# Developer HOWTO¶

This page is a collection of notes for Gammapy contributors and maintainers, in the form of short “How to” or “Q & A” entries.

## How to clean up old files¶

TODO: Gammapy now has a Makefile … this section should be expanded to a page about setup.py and make.

Many projects have a Makefile to build and install the software and do all kinds of other tasks. In Astropy and Gammapy and most Python projects, there is no Makefile, but the setup.py file and you’re supposed to type python setup.py <cmd> and use --help and --help-commands to see all the available commands and options.

There’s one common task, cleaning up old generated files, that’s not done via setup.py. The equivalent of make clean is:

$rm -r build docs/_build docs/api htmlcov docs/notebooks docs/_static/notebooks  These folders only contain generated files and are always safe to delete! Most of the time you don’t have to delete them, but if you e.g. remove or rename files or functions / classes, then you should, because otherwise the old files will still be around and you might get confusing results, such as Sphinx warnings or import errors or code that works locally because it uses old things, but fails on travis-ci or for other developers. • The build folder is where python setup.py build or python setup.py install generate files. • The docs/api folder is where python setup.py build_docs generates [RST] files from the docstrings (temporary files part of the HTML documentation generation). • The docs/_build folder is where python setup.py build_docs generates the HTML and other Sphinx documentation output files. • The htmlcov folder is where python setup.py test --coverage generates the HTML coverage report. • The docs/notebooks folder is where the source Jupyter notebooks are placed before conversion into Sphinx formatted HTML documents. • The docs/_static/notebooks folder is where raw .ipynb files and .py scripts versions of Jupyter notebooks are placed so they may be downloaded from links in the documentation. If you use python setup.py build_ext --inplace, then files are generated in the gammapy source folder. Usually that’s not a problem, but if you want to clean up those generated files, you can use git clean: $ git status
# The following command will remove all untracked files!
# If you have written code that is not committed yet in a new file it will be gone!
# So use with caution!
$git clean -fdx  At least for now we prefer not to add a Makefile to Gammapy, because that splits the developers into those that use setup.py and those that use make, which can grow into an overall more complicated system where some things are possible only with setup.py and others only with make. ## Where should I import from?¶ You should import from the “end-user namespaces”, not the “implementation module”. from gammapy.data import EventList # good from gammapy.data.event_list import EventList # bad from gammapy.stats import cash # good from gammapy.stats.fit_statistics import cash # bad  The end-user namespace is the location that is shown in the API docs, i.e. you can use the Sphinx full-text search to quickly find it. To make code maintenance easier, the implementation of the functions and classes is spread across multiple modules (.py files), but the user shouldn’t care about their names, that would be too much to remember. The only reason to import from a module directly is if you need to access a private function, class or variable (something that is not listed in __all__ and thus not imported into the end-user namespace. Note that this means that in the definition of an “end-user namespace”, e.g. in the gammapy/data/__init__.py file, the imports have to be sorted in a way such that modules in gammapy/data are loaded when imported from other modules in that sub-package. ## Functions returning several values¶ Functions that return more than a single value shouldn’t return a list or dictionary of values but rather a Python Bunch result object. A Bunch is similar to a dict, except that it allows attribute access to the result values. The approach is the same as e.g. the use of OptimizeResult. An example of how Bunches are used in gammapy is given by the SkyImageList class. ## Python 2 and 3 support¶ We support Python 2.7 and 3.4 or later using a single code base. This is the strategy adopted by most scientific Python projects and a good starting point to learn about it is here and here. For developers, it would have been nice to only support Python 3 in Gammapy. But the CIAO and Fermi Science tools software are distributed with Python 2.7 and probably never will be updated to Python 3. Plus many potential users will likely keep running on Python 2.7 for many years (see e.g. this survey). The decision to drop Python 2.6 and 3.2 support was made in August 2014 just before the Gammapy 0.1 release, based on a few scientific Python user surveys on the web that show that only a small minority are still using such an old version, so that it’s not worth the developer and maintainer effort to test these old versions and to find workarounds for their missing features or bugs. Python 3.3 support was dropped in August 2015 because conda packages for some of the affiliated packages weren’t available for testing on travis-ci. ## Skip unit tests for some Astropy versions¶ import astropy import pytest ASTROPY_VERSION = (astropy.version.major, astropy.version.minor) @pytest.mark.xfail(ASTROPY_VERSION < (0, 4), reason="Astropy API change") def test_something(): ...  ## Fix non-Unix line endings¶ In the past we had non-Unix (i.e. Mac or Windows) line endings in some files. This can be painful, e.g. git diff and autopep8 behave strangely. Here’s to commands to check for and fix this (see here): $ git clean -fdx
$find . -type f -print0 | xargs -0 -n 1 -P 4 dos2unix -c mac$ find . -type f -print0 | xargs -0 -n 1 -P 4 dos2unix -c ascii
$git status$ cd astropy_helpers && git checkout -- . && cd ..


