An SELinux policy prevents this sender from sending this message to this recipient, 0 matched rules: type="method_call", sender="(null)" (inactive) interface="org.freedesktop.Dbus" member="Hello" error name="(unset)" requested_reply="0" destination="org.freedesktop.Dbus" (bus)

name: precise
encrypted: no
Entering /mnt/stateful_partition/crouton/chroots/precise...
crouton: version 1-20161028111244~master:948ee6dc
release: precise
architecture: amd64
xmethod: xorg
targets: xorg,xiwi,extension,unity,touch
host: version 8743.83.0 (Official Build) stable-channel samus
kernel: Linux localhost 3.14.0 #1 SMP PREEMPT Thu Nov 3 00:10:43 PDT 2016 x86_64 x86_64 x86_64 GNU/Linux
freon: yes
Unmounting /mnt/stateful_partition/crouton/chroots/precise...

Entering /mnt/stateful_partition/crouton/chroots/precise...

_XSERVTransmkdir: Owner of /tmp/.X11-unix should be set to root

X.Org X Server 1.15.1
Release Date: 2014-04-13
X Protocol Version 11, Revision 0
Build Operating System: Linux 2.6.42-75-generic x86_64 Ubuntu
Current Operating System: Linux localhost 3.14.0 #1 SMP PREEMPT Thu Nov 3 00:10:43 PDT 2016 x86_64
Kernel command line: cros_secure console= loglevel=7 init=/sbin/init cros_secure oops=panic panic=-1 root=/dev/dm-0 rootwait ro dm_verity.error_behavior=3 dm_verity.max_bios=-1 dm_verity.dev_wait=1 dm="1 vroot none ro 1,0 3584000 verity payload=PARTUUID=8f7f09fd-3fa9-c04b-8582-dd9473131ef9/PARTNROFF=1 hashtree=PARTUUID=8f7f09fd-3fa9-c04b-8582-dd9473131ef9/PARTNROFF=1 hashstart=3584000 alg=sha1 root_hexdigest=1675bf78df1cf9a151854dc01db5c8e214df3981 salt=97e4c32eee8afade09d049e69514b5ef06798440da0283a3274040d692719bd4" noinitrd vt.global_cursor_default=0 kern_guid=8f7f09fd-3fa9-c04b-8582-dd9473131ef9 add_efi_memmap boot=local noresume noswap i915.modeset=1 tpm_tis.force=1 tpm_tis.interrupts=0 nmi_watchdog=panic,lapic i915.enable_psr=1
Build Date: 12 February 2015  03:37:52PM
xorg-server 2:1.15.1-0ubuntu2~precise5 (For technical support please see http://www.ubuntu.com/support)
Current version of pixman: 0.30.2
        Before reporting problems, check http://wiki.x.org
        to make sure that you have the latest version.
Markers: (--) probed, (**) from config file, (==) default setting,
        (++) from command line, (!!) notice, (II) informational,
        (WW) warning, (EE) error, (NI) not implemented, (??) unknown.
(++) Log file: "/tmp/Xorg.crouton.1.log", Time: Sun Nov 20 21:46:44 2016
(==) Using system config directory "/usr/share/X11/xorg.conf.d"
setversion 1.4 failed: Permission denied
Initializing built-in extension Generic Event Extension
Initializing built-in extension SHAPE
Initializing built-in extension MIT-SHM
Initializing built-in extension XInputExtension
Initializing built-in extension XTEST
Initializing built-in extension BIG-REQUESTS
Initializing built-in extension SYNC
Initializing built-in extension XKEYBOARD
Initializing built-in extension XC-MISC
Initializing built-in extension SECURITY
Initializing built-in extension XINERAMA
Initializing built-in extension XFIXES
Initializing built-in extension RENDER
Initializing built-in extension RANDR
Initializing built-in extension COMPOSITE
Initializing built-in extension DAMAGE
Initializing built-in extension MIT-SCREEN-SAVER
Initializing built-in extension DOUBLE-BUFFER
Initializing built-in extension RECORD
Initializing built-in extension DPMS
Initializing built-in extension X-Resource
Initializing built-in extension XVideo
Initializing built-in extension XVideo-MotionCompensation
Initializing built-in extension XFree86-VidModeExtension
Initializing built-in extension XFree86-DGA
Initializing built-in extension XFree86-DRI
Initializing built-in extension DRI2
Loading extension GLX
xf86EnableIOPorts: failed to set IOPL for I/O (Operation not permitted)
setversion 1.4 failed: Permission denied
crouton: version 1-20161028111244~master:948ee6dc
release: precise
architecture: amd64
xmethod: xorg
targets: xorg,xiwi,extension,unity,touch
host: version 8743.83.0 (Official Build) stable-channel samus
kernel: Linux localhost 3.14.0 #1 SMP PREEMPT Thu Nov 3 00:10:43 PDT 2016 x86_64 x86_64 x86_64 GNU/Linux
freon: yes
Running exit commands...
/usr/bin/xinit: connection to X server lost

waiting for X server to shut down Hangup
Hangup
Hangup
(EE) Server terminated successfully (0). Closing log file.

Unmounting /mnt/stateful_partition/crouton/chroots/precise..

Please describe your issue:
When I sign in I get the following error:
Could not connect to session bus: An SELinux policy prevents this sender from sending this message to this recipient, 0 matched rules: type="method_call", sender="(null)" (inactive) interface="org.freedesktop.Dbus" member="Hello" error name="(unset)" requested_reply="0" destination="org.freedesktop.Dbus" (bus) in a popup and a button that says Log out. I cannot find a solution to this.
I just installed a fresh unity chroot and even after a powerwash and reinstall, I cannot remove this error.

@spafindoople,
Please try the 'trusty' or 'xenial' release by using the '-r [RELEASE]' option when installing.
'precise' is quite old and will soon be replaced by 'xenial' once some issues have been worked out.
Hope this helps,
-DennisL

Ok, I will try that.
Thanks a lot.


cl21: Fix Issue #35 - ask for capability size. Too small size could cause errors

Fix Issue https://github.com/KhronosGroup/OpenCL-CTS/issues/35

Force pushed because of rebase needed and solving merge issues.


pari: 2.15.4 -> 2.15.5

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://repology.org/project/pari/versions.
meta.description for pari is: Computer algebra system for high-performance number theory computations
meta.homepage for pari is: http://pari.math.u-bordeaux.fr
Updates performed

Version update

To inspect upstream changes
Impact
Checks done

built on NixOS
The tests defined in passthru.tests, if any, passed
found 2.15.5 with grep in /nix/store/dffr7l81wnmp2y94fwnfmc85qijf2c90-pari-2.15.5
found 2.15.5 in filename of file in /nix/store/dffr7l81wnmp2y94fwnfmc85qijf2c90-pari-2.15.5

Rebuild report (if merged into master) (click to expand)

26 total rebuild path(s)

26 package rebuild(s)

First fifty rebuilds by attrpath
cantor
eclib
gap
gap-full
gap-minimal
giac
giac-with-xcas
gp2c
labplot
lcalc
libsForQt5.cantor
pari
plasma5Packages.cantor
python311Packages.cypari2
python311Packages.cysignals
python311Packages.fpylll
python311Packages.pplpy
python311Packages.primecountpy
python312Packages.cypari2
python312Packages.cysignals
python312Packages.fpylll
python312Packages.pplpy
python312Packages.primecountpy
sage
sageWithDoc
sympow

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/dffr7l81wnmp2y94fwnfmc85qijf2c90-pari-2.15.5 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A pari https://github.com/r-ryantm/nixpkgs/archive/9b570f04bcbb8b2a7f6512708042229519947a94.tar.gz

Or:
nix build github:r-ryantm/nixpkgs/9b570f04bcbb8b2a7f6512708042229519947a94#pari

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/dffr7l81wnmp2y94fwnfmc85qijf2c90-pari-2.15.5
ls -la /nix/store/dffr7l81wnmp2y94fwnfmc85qijf2c90-pari-2.15.5/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
nixpkgs-review took longer than 45m and timed out

Maintainer pings
cc @ertes @timokau @omasanori @7c6f434c @collares for testing.

@ofborg build sageWithDoc


Automatically mark format for the location properties and parameters

Should be resolved by https://github.com/Azure/autorest.csharp/pull/2318


rawpixel catalog

Is your feature request related to a problem? Please describe.
It's difficult to find the public domain images rawpixel produced.
Describe the solution you'd like
We would love to see rawpixel listed with others catalogs in this section https://search.creativecommons.org/collections

@aldenstpage & @kgodey 👆

Moving this to the API repo since catalog work is done.

The catalog work is done here, we just need to add it to the API and frontend.

Hi, thanks to take care of this, I noticed the list of providers here too https://search.creativecommons.org/about would be good to add rawpixel there too

rawpixel was hidden due to https://github.com/creativecommons/cccatalog-frontend/issues/395; it shows up in the about page and search results now


With -n avoid injecting blank lines (and unnecessary newlines)

It seems a shame when trimming the CSS to a minimum to leave in (in fact, add!) redundant blank lines and newlines; all the filtered CSS should be on a single line with no breaks.
(Thanks for an interesting tool: I'm writing up my comparison for my site against purifycss here: http://www.earth.org.uk/note-on-site-technicals-4.html#uncss )
Rgds
Damon

@DamonHD I ran some quick tests and I'm not seeing the behavior you described. Can you post a minimal reproducible test case?

% cat .note-on-carbon-cost-of-CDN.html | uncss -t 0 -n -m screen,print -i .container,.sidebar,.sml,.noprint,.ad -s img/css/base-20171001.css,img/css/desktop-20170906.css,img/css/fullw-20170606.css,img/css/table-20170622.css
h1,h2,h3{font-family:sans-serif}.sidebar{color:#000;background-color:#cfc;margin:1em 0}.sml{font-size:small}.pgdescription{font-weight:700;margin:1em 0}.ad{border:1px dotted red;margin:1em;padding:3px}.resp{max-width:100%;height:auto}.respfloatr{float:right;clear:right;height:auto;margin:1em 0 1em 1.5em}.respfloatr{max-width:50%}.respfloatr{border-radius:4px}@media screen and (min-width:640px){.container{max-width:800px;margin:auto}}@media print{.ad,.noprint{display:none!important}}
@media screen and (min-width:800px){.container{font-size:1.2rem}}

.fullwcdisp{width:100%;width:100vw;position:relative;left:50%;right:50%;margin-left:-50%;margin-left:-50vw;margin-right:-50%;margin-right:-50vw}.fullwcdisp{clear:both;text-align:center}

table.tb1,table.tb1 td,table.tb1 th{border:1px solid}

Note the blank lines between preserved CCS fragments for the separate files (currently I am stripping this out with an additional step in the pipeline.
Secondary issue: note the trailing \n not being stripped from the end of each file's preserved content even when it could be.

If each of your files contain a trailing newline, uncss will preserve that.

Uncss is adding a newline of its own between files (esp if all CSS dropped from that file) it seems.
It's a shame for a minifier not to apply its built-in rules to eliminate trailing whitespace!  Another tool that shall remain nameless does eliminate whitespace between files (where safe).

Rgds
Damon

I'm still unable to reproduce; I'll need you to make a reduced test case repo for me to be able to investigate.

If I bundled up my input files and the result in a ZIP, along with the version output, would that be helpful?
FWIW I am getting the same behaviour on a RPi (ie Linux) and on macOS, so I don't think that it's a weird one-off!
Rgds
Damon

I'd prefer if you pushed the files to a github repository.

OK.  A new one for this purpose?

Yep

OK, see if this helps:
https://github.com/DamonHD/201710-uncss-bug
Rgds
Damon

OK, now I see what you mean. I assumed you were saying uncss inserts blank lines between stylesheets; it doesn't do that, it just puts each sheet on a new line. I'd be open to changing this to not add the newline when -n is passed, though I don't think uncss should ever trim existing trailing whitespace from stylesheets.
This would be a breaking change, but we're currently working on a semver-major release anyhow, so I'll try to get it in.

For my own reference, we need to change https://github.com/giakki/uncss/blob/master/src/uncss.js#L143 to fix this.

Why don't you think trimming excess whitespace (leading, trailing, etc) in a non-breaking way isn't the obvious thing to do when you are otherwise helping removing redundancy and doing some fairly deep analysis?  I'm puzzled!  B^>
Anyhow, thanks for looking into this!
Rgds
Damon

Some people want to use this on their existing stylesheets, to remove old/deprecated code they no longer need.
But also, I'm a fan of "each tool does one thing well". There are other tools that trim whitespace, minimize, or do other space-saving operations. I'd rather them do that (with the plethora of configuration options they then require), rather than have this slowly grow ("oh hey, I noticed you trim newlines at the end, can you trim newlines in the middle of stylesheets too, please? You're already trimming, so I don't see why you wouldn't want to trim more useless stuff. kthxbye").
I was a fan of unix's small tools that then get combined with pipes, and view PostCSS and this JS ecosystem somewhat similarly.

Yes, that I understand.  And indeed if you look at my example you see there's now three adjacent blocks that could easily be merged, maybe we can talk about that one another time!  B^>
But you're (a) adding new whitespace here that I then need to run an extra process to remove incompletely and (b) as I understand it you're reconstituting the CSS from the semantics, so I'd suggest that preserving or re-injecting any non-functional content is the odd thing to do.
Rgds
Damon

We're not reconstituting the CSS; we're removing parts of it that aren't used by the HTML.
Not inserting a blank newline; yes, I can see doing that. Removing newlines: No. That's what CSS minifiers are for; run a minifier after uncss.

OK, I hear you.
I'll be happy with uncss not adding a \n and I'll ensure that my new minification process will not leave a trailing one in the fragment .css files.  All should be happy.
Thank you.
Rgds
Damon

I guess I'll call this PR welcome, since I don't have the time to do this myself.


BUG with multi Include

There are three Entities:
class ProductTheme
{
   int Id {get;set;}
   int ProductId {get;set;}
   ...
   ICollection<Part> Parts {get;set;}
   Poster Poster {get;set;}
}

class ProductPoster
{
     int Id {get;set;}
     ...
     Theme Theme {get;set;}
}

class ProductThemePart
{
     int Id{get;set;}
     ....
     int ThemeId  {get;set;}
}

Mapping relation like below:
Poster:  (Poster and Theme 1: 1)
builder.HasOne(p=>p.Theme).WithOne(t=>t.Poster).ForeignKey<Poster>(p => p.Id)

Theme:  (Theme and Part 1 : x)
builder.HasMany(p => p.Parts).WithOne().ForeignKey(pt => pt.ThemeId);

Query:
           var query = from t in _themeRepository.TableNoTracking
                                where t.ProductId == productId && t.Deleted == false
                                select t;

            query = query.Include(t => t.Poster);
            query = query.Include(t => t.Parts);

            var  list = await query.ToArrayAsync();
            return list;

Wrong T-Sql generated:
exec sp_executesql N'SELECT [t].[Id], .....[p].[ProductId], ....
FROM [ProductThemes] AS [t]
LEFT JOIN [ProductPosters] AS [p] ON [p].[Id] = [t].[Id]
WHERE ([t].[ProductId] = @__productId_0) AND ([t].[Deleted] = 0)
ORDER BY [p].[Id]',N'@__productId_0 int',@__productId_0=101
go
exec sp_executesql N'SELECT [p].[Id],  [p].[ThemeId] ...
FROM [ProductThemeParts] AS [p]
INNER JOIN (
SELECT DISTINCT [p].[Id]             ------------------------------> this line exists bug
FROM [ProductThemes] AS [t]
LEFT JOIN [ProductPosters] AS [p] ON [p].[Id] = [t].[Id]
WHERE ([t].[ProductId] = @__productId_0) AND ([t].[Deleted] = 0)
) AS [p0] ON [p].[ThemeId] = [p0].[Id]
ORDER BY [p0].[Id]',N'@__productId_0 int',@__productId_0=101
go
Looks like alias "[p]" appears conflict,  "SELECT DISTINCT [p].[Id]" change to "SELECT DISTINCT [t].[Id]" will work fine!

@endink which version of EF7 are you using?

@maumar version is beta8

@endink does this error still reproduce for you in a newer version of EF? I wrote this test to reproduce but it passes on SQL Server/Sqlite.

Closing due to lack of response and inability to reproduce error.


Events

Codecov Report

Merging #1 (10e873d) into main (e1cba3a) will decrease coverage by 1.12%.
The diff coverage is 87.80%.

@@             Coverage Diff              @@
##               main       #1      +/-   ##
============================================
- Coverage     95.23%   94.11%   -1.13%
- Complexity       77       97      +20
============================================
  Files            15       16       +1
  Lines           231      272      +41
============================================
+ Hits            220      256      +36
- Misses           11       16       +5

Impacted Files
Coverage Δ

src/LaravelElasticaBridgeServiceProvider.php
80.95% <63.63%> (-19.05%)
:arrow_down:

src/Services/ModelEvent.php
94.44% <94.44%> (ø)

src/Client/ElasticaClient.php
100.00% <100.00%> (ø)

src/Index/AbstractIndex.php
97.05% <100.00%> (+0.18%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update e1cba3a...10e873d. Read the comment docs.


Feature: Secret-Storage Sync if credentials are not available

sync robot-credentials to secret-storage if they are not available ( on registry commands , ... )
setup endpoint for secret-storage ( engines, ...)


Too much Loading!!!!!!!!

Issue Type: Bug
I need quick response to solve this bug.
My laptop will take load some time,
When i start server,Litrelly much much more time to take load.
Need Solution!
VS Code version: Code 1.63.0 (7db1a2b88f7557e0a43fec75b6ba7e50b3e9f77e, 2021-12-07T06:26:56.179Z)
OS version: Windows_NT x64 10.0.19042
Restricted Mode: No

System Info

Item
Value

CPUs
Intel(R) Core(TM) i3-7020U CPU @ 2.30GHz (4 x 2304)

GPU Status
2d_canvas: enabledgpu_compositing: enabledmultiple_raster_threads: enabled_onoop_rasterization: enabledopengl: enabled_onrasterization: enabledskia_renderer: enabled_onvideo_decode: enabledvulkan: disabled_offwebgl: enabledwebgl2: enabled

Load (avg)
undefined

Memory (System)
3.91GB (0.49GB free)

Process Argv
--crash-reporter-id f198afd2-98ba-4ee4-9ef2-be4aaa59185b

Screen Reader
no

VM
0%

Extensions (5)

Extension
Author (truncated)
Version

simple-react-snippets
bur
1.2.5

es7-react-js-snippets
dsz
3.1.1

sublime-keybindings
ms-
4.0.10

material-icon-theme
PKi
4.11.0

LiveServer
rit
5.6.1

A/B Experiments
vsliv368cf:30146710
vsreu685:30147344
python383cf:30185419
vspor879:30202332
vspor708:30202333
vspor363:30204092
vstes627:30244334
pythontb:30283811
pythonptprofiler:30281270
vshan820:30294714
vstes263cf:30335440
pythondataviewer:30285071
vscod805:30301674
pythonvspyt200:30340761
binariesv615:30325510
bridge0708:30335490
bridge0723:30353136
pythonrunftest32:30373476
pythonf5test824:30373475
javagetstartedt:30391933
pythonvspyt187:30373474
vsaa593cf:30376535
pythonvs932:30410667
vscop804cf:30404767
vscop940:30404999
vsrem710cf:30405999
vscexrecpromp3c:30407761

/needsMoreInfo

When i start server,Litrelly much much more time to take load.

Which server? If you are launching using LiveServer extension - you should report this issue there (with more details) or look for alternatives (as LiveServer hasn't been updated in 2 years). Maybe https://marketplace.visualstudio.com/items?itemName=ms-vscode.live-server#features
If you are using Run/Debug - you need to provide more details and your report has no info that would help to reproduce issue.


Network.getPositions() TS type/documentation does not align with actual implementation.

Hello there!
One of our projects was working on hooking into the afterDrawing event and drawing some more objects around a given node. In doing this, we were attempting to use Network.getPositions in the manner defined by the ts definitions/documentation:
  /**
   * Returns the x y positions in canvas space of the nodes with the supplied nodeIds as an object.

