How about here! The corresponding documentation reference is configuration-dictionary-schema.

LOGGING_CONFIG = { 
    'version': 1,
    'disable_existing_loggers': True,
    'formatters': { 
        'standard': { 
            'format': '%(asctime)s [%(levelname)s] %(name)s: %(message)s'
        },
    },
    'handlers': { 
        'default': { 
            'level': 'INFO',
            'formatter': 'standard',
            'class': 'logging.StreamHandler',
            'stream': 'ext://sys.stdout',  # Default is stderr
        },
    },
    'loggers': { 
        '': {  # root logger
            'handlers': ['default'],
            'level': 'WARNING',
            'propagate': False
        },
        'my.packg': { 
            'handlers': ['default'],
            'level': 'INFO',
            'propagate': False
        },
        '__main__': {  # if __name__ == '__main__'
            'handlers': ['default'],
            'level': 'DEBUG',
            'propagate': False
        },
    } 
}

Usage:

import logging.config

# Run once at startup:
logging.config.dictConfig(LOGGING_CONFIG)

# Include in each module:
log = logging.getLogger(__name__)
log.debug("Logging is configured.")

In case you see too many logs from third-party packages, be sure to run this config using logging.config.dictConfig(LOGGING_CONFIG) before the third-party packages are imported.

To add additional custom info to each log message using a logging filter, consider this answer.

eval is evil

eval("__import__('os').remove('important file')") # arbitrary commands
eval("9**9**9**9**9**9**9**9", {'__builtins__': None}) # CPU, memory

Note: even if you use set __builtins__ to None it still might be possible to break out using introspection:

eval('(1).__class__.__bases__[0].__subclasses__()', {'__builtins__': None})

Evaluate arithmetic expression using ast

import ast
import operator as op

# supported operators
operators = {ast.Add: op.add, ast.Sub: op.sub, ast.Mult: op.mul,
             ast.Div: op.truediv, ast.Pow: op.pow, ast.BitXor: op.xor,
             ast.USub: op.neg}

def eval_expr(expr):
    """
    >>> eval_expr('2^6')
    4
    >>> eval_expr('2**6')
    64
    >>> eval_expr('1 + 2*3**(4^5) / (6 + -7)')
    -5.0
    """
    return eval_(ast.parse(expr, mode='eval').body)

def eval_(node):
    if isinstance(node, ast.Num): # <number>
        return node.n
    elif isinstance(node, ast.BinOp): # <left> <operator> <right>
        return operators[type(node.op)](eval_(node.left), eval_(node.right))
    elif isinstance(node, ast.UnaryOp): # <operator> <operand> e.g., -1
        return operators[type(node.op)](eval_(node.operand))
    else:
        raise TypeError(node)

You can easily limit allowed range for each operation or any intermediate result, e.g., to limit input arguments for a**b:

def power(a, b):
    if any(abs(n) > 100 for n in [a, b]):
        raise ValueError((a,b))
    return op.pow(a, b)
operators[ast.Pow] = power

Or to limit magnitude of intermediate results:

import functools

def limit(max_=None):
    """Return decorator that limits allowed returned values."""
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            ret = func(*args, **kwargs)
            try:
                mag = abs(ret)
            except TypeError:
                pass # not applicable
            else:
                if mag > max_:
                    raise ValueError(ret)
            return ret
        return wrapper
    return decorator

eval_ = limit(max_=10**100)(eval_)

Example

>>> evil = "__import__('os').remove('important file')"
>>> eval_expr(evil) #doctest:+IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TypeError:
>>> eval_expr("9**9")
387420489
>>> eval_expr("9**9**9**9**9**9**9**9") #doctest:+IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
ValueError:

You have to create conftest.py file and override pytest_keyboard_interrupt in that file:

# content of conftest.py

def pytest_keyboard_interrupt(excinfo):
    # Calling tearDown.

NOTE: Not duplicate question, however answer may be use full for your case to: py.test: get KeyboardInterrupt to call teardown

Thanks to comments and guidance in the comments from @hoefling.

I could attach the cassette fixture to the request.node in a conftest.py local plugin overriding the pytest-vcr marker...

@pytest.fixture(autouse=True)
def _vcr_marker(request):
    marker = request.node.get_closest_marker("vcr")
    if marker:
        cassette = request.getfixturevalue("vcr_cassette")
        vcr = request.getfixturevalue("vcr")
        request.node.__vcr_fixtures = dict(vcr_cassette=cassette, vcr=vcr)
    yield

But I needed more than the cassette to get to my solution.

Ingredients

• Use the pytest_exception_interact hook
• Pull request to VCR.py https://github.com/kevin1024/vcrpy/pull/446
  • PR #439 and PR #441 by arthurHamon2 has been huge to fix the test suite and also integrate matcher differencing outputs.
• BONUS: Use the raise ... from ... form of throwing an exception

Recipe

Get latest VCRpy

These patches were released in vcrpy v2.1.0

pip install vcrpy==2.1.0

Override the pytest_exception_interact hook

In the root of your test directory create a conftest.py to create a local plugin that overrides the pytest_exception_interact hook.

@pytest.hookimpl(hookwrapper=True)
def pytest_exception_interact(node, call, report):
    """Intercept specific exceptions from tests."""
    if report.when == "call" and isinstance(call.excinfo.value, CannotOverwriteExistingCassetteException):
        __handle_cassette_exception(node, call, report)

    yield

Extract the Cassette and the Request from the exception.