## Other codes¶

These projects are on Github, which is great because it has full-text search and git history view:

These are unofficial, unmaintained copies on open codes on Github:

Actually at this point we welcome experimentation, so you can use cool new technologies to implement some functionality in Gammapy if you like, e.g.

## What checks and conversions should I do for inputs?¶

In Gammapy we assume that “we’re all consenting adults”, which means that when you write a function you should write it like this:

def do_something(data, option):
"""Do something.

Parameters
----------
data : numpy.ndarray
Data
option : {'this', 'that'}
Option
"""
if option == 'this':
out = 3 * data
elif option == 'that':
out = data ** 5
else:
ValueError('Invalid option: {}'.format(option))

return out

• Don’t always add isinstance checks for everything … assume the caller passes valid inputs, … in the example above this is not needed:

assert isinstance(option, str)

• Don’t always add numpy.asanyarray calls for all array-like inputs … the caller can do this if it’s really needed … in the example above document data as type ndarray instead of array-like and don’t put this line:

data = np.asanyarray(data)

• Do always add an else clause to your if-elif clauses … this is boilerplate code, but not adding it would mean users get this error if they pass an invalid option:

UnboundLocalError: local variable 'out' referenced before assignment


Now if you really want, you can add the numpy.asanyarray and isinstance checks for functions that end-users might often use for interactive work to provide them with better exception messages, but doing it everywhere would mean 1000s of lines of boilerplate code and take the fun out of Python programming.

## Float data type: 32 bit or 64 bit?¶

Most of the time what we want is to use 32 bit to store data on disk and 64 bit to do computations in memory.

Using 64 bit to store data and results (e.g. large images or cubes) on disk would mean a factor ~2 increase in file sizes and slower I/O, but I’m not aware of any case where we need that precision.

On the other hand, doing computations with millions and billions of pixels very frequently results in inaccurate results … e.g. the likelihood is the sum over per-pixel likelihoods and using 32-bit will usually result in erratic and hard-to-debug optimizer behaviour and even if the fit works incorrect results.

Now you shouldn’t put this line at the top of every function … assume the caller passes 64-bit data:

data = np.asanyarray(data, dtype='float64')


But you should add explicit type conversions to 64 bit when reading float data from files and explicit type conversions to 32 bit before writing to file.

## Clobber or overwrite?¶

In Gammapy we use on overwrite bool option for gammapy.scripts and functions that write to files.

Why not use clobber instead? After all the FTOOLS always use clobber.

The reason is that overwrite is clear to everyone, but clobber is defined by the dictionary (e.g. see here) as “to batter severely; strike heavily. to defeat decisively. to denounce or criticize vigorously.” and isn’t intuitively clear to new users.

Astropy has started the process of changing their APIs to consistently use overwrite and deprecated the use of clobber. So we do the same in Gammapy.

## Pixel coordinate convention¶

All code in Gammapy should follow the Astropy pixel coordinate convention that the center of the first pixel has pixel coordinates (0, 0) (and not (1, 1) as shown e.g. in ds9). It’s currently documented here but I plan to document it in the Astropy docs soon (see issue 2607).