   * When a String is supplied, the position of the node corresponding to the ID is returned.
   * When nothing is supplied, the positions of all nodes are returned.
   */
  getPositions(nodeIds?: IdType[]): { [nodeId: string]: Position };
  getPositions(nodeId: IdType): Position;

The method getPositions(nodeId): Position is not actually implemented as defined: https://github.com/visjs/vis-network/blob/93a79d73099a8f13088ea93c6ad0210616b1a177/lib/network/modules/NodesHandler.js#L403-L428
So, either the ts definition for that overload should be removed/first one modified to accept the string only and return keyed position, or the getPositions() method on line 417 should be altered to return the x/y position object directly, rather than the dataArray object.
Or a new method getPosition(nodeId: IdType): Position rather than the overload and split return type?
I'm happy to open a PR with whatever direction you guys want to go.

I'd personally go with:
getPositions(nodeIds?: IdType | IdType[]): Record<string, Position>;
getPosition(nodeId: IdType): Position;

It wouldn't break anything and it would provide a convenient and intuitive way of getting the position of a single node.


AR doesn't work on chrome with android

AR doesn't work on chrome neither with android, just me?
if I insert the code on wordpress I have this problem, even on the html page, while on Wix it works, how come?

Good question; can you post links here where it does and doesn't work? I'd be happy to take a look. Also, does the javascript console show any interesting errors or warnings? A possible issue is not having the proper CORS headers on the glTF.

Hello, I wanted to share my experience and I think we have the same symptom.
I have tested the following two links with model-viewer using WebXR and SceneViwer, I have also downloaded the same demos from GITHUB and uploading them to my server the same thing happens (The launch AR button dont start the AR).
Links:

(WEBXR) https://modelviewer.dev/examples/webxr.html
(WEBXR + SCENEVIEWER) https://modelviewer.dev/examples/augmented-reality.html

Using model-viewer:

version 1.0

Browsers version and device:

Samsung browser (<IP_ADDRESS> GalaxyS7 and <IP_ADDRESS> GalaxyS10) in both case happen the same:
WebXR - AR button dont start the AR.
Sceneview - AR button dont start the AR.

Google Chrome browser (83.0.4 GalaxyS7 and 83.0.41 GalaxyS10) in both case happen the same:
WebXR - This is the ONLY case where the button start the AR.
Sceneview - AR button dont start the AR.

This start since model-viewer version 1.0, it was working in 0.10

@FrankHiguera Thank you for the details! We've managed to repro all of this on an S8+.
As a side note, modelviewer.dev serves the latest master, which is now ahead of 1.0.


Remove respChan and writeLoop, use response callback directly

This alleviates the backpressure observed during
times of high load, and simplifies the Peer structure.
Fixes #1070

A run on devnet did not actually relieve the memory leak. A ring buffer may be a more suitable solution, since we should manage the amount of running goroutines.


Using a custom icon instead of the x character for the remove button

How can I customize rendering the remove button, so as to use a custom icon instead of the character x?

@mgoldenbe you can use the removeComponent


Bus Icon Bugs

Two bugs observed with the bus icons:
First is that some bus stops have null titles in there. Ellie says these bus stops don't exist now (or maybe never have), so we need to figure out what's the deal with these.

The other is that rail stops seem to be appearing twice. Here, for example, both of these icons, when clicked on, show the same name.

Got confirmation from @hellonewman on this -- these actually just represent the inbound and outbound for the same stop, so not a bug! 🐛 🚫


BUG -  General Feedback Message sent pop up still remains when you re visit the tree

Related Feature
Feature request: issue number
Description
Message sent pop up still displays when you re visit the tree
Steps to Reproduce
Steps to reproduce the behavior:

Open GA
Select Fiori tools tree
Select out come
Submit General Feedback  - Message sent appears and then fades away
Navigate back to the home screen
Select any tree

Expected results
Guide appears
Actual results
Guide appears with the 'Message Sent' pop up displaying which fades away.
Screenshots
Version/Components/Environment
Add any other context about the problem here
OS:

[ ] Mac OS
[ ] Windows
[ ] Other

Root Cause Analysis
Problem
{describe the problem}
Fix
{describe the fix}
Why was it missed
{Some explanation why this issue might have been missed during normal development/testing cycle}
How can we avoid this
{if we don’t want to see this type of issues anymore what we should do to prevent}

FYI

@theStiggySpd FYI

this is fixed. Tested on both BAS and VSCode with version https://github.com/SAP/guided-answers-extension/releases/tag/sap-guided-answers-extension%401.4.0

@unseen1980 @Klaus-Keller Thanks for your help.


undefined reference to 'soc_interface_AllocateInOutNodeBuffers'  Hexagon build failed with latest commit

OS: Ubuntu 16.04 64bits
Android Version: 7.1 (Nougat)
NDK Version: android-ndk-r12b
HEXAGON SDK: 3.1
nnlib source: https://source.codeaurora.org/quic/hexagon_nn/nnlib
I have cloned to git commit id - 43a819e1386195f7010f8ca9c74ed96ab81913d8
when I try to build executable for hexagon,
CC_PREFIX=${CC_PREFIX} NDK_ROOT=${NDK_ROOT} "${BUILD_ALL_ANDROID_PATH}" -x "${GEN_LIBS_DIR}" -s "${TF_ROOT_DIR}/tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in" -t hexagon_graph_execution
I am getting build error,
tensorflow/tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a(hexagon_control_wrapper.o):hexagon_control_wrapper.cc:function tensorflow::HexagonControlWrapper::Init(tensorflow::RemoteFusedGraphExecuteInfo const&): error: undefined reference to 'soc_interface_AllocateInOutNodeBuffers'
collect2: error: ld returned 1 exit status

build changes are moved partially out of tensorflow/contrib/makefile/sub_makefiles/hexagon_graph_execution/Makefile.in
is this the possible cause for error.
I am not sure I can pull out latest commit and build over that, but right now I am stuck at build fail.
Help me with a fix .

@Satok provide pointers.

Is this issue still open?

Sorry, I'm on it.  Will be fixed asap.

@satok16, keep me posted.

It's fixed last week.

@satok16 , I missed it, I guess this is the commit id-06d25a7e, will check with this,
thanks.


Order of Groups

Hello, just started using this (fantastic) plugin today and am not sure if this behaviour is intentional: the order of tab groups reverts each time I reopen my vault. Example of what it looks like initially:

and this is what it looks like after reopening (and collapsing all groups):

I noticed that the group order is the same every time I reopen but I wasn't able to determine the sorting logic (it's not alphabetical nor based on the number of pinned notes or anything like that at least). Is there a logic behind this sorting or is it more on the metadata level somewhere?
Best,
shefski

Thank you for reporting the issue.

Is there a logic behind this sorting?

Whenever Vertical Tabs reloads, it sorts the groups based on their hierarchy. Under the hood, each group is a workspace split of Obsidian. When you enabled the Zen Mode, the structure is hidden from you since only one split is visible on screen. You may see the full layout by temporarily disabling the Zen Mode. The order of these splits (or groups) is managed natively by Obsidian and Vertical Tabs only displays it.
When you manually reorder the groups, the original split hierarchy will not be modified by design. This is because order synchronization for splits has no uniform behavior and will incur huge performance overhead. Besides, it will also break some users' workflow.
As the native hierarchy and order may have little value for most users, I'll make some changes to ensure the persistence of group orders ASAP.

Thanks a lot for the quick response! I was not familiar with workspace splits as I only ever look at one tab at the same time.

This issue has been fixed in the latest version. Please update to v0.9.0. Thank you.


Flagged visibilities is inaccurate with MS describe

It appears that the ms describe function is not correctly picking up the number of flagged visibilities.

This has been fixed. It was ultimately a ruff suggestion that I accepted without thinking. I have reverted and added a couple of NoQA


Need a system to handle controls attached to entities

Right now 3 raycasts are being done every frame on the client: do_after, popups, and chat. Ideally these would just be controlled by a system that does 1 raycast and toggles control visibility (this would also make stuff like fading it in / out much easier).
This is probably going to require some decent work in the 3 systems and would be better waiting for smez's hud refactor first to avoid conflicts.

This also ties into status indicators (e.g. alerts / medical hud).


[SUPPORT] Different keygen class assigned by Hudi in 0.11.1 and 0.12.1 while creating a table with multiple primary keys

Hi Team,
whenever I am trying to create hudi table with mutiple primary key , generating :
hoodie.table.keygenerator.class=org.apache.hudi.keygen.SimpleKeyGenerator in hudi .12.1
but the same create statement in hudi .11.1 , keygenerator class as complex one. Any idea ?
Sample code:
` spark-shell
--packages org.apache.hudi:hudi-spark-bundle_2.12:0.12.1
--conf "spark.serializer=org.apache.spark.serializer.KryoSerializer"
--conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension'
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceReadOptions
import org.apache.hudi.QuickstartUtils._
import org.apache.spark.sql.DataFrame
spark.sql("""create  table .f_test5(
id string,
name string,
age string,
salary string,
upd_ts string,
job string
)
using hudi
partitioned by (job)
location 'gs:///HUDI/f_test5/'
options (
type = 'cow',
primaryKey = 'id,name',
preCombineField = 'upd_ts'
)""")
Colla`
Discussion in Hudi Slack channel:
https://apache-hudi.slack.com/archives/C4D716NPQ/p1668596344997409

Looks like the behavior changed in https://github.com/apache/hudi/commit/6fee77b76fa25be677b324804eae9801ca6b9f4c
I think we should keep the complex keygen while overriding timestamp-type handling.
cc @alexeykudinkin @xiarixiaoyao Any idea why did we remove ComplexKeyGenerator as base class?

while we are at it. can you also explore how this change would work for existing tables.
for eg,
with 0.11.0 if a user did not explicitly set the key gen, it would pick ComplexKeyGen.
and if the user upgrades to 0.12.1, wouldn't the default will be chosen as SimpleKeyGen. or is that taken care of already?

