Use utfsimd when appending UTF-16 to StringBuilder

Adds a fast path for valid UTF-16 using utfsimd, and fall back to the slow path for unmatched surrogates.
111.48% faster for random ASCII
111.51% faster for random (99% ASCII)
135.38% faster for random (90% ASCII)
197.82% faster for random (75% ASCII)
252.42% faster for random (50% ASCII)
187.54% faster for random BMP
114.49% faster for random
257.53% faster for random surrogates
  2.64% faster for random high surrogates
  6.30% faster for random (50% high surrogates, 50% BMP)

I believe it's called simdutf 😅

whoops!


how can i handle the nested json into the fields

[{"_id":"557f7d8bc6b7a51790000003","baseagent":{"address":"testa dd","company":"shenzhen","email":"12@111.com","license":"12222222","name":"1111111111111111","phone":"233333333"},"posted":"2015-06-26T17:47:34.441+08:00"}]
i want to get the baseagent.name,but i don't know how to do ,
i try :
nga.field('baseagent["name"]')
but that can't work.thanks

Duplicate of #525

wow,thanks a lot.


HIVE-21599: Remove predicate on partition columns from Table Scan operator

What changes were proposed in this pull request?
Partition columns are getting removed from Parquet metadata (schema).
Why are the changes needed?
When a Parquet data file contains partition columns, and the query filters on those partition columns, we can get wrong results. By removing the partition columns from the schema, we avoid creating Filter predicates on those columns.
Does this PR introduce any user-facing change?
No
How was this patch tested?
mvn test  -Dtest=TestMiniLlapLocalCliDriver -Dtest.output.overwrite=true -Dqfile=parquet_partition_col.q
This test returns correct results.

A note about the commit message: the summary says 'remove predicate ..' which is not true any more with this patch. So best to reword it.  Also remove reference to virtual columns since the patch is not making any changes for that.

@zabetak LGTM. Thanks for cleaning this up!


(#11) Automatically inject devtools into the DOM in the preload namespace.

Nice, so the panel works fine without being attached to the main app? I'm a little bit worried about exception handling stuff, but this is a really big win to make it easy to add to existing apps with way less effort.


Renamed the file for astro.files.type to astro.files.types

Replaced to Type with type.

Codecov Report

Merging #388 (cf296c1) into main (a876062) will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main     #388   +/-   ##
=======================================
  Coverage   89.85%   89.85%
=======================================
  Files          53       53
  Lines        1804     1804
  Branches      282      282
=======================================
  Hits         1621     1621
  Misses        143      143
  Partials       40       40

Impacted Files
Coverage Δ

src/astro/files/types/base.py
80.00% <ø> (ø)

src/astro/files/base.py
92.68% <100.00%> (ø)

src/astro/files/types/__init__.py
86.20% <100.00%> (ø)

src/astro/files/types/csv.py
100.00% <100.00%> (ø)

src/astro/files/types/json.py
100.00% <100.00%> (ø)

src/astro/files/types/ndjson.py
96.00% <100.00%> (ø)

src/astro/files/types/parquet.py
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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EFS provisioner does not work when in a different Availability Zone

When either the provisioner or a pod requesting an EFS PVC is NOT in the same AZ as the EFS mount point, the DNS name will not resolve and will result in output similar to the following:
Output: mount.nfs: Failed to resolve server fs-aaaaaaaa.efs.us-east-1.amazonaws.com: No address associated with hostname

The DNS name for a mount point is az.fs-aaaaaaaa.efs.us-east-1.amazonaws.com, when attempting to reference it from another AZ (link http://docs.aws.amazon.com/efs/latest/ug/mounting-fs-mount-cmd-dns-name.html) .
What is the recommended configuration when Kubernetes nodes are distributed across availability zones? Should pods that require EFS be forced in the same AZ as the EFS mount point (nodeselector)?
Would it be possible to support adding the AZ to the mount point DNS name?

It should work, Amazon claims that instances across zones can access it as long as they're in the same region and VPC. Is there a "mount target" in every AZ http://docs.aws.amazon.com/efs/latest/ug/accessing-fs.html?

Ugh.  apologies.  Closing the ticket.
@wongma7 thank you very much. I forgot to add mount targets to each subnet in the different AZ. Doh.


Change Blade Delimiters Feature Request

When developing Laravel with AngularJS, there is a need to change blade delimiters. A good tutorial is here: http://laravelsnippets.com/snippets/using-laravel-blade-with-angularjs-templates-redux-alternate-method
Hope this syntax package come with the features allow us to change it so that we can use this syntax package when developing Laravel with AngularJS.

I don't think that this can be achieved, simply because the syntax needs to know at run-time what tags it's looking for.


BIG LOSS

It seems that the loss keeps around 0.8 during training. It is right?

My loss is 0.4~0.5 in pascal2007 and vgg16

how can i reduce the loss , any idea please help me


Fixed the issue of metrics for golang runtime

Issue Ref: https://github.com/kubeless/kubeless/issues/1020
Description: Removed promhttp.Handler from kubeless/runtime/stable/golang/kubeless.tpl.go and put it inside the pkg/function-proxy/utils/proxy-utils.go so that it can scrape the registered metrics.
This should make it possible to get the registered metrics by doing a simple curl on the http-function-port/metrics
TODOs:

[ ] Ready to review
[ ] Automated Tests
[ ] Docs

anyone looking for the fix can apply this in their cluster in the meantime while there is no new release:
https://2824-73902337-gh.circle-artifacts.com/0/build-manifests/kubeless.yaml and restart the kubeless-controller-manager pod.


Library returns default value for each second request

I noticed strange thing - library returns some default value for each even request. For example, -23 for ambient air temp and engine coolant temp, -1 for RPM and 17 for speed. I copied the OBD service from the sample project. Also, sample project returns the same values. Is it the issue in library or I should handle defaults by myself?

Where do you see the code that returns those default values?


mpv output doesn't update

MPV seems to be stuck at one of the first frames, this is with mpv 0.27.2, which used to worked.
Debug log doesn't look like done events are missing. Rather like the surface buffer is never updated on the wlroots end.

Forcing https://github.com/swaywm/wlroots/blob/master/types/wlr_surface.c#L412 to be true fixes the issues

That bool just makes wlroots reupload the buffer all the time...
We seem to be in the 2nd branch of wlr_surface_apply_damage with wlr_renderer_resource_is_wl_drm_buffer... Where we don't consider damage at all.
It looks like we're supposed to reimport the damaged area from drm in that case?

This is a #744 regression.

Indeed, now I'm looking at the old wlr_surface_apply_damage, it used to unconditionally call wlr_texture_upload_drm without any check on previous image in the wl_drm case. Same with dmabuf.
That's pretty trivial to revert this behaviour (just remove the if (!surface->texture || reupload_buffer) check after wl_shm handling in wlr_surface_apply_damage; and it obviously "works"), but I wonder if there's a way to only fetch the damaged portion of these buffers somehow, which doesn't look possible atm but am not sure how realisable it would be in the first place.
@emersion I suppose I'd rather let you look into it

I wonder if there's a way to only fetch the damaged portion of these buffers somehow

When using wl_drm or linux_dmabuf, we're not copying pixels around - the client is directly updating the texture. We don't need to "only upload the damaged region" since we don't upload anything.
What we do need to do is to re-create the texture when the client attaches a new buffer.


Fix ConsoleMessage Datetime for betterproto 2.0.0b7

This PR fixes the error of python udf stuck when betterproto is at version 2.0.0b7
This fix is compatible with betterproto 2.0.0b3 to 2.0.0b7.
The cause of the bug
When betterproto is at version 2.0.0b7, timestamp requires the timezone information. Otherwise, there will be exceptions if print statements are in the python udf's code.
Before the fix, here is the code snippet of creating ConsoleMessage in pytexera:
ConsoleMessage(
                        worker_id=self.context.worker_id,
                        timestamp=datetime.datetime.now(),
                        msg_type=ConsoleMessageType.DEBUGGER,
                        source="(Pdb)",
                        title=debug_event,
                        message="",
                    )

How the fix is done
By changing the timestamp=datetime.datetime.now() to timestamp=datetime.datetime.now(datetime.timezone.utc) in all occurrence of ConsoleMessage constructor, i.e. in replace_print.py, data_processor.py and main_loop.py. This problem got fixed.

@bobbai00 please make sure you update the PR description accordingly.


