Python notes - semi-sorted

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speed, memory, debugging, profiling · Python extensions · semi-sorted



functools notes

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.


functools.partial

Setting the process name

It's OS-specific stuff, so there is no short portable way.

The easiest method is to install/use the setproctitle module - it aims to be portable and try its best on various platforms. Example use: 

try:
    import setproctitle
    setproctitle.setproctitle( os.path.basename(sys.argv[0]) )
except ImportError:
    pass

The above often means 'use the filesystem name that was used to run this' - but not always, so a hardcoded string can make sense.

Temporary files

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

Python's standard library module tempfile has a number of choices:


creates a temporary file
returns (open_file_descriptor, abspath)
the open file handle is fairly secure in the sense that others cannot easily get that handle(verify)
but if you only wanted a unique file *name* you wouldn't care about that
you can ask it to add a a prefix and/or suffix (both default to none)
you can ask for it to be placed in a specific directory (defaults to system default)
you are responsible for cleanup
(if you want that to be automatic, look to TemporaryFile)
if the OS implements O_EXCL, that file is accessible only for the creating user (plus admins(verify))
creates a temporary directory
with similar 'only accessible for creating user'
returns the abspath
you are responsible for cleanup


More help, and context managers

  • TemporaryFile[3]
acts much like mkstemp
...but returns file-like object (rather than file handle)
that is destroyed on close / gc
depending on OS/filesystem, the directory entry is either not created at all, or removed immediately after creation
helps security a little (but not hard guarantee)
implies the filesystem will clean up the backing data only after you close (what behind the scenes is still) the file handle


  • NamedTemporaryFile[4]
like TemporaryFile but does specifically have a directory entry
its filename is in .name


  • SpooledTemporaryFile[5]
like TemporaryFile, but makes an attempt to buffer contents in memory a bit longer


  • TemporaryDirectory[6]
acts much like mkstemp
returns a stringy object that can be used as a context manager
removes contents and directory (fundamentally best-effort - there are ways to break that)


These are all usable as context managers, so instead of e.g.: 

tmp = tempfile.NamedTemporaryFile()
print('temp.name:', tmp.name)
   tmp.write(...)
tmp.close()

you can write: 

with tempfile.NamedTemporaryFile() as tmp:
   print(tmp.name)
   tmp.write(...)

Notes:

  • since more is handled for you, there are some more hidden edge cases, and hidden platform-specific details


  • gettempdir() - finds the directory we put temporary files in (basically what is used if you supply dir=None to the above)

Temporary files are created in tempfile.gettempdir() unless you

hand in dir= to every call
set tempfile.tempdir to a default directory you want the module to use (has some details, so not necessarily recomended)

array, deque

A list can be used as a sort of deque, like: 

append(val)     # insert on right side 
pop()           # take from right side
insert(0,val)   # insert on left side
pop(0)          # take from left side

However, list is primarily efficient for stack-like use - list.pop(0) and list.insert(0, val) are O(n) operations,

...while those are O(1) operations on collections.deque (added in 2.4). deque also has appendleft(), extendleft(), and popleft(), and some others that can be convenient and/or more readable.

You may also wish to know about the queue nodule, a multi-producer, multi-consumer, thread-safe queue.

See also:

Some library notes

pytest notes

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.


Pytest wants to find tests itself - this is an inversion of control thing, so no matter how complex tests get, pytest can do everything for us without us having to hook it in specifically.


What files does pytest pick up to be tested?

  • the filenames specified
  • if none specified:
filenames named like test_*.py or *_test.py on the directory tree under the current dir(verify)
You can control this discovery, see e.g. https://docs.pytest.org/en/6.2.x/example/pythoncollection.html


How does pytest decide what code to run as tests?

  • functions prefixed test at module scope (verify)


  • classes prefixed Test, and then functions prefixed test inside them
...but only if that class does not have a constructor (__init__).
These classes are not intended to be classes with state (and it does not actually instantiate the class(verify))
just to collect functions (and potentially pollute a namespace less).
  • classes subclassed from unittest.TestCase (see unittest)


  • by marker, e.g. pytest -m slow picks up things decorated with @pytest.mark.slow
useful to define groups of tests, and run specific subsets


What does pytest actually consider success/failure?

