H2PushResource lacks "as" parameter for Link

Hi,
Is there a way to tell to H2PushResource to add the "as" parameter to the header Link of the request ?
Currently it's more efficient to manually add Link header to the response. Using H2PushResource (so without "as") force the browser to download twice the resource.
Bye.

Currently, there is no way to do that. Seems like a good addition to the config.

Hi,
@bazzadp you are right, i forgot to remove a duplicated link, my bad.
BUT, pushing with H2PushResource is still less efficient than directly adding a Link Header. I suppose it's due to the lack of "as" parameter.
I've done some performance audits using chrome and it seems that without this "as" parameter, the browser do not render immediately after reading pushed stylesheets. See screenshots for example:
I'm trying to get the best results for critical rendering path
with: Link Header
with: H2PushResource

@tlechauve I don't know if I would be as quick to close the issue. Technically, there should be no need for the as link, as I stated, but if browsers are handling the response differently based on this attribute then perhaps there is a case to add this.
There is no downside to having it (other than the work to implement it) and it could avoid confusion, so I would actually be in favour of your proposal. Apologies if my comment made it sound like I wasn't, I just was clarifying the technical side of this.
Saying that, can you confirm if the resource is definitely being pushed in both instances? Does it show as Pushed in the Network Tab->Initiator column? I've seen many instances were push is ignored, especially in development environments, as a self-signed certificate is used and so the domain is "untrusted" and the cache is therefore not used. In this case the as attribute might cause the resource still to be preloaded, whereas without it is not preloaded (another reason to support this change). So perhaps push is not being used in either case? It's kind of difficult to see from your screenshots.
Ultimately H2PushResource should be faster (http://icing.github.io/mod_h2/earlier.html) though it served from local disk the difference will be negligible.

Re-opening for future implementation of the feature. Seems easy to do.

HTTP/2 push has been turned off in Chrome.
However 103 Early Hints is being pushed (pun totally intended!!) as a replacement. This doesn't actually send the resource, but instead sends the preload hint. The browser then makes a request if it wants.
This means that having the right as attribute is actually more important - so the browser can use all these things for the subsequent request, whereas it wasn't as important for the Push case.

Coming back to this after some time, I made #252 where you can configure headers to be added in a 103 response. What you put in there is totally up to the config writers and they may follow the web innovations as closely as they like.

Great news! Thanks @icing !

Merged #252.


[Error: android.health.connect.HealthConnectException: java.lang.SecurityException: Caller doesn't have android.permission.health.READ_SLEEPto read to record typeclass android.health.connect.datatypes.SleepSessionRecord]

Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior:

Go to ''

Click on '....'

Scroll down to '....'

See error

Expected behavior
A clear and concise description of what you expected to happen.
Screenshots
If applic

able, add screenshots to help explain your problem.
Minimal Reproducible

Link to minimal reproducible code. It would be much easier for us to investigate the issue!
Environment:

Health Connect Version: "^2

.1.0",
React Native Version:0.71.7
New architecture enabled: No
Using Expo: No
Android API Level: API 34 (Android 14)

Please fix the template of the issue so I can investigate. Thanks for your understanding.


Allow connecting to an existing nREPL port

Proto REPL now uses nREPL as the backend but it still expects to start up the REPL itself. It should support an additional way of starting the REPL by connecting to a specified REPL host and port. This will need to display some kind of prompt to allow the user to enter the host and port number.

The go-to-line package prompts for user input to type in a line number. A similar approach can be taken to prompt for host and port.

+1


Determinate Progress Spinner should draw incomplete portion of circle

Bug, feature request, or proposal:
Bug
What is the expected behavior?
The progress completed is shown as it currently works, with the incomplete section optionally drawn in a different hue/color.
What is the current behavior?
Only the completed progress is drawn. There is no option to draw the rest of the circle.
What are the steps to reproduce?
https://material.angular.io/components/component/progress-spinner and set to "Determinate"
What is the use-case or motivation for changing an existing behavior?
I think it would look a lot better. A slow progress circle looks odd with very little completed - doesn't even look much like a circle. The progress bar follows this pattern, so there is also an issue with consistency. I think even the indeterminate variation would look better this way. The Material Design guidelines show an example of this.
Which versions of Angular, Material, OS, browsers are affected?
Material 2.0.0 beta3
Is there anything else we should know?

The progress spinner is implemented to match the Material Design spec for spinners, so this is not likely to change.

But it doesn't really match the "spec" as it clearly shows a circle with the incomplete section drawn in a contrasting hue, which can't be done currently. See the first "Circular with integration" example.

@gtranter The one you're pointing out is a specific integration with a FAB, not a normal progress circle.  See the "circular" example right above that for an example of a progress circle on its own.
It's worth noting this also matches what was done in AngularJS Material.

With the FAB it's still a progress circle. If you don't support the integration version then you haven't fully met the "spec."
"Isn't likely" is not the same as definitely won't, so can you please keep this open as a feature request for an option? The "spec" is really just a guideline. The interpretation of the guideline is what becomes the spec which turns into an implementation, and I think this implementation has missed the boat here. It looks fine the way it is for indeterminate or for fast running progressions, but for slow progressions, such as a battery charge indicator, it really looks terrible. There are many implementations of material progress circles that draw the incomplete portion of the circle.

We already have an issue for FAB integration with progress circle here. You can follow that issue if you'd like updates.
It's our goal to build reference implementations of what's explicitly in the spec without going on too much of a tangent from what's defined. In the spec, the progress circle on its own looks like what we have, so again, this will not change.


Chore make check_input_type adapt with array distance new signature

Proposed changes
make check_input_type adapt with array distance new signature
the function signature modified by #35136

run buildall


LSTM version of first model per electrode

Hi Anil,
I created an LSTM version but I'm new to neon so I'm not sure its implemented correctly.
In comparison to your biRNN  model, do you think there is a significant difference in the two models to pursue optimizing (spending time) the LSTM network?
Here is my code...
https://gist.github.com/bigsnarfdude/cbe9703a2ad1875dbb0e60b9248ced17

Looks correct to me. I don't have any intuition as to which topology is likely to perform better. But I'm pretty sure a BiLSTM would work better than an LSTM.

Thanks I will refactor and test. Thanks again


Remove unnecessary package references

@pakrym,
Thanks for having already signed the Contribution License Agreement. Your agreement was validated by .NET Foundation. We will now review your pull request.
Thanks,
.NET Foundation Pull Request Bot


DribbblishDynamic theme downloading message position fixed.

@JulienMaille

@JulienMaille

Is it difficult to display in on a single line at the bottom? Otherwise looks good to me

Is it difficult to display in on a single line at the bottom? Otherwise looks good to me

Is it difficult to display in on a single line at the bottom? Otherwise looks good to me

I want single line too but i don't know css very well. I'll happy to if did. 👍

Is it difficult to display in on a single line at the bottom? Otherwise looks good to me

I want single line too but i don't know css very well. I'll happy to if did. 👍

ok, looks good to me

ok, looks good to me

@morpheusthewhite I gave it a look, it's harder than expected, the text stays behind the player bar.

Handle this as you prefer


Icons are not shown any longer.

Hello,
The programming languages's icons seem to not appear anymore! Any ideas on why this is happening?

Closing as it seems to be resolved after last build ✨


Dialog crashes on Desktop when removed from hierarchy

kotlin.version=1.7.20
compose.version=1.2.2
Crashing on Windows 11, haven't tested other OSs.
Having a Dialog in part of the hierarchy where it is added and removed, causes a crash:
hs_err_pid19892.log
Actually it looks like even just showing and hiding the dialog via something like this will crash:
    if(showDialog) {
        Dialog(
            onCloseRequest = { showDialog = false },
        ) {
            Button(onClick = { showDialog = false }) {
                Text("Hide")
            }
        }
    }

Example Project:
DialogCrashProject.zip

I also encountered a similar problem.
When I run the program on Windows 11 22621.1105, as long as a new Window or Dialog pops up, the original Window of the program will get stuck, and then the program crash.
Switch to one of my old laptops with Windows 11 22610.1, and the program will run normally.
I also tried other JDK versions like 11, 18, and 19.
hs_err_pid20368.log

I also encountered a similar problem.
When I run the program on Windows 11 22621.1105, as long as a new Window or Dialog pops up, the original Window of the program will get stuck, and then the program crash.
Switch to one of my old laptops with Windows 11 22610.1, and the program will run normally.
I also tried other JDK versions like 11, 18, and 19.
hs_err_pid20368.log

I found that my case is related to https://github.com/JetBrains/compose-jb/issues/610. After adding System.setProperty("skiko.renderApi", "OPENGL"), it works fine.

Please check the following ticket on YouTrack for follow-ups to this issue. GitHub issues will be closed in the coming weeks.


Update index.md

Add frameworks support for quest

Please make sure to give it a meaningful merge message


Add a new kind dialog should have x at the top left

Describe the bug
X should be top left of the dialog and cancel should be removed.
To Reproduce
Steps to reproduce the behavior:

Drag 'Create a sprite' blog
Click on 'kind'
Add a new kind

Screenshots

Desktop (please complete the following information):

OS: Windows 10
Browser: Edge Beta

This is the Blockly prompt. It is consistent with the variable creation dialog etc... If you fix this, all blocky prompt will be modified as well.


Create Swagger API documentation

Suggestion to create a swagger api documentation for louis for better tracking different api endpoints, with the application scope expanding and also making testing easier.

Hi JoshBuch
I am here trying to work on this issue although it can take some time as I am a newbie to the topic.


Schedule expert interviews

We talked about not just protocol level people but also people and colleagues for instance working in privacy and a regular developer to try to understand concerns, issues and ideas about the URL bar

@autonome Can you send the emails? By the way, just booked another call with Ed Silverton, who is technically an expert even though I tried to get another non-expert. Apologies, he's another British, white male. He would be super interesting to talk to though.