You should use origin=0 when calling any of the pixel to world or world to pixel coordinate transformations in astropy.wcs.

## When to use C or Cython or Numba for speed¶

Most of Gammapy is written using Python and Numpy array expressions calling functions (e.g. from Scipy) that operate on Numpy arrays. This is often nice because it means that algorithms can be implemented with few lines of high-level code,

There is a very small fraction of code though (one or a few percent) where this results in code that is either cumbersome or too slow. E.g. to compute TS or upper limit images, one needs to do a root finding method with different number of iterations for each pixel … that’s impossible (or at least very cumbersome / hard to read) to implement with array expressions and Python loops over pixels are slow.

In these cases we encourage the use of Cython or Numba, or writing the core code in C and exposing it to Python via Cython. These are popular and simple ways to get C speed from Python.

To use several CPU cores consider using the Python standard library multiprocessing module.

Note that especially the use of Numba should be considered an experiment. It is a very nice, but new technology and no-one uses it in production. Before the Gammapy 1.0 release we will re-evaluate the status of Numba and decide whether it’s an optional dependency we use for speed, or whether we use the much more established Cython (Scipy, scikit-image, Astropy, … all use Cython).

At the time of writing (April 2015), the TS map computation code uses Cython and multiprocessing and Numba is not used yet.

## What belongs in Gammapy and what doesn’t?¶

The scope of Gammapy is currently not very well defined … if in doubt whether it makes sense to add something, please ask on the mailing list or via a Github issue.

Roughly the scope is high-level science analysis of gamma-ray data, starting with event lists after gamma-hadron separation and corresponding IRFs, as well as source and source population modeling.

For lower-level data processing (calibration, event reconstruction, gamma-hadron separation) there’s ctapipe. There’s some functionality (event list processing, PSF or background model building, sensitivity computations …) that could go in either ctapipe or Gammapy and we’ll have to try and avoid duplication.

SED modeling code belongs in naima.

A lot of code that’s not gamma-ray specific belongs in other packages (e.g. Scipy, Astropy, other Astropy-affiliated packages, Sherpa). We currently have quite a bit of code that should be moved “upstream” or already has been, but the Gammapy code hasn’t been adapted yet.

## Assert convention¶

When performing tests, the preferred numerical assert method is numpy.testing.assert_allclose. Use

from numpy.testing import assert_allclose


at the top of the file and then just use assert_allclose for the tests. This makes the lines shorter, i.e. there is more space for the arguments.

assert_allclose covers all use cases for numerical asserts, so it should be used consistently everywhere instead of using the dozens of other available asserts from pytest or numpy in various places.

In case of assertion on arrays of quantity objects, such as Quantity or Angle, the following method can be used: astropy.tests.helper.assert_quantity_allclose. In this case, use

from astropy.tests.helper import assert_quantity_allclose


at the top of the file and then just use assert_quantity_allclose for the tests.

## Random numbers¶

All functions that need to call a random number generator should take a random_state input parameter and call the get_random_state utility function like this (you can copy & paste the three docstring lines and the first code line to the function you’re writing):

from gammapy.utils.random import get_random_state

def make_random_stuff(X, random_state='random-seed'):
"""...

Parameters
----------
random_state : {int, 'random-seed', 'global-rng', ~numpy.random.RandomState}
Defines random number generator initialisation.
Passed to ~gammapy.utils.random.get_random_state.
"""
random_state = get_random_state(random_state)
data = random_state.uniform(low=0, high=3, size=10)
return data


This allows callers flexible control over which random number generator (i.e. which numpy.random.RandomState instance) is used and how it’s initialised. The default random_state='random-seed' means “create a new RNG, seed it in a random way”, i.e. different random numbers will be generated on every call.

There’s a few ways to get deterministic results from a script that call functions that generate random numbers.

