NavSat (GPS) map plugin

🎉 New feature

Part of https://github.com/ignitionrobotics/ign-sensors/issues/23
Requires https://github.com/ignitionrobotics/ign-msgs/pull/206
Required by https://github.com/ignitionrobotics/ign-gazebo/pull/1248

Summary

Ported @Sarath18 's GPS plugin from ign-rviz to ign-gui, see

https://github.com/ignitionrobotics/ign-rviz/pull/66
https://github.com/ignitionrobotics/ign-rviz/issues/65

I had to make a few changes to the QML for it to work for me on Bionic:

I couldn't get the mapbox plugin to work, so I went with osm
supportedMapTypes wasn't working for me so I removed it
The unicode symbol ⌖ won't render for me when I load the GUI, mysterious... So I went with a different one for now which is not as nice: ✲

I can think of a few different features to add, like showing multiple vehicles on the same map, marking the world origin, clicking on the map to choose the location to spawn a model... The possibilities are endless! This is just the first MVP.
TODO

[x] Fix warnings [GUI] [Wrn] [Application.cc:674] [QT] file::/NavSatMap/NavSatMap.qml:97:3: QML Map: Binding loop detected for property "center"

Test it

It works with https://github.com/ignitionrobotics/ign-gazebo/pull/1248:

Checklist

[x] Signed all commits for DCO
[ ] Added tests
[x] Added example and/or tutorial - on Gazebo
[x] Updated documentation (as needed)
[x] Updated migration guide (as needed)
[x] codecheck passed (See contributing)
[x] All tests passed (See test coverage)
[x] While waiting for a review on your PR, please help review another open pull request to support the maintainers

Note to maintainers: Remember to use Squash-Merge and edit the commit message to match the pull request summary while retaining Signed-off-by messages.
🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸🔸

I don't get this warning on focal

I can't reproduce it anymore either :smile:

Although I do get it for:

Ah yeah that's a pre-existing one. We should look into this when working on OOBE soon

This pull request has been mentioned on Gazebo Community. There might be relevant details there:
https://community.gazebosim.org/t/new-ignition-releases-2022-01-10/1228/1


Fixing URL for Tekton plugin icon

Hey, I just made a Pull Request!
Changed the iconUrl in the yaml, so that the icon for this plugin will display on the /plugins page.
(Should be a simple update, but… Originally, I was trying to fix the icon for the TechRadar plugin, but I failed at finding a quick fix for that. So I decided to fix this icon instead, in order to redeem myself. But then, being a marketing hack who has not made a PR in a long time, I had a little adventure undoing a commit to master (that was also missing my DCO sign-off). Hope I was successful in fully undoing that, and this new one is OK.)
:heavy_check_mark: Checklist

[x] Screenshots attached (for UI changes)
[x] All your commits have a Signed-off-by line in the message. (more info)

Need DCO sign-off though @fengypants!

Is there some reason I'm not passing the DCO check? I have the Signed-off-by line in the commit.

There's some more info right at the bottom of the build report

Thank you! I did not read to the bottom of the error message. Then I had to learn how to fix a commit. And then I had to learn vi. And then I bravely ignored the scary warnings about rebasing. But I fixed it! And without resorting to usual move, which is nuking everything and starting over from scratch.

Nice work! ✨

Ha, well, it's still altogether embarrassing. But I'll take it!


Cards not recognized after pushing to App Store/TestFlight

If I do a local push from Xcode all function calls work as expected, but as soon as I push the same build of the app through TestFlight or through the App Store it always says that there are no supported cards. I have these same results with my production app, as well as a Ionic blank starter.
cordova-ios: 4.5.4
cordova-plugin-applepay: 3.0.0
Not the same issue as https://github.com/samkelleher/cordova-plugin-applepay/issues/14
as the Apple Pay capability is ON and the merchant id is selected. (And all three associated checkmarks are checked)
Not the same issue as https://github.com/samkelleher/cordova-plugin-applepay/issues/10
since the merchant ID has been added.
Not the same issue as https://github.com/samkelleher/cordova-plugin-applepay/issues/22
since I am not dealing with test sandbox accounts or Xcode capability issues.
This issue is also brought up here on the ionic forms, https://forum.ionicframework.com/t/has-anyone-successfully-integrated-apple-pay-with-ionic-2/84002
Does anyone know where I could look to find the cause of this?

Support @Barlocker-Garrett not work in production, pushing to App Store/TestFlight

Hi, Have you found a solution, I have the same issue!

I have not found a working solution, sorry.

It turns out that the xCode was not adding the correct entitlements to the Entitlements.plist file. On submitting the app to app store check the list of entitlements the apple pay and merchant ID should be there if not edit the Entitlements.plist file and add them.

Where to find and edit the Entitlements.plist file after submitting the app to app store

This plugin does not update the entitlements on its own. I have updated the documentation:
https://github.com/samkelleher/cordova-plugin-applepay/blob/4bf3eac62f8a4e67021829a1e15ac50d3a9dae5e/README.md#updating-entitlements
Let me know if there are issues with this.
An example of resigning with entitlements would be https://stackoverflow.com/questions/36888535/how-do-i-resign-app-with-entitlements

Just to note, I've also had this issue but it was linked to this issue: https://stackoverflow.com/questions/49393514/apple-pay-error-add-merchant-id-to-your-app-id

Hi,  I have the same issue , can found solution ?


6.2.0: cherry-pick thermocycler gen2 support commits

This branch adds commits that add support for the thermocycler GEN2, and their dependents. Mostly they applied cleanly; there were some changes that needed to be made. I don't know that they're all correct!
This branch will be rebase merged onto develop-6.2.0 to preserve history.
Reviewing
The only way to review this is to glance through commits, unfortunately.
Testing

Do it
A lot of it

Todo

we need the app support PRs, and because those PRs were larger there are many more conflicts with them and they require just as many previous commits, which tend to add more things we can't tolerate in this release. The PRs are https://github.com/Opentrons/opentrons/pull/11522 and https://github.com/Opentrons/opentrons/pull/11492 and I think we may just need to fix up the conflicts rather than pull in their deps.

Going with an edge based release.


why can not use this ?

..

if cannot use this, do you have a good recommend for cas(compareAndSet) of python module?
thanks in advance.


Consolidate flushes even when no read in progress

Motivation:
Currently FlushConsolidationHandler only consolidates if a read loop is
active for a Channel, otherwise each writeAndFlush(...) call will still
be flushed individually. When these calls are close enough, it can be
beneficial to consolidate them even outside of a read loop.
Modifications:
When we allow a flush to "go through", don't perform it immediately, but
schedule it on the channel's executor with a timeout of 0. Under high
pressure, this gives other writes a chance to enqueue before the task
gets executed, and so we flush multiple writes at once.
Result:
Lower CPU usage and less context switching.

Can you share a benchmark or some numbers?

@normanmaurer Let me check with my management what I can share publicly.

Thx if not you know how to find me

Am 09.09.2016 um 19:19 schrieb Olivier Michallat<EMAIL_ADDRESS>@normanmaurer Let me check with my management what I can share publicly.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

I addressed all your comments. Sorry for the delay, I wanted to run a few more tests.
I thought I saw a difference between submit and schedule, but it was just noise in my tests, over a few runs the differences are not significant.
I do however see a clear difference in favor of the custom flusher we are currently using in the Datastax Java driver: FlushConsolidationHandler triggers approximately 15% more context switches, and uses a bit more CPU. One thing that the Java driver code does differently is that it coalesces not only the flush() call, but also the writes themselves: nothing is written to the channel until we decide to flush (the messages are enqueued). Before we flush, we execute all the pending writes from the channel's event loop. I think doing the writes sequentially instead of submitting them as individual tasks could explain the reduced context switching, does that make sense?

Actually I realize the same behavior could be achieved in the handler by overriding write, I'll give it a try.

Actually I realize the same behavior could be achieved in the handler by overriding write, I'll give it a try.

Forget that last comment, processing a write in the handler implies that it's been submitted as a task already, so that wouldn't solve the issue.

good idea @olim7t ! lgtm

@olim7t can you please squash your commits ?

@olim7t will merge soon. Just want to make one more test.

Sounds good to me, I will try to work on that today.

@normanmaurer - providing this as optional behavior sgtm.

@olim7t awesome! Squash and I will merge it :)

Squashed and rebased on current 4.0.

Cherry-picked into 4.0 (11f09bb5a6bac48c1da73cba3442652f8a8e6a1d) and 4.1 (3a8b8c9219901bb3fe6dd8b8b87b41eec3e275fc)

@olim7t thanks!


