Useful python stuff

Only when you have already read all the content of this page several times, and you are looking for new ideas

• Ultimate Python Cheat Sheet: Practical Python For Everyday Tasks

>>> os.environ['TMPDIR']
'/data/jypmce/climafcache'
>>> os.environ.get('SCRATCHDIR', '/data/jypmce/some_scratch_stuff')
'/data/jypmce/some_scratch_stuff'
>>> os.environ['temporary_env_var_for_THIS_script'] = 'some value'
>>> os.environ['temporary_env_var_for_THIS_script']
'some value'

This will stop the script, unless it is called in a function, and the code calling the function explicitely catches and deals with errors

• raise RuntimeError('\n\nOMG! An error! :-(\nAborting script...')

• Errors and Exceptions tutorial
• Built-in Exceptions reference

A user can use CTRL-C or kill to stop a script, or CTRL-Z to suspend it temporarily (use fg to resume a suspended script). The code below can be used by the script itself to interrupt its execution, instead of raising an error

sys.exit('Some optional message about why we are stopping')

>>> os.access('/', os.W_OK)
False
>>> os.access('/home/jypmce/.bashrc', os.W_OK)
True

It's easy to split a string with multiple blank delimiters, or a specific delimiter, but it can be harder to deal with sub-strings

>>> str_with_blanks = 'one    two\t3\t\tFOUR'
>>> str_with_blanks.split()
['one', 'two', '3', 'FOUR']

>>> str_with_simple_delimiters = '1,2,3.14,  4'
>>> str_with_simple_delimiters.split(',')
['1', '2', '3.14', '  4']

>>> complex_string='-o 1 --long "A string with accented chars: é è à ç"'
>>> complex_string.split()
['-o', '1', '--long', '"A', 'string', 'with', 'accented', 'chars:', '\xc3\xa9', '\xc3\xa8', '\xc3\xa0', '\xc3\xa7"']

>>> import shlex
>>> shlex.split(complex_string)
['-o', '1', '--long', 'A string with accented chars: \xc3\xa9 \xc3\xa8 \xc3\xa0 \xc3\xa7']

If you are in a hurry, you can just use string functions to work with paths and file names.

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

• os.path: common pathname manipulations
  • Available since… a long time! Use this if you want to avoid backward compatibility problems
  • Some functions are directly in os Miscellaneous operating system interfaces
    e.g. os.remove and os.rmdir
• pathlib: a more recent object-oriented way to deal with filesystem paths
  • Available since Python version 3.4
  • Matching pathlib, and os or os.path functions
• shutil: High-level file operations, e.g copy/move a file or directory tree

Note: the actual python may be different from the default python!

$ which python
/usr/bin/python

$ /home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python
>>> import sys, shutil
>>> shutil.which('python')
'/usr/bin/python'
>>> sys.executable
'/home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python'

>>> import os
>>> os.getcwd()
'/home/jypmce/PMIP4'
>>> os.path.exists('./argv_test.py')
True
>>> os.path.abspath('./argv_test.py')
'/home/jypmce/PMIP4/argv_test.py'
>>> os.path.exists('/home/jypmce/PMIP4/argv_test.py')
True

Note: the following example was generated on a Linux server and uses a / character as a path separator

>>> 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']

$ cd /data/jypmce/TestDir
$ ls -l
total 72
-rw-r--r-- 1 jypmce ipsl 18147 Jun 25  2012 get_TS_cmip5.py
-rw-r--r-- 1 jypmce ipsl 16152 Jun 21  2012 get_TS_cmip5.py~
-rw-r--r-- 1 jypmce ipsl 13954 Jul  3  2012 get_TS_cmip5_regular.py
-rw-r--r-- 1 jypmce ipsl 16539 Jun 22  2012 get_TS_cmip5_regular.py~

>>> os.chdir('/data/jypmce/TestDir')
>>> print(os.getcwd())
/data/jypmce/TestDir
>>> files_list = os.listdir()
>>> files_list
['get_TS_cmip5.py~', 'get_TS_cmip5_regular.py', 'get_TS_cmip5_regular.py~', 'get_TS_cmip5.py']
>>> files_sizes = list(map(os.path.getsize, files_list))
>>> files_sizes
[16152, 13954, 16539, 18147]
>>> sum(files_sizes)
64792

>>> import time
>>> plot_version = time.strftime('%Y%m%d_%H%M')
>>> f_name = 'test_%s.nc' % (plot_version,)
>>> f_name
'test_20210827_1334.nc'