I work in the GLAM (Galleries, Libraries, Archives, and Museums) sector where usage of the iiif.io standards is quite widespread.
I've presented at the British Library and Internet Archive Decentralised Web Summit on ways to combine IIIF with P2P protocols:
https://docs.google.com/presentation/d/1YmBvOZhokhkpn_dHKzJR5gH4W7qV0ncVY_fjzaPhyws/edit?usp=sharing
Jim Pick (Protocol Labs) used my "biiif" library for his demo at Tokyo Nodefest 2018: https://twitter.com/jimpick/status/1065974535952855040
I'm fairly preoccupied with finding ways to make it as easy as possible for people in the GLAM sector to use P2P technologies

By the way, just booked another call with Ed Silverton, who is technically an expert even though I tried to get another non-expert. Apologies, he's another British, white male. He would be super interesting to talk to though.

I work in the GLAM (Galleries, Libraries, Archives, and Museums) sector where usage of the iiif.io standards is quite widespread.
I've presented at the British Library and Internet Archive Decentralised Web Summit on ways to combine IIIF with P2P protocols:
https://docs.google.com/presentation/d/1YmBvOZhokhkpn_dHKzJR5gH4W7qV0ncVY_fjzaPhyws/edit?usp=sharing
Jim Pick (Protocol Labs) used my "biiif" library for his demo at Tokyo Nodefest 2018: https://twitter.com/jimpick/status/1065974535952855040
I'm fairly preoccupied with finding ways to make it as easy as possible for people in the GLAM sector to use P2P technologies

Also, @autonome I was through a Slack community I'm involved with suggested I get in touch with Mozilla, which I suppose we already are. In particular was mentioned:

Might be worth talking to Mozilla about this, as their decentralised workstream fits this perfectly. I would recommend talking to Irina Bolychevsky for sure and Danielle Robinson

[x] Ed Silverton (OpenGLAM developer, user of IPFS)
[ ] Rob van Kranenburg (privacy and IoT advocate, EU technology advisor)

Tim Taubert scheduled for tomorrow
@autonome If you have any more, send them my way.
I was going to try and get Rob van Kranenburg as well.

Stephen booked


window.L not set if using a module system

With the new Rollup build, the UMD loader will not set window.L if you're using a module system, like when building with Browserify.
This is a breaking change, and breaks a number of plugins, which rely on window.L being set instead of explicitly importing or requiring Leaflet.
Related discussion in #2364, where the conclusion was that 1.0 (or 0.8 as it was back then) should set window.L.

Hi,
FWIW, many developers that I see asking questions (SO, GIS SE…) and using build tools (not so many people in the end, to be honest) indeed seem to expect window.L to be set.
E.g. see this post on SO, and even https://github.com/Leaflet/Leaflet/issues/4964.
And so do many plugins, as already mentioned.
Here is an interesting post that does propose a workaround to expose globally even through the UMD wrapper (it also cites the d3 example BTW).
Unfortunately I guess that would require making a Feature Request to Rollup?
As a side note, I think it could be interesting to provide a simple ES module entry file for power users actually using an ES module loader and expecting their build tool to optimize the bundle (I guess that it is what is implied in that comment).
Here is a quick example in SystemJS.
It basically uses an entry file almost like src/Leaflet.js, but without the version import from package.json (which would require a special plugin for JSON in SystemJS, if I understand correctly) nor the noConflict code.
And with a simple plugin wrapped with a new import and export wrapper, instead of the standard UMD.
(using Leaflet.FeatureGroup.SubGroup plugin, but could probably be applied to most plugins that have a build process)
As previously, even that example demonstrates that the biggest drawback is breaking plugins that do not adapt to the new pattern…
But at least this is just an extra option that is cheap to add.
And I feel this was one of the potential benefits of switching to Rollup?

@ghybs I read your post above in detail first now. I haven't read carefully enough to get all the details, but I think this sounds very good.
Is this something you think you could make a PR of, so that we have something more to discuss around going forward? It seems we need to come up with a plan on how to move forward with ES6 modules, plugins and bundlers.

Hi,
Sure let me set up something simple.
I feel we could get a lot of inspiration from d3 build process.


chore: Configure Renovate

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.github/workflows/codeql.yml (github-actions)
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.github/workflows/pr.yml (github-actions)
.github/workflows/scorecard.yml (github-actions)
package.json (npm)
.nvmrc (nvm)

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chore(deps): update github/codeql-action action to v3.27.9

Schedule: ["on monday"]
Branch name: renovate/github-codeql-action-3.27.x
Merge into: main
Upgrade github/codeql-action to df409f7d9260372bd5f19e5b04e83cb3c43714ae

chore(deps): update patch updates (patch)

Schedule: ["on monday"]
Branch name: renovate/patch-all-patch-patch
Merge into: main
Upgrade @types/node to 22.10.2
Upgrade eslint-plugin-jsdoc to 50.6.1
Upgrade lint-staged to 15.2.11
Upgrade prettier to 3.4.2

fix(deps): update dependency shiki to v1.24.3

Schedule: ["on monday"]
Branch name: renovate/patch-shiki-monorepo
Merge into: main
Upgrade shiki to 1.24.3

chore(deps): update actions/upload-artifact action to v4.5.0

Schedule: ["on monday"]
Branch name: renovate/actions-upload-artifact-4.x
Merge into: main
Upgrade actions/upload-artifact to 6f51ac03b9356f520e9adb1b1b7802705f340c2b

chore(deps): update dependency globals to v15.14.0

Schedule: ["on monday"]
Branch name: renovate/all-minor-updates
Merge into: main
Upgrade globals to 15.14.0

chore(deps): update eslint monorepo to v9.17.0 (minor)

Schedule: ["on monday"]
Branch name: renovate/eslint-monorepo
Merge into: main
Upgrade @eslint/js to 9.17.0
Upgrade eslint to 9.17.0

chore(deps): update github/codeql-action action to v3.28.0

Schedule: ["on monday"]
Branch name: renovate/github-codeql-action-3.x
Merge into: main
Upgrade github/codeql-action to 48ab28a6f5dbc2a99bf1e0131198dd8f1df78169

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We actually already have dependabot


Re-enable configuration of multiple clusters

LGTM.

Successful build: https://jenkins.mesosphere.com/service/jenkins/job/cassandra/job/0-cassandra-trigger-pr/36/


Telegram bot action: Send photo - Can't send file with kwargs

I try to send a screenshot using the Telegram bot action: Send photo.
Every time in the HA logs I have:
Can't send file with kwargs: {'authentication': 'digest', 'parse_mode': 'html', 'url': 'http://<IP_ADDRESS>:1984/api/frame.jpeg?src=camera.garaz_brama_profile000_mainstream'}
Can't send file with kwargs: {'url': 'http://<IP_ADDRESS>:1984/api/frame.jpeg?src=camera.garaz_brama_profile000_mainstream'}
Can't send file with kwargs: {'url': 'http://<IP_ADDRESS>:1984/api/frame.jpeg?src=camera.garaz_brama_profile000_mainstream', 'authentication': 'bearer_token'}

I tried frame.mp4 and frame.jpeg - no difference.
Camera Reolink E1 Pro

Are you sure you has this camera in the go2rtc config?

Maybe your Hass can't get this url, but your browser can

Camera preview in the tab works.

Don't understand what you mean

I mean that the camera preview works on the tab.
What did you mean: "Hass can't get this url"?

Perhaps Hass can't get this URL: http://<IP_ADDRESS>:1984/api/frame.jpeg?src=camera.garaz_brama_profile000_mainstream.
Or you are writing the service call incorrectly.

Onwif integration helped.
Sending a photo directly to Telegram doesn't work either.
That's why I save the snapshot to disk and then send it.


Large Gaps Between Messages Sent in a Short Amount of Time

Describe the bug
The bug makes it so there is large gaps when a user sends more than one message in a short amount of time.
To Reproduce
Send Multiple Messages
Screenshots
*

*
Infomation (please complete the following information):
Discord channel: Canary
OS: Windows
Mod: Powercord
Discord language: US English
Additional context
I booted up Discord this evening to find this bug

Yep, and there's nothing I can do about it for now.
Discord has just change how the messages are rendered meaning it's not possible to detected which messages are start of a message group or not.

Fixed.

Thank you so much for this fix


__half has no member x

any help highly appreciated, I know nothing about cmake someone said that I should add a build directive __CUDA_NO_HALF_OPERATORS__ no idea how.

oh well the installation succeeded after downloading cuda 8, the error happened with 9.


libceed solvers improvements

This PR includes several features and improvements to the libCEED-based algebraic p-multigrid solver in MFEM. Most of these I was working on during the review process for PR #1763 but didn't want to derail the process by making too many changes. In my view some of these changes (but maybe not all) should definitely be included in MFEM at some point but that may end up being someone else's decision.
The changes include:

Rely on full assembly of CeedOperator in libCEED rather than implementing in MFEM. This PR relies on the very newest libCEED that includes full assembly of operators - previously we implemented this in MFEM. In addition to putting this code where it belongs, it is now a bit more general, allowing for nonsymmetric Ceed operators (ie ConvectionIntegrator) which the current MFEM assembly does not.
Coarsening of integration points. The code in #1763 did not coarsen quadrature points, only basis functions.
AMGx. The algebraic p-multigrid now supports AMGx on CUDA for the coarsest level, which speeds things up significantly.
Reorganize for "adaptive" coarsening. The way levels are constructed is reorganized so the code can query the D operator at each level to decide how to coarsen. What's included basically does order p to p/2 for constant coefficients and p to p-1 for ugly coefficients, but a great deal more could be done (change when you switch to AMG, different policy on GPU/CPU, etc).
Collocated quadrature on coarse levels. The coarse levels use Gauss-Lobatto quadrature so the mass part of the B operator is the identity. You would need specialized kernels to really take advantage of this, but even as is it provides a small performance advantage.
Better include dependence. Previously libceed-backend.h was included in .hpp files, now it is only included in .cpp files, so these backend functions don't pollute the namespace for everybody.

