Include the text of the Contributor Covenant (1.1.0) in
CONTRIBUTING.rst, and add a link to the document to README.md.
Signed-off-by: Gergely Nagy <algernon@madhouse-project.org>
As reported in issue #748, there was a bug in which passing a lambda
as the value of a :keyword argument would fail—
$ hy --spy
hy 0.10.1 using CPython(default) 3.4.0 on Linux
=> (sorted (range 10) :key (fn [x] (- x)))
from hy.core.language import range
sorted(range(10), key=_hy_anon_fn_1)
Traceback (most recent call last):
File "<input>", line 1, in <module>
NameError: name '_hy_anon_fn_1' is not defined
The function call would appear in the generated AST without being
preceded by the appropriate function definition corresponding to the
anonymous function argument value in the Hy source, causing either a
NameError (as in the example above), or erroneous reuse of whatever
function was already pointed to by the `_hy_anon_fn_` name referenced
in the list of keywords passed to `ast.Call`.
This commit aims to fix the problem by handling it in same way that
the expression/statement gap is bridged many other places in the
compiler, by adding the compiled value of the keyword argument to the
Result object being built during `_compile_collect`, with the
understanding that any Python statements implied by the argument value
will be appropriately preserved therein.
Python 3.5 will have a new commercial-at infix operator with the magic
methods __matmul__, __rmatmul__, and __imatmul__, unused as yet in the
standard library, but intended to represent matrix multiplication in
numerical code; see PEP 465 (https://www.python.org/dev/peps/pep-0465/)
for details. This commit (developed against Python 3.5 alpha 3) brings
support for this operator to Hy when running under Python 3.5 (or,
hypothetically as yet, greater). For Hy under Python <= 3.4, attempting
to use `@` in function-call position currently results in a NameError;
this commit does not change that behavior.
This is intended to resolve#668.
Using travis' container based infra which is faster & allows caching pip
and is faster, also after the environment update hy seems happy with
pypy2.5 so removing the hacks needed to pass that.
