other:python:misc_by_jyp
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other:python:misc_by_jyp [2023/05/03 15:56] jypeter [Numerical values] |
other:python:misc_by_jyp [2024/04/17 09:25] jypeter [Useful python stuff] Added the Extra tutorials section |
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| Line 5: | Line 5: | ||
| </WRAP> | </WRAP> | ||
| + | ===== Extra tutorials ===== | ||
| + | Only **when you have already read all the content of this page several times**, and you are looking for new ideas | ||
| + | |||
| + | * [[https://medium.com/@yaduvanshineelam09/ultimate-python-cheat-sheet-practical-python-for-everyday-tasks-8a33abc0892f|Ultimate Python Cheat Sheet: Practical Python For Everyday Tasks]] | ||
| ===== Reading/setting environments variables ===== | ===== Reading/setting environments variables ===== | ||
| Line 65: | Line 69: | ||
| ==== Working with paths and filenames ==== | ==== Working with paths and filenames ==== | ||
| - | If you are in a hurry, you can just use string functions to work with path and file names. But you will need some specific functions to check if a file exists, and similar operations. All these are available in 2 libraries that have similar functions. Both of these libraries can deal with Unix-type paths on Linux computers, and Windows-type paths on Windows computers | + | If you are in a hurry, you can just use string functions to work with paths and file names. |
| - | * [[https://docs.python.org/3/library/os.path.html|os.path]] //Common pathname manipulations// | + | |
| + | You will need some specific objects and functions to check if a file exists, and similar operations. Check the libraries listed below, that can automatically deal with Unix-type paths on Linux and MacOS computers, and Windows-type paths on Windows computers | ||
| + | |||
| + | * [[https://docs.python.org/3/library/os.path.html|os.path]]: //common pathname manipulations// | ||
| * Available since... a long time! Use this if you want to avoid backward compatibility problems | * Available since... a long time! Use this if you want to avoid backward compatibility problems | ||
| * Some functions are directly in [[https://docs.python.org/3/library/os.html|os]] //Miscellaneous operating system interfaces//\\ e.g. [[https://docs.python.org/3/library/os.html#os.remove|os.remove]] and [[https://docs.python.org/3/library/os.html#os.rmdir|os.rmdir]] | * Some functions are directly in [[https://docs.python.org/3/library/os.html|os]] //Miscellaneous operating system interfaces//\\ e.g. [[https://docs.python.org/3/library/os.html#os.remove|os.remove]] and [[https://docs.python.org/3/library/os.html#os.rmdir|os.rmdir]] | ||
| - | * [[https://docs.python.org/3/library/pathlib.html|pathlib]] //Object-oriented filesystem paths// | + | * [[https://docs.python.org/3/library/pathlib.html|pathlib]]: a **more recent** //object-oriented// way to deal with //filesystem paths// |
| * Available since Python version 3.4 | * Available since Python version 3.4 | ||
| * [[https://docs.python.org/3/library/pathlib.html#correspondence-to-tools-in-the-os-module|Matching pathlib, and os or os.path functions]] | * [[https://docs.python.org/3/library/pathlib.html#correspondence-to-tools-in-the-os-module|Matching pathlib, and os or os.path functions]] | ||
| - | * [[https://docs.python.org/3/library/shutil.html|High-level file operations]] | + | * [[https://docs.python.org/3/library/shutil.html|shutil]]: High-level file operations, e.g copy/move a file or directory tree |
| - | === Example: getting the full path of the Python used === | + | === Example: getting the full path of the Python executable used === |
| Note: the actual python may be different from the default python! | Note: the actual python may be different from the default python! | ||
| Line 83: | Line 90: | ||
| /usr/bin/python | /usr/bin/python | ||
