def _GetPatch(self) -> Patch:
random.seed(self.seed)
data_table = DataTable()
data_table.ResetToVanilla()
level_generator = LevelGenerator(data_table)
level_generator.Generate()
item_randomizer = ItemRandomizer(data_table, self.settings)
item_randomizer.Randomize()
validator = Validator(data_table, self.settings)
while not validator.IsSeedValid():
item_randomizer.Randomize()
patch = data_table.GetPatch()
patch.AddData(0x16fd8, [
0xFF, 0xA5, 0xEC, 0x30, 0x0B, 0x49, 0x80, 0xCD, 0xA1, 0x6B, 0xD0, 0x09, 0xA4, 0x10, 0xF0,
0x05, 0x85, 0xEC, 0x4C, 0x47, 0xB5, 0x4C, 0x59, 0xB5, 0xAC, 0xBB, 0x6B, 0xB9, 0xF1, 0xAF,
0x85, 0x98, 0xB9, 0xF6, 0xAF, 0x85, 0x70, 0xB9, 0xFB, 0xAF, 0x60, 0x00, 0x04, 0x08, 0x01,
0x02, 0x78, 0x78, 0x78, 0x00, 0xF0, 0x8D, 0x3D, 0xDD, 0x8D, 0x8D
])
patch.AddData(0x17058, [0xA9, 0x78, 0x85, 0x70, 0x20, 0xE0, 0xAF, 0x85])
patch.AddData(0x17550, [0x20, 0xC0, 0xB8, 0x4C, 0xC9, 0xAF, 0x12, 0x20])
patch.AddData(0x178D0, [0xAD, 0x22, 0x05, 0xC9, 0x01, 0xF0, 0x03, 0x4C, 0x2F, 0x75, 0x60])
patch.AddData(0x1934D, [0x00])
# Fix for ring/tunic colors
patch.AddData(0x6BFB, [0x20, 0xE4, 0xFF])
patch.AddData(0x1FFF4, [0x8E, 0x02, 0x06, 0x8E, 0x72, 0x06, 0xEE, 0x4F, 0x03, 0x60])
self._AddExtras(patch)
return patch
def __analyze(self):
magic_string = peekFixedLengthString(self.file, 15, position=0)
(magic, archname) = magic_string.split()
if magic != 'dyld_v1':
raise MachOError('Invalid magic "{0}"'.format(magic_string))
self.arch = Arch(archname)
if self.endian is None:
self.endian = self.arch.endian
if self.endian is None:
raise MachOError(
'Cannot guess endian from architecture "{0}"'.format(
archname))
(mappingOffset, mappingCount, imagesOffset, imagesCount, self.dyldBaseAddress) = \
peekStruct(self.file, Struct(self.endian + '4LQ'), position=16)
self.mappings = MappingSet(
self.__analyzeMappings(mappingOffset, mappingCount))
self.mappings.freeze()
images = DataTable('!address', '!name', '!path')
for image in self.__analyzeImages(imagesOffset, imagesCount):
path = image.path
bn = basename(path)
while True:
(stem, ext) = splitext(bn)
if not ext:
break
bn = stem
images.append(image, address=image.address, name=bn, path=path)
self.images = images
def _loadSections(self, machO):
segStruct = machO.makeStruct('16s4^2i2L')
sectStruct = machO.makeStruct(Section.STRUCT_FORMAT)
(segname, self.vmaddr, self._vmsize, self._fileoff, self._filesize,
self.maxprot, self.initprot, nsects,
_) = readStruct(machO.file, segStruct)
self.segname = fromStringz(segname)
machO_fileOrigin = machO._fileOrigin
sectVals = peekStructs(machO.file, sectStruct,
count=nsects) # get all section headers
sectionsList = (Section.createSection(i) for i in sectVals
) # convert all headers into Section objects
sections = DataTable('className', 'sectname', 'ftype')
for s in sectionsList:
if s.offset < machO_fileOrigin:
s.offset += machO_fileOrigin
sections.append(s,
className=type(s).__name__,
sectname=s.sectname,
ftype=s.ftype)
self.sections = sections
self._hasAnalyzedSections = False
self._shouldImportMappings = machO.mappings.mutable
def test_data_table(self):
"""test regular operations for DataTable"""
with self.assertRaises(TypeError):
DataTable("")
with self.assertRaises(TypeError):
DataTable(1, ["header1"])
table = DataTable([[1, 2], [3]], ["header1", "header2"])
rows = list(table.dict_records())
self.assertEqual(rows[0]["header2"], 2)
self.assertEqual(rows[1]["header1"], 3)
with self.assertRaises(KeyError):
a = rows[1]["header2"]
table = DataTable([1, 2], ["header1", "header2"])
with self.assertRaises(TypeError):
rows = list(table)
def main():
logging.basicConfig(level=logging.INFO)