LastConsumed behaviuor seems a little broken

Is your feature request related to a problem? Please describe.
Hello!
I've found that LastConsumed offset would replay the last message at the stream.
Here is a repro:
package main

import (
	"context"
	"fmt"
	"os"
	"os/signal"
	"strconv"
	"time"

	"github.com/google/uuid"
	"github.com/rabbitmq/rabbitmq-stream-go-client/pkg/amqp"
	"github.com/rabbitmq/rabbitmq-stream-go-client/pkg/stream"
)

func CheckErr(err error) {
	if err != nil {
		fmt.Printf("%s ", err)
		os.Exit(1)
	}
}

func main() {
	ctx, stop := signal.NotifyContext(context.Background(), os.Interrupt, os.Kill)
	defer stop()
	env, err := stream.NewEnvironment(
		stream.NewEnvironmentOptions().
			SetHost("localhost").
			SetPort(5552).
			SetUser("guest").
			SetPassword("guest").SetRequestedHeartbeat(time.Second * 10))
	CheckErr(err)

	streamName := uuid.New().String()
	err = env.DeclareStream(streamName,
		&stream.StreamOptions{
			MaxLengthBytes: stream.ByteCapacity{}.MB(500),
		},
	)
	CheckErr(err)

	consName := "consumer_"
	producer, err := env.NewProducer(streamName, nil)
	CheckErr(err)

	go func() {
		for i := 0; i < 1; i++ {
			err := producer.Send(amqp.NewMessage([]byte("hello_world_" + strconv.Itoa(i))))
			CheckErr(err)
		}
	}()

	msgChan := make(chan string)

	handleMessages := func(consumerContext stream.ConsumerContext, message *amqp.Message) {
		err := consumerContext.Consumer.StoreCustomOffset(consumerContext.Consumer.GetOffset())
		if err != nil {
			CheckErr(err)
		}
		fmt.Printf("offset %d\n", consumerContext.Consumer.GetOffset())
		msgChan <- string(message.GetData())
	}

	cons, err := env.NewConsumer(streamName, handleMessages, stream.
		NewConsumerOptions().
		SetManualCommit().
		SetOffset(stream.OffsetSpecification{}.LastConsumed()).SetConsumerName(consName),
	)
	CheckErr(err)
	fmt.Println(<-msgChan)
	cons.Close()

	consCopy, err := env.NewConsumer(streamName, handleMessages, stream.
		NewConsumerOptions().
		SetManualCommit().
		SetOffset(stream.OffsetSpecification{}.LastConsumed()).SetConsumerName(consName),
	)
	CheckErr(err)
	fmt.Println(<-msgChan)
	defer consCopy.Close()

	<-ctx.Done()
}

The output is next:
offset 0
hello_world_0
offset 0
hello_world_0

Is it an expected behaviour?
Describe the solution you'd like
In my code i have to use Offset specification instead of LastConsumed, and inside the callback check if consumer context offset equals QueryOffset at the start of consumer, than this message was processed and it should be skipped
But is it correct to make someting like this: query offset before starting of consumer, and if offset exists increment it:
offset, err := env.QueryOffset(consName, streamName)
//handle error
if offsetExists {
	offset++ // is this increment correct
}
consCopy, err := env.NewConsumer(streamName, handleMessages, stream.
	NewConsumerOptions().
	SetManualCommit().
	SetOffset(stream.OffsetSpecification{}.Offset(offset)).SetConsumerName(consName),
)

If this solution is correct, may be it's worth to setup lastOffset + 1 here
Describe alternatives you've considered
No response
Additional context
No response

Hi @HustonMmmavr,
LastConsumed API was meant to help the user to restart from the last stored message.
Over the years, I have seen that it created more confusion than helping.
Also the name is misleading.
This is why I decided to deprecate the API,  The correct way is what you have done:
offset, err := env.QueryOffset(consName, streamName)
//handle error
if offsetExists {
	offset++ // is this increment correct
}

Got it! Thank you)

The method is deprecated now https://github.com/rabbitmq/rabbitmq-stream-go-client/pull/324.
Thank you @HustonMmmavr


Integration Test - Release - 2022/01/19 07:08:51

Auto create for integration test. IssueCommandFlow

@prmerger-dev-1 : Thanks for your contribution! The author(s) have been notified to review your proposed change.

Docs Build status updates of commit 68a40c4:
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Change xvfb command to bundle exec rake spec

This is probably a typo, bundle exec spec isn't a common command.

spec was the command back in rspec 1.x days - The equivalent nowadays would be bundle exec rspec nowadays.  If you make that change I'll merge it.

I think this PR is a fix for this: https://github.com/thoughtbot/capybara-webkit/pull/1054

Closing in favor of PR #1054


Bot Recording from form page

Hello
we added recording solution into our application. its recording the meeting but its stuck on join form page and recording all meeting from there.

Hey @rohit0337
Can you share some more details about the issue. May be some ECS logs and screenshot of the issue would help.

Closing the issue for now. Feel free to reopen it if you still have problems.


Add calibration for haz to nav and sci cam extrinsics, and add a warning for loading very old maps

@marinagmoreira Could you merge this after the code freeze is over? (I think after we merge back from master to develop it is done, right?)


Improve Metadata and SEO for Picwise

The metadata is missing several important tags that can improve SEO and the appearance of shared links on social media:

keywords: A tag to help improve SEO with relevant search terms.
Additional Open Graph Tags: Including og:type, og:url, and og:site_name.
Twitter Meta Tags: Adding twitter:card, twitter:title, twitter:description, and twitter:image for better link previews on Twitter.

@Aryainguz Please assign this to me (myselfshivams) labels gssoc, hackoberfest.

Hey @myselfshivams let me know if you’re still on this or not

Hey @myselfshivams please let me know if you’re still on this or not

Hey @Aryainguz, yes, I’m still on it!...sorry for the delay I will create PR today only

Cool

Cool

Hey @Aryainguz ,  Could you please add level 2 or 3 label.


Unhelpful error message when country code exceeds 2 characters in openssl tool

Specifying a -subj /C=Foo/ST=St/L=Loc/O=Org/OU=mlaunch/CN=localhost when generating a certificate via the openssl command line tool yields the following error:

140007147442368:error:0D07A097:asn1 encoding routines:ASN1_mbstring_ncopy:string too long:../crypto/asn1/a_mbstr.c:109:maxsize=2

This error does not indicate which field the problem is in, making this error extremely difficult to figure out just going by the error message.

I have same issue like as you and find it on this page.
https://github.com/servisys/ispconfig_setup/issues/279

@p-mongo is this still a problem on 3.3?  If so would you be willing to write a PR to improve this?  I'm happy to assign it to you as a community issue


xDnsServerDiagnostics: Added this resource to configure additional diagnostic settings. Take 2.

Pull Request (PR) description
Added the xDnsServerDiagnostics resource to configure additional diagnostic settings. Tests have been added.
This Pull Request (PR) fixes the following issues
- Fixes #86

Task list

[x] Added an entry under the Unreleased section of the change log in the README.md.
Entry should say what was changed, and how that affects users (if applicable).
[x] Resource documentation added/updated in README.md.
[x] Resource parameter descriptions added/updated in README.md, schema.mof
and comment-based help.
[x] Comment-based help added/updated.
[x] Localization strings added/updated in all localization files as appropriate.
[x] Examples appropriately added/updated.
[x] Unit tests added/updated. See DSC Resource Testing Guidelines.
[x] Integration tests added/updated (where possible). See DSC Resource Testing Guidelines.
[x] New/changed code adheres to DSC Resource Style Guidelines and Best Practices.

This change is

Codecov Report

Merging #92 into dev will increase coverage by 2.07%.
The diff coverage is 89.47%.

@@            Coverage Diff             @@
##              dev      #92      +/-   ##
==========================================
+ Coverage   72.88%   74.95%   +2.07%
==========================================
  Files          10       11       +1
  Lines         520      563      +43
==========================================
+ Hits          379      422      +43
  Misses        141      141

Impacted Files
Coverage Δ

...sServerDiagnostics/MSFT_xDnsServerDiagnostics.psm1
100% <100%> (ø)

...MSFT_xDnsServerSetting/MSFT_xDnsServerSetting.psm1
75.75% <75.75%> (ø)
: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 7f06722...972d137. Read the comment docs.

DSCResources/MSFT_xDnsServerDiagnostics/MSFT_xDnsServerDiagnostics.psm1, line 21 at r2 (raw file):
Previously, nehrua (Nehru Ali) wrote…
Shouldn't this be xDnsServer?

No, it is referencing the module name for the DNS Role


Can't close window when opening project with -p specified

Godot version: v3.2.beta.custom_build.83fe471c3
OS/device including version: macOS Catalina 10.15.2 / MacBook Pro (Retina, 13-inch, Early 2015)
Issue description:
This happens when a project is specified (either it's in the current working directory or by the --path option), and Godot is run with both -e and -p on the command line.
The window size is the same as the editor, and the title shows the specified project, but the actual UI is the project manager.
When I try to close the window, the window dims, but never close.
Steps to reproduce:

Run the command: path/to/godot -e -p --path path/to/project
Try to close the window

It's macOS specific, same happens on Windows.
-p flag is for project manager, -e for the editor, so I guess it's loading editor and project manager on top of it. Dimmed window is from editor exit dialog, but it's child of editor node and is covered by PM UI.

@bruvzg Both the editor and project manager feature dimming when quitting. (This is independent from the dimming displayed when a pop-up dialog appears.)

I have added alpha to the PM root node and my assumption was correct. PM is overlaid on to of editor, it crashes when Quit is pressed.

