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#!/usr/bin/env python
__description__ = 're extra'
__author__ = 'Didier Stevens'
__version__ = '0.0.4'
__date__ = '2018/07/15'
"""
History:
2014/04/04: refactoring from proxy-snort.py
2017/05/17: 0.0.2 added extra=P
2017/06/13: 0.0.3 added Script and Execute
2018/07/15: 0.0.4 made decode_base58 more robust
Todo:
"""
import re
import pickle
import math
import os
import glob
import datetime
import hashlib
def File2Strings(filename, comment=None):
try:
f = open(filename, 'r')
except:
return None
try:
return [line for line in map(lambda line:line.rstrip('\n'), f.readlines()) if comment == None or not line.startswith(comment)]
except:
return None
finally:
f.close()
def GFile2Strings(argument):
if isinstance(argument, str):
try:
f = open(argument, 'r')
except:
return
else:
f = argument
try:
for line in f.readlines():
yield line.rstrip('\n')
except:
return
finally:
if isinstance(argument, str):
f.close()
def File2StringsFiltered(filename):
"""\
Read a text file and return a list of strings,
excluding comments and temporary lines.
Returns None when an error occured.
Comment lines (first character #) are not included in the returned
list of strings.
Temporary lines can be excluded from the returned list of strings,
depending on the current date.
Temporary lines are preceeded by a comment with this syntax:
#begin:YYYY-MM-DD
#begin:YYYY-MM-DD+##
#end:YYYY-MM-DD
#end:YYYY-MM-DD+##
#within:YYYY-MM-DD~YYYY-MM-DD
Temporary lines preceeded by #begin are included if the current date
is equal or later than the date YYYY-MM-DD or the date YYYY-MM-DD
+ ## number of days
Temporary lines preceeded by #end are included if the current date
is equal or earlier than the date YYYY-MM-DD or the date YYYY-MM-DD
+ ## number of days
Temporary lines preceeded by #within are included if the current date
falls within the dates YYYY-MM-DD
"""
commentCharacter = '#'
try:
f = open(filename, 'r')
except:
return None
result = []
try:
skip = False
for line in map(lambda line:line.rstrip('\n'), f.readlines()):
if not skip and not line.startswith(commentCharacter):
result.append(line)
elif line.startswith(commentCharacter):
oMatchBeginEnd = re.match(r'(begin|end):(\d{4})-(\d{2})-(\d{2})(\+\d+)?', line[1:], re.IGNORECASE)
oMatchWithin = re.match(r'within:(\d{4})-(\d{2})-(\d{2})~(\d{4})-(\d{2})-(\d{2})', line[1:], re.IGNORECASE)
if oMatchBeginEnd:
oDate = datetime.date(int(oMatchBeginEnd.group(2)), int(oMatchBeginEnd.group(3)), int(oMatchBeginEnd.group(4)))
if oMatchBeginEnd.group(5) != None:
oDate += datetime.timedelta(int(oMatchBeginEnd.group(5)[1:]))
if oMatchBeginEnd.group(1) == 'begin' and oDate > datetime.date.today():
skip = True
if oMatchBeginEnd.group(1) == 'end' and oDate < datetime.date.today():
skip = True
elif oMatchWithin:
oDateBegin = datetime.date(int(oMatchWithin.group(1)), int(oMatchWithin.group(2)), int(oMatchWithin.group(3)))
oDateEnd = datetime.date(int(oMatchWithin.group(4)), int(oMatchWithin.group(5)), int(oMatchWithin.group(6)))
if oDateBegin > datetime.date.today() or oDateEnd < datetime.date.today():
skip = True
elif skip:
skip = False
except:
return None
finally:
f.close()
return result
def decode_base58(bc, length):
digits58 = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'
n = 0
for char in bc:
if not char in digits58:
return None
n = n * 58 + digits58.index(char)
# print(n.to_bytes(length, 'big'))
# return n.to_bytes(length, 'big')
return ''.join([chr((n >> i*8) & 0xff) for i in reversed(range(length))])