Onnx eyelike

This PR implements the onnx eyelike operator and it's optional components.
@masahi @jwfromm

There is still a lint error.

thanks @CircleSpin


add 1stbranch 0000-01-02 ishcode

layout: slide
title: "Welcome to our second slide!"
ishabb sliding into github with git/github
Use the left arrow to go back!

posts/0000-01-02-ishcode.md  wrong filename was used. had to change to the right filename to merge request...I think lol!

changesnmade

` _posts/0000-01-02-ishcode.md.git
fetch origin
git checkout -b my-first-branch origin/my-first-branch
git merge master
git checkout master
git merge --no-ff my-first-branch
git push origin master


Add OpenTracing module

What was changed:
This PR adds a new temporal-opentracing module with support for OpenTracing context propagation from the client to workflow and to activity execution.
Why?
We want to be able to tie all the parts of the workflow execution under the same client parent OpenTracing span for tracing and debugging.
How it can be used?
OpenTracingClientInterceptor should be supplied as a client interceptor
OpenTracingWorkerInterceptor should be supplied as a worker interceptor

There will be a couple more things later today:

moving options into optional input parameters
failures logging into Span

But this is ready for the main review and feedback.

@vitarb Thanks for your review!

Addressed PR comments
Implemented logging of workflow/activity failures
Reworked and exposed OpenTracingOptions

Ready to be revisited.


Support for an external exportStar helper

Adds support for using an external __exportStar for use with --importHelpers per this suggestion.

@mhegazy, @DanielRosenwasser, @vladima, can you take a look? We have already merged the external __exportStar helper into tslib.


adding /plan endpoint to search bar

Please always provide the GitHub issue(s) your PR is for, as well as test URLs where your change can be observed (before and after):
Fix #<129
Test URLs:

Before: https://main--danaher-ls-aem--hlxsites.hlx.page/
After: https://129-plan-coveo-api--danaher-ls-aem--hlxsites.hlx.page/

Can be tested by searching for 'about us curated'. Should redirect user to the about-us page on stage.


fix: launcher.WithSampler doesn't get passed all the way through

Which problem is this PR solving?
Currently passing launcher.WithSampler(myCustomSampler) into  launcher.ConfigureOpenTelemetry & friends doesn't do anything.
Short description of the changes
This PR ensures that when constructing the pipelines.PipelineConfig it passes in the provided launcher.Config.Sampler instead of not.
How to verify that this has the expected result
Added a test using the dummyGRPCServer & an extended testSampler that shows that the sampler isn't being used. Then updated the pipelines.NewTracePipeline(pipelines.PipelineConfig{...}) to include the passed in Sampler and watched the test start passing.

The CircleCI integration seems to be having a sad. Here's it working locally xD

I would also really appreciate it if you'd consider cutting a release after this merges so I can get off my fork <3

Thanks a bunch for the review @MikeGoldsmith ❤


Limits file not found

Hi.
I've installed RNSVG via cocoapods. Also I've installed Folly library.
And in that case Folly has "Math.h" file and RNSVG linked on "math.h" file and I RNSVG tries wrong path.
In RNSVGPathParser.m file I've changed #import "math.h" to #import <math.h>
And it works.
Could you explain is it a my bug or you should change this file?
Thank you.

@akantsevoi Have you added the path to your Header Search Paths in Xcode? This would solve it without the need to change it to a framework import.

Not sure about this really, will close this for now. Reopen if its still an issue.

hey @dustinsavery could you please share what to add and where?


scripts: Add a python script to generate Rust bindings

Signed-off-by: Jinank Jain<EMAIL_ADDRESS>Summary of the PR
Adding a python script to automatically generate Rust bindings given a kernel source tree.
Fixes #36
Requirements
Before submitting your PR, please make sure you addressed the following
requirements:

[Done] All commits in this PR are signed (with git commit -s), and the commit
message has max 60 characters for the summary and max 75 characters for each
description line.
[Done] All added/changed functionality has a corresponding unit/integration
test.
[Done] Any newly added unsafe code is properly documented.

I don't have time to do a proper review but I fully support the addition of this script. :-)

This is a very basic script but a good starting point for modifying it further to fix up the missing details.


UI: Volume list page: Batch detach should be allowed if volume is in attaching state

If volume stuck in attaching state, we should allow user to detach it.

Fixed.


Is the inertia calculation for the 3D Link correct?

It probably is, but thought to open an issue just in case :)
I_len = (self.mass_sym * self.radius_sym**2)/2
I_wid = (self.mass_sym * self.length_sym**2)/12
if aligned_along.endswith('x'):
    self.inertia = sp.diag(I_len, I_wid, I_wid)
    offset_b = Mat([-self.length_sym/2, 0, 0])
elif aligned_along.endswith('y'):
    self.inertia = sp.diag(I_wid, I_len, I_wid)
    offset_b = Mat([0, -self.length_sym/2, 0])
else:
    self.inertia = sp.diag(I_wid, I_wid, I_len)
    offset_b = Mat([0, 0, -self.length_sym/2])

The above code is responsible for calculating the inertia of a 3D link that is modelled as a rigid cylinder. I haven't done extensive research and don't have any experience with inertia, but the following link suggests that the inertia should be calculated as (aligned with the z-axis):
I_z = (1/2)*m*r^2
I_x = I_y = (1/4)*m*r^2 + (1/12)*L^2

However, this doesn't match the above? The axis aligned calculation is the same but not the others. Can you explain the above?

@alknemeyer Amir seems to think that you modelled the inertia for the perpendicular axis as an "infinitely thin rod" i.e. r -> 0. This would make I_x and I_y (1/12)*L^2 for a z-axis aligned rod. For reference: https://www.physicsforums.com/threads/question-about-the-inertia-matrix-of-a-bar.909598/. Does this sound familiar? Then I think we would need to set the inertia for the aligned axis to 0 and not model links as cylinders and rather thin rods?

That rings a bell actually! I imagine I got mixed up with infinitely thin rod vs cylinder

Yes, that does seem to be the case. I suppose having the cylinder modelled, we could model a thin rod by using r=0. Probably something to note if a simpler model is wanted.
In any case, this issue has been resolved now. Thanks.


[feat] Support conditional execution

close #310
Changes

Add results & parameter & when field for conditional execution
TODO : add docs & examples to use when field

Submitter Checklist

[ ] Docs included if needed
[ ] Tests included if needed
[x] Follows the good commit messages standard

/test

/test


my data has been added

Added my name and links of accounts.

Good job @abhishek0777
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Update mixer client tests to expect destionation.ip

Need to update mixer client tests after https://github.com/istio/proxy/pull/1327

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@qiwzhang: The following test failed, say /retest to rerun them all:

Test name
Commit
Details
Rerun command

prow/istio-pilot-e2e-v1alpha3.sh
272b7575377d44606accb928fa87e13927dc284c
link
/test istio-pilot-e2e-v1alpha3

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/lgtm
/approve

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release builds timing out

The last two release builds have timed out trying to publish to bintray:
:bintrayUpload:bintrayUpload: Could not find publication: nebula.
 FAILED
FAILURE: Build failed with an exception.
* What went wrong:
Execution failed for task ':bintrayUpload'.
> Could not publish 'netflixoss/maven/spectator/0.34.1': HTTP/1.1 408 Request Timeout [message:Request timeout for publishing /netflixoss/maven/spectator/0.34.1]

They make it to jcenter:
http://jcenter.bintray.com/com/netflix/spectator/spectator-api/
But not maven:
http://repo1.maven.org/maven2/com/netflix/spectator/spectator-api/

Perhaps a good time to consider dropping the maven sync (#228).

Manually ran the sync to maven central for 0.34.1. It should show up soon.

@rspieldenner any ideas why this started failing? I see hystrix had some similar errors a while back (https://github.com/Netflix/Hystrix/issues/979).

Push to backlog, if 0.37 release build fails we'll go ahead and disable the sync for versions past that.
Main benefit of syncing with maven central is it is the default so it avoids users needing to add a jcenter resolver. Also, according to some maven central has better availability for reads than jcenter.

v0.37.0 build passed successfully. Closing this issue for now.


Tact switch for pause/play and volume up/down

Hi, How I can add a tact switches for pause/play and volume up/down?

If you have free inputs, you can.  Read the documentation.


Remove extra spaces.

Proposed changes

Unreleased project version (optional)

Related issues (optional)

Thanks, @yanzay :)


Error nerdctl commit, "failed to create a tmp single-platform image..., content digest ... not found"

Description
I am using Kubernetes, and the container runtime is containerd.
I want to commit an image from within a pod.
Inside the pod, nerdctl is installed, and the pod has mounted the node's /run/containerd and /var/lib/containerd directories as hostPath volumes.
When I try to commit using nerdctl, I encounter the following error:
"time="2024-09-05T08:09:43Z" level=fatal msg="failed to create a tmp single-platform image "registry:tag-tmp-reduced-platform": content digest sha256:602d8ad51b8130f3fcd71cb936dea612ebc799666136abf2e5914585b3178a4a: not found"
The registry and tag seem fine.
I've also tried pulling the base image before running nerdctl.
I already attempted using the --unpack=false flag as well.
Do you know of any solution for this issue?
Steps to reproduce the issue
No response
Describe the results you received and expected
complete commit
What version of nerdctl are you using?
nerdctl commit
Are you using a variant of nerdctl? (e.g., Rancher Desktop)
Others
Host information
No response

Possibly a duplicate of #827 ?