Screenshot

Crash log
arguments
0: godot
1: -p
2: -e
3: --path
4: ./win_max_min_size
Current path: /Volumes/Seagate/SDK/godot_sdk
Godot Engine v3.2.beta.custom_build.e7b7dc57f - https://godotengine.org
OpenGL ES 3.0 Renderer: AMD Radeon HD - FirePro D300 OpenGL Engine

ERROR: _load: Invalid tag in doc file: demos.
   At: editor/doc/doc_data.cpp:986.
handle_crash: Program crashed with signal 11
Dumping the backtrace. Please include this when reporting the bug on https://github.com/godotengine/godot/issues
[1] 1   libsystem_platform.dylib            0x00007fff7138e42d _sigtramp + 29
[2] 2   ???                                 0x3f8000003f70a3d7 0x0 +<PHONE_NUMBER>472770519
handle_crash: Program crashed with signal 11
Dumping the backtrace. Please include this when reporting the bug on https://github.com/godotengine/godot/issues
[1] 1   libsystem_platform.dylib            0x00007fff7138e42d _sigtramp + 29
[3] String::String(String const&) (in Godot) (ustring.h:357)
[2] String::String(String const&) (in Godot) (ustring.h:357)
[4] String::String(String const&) (in Godot) (ustring.h:357)
[3] EditorExportPlatformAndroid::_device_poll_thread(void*) (in Godot) (export.cpp:245)
[5] EditorSettings::get_settings_dir() const (in Godot) (editor_settings.cpp:1197)
[4] ThreadPosix::thread_callback(void*) (in Godot) (thread_posix.cpp:76)
[5] 5   libsystem_pthread.dylib             0x00007fff71399e65 _pthread_start + 148
[6] 6   libsystem_pthread.dylib             0x00007fff7139583b thread_start + 15
-- END OF BACKTRACE --
zsh: abort      godot -p -e --path ./win_max_min_size


Surprising feature on MacOS: empty service/account strings are wildcards

The underlying Security Framework functions take optional arguments for the service and account strings, but this bubbles up to keyring as plain &strs. Passing an empty string is then treated as a non-provided optional argument rather than an explicit empty value. So, for example:
    #[test]
    fn secret_wildcard() {
        let name = "test";
        let entry = keyring::Entry::new("my_service", name);
        entry.set_password("abc").unwrap();

        let entry = keyring::Entry::new("my_service", name);
        let pass = entry.get_password().unwrap();
        assert_eq!("abc", pass);

        // Can also access via empty string(s)!
        let entry = keyring::Entry::new("my_service", "");
        let pass = entry.get_password().unwrap();
        assert_eq!("abc", pass);

        // And can use the empty-string entry to overwrite the original!
        entry.set_password("def").unwrap();

        let entry = keyring::Entry::new("my_service", name);
        let pass = entry.get_password().unwrap();
        assert_eq!("def", pass);

        entry.delete_password().unwrap();
    }

I've only tested this on MacOS but I suspect the behavior is the same on iOS.
This makes some sense as a reflection of the underlying API, but just reading the docs for keyring, I think it's a very surprising behavior. I think this should either a) get clear documentation somewhere, b) be forbidden (just fail on empty values somewhere) or c) be reflected in the API by making the service/account params Options.

Great catch, @cvermilion, thanks!  I'm inclined to simply refuse empty user names, since that could easily be made consistent behavior across all platforms, and it would conceal this Apple-specific "wildcard" feature (which can't be exposed by keyring in a cross-platform way. because it doesn't exist on Windows).

Yeah, I think that's a good plan. The "search if empty" feature sort of makes sense if you're familiar with the Apple APIs but otherwise is probably unexpected, so I don't think forbidding empty fields is a big loss. If someone wants this behavior on Mac/iOS they can use security_framework directly.

So looking at the code, there's really no way of putting a general fix in without changing the API or allowing the code to panic.  This is because the signatures for Entry::new and friends don't allow for argument validation and graceful failure, so I can't easily refuse to allow credentials with empty service or user names.  Here are some options I'm considering:

Up the major version and allow Entry::new and friends to fail.
Up the minor version and add new Entry::try_new and friends which fail (note this leaves the bug unfixed).
Leave the version alone and just have all platform operations return a NoEntry error if there is an empty string in target, service, or username.  Then document this special case in the docs.

I'm inclined to the latter, as this doesn't seem like it's worth altering the API for.  @hwchen what do you think?

FWIW, I think that's a sensible approach, and #87 looks fine to me. Allowing Entry::new to fail might be worth considering for the next major version but fixing this particular edge case is probably not worth an API change on its own.
I'm not sure whether it's worth worrying about users who relied on this behavior -- it seems possible that someone who worked exclusively on Mac/iOS might have expected and used it. But given that it's undocumented and non-portable, I'd say it's probably ok to remove?

Thanks for the review and the thoughtful comments!
It's hard to believe anyone is actually relying on this on macOS/iOS, since it only works if the credential you're working with is either the only one with that service name or magically the "first" one the keyring decides to return.
My bigger concern is that someone might explicitly be using an empty username or service name in order to be compatible with a 3rd-party application.  If so, they are going to be a bit surprised.  But, since the behavior in that case is undocumented and varies across platforms, I feel OK about clarifying it.  They can still access the existing functionality by using the new_with_credential entry point and creating the same platform-specific credential that would have been created before this change.
This discussion makes me think, however, that this should probably be a minor version release not a patch release, and I should add some explicit deprecation documentation to the release notes, as well as a warning that the next major release will probably do validation at Entry creation time.

@cvermilion @hwchen I've updated the PR #87 per my last comment.  I'll merge later today or tomorrow unless someone objects.


FAB margins are wrong on pre-lollipop

[x] I have verified there are no duplicate active or recent bugs, questions, or requests
[x] I have verified that I am using the latest version of FABsMenu.

Device/dashboard info:

FABsMenu version: 1.1.2
Android version: API 16 to 27
Device Manufacturer: Google
Device Name: Emulator

Reproduction Steps

Build the sample app
Run it on API < 21 (fix for the crash available here)
Click on the FAB

Expected Result
All the FABs are using the same margin on every API level
Actual Result
API < 21 have different margins than API >= 21

Can replicate in 1.1.3.

@EmmanuelMess
The issue is still open, so it for sure hasn't been fixed. When fixed, I will close the issue.
Sorry for this taking too long, but I'm quite busy with University and work 😕

Probably related to this.
Anyways, I'm deprecating this library. Sorry


Fix ACL modifications

This is a regression from https://github.com/project-chip/rs-matter/pull/170
I've changed then AclEntry::fab_idx from Option to mandatory in an effort to make it clear that when persisted in non-volatile storage, the fabric index should always be there.
What I did not realize back then is that when the AclEntry struct is coming from the other peer, this field will not be set, as the fabric index is coming from the session access context instead.
For the time being and while we use the AclEntry struct for both de-serializing incoming peer TLV, as well as for the on-disk/storage format, we'll have to have this field as optional. This might change once I introduce the long-in-the-works optimizations on fabric and NOC cluster operations.

@ivmarkov we could switch some map().unwrap_or into map_or calls throughout, however since this can be done in many places (it seems even existing ones) maybe this could be a followup. I will merge as-is.


Any alternative way to maneuver vehicles in curved road

I am trying to maneuver external actors (traffic vehicles) in a 2000m long curved road.
Currently I am using a tag named "AssignRouteAction" and  have manually provided the worldposition(x,y) to move the vehicle in the curved path.
Is there any other possible way to maneuver the external actors in curved road without providing worldposition in .xosc?

OS: [e.g. Ubuntu 18.04]
CARLA Version [e.g. CARLA 0.9.9]
Python version [e.g. 3.6]

Hey @vikilancer, there are basically two options to move a vehicle:
The first is one is to use CARLA's autopilot, which you can set by ActivateControllerAction. With that the vehicle will randomly move around the town. The main disadvantage is that you can't it's route so at intersections, the actor will choose a random exit.
The second one is to use an AssignRouteAction (which you are already using) + a controller. Waypoints can be as far away as you want as the route between them is interpolated, so you only need to specify the important ones (such as the waypoints after an intersection).
Additionally, here, you have the level of support of OpenSCENARIO 1.0, and at the end, all the types of positions you can use.

The second one is to use an AssignRouteAction (which you are already using) + a controller. Waypoints can be as far away as you want as the route between them is interpolated, so you only need to specify the important ones (such as the waypoints after an intersection).

This is only partially correct. For the shortest routingStrategy this does not happen. There really a direct connection is used. However, by a real coincidence, I required this feature today, so there is now a PR for this #673


Illogical way to handle URL in logging in

Describe the bug
When user types in other URL in the first fragment, they shouldn't have to type in the URL again
Screenshots

Would you like to work on the issue?
Open to all

Save other URL in shared preference


fix: update puppet guide article after review

[x ] I have read freeCodeCamp's contribution guidelines.
[ x] My pull request has a descriptive title (not a vague title like Update index.md)
[ x] My pull request targets the master branch of freeCodeCamp.
[ x] None of my changes are plagiarized from another source without proper attribution.
[ x] My article does not contain shortened URLs or affiliate links.

If your pull request closes a GitHub issue, replace the XXXXX below with the issue number.
Closes #XXXXX

Congrats @asarolia on your first pull request (PR)! 🎉
Thank you for your contribution to the page! 👍
We're happy to accept these changes, and look forward to future contributions. 📝


Add pages for new documentation #25

#25

Start at: https://github.com/metafacture/metafacture-documentation/blob/25-StartNewDocumentation/Home.md


run multilayer in qt gui with detect mode

Hi
in your qt gui we don't have any option to active detect mode
it is only on learn mode
and in learn mode it dont produce ,yml file.
can you explain how should i use it?
if it needed to write our main, how it should be? (in c++)

Hi @sabrabano0 , I'm sorry for my delayed feedback to you.
Yes, you're right. In fact, when you run the algorithm it produces a configuration file in the config folder. You can open it, and change the from learn to detect mode. Hope this helps you.

thank you that solved my  problem.


Record failed TLS client handshakes in UI and to mitm dump file (not only in log)

Problem Description
More and more mobile apps use certificate pinning on connections which let the connection fail in the TLS handshake phase on client side. Those connection attempts are currently only logged, however in my opinion it should be recorded similar to successful connections so that they were included when the recorded traffic is saved to a file, as the app tried to establish a connection with a server it most likely just failed because of mitmproxy.
Proposal
TLS client handshake failed errors should be recorded as flow so that they can be saved to the mitmproxy dump file.

refs https://github.com/mitmproxy/mitmproxy/issues/6421#issuecomment-1799035357


Cannot allocate memory

Hi, I call several time a little query that return an array freeze(), every results has > 3000 records:
Note. update today the v5.3.6 but this issue continue
let realm = try! Realm()
  realm.refresh()
  var metadatas: [tableMetadata] = []

  let results = realm.objects(tableMetadata.self).filter(predicate).sorted(byKeyPath: sorted, ascending: ascending)

  if results.count > 0 {
      return Array(results.freeze())

after 100/200 or 300 time crashed with;
Could not write to database: Error Domain=io.realm Code=9 "mmap() failed: Cannot allocate memory size: 23887872 offset: 201326592" UserInfo={NSLocalizedDescription=mmap() failed: Cannot allocate memory size: 23887872 offset: 201326592, Error Code=9}
Possible that the issue is before this call when I write in database but 23887872 is not a big database ... whats happens?

