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other:python:misc_by_jyp

Useful python stuff

You will find on this page some useful, but unsorted, python tips and tricks that can't fit in a section of the main JYP's recommended steps for learning python page

Reading/setting environments variables

>>> 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'

Generating (aka raising) an error

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

Stopping a script

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')

Checking if a file/directory is writable by the current user

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

Playing with strings

Filenames, etc...

Splitting strings

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']

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

Example: getting the full path of the Python used

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

$ which python
/usr/bin/python

$ /modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python
>>> import sys, shutil
>>> shutil.which('python')
'/usr/bin/python'
>>> sys.executable
'/modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python'

Example: getting the full path of a script

>>> 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

Example: getting the size(s) of all the files in a directory

$ 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

Generating file names

Name depending on the current date/time

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

Temporary file

>>> 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)

Using command-line arguments

The extremely easy but non-flexible way: sys.argv

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

The C-style way: getopt

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

The deprecated Python way: optparse

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)

The current Python way: argparse

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

Using ordered dictionaries

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

Using sets

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.

Printing a readable version of long lists or dictionaries

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']

Sorting

  • When dealing with numerical values, you should use the numpy sorting, searching, and counting routines!
  • 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']

Using a numpy array to store arbitrary objects

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)

Dealing with a variable number of indices

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.]])

Finding and counting unique values

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. ])

Applying a ufunc over all the elements of an array

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)

Applying a ufunc over specified sections of an array

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. ])





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other/python/misc_by_jyp.txt · Last modified: 2022/07/08 14:00 by jypeter