pytest has a hookspec pytest_deselected for accessing the deselected tests. Example: add this code to conftest.py in your test root dir:

def pytest_deselected(items):
    if not items:
        return
    config = items[0].session.config
    reporter = config.pluginmanager.getplugin("terminalreporter")
    reporter.ensure_newline()
    for item in items:
        reporter.line(f"deselected: {item.nodeid}", yellow=True, bold=True)

Running the tests now will give you an output similar to this:

$ pytest -vv
...
plugins: cov-2.8.1, asyncio-0.10.0
collecting ...
deselected: test_spam.py::test_spam
deselected: test_spam.py::test_bacon
deselected: test_spam.py::test_ham
collected 4 items / 3 deselected / 1 selected
...

If you want a report in another format, simply store the deselected items in the config and use them for the desired output somewhere else, e.g. pytest_terminal_summary:

# conftest.py

import os

def pytest_deselected(items):
    if not items:
        return
    config = items[0].session.config
    config.deselected = items


def pytest_terminal_summary(terminalreporter, exitstatus, config):
    reports = terminalreporter.getreports('')
    content = os.linesep.join(text for report in reports for secname, text in report.sections)
    deselected = getattr(config, "deselected", [])
    if deselected:
        terminalreporter.ensure_newline()
        terminalreporter.section('Deselected tests', sep='-', yellow=True, bold=True)
        content = os.linesep.join(item.nodeid for item in deselected)
        terminalreporter.line(content)

gives the output:

$ pytest -vv
...
plugins: cov-2.8.1, asyncio-0.10.0
collected 4 items / 3 deselected / 1 selected                                                     

...

---------------------------------------- Deselected tests -----------------------------------------
test_spam.py::test_spam
test_spam.py::test_bacon
test_spam.py::test_ham
================================= 1 passed, 3 deselected in 0.01s =================================

Yes, it is possible.

If you have the test and all the fixtures in 1 file: test.py

import pytest

@pytest.fixture
def foo():
    return "foo"

@pytest.fixture
def bar(foo):
    return foo, "bar"

def test_foo_bar(bar):
    expected = ("foo", "bar")
    assert bar == expected

and run pytest test.py then Success!!!

======================================= test session starts ========================================
platform darwin -- Python 3.6.8, pytest-4.3.0
collected 1 item                                                                                   

test.py .                                                                                    [100%]

===================================== 1 passed in 0.02 seconds =====================================

But if you put the fixtures in a different file: test_foo_bar.py

from test import bar

def test_foo_bar(bar):
    expected = ("foo", "bar")
    assert bar == expected

and run pytest test_foo_bar.py expecting (like I did) that importing only the bar fixture is enough since on importing it would already have executed the foo fixture then you get the error you are getting.

======================================= test session starts ========================================
platform darwin -- Python 3.6.8, pytest-4.3.0
collected 1 item                                                                                   

test2.py E                                                                                   [100%]

============================================== ERRORS ==============================================
__________________________________ ERROR at setup of test_foo_bar __________________________________
file .../test_foo_bar.py, line 3
  def test_foo_bar(bar):
.../test.py, line 7
  @pytest.fixture
  def bar(foo):
E       fixture 'foo' not found
>       available fixtures: TIMEOUT, bar, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, cov, doctest_namespace, monkeypatch, no_cover, once_without_docker, pytestconfig, record_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory
>       use 'pytest --fixtures [testpath]' for help on them.

.../test.py:7
===================================== 1 error in 0.03 seconds ======================================

To fix this also import the foo fixture in the test_foo_bar.py module.

For the string variable a, the only solution is global a. If you look at the Python 2 and Python 3 source code, setupModule() doesn't appear to do anything magical, so all the usual namespace rules apply.

If a were a mutable variable, like a list, you could define it at global scope and then append to it within setupModule.

Variable b is easier to work with because it is defined within a class. Try this:

@classmethod
def setUpClass(cls):
    cls.b = "Setup Class variable"

def test_1(self):
    print self.b

This answer presents two compelling use-cases for a TestClass in pytest:

• Joint parametrization of multiple test methods belonging to a given class.
• Reuse of test data and test logic via subclass inheritance

Joint parametrization of multiple test methods belonging to a given class.