def BTCValidate(bc):
bcbytes = decode_base58(bc, 25)
if bcbytes == None:
return False
return bcbytes[-4:] == hashlib.sha256(hashlib.sha256(bcbytes[:-4]).digest()).digest()[:4]
def CountUniques(data):
dCount = {}
for b in data:
dCount[b] = True
return len(dCount)
def Script(filename):
execfile(filename, globals(), globals())
def Execute(pythoncode):
exec(pythoncode, globals())
class cGibberishDetector():
def __init__(self, filenamePickle='', acceptedCharacters='abcdefghijklmnopqrstuvwxyz '):
self.filenamePickle = filenamePickle
self.acceptedCharacters = acceptedCharacters
self.ngramSize = 2
self.pos = dict([(char, idx) for idx, char in enumerate(self.acceptedCharacters)])
self.modelMatrix = None
pickledata = """\
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S'acceptedCharacters'
p1
S'abcdefghijklmnopqrstuvwxyz '
p2
sS'modelProbabilityThreshold'
p3
F0.018782003473122023
sS'ngramSize'
p4
I2
sS'modelMatrix'
p5
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aF-3.9369332597631863
aF-3.220670162697391
aF-3.0482479869676102
aF-6.052279063336297
aF-4.69956099775001
aF-3.9941585968087816
aF-6.710407217596661
aF-3.2453041060602184
aF-7.060740255010108
aF-4.512283359624297
aF-2.4997201529644935
aF-3.642636781640966
aF-1.5707462805725019
aF-7.978468801653891
aF-3.8936418102220776
aF-9.821900281426267
aF-2.3025283782801376
aF-2.348366425382398
aF-1.9448651421947813
aF-4.539158126663701
aF-3.871849760115083
aF-4.706359120463831
aF-6.560313338465017
aF-3.649725323207633
aF-6.641954302926283
aF-2.7134701747591117
aa(lp8
F-2.5528619980785
aF-5.139226208055755
aF-6.049719822245583
aF-6.219404795035026
aF-1.173596307609444
aF-8.563954087128105
aF-8.805116143944993
aF-8.494961215641153
aF-3.3328454702735173
aF-5.004532700251055
aF-8.805116143944993
aF-2.139085063222716
aF-6.121607051758899
aF-6.808562262070924
aF-2.1459387811703974
aF-8.312639658847198
aF-8.900426323749317
aF-2.719375008855968
aF-3.788438535392774
aF-4.703224376087508
aF-2.137350056136624
aF-6.320209494156992
aF-7.676650892127201
aF-8.900426323749317
aF-2.3611294272462486
aF-8.900426323749317
aF-4.738423113053401
aa(lp9
F-2.08946313988477
aF-9.398284978640158
aF-3.8466182187208457
aF-7.678499009037193
aF-1.7391136109740999
aF-8.792149175069843
aF-9.580606535434113
aF-1.9093388132314668
aF-2.9331775988939013
aF-9.485296355629789
aF-3.343650033348159
aF-3.276523430584318
aF-8.515895798441685
aF-8.838669190704735
aF-1.6000355228278762
aF-9.485296355629789
aF-6.386023403134956
aF-3.377666241879603
aF-5.838186314392146
aF-2.3909106346293085
aF-3.2184408726543063
aF-9.485296355629789
aF-8.838669190704735
aF-9.580606535434113
aF-4.621264535725407
aF-8.299672689972049
aF-3.8668737299247438
aa(lp10
F-3.7200475908408235
aF-7.418434550575756
aF-7.88681348409449
aF-4.564865474086475
aF-1.9699465394791131
aF-6.796896902393934
aF-5.430448837645974
aF-6.754155354016663
aF-2.4627220077183942
aF-6.277375571660389
aF-7.666270714480338
aF-4.558326450319418
aF-5.5188785108919935
aF-5.946484744877372
aF-3.117009722931763
aF-8.380471304238116
aF-8.136274343726074
aF-3.6243165394601604
aF-3.6811521539816408
aF-7.1936663035345445
aF-3.9941983786827824
aF-5.568764165402863
aF-7.077327311450395
aF-9.928033812954128
aF-4.613843097249801
aF-9.745712256160173
aF-0.5394468422260206
aa(lp11
F-3.0956911156796725
aF-6.3279563530093395
aF-3.711394117529345
aF-2.414309096615009
aF-3.7280101535107066