TODO:

[x] will not build without Ceed, tries to include Ceed headers (need to fix #ifdef guards)
[x] the "adaptive" coarsening does not communicate heuristics across processors, so will not work in general in parallel
[x] collocated quadrature should be optional; it is currently hard-coded at algebraic.cpp line 432.
[ ] output, print_level, etc. is a bit rough and inconsistent, especially in parallel.
[ ] the "adaptive" coarsening is an ugly hack, the "heuristic" has no theoretical grounding and doesn't even make sense (everything is driven by the quadrature weight, not the coefficient), the thresholds are just made up, and someone needs to actually think about this and do something reasonable.

PR
Author
Editor
Reviewers
Assignment
Approval
Merge

#2123
@barker29
@v-dobrev
@YohannDudouit + @pazner
04/06/21
⌛due 04/20/21
⌛due 04/27/21

At the suggestion of @v-dobrev , I am marking this ready-for-review even though under normal circumstances it would not be quite ready. It is close, though.

@v-dobrev, should we start the formal review process?

This PR is now under review (see the table in the PR description). To help with the review process, please do not force push to the branch.

Closing for now, we can re-open if needed.


Links to Spring Cloud Config docs on spring.io are broken

Links to Reference Doc and API Doc on https://spring.io/projects/spring-cloud-config#learn are broken for:
3.0 0-M3
3.0.0 M1
2.2.4
2.1.9
2.1.8
I apologize if this isn't the place to report documentation issues - if that's the case, could you help direct me to which repo to create an issue on?

This has been fixed.


New package for Matplotlib 1.4

Adds updated package for matplotlib 1.4

New parser that supports numpy docstrings
New codepath is matplotlib14. Old package has unchanged codepath (So it will still be used when importing to other packages).
Old and  new package will be loaded on matplotlib <v1.4 and >=v1.4, respectively.
New matplotlib package version is 1.1.0
Upgrading from old package should in most cases work automatically.
Downgrading is not supported.
Currently you have to load the correct package version manually if the wrong one is tried first. Maybe we should try to load all codepaths for a package until one succeeds?

Might work to see version number in the list if there are two packages with the same name. For example

matplotlib (1.3)
matplotlib (1.4)

As a heads up, mpl 1.5 will be coming out in the next week or so as well.

The package works on the matplotlib 1.5 RC, but the parser needs to be updated. I fixed a bug where signatures in the documentation were not detected correctly. This is needed because a lot of autogenerated signature arguments now have None as their default value (like psd).

I added sorting for the modulespecs. That doesn't seem to be enough, attributes are still not ordered, and in fact I get more than ordering differences when I generate twice in a row on my machine...
The diff between two consecutive runs of update.py (with matplotlib 1.5.0rc2+57.ga83dead) contains:
--- a/vistrails/packages/matplotlib14/artists.py
+++ b/vistrails/packages/matplotlib14/artists.py
@@ -3757,2 +3757,2 @@ class MplTickProperties(MplArtistProperties):
-              ("labelcolor", "basic:String",
-                {'optional': True}),
+              ("label", "basic:String",
+                {'optional': True, 'docstring': 'Set the text of ticklabel'}),
@@ -3855,2 +3855,2 @@ class MplTickProperties(MplArtistProperties):
-        if self.has_input('labelcolor'):
-            artist.constructor_props['labelcolor'] = self.get_input('labelcolor')
+        if self.has_input('label'):
+            artist.props['label'] = self.get_input('label')
--- a/vistrails/packages/matplotlib14/mpl_artists.xml
+++ b/vistrails/packages/matplotlib14/mpl_artists.xml
@@ -1854,1 +1854,3 @@ None, to remove the clipping path
-    <inputPortSpec arg="labelcolor" arg_pos="9" constructor_arg="True" name="labelcolor" />
+    <inputPortSpec arg="label" name="label">
+      <docstring>Set the text of ticklabel</docstring>
+    </inputPortSpec>
--- a/vistrails/packages/matplotlib14/mpl_artists_diff.xml
+++ b/vistrails/packages/matplotlib14/mpl_artists_diff.xml
@@ -15,1 +15,1 @@
-  <deletePortSpec code_ref="matplotlib.axis.Tick" port="clip_path" type="input" />
+  <deletePortSpec code_ref="matplotlib.axis.Tick" port="label" type="input" />

The module sorting needs to preserve the subclass hierarchy when generating the classes. This probably makes sense to keep in the xml spec as well, so I added this.
The diff you are seeing looks wrong. It looks like the diff code is sensitive to the order of ports, but I need to look more into that...

The faulty diff is fixed by bc096e4d58e69133d76e677f761e781ab857e43e. An indexing bug caused the wrong spec to get deleted. I am surprised this bug has not been detected before. The new code must cause more deletions or index rearrangements.


text fix

Sorry I copied the wording from the rollback task into the migrate task in the last PR. This fixes it.

Thanks! 💚


Add episode images

@afuh

Hi, thanks for the PR.
At the moment we are in the middle of an update (4th season) and, with the exception of new characters, we won't add images to location/episodes to this update.
We've already considered it in the past, however at the moment we are delaying that feature for future releases.
Thanks again!


Using ext_method

How come something like this doesn't work?
scoped_search on: [:born_at, :death_at], alias: :dead_at, ext_method: :dead_at
def dead_at(key, operator, value)
{ conditions: "DATEDIFF(death_at, born_at) #{operator} #{value}" }
end

Forgot self.dead_at


Correct FQDN for DockerHub.

Minor improvement: FQDN Value of --docker-server is https://index.docker.io/v1/ instead of https://index.docker.io/v2/ in Pull an Image from a Private Registry task.

Hi @daminisatya @kbarnard10
PTAL

/assign @jimangel

Hi @jimangel
PTAL!

/hold
Until there's an upstream fix

I'm going to approve this PR considering a couple things:

The task is about setting up a private repository which has implications of unknown and custom alterations to the tutorial
The doc today has conflicting information, The "output is similar to this" section (currently v1) does not match the area of concern (currently v2).
It's my understanding that v1 and v2 are two different types of storing images and are dependent on your private registry (back to point 1).
The PR is a single commit and ready to merge as is

I don't really have an opinion whether it's v1 or v2 in this doc, but today it's inconsistent and merging this PR fixes that. Feel free to PR a change to make it all v2, including in example outputs, if folks disagree.
/unhold
/lgtm
/approve


First time Network synchronization hangs

version: BIO-v1.0.0.build-g-bdb-gcc
OS: Ubuntu 16.04
Synchronization process hangs at some point and resumed only after wallet restart.

this is reproduced only within first synchronization
UPD:
the "blocks remaining" number showed on screenshot did not change within a few minutes, but before it rapidly diminished

This problem will soon be removed with an update. While you can put the daemon on a restart or push “rescan” in the console command


fix: Userid columns transformation from Postgres data to Oracle database

Description
Closes #1165
Changelog
N/A
Changed

nr-spar\sync\config\SQL\SPAR\POSTGRES_SEEDLOT_EXTRACT.sql (including transformation to all userid columns)
nr-spar\sync\config\SQL\SPAR\ORACLE_SEEDLOT_EXTRACT.sql (including transformation to DECLARED_USERID missed column)

Removed

How was this tested?

[ ] 🧠 Not needed
[ ] 👀 Eyeball
[x] 🤖 Unit tests

What gif/image best describes this PR or how it makes you feel?

Thanks for the PR!
Deployments, as required, will be available below:

Frontend
Backend
Oracle-API

Please create PRs in draft mode.  Mark as ready to enable:

Analysis Workflow

After merge, new images are deployed in:

Merge Workflow

Thank you @DerekRoberts . Merging it!


Lab 09 Issue with npm run build

Lab: 09
Task: 1.3
Step: 11
Description of issue:
Encountering the below issues when attempting to run "npm run build":
[pcf-1014] [Error] Manifest validation problem: instance.manifest.control[0].resources[0] is not allowed to have the additional property "_"
I have repeated all steps twice and also used the completed forms provided in the latest pull request and still encountering this issue. Any help would be greatly appreciated.

Hello,
I tried npm audit fix --force  and then npm run build  about two or three times and it build the code sucessfully.
Let me know if this works.

Have retested the lab and could not reproduce the error and as had no response I am closing

I fixed it..
Just update ur manifest file with this one

<!-- dataset node represents a set of entity records on CDS; allow more than one datasets -->    <data-set name="timelineDataSet" display-name-key="Dataset_Display_Key">      <!-- 'property-set' node represents a unique, configurable property that each record in the dataset must provide. -->      <!-- Uncomment to add a property-set node -->      <!-- <property-set name="samplePropertySet" display-name-key="Property_Display_Key" description-key="Property_Desc_Key" of-type="SingleLine.Text" usage="bound" required="true" /> -->    </data-set>    <resources>      <!-- Define the TypeScript file for the control -->      <code path="index.ts" order="1"/>            <!-- Uncomment to add more resources -->      <css path="css/timelinecontrol.css" order="1" />      <css path="..\node_modules\vis-timeline\dist\vis-timeline-graph2d.min.css" order="2" />      <!-- Uncomment to add localized resources -->      <!-- <resx path="strings/timelinecontrol.1033.resx" version="1.0.0" /> -->    </resources>    <!-- Uncomment to enable the specified API features -->    <!--     <feature-usage>      <uses-feature name="Device.captureAudio" required="true" />      <uses-feature name="Device.captureImage" required="true" />      <uses-feature name="Device.captureVideo" required="true" />      <uses-feature name="Device.getBarcodeValue" required="true" />      <uses-feature name="Device.getCurrentPosition" required="true" />      <uses-feature name="Device.pickFile" required="true" />      <uses-feature name="Utility" required="true" />      <uses-feature name="WebAPI" required="true" />    </feature-usage>    -->  </control></manifest>

//You need to replace the require statement with an import statement. Here's how you can resolve this issue
//use only  these imports in the  index.ts file.
import { IInputs, IOutputs } from "./generated/ManifestTypes";
import DataSetInterfaces = ComponentFramework.PropertyHelper.DataSetApi;
import { Timeline } from "vis-timeline/standalone";
import * as visTimeline from 'vis-timeline';
Done run npm run build...it will fix
Additionally if not fixed:
pac install latest you can run before the above steps.


