html.etree\n", + "from helper.utils import get_elements,get_one_element\n", + "from typing import List" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "## parse the dedicated file!\n", + "filePath = os.path.join(ROOT_DIR,\"test/page/thread/thread_turn_page_example.html\")\n", + "\"test/page/forumdisplay.phpfid=35.html\"\n", + "\"test/page/thread/thread_turn_page_example.html\"\n", + "file = open(filePath,\"r\")\n", + "text = file.read()\n", + "html = etree.HTML(text)\n", + "#print(get_elements(html,\"/html/body/div[1]/div[2]/div/table[2]\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "## execute xpath\n", + "post_xpath = [\n", + " \"/html/body/div[1]/div[2]/div/table/tr[2]/td/div/div\"\n", + "]\n", + "href:List[BeautifulSoup] = []\n", + "for xpath in post_xpath:\n", + " newList = get_elements(html,xpath)\n", + " if newList is not None:\n", + " for item in newList:\n", + " href.append(item)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[, , , , , , , , , ]\n", + "10\n" + ] + } + ], + "source": [ + "print(href)\n", + "print(len(href))" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1 ['Century Child']\n", + "0 0 []\n", + "0 1 ['Century Child']\n", + "0 0 []\n", + "0 0 []\n", + "0 1 ['Century Child']\n", + "0 0 []\n", + "0 0 []\n", + "0 0 []\n", + "0 1 ['Century Child']\n", + "['Century Child', 'Century Child', 'Century Child', 'Century Child']\n" + ] + } + ], + "source": [ + "# test relative path\n", + "relative_path = \"./div[1]/div[2]/strong/span/a/span/strong/text()\"\n", + "\n", + "relative_sum = []\n", + "cnt=0\n", + "for re_ht in href:\n", + " sig = get_elements(re_ht,relative_path)\n", + " print(cnt,len(sig),sig)\n", + " for sig1 in sig:\n", + " relative_sum.append(sig1)\n", + "#print()\n", + "#relative_author = get_elements(relative_html,relative_path)\n", + "print(relative_sum)\n", + "#print(tostring(relative_author[0]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "parse_result 7\n" + ] + } + ], + "source": [ + "import re\n", + "\n", + "def parse_href(href_str,regex_str):\n", + " regex_result= re.search(regex_str,href_str)\n", + "\n", + " if regex_result:\n", + " print(regex_result)\n", + " href_str = regex_result.group()\n", + " return int(re.search(r'\\d+',href_str).group())\n", + "\n", + "regex_str = 'page=\\d+'\n", + "res = parse_href(href[0],regex_str)\n", + "print(\"parse_result\",res)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/lab-miots/darknet-market/test/proxy_tester.py b/lab-miots/darknet-market/test/proxy_tester.py new file mode 100644 index 0000000000000000000000000000000000000000..c419c211c974525d297b5525012c88a83896b36f --- /dev/null +++ b/lab-miots/darknet-market/test/proxy_tester.py @@ -0,0 +1,23 @@ +from sites.proxy.tor import TorProxy +from const import CONFIG +from urllib.parse import urlencode + +class Proxifier(TorProxy): + def __init__(self): + super().__init__() + + def main_routine(self,url:str,params:dict): + resp = self.get(url,params=params) + print(resp.text) + +forum:dict = CONFIG['forum'] +crypt_forum = forum["Crypt BB"] + +pro = Proxifier() +pro.set_cookie(crypt_forum['cookie']) +params = dict() +params["page"]=2 +#pro.main_routine("http://cryptbbtg65gibadeeo2awe3j7s6evg7eklserehqr4w4e2bis5tebid.onion/forumdisplay.php?fid=86",params=params) +good_id_params = { + "id":id +} diff --git a/lab-miots/darknet-market/test/xpath.ipynb b/lab-miots/darknet-market/test/xpath.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8e01876dcd332813e946dde148c5a51c8c7bcc5e --- /dev/null +++ b/lab-miots/darknet-market/test/xpath.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from lxml.html import tostring\n", + "from bs4 import BeautifulSoup\n", + "import os\n", + "from lxml import html\n", + "from helper.utils import get_elements,get_one_element\n", + "etree = html.etree" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "html_source = '''\n", + "