| - | $ /modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python | + | $ /home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python |
| >>> import sys, shutil | >>> import sys, shutil | ||
| >>> shutil.which('python') | >>> shutil.which('python') | ||
| '/usr/bin/python' | '/usr/bin/python' | ||
| >>> sys.executable | >>> sys.executable | ||
| - | '/modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python'</code> | + | '/home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python'</code> |
| Line 104: | Line 111: | ||
| </code> | </code> | ||
| + | |||
| + | === Example: system independent paths with pathlib === | ||
| + | |||
| + | Note: the following example was generated on a Linux server and uses a <wrap em>/</wrap> character as a path separator | ||
| + | |||
| + | <code>>>> my_home = Path.home() | ||
| + | >>> my_home | ||
| + | PosixPath('/home/users/my_login') | ||
| + | >>> my_conf = my_home / '.config' / 'evince' | ||
| + | >>> my_conf | ||
| + | PosixPath('/home/users/my_login/.config/evince') | ||
| + | >>> my_conf.is_dir() | ||
| + | True | ||
| + | >>> my_conf.is_file() | ||
| + | False | ||
| + | >>> list(my_conf.glob('*')) | ||
| + | [PosixPath('/home/users/my_login/.config/evince/evince_toolbar.xml'), PosixPath(' /home/users/my_login/.config/evince/accels')] | ||
| + | >>> [ ff.name for ff in my_conf.glob('*') ] | ||
| + | ['evince_toolbar.xml', 'accels'] | ||
| + | </code> | ||
| === Example: getting the size(s) of all the files in a directory === | === Example: getting the size(s) of all the files in a directory === | ||
| Line 290: | Line 317: | ||
| ['c', 'd', 'b', 'a']</code> | ['c', 'd', 'b', 'a']</code> | ||
| + | |||
| + | ===== Efficient looping with numpy, map, itertools and list comprehension ===== | ||
| + | |||
| + | <wrap hi>Big, nested, explicit ''for'' loops should be avoided at all cost</wrap>, in order to reduce a script execution time! | ||
| + | |||
| + | * **''numpy'' arrays** should be used when dealing with //numerical data// | ||
| + | * **Masked arrays** can be used to deal with //special cases// and remove tests from loops | ||
| + | |||
| + | * The built-in [[https://docs.python.org/3/library/functions.html?highlight=map#map|map]] function (and similar functions like [[https://docs.python.org/3/library/functions.html?highlight=zip#zip|zip]], [[https://docs.python.org/3/library/functions.html?highlight=filter#filter|filter]], ...) can be used to efficiently apply a function (possibly a //simple// [[https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions|lambda]] function) to all the elements of a list | ||
| + | * <code>>>> my_ints = [1, 2, 3] | ||
| + | |||
| + | >>> map(str, my_ints) | ||
| + | ['1', '2', '3'] | ||
| + | |||
| + | >>> map(lambda ii: str(10*ii + 5), my_ints) | ||
| + | ['15', '25', '35']</code> | ||
| + | |||
| + | * The [[https://docs.python.org/3/library/itertools.html|itertools]] module defines many more fancy iterators that can be used for efficient looping | ||
| + | * Example: replacing nested loops with [[https://docs.python.org/3/library/itertools.html#itertools.product|product]] | ||
| + | * <code>>>> it.product('AB', '01') | ||
| + | <itertools.product object at 0x2b35a7b5f100> | ||
| + | |||
| + | >>> list(it.product('AB', '01')) | ||
| + | [('A', '0'), ('A', '1'), ('B', '0'), ('B', '1')] | ||
| + | |||
| + | >>> for c1, c2 in it.product('AB', '01'): | ||
| + | ... print(c1 + c2) | ||
| + | ... | ||
| + | A0 | ||
| + | A1 | ||
| + | B0 | ||
| + | B1 | ||
| + | |||
| + | >>> for c1, c2 in it.product(['A', 'B'], ['0', '1']): | ||
| + | ... print(c1 + c2) | ||
| + | ... | ||
| + | A0 | ||
| + | A1 | ||
| + | B0 | ||
| + | B1 | ||
| + | |||
| + | >>> for c1, c2, c3 in it.product('AB', '01', '$!'): | ||
| + | ... print(c1 + c2 + c3, end=', ') | ||
| + | ... | ||