Roughly: each test function will be a

  • success:
if it returns, AND
if all asserts contained (if any) are successful
  • failure: on the first failing assert
  • failure: on the first exception

There are "assert for me" functions, including:

unittest.assertEqual(a, b)
unittest.assertNotEqual(a, b)
unittest.assertTrue(x)
unittest.assertFalse(x)
unittest.assertIs(a, b)
unittest.assertIsNot(a, b)
unittest.assertIsNone(x)
unittest.assertIsNotNone(x)
unittest.assertIn(a, b)
unittest.assertNotIn(a, b)
unittest.assertIsInstance(a, b)
unittest.assertNotIsInstance(a, b)

...but many of those are shorter to write in your own assert


How do I test that something should throw an exception(/warning)?

Has a few alternatives, also varying a little with whether you're testing that it should raise an error or that it doesn't.

The context manager form seems a brief-and-more-flexible way to filter for a specific error type and specific error text: 

with pytest.raises(ValueError, match=r'.*found after.*'):
    # code that whines about some value parsing


You could also e.g. catch the specific error as you normally would, and

If you want to add a more useful message, you can then use pytest.fail to signal that is a failure. 

try:
   calculate_average( data )
except ZeroDivisionError as exc:
   pytest.fail("a division by zero in this code suggests preprocessing removed all cases")

If you were testing that it does 

try:
   calculate_average( data )
   pytest.fail("test should have borked out with a division by zero")
except ZeroDivisionError as exc:
   pass # this is what we want

...but consider the context manager form mentioned above, its signaled intent is often clearer.


For warnings, pytest.warns can be used as a context manager that works much the same as that exception one above

and the context object variant is probably easiest here
note: warnings.warn() by default emits a UserWarning - see https://docs.python.org/3/library/warnings.html#warning-categories
with pytest.warns(UserWarning, match=r'.*deprecated.*'):  #


Showing details

For example, asserts on simpler comparisons will lead pytest to pick up the values that didn't compare as you wanted:

comparing long strings: a context diff is shown
comparing long sequences: first failing index is shown
comparing dicts: different entries are shown

This can be customized, which is sometimes worth it to get more useful output from pytest runs.



On fixtures/mocking

Benchmarking,_performance_testing,_load_testing,_stress_testing,_etc.#Mocking.2C_monkey_patching.2C_fixtures Fixtures create reusable state/helpers for tests, and are great if you use the same data/objects.

In pytest, they are functions that are called before your function.

pytest has a few different things you could call fixtures.


Some given fixtures

Having a test function with a specifically named keyword, you get in some extra behaviour when pytest runs this test. Consider: 

def test_download_to_file( tmp_path ):
    tofile_path = tmp_path / "testfile"  # this syntax works because tmp_path is a pathlib.Path object
    download('https://www.example.com', tofile_path=tofile_path)
    assert os.path.exists( tofile_path )

tmp_path means "create directory for you, hand it in for you to use, and we will clean it up afterwards", which is a great helper you would otherwise have to write yourself (and test in itself).

For some other given fixtures, see e.g.

https://docs.pytest.org/en/6.2.x/fixture.html
https://levelup.gitconnected.com/a-comprehensive-guide-to-pytest-3676f05df5a0


There is also @pytest.fixture decorator, which marks a function as a fixture To steal an example from [7], consider: 

import pytest

@pytest.fixture
def hello():
    return 'hello'

@pytest.fixture
def world():
    return 'world'

def test_hello(hello, world):
    assert "hello world" == hello + ' ' + world

To keep this the first example short, this is only remembering some values for us and handing them into functions. Which is fairly pointless.

In the real world this is probably mostly useful more useful for setup and teardown.