Attempted to reproduce with below script (note that I created table with 0.11.1 and did insert with 0.12.1), the issue described by the OP is reproducible but upgrade doesn't override the keygen prop in hoodie.properties.
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceReadOptions
import org.apache.hudi.QuickstartUtils._
import org.apache.spark.sql.DataFrame

spark.sql("""create table f_test111(
id string,
name string,
age string,
salary string,
upd_ts string,
job string
)
using hudi
partitioned by (job)
location 'file:///tmp/f_test111/'
options (
type = 'cow',
primaryKey = 'id,name',
preCombineField = 'upd_ts'
)""")

spark.sql("""insert into f_test111 values('a1', 'sagar', '32', '1000', '100', 'se')""")

I put this test in TestCreateTable.scala and it succeeds without error
  test("Test Multiple Primary Key Default Keygen") {
    withTempDir { tmp =>
      val tableName = generateTableName
      spark.sql(
        s"""
           |create table $tableName (
           |  id int,
           |  name string,
           |  price double,
           |  ts long
           |) using hudi
           | partitioned by (name)
           | tblproperties (
           |  primaryKey = 'id,price',
           |  preCombineField = 'ts',
           |  type = 'cow'
           | )
           | location '${tmp.getCanonicalPath}'
       """.stripMargin)
      val table = spark.sessionState.catalog.getTableMetadata(TableIdentifier(tableName))
      val tablePath = table.storage.properties("path")
      val metaClient = HoodieTableMetaClient.builder()
        .setBasePath(tablePath)
        .setConf(spark.sessionState.newHadoopConf())
        .build()
      val tableConfig = metaClient.getTableConfig.getProps.asScala.toMap
      assertResult("org.apache.hudi.keygen.ComplexKeyGenerator")(tableConfig(HoodieTableConfig.KEY_GENERATOR_CLASS_NAME.key()))
      val source = scala.io.Source.fromFile(tmp.getCanonicalPath + "/.hoodie/hoodie.properties")
      val lines = try source.mkString finally source.close()
      assertResult(lines.contains("hoodie.table.recordkey.fields=id,price"))(true)
      assertResult(lines.contains("hoodie.table.keygenerator.class=org.apache.hudi.keygen.ComplexKeyGenerator"))(true)
    }
  }

I just tried out my test with options instead of tblproperties and it still passed. So not sure what else there is to try

@nikhilindikuzha : looks like we could not reprod. could you give us a reproducible script/runbook. Feel free to close if you could not reproduce.

@nikhilindikuzha : any updates here please.


Refactor mobile modals

button colors according to https://app.zeplin.io/project/59e4cf99fc0bb2f99a89551b/screen/5c335ee1c449feaf64960441

need for #492, #486


Possible Capitalization Error on the Curriculum

Describe your problem and - if possible - how to reproduce it
I noticed that "Apis And Microservices Certification" did not have the "pi" capitalized. I realize that could interfere with the intended acronym of the certification (AMC). but I would suggest using a smaller size yet still capitalized HTML font.
Add a Link to the page with the problem
https://learn.freecodecamp.org/
Tell us about your browser and operating system

Browser Name: Google Chrome
Browser Version: Chrome 66
Operating System: Windows NT 10.0

If possible, add a screenshot here

We are closing this issue as it has already been fixed (view on our beta).  This change will be shipping to production soon.


Fix nightly release

Motivation
Nightly release is broken.
Also the version is so long that NPM breaks, see https://github.com/ChainSafe/lodestar/issues/2703
Description

Use --no-git-reset flag to access version artifacts in next step
Use shorter commit sha

@wemeetagain @mpetrunic is the prepublishOnly script necessary? It causes an unnecessary double build only in some packages.

@wemeetagain @mpetrunic is the prepublishOnly script necessary? It causes an unnecessary double build only in some packages.

Probably not but you never know what some package needs.


Wrong test - not sure what it should be there...

In the test https://github.com/jelovirt/dita-ot-markdown/blob/master/src/test/resources/test.md file, we have at the end of a table:
[Prototype table][reference_table]

which generates the following invalid DITA, plus a ] after Property table, which should probably not be there:
<title class="- topic/title ">Prototype table]<xref class="- topic/xref " keyref="reference_table"/>
</title>

As the title says, I am not sure what the fix should be, shall we just remove the [reference table] from the Markdown file?

IIRC it's trying to be title + anchor for the table, but I don't remember where I got the syntax. Keeping the test, even if the parser will not handle it.


Implement Kp Push Triggers

We need to polish the Kp push notification trigger system so that we can change the settings and then get new push notifications on those Kp changes.

DUPLICATE ISSUE, WOOPS.


[FEATURE-REQ]ACL-Limit specified group can only pub or sub specified topic

- accessKey: xxx
  secretKey: xxx
  admin: false
  topicPerms:
  - Topic_A=PUB|SUB
  - Topic_B=PUB|SUB
  groupPerms:
  - Group_A=SUB

this acl config means that Group_A can pub or sub the both topic_a and topic_b，but sometimes business need to limit Group_A  can only sub the toipc_a's topic instesad of both a and b

@coder-zzzz why need to specified the consumer group, can you desc some scenes?

I didn't meet this scene until now , so i think can close this issue .


replace currentVersion with currentPatchVersion in concepts

As suggested by @sftim, I have attempted to replace all {{< skew currentVersion >}} with {{< skew currentPatchVersion >}} in the concepts folder.
If this change is acceptable, we can proceed to include additional files as needed.

Ah, I don't recommend replacing all the occurrences. Only change it where it makes sense to do so!

-_! seems tricky. Only two remained now. Fortunately, not many were changed in the original commit.


Not possible to change PIO settings in makefile

In its current form there is no (obvious) way to have a project with the pico-sdk as a dependency, where the project would be able to define things like the SPI_PROGRAM_NAME or the CLOCK_DIV - things that are essential to e.g. have a project that is power efficient.
It would be nice if it would be possible to define these things in a makefile. The fix is trivial and a pull request will follow, but the instructions said that an issue should be logged first.

Related to #1392 ?


Implement coverage report

Ensure we have something for each method.
And, of course, get better with coverage.py.   :)

Now a part of Makefile.


SharqIQ Fails to start when Homebridge is installed on DSM

Describe The Bug:
SharkIQ Plugin fails to launch Chromium due to a missing library when Homebridge is installed on DSM
To Reproduce:

Install Homebridge on DSM (x86 Arch).
Install and configure the SharqIQ plugin.
Restart Homebridge
Observe the error in the logs.

Expected behavior:
I expect to see the login URL like the ReadMe page states.
Logs:
[12/3/2024, 1:17:37 PM] [SharkIQ] If you received the error Syntax error: Unterminated quoted string, please use the OAuth code login method. This is a known issue with arm64 devices like Raspberry Pi.
[12/3/2024, 1:17:37 PM] [SharkIQ] To login using the OAuth code method, please remove the email and password from your Homebridge configuration and restart Homebridge.
[12/3/2024, 1:17:37 PM] [SharkIQ] Error with login.
[12/3/2024, 1:17:37 PM] [SharkIQ] Error: Error: Failed to launch the browser process!
/var/packages/homebridge/home/.cache/puppeteer/chrome/linux-130.0.6723.69/chrome-linux64/chrome: error while loading shared libraries: libatk-1.0.so.0: cannot open shared object file: No such file or directory
Plugin Config:
{
    "name": "SharkIQ",
    "email"<EMAIL_ADDRESS>    "password": "*****************",
    "vacuums": [
        "AC000W0226********"
    ],
    "europe": false,
    "invertDockedStatus": false,
    "dockedUpdateInterval": 30000,
    "platform": "SharkIQ",
    "_bridge": {
        "username": "0E:0B:58:0A:**:**",
        "port": 40080
    }
}

Screenshots:
None
Environment:
OS | Synology DSM
DS720Plus2
homebridge

<EMAIL_ADDRESS>/volume1/homebridge
/volume1/homebridge/config.json
/volume1/homebridge/node_modules
GMT-0700

Plugin Version: 1.3.2
Homebridge Version:  1.8.5
Node.js Version: 20.9.0
NPM Version: 10.1.0
Operating System: DSM

@Romeo1984 Check #24

-MarkSent from my iPhoneOn Dec 17, 2024, at 1:09 PM, Bubba8291 @.***> wrote:Why was this closed? Most of the commands in #24 can’t be run on DSM. Please let me trouble shoot before closing.
@Romeo1984 Check #24
—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: @.***>

@Romeo1984 It looks like your DSM doesn't have libatk installed, which is a system package.
I do not have a Synology, but if there isn't a way to manually add that package (through a web ui or a terminal interface), try to login with the OAuth login

@Bubba8291 homebridge-sharkiq @.***>  I
already looked for it. It is not available for DSM to install.
On Tue, Dec 17, 2024 at 4:25 PM Bubba8291 @.***> wrote:

@Romeo1984 https://github.com/Romeo1984 It looks like your DSM doesn't
have libatk installed, which is a system package.
I do not have a Synology, but if there isn't a way to manually add that
package (through a web ui or a terminal interface), try to login with the
OAuth login
—
Reply to this email directly, view it on GitHub
https://github.com/Bubba8291/homebridge-sharkiq/issues/33#issuecomment-2549883427,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AIJGSZURLBAUFKPW7FIP5A32GCXHXAVCNFSM6AAAAABS6UPL2GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKNBZHA4DGNBSG4
.
You are receiving this because you were mentioned.Message ID:
@.***>

@Bubba8291 I was successful in getting OAuth working. That needs to be the solution if Homebridge is installed on any device that can't use apt to update packages. Thanks.

@Romeo1984 Awesome! Glad to hear it is working.
I am not sure which range of distros do not come with the required package for puppeteer by default. If there was an efficient way to, I would add it.
For now, I could add more clear error logs


How does Masking work?

I'm wondering how Masking Layer works.
I try to write simple model to test Masking on Activation Layer
from keras.models import Model
import numpy as np
from keras.layers import Masking, Activation, Input
a = np.array([[3.,1.,2.,2.,0.,0.]])

inputs = Input(shape=(6,))
mask = Masking(mask_value=0.0)(inputs)
softmax = Activation('softmax')(mask)
model = Model(input=inputs,output=softmax)
model.predict(a)

and the result of prediction is
array([[ 0.50744212,  0.06867483,  0.18667753,  0.18667753,  0.02526405,
         0.02526405]])

Is this the correct behavior?
My keras version is 1.0.5

I'm also interested with this question. It seems to me you expected to get something like the following: [[ 0.53444666  0.07232948  0.19661194  0.19661194]] ?