aF-4.555095809949321
aF-4.9582369569836615
aF-6.340824177620733
aF-4.465115884463944
aF-8.043262266521952
aF-7.012917948982845
aF-3.4395652463282116
aF-3.7489856294972617
aF-2.389153595060143
aF-5.281198278652138
aF-4.426598784036354
aF-6.265131628349503
aF-1.976271246918681
aF-2.519225987547763
aF-3.7331106440131854
aF-6.05129367580717
aF-4.115226028040922
aF-4.719249301196115
aF-4.451178867151557
aF-4.535588241035776
aF-7.731754615658102
aF-1.1291364595675442
aa(lp12
F-2.7634422911455703
aF-7.9114774546442685
aF-7.529542843946299
aF-8.494623739989885
aF-2.451100566435661
aF-2.926120215794328
aF-7.612234559791412
aF-8.53718335440868
aF-2.4505074466080714
aF-9.033620240722573
aF-8.72823859117139
aF-3.748243385450271
aF-8.839464226281615
aF-7.66534438510536
aF-1.9043799278633868
aF-8.305381740351358
aF-9.370092477343785
aF-2.3558173549839756
aF-5.929674382528349
aF-3.315653131074415
aF-3.5058932812816797
aF-9.370092477343785
aF-7.801476559429941
aF-9.370092477343785
aF-6.135343303319295
aF-9.370092477343785
aF-0.9976707550554195
aa(lp13
F-2.681117318742622
aF-8.560252680876685
aF-8.083328608786376
aF-6.8862762473050125
aF-1.9631827738890724
aF-7.924263914156688
aF-4.61975020412667
aF-2.2213641819441556
aF-2.8612966495974126
aF-9.148039345778804
aF-8.560252680876685
aF-3.35807917488155
aF-6.075346031088684
aF-3.7399711152716475
aF-2.831958278425978
aF-7.954116877306369
aF-9.052729165974478
aF-2.5526687518888194
aF-4.062915199691808
aF-4.943346726387837
aF-3.494498087559349
aF-9.148039345778804
aF-7.579423427864958
aF-9.148039345778804
aF-5.882279935011752
aF-8.617411094716633
aF-1.0302198528344138
aa(lp14
F-1.8949866865832032
aF-7.38720654177459
aF-8.05310407988716
aF-7.542278456121169
aF-0.7286353651447546
aF-7.858315754328075
aF-9.589971299486425
aF-8.779041083270096
aF-1.9951372153459488
aF-10.100796923252416
aF-7.885223207247999
aF-6.635061020452689
aF-6.261344610659105
aF-6.705170586639715
aF-2.5617698674284206
aF-9.253499062865211
aF-10.100796923252416
aF-4.5720294302077304
aF-6.222675469499951
aF-3.764561336708525
aF-4.622243506401445
aF-9.541181135316993
aF-7.3816968859636205
aF-10.28311848004637
aF-5.024581983510178
aF-10.187808300242045
aF-2.3623088007577695
aa(lp15
F-3.712244886548001
aF-4.717475282130211
aF-2.783984370515817
aF-3.2216013768067646
aF-3.1683365236496233
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aF-9.119304544100272
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aF-5.240838322200969
aF-3.0795261367137394
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aF-9.672689782285058
aF-6.168308164387673
aF-10.410288725415837
aF-5.511770729440288
aF-3.788398987959084
aa(lp16
F-2.3427609160575655
aF-6.1024094410597085
aF-6.037870919922137
aF-6.507874549167873
aF-1.4153520955994334
aF-6.245510284700382
aF-6.17140231254666
aF-6.325552992373918
aF-5.51462277615759
aF-6.507874549167873
aF-6.245510284700382
aF-6.412564369363548
aF-6.325552992373918
aF-6.507874549167873
aF-1.2783714986201964
aF-6.412564369363548
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aF-6.1024094410597085
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aF-6.245510284700382
aF-1.1002544477293865
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aF-6.507874549167873
aF-6.507874549167873
aF-6.507874549167873
aF-4.859215923580492
aa(lp17
F-3.6194933584945135
aF-7.047703539402737
aF-6.062419936041631
aF-7.671857848475732
aF-1.2114318817004575
aF-6.285563487355841