Hi @marinofaggiana
Is it happening on your physical device (what kind of it) or is it on iOS simulator?
Thanks

Hi @pavel-ship-it ,
I can reproduce this issue only with physical device because I need a big amount of images
Thanks !

Hi @marinofaggiana,
In this case the memory could be arranged for the images - each image will be expanded in to a bitmap in memory and may occupy significant piece if space.
Could you please check this case?
You could roughly estimate a memory usage by multiplying image's width by height by 4 bytes. Available memory could vary depending on the device model.

Hi @pavel-ship-it , thanks for your answer, no the memory of images are under control (the images are (files) only send via network), but I have found the issue:
replaced: result.freeze() with table.init(value: result)
So, as I imagined, the freeze() is totally unreliable, the table.init(value: result) occupy more memory in my task but is correctly released.

Thank you for getting back with it.
freeze() needs a space to create a copy of required data. If youк device haven't enough space it will cause a crash. Could you please check it?

Hi, my phone has over 9 GB free ... I don't think that's the cause

@marinofaggiana It would be great if you could get a sample app to us that reproduces this issue. Also make sure you are not keeping a Realm instance alive when it is not needed as that will also take up memory. Version 5.4.6 is out now I would recommend updating too.

This issue has been closed because there has been no response to our request for more information from the original author. With only the information that is currently in the issue, we don't have enough information to take action. Please reach out if you have or find the answers we need so that we can investigate further.


Expose the adapters

Rationale: I use a library that serializes objects to Json using Gson without exposing a GsonBuilder to me for configuration (https://github.com/karsaig/approvalcrest). Instead, it accepts a configuration object.
I wish to use vavr types with this library, so I must provide type adapters. The type adapters are not exported from this library, so I can't use them as-is.
This PR addresses the issue by exposing 2 maps typeAdapters() and typeHierarchyAdapters() for general access to type adapters in this module without requiring a GsonBuilder. Internally, these maps are also used for registering type adapters with a GsonBuilder, in order to avoid code duplication.

Codecov Report

Merging #6 into master will increase coverage by 0.08%.
The diff coverage is 100%.

@@             Coverage Diff              @@
##             master       #6      +/-   ##
============================================
+ Coverage     99.44%   99.52%   +0.08%
- Complexity       85      101      +16
============================================
  Files             9        9
  Lines           179      210      +31
  Branches          8        8
============================================
+ Hits            178      209      +31
  Misses            1        1

Impacted Files
Coverage Δ
Complexity Δ

src/main/java/io/vavr/gson/VavrGson.java
99.08% <100%> (+0.36%)
49 <48> (+16)
:arrow_up:

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Add code coverage badge

Now nyc from #181 shows 93.15% coverage. I think we can make it 💯
Badge and PR checker from codecov.io or coveralls.io would be awesome.

Also, seems like we have tests only for server.js:
-----------|----------|----------|----------|----------|----------------|
File       |  % Stmts | % Branch |  % Funcs |  % Lines |Uncovered Lines |
-----------|----------|----------|----------|----------|----------------|
All files  |    93.24 |    74.36 |      100 |    92.96 |                |
 server.js |    93.24 |    74.36 |      100 |    92.96 |... ,65,110,131 |
-----------|----------|----------|----------|----------|----------------|

CLI and other files should be tested too.


Globber exception when using a path with a dollar sign

Same drill as https://github.com/cake-build/cake/issues/1323

Not only with dollar sign, but also with many special characters.
What version of Cake are you using?
Version 0.17
What environment are you running on? Windows? Linux? Mac?
Windows 10
Are you running on a CI Server? If so, which one?
Local (Windows)
How Did You Get This To Happen? (Steps to Reproduce)
Create a directory with a [á|é|í|ó|ú] in the Directory Name
Bad output log

Good output log (symbolic link)

@devlead


Aqueduct and the future of DartVM ?

I was wondering what does the Aqueduct team think about Google's intentions with DartVM
I know you have a major DartVM investment in the Aqueduct framework
I also have been working heavily on server-side Dart
I'm a little bit worried about its future because Google seems to be obsessed about the front-end web and Flutter implementations
the latest developers' conference didn't almost mention DartVM ...

This all seems overblown. The marketing has narrowed to better onboard people into the language, but nothing technical is changing. Server-side Dart and Aqueduct will continue to exist. We are continuing to invest in Aqueduct, and you can see the progress on the 3.0 branch.
Members of our team spoke at the latest DartConf on server-side Dart. The Dart team has been very supportive of Aqueduct. They have been great about our feedback and communicating their direction, and I have participated in a regularly scheduled meeting with them for some time.
The best thing anyone can do for Dart server-side is to use, talk about, or contribute to Aqueduct.

thank you very much for reassuring us !

👍


[DEV] Translation of user generated text

Overview doc: https://docs.google.com/document/d/121UEkZNXQyn9wVO4aw96CCuhjEmAASl9lXAChIHicDU/edit#
User flows
A new entry is added

a user adds a new entry and the original language is saved as the language they are viewing the site in (this is already done)
[x] [pan] when it is saved, the user entered text fields (title, description, body) is machine translated in to our other languages, from this point the user entered text is forked, and different versions exist for title, body, and description for each language.
----  title, description, body fields are saved in a table called localized_texts. this table already exists

An entry is edited in its original language

no changes needed

An entry is edited in another language

[ ] [alanna] an alert is shown that says “This entry was originally added in {original_language}. [link]View this entry in {original_language}.”
[ ] [jesi] draft copy for info modal
---- Entry saved for 1st time it is machine translated into these languages
---- Each language is a version, but they share the same data (title description and body are different versions)
[x] [pan] title, description, body fields on the entry object are pulled from the localized_texts table in the language being viewed, not in the original language
[ ] [nice-to-have/future feature (alanna & pan)] if a user is editing an entry in a language that is not the original language, and the original language has a description/or body saved, add a prompt/button above respective input/textarea -- "Update this field with a machine translation from the original language". If the additional language version has some content in either description/body, this button is not shown.

An entry is viewed in its original language

no changes needed

An entry is viewed in a language that is not it’s original language

[x] [pan] The title, description, body fields on the entry object are pulled from the localized_texts table in the language the site is being viewed in
[ ] [alanna] an alert is shown that says “This entry was originally added in {original_language}. [link] View this entry in {original_language}.”

Other tasks

[x] [pan] node script to machine translate all existing entries user generated fields (title, description, body) and save to localized_texts table. localized_texts contains versioned entries
[x] [pan] Make sure not to run the script twice on the whole set of data (expensive)
[ ] [alanna] add functionality so that if a locale={language_code} param is present in the url, view the site in that language. always show the locale={language_code} in the url so that when entry urls are shared, the language that is being viewed by the sharer is being shared as well.
[x] [alanna] spin up a new localization-staging server and database so we can work and test this project independently of our current staging server.  done: https://participedia-i18n-staging.herokuapp.com/

currently in progress: [pan] node script to machine translate all existing entries user generated fields (title, description, body) and save to localized_texts table. localized_texts contains versioned entries

@ascott i've completed my part of the checklist. Is this ready to go to stage or are we waiting for your feature to be completed before we test it there?

Drafting modal text here: https://docs.google.com/document/d/1F6CeUeuh-uah7q4-JeU_F44ni1isrpUwjXSqlafa4aI/edit?usp=sharing

@paninee thanks for your work on this!! i will do my work on this branch and test on our i18n server before going to staging.

Rollout plan

Merge code to master, which will allow new entries to be translated.
We may want to test run that for a few weeks. Make sure it's stable.
Once the new entries translation has proven stable, increase the Translation API quota temporarily to run the script to translate existing data.
Run scripts/generate-entry-i10n.js

a couple more todos

[ ] when a collection is added, it is not translated like a case, method and org is @paninee
[ ] add faq item @ascott

pre roll out todos:

[ ] review PR comments and make small changes requests
[ ] ensure all needed ENV variables are set on ppedia-stage and participedia-api app on heroku
[ ] get tests passing on participedia-i18n-staging

roll out plan

[ ] merge this PR into master https://github.com/participedia/api/pull/876
[ ] deploy to staging
[ ] run needed migrations on staging
[ ] test on staging
[ ] put prod into maintenance mode
[ ] deploy to prod
[ ] run migrations
[ ] take prod out of maintenance mode
[ ] test app functionality
[ ] run script to translate all (non-hidden) entries


#MAPP-84 fixed

Fixed Issue MAPP-84
Description
MAPP-84: "Pickup & dropoff pins are shown before ordering a pickup"
When order form was canceled the state was saved along with the dropoff point which caused the UI to display it.
Cancel now will not save the dropoff point.
Types of changes

[ x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[ x] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ x] I have read the CONTRIBUTING document.
[ ] I have added tests to cover my changes.
[ ] All new and existing tests passed.

🎉 !


Add header images and background colors. Fixes #52

See my comment at
https://github.com/duilio/pelican-octopress-theme/issues/52#issuecomment-217273440
for implementation specific details

ping, any updates here? This can still be merged directly.