logging.info('=== NORMAL START ===' + '=' * 111)
chapter(f"Process arguments - configure")
conf = configure()
pprint.pprint(conf, width=132, indent=10, depth=2)
chapter(f"Load left csv file [{conf['left']}]")
left_data = DataTable(displayfield=conf['keyfield'])
left_data.load_csv(conf['left'], conf['fielddelimiter'], conf['limit'])
# print(left_data.records)
# left_data.show_fields()
chapter(f"Load right csv file [{conf['right']}]")
right_data = DataTable(displayfield=conf['keyfield'])
right_data.load_csv(conf['right'], conf['fielddelimiter'], conf['limit'])
# print(left_data.records)
# right_data.show_fields(('Name', 'Category', 'Type'))
chapter(f"Check key is unique in both files")
if left_data.is_unique_field(conf['keyfield']):
logging.info(f"[{conf['keyfield']}] is unique in left file - OK")
else:
logging.error(
f"[{conf['keyfield']}] is NOT unique in left file - exiting")
exit(3)
if right_data.is_unique_field(conf['keyfield']):
logging.info(f"[{conf['keyfield']}] is unique in right file - OK")
else:
logging.error(
f"[{conf['keyfield']}] is NOT unique in right file - exiting")
exit(3)
chapter(f"Diff them csv files")
diff = diff_data(left_data, right_data, conf['keyfield'],
conf['fieldlist'])
# print(diff)
logging.info('=== NORMAL END ===' + '=' * 113)
exit(0)
def main():
logging.basicConfig(level=logging.INFO)
chapter(f" NORMAL START ", filler='=')
chapter(f"Process arguments - configure")
conf = configure()
chapter(f"Load csv [{conf['inputfile']}]")
input_data = DataTable(displayfield='Name')
input_data.load_csv(conf['inputfile'], conf['delimiter'], conf['limit'])
# input_data.show_fields(('Active', 'Name', 'Description'))
input_data.show_fields()
chapter(f"Remove unwanted records")
input_data.remove_records({'Active': 'N'})
chapter(f"Replace some shizzle - newlines, semicolons, etc")
# input_data.replace_in_field(field='Description', frompart='\n', topart='<br/>')
input_data.replace_in_field(field='Description', frompart=';', topart='.')
# input_data.show_fields(('Active', 'Name', 'Description'))
chapter(f"Checking uniqueness of field Name")
isUnique = input_data.is_unique_field(fieldname='Name')
chapter(f"Add some extra fields")
input_data.add_counter_field('gen-id', initialvalue=10001)
input_data.copy_field(sourcefield='Description', newfield='gen-short-description')
input_data.add_fixed_field('fix-text-1', 'from')
input_data.add_combined_field(newfield='gen-subtitle', fieldstocombine=['Type', 'fix-text-1', 'Country'], delimiter=' ')
chapter(f"Generate complex combined feature field from the feature fields")
input_data.add_combined_features_field('combined-features', conf['featurelist'])
# input_data.show_fields(('Name', 'Product', 'combined-features'))
chapter(f"Combine the category fields into one category (list)field")
input_data.add_fixed_field('fix-maincat', 'Inhabitants')
input_data.add_combined_categories_field('combined-categories', conf['categorieslist'])
# input_data.show_fields(('Name', 'Product', 'combined-categories'))
chapter(f"Re-map fields for Prestashop")
output_data = input_data.re_map_table(conf['finalfieldmap'], displayfield='NAME')
chapter(f"Some tests - output to screen")
output_data.show_fields(('UNIQUE-ID', 'NAME', 'TYPE', 'CATEGORIES', 'FEATURES'))
# output_data.show_record('Bollie')
isUnique = output_data.is_unique_field(fieldname='NAME')
# Finally we write the resulting products csv file
chapter(f"Writing resulting csv - [{conf['outputfile']}]")
output_data.write_csv(conf['outputfile'], delimiter=';')
chapter(f" NORMAL END ", filler='=')
exit(0)
def addSymbols(self, symbols):
'''Add an iterable of :class:`~sym.Symbol`\\s to this Mach-O object.'''
