Developer How To#

General conventions#

Python version support#

In Gammapy we currently support Python 3.8 or later.

Coordinate and axis names#

In Gammapy, the following coordinate and axis names should be used.

This applies to most of the code, ranging from IRFs to maps to sky models, for function parameters and variable names.

  • time - time

  • energy - energy

  • ra, dec - sky coordinates, radec frame (i.e. icrs to be precise)

  • glon, glat - sky coordinates, galactic frame

  • az, alt - sky coordinates, altaz frame

  • lon, lat for spherical coordinates that aren’t in a specific frame.

For angular sky separation angles:

  • psf_theta - offset wrt. PSF center position

  • fov_theta - offset wrt. field of view (FOV) center

  • theta - when no PSF is involved, e.g. to evaluate spatial sky models

For the general case of FOV coordinates that depend on angular orientation of the FOV coordinate frame:

  • fov_{frame}_lon, fov_{frame}_lat - field of view coordinates

  • fov_theta, fov_{frame}_phi - field of view polar coordinates

where {frame} can be one of radec, galactic or altaz, depending on with which frame the FOV coordinate frame is aligned.

Notes:

  • In cases where it’s unclear if the value is for true or reconstructed event parameters, a postfix _true or _reco should be added. In Gammapy, this mostly occurs for energy_true and energy_reco, e.g. the background IRF has an axis energy_reco, but effective area usually energy_true, and energy dispersion has both axes. We are not pedantic about adding _true and _reco everywhere. Note that this would quickly become annoying (e.g. source models use true parameters, and it’s not clear why one should write ra_true). E.g. the property on the event list energy matches the ENERGY column from the event list table, which is for real data always reco energy.

  • Currently, no sky frames centered on the source, or non-radially symmetric PSFs are in use, and thus the case of “source frames” that have to be with a well-defined alignment, like we have for the “FOV frames” above, doesn’t occur and thus doesn’t need to be defined yet (but it would be natural to use the same naming convention as for FOV if it eventually does occur).

  • These definitions are mostly in agreement with the Gamma Astro Data Formats specifications. We do not achieve 100% consistency everywhere in the spec and Gammapy code. Achieving this seems unrealistic, because legacy formats have to be supported, we are not starting from scratch and have time to make all formats consistent. Our strategy is to do renames on I/O where needed, to and from the internal Gammapy names defined here, to the names used in the formats. Of course, where formats are not set in stone yet, we advocate and encourage the use of the names chosen here.

  • Finally, we realise that eventually probably CTA will define this, and Gammapy is only a prototype. So if CTA chooses something else, probably we will follow suite and do one more backward-incompatible change at some point to align with CTA.

Clobber or overwrite?#

In Gammapy we consistently use an overwrite bool option for gammapy.scripts and functions that write to files. This is in line with Astropy, which had a mix of clobber and overwrite in the past, and has switched to uniform overwrite everywhere.

The default value should be overwrite=False, although we note that this decision was very controversial, several core developers would prefer to use overwrite=True. For discussion on this, see GH 1396.

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).

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

BSD or GPL license?#

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 of 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.

How to write code#

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#

It is up to the developer to decide how to return multiple things from functions and methods. For up to three things, if callers will usually want access to several things, using a tuple or collections.namedtuple is ok. For three or more things, using a Python dict instead should be preferred.

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.

How to use 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 randomly”, 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').

How to use 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 …

It is worth mentioning that important logs returned to the user should be captured and tested using caplog fixture, see the section Caplog fixture above

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.

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.

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 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 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 – Less “safe”. Extrapolate with zero. Can be very convenient for the caller to avoid dealing with NaN, but if the data values can also be zero you will lose track of invalid pixels.

  • bounds_error=False, fill_value=None – “Unsafe”. If fill_value is None, values outside the domain are extrapolated. Can 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.

How to write tests#

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.

For assertions on Quantity objects, you can do this to assert on the unit and value separately:

from numpy.testing import assert_allclose
import astropy.units as u

actual = 1 / 3 * u.deg
assert actual.unit == 'deg'
assert_allclose(actual.value, 0.33333333)

Note that Quantity can be compared to unit strings directly. Also note that the default for assert_allclose is atol=0 and rtol=1e-7, so when using it, you have to give the reference value with a precision of rtol ~ 1e-8, i.e. 8 digits to be on the safe side (or pass a lower rtol or set an atol).

The use of assert_quantity_allclose is discouraged, because it only requires that the values match after unit conversions. This is not so bad, but units in test cases should not change randomly, so asserting on unit and value separately establishes more behaviour.

If you don’t like the two separate lines, you can use gammapy.utils.testing.assert_quantity_allclose, which does assert that units are equal, and calls numpy.testing.assert_equal for the values.

Testing of plotting functions#

Many of the data classes in Gammapy implement .plot() or .peek() methods to allow users a quick look in the data. Those methods should be tested using the mpl_check_plot() context manager. The context manager will take care of creating a new figure to plot on and writing the plot to a byte-stream to trigger the rendering of the plot, which can raise errors as well. Here is a short example:

from gammapy.utils.testing import mpl_plot_check

def test_plot():
    with mpl_plot_check():
        plt.plot([1., 2., 3., 4., 5.])

With this approach we make sure that the plotting code is at least executed once and runs completely (up to saving the plot to file) without errors. In future, we will maybe change to something like https://github.com/matplotlib/pytest-mpl to ensure that correct plots are produced.

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():
   ...