\n", + "\n", + "
\n", + "\n", + "'''\n", + "ele = html.fromstring(html_source)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "\n", + "\n" + ] + } + ], + "source": [ + "xpath = 'substring-after(//div/a/text(),\"/u/\")'\n", + "# username = ele.xpath(xpath)\n", + "# print(username)\n", + "lists = get_elements(ele,xpath) or \"\"\n", + "if isinstance(lists,str) and lists != \"\":\n", + " print(\"hello !\")\n", + "print(isinstance(lists,str))\n", + "print(type(lists))\n", + "print(lists)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab-miots/java_sca.py b/lab-miots/java_sca.py new file mode 100644 index 0000000000000000000000000000000000000000..4347793edf9b6bd690916ccb9d8adb02c67aa721 --- /dev/null +++ b/lab-miots/java_sca.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +#import src_analysis +import lib_analysis +import vuln_check +import re +from library_list import known_lib_list +from prettytable import PrettyTable +""" +codes analysis java_src_lib and return vulns +""" + +""" +[lib_module]: module_info from lib_analysis +[known_lib_list]: known usual used libraries list +[return]: module called by lib_info +""" +def moduleFetch(lib_module: list, known_lib_list: list): + module_to_check = [] + # traverse lib + for module in lib_module: + for name2 in known_lib_list: + if re.match(module[0],name2,re.I) != None: + module_to_check.append(module) + + return module_to_check + +""" +[prog_dir]: dir of prog with src and pkg +[return]: module with vuln +""" +def javaSca(prog_dir): + print("checking...") + # fetch lib_module + lib_dir = prog_dir #+'lib/' + lib_module = lib_analysis.libAnalysis(lib_dir) + # filter module_to_check and extract component_list + component_list = moduleFetch(lib_module, known_lib_list) + + #return component_list + + # check for vuln + module_vuln_list = vuln_check.vulnCheck('./vuln_data/nvdcve_2020.json', component_list) + return module_vuln_list + + #return module_to_check + +""" +test for java_sca +""" +def main(): + + Vstrlist = [] + vulns = javaSca("./test_prog/") + for vuln in vulns: + print("--------------------------") + V = [] + #print(vuln) + for key, value in vuln.items(): + print(f"[{key}]: {value}\n") + V.append(value) + Vstr = ' '.join(V) + '\n' + Vstrlist.append(Vstr) + with open('./../result.txt','w',encoding='utf-8') as f: + f.writelines(Vstrlist) + + x = PrettyTable(["组件名称","组件版本","调用路径","CVE编号","影响范围"]) + x.align["组件名称"] = "1" + x.padding_width = 1 + for vuln in vulns: + x.add_row([vuln["name"],vuln["version"],vuln["src_path"].replace('\\','/'),vuln["cve_id"],vuln["affect_version"]]) + print(x) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/lab-nss/small-projects/Safebox/.keep b/lab-nss/small-projects/Safebox/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-nss/small-projects/Safebox/gui/.keep b/lab-nss/small-projects/Safebox/gui/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-nss/small-projects/Safebox/gui/gui.zip b/lab-nss/small-projects/Safebox/gui/gui.zip new file mode 100644 index 0000000000000000000000000000000000000000..43278e02635f48b14e0c0dd6e2d11830c74db18d Binary files /dev/null and b/lab-nss/small-projects/Safebox/gui/gui.zip differ diff --git a/lab-nss/small-projects/Safebox/krnl/.keep b/lab-nss/small-projects/Safebox/krnl/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lab-nss/small-projects/Safebox/krnl/Makefile b/lab-nss/small-projects/Safebox/krnl/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..fb76e474175578a0720b56be2bd82509bfbb3138 --- /dev/null +++ b/lab-nss/small-projects/Safebox/krnl/Makefile @@ -0,0 +1,9 @@ +obj-m:=SafeboxModule.o +SafeboxModule-objs :=sdthook.o syscalltable.o netlinkp.o check.o +KDIR := /lib/modules/$(shell uname -r)/build +PWD := $(shell pwd) +default: + $(MAKE) -C $(KDIR) SUBDIRS=$(PWD) modules +clean: + $(RM) -rf .