One option is to create a single RandomState object seeded with an integer and then pass that random_state object to every function that generates random numbers:

from numpy.random import RandomState
random_state = RandomState(seed=0)

stuff1 = make_some_random_stuff(random_state=random_state)
stuff2 = make_more_random_stuff(random_state=random_state)


Another option is to pass an integer seed to every function that generates random numbers:

seed = 0
stuff1 = make_some_random_stuff(random_state=seed)
stuff2 = make_more_random_stuff(random_state=seed)


This pattern was inspired by the way scikit-learn handles random numbers. We have changed the None option of sklearn.utils.check_random_state to 'global-rng', because we felt that this meaning for None was confusing given that numpy.random.RandomState uses a different meaning (for which we use the option 'global-rng').

## Logging¶

Gammapy is a library. This means that it should never contain print statements, because with print statements the library users have no easy way to configure where the print output goes (e.g. to stdout or stderr or a log file) and what the log level (warning, info, debug) and format is (e.g. include timestamp and log level?).

So logging is much better than printing. But also logging is only rarely needed. Many developers use print or log statements to debug some piece of code while they write it. Once it’s written and works, it’s rare that callers want it to be chatty and log messages all the time. Print and log statements should mostly be contained in end-user scripts that use Gammapy, not in Gammapy itself.

That said, there are cases where emitting log messages can be useful. E.g. a long-running algorithm with many steps can log info or debug statements. In a function that reads and writes several files it can make sense to include info log messages for normal operation, and warning or error log messages when something goes wrong. Also, command line tools that are included in Gammapy should contain log messages, informing the user about what they are doing.

Gammapy uses the Python standard library logging module. This module is extremely flexible, but also quite complex. But our logging needs are very modest, so it’s actually quite simple …

### Generating log messages¶

To generate log messages from any file in Gammapy, include these two lines at the top:

import logging
log = logging.getLogger(__name__)


This creates a module-level logging.Logger object called log, and you can then create log messages like this from any function or method:

def process_lots_of_data(infile, outfile):

log.info('Starting processing data ...')

# do lots of work

log.info('Writing {}'.format(outfile))


You should never log messages from the module level (i.e. on import) or configure the log level or format in Gammapy, that should be left to callers … except from command line tools …

There is also the rare case of functions or classes with the main job to check and log things. For these you can optionally let the caller pass a logger when constructing the class to make it easier to configure the logging. See the EventListDatasetChecker as an example.

### Configuring logging from command line tools¶

Every Gammapy command line tool should have a --loglevel option:

parser.add_argument("-l", "--loglevel", default='info',
choices=['debug', 'info', 'warning', 'error', 'critical'],
help="Set the logging level")


This option is then processed at the end of main using this helper function:

set_up_logging_from_args(args)


This sets up the root logger with the log level and format (the format isn’t configurable for the command line scripts at the moment).

See gammapy/scripts/find_obs.py as an example.

## Command line tools using click¶

Command line tools that use the click module should disable the unicode literals warnings to clean up the output of the tool:

import click
click.disable_unicode_literals_warning = True


See here for further information.

Gammapy is BSD licensed (same license as Numpy, Scipy, Matplotlib, scikit-image, Astropy, photutils, yt, …).

We prefer this over the GPL3 or LGPL license because it means that the packages we are most likely to share code with have the same license, e.g. we can take a function or class and “upstream” it, i.e. contribute it e.g. to Astropy or Scipy if it’s generally useful.

Some optional dependencies of Gammapy (i.e. other packages like Sherpa or Gammalib or ROOT that we import in some places) are GPL3 or LGPL licensed.

Now the GPL3 and LGPL license contains clauses that other package that copy or modify it must be released under the same license. We take the standpoint that Gammapy is independent from these libraries, because we don’t copy or modify them. This is a common standpoint, e.g. astropy.wcs is BSD licensed, but uses the LGPL-licensed WCSLib.

Note that if you distribute Gammapy together with one of the GPL dependencies, the whole distribution then falls under the GPL license.

## Changelog¶

In Gammapy we keep a Changelog with a list of pull requests. We sort by release and within the release by PR number (largest first).

As explained in the Updating and Maintaining the Changelog section in the Astropy docs, there are (at least) two approaches for adding to the changelog, each with pros and cons.

