It seems that non-python resources are included in python distribution packages one of 4 ways:
Manifest.in file (I'm not sure when this is preferred over package_data or data_files)
package_data in setup.py (for including resources within python import packages)
data_files in setup.py (for including resources outside python import packages)
something called setuptools-scm (which I believe uses your version control system to find resources instead of manifest.in or something)
Which of these are accessible from importlib.resources?
(It is my understanding that importlib.resources is the preferred way to access such resources.) If any of these are not accessible via importlib.resources, then how could/should one access such resources?
Other people online have been scolded for the suggestion of using __file__ to find the path to a resource because installed wheel distributions may be stored as zip files and therefore there won't even be a proper path to your resources. When are wheels extracted into site-packages and when do they remain zipped?
All of (1)-(3) will put files into your package (don't know about (4)).
At runtime, importlib.resources will then be able to access any data in your package.
At least with Python 3.9, which can access resources in subdirectories.
Before, you had to make each subdirectory a package by adding a __init__.
As for why not use __file__: Pythons import system has some weird ways to resolve packages. For example it can look them up in a zip file if you use Zipapp.
You may even have a custom loader for a package you are asked to load some resources from.
Who knows where those resources are located? Ans: importlib.resources.
Afaik, wheels are not a contender as those are unpacked.
Related
I'm trying to write a plugin for Sublime Text 3.
I have to use several third party packages in my code. I have managed to get the code working by manually copying the packages into /home/user/.config/sublime-text-3/Packages/User/, then I used relative imports to get to the needed code. How would I distribute the plugin to the end users? Telling them to copy the needed dependencies to the appropriate location is certainly not the way to go. How are 3rd party modules supposed to be used properly with Sublime Text plugins? I can't find any documentation online; all I see is the recommendation to put the modules in the folder.
Sublime uses it's own embedded Python interpreter (currently Python 3.3.6 although the next version will also support Python 3.8 as well) and as such it will completely ignore any version of Python that you may or may not have installed on your system, as well as any libraries that are installed for that version.
For that reason, if you want to use external modules (hereafter dependencies) you need to do extra work. There are a variety of ways to accomplish this, each with their own set of pros and cons.
The following lists the various ways that you can achieve this; all of them require a bit of an understanding about how modules work in Python in order to understand what's going on. By and large except for the paths involved there's nothing too "Sublime Text" about the mechanisms at play.
NOTE: The below is accurate as of the time of this answer. However there are plans for Package Control to change how it works with dependencies that are forthcoming that may change some aspect of this.
This is related to the upcoming version of Sublime supporting multiple versions of Python (and the manner in which it supports them) which the current Package Control mechanism does not support.
It's unclear at the moment if the change will bring a new way to specify dependencies or if only the inner workings of how the dependencies are installed will change. The existing mechanism may remain in place regardless just for backwards compatibility, however.
All roads to accessing a Python dependency from a Sublime plugin involve putting the code for it in a place where the Python interpreter is going to look for it. This is similar to how standard Python would do things, except that locations that are checked are contained within the area that Sublime uses to store your configuration (referred to as the Data directory) and instead of a standalone Python interpreter, Python is running in the plugin host.
Populate the library into the Lib folder
Since version 3.0 (build 3143), Sublime will create a folder named Lib in the data directory and inside of it a directory based on the name of the Python version. If you use Preferences > Browse Packages and go up one folder level, you'll see Lib, and inside of it a folder named e.g. python3.3 (or if you're using a newer build, python33 and python38).
Those directories are directly on the Python sys.path by default, so anything placed inside of them will be immediately available to any plugin just as a normal Python library (or any of those built in) would be. You could consider these folders to be something akin to the site-packages folder in standard Python.
So, any method by which you could install a standard Python library can be used so long as the result is files ending up in this folder. You could for example install a library via pip and then manually copy the files to that location from site-packages, manually install from sources, etc.