@ipoletaev
yes, sure. However keras can also return [[ 0.53444666, 0.07232948, 0.19661194, 0.19661194, 0.0, 0.0 ]] by padding it to keep shape.
Here is another example about Masknig on bi-LSTM layer but sum two layer
from keras.models import Model
import numpy as np
from keras.layers import Masking, Activation, Input, LSTM, merge
a = np.array([[[.3,.1,.2,.2,.1,.1],[.2,.3,.3,.3,.3,.1],[0,0,0,0,0,0]]])

inputs = Input(shape=(3,6))
mask = Masking(mask_value=0.0)(inputs)
fw = LSTM(1,return_sequences=True)(mask)
bw = LSTM(1,return_sequences=True,go_backwards=True)(mask)
merged = merge([fw,bw],mode='sum')
model = Model(input=inputs,output=fw)
model2 = Model(input=inputs,output=bw)
model3 = Model(input=inputs,output=merged)

what fw's output is
array([[[-0.07041532],         [-0.12203699],         [-0.12203699]]])
what bw's output is
array([[[ 0.        ],         [-0.03112165],         [ 0.02271803]]])
what merge's output is
array([[[-0.07041532],         [-0.15315863],         [-0.09931896]]])
but I think it should be (Here it also can padding 0 to keep shape.)
array([[[-0.10153697],         [-0.09931896]]])
which -0.10153697 = (-0.07041532) + (-0.03112165) and -0.09931896 = -0.12203699 + 0.02271803
Is is anything wrong on Keras?

@poyuwu

However keras can also return [[ 0.53444666, 0.07232948, 0.19661194, 0.19661194, 0.0, 0.0 ]] by padding it to keep shape.

Hmm... I don't know how to make such out only through the Keras.
About your example: I think it's similar to the aforementioned example, so you should get array([[[-0.07041532], [-0.15315863], [0.02271803]]]) . And it's really strange that bw works right but bw doesn't, because of its third output is not equal to zero, but it must.

Hi guys, I got this question too.. Especially for LSTM (BRNN).
Masking Layer gives an masked vector, only work for the inputs, not for inner states.
So in @poyuwu 's example, the fw's output still has value in step 3.
This might be correct because inputs are surely masked.
While, I want to find a way to skip the calculation step when coming masked value.
Using Masking layer in bidirectional RNN for sequences with different lengths may be totally wrong.

@lomizandtyd

So in @poyuwu 's example, the fw's output still has value in step 3.

Yes, it's logically, but in any case we want get zero at the third place, isn't it?

While, I want to find a way to skip the calculation step when coming masked value.

I think it doesn't matter because of as I understood you should specify output sample weights in fit() in order to skip necessary timesteps with all zeros in feature vector.

@ipoletaev  Wow, thanks a lot for this!
Yes,  we want to get zero at the masked position.
The problem is we also want to keep the inner states across the masked step.
Maybe we can deliver another sample_weights in the predict() function?.
If do so, BRNN is still wrong...

@lomizandtyd

...keep the inner states across the masked step.

I think it's not necessary, because the network shouldn't remember what responses it need to get at empty vectors...

Maybe we can deliver another sample_weights in the predict() function?.

I don't understand for what task you want to use it? After all you always know in advance what data you process, and you respectively know - which output of the network corresponds to the empty vector, so you can just skip such positions in output, I think.

from keras.models import Model
import numpy as np
from keras.layers import Masking,Input,TimeDistributed,Dense
a = np.array([[[3,1,2,2,0,0],[0,0,0,0,0,0],[2,1,1,2,0,0]]])
input = Input(shape=(3,6))
mask = Masking(mask_value=0)(input)
out = TimeDistributed(Dense(1,activation='linear'))(mask)
model = Model(input=input,output=out)
q = model.predict(a)
print (q[0])

@ipoletaev I think it's just Dense Layer that have zero inputs, so that its output is 0. If you change activation function to softmax, then you will get wrong answer.
Besides, batch_size set None on time steps will raise other error in some case (especially on merge layer).
In lasagne, it seems to use Masking matrix to deal with padding. (I do not test its accuracy)

@poyuwu : yes, I had checked it - and you are right. It means,as I understood, that and simple Dense doesn't keep masked values...
I write again what does not converge with the expectations:

Forward RNN doesn't keep mask values, backward does it. It's strange.
Is this task solving with batch_size = 1?
How to specify correctly what timesteps the network should to skip.

@ipoletaev I don't think

Forward RNN doesn't keep mask values, backward does it. It's strange.

this statement is true.
That's because padding argument is 'post', not 'pre'. Hence, the reason is the same as Dense layer I said.

How to specify correctly what timesteps the network should to skip.

As I said, in lasagne, we provide a mask numpy.array (the same shape as input) to deal with it. Besides, if go_backwards=True, it needs to keep padding argument the same.

@poyuwu so you want to say that now, there's no way to solve this issue with Keras?

Same here. It seems masking mechanism in Keras is not fully supported.

Does the merge function after masking and pre-padding already work?

I don't think Masking masks input values (neither during forward or back-propagation). It just skips a time-step where all features are equal to the mask value (i.e. when you pad a sequence). You can confirm this by:
from keras.models import Model
import numpy as np
from keras.layers import Masking, Activation, Input, TimeDistributed, Dense

if __name__ == "__main__":
	a = np.array([[[3,1,2,2,0.1,0.1],[0,0,0,0,0,0],[2,1,1,2,0.1,0.1]]])
	print 'Input array:'
	print a
	print ''
	input = Input(shape=(3,6))
	mask = Masking(mask_value=0.1)(input)
	out = TimeDistributed(Dense(1, activation='linear'))(mask)
	model = Model(input=input, output=out)

	model.set_weights([np.array([[ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1.]], dtype=np.float32),
	                   np.array([ 0.], dtype=np.float32)])

	print 'Weights'
	print model.get_weights()
	q = model.predict(a)
	print q

The answer is:
Input array:
[[[ 3.   1.   2.   2.   0.1  0.1]
  [ 0.   0.   0.   0.   0.   0. ]
  [ 2.   1.   1.   2.   0.1  0.1]]]

Weights
[array([[ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.]], dtype=float32), array([ 0.], dtype=float32)]
[[[ 8.20000076]
  [ 0.        ]
  [ 6.19999981]]]

If it masked the inputs of value 0.1, you would expect result to be
[[[ 8.       ]
  [ 0.        ]
  [ 6.        ]]]

Actually Masking works exactly as expected. The problem is that you are working with the wrong dimensions: in input = (3,6) the 3 is the time dimensions, so masking masks only along that dimension.
import keras
from keras.utils.visualize_util import plot
from keras.layers import *
from keras.models import Model

net_input = Input(shape = ( 3, 10))
mask = Masking(mask_value = 0.5)(net_input)
conv = TimeDistributed(Dense(1, activation = 'linear', init='one'))(mask)
#perm = Permute((2,1,3,4))(conv)
out = LSTM(1, init='one', inner_init='one',activation='tanh', inner_activation='tanh',)(conv)
model = Model(net_input, out)

print('W: ' + str(model.get_weights()))

in = np.ones((1,3, 10))
val = 0.5
in[0, 2, :] = val
out = model.predict(in)
print('Input: ' + str(in))
print('Output: ' + str(out))

In this case the answers are:
mask = 0.5 val = 0.0 : 0.73566443
mask = 0.0 val = 0.0 : 0.96402758
mask = 0.0 val = 0.5 : 0.99504161
mask = 0.5 val = 0.5 : 0.96402758

so from here you can see that when we mask val we get  the same result.

Moreover, I just tested, if you have a Multi-input net (with multiple input branches) and you have a masking layer on each branch, it is enough that just one of the inputs at time step t is equal to the masked value that all the time step is skipped.
If you want to skip the time step only when all the inputs are equal to the masked value, you need to have a shared Masking layer.

Hi Fragore, I have a similar question to you about masking with multiple inputs. I have two input branches and all I want to do is mask 0 from both. Am I right in thinking that adding a mask to the end of each branch is equivalent to adding a single mask AFTER the inputs are merged? here's my example
`input1 = Sequential()
input1.add(TimeDistributed(Dense(50), input_shape=(MAX_SEQUENCE_LENGTH,48)))
input1.add(keras.layers.core.Masking(mask_value=0.0)) heres where I would add a pre-merge mask
input2 = Sequential()
input2.add(Embedding(nb_words+2,EMBEDDING_DIM,weights=[embedding_matrix],trainable=False,input_length=MAX_SEQUENCE_LENGTH)) #then I could add mask_zero = True here
model = Sequential()
model.add(keras.engine.topology.Merge([input1,input2],mode='concat',concat_axis=-1))
model.add(keras.layers.core.Masking(mask_value=0.0))
model.add(LSTM(1024,dropout_W=.2,dropout_U=.2,return_sequences=True))
model.add(LSTM(1024,dropout_W=.2,dropout_U=.2,return_sequences=True))
model.add(LSTM(512,dropout_W=.2,dropout_U=.2,return_sequences=True))
model.add(TimeDistributed(Dense(nb_words + 1)))
model.add(Activation('softmax'))
`

Wait, you want to mask the output of the branches that are 0? In that case both of your approaches should give you the same result. But usually you mask inputs, this means to put the mask layer as input of the net.
Ps it may also be more convenient to use the functional API :)

I've been experimenting with an without masking for a little bit now and I have finally figured out what the Masking layer actually does. It doesn't actually "skip" the timepoint that has all masked values, it just forces all the values for that timepoint to be equal to 0... So effectively Masking(mask_value=0.) does nothing. That is why in the example provided by @GPaolo  above the results for mask_value=0 and mask_value=0.5 are the same when val matches them.
Here is some easy code to demonstrate what I mean.
Model:
`input1 = Input(batch_shape=(1,1,10)
mask1 = Masking(mask_value=2)(input1)
dense_layer1 = Dense(1, activation='sigmoid')
dense_layer1.setattr('supports_masking', True)
output1 = dense_layer1(mask1)
model = Model(input1, output1)
model.compile(optimizer='adam', loss='binary_crossentropy')
`
Data:
`data = np.ones((10, 1, 10), dtype='float32')
#set half of the data equal to mask value
for index in range(5,10):
data[index,0,:] = 2
#set first data point equal to mask value to show that this line is uneffected
data[0,0,0] = 2`
print outputs:
`get_mask_output = K.function([model.layers[0].input], [model.layers[1].output])
mask_output = get_mask_output([data])[0]
print(data)
print(mask_output)
[[[ 2.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]]
[[[ 2.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]]`
Predictions:
`test_data = np.ones((5,1,10))
test_data[1,0,:] = 2
test_data[2,0,:] = 0
predictions = model.predict(test_data, batch_size=1)
print(test_data)
print(predictions)
`
Results:
`[[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]]
[[[ 0.09200736]]
[[ 0.5       ]]
[[ 0.5       ]]
[[ 0.09200736]]
[[ 0.09200736]]]`
As you can imagine, "masking" values by setting them to 0 and still calculating the results for those lines in layers causes some mistakes from backpropagation (treating unknown values as a real result) as well as added unneeded computation time. I'm going to try to rework how masking is done in Keras a bit...