[RESTEASY-2872] Multipart annotation ignored when using async rest client

https://issues.redhat.com/browse/RESTEASY-2872

We require all contributions to be made under the terms of the ASL 2.0 License: http://www.apache.org/licenses/LICENSE-2.0
Do you agree to these terms?

We require all contributions to be made under the terms of the ASL 2.0 License: http://www.apache.org/licenses/LICENSE-2.0
Do you agree to these terms?
Yes

These failures don't seem relevant to this PR. I'm testing a surefire upgrade on #2762 to see if it works. Hopefully that will make these issues go away.

@jamezp I agree, looks like the failures were due to the surefire changes.   Will someone need to manually kick this off again when that is resolved?


Button up/down events are not emitted on global input level

This Issue is about (delete as applicable)

A bug in the API (always say which version you're using!)

Version:

3.2.1

Following works on Scene level:
class BasicWorld extends Phaser.Scene {

    constructor() {
        super({
            key: 'BasicWorld'
        });
    }

    preload() {
    }

    create() {
        this.input.keyboard.on('keyup_S', function (event) {
            console.log('pressed S !!!');
        });
    }
}

The - "pressed S" string is printed out.
However, the documentation says:
The Input Manager consists of two parts: The Global Input Manager, which is owned by the Game itself, and the Input Manager, which is a Scene level system.
Hence I assumed it would analogically work on global level, but it doesn't. Following code doesn't print anything when "R" is pressed.

const config: GameConfig = {
    type: Phaser.CANVAS,
    parent: 'content',
    width: 640,
    height: 480,
    resolution: 1,
    backgroundColor: '#EDEEC9',
    scene: [
        TestScene
    ]
};

const game = new Phaser.Game(config);

game.input.keyboard.on('keyup_R', function (event) {
    console.log('pressed R !!!');
}, game);

Is this a bug or expected behaviour?
Apart from the above, one side question:
In phaser2, the way of reacting to key up/down events was this
let keyLeft = game.input.keyboard.addKey(Phaser.Input.Keyboard.KeyCodes.LEFT);
keyLeft.onDown(function() { console.log('LEFT PRESSED'); });

I liked the fact that one could set up event callback using the KeyCodes enumeration - any plans for getting that into Phaser3? I think that would be nicer than "keyup_R" magic strings.

This is absolutely correct. All input should be handled via the Scene.
You can create a Key object in v3 using exactly the same call as you did in v2: keyA = this.input.keyboard.addKey(Phaser.Input.Keyboard.KeyCodes.A);


OSC Ip configuration on PC&Quest

What's the idea?*
Ability to change OSC ip configuration (remote OSC ip/port) in menu or by OSC commands,
currently it's defined in launch args and not usable in the Quest version.
Quest version have only OSC binding input on the QuestIP:9000, the ability to use OSC output (udp 9001) by menu configuration or by OSC command (for easy conf, but require OSC string support) could be great.

I think this is a basic feature not an enhancement and should be implemented. Otherwise I am unable to read headset position for standalone fullbody tracking.


updating the sdk breaks some tests

WIP, i still have one breaking test.

Fixed that one test I guess :(


Update readme

Updated the readme to better reflect the current status of std::simd.

Sorry if this has been addressed, but does the unspecified layout affect the veracity of the docs for to_int():
https://rust-lang.github.io/portable-simd/core_simd/struct.Mask.html#method.to_int

no, m.to_int() is entirely equivalent to m.select(Simd::splat(-1), Simd::splat(0)), layout doesn't affect that -- just like the Vec struct's layout being unspecified doesn't mean you can't use Vec<bool>

Sorry if this has been addressed, but does the unspecified layout affect the veracity of the docs for to_int():
https://rust-lang.github.io/portable-simd/core_simd/struct.Mask.html#method.to_int

no, m.to_int() is entirely equivalent to m.select(Simd::splat(-1), Simd::splat(0)), layout doesn't affect that -- just like the Vec struct's layout being unspecified doesn't mean you can't use Vec<bool>

Phew, thanks for the clarification and all that's been accomplished too!


Scrub test case name.

Remove all instances of Validate for Step.  This addresses issue #162.

Hi @boumenot, I'm your friendly neighborhood Microsoft Pull Request Bot (You can call me MSBOT). Thanks for your contribution!
You've already signed the contribution license agreement. Thanks!
The agreement was validated by Microsoft and real humans are currently evaluating your PR.
TTYL, MSBOT;

well, it's more consistent. I still have trouble reading TestStepDeployTemplateShouldFailIfStepFails and thinking what is this second 'Step' in this sentence? because it can't be 'StepDeployTemplate' because then is would be trivial. Maybe it means TestStepDeployTemplateShouldFailIf*Deployment*Fails?
:wink:

Beauty is in the eye of the beholder.
I'll make another attempt at this.


Not sure where to put this so I will put it here..

 Is there any way the author of this tweak could make a small one to enable it? It only works on 2013 or newer.

Not possible without a 802.11ac Wi-Fi capable card.


Convention plugin says property 'factories.$0.value.main' doesn't have a configured value

Error logs
When executing shadowJar or mainResourceFactory manually:
> Task :checkKotlinGradlePluginConfigurationErrors SKIPPED
> Task :api:checkKotlinGradlePluginConfigurationErrors SKIPPED
> Task :api:compileKotlin NO-SOURCE
> Task :api:compileJava NO-SOURCE
> Task :mainResourceFactory FAILED
> Task :compileKotlin
2 actionable tasks: 2 executed

FAILURE: Build failed with an exception.

* What went wrong:
A problem was found with the configuration of task ':mainResourceFactory' (type 'ExecuteResourceFactories').
  - In plugin 'xyz.jpenilla.resourcefactory.ResourceFactoryPlugin$Inject' type 'xyz.jpenilla.resourcefactory.ExecuteResourceFactories' property 'factories.$0.value.main' doesn't have a configured value.

    Reason: This property isn't marked as optional and no value has been configured.

    Possible solutions:
      1. Assign a value to 'factories.$0.value.main'.
      2. Mark property 'factories.$0.value.main' as optional.

    For more information, please refer to https://docs.gradle.org/8.7/userguide/validation_problems.html#value_not_set in the Gradle documentation.

* Try:
> Run with --stacktrace option to get the stack trace.
> Run with --info or --debug option to get more log output.
> Run with --scan to get full insights.
> Get more help at https://help.gradle.org.

BUILD FAILED in 3s

Configurations
I've already imported the plugins via libs.versions.toml:
resource-factory = { id = "xyz.jpenilla.resource-factory-bukkit-convention", version = "1.1.0" }

build.gradle.kts:
import xyz.jpenilla.resourcefactory.bukkit.bukkitPluginYaml

plugins {
    id("java")
    id("java-library")
    alias(libs.plugins.kotlin)
    alias(libs.plugins.kotlin.serialization)
    alias(libs.plugins.shadow)
    alias(libs.plugins.runpaper)
    alias(libs.plugins.resource.factory)
}

fun kotlin(s: String): String {
    return "org.jetbrains.kotlin.$s"
}

allprojects {
    apply {
        plugin("java")
        plugin("java-library")
        plugin(kotlin("jvm"))
        plugin(kotlin("plugin.serialization"))
        plugin("com.github.johnrengelman.shadow")
    }

    group = "ink.pmc.satellite"
    version = "1.0.0"

    repositories {
        mavenCentral()
        mavenLocal()
        maven(uri("https://repo.papermc.io/repository/maven-public/"))
        maven(uri("https://maven.nostal.ink/repository/maven-public"))
    }

    dependencies {
        compileOnly(rootProject.libs.bundles.kotlin)
        compileOnly(rootProject.libs.bundles.nightconfig)
        compileOnly(rootProject.libs.bundles.cloud)
        compileOnly(rootProject.libs.bundles.mccoroutine)
        compileOnly(rootProject.libs.bundles.ktor)
        compileOnly(rootProject.libs.paper.api)
        compileOnly(rootProject.libs.caffeine)
        compileOnly(rootProject.libs.catppuccin)
        compileOnly(rootProject.libs.dynmap)
        compileOnly(rootProject.libs.common.utils)
    }
}

dependencies {
    implementation(project(":api"))
}

bukkitPluginYaml {
    main = "ink.pmc.satellite.Satellite"
    author = "Nostal Yuu"
    // depend.add("dynmap")
}

Use manually method doesn't work either:
val yaml = bukkitPluginYaml {
    main = "ink.pmc.satellite.Satellite"
    author = "Nostal Yuu"
    // depend.add("dynmap")
}

sourceSets.main {
    resourceFactory {
        factory(yaml.resourceFactory())
    }
}

I'm using Java 21 and Gradle 8.7 with the plugin.

You should not import the extension function if you are trying to use the extensions provided by the convention plugins. The extension function creates a new plugin yaml. So you are not configuring the default one added by the bukkit convention.


Interesting...

...but of any practical use to developers? I came here hoping to find advice on creating a Xamarin.Forms splash screen.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a5a4e93b-006f-176e-cf02-108f48bff807
Version Independent ID: 2d677054-ae70-1eb1-903d-70b4f6cdfc50
Content: Xamarin.Forms splash screen - Xamarin
Content Source: docs/xamarin-forms/user-interface/splashscreen.md
Product: xamarin
Technology: xamarin-forms
GitHub Login: @profexorgeek
Microsoft Alias: jusjohns

Hi @BigPaul,
Typically the purpose of a splash screen is to offer users some type of branded experience while the platform operating system (such as Android) loads your application. Once Xamarin.Forms is fully initialized and loads your application, this delay has already elapsed. This means that splash screens must be implemented at a lower level so they can show before Xamarin.Forms even starts loading!
So "creating a Xamarin.Forms splash screen" really means creating a native screen that shows while Xamarin.Forms loads your application. This article attempts to introduce that concept and provide links to articles for each platform.


