other:python:misc_by_jyp
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other:python:misc_by_jyp [2023/04/27 09:47] jypeter [Data representation] Improved |
other:python:misc_by_jyp [2023/04/28 13:55] jypeter [Data representation] Improved, started a Strings section |
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| ===== Data representation ===== | ===== Data representation ===== | ||
| - | A few notes for a future section or page about about data representation (bits and bytes) on disk and in memory, vs data format | + | A few notes for a future section or page about about //data representation// (bits and bytes) on disk and in memory, vs //data format// |
| + | |||
| + | |||
| + | ==== Numerical values ==== | ||
| * Binary data representation of some numbers: | * Binary data representation of some numbers: | ||
| Line 41: | Line 44: | ||
| * 4-byte integers (''numpy.int32''): −2,147,483,648 to 2,147,483,647 | * 4-byte integers (''numpy.int32''): −2,147,483,648 to 2,147,483,647 | ||
| * 8-byte integers (''numpy.int64''): −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 | * 8-byte integers (''numpy.int64''): −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 | ||
| - | * Note: using [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for negative integers | + | * Tech note: signed integers use [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for coding negative integers |
| * [[https://en.wikipedia.org/wiki/IEEE_754|Floating point numbers]] (//IEEE 754// standard aka //IEEE Standard for Binary Floating-Point for Arithmetic//) | * [[https://en.wikipedia.org/wiki/IEEE_754|Floating point numbers]] (//IEEE 754// standard aka //IEEE Standard for Binary Floating-Point for Arithmetic//) | ||
| * Range: | * Range: | ||
| * 4-byte float (''numpy.float32''): ~8 significant digits * 10E±38 | * 4-byte float (''numpy.float32''): ~8 significant digits * 10E±38 | ||
| - | * See also [[https://en.wikipedia.org/wiki/Single-precision_floating-point_format|Single-precision floating-point format|Single-precision floating-point format]] | + | * See also [[https://en.wikipedia.org/wiki/Single-precision_floating-point_format|Single-precision floating-point format]] |
| * 8-byte float (''numpy.float64''): ~15 significant digits * 10E±308 | * 8-byte float (''numpy.float64''): ~15 significant digits * 10E±308 | ||
| * Special values: | * Special values: | ||
| * [[https://en.wikipedia.org/wiki/NaN|NaN]] (''numpy.nan''): //Not a Number// | * [[https://en.wikipedia.org/wiki/NaN|NaN]] (''numpy.nan''): //Not a Number// | ||
| * Infinity (''-numpy.inf'' and ''numpy.inf'') | * Infinity (''-numpy.inf'' and ''numpy.inf'') | ||
| + | * Note: it is cleaner to use masks (and [[https://numpy.org/doc/stable/reference/maskedarray.generic.html|Numpy masked arrays]]) than NaNs, when you have to deal with missing values ! | ||
| * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]] | * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]] | ||
| * [[https://en.wikipedia.org/wiki/Endianness|Endianness]] | * [[https://en.wikipedia.org/wiki/Endianness|Endianness]] | ||
| + | * A rather technical example: we //play// with a numpy 4-byte integer scalar | ||
| + | * <code>>>> one_int32 = np.int32(1) | ||
| + | >>> one_int32 | ||
| + | 1 | ||
| + | >>> type(one_int32) | ||
| + | <class 'numpy.int32'> | ||
| + | >>> one_int32.dtype | ||
| + | dtype('int32') | ||
| + | >>> one_int32.shape # A numpy SCALAR, is an ARRAY WITH NO SHAPE ! | ||
| + | () | ||
| + | >>> one_int32[0] | ||
| + | Traceback (most recent call last): | ||
| + | File "<stdin>", line 1, in <module> | ||
| + | IndexError: invalid index to scalar variable. | ||