Caplog fixture#

Inside tests, we have the possibility to change the log level for the captured log messages using the caplog fixture which allow you to access and control log capturing. When logging is part of your function, and you want to verify the right message is logged with the expected logging level:

import pytest

def test_something(caplog):
    """Test something.

    Parameters
    ----------
    caplog : caplog fixture that give you access to the log level, the logger, etc.,
    """
    assert "WARNING" in [_.levelname for _ in caplog.records]
    assert "warning message" in [_.message for _ in caplog.records]

How to make a pull request#

Making a pull request with new or modified datasets#

Datasets used in tests are hosted in the gammapy-data GitHub repository. It is recommended that developers have $GAMMAPY_DATA environment variable pointing to the local folder where they have fetched the gammapy-data GitHub repository, so they can push and pull eventual modification of its content.

Making a pull request which skips GitHub Actions#

For minor PRs (eg: correcting typos in doc-strings) we can skip GitHub Actions. Adding [ci skip] in a specific commit message will skip CI for that specific commit which can be useful for draft or incomplete PR. For details, see here.

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 ..

Release notes#

In Gammapy we keep Release notes with a list of pull requests. We sort by release and within the release by PR number (the 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 releases, each with pros and cons.

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

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

Others#

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.

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.

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 text repr, str and info#

In Python, by default objects don’t have a good string representation. This section explains how Python repr, str and print work, and gives guidelines for writing __repr__, __str__ and info methods on Gammapy classes.

Let’s use this as an example:

class Person:
    def __init__(self, name='Anna', age=8):
        self.name = name
        self.age = age

The default repr and str are this:

p = Person()
repr(p)
'<__main__.Person object at 0x105fe3b70>'
p.__repr__()
'<__main__.Person object at 0x105fe3b70>'
str(p)
'<__main__.Person object at 0x105fe3b70>'
p.__str__()

Users will see that. If they just give an object in the Python REPL, the repr is shown. If they print the object, the str is shown. In both cases without the quotes seen above.

p <__main__.Person at 0x105fd0cf8> print(p) <__main__.Person object at 0x105fe3b70>

There are ways to make this better and avoid writing boilerplate code, specifically attrs and dataclasses. We might use those in the future in Gammapy, but for now, we don’t.

If you want a better repr or str for a given object, you have to add __repr__ and / or __str__ methods when writing the class. Note that you don’t have to do that, it’s mainly useful for objects users interact with a lot. For classes that are mainly used internally, developers can e.g. just do this to see the attributes printed nicely:

p.__dict__
{'name': 'Anna', 'age': 8}

Here’s an example how to write __repr__:

def __repr__(self):
    return '{}(name={!r}, age={!r})'.format(
        self.__class__.__name__, self.name, self.age
    )

Note how we use {!r} in the format string to fill in the repr of the object being formatted, and how we used self.__class__.__name__ to avoid duplicating the class name (easier to refactor code, and shows sub-class name if repr is inherited).

This will give a nice string representation. The same one for repr and str, you don’t have to write __str__:

p = Person(name='Anna', age=8)
p
Person(name='Anna', age=8)
print(p)
Person(name='Anna', age=8)

The string representation is usually used for more informal or longer printout. Here’s an example:

def __str__(self):
    return (
        "Hi, my name is {} and I'm {} years old.\n"
        "I live in Heidelberg."
    ).format(self.name, self.age)

If you need text representation that is configurable, i.e. tables arguments what to show, you should add a method called info. To avoid code duplication, you should then call info from __str__. Example:

class Person:
    def __init__(self, name='Anna', age=8):
        self.name = name
        self.age = age

    def __repr__(self):
        return '{}(name={!r}, age={!r})'.format(
            self.__class__.__name__, self.name, self.age
        )

    def __str__(self):
        return self.info(add_location=False)

    def info(self, add_location=True):
        s = ("Hi, my name is {} and I'm {} years old."
            ).format(self.name, self.age)
        if add_location:
            s += "\nI live in Heidelberg"
        return s

This pattern of returning a string from info has some pros and cons. It’s easy to get the string, and do what you like with it, e.g. combine it with other text, or store it in a list and write it to file later. The main con is that users have to call print(p.info()) to see a nice printed version of the string instead of \n:

p = Person()
p.info()
"Hi, my name is Anna, and I'm 8 years old.\nI live in Heidelberg"
print(p.info())
Hi, my name is Anna, and I'm 8 years old.
I live in Heidelberg

To make info print by default, and be re-usable from __str__ and make it possible to get a string (without having to monkey-patch sys.stdout), would require adding this show option and if-else at the end of every info method:

def __str__(self):
    return self.info(add_location=False, show=False)

def info(self, add_location=True, show=True):
    s = ("Hi, my name is {} and I'm {} years old."
         ).format(self.name, self.age)
    if add_location:
        s += "\nI live in Heidelberg"

    if show:
        print(s)
    else:
        return s

To summarise: start without adding and code for text representation. If there’s a useful short text representation, you can add a __repr__. If really useful, add a __str__. If you need it configurable, add an info and call info from str. If repr and str are similar, it’s not really useful: delete the __str__ and only keep the __repr__.

It is common to have bugs in __repr__, __str__ and info that are not tested. E.g. a NameError or AttributeError because some attribute name changed, and updating the repr / str / info was forgotten. So tests should be added that execute these methods once. You can write the reference string in the output, but that is not required (and actually very hard for cases where you have floats or Numpy arrays or str, where formatting differs across Python or Numpy version). Example what to put as a test:

def test_person_txt():
    p = Person()
    assert repr(p).startswith('Person')
    assert str(p).startswith('Hi')
    assert p.info(add_location=True).endswith('Heidelberg')