We’ve had some pain due to merge conflicts in the changelog and having to wait until the contributor rebases (and having to explain git rebase to new contributors).

So our recommendation is that changelog entries are not added in pull requests, but that the core developer adds a changelog entry after right after having merged a pull request (you can add [skip ci] on this commit).

## File and directory path handling¶

In Gammapy use Path objects to handle file and directory paths.

from gammapy.extern.pathlib import Path

dir = Path('folder/subfolder')
filename = dir / 'filename.fits'
dir.mkdir(exist_ok=True)
table.write(str(filename))


Note how the / operator makes it easy to construct paths (as opposed to repeated calls to the string-handling function os.path.join) and how methods on Path objects provide a nicer interface to most of the functionality from os.path (mkdir in this example).

One gotcha is that many functions (such as table.write in this example) expect str objects and refuse to work with Path objects, so you have to explicitly convert to str(path).

Note that pathlib was added to the Python standard library in 3.4 (see here), but since we support Python 2.7 and the Python devs keep improving the version in the standard library (by adding new methods and new options for existing methods), we decided to bundle the latest version (from here) in gammapy/extern/pathlib.py and that should always be used.

## Bundled gammapy.extern code¶

We bundle some code in gammapy.extern. This is external code that we don’t maintain or modify in Gammapy. We only bundle small pure-Python files (currently all single-file modules) purely for convenience, because having to explain about these modules as Gammapy dependencies to end-users would be annoying. And in some cases the file was extracted from some other project, i.e. can’t be installed separately as a dependency.

For gammapy.extern we don’t generate Sphinx API docs. To see what is there, check out the gammapy/extern directory locally or on Github. Notes on the bundled files are kept in the docstring of gammapy/extern/__init__.py.

## Interpolation and extrapolation¶

In Gammapy, we use interpolation a lot, e.g. to evaluate instrument response functions (IRFs) on data grids, or to reproject diffuse models on data grids.

Note: For some use cases that require interpolation the NDDataArray base class might be useful.

The default interpolator we use is scipy.interpolate.RegularGridInterpolator because it’s fast and robust (more fancy interpolation schemes can lead to unstable response in some cases, so more careful checking across all of parameter space would be needed).

You should use this pattern to implement a function of method that does interpolation:

def do_something(..., interp_kwargs=None):
"""Do something.

Parameters
----------
interp_kwargs : dict or None
Interpolation parameter dict passed to scipy.interpolate.RegularGridInterpolator.
If you pass None, the default interp_params=dict(bounds_error=False) is used.
"""
if not interp_kwargs:
interp_kwargs = dict(bounds_error=False)

interpolator = RegularGridInterpolator(..., **interp_kwargs)


Since the other defaults are method='linear' and fill_value=nan, this implies that linear interpolation is used and NaN values are returned for points outside of the interpolation domain. This is a compromise between the alternatives:

• bounds_error=True – Very “safe”, refuse to return results for any points if one of the points is outside the valid domain. Can be annoying for the caller to not get any result.
• bounds_error=False, fill_value=nan – Medium “safe”. Always return a result, but put NaN values to make it easy for analysers to spot that there’s an issue in their results (if pixels with NaN are used, that will usually lead to NaN values in high-level analysis results.
• bounds_error=False, fill_value=0 or bounds_error=False, fill_value=None – Least “safe”. Extrapolate with zero or edge values (this is what None means). Can be very convenient for the caller, but can also lead to errors where e.g. stacked high-level analysis results aren’t quite correct because IRFs or background models or … were used outside their valid range.

Methods that use interpolation should provide an option to the caller to pass interpolation options on to RegularGridInterpolator in case the default behaviour doesn’t suit the application.

TODO: we have some classes (aeff2d and edisp2d) that pre-compute an interpolator, currently in the constructor. In those cases the interp_kwargs would have to be exposed e.g. also on the read and other constructors. Do we want / need that?

## Locate origin of warnings¶

By default, warnings appear on the console, but often it’s not clear where a given warning originates (e.g. when building the docs or running scripts or tests) or how to fix it.