| + | A0$, A0!, A1$, A1!, B0$, B0!, B1$, B1!,</code> | ||
| + | |||
| + | * The [[https://docs.python.org/3/tutorial/datastructures.html?highlight=comprehension#list-comprehensions|list comprehension]] (aka //implicit loops//) can also be used to generate lists from lists | ||
| + | * Example: converting a list of integers to a list of strings\\ Note: in that case, you should rather use the ''map'' function detailed above | ||
| + | * <code>>>> my_ints = [1, 2, 3] | ||
| + | |||
| + | >>> [ str(ii) for ii in my_ints ] | ||
| + | ['1', '2', '3']</code> | ||
| ===== numpy related stuff ===== | ===== numpy related stuff ===== | ||
| Line 452: | Line 531: | ||
| array([3. , 4.5, 8. ])</code> | array([3. , 4.5, 8. ])</code> | ||
| + | ==== Exercise your brain with numpy ==== | ||
| + | |||
| + | Have a look at [[https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb|100 numpy exercises]] | ||
| ===== matplotlib related stuff ===== | ===== matplotlib related stuff ===== | ||
| Line 467: | Line 549: | ||
| FIXME Add parts (pages 28 to 37) of this [[https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:jyp_steps#part_2|old tutorial]] to this section | FIXME Add parts (pages 28 to 37) of this [[https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:jyp_steps#part_2|old tutorial]] to this section | ||
| + | |||
| + | ==== Base notions ==== | ||
| + | |||
| + | * **Never forget** that all the bits and pieces of information we use are coded in [[https://en.wikipedia.org/wiki/Binary_number#Counting_in_binary|base 2]] (''0''s and ''1''s ...), grouped in bytes! | ||
| + | * Some things can be stored exactly (integers, characters, ...) | ||
| + | * In other cases (**//real// numbers** that we work with all the time, compressed images/videos/music) we only store **//good enough approximation//** | ||
| + | |||
| + | * 1 byte <=> 8 bits | ||
| + | * ''REAL*4'' <=> 4 bytes <=> 32 bits | ||
| + | * For easier written/displayed representation, 1 byte is usually split into 2 groups of 4 bits, and displayed using base 16 and [[https://en.wikipedia.org/wiki/Hexadecimal|hexadecimal representation]] (characters ''0'', ''1'', ..., ''A'', ''B'', ..., ''F'') | ||
| + | * ''0000'' <=> ''0'',\\ ''0010'' <=> ''1'', ...,\\ ''1111'' <=> ''F'' | ||
| + | * ''1101'' <=> ''D'' in hexadecimal <=> ''13'' in decimal (''**1** * 8 + **1** * 4 + **0** * 2 + **1** * 1'') | ||
| + | * ''11111101'' in //base 2// <=> ''1111 1101'' <=> ''FD'' in //hexadecimal// <=> ''253'' (''15 * 16 + 13'') in //decimal// | ||
| + | |||
| + | * Base conversion with Python | ||
| + | * <code>>>> hex(13) # Decimal to Hexadecimal conversion | ||
| + | '0xd' | ||
| + | >>> hex(253) | ||
| + | '0xfd' | ||
| + | >>> hex(256) | ||
| + | '0x100' | ||
| + | >>> int('0x100', 16) # Hexadecimal to Decimal conversion | ||
| + | 256 | ||
| + | >>> int('1111', 2) # Binary to Decimal conversion | ||
| + | 15 | ||
| + | >>> int('11111101', 2) # '11111101' <=> '1111 1101' <=> 'FD' <=> 15 * 16 + 13 = 253 | ||
| + | 253 | ||
| + | >>> 013 # DANGER! Python considers an integer to be in OCTAL base if it starts with a 0 | ||
| + | 11 | ||
| + | >>> int('13', 8) # 1*8 + 3 | ||
| + | 11</code> | ||
| + | |||
| + | * More technical topics | ||
| + | * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]]: the art of ordering bits, everything about MSB (Most Significant Byte) and LSB (Least Significant Byte) | ||
| + | * [[https://en.wikipedia.org/wiki/Endianness|Endianness]]: the art of ordering bytes | ||
| ==== Numerical values ==== | ==== Numerical values ==== | ||
| - | * Binary data representation of some numbers (not everythin is listed here): | + | * Binary data representation of some numbers (only some common types are listed here): |
| + | * Languages and packages **references** used below: | ||
| + | * Python: [[https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases|NumPy Sized aliases]] | ||