"failed to create a tmp single-platform image"
-> Solved issue, using the '--all-platforms' option
However, " content digest ... not found" still remains.

@AkihiroSuda we need to fix this.
We got many reports / variants of it.
I believe this is flaky - sometimes the Kube test would pass, sometimes it would fail.
Someone mentioned it could be containerd garbage collecting layers.
Tagging @lingdie who did some work already on this, and @fahedouch who might have an idea what's going on (IIRC he mentioned before that this was happening as well outside of kube?).
Anyone has a good lead to start investigating? Or at least a simpler way to repro without Kube?

Are there any issues when committing a pod running an image built from a Dockerfile? Committing a pod running an image from NVIDIA GPU Cloud worked fine

There are issues when committing a pod running an image built from a Dockerfile.
Could the issue be caused by the image built from the Dockerfile?
Committing a pod running an image from NVIDIA GPU Cloud worked fine

There are issues when committing a pod running an image built from a Dockerfile. Could the issue be caused by the image built from the Dockerfile? Committing a pod running an image from NVIDIA GPU Cloud worked fine

Unfortunately, we reproduced with prebuilt images - so I would say that this is unlikely - but then again, we don't know at this point...

I'm not sure, but I do know that crt can push images that have been committed using nerdctl.

When there is an error, the content digest in the error message is not in the path (/var/lib/containerd/io.containerd.content.v1.content/blobs/sha256)
Anyway, I will try ctr. Thank you.

I'm not sure, but I do know that crt can push images that have been committed using nerdctl.

The same issue arises.
"content digest sha256:...: not found"

When "nerdctl pull" tried, image was not pulled completed.
I thought it was pulled image when using nerdctl.
But, when I check "ctr image check" option, Image imcomplete
acr-0-jworker-6:/# ctr --namespace=k8s.io i check | grep ubun <IP_ADDRESS>:30500/jfb/jbi_ubuntu-20.04.6:latest                                                                              application/vnd.docker.distribution.manifest.v2+json      sha256:77649be7ae3b6950e0d6383c23f251e15cf58624d5ff64b40e35572663ce8552 incomplete (10/11) 930.8 MiB/957.0 MiB true
I used ctr image pull. image pull completed

Steps

nerdctl --namespace k8s.io pull --unpack=false {pod image}
nerdctl --namespace k8s.io commit {nerdctl pod container id}
nerdctl push --all-platforms

Error

nerdctl push error message : content digest ... not found
When there is an error, the content digest in the error message is not in the path (/var/lib/containerd/io.containerd.content.v1.content/blobs/sha256)
I check "ctr image check" option, Image imcompleted,

acr-0-jworker-6:/# ctr --namespace=k8s.io i check | grep ubun
<IP_ADDRESS>:30500/jfb/jbi_ubuntu-20.04.6:latest              application/vnd.docker.distribution.manifest.v2+json      sha256:77649be7ae3b6950e0d6383c23f251e15cf58624d5ff64b40e35572663ce8552 incomplete (10/11) 930.8 MiB/957.0 MiB true

but no error message nerdctl pull
Solution

ctr --namespace k8s.io image pull
nerdctl commit --namespace k8s.io
ctr push --all-platforms

Isn't there a problem with "nerdctl pull"?

not nerdctl pull, bug containerd pull.
see: https://github.com/containerd/containerd/issues/8973


How do I remove dot dropping?

Hi!
I am trying to create a population map, similar to https://www.mapbox.com/blog/twitter-map-every-tweet/. I have one dot per avg population in each block. However, I see that many of my dots do not show up in a zoomed out view. I went through tippecanoe documentation and tried some options for avoiding dot dropping.
I tried using -pf and -pk to disable tile size limits.
tippecanoe -f -pf -pk -o points.mbtiles -l "avg_estimated_population" points.geojson
I also tried adding -r1 --drop-fraction-as-needed to turn off normal dot dropping.
tippecanoe -f -pf -pk -r1 --drop-fraction-as-needed -o points.mbtiles -l "avg_estimated_population" points.geojson
Neither of these methods worked. Am I missing something?
Here's my mapbox gl code to render the dots :
map.on('load', function() {
  map.addSource('population-data', {
    'type': 'vector',
    'tiles': ['http://localhost:7778/population/{z}/{x}/{y}.pbf'],
    'maxzoom': 14,
    'interactive': false
  });

  map.addLayer({
      "id": "markers",
      "source": "population-data",
      "source-layer": "avg_estimated_population",
      "type": "symbol",
      "paint": {
          "icon-opacity": 0.8
      },
      'layout': {
        "icon-size": {
          "stops": [
            [5, 0.1], [6, 0.1], [7, 0.1], [8, 0.1], [9, 0.1], [10, 0.2], [11, 0.2], [12, 0.2], [13, 0.9], [14, 1]
          ]
        },
        "icon-image": "circle-11",
        "icon-allow-overlap": true,
        "icon-ignore-placement": true
      }
  });
});

I serve my tiles on localhost:7778.
Thanks!

If you want to keep all the Point features at every zoom level, -r1 -pk -pf should do that. (--drop-fraction-as-needed should have no effect if -pk and -pf are set.)
Can you be more specific about the problem you are running into?

Some dots appear only at higher zoom levels. But, I want all dots to show at all zoom levels. Here are two screen shots at zoom levels 9 and 10. Note that there are lesser number of dots in zoom level 9 compared to zoom level 10. Here, marker widths at both zoom levels are the same, but I intend to change the marker widths for different zoom levels.

I don't understand how that could be. If I do
 tippecanoe -X -f -o foo.mbtiles -b0 -r1 -pk -pf

(with -b0 to eliminate the tile buffer, to avoid double-counting) and then
$ tippecanoe-decode -z9 -Z9 foo.mbtiles | grep '"Feature"' | wc -l
  250028
$ tippecanoe-decode -z10 -Z10 foo.mbtiles | grep '"Feature"' | wc -l
  250028

I've got the same 250,028 features in each zoom level. Are you sure you don't have old tiles in the browser cache or something?

Turned out that I had an older version of tippecanoe, I updated it and ran it again and now I have approx equal number of features at different zoom levels. :) thanks for the help, I now know how to debug with tippecanoe-decode.

Glad to hear it's working for you now!

Hi @ericfischer ! I don't think this completely worked for me :( So, I am not able to see visual differences, but when i do tippecanoe-decode, I see that there are different number of features at different zooms. When i did it before, the difference was very small, but now I see large differences in my greater zoom levels.
I first use tippecanoe to make my mbtiles file from a geojson file(having mutipoint geometries) that i have. Then, I use tippecanoe-decode to decode the number of features from the mbtiles file at various zooms.
tippecanoe -X -f -o points_avg_pop_6.mbtiles -b0 -r1 -pk -pf -l "avg_estimated_population" -z22 -Z15 points_avg_pop_6.geojson

tippecanoe-decode -z16 -Z16 points_avg_pop_6.mbtiles | grep '"Feature"' | wc -l
 7578

tippecanoe-decode -z17 -Z17 points_avg_pop_6.mbtiles | grep '"Feature"' | wc -l
9582

Am I missing something here?

I'm not sure what is going on here. Can you post your GeoJSON file somewhere so I can try it myself and see if I get the same results?

This is the geojson file i used - https://gist.githubusercontent.com/divya1c/fca4e46bd8abf5a6980c81e7946d974b/raw/763be52bae53051b57e75d79416f1dd2f1a501bb/points.geojson
I use this to make my mbtiles :
tippecanoe -X -f -o points.mbtiles -b0 -r1 -pk -pf -l "avg" -z22 -Z15 points.geojson
Then , I decode it this way :
tippecanoe-decode -z16 -Z16 points.mbtiles | grep '"Feature"' | wc -l
tippecanoe-decode -z17 -Z17 points.mbtiles | grep '"Feature"' | wc -l
I observe that the projection in my geojson file is EPSG::4269, will that cause a problem? Thanks a lot for the help!

EPSG:4629 should be OK, since it looks like it is effectively an alias for WGS84, even though it  is meant to be used in French Polynesia, not San Francisco.
I am getting different numbers of features in these tiles too. I don't understand why yet, but I'll investigate and figure it out. Thanks for the bug report.

