Writing and Reading Binary Data in Java

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Writing and Reading Binary Data
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Binary formats, even without compression, usually take up the least amount of disk space and are usually the fastest to save and load Easiest of all is to use pickles, although handling binary data manually should produce the smallest le sizes
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Pickles with Optional Compression
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Pickles offer the simplest approach to saving and loading data from Python programs, but as we noted in the preceding chapter, pickles have no security mechanisms (no encryption, no digital signature), so loading a pickle that comes from an untrusted source could be dangerous The security concern arises because pickles can import arbitrary modules and call arbitrary functions, so we could be given a pickle where the data has been manipulated in such a way as to, for example, make the interpreter execute something harmful when the pickle is loaded Nonetheless, pickles are often ideal for handling ad hoc data, especially for programs for personal use It is usually easier when creating le formats to write the saving code before the loading code, so we will begin by seeing how to save the incidents into a pickle
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def export_pickle(self, filename, compress=False): fh = None try: if compress: fh = gzipopen(filename, "wb") else: fh = open(filename, "wb")
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Writing and Reading Binary Data The Bytes and Bytearray Data Types
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Python provides two data types for handling raw bytes: bytes which is immutable, and bytearray which is mutable Both types hold a sequence of zero or more 8-bit unsigned integers (bytes) with each byte in the range 0 255 Both types are very similar to strings and provide many of the same methods, including support for slicing In addition, bytearrays also provide some mutating list-like methods All their methods are listed in Tables 71 ( 299) and 72 ( 300) Whereas a slice of a bytes or bytearray returns an object of the same type, accessing a single byte using the item access operator ([]) returns an int the value of the speci ed byte For example:
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word = b"Animal" x = b"A" word[0] == x # returns: False word[:1] == x # returns: True word[0] == x[0] # returns: True
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str translate()
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77
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# word[0] == 65; x == b"A" # word[:1] == b"A"; x == b"A" # word[0] == 65; x[0] == 65
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Here are some other bytes and bytearray examples:
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data = b"5 Hills \x35\x20\x48\x69\x6C\x6C\x73" dataupper() # returns: b'5 HILLS 5 HILLS' datareplace(b"ill", b"at") # returns: b'5 Hats 5 Hats' bytesfromhex("35 20 48 69 6C 6C 73") # returns: b'5 Hills' bytesfromhex("352048696C6C73") # returns: b'5 Hills' data = bytearray(data) # data is now a bytearray datapop(10) # returns: 72 (ord("H")) datainsert(10, ord("B")) # data == b'5 Hills 5 Bills'
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Methods that make sense only for strings, such as bytesupper(), assume that the bytes are encoded using ASCII The bytesfromhex() class method ignores whitespace and interprets each two-digit substring as a hexadecimal number, so "35" is taken to be a byte of value 0x35, and so on
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pickledump(self, fh, pickleHIGHEST_PROTOCOL) return True except (EnvironmentError, picklePicklingError) as err: print("{0}: export error: {1}"format( ospathbasename(sysargv[0]), err)) return False finally: if fh is not None: fhclose()
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7 File Handling
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If compression has been requested, we use the gzip module s gzipopen() function to open the le; otherwise, we use the built-in open() function We must use write binary mode ("wb") when pickling data in binary format In Python 30 and 31, pickleHIGHEST_PROTOCOL is protocol 3, a compact binary pickle format This is the best protocol to use for data shared among Python 3 programs For error handling we have chosen to report errors to the user as soon as they occur, and to return a Boolean to the caller indicating success or failure And we have used a finally block to ensure that the le is closed at the end, whether there was an error or not In 8 we will use a more compact idiom to ensure that les are closed that avoids the need for a finally block This code is very similar to what we saw in the preceding chapter, but there is one subtle point to note The pickled data is self, a dict But the dictionary s values are Incident objects, that is, objects of a custom class The pickle module is smart enough to be able to save objects of most custom classes without us needing to intervene In general, Booleans, numbers, and strings can be pickled, as can instances of classes including custom classes, providing their private __dict__ is picklable In addition, any built-in collection types (tuples, lists, sets, dictionaries) can be pickled, providing they contain only picklable objects (including collection types, so recursive structures are supported) It is also possible to pickle other kinds of objects or instances of custom classes that can t normally be pickled (eg, because they have a nonpicklable attribute), either by giving some help to the pickle module or by implementing custom pickle and unpickle functions All the relevant details are provided in the pickle module s online documentation To read back the pickled data we need to distinguish between a compressed and an uncompressed pickle Any le that is compressed using gzip compression begins with a particular magic number A magic number is a sequence of one or more bytes at the beginning of a le that is used to indicate the le s type For gzip les the magic number is the two bytes 0x1F 0x8B, which we store in a bytes variable:
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GZIP_MAGIC = b"\x1F\x8B"
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__dict__
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