"Token used before issued" when checking 'iat'

I have out of sync problem.
Time on php server is different for a fraction of a second and because of this there is a problem when checking the token Token used before issued.
Is it possible to make the check less strict?
func verifyIat(iat int64, now int64, required bool) bool {
	if iat == 0 {
		return !required
	}
	return now >= iat
}

I used this workaround:
jwt.TimeFunc = func() time.Time {
		return time.Now().Add(time.Second)
	}

What would you advise me?

Something to add to this conversation perhaps, I noticed that changing the local machines time to Apple America/U.S solves my problem...

I originally was a few milliseconds off (the now and iat stamps), now they are consistently the same.

Issued at checking will be removed in v4, per the jwt spec.

@dgrijalva is there a workaround available now? I'm also seeing this issue on a small amount of requests.

Simplest workaround is probably to wrap your claims type in something custom that bypasses the IAT check. Here's a lazy example I didn't actually test. Something like the following should work:
type MyClaims struct {
*jwt.StandardClaims
}

func (c *MyClaims) Valid()bool {
    var leeway = 10
    c.StandardClaims.IssuedAt -= leeway
   valid := c.StandardClaims.Valid()
   c.StandardClaims.IssuedAt += leeway
  return valid
}

FWIW - I had this problem and it turned out to be the internal clock on my EC2 instance. Following this guide solved the problem.

Ran into this as well, any plans to support some amount of clock drift? 0 - 500ms isn't that uncommon with ntp.

Another workaround:
token, err := jwt.Parse(t, func(token *jwt.Token) (interface{}, error) {
...
}
if err != nil {
	e, ok := err.(*jwt.ValidationError)
	if !ok || ok && e.Errors & jwt.ValidationErrorIssuedAt == 0 { // Don't report error that token used before issued.
		return NewError(http.StatusUnauthorized, err.Error())
	}
}

There's a caveat for @pawelkowalak's suggestion in that .Valid needs to be set to true if that's the only error. Otherwise you may later do if token.Valid { and find that it fails despite a lack of err.
Another workaround is to remove iat from the token prior to validation:
request.ParseFromRequest(..., func(token *jwt.Token) (interface{}, error) {
	mapClaims := token.Claims.(jwt.MapClaims)
	delete(mapClaims, "iat")
	// ...
})

This approach works because the keyFunc callback is called internally in the library before running validation. This allows using the callback to modify the claims after it has been created but before it's being validated.
You can also chose to simply modify it directly instead of deleting it with this pseudo code: (mapClaims["iat"].(float64)) -= 5 which subtracts 5 seconds from the issue time.


Add RequiresXPACK=true only for WotC mods

Seems reasonable to me.

@robojumper Thanks for your review! Does anything remain to be done for this to be merged?

@robojumper Thanks for your review! Does anything remain to be done for this to be merged?


HBASE-24550 Passing '-h' or '--help' to bin/hbase doesn't do as expected

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https://github.com/apache/hbase/pull/1949

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https://builds.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-1949/1/console

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https://github.com/apache/hbase/pull/1949

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@WenFeiYi usually we commit to branch-1 only as it is the active one but since this is good fix to have, we can commit to more branches.
As of now, the build is broken for this PR.

I got it, I will be close the PR.


ign -> gz CLI Migration : gz-tools

See: https://github.com/gazebo-tooling/release-tools/issues/739

The CMakeLists.txt changes are quite scuffed.
I needed to contort it because execute_process isn't expanding the $ generator expression...


Write pseudocode for the baby grabber

Checklist of Functions

[ ] Detect if aligned
[ ] Extend grabber
[ ] Retract grabber

Daniel is doing this

pseudocode for baby grabber.py.docx

What happens if the robot finds red?

Pseudocode for red is the same as other colors, but looking for a red boolean from the vision software.


Consider: Additional output formats for documents that can be rendered (md, rst, dot, etc.)

Some potential additions:

[ ] md

[ ] -> pdf
[ ] -> png/jpg
[ ] -> styled HTML

[ ] rst

[ ] -> pdf
[ ] -> png/jpg
[ ] -> styled HTML

[ ] dot

[ ] -> pdf
[ ] -> png/jpg
[ ] -> styled html
[ ] -> svg
[ ] -> monospaced box drawing

[ ] (see: graph-easy, a perl project)

For each additional render type, I'll need to lock down dangerous execution possibilities.

I've decided this isn't in scope.


HL-15,16,44,46: Layout, stepper, generic form and wizard

Description :sparkles:

generic stepper implemented with independent action buttons
styles and common components for forms
form implementation for applicant step 1
applicant wizard steps with steps with sections and basic translations

Issues :bug:
Testing :alembic:
Screenshots :camera_flash:
Additional notes :spiral_notepad:

Codecov Report

Merging #73 (1faaa5d) into main (835bfaf) will decrease coverage by 43.67%.
The diff coverage is 12.66%.

@@             Coverage Diff             @@
##             main      #73       +/-   ##
===========================================
- Coverage   61.45%   17.77%   -43.68%
===========================================
  Files         119      104       -15
  Lines        1754     1243      -511
  Branches      108      179       +71
===========================================
- Hits         1078      221      -857
- Misses        648      990      +342
- Partials       28       32        +4

Impacted Files
Coverage Δ

frontend/benefit/applicant/.eslintrc.js
0.00% <ø> (ø)

...applicant/src/components/applications/constants.ts
0.00% <0.00%> (ø)

...ns/forms/application/deMinimisAid/DeMinimisAid.tsx
0.00% <0.00%> (ø)

...cations/forms/application/deMinimisAid/extended.ts
0.00% <0.00%> (ø)

...s/forms/application/step1/ApplicationFormStep1.tsx
0.00% <0.00%> (ø)

...s/applications/forms/application/step1/extended.ts
0.00% <0.00%> (ø)

...s/forms/application/step2/ApplicationFormStep2.tsx
0.00% <0.00%> (ø)

...s/forms/application/step3/ApplicationFormStep3.tsx
0.00% <0.00%> (ø)

...s/forms/application/step4/ApplicationFormStep4.tsx
0.00% <0.00%> (ø)

...s/forms/application/step5/ApplicationFormStep5.tsx
0.00% <0.00%> (ø)

... and 103 more

Continue to review full report at Codecov.

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[SPARK-25002][SQL] Avro: revise the output record namespace

What changes were proposed in this pull request?
Currently the output namespace is starting with ".", e.g. .topLevelRecord
Although it is valid according to Avro spec, we should remove the starting dot in case of failures when the output Avro file is read by other lib:
https://github.com/linkedin/goavro/pull/96
How was this patch tested?
Unit test

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/testing-k8s-prb-make-spark-distribution-unified/1683/
Test PASSed.

Merged build finished. Test PASSed.

Test build #94035 has started for PR 21974 at commit 70ceb84.

Test build #94035 has finished for PR 21974 at commit 70ceb84.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
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Test PASSed.

thanks, merging to master!


[Question] line 318 on reconftw.sh

Hi,
What happens to passive_subs.txt after? Is it not run through anything after this or am I missing something.
after (line 318) NUMOFLINES=$(cat .tmp/_psub.txt 2>>"$LOGFILE" | sed "s/.//" | anew .tmp/passive_subs.txt | wc -l)
I can't find a reference to passive_subs.txt
Should it not put the contents of .tmp/passive_subs.txt in to webs.txt to scan all these subs? (ran through httpx first)
It seems it's missing alot of subs compaired to my usual slow way of gathering subs. (tried full also)
Again... I could be missing something.
Thanks

The passive subs file and crt.sh file are combined and  then sent to DNS resolution.
Check out line number 348 https://github.com/six2dez/reconftw/blob/main/reconftw.sh#L348.
Here we have used a regular expression "*" to get all the contains of file ending with _subs.txt into one single file and then send it for resolution.

Thus you didn't get the required file "passive_subs.txt" upon searching as we use a regular expression (*_subs.txt) so that the file comes into consideration.

Ah okay, thanks for the quick reply :) Great work you guys are doing btw.