aF-6.824559988088527
aF-3.6311485020862624
aF-1.7851984157338754
aF-7.814958692116405
aF-7.489536291681777
aF-3.88766821455747
aF-6.036102627724257
aF-2.3543916630889337
aF-3.8417682258102714
aF-7.546694705521725
aF-7.9820127767795706
aF-5.48206824962703
aF-3.0008999219496366
aF-6.6910285954640045
aF-3.73872587983735
aF-7.2444138336487915
aF-5.438265626968637
aF-8.077322956583895
aF-4.682814563072537
aF-8.077322956583895
aF-1.4288566755523215
aa(lp18
F-2.269183388314388
aF-6.573297799694782
aF-5.745479365449931
aF-2.8596367214634224
aF-1.7841347050080587
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aa(lp19
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aa(lp20
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aa(lp21
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aa(lp22
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aa(lp23
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aF-6.182291496945648
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aF-6.182291496945648
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aF-6.182291496945648
aF-6.182291496945648
aF-6.182291496945648
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aF-0.057170565024993084
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aF-5.540437610773254
aa(lp24
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aF-4.850464566831816
aF-4.673556599383159
aF-3.74500437214974
aF-3.916576116332641
aF-2.322164557359624
aF-5.413979406021222
aF-9.0405400957359
aF-3.636862213530037
aF-2.9016138569514562
aF-3.2345687965875074
aF-3.9816438780223904
aF-4.905818082232694
aF-6.257588586460572
aF-10.13915238440401
aF-3.2585970253485272
aF-8.070182142591468
aF-1.7281348556024396
aa(lp25
F-3.2090119684802527
aF-6.334899523154303
aF-4.093299064417374
aF-7.59203503906228
aF-2.1586345703035548
aF-6.182995193953419
aF-7.849503332917564
aF-2.915257814589145
aF-2.7754710419906368
aF-9.550291023939893
aF-4.551024457450397
aF-4.707131510786311
aF-4.589272322367449
aF-6.243245073400844
aF-2.968928515714967
aF-3.853927304671137
aF-7.00903143760076
aF-8.243133983378726
aF-2.8108514947509753
aF-2.1177557637833035
aF-3.256550330868065
aF-7.865503674264006
aF-5.380597024476005
aF-10.383200146874996
aF-5.201416596582911
aF-9.913196517629261
aF-0.9911300598753203
aa(lp26
F-3.1682706209020797
aF-8.217706195099385
aF-5.878899838671022
aF-9.525219678366163
aF-2.3388918749787213
aF-7.172402459885784
aF-8.478432457562638
aF-1.1074718716074006
aF-2.3662341497302157
aF-10.456777882371107
aF-9.689522729657439
aF-4.418539855249544
aF-6.032391973858083
aF-7.21960886445558
aF-2.3367153961191565
aF-8.16191483547097
aF-10.719142146838596
aF-3.4624215669877803
aF-3.6797447588047447
aF-4.0251990917417855
aF-3.9002180815630756
aF-9.555991337032916
aF-5.18259559155831
aF-10.131355481936478
aF-4.204725796166784
aF-7.984774637419013
aF-1.5835253210583506
aa(lp27
F-3.6907063474960813
aF-3.7341336409281
aF-3.2454721262149633
aF-4.020608712629845
aF-3.281297978688059
aF-5.0172437658127595
aF-3.1933271849501734
aF-7.7963898885987
aF-3.73111886626001
aF-9.35453450664525
aF-6.208229374611885
aF-2.257675168572427
aF-3.4112977818505237
aF-2.089862646254399
aF-6.096437968623768
aF-3.100705695069777
aF-8.949069398537086
aF-1.905375903432984
aF-1.9722545836234537
aF-1.9648676140617645
aF-9.274491798971713
aF-6.7831953510849425
aF-9.35453450664525
aF-7.31765257938421
aF-7.3735330377786665
aF-5.267158613739243
aF-3.2700350935700784
aa(lp28
