Known Issues¶
While most bugs and issues are managed using the astropy issue tracker, this document lists issues that are too difficult to fix, may require some intervention from the user to work around, or are caused by bugs in other projects or packages.
Issues listed on this page are grouped into two categories: The first is known
issues and shortcomings in actual algorithms and interfaces that currently do
not have fixes or workarounds, and that users should be aware of when writing
code that uses astropy
. Some of those issues are still platform-specific,
while others are very general. The second category is of common issues that come
up when configuring, building, or installing astropy
. This also includes
cases where the test suite can report false negatives depending on the context/
platform on which it was run.
Known Deficiencies¶
Quantities Lose Their Units with Some Operations¶
Quantities are subclassed from NumPy’s ndarray
and in some NumPy
operations (and in SciPy operations using NumPy internally) the subclass is
ignored, which means that either a plain array is returned, or a
Quantity
without units.
E.g., prior to astropy 4.0 and numpy 1.17:
>>> import astropy.units as u
>>> import numpy as np
>>> q = u.Quantity(np.arange(10.), u.m)
>>> np.dot(q,q)
285.0
>>> np.hstack((q,q))
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 0., 1., 2., 3., 4., 5.,
6., 7., 8., 9.] (Unit not initialised)>
And for all versions:
>>> ratio = (3600 * u.s) / (1 * u.h)
>>> ratio
<Quantity 3600. s / h>
>>> np.array(ratio)
array(3600.)
>>> np.array([ratio])
array([1.])
Workarounds are available for some cases. For the above:
>>> q.dot(q)
<Quantity 285. m2>
>>> np.array(ratio.to(u.dimensionless_unscaled))
array(1.)
>>> u.Quantity([q, q]).flatten()
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 0., 1., 2., 3., 4., 5.,
6., 7., 8., 9.] m>
An incomplete list of specific functions which are known to exhibit this behavior (prior to astropy 4.0 and numpy 1.17) follows:
numpy.hstack
,numpy.vstack
,numpy.c_
,numpy.r_
,numpy.append
pandas DataFrame(s)
See: https://github.com/astropy/astropy/issues/1274
Care must be taken when setting array slices using Quantities:
>>> a = np.ones(4)
>>> a[2:3] = 2*u.kg
>>> a
array([1., 1., 2., 1.])
>>> a = np.ones(4)
>>> a[2:3] = 1*u.cm/u.m
>>> a
array([1., 1., 1., 1.])
Either set single array entries or use lists of Quantities:
>>> a = np.ones(4)
>>> a[2] = 1*u.cm/u.m
>>> a
array([1. , 1. , 0.01, 1. ])
>>> a = np.ones(4)
>>> a[2:3] = [1*u.cm/u.m]
>>> a
array([1. , 1. , 0.01, 1. ])
Both will throw an exception if units do not cancel, e.g.:
>>> a = np.ones(4)
>>> a[2] = 1*u.cm
Traceback (most recent call last):
...
TypeError: only dimensionless scalar quantities can be converted to Python scalars
Numpy array creation functions cannot be used to initialize Quantity¶
Trying the following example will throw an UnitConversionError on NumPy before version 1.20 and ignore the unit in later versions:
>>> my_quantity = u.Quantity(1, u.m)
>>> np.full(10, my_quantity)
Traceback (most recent call last):
...
UnitConversionError: 'm' (length) and '' (dimensionless) are not convertible
A workaround for this at the moment would be to do:
>>> np.full(10, 1) << u.m
<Quantity [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.] m>
Quantities Lose Their Units When Broadcasted¶
When broadcasting Quantities, it is necessary to pass subok=True
to
broadcast_to
, or else a bare ndarray
will be returned:
>>> q = u.Quantity(np.arange(10.), u.m)
>>> b = np.broadcast_to(q, (2, len(q)))
>>> b
array([[0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]])
>>> b2 = np.broadcast_to(q, (2, len(q)), subok=True)
>>> b2
<Quantity [[0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]] m>
This is analogous to the case of passing a Quantity to array
:
>>> a = np.array(q)
>>> a
array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
>>> a2 = np.array(q, subok=True)
>>> a2
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] m>
Quantities Float Comparison with np.isclose Fails¶
Comparing Quantities floats using the NumPy function isclose
fails on
NumPy versions before 1.17 as the comparison between a
and b
is made using the formula
This will result in the following traceback when using this with Quantities:
>>> from astropy import units as u, constants as const
>>> import numpy as np
>>> np.isclose(500 * u.km/u.s, 300 * u.km / u.s)
Traceback (most recent call last):
...
UnitConversionError: Can only apply 'add' function to dimensionless quantities when other argument is not a quantity (unless the latter is all zero/infinity/nan)
If one cannot upgrade to numpy 1.17 or later, one solution is:
>>> np.isclose(500 * u.km/u.s, 300 * u.km / u.s, atol=1e-8 * u.mm / u.s)
False
Quantities in np.linspace Failure on NumPy 1.10¶
linspace
does not work correctly with quantities when using NumPy
1.10.0 to 1.10.5 due to a bug in NumPy. The solution is to upgrade to NumPy
1.10.6 or later, in which the bug was fixed.
mmap Support for astropy.io.fits
on GNU Hurd¶
On Hurd and possibly other platforms, flush()
on memory-mapped files are not
implemented, so writing changes to a mmap’d FITS file may not be reliable and is
thus disabled. Attempting to open a FITS file in writeable mode with mmap will
result in a warning (and mmap will be disabled on the file automatically).