Possible race if PR is reconciled before Pipeline

When creating a PipelineRun and Pipeline in quick succession you can get the following error:
    types.go:25: time: 2022-10-04 21:36:41.377386386 +0000 UTC m=+47.592144259: non-normal event reason Failed about obj PipelineRun:sample-component-build-bh8mx message Pipeline jvm-build-service-test-namespace-mzhhd/sample-component-build can't be Run; it has an invalid spec: "" type does not match default value's type: "string": finally.params[app-path].default.type, finally.params[app-path].type, finally.params[context].default.type, finally.params[context].type, finally.params[revision].default.type, finally.params[revision].type, tasks.params[app-path].default.type, tasks.params[app-path].type, tasks.params[context].default.type, tasks.params[context].type, tasks.params[revision].default.type, tasks.params[revision].type

e.g.  as at https://prow.ci.openshift.org/view/gs/origin-ci-test/pr-logs/pull/redhat-appstudio_jvm-build-service/290/pull-ci-redhat-appstudio-jvm-build-service-main-jvm-build-service-in-repo-e2e/1577408946094739456
I had a look at the code and I am pretty sure this happens if the PR is reconciled first, and the pipeline has parameters that have a default but no explicit type set. Normally the first reconcile of the Pipeline would set this type, however if the PR is reconciled before the pipeline this won't happen (note that this is just my theory, I have no real knowledge of the code base, so I may be wrong).
Steps to Reproduce the Problem
It is racy, so it is hard to reproduce consistently. I have seen it multiple times, but fairly infrequently. It usually happens if you use a script to both install tekton, the pipeline and the pipelinerun straight after each other.
Generally once it happens you need to delete the pipeline and start again.
Additional Info

Kubernetes version:

Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v0.21.0-beta.1", GitCommit:"0d10c3f72592addce965b9bb34992eb6fc283a3b", GitTreeState:"clean", BuildDate:"2021-08-31T22:03:33Z", GoVersion:"go1.16.6", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"23", GitVersion:"v1.23.5+012e945", GitCommit:"3c28e7a79b58e78b4c1dc1ab7e5f6c6c2d3aedd3", GitTreeState:"clean", BuildDate:"2022-07-13T08:38:41Z", GoVersion:"go1.17.12", Compiler:"gc", Platform:"linux/amd64"}
(paste your output here)

Tekton Pipeline version:

Client version: 0.26.0
Chains version: v0.9.0
Pipeline version: v0.37.4
Triggers version: v0.20.2
Operator version: v0.60.1
(note that this is from my personal openshift, I am not sure what version is run on openshift CI above).

Hey @stuartwdouglas, thanks for the issue. One thing though, Pipeline are not "reconciled" at all, only PipelineRun and TaskRun are — but Pipeline and Task (and other type) do have a mutating admission controller though.
Any specifics on the Pipeline used ? (like using pipelineref with oci bundles, or something like that).
Also, where does this errors pops out ? in the controller logs or in the webhook logs ?
If it sometimes works, and sometimes it doesn't, and given the names in the logs, one thing that "could" happen here is, that some object are created before the admission controller registered itself, making it bypass any validation or "defaulting". Essentially this would mean some object are created before the pipeline instance is fully ready to work.

Ah, looking again I seem to have misunderstood what what going on here.
Looking at the actual code I don't actually understand what is going on, as far as I can see the message is generated here: https://github.com/tektoncd/pipeline/blob/a11c38585b924d9b07b93bc38524889c7413ce1d/pkg/apis/pipeline/v1beta1/task_validation.go#L305
The message "" type does not match default value's type: "string" implies that p.Type is the empty string, however the code above that should have caught that problem.
Looking at the code I have no idea how that message could be generated, but it clearly is as shown in the logs.

So we looked at this offline with @stuartwdouglas, and this is indeed a "timing" issue.
Because the webhook registers itself, there is a small (but possibly long) period of time, where the CRDs are available but the webhook is not registered (or has no rules). This is not really a problem in a "normal" usage, but for things like automated e2e tests, it could be a problem, as one could create some object before the admission controller are ready, making it bypass validation or mutation (defaulting, conversion).
Initially, I thought we could check for the webhook pod readiness, but that doesn't work either. As of today (see https://github.com/tektoncd/pipeline/blob/main/cmd/webhook/main.go#L219), we mark the webhook pod as ready as soon as it start. Ideally we should mark it ready when we registered. This might need some knative/pkg changes though.
/cc @imjasonh @abayer

/remove-lifecycle stale

/remove-lifecycle rotten

/lifecycle frozen

Created https://github.com/tektoncd/pipeline/issues/7353 to track fixing the readiness of the webhook. I am going to close this issue as, unless we fix #7353, this can be workaround by waiting a bit more (on automated e2e tests).


add: Giulia Rappo

#938 add my profile

@giuliarappo Thank you so much for your contribution! Your efforts are greatly appreciated and will go a long way in improving our project. Please feel free to share it with others and help us grow even further!


add option -i for ips to display interface(s)

I have made a lot of test with the files in the attachment. Please review the code.
Edit (nwinkler): Removed zip attachment

I don't mege this PR. beacause g these two commands are so dependent on the OS.  The code is difficult to cover all cases.

the result of running "ifconfig" on CentOS:
eth0      Link encap:Ethernet  HWaddr 00:0C:29:8B:5F:2F
inet addr:<IP_ADDRESS>  Bcast:<IP_ADDRESS>  Mask:<IP_ADDRESS>
inet6 addr: fe80::20c:29ff:fe8b:5f2f/64 Scope:Link
UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
RX packets:21332922 errors:0 dropped:0 overruns:0 frame:0
TX packets:4651204 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:2490502766 (2.3 GiB)  TX bytes:1458751886 (1.3 GiB)
eth1      Link encap:Ethernet  HWaddr 00:0C:29:8B:5F:39
inet addr:<IP_ADDRESS>  Bcast:<IP_ADDRESS>  Mask:<IP_ADDRESS>
inet6 addr: fe80::20c:29ff:fe8b:5f39/64 Scope:Link
UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
RX packets:10924782 errors:0 dropped:0 overruns:0 frame:0
TX packets:9648544 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:2826373567 (2.6 GiB)  TX bytes:3626977407 (3.3 GiB)
eth2      Link encap:Ethernet  HWaddr 00:0C:29:8B:5F:4D
inet addr:<IP_ADDRESS>  Bcast:<IP_ADDRESS>  Mask:<IP_ADDRESS>
inet6 addr: fe80::20c:29ff:fe8b:5f4d/64 Scope:Link
UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
RX packets:2508744 errors:0 dropped:0 overruns:0 frame:0
TX packets:3140759 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:1954971282 (1.8 GiB)  TX bytes:1513674181 (1.4 GiB)
eth3      Link encap:Ethernet  HWaddr 00:0C:29:8B:5F:43
inet addr:<IP_ADDRESS>  Bcast:<IP_ADDRESS>  Mask:<IP_ADDRESS>
inet6 addr: fe80::20c:29ff:fe8b:5f43/64 Scope:Link
UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
RX packets:8801251 errors:0 dropped:0 overruns:0 frame:0
TX packets:8974794 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:2533772506 (2.3 GiB)  TX bytes:2796157298 (2.6 GiB)
gre2      Link encap:UNSPEC  HWaddr 02-02-02-02-FF-FF-E0-E6-00-00-00-00-00-00-00-00
inet addr:<IP_ADDRESS>  P-t-P:<IP_ADDRESS>  Mask:<IP_ADDRESS>
UP POINTOPOINT RUNNING NOARP  MTU:1476  Metric:1
RX packets:4045 errors:0 dropped:0 overruns:0 frame:0
TX packets:4045 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:0
RX bytes:339780 (331.8 KiB)  TX bytes:355960 (347.6 KiB)
lo        Link encap:Local Loopback
inet addr:<IP_ADDRESS>  Mask:<IP_ADDRESS>
inet6 addr: ::1/128 Scope:Host
UP LOOPBACK RUNNING  MTU:16436  Metric:1
RX packets:5302709 errors:0 dropped:0 overruns:0 frame:0
TX packets:5302709 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:0
RX bytes:253434162 (241.6 MiB)  TX bytes:253434162 (241.6 MiB)
lo:1      Link encap:Local Loopback
inet addr:<IP_ADDRESS>  Mask:<IP_ADDRESS>
UP LOOPBACK RUNNING  MTU:16436  Metric:1
lo:2      Link encap:Local Loopback
inet addr:<IP_ADDRESS>  Mask:<IP_ADDRESS>
UP LOOPBACK RUNNING  MTU:16436  Metric:1
tun_10_45 Link encap:UNSPEC  HWaddr 00-00-00-00-00-00-00-00-00-00-00-00-00-00-00-00
inet addr:<IP_ADDRESS>  P-t-P:<IP_ADDRESS>  Mask:<IP_ADDRESS>
UP POINTOPOINT RUNNING NOARP MULTICAST  MTU:1500  Metric:1
RX packets:4412849 errors:0 dropped:0 overruns:0 frame:0
TX packets:3104433 errors:0 dropped:1864 overruns:0 carrier:0
collisions:0 txqueuelen:100
RX bytes:429789680 (409.8 MiB)  TX bytes:1431382085 (1.3 GiB)
tun_10_47 Link encap:UNSPEC  HWaddr 00-00-00-00-00-00-00-00-00-00-00-00-00-00-00-00
inet addr:<IP_ADDRESS>  P-t-P:<IP_ADDRESS>  Mask:<IP_ADDRESS>
UP POINTOPOINT RUNNING NOARP MULTICAST  MTU:1500  Metric:1
RX packets:2034172 errors:0 dropped:0 overruns:0 frame:0
TX packets:3548421 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:100
RX bytes:167736430 (159.9 MiB)  TX bytes:266262913 (253.9 MiB)