F-2.4677551327310105
aF-8.46601472297182
aF-8.561324902776146
aF-7.868177722216201
aF-0.5192363285514866
aF-8.561324902776146
aF-8.298960638308655
aF-8.46601472297182
aF-1.7429474349452256
aF-8.46601472297182
aF-7.919471016603751
aF-5.443374996497906
aF-8.561324902776146
aF-4.566800675836256
aF-2.7892609207035393
aF-8.561324902776146
aF-8.561324902776146
aF-6.268790145635601
aF-4.407140340198028
aF-7.973538237874027
aF-6.289199017266808
aF-8.379003345982191
aF-8.030696651713976
aF-8.46601472297182
aF-5.258107929474194
aF-8.561324902776146
aF-3.1228109057348257
aa(lp29
F-1.596798460957614
aF-7.6001421705956735
aF-7.640964165115928
aF-5.344648685135478
aF-1.8921021056868312
aF-6.839336341561913
aF-8.110967794361665
aF-1.6228272590921338
aF-1.7628783045649916
aF-8.87310784640856
aF-7.001305669506969
aF-5.467159861987808
aF-8.179960665848615
aF-3.2183655671770013
aF-2.524218636471301
aF-8.293289351155618
aF-9.209580083029774
aF-4.57000847032435
aF-4.2425484264156506
aF-5.65423202154036
aF-7.560921457442392
aF-9.209580083029774
aF-7.307472556632853
aF-9.209580083029774
aF-6.877436187794184
aF-9.02725852623582
aF-2.172728230713227
aa(lp30
F-2.254780968033424
aF-6.898209866138606
aF-2.027603216646053
aF-6.492744758030441
aF-2.462642464536694
aF-5.833499129146177
aF-6.898209866138606
aF-4.310445830910898
aF-2.0731012597852527
aF-6.898209866138606
aF-6.898209866138606
aF-6.42820623689287
aF-6.715888309344651
aF-6.898209866138606
aF-4.4558628307694015
aF-1.4955324842663262
aF-6.3675816150764355
aF-4.636446767664815
aF-5.766807754647505
aF-1.8703897472882491
aF-4.37248122183035
aF-4.348764695213034
aF-6.802899686334281
aF-4.473407140420311
aF-5.175443268397502
aF-6.898209866138606
aF-2.5557039896270073
aa(lp31
F-3.872874353532438
aF-6.07983833755968
aF-5.605956228985375
aF-6.1495716755743555
aF-2.9181087352006836
aF-5.833140189836589
aF-6.998723104610883
aF-7.317176835729417
aF-3.8266394412494407
aF-8.857621876676566
aF-7.45070822835394
aF-4.745382824577915
aF-4.447858487031085
aF-5.448125692199715
aF-2.2255103117197566
aF-4.737230605516364
aF-9.03994343347052
aF-5.751541545953709
aF-3.132676045163294
aF-4.230201081753655
aF-7.038463433260397
aF-7.65364907235063
aF-6.230540738108023
aF-7.70494236673818
aF-8.16447469611662
aF-7.731610613820342
aF-0.3817890191036424
aa(lp32
F-2.531179599331457
aF-6.00314605188182
aF-5.907835872077495
aF-4.694813232231641
aF-0.9292230185496455
aF-5.907835872077495
aF-6.00314605188182
aF-3.199785670975285
aF-2.3395844057521735
aF-6.00314605188182
aF-5.666673815260607
aF-3.886890537079268
aF-4.0016660516716955
aF-5.009894278871537
aF-1.7362497244615696
aF-6.00314605188182
aF-6.00314605188182
aF-6.00314605188182
aF-5.820824495087865
aF-5.907835872077495
aF-3.1183453390351104
aF-5.666673815260607
aF-5.907835872077495
aF-6.00314605188182
aF-4.568061526592497
aF-3.7731316517226094
aF-3.1699327078256037
aa(lp33
F-2.154456318300654
aF-3.132028909232904
aF-3.204240273221435
aF-3.554775966080049
aF-3.8320798875631166
aF-3.2625149066892174
aF-4.1318426237305275
aF-2.7847122791384975
aF-2.7534204117779435
aF-5.68607428491371
aF-5.271518205104294
aF-3.779792629468334
aF-3.352468498262208
aF-3.8123859357235683
aF-2.644532475328425
aF-3.3676057306367433
aF-6.237872536056438
aF-3.6809218434868227
aF-2.7030074975159986
aF-1.86142386740762
aF-4.4723885304095425
aF-4.918696830481552
aF-2.8042850405884527
aF-7.783949528282384
aF-4.702039558487341
aF-8.486442571260568
aF-3.2910629924454486
aas.
