Python setuptools package directory

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Python setuptools package directory

Python packages are collections of modules (reusable code) that extend and enhance the functionality of the core Python language. Python developers contribute to the official Python Package Index (PyPI) repository, making their packages available to the Python community under open source license terms. The Python Packaging Authority (PyPA) manages the repository, and maintains a standard set of tools for building, distributing, and installing Python packages. On Indiana University's research supercomputers, many third-party packages already are installed to supplement commonly used Python builds. If you have a unique need for a third-party Python package that is not already installed, you can use pip or setup.py to install the package in your home directory. If you know several researchers are interested in using a Python package that is not already installed, you can request to have it installed as a system-wide site package. Install Python packages for personal use Set up your user environment To install Python packages, you must have Python added to your user environment. The IU research supercomputers use module-based environment management systems that provide a convenient method for dynamically customizing your software environment. To check which modules are currently loaded; on the command line, enter: module list If Python is not among the list of currently loaded modules, use the module load command to add it; for example: To add the default version, on the command line, enter: module load python To add a non-default version: Check which versions are available; on the command line, enter: module avail python Load the preferred version; on the command line, enter (replace version_number with the preferred version number): module load python/version_number If Python is listed among the currently loaded modules, but you prefer or need to use another version, you must remove the currently loaded module before loading the other version. To do this with one command, use module switch; for example, on the command line, enter (replace current_version with the version number of the currently loaded python module and new_version with the preferred version number): module switch python/current_version python/new_version You can save your customized user environment so that it loads every time you start a new session; for instructions, see Use modules to manage your software environment on IU research supercomputers. Install a package using pip The pip package management tool, one of the standard tools maintained by the Python Package Authority (PyPA), is the recommended tool for installing packages from the Python Package Index (PyPI) repository. To install a package from the PyPI repository (for example, foo), use the pip install command with the --user flag; for example: To install: Use the command: The latest version pip install foo --user A particular version (for example, foo 1.0.3) pip install foo==1.0.3 -user A minimum version (for example, foo 2.0) pip install 'foo>=2.0' --user The --user option directs pip to download and unpack the source distribution for your package (for example, foo) in the user site-packages directory for the running Python; for example: ~/.local/lib/python3.6/site-packages/foo Python automatically searches this directory for modules, so prepending this path to the PYTHONPATH environmental variable is not necessary. If you omit the --user option, pip will try to install your package in the global site-packages directory (where you do not have the necessary permissions); as a result, the installation will fail. For more about using pip, see the pip install page in the pip User Guide. Install a package using its setup.py script To install a Python package from a source other than the PyPI repository, you can download and unpack the source distribution yourself, and then use its setup.py script to install the package in the user site-packages directory: Set up your user environment (as described in the previous section). Use the wget command to download the distribution archive (for example, foo-1.0.3.gz) from the source (for example, ); for example: wget Use tar to unpack the archive (for example, foo-1.0.3.gz); for example: tar -xzf foo-1.0.3.gz The distribution should unpack into a similarly-named directory in your home directory (for example, ~/foo-1.0.3). Change (cd) to the new directory, and then, on the command line, enter: python setup.py install --user The --user option directs setup.py to install the package (for example, foo) in the user site-packages directory for the running Python; for example: ~/.local/lib/pythonX.Y/site-packages/foo Python automatically searches this directory for modules, so prepending this path to the PYTHONPATH environmental variable is not necessary. If you omit the --user option, setup.py will try to install the package in the global site-packages directory (where you do not have the necessary permissions); as a result, the installation will fail. Alternatively, you can use the --home or --prefix option to install your package in a different location (where you have the necessary permissions); for example, to install your package in a subdirectory (for example, python-pkgs): Within your home directory, enter: python setup.py install --home=~/python-pkgs In your Slate storage space, enter: python setup.py