if not hasattr(self, 'symbols'):
self.symbols = DataTable('name', 'addr', '!ordinal')
self_symbols_append = self.symbols.append
for sym in symbols:
self_symbols_append(sym,
name=sym.name,
addr=sym.addr,
ordinal=sym.ordinal)
def readCategoryList(machO, addresses, classes, protoRefsMap):
"""Read categories from an iterable of *addresses*, and return a
:class:`~data_table.DataTable` of :class:`~objc.category.Category`\\s with
the following column names:
* ``'name'`` (string, the name of the category)
* ``'base'`` (string, the name of the class the category is patching)
"""
cats = DataTable('name', 'base')
for vmaddr in addresses:
cat = readCategory(machO, vmaddr, classes, protoRefsMap)
cats.append(cat, name=cat.name, base=cat.class_.name)
return cats
def __init__(self, filename, arch="armv7", lenientArchMatching=False):
from .vmaddr import MappingSet
self.filename = filename
self._arch = Arch(arch)
self._lenientArchMatching = lenientArchMatching
self.fileno = -1
self.file = None
self.loadCommands = DataTable('className', 'cmd')
self.is64bit = False
self.endian = '<'
self._structCache = {}
self._fileOrigin = 0
self.mappings = MappingSet()
def readProtocolList(machO, addresses):
"""Read protocols from an iterable of *addresses*, and return a
:class:`~data_table.DataTable` of :class:`~objc.protocol.Protocol`\\s with
the following column names:
* ``'name'`` (unique, string, the name of the protocol)
* ``'addr'`` (unique, integer, the VM address to the protocol)
"""
# read protocols from the Mach-O binary.
protos = DataTable('!name', '!addr')
refs = []
for vmaddr in addresses:
(proto, protocolRefs) = readProtocol(machO, vmaddr)
protos.append(proto, name=proto.name, addr=vmaddr)
refs.append(protocolRefs)
# connect the protocols.
for proto, protocolRefs in zip(protos, refs):
connectProtocol(proto, protocolRefs, protos)
return protos
def to_data_table(self):
headers = [
"department_id", "number_of_orders", "number_of_first_orders",
"percentage"
]
records = self.counts.values()
# remove the rows with no order count
records = filter(lambda row: row["number_of_orders"] > 0, records)
def append_ratio(record):
row = [
record["department_id"], record["number_of_orders"],
record["number_of_first_orders"], 0
]
row[3] = "{:.2f}".format(row[2] / row[1])
return row
records = map(append_ratio, records)
# put rows in increasing order of department's id
records = sorted(records, key=lambda row: int(row[0]))
return DataTable(records, headers)
def readClassList(machO, addresses, protoRefsMap):
"""Read classes from an iterable of *addresses*, and return a
:class:`~data_table.DataTable` of :class:`~objc.class_.Class`\\s with
the following column names:
* ``'name'`` (unique, string, the name of the class)
* ``'addr'`` (unique, integer, the VM address to the class)
"""
classes = DataTable('!name', '!addr')
supers = []
for vmaddr in addresses:
(cls, superPtr) = readClass(machO, vmaddr, protoRefsMap)
supers.append(superPtr)
classes.append(cls, name=cls.name, addr=vmaddr)
for cls, superPtr in zip(classes, supers):
if not cls.isRoot:
cls.superClass = classAt(machO, superPtr, classes)
return classes
import traceback
import os
import sys
sys.path.append('../../')
if __name__ == "__main__":
hier_meta_dict = {}
data_folder = '../data_news/used'
hier_meta_dict['Location'] = data_folder + '/Location.hier'
# hier_meta_dict['Topic'] = data_folder + '/Topic.hier'
data_file = data_folder + "/data_table.csv"
doc_folder = data_folder + "/docs_linked/" # the segphrased version
splitter = "\t@@\t"
dt = DataTable(data_file, hier_meta_dict)
# used to store the constructing cells
queries = []
# if we want to sample at most K document from the cell for experiment
K = 100000
# group_name = 'Topics'
# queries.append({'Topic':'Business'})
# queries.append({'Topic':'Arts'})
# queries.append({'Topic':'Travel'})
# queries.append({'Topic':'World'})
# queries.append({'Topic':'Science'})
# queries.append({'Topic':'Health'})
# queries.append({'Topic':'Technology'})
def imprimir(self):
DataTable(['Lexeme', 'Token', 'Type', 'Value'], self.tabla_simbolos).print()