Bug with Unicode Endianness in io.fits
for Big Endian Processors¶
On big endian processors (e.g. SPARC, PowerPC, MIPS), string columns in FITS
files may not be correctly read when using the Table.read
interface. This
will be fixed in a subsequent bug fix release of astropy
(see bug report here).
Color Printing on Windows¶
Colored printing of log messages and other colored text does work in Windows, but only when running in the IPython console. Colors are not currently supported in the basic Python command-line interpreter on Windows.
numpy.int64
does not decompose input Quantity
objects¶
Python’s int()
goes through __index__
while numpy.int64
or numpy.int_
do not go through __index__
. This
means that an upstream fix in numpy` is required in order for
``astropy.units
to control decomposing the input in these functions:
>>> np.int64((15 * u.km) / (15 * u.imperial.foot))
1
>>> np.int_((15 * u.km) / (15 * u.imperial.foot))
1
>>> int((15 * u.km) / (15 * u.imperial.foot))
3280
To convert a dimensionless Quantity
to an integer, it is
therefore recommended to use int(...)
.
Inconsistent behavior when converting complex numbers to floats¶
Attempting to use float
or NumPy’s numpy.float
on a standard
complex number (e.g., 5 + 6j
) results in a TypeError
. In
contrast, using float
or numpy.float
on a complex number from
NumPy (e.g., numpy.complex128
) drops the imaginary component and
issues a numpy.ComplexWarning
. This inconsistency persists between
Quantity
instances based on standard and NumPy
complex numbers. To get the real part of a complex number, it is
recommended to use numpy.real
.
Build/Installation/Test Issues¶
Anaconda Users Should Upgrade with conda
, Not pip
¶
Upgrading astropy
in the Anaconda Python distribution using pip
can result
in a corrupted install with a mix of files from the old version and the new
version. Anaconda users should update with conda update astropy
. There
may be a brief delay between the release of astropy
on PyPI and its release
via the conda
package manager; users can check the availability of new
versions with conda search astropy
.
Locale Errors in MacOS X and Linux¶
On MacOS X, you may see the following error when running pip
:
...
ValueError: unknown locale: UTF-8
This is due to the LC_CTYPE
environment variable being incorrectly set to
UTF-8
by default, which is not a valid locale setting.
On MacOS X or Linux (or other platforms) you may also encounter the following error:
...
stderr = stderr.decode(stdio_encoding)
TypeError: decode() argument 1 must be str, not None
This also indicates that your locale is not set correctly.
To fix either of these issues, set this environment variable, as well as the
LANG
and LC_ALL
environment variables to e.g. en_US.UTF-8
using, in
the case of bash
:
export LANG="en_US.UTF-8"
export LC_ALL="en_US.UTF-8"
export LC_CTYPE="en_US.UTF-8"
To avoid any issues in future, you should add this line to your e.g.
~/.bash_profile
or .bashrc
file.
To test these changes, open a new terminal and type locale
, and you should
see something like:
$ locale
LANG="en_US.UTF-8"
LC_COLLATE="en_US.UTF-8"
LC_CTYPE="en_US.UTF-8"
LC_MESSAGES="en_US.UTF-8"
LC_MONETARY="en_US.UTF-8"
LC_NUMERIC="en_US.UTF-8"
LC_TIME="en_US.UTF-8"
LC_ALL="en_US.UTF-8"
If so, you can go ahead and try running pip
again (in the new
terminal).
Failing Logging Tests When Running the Tests in IPython¶
When running the Astropy tests using astropy.test()
in an IPython
interpreter, some of the tests in the astropy/tests/test_logger.py
might
fail depending on the version of IPython or other factors.
This is due to mutually incompatible behaviors in IPython and pytest, and is
not due to a problem with the test itself or the feature being tested.
Some Docstrings Can Not Be Displayed in IPython < 0.13.2¶
Displaying long docstrings that contain Unicode characters may fail on some platforms in the IPython console (prior to IPython version 0.13.2):
In [1]: import astropy.units as u
In [2]: u.Angstrom?
Out[2]: ERROR: UnicodeEncodeError: 'ascii' codec can't encode character u'\xe5' in
position 184: ordinal not in range(128) [IPython.core.page]
This can be worked around by changing the default encoding to utf-8
by adding the following to your sitecustomize.py
file:
import sys
sys.setdefaultencoding('utf-8')
Note that in general, this is not recommended, because it can hide other Unicode encoding bugs in your application. However, if your application does not deal with text processing and you just want docstrings to work, this may be acceptable.
The IPython issue: https://github.com/ipython/ipython/pull/2738
Compatibility Issues with pytest 3.7 and later¶
Due to a bug in pytest related to test collection,
the tests for the core astropy
package for version 2.0.x (LTS), and for
packages using the core package’s test infrastructure and being tested against
2.0.x (LTS), will not be executed correctly with pytest 3.7, 3.8, or 3.9. The
symptom of this bug is that no tests or only tests in RST files are collected.
In addition, astropy
2.0.x (LTS) is not compatible with pytest 4.0 and above,
as in this case deprecation errors from pytest can cause tests to fail.
Therefore, when testing against astropy
v2.0.x (LTS), pytest 3.6 or earlier
versions should be used. These issues do not occur in version 3.0.x and above of
the core package.
There is an unrelated issue that also affects more recent versions of
astropy
when testing with pytest 4.0 and later, which can
cause issues when collecting tests — in this case, the symptom is that the
test collection hangs and/or appears to run the tests recursively. If you are
maintaining a package that was created using the Astropy
package template, then
this can be fixed by updating to the latest version of the _astropy_init.py
file. The root cause of this issue is that pytest now tries to pick up the
top-level test()
function as a test, so we need to make sure that we set a
test.__test__
attribute on the function to False
.