[SPARK-16817][CORE][WIP] Use Alluxio to improve stability of shuffle by replication of shuffle data

What changes were proposed in this pull request?
(In the PR,  I propose to use Alluxio to help store shuffle data in order to  improve the stability of complicated OLAP task.
Motivation
In original ways, when there is a shuffle fetch failure (NodeManager(shuffle service) crashed), spark will rerun previous stage to reproduce shuffle data. This way works well, but in some cases we cannot accept the recalculation price.
In this PR, when there is a  shuffle fetch failure , reduce will retry fetch shuffle data from Alluxio to avoid recalculation
Usage

Enable this feature in spark-default.conf.
spark.alluxio.shuffle.enabled ture

How was this patch tested?
manual tests

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

I believe such kind of PR requires SPIP and community discussion first.


Promise API does not seem to work as intended with custom toasts

I was attempting to make use of the promise API to handle network requests at all stages, however two toasts began popping up when I began using custom Toast Components. I tried giving the components Id's to see if that would help rectify the issue but it still did not have the desired effect. I then checked that I was using the API correctly with custom and I was able to get what i wanted it to look like.
Example not working code:
toast.promise(useFetch(url, "POST", SEND), {
loading: toast.loading("Awaiting Response...", { id: "response" }),
success: toast.custom(
(t) =>
 Success({
 message: 'Save your ${title} preferences.',
 t: t,
 }),
 { id: "response" }
 ),
 error: toast.error("Unable to update your prefrences", {
id: "response",
}),
 });
Example not working result:

Example working code:
toast.loading("Awaiting Response...", { id: "response" });
await sleep(500); // sleep is here to make the effect more obvious
const DATA = await useFetch(url, "POST", SEND);
 DATA.success
 ? toast.error("Unable to update your prefrences.", {
  id: "response",
    })
: toast.custom(
(t) => (
 <Success
     message={'Saved your ${title} preferences.'}
     t={t}
     />
 ),
  { id: "response" }
   );
Example working result:

Did some digging through other issues and found this #67. Seems it's known not to work with the current API but can be made with some extra abstraction layer mentioned in the post. Much appreciated to @RomanFausek for his solution on that post


Some openId Scopes are not excluded for the id token disabled custom grant types

Describe the issue:
When obtaining a token from a custom grant type which has id token disabled, IS will only drop the OIDC scope instead of dropping all the OIDC scopes.
How to reproduce:

Add a new grant type following https://is.docs.wso2.com/en/latest/references/extend/oauth2/write-a-custom-oauth-2.0-grant-type/ and set IdTokenAllowed=false
Send a request to token endpoint with grant type as custom grant type and several OIDC scopes
Response will contain all requested OIDC scopes except for the openid scope.

Expected behavior:
If the id token is disabled for a grant type, it should drop all the OIDC scopes
Possible Solution
Update following code to drop all the registered OIDC scopes in the IS.
https://github.com/wso2-extensions/identity-inbound-auth-oauth/blob/44c9aefd44b9e5bcf3794b5797a30086a8c880b4/components/org.wso2.carbon.identity.oauth/src/main/java/org/wso2/carbon/identity/oauth2/validators/OIDCScopeHandler.java#L53-L61

This issue is being closed due to extended inactivity. Please feel free to reopen it if further attention is needed. Thank you for helping us keep the issue list relevant and focused!


Link Course to Person

Closes #52.
This pull request does not modify the GUI to include Course.

Adds Course into Person and its usages, such as EditCommand, AddCommandParser and more.
Updates affected testcases.

LGTM


SendBeacon() doesn't send  beacon

Here is my usecase:

I have some data, then I want to customize beacon data myself and I need sending beacons several >times during one page. like:
User click a button ---------> generate some data and the beacon   ------->  send beacon.

When I use SendBeacon(), it doesn't fire the process because the RT plugin is  not 'Complete'. I have read some docs and source code, but I don't find an approach to make the RT plugin 'Complete'. I don't know what complete status exactly means because it is always not complete.
Any help?

Plugins' 'complete' status are managed by themselves.


Sencha adapter "filter" key can't use "id" property

Matching against "id" property works (as it should) for "cayenneExp" protocol key. E.g.:
target("/e2").queryParam("include", "id")
   .queryParam("cayenneExp", urlEnc("{\"exp\":\"id in (2,3)\"}")).request().get();

An equivalent query using Sencha Adapter "filter" key blows up with Cayenne error (as "id" property leaks down to Cayenne) :
filter=[{"exactMatch":true,"disabled":false,"property":"id","operator":"in","value":[73,77,128,129]}]

CayenneRuntimeException [v.4.0.M3.28c7db9 Oct 23 2015 16:20:54] Can't resolve path component: [MyEntity.id].
So we need to port ExpressionPostProcessor inner class located in CayenneExpProcessorWorker to the Sencha adapter, to do property cleansing.

PR is applied. While we can have more enhancements to this implementation, the bug is fixed. So closing.

@atomashpolskiy : can we add a unit test with Sencha adapter using a filter matching on id? Doesn't look like we have such test (or do we?)

@andrus , yes, I added such test in 006b987

Great thanks. Will start merging it once Travis finishes a CI build


ENH: allow button-box input to TDT in RZ6 circuit

Circuit changes were implemented in #266 (lots of discussion on that thread), but hardware issues with the buttons and switchbox made testing difficult at the time.  Needs testing / debugging / possibly more circuit changes.  cc @rkmaddox @mfreilly

At present, the switchbox passes presses to expyfun via USB, by presenting itself as a keyboard.
This proposed enhancement is possible as far as expyfun is concerned, by passing response_device='tdt' when ExperimentController is instantiated (or by changing the machine config file), and I'm assuming that the updated RZ6 circuit is configured to accept button presses (right @Eric89GXL?)  The bottleneck seemed to be getting the switchbox to pass the signals to the TDT reliably.  @mfreilly did you ever re-test this after the switchbox got upgraded?

I think that was the issue, yes.

I have not tested it since the fix, I'll look at this today. I assume the output of the Elekta trigger box goes to TDT digital inputs. If so which ones?

It probably needs to be connected to the first four ports of block A. If that doesn't work, try B. If that doesn't work, try the four ports of block C that are not connected as outputs to the TDT.
The output trigger lines are connected to the first four ports of block C, right?

A is the output of the TDT to the Elekta trigger inputs. I'll try B and C. Nothing is connected to C now.

Tried response_device='tdt' and patched the Trigger box outputs to TDT A, B, and C.
Ran keypress_demo.py, got 'no keys pressed'

@mfreilly in the keypress demo script, did you edit it to specify response_device='tdt' when ExperimentController is instantiated?  If not, expyfun will default to 'keyboard' based on NINTOK's config file, and wouldn't register the responses even if they were making it through to the TDT.

@mfreilly never mind, I misread your comment.

@drammock didn't we have it in a state where some but not all of the triggers came through? I wonder if we broke something.
@mfreilly did you make sure that the version of expyfun being imported/used is the one from latest master?

prior to the switchbox upgrade, we did get it working such that button presses would register on the TDT sometimes, but not always.  It wasn't "some buttons and not others"... it seemed to depend on how long/hard you pressed the button.  For quick presses you would see LED flashes on the switchbox, but not on the TDT input indicator LEDs.

Hmm... I suspect that the TDT indicator LEDs should come on almost regardless of what port they're plugged into (at least as long as they're in an input port). @mfreilly did you see any lights on the TDT come on when you pressed buttons? I assume you did see lights on the Elekta box?

Will check things this morning

From the other issue:

TDT front panel Digital I/O LEDs 4 5 6 7 light when buttons pressed.
TDT input is A5 A6 A7 A8.
keypress_demo.py does not respond to press.
expyfun commit version: can't tell, is Git installed on Nintok? I don't see it.
But looks like Dan installed expyfun 11/15/16.

Within the circuit, port C0-3 are the only ones marked as "output", all other ports (all of A, B, and C4-7) are all marked as "input". @mfreilly I wonder if there is some mis-labeling or swapped cables.
Do any LED lights come on when you connect it to B ports?

In the circuit there is even a comment "This will access only 4 bits of Port-C of the Digital IO." for the TTL line outputs. It seems like the correct configuration would be to have the first 4 ports of C outgoing to the Neuromag...
I wonder if the labels A/C are swapped. @mfreilly can you try with the button box plugged into the first four ports of B, and then of C? Do any lights come on? Any different behavior of the circuit?

Sorry, responded on wrong issue.
Buttons in B1 -B8, C1 - C8. No response

To confirm what you mean by no response -- the script did not respond and there were no LEDs that came on the TDT, right?
I think this will require local debugging of the circuit unfortunately.
@mfreilly on the experimental computer, can you:

make a new folder, say "testing"
Copy the latest version of expyfun master in it, probably easiest to do expyfun.download_version() on some computer and copy the result
Put plot_keypress_demo.py in the testing folder
Run plot_keypress_demo.py, quit out of it, and do print(expyfun) within Python to ensure that it is importing it from the local directory
Open the testing\expyfun\data\expCircuitF32_RZ6.rcx file (it should automatically open in RPvdsEx)
Go to the "waitForButtonPress" tab at the bottom
Try doing menu Implement->Build, Load and Run. This gets the circuit loaded and running on the TDT.
See if the rightmost (middle center) tag in the circuit does anything when pressing buttons
Stop the circuit (Implement->Halt circuit), change the bitmask (leftmost component) from 255 to 65280, and repeat steps 7-8
Stop the circuit, change the bitmask to 16711680, repeat steps 7-8

Hopefully one of these steps produces a non-zero output on the monitor. If it doesn't, I can make a simpler circuit for testing.