Sometimes putting this in gammapy/__init__.py can help:

import numpy as np
np.seterr(all='raise')


Following the advice here, putting this in docs/conf.py can also help sometimes:

import traceback
import warnings
import sys

def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
traceback.print_stack()
log = file if hasattr(file,'write') else sys.stderr
log.write(warnings.formatwarning(message, category, filename, lineno, line))

warnings.showwarning = warn_with_traceback


## Object summary info string¶

If you want to add a method to provide some basic information about a class instance, you should use the Python __str__ method.

class Spam(object):
def __init__(self, ham):
self.ham = ham

def __str__(self):
ss = 'Summary Info about class Spam\n'
ss += '{:.2f}'.format(self.ham)
return ss


If you want to add configurable info output, please provide a method summary, like here. In this case the __str__ method should be a call to summary with default parameters. Do not use an info method, since this would lead to conflicts for some classes in Gammapy (e.g. classes that inherit the info method from astropy.table.Table.

## Validating H.E.S.S. FITS exporters¶

The H.E.S.S. experiment has 3 independent analysis chains, which all have exporters to the Data formats for gamma-ray astronomy format. The Gammapy tests contain a mechanism to track changes in these exporters.

In the gammapy-extra repository there is a script test_datasets/reference/make_reference_files.py that reads IRF files from different chains and prints the output of the __str__ method to a file. It also creates a YAML file holding information about the datastore used for each chain, the observations used, etc.

The test gammapy/irf/tests/test_hess_chains.py load exactly the same files as the script and compares the output of the __str__ function to the reference files on disk. That way all changes in the exporters or the way the IRF files are read by Gammapy can be tracked. So, if you made changes to the H.E.S.S. IRF exporters you have to run the make_reference_files.py script again to ensure the passing of all Gammapy tests.

If you want to compare the IRF files between two different datastores (to compare between to chains or fits productions) you have to
manually edit the YAML file written by make_reference_files.py and include the info which datastore should be compared to which reference file.

## Using the NDDataArray¶

Gammapy has a class for generic n-dimensional data arrays, NDDataArray. Classes that represent such an array should use this class. The goal is to reuse code for interpolation and have an coherent I/O interface, mainly in irf.

A usage example can be found in :gp-extra-notebooks:nddata_demo.

Also, consult Interpolation and extrapolation if you are not sure how to setup your interpolator.

## Write a test for an IPython notebook¶

There is a script called test_notebooks.py in the gammapy main folder. It exectues all notebooks listed in file notebook.yaml in gammapy-extra/notebooks.yaml using runipy. So if you edit an existing notebook or make changes to gammapy that break an existing notebook, you have to run test_notebooks.py until all notebooks run without raising an error. If you add a new notebook and want it to be under test (which of course is what you want) you have to add it to gammapy-extra/notebooks/notebooks.yaml. Note that there is also the command make test-notebooks which is used for

continuous integration on travis CI. It is not recommended to use this locally, since it overwrides your gammapy installation (see issue 727).

## Sphinx docs build¶

Generating the HTML docs for Gammapy is straight-forward:

python setup.py build_docs
open docs/_build/html/index.html


Generating the PDF docs is more complex. This should work:

python setup.py build_docs -b latex
cd docs/_build/latex
makeindex -s python.ist gammapy.idx
pdflatex -interaction=nonstopmode gammapy.tex
open gammapy.pdf


You need a bunch or LaTeX stuff, specifically texlive-fonts-extra is needed.

Jupyter notebooks present in the gammapy-extra repository are by default copied to the docs/notebooks and docs/_static/notebooks folders during the process of generating HTML docs. This triggers its conversion to Sphinx formatted HTML files and .py scripts. The Sphinx formatted versions of the notebooks provide access to the raw .ipynb Jupyter files and .py script versions stored in docs/_static/notebooks folder.