"""
def Normalize(self, line):
""" Return only the subset of chars from acceptedCharacters.
This helps keep the model relatively small by ignoring punctuation,
infrequenty symbols, etc. """
return [c for c in line.lower() if c in self.acceptedCharacters]
def Ngram(self, line):
""" Return all ngramSize grams from line """
for start in range(0, len(line) - self.ngramSize + 1):
yield ''.join(line[start:start + self.ngramSize])
def AverageTransitionProbability(self, line):
""" Return the average transition prob from line through self.modelMatrix. """
log_prob = 0.0
transition_ct = 0
for a, b in self.Ngram(self.Normalize(line)):
log_prob += self.modelMatrix[self.pos[a]][self.pos[b]]
transition_ct += 1
# The exponentiation translates from log probs to probs.
return math.exp(log_prob / (transition_ct or 1))
def Train(self, filenameReferencetext, filenameSensical, filenameGibberish):
""" Write a simple model as a pickle file """
k = len(self.acceptedCharacters)
# Assume we have seen 10 of each character pair. This acts as a kind of
# prior or smoothing factor. This way, if we see a character transition
# live that we've never observed in the past, we won't assume the entire
# string has 0 probability.
countsMatrix = [[10 for i in xrange(k)] for i in xrange(k)]
# Count transitions from big text file, taken
# from http://norvig.com/spell-correct.html
countNgrams = 0
dCharacters = {}
for line in GFile2Strings(filenameReferencetext):
for a, b in self.Ngram(self.Normalize(line)):
countsMatrix[self.pos[a]][self.pos[b]] += 1
countNgrams += 1
for character in line.lower():
if character in dCharacters:
dCharacters[character] += 1
else:
dCharacters[character] = 1
# Normalize the countsMatrix so that they become log probabilities.
# We use log probabilities rather than straight probabilities to avoid
# numeric underflow issues with long texts.
# This contains a justification:
# http://squarecog.wordpress.com/2009/01/10/dealing-with-underflow-in-joint-probability-calculations/
self.modelMatrix = [[math.log(count / float(sum(row))) for count in row] for row in countsMatrix]
# Find the probability of generating a few arbitrarily choosen sensical and gibberish phrases.
probabilitiesSensical = [self.AverageTransitionProbability(line) for line in GFile2Strings(filenameSensical)]
probabilitiesGibberish = [self.AverageTransitionProbability(line) for line in GFile2Strings(filenameGibberish)]
# Assert that we actually are capable of detecting the junk.
assert min(probabilitiesSensical) > max(probabilitiesGibberish)