Building on @slaterb1  and @GPaolo 's snippets  I tried digging around to see the benefits of masking but haven't found it yet. It feels like I'm missing something.

It does not seem to propagate numerically sound values through time
It propagates np.nan, see gist
Feels (TODO:test) quite numerically unstable to propagate possibly absurd values down the network?
It has to test each input
Quick testing (see gist) seem to show that there's no immediate performance gains

Does anyone have an idea about if/when it gives performance gains? I didn't have time to run for long and I'm not comfortable about how Python/Keras/Tensorflow/Theano compiles
Have I misunderstood or is mask an intricate way of doing what I think weights would to be doing? I.e multiplying with the loss and dividing by sum of weights in batch?
It's literally what seems to be done here anyway:
https://github.com/fchollet/keras/blob/master/keras/engine/training.py#L453
Does it actually halt any execution?

@ragulpr, it's my understanding that masking does do more than just scale the loss. If a timestep has been masked, the previous output and state will be reused. See here and here.

@ragulpr, I'm not sure about performance gains but Theano is pretty smart about knowing what it needs to hang on to and what it doesn't (based on the API doc: http://deeplearning.net/software/theano/library/scan.html)
More specifically this line: "Note that there is an optimization, that at compile time will detect that you are using just the last value of the result and ensure that scan does not store all the intermediate values that are used. So do not worry if A and k are large."
So after compiling the model it might pass over the masked values (or at least not hold them in memory as long), but that is pure speculation based on similarities in the underlying code.
@carlthome, I came across the mask snippet in the "theano_backend.py" as well and you are right that the masking  has a direct effect on how the states are evaluated and passed on (T.switch). Maybe this is too general a question but how does this layer accept the mask? Just to give an example, if I have a model with multiple layers, defined as so:
model = Model(input1, output1)
I understand that Theano wraps this up as a mathematical equation to calculate:
output1 = input1 -> [ layers[0] -> layers[1] -> ... layers[N] ]
but if I have somewhere in the middle:
prev_layer -> Masking_layer -> RNN_layer
The output from the Masking_layer gets put into the RNN_layer as input ("x"). Does the "supports_masking" attribute tell the RNN_layer to figure out the mask? I could not find anywhere in the code where the mask is evaluated or interpreted by the RNN_layer, except that I can pass in a "mask" variable via the call() method of the Recurrent(Layer) object.
I tried calling RNN_layer(prev_layer, mask=Masking_layer) but it didn't do anything different. The last comment in the thread, https://github.com/fchollet/keras/issues/176 suggests that it has to be called with a mask but I'm not sure how to do that... Any thoughts?

I could not find anywhere in the code where the mask is evaluated or interpreted by the RNN_layer

Each Keras layer declares if it supports masking. Each layer is also responsible for using the mask in a sensible way (which I believe is the primary source of confusion: that the masking functionality is implemented across a bunch of different classes). For RNN layers in particular, they rely on the fact that the underlying K.rnn operation has mask support so if you're looking for where precisely the logic is, you'll note that the RNN layers simply pass the mask argument into the backend, where the magic happens (see here).

@carlthome, I saw that in the code but was not able to get the mask to work in my RNN network. For clarity I was trying to rework stuff in RecurrentShop to setup an encoder decoder network that adjusts the next input based on a prediction made on the previous state from both the encoder and the decoder (a custom RNN that uses a .single_step_rnn() instead of the regular .rnn() ).
But based on your advice, I tried to just build a basic LSTM network to act as a NOT Gate (pointless but simple) and it does interpret the mask correctly, when the it is passed a mask mid network! I'm including the gist. It shows that masking works for both return_sequences=True and return_sequences=False. It also shows that if you train the network with data that does not have 'masked' input, 'masked' lines in the test data will still get masked appropriately. Hope that helps people understand the masking stuff better!
This is the gist

@fferroni apparently, the TimeDistributed layer didn't support masking, since this feature has been added in Pull #6401?

Hey Guys, there is a seq2seq example which it's input is a string (sequence) like '5+9' and output is another string '14'.
The author used pre padding to have sequences with same lengths at input but he didn't use masking.
I add a simple line to add masking to his model and there is about 8 percent improvement in accuracy.
Is my case a correct use of masking?
this is main code:
`from random import seed
from random import randint
from numpy import array
from math import ceil
from math import log10
from math import sqrt
from numpy import argmax
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import TimeDistributed
from keras.layers import RepeatVector
generate lists of random integers and their sum
def random_sum_pairs(n_examples, n_numbers, largest):
X, y = list(), list()
for i in range(n_examples):
in_pattern = [randint(1,largest) for _ in range(n_numbers)]
out_pattern = sum(in_pattern)
X.append(in_pattern)
y.append(out_pattern)
return X, y
convert data to strings
def to_string(X, y, n_numbers, largest):
max_length = n_numbers * ceil(log10(largest+1)) + n_numbers - 1
Xstr = list()
for pattern in X:
strp = '+'.join([str(n) for n in pattern])
strp = ''.join([' ' for _ in range(max_length-len(strp))]) + strp
Xstr.append(strp)
max_length = ceil(log10(n_numbers * (largest+1)))
ystr = list()
for pattern in y:
strp = str(pattern)
strp = ''.join([' ' for _ in range(max_length-len(strp))]) + strp
ystr.append(strp)
return Xstr, ystr
integer encode strings
def integer_encode(X, y, alphabet):
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
Xenc = list()
for pattern in X:
integer_encoded = [char_to_int[char] for char in pattern]
Xenc.append(integer_encoded)
yenc = list()
for pattern in y:
integer_encoded = [char_to_int[char] for char in pattern]
yenc.append(integer_encoded)
return Xenc, yenc
one hot encode
def one_hot_encode(X, y, max_int):
Xenc = list()
for seq in X:
pattern = list()
for index in seq:
vector = [0 for _ in range(max_int)]
vector[index] = 1
pattern.append(vector)
Xenc.append(pattern)
yenc = list()
for seq in y:
pattern = list()
for index in seq:
vector = [0 for _ in range(max_int)]
vector[index] = 1
pattern.append(vector)
yenc.append(pattern)
return Xenc, yenc
generate an encoded dataset
def generate_data(n_samples, n_numbers, largest, alphabet):
# generate pairs
X, y = random_sum_pairs(n_samples, n_numbers, largest)
# convert to strings
X, y = to_string(X, y, n_numbers, largest)
# integer encode
X, y = integer_encode(X, y, alphabet)
# one hot encode
X, y = one_hot_encode(X, y, len(alphabet))
# return as numpy arrays
X, y = array(X), array(y)
return X, y
invert encoding
def invert(seq, alphabet):
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
strings = list()
for pattern in seq:
string = int_to_char[argmax(pattern)]
strings.append(string)
return ''.join(strings)
define dataset
seed(1)
n_samples = 1000
n_numbers = 2
largest = 10
alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', ' ']
n_chars = len(alphabet)
n_in_seq_length = n_numbers * ceil(log10(largest+1)) + n_numbers - 1
n_out_seq_length = ceil(log10(n_numbers * (largest+1)))
define LSTM configuration
n_batch = 10
n_epoch = 10
create LSTM
model = Sequential()
model.add(LSTM(100, input_shape=(n_in_seq_length, n_chars)))
model.add(RepeatVector(n_out_seq_length))
model.add(LSTM(50, return_sequences=True))
model.add(TimeDistributed(Dense(n_chars, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
train LSTM
for i in range(n_epoch):
X, y = generate_data(n_samples, n_numbers, largest, alphabet)
print(i)
model.fit(X, y, epochs=1, batch_size=n_batch)
evaluate on some new patterns
X, y = generate_data(n_samples, n_numbers, largest, alphabet)
result = model.predict(X, batch_size=n_batch, verbose=0)
calculate error
expected = [invert(x, alphabet) for x in y]
predicted = [invert(x, alphabet) for x in result]
show some examples
for i in range(20):
print('Expected=%s, Predicted=%s' % (expected[i], predicted[i]))`
and I just change this part:
model = Sequential() model.add(LSTM(100, input_shape=(n_in_seq_length, n_chars)))
to this part:
from keras.layers import Masking model = Sequential() model.add(Masking(mask_value = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], input_shape=(n_in_seq_length, n_chars))) model.add(LSTM(100))
sources:
http://machinelearningmastery.com/learn-add-numbers-seq2seq-recurrent-neural-networks/#comment-400854