Oh, it's because your features are MultiPoints, and the higher the zoom level gets, the more of them get split into two separate Points on either side of a tile boundary.
If you pull out just the coordinate pairs from the features, there are the same number of unique points in each zoom level.
$ tippecanoe-decode -z15 -Z15 points.mbtiles | grep '"Feature"' | sed 's/.*"coordinates"://' | sed 's/, /,/g' | tr '[]' '\012\012' | grep '[0-9]' | sort | uniq -c | wc -l
   33869

$ tippecanoe-decode -z16 -Z16 points.mbtiles | grep '"Feature"' | sed 's/.*"coordinates"://' | sed 's/, /,/g' | tr '[]' '\012\012' | grep '[0-9]' | sort | uniq -c | wc -l
   33869

$ tippecanoe-decode -z17 -Z17 points.mbtiles | grep '"Feature"' | sed 's/.*"coordinates"://' | sed 's/, /,/g' | tr '[]' '\012\012' | grep '[0-9]' | sort | uniq -c | wc -l
   33869

(There must be a cleaner way to write those shell commands, but I hope it gets the point across.)

ohh! I couldn't see any difference while seeing on the map. I could only see the difference through tippecanoe-decode! makes sense now.


Overlapping issue in SOS Report page

Describe the bug
Header is being overlapped by the navbar in SOS Report page
To Reproduce
Steps to reproduce the behavior:

Go to 'SOS Report' page
See error

Expected behavior
"Report any Critical stray animal to the nearest Animal Care centres" should be visible.
Screenshots

Desktop (please complete the following information):

OS: Windows
Browser : Chrome
Version: 112.0.5615.138

Additional context
Overlapping issue exists for all screen sizes.
Please assign me this issue under Gssoc'23 @akshitagupta15june

I am interested to work on this issue under GSSoC 2023 as a contributor


example_state is not defined on Windows PC

Server Error occurs when following State turorial
I followed State tutorial and created State source in my local environment.
When I run the codes, the Sever Error happens.
To Reproduce
I create hello folder with init.py and hello.py.
In pcconfig.py, I set app_name to hello.
And I run commands, pc init and pc run.
Expected behavior
I think example_state will be made.
Screenshots

source codes

pages\index.js

import {useEffect, useRef, useState} from "react"

import {useRouter} from "next/router"

import {E, connect, updateState} from "/utils/state"

import "focus-visible/dist/focus-visible"

import {Heading} from "@chakra-ui/react"

import NextHead from "next/head"

const EVENT = "ws://localhost:8000/event"

export default function Component() {

const [default_state, setDefault_state] = useState({"events": [{"name": "default_state.hydrate"}]})

const [result, setResult] = useState({"state": null, "events": [], "processing": false})

const router = useRouter()

const socket = useRef(null)

const { isReady } = router;

const Event = events => setDefault_state({

  ...default_state,

  events: [...default_state.events, ...events],

})

useEffect(() => {

  if(!isReady) {

    return;

  }

  if (!socket.current) {

    connect(socket, default_state, setDefault_state, result, setResult, router, EVENT)

  }

  const update = async () => {

    if (result.state != null) {

      setDefault_state({

        ...result.state,

        events: [...default_state.events, ...result.events],

      })

      setResult({

        state: null,

        events: [],

        processing: false,

      })

    }

    await updateState(default_state, setDefault_state, result, setResult, router, socket.current)

  }

  update()

})

return (

<Heading onClick={() => Event([E("example_state.next_color", {})])}

sx={{"color": example_state.color, "_hover": {"cursor": "pointer"}}}>{`Welcome to Pynecone!`}

<NextHead><title>{`Pynecone App`}</title>

<meta content="A Pynecone app."

name="description"/>

<meta content="favicon.ico"

property="og:image"/></NextHead></Heading>

)

}

hello\hello.py

import pynecone as pc

class ExampleState(pc.State):

    # The colors to cycle through.
    colors = ["black", "red", "green", "blue", "purple"]

    # The index of the current color.
    index = 0

    def next_color(self):
        """Cycle to the next color."""
        self.index = (self.index + 1) % len(self.colors)

    @pc.var
    def color(self):
        return self.colors[self.index]

def index():
    return pc.heading(
        "Welcome to Pynecone!",
        on_click=ExampleState.next_color,
        color=ExampleState.color,
        _hover={"cursor": "pointer"},
    )

app = pc.App()
app.add_page(index)
app.compile()

** Specifics:**

Python Version: 3.10
Pynecone Version: 0.1.13
OS: Windows 10
Browser (Optional): Chrome

Maybe you forgot to pass the state argument to pc.App?

@FHU-yezi True. The page was showed without error.

Ok thanks @FHU-yezi closing the issue


Enqueue controller if still in upgrading state

While upgrading k3s clusters the controller may cause updates that do not change the cluster object. This means that the controller will not be run again to exit the updating state. In these cases the controller should re-enqueue itself.
Addresses https://github.com/rancher/rancher/issues/26286

LGTM with a nit: could probably explain enqueue or update reasoning in a comment

Do you think the commit message is enough?


Element not visible even after using browser.wait()

I am using ng-hide and ng-show for my login page. While testing I get element not visible, I tried browser.wait() but it says mulitple elements found.
My code is available at
https://github.com/Leo-G/Flask-Scaffold/blob/develop/app/templates/login.html
Any help will much appreciated.

Please direct general support questions like this one to an appropriate support channel, see https://github.com/angular/protractor/blob/master/CONTRIBUTING.md#questions
Thank you!


Add support for MicroProfile JWT auth

As part of MicroProfile 1.2 implementation JWT Auth support must be added to Helidon. Consider implementing the latest versio of JWT Auth.
http://microprofile.io/project/eclipse/microprofile-jwt-auth

TCK now requires Servlet, so we cannot pass it. See: https://github.com/eclipse/microprofile/issues/59 for reference.

Passed most TCK - opened issues with MP JWt-Auth for those we could not.
Merged into master

Hi @tomas-langer, were you able to run the JWT TCK in the end, even if it requires the servlet API? Or did you work around it somehow? I'd like to raise an issue for MP JWT to remove the servlet dependency from the TCK and use JAX-RS instead. Other specs have done a similar thing already.

I've raised an issue to make the MP JWT Servlet test optional if Servlets are not supported by the implementation: https://github.com/eclipse/microprofile-jwt-auth/issues/133


What does yellow mean?

I see some things match and then show as yellow and don't consume the stream. You don't indicate what yellow is supposed to mean in the readme. Can you please update the readme to explain it?

@fasterthanlime : Is that the correct way to understand it?

It's been so long since I touched this codebase, your guess is as good as mine!

ok, then my (educated) guess is encoded in #6 ;)

Thanks for merging. I think we can now close this, @fasterthanlime 😊


Loading firmware manifest clobbers FHT

The ImageManifest structure is 4464 bytes, but we only have 4096 bytes allocated for it in DCCM (MAN2 region). The trailing bytes overlap with the FHT.
On update reset this clobbers the old FHT. ROM does not know all of the correct values for the FHT on update reset and must use some values leftover from the last cold reset.
We should give the manifest some more space to avoid this conflict.

Never mind, I see this is fixed in https://github.com/chipsalliance/caliptra-sw/pull/440


Update SCNMaterialProperty.xml (Grammatical/spelling mistake in property description)

Grammatical/spelling error in the description of the ContentsTransform property

Docs Build status updates of commit 2865d95:
:clock10: Full build: the average full build time is 70 min(s) 14 sec(s), this is based on the last 15 build(s) for this repository.
:warning: This is a FULL build due to a recent Docs Build service upgrade, normally no action is required on your part. In the result you find subsequent builds still performing full builds, please contact your repository admin to manually perform a full build on the 'master' branch. For more detailed info, please click here.

Docs Build status updates of commit 2865d95:
:x: Validation status: errors
Please follow instructions here which may help to resolve issue.

