SDSS Python Template version 1


This documentation is for version 1 of the Python Template. Python Template 2 provides multiple new features. The documentation for version 2 can be found here.

This page describes the SDSS Python Template as well as the coding standards.

See what’s new.

What you get with this template

Directory Contents

  • cextern: The directory for placing C code to be compiled
  • docs: The directory for Sphinx documentation and other docu-related files
  • etc: The directory containing your SDSS modulefile and other etc
  • python: Your new python package directory
  • python/package_name/core: A directory for high-level core classes used in your product. Contains a set of custom python Exceptions.
  • python/package_name/etc: An etc directory with text files that will be installed with the product. Contains a YAML configuration file that is ready by the package when imported.
  • python/package_name/utils: General-use tools, including a custom logger and colour printing routines.
  • python/package_name/tests: The directory containing the tests for the package. Includes a file with basic configuration using pytest.
  • CHANGELOG.rst: A file documenting changes to your code, e.g. new features, fixed issues, or bug-fixes.
  • CODEOWNERS: A file assigning ownership of the code to the package or components of the package to various users
  • README.rst: A file describing your package. This will be the main display on Github.
  • STYLE.rst: The SDSS style guide for best coding practices.
  • The open source license for your product. DO NOT DELETE.
  • The setup for your pip-deployable product. Also used when installing manually with python install.
  • A list of all invoke tasks available.
  • requirements[_xxx].txt: These files list all Python packages that are dependencies for your product. Needed by pip.
  • readthedocs.yml: The configuration file for Read The Docs.
  • .travis.yml: The configuration file for Travis CI.
  • .bumpversion.cfg: The configuration file for Bumpversion.
  • .coveragerc: The configuration file for python code coverage and Coveralls.

Creating a new product

To install and initialize a new product from the template run

pip install invoke
pip install bumpversion
pip install cookiecutter
cookiecutter --checkout python-template-v1

During the installation cookiecutter will ask you a series of prompts to specify options and variable names, e.g. your name, the repository/folder name, the package name (which can be identical to the repository name), etc. These definitions will be inserted into the package in designated places to customise it for you.

The create_git_repo prompt asks do you want to create a git repository out of your new package?. If you answer yes, the product will be initialised as a git repository. The final prompts ask did you already create a new repository on Github? and what is your Github username?. If you answer yes, and specify a name, a remote origin will be added to your new git repository and will be pushed to Github. If not, create a blank GitHub repository (either at the SDSS organisation or in your personal account) and copy the URL provided by GitHub. Make sure the Github repository is initially empty. In the root of your local product run

git remote add origin GITHUB_URL
git push

The new product can be installed in your system by running python install. For development, however, it is usually better to add the product path to your PYTHONPATH. In bash add the following line to your ~/.bashrc (modify accordingly for csh or other shells)

export PYTHONPATH=/path/to/your/product/python:$PYTHONPATH

Now you have a totally functional, if very simple, Python package connected to a GitHub repository. The following sections explain how to use the features included in the template and how to connect it with different online services. Before you continue, this may be a good time to read the SDSS coding standards and make sure your code complies with them.

Bumping a version

The python template you cookiecut uses bumpversion to increase the version of your product. First, you need to install bumpversion by doing

pip install bumpversion

The bumpversion configuration is defined in the .bumpversion.cfg file in your new product. You should read the bumpversion documentation for details, but usually your workflow will be as follows: once you are ready to start working on a new version do

bumpversion patch

This will increase your version from X.Y.Z to X.Y.(Z+1)dev (e.g., 1.2.3 to 1.2.4dev) everywhere in your product and commit the changes. You can alternatively do bumpversion minor or bumpversion major to change the minor or major version. Once you are ready to release the version, do

bumpversion release

to remove the dev suffix. You can also do bumpversion patch release to release a new patch version without passing through the dev step.