>>> import tempfile, os
>>> f_tmp = tempfile.NamedTemporaryFile(mode='w', suffix='.nc', delete=False)
>>> f_tmp
<tempfile._TemporaryFileWrapper object at 0x2b5614743820>
>>> f_tmp.name
'/tmp/tmpi6uk9hre.nc'
>>> f_tmp.close()
>>> os.remove(f_tmp.name)

The name of a script, the number of arguments (including the name of the script), and the arguments (as strings) can be accessed through the sys.argv strings' list

Simple argv_test.py test script:

#!/usr/bin/env python
import sys
nb_args = len(sys.argv)
print('Number of script arguments (including script name) =', nb_args)
for idx, val in enumerate(sys.argv):
    print(idx, val)

$ python argv_test.py
Number of script arguments (including script name) = 1
0 argv_test.py

$ python argv_test.py tas tas_tes.nc
Number of script arguments (including script name) = 3
0 argv_test.py
1 tas
2 tas_tes.nc

Use getopt (C-style parser for command line options)

optparse (parser for command line options) is deprecated since Python version 3.2! You should now use argparse (check Upgrading optparse code for converting from optparse to argparse)

argparse (parser for command-line options, arguments and sub-commands) is available since Python version 3.2

Dictionary order is guaranteed to be insertion order! Note that the usual Python dictionary also guarantees the order since version 3.6

Check the OrderedDict class (from collections import OrderedDict) and the OrderedDict vs dict in Python: The Right Tool for the Job tutorial

Python sets are groups of unique elements. They can be used to easily find all the unique elements of something and you can easily determine the intersection, union (and other similar operations) of sets.

The pprint module can be used for pretty printing objects (lists, dictionaries, …). It will wrap long lines in a meaningful way

>>> import pprint

>>> test_dic = {'AWI-ESM-1-1-LR_AWI':{'r1i1p1f1': {'grid': 'gn'}}, 'CESM2_NCAR':{'r1i1p1f1': {'grid': 'gn'}}, 'IPSL-CM6A-LR_IPSL':{'r1i1p1f1': {'grid': 'gr'}, 'r1i1p1f2': {'grid': 'gr'}, 'r1i1p1f3': {'grid': 'gr'}, 'r1i1p1f4': {'grid': 'gr'}}}

>>> print(test_dic)
{'AWI-ESM-1-1-LR_AWI': {'r1i1p1f1': {'grid': 'gn'}}, 'CESM2_NCAR': {'r1i1p1f1': {'grid': 'gn'}}, 'IPSL-CM6A-LR_IPSL': {'r1i1p1f1': {'grid': 'gr'}, 'r1i1p1f2': {'grid': 'gr'}, 'r1i1p1f3': {'grid': 'gr'}, 'r1i1p1f4': {'grid': 'gr'}}}

>>> pprint.pprint(test_dic)
{'AWI-ESM-1-1-LR_AWI': {'r1i1p1f1': {'grid': 'gn'}},
 'CESM2_NCAR': {'r1i1p1f1': {'grid': 'gn'}},
 'IPSL-CM6A-LR_IPSL': {'r1i1p1f1': {'grid': 'gr'},
                       'r1i1p1f2': {'grid': 'gr'},
                       'r1i1p1f3': {'grid': 'gr'},
                       'r1i1p1f4': {'grid': 'gr'}}}
                       
>>> dir(test_dic)
['__class__', '__contains__', '__delattr__', [... lots of unreadable stuff removed...] 'setdefault', 'update', 'values']

>>> pprint.pprint(dir(test_dic))
['__class__',
 '__contains__',

[... lots of lines removed in this example ]

 'setdefault',
 'update',
 'values']

The built-in shelve module can be easily used for storing temporary/intermediate data

More options:

• Some non-NetCDF file formats
• Working with NetCDF files

The built-in configparser module can be easily used for reading (and writing!) text configuration files.

Note: a configuration file is also a way to easily store and exchange text data !

There is a good chance you don't actually want/need a global variable. Be sure to use the global statement correctly if you want to avoid side-effects…

• Using (and changing) a global variable inside a script or module
  • Simple module example
    

    _myvar = 10
    
    def set_myvar(new_val):
        # Note: need to explicitly define a global variable (of a module)
        # as 'global' BEFORE changing its value in a function!
        # Otherwise, the value will not be REdefined outside the function
        global _myvar
        _myvar = new_val
    
    def get_myvar():
        return _myvar
    
    def myfunc(nb_repeat = 10):
        print(nb_repeat * _myvar)