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Preview URL
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en/api/Accelerate.Pixel8888.A.yml
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en/api/AppKit.LocalEventHandler.yml
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en/api/AppKit.NSGestureEvent.yml
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en/api/AddressBook.ABAuthorizationStatus.yml
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en/api/Accounts.ACAccountStore.Notifications.yml
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en/api/AddressBook.ABRecordType.yml
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en/api/AppKit.NSSavePanelComplete.yml
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en/api/AppKit.NSAlertStyle.yml
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en/api/AppKit.NSSharingServicePickerSharingServicesForItems.yml
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en/api/Accelerate.vImage.ConvolveMultiKernelARGB8888.yml
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en/api/AppKit.NSSharingServiceSourceFrameOnScreenForShareItem.yml
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en/api/AppKit.NSSharingServiceSourceWindowForShareItems.yml
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en/api/Accelerate.vImage.MatrixMultiplyARGB8888.yml
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en/api/Accounts.AccountStoreOptions.yml
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en/api/AppKit.NSSharingServiceTransitionImageForShareItem.yml
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en/api/AddressBook.ABPersonAddressKey.yml
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en/api/Accelerate.vImage.TentConvolvePlanar8.yml
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en/api/AppKit.NSAlertType.yml
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en/api/AppKit.NSTouchBarMakeItem.yml
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en/api/AddressBook.ABPersonPhoneLabel.yml
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en/api/Accelerate.vImageAffineTransformDouble.ty.yml
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en/api/AppKit.NSTouchEvent.yml
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en/api/AppKit.NSApplicationDelegateReply.yml
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en/api/Accelerate.vImageBuffer.BytesPerRow.yml
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en/api/AddressBook.ABRecord.yml
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en/api/AudioToolbox.MusicSequenceUserCallback.yml
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en/api/AppKit.NSBorderType.yml
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en/api/AddressBookUI.ABNewPersonCompleteEventArgs.yml
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en/api/AudioUnit.AURenderPullInputBlock.yml
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en/api/AppKit.NSCollectionViewScrollDirection.yml
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en/api/Accounts.ACAccount.-ctor.yml
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en/api/AddressBookUI.ABPeoplePickerNavigationControllerDelegate_Extensions.yml
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en/api/AudioUnit.AUScheduledAudioFileRegionCompletionHandler.yml
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en/api/Accounts.ACAccountStore.Dispose.yml
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en/api/AudioUnit.AUScheduleParameterBlock.yml
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en/api/AppKit.NSCollectionViewScrollPosition.yml
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en/api/Accounts.ACAccountStore.FindAccount.yml
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en/api/AudioUnit.InputDelegate.yml
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en/api/AppKit.NSColorSpaceModel.yml
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en/api/Accounts.ACAccountType.Description.yml
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en/api/AudioUnit.RenderDelegate.yml
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en/api/AppKit.NSControlCharacterAction.yml
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en/api/AVFoundation.AVAudioNodeTapBlock.yml
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This comment lists only the first 50 files in the pull request.

en/api/Accelerate.Pixel8888.A.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.LocalEventHandler.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.Pixel8888.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.PixelARGB16S.Zero.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSGestureEvent.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBook.ABAuthorizationStatus.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccountStore.Notifications.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.PixelFFFF.G.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSGestureProbe.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBook.ABRecordType.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSPasteboardPredicate.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSAlertButtonReturn.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSSavePanelComplete.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSAlertStyle.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSSharingServicePickerSharingServicesForItems.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.vImage.ConvolveMultiKernelARGB8888.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSSharingServiceSourceFrameOnScreenForShareItem.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSSharingServiceSourceWindowForShareItems.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.vImage.MatrixMultiplyARGB8888.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.AccountStoreOptions.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSSharingServiceTransitionImageForShareItem.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBook.ABPersonAddressKey.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.vImage.TentConvolvePlanar8.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSAlertType.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSTouchBarMakeItem.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBook.ABPersonPhoneLabel.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.vImageAffineTransformDouble.ty.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSTouchEvent.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSApplicationDelegateReply.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accelerate.vImageBuffer.BytesPerRow.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBook.ABRecord.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'derivedClassesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'derivedClassesWithMoniker'.
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioToolbox.MusicSequenceUserCallback.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSBorderType.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBookUI.ABNewPersonCompleteEventArgs.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioUnit.AURenderPullInputBlock.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSCollectionViewScrollDirection.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccount.-ctor.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AddressBookUI.ABPeoplePickerNavigationControllerDelegate_Extensions.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetType' (https://static.docs.com/ui/latest/schemas/NetType.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioUnit.AUScheduledAudioFileRegionCompletionHandler.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccountStore.Dispose.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioUnit.AUScheduleParameterBlock.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSCollectionViewScrollPosition.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccountStore.FindAccount.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioUnit.InputDelegate.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSColorSpaceModel.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccountType.Description.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AudioUnit.RenderDelegate.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/Accounts.ACAccountType.EncodeTo.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetMember' (https://static.docs.com/ui/latest/schemas/NetMember.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AppKit.NSControlCharacterAction.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetEnum' (https://static.docs.com/ui/latest/schemas/NetEnum.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

en/api/AVFoundation.AVAudioNodeTapBlock.yml

[Error]

Schema validation failed. Please validate the file and make sure it conforms to schema 'NetDelegate' (https://static.docs.com/ui/latest/schemas/NetDelegate.schema.json):
Property 'assembliesWithMoniker' has not been defined and the schema does not allow additional properties. Path 'assembliesWithMoniker'.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


NICS option in compose.yaml

I can't work out the format for this option as I can't find it documented anywhere. Currently on my rpi it tries to use an interface called "tap0" but it should be using "eth0" as "tap0" doesn't even exist under ip a or ifconfig so not sure where it's getting that from.
This is what I have in my compose.yaml file.

What's your output of cat /proc/net/dev?

What do the logs say?
Please try the binary if you can to see if that picks up eth0.

Logs don't say anything relating to it when running with docker compose up.
Ended up using it outside of docker by making a systemd service and it picked it up without change. Seems to be an issue with docker?

The agent just reads /proc/net/dev to find interfaces.
Docker blocks access to the host's /proc/net by default, even if it's mounted in the container. That's why the agent needs to use network_mode: host.
So my best guess is that network_mode: host is not working properly on your device, or there's some other configuration with docker / networking that's preventing the default behavior.
There's no shell in the beszel-agent container, but you can try to debug by running some other container -- like base alpine -- exec into it and run cat /proc/net/dev with host network mode and without it. See if it has the host's interfaces in host mode.


v2: add CI and linting

Copies the siafoundation/core golangci and test files
Fixes lint errors

Race should be fixed by https://github.com/SiaFoundation/siad/commit/5232b092581b3890bc899186bf80a1cb44513db5; try rebasing

Race should be fixed by 5232b09; try rebasing

almost

gah, ok, I tested https://github.com/SiaFoundation/siad/commit/d09032df7efb173c598ac5fdf9218f628beeacd7 locally and it didn't trigger the race detector, so hopefully that works.

I added some locking and extended the timeout on the failing test. Try again? 🙃
More broadly, it looks like I should really revisit the ChainManager API. It was a decent design for the single-threaded message-queue p2p code of sunyata, but I don't think it's a good fit for a highly-concurrent RPC-based architecture.

Using go test -failfast -count=10 ./...,  TestNetwork fails almost every time on localhost. Getting all 6 jobs to pass on the same run seems unlikely.

dang. Well, I certainly don't want to go back to the dark ages of rerunning tests over and over because they fail nondeterministically. I guess we'll have to actually get to the bottom of why TestNetwork is failing.

If we're going to be redoing parts of the p2p, do you think we should go ahead and merge this? It would help keep new inconsistent tests out while we sort out the p2p code.

I'm gonna try to fix the NDF first; no sense merging this if it's gonna fail on every PR

phew, ok, I think I really tracked it down this time. Since we're mining at low difficulty, there was a chance that two different chains could have exactly the same amount of total work. Nodes will only reorg to a competing chain if it has strictly more work than their own, so the two test nodes would both reject each other's chains and remain desynced.
In real life, this isn't a problem because A) the work values are much larger, and so the probability of an exact match is much lower, and B) if there is an exact match, it will resolve itself once another block is mined. AFAIK most blockchain implementations, including Bitcoin, don't even bother handling the case where work is exactly equal.
I also fixed a race condition in the txpool. I've run TestNetwork 100 times locally without any failures so... I would say we're good now, but I'm afraid to anger the gods any further.


sort and sortBy but only group

Should there be a groupBy to go with group like sortBy for sort?
group is not terribly useful, groupBy is usually what you want.

Fixed in 1b1f82b749b100f48c8f9f91e90177d14791e6c0


Added: CountOfFlashcards property in Deck model and DeckListView (bel…

…ow the deck name)
w/o backend connection atm.

added:  STUDYBOX-427 (BUG) fix

added: STUDYBOX-428 (BUG) fix


what are you doing?

why did you take down my issue? also, none of this is yours.. :joy:

Also just for your knowledge i have a thing with all these hacks, and you forgot to credit someone :joy: Good luck trying to survive what's going to happen next

I'm gliz and it literally says on my hacks, I make all of these and provide them to schoolcheats so idk what your up on.

no your not.


Flow and actionBar

In a single Activity + flow  setting  (with optionnaly mortar + dagger).
What is the prefered way/best practice to handle the actionBar/Menu ?
delegate the onCreateOptionsMenu and onPrepareOptionsMenu methods of the activity
and handling them depending on the current view ?
If a flow app naviguates between CustomViews, which are UI states, aren't menu configuration supposed to be part of that state ?

My 2 cents : Do yourself a huge favor and use the standalone toolbar. Then things are properly seperated, and you don't need to keep something in sync when switching between views.

