* fixes#12015 by also checking kind of `typeNode`
If a tuple field is aliased it'll appear the same as a ref type in a
call to `getType` if only for the kind of the resulting `NimNode` is
checked (that is a `nnkBracketExpr`)
* fix test case due to #12017 and add more realistic test case
Adds an additional test case, which includes generics and is closer to
the real failure I encountered
* remove previous fix and fix differently after all
The previous fix was incomplete, because it failed for generics.
Note that the `of "tuple"` is not actually needed, the
`nnkBracketExpr` branch in the `else` branch would catch it too, but I
decided to introduce it for clarity. However, the latter is actually
needed, because it seems for aliases of `seq` we end up in it.
* update comment about global `%` proc in json test
* Add patch by @xenogenesi
* Async test for HTTP/1.1 without Content-Length
* Apply suggestions from code review
Co-Authored-By: Dominik Picheta <dominikpicheta@googlemail.com>
* Allow for locale-based parsing/formatting of dates
* Updates based on review feedback of PR 11170
DateTimeLocale arrays are now indexed by Month and WeekDay enums.
More sane date used for testing.
Documentation newline.
Case change of DefaultLocale (and make it public)
* Add changelog entry for DateTimeLocale addition to times module
* Use pattern symbols for DateTimeLocale attribute names
* s/exitStatus(...)/exitStatusLikeShell(...)/
* fix#10273 execShellCmd now returns nonzero when child exits with signal
* test case for #10249 and explanation for the bug
* fix test failure
* add tests/nim.cfg
Darwin has long deprecated the wait union, but their macros still assume
it unless you define _POSIX_C_SOURCE. This trips up C++ compilers.
This commit duplicates the behavior of WEXITSTATUS when _POSIX_C_SOURCE
is defined.
* Resolve things raised in https://github.com/nim-lang/Nim/issues/10081 ?
CDF is a standard ident in all things related to random numbers/sampling,
and full words "cumulativeDistributionFunction" would be silly long, in
this case, IMO. We use lowercase `cdf` to make it not look like a type,
remove all looping from `sample` letting callers do it. Besides just
side-stepping any `sampleSize` name choice, callers may want to filter
out samples anyway which this makes slightly simpler.
Also add two variants of `cumsum`, value return and in-place update
distinguished by the var-ness of the first argument. Add tests for
`int` and `float` for both `cumsum` and the new `sample`. (The sample
tests exercise the value return mode of `cumsum`.)
Functionality pre-this-PR `sample(a, w)` is now the almost as simple
`for i in 0..<n: sample(a, w.cumsum)`, but this new code factoring is
almost surely better. The statistical tests pass, as before.
* Address Araq comment in https://github.com/nim-lang/Nim/pull/10084
We can always add in some `var` version later if desired to save
memory, but this change now at least firms up the `sample` interface.
* Rename `cumsum` -> `cumsummed` to honor NEP1 style. Re-instate `cumsum` as
the in-place transformation. Test both in `tests/stdlib/tmath.nim` and use
`cumsummed` in the example code for sample since that's a simpler example.
* Fix requests from https://github.com/nim-lang/Nim/pull/10084 :
example in lib/pure/math.nim and comment whitespace in lib/pure/random.nim
* Add the ability to sample elements from an openArray according to a parallel
array of weights/unnormalized probabilities (any sort of histogram, basically).
Also add a non-thread safe version for convenience.
* Address Araq comments on https://github.com/nim-lang/Nim/pull/10072
* import at top of file and space after '#'.
* Put in a check for non-zero total weight.
* Clarify constraint on `w`.
* Rename `rand(openArray[T])` to `sample(openArray[T])` to `sample`, deprecating
old name and name new (openArray[T], openArray[U]) variants `sample`.
* Rename caller-provided state version of rand(openArray[T]) and also clean
up doc comments.
* Add test for new non-uniform array sampler. 3 sd bound makes it 99% likely
that it will still pass in the future if the random number generator changes.
We cannot both have a tight bound to check distribution *and* loose check to
ensure resilience to RNG changes. (We cannot *guarantee* resilience, anyway.
There's always a small chance any test hits a legitimate random fluctuation.)