• Sharing global variables across modules

• When dealing with numerical values, you should use the numpy sorting, searching, and counting routines!
• Sorting HOW TO
• Example: sorting the keys and the values of a dictionary, and then using the key parameter to sort the keys of a dictionary according to the value associated with the key
  • If we provide a key function, the sort function will sort the elements by the values returned by the function, instead of sorting by the initial values. The function used for generating the key below is very simple and we can use a lambda (i.e in place) function

  • >>> demo_dic = {'a':10, 'b':5, 'c':-1, 'd':0}
    
    >>> sorted(demo_dic.keys())
    ['a', 'b', 'c', 'd']
    
    >>> sorted(demo_dic.values())
    [-1, 0, 5, 10]
    
    >>> sorted(demo_dic.keys(), key=lambda key_name:demo_dic[key_name])
    ['c', 'd', 'b', 'a']

Big, nested, explicit for loops should be avoided at all cost, 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 map function (and similar functions like zip, filter, …) can be used to efficiently apply a function (possibly a simple lambda function) to all the elements of a list

  • >>> my_ints = [1, 2, 3]
    
    >>> map(str, my_ints)
    ['1', '2', '3']
    
    >>> map(lambda ii: str(10*ii + 5), my_ints)
    ['15', '25', '35']

• The itertools module defines many more fancy iterators that can be used for efficient looping
  • Example: replacing nested loops with product

    • >>> 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!,

• The 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

    • >>> my_ints = [1, 2, 3]
      
      >>> [ str(ii) for ii in my_ints ]
      ['1', '2', '3']

The numpy arrays are usually used to store scalars of the same type (see also the Data type objects (dtype)), very often numerical values.

It is also possible to store arbitrary Python objects in an array, rather than using nested lists or dictionaries!

>>> some_array = np.empty((2, 3), dtype=object)
>>> some_array
array([[None, None, None],
       [None, None, None]], dtype=object)
>>> some_array.shape
(2, 3)
>>> print(some_array[-1, -1])
None
>>> some_array[-1, 0] = filled_contour # e.g. save an existing cartopy filled contour object
>>> some_array
array([[None, None, None],
       [<cartopy.mpl.contour.GeoContourSet object at 0x2ab679e8bf10>,
        None, None]], dtype=object)

Official reference

>>> i10 = np.identity(10)
>>> i10
array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
...
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
>>> i10.shape
(10, 10)

>>> i10[3:7, 4:6]
array([[0., 0.],
       [1., 0.],
       [0., 1.],
       [0., 0.]])
       
>>> s0 = slice(3, 7)
>>> s1 = slice(4, 6)
>>> i10[s0, s1]
array([[0., 0.],
       [1., 0.],
       [0., 1.],
       [0., 0.]])
       
>>> my_slices = (s0, s1)
>>> i10[my_slices]
array([[0., 0.],
       [1., 0.],
       [0., 1.],
       [0., 0.]])
       
>>> my_fancy_slices = (s0, Ellipsis)
>>> i10[my_fancy_slices]
array([[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]])
>>> i10[my_fancy_slices].shape
(4, 10)

>>> # WARNING! DANGERRRR! NEVER forget that a VIEW is NOT A COPY
>>> # and that you can change the content of the original array by mistake
>>> my_view = i10[my_slices]
>>> my_view[:, :] = -1
>>> my_view
array([[-1., -1.],
       [-1., -1.],
       [-1., -1.],
       [-1., -1.]])
>>> i10
array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1., -1., -1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0., -1., -1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0., -1., -1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0., -1., -1.,  1.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]])

Use np.unique, do not try to use histogram related functions!

>>> vals = np.random.randint(2, 5, (10,)) * 0.5 # Get 10 discreet float values
>>> vals
array([1. , 2. , 1. , 2. , 2. , 1.5, 1. , 1.5, 2. , 1.5])

>>> np.unique(vals)
array([1. , 1.5, 2. ])
>>> unique_vals, nb_unique = np.unique(vals, return_counts=True)
>>> unique_vals
array([1. , 1.5, 2. ])
>>> nb_unique
array([3, 3, 4])

>>> sorted_vals = np.sort(vals) # Sorted copy, in order to check the result
>>> sorted_vals
array([1. , 1. , 1. , 1.5, 1.5, 1.5, 2. , 2. , 2. , 2. ])

There are all sorts of ufuncs (Universal Functions), and we will just use below add from the math operations, applied on the arrays defined in Finding and counting unique values

# Get the sum of all the elements of 'vals'
>>> np.add.reduce(vals)
15.5
>>> np.add.reduce(sorted_vals)
15.5
>>> vals.sum() # The usual and easy way to do it
15.5

# Compute the sum of the elements of 'nb_unique'
# AND keep (accumulate) the intermediate results
>>> nb_unique
array([3, 3, 4])
>>> np.add.accumulate(nb_unique)
array([ 3,  6, 10])

# The accumulated values can be used as indices to separate the different groups of sorted values!
>>> sorted_vals
array([1. , 1. , 1. , 1.5, 1.5, 1.5, 2. , 2. , 2. , 2. ])
>>> sorted_vals[0:3]
array([1., 1., 1.])
>>> sorted_vals[3:6]
array([1.5, 1.5, 1.5])
>>> sorted_vals[6:10]
array([2., 2., 2., 2.])