The pytest parametrization decorator, @pytest.mark.parametrize, can be used to make inputs available to multiple methods within a class. In the code below, the inputs param1 and param2 are available to each of the methods TestGroup.test_one and TestGroup.test_two.

"""test_class_parametrization.py"""
import pytest

@pytest.mark.parametrize(
    ("param1", "param2"),
    [
        ("a", "b"),
        ("c", "d"),
    ],
)
class TestGroup:
    """A class with common parameters, `param1` and `param2`."""

    @pytest.fixture
    def fixt(self):
        """This fixture will only be available within the scope of TestGroup"""
        return 123

    def test_one(self, param1, param2, fixt):
        print("\ntest_one", param1, param2, fixt)

    def test_two(self, param1, param2):
        print("\ntest_two", param1, param2)

$ pytest -s test_class_parametrization.py
================================================================== test session starts ==================================================================
platform linux -- Python 3.8.6, pytest-6.2.1, py-1.10.0, pluggy-0.13.1
rootdir: /home/jbss
plugins: pylint-0.18.0
collected 4 items

test_class_parametrization.py
test_one a b 123
.
test_one c d 123
.
test_two a b
.
test_two c d
.

=================================================================== 4 passed in 0.01s ===================================================================

Reuse of test data and test logic via subclass inheritance

I'll use a modified version of code taken from another answer to demonstrate the usefulness of inheriting class attributes/methods from TestClass to TestSubclass:

# in file `test_example.py`
class TestClass:
    VAR = 3
    DATA = 4

    def test_var_positive(self):
        assert self.VAR >= 0


class TestSubclass(TestClass):
    VAR = 8

    def test_var_even(self):
        assert self.VAR % 2 == 0

    def test_data(self):
        assert self.DATA == 4

Running pytest on this file causes four tests to be run:

$ pytest -v test_example.py
=========== test session starts ===========
platform linux -- Python 3.8.2, pytest-5.4.2, py-1.8.1
collected 4 items

test_example.py::TestClass::test_var_positive PASSED
test_example.py::TestSubclass::test_var_positive PASSED
test_example.py::TestSubclass::test_var_even PASSED
test_example.py::TestSubclass::test_data PASSED

In the subclass, the inherited test_var_positive method is run using the updated value self.VAR == 8, and the newly defined test_data method is run against the inherited attribute self.DATA == 4. Such method and attribute inheritance gives a flexible way to re-use or modify shared functionality between different groups of test-cases.

Yes, it was added in version 2.5. The expression syntax is:

a if condition else b

First condition is evaluated, then exactly one of either a or b is evaluated and returned based on the Boolean value of condition. If condition evaluates to True, then a is evaluated and returned but b is ignored, or else when b is evaluated and returned but a is ignored.

This allows short-circuiting because when condition is true only a is evaluated and b is not evaluated at all, but when condition is false only b is evaluated and a is not evaluated at all.

For example:

>>> 'true' if True else 'false'
'true'
>>> 'true' if False else 'false'
'false'

Note that conditionals are an expression, not a statement. This means you can't use statements such as pass, or assignments with = (or "augmented" assignments like +=), within a conditional expression:

>>> pass if False else pass
  File "<stdin>", line 1
    pass if False else pass
         ^
SyntaxError: invalid syntax

>>> # Python parses this as `x = (1 if False else y) = 2`
>>> # The `(1 if False else x)` part is actually valid, but
>>> # it can't be on the left-hand side of `=`.
>>> x = 1 if False else y = 2
  File "<stdin>", line 1
SyntaxError: cannot assign to conditional expression

>>> # If we parenthesize it instead...
>>> (x = 1) if False else (y = 2)
  File "<stdin>", line 1
    (x = 1) if False else (y = 2)
       ^
SyntaxError: invalid syntax

(In 3.8 and above, the := "walrus" operator allows simple assignment of values as an expression, which is then compatible with this syntax. But please don't write code like that; it will quickly become very difficult to understand.)