Once the documentation built you can optimize the speed of re-building processes, for example in case you are modifying or creating new docs and you would like to check these changes are displayed nicely. For that purpose, if your modified doc file does not contain links to notebooks, you may set the flag build_notebooks to False in the setup.cfg file, so they are not re-written again by Sphinx.

## Documentation guidelines¶

Like almost all Python projects, the Gammapy documentation is written in a format called restructured text (RST) and built using Sphinx. We mostly follow the Astropy documentation guidelines, which are based on the Numpy docstring standard, which is what most scientific Python packages use.

There’s a few details that are not easy to figure out by browsing the Numpy or Astropy documentation guidelines, or that we actually do differently in Gammapy. These are listed here so that Gammapy developers have a reference.

Usually the quickest way to figure out how something should be done is to browse the Astropy or Gammapy code a bit (either locally with your editor or online on Github or via the HTML docs), or search the Numpy or Astropy documentation guidelines mentioned above. If that doesn’t quickly turn up something useful, please ask by putting a comment on the issue or pull request you’re working on on Github, or send an email to the Gammapy mailing list.

### Functions or class methods that return a single object¶

For functions or class methods that return a single object, following the Numpy docstring standard and adding a Returns section usually means that you duplicate the one-line description and repeat the function name as return variable name. See astropy.cosmology.LambdaCDM.w or astropy.time.Time.sidereal_time as examples in the Astropy codebase. Here’s a simple example:

def circle_area(radius):
"""Circle area.

Parameters
----------
radius : ~astropy.units.Quantity

Returns
-------
area : ~astropy.units.Quantity
Circle area
"""
return 3.14 * (radius ** 2)


In these cases, the following shorter format omitting the Returns section is recommended:

def circle_area(radius):
"""Circle area (~astropy.units.Quantity).

Parameters
----------
radius : ~astropy.units.Quantity
"""
return 3.14 * (radius ** 2)


Usually the parameter description doesn’t fit on the one line, so it’s recommended to always keep this in the Parameters section.

This is just a recommendation, e.g. for gammapy.cube.SkyCube.spectral_index we decided to use this shorter format, but for gammapy.cube.SkyCube.flux we decided to stick with the more verbose format, because the return type and units didn’t fit on the first line.

A common case where the short format is appropriate are class properties, because they always return a single object. As an example see gammapy.data.EventList.radec, which is reproduced here:

@property
"""Event RA / DEC sky coordinates (~astropy.coordinates.SkyCoord).
"""
lon, lat = self['RA'], self['DEC']
return SkyCoord(lon, lat, unit='deg', frame='icrs')


### Class attributes¶

Class attributes (data members) and properties are currently a bit of a mess, see SkyCube as an example. Attributes are listed in an Attributes section because I’ve listed them in a class-level docstring attributes section as recommended here. Properties are listed in separate Attributes summary and Attributes Documentation sections, which is confusing to users (“what’s the difference between attributes and properties?”).

One solution is to always use properties, but that can get very verbose if we have to write so many getters and setters. I don’t have a solution for this yet … for now I’ll go read this and meditate.

TODO: make a decision on this and describe the issue / solution here.

### Constructor parameters¶

TODO: should we put the constructor parameters in the class or __init__ docstring?

## Different versions of notebooks in Binder¶

Jupyter notebooks may be accessed and executed on-line in the Gammapy Binder space. Each fixed-text sphinx formatted notebook present in the documentation has its own link pointing to its specific space in Gammapy Binder. Since notebooks are evolving with Gammapy functionalities and documentation, it is possible to link the different versions of the notebooks stored in GitHub repository gammapy-extra to the same versions built in Gammapy Binder. For this purpose just edit the variable git_commit in setup.cfg file and provide the branch, tag or commit of GitHub repository gammapy-extra that will be used to access the same version of the notebook in Gammapy Binder.

## Include images from gammapy-extra into the docs¶

Similar to the gp-extra-notebook role, Gammapy has a gp-extra-image directive.

To include an image from gammapy-extra/figures/, use the gp-extra-image directive instead of the usual Sphinx image directive like this:

.. gp-extra-image:: detect/fermi_ts_image.png
:scale: 100%