# Compute the sum of each equal-value group
>>> sorted_vals[0:3].sum(), sorted_vals[3:6].sum(), sorted_vals[6:10].sum()
(3.0, 4.5, 8.0)

The reduceat function can be used to avoid explicit python loops, and improve the speed (but not the readability…) of a script. The example below improves what has been shown above

# Define a list with the boundaries of the intervals we want to apply the 'add' function to
# We need to add the beginning index (0), AND remove the last index
# (reduceat will automatically go to the end of the input array
>>> nb_unique
array([3, 3, 4])
>>> slices_indices = [0] + list(np.add.accumulate(nb_unique))
>>> slices_indices.pop() # Remove last element
10
>>> slices_indices
[0, 3, 6]

# Compute the sums over the selected intervals with just one call
>>> np.add.reduceat(np.sort(vals), slices_indices)
array([3. , 4.5, 8. ])

Have a look at 100 numpy exercises

If you have problems setting the limits of a time axis, choosing the ticks' locations, or specifying the style of the labels, you should check the:

• Ticks examples' gallery
• Date tick labels example

A few notes for a future section or page about about data representation (bits and bytes) on disk and in memory, vs data format

Add parts (pages 28 to 37) of this old tutorial to this section

• Never forget that all the bits and pieces of information we use are coded in base 2 (0s and 1s …), 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 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

  • >>> 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

• More technical topics
  • Bit numbering: the art of ordering bits, everything about MSB (Most Significant Byte) and LSB (Least Significant Byte)
  • Endianness: the art of ordering bytes

• Binary data representation of some numbers (only some common types are listed here):
  • Languages and packages references used below:
    • Python: NumPy Sized aliases
    • NetCDF: Data Types, Fortran related Data Types, CDL Data Types
    • Fortran: Intel Fortran Compiler Intrinsic Data Types
  • Integers
    • Range:
      • 4-byte signed integers: −2,147,483,648 to 2,147,483,647
        • Python: numpy.int32
        • NetCDF: int, NC_INT or NC_LONG, NF90_INT
        • Fortran: INTEGER*4
      • 8-byte signed integers: −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807
        • Python: numpy.int64
        • NetCDF: int64, NC_INT64
        • Fortran: INTEGER*8
    • Tech note: signed integers use two's complement for coding negative integers
  • Floating point numbers (IEEE 754 standard aka IEEE Standard for Binary Floating-Point for Arithmetic)
    • Range:
      • 4-byte float: ~8 significant digits * 10E±38
        • Python: numpy.float32
        • NetCDF: float, NC-FLOAT, NF90_FLOAT
        • Fortran:REAL*4
        • See also Single-precision floating-point format
      • 8-byte float: ~15 significant digits * 10E±308
        • Python: numpy.float64
        • NetCDF: double, NC_DOUBLE, NF90_DOUBLE
        • Fortran: REAL*8
    • Special values:
      • NaN: Not a Number
        • Python: numpy.nan
      • Infinity
        • Python: -numpy.inf and numpy.inf
      • Note: it is cleaner to use masks (and Numpy masked arrays) rather than NaNs, when you have to deal with missing values !
    • The RISKS of working with (the wrong) floats:
      • Round-off error
      • Catastrophic cancellation
        • What Every Computer Scientist Should Know About Floating-Point Arithmetic
  • A rather technical example: we play with a numpy 4-byte integer scalar

    • >>> 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'

  • 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

    • >>> 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'

• Manipulating binary data with bytes, bytearray, memoryview

• Array addressing
  • Calculation of address of element of 1-D, 2-D, and 3-D using row-major and column-major order
    • In other words: using indices to go from 1-D to n-Dimnensions data
  • The array structure
  • 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?)
  • du, df, cat /proc/meminfo, top

• understanding and reverse-engineering binary format
  • od, strings

• binary vs text format: ascii, utf, raw
  • text related functions in python: str, int, float, ord, …
    • lists conversion with map and join

• Misc : md5sum

• Encoding, ASCII, unicode, UTF-8, …

• Getting the binary representation of a string

  • >>> 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'

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