Correct: the script did not respond and there were no LEDs that came on the TDT
I'll do the setup

keypad connected to B1 B2 B3 B4
Installed expyfun in /keypress_test
print(expyfun) result = <module 'expyfun' from 'expyfun_init_.py'>
On  keypresses there is no visible change in the circuit.

The print result was blank?
That was steps 1 through 4 essentially, right?

Although the last line makes it sound like you also did through step8

The print result is blank.

You are doing it within the python interpreter? I'm not sure how it's
possible it is blank with no error or anything. Does print(1+1) give
anything?

print(1+1) result = 2
Also on CTRL-C it stops with KeyboardInterrupt but there is no command line open in the console.

Try doing
import expyfun
print(expyfun)
Maybe it wasn't imported by the script you are running and Spyder just hid
the error
On Nov 18, 2016 12:22, "mfreilly"<EMAIL_ADDRESS>wrote:

print(1+1) result = 2
Also on CTRL-C it stops with KeyboardInterrupt but there is no command
line open in the console.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/LABSN/expyfun/issues/282#issuecomment-261589042, or mute
the thread
https://github.com/notifications/unsubscribe-auth/ACQZXnWM2EtvKa0Cq3e616kr6rAEIElNks5q_d7BgaJpZM4I-M5N
.

import expyfun
print(expyfun)
<module 'expyfun' from 'expyfun_init_.pyc'>
Also:
import numpy
print(numpy)
<module 'numpy' from 'C:\WinPython-32bit-<IP_ADDRESS>\python-2.7.6\lib\site-packages\numpy_init_.pyc'>
assert in keypress_tesst.py
assert_version('aaaaaaa')
AssertionError: Requested version aaaaaaa does not match current version 25b25ea
This is the commit in the test folder

Okay, so we're at least on the latest master. Have you tried steps 5+ yet?

Did steps 5 - 10. No changes seen in waitForButtonPress

Hmmm.... how about this. First modify the components in waitForButtonPress -- instead of reading out on the rightmost part of the For each mask value (255, 65280, 16711680):

Add a Watch (Components->Common->Watch)
Hook up the input component (leftmost component) to the ParWatch by by double-clicking the output triangle of the 10110100-triangular component, and then clicking the input triangle of the ParWatch
Stop the circuit (if it's running)
Change the mask value
Load and run the circurt
Press buttons, see if the ParWatch changes
Try steps 3-6 for all set-of-four input connections (A5-A8, B1-B4, B5-B8, C1-C4, C5-C8)

It seems like one of these must work...
Sorry this is such a pain

Actually to make it really easy you could just add three inputs (Componetns->Input/Output; WordIn) with the three different mask values, each hooked up to their own ParWatch. Then once that circuit is running, you only need to switch the cables to each location once!

Progress, the new watch changes with inputs on C1 thru C8.
Not a pain, a good intro to circuits

Excellent! @mfreilly let's iterate on Gitter so we don't slam GitHub notifications
https://gitter.im/LABSN/expyfun

It looks like this works as long as all 8 buttons are connected to input B. The problem was that B5-8 being disconnected appeared as HIGH to the TDT, so it looked like all those buttons were permanently pressed (value 240 was constant).


Add staffbase doc

Type of Change

[ ] Bugfix
[ ] Enhancement / new feature
[ ] Refactoring
[X] Documentation

Description
For a better recognition of Staffbase as an employer, it was proposed that we add a brand labeling to our public repositories.
Checklist

[ ] Write tests
[ ] Make sure all tests pass
[X] Update documentation
[ ] Review the Contributing Guideline and sign CLA
[ ] Reference relevant issue(s) and close them after merging

I have read the CLA Document and I hereby sign the CLA


Wordwar voice channel

The idea for this is to have a set of voice prompts trigger in a voice channel when a wordwar is happening. Would not scale very well to multiple concurrent wordwars, but most of ours are "one at a time", even if many may be planned.
Can use IBM Watson's demo to generate the speech samples: https://text-to-speech-demo.ng.bluemix.net/
Prompts:

30 seconds out
3-2-1 countdown when starting
Halfway point
30 seconds left
10 seconds
Stop

The rationale is that this provides:

a richer experience, with the 3,4,5 prompts added rather than just start/finish
a distinct alert, rather than a generic mention ping, which helps disambiguate in the case of people pinging you for other reasons
with the volume up, the prompts could be heard further than locally, for write-ins or when going away from keyboard for a bit

As Rogare runs on Heroku, and Heroku does not support UDP traffic, the bot cannot itself speak. An external service is needed. We could have something running on a VPS, but I believe Lambda functions would work much better (and cost less).
Rogare runs on Heroku US, aka AWS N. Virginia. The lambda functions would have to run there, too.
The idea is to have a function that opens a connection to a voice channel, unmutes itself, plays a named sample, mutes, and finishes. The channel and sample would be parameters to the function/endpoint.
For added timing precision, a timestamp optional parameter could be used. In this case, the function is called at least 15 seconds in advance and up to 30. It establishes the connection, then two seconds before mark it unmutes, plays the sample on the mark, then finishes as usual.
For added ergonomy, there could be a boolean "trailing edge" parameter in addition to the timestamp option. In that case, the lambda figures out how long the sample plays for, and arranges the timing so that the sample finishes at the mark, rather than starts.
That service could be shared for other purposes too.

Apex: http://apex.run/ may be used to structure and manage the Lambda.
There are a variety of languages that could be used, although the need for sodium and opus libraries will likely dictate what is easiest. A few tests would be needed, to determine how and what is possible.
For rust: https://github.com/serenity-rs/serenity/ building on musl and vendoring the libraries.
For js: the usual discord library, with ffmpeg-binaries from npm and vendored libs for sodium etc.

Probably a better choice than Apex for Rust: https://github.com/srijs/rust-aws-lambda

No need for any of this mess! Sassbot is now hosted on a UDP-capable server, so we can fire away directly.


IE 9 ISSUE

#6024 ```
I am running with ng serve .
It's working fine for chrome and firefox but not for IE9 + versions.
Please provide the suggesitions.
Thanks in advance.

Hi @tata-abhi ,
Have you had a look at the polyfills.ts file with the src folder?
You need to follow a set of instructions in there on how you can make your application available for IE 9, 10 & 11. Simply read the inline comments for this information.

Yes, the comments in the polyfills file will lead you to the appropriate changes.

Thanks @RicardoVaranda , @Brocco.... Its working..


Customized loss can't get the shape of input data (BUG?)

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

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): YES
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.0.0-beta
Python version:
Bazel version (if compiling from source): 3.7.3
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: cuda 10.0 with cudnn 7.6
GPU model and memory: 12GB

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
Describe the current behavior
I write a custom loss function (dice coefficient) with following code:
def dice_coef(y_true, y_pred, smooth=1e-7):
    _, W, H, D, C = y_pred.get_shape()

    y_pred = tf.reshape(y_pred, shape=[-1, W * H * D * C])
    y_true = tf.reshape(y_true, shape=[-1, W * H * D * C])

    y_pred = tf.cast(y_pred, dtype=tf.float32)
    y_true = tf.cast(y_true, dtype=tf.float32)

    dice_numerator = 2.0 * tf.reduce_sum(y_true * y_pred, axis=1) + smooth
    dice_denominator = tf.reduce_sum(y_pred + y_true, axis=1) + smooth

    dice_coefficient = dice_numerator / dice_denominator
    dice_coefficient = tf.reduce_mean(dice_coefficient)
    return dice_coefficient

when i run code like this:
mirrored_strategy = tf.distribute.MirroredStrategy()
    with mirrored_strategy.scope():
        model = unet3d(input_shape=input_shape, num_class=num_class, activation='sigmoid')
        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
                      loss=bce_dice,
                      metrics=[
                          tf.keras.metrics.BinaryAccuracy(),
                          tf.keras.metrics.BinaryCrossentropy(),
                      ])

i got the following error
Traceback (most recent call last):
  File "/tmp/pycharm_project_317/opt/train.py", line 84, in <module>
    epochs=50)
  File "/tmp/pycharm_project_317/opt/train.py", line 21, in train
    callbacks=callbacks)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 643, in fit
    use_multiprocessing=use_multiprocessing)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_distributed.py", line 681, in fit
    steps_name='steps_per_epoch')
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 294, in model_iteration
    batch_outs = f(actual_inputs)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/distribute/distributed_training_utils.py", line 813, in execution_function
    return [out.numpy() for out in distributed_function(input_fn)]
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 416, in __call__
    self._initialize(args, kwds, add_initializers_to=initializer_map)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 359, in _initialize
    *args, **kwds))
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 309, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 706, in wrapper
    raise e.ag_error_metadata.to_exception(type(e))
TypeError: in converted code:

    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/distribute/distributed_training_utils.py:804 distributed_function  *
        outputs = strategy.experimental_run_v2(
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:708 experimental_run_v2
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1710 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/distribute/mirrored_strategy.py:708 _call_for_each_replica
        fn, args, kwargs)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/distribute/mirrored_strategy.py:195 _call_for_each_replica
        coord.join(threads)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/training/coordinator.py:389 join
        six.reraise(*self._exc_info_to_raise)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/six.py:693 reraise
        raise value
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception
        yield
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/distribute/mirrored_strategy.py:926 run
        self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:908 train_on_batch
        output_loss_metrics=self._output_loss_metrics)
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_eager.py:307 train_on_batch
        output_loss_metrics=output_loss_metrics))
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_eager.py:237 _process_single_batch
        training=training))
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_eager.py:152 _model_loss
        per_sample_losses = loss_fn.call(targets[i], outs[i])
    /home/tanjl/anaconda3/envs/tensorflow2/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:215 call
        return self.fn(y_true, y_pred, **self._fn_kwargs)
    /tmp/pycharm_project_317/app/losses/binary.py:26 bce_dice
        return -dice_coef(y_true, y_pred, smooth) + \
    /tmp/pycharm_project_317/app/losses/binary.py:7 dice_coef
        y_pred = tf.reshape(y_pred, shape=[-1, W * H * D * C])

    TypeError: unsupported operand type(s) for *: 'NoneType' and 'NoneType'

bash: 行 1:  7597 段错误               (核心已转储) env "PYCHARM_HOSTED"="1" "PYTHONUNBUFFERED"="1" "PYTHONIOENCODING"="UTF-8" "PYCHARM_MATPLOTLIB_PORT"="52949" "JETBRAINS_REMOTE_RUN"="1" "PYTHONPATH"="/home/tanjl/.pycharm_helpers/pycharm_matplotlib_backend:/tmp/pycharm_project_317" /home/tanjl/anaconda3/envs/tensorflow2/bin/python3.7 -u /tmp/pycharm_project_317/opt/train.py

Describe the expected behavior
it have no error when i run code with tf-2.0.0-alpha but the error happend with beta version tensorflow
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
it can't get the input shape in loss function, is that right? and i wish u could help me, thanks

Please provide us complete code to reproduce the issue.Thanks.