Failed Regression tests

Installing the package in development mode and running py.test gives an error. Please fix.

(grmToolbox) peisenha@pontos:~/grmToolbox/grmpy$ pip install -e .
Obtaining file:///home/peisenha/ownCloud/office/workspace/software/repositories/organizations/grmToolbox/grmpy
Requirement already satisfied: numpy in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from grmpy==0.0.5.dev0)
Requirement already satisfied: scipy in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from grmpy==0.0.5.dev0)
Requirement already satisfied: pytest in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from grmpy==0.0.5.dev0)
Requirement already satisfied: pandas in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from grmpy==0.0.5.dev0)
Requirement already satisfied: statsmodels in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from grmpy==0.0.5.dev0)
Requirement already satisfied: py>=1.4.33 in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from pytest->grmpy==0.0.5.dev0)
Requirement already satisfied: setuptools in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from pytest->grmpy==0.0.5.dev0)
Requirement already satisfied: python-dateutil>=2 in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from pandas->grmpy==0.0.5.dev0)
Requirement already satisfied: pytz>=2011k in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from pandas->grmpy==0.0.5.dev0)
Requirement already satisfied: patsy in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from statsmodels->grmpy==0.0.5.dev0)
Requirement already satisfied: six>=1.5 in /home/peisenha/.envs/grmToolbox/lib/python3.5/site-packages (from python-dateutil>=2->pandas->grmpy==0.0.5.dev0)
Installing collected packages: grmpy
  Found existing installation: grmpy 0.0.5.dev0
    Uninstalling grmpy-0.0.5.dev0:
      Successfully uninstalled grmpy-0.0.5.dev0
  Running setup.py develop for grmpy
Successfully installed grmpy
(grmToolbox) peisenha@pontos:~/grmToolbox/grmpy$ py.test
============================= test session starts ==============================
platform linux -- Python 3.5.2, pytest-3.2.1, py-1.4.34, pluggy-0.4.0
rootdir: /home/peisenha/ownCloud/office/workspace/software/repositories/organizations/grmToolbox/grmpy, inifile:
collected 7 items

grmpy/test/test_integration.py .F
grmpy/test/test_unit.py .....

================================================================================================= FAILURES =================================================================================================
_____________________________________________________________________________________________ TestClass.test2 ______________________________________________________________________________________________

self = <grmpy.test.test_integration.TestClass object at 0x7f6f388a5048>

    def test2(self):
        """The test takes a subsample of 5 random entries from the regression battery test list
            (resources/regression_vault.grmpy.json), simulates the specific output again, sums the
            resulting data frame up and checks if the sum is equal to the regarding entry in the test
            list eement.
            """
        tests = json.load(
>           open('{}'.format(os.getcwd()) + '/test/resources/regression_vault.grmpy.json', 'r'))
E       FileNotFoundError: [Errno 2] No such file or directory: '/home/peisenha/ownCloud/office/workspace/software/repositories/organizations/grmToolbox/grmpy/test/resources/regression_vault.grmpy.json'

grmpy/test/test_integration.py:35: FileNotFoundError
=================================================================================== 1 failed, 6 passed in 11.27 seconds ====================================================================================
(grmToolbox) peisenha@pontos:~/grmToolbox/grmpy$

It should work now.

Great, thanks!


CursorMoved and MouseInput events happening in title bar

On macOS High Sierra, it seems like I am getting window events that I shouldn't be, when clicking on the title bar / window decoration area.
Is this a bug or intentional? I think these window events should be restricted to happen only in the inner window. I even loaded up a C++ SDL project to check how they handle it, and it does ignore the equivalent events in the title bar.

This should be reported to winit instead.

Okay, will do.


Code coverage with Cypress and Angular Esbuild

Command
e2e
Description
Hello,
We are trying currently trying to migrate our applications to the new esbuild.
The last issue we are facing is that it seems to have no solution to keep code coverage and esbuild for Cypress tests.
All solutions (for example this one https://lukas-klement.medium.com/implementing-code-coverage-with-angular-and-cypress-6ed08ed7e617) are using a custom webPack configuration which is no more allowed.
Did someone already faced this issue and found a solution ?
Describe the solution you'd like
A way to keep code coverage with Cypress and esbuild.
Describe alternatives you've considered
For now we are keeping the old Angular builder.

Hello, we reviewed this issue and determined that it doesn't fall into the bug report or feature request category. This issue tracker is not suitable for support requests, please repost your issue on StackOverflow using tag angular-cli.
If you are wondering why we don't resolve support issues via the issue tracker, please check out this explanation.


glTF model clippingPlanes  removeall error

Cesium version : 1.68
chrome version :  80.0.3987.149
when excute glTFModel.clippingPlanes.removeAll() i got a  rendering error  :  gltf_clippingPlanes  UniformSampler   value is undefined
I've tried other models . I think the multi Bounding Volume model has this problem.
model link : https://sketchfab.com/3d-models/white-silver-green-residence-ec516ba32f03403f97954472c5d4b2cf#download

window.viewer = new Cesium.Viewer("cesiumContainer", {
  // terrainProvider: createWorldTerrain()
  imageryProvider: false,
  animation: false,
  baseLayerPicker: false,
  geocoder: false,
  timeline: false,
  sceneModePicker: false,
  navigationHelpButton: false,
  infoBox: false,
  fullscreenButton: false,
  homeButton: false,
  selectionIndicator: false,
  navigationInstructionsInitiallyVisible: false,
  shouldAnimate: true,
});

const options = {
  id: "example-model",
  // url: '/model/Duck.glb',
  // scale:400000,
  /*-------------------------------------------------------- */
  url: "/model/white_silver_green_residence/scene.gltf",
  scale: 400,
  position: [108.947048, 34.259415],
};

window.model = Cesium.Model.fromGltf({
  modelMatrix: Cesium.Transforms.headingPitchRollToFixedFrame(
    Cesium.Cartesian3.fromDegrees.call(null, ...options.position),
    new Cesium.HeadingPitchRoll(
      Cesium.Math.toRadians(0),
      Cesium.Math.toRadians(0),
      Cesium.Math.toRadians(0)
    )
  ),
  ...options,
  url: options.url,
  clippingPlanes: new Cesium.ClippingPlaneCollection({
    edgeWidth: 1,
    planes: [new Cesium.ClippingPlane(new Cesium.Cartesian3(-1, 0, 0), 0)],
  }),
});

viewer.scene.primitives.add(model);

viewer.camera.setView({
  destination: new Cesium.Cartesian3(
    -1953829.2409311095,
    6618724.811636659,
    3227893.906284791
  ),
  orientation: {
    heading: 0.24165191356149585,
    pitch: -0.9933975711287646,
    roll: 6.283006645076183,
  },
});

There does seem to be something about this model that causes the renderer to be in an invalid state when the clipping plane is removed. Running it myself I get a different stack trace sometimes:
TypeError: Cannot read property '6' of undefined
TypeError: Cannot read property '6' of undefined
    at regenerateShaders (http://localhost:8080/Source/Scene/Model.js:4981:49)
    at Model.update (http://localhost:8080/Source/Scene/Model.js:4835:17)
    at PrimitiveCollection.update (http://localhost:8080/Source/Scene/PrimitiveCollection.js:374:27)
    at updateAndRenderPrimitives (http://localhost:8080/Source/Scene/Scene.js:3010:27)
    at executeCommandsInViewport (http://localhost:8080/Source/Scene/Scene.js:2836:13)
    at Scene.updateAndExecuteCommands (http://localhost:8080/Source/Scene/Scene.js:2644:13)
    at render (http://localhost:8080/Source/Scene/Scene.js:3319:15)
    at tryAndCatchError (http://localhost:8080/Source/Scene/Scene.js:3338:13)
    at Scene.render (http://localhost:8080/Source/Scene/Scene.js:3413:13)
    at CesiumWidget.render (http://localhost:8080/Source/Widgets/CesiumWidget/CesiumWidget.js:704:25)

I think it is related. There is a list of uniforms out of which there is one UniformSampler named "model_clipping_planes" with value as undefined.

@prasuncct or @changqingom could you check if this is fixed for you in the latest release CesiumJS 1.97? We refactored the model loading code and this may have been fixed.

@lilleyse I am not loading the gltfmodel but the pointcloud (las). It does not seem to be fixed yet.

Point clouds and glTF models now use the same Model class under the hood so the behavior should be similar.
Would you be able to put together a sandcastle that demonstrates the problem?

Can you see this sandcastle?
Click the removeAll button twice , all  types fail
Hello World - Cesium Sandcastle

Point clouds and glTF models now use the same Model class under the hood so the behavior should be similar.
Would you be able to put together a sandcastle that demonstrates the problem?

Can you see this sandcastle?
Click the removeAll button twice , all  types fail
Hello World - Cesium Sandcastle

Point clouds and glTF models now use the same Model class under the hood so the behavior should be similar.
Would you be able to put together a sandcastle that demonstrates the problem?


fix(boot): return signature error when no features

Fixes #3339
Always return signature error in verify_and_mark_updated when no signature features are enabled.

Done!


HUDCE-Customized-Paella upgrade to UPV Paella 6.2.2

HUDCE specific changes to Opencast engage-player-paella module.
For the purpose of UPV peer review.
The player uses HUDCE specific auth strategy embedded into Opencast search endpoints and supporting custom modules not represented in this pull.