Lib/python3.3/
|-- librarya
| `-- file1.py
|-- libraryb
| `-- file2.py
`-- singlefile.py
Version restrictions apply here; the dependency that you want to use must support the version of Python that Sublime is using, or it won't work. This is particularly important for Python libraries with a native component (e.g. a .dll, .so or .dylib), which may require hand compiling the code.
This method is not automatic; you would need to do it to use your package locally, and anyone that wants to use your package would also need to do it as well. Since Sublime is currently using an older version of Python, it can be problematic to obtain a correct version of libraries as well.
In the future, Package Control will install dependencies in this location (Will added the folder specifically for this purpose during the run up to version 3.0), but as of the time I'm writing this answer that is not currently the case.
Vendor your dependencies directly inside of your own package
The Packages folder is on the sys.path by default as well; this is how Sublime finds and loads packages. This is true of both the physical Packages folder, as well as the "virtual" packages folder that contains the contents of sublime-package files.
For example, one can access the class that provides the exec command via:
from Default.exec import ExecCommand
This will work even though the exec.py file is actually stored in Default.sublime-package in the Sublime text install folder and not physically present in the Packages folder.
As a result of this, you can vendor any dependencies that you require directly inside of your own package. Here this could be the User package or any other package that you're creating.
It's important to note that Sublime will treat any Python file in the top level of a package as a plugin and try to load it as one. Hence it's important that if you go this route you create a sub-folder in your package and put the library in there.
MyPackage/
|-- alibrary
| `-- code.py
`-- my_plugin.py
With this structure, you can access the module directly:
import MyPackage.alibrary
from MyPackage.alibrary import someSymbol
Not all Python modules lend themselves to this method directly without modification; some code changes in the dependency may be required in order to allow different parts of the library to see other parts of itself, for example if it doesn't use relative import to get at sibling files. License restrictions may also get in the way of this as well, depending on the library that you're using.
On the other hand, this directly locks the version of the library that you're using to exactly the version that you tested with, which ensures that you won't be in for any undue surprises further on down the line.
Using this method, anything you do to distribute your package will automatically also distribute the vendored library that's contained inside. So if you're distributing by Package Control, you don't need to do anything special and it will Just Work™.
Modify the sys.path to point to a custom location
The Python that's embedded into Sublime is still standard Python, so if desired you can manually manipulate the sys.path that describes what folders to look for packages in so that it will look in a place of your choosing in addition to the standard locations that Sublime sets up automatically.
This is generally not a good idea since if done incorrectly things can go pear shaped quickly. It also still requires you to manually install libraries somewhere yourself first, and in that case you're better off using the Lib folder as outlined above, which is already on the sys.path.
I would consider this method an advanced solution and one you might use for testing purposes during development but otherwise not something that would be user facing. If you plan to distribute your package via Package Control, the review of your package would likely kick back a manipulation of the sys.path with a request to use another method.
Use Package Control's Dependency system (and the dependency exists)
Package control contains a dependency mechanism that uses a combination of the two prior methods to provide a way to install a dependency automatically. There is a list of available dependencies as well, though the list may not be complete.
If the dependency that you're interested in using is already available, you're good to go. There are two different ways to go about declaring that you need one or more dependencies on your package.
NOTE: Package Control doesn't currently support dependencies of dependencies; if a dependency requires that another library also be installed, you need to explicitly mention them both yourself.
The first involves adding a dependencies key to the entry for your package in the package control channel file. This is a step that you'd take at the point where you're adding your package to Package Control, which is something that's outside the scope of this answer.
While you're developing your package (or if you decide that you don't want to distribute your package via Package Control when you're done), then you can instead add a dependencies.json file into the root of your package (an example dependencies.json file is available to illustrate this).
Once you do that, you can choose Package Control: Satisfy Dependencies from the command Palette to have Package Control download and install the dependency for you (if needed).
This step is automatic if your package is being distributed and installed by Package Control; otherwise you need to tell your users to take this step once they install the package.
Use Package Control's Dependency system (but the dependency does not exist)
The method that Package Control uses to install dependencies is, as outlined at the top of the question subject to change at some point in the (possibly near) future. This may affect the instructions here. The overall mechanism may remain the same as far as setup is concerned, with only the locations of the installation changing, but that remains to be seen currently.