It is recommended to always do a dry run of your bump before the real thing to make sure it will go smoothly. You can do it with:

bumpversion patch --dry-run --verbose

The default configuration of bumpversion is to always perform a commit whenever you bump to the next version. You can specify to also create a new tag of your version with:

bumpversion patch --tag

This will create a new tag locally with the new bumped version as the tag name. You can push the tag to Github with:

git push origin [tagname]

If you release and tag a new version, don’t forget to do bumpversion patch to increment to the next dev version.

Writing and running tests

The tests directory contains some examples on how to write and run tests for your package using pytest. Use the file to define fixtures and other pytest-specific features. cd’ing to the tests directory and typing pytest will recursively run all the tests in files whose filename starts with test_.

If you prefer to use unittest or nose feel free to remove those files.

Connecting your product to Travis

The template includes a basic setup for Travis CI and Coveralls. The configuration is defined in the .travis.yml and .coveragerc files.

Once you have created the GitHub repository for the product, you can go to your Travis CI account (create one if you don’t have it) and click on Add a new repository. Then search for the new product and flip the switch to initiate the integration. You can do the same for Coveralls. Each new push to the repository will trigger a Travis run that, if successful, will update the coverage report.

Using invoke

The product includes several macros to automate frequent tasks using Invoke. To get a list of all the available tasks, from the root of your cookiecut project, do

invoke -l

The documentation can be compiled by doing invoke and then shown in your browser with invoke Another useful macro, invoke deploy, automates the process of deploying a new version by creating new distribution packages and uploading them to PyPI (see deploying-section-v1).

You can add new tasks to the file.

How to build Sphinx Documentation

This template includes Sphinx documentation, written using the reStructuredText format. The documentation is located inside your python package, in a docs/sphinx/ directory. You can build the existing Sphinx documentation using invoke


Alternatively, navigate to your python package’s docs/sphinx/ directory and type:

make html

This will build your documentation, converting the rst files into html files. The output html files live in the docs/sphinx/_build subdirectory. To both build and display the documentation, type:

# builds and displays

The main page of your documentation lives at docs/sphinx/_build/html/index.html. New documentation must be written in the rst syntax for Sphinx to understand and properly build html files.

The template includes an example on how to automatically document the docstrings in your code. In docs/sphinx/api.rst you’ll see the lines

.. automodule:: mypython.main

You can add similar blocks of code for other modules. See the Sphinx autodoc for more details. The coding standards include a section on how to write good docstrings to document your code.

Connecting your product to Read The Docs

The cookiecut product documentation is ready to be built and integrated with Read The Docs. As with Travis and Coveralls above, you will need to commit the products to a GitHub repository first. SDSS has a Read The Docs account that is the preferred place to integrate the documentation. You can request access to the account by emailing admin[at]sdss[dot]org. Alternatively, you can deploy your product in your own Read the Docs account and add the user sdss as a maintainer from the admin menu.

Probably you will receive a message saying that the integration of the product is not complete and that you need to set up a webhook. To do that, got to the admin setting of the new Read The Docs project. In Intergations add a new integration and copy the link to the webhook. Then go to the GitHub repository settings and in the Webhooks section add a new webhook with the URL you just copied. Once you submit, any push to the main branch of the GitHub repo should produce a new built of the documentation. You can find more details on the webhook set up here.

The product configuration for Read The Docs can be found in readthedocs.yml. By default, the Sphinx documentation will be built using Python 3.6 and using the requirements specified in requirements_doc.txt. You can change those settings easily.

Configuration file and logging

Your new product contains a YAML configuration file in the python/[product_name]/etc/ directory. YAML is significantly superior to other alternatives such as configparser; it provides typed values, a clear data structure, and powerful parsing libraries. When you import the package, the configuration can be accessed as a dictionary using the config attribute. For example

import mypython
>>> 2.0
>>> 'some text'

If the user creates a custom configuration file in ~/.config/mypython/mypython.yml, the contents of that file will be used to update the default options. For instance, if you create a file with the contents

    suboption2: "a different text"

the code above would return

>>> 2.0
>>> 'a different text'

Another possibility is to define an environment variable $MYPYTHON_CONFIG_PATH pointing to the user configuration file to use. If the environment variable is set, it overrides the default location for the user configuration file.