@ferserc1 I'm adding you to this repo so you can review this pull in situ.

closing old outdated informational pull


Re-add SaaS template update script to startup hook

Re-add SaaS template update script on startup that had been removed accidentally in merging over to unified Fides.
Closes #1195
Code Changes

execute update script in startup hook
include a new session getter dependency function to allow a session to be retrieved directly, as is needed for the update script execution

Steps to Confirm

[ ] list any manual steps taken to confirm the changes

Pre-Merge Checklist

[ ] All CI Pipelines Succeeded
Documentation Updated:

[ ] documentation complete, or draft/outline provided (tag docs-team to complete/review on this branch)
[ ] documentation issue created (tag docs-team to complete issue separately)

[ ] Issue Requirements are Met
[ ] Relevant Follow-Up Issues Created
[ ] Update CHANGELOG.md

Description Of Changes
Context:
In fidesops, on webserver bootup we ran an update "script" for saas configs to pick up any updates coming from a new version of their corresponding templates. See https://github.com/ethyca/fidesops/pull/1307 and associated issue https://github.com/ethyca/fidesops/issues/1098 for more context.

@pattisdr @ThomasLaPiana would either of you be able to take a quick look to tell me if this looks ok? specifically getting the db session. i'm taking a bit of a stab in the dark here, not super familiar with how session management works here, and also still getting acquainted with unified fides landscape...

@pattisdr thanks for your quick feedback - i think this should be good to go now based on our above conversation. let me know if you think it needs any further tuning.
also, apologies in advance if this creates a merge conflict with your pending PR :/

ah sorry created the conflict on your end, I'll review and merge when that's resolved -

ah sorry created the conflict on your end, I'll review and merge when that's resolved -

all good, i knew what i was doing, that was only fair 😄

OK, conflict resolved, should be good for review @pattisdr !


Improved implementation to kill a job

The previous implementation was not sufficient for killing jobs.
The invocation using the host ocrd-managerdid not work, because the browser sends requests from the host, and here localhost was the correct host.
However, this leads to a CORS problem in the browser.
Therefore, the monitor was extended with the URL /jobs/kill/%PID% and sends an HTTP request to the manager. From the monitor's perspective, the manager can be found under the host ocrd-manager.
The URL to the manager's endpoint is passed to the monitor as an environment variable when it starts.

The invocation using the host ocrd-managerdid not work, because the browser sends requests from the host, and here localhost was the correct host.
However, this leads to a CORS problem in the browser.

Good catch!


Skipped un-escaping when url contains escaped /

Describe the bug
Skipping unescaping of %2F causes the need to do additional decoding of parameters in controller.
To Reproduce
invoke any endpoint with an escaped parameter including an escaped / will result in incomplete decoding of parameter.
for example:
curl -X GET "https://localhost:5001/test%20space%2Fslash"
in the controller will be decoded as:
        [HttpGet("{test}")]
        public string Get(string test)
        {
            var expected = "test space/slash";
            return $"Expecting: {expected} But got: {test}";
        }

The code responsible for this is:
https://github.com/aspnet/AspNetCore/blob/master/src/Shared/UrlDecoder/UrlDecoder.cs#L340
Please advise if this is the intended behavior or this is an unintended consequence.

@Tratcher

Yes, this is the intended behavior. Un-escaping %2F would incorrectly change your routing results. %2F can only be safely un-escaped after routing (e.g. during model binding?).


[Core.VarInfo] Allow adding right-hand-side information to VarName

The current VarName in VarInfo is only dependent on left-hand-side information, we might want to generalise this to include RHS information to distinguish variables with the same symbol but live in a different scope. A example design could be
x ~ Normal(0,1; lhs_vn = unique_id)

I think a solution is to introduce another filed to VarInfo named mark, and introduce different codes to mark variables. This also solves https://github.com/yebai/Turing.jl/issues/391 as now we can mark then can deleted variables.

Closed by #407


Inference from frozen_graph yields all black result despite deeplab/vis.py works as intended (frozen_graph uses the same checkpoint)

Please make sure that this is a bug. As per our
GitHub Policy,
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:bug_template
System information

Stock code
Both on Windows 10 and Google colab
TensorFlow 1.x on google colab.
TensorFlow 2.6.2
TensorFlow version (use command below):

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with:

TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

I used the deeplabibrary to train my models.
I tried to export the checkpoint using this:
%tensorflow_version 1.x !pip install tf_slim from google.colab import drive drive.mount('/content/drive',force_remount=True) !python /content/drive/MyDrive/models/research/deeplab/export_model.py --logtostderr \   --model_variant="xception_65" \    --atrous_rates=6 \    --atrous_rates=12 \    --atrous_rates=18 \    --output_stride=16 \    --crop_size=513 \ --crop_size=513\    --num_classes=21\    --export_path="/content/drive/MyDrive/models/frozen_graph/prova21.pb"\    --checkpoint_path="/content/drive/MyDrive/models/checkpoint_exc_all2/model.ckpt-30000" \
But when loading and using the model for inference i get an all blank(0) result. This is the script i use:
`import os
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import time
import cv2
from tqdm import tqdm
import tensorflow as tf
Needed to show segmentation colormap labels
flags = tf.compat.v1.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('model_dir', r"C:\Users\User\PycharmProjects\test_model_seg\models\exc65.pb", 'Where the model is')
flags.DEFINE_string('image_dir', "0000631.jpg", 'Where the image is')
flags.DEFINE_string('save_dir', None, 'Dir for saving results')
flags.DEFINE_string('image_name', None, 'Image name')
class DeepLabModel(object):
"""Class to load deeplab model and run inference."""
INPUT_TENSOR_NAME = 'ImageTensor:0'
OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
INPUT_SIZE = 513

def __init__(self, model_dir):
    """Creates and loads pretrained deeplab model."""
    self.graph = tf.Graph()

    graph_def = None
    # Extract frozen graph from tar archive.
    model_filename = FLAGS.model_dir
    with tf.compat.v1.gfile.FastGFile(model_filename, 'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(open("models\exc65.pb", "rb").read())

    if graph_def is None:
        raise RuntimeError('Cannot find inference graph in tar archive.')

    with self.graph.as_default():
        tf.import_graph_def(graph_def, name='')

    self.sess = tf.compat.v1.Session(graph=self.graph)

def run(self, image):
    """Runs inference on a single image.

    Args:
        image: A PIL.Image object, raw input image.

    Returns:
        resized_image: RGB image resized from original input image.
        seg_map: Segmentation map of `resized_image`.
    """
    width, height = image.size
    resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
    target_size = (int(resize_ratio * width), int(resize_ratio * height))
    resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
    print('Image resized')
    start_time = time.time()
    batch_seg_map = self.sess.run(
        self.OUTPUT_TENSOR_NAME,
        feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
    print('Image processing finished')
    print('Elapsed time : ' + str(time.time() - start_time))
    seg_map = batch_seg_map[0]
    return resized_image, seg_map

model = DeepLabModel(FLAGS.model_dir)
print('Model created successfully')
img = Image.open("0000631.jpg")
or_img, seg = model.run(img)
for x in seg:
print(x)`
Some side notes that may be usefull:
I used transfer training to segment the skin. My dataset is composed of nearly 32k images/segmented mask. I only have 2 classes in the segmented mask.(skin and background). I don't really know whats wrong with my code, perhaps is the way i import it?
Thanks in advance

Hi @Tix00! Can you please share the model file too.

Hi @Tix00! Could you please share the model file or update the error trace in above template? Thanks!

Do you need the .pb file or the checkpoint?
And there is no error trace. It simply output and all blank(zeros) result. While it is working with the checkpoint model.
https://drive.google.com/file/d/1-5ad_AcTyfEmUWh5Gu7_JCm-Vu7ZPa8P/view?usp=sharing

Hi @Tix00! Could you please share the model file or update the error trace in above template? Thanks!

Am i missing something? Do you need more information?

@Tix00 !  Its 1.x code actually which you might be trying to run compatibility mode in 2.6 which might be cause of issue. Can you upgrade your code base to 2.7 version and let us know ? Thanks!

The whole code base(so redoing the preprocessing/training/export_graph) or
just trying to use model .pb file?
Because otherwise I already tried using the latest version of tensor flow
while trying to make an inference Frome the .pb
Does it gives different results to you?
Il mar 18 gen 2022, 05:10 mohantym @.***> ha scritto:

@Tix00 https://github.com/Tix00 ! Its 1.x code actually which you might
be trying to run compatibility mode in 2.6 which might be cause of issue.
Can you upgrade your code base to 2.7 version and let us know ? Thanks!
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Can you please share the colab gist with results? Thank you!

Yes of course.
https://gist.github.com/Tix00/156ab9226f951475d9684d3f4e7708c6
Thank you for your help
On Tue, Jan 18, 2022 at 12:29 PM mohantym @.***> wrote:

Can you please share the colab gist with results? Thank you!
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@Saduf2019 ! Could you please look at this issue ? Attaching gist for reference.

@Tix00 Session is deprecated. We see that you're using Session which does not work with  eager execution
(TF V2). To migrate code that uses sessions to TF2, rewrite the code without it. Please see the migration guide to migrate from 1.x to 2.x.
tf.compat.v1.GraphDef   is also designed for TensorFlow v1. Please refer this   .
TF v1.x is currently  not actively supported so please try to upgrade to TF v2.4 or later versions.
For any further queries regarding TF v1.x specific issues please post it in TF forum where there is a larger community to get you the right help.
Thanks!

Thanks for your concern. I resolved the issue.
--save_inference_graph=True \
Adding this flag in the export model script will do the trick.
Have a nice day and thanks for your time


Potentially change working directory's path

Maybe. I don't personally like /3ds/data. I'll think about it. Later.

I don't personally like /3ds/data.

Why?
It makes the most sense for what it's doing, and reduces SD root clutter.

I don't like it.
I prefer keeping certain things at root. That's my preference.


Prep for Cloud Conf

I revisited this and did some maintenance. Resolves #94 and resolves #97. Also addressed some of #104.