| + | * NetCDF: [[https://docs.unidata.ucar.edu/nug/current/md_types.html|Data Types]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|Fortran related Data Types]], [[https://docs.unidata.ucar.edu/nug/current/_c_d_l.html#cdl_data_types|CDL Data Types]] | ||
| + | * Fortran: Intel Fortran Compiler [[https://www.intel.com/content/www/us/en/docs/fortran-compiler/developer-guide-reference/2023-1/intrinsic-data-types.html|Intrinsic Data Types]] | ||
| * [[https://en.wikipedia.org/wiki/Integer_(computer_science)|Integers]] | * [[https://en.wikipedia.org/wiki/Integer_(computer_science)|Integers]] | ||
| * Range: | * Range: | ||
| - | * 4-byte integers: −2,147,483,648 to 2,147,483,647 | + | * 4-byte //signed// integers: ''−2,147,483,648'' to ''2,147,483,647'' |
| * Python: ''numpy.int32'' | * Python: ''numpy.int32'' | ||
| - | * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: ''int'', ''NC_INT64'', ''NF90_INT'' | + | * NetCDF: ''int'', ''NC_INT'' or ''NC_LONG'', ''NF90_INT'' |
| - | * Fortran: | + | * Fortran: ''INTEGER*4'' |
| - | * 8-byte integers: −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 | + | * 8-byte //signed// integers: ''−9,223,372,036,854,775,808'' to ''9,223,372,036,854,775,807'' |
| * Python: ''numpy.int64'' | * Python: ''numpy.int64'' | ||
| - | * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]]: ''int64'', ''NC_INT64'' | + | * NetCDF: ''int64'', ''NC_INT64'' |
| - | * Fortran: | + | * Fortran: ''INTEGER*8'' |
| * Tech note: signed integers use [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for coding 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: ~8 significant digits * 10E±38 | + | * 4-byte float: ''~8 significant digits * 10E±38'' |
| * Python: ''numpy.float32'' | * Python: ''numpy.float32'' | ||
| - | * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: | + | * NetCDF: ''float'', ''NC-FLOAT'', ''NF90_FLOAT'' |
| - | * Fortran: | + | * Fortran:''REAL*4'' |
| * See also [[https://en.wikipedia.org/wiki/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: ~15 significant digits * 10E±308 | + | * 8-byte float: ''~15 significant digits * 10E±308'' |
| * Python: ''numpy.float64'' | * Python: ''numpy.float64'' | ||
| - | * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: | + | * NetCDF: ''double'', ''NC_DOUBLE'', ''NF90_DOUBLE'' |
| - | * Fortran: | + | * Fortran: ''REAL*8'' |
| - | * Special values: | + | * **Special values**: |
| - | * [[https://en.wikipedia.org/wiki/NaN|NaN]] (''numpy.nan''): //Not a Number// | + | * [[https://en.wikipedia.org/wiki/NaN|NaN]]: //Not a Number// |
| - | * Infinity (''-numpy.inf'' and ''numpy.inf'') | + | * Python: ''numpy.nan'' |
| - | * 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 ! | + | * Infinity |
| - | * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]] | + | * Python: ''-numpy.inf'' and ''numpy.inf'' |
| - | * [[https://en.wikipedia.org/wiki/Endianness|Endianness]] | + | * Note: it is cleaner to use masks (and [[https://numpy.org/doc/stable/reference/maskedarray.generic.html|Numpy masked arrays]]) rather than ''NaN''s, when you have to deal with missing values ! |
| + | * <wrap hi>The RISKS of working with (the wrong) floats</wrap>: | ||
| + | * [[https://en.wikipedia.org/wiki/Round-off_error|Round-off error]] | ||
| + | * [[https://en.wikipedia.org/wiki/Catastrophic_cancellation|Catastrophic cancellation]] | ||
| + | * [[https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html|What Every Computer Scientist Should Know About Floating-Point Arithmetic]] | ||
| * A rather technical example: we //play// with a numpy 4-byte integer scalar | * A rather technical example: we //play// with a numpy 4-byte integer scalar | ||
| * <code>>>> one_int32 = np.int32(1) | * <code>>>> one_int32 = np.int32(1) | ||
other/python/misc_by_jyp.txt · Last modified: 2024/04/19 12:02 by jypeter
