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: astropy/astropy#7582
Multiplying a pandas.Series
with an Unit
does not produce a Quantity
#
Quantities may work with certain operations on Series
but
this behaviour is not tested.
For example, multiplying a Series
instance
with a unit will not return a Quantity
. It will return a Series
object without any unit:
>>> 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:
>>> 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:
>>> 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.
Using Numpy array creation functions 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.])
However, the following works as one would expect
>>> np.full(10, 1.0, like=u.Quantity([], u.m))
<Quantity [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.] m>
and is equivalent to:
>>> np.full(10, 1) << u.m
<Quantity [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.] m>
zeros
, ones
, and empty
behave similarly.
arange
also supports the like
keyword argument
>>> np.arange(0 * u.cm, 1 * u.cm, 1 * u.mm, like=u.Quantity([], u.cm))
<Quantity [0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] cm>
Also note that the unit of the output array is dictated by that of the stop
argument, and that, like for quantities generally, the data has a floating-point
dtype. If stop
is a pure number, the unit of the output will default to that
of the like
argument.
As with ~numpy.full
and similar functions, one may alternatively move the
units outside of the call to arange
:
>>> np.arange(0, 10, 1) << u.mm
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] mm>
Or use linspace
:
>>> np.linspace(0 * u.cm, 9 * u.mm, 10)
<Quantity [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] mm>
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: astropy/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
np.True_
What the second comparison is really doing is this:
>>> (0 * u.Celsius == 0) and (0 == 0 * u.m)
np.True_
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/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))
np.int64(1)
>>> np.int_((15 * u.km) / (15 * u.imperial.foot))
np.int64(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(...)
.
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).