*.cmd *.mod.c *.o *.ko .tmp* modules.order Module.symvers sdthook.o.ur-safe + diff --git a/lab-nss/small-projects/Safebox/krnl/check.c b/lab-nss/small-projects/Safebox/krnl/check.c new file mode 100644 index 0000000000000000000000000000000000000000..a6259637d8389157fa59f30b703de9b868993c04 --- /dev/null +++ b/lab-nss/small-projects/Safebox/krnl/check.c @@ -0,0 +1,136 @@ +#include +#include +#include +#include +#include + +#define SAFEBOXPATH "/home/safebox" + +#define TASK_COMM_LEN 16 +#define MAX_PATH_LEN 256 +#define MAX_LENGTH 256 +#define COMP_MAX 50 + +#define ispathsep(ch) ((ch) == '/' || (ch) == '\\') +#define iseos(ch) ((ch) == '\0') +#define ispathend(ch) (ispathsep(ch) || iseos(ch)) + +extern int recv_pid; + + +// path normalization +char *normpath(char *out, const char *in) +{ + char *pos[COMP_MAX], **top = pos, *head = out; + int isabs = ispathsep(*in); + + if (isabs) *out++ = '/'; + *top++ = out; + + while (!iseos(*in)) + { + while (ispathsep(*in)) ++in; + + if (iseos(*in)) + break; + + if (memcmp(in, ".", 1) == 0 && ispathend(in[1])) + { + ++in; + continue; + } + + if (memcmp(in, "..", 2) == 0 && ispathend(in[2])) + { + in += 2; + if (top != pos + 1) + out = *--top; + else if (isabs) + out = top[-1]; + else { + strcpy(out, "../"); + out += 3; + } + continue; + } + + if (top - pos >= COMP_MAX) + return NULL; /* path to complicate */ + + *top++ = out; + while (!ispathend(*in)) + *out++ = *in++; + if (ispathsep(*in)) + *out++ = '/'; + } + + *out = '\0'; + if (*head == '\0') + strcpy(head, "./"); + return head; +} + +// get fullpath of openat file +void get_fullpath(const char *pathname, char *fullpath) +{ + char buf[MAX_LENGTH]; memset(buf, 0, MAX_LENGTH); + char raw_fullpath[MAX_PATH_LEN]; memset(raw_fullpath, 0, MAX_PATH_LEN); + struct dentry *parent_dentry = current->fs->pwd.dentry; + + if (strncmp(pathname, "/", 1) == 0) + { + // pathname is abspath + strcpy(fullpath, pathname); + return; + } + + // pathname is relpath + if (*(parent_dentry->d_name.name)=='/') + { + strcpy(raw_fullpath, pathname); + } + else + { + while(1) + { + if (strcmp(parent_dentry->d_name.name,"/")==0) + buf[0]='\0';//reach the root dentry. + else + strcpy(buf,parent_dentry->d_name.name); + strcat(buf,"/"); + strcat(buf, raw_fullpath); + strcpy(raw_fullpath, buf); + + if ((parent_dentry == NULL) || (*(parent_dentry->d_name.name)=='/')) + break; + + parent_dentry = parent_dentry->d_parent; + } + + strcat(raw_fullpath, pathname); + } + + normpath(fullpath, raw_fullpath); +} + + +int SafeboxCheckPath(struct pt_regs *regs, char *pathname) +{ + char fullpath[MAX_PATH_LEN]; memset(fullpath, 0, MAX_PATH_LEN); + char safebox_path[20] = SAFEBOXPATH; + + // process the pathname, get the normalized full path + get_fullpath(pathname, fullpath); + + // pass if not SAFEBOXPATH + if (strncmp(fullpath, safebox_path, strlen(safebox_path)) != 0) + return 0; + + // in SAFEBOXPATH + if (recv_pid == 0 || recv_pid != current->pid) + return -1; + + printk("Info: recv_pid is %d, current->pid is %d;\n", recv_pid, current->pid); + return 0; +} + diff --git a/lab-nss/small-projects/Safebox/krnl/netlinkp.c b/lab-nss/small-projects/Safebox/krnl/netlinkp.c new file mode 100644 index 0000000000000000000000000000000000000000..5ee15cd8f6aac8562b8c905a81014bc52b2f30d5 --- /dev/null +++ b/lab-nss/small-projects/Safebox/krnl/netlinkp.c @@ -0,0 +1,77 @@ +#include +#include +#include +#include +#include +#include +#include + +#define NETLINK_SAFEBOX 29 + +u32 recv_pid=0; +struct sock *nl_sk = NULL; + +// 发送netlink消息message +int netlink_sendmsg(const void *buffer, unsigned int size) +{ + struct sk_buff *skb; + struct nlmsghdr *nlh; + int len = NLMSG_SPACE(1200); + if((!buffer) || (!nl_sk) || (pid == 0)) return 1; + skb = alloc_skb(len, GFP_ATOMIC); // 分配一个新的sk_buffer + if (!skb){ + printk(KERN_ERR "net_link: allocat_skb failed.