Similarly, because it is an expression, the else part is mandatory:

# Invalid syntax: we didn't specify what the value should be if the 
# condition isn't met. It doesn't matter if we can verify that
# ahead of time.
a if True

You can, however, use conditional expressions to assign a variable like so:

x = a if True else b

Or for example to return a value:

# Of course we should just use the standard library `max`;
# this is just for demonstration purposes.
def my_max(a, b):
    return a if a > b else b

Think of the conditional expression as switching between two values. We can use it when we are in a 'one value or another' situation, where we will do the same thing with the result, regardless of whether the condition is met. We use the expression to compute the value, and then do something with it. If you need to do something different depending on the condition, then use a normal if statement instead.

Keep in mind that it's frowned upon by some Pythonistas for several reasons:

• The order of the arguments is different from those of the classic condition ? a : b ternary operator from many other languages (such as C, C++, Go, Perl, Ruby, Java, JavaScript, etc.), which may lead to bugs when people unfamiliar with Python's "surprising" behaviour use it (they may reverse the argument order).
• Some find it "unwieldy", since it goes contrary to the normal flow of thought (thinking of the condition first and then the effects).
• Stylistic reasons. (Although the 'inline if' can be really useful, and make your script more concise, it really does complicate your code)

If you're having trouble remembering the order, then remember that when read aloud, you (almost) say what you mean. For example, x = 4 if b > 8 else 9 is read aloud as x will be 4 if b is greater than 8 otherwise 9.

Official documentation:

• Conditional expressions
• Is there an equivalent of C’s ”?:” ternary operator?

It depends how it was split.

MP3 files are a header block followed by some data, you can split the file at any header block and combine them by just concating them together. There isn't necessarily anything in the header block to say what order it's in. ( http://en.wikipedia.org/wiki/MP3#File_structure )

If the MP3 files are separate tracks from an album they will have ID tags that list the track number. There are a few python MP3 libraries - see Accessing mp3 Meta-Data with Python

edit: if you mean analyse the music so that you can tell if one note is supposed to follow another, that's a little out of my expertise!

Your example is coming out as gibberish, it's much worse than just some missing stop words.

If you re-read the docs for the start_char, oov_char, and index_from parameters of the [keras.datasets.imdb.load_data](https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification ) method they explain what is happening:

start_char: int. The start of a sequence will be marked with this character. Set to 1 because 0 is usually the padding character.

oov_char: int. words that were cut out because of the num_words or skip_top limit will be replaced with this character.

index_from: int. Index actual words with this index and higher.

That dictionary you inverted assumes the word indices start from 1.

But the indices returned my keras have <START> and <UNKNOWN> as indexes 1 and 2. (And it assumes you will use 0 for <PADDING>).

This works for me:

import keras
NUM_WORDS=1000 # only use top 1000 words
INDEX_FROM=3   # word index offset

train,test = keras.datasets.imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)
train_x,train_y = train
test_x,test_y = test

word_to_id = keras.datasets.imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3

id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in train_x[0] ))

The punctuation is missing, but that's all:

"<START> this film was just brilliant casting <UNK> <UNK> story
 direction <UNK> really <UNK> the part they played and you could just
 imagine being there robert <UNK> is an amazing actor ..."

In the client link you provided, states that the client is an instance of the django.test.Client so in reality it doesn't do anything special there and shouldn't be a problem.

You need to setup your environment as you correctly stated.
Let's have a look at the error now:

from the setup_test_environment() source code:

if hasattr(_TestState, 'saved_data'):
      # Executing this function twice would overwrite the saved values.
      raise RuntimeError(
          "setup_test_environment() was already called and can't be called "
          "again without first calling teardown_test_environment()."
)

And that is what raises your RuntimeError above.

Let's look now at the teardown_test_environment() method:

...
del _TestState.saved_data

So it deletes the culprit of the aforementioned exception.

Thus:

from django.test.utils import teardown_test_environment, setup_test_environment

try:
    # If setup_test_environment haven't been called previously this
    # will produce an AttributeError.
    teardown_test_environment()
except AttributeError:
    pass

setup_test_environment() 

...

You can check for this using isinstance and collections.Iterable

>>> from collections.abc import Iterable # for python >= 3.6
>>> l = [1, 2, 3, 4]
>>> isinstance(l, Iterable)
True

Note: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working.