Package Control installs dependencies via a special combination of vendoring and also manipulation of the sys.path to allow things to be found. In order to do so, it requires that you lay out your dependency in a particular way and provide some extra metadata as well.
The layout for the package that contains the dependency when you're building it would have a structure similar to the following:
Packages/my_dependency/
├── .sublime-dependency
└── prefix
└── my_dependency
└── file.py
Package Control installs a dependency as a Package, and since Sublime treats every Python file in the root of a package as a plugin, the code for the dependency is not kept in the top level of the package. As seen above, the actual content of the dependency is stored inside of the folder labeled as prefix above (more on that in a second).
When the dependency is installed, Package Control adds an entry to it's special 0_package_control_loader package that causes the prefix folder to be added to the sys.path, which makes everything inside of it available to import statements as normal. This is why there's an inherent duplication of the name of the library (my_dependency in this example).
Regarding the prefix folder, this is not actually named that and instead has a special name that determines what combination of Sublime Text version, platform and architecture the dependency is available on (important for libraries that contain binaries, for example).
The name of the prefix folder actually follows the form {st_version}_{os}_{arch}, {st_version}_{os}, {st_version} or all. {st_version} can be st2 or st3, {os} can be windows, linux or osx and {arch} can be x32 or x64.
Thus you could say that your dependency supports only st3, st3_linux, st3_windows_x64 or any combination thereof. For something with native code you may specify several different versions by having multiple folders, though commonly all is used when the dependency contains pure Python code that will work regardless of the Sublime version, OS or architecture.
In this example, if we assume that the prefix folder is named all because my_dependency is pure Python, then the result of installing this dependency would be that Packages/my_dependency/all would be added to the sys.path, meaning that if you import my_dependency you're getting the code from inside of that folder.
During development (or if you don't want to distribute your dependency via Package Control), you create a .sublime-dependency file in the root of the package as shown above. This should be a text file with a single line that contains a 2 digit number (e.g. 01 or 50). This controls in what order each installed dependency will get added to the sys.path. You'd typically pick a lower number if your dependency has no other dependencies and a higher value if it does (so that it gets injected after those).
Once you have the initial dependency laid out in the correct format in the Packages folder, you would use the command Package Control: Install Local Dependency from the Command Palette, and then select the name of your dependency.
This causes Package Control to "install" the dependency (i.e. update the 0_package_control_loader package) to make the dependency active. This step would normally be taken by Package Control automatically when it installs a dependency for the first time, so if you are also manually distributing your dependency you need to provide instructions to take this step.
Is there a way to convert a python package, i.e. is a folder of python files, into a single file that can be copied and then directly imported into a python script without needing to run any extra shell commands? I know it is possible to zip all of the files and then unzip them from python when they are needed, but I'm hoping that there is a more elegant solution.
It's not totally clear what the question is. I could interpret it two ways.
If you are looking to manage the symbols from many modules in a more organized way:
You'll want to put an __init__.py file in your directory and make it a package. In it you can define the symbols for your package, and create a graceful import packagename behavior. Details on packages.
If you are looking to make your code portable to another environment:
One way or the other, the package needs to be accessible in whatever environment it is run in. That means it either needs to be installed in the python environment (likely using pip), copied into a location that is in a subdirectory relative to the running code, or in a directory that is listed in the PYTHONPATH environment variable.
The most straightforward way to package up code and make it portable is to use setuptools to create a portable package that can be installed into any python environment. The manual page for Packaging Projects gives the details of how to go about building a package archive, and optionally uploading to PyPi for public distribution. If it is for private use, the resulting archive can be passed around without uploading it to the public repository.
There are python tools like check-manifest, to verify that all your files under your vcs are included also in your MANIFEST.in. And releasing helpers like zest.releaser recommend you to use them.
I think files in tests or docs directories are never used directly from the python package. Usually services like read the docs or travis-ci are going to access that files, and they get the files from the vcs, not from the package. I have seen also packages including .travis.yml files, what makes even less sense to me.