\n"); + return 1; + } + nlh = nlmsg_put(skb,0,0,0,1200,0); + NETLINK_CB(skb).creds.pid = 0; /* from kernel */ + + // 下面必须手动设置字符串结束标志\0，否则用户程序可能出现接收乱码 + memcpy(NLMSG_DATA(nlh), buffer, size); + + // 使用netlink单播函数发送消息 + if( netlink_unicast(nl_sk, skb, pid, MSG_DONTWAIT) < 0){ + + // 如果发送失败，则打印警告并退出函数 + printk(KERN_ERR "net_link: can not unicast skb \n"); + return 1; + } + return 0; +} + +void nl_recv_pid(struct sk_buff *skb) +{ + struct nlmsghdr *nlh; + printk(KERN_INFO "Entering: %s\n", __FUNCTION__); + + // read the pid data received + nlh = (struct nlmsghdr *)skb->data; + + // store the received pid into recv_pid + recv_pid = nlh->nlmsg_pid; + + printk(KERN_INFO "Netlink received pid %d\n", recv_pid); // for test +} + +void netlink_init(void) +{ + struct netlink_kernel_cfg cfg = + { + .input = nl_recv_pid, + }; + + nl_sk=netlink_kernel_create(&init_net,NETLINK_SAFEBOX, &cfg); + + if (!nl_sk){ + printk(KERN_ERR "net_link: Cannot create netlink socket.\n"); + if (nl_sk != NULL) + sock_release(nl_sk->sk_socket); + } + else printk("net_link: create socket ok.\n"); +} + +void netlink_release(void) +{ + if (nl_sk != NULL) + sock_release(nl_sk->sk_socket); +} diff --git a/lab-nss/small-projects/Safebox/krnl/sdthook.c b/lab-nss/small-projects/Safebox/krnl/sdthook.c new file mode 100644 index 0000000000000000000000000000000000000000..9c7f77e34ca2d1065168e06dbd56fef08ce3cb8a --- /dev/null +++ b/lab-nss/small-projects/Safebox/krnl/sdthook.c @@ -0,0 +1,204 @@ +#ifndef _LARGEFILE64_SOURCE +#define _LARGEFILE64_SOURCE +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +// module macros +MODULE_LICENSE("GPL"); +MODULE_AUTHOR("sjtu/ljl/zzm/wcl"); +MODULE_DESCRIPTION("hook sys_call_table"); + +// typedef +typedef void (* demo_sys_call_ptr_t)(void); +typedef asmlinkage long (*orig_openat_t)(struct pt_regs *regs); +typedef asmlinkage long (*orig_chdir_t)(struct pt_regs *regs); +typedef asmlinkage long (*orig_mkdir_t)(struct pt_regs *regs); +typedef asmlinkage long (*orig_symlinkat_t)(struct pt_regs *regs); +typedef asmlinkage long (*orig_linkat_t)(struct pt_regs *regs); +typedef asmlinkage long (*orig_rename_t)(struct pt_regs *regs); + +// functions +void netlink_release(void); +void netlink_init(void); +int SafeboxCheckPath(struct pt_regs*, char*); +demo_sys_call_ptr_t* get_sys_call_table(void); + +// vars +demo_sys_call_ptr_t* sys_call_table = NULL; +orig_openat_t orig_openat = NULL; +orig_chdir_t orig_chdir = NULL; +orig_mkdir_t orig_mkdir = NULL; +orig_symlinkat_t orig_symlinkat = NULL; +orig_linkat_t orig_linkat = NULL; +orig_rename_t orig_rename = NULL; +unsigned int level; +pte_t *pte; + + +asmlinkage long hooked_openat(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + // di, si: filepath, dx: flags + nbytes = strncpy_from_user(buffer, (char*)regs->si, PATH_MAX); + + // check if it's visiting SAFEBOX + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + // original sys_openat + sys_ret = orig_openat(regs); + return sys_ret; +} + +asmlinkage long hooked_chdir(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + nbytes = strncpy_from_user(buffer, (char*)regs->di, PATH_MAX); + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + sys_ret = orig_chdir(regs); + return sys_ret; +} + +asmlinkage long hooked_mkdir(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + nbytes = strncpy_from_user(buffer, (char*)regs->di, PATH_MAX); + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + sys_ret = orig_mkdir(regs); + return sys_ret; +} + +asmlinkage long hooked_symlinkat(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + nbytes = strncpy_from_user(buffer, (char*)regs->di, PATH_MAX); + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + sys_ret = orig_symlinkat(regs); + return sys_ret; +} + +asmlinkage