the result of running "ifconfig" on Ubuntu:
enp8s0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
inet <IP_ADDRESS>  netmask <IP_ADDRESS>  broadcast <IP_ADDRESS>
inet6 fe80::845f:b85f:5b3a:152b  prefixlen 64  scopeid 0x20
ether 08:9e:01:9d:06:20  txqueuelen 1000  (Ethernet)
RX packets 6716151  bytes<PHONE_NUMBER> (8.7 GB)
RX errors 192  dropped 0  overruns 192  frame 0
TX packets 1354429  bytes<PHONE_NUMBER> (1.0 GB)
TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
device interrupt 17
lo: flags=73<UP,LOOPBACK,RUNNING>  mtu 65536
inet <IP_ADDRESS>  netmask <IP_ADDRESS>
inet6 ::1  prefixlen 128  scopeid 0x10
loop  txqueuelen 1000  (Local Loopback)
RX packets 119607  bytes<PHONE_NUMBER> (1.6 GB)
RX errors 0  dropped 0  overruns 0  frame 0
TX packets 119607  bytes<PHONE_NUMBER> (1.6 GB)
TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
vmnet1: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
inet <IP_ADDRESS>  netmask <IP_ADDRESS>  broadcast <IP_ADDRESS>
inet6 fe80::250:56ff:fec0:1  prefixlen 64  scopeid 0x20
ether 00:50:56:c0:00:01  txqueuelen 1000  (Ethernet)
RX packets 370  bytes 0 (0.0 B)
RX errors 0  dropped 0  overruns 0  frame 0
TX packets 1388  bytes 0 (0.0 B)
TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
vmnet8: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
inet <IP_ADDRESS>  netmask <IP_ADDRESS>  broadcast <IP_ADDRESS>
inet6 fe80::250:56ff:fec0:8  prefixlen 64  scopeid 0x20
ether 00:50:56:c0:00:08  txqueuelen 1000  (Ethernet)
RX packets 3469  bytes 0 (0.0 B)
RX errors 0  dropped 0  overruns 0  frame 0
TX packets 1110  bytes 0 (0.0 B)
TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
wlp7s0: flags=4099<UP,BROADCAST,MULTICAST>  mtu 1500
ether f6:4e:c4:29:04:c4  txqueuelen 1000  (Ethernet)
RX packets 0  bytes 0 (0.0 B)
RX errors 0  dropped 0  overruns 0  frame 0
TX packets 0  bytes 0 (0.0 B)
TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

the result of running "ip addr" on CentOS:
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 16436 qdisc noqueue state UNKNOWN
link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00
inet <IP_ADDRESS>/8 scope host lo
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global lo:1
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global secondary lo:2
inet6 ::1/128 scope host
valid_lft forever preferred_lft forever
2: eth1: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP qlen 1000
link/ether 00:0c:29:8b:5f:39 brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global eth1
inet6 fe80::20c:29ff:fe8b:5f39/64 scope link
valid_lft forever preferred_lft forever
3: eth3: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP qlen 1000
link/ether 00:0c:29:8b:5f:43 brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global eth3
inet6 fe80::20c:29ff:fe8b:5f43/64 scope link
valid_lft forever preferred_lft forever
4: eth2: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP qlen 1000
link/ether 00:0c:29:8b:5f:4d brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global eth2
inet6 fe80::20c:29ff:fe8b:5f4d/64 scope link
valid_lft forever preferred_lft forever
5: eth0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UP qlen 1000
link/ether 00:0c:29:8b:5f:2f brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global eth0
inet6 fe80::20c:29ff:fe8b:5f2f/64 scope link
valid_lft forever preferred_lft forever
21: gre0:  mtu 1476 qdisc noop state DOWN
link/gre <IP_ADDRESS> brd <IP_ADDRESS>
22: gretap0: <BROADCAST,MULTICAST> mtu 1476 qdisc noop state DOWN qlen 1000
link/ether 00:00:00:00:00:00 brd ff:ff:ff:ff:ff:ff
23: gre2@NONE: <POINTOPOINT,NOARP,UP,LOWER_UP> mtu 1476 qdisc noqueue state UNKNOWN
link/gre <IP_ADDRESS> peer <IP_ADDRESS>
inet <IP_ADDRESS> peer <IP_ADDRESS>/32 scope global gre2
24: tun_10_45: <POINTOPOINT,MULTICAST,NOARP,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UNKNOWN qlen 100
link/[65534]
inet <IP_ADDRESS> peer <IP_ADDRESS>/32 scope global tun_10_45
26: tun_10_47: <POINTOPOINT,MULTICAST,NOARP,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UNKNOWN qlen 100
link/[65534]
inet <IP_ADDRESS> peer <IP_ADDRESS>/32 scope global tun_10_47

the result of running "ip addr" on Ubuntu:
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000
link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00
inet <IP_ADDRESS>/8 scope host lo
valid_lft forever preferred_lft forever
inet6 ::1/128 scope host
valid_lft forever preferred_lft forever
2: enp8s0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP group default qlen 1000
link/ether 08:9e:01:9d:06:20 brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global dynamic enp8s0
valid_lft 35682sec preferred_lft 35682sec
inet6 fe80::845f:b85f:5b3a:152b/64 scope link
valid_lft forever preferred_lft forever
3: wlp7s0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc mq state DOWN group default qlen 1000
link/ether f6:4e:c4:29:04:c4 brd ff:ff:ff:ff:ff:ff
4: vmnet1: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UNKNOWN group default qlen 1000
link/ether 00:50:56:c0:00:01 brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global vmnet1
valid_lft forever preferred_lft forever
inet6 fe80::250:56ff:fec0:1/64 scope link
valid_lft forever preferred_lft forever
5: vmnet8: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UNKNOWN group default qlen 1000
link/ether 00:50:56:c0:00:08 brd ff:ff:ff:ff:ff:ff
inet <IP_ADDRESS>/24 brd <IP_ADDRESS> scope global vmnet8
valid_lft forever preferred_lft forever
inet6 fe80::250:56ff:fec0:8/64 scope link
valid_lft forever preferred_lft forever

Sorry, not sure what to do with these comments. Before merging this, we need to have some assurance that the changes work reasonably well on the major operating systems.
Please let me know how you want to proceed.

I will try to modify as your comments to make sure this command run well on the major OS.

@YumingMa want to take another stab at this?

If this isn't picked back up, I vote this function be removed or moved to a plugin called "misc", or something (open to suggestions). Relates to #1640
@nwinkler @NoahGorny @davidpfarrell

If this isn't picked back up, I vote this function be removed

I also think that this should be removed from the base plugins.
I could see it being its own stand-alone plugin.
I don't know how popular it is, but there's only one use within bash-it itself (not counting the base plugin test).  We'd probably want to update that theme to include a command_exists check.
Side note: Additionally,  the phoenix alias set creates an ips alias (not related to IP address)
As for the code: It does seem like it could be simplified - I'd probably consider that a post-move action, separated from the act of moving this into its own plugin.

I'm fine with moving it to a separate plugin. I'm not a huge fan of a misc plugin, since it will simply become another collection of odds and ends without any real purpose. Maybe a network plugin?
The usage in the theme is a bit problematic anyway, the theme relies on the function to be available. At the moment, that's OK, since most people have the base plugin enabled by default. Moving the function will require another plugin to be enabled...

closing this as this is stale


Update NR download data link

[ ] Move button to bottom
[x] Update to EHDP-data repo
[ ] Include report-specific or neighborhood-specific filtering?