install --prefix=/N/slate/$USER/python-pkgs For more on using setup.py to install packages, see Installing Python Modules (Legacy version). Understand the module search order Knowing how the Python interpreter responds to import statements can help you determine why a particular module or package isn't loading, or why an unexpected version of a package is loading, even though the correct version is installed and the path to its location is listed in your PYTHONPATH environment variable. When Python launches, it searches the paths found in sys.path, a list of directories that determines the interpreter's search path for modules. The sys.path variable is initialized from the following locations, in this order: The directory containing the script used to invoke the Python interpreter (if the interpreter is invoked interactively or the script is read from standard input, this first item, path[0], remains an empty string, which directs Python to search modules in the current working directory first) The directories listed in PYTHONPATH The version-specific site-packages directory for the running Python installation; for example: /lib/pythonX.Y/site-packages In this example, is the path to the running Python installation; X.Y is the version number (for example, 3.8) of the running Python installation. By default, Python also imports the site.py module upon initialization, which adds site-specific paths to the module search path (sys.path), including the path to your user site-packages directory within in your home directory; for example (X.Y will be the version number of the running Python installation): ~/.local/lib/pythonX.Y/site-packages As site.py adds paths to sys.path, it scans them for path configuration (.pth) files, which contain additional directories that are added to sys.path. If a directory contains multiple .pth files, site.py processes them in alphabetical order. However, some .pth files contain embedded commands that insert directory entries at the beginning of the module search path (ahead of the standard library path). As a result, a module from one of the inserted directories will load instead of the module of the same name from the standard library directory. This can be undesired and confusing behavior unless such a replacement is intended. To see which directories Python scans when you issue import commands, on the command line, enter: python -c "import sys; print (''.join(sys.path))" Alternatively, launch Python in interactive mode, and then invoke the same commands in this order (>>> is the Python primary prompt): >>>import sys >>>print (''.join(sys.path)) Use Python on IU research supercomputers About Python Putting tests into an extra directory outside your actual application code might be useful if you have many functional tests or for other reasons want to keep tests separate from actual application code (often a good idea): setup.py mypkg/ __init__.py app.py view.py tests/ test_app.py test_view.py ... This has the following benefits: Your tests can run against an installed version after executing pip install .. Your tests can run against the local copy with an editable install after executing pip install --editable .. If you don't have a setup.py file and are relying on the fact that Python by default puts the current directory in sys.path to import your package, you can execute python -m pytest to execute the tests against the local copy directly, without using pip. Note that this scheme has a drawback if you are using prepend import mode (which is the default): your test files must have unique names, because pytest will import them as top-level modules since there are no packages to derive a full package name from. In other words, the test files in the example above will be imported as test_app and test_view top-level modules by adding tests/ to sys.path. If you need to have test modules with the same name, you might add __init__.py files to your tests folder and subfolders, changing them to packages: setup.py mypkg/ ... tests/ __init__.py foo/ __init__.py test_view.py bar/ __init__.py test_view.py Now pytest will load the modules as tests.foo.test_view and tests.bar.test_view, allowing you to have modules with the same name. But now this introduces a subtle problem: in order to load the test modules from the tests directory, pytest prepends the root of the repository to sys.path, which adds the side-effect that now mypkg is also importable. This is problematic if you are using a tool like tox to test your package in a virtual environment, because you want to test the installed version of your package, not the local code from the repository. In this situation, it is strongly suggested to use a src layout where application root package resides in a sub-directory of your root: setup.py src/ mypkg/ __init__.py app.py view.py tests/ __init__.py foo/ __init__.py test_view.py bar/ __init__.py test_view.py This layout prevents a lot of common pitfalls and has many benefits, which are better explained in this excellent blog post by Ionel Cristian Mrie. Note The new --import-mode=importlib (see Import modes) doesn't have any of the drawbacks above because sys.path and sys.modules are not changed when importing test modules, so users that run into this issue are strongly encouraged to try it and report if the new option works well for them. The src directory layout is still strongly recommended however. Inlining test directories into your application package is useful if you have