Thanks for the advice. I'll look into that.

We've also largely solved this by using separate instances, and not sharing actionbars at all.


Pineline of the framework.

I'm wondering if PaintsUndo has corresponding paper or technical illustration of how it works and details of  training / inference?

Haha, to be honest, if you can be patient enough, you can analyze all the *.py file in the paints-undo\diffusers_vdm\ folder. So you can get a conclusion. XD


Make Sourceror.get_range/1 return the range to the end of the last token

Currently, with the only exception of end tokens, Sourceror.get_range/1 returns a range from the start of the first token in the node(the :line and :column metadata fields) to the start of the last token(end, closing or end_of_expression positions).
This is too limited and renders the function practically useless.
One option would be to use Sourceror.parse_expression/2 starting at the position of the node, and get the document offsets where the parsing started and succeeded to build the range. This approach may also allow us to make the offset index configurable(utf-8 vs utf-16), which would make it more useful for tools like Elixir LS. The disadvantage is that the original document would be needed, which makes this function useless for any use case that requires non-utf8 encoded offsets. And this is a very deep rabbit hole, I can already feel it.
The other option I can think of is to infer the end position by converting terminals to strings and measuring their length. It should take into account delimiters, the original token representation, and so on. Loads of tests would need to be added to ensure what we return is the actual position and not just a "best effort" janky output.

I decided to go with the second option in #3
I will probably need to ask the elixir core maintainers if it would be possible to add more detadata to certain nodes.
This is valid elixir:
foo.
bar

But we have no way to know if the bar is in the first or second line because such metadata is discarded by the parser. The same happens for the segments of a module alias. I'm going to ask when I find enough cases, in the meantime I'm just going to assume they're in the same line.

#3 was merged, so this is mostly complete now. The only thing left to figure out is the issue with qualified identifiers and module aliases, that don't have any metadata about their right sides and it is impossible to figure out at which line is which segment.
Experimenting a bit I found that that metaata is being discarded in the build_dot and build_dot_alias of the Elixir parser. Adding it should't be hard, but it's a change that needs to be made in Elixir itself, so I will ask in the elixir mailing list if such change would be accepted.
We can always change it in our vedored version and only use our custom parser, but I'd prefer for Sourceror to build on top of Elixir and keep everything as vanilla as possible.


Added animation to target with # in link

I added an animation to targets referenced with a # in a link, to make the seat/position easily findable. Currently, adding #f1r6s11 to a link would scroll to that position on the cluster map, but it would still be unclear which seat it is. By merging this pull request, the position will then blink 12 times, to make that position as clear as possible.
Empty seats would also blink red to make them even more easily findable. Taken seats would just grow big and small, no color change there.
The reason for opening this pull request, is that for my Dark Mode for Intranet extension, I now link to this cluster map when a person is available and logged in to a computer at Codam, passing the logged in position as a hash like described above.
By the way: I don't know elm, so I hope I added this animation in the right file and that this CSS file was not generated by elm itself. If not, please point me in the right direction and I'll add the change myself! :)

Also uploaded the new css to the server, so if you disable cache this should work now.

Cool, thanks! I found that it only works when you send the hash in the URL with a little delay, since the cluster map needs to have been loaded in order for the browser to find the location. Should be fixable on my end. Apart from that, I found it works really well. Thanks again!


Add option of existingType to .enum method to support repeated use of enums

I was super happy to see knex supporting native enums in postgres, but when I had an enum that I used repeatedly across different columns in different tables, I was running into errors as the enum method created the type enum for you each time. The type would already exist so it would error. I only wanted to create the type once, afterwards I just wanted to use that enum.
Could we add a flag in the options param to not run the sql that creates the type enum?
The first time you run it you could do
table.enu('current_mood', ['sad', 'ok', 'happy'], { useNative: true, enumName: 'mood' })

And then the other times you can run
table.enu('mood', ['sad', 'ok', 'happy'], { useNative: true, enumName: 'mood', existingType: true })

I second this proposition and motion for a move to merge :P

Sounds reasonable, I cannot think any better API for handling this situation.

@ekeric13 Would you consider finishing this PR?

Yeah for sure. @heisian 	and I have a working implementation, just need to write tests for it.

Awesome! Also PR for documentation in https://github.com/knex/documentation would be most appreciated :)

@ekeric13 Thank you very much! It's 0:30 here already, it was a long day; I'll take a look at it tomorrow and comment ASAP, sorry for the delay.

So what is the process after this? Just will be merged in for the next release?

@ekeric13 Probably can be merged within 0.16.0 window, but before that I assume we should consider the alternative improved syntax that you've suggested?..

@ekeric13 Looked at previous and current syntax and I think that it makes perfect sense, would you do the change?

Okay, added the proposed ternary operator to make the function type agnostic of the values in the case where the values aren't being used.
Updated the docs as well for further clarification:
https://github.com/knex/documentation/pull/147/commits/e3d6f48bb6f81c4bcb5aa56fd7d813d72dfb1af1

@ekeric13 Awesome work, thank you!

🎊


Google Keep, Save to Google Drive...

These extensions don't work, they just present a pop-up that says you have to sign in but the link or ability to sign is not working.

@ordaj Unfortunately, we can't fix this issue, because it's looks like the fix should be done in extension itself. Please, report an error on Google Keep page.


getNearObject - found object is not residing in objectPoint

I had the same issue as @AndrewEastwood in #105 where Uncaught TypeError: Cannot read property 'x' of undefined. Sometimes, obj resolves to an object which does not reside in objectPoint, which results in calculating the square distance of an undefined and a defined point (point).

This isn't a fix for the issue, this is a hack around. Not merging.


enable 3d not working in debian 8.x

could anyone else try this plugin on Debian 8.x in a VMWare VM?
Enable 3D doesn't seem to work for me.

i have Debian 8 on my PC, but i'd remember that the 3D is enabled by default in the freshplayerplugin, since 12add65.

I get a black screen with audio when the 3D is turned on - which is default.
I've had to turn 3D off to get any picture.

thats rare.
which graphic adapter do you have?

r9 290x
VMware has it's own Virtualized Graphics
oddly though, it works just fine in a Kubuntu VM.

that's rare.
i'll try to VM a jessie latter, and i tell u how it works for me.

Reproduced with Debian 8 in VmWare Player. Black screen indeed. I'll take a look.

any progress made regarding this?

Nope, not yet. I've been busy recently.

I have a physical copy of debian 8 on my laptop. I can check it out if someone can send me a flash you're having trouble with.

try any flash video with 3d turned on, you should get a black box where the video is.
This is observed in a VMWare VM though, it might be different on real hardware.

OK, I tried again, with a test app. And it looks like offscreen rendering doesn't work for some reason. I actually already had a test app, in tests/util_glx_pixmap which can be built by calling make util_glx_pixmap.
That app does essentially the same freshwrapper does for rendering. Creates an (offscreen) X Pixmap, GLX Pixmap, acquires GL context, calls glClear() twice, first time with red color, then with green color. Then copies that pixmap content to a visible window.
In ideal case you should see a window with green a box. If you have glClear() bug, box will be red. And if you don't have offscreen rendering working, box will be either black or there will be no box at all, only window background, which is a tint of blue.
With such simple app failing to render correctly, it's hardly possible to make anything about that in freshwrapper.

So there is no possible fix for this?

Maybe downgrading Mesa version will help. I've tried to install Mesa packages from Debian testing, but that had no effect.

I'm not entirely sure how to downgrade mesa.

One way is to install corresponding packages from Debian Wheezy, which is previous stable. Another option is to checkout Mesa git repository and try to build different versions.

I tried out debian 7 wheezy, I can get a picture, but hardware accelerated decoding won't work, even when enabled in the config.
maybe i should switch back to ubuntu.

but hardware accelerated decoding won't work

Try newest version; configure with
cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DWITH_GLES2=0 ..

that made no difference for debian 8, i will try 7

I tried on debian 7, it seems to work, it said accelerated video decoding in stats for nerds, but the FPS was worse than software rendering, could not get above 10 FPS.
maybe I configured mine wrong?

maybe I configured mine wrong?

Maybe something wrong with driver. Or maybe something wrong with my code, but that only reveals on your hardware.
By the way, are you still using VMware virtual machine? If so, it's hardly possible VA-API to be available under VMware.

yes, I'm still using a VMWare VM.

I think there is no easy solution. I'll need to investigate it further.

It's probably easier if I just leave debian and switch to another distro

This is another issue which I can't fix. I still have similar issue on my own machine, plugin content is all black with using discrete adapter via DRI_PRIME=1. Perhaps reasons are also similar. I've tracked mine to DRI2 subsystem in X.Org server. It could be X server itself, or radeon driver, or maybe even something somewhere else.


