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

See: https://github.com/astropy/astropy/issues/7582

Numpy array creation functions cannot be used to initialize Quantity ¶

Trying the following example will ignore the unit:

>>> np.full(10, 1 * u.m)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

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>

See: https://github.com/astropy/astropy/issues/7832

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).

See: https://github.com/astropy/astropy/issues/968

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.

See: https://github.com/astropy/astropy/issues/717

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Known Issues¶