direct relation between tests and application modules and want to distribute them along with your application: setup.py mypkg/ __init__.py app.py view.py test/ __init__.py test_app.py test_view.py ... In this scheme, it is easy to run your tests using the --pyargs option: pytest will discover where mypkg is installed and collect tests from there. Note that this layout also works in conjunction with the src layout mentioned in the previous section. Note You can use Python3 namespace packages (PEP420) for your application but pytest will still perform test package name discovery based on the presence of __init__.py files. If you use one of the two recommended file system layouts above but leave away the __init__.py files from your directories it should just work on Python3.3 and above. From "inlined tests", however, you will need to use absolute imports for getting at your application code. Note In prepend and append import-modes, if pytest finds a "a/b/test_module.py" test file while recursing into the filesystem it determines the import name as follows: determine basedir: this is the first "upward" (towards the root) directory not containing an __init__.py. If e.g. both a and b contain an __init__.py file then the parent directory of a will become the basedir. perform sys.path.insert(0, basedir) to make the test module importable under the fully qualified import name. import a.b.test_module where the path is determined by converting path separators / into "." characters. This means you must follow the convention of having directory and file names map directly to the import names. The reason for this somewhat evolved importing technique is that in larger projects multiple test modules might import from each other and thus deriving a canonical import name helps to avoid surprises such as a test module getting imported twice. With --import-mode=importlib things are less convoluted because pytest doesn't need to change sys.path or sys.modules, making things much less surprising. Page 2 pytest supports running Python unittest-based tests out of the box. It's meant for leveraging existing unittest-based test suites to use pytest as a test runner and also allow to incrementally adapt the test suite to take full advantage of pytest's features. To run an existing unittest-style test suite using pytest, type: pytest will automatically collect unittest.TestCase subclasses and their test methods in test_*.py or *_test.py files. Almost all unittest features are supported: @unittest.skip style decorators; setUp/tearDown; setUpClass/tearDownClass; setUpModule/tearDownModule; Up to this point pytest does not have support for the following features: load_tests protocol; subtests; Running your unittest with pytest allows you to use its fixture mechanism with unittest.TestCase style tests. Assuming you have at least skimmed the pytest fixture features, let's jump-start into an example that integrates a pytest db_class fixture, setting up a class-cached database object, and then reference it from a unittest-style test: # content of conftest.py # we define a fixture function below and it will be "used" by # referencing its name from tests import pytest @pytest.fixture(scope="class") def db_class(request): class DummyDB: pass # set a class attribute on the invoking test context request.cls.db = DummyDB() This defines a fixture function db_class which - if used - is called once for each test class and which sets the class-level db attribute to a DummyDB instance. The fixture function achieves this by receiving a special request object which gives access to the requesting test context such as the cls attribute, denoting the class from which the fixture is used. This architecture de-couples fixture writing from actual test code and allows re-use of the fixture by a minimal reference, the fixture name. So let's write an actual unittest.TestCase class using our fixture definition: # content of test_unittest_db.py import unittest import pytest @pytest.mark.usefixtures("db_class") class MyTest(unittest.TestCase): def test_method1(self): assert hasattr(self, "db") assert 0, self.db # fail for demo purposes def test_method2(self): assert 0, self.db # fail for demo purposes The @pytest.mark.usefixtures("db_class") class-decorator makes sure that the pytest fixture function db_class is called once per class. Due to the deliberately failing assert statements, we can take a look at the self.db values in the traceback: $ pytest test_unittest_db.py =========================== test session starts ============================ platform linux -- Python 3.x.y, pytest-6.x.y, py-1.x.y, pluggy-0.x.y cachedir: $PYTHON_PREFIX/.pytest_cache rootdir: $REGENDOC_TMPDIR collected 2 items test_unittest_db.py FF [100%] ================================= FAILURES ================================= ___________________________ MyTest.test_method1 ____________________________ self = def test_method1(self): assert hasattr(self, "db") > assert 0, self.db # fail for demo purposes E AssertionError: E assert 0 test_unittest_db.py:10: AssertionError ___________________________ MyTest.test_method2 ____________________________ self = def test_method2(self): > assert 0, self.db # fail for demo purposes E AssertionError: E assert 0 test_unittest_db.py:13: AssertionError ========================= short test summary info ========================== FAILED test_unittest_db.py::MyTest::test_method1 - AssertionError:

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