I'm working on this, but was briefly confused by the fact that the download button grabs a variable called data_download_loc which I could not find referenced or declared in our source code. Momentarily forgot that we are storing the report x neighborhood json spec at EHDP-data.
The report x neighborhood json spec define data_download_loc. @cgettings, could you please update and re-export, changing those to the following?

Active Design: https://raw.githubusercontent.com/nychealth/EHDP-data/prod/neighborhood-reports/data/Active Design Physical Activity and Health_data.csv
Asthma: https://raw.githubusercontent.com/nychealth/EHDP-data/prod/neighborhood-reports/data/Asthma and the Environment_data.csv
Climate: https://raw.githubusercontent.com/nychealth/EHDP-data/prod/neighborhood-reports/data/Climate and Health_data.csv
Housing: https://raw.githubusercontent.com/nychealth/EHDP-data/prod/neighborhood-reports/data/Housing and Health _data.csv
Outdoor Air: https://raw.githubusercontent.com/nychealth/EHDP-data/prod/neighborhood-reports/data/Outdoor Air and Health_data.csv

I'll start by moving the button; when you have a chance to export revised report x neighborhood json, I can add a little JS that ingests the CSV, filters by selected neighborhood, and downloads that as a CSV.

@mmontesanonyc paths changed, and also edited a bit.

I moved the button, ingested the csv, filtered by neighborhood, and triggered a download. Happy with this.


mountwizzard4 v1.0.5 is broken on pypi

Hi all,
The mountwizzard4 is broken on ARM (Jetson Nano) devices due to the issue with NumPy library 1.19.3
https://pypi.org/project/mountwizzard4/1.0.5/
This is the current error:
(venv) astronomer@jetson-observatory:~/mountwizzard4$ python ./venv/lib/python3.8/site-packages/mw4/loader.py
RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
Traceback (most recent call last):
  File "./venv/lib/python3.8/site-packages/mw4/loader.py", line 36, in <module>
    import matplotlib
  File "/home/astronomer/mountwizzard4/venv/lib/python3.8/site-packages/matplotlib/__init__.py", line 174, in <module>
    _check_versions()
  File "/home/astronomer/mountwizzard4/venv/lib/python3.8/site-packages/matplotlib/__init__.py", line 159, in _check_versions
    from . import ft2font
ImportError: numpy.core.multiarray failed to import

To resolve it on the installation, consider forcing the upgrade of NumPy as the following:
pip uninstall numpy && pip install numpy==1.20.1

I have moved the issue to the correct repo #https://github.com/mworion/MountWizzard4/issues/121


import or export an .html file

Hello
from an internet browser, we can export our favorites or bookmarks in an HTML file.
I would like your application to be able to import these favorites or bookmarks.
being able to create an HTML file containing the bookmarks would be nice from your application.
Thank you

Thank you for your suggestion, its a good idea but first, we need to check the exported file from many browsers to write a general exporter and maybe we can target specific browser to import bookmarks for it

from a computer, most internet browsers have the function to export in HTML and also to import.
On Android, it's different. But generally speaking, open-source internet browsers have this possibility.

Most applications similar to yours offer to import bookmarks from an HTML file

Implemented in 1.6.0


BlockFormat check: allow comments after opening brace

Hi! Thanks for your tool, it's really one of the things Haxe needs most, I am looking into integrating it into our company workflow, however I found quite a bit of code like the following:
if (someComplexCheck) { // description of what the check actually means
   // ...
}

And I think it's not unreasonable to place those comments after the opening brace, so do you think checkstyle could have an option for the BlockFormat checker to allow comments after the opening brace?

BlockFormat had some support for comments, but it was broken, it should work now on the dev branch.
dev also contains a few new tests that are based on an experimental token tree builder, which might break on some code fragments. Let us know, if you see stack traces when activating them (if you can pinpoint the location and/or provide code that breaks, that would be helpful).

Thanks, I will. Oh, while we're at it, I also had problems with import haxe.extern.EitherType - probably because haxeparser (checkstyle uses it, right?) doesn't allow extern (and probably macro) in a type path, however Haxe allows it. cc @Simn

@nadako just released BlockFormat bug fix to haxelib v1.2.1


FOP-2847: fix PNGs with index color model did not render SMask

java.awt.image.BufferedImage.getAlphRaster() return null if an
IndexColorModel is used. This resulted in those PNGs missing the
SMask and had broken transparency effects.

superseded by
https://github.com/apache/fop/pull/55


Add spring boot version suffix in rt guide

add suffix .RELEASE where SB version is used.

@ccopelloRH @rhoads-zach version has been updated in guide to match RN and confirmed with @jclingan.


Create outcome per path for France Marriage Abroad

Trello card
https://trello.com/c/tKI6NBOS
Motivation
Marriage abroad is the most complex Smart Answer to update. It's also one of the most frequent Smart Answers to be updated. We want to simplify the updates so they are quicker, less complex and reduce the need for Developers to make the update. By creating one template per outcome for a country we greatly reduce the mount of logic in the templates.
France already has one outcome per path, but it shares them with the French Territories. This PR add templates only used for France in a structure similar to the outcomes for Italy and Sweden.
Factcheck
marriage-abroad France
Expected changes
URL on GOV.UK

Change the question asked after the user selects France.
Change how the outcome templates for France are structured in the code base.

Before

After

@ikennaokpala I've updated the commit structure as you recommended, and expanded some of the commit notes.

I am updating this PR from its last passing commit 4b2e852 and update from @sistephens 1b87e37


Cyber Security of smart contracts

Research Cyber Security of smart contracts, and related security issue

using 8 hrs, 完成[KM]https://github.com/CAFECA-IO/KnowledgeManagement/blob/master/smartContract/security.md


NuGet Package 1.6.0 Lacks of StrongName

Hi there,
the current available nuget package version 1.6.0 lacks of strong name. Is there any good reason why not signing it?
The same issue targets the new dependency ControlsEx.
Exception on application start:
Additional information: Could not load file or assembly 'MahApps.Metro, Version=<IP_ADDRESS>2, Culture=neutral, PublicKeyToken=null' or one of its dependencies. A strongly-named assembly is required. (Exception from HRESULT: 0x80131044)

Please see the release notes for v1.6:

Strong naming
MahApps will no longer be strong-named beginning with this version. #3029
Why?
Strong-named assemblies are only useful in some rare scenarios. If you need strong-named assembly then you can compile the source by yourself ore use the Strong Namer from Daniel Plaisted @dsplaisted or Strong-Name Signer from Werner van Deventer @brutaldev.
More informations about the reason of this decision can be found here:

Still Strong-Naming your Assemblies? You do know it’s 2016, right?
XAML Behaviors UWP Managed 1.1.0
Strong-Named Assemblies


[REQUEST] For binding to certain IP addresses

[REQUEST] For binding to certain IP addresses on multi-homed boxes. Currently listens on all available IP addresses

https://github.com/toc-irc/jbnc/commit/91aba1bd7b29a922a02dcb8e6d5180f9b3bea73a
Use "bouncerIp" in config like "bouncerIp":"<IP_ADDRESS>" or "bouncerIp":null for all ips.


Add MAKEOPT env to specify make options.

I love to see the rake-compiler can have an environment variable to specify the make command options.
Because I want to compile extconf.rb with make -v to see the compiling command lines such as compiler flags and macros. It makes us to easy to debug.
Right now we can do this by setting MAKE="make -v" in Rakefile or tasks/compile.rake.
But someone might have to use gmake rather than make.
https://github.com/rake-compiler/rake-compiler/blob/master/lib/rake/extensiontask.rb#L501
So, I think it's convenient to separate the MAKE into MAKE and MAKEOPT.
The environment variable name MAKEOPT is inspired from RUBYOPT in Ruby.
What do you think?

Or just adding the accessor in extensiontask.rb might be better without the environment variable.
attr_accessor :make_opt

Here is the patch to have the accessor and return a value by the environment variable if the class variable is empty.
https://github.com/junaruga/rake-compiler/commit/363d7111ddf70a77e51421b822abb200cfed93bd

I want to ask you if you like this PR before testing the PR by myself.

How about using MAKEFLAGS environment variable? It works with GNU Make and BSD make.
MAKEFLAGS="V=1" rake ...

@kou Oh! I did not know that. Thanks for sharing it. OK. So far I am okay for your suggested way, as I am not sure about nmake.

OK.
I close this for now.


