whole | Whole Platform Language Workbench | BPM library

This issue was fixed by Google (10 February 2022).

You can now update Android Studio normally.

Thank you all for helping to bring this problem to Google's attention.

The functions provided in the Python re module do not optimize based on anchors. In particular, functions that try to apply a regex at every position - .search, .sub, .findall etc. - will do so even when the regex can only possibly match at the beginning. I.e., even without multi-line mode specified, such that ^ can only match at the beginning of the string, the call is not re-routed internally. Thus:

Your split splits the list in two ordered halves, so merge consumes its first argument first and then just produces the second half in full. In other words it is equivalent to ++, doing redundant comparisons on the first half which always turn out to be True.

In the true mergesort the merge actually does twice the work on random data because the two parts are not ordered.

The split though spends some work on the partitioning whereas an online bottom-up mergesort would spend no work there at all. But the built-in sort tries to detect ordered runs in the input, and apparently that extra work is not negligible.

For example, git diff --word-diff=color:

and git diff --word-diff=plain:

There's also --word-diff-regex=

See git help diff for more info.

There is an update to mini-css-extract-plugin in version 2.5.0. I temporarily fixed it by adding in package.json:

This is what solved it for me on my M1.

1. Go to Android Studio Preview and download the latest Canary build for Apple chip (Chipmunk). Don't worry this is just to get through the initial setup.
2. Unpack it, run it, let it install all the SDK components, accept licenses, etc as usual.
3. Once it's done, simply close it and delete it.

Now when you start your stable Android Studio (Arctic Fox) you should not see the error.

The active CWG issue 1647 mentions exactly this case of specializing an int non-type template parameter to a char.

It also mentions that the standard is currently lacking wording to handle type mismatches between non-type template parameters in primary templates and their partial specializations and that there is implementation divergence on the issue.

Since you mention executing the same files (with proper performance) from within Git Bash, I'm going to make an assumption here. Correct me if I'm wrong on this, and I'll delete the answer and look for another possibility.

This would be explained (and expected) if your files are stored on /mnt/c (a.k.a. C:, or /C under Git Bash) or any other Windows drive, as they would likely need to be to be accessed by Git Bash.

WSL2 uses the 9P protocol to access Windows drives, and it is currently known to be very slow when compared to:

• Native NTFS (obviously)
• The ext4 filesystem on the virtual disk used by WSL2
• And even the performance of WSL1 with Windows drives

I've seen a git clone of a large repo (the WSL2 Linux kernel Github) take 8 minutes on WSL2 on a Windows drive, but only seconds on the root filesystem.

Two possibilities:

• If possible (and it is for most Node projects), convert your WSL to version 1 with wsl --set-version 1. I always recommend making a backup with wsl --export first.

  And since you are making a backup anyway, you may as well just create a copy of the instance by wsl --importing your backup as --version 1 (as the last argument). WSL1 and WSL2 both have their uses, and you may find it helpful to keep both around.

  See this answer for more details on the exact syntax..

• Or just move the project over to somewhere under the WSL root, such as /home/username/src/.

You can do calculations within a function for the x and y values to construct the ggplot which extends the circle all the way round and gives labels correct heights.

I've adapted a function to work with other datasets. This takes a dataset in a tidy format, with:

• a 'year' column
• one row per 'event'
• a grouping variable (such as country)

I've used Nobel laurate data from here as an example dataset to show the function in practice. Data setup: