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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 ~numpy.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 # doctest: +FLOAT_CMP
array([1., 1., 2., 1.])

>>> a = np.ones(4)
>>> a[2:3] = 1*u.cm/u.m
>>> a # doctest: +FLOAT_CMP
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 # doctest: +FLOAT_CMP
array([1.  , 1.  , 0.01, 1.  ])

>>> a = np.ones(4)
>>> a[2:3] = [1*u.cm/u.m]
>>> a # doctest: +FLOAT_CMP
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: astropy#7582

Multiplying a pandas.Series with an ~astropy.units.Unit does not produce a |Quantity|

Quantities may work with certain operations on ~pandas.Series but this behaviour is not tested. For example, multiplying a ~pandas.Series instance with a unit will not return a |Quantity|. It will return a ~pandas.Series object without any unit:

.. doctest-requires:: pandas>=1.5

   >>> import pandas as pd
   >>> import astropy.units as u
   >>> a = pd.Series([1., 2., 3.])
   >>> a * u.m
   0    1.0
   1    2.0
   2    3.0
   dtype: float64

To avoid this, it is best to initialize the |Quantity| directly:

.. doctest-requires:: pandas>=1.5

    >>> u.Quantity(a, u.m)
    <Quantity [1., 2., 3.] m>

Note that the overrides pandas provides are not complete, and as a consequence, using the (in-place) shift operator does work:

.. doctest-requires:: pandas>=1.5

   >>> b = a << u.m
   >>> b
   <Quantity [1., 2., 3.] m>
   >>> a <<= u.m
   >>> a
   <Quantity [1., 2., 3.] m>

But this is fragile as this may stop working in future versions of pandas if they decide to override the dunder methods.

See: astropy#11247

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 ~numpy.full one cannot do ~numpy.zeros, ~numpy.ones, and ~numpy.empty.

The ~numpy.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 ~numpy.arange:

>>> np.arange(0, 10, 1) * u.m
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] m>

Also, ~numpy.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 ~numpy.broadcast_to, or else a bare ~numpy.ndarray will be returned:

>>> q = u.Quantity(np.arange(10.), u.m)
>>> b = np.broadcast_to(q, (2, len(q)))
>>> b # doctest: +FLOAT_CMP
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 # doctest: +FLOAT_CMP
<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 ~numpy.array:

>>> a = np.array(q)
>>> a # doctest: +FLOAT_CMP
array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
>>> a2 = np.array(q, subok=True)
>>> a2 # doctest: +FLOAT_CMP
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] m>

See: astropy#7832

Chained Quantity comparisons to dimensionless zero can be misleading

When chaining comparisons using Quantities and dimensionless zero, the result may be misleading:

>>> 0 * u.Celsius == 0 * u.m  # Correct
False
>>> 0 * u.Celsius == 0 == 0 * u.m  # Misleading
True

What the second comparison is really doing is this:

>>> (0 * u.Celsius == 0) and (0 == 0 * u.m)
True

See: astropy#15103

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: astropy#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 ~astropy.units.Quantity to an integer, it is therefore recommended to use int(...).

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: astropy#717