Add documentation for RandomSignature

https://github.com/macros4outlook/quotefixmacro/pull/6 added RandomSignature.bas. There currently is no documentation for that feature. That should be added.

An initial hint is put at https://macros4outlook.github.io/quotefixmacro/advanced-features.html#random-signature-generation.


Update README.md

fixed links
closes #1031

@sanjay-kv  sir I have updated the necessary

@MitulSonagara please merge, the deadline is close @MastanSayyad


copying + downloading bib

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Please upload report for BASE (master@7e08e30). Learn more about missing BASE report.
Report is 6 commits behind head on master.

Additional details and impacted files
@@            Coverage Diff            @@
##             master      #58   +/-   ##
=========================================
  Coverage          ?   78.72%
=========================================
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  Branches          ?        1
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  Misses            ?        9
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Help needed: Wireless Crafting Grid in the Nether

Used the WIKI to put up a second RS-System in the Nether. The Crafting Grid in the Nether is up and running and shows all my items and blocks which are stored into the system in the Overworld.
This means, the system is successfully set up with receiver, transmitter, network card, interdim upgrade and some chunk loaders in the Overworld as well as in the Nether.
In the Overworld, the wireless Crafting Grid works with unlimited range. In the Nether it tells "Controller not found". But there is no controller in the Nether. I crafted a new wireless crafting grid and tried to connect it to the receiver as the WIKI tells me to think of a receiver as of a controller. There's also an infinite wireles transmitter installed in the Nether.
I'm sure I'm missing something....

Ok I reconnected the wireless crafting grid another time and now it works everywhere as expected. Shame on me - it seems after the chunk loaders were installed and after the system was set up that the wireless crafting grid had lost connection in all worlds but I only checked the Nether . After the re-connect to the overworld controller everthing works out perfectly.


Give support for selecting Chromium profile language instead of system default

Puppeteer version: 0.10.2
Platform / OS version: macOS

Hi!
I'm trying to run the browser of puppeteer in Spanish, but my system is on English. Even I am Spanish I like to use my OS in English.
But some mates in my team are using the OS in Spanish, so the webpage content is different for me and some of them! How can I normalize this by setting the language of the profile?
As on every launch puppeteer is creating a new profile, I can not configure it to choose a profile with Spanish, and also I can not specify the language by parameter. I tried --lang=ES and --profile-directory but as you know it removes the previous profile created, so even creating a new profile with headless to false, the next time won't be available.

You can try to pre-create an empty folder (say, es-profile) and launch the browser for the first time this way:
puppeteer.launch({ headless: false, userDataDir: 'es-profile' });

not calling browser.close().
Then set the profile language and send Ctrl+C to the node console to close the browser and exit script gracefully.
Then you can use the same userDataDir option in any other scripts if needed.

Thanks a lot! It works.


fix(collapse): revert change to transition css

Revert change to getScrollFromElement function

Incorrectly changed one part prior, this reverses it to completely agree with Bootstrap's implementation.
This results in some jank for the situation in the situation in #6045, but it looks like Bootstrap also would have that same problem.
This fixes the accordion regression, with fix as seen here.
This should fix #6194

LGTM


One salt-proxy for each host?

Have I understood it correctly that you’ll have one salt-proxy running for each device you want to utilize napalm against? That seems a bit rough say in a network of 2-3000 devices managed.

Hi @kheide. You understanding is correct: you need as many Proxy Minion as devices to manage. I assume you not are planning to manage these processes manually (the same goes with any process, whatever its nature would be)... in that case, managing 1 process or thousands becomes equally simple, as I tried to explain in https://mirceaulinic.net/2018-09-27-network-automation-at-scale/. Please let me know if you have any questions!
Additionally, I'm also working on an alternative "proxyless" implementation (almost ready, will write up about it soon-ish) which only requires you to have just one single process, the Salt Master and nothing else. Hope this helps! :-)


rtd remove unshallow

🚀 Pull Request
Description
This PR resolves this issue on RTD building for main:

Which didn't present itself whilst testing on a private branch for #271.
Here is the successful build of the documentation for this pull-request.
Closes #267

@trexfeathers I can't quite explain this... don't quite understand why this issue didn't happen for #271

@trexfeathers I can't quite explain this... don't quite understand why this issue didn't happen for #271 when testing.

@bjlittle I also want to understand why it's not a problem for Iris - see iris/.readthedocs.yml#L8-10

This could plausibly be down to a difference in config within RTD. I set up cf-units as bare-bones as I could, so perhaps Iris has a better config that we should be mimicking.

@trexfeathers I think it's a difference in the repos. iris has more history.
Also, it's clear that you do need to do git fetch --unshallow for iris otherwise the version is wrong.


Changed: Reduce .NET Runtime requirement to net5.0

Based on API availability, there isn't a good reason as to why we should prohibit the running of the library on .NET 5, even if the runtime itself is itself officially out of support as there isn't anything in the library that strictly requires net6.0 or above.
This PR restores support for the net5.0 TFM and runtime in the library, including Unit Tests and CI/CD.
My previous PRs were much more elaborate; hopefully this one should be pretty trivial.
I'm personally stuck on .NET 5 in one of my projects due to a runtime issue in .NET 6 involving an optimisation that skips the Managed->Native transition during ICalls/FCalls. Thankfully it appears to have been fixed in .NET 7.
[A bit anticlimactic for PR #255, but it is what it is]

Thanks for proposing this change. However, I don’t plan to revert back to .NET 5. .NET 5 is no longer supported by Microsoft. I don’t intend to support it either. If you are unable to upgrade your projects to .NET 6, you can keep using an 1.x version of DotNetCorePlugins from NuGet.

As much as I understand the concept of not wishing to explicitly support an out of support framework, I don't understand the concept of restricting end-users from using a library on said framework by using a newer TFM when there are no valid technical or engineering reasons to do so.
If the goal was to clean up the codebase, removing support from netcoreapp3.1 made sense (although it still is officially supported); supporting net5.0 requires no code changes on the library part.
Would keeping the readme listing net6.0 as minimum TFM and refusing support for anyone below net6.0 not be a viable compromise?

If you are unable to upgrade your projects to .NET 6, you can keep using an 1.x version of DotNetCorePlugins from NuGet.

I don't mind keeping my own fork of the project around until I eventually go to .NET 7 in a few months. I'm familiar with the codebase and have done so in the past while waiting for PRs on this repository. It's just that I don't see excluding compatibility for a TFM as a necessary action.

The reason for dropping .NET 5 is straightforward here: I have chosen not to support a version of .NET which is past its official end of life date. https://dotnet.microsoft.com/en-us/platform/support/policy/dotnet-core
As there are currently no new features in the 2.0-beta version of this library, I think using the stable 1.x versions is the simplest solution for you. I have not committed to a timeframe for marking 2.0 as the new stable version. I am tentatively waiting until the end of 2022 when .NET Core 3.1 also reaches end of life, at which point I also expect .NET 7 would be available.


Add status:pending|success|failure dropdown filter for pull request.

Description
In github search fields for PRs it is possible to filter by CI/Commit status.
I never remember the syntax, if it's status: or ci:, or action:, and wether the values are  pass, passing, ok. I'd love a dropdown like this:

Example URLs
I guess it should appear on any pages where PRs can be filtered.
https://github.com/napari/napari/pulls or any ORG/REPO/pull
https://github.com/pulls (that list all your own PRs), that would make it easy to find all my PRs that need a a fix.

This feature already exists: https://github.com/refined-github/refined-github/blob/main/source/features/pr-filters.tsx

Tracked in https://github.com/refined-github/refined-github/issues/5168


Fixed japanese realText and text.

the generated Japanese is unnatural, I fixed it.

I don't understand, did you fix the base text, which is a public resource?

I deleted ruby data on Kanji at public resource.
You need to turn off ruby in Japanese text resources.

これは黒い星座の図です。
→ This is a picture of a black constellation.
これは黒い星座せいざの図です。
→ This is a picture of a black constellation constellation.

Has the question been resolved?


Frigate installer failing during install

Please verify that you have read and understood the guidelines.
yes
A clear and concise description of the issue.
Tried installing a few times and keep getting errors during install on the same place. Seems to be related to openvino and ssdlite_mobilenet_v2
I am running an old server without AVX support, although SSE 4.2 should be supported. CPU info
8 x Intel(R) Xeon(R) CPU E5620 @ 2.40GHz (1 Socket)
--
Kernel Version Linux 6.8.12-4-pve (2024-11-06T15:04Z)
Boot Mode Legacy BIOS
Manager Version pve-manager/8.2.8/a577cfa684c7476d

My plan is to run frigate with USB Google Coral - no real need for iGPU support in my case. I have also tried installing with default settings.
I found a related  issue but don't understand how it was solved
What settings are you currently utilizing?