Consider an example from [8] 

@pytest.fixture
def app_without_notes():
    app = NotesApp()
    return app

@pytest.fixture
def app_with_notes():
    app = NotesApp()
    app.add_note("Test note 1")
    app.add_note("Test note 2")
    return app

...which comes from a basic "soooo I spend the first lines of every test just instantiating my application, can't I move that out?"


Doing teardown seems to be done by using a generator (this is a little creative syntax-wise, but lets pytest does most of the work for you) 

@pytest.fixture
def app_with_notes(app):
    app.add_note("Test note 1")
    app.add_note("Test note 2")
    yield app # state handed to the test
    app.notes_list = [] # clean up test's data


See also https://docs.pytest.org/en/7.1.x/how-to/fixtures.html


On coverage

To do coverage checking at all, add 

--cov=dirname/

This mostly gets you a summary.


To see which lines aren't covered, read https://pytest-cov.readthedocs.io/en/latest/reporting.html

If you want it to generate a browsable set of HTML pages, try: 

--cov-report html:coverage-report



https://docs.pytest.org/en/latest/how-to/usage.html


On parallel tests

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

Tests take a while, so it would be nice if you

They should be isolated things, right, right?


It's not a standard feature, presumably so that you don't blame pytest for bad decisions in threading and nondeterminism, whether it is your own or that in a library you use (consider e.g. that selenium isn't thread-safe).

That said there is:

  • pytest-xdist
  • pytest-parallel

typer

Rich

3D

PyGame

Win32 interface

pywin32, previously known as win32all, provides hooks into various parts of windows. Apparently with central module win32api. (see also its help file)

downloadable from sourceforge and with a homepage here.


Some code for this:


GPGPU

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

Reikna

Clyther

PyOpenCL


PyStream


cudamat

scikits.cuda


gnumpy

Theano


Unsorted

zipfile notes

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

The zipfile library,


Creating ZIP files (in-memory)

We can take in-memory data and write to a StringIO objects, so we never need to touch the filesystem.

For context, you can add content with either:

  • ZipFile.write - takes a filename, it will open it itself.
  • ZipFile.writestr takes a filename/ZipInfo and a bytestring
the filesystem isn't touched, which is probably what you want when adding in-memory data to in-memory zip files


# Just an example snippet
import io, zipfile

zip_bio = io.BytesIO()
z = zipfile.ZipFile(zip_bio, "w", zipfile.ZIP_DEFLATED)  # or another compression level

for filename,filedata in (('filename1.txt', b'foo'),
                          ('filename2.txt', b'bar')):
   z.writestr(filename,filedata)
z.close()

zip_bio.getvalue() # zip file contents as bytes

FFTs

There are a few implementations/modules, including:

  • fftpack: used by numpy.fft, scipy.fftpack


Speed-wise: FFTW is faster, numpy is slower, scipy is slower yet (not sure why the np/sp difference when they use the same code)

Think a factor 2 or 3 (potentially), though small cases can be drowned in overhead anyway.

Also, Optimized_number_crunching#On_plans_and_wisdom matters.


It does matter how the coupling works - there for example are more and less direct (overhead-wise) ways of using FFTW.

TODO: figure out threading, MPI stuff.


See also:

Other extensions

Bytecode / resolve-related notes

  • .py - source text
  • .pyc - compiled bytecode
  • .pyo - compiled bytecode, optimized. Written when python is used with -O. The difference with pyc is currently usually negligible.
  • .pyd - a (windows) dll with some added conventions for importing
(and path-and-import wise, it acts exactly like the above, not as a linked library)
note: native code rather than bytecode
  • (.pyi files are unrelated)


All of the above are searched for by python itself.

Python it will generate pyc or pyo

when they are imported them (not when the modules are run directly)
...with some exceptions, e.g. when importing from an egg, or zip file it will not alter those
...which means it can, for speed reasons, be preferable to distribute those with pyc/pyo files in them


There is some talk about changing these, see e.g. PEP 488


PYTHONDONTWRITEBYTECODE (or parameter -B) asks python not to create pyc and such, which is potentially useful...