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!

I'm having the same error. The TF example VAE code doesn't run with distribute strategy (see code below). I'm on tf<IP_ADDRESS>-beta1 (tried rc0 but it has too many issues at this stage to upgrade, I have to stick with beta).
Error:

per_sample_losses = loss_fn.call(targets[i], outs[i])
IndexError: list index out of range

(See full error stack in the bottom)
Any workaround to this?

import numpy as np
import os
import tensorflow_probability as tfp
tfd = tfp.distributions
import tensorflow as tf
from tensorflow.keras import layers

load_model = 1

latent_dim = 8
learning_rate = 1e-4

BATCH_SIZE = 100
TEST_BATCH_SIZE = 10

color_channels = 1
(train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()

train_images = train_images[:5000,::]
test_images = test_images[:1000,::]
n_trainsamples = np.shape(train_images)[0]
n_testsamples = np.shape(test_images)[0]
imsize = np.shape(train_images)[1]
np.random.shuffle(train_images)
train_images = train_images.reshape(-1, imsize, imsize, 1).astype('float32')
test_images = test_images.reshape(-1, imsize, imsize, 1).astype('float32')
train_images /= 255.
test_images /= 255.
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(n_trainsamples).batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.from_tensor_slices((test_images)).shuffle(n_testsamples).batch(TEST_BATCH_SIZE)

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():

    optimizer = tf.keras.optimizers.Adam(learning_rate)

    class Sampling(tf.keras.layers.Layer):
        """Uses (z_mean, z_log_var) to sample z, the vector encoding an image."""
        def call(self, inputs):
            z_mean, z_log_var = inputs
            batch = tf.shape(z_mean)[0]
            dim = tf.shape(z_mean)[1]
            epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
            return z_mean + tf.exp(0.5 * z_log_var) * epsilon

    original_dim = 784
    intermediate_dim = 64
    latent_dim = 32

    # Define encoder model.
    original_inputs = tf.keras.Input(shape=(imsize, imsize, color_channels), name='encoder_input')
    x = layers.Dense(intermediate_dim, activation='relu')(original_inputs)
    z_mean = layers.Dense(latent_dim, name='z_mean')(x)
    z_log_var = layers.Dense(latent_dim, name='z_log_var')(x)
    z = Sampling()((z_mean, z_log_var))
    encoder = tf.keras.Model(inputs=original_inputs, outputs=z, name='encoder')

    # Define decoder model.
    latent_inputs = tf.keras.Input(shape=(latent_dim,), name='z_sampling')
    x = layers.Dense(intermediate_dim, activation='relu')(latent_inputs)
    outputs = layers.Dense(original_dim, activation='sigmoid')(x)
    outputs = tf.keras.layers.Reshape(target_shape=(imsize, imsize, color_channels))(x)
    decoder = tf.keras.Model(inputs=latent_inputs, outputs=outputs, name='decoder')

    # Define VAE model.
    outputs = decoder(z)
    vae = tf.keras.Model(inputs=original_inputs, outputs=outputs, name='vae')

    # Add KL divergence regularization loss.
    kl_loss = - 0.5 * tf.reduce_mean(
        z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1)
    vae.add_loss(kl_loss)

    # Train.
    optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
    vae.compile(optimizer, loss=tf.keras.losses.MeanSquaredError())
    vae.fit(train_dataset, epochs=n_trainsamples//BATCH_SIZE)

Logs:

2019-09-01 05:47:53.856027: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-09-01 05:47:53.860170: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 7a50:00:00.0
2019-09-01 05:47:53.860377: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-09-01 05:47:53.861509: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-09-01 05:47:53.862528: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-09-01 05:47:53.862808: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-09-01 05:47:53.864211: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-09-01 05:47:53.865345: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-09-01 05:47:53.868965: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-09-01 05:47:53.869575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-09-01 05:47:53.869944: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-01 05:47:53.948861: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x52c0430 executing computations on platform CUDA. Devices:
2019-09-01 05:47:53.948905: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7
2019-09-01 05:47:53.951820: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-09-01 05:47:53.952412: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5330710 executing computations on platform Host. Devices:
2019-09-01 05:47:53.952439: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): ,
2019-09-01 05:47:53.952837: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 7a50:00:00.0
2019-09-01 05:47:53.952893: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-09-01 05:47:53.952914: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-09-01 05:47:53.952931: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-09-01 05:47:53.952948: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-09-01 05:47:53.952965: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-09-01 05:47:53.952981: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-09-01 05:47:53.952999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-09-01 05:47:53.953570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-09-01 05:47:53.953614: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-09-01 05:47:53.955775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-01 05:47:53.955803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2019-09-01 05:47:53.955816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2019-09-01 05:47:53.956462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 224 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 7a50:00:00.0, compute capability: 3.7)
2019-09-01 05:47:53.970613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 7a50:00:00.0
2019-09-01 05:47:53.970682: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-09-01 05:47:53.970703: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-09-01 05:47:53.970720: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-09-01 05:47:53.970736: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-09-01 05:47:53.970751: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-09-01 05:47:53.970767: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-09-01 05:47:53.970783: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-09-01 05:47:53.971338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-09-01 05:47:53.971377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-01 05:47:53.971391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2019-09-01 05:47:53.971401: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2019-09-01 05:47:53.971990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/device:GPU:0 with 224 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 7a50:00:00.0, compute capability: 3.7)
Train on 50 steps
Epoch 1/50
Traceback (most recent call last):
File "/home/kristofgiber/pycharm_project/VAE/save_issue_reproduction.py", line 82, in
vae.fit(train_dataset, epochs=n_trainsamples//BATCH_SIZE)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training.py", line 643, in fit
use_multiprocessing=use_multiprocessing)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_distributed.py", line 681, in fit
steps_name='steps_per_epoch')
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_arrays.py", line 294, in model_iteration
batch_outs = f(actual_inputs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py", line 813, in execution_function
return [out.numpy() for out in distributed_function(input_fn)]
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/def_function.py", line 416, in call
self._initialize(args, kwds, add_initializers_to=initializer_map)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/def_function.py", line 359, in _initialize
*args, **kwds))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/function.py", line 1360, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/function.py", line 1648, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/function.py", line 1541, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/func_graph.py", line 716, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/eager/def_function.py", line 309, in wrapped_fn
return weak_wrapped_fn().wrapped(*args, **kwds)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/func_graph.py", line 706, in wrapper
raise e.ag_error_metadata.to_exception(type(e))
tensorflow.python.autograph.impl.api.StagingError: in converted code:
relative to /usr:
local/lib/python3.5/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py:804 distributed_function  *
    outputs = strategy.experimental_run_v2(
local/lib/python3.5/dist-packages/tensorflow/python/distribute/distribute_lib.py:708 experimental_run_v2
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
local/lib/python3.5/dist-packages/tensorflow/python/distribute/distribute_lib.py:1710 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
local/lib/python3.5/dist-packages/tensorflow/python/distribute/mirrored_strategy.py:708 _call_for_each_replica
    fn, args, kwargs)
local/lib/python3.5/dist-packages/tensorflow/python/distribute/mirrored_strategy.py:195 _call_for_each_replica
    coord.join(threads)
local/lib/python3.5/dist-packages/tensorflow/python/training/coordinator.py:389 join
    six.reraise(*self._exc_info_to_raise)
lib/python3/dist-packages/six.py:686 reraise
    raise value
local/lib/python3.5/dist-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception
    yield
local/lib/python3.5/dist-packages/tensorflow/python/distribute/mirrored_strategy.py:926 run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training.py:908 train_on_batch
    output_loss_metrics=self._output_loss_metrics)
local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_eager.py:307 train_on_batch
    output_loss_metrics=output_loss_metrics))
local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_eager.py:237 _process_single_batch
    training=training))
local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_eager.py:152 _model_loss
    per_sample_losses = loss_fn.call(targets[i], outs[i])

IndexError: list index out of range

Process finished with exit code 1

I have same error


webpack-serve serves files from src folder, overriding generated files

Operating System: Mac
Node Version: v8.10.0
NPM Version: yarn 1.7.0
webpack Version: 4.10.2
webpack-serve Version: 1.0.2

This issue is for a:

[x] bug
[ ] feature request
[ ] modification request

Code
Minimal repro at https://github.com/BernieSumption/webpack-serve-bug-demo
The issue
I discovered this while trying to figure out why html-webpack-plugin worked in production builds but not with webpack-serve. webpack-serve serves files from the source directory. This breaks the development experience when there are files in the source directory that have the same name as files in the output directory. Also, serving these files at all allows developers to write code that works in development with webpack-serve but fails in a production build as it directly refers to files in the source directory that aren't copied to the output directory.
Expected Behavior
In general, webpack-serve should serve exactly the same content as if you did a webpack build then served the output folder using a static file server.

http://localhost:8080/ should serve the index.html file created by html-webpack-plugin
http://localhost:8080/bundle.js (or whatever file name is specified in webpackConfig.output.bundle) should serve the webpack bundle, regardless of whether there is a src/bundle.js.
http://localhost:8080/index.js should give a 404

Actual Behavior

http://localhost:8080/ serves src/index.html - the template used by html-webpack-plugin, not the output of the plugin
If you create a file src/bundle.js, this file overrides the webpack bundle
http://localhost:8080/index.js serves src/index.js.