What is the advantage of including all the files in the python package?
About the only reason I can think of to distribute a python package as an egg is so that you can not include the .py files with your package (and only include .pyc files, which is a dubious way to protect your code anyway). Aside from that, I can't really think of any reason to upload a package as an egg rather than an sdist. In fact, pip doesn't even support eggs.
Is there any real reason to use an egg rather than an sdist?
One reason: eggs can include compiled C extension modules so that the end user does not need to have the necessary build tools and possible additional headers and libraries to build the extension module from scratch. The drawback to that is that the packager may need to supply multiple eggs to match each targeted platform and Python configuration. If there are many supported configurations, that can prove to be a daunting task but it can be effective for more homogenous environments.
I've read some about .egg files and I've noticed them in my lib directory but what are the advantages/disadvantages of using then as a developer?
From the Python Enterprise Application Kit community:
"Eggs are to Pythons as Jars are to Java..."
Python eggs are a way of bundling
additional information with a Python
project, that allows the project's
dependencies to be checked and
satisfied at runtime, as well as
allowing projects to provide plugins
for other projects. There are several
binary formats that embody eggs, but
the most common is '.egg' zipfile
format, because it's a convenient one
for distributing projects. All of the
formats support including
package-specific data, project-wide
metadata, C extensions, and Python
code.
The primary benefits of Python Eggs
are:
They enable tools like the "Easy Install" Python package manager
.egg files are a "zero installation" format for a Python
package; no build or install step is
required, just put them on PYTHONPATH
or sys.path and use them (may require
the runtime installed if C extensions
or data files are used)
They can include package metadata, such as the other eggs they depend on
They allow "namespace packages" (packages that just contain other
packages) to be split into separate
distributions (e.g. zope., twisted.,
peak.* packages can be distributed as
separate eggs, unlike normal packages
which must always be placed under the
same parent directory. This allows
what are now huge monolithic packages
to be distributed as separate
components.)
They allow applications or libraries to specify the needed
version of a library, so that you can
e.g. require("Twisted-Internet>=2.0")
before doing an import
twisted.internet.
They're a great format for distributing extensions or plugins to
extensible applications and frameworks
(such as Trac, which uses eggs for
plugins as of 0.9b1), because the egg
runtime provides simple APIs to locate
eggs and find their advertised entry
points (similar to Eclipse's
"extension point" concept).
There are also other benefits that may come from having a standardized
format, similar to the benefits of
Java's "jar" format.
-Adam
One egg by itself is not better than a proper source release. The good part is the dependency handling. Like debian or rpm packages, you can say you depend on other eggs and they'll be installed automatically (through pypi.python.org).
A second comment: the egg format itself is a binary packaged format. Normal python packages that consist of just python code are best distributed as "source releases", so "python setup.py sdist" which result in a .tar.gz. These are also commonly called "eggs" when uploaded to pypi.
Where you need binary eggs: when you're bundling some C code extension. You'll need several binary eggs (a 32bit unix one, a windows one, etc.) then.
Eggs are a pretty good way to distribute python apps. Think of it as a platform independent .deb file that will install all dependencies and whatnot. The advantage is that it's easy to use for the end user. The disadvantage are that it can be cumbersome to package your app up as a .egg file.
You should also offer an alternative means of installation in addition to .eggs. There are some people who don't like using eggs because they don't like the idea of a software program installing whatever software it wants. These usually tend to be sysadmin types.
.egg files are basically a nice way to deploy your python application. You can think of it as something like .jar files for Java.
More info here.
Whatever you do, do not stop distributing your application, also, as a tarball, as that is the easiest packagable format for operating systems with a package sysetem.
For simple Python programs, you probably don't need to use eggs. Distributing the raw .py files should suffice; it's like distributing source files for GNU/Linux. You can also use the various OS "packagers" (like py2exe or py2app) to create .exe, .dmg, or other files for different operating systems.
More complex programs, e.g. Django, pretty much require eggs due to the various modules and dependencies required.