The `__future__` we relied on is now, where the 3 specific things are
all included [since Python 3.0](https://docs.python.org/3/library/__future__.html):
* absolute_import
* print_function
* unicode_literals
* division
These import statements are no-ops and are no longer necessary.
Timers are just too noisy to give reliable results. Previously, the
timers were not considered due to a bug in process-stats-dir that would
(almost) always output 0. This bug has been fixed in 92073c671e.
During the Python 2 to Python 3 conversion, the difference in encoding
became apparent. Explicitly handle the encoding by opening the file
with an explicit encoding. This prevents falling back to the `C` locale
which will use ASCII for UTF-8 which fails.
* More Python3 lint fixes
Some of the issues addressed include:
* Don't use `l` as a variable name (confusable with `1` or `I`)
* `print` statement does not exist in Py3, use `print` function instead
* Implicit tuple deconstruction in function args is no longer supported,
use explicit splat `*` at the call site instead
* `xrange` does not exist in Py3, use `range` instead
* Better name per review feedback
`zip` will return a generator in Python 3 rather than the zipped list.
This results in the Nelder-Mead Simplex to fail as it does not actually
perform the optimization of the data. Explicitly convert the data to a
form which can be consumed.
- Outlaw duplicate input files, fix driver, fix tests, and add test.
- Reflect that no buffer is present without a (possibly pseudo) named file.
- Reflect fact that every input has a (possible pseudo) name.
- Break up CompilerInstance::setup.
Don't bail on dups.
This allows invocations that fail with a specific exit code, e.g. 0 is the old
behaviour, for success, but also, for instance, 1 for measuring examples that
fail to typecheck.
This, somewhat questionably, fits the polynomial model and the
exponential model, and then chooses the one with the best R^2. However,
no matter how statistically valid this is, it works reasonably in
practice.
Slow growing things sometimes get classified as 1.0^n or 1.1^n, but
these are either spurious or not relevant, and so a similar thresholding
to the polynomial fit is used.
Erik Eckstein
Jamie
Slava Pestov
Josh Soref
YOCKOW
Daniel Duan
Alex Hoppen
Saleem Abdulrasool
tbkka
Ross Bayer
Alex Hoppen
Andrew Trick
Pavol Vaskovic
Xi Ge
David Ungar
Graydon Hoare
Mark Lacey
swift-ci
Max Moiseev
Huon Wilson
Hugh Bellamy
David Farler