Trade modal : display trading fees as cryptoAmount if available

Overview
Currently we are displaying trading fees only by their fiat values (eg $3.30). See screenshot
For CowSwap for example, the trading fees are taken by the platform as a portion of the sold token during a swap.
We want to be able to display this trading fee as a cryptoAmount (eg 50 FOX ≈ $3.30)
Changes are required in lib and web to do this.
References and additional details
Current implementation

Acceptance Criteria
Trading fee should be displayed as cryptoAmount ≈ fiat value when possible
No regression for swappers not concerned by this change
Need By Date
No response
Screenshots/Mockups
No response
Estimated effort
2 hours

Gm @chxash ser!
After much discussion on handling Cowswap fees, we're going to go with the implementation outlined in #2265. It doesn't show crypto values but provides the user with a much more detailed breakdown of fees. Let me know if this makes sense and we can close this ticket!


Python script for maintenance (sorting by deadline, cleaning passed conferences.)

Process.py will sort all conferences by deadline. It can also clean up
passed conferences. It will prompt the user if modified data should
overwrite existing data. Default is no, then transfer has to be made
manually.
Just a little tool to make the list easier to maintain.

This is very useful @JesperDramsch, thank you very much!


Poor results for 360 captured scene

First of all, thanks for the great implementation!
I've managed to get good results with the code in this repository for frontal scenes but am struggling to have it properly work with 360 captures. Attached is an example capture of a fountain taken from a variety of different angles (and where the camera poses are "ground truth" poses gathered from the simulation): https://drive.google.com/file/d/1FbtrupOXURc0eTDtDOmD1oKZz5e2MIAE/view?usp=sharing
Below are the results after 6 epochs of training (I've trained for longer but it never converges to anything useful).

In contrast, other nerf implementations such as https://github.com/google-research/google-research/tree/master/jaxnerf seem to provide more sensible results even after a few thousand iterations:

I'm using the spherify flag and have tried both with and without the use_disp option. I've also tried setting N_importance and N_samples to match the config of the other NeRF implementation that I tried (https://github.com/google-research/google-research/blob/master/jaxnerf/configs/llff_360.yaml). Would you have any pointers as to where the difference could be coming from?

There are two reasons:

Originally I assumed the 360 images are taken in just one round, but here your data is taken for 3 rounds:

Hence, the closest depth is computed from the upper cameras (because they are closer to the scene). In this case, the predefined object range
https://github.com/kwea123/nerf_pl/blob/748d8174e7e83f916e62b439b6f5026846bc277d/datasets/llff.py#L244-L245
DOES NOT include the object at all for the images taken at the lower two rounds (because they are farther from the object, and the far value is set too small)!
Therefore, the model learns a weird 3d structure.

The second reason is somewhat related to the first. Because I normalize the poses w.r.t. the closest depth,
https://github.com/kwea123/nerf_pl/blob/748d8174e7e83f916e62b439b6f5026846bc277d/datasets/llff.py#L208-L211
the poses at the lower rounds are not included in the range [-1, 1] (e.g. some has position of value ~50). These large values make the positional embedding fail, because the embedding requires the values to be in range [-1, 1] (or probably larger, [-2, 2] or something, but definitely not 50)

To solve the issue due to the above two reasons, you can:

enlarge the far range (in both train and val, test, so 2 places to modify in llff.py), e.g.

far = self.bounds.max()

scale down the poses more, e.g.

scale_factor = near_original*100

After making the above changes, and use the following command to train
python train.py --dataset_name llff \
--root_dir /home/ubuntu/data/nerf_example_data/my/fountain/ \
--N_importance 64 --img_wh 200 200 \
--num_epochs 20 --batch_size 1024 \
--optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name fountain --spheric_poses

I get this after one epoch:

You could do better by training more epochs or increasing number of samples on the ray or increasing the resolution.
FYI, if you don't use colab, the latest code can be found on the dev branch (you still need to apply the above changes yourself).
Finally, the other implementation doesn't fail because

it sets the near and far in the config instead of compute from the script, so it is manually set larger
it additionally scales the poses according to the largest circle radius here which I didn't think of.

Let me know if there's still issue (my result is more noisy than I think, I'd like to know if it gets resolved if you train longer at higher resolution)

There are two reasons:

Originally I assumed the 360 images are taken in just one round, but here your data is taken for 3 rounds:

Hence, the closest depth is computed from the upper cameras (because they are closer to the scene). In this case, the predefined object range
https://github.com/kwea123/nerf_pl/blob/748d8174e7e83f916e62b439b6f5026846bc277d/datasets/llff.py#L244-L245
DOES NOT include the object at all for the images taken at the lower two rounds (because they are farther from the object, and the far value is set too small)!
Therefore, the model learns a weird 3d structure.

The second reason is somewhat related to the first. Because I normalize the poses w.r.t. the closest depth,
https://github.com/kwea123/nerf_pl/blob/748d8174e7e83f916e62b439b6f5026846bc277d/datasets/llff.py#L208-L211
the poses at the lower rounds are not included in the range [-1, 1] (e.g. some has position of value ~50). These large values make the positional embedding fail, because the embedding requires the values to be in range [-1, 1] (or probably larger, [-2, 2] or something, but definitely not 50)

To solve the issue due to the above two reasons, you can:

enlarge the far range (in both train and val, test, so 2 places to modify in llff.py), e.g.

far = self.bounds.max()

scale down the poses more, e.g.

scale_factor = near_original*100

After making the above changes, and use the following command to train
python train.py --dataset_name llff \
--root_dir /home/ubuntu/data/nerf_example_data/my/fountain/ \
--N_importance 64 --img_wh 200 200 \
--num_epochs 20 --batch_size 1024 \
--optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name fountain --spheric_poses

I get this after one epoch:

You could do better by training more epochs or increasing number of samples on the ray or increasing the resolution.
FYI, if you don't use colab, the latest code can be found on the dev branch (you still need to apply the above changes yourself).
Finally, the other implementation doesn't fail because

it sets the near and far in the config instead of compute from the script, so it is manually set larger
it additionally scales the poses according to the largest circle radius here which I didn't think of.

Let me know if there's still issue (my result is more noisy than I think, I'd like to know if it gets resolved if you train longer at higher resolution)

Thanks for the quick reply! I removed the first part (ie: removed the min on "min(8 * near, self.bounds.max())"). I also tried setting the scale factor as follows:
    if not self.spheric_poses:
        scale_factor = near_original*0.75 # 0.75 is the default parameter
                                      # the nearest depth is at 1/0.75=1.33
    else:
        scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center))))

Which gives a scale factor of 87.20806618688762 vs something around 70 for the jaxnerf repo. Is this derivation correct / something that you'd like me to submit a PR for, or should it be further refined to reflect the values of the other repository?
I also noted that in the suggested command above you are not using the use_disp flag - is that intentional or an oversight/flag that I should indeed be using?
In terms of further improving the quality of the reconstruction, I've tried using --N_importance and --N_samples flags to values that reflect that of https://github.com/google-research/google-research/blob/master/jaxnerf/configs/llff_360.yaml, but the GPU memory demands seem to be very high, even when using small batch sizes (820) and chunk sizes (1024), and generally slower than the other repo's implementation. With 4 2080Ti GPUs running an epoch seems to take 10 hours and I actually ended up getting a segmentation fault near the end :(
Epoch 0:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████▎      | 13578/14441 [9:48:04<37:22,  2.60s/it, loss=0.024, val/psnr=8.02, train/psnr=20.9]Segmentation fault
And when trying to use 10 2080Ti gpus instead of 4, the initial validation sanity check takes an extremely long time (about 1 hour). Do you have suggestions on what might be happening and how to make things more performant?

Thanks for the quick reply! I removed the first part (ie: removed the min on "min(8 * near, self.bounds.max())"). I also tried setting the scale factor as follows:
    if not self.spheric_poses:
        scale_factor = near_original*0.75 # 0.75 is the default parameter
                                      # the nearest depth is at 1/0.75=1.33
    else:
        scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center))))

Which gives a scale factor of 87.20806618688762 vs something around 70 for the jaxnerf repo. Is this derivation correct / something that you'd like me to submit a PR for, or should it be further refined to reflect the values of the other repository?
I also noted that in the suggested command above you are not using the use_disp flag - is that intentional or an oversight/flag that I should indeed be using?
In terms of further improving the quality of the reconstruction, I've tried using --N_importance and --N_samples flags to values that reflect that of https://github.com/google-research/google-research/blob/master/jaxnerf/configs/llff_360.yaml, but the GPU memory demands seem to be very high, even when using small batch sizes (820) and chunk sizes (1024), and generally slower than the other repo's implementation. With 4 2080Ti GPUs running an epoch seems to take 10 hours and I actually ended up getting a segmentation fault near the end :(
Epoch 0:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████▎      | 13578/14441 [9:48:04<37:22,  2.60s/it, loss=0.024, val/psnr=8.02, train/psnr=20.9]Segmentation fault
And when trying to use 10 2080Ti gpus instead of 4, the initial validation sanity check takes an extremely long time (about 1 hour). Do you have suggestions on what might be happening and how to make things more performant?