long hooked_linkat(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + nbytes = strncpy_from_user(buffer, (char*)regs->si, PATH_MAX); + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + sys_ret = orig_linkat(regs); + return sys_ret; +} + +asmlinkage long hooked_rename(struct pt_regs *regs) +{ + long sys_ret, safe_ret; + char buffer[PATH_MAX]; + long nbytes; + + // di, si: filepath, dx: flags + nbytes = strncpy_from_user(buffer, (char*)regs->di, PATH_MAX); + + // check if it's visiting SAFEBOX + safe_ret = SafeboxCheckPath(regs, buffer); + if (safe_ret == -1) + return safe_ret; + + // original sys_openat + sys_ret = orig_rename(regs); + return sys_ret; +} + +static int __init safebox_init(void) +{ + // get sys_call_table + sys_call_table = get_sys_call_table(); + printk("Info: sys_call_table found at %lx\n",(unsigned long)sys_call_table) ; + + // find sys_openat, sys_chdir, sys_mkdir, sys_symlinkat, sys_linkat + orig_openat = (orig_openat_t) sys_call_table[__NR_openat]; + orig_chdir = (orig_chdir_t) sys_call_table[__NR_chdir]; + orig_mkdir = (orig_mkdir_t) sys_call_table[__NR_mkdir]; + orig_symlinkat = (orig_symlinkat_t) sys_call_table[__NR_symlinkat]; + orig_linkat = (orig_linkat_t) sys_call_table[__NR_linkat]; + orig_rename = (orig_rename_t) sys_call_table[__NR_rename]; + + printk("Info: sys_openat: %lx; sys_chdir: %lx; sys_mkdir: %lx; sys_symlinkat: %lx; sys_linkat: %lx;; sys_rename: %lx;\n",\ + (long)orig_openat, (long)orig_chdir, (long)orig_mkdir, (long)orig_symlinkat, (long)orig_linkat, (long)orig_rename); + + // change PTE to allow writing + pte = lookup_address((unsigned long)sys_call_table, &level); + set_pte_atomic(pte, pte_mkwrite(*pte)); + printk("Info: Disable write-protection of page with sys_call_table\n"); + // hook sys_openat, sys_chdir, sys_mkdir, sys_symlinkat, sys_linkat + sys_call_table[__NR_openat] = (demo_sys_call_ptr_t) hooked_openat; + sys_call_table[__NR_chdir] = (demo_sys_call_ptr_t) hooked_chdir; + sys_call_table[__NR_mkdir] = (demo_sys_call_ptr_t) hooked_mkdir; + sys_call_table[__NR_symlinkat] = (demo_sys_call_ptr_t) hooked_symlinkat; + sys_call_table[__NR_linkat] = (demo_sys_call_ptr_t) hooked_linkat; + sys_call_table[__NR_rename] = (demo_sys_call_ptr_t) hooked_rename; + set_pte_atomic(pte, pte_clear_flags(*pte, _PAGE_RW)); + + printk("Info: sys_call_table hooked!\n"); + + netlink_init(); + return 0; +} + + +static void __exit safebox_exit(void) +{ + pte = lookup_address((unsigned long) sys_call_table, &level); + set_pte_atomic(pte, pte_mkwrite(*pte)); + // remove hook + sys_call_table[__NR_openat] = (demo_sys_call_ptr_t) orig_openat; + sys_call_table[__NR_chdir] = (demo_sys_call_ptr_t) orig_chdir; + sys_call_table[__NR_mkdir] = (demo_sys_call_ptr_t) orig_mkdir; + sys_call_table[__NR_symlinkat] = (demo_sys_call_ptr_t) orig_symlinkat; + sys_call_table[__NR_linkat] = (demo_sys_call_ptr_t) orig_linkat; + sys_call_table[__NR_rename] = (demo_sys_call_ptr_t) orig_rename; + set_pte_atomic(pte, pte_clear_flags(*pte, _PAGE_RW)); + + netlink_release(); +} + +module_init(safebox_init); +module_exit(safebox_exit); diff --git a/lab-nss/small-projects/Safebox/krnl/syscalltable.c b/lab-nss/small-projects/Safebox/krnl/syscalltable.c new file mode 100644 index 0000000000000000000000000000000000000000..604f2fa8828daefbb5fff385e95fa8c705ff9f14 --- /dev/null +++ b/lab-nss/small-projects/Safebox/krnl/syscalltable.c @@ -0,0 +1,29 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +typedef void (* demo_sys_call_ptr_t)(void); + +demo_sys_call_ptr_t * get_sys_call_table(void) +{ + demo_sys_call_ptr_t * _sys_call_table=NULL; + + _sys_call_table=(demo_sys_call_ptr_t *)kallsyms_lookup_name("sys_call_table"); + + //Print sys_call_table address + printk("Info: _sys_call_table is at %lx\n", (long) _sys_call_table); + + return _sys_call_table; +} + +