I don't think Masking masks input values (neither during forward or back-propagation). It just skips a time-step where all features are equal to the mask value (i.e. when you pad a sequence). You can confirm this by:
from keras.models import Model
import numpy as np
from keras.layers import Masking, Activation, Input, TimeDistributed, Dense

if __name__ == "__main__":
	a = np.array([[[3,1,2,2,0.1,0.1],[0,0,0,0,0,0],[2,1,1,2,0.1,0.1]]])
	print 'Input array:'
	print a
	print ''
	input = Input(shape=(3,6))
	mask = Masking(mask_value=0.1)(input)
	out = TimeDistributed(Dense(1, activation='linear'))(mask)
	model = Model(input=input, output=out)

	model.set_weights([np.array([[ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1. ],
							     [ 1.]], dtype=np.float32),
	                   np.array([ 0.], dtype=np.float32)])

	print 'Weights'
	print model.get_weights()
	q = model.predict(a)
	print q

The answer is:
Input array:
[[[ 3.   1.   2.   2.   0.1  0.1]
  [ 0.   0.   0.   0.   0.   0. ]
  [ 2.   1.   1.   2.   0.1  0.1]]]

Weights
[array([[ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.]], dtype=float32), array([ 0.], dtype=float32)]
[[[ 8.20000076]
  [ 0.        ]
  [ 6.19999981]]]

If it masked the inputs of value 0.1, you would expect result to be
[[[ 8.       ]
  [ 0.        ]
  [ 6.        ]]]

Mask layer will work only when all feature of a timestep equals to the mask value.In you case,the input a is a 3d matrix with the shape(1,3,6),1 means batch_size,3 means timesteps,and 10 means the feature of that timestep.Mask will work when the feature of a timestep all equal to 0.1.if you change a to:
a = np.array([[[3,1,2,2,0.1,0.1],[0,0,0,0,0,0],[0.1,0.1,0.1,0.1,0.1,0.1]]])
you will get the output like:
[[[8.200001]   [0.      ]   [0.      ]]]

Hi,
I struggled a lot with this recently, and here is some experience I learnt. I hope it would be useful for people.

Masking is extremely powerful. I found it perhaps the only way to deal with several "hard" problems that are with sequence of missing inputs, missing outputs as follows.

Masking is not that complicated if we understand how the loss is computed with masking. For instance let us assume we have a sequence with length 256. From this sequence we have a masking with only 4 elements that are with masking of 1 (others are with masking 0). I thought the loss is computed as the average between these 4 elements. Guess what - it is not! The average loss will be divided by 256 instead. For this reason sometimes the loss will be extremely small (0.0something) if we have only few 1 elements and long sequence.
Does it matter? I guess not, as what we need is the gradient of loss, rather than the loss itself.

When we use softmax as the last layer, the denominator would be the sum of exponential of all elements, regarding whether their masking is 1 or 0.

I thought the output of masking inputs is zeros all the time in LSTM. But it is not the case. Let us assume we have a masking:

0 0 0 1 1 0 0 0
With this case, the three first elements with masking zero has output of 0. However, the three last zeros have output that is as the same as the output of the last element with masking 1.

Meanwhile, Keras is very convenient in the sense that the loss it computes will be based on only elements with masking of 1. I found this is a big plus of using Keras, something a bit too good too be true as I guess implementing this is not that easy.

However, the accuracy in Keras is not computed that way. It is thus not trivial in keras to write a custom metric (for fit). There is something very mysterious to me. I am pretty sure my code for writing custom metric is correct but somehow it does not give me accurate result. Because of this I think it is much much easier if we write such an accuracy function with a custom callback class.

That is it, I hope it is helpful!

Hi,
I struggled a lot with this recently, and here is some experience I learnt. I hope it would be useful for people.

Masking is extremely powerful. I found it perhaps the only way to deal with several "hard" problems that are with sequence of missing inputs, missing outputs as follows.

Masking is not that complicated if we understand how the loss is computed with masking. For instance let us assume we have a sequence with length 256. From this sequence we have a masking with only 4 elements that are with masking of 1 (others are with masking 0). I thought the loss is computed as the average between these 4 elements. Guess what - it is not! The average loss will be divided by 256 instead. For this reason sometimes the loss will be extremely small (0.0something) if we have only few 1 elements and long sequence.
Does it matter? I guess not, as what we need is the gradient of loss, rather than the loss itself.
When we use softmax as the last layer, the denominator would be the sum of exponential of all elements, regarding whether their masking is 1 or 0.
I thought the output of masking inputs is zeros all the time in LSTM. But it is not the case. Let us assume we have a masking:

0 0 0 1 1 0 0 0
With this case, the three first elements with masking zero has output of 0. However, the three last zeros have output that is as the same as the output of the last element with masking 1.

Meanwhile, Keras is very convenient in the sense that the loss it computes will be based on only elements with masking of 1. I found this is a big plus of using Keras, something a bit too good too be true as I guess implementing this is not that easy.
However, the accuracy in Keras is not computed that way. It is thus not trivial in keras to write a custom metric (for fit). There is something very mysterious to me. I am pretty sure my code for writing custom metric is correct but somehow it does not give me accurate result. Because of this I think it is much much easier if we write such an accuracy function with a custom callback class.

That is it, I hope it is helpful!

Hi @hoangcuong2011 , thanks for your explanations. I've validated your second point and indeed it's exactly what you said. I'm currently trying to implement a LSTM-autoencoder model to encode sequence into sequence, in which it involves a LSTM layer with return_sequence = False and then RepeatVector layer to copy that back to the previous timestep dimension. However, the mask get lost right after the LSTM because return_sequence = False (if True it returns the input_mask), then I'm wondering how I can get back the mask so that the loss will also ignore the padded timesteps? Thanks!

@zhangwj618 I am not really sure what your question is about. I guess you would like to write a custom masking layer. If you explain the question in more detail, I think I can help. Thx!

I thought the output of masking inputs is zeros all the time in LSTM. But it is not the case. Let us assume we have a masking:

0 0 0 1 1 0 0 0
With this case, the three first elements with masking zero has output of 0. However, the three last zeros have output that is as the same as the output of the last element with masking 1.

Meanwhile, Keras is very convenient in the sense that the loss it computes will be based on only elements with masking of 1. I found this is a big plus of using Keras, something a bit too good too be true as I guess implementing this is not that easy.

I think it is actually bad if it is implemented that way, because it reveals coupled code. In my case specifically i am still struggling with a right loss implementation right now, and it feels like the only way to go is to split training samples based on their sequence length, and avoid masking, which i would not want to do obviously.
Another approach i am thinking about is pre-pad my sequences, instead of post-pad, have not worked it out yet though. Luckily i have either left or right padding with my problem.
Can anyone please shed some light why keras (and now tensorflow.keras) masking is implemented (and what is more importanlty - kept) that way?


make Output class more redable

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docs: Add app / code review tool attestations

Adds app / code review tool attestations as an extension doc after some discussions with @wlynch re https://github.com/gittuf/github-app.

#626 is a first pass at supporting this in the policy metadata, we could use that to discuss the changes proposed here.


Issue workflow

issue #120 solved

@Yashshukla11 can you review thia PR?

Could you please assign the appropriate labels and review this PR?


Projectile despawn animation seems to be tied to FPS

When spawning multiple projectiles (number in the thousands), the game lags for a second or so, after which all the projectiles despawn some pixels away from where they spawned.
I'd expect the projectiles to shoot off normally after the lag spike is over and despawn after the animation time passes ingame instead of realtime.

Fixed with #145


Add myself as a recipe maintainer

I like helping out in the Jupyter ecosystem. Merging automatic PRs that builds okay after releasing to pip is something I'd be happy to do.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

Ping @minrk, I understand this is a notification you got while swamped with other work.
I made this PR as I figured it could be useful to have another person or so to be able to get the conda releases out following a PyPI release.

Sorry, missed this one!


HHH-18132 Add support for skip locked for HANA.

https://hibernate.atlassian.net/browse/HHH-18132

I forgot to create the topic branch in my fork. I will create a new PR as soon as I've fixed it. Sorry about this.

Also, you need a space before the ignore, otherwise you will generate wrong SQL like select l1_0.id,l1_0.name,l1_0.version from Lock_ l1_0 where l1_0.id=? for updateignore locked


Comment puzzle

In the online editor the comment gets preserved:
runtime-benchmarks = [
  # a comment
]

but master cli doesn't preserve it with default config:
runtime-benchmarks = []

Any ideas?

Looks like I forgot to deploy a new version of the site with the latest releases.
This seems to be a bug in the new formatter though.

If there's an element before the comment or after it seems to keep the comment so it's just if there's no elements in the array.
I'm blown away by the align_entries = true btw - it makes such a difference to readability. This is all so much good work that you've done.

Thanks, I'm glad you find it useful!


crypto: Implement b64 encoding for genesis hash

I added base64 encoding to the genesis hash. This aligns with how Algorand's REST endpoints serialize the genesis information.

Hi. What endpoint are you trying to fix? This breaks the msg pack transaction serialization (reference, which doesn't have to be encoded). Unfortunately our test coverage isn't that good yet and didn't catch this.

Oh, my bad! I was thinking of transactions/params (reference). You're totally right, the change doesn't belong in algonaut_crypto.
How about instead, we change suggested_transaction_params here https://github.com/manuelmauro/algonaut/blob/75bce542b0a386b8fdd7cd97900b90040eff0f1f/src/algod/v2/mod.rs#L220-L233.
so instead of genesis_hash: params.genesis_hash we convert it to base64. Would that be acceptable?

Hmm, the serialization in TransactionParams is indeed incorrect. This should be fixed ideally at SDK level (adding serialize_with). We seem to have this problem in Version as well.


it would be nice if the back button was on the left side instead of the right

what do you think @nylira ?

I think that makes sense.

@nylira my mind has changed about this ticket. i am totally comfortable with the back button in the toolbar on the right. it seems to me that we don't have any routes that are more than one or two deep anyways, so using "back" will be just as easy clicking the route in the sidebar.
that being said, i will make sure it's on every page and in the same place. i think for now, this should be ok.
👍 😆 🙃


Update install and upgrade docs

Issue #, if available:
N/A
Description of changes:

Update installation instructions as without deleting and re-creating main branch the tag is not deployed

git checkout tags/v2.3.0 # Checkout tag
This command checks out the tag but doesn't reset main branch to be at this tag

Update upgrade instructions as:-

git checkout upstream/v2.2.0 -b v2.2.0 is not valid
git checkout origin/main results in a detached head state rather than checking out main
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Hey @trevorrea Thank you for this work.
I fully agree with the upgrade docs and confirm they work.
As for the install docs, deleting the branch feels a bit wrong, as there may be mistakes in deleting. I've updated instead to specify the local source branch rather than assuming main, into remote main.
[cloudshell-user@ip-10-132-27-211 document-translation]$ git checkout tags/v2.0.1
Note: switching to 'tags/v2.0.1'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at fdbc469 Return item with nested list for models
[cloudshell-user@ip-10-132-27-211 document-translation]$ git branch -a
* (HEAD detached at v2.0.1)
  main
  remotes/codecommit/main
  remotes/origin/HEAD -> origin/main
  remotes/origin/main
  remotes/origin/update-docs-only-on-change
[cloudshell-user@ip-10-132-27-211 document-translation]$ git push  codecommit v2.0.1:main


Light refactoring of TUI pane and instructions.