  • ...around docker, to reduce image size, and let containers be read-only
  • ...when you know the pyc files are unlikely to match (e.g. distribution into a different python version)
or if the same direcory is run by different python versions, which would overwrite the pyc files all the time (would not apply to dist-packages)
  • ...when you edit the code continuously edited, because there are cases where py and pyc race to mismatch
except this is not common, and py3 seems to be much better about this anyway


See also:

pyi files

These are called stub files, and are part of type-checker tooling.

These should only be read for their function signatures. They are syntactically valid python but they should be considered metadata, and you should have no runtime behaviour of them.

IDEs have been known to store what they managed to infer in these files. (and I looked them up because I wanted to know the reason vscode often doesn't show me the actual code when I ask for definition (F12 / Ctrl-click). This seems to have been broken / half-fixed in ways only some people understand for a few years now)



They are metnioned in Type hints PEP (https://peps.python.org/pep-0484/ PEP-484)

Documentation

docstrings

documentation generators

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

https://wiki.python.org/moin/DocumentationTools


docstring formats

Not standardized. Options include:

now more common
see also PEP 287
earlier?


http://daouzli.com/blog/docstring.html

Further notes

!=None versus is not None

tl;dr:

  • 99% of the time it makes no difference
  • ...but in some cases it does. If you want a singular good habit that covers those cases too, it's is


Why?

PEP8 says that comparisons to singletons should be done with is resp. is not, not with equality (==).


Part of the reason is roughly that a class is free to implement the equality operator (==) any way it wants, whereas is is defined by the language and will always do what you think.

Another is that you may not have perfect overview of what coercion does

consider that 1 == True but 1 is not True.


Whenever a class doesn't redefine the equality operator, then == is often just as good.

Even when it does redefine it, it is still not necessarily a problem, due to what people usually use the equality comparison operator for.

Still, in general practice, is is cleaner.


One real-world example is numpy

Where you write 

if ary==None:

What you probably wanted to check whether the variable has an array (or any object) assigned at all.

But numpy redefines the == comparison so that you can e.g. compare an array to an array, and an array to a scalar, evaluated element-wise -- and get an ndarray of bools, rather than a single python bool: 

>>> numpy.array( [(1, 2), (2, 1)]  ) == 2
array([[False,  True],
       [ True, False]])

And ary==None would almost always give an all-False matrix (...because numpy won't even be able to store None unless you use dtype=object, which is often specifically avoided in number crunching if you can).



global

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

(We are skipping the "never use globals" topic here)


tl;dr:


When you want to assign to a name, and you want that name to be a global, you need to say so explicitly

...basically to make the distinction between "I want to create a new local variable with that name" or "I want you to look for a global".

So python insists you make yourself clear.

Until told olderwise, variable writes go to local scope.

So if you want to write to global scope, you have to say so: 

global nameatglobalcope


As this FAQ notes, this can be a little surprising, because you can always read variables from global scope (and other outer scopes)(verify) without asking for it. Or the fact that you can is surprising in itself.

One argument is that yes, maybe reaching into other scopes would be clearer when always explicit, but then you would either be doing that a load, or would be treating variables differently from imported modules and their functions, built-ins, and more - those are all effectively global too.

Instead, it may have more function to have to say 'global' only when you are doing something unusual -- so that it has specific debugging value.

You can argue that even if you only read, you can still use global to indicate you are accessing globals. Not because it does anything, but because it communicates to others you are mixing scopes. (eager linters consider this meh, though)


At a more technical level, global has two possible effects: (verify)

  • if the variable did not exist in global scope, it now declares it there, (and binds it in your more local scope as it always would)
  • if the variable already existed in global scope, it just binds it in your more local scope


nonlocal

If you understand the need for global, you may have also run into cases where that is crude, in the sense that you e.g. might just want a local function to reach the outer function's state, and didn't want to do that via an actual global.

Python 3 introduced nonlocal, which binds to the nearest containing scope that has the name defined.


https://docs.python.org/3/reference/simple_stmts.html#the-nonlocal-statement

https://stackoverflow.com/questions/5218895/python-nested-functions-variable-scoping

dunder methods

dataclass

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