PERF: leverage tzlocal package to provide 2000x speedup for dateutil.tz.tzlocal operations
State: closed

This is a follow-up to https://github.com/pandas-dev/pandas/pull/24491, where it was noted that `tzlocal` operations are notably slower:
```
[ 53.12%] ··· timeseries.DatetimeIndex.time_add_timedelta                                                                                                                 ok
[ 53.12%] ··· ============ =============
               index_type
              ------------ -------------
                  dst         471±8μs
                repeated    3.52±0.05ms
                tz_aware    3.78±0.07ms
              **tz_local     5.77±0.01s**
                tz_naive    1.98±0.03ms
              ============ =============
```
This PR makes them indistinguishable from other timezones:
```
[ 60.00%] ··· timeseries.DatetimeIndex.time_add_timedelta                                                                                                                 ok
[ 60.00%] ··· ============ =============
               index_type
              ------------ -------------
                  dst         465±8μs
                repeated    3.55±0.07ms
                tz_aware    3.65±0.05ms
                tz_local    3.68±0.04ms
                tz_naive    1.81±0.05ms
              ============ =============
```
To do this, we add a new optional dependency: `tzlocal`. This package queries the OS to try and identify a `pytz` compatible timezone name for the local timezone. If this is successful, we can use the `pytz` timezone instead of `dateutil.tz.tzlocal()`, yielding a huge speedup as the latter invokes `datetime.datetime()`. In cases where the local timezone is also UTC, further speedups are possible.

In all cases, the `tzlocal()` object is maintained as the `.tz` to preserve any semantic meaning.

One unresolved issue is an incompatibility with `python-dateutil` versions prior to `2.6.0`. I'm unable to reproduce the error locally, but can repeatably get this error on Travis:
```
    def test_timetz_accessor(self, tz_naive_fixture):
        # GH21358
        tz = timezones.maybe_get_tz(tz_naive_fixture)
    
        expected = np.array([time(10, 20, 30, tzinfo=tz), pd.NaT])
    
        index = DatetimeIndex(['2018-06-04 10:20:30', pd.NaT], tz=tz)
        result = index.timetz
    
>       tm.assert_numpy_array_equal(result, expected)
pandas/tests/indexes/datetimes/test_timezones.py:824: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
pandas/core/dtypes/missing.py:404: in array_equivalent
    ensure_object(left.ravel()), ensure_object(right.ravel()))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   if not (PyObject_RichCompareBool(x, y, Py_EQ) or
E   TypeError: can't compare offset-naive and offset-aware times
```
While this only shows up on the 2.7 build, I believe this is due to it being the only one actually building against `python-dateutil=2.5.0`. Any version `>=2.6.0` does not exhibit this error.

Finally, here is the `asv` benchmark against `v0.24.0rc1`:
```
$ asv compare v0.24.0rc1 HEAD -s --sort ratio --only-changed
       before           after         ratio
     [fdc4db25]       [56e5cc0d]
     <v0.24.0rc1^0>       <tzlocal>
-        68.4±5μs         17.9±1μs     0.26  timeseries.DatetimeIndex.time_get('tz_local')
-      5.54±0.02s          188±2ms     0.03  timeseries.DatetimeIndex.time_to_pydatetime('tz_local')
-      7.13±0.02s       30.2±0.7ms     0.00  timeseries.DatetimeIndex.time_normalize('tz_local')
-      6.00±0.05s       23.7±0.3ms     0.00  timeseries.DatetimeIndex.time_to_time('tz_local')
-      6.00±0.01s       21.3±0.3ms     0.00  timeseries.DatetimeIndex.time_to_date('tz_local')
-      5.98±0.06s      3.68±0.04ms     0.00  timeseries.DatetimeIndex.time_add_timedelta('tz_local') 
-      5.96±0.05s      2.49±0.05ms     0.00  timeseries.DatetimeIndex.time_timeseries_is_month_start('tz_local')
```

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry


Labels:
name: Datetime
description: Datetime data dtype
name: Performance
description: Memory or execution speed performance
name: Timezones
description: Timezone data dtype

Comments:
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=h1) Report
> Merging [#24737](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/fdc4db25a9988a7b595a3756760d05a6c177123d?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/24737/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #24737      +/-   ##
==========================================
- Coverage   92.39%   92.38%   -0.01%     
==========================================
  Files         166      166              
  Lines       52358    52358              
==========================================
- Hits        48374    48373       -1     
- Misses       3984     3985       +1
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.81% <ø> (ø)` | :arrow_up: |
| #single | `43.07% <ø> (-0.02%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `88% <0%> (-0.1%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=footer). Last update [fdc4db2...5f9fb96](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).

# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=h1) Report
> Merging [#24737](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/e3b095074c5c3b29ec25a994cd204349b872d961?src=pr&el=desc) will **decrease** coverage by `49.61%`.
> The diff coverage is `9.77%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/24737/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=tree)

```diff
@@             Coverage Diff             @@
##           master   #24737       +/-   ##
===========================================
- Coverage   92.37%   42.75%   -49.62%     
===========================================
  Files         166      171        +5     
  Lines       52408    52582      +174     
===========================================
- Hits        48412    22484    -25928     
- Misses       3996    30098    +26102
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `?` | |
| #single | `42.75% <9.77%> (-0.11%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/\_libs/src/tzlocal/win32.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL19saWJzL3NyYy90emxvY2FsL3dpbjMyLnB5) | `0% <0%> (ø)` | |
| [pandas/\_libs/src/tzlocal/windows\_tz.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL19saWJzL3NyYy90emxvY2FsL3dpbmRvd3NfdHoucHk=) | `0% <0%> (ø)` | |
| [pandas/\_libs/src/tzlocal/unix.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL19saWJzL3NyYy90emxvY2FsL3VuaXgucHk=) | `10.41% <10.41%> (ø)` | |
| [pandas/\_libs/src/tzlocal/utils.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL19saWJzL3NyYy90emxvY2FsL3V0aWxzLnB5) | `28.57% <28.57%> (ø)` | |
| [pandas/\_libs/src/tzlocal/\_\_init\_\_.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL19saWJzL3NyYy90emxvY2FsL19faW5pdF9fLnB5) | `75% <75%> (ø)` | |
| [pandas/io/formats/latex.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL2lvL2Zvcm1hdHMvbGF0ZXgucHk=) | `0% <0%> (-100%)` | :arrow_down: |
| [pandas/core/categorical.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvY2F0ZWdvcmljYWwucHk=) | `0% <0%> (-100%)` | :arrow_down: |
| [pandas/io/sas/sas\_constants.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL2lvL3Nhcy9zYXNfY29uc3RhbnRzLnB5) | `0% <0%> (-100%)` | :arrow_down: |
| [pandas/tseries/plotting.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL3RzZXJpZXMvcGxvdHRpbmcucHk=) | `0% <0%> (-100%)` | :arrow_down: |
| [pandas/tseries/converter.py](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree#diff-cGFuZGFzL3RzZXJpZXMvY29udmVydGVyLnB5) | `0% <0%> (-100%)` | :arrow_down: |
| ... and [133 more](https://codecov.io/gh/pandas-dev/pandas/pull/24737/diff?src=pr&el=tree-more) | |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=footer). Last update [e3b0950...59c1cb6](https://codecov.io/gh/pandas-dev/pandas/pull/24737?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).

@mroeschke Agreed, it's worth mentioning that this mostly fixes the top performance regression between `v0.23.4` and `v0.24.0rc1`:
```
       before           after         ratio
     [04095216]       [fdc4db25]
     <v0.23.4^0>       <v0.24.0rc1^0>
+     3.91±0.01ms       18.8±0.01s  4819.04  timeseries.DatetimeIndex.time_add_timedelta('tz_local')
```
It'd be relatively simple to vendor it as it's only a handful of files. It's MIT licensed, so should be fine but worth mentioning now.

I haven't been able to reproduce the `python-dateutil` conflict locally thus far, but I'll dig into that further to see if there's an easy workaround.


if we bump to 2.6.0 would u still need tzlocal?
I generally like the idea of avoiding returning `dateutil.tz.tzlocal()`.  Besides the performance issue, it hurts portability.

Do we have any read on how accurate/canonical tzlocal.get_localzone is?  For instance I just called it and got "America/Los_Angeles" back, while I was expecting to get "US/Pacific".  Do/should we care about that distinction?

Also FWIW, the way dateutil decides to return a tzlocal() object is based on looking at `time.tzname`, which for me returns a tuple `('PST', 'PDT')`.
@jreback Yes, there's just a hard-to-reproduce conflict with 2.5.0-2.5.3. Not sure how serious it is at this point.

@jbrockmendel Regarding accuracy, it's pulling from system files like `/etc/timezone`, `/var/db/zoneinfo`, etc so should accurately reflect the system timezone. However, given that it's checking so many places it's entirely possible a user has different timezones configured in different files.

There's no distinction between `America/Los_Angeles` and `US/Pacific` other than the latter being deprecated (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones).
Although tzlocal hurts portability (unless that's the intension), I think pandas should ensure that timezones in == timezones out and shouldn't make any assumptions or transformation when returning the timezone (i.e tzlocal in, tzlocal out; US/Pacific in, US/Pacific out), which I can see your PR does @qwhelan .

Overall agreed that I'd be great to address this regression. Shot in the dark: I think the proper way to compare dateutil tzs is with `is` and not `==`, so maybe that's why you're seeing that error?

> Overall agreed that I'd be great to address this regression. Shot in the dark: I think the proper way to compare dateutil tzs is with is and not ==, so maybe that's why you're seeing that error?

use ``tz_compare`` for this purpose
Hello @qwhelan! Thanks for updating the PR.