How Do We Reproduce?
Clone https://github.com/BernieSumption/webpack-serve-bug-demo then yarn && yarn start to launch webpack-serve. yarn build to do a webpack build to the dist folder.

See #114. This isn't a bug. This is a symptom of your environment. You'll need to configure webpack-serve correctly for your environment.


Terraform Import with index

Hi,
Have you tried using the terraform plugin with the import command with an index on the resource like this:

task: TerraformTaskV3@3
displayName: 'Terraform import'
inputs:
provider: 'azurerm'
command: 'custom'
workingDirectory: '$(System.DefaultWorkingDirectory)'
commandOptions: '--var-file myvarfile.tfvars azurerm_route_table.tr-tblroute["index"] /subscriptions/id/resourceGroups/rg/providers/Microsoft.Network/routeTables/tr-routetable'
outputTo: 'console'
customCommand: 'import'
environmentServiceNameAzureRM: serviceName

Looks like the plugin doesn't allow putting an index on the option line?
Is it a bug?
thanks

More details about my tests :
commandOptions: '--var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route["pr-svcpartage-commun-001"] ${{ parameters.resourceId }}'
output :
/usr/bin/terraform import --var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route[pr-svcpartage-commun-001] ...
Index brackets must contain either a literal number or a literal string.
If I add " like this :
commandOptions: '--var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route["pr-svcpartage-commun-001"] ${{ parameters.resourceId }}'
output:
/usr/bin/terraform import --var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route[\pr-svcpartage-commun-001"] ...
the first " of the index has disappeared ans I dont know why

Thanks for reporting, we will look into this and get back to you sooner.

Hi.  Have you tried to escape the quotes?  like commandOptions: '--var-file myvarfile.tfvars azurerm_route_table.tr-tblroute["index"]

Yes I have tried like this :
commandOptions: '--var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route["pr-svcpartage-commun-001"] ${{ parameters.resourceId }}'
and the output is :
usr/bin/terraform import --var-file NPR.AZ.COMMUN.01.tfvars azurerm_route.tr-route[\pr-svcpartage-commun-001"] ...
the first " of the index has disappeared

ok. that is strange. let me look around and see what I can come up with.  I think the issue here is that  the extension use string input using another tool lib , azure-pipelines-task-lib/task, to parse the quotes ETC.

Ok found another reported issue on azure-pipelines-task-lib.  that stated if you want literal "b" you should escape it like this "\"b\""
that would mean  commandOptions: '--var-file myvarfile.tfvars azurerm_route_table.tr-tblroute["\"index\""]

Nice! It works with the way you wrote the index:
["\"index\""]
Do you plan to correct this problem or I must thinking to write the " with the way you indicated to me?
Thanks!

As this is not specific to the extension but rather behavior of the  azure-pipelines-task-lib we will not fix this.  I will however updated the documentation on escaping quotes.


Tutorial 09: Issues converting encoder and decoder models to CPUs pytorch 0.1.11

Thanks for a fantastic tutorial. Really clear and easy to follow!
I'd like to run the pretrained models on a CPU, and am trying to convert the models as follows:
encoder.load_state_dict(torch.load(args.encoder_path, map_location=lambda storage, loc: storage))
decoder.load_state_dict(torch.load(args.decoder_path, map_location=lambda storage, loc: storage))
I also tried:
encoder = torch.load(args.encoder_path, map_location=lambda storage, loc: storage)
decoder = torch.load(args.decoder_path, map_location=lambda storage, loc: storage)
as advised by the pytorch developers (https://discuss.pytorch.org/t/on-a-cpu-device-how-to-load-checkpoint-saved-on-gpu-device/349/8)
Based on the discussions it seems that it might be a pytorch versioning issue. Could you let me know what version of pytorch you used to train and save the models? Or if it's something else, would really appreciate any guidance in getting it fixed.
Many thanks!

This is PyTorch version issue. You can see here. You can solve this problem if you use Python 3.5 instead of 2.7. Also, you can fix this problem by replacing 'requires_grad' to 'volatiles'.
This issue is automatically resolved when a new version of PyTorch is released.

Thanks a lot for letting me know the version. Also, I'll try switching to python 3.5 and see if that helps.


Update DG: Effort Section & Planned Enhancements

To be added if relevant for deferred bugs etc.

Effort section: Chuaaennn
Planned Enhancements: As required

Closed by PR #198 #199 #201


style: changed the login design

made changes to the css of the login page

Hey @chesh-ire Thankyou for the contribution.
Changes were good but can you also update the placeholders to have capitalized first letters.

Made the requested changes . Please look into it .

i tested them they're good to go
thankyou :)


EPMRPP-86229 || Adjust plugin to the new mechanism

From my side, everything looks good!
I have no linter errors and everything builds and serves correctly!


Update react-dnd to the latest version

Update react-dnd to 2.4.0 to remove Warning: Accessing PropTypes via the main React package is deprecated warning and make compatible for React 16
Closes #94

Couldn't this issue just be fixed by running npm update on the repo using the tree component? The existing version string should also support 2.x versions of react-dnd


Example Issue

In this article
Example
Example
Example
Example
Example
Example

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 870ea49c-ad84-f940-8d27-17c8b25beb97
Version Independent ID: 285ef531-525a-8703-0f16-73a3c9e497b0
Content: Try...Catch...Finally Statement (Visual Basic)
Content Source: docs/visual-basic/language-reference/statements/try-catch-finally-statement.md
Product: dotnet-visualbasic
GitHub Login: @KathleenDollard
Microsoft Alias: kdollard

Thanks for pointing out the abundance of Example headings. We've opened #9485 to address the issue by creating a single Examples section.


Use async synchronisation primitives

This is a proposal of changes needed for use of the futures friendly mutex and mpsc channel mentioned in #73.
Tests will not compile yet, because Actor trait was not updated to have async methods.
To make this work multiple api functions had to be changed to async. Also I have changed few traits to have async methods with the help of async-trait crate.
@leenozara are you ok with the use of the async-trait crate?

Hi @mrjoe7, thanks, let me dig in and see the overall impact on the API.

@leenozara I have pushed final proposal how fully async system could look like. Please let me know what do you think.

I was looking at performance of the proposed async actors and noticed that tell and try_tell are now approximately 2-3x times slower. Not sure if I like that. Also release compilation takes like forever because compiler has to do lots of optimizations.


Item deletion dialog button hover colors

Gitea version (or commit ref): 1.4.0rc1
Can you reproduce the bug at https://try.gitea.io: Yes

Description
The button hover colors for item deletion dialogs are counter-intuitive:

the 'Yes' button confirms deletion and is colored green;
the 'No' button cancels deletion and is colored red.

We should either:

reword all dialogs to have macOS style button labels like 'Delete' and 'Don't Delete' (or 'Cancel') or;
use neutral hover colors for these dialogs.

I'm also not sure if there even any positively-phrased dialogs in Gitea where 'yes' is the non-intrusive action, so maybe both options can even work together.
Screenshots

(Wording on this dialog is from my private branch, but Gitea releases are similar.)

I would prefer Cancel (white) and Delete (red) in this case

I agree, this is bad UX.
Since there is a label „reviewed/confirmed”, I assume this is good to pick up.
Changing the wording would require going through translation. I don't know how macOS handle it (I'm running a Linux).
That being said, if there was a rewording, I would add something

This cannot be undone.

The effect is already described, so that should be okay.
The relevant code part is
https://github.com/go-gitea/gitea/blob/73382d2132f8f242245d83f66c92761e0b477b91/templates/user/settings/applications.tmpl#L57-L75
I would switch the hover colours to white and red in a first PR: https://github.com/go-gitea/gitea/issues/3589#issuecomment-368626021
Following https://uxplanet.org/ghost-buttons-in-ux-design-4cf3717334f8 ghost buttons should not be used for primary actions.
In order to prevent accidental action, I'm going to turn the „No” button into a filled UI (by removing basic inverted classes) and leave the „Yes” button a ghost one. This way, there is conscious effort required to actually do delete an access token.

Looking at https://fomantic-ui.com/elements/button.html#colored I cannot see a white option. Basic adds transparency in our case (so in dark mode, it would appear as a ghost button).


Resizing item goes out of parent grid boundaries

Hi,
When i resize the item it overflows/goes out of grid , can i restrain it to be within the grid like in case of drag.

I think is caused by this line in NgGrid.ts:
this._renderer.setElementStyle(this._ngEl.nativeElement, 'width', '100%');//(maxCol * (this.colWidth + this.marginLeft + this.marginRight))+'px');
That 100% is causing the problem.  If you put all that commented column math back it it seems to work fine.

no that didn't work .. it still goes out of parent grid when i resize and there is white space you can see

Resizing grid item goes outside of grid.

Any solution for this ?


added two circular vizualizations and ability to swap between with pressing numbers or combine

There are two additional circular viz types, you can change to them while music is playing by pressing 2 or 3. 1 - is a default FFT viz.
You also can combine multiple vizs, while pressing a number key also hold SHIFT key to toggle given viz.
Recording will be done with whatever setup was set before starting the recording (e.g. preserves state)
https://github.com/tsoding/musializer/assets/16069758/19bc5c07-8a9a-43ba-b7fb-8d22ee18e149

Thanks for the suggestion! My plan is to keep all the custom visualization in custom separate plugins (when we have the plugin system). So this repo is gonna be just the "engine" sort of speak.
Cool visualization btw :+1: