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other:python:misc_by_jyp [2021/07/07 13:07] jypeter Added the paths/filenames section |
other:python:misc_by_jyp [2022/02/21 16:31] jypeter [numpy related stuff] Added ufuncs |
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<WRAP center round tip 60%> | <WRAP center round tip 60%> | ||
- | You will find on this page some useful, but unsorted, python tips and tricks that can't fit in a section of the main [[other:python:jyp_steps|JYP's recommended steps for learning python]] page | + | You will find on this page some **useful, but unsorted, python tips and tricks** that can't fit in a section of the main [[other:python:jyp_steps|JYP's recommended steps for learning python]] page |
</WRAP> | </WRAP> | ||
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* 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]] | ||
+ | |||
+ | |||
+ | === Example: getting the full path of the Python used === | ||
+ | |||
+ | Note: the actual python may be different from the default python! | ||
+ | |||
+ | <code>$ 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'</code> | ||
+ | |||
+ | |||
+ | === Example: getting the full path of a script === | ||
+ | |||
+ | <code>>>> 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 | ||
+ | </code> | ||
+ | |||
+ | |||
+ | === Example: getting the size(s) of all the files in a directory === | ||
+ | |||
+ | <code>$ 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~</code> | ||
+ | |||
+ | <code>>>> 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</code> | ||
+ | |||
+ | ==== Generating file names ==== | ||
+ | |||
+ | === Name depending on the current date/time === | ||
+ | |||
+ | <code>>>> import time | ||
+ | >>> plot_version = time.strftime('%Y%m%d_%H%M') | ||
+ | >>> f_name = 'test_%s.nc' % (plot_version,) | ||
+ | >>> f_name | ||
+ | 'test_20210827_1334.nc' | ||
+ | </code> | ||
+ | |||
+ | === Temporary file === | ||
+ | |||
+ | <code>>>> 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)</code> | ||
==== Using command-line arguments ==== | ==== Using command-line arguments ==== | ||
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[[https://docs.python.org/3/library/argparse.html|argparse]] (//parser for command-line options, arguments and sub-commands//) is available since Python version 3.2 | [[https://docs.python.org/3/library/argparse.html|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 [[https://docs.python.org/3/library/stdtypes.html#dict|usual Python dictionary]] also guarantees the order since version **3.6** | ||
+ | |||
+ | Check the [[https://docs.python.org/3/library/collections.html#collections.OrderedDict|OrderedDict class]] (''from collections import OrderedDict'') and the [[https://realpython.com/python-ordereddict/|OrderedDict vs dict in Python: The Right Tool for the Job]] tutorial | ||
+ | |||
+ | ==== Using sets ==== | ||
+ | |||
+ | [[https://docs.python.org/3/tutorial/datastructures.html#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 [[https://docs.python.org/3/library/pprint.html|pprint]] module can be used for //pretty printing// objects (lists, dictionaries, ...). It will wrap long lines in a meaningful way | ||
+ | |||
+ | <code>>>> 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'] | ||
+ | |||
+ | </code> | ||
+ | |||
+ | ==== Sorting ==== | ||
+ | |||
+ | * When dealing with **numerical values**, you should use the [[https://numpy.org/doc/stable/reference/routines.sort.html|numpy sorting, searching, and counting routines]]! | ||
+ | * [[https://docs.python.org/3/howto/sorting.html|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 | ||
+ | * <code>>>> 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']</code> | ||
+ | |||
+ | ==== numpy related stuff ==== | ||
+ | |||
+ | === Finding and counting unique values === | ||
+ | |||
+ | Use ''np.unique'', do **not** try to use histogram related functions! | ||
+ | |||
+ | <code>>>> 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. ])</code> | ||
+ | |||
+ | === 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 [[https://numpy.org/doc/stable/reference/ufuncs.html#math-operations|math operations]], applied on the arrays defined in [[#finding_and_counting_unique_values|Finding and counting unique values]] | ||
+ | |||
+ | <code># 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)</code> | ||
/* | /* |