Update dependabot
Update Terraform version
update actions
Add buildkit
Update Terraform
Update tf state and versions

@blakemorgan Y'all are quick with these merges.


Respeccing main character doesnt apply any changes

Describe the bug
When respeccing my main character and going through all the menus no changes are applied. Class and feats and everything remain the same as it was before the respec. MC is an angel oracle with a pet if that matters. Neither respeccing into same class, other class, or same class with dips didnt apply.
To Reproduce
Steps to reproduce the behavior:

Open toybox
Go to party tab
Respec main character
Go through respec windows
No changes applied to main character

Expected behavior
Expected outcome - changes from respec applied to main character.
Save Files
Manual_31_lvl_19_respec.zip
Screenshots
If applicable, add screenshots to help explain your problem.
Settings
Please attach your settings.xml file from the mod folder
Settings.zip
Version Info:

ToyBox Version: 1.4.21b
OS: Win10
Version Version 10.0.19044 Build 19044
Wotr version 2.1.0u, 2.1.0w

Additional context
Found this in the logs, which may be helpful:
InvalidOperationException: Collection was modified; enumeration operation may not execute.   at System.Collections.Generic.Dictionary2+ValueCollection+Enumerator[TKey,TValue].MoveNext () [0x00013] in <695d1cc93cca45069c528c15c9fdd749>:0
at Kingmaker.UI.Selection.UnitMarkManager.LateUpdate () [0x00095] in <7233d069edc947ad9245aca7f2d9f2b6>:0 `

I don't see an issue respeccing. If you have Barley's excellent respec mod, please update that to the most recent version.

I think I encountered the same issue recently. The issue was in having something equipped on character, and I was getting
[RespecWrath] [Error] System.InvalidOperationException: Collection was modified; enumeration operation may not execute.
  at System.ThrowHelper.ThrowInvalidOperationException (System.ExceptionResource resource) [0x0000b] in <695d1cc93cca45069c528c15c9fdd749>:0
  at System.Collections.Generic.List`1+Enumerator[T].MoveNextRare () [0x00013] in <695d1cc93cca45069c528c15c9fdd749>:0
  at System.Collections.Generic.List`1+Enumerator[T].MoveNext () [0x0004a] in <695d1cc93cca45069c528c15c9fdd749>:0
  at RespecWrath.RespecClass.RespecOnCommit2 (Kingmaker.EntitySystem.Entities.UnitEntityData targetUnit, Kingmaker.EntitySystem.Entities.UnitEntityData tempUnit, Kingmaker.EntitySystem.Entities.UnitEntityData[] petsToRemove, System.Action successCallback, System.Collections.Generic.List`1[T] scrollstoadd) [0x00304] in <fbdf32d55d994177b57bf10dde30d74d>:0

I take it that in RespecOnCommit2 function mod is trying to remove remaining equipped items in foreach cycle, which breaks if this action affects underlying container and invalidates enumerator


Option to switch back navigation bar material theme on true black

Describe the problem
Navigation bar material theme is too distracting on true black to previous version. Option to switch back.
Describe the solution
Latest

Previous

This is already implemented in Quacker

The version v3.8.2 implements this feature.
Here is how it looks now, no more background color on selected icon:

I think they mean't the color of the navigation bar


Chore: fix 'weather' -> 'whether' typo.

Description of what the PR does, such as fixes # {issue number}

Fixes a small typo in a couple files' docstrings.

Looks good! Thanks


RecursionError: maximum recursion depth exceeded while calling a Python object

I'm getting following error while trying to train a model.
My training config is as follows:
# https://mmdetection.readthedocs.io/en/latest/tutorials/config.html#an-example-of-mask-r-cnn
model = dict(
    type='FasterRCNN',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True)),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8], # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes
            ratios=[0.05, 0.5, 1.0, 2.0, 20.0], # The ratio between height and width.
            strides=[4, 8, 16, 32, 64, 128]), # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set.
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict( # RoIHead encapsulates the second stage of two-stage/cascade detectors.
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=6, # Number of classes for classification
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_across_levels=False,
            nms_pre=2000,
            nms_post=1000,
            max_num=1000,
            nms_thr=0.7,
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_across_levels=False,
            nms_pre=1000,
            nms_post=1000,
            max_num=1000,
            nms_thr=0.7,
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))

# Modify dataset related settings
# dataset_type = None
# data_root = None
img_norm_cfg = None #dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    # dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    # dict(
    #     type='Normalize',
    #     mean=[123.675, 116.28, 103.53],
    #     std=[58.395, 57.12, 57.375],
    #     to_rgb=True),
    # dict(type='Pad', size_divisor=32),
    # dict(type='DefaultFormatBundle'),
    # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    # dict(
    #     type='MultiScaleFlipAug',
    #     img_scale=(1333, 800),
    #     flip=False,
    #     transforms=[
    #         dict(type='Resize', keep_ratio=True),
    #         dict(type='RandomFlip'),
    #         dict(
    #             type='Normalize',
    #             mean=[123.675, 116.28, 103.53],
    #             std=[58.395, 57.12, 57.375],
    #             to_rgb=True),
    #         dict(type='Pad', size_divisor=32),
    #         dict(type='ImageToTensor', keys=['img']),
    #         dict(type='Collect', keys=['img'])
    #     ])
]

classes = ('big_crack_and_cuts','decayed','major_hole','shriveled','sprouted','white_decay')
data = dict(
	samples_per_gpu=4,  # Batch size of a single GPU
    workers_per_gpu=4,  # Worker to pre-fetch data for each single GPU
    train=dict(
        type='CocoDataset',
        img_prefix='/home/deploy/ved/dataset/sort_potato_l2/train/images/',
        classes=classes,
        ann_file='/home/deploy/ved/dataset/sort_potato_l2/train/train.json',
        pipeline=train_pipeline,
        ),
    val=dict(
        type='CocoDataset',
        img_prefix='/home/deploy/ved/dataset/sort_potato_l2/val/images/',
        classes=classes,
        ann_file='/home/deploy/ved/dataset/sort_potato_l2/val/val.json',
        pipeline=test_pipeline),
    test=None
    )

evaluation = dict(  # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
    interval=1,  # Evaluation interval
    metric=['bbox'])  # Metrics used during evaluation
optimizer = dict(  # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
    type='SGD',  # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details
    lr=0.004,  # Learning rate of optimizers, see detail usages of the parameters in the documentaion of PyTorch
    momentum=0.9,  # Momentum
    weight_decay=0.0001)  # Weight decay of SGD
optimizer_config = dict(  # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
    grad_clip=None)  # Most of the methods do not use gradient clip
lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
    policy='step',  # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
    warmup='linear',  # The warmup policy, also support `exp` and `constant`.
    warmup_iters=500,  # The number of iterations for warmup
    warmup_ratio=
    0.001,  # The ratio of the starting learning rate used for warmup
    step=[8, 11])  # Steps to decay the learning rate
total_epochs = 50  # Total epochs to train the model
checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
    interval=1,
    max_keep_ckpts=5)  # The save interval is 1
log_config = dict(  # config to register logger hook
    interval=50,  # Interval to print the log
    hooks=[
        dict(type='TensorboardLoggerHook')  # The Tensorboard logger is also supported
        # dict(type='TextLoggerHook')
    ])  # The logger used to record the training process.
dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO'  # The level of logging.
load_from = None  # load models as a pre-trained model from a given path. This will not resume training.
resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.
work_dir = '/home/deploy/ved/faster_rcnn_r50_fpn_mdconv_c3-c5_group4'  # Directory to save the model checkpoints and logs for the current experiments.
gpu_ids = [0]

I'm using a custom dataset.
Environment
fatal: not a git repository (or any of the parent directories): .git
sys.platform: linux
Python: 3.7.9 (default, Aug 31 2020, 12:42:55) [GCC 7.3.0]
CUDA available: True
GPU 0: Tesla P100-PCIE-16GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.6.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.7.0
OpenCV: 4.5.1
MMCV: 1.2.6
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.9.0+

Error traceback
2021-02-06 08:03:34,562 - mmdet - INFO - load model from: torchvision://resnet50
2021-02-06 08:03:34,766 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

missing keys in source state_dict: layer2.0.conv2.conv_offset.weight, layer2.0.conv2.conv_offset.bias, layer2.1.conv2.conv_offset.weight, layer2.1.conv2.conv_offset.bias, layer2.2.conv2.conv_offset.weight, layer2.2.conv2.conv_offset.bias, layer2.3.conv2.conv_offset.weight, layer2.3.conv2.conv_offset.bias, layer3.0.conv2.conv_offset.weight, layer3.0.conv2.conv_offset.bias, layer3.1.conv2.conv_offset.weight, layer3.1.conv2.conv_offset.bias, layer3.2.conv2.conv_offset.weight, layer3.2.conv2.conv_offset.bias, layer3.3.conv2.conv_offset.weight, layer3.3.conv2.conv_offset.bias, layer3.4.conv2.conv_offset.weight, layer3.4.conv2.conv_offset.bias, layer3.5.conv2.conv_offset.weight, layer3.5.conv2.conv_offset.bias, layer4.0.conv2.conv_offset.weight, layer4.0.conv2.conv_offset.bias, layer4.1.conv2.conv_offset.weight, layer4.1.conv2.conv_offset.bias, layer4.2.conv2.conv_offset.weight, layer4.2.conv2.conv_offset.bias

fatal: not a git repository (or any of the parent directories): .git
2021-02-06 08:03:36,760 - mmdet - INFO - Start running, host: deploy@GPU-NISHANT-VM5, work_dir: /home/deploy/ved/faster_rcnn_r50_fpn_mdconv_c3-c5_group4
2021-02-06 08:03:36,760 - mmdet - INFO - workflow: [('train', 1)], max: 50 epochs
Traceback (most recent call last):
  File "/home/deploy/mmdetection/tools/train.py", line 187, in <module>
    main()
  File "/home/deploy/mmdetection/tools/train.py", line 183, in main
    meta=meta)
  File "/home/deploy/mmdetection/mmdet/apis/train.py", line 150, in train_detector
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
    for i, data_batch in enumerate(self.data_loader):
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 363, in __next__
    data = self._next_data()
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 989, in _next_data
    return self._process_data(data)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1014, in _process_data
    data.reraise()
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
RecursionError: Caught RecursionError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 81, in collate
    for key in batch[0]
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 81, in <dictcomp>
    for key in batch[0]
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 81, in collate
    for key in batch[0]
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 81, in <dictcomp>
    for key in batch[0]
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 77, in collate
    return [collate(samples, samples_per_gpu) for samples in transposed]
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 77, in <listcomp>
    return [collate(samples, samples_per_gpu) for samples in transposed]
  ...