I answer the last question first, the validation should run the same time no matter how many gpus are used, maybe there's synchronization problem between the gpus (some run slower than others)?

I answer the last question first, the validation should run the same time no matter how many gpus are used, maybe there's synchronization problem between the gpus (some run slower than others)?

Re: the slowness with 10 gpus, could this just be due to how long it takes to copy the validation data to the gpus (which is being done sequentially).
The more vexing issue to me is the blurriness in the results relative to other implementations like the jaxnerf one. For an easier reproduction, consider the training set at https://drive.google.com/file/d/1QqRfYaKNrH98VGl8GAd9SYds88iNEyM0/view?usp=sharing. Here's the captured poses should be just one orbit unlike the fountain set in the original post.
Training with a scale factor of 4 (320x180 instead of 1280x720) and the same sample settings (256 coarse and 512 fine samples) trains pretty quickly in both implementations however the quality varies significantly. In this repo I get a low-quality background after 30 epochs even with the default settings, removing the min(8 * near) section you mentioned earlier, manually setting near and far to 0.2 and 100 as in the jaxnerf implementation, training for more epochs than 30, etc:

Whereas the jaxnerf implementation very quickly converges to a nearly perfect result:

My full training command is:
python train.py --dataset_name llff --root_dir /data/airsim_vehicles/hatchback_90_90/train --img_wh 320 180 --num_epochs 100 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler steplr --decay_step 10 20 --decay_gamma 0.5 --exp_name meta_hatchback_90_90 --num_gpus 4 --N_importance 512 --N_samples 256 --spheric --use_disp --chunk 8192

Re: the slowness with 10 gpus, could this just be due to how long it takes to copy the validation data to the gpus (which is being done sequentially).
The more vexing issue to me is the blurriness in the results relative to other implementations like the jaxnerf one. For an easier reproduction, consider the training set at https://drive.google.com/file/d/1QqRfYaKNrH98VGl8GAd9SYds88iNEyM0/view?usp=sharing. Here's the captured poses should be just one orbit unlike the fountain set in the original post.
Training with a scale factor of 4 (320x180 instead of 1280x720) and the same sample settings (256 coarse and 512 fine samples) trains pretty quickly in both implementations however the quality varies significantly. In this repo I get a low-quality background after 30 epochs even with the default settings, removing the min(8 * near) section you mentioned earlier, manually setting near and far to 0.2 and 100 as in the jaxnerf implementation, training for more epochs than 30, etc:

Whereas the jaxnerf implementation very quickly converges to a nearly perfect result:

My full training command is:
python train.py --dataset_name llff --root_dir /data/airsim_vehicles/hatchback_90_90/train --img_wh 320 180 --num_epochs 100 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler steplr --decay_step 10 20 --decay_gamma 0.5 --exp_name meta_hatchback_90_90 --num_gpus 4 --N_importance 512 --N_samples 256 --spheric --use_disp --chunk 8192

Yes, I just found that using multigpu will slow down when reading the data from the disk because of concurrent reading. If your data is big, it could be better to do one data caching (saving the rays and rgbs into a .npy file) and read them on every gpu just once.
Honestly when I tried training fountain using jaxnerf, I didn't get better result than mine visually: this is what I get after 105k steps, which took 3hr on my 1 GPU machine

Anyway, I will try your hatchback-train next, since it seems more like my assumption on the data.

Yes, I just found that using multigpu will slow down when reading the data from the disk because of concurrent reading. If your data is big, it could be better to do one data caching (saving the rays and rgbs into a .npy file) and read them on every gpu just once.
Honestly when I tried training fountain using jaxnerf, I didn't get better result than mine visually: this is what I get after 105k steps, which took 3hr on my 1 GPU machine

Anyway, I will try your hatchback-train next, since it seems more like my assumption on the data.

For the hatchback-train data, it seems the data in poses_bounds.npy is wrong. Namely the image size was saved in W, H, focal order (line 189 in llff.py) while normally it should be in H, W, focal. The original LLFF code didn't change, so I don't know why you get a different order. Btw jaxnerf reads the image size from image files, not by poses_bounds.npy so it doesn't have this problem.
After inversing the W, H in poses_bounds.npy (inverse columns 4 and 9) and use the code in dev branch without any change, I can successfully train and the result looks decent (just 50k steps ie 10 epochs, the PSNR reached 29.36):

The number of samples is small for a quick demo:
python train.py --dataset_name llff --root_dir /home/ubuntu/data/nerf_example_data/my/train/ \
--N_importance 64 --img_wh 320 180 \
--num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name train --spheric_poses

Btw, from my experiments I didn't find --use_disp to be useful if the scene is small (near and far bounds are close). I think it's only useful if the scene contains a big portion of far background.
 I will try your
scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center))))

next.

For the hatchback-train data, it seems the data in poses_bounds.npy is wrong. Namely the image size was saved in W, H, focal order (line 189 in llff.py) while normally it should be in H, W, focal. The original LLFF code didn't change, so I don't know why you get a different order. Btw jaxnerf reads the image size from image files, not by poses_bounds.npy so it doesn't have this problem.
After inversing the W, H in poses_bounds.npy (inverse columns 4 and 9) and use the code in dev branch without any change, I can successfully train and the result looks decent (just 50k steps ie 10 epochs, the PSNR reached 29.36):

The number of samples is small for a quick demo:
python train.py --dataset_name llff --root_dir /home/ubuntu/data/nerf_example_data/my/train/ \
--N_importance 64 --img_wh 320 180 \
--num_epochs 20 --batch_size 1024 --optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name train --spheric_poses

Btw, from my experiments I didn't find --use_disp to be useful if the scene is small (near and far bounds are close). I think it's only useful if the scene contains a big portion of far background.
 I will try your
scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center))))

next.

The scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center)))) here yields roughly the same result as I commented above...
To sum up, for fountain scene you'd need to scale the poses fairly largely, and set the far plane farther since it has a large portion of background. The scale_factor could be scale_factor = near_original*100 or scale_factor = np.sqrt(np.mean(np.sum(np.square(distances_from_center)))). And far = self.bounds.max().
I will not apply these changes to the branches because it will cause the pretrained models to break. Finally, there is nerf++ that better handles this kind of far backgrounds than NeRF that you might want to try.

@hturki @kwea123 I used the same code as you for my own 360 scenes for training. The PSNR stuck around 24 dB. The result is blur like this,

You can see that this is no background that can affect the visual reconstruction. Why cannot I train NeRF to have sharp visual results?


freebayes stucks at chrY

Hello,
I am trying to use freebayes for a 40MB bam file but it gets stuck. I have tried with --use-best-n-alleles 6 and with max-coverage but there is no way I get results.
I attach the bam file in the link below.
https://we.tl/wiANZHDkNR
Best

Hello,
I've checked that with freebayes 1.2 the error happens but with freebayes v0.9.18 it does not happen.
Best

Hello,
any news about this issue? did anyone try this?
Thanks


Update sbt-http4s-org to 0.13.0

Updates org.http4s:sbt-http4s-org from 0.2.1 to 0.13.0.
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Superseded by #173.


Suddenly not working?

Not sure why but this was working magically then all of the sudden stopped. Here is the site that this is implemented on:
http://attunecommunications.com/
The latest tweet should be in the header, in the little birdies talking bubble.

That site works for me. My guess is that you've exceeded the twitter API rate limiting, which is something like 150 req/hr per source IP. This is prone to happening when testing intensively on a local machine, but unlikely to affect end users of any site into which you might integrate tweet.js.

You see the latest tweet? Weird, I still cannot see it. Is the twitter API limit is by server IP or client IP? I assume it is by client IP address because this is all done client-side. In that case do you think I'm not seeing the tweet due to the fact that I'm on a corporate connection (400+ internet users) and maybe there are other users making calls to the twitter API using the same source IP address? If that's the case, it may be helpful to have a feature that caches the feed...or at a minimum, a message that states that you hit the limit.

Limiting is by client IP, so it's entirely possible that the size of your organization. There's no way to cache the feed - that's been discussed before in older issues. Also, detecting when the rate limiting has kicked in is not easy. The only workaround is to instead install server-side scripts that fetch the tweets periodically, store them on your server, and then render them into html for every visitor -- much less convenient!
-Steve

Thanks, that's what I figured.


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