# And pick a threshold halfway between the worst good and best bad inputs.
self.modelProbabilityThreshold = (min(probabilitiesSensical) + max(probabilitiesGibberish)) / 2
pickle.dump({'modelMatrix': self.modelMatrix, 'modelProbabilityThreshold': self.modelProbabilityThreshold, 'ngramSize': self.ngramSize, 'acceptedCharacters':self.acceptedCharacters}, open(self.filenamePickle, 'wb'))
print('Different characters in reference file: %d' % len(dCharacters))
for items in sorted(dCharacters.iteritems(), key=operator.itemgetter(1)):
print(' %s: %d' % items)
print('Number of ngrams in reference file: %d' % countNgrams)
print('Highest probability from gibberish file: %f' % max(probabilitiesGibberish))
print('Lowest probability from sensical file: %f' % min(probabilitiesSensical))
print('Probability threshold: %f' % self.modelProbabilityThreshold)
def LoadModel(self):
if self.filenamePickle == '':
model_data = pickle.loads(cGibberishDetector.pickledata)
else:
model_data = pickle.load(open(self.filenamePickle, 'rb'))
self.modelMatrix = model_data['modelMatrix']
self.modelProbabilityThreshold = model_data['modelProbabilityThreshold']
self.ngramSize = model_data['ngramSize']
self.acceptedCharacters = model_data['acceptedCharacters']
self.pos = dict([(char, idx) for idx, char in enumerate(self.acceptedCharacters)])
def Sensical(self, line):
if self.modelMatrix == None:
self.LoadModel()
return self.AverageTransitionProbability(line) > self.modelProbabilityThreshold
class cExtraSensical():
def __init__(self, sensical, sensicalPickle=''):
self.sensical = sensical
self.oGibberishDetector = cGibberishDetector(sensicalPickle)
def Test(self, data):
sensical = self.oGibberishDetector.Sensical(data)
if self.sensical:
return sensical
else:
return not sensical
class cExtraList():
def __init__(self, include, identifyer, dLists):
self.include = include
if not identifyer in dLists:
raise Exception('cExtraList identifyer not in lists')
self.datalist = File2StringsFiltered(dLists[identifyer])
def Test(self, data):
found = data.lower() in self.datalist
if self.include:
return found
else:
return not found
class cExtraPython():
def __init__(self, functionname):
self.function = eval(functionname)
def Test(self, data):
return self.function(data)
class cREExtra():
def __init__(self, regex, flags, sensicalPickle='', listsDirectory=''):
self.regex = regex
self.flags = flags
self.listsDirectory = listsDirectory
self.oRE = re.compile(self.regex, self.flags)
self.extra = None
self.conditions = []
if not self.regex.startswith('(?#extra='):
return
iRightParanthesis = regex.find(')')
if iRightParanthesis == -1:
raise Exception('Error extra regex comment: 1')
self.extra = regex[9:iRightParanthesis]
dLists = {os.path.basename(filename):filename for filename in sum(map(glob.glob, [os.path.join(listsDirectory, '*')]), [])}
for condition in self.extra.split(';'):
if condition.startswith('S:'):
if condition[2:] != 'g' and condition[2:] != 's':
raise Exception('Error extra regex comment: 3')
self.conditions.append(cExtraSensical(condition[2:] == 's', sensicalPickle))
elif condition.startswith('E:'):
if condition[2:] == '':
raise Exception('Error extra regex comment: 4')
self.conditions.append(cExtraList(False, condition[2:], dLists))
elif condition.startswith('I:'):
if condition[2:] == '':
raise Exception('Error extra regex comment: 5')
self.conditions.append(cExtraList(True, condition[2:], dLists))
elif condition.startswith('P:'):
if condition[2:] == '':
raise Exception('Error extra regex comment: 6')
self.conditions.append(cExtraPython(condition[2:]))
else:
raise Exception('Error extra regex comment: 2')
def Test(self, data):
return all([oCondition.Test(data) for oCondition in self.conditions])
def Findall(self, line):
found = self.oRE.findall(line)
results = []
for result in found:
if isinstance(result, str):
if self.Test(result):
results.append(result)
if isinstance(result, tuple):
results.append(result)
return results
def Search(self, line, flags=0):
oMatch = self.oRE.search(line, flags)
if oMatch == None:
return None
if self.Test(oMatch.group(0)):
return oMatch
else:
return None