 - In the file [`pandas/_libs/src/tzlocal/unix.py`](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py), following are the PEP8 issues :

> [Line 63:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L63): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (81 > 79 characters)
> [Line 121:33](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L121): [E226](https://duckduckgo.com/?q=pep8%20E226) missing whitespace around arithmetic operator
> [Line 128:37](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L128): [E226](https://duckduckgo.com/?q=pep8%20E226) missing whitespace around arithmetic operator
> [Line 131:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L131): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (81 > 79 characters)
> [Line 134:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L134): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (80 > 79 characters)
> [Line 160:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/unix.py#L160): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (89 > 79 characters)

 - In the file [`pandas/_libs/src/tzlocal/utils.py`](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/utils.py), following are the PEP8 issues :

> [Line 12:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/utils.py#L12): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (80 > 79 characters)
> [Line 27:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/utils.py#L27): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (80 > 79 characters)
> [Line 37:16](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/utils.py#L37): [E126](https://duckduckgo.com/?q=pep8%20E126) continuation line over-indented for hanging indent

 - In the file [`pandas/_libs/src/tzlocal/win32.py`](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/win32.py), following are the PEP8 issues :

> [Line 90:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/win32.py#L90): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (96 > 79 characters)
> [Line 100:80](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/win32.py#L100): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (89 > 79 characters)

 - In the file [`pandas/_libs/src/tzlocal/windows_tz.py`](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py), following are the PEP8 issues :

> [Line 4:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L4): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 5:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L5): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 6:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L6): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 7:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L7): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 8:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L8): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 9:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L9): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 10:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L10): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 11:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L11): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 12:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L12): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 13:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L13): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 14:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L14): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 15:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L15): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 16:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L16): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 17:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L17): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 18:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L18): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 19:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L19): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 20:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L20): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
> [Line 21:2](https://github.com/pandas-dev/pandas/blob/59c1cb619a959c4d354db21817fb689fe25d9cd7/pandas/_libs/src/tzlocal/windows_tz.py#L21): [E128](https://duckduckgo.com/?q=pep8%20E128) continuation line under-indented for visual indent
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##### Comment last updated on February 05, 2019 at 07:53 Hours UTC
@jreback I'm finally able to reproduce the `dateutil` 2.5.0 issue locally and think I have it resolved without needing to bump the dependency. Where would be the appropriate directory to vendor `tzlocal`?
put in pandas/tseries/_tzlocal.py i think - see how that works
@jreback How about `pandas/_libs/tslibs/*`? Could give way to cythonize some things?
sure that’s ok too
> Could give way to cythonize some things?

Is there anything in particular that bears cythonizing?

Haven't looked closely, but I imagine most all functions here can be `cdef`'d given that it just interacts in `timezones.pyx`?
@qwhelan can you merge master and address build failures?
can you merge master and update
can you merge master and update
closing as stale. nice idea though, pls ping if you can continue.