The package also includes a logging object built around Python’s logging module. Our custom logger allows to file and screen at the same time and provides more colourful tracebacks and warnings. From anywhere in your code you can do

from mypython import log'Some information that we want to log')
>>> [INFO]: Some information that we want to log

Available levels are .debug, .info, .error, and .critical. For warnings, use warnings module.

By default, the file logger is not enabled. To start logging to file do


where '~/.mypython/mypython.log' is the path of the file to which we want to log. If the file exists, the previous file is backed up by adding a timestamp to the extension. File logs are automatically backed up at midnight (see TimedRotatingFileHandler).

On initialisation, the screen logger will only show messages with level INFO or above. The file logger default level is DEBUG. Levels can be changed in runtime

# Sets the screen minimum level to DEBUG
import logging

# Sets the file level to CRITICAL

The current log can be saved as


Deploying your product

This section explains how to deploy a new version of your product to PyPI so that it becomes pip-installable. All SDSS products should be deployed to the SDSS dedicated PyPI account, access to which can be requested to admin[at]sdss[dot]org. First you will need to create a ~/.pypirc file with the following content


repository =
username = sdss
password = [request this password]

To deploy a new release you will need twine. To install it

pip install twine

Then, from the root of your product, run

invoke deploy

which will create source and wheel distributions of your package and upload them to PyPI. The command above is equivalent to running

python sdist bdist_wheel --universal
twine upload dist/*

The NAME argument inside your specifies the name of the package as it appears in PyPi and how it will be installed. To avoid potential conflicts with existing packages, all SDSS package pip-names should adhere to the format sdss-[pkgname]. E.g. the Python package tree would be called sdss-tree. The python package sdss_access would be called sdss-access.

How to modify this template

This template is built using Cookiecutter. To add content to or expand this template, you must first check out the main template product using git:

git clone

Now you have the development version of this template. The two main components need are a cookiecutter.json file and a {{cookiecutter.package_name}} directory. Cookiecutter templates use the Jinja2 templating language to define variable substitution, using double bracket notation, e.g. {{variable_name}}. All customizable content to be inserted by the user is defined using this notation.

  • {{cookiecutter.package_name}}: the top-level directory defining the installed python package. Everything below this directory belongs to the Python package that gets installed by the user.
  • cookiecutter.json: A JSON file containing a dictionary of key:value pairs of variables defined in the template, with their default values. These keys are referenced throughout the template with {{cookiecutter.key}}.

Upon installation of the template by a user, the variables defined in the cookiecutter.json file, or by the user during install, get substituted into their respective reference places.

Please, do not modify the main branch directly unless otherwise instructed. Instead, develop your changes in a branch or fork and, when ready to merge, create a pull request.

SDSS tree and sdss_access

This template includes the SDSS tree and sdss_access Python packages. This template adds these products as required dependencies in your installed project’s requirements.txt file. We encourage you to use these packages inside your code. The tree package is designed to set up the SDSS SAS environment system dynamically within your Python environment. The sdss_access package is designed to provide local and remote filesystem path generation and downloading. To use these yourself, you may need to install them:

pip install sdss-tree
pip install sdss-access

See the tree and sdss_access readthedocs for full documentation on each package, but in brief, to use the tree:

# loads the full SAS using the sdsswork configuration.  You only need to do this one per Python session.
from tree import Tree
my_tree = Tree()

and to use sdss_access:

# generate a local path to a file
from sdss_access.path import Path
path = Path()
filepath = path.full('mangacube', drpver='v2_3_1', plate='8485', '1901')