| + | >>> one_int32[()] # Note how to access the single element, when there is NO SHAPE | ||
| + | 1 | ||
| + | >>> one_int32.ndim # NO SHAPE means no dimensions, but there is ONE element | ||
| + | 0 | ||
| + | >>> one_int32.size | ||
| + | 1 | ||
| + | >>> one_int32.nbytes # The element requires 4 bytes of storage | ||
| + | 4 | ||
| + | >>> hex(one_int32) # We can print the hexadecimal representation for INTEGERS scalars and arrays | ||
| + | '0x1' | ||
| + | >>> hex(one_int32 * 15) | ||
| + | '0xf' | ||
| + | >>> hex(one_int32 * 16) | ||
| + | '0x10' | ||
| + | |||
| + | # 'Serialize' the data (i.e. change the data to a series of bytes) | ||
| + | # Note: the serialized data seems to be printed in the reverse order of 'hex(one_int32)' | ||
| + | >>> one_int32_serialized = one_int32.tobytes() | ||
| + | >>> type(one_int32_serialized) | ||
| + | <class 'bytes'> | ||
| + | >>> len(one_int32_serialized) | ||
| + | 4 | ||
| + | >>> one_int32_serialized | ||
| + | b'\x01\x00\x00\x00' | ||
| + | >>> one_int32_serialized.hex(' ') # Another way to print the hexadecimal values | ||
| + | '01 00 00 00' | ||
| + | |||
| + | # Use the following in the unlikely case where you need to change the endianness (bytes ordering) | ||
| + | >>> one_int32_reversed_endian = one_int32.byteswap() | ||
| + | >>> one_int32_reversed_endian # Same bytes in a different order represent a different number (of course) | ||
| + | 16777216 | ||
| + | >>> hex(one_int32_reversed_endian) # Compare to the output of hex(one_int32) above | ||
| + | '0x1000000' | ||
| + | >>> one_int32_reversed_endian.tobytes() | ||
| + | b'\x00\x00\x00\x01'</code> | ||
| + | * Another technical example: we use an array of 2 integers\\ When using ''byteswap()'', notice how bytes are swapped by groups of 4 bytes, because int32 use 4 bytes | ||
| + | * <code>>>> array_example = np.asarray((3, 17), dtype=np.int32) | ||
| + | >>> array_example | ||
| + | array([ 3, 17], dtype=int32) | ||
| + | >>> array_example.shape, array_example.ndim, array_example.size, array_example.nbytes | ||
| + | ((2,), 1, 2, 8) | ||
| + | >>> array_example.tobytes().hex(' ', 4) | ||
| + | '03000000 11000000' | ||
| + | >>> array_example.byteswap().tobytes().hex(' ', 4) | ||
| + | '00000003 00000011' | ||
| + | </code> | ||
| * Array addressing | * Array addressing | ||
| + | * python/C vs Fortran... | ||
| * disk and ram usage: how to check the usage (available ram and disk), best practice on multi-user systems (how much allowed?) | * disk and ram usage: how to check the usage (available ram and disk), best practice on multi-user systems (how much allowed?) | ||
| Line 66: | Line 131: | ||
| * Misc : ''md5sum'' | * Misc : ''md5sum'' | ||
| + | |||
| + | ==== Strings ==== | ||
| + | |||
| + | * Encoding, [[https://en.wikipedia.org/wiki/ASCII|ASCII]], [[https://en.wikipedia.org/wiki/Unicode|unicode]], [[https://en.wikipedia.org/wiki/UTF-8|UTF-8]], ... | ||
| + | |||
| + | * Getting the binary representation of a string | ||
| + | * <code>>>> test_string = 'A B 0 1 à µ' | ||
| + | >>> type(test_string) | ||
| + | <class 'str'> | ||
| + | >>> len(test_string) | ||
| + | 11 | ||
| + | >>> test_string_bin = test_string.encode('utf-8') | ||
| + | >>> test_string_bin | ||
| + | b'A B 0 1 \xc3\xa0 \xc2\xb5' | ||
| + | >>> type(test_string_bin) | ||
| + | <class 'bytes'> | ||
| + | >>> len(test_string_bin) | ||
| + | 13 | ||
| + | >>> test_string_bin.hex('-') | ||
| + | '41-20-42-20-30-20-31-20-c3-a0-20-c2-b5' | ||
| + | </code> | ||
| + | |||
| ===== Checking if a file/directory is writable by the current user ===== | ===== Checking if a file/directory is writable by the current user ===== | ||
other/python/misc_by_jyp.txt · Last modified: 2024/04/19 12:02 by jypeter