Description
A proposal for some light TUI refactors. I like that there's less visual clutter but also could see that there's not enough visual feedback indication when you've entered the task.
Mentally prepared to hear either way. I think I prefer the fewer borders but I don't want the behavior to be too opaque. Alternatively, if we could get the red-to-blue gradient on the left sidebar when interactive... :chefs-kiss: - but I haven't looked at how to do that yet.
I've also "localized" the instructions for moving up and down the task list to the header of the task list itself.
Testing Instructions
👀

Removing my review request from here, because I really don't feel like I can be valuable here


@conda-forge-admin, please add bot automerge

@conda-forge-admin, please add bot automerge

Hi! This is the friendly automated conda-forge-webservice.
I just wanted to let you know that I added bot automerge in conda-forge/crayons-feedstock#3.


How to enable and configure svgo?

As i understood, svgo is disabled by default. I would like to enable and configure it, but .svgrrc doesn't work. Is there a way I can achieve it? Or mb should I just use svgo/cli before saving svg-files.

We don't allow you to configure SVGR so running svgo/cli on your SVG files is probably your best option.


Slow Path IntelliSense in TS 2.6.1

From https://github.com/Microsoft/vscode/issues/36825

TypeScript Version:  2.6.1
Problem
Users are reporting slow path IntelliSense in some cases with TS 2.6.1. Suggestions in import paths have a noticeable delay and also sometimes fail to return. See original issue for more details. I'll move over the relevant logs and comments as well

@jankalfus Originally reported:

Try to use path intellisense for relative path
VS Code is brutally slow at first, it gets worse with every level, until it stops suggesting completely at 4th level (import Something from '../../../../<here, nothing happens>'). Then it never starts working again, even after writing a completely new import statement. The only way to get it "working" is by restarting the editor. But then, all the issues start again.

Here are the logs for the suggestion:
Info 218  [12:49:31.876] request: {"seq":72,"type":"request","command":"completions","arguments":{"file":"/*censored*/","line":12,"offset":27}}
Info 219  [12:49:32.586] Scheduling throttled operation: {"projectName":"/dev/null/inferredProject1*","fileNames":[/*censored list of 489 files*/],"compilerOptions":{"module":1,"target":2,"allowSyntheticDefaultImports":true,"allowNonTsExtensions":true,"allowJs":true,"jsx":1,"checkJs":false,"noEmitForJsFiles":true,"maxNodeModuleJsDepth":2},"typeAcquisition":{"enable":true,"include":[],"exclude":[]},"unresolvedImports":["child_process","core-js","crypto","events","fs","http","https","net","node-notifier","path","readline","stream","tls","url","util","vm","zlib"],"projectRootPath":"","kind":"discover"}
Info 220  [12:49:32.587] Scheduling request for: /dev/null/inferredProject1*
Perf 221  [12:49:32.591] 72::completions: elapsed time (in milliseconds) 713.8218
Info 222  [12:49:32.591] response: {"seq":0,"type":"response","command":"completions","request_seq":72,"success":true,"body":[{"name":"index","kind":"script","kindModifiers":"","sortText":"index","replacementSpan":{"start":{"line":12,"offset":26},"end":{"line":12,"offset":27}}},{"name":"js","kind":"directory","kindModifiers":"","sortText":"js","replacementSpan":{"start":{"line":12,"offset":26},"end":{"line":12,"offset":27}}},{"name":"scss","kind":"directory","kindModifiers":"","sortText":"scss","replacementSpan":{"start":{"line":12,"offset":26},"end":{"line":12,"offset":27}}}]}

BTW I couldn't find any errors or stack traces.

Also, it looks like the logging stopped just after I typed the last / in the import and the suggestion stopped working. There are no more log items after that.

@nicksnyder also this issue:

Video:
https://cl.ly/0o0F1H170j0S

Attached is the log from this session.
tsserver.log

This is probably related more to the original issue, but whatever, I'll put it here, since this one is still open:
I noticed yesterday that vscode-js-import plugin started working again, probably after the VS Code 1.8.1 update. It also looks like I'm not having any performance issues with the imports anymore.

thanks @jankalfus. please let us know if you see issues in the future.

I recently upgraded to VSC 1.21 and have started experiencing this same issue in Javascript. No errors/warning are being shown in the developer tools console. I have tried disabling all extensions, and I have the same thing. I am using Ubuntu 16.04. I never used to have this issue on the jS project I am working on. I also tried installing the vscode-js-import plugin mentioned above to see if that fixed it for me, but no, the issue continues. My issue is slightly different to the original poster in that no matter how deep I drill into the path, it does eventually return a list of files/folders I can choose.

@charlesdeb can you file a new ticket and share some context about your project? and upload your tsserver logs.

Sure @mhegazy, I will try and do that later this week. Thanks for getting back to me.

OK, I have had a chance at looking at the tsserver.log file for trying an import code completion.
 There are actually about 45 of these errors right after this output - and then another 35 or so a bit further on. You can also see from the top of the snippet that the async completions request took 47738 milliseconds.
Note that in order to trigger an autocompletion now, I need to press alt-space (or some other key combo - can't quite remember which) whereas before I did the upgrade to 1.21, I think path completion would just work automatically within import statements.
Hopefully this is helpful. I'd really like to get completion working in a more timely fashion again! O/wise, I am a huge fan of VCS.

the two completion requests in this log took  18.2173 and 2.9179 milliseconds, so not sure where is the slowness.
The open file is the one that took the 47738 , and not the completion. looks like we are reading too many files/looking in too many directories for modules.
can you file a new issue, and upload your full tsserver log.

Sure - but can you suggest exactly what I call this issue.

"slow file open" ?


this bug was happened when i enabled it

could you please describe it clearly with some basic info of your environment (such as flarum version, extensions enabled, etc.) ?


Merge functionalities of several projects

Merge functionalities of several projects
Context
Currently, the functionalities are attach to a given project, but for a given reason, we might need to attach functionalities to several projects without having to rewrite them manually.
Expected
We can add the ability to merge functionalities of several projects with something like a project group concept. Only accessible by ARA administrators (which makes this issue depends on #19 ).

This feature was delivered in version 4.1.0 (using the cartography export feature in ARA format, and import it in another project)


fix: hooks v3 testnet

hooks v2 is shutdown

@justinr1234 there appears to be some sort of format issue in the new faucet
File "/home/runner/work/xrpl-py/xrpl-py/tests/integration/sugar/test_wallet.py", line 159, in test_fund_given_wallet_hooks_v3_testnet_async_websockets
    balance = int(result.result["account_data"]["Balance"])
KeyError: 'account_data'

@justinr1234 there appears to be some sort of format issue in the new faucet
File "/home/runner/work/xrpl-py/xrpl-py/tests/integration/sugar/test_wallet.py", line 159, in test_fund_given_wallet_hooks_v3_testnet_async_websockets
    balance = int(result.result["account_data"]["Balance"])
KeyError: 'account_data'

Yeah I haven’t figured it out yet, because it appears to be identical to the passing JavaScript code

@justinr1234 there appears to be some sort of format issue in the new faucet
File "/home/runner/work/xrpl-py/xrpl-py/tests/integration/sugar/test_wallet.py", line 159, in test_fund_given_wallet_hooks_v3_testnet_async_websockets
    balance = int(result.result["account_data"]["Balance"])
KeyError: 'account_data'

Yeah I haven’t figured it out yet, because it appears to be identical to the passing JavaScript code

I assume it's because the call is failing.


Support negative numbers in query language

[Current coverage][cc-pull] is 62.04% (diff: 100%)

Merging [#315][cc-pull] into [filter-interface][cc-base-branch] will not change coverage

@@           filter-interface       #315   diff @@
==================================================
  Files                    24         24
  Lines                   627        627
  Methods                   0          0
  Messages                  0          0
  Branches                  0          0
==================================================
  Hits                    389        389
  Misses                  238        238
  Partials                  0          0

Powered by Codecov. Last update [92df20e...b891fb9][cc-compare]
[cc-base-branch]: https://codecov.io/gh/Kitware/candela/branch/filter-interface?src=pr
[cc-compare]: https://codecov.io/gh/Kitware/candela/compare/92df20eb0df4b0aaa5c8773ea934e9103a133c34...b891fb91fde667c3f26d978667c5e3ae667f6593?src=pr
[cc-pull]: https://codecov.io/gh/Kitware/candela/pull/315?src=pr

LGTM!


docs(quick-start): add more "X" to template to fix some distros

Some distros like latest Alpine 3.18 + Alpine 3.19 requires at least 6 "X"s

Thanks!


Support for Deployments

Most of our kubernetes specifications are of kind Deployment or Service.  Therefore the containers are actually in the deployment yamls.
For example:
kind: Deployment
metadata:
  name: heapster
  namespace: kube-system
spec:
  replicas: 1
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: heapster
    spec:
      containers:
      - name: heapster
        resources:
            limits:
              cpu: 100m
              memory: 100Mi
            requests:
              cpu: 100m
              memory: 100Mi
        image: gcr.io/google_containers/heapster-amd64:v1.3.0-beta.1
        imagePullPolicy: IfNotPresent
        command:
        - /heapster
        - --source=kubernetes:https://kubernetes.default
        - --sink=influxdb:http://monitoring-influxdb:8086```

This will be next to be implemented for sure. Thanks for the feedback.

Ingress is high up on my list as well.. thanks for the tool @viglesiasce!