# Define new exception to throw
class RequestNotFoundCassetteException(Exception):
   ...

def __handle_cassette_exception(node, call, report):
    # Safely check for attributes attached to exception
    vcr_request = None
    cassette = None
    if hasattr(call.excinfo.value, "cassette"):
        cassette = call.excinfo.value.cassette
    if hasattr(call.excinfo.value, "failed_request"):
        vcr_request = call.excinfo.value.failed_request

    # If we have the extra context available...
    if cassette and vcr_request:

        match_properties = [f.__name__ for f in cassette._match_on]
        this_req, req_str = __format_near_match(cassette.requests, vcr_request, match_properties)

        try:
            raise RequestNotFoundCassetteException(f"{this_req}\n\n{req_str}\n") from call.excinfo.value
        except RequestNotFoundCassetteException as e:
            call.excinfo._excinfo = (type(e), e)
            report.longrepr = node.repr_failure(call.excinfo)

I figured it out finally. I had this environment variable set:

PYTEST_ADDOPTS=-m networks and level1

I was experimenting with env variables a while back but never had an issue until I did a reboot. Hopefully this helps someone in the future not be an idiot like myself lol.

Though classmethod and staticmethod are quite similar, there's a slight difference in usage for both entities: classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.

Example

class Date(object):
    
    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

date2 = Date.from_string('11-09-2012')
is_date = Date.is_date_valid('11-09-2012')

Explanation

Let's assume an example of a class, dealing with date information (this will be our boilerplate):

class Date(object):
    
    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

This class obviously could be used to store information about certain dates (without timezone information; let's assume all dates are presented in UTC).

Here we have __init__, a typical initializer of Python class instances, which receives arguments as a typical instance method, having the first non-optional argument (self) that holds a reference to a newly created instance.

Class Method

We have some tasks that can be nicely done using classmethods.

Let's assume that we want to create a lot of Date class instances having date information coming from an outer source encoded as a string with format 'dd-mm-yyyy'. Suppose we have to do this in different places in the source code of our project.

So what we must do here is:

1. Parse a string to receive day, month and year as three integer variables or a 3-item tuple consisting of that variable.
2. Instantiate Date by passing those values to the initialization call.

This will look like:

day, month, year = map(int, string_date.split('-'))
date1 = Date(day, month, year)

For this purpose, C++ can implement such a feature with overloading, but Python lacks this overloading. Instead, we can use classmethod. Let's create another constructor.

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

date2 = Date.from_string('11-09-2012')

Let's look more carefully at the above implementation, and review what advantages we have here:

1. We've implemented date string parsing in one place and it's reusable now.
2. Encapsulation works fine here (if you think that you could implement string parsing as a single function elsewhere, this solution fits the OOP paradigm far better).
3. cls is the class itself, not an instance of the class. It's pretty cool because if we inherit our Date class, all children will have from_string defined also.

Static method

What about staticmethod? It's pretty similar to classmethod but doesn't take any obligatory parameters (like a class method or instance method does).

Let's look at the next use case.

We have a date string that we want to validate somehow. This task is also logically bound to the Date class we've used so far, but doesn't require instantiation of it.

Here is where staticmethod can be useful. Let's look at the next piece of code:

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

# usage:
is_date = Date.is_date_valid('11-09-2012')

So, as we can see from usage of staticmethod, we don't have any access to what the class is---it's basically just a function, called syntactically like a method, but without access to the object and its internals (fields and other methods), which classmethod does have.

Here's the hack that I'm using for now in case others need to circumvent this in the short run. I'll post back if I encounter issues with it; it's only minimally tested so far. Basically, if you're on Windows, I just write the command into a .bat file, then I run the .bat file (as a single-line command).

import invoke
import platform
from pathlib import Path
from tempfile import TemporaryDirectory


def _fixed_run(ctx, cmd: str, *args, **kwargs):
    if platform.system() != "Windows":
        return ctx._old_run(cmd, *args, **kwargs)

    with TemporaryDirectory() as tmp_dir:
        tmp_file = Path(tmp_dir) / "tmp.bat"
        tmp_file.write_text("@echo off\r\n" + cmd)
        return ctx._old_run(str(tmp_file), *args, **kwargs)

invoke.Context._old_run = invoke.Context.run
invoke.Context.run = _fixed_run

To use this easily, save it into a file (e.g. fix_invoke.py and then just do import fix_invoke whenever you need this fix).

I'd be glad to hear of a real solution, though, if anybody has one!

The docs describe the attributes available on the request object (from flask import request) during a request. In most common cases request.data will be empty because it's used as a fallback:

request.data Contains the incoming request data as string in case it came with a mimetype Flask does not handle.

• request.args: the key/value pairs in the URL query string
• request.form: the key/value pairs in the body, from a HTML post form, or JavaScript request that isn't JSON encoded
• request.files: the files in the body, which Flask keeps separate from form. HTML forms must use enctype=multipart/form-data or files will not be uploaded.
• request.values: combined args and form, preferring args if keys overlap
• request.json: parsed JSON data. The request must have the application/json content type, or use request.get_json(force=True) to ignore the content type.

All of these are MultiDict instances (except for json). You can access values using:

• request.form['name']: use indexing if you know the key exists
• request.form.get('name'): use get if the key might not exist
• request.form.getlist('name'): use getlist if the key is sent multiple times and you want a list of values. get only returns the first value.

Yes, there is a way:

@pytest.fixture
def your_fixture(request):
    print(request.module)
    print(request.cls)
    print(request.function)


def test_something(your_fixture):
    pass

output:

test.py <module 'test' from '/path/to/file/test.py'>
None
<function test_something at 0x7fa5205bd140>

Read more info here: #_pytest.python.FixtureRequest