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 while we have
ensured that numpy functions will work well with them, they do not always
work in functions from scipy or other packages that use numpy
internally, but ignore the subclass. Furthermore, at a few places in numpy
itself we cannot control the behaviour. For instance, care must be taken when
setting array slices using Quantities:
>>> import astropy.units as u
>>> import numpy as np
>>> 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>
As well as with full one cannot do zeros, ones, and empty.
The arange function does not work either:
>>> np.arange(0 * u.m, 10 * u.m, 1 * u.m)
Traceback (most recent call last):
...
TypeError: only dimensionless scalar quantities can be converted to Python scalars
Workarounds include moving the units outside of the call to
arange:
>>> np.arange(0, 10, 1) * u.m
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] m>
Also, linspace does work:
>>> np.linspace(0 * u.m, 9 * u.m, 10)
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] 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>
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).
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.
Structured units deserialization segfaults in big-endian¶
Structured units deserialization with pickle may cause segmentation
fault in big-endian machine with numpy<1.21.1.
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.