  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmcv/parallel/collate.py", line 23, in collate
    if not isinstance(batch, Sequence):
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/abc.py", line 139, in __instancecheck__
    return _abc_instancecheck(cls, instance)
  File "/home/deploy/miniconda3/envs/mmdetection/lib/python3.7/abc.py", line 143, in __subclasscheck__
    return _abc_subclasscheck(cls, subclass)
RecursionError: maximum recursion depth exceeded while calling a Python object

My custom training json is this.

The problem was with the data pipeline. When I changed it to the following, it worked:
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(750, 750), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(750, 750),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize',**img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]


added page for missing compiler error cs9026

There is currently no documentation for compiler error cs9026.  This pull request adds documentation for this compiler error.

@BillWagner This is a draft page for CS9026.  It is missing from the docs, and is about a proposed C# 11 feature.  I chose this one as a first pull request (regarding adding missing compiler message docs) because it is a proposed feature and likely fresh in people's minds and probably lower priority in terms of documentation to add.  I hope to get feedback on the structure and style of the content.

The one section that I've been working to add in this area is "how to fix the error".

Is a distinct section preferred?  There seems to be a mix of styles, like "How to fix violations" or "How to correct this error".  Any expectations there?

Also @BillWagner:

is ms.date the date the page is created?
Where should these pages be located?  I've picked docs/csharp/language-reference/compiler-messages here, but is that correct?  (There's also docs/docs/csharp/misc.) If it's message-specific, what are the criteria?
Are there any other files that need to be updated when a message page is added? like toc.yml?
Is pinching samples from the tests okay?
Thoughts on a "See also" section?  Nice-to-have? Expectations?

The one section that I've been working to add in this area is "how to fix the error".

Is a distinct section preferred? There seems to be a mix of styles, like "How to fix violations" or "How to correct this error". Any expectations there?

Either works. We don't have a style guide that detailed.

Also @BillWagner:

is ms.date the date the page is created?
It's the latest day the article was updated.
Where should these pages be located?  I've picked docs/csharp/language-reference/compiler-messages here, but is that correct?  (There's also docs/docs/csharp/misc.) If it's message-specific, what are the criteria?
docs/csharp/language-reference/compiler-messages. The docs/csharp/misc is an artifact from a migration.
Are there any other files that need to be updated when a message page is added? like toc.yml?
Yes, the TOC in the compiler-messages folder should add the new page.
Is pinching samples from the tests okay?
Yes, it's encouraged!
Thoughts on a "See also" section?  Nice-to-have? Expectations?
I typically don't add a "See also" section on the warning and error pages. The traffic patterns indicate readers land here from F1, fix their warning/error, then close the page. "See also" links get minimal traffic.

Also @BillWagner:

is ms.date the date the page is created?
It's the latest day the article was updated.
Where should these pages be located?  I've picked docs/csharp/language-reference/compiler-messages here, but is that correct?  (There's also docs/docs/csharp/misc.) If it's message-specific, what are the criteria?
docs/csharp/language-reference/compiler-messages. The docs/csharp/misc is an artifact from a migration.
Are there any other files that need to be updated when a message page is added? like toc.yml?
Yes, the TOC in the compiler-messages folder should add the new page.
Is pinching samples from the tests okay?
Yes, it's encouraged!
Thoughts on a "See also" section?  Nice-to-have? Expectations?
I typically don't add a "See also" section on the warning and error pages. The traffic patterns indicate readers land here from F1, fix their warning/error, then close the page. "See also" links get minimal traffic.

Awesome, thanks!


switch CI to using Open MPI

PULL REQUEST
Purpose and Content
Switch CI from MPICH to Open MPI
Benefits and Risks
This is required to use parallel HDF5 (the cluster currently only has it built against Open MPI).
Linked Issues
A prerequisite for #752.
PR Checklist

[x] This PR has a corresponding issue OR is linked to an SDI.
[x] I have followed CliMA's codebase contribution and style guidelines OR N/A.
[x] I have followed CliMA's documentation policy.
[x] I have checked all issues and PRs and I certify that this PR does not duplicate an open PR.
[x] I linted my code on my local machine prior to submission OR N/A.
[x] Unit tests are included OR N/A.
[x] Code used in an integration test OR N/A.
[x] All tests ran successfully on my local machine OR N/A.
[x] All classes, modules, and function contain docstrings OR N/A.
[x] Documentation has been added/updated OR N/A.

bors r+


fixed 'H264 Payload index out of range'

Description
return when the slice is empty to avoid 'index out of range'
Reference issue
Fixes #27

Great work! Merged with 124ecad946ecbb3190897d657def6d5862958429
Thank you so much for your contribution @lxb531 I hope to see more in the future :)


[WIP] search blog post

https://electron-website-pr-1283.herokuapp.com/blog/search
// @echjordan @vanessayuenn

This is getting closer. Ready for feedback.

@ckerr thanks for all the feedback. I made updates.


windows 2022 image: AzureCosmosDbEmulator Checksum verification failed

Description
AzureCosmosDbEmulator Checksum verification failed when building windows 2022 image
Build error log :

Provisioning with powershell script: /home/vsts/work/1/s/Packer/runner-images/images/windows/scripts/build/Install-AzureCosmosDbEmulator.ps1
Downloading package from https://aka.ms/cosmosdb-emulator to C:\Users\packer\AppData\Local\Temp\g5ja4od5.msi...
Package downloaded in 591.82 seconds
Checksum verification failed: expected 1EF64CF9CD13C611AAC394113A703AF6F525C7C39BFB6B4C3F884D57F6F1D1CF, got
5E4F4086F02B7FB12334D4E6494DCD2081F284B76B264E035376A9334E106B6B
At C:\Program Files\WindowsPowerShell\Modules\ImageHelpers\InstallHelpers.ps1:919 char:9
+         throw "Checksum verification failed: expected $expectedHash,  ...
+         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    + CategoryInfo          : OperationStopped: (Checksum verifi...376A9334E106B6B:String) [], RuntimeException
    + FullyQualifiedErrorId : Checksum verification failed: expected 1EF64CF9CD13C611AAC394113A703AF6F525C7C39BFB6B4C3
   F884D57F6F1D1CF, got 5E4F4086F02B7FB12334D4E6494DCD2081F284B76B264E035376A9334E106B6B

Provisioning step had errors: Running the cleanup provisioner, if present...

Platforms affected

[X] Azure DevOps
[ ] GitHub Actions - Standard Runners
[ ] GitHub Actions - Larger Runners

Runner images affected

[ ] Ubuntu 20.04
[ ] Ubuntu 22.04
[ ] Ubuntu 24.04
[ ] macOS 12
[ ] macOS 13
[ ] macOS 13 Arm64
[ ] macOS 14
[ ] macOS 14 Arm64
[ ] macOS 15
[ ] macOS 15 Arm64
[ ] Windows Server 2019
[X] Windows Server 2022

Image version and build link
N/A
Is it regression?


Define in README.md what happens if a time string is invalid

Without this tiny piece of documentation it is inconvenient for a developer to use the methods. If css-time is used as a part of another library, the time parameter probably comes from unknown code. Therefore developers want to know should they validate the parameter before using the methods or should they expect an error to be thrown. Of course a dev can read the code but relying on that kind of thrashes the purpose of documentation in the first place.

Yep, fair point. Will update.

Great, thanks!

Done. :)


Dismiss intellisense in interactive

Hi
I noticed that there are a couple of conflicting responses in the interactive window with intellisense. My primary issue is that ctrl+enter does two things in interactive: first it autocompletes what I typed, and then submits it. I tried rebinding ExecuteOrPasteInInteractive to avoid intellisense completion, but the new key combination also ends up doing an autocomplete.
If I try to use escape to dismiss the suggestion list (and thereby remove autocomplete) it clears the whole line, which isn't useful either.
Is there a way that I can have an execute without autocomplete in the interactive window? I typically use tab to autocomplete when I need it.
Thanks

ESC clearing the whole line is a bug, and a fix for it went in yesterday.
If you are already in the interactive window, I'm not sure what you gain by using ctrl+enter rather than just enter.
If you turn off "Enter commits current completion" and use enter to execute code, I checked that it won't auto complete. That might be enough of a workaround until the fix for ESC becomes available.

Hi
I have "Enter commits current completion" as unchecked. The reason I use Ctrl+Enter, however, is because when I type something in the middle of a line and press enter, it acts as a CRLF instead of executing the code.
In any case, thank you for the bug fix. I have something to really look forward to in the next version :)

Strange that Ctrl+Enter doesn't respect that option while plain Enter does, but for some reason it's the case.

I can take it