[ ] Default Settings
[X] Advanced Settings

Which Linux distribution are you employing?
Debian 11
If relevant, including screenshots or a code block can be helpful in clarifying the issue.
Beginning of script:
    ______     _             __
   / ____/____(_)___ _____ _/ /____
  / /_  / ___/ / __ `/ __ `/ __/ _ \
 / __/ / /  / / /_/ / /_/ / /_/  __/
/_/   /_/  /_/\__, /\__,_/\__/\___/
             /____/

Using Advanced Settings
Using Distribution: debian
Using debian Version: 11
Using Container Type: 0
Using Root Password: ********
Container ID: 102
Using Hostname: frigate
Using Disk Size: 20
Allocated Cores: 4
Allocated RAM: 8096
Using Bridge: vmbr0
Using IP Address: dhcp
Using Gateway IP Address: Default
Using APT-Cacher IP Address: Default
Disable IPv6: yes
Using Interface MTU Size: Default
Using DNS Search Domain: Host
Using DNS Server IP Address: Host
Using Vlan: Default
Enable Root SSH Access: yes
Enable Verbose Mode: yes
Creating a Frigate LXC using the above advanced settings
 ✓ Using local for Template Storage.
 ✓ Using local-lvm for Container Storage.
 ✓ Updated LXC Template List
 / Creating LXC Container

Last couple of rows with the error:
-dtypes, h5py, grpcio, google-pasta, gast, defusedxml, astunparse, absl-py, openvino-dev, markdown, keras, tensorboard, tensorflow
 /  Attempting uninstall: openvino
    Found existing installation: openvino 2024.1.0
 -    Uninstalling openvino-2024.1.0:
      Successfully uninstalled openvino-2024.1.0
 /Successfully installed absl-py-2.1.0 astunparse-1.6.3 defusedxml-0.7.1 gast-0.6.0 google-pasta-0.2.0 grpcio-1.68.0 h5py-3.12.1 keras-3.6.0 libclang-18.1.1 markdown-3.7 ml-dtypes-0.4.1 namex-0.0.8 networkx-3.1 openvino-2024.5.0 openvino-dev-2024.5.0 opt-einsum-3.4.0 optree-0.13.1 tensorboard-2.18.0 tensorboard-data-server-0.7.2 tensorflow-2.18.0 tensorflow-io-gcs-filesystem-0.37.1 termcolor-2.5.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
 \################|| Downloading ssdlite_mobilenet_v2 ||################

========== Downloading /opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
... 100%, 49829 KB, 33511 KB/s, 1 seconds passedd
 \
========== Unpacking /opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
 |
 |========== Converting ssdlite_mobilenet_v2 to IR (FP16)
Conversion command: /usr/bin/python3 -- /usr/local/bin/mo --framework=tf --output_dir=/opt/frigate/models/public/ssdlite_mobilenet_v2/FP16 --model_name=ssdlite_mobilenet_v2 --input=image_tensor --reverse_input_channels --output=detection_scores,detection_boxes,num_detections --transformations_config=/usr/local/lib/python3.9/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config=/opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config --input_model=/opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb '--layout=image_tensor(NHWC)' '--input_shape=[1, 300, 300, 3]' --compress_to_fp16=True

 -[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
 \FAILED:
ssdlite_mobilenet_v2
 -
[ERROR] in line 114: exit code 0: while executing command $STD /usr/local/bin/omz_converter --name ssdlite_mobilenet_v2 --precision FP16 --mo /usr/local/bin/mo
root@proxmox:~#

Please provide detailed steps to reproduce the issue.
running root@proxmox:~# bash -c "$(wget -qLO - https://github.com/community-scripts/ProxmoxVE/raw/main/ct/frigate.sh)" in the proxmox shell - error occurs with both default and advanced settings

I wonder if Frigate is too much of a moving target to support since the recommended installation method is via Docker? There's also still breaking changes on pretty much each major release. I've been running Frigate on Docker within a Debian LXC well over a year (originally installed using @tteck's "Docker LXC" script). Even with the nested virtualization, I've been surpised on how good the performance is.

I wonder if Frigate is too much of a moving target to support since the recommended installation method is via Docker? There's also still breaking changes on pretty much each major release. I've been running Frigate on Docker within a Debian LXC well over a year (originally installed using @tteck's "Docker LXC" script). Even with the nested virtualization, I've been surpised on how good the performance is.

I've often asked myself the same question, and this is by far the script that causes the most problems. tteck had already seen it that way and wanted to remove it several times, but then many users complained.

I actually managed to get past this by forking the repo and commenting out the Openvino install part in frigate-install.sh
Suggestion to the maintainers is to make optional installs deselectable if possible without to much hassle

I'm suspecting it's the lack of AVX support. Unfortunately, I can't test further. I have a recent CPU and the default installation ran fine.
@jappish84 Can you log into the container and run this? please paste the detailed logs
cd /opt/frigate/models && /usr/local/bin/omz_converter --name ssdlite_mobilenet_v2 --precision FP16 --mo /usr/local/bin/mo

i got the same Error, CPU is Intel Celeron J4105 with integrated Intel® UHD Graphics 600 (Generation 9)
result of your code:
========== Converting ssdlite_mobilenet_v2 to IR (FP16)
Conversion command: /usr/bin/python3 -- /usr/local/bin/mo --framework=tf --output_dir=/opt/frigate/models/public/ssdlite_mobilenet_v2/FP16 --model_name=ssdlite_mobilenet_v2 --input=image_tensor --reverse_input_channels --output=detection_scores,detection_boxes,num_detections --transformations_config=/usr/local/lib/python3.9/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config=/opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config --input_model=/opt/frigate/models/public/ssdlite_mobilenet_v2/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb '--layout=image_tensor(NHWC)' '--input_shape=[1, 300, 300, 3]' --compress_to_fp16=True

[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
FAILED:
ssdlite_mobilenet_v2

As mentioned, I forked the repo and excluded openvino - frigate then installed without errors.
`
root@frigate:~# cd /opt/frigate/models && /usr/local/bin/
omz_converter --name ssdlite_mobilenet_v2 --precision FP1
6 --mo /usr/local/bin/mo
-bash: /usr/local/bin/omz_converter: No such file or dire
ctory
root@frigate:/opt/frigate/models#
`
@Dino0811 you could use my fork if your cpu doesn't support AVX.
@remz1337 I could try to modify the script to prompt the user to choose whether or not to install openvino instead of just skipping it and create a PR, let me know if that's how you'd like to go around the issue.
Another approach is to better check CPUs compability maybe?

I've removed the SSE 4.2 supports. Should fix this issue.


1032 allow referred search terms

Fixes #1032
Were any Django, import pipeline, table_schema, or dictionary files modified? Y

If YES:

Database used: import on an empty database
All Django tests pass: Y
FLAKE8 run on modified code: Y

Were any JavaScript or CSS files modified? Y

If YES:

JSHINT run on all affected files: Y (menu.js)
Tested on Chrome, Firefox, Safari, Edge, and iOS: Y
(Remember to test all browser sizes)
Tested for race conditions (with delayed API calls if applicable): NA

Description of changes:

In search sidebar, select metadata menu:

Add Observed Ring Radius under Occultation Constraints
Add Exposure Duration under Cassini ISS
Add Ring Center Opening Angle to Sun under Occultation Constraints
Add Ring Center Opening Angle to Observer under Occultation Constraints

Known problems: None

No columns added in the corresponding database tables for referred fields. (except param info table)
Make referred fields show up in detail tab under corresponding categories.
Add a link icon for referred fields at the end of the menu label in search sidebar and select matedata menu.
Include referred fields in fields.json

Add a field "referred" (true/false) to identify if this is a referred slug field

Add referred slug under the following categories:

Add Exposure Duration under Hubble Mission Constraints
Add Exposure Duration under Galileo SSI Constraints
Add Exposure Duration under Voyager ISS Constraints
Add Exposure Duration under New Horizons LORRI Constraints

Change fields.json category name to real name and also change "referred" to "linked"
Add link icon at the end for referred field in detail tab & "+" menu in table view/slideshow
Add tooltips for link icons
Add info (blue text) in the widgets of Cassini ISS filter and Hubble Filter Name
Update based on comments

Database used: New
All Django tests pass: Y
JSHINT run on all affected files: Y
FLAKE8 run on all affected files: Y
Tested on Chrome, Firefox, Safari, and iOS: NA
(Remember to test all browser sizes)
Tested for race conditions (with delayed API calls if applicable): NA


refactor: transport injection

Refactor transport injection

Redefine the method name
Optimize the injection method

Thanks for the PR. This PR however introduces multiple breaking API changes, which for backward compatibility reasons are not preferred.
What is the problem exactly with the current behavior?

I'm very sorry for ignoring the naming rigor issue due to the PR I merged earlier.
As a result, when using it, the naming was found to be unreasonable, so a correction was proposed.

@Davincible
Hello, may I ask if this PR can be incorporated?

There are still breaking API changes

We can make any non breaking changes now, breaking changes will be taken into v5

ok thanks

Sorry for not being any help now, I'd like to not make breaking changes in minor version upgrades.
If there is any other way to make your life with micro easier please let me know :)


test_aero_data and test_get_airfoil

test_calculate_aero_data_inviscid, test_calculate_aero_data_viscous, and test_get_airfoil

Nice! Can you change the merging branch from "master" to "dev"? Going to close this PR, feel free to open a new one with this change


[cherry-pick]test add table design example in doris's doc to regression test (#42444)

(cherry picked from commit 051128f172a0e6c26109160752293d95746e29f8)
What problem does this PR solve?
Issue Number: close #xxx
Related PR: #42444
Problem Summary:

run buildall

run buildall


Readme fix for Scala2

The Typeclass type alias is confusing, as it's unused. When I read the documentation, I wondered if that type alias is needed by the macro or something 🙂

It is actually used and required:  https://github.com/softwaremill/magnolia/blob/scala2/core/src/main/scala/magnolia1/magnolia.scala#L69
If you try to remove the Typeclass type alias from the derivation it will fail (e.g. in the tests). I suspect the motivation might be supporting types with more than one type parameter, so that magnolia knows which parameter to vary. Not sure if it could be inferred.

Interesting. In my own code I removed the alias without consequences, not sure what happened there.
I'll double check, but will close this PR for now.


rewrite in julia

JS is uuglyyy. Just need to wait until julia is compilable so startup times don't regress

turns out is so much faster that a 10s boot time doesn't matter


docs-bug(Google Maps): How do you load directions dynamically?

Documentation Feedback
The current instructions are supposing you are using this type of coding:
<!-- index.html -->
<!doctype html>
<head>
  ...
  <script src="https://maps.googleapis.com/maps/api/js?key=YOUR_API_KEY"></script>
</head>

However, what if you use the other option?
export class GoogleMapsDemoComponent {
  apiLoaded: Observable<boolean>;

  constructor(httpClient: HttpClient) {
    this.apiLoaded = httpClient.jsonp('https://maps.googleapis.com/maps/api/js?key=YOUR_KEY_HERE', 'callback')
        .pipe(
          map(() => true),
          catchError(() => of(false)),
        );
  }
}

I cannot find any example with this option. Also, it doesn't support the then or other alternatives. My new app relies on maps, but it's not the core and it's not going to be shown everywhere, the second alternative is the best one since I'll load them on demand.
Currently, I cannot find how to combine the second approach with the suggested code:
center: google.maps.LatLngLiteral = {lat: 24, lng: 12};
  zoom = 4;

  readonly directionsResults$: Observable<google.maps.DirectionsResult|undefined>;

  constructor(mapDirectionsService: MapDirectionsService) {
    const request: google.maps.DirectionsRequest = {
      destination: {lat: 12, lng: 4},
      origin: {lat: 14, lng: 8},
      travelMode: google.maps.TravelMode.DRIVING
    };
    this.directionsResults$ = mapDirectionsService.route(request).pipe(map(response => response.result));
  }

Since I should be able to load this after the previous code was loaded on my first page.
Thanks.
Affected documentation page
https://github.com/angular/components/tree/master/src/google-maps/map-directions-renderer

Please keep GitHub issues for bug reports / feature requests. Better avenues for troubleshooting / questions are stack overflow, gitter, mailing list, etc.


Yolo training does not stop, and what type of images can be used

Hi
I am new to working with YOLO and Neural networks
I used your example data to run with yolo no gpu.
this is running for about 4 days and not stopping.
command:
darknet_no_gpu.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23
output started  like this:
progress of learning
Loading weights from darknet19_448.conv.23...Done!
Learning Rate: 0.0001, Momentum: 0.9, Decay: 0.0005
Loaded: 1.588000 seconds
Region Avg IOU: 0.120223, Class: 0.362698, Obj: 0.612478, No Obj: 0.513480, Avg Recall: 0.000000,  count: 9
Region Avg IOU: 0.238924, Class: 0.439833, Obj: 0.548626, No Obj: 0.514007, Avg Recall: 0.058824,  count: 34
Region Avg IOU: 0.200323, Class: 0.519404, Obj: 0.561993, No Obj: 0.512736, Avg Recall: 0.166667,  count: 12
Region Avg IOU: 0.196986, Class: 0.321407, Obj: 0.634472, No Obj: 0.513765, Avg Recall: 0.000000,  count: 18
Region Avg IOU: 0.187150, Class: 0.526126, Obj: 0.575120, No Obj: 0.513986, Avg Recall: 0.083333,  count: 24
Region Avg IOU: 0.147942, Class: 0.573413, Obj: 0.527868, No Obj: 0.513225, Avg Recall: 0.000000,  count: 11
Region Avg IOU: 0.147899, Class: 0.419107, Obj: 0.490241, No Obj: 0.512636, Avg Recall: 0.000000,  count: 18
Region Avg IOU: 0.155855, Class: 0.431433, Obj: 0.527465, No Obj: 0.512580, Avg Recall: 0.000000,  count: 11
1: 23.719202, 23.719202 avg, 0.000100 rate, 1525.245972 seconds, 64 images
Now after 4 days it is:
Region Avg IOU: 0.776839, Class: 0.999406, Obj: 0.881055, No Obj: 0.007709, Avg Recall: 1.000000,  count: 17
Region Avg IOU: 0.789834, Class: 0.998349, Obj: 0.804908, No Obj: 0.010932, Avg Recall: 1.000000,  count: 32
Region Avg IOU: 0.716955, Class: 0.998312, Obj: 0.747980, No Obj: 0.008201, Avg Recall: 0.863636,  count: 22
Region Avg IOU: 0.791601, Class: 0.999399, Obj: 0.838217, No Obj: 0.008875, Avg Recall: 1.000000,  count: 21
Region Avg IOU: 0.774943, Class: 0.997678, Obj: 0.875351, No Obj: 0.006102, Avg Recall: 1.000000,  count: 9
Region Avg IOU: 0.764226, Class: 0.998302, Obj: 0.723759, No Obj: 0.011918, Avg Recall: 0.969697,  count: 33
Region Avg IOU: 0.760802, Class: 0.998138, Obj: 0.750877, No Obj: 0.014727, Avg Recall: 0.976744,  count: 43
Region Avg IOU: 0.755867, Class: 0.999586, Obj: 0.833746, No Obj: 0.005354, Avg Recall: 1.000000,  count: 11
207: 0.218326, 0.224815 avg, 0.001000 rate, 1554.923950 seconds, 13248 images
Loaded: 0.000000 seconds
Region Avg IOU: 0.811563, Class: 0.997593, Obj: 0.791255, No Obj: 0.012824, Avg Recall: 1.000000,  count: 31
Region Avg IOU: 0.763254, Class: 0.998242, Obj: 0.847682, No Obj: 0.008106, Avg Recall: 1.000000,  count: 19
Region Avg IOU: 0.785790, Class: 0.997373, Obj: 0.738193, No Obj: 0.013735, Avg Recall: 1.000000,  count: 37
And not stopping.
Again I used your aeroplanes  and birds data, as is.
Is this normal. do I have a problem?
Also wanted to ask if you can explain the output
Region Avg IOU:
Class:
Obj:
No Obj:
Avg Recall:
count:
and the line that comes after every few "interations?"
207: 0.218326, 0.224815 avg, 0.001000 rate, 1554.923950 seconds, 13248 images
Also wanted to ask, Can I use images that have several classes in them, in your case an image with a bird and a aeroplane? (marking different rectangles with different class tags. can this be in the same image?)
Thanks in advance
m.

@mwindowshz Hi,
About output explain: https://timebutt.github.io/static/understanding-yolov2-training-output/

Also wanted to ask, Can I use images that have several classes in them, in your case an image with a bird and a aeroplane? (marking different rectangles with different class tags. can this be in the same image?)

Yes, you can use image with marked many different object on the same image. Also you can use image without objects at all, I use it to avoid false-positive detections.

Is this normal. do I have a problem?

There is no problem. But for good detection you should use ~500 - 2000 images per class. And you should train 2000 iterations per class. So on CPU (without GPU) you will train 4000 iterations (for 2 classes) very much time :)

Hi Thank you for the information, it was very helpful

There is no problem. But for good detection you should use ~500 - 2000 images per class. And you should train 2000 iterations per class. So on CPU (without GPU) you will train 4000 iterations (for 2 classes) very much time :)

Is there a way to set number of iterations per class? From running the code I found that the images are passed in random order, for better training. but is there some parameter counting iterations for each class?
Also I have a data set of small images.  around  187x187.
Do I have to set the cfg file to reflect this? (setting the width and height in cfg file to be different then 416)
Also can I still use the pre trained weights ? darknet19_448.conv.23
I am running and training the images, (Slow on CPU for now) sometimes I get
Region Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.001821, Avg Recall: -nan(ind),  count: 0
I guess this means the network could not find anything in the image?
Another Question about testing. is there a build way to pass a set of images and get the results listed so I can have statistics of how many objects found in image and were classified correctly?
It is always possible to write something but if it is already present....
Again I appreciate your help. thanks Allot!

Hi,

Is there a way to set number of iterations per class? From running the code I found that the images are passed in random order, for better training. but is there some parameter counting iterations for each class?

No, but you can set total iterations: https://github.com/AlexeyAB/darknet/blob/576dbe12e64697f10aaa15f6ef23e0a3d5eea5b5/cfg/yolo-voc.2.0.cfg#L15
So if you set classes=5 in cfg-file, then set max_batches = 10000.

Also I have a data set of small images. around 187x187.
Do I have to set the cfg file to reflect this? (setting the width and height in cfg file to be different then 416). I know there is a resize of the image but stretching the image can cause it to be blurey.
Also If I change the size in cfg can I still use the pre trained weights ? darknet19_448.conv.23

You can use darknet19_448.conv.23 in this case. If most of your images in the training-dataset is around 187x187, then you should set width=192 height=192 during training.

Also on the other hand , if I use very large images, Full HD with small objects, then resize to 416x416 can make the objects much smaller and they would not be detected.
Are these considerations right?

You can train netowrk on 192x192, but after training you can change width=608 height=608 or 800x800 in cfg-file and use this cfg-file for detection.

I am running and training the images, (Slow on CPU for now) sometimes I get
Region Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.001821, Avg Recall: -nan(ind), count: 0
I guess this means the network could not find anything in the image?

If you get nan only sometimes - then that's fine. But if you get nan always, then you dataset is worng labeled.

Another Question about testing. is there a build way to pass a set of images and get the results listed so I can have statistics of how many objects found in image and were classified correctly?
It is always possible to write something but if it is already present....
Again I appreciate your help. thanks Allot!

So you need for True Positives. Yolo it show as Recall instead of really recall: https://github.com/AlexeyAB/darknet/issues/35#issuecomment-284221184
You can do: darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights as described here: https://github.com/AlexeyAB/darknet#when-should-i-stop-training
It can give you such information: 7586 7612 7689 RPs/Img: 68.23 IOU: 77.86% Recall:99.00% where Recall is what you need. (but I would advise you to look at the parameter IoU - the bigger, the better, this is a more general metric)
In the code: fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
https://github.com/AlexeyAB/darknet/blob/576dbe12e64697f10aaa15f6ef23e0a3d5eea5b5/src/detector.c#L435

IoU - Yolo calculates the best IoUs for each image:
and get average:  avg_iou*100/total
Recall - Yolo calculates True positives
and get average:  100.*correct/total

Hello!
You say:

Yes, you can use image with marked many different object on the same image. Also you can use image without objects at all, I use it to avoid false-positive detections.

but each image must correspond to the text file with the same name containing     . For jpg-file with no object for detect  what you need to write in txt file?

@ivanevgenyev You should have empty .txt-file for these images.

Hi,
I have problem that Avg Recall: 0.000000 is always zero i started training around 2 hours and it is still zero and Region Avg IOU: 0.027899 for example, almost less than 2.
my images are 30003000 and I have around 1000 objects in each image - just one class.
I followed this tutorial
https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/
Any hint?

For your network resolution 2976x2976 there shouldn't be any problem to detect 1000 objects on one image, because you can detect (2976/13)*(2976/13)*5 = 43245 objects .

Try to base you cfg-file on yolo-voc.2.0.cfg instead of yolo-voc.cfg

Change 4 lines in the source code - set 1000 instead of 30 in these 4 lines, then recompile, and train again:

https://github.com/AlexeyAB/darknet/blob/75c39f57507ed81f33571271b939de400a69adf0/src/region_layer.c#L30
https://github.com/AlexeyAB/darknet/blob/75c39f57507ed81f33571271b939de400a69adf0/src/region_layer.c#L190
https://github.com/AlexeyAB/darknet/blob/75c39f57507ed81f33571271b939de400a69adf0/src/region_layer.c#L222
https://github.com/AlexeyAB/darknet/blob/75c39f57507ed81f33571271b939de400a69adf0/src/region_layer.c#L259

After changing the 4 lines in source code and recompiling i got segmentation fault 11
./darknet detector train cfg/obj.data cfg/yolo-voc.2.0.cfg darknet19_448.conv.23
yolo-voc
layer     filters    size              input                output
0 conv     32  3 x 3 / 1  2976 x2976 x   3   ->  2976 x2976 x  32
1 max          2 x 2 / 2  2976 x2976 x  32   ->  1488 x1488 x  32
2 conv     64  3 x 3 / 1  1488 x1488 x  32   ->  1488 x1488 x  64
3 max          2 x 2 / 2  1488 x1488 x  64   ->   744 x 744 x  64
4 conv    128  3 x 3 / 1   744 x 744 x  64   ->   744 x 744 x 128
5 conv     64  1 x 1 / 1   744 x 744 x 128   ->   744 x 744 x  64
6 conv    128  3 x 3 / 1   744 x 744 x  64   ->   744 x 744 x 128
7 max          2 x 2 / 2   744 x 744 x 128   ->   372 x 372 x 128
8 conv    256  3 x 3 / 1   372 x 372 x 128   ->   372 x 372 x 256
9 conv    128  1 x 1 / 1   372 x 372 x 256   ->   372 x 372 x 128
10 conv    256  3 x 3 / 1   372 x 372 x 128   ->   372 x 372 x 256
11 max          2 x 2 / 2   372 x 372 x 256   ->   186 x 186 x 256
12 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
13 conv    256  1 x 1 / 1   186 x 186 x 512   ->   186 x 186 x 256
14 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
15 conv    256  1 x 1 / 1   186 x 186 x 512   ->   186 x 186 x 256
16 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
17 max          2 x 2 / 2   186 x 186 x 512   ->    93 x  93 x 512
18 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
19 conv    512  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x 512
20 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
21 conv    512  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x 512
22 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
23 conv   1024  3 x 3 / 1    93 x  93 x1024   ->    93 x  93 x1024
24 conv   1024  3 x 3 / 1    93 x  93 x1024   ->    93 x  93 x1024
25 route  16
26 reorg              / 2   186 x 186 x 512   ->    93 x  93 x2048
27 route  26 24
28 conv   1024  3 x 3 / 1    93 x  93 x3072   ->    93 x  93 x1024
29 conv     30  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x  30
30 detection
mask_scale: Using default '1.000000'
Loading weights from darknet19_448.conv.23...Done!
Learning Rate: 0.0001, Momentum: 0.9, Decay: 0.0005
Loaded: 23.269316 seconds
Segmentation fault: 11

yolo-voc.2.0.cfg
[net]
batch=64
subdivisions=8
height=2976
width=2976
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.0001
max_batches = 45000
policy=steps
steps=100,25000,35000
scales=10,.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
#######
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[route]
layers=-9
[reorg]
stride=2
[route]
layers=-1,-3
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=0

on my older yolo-obj.cfg, the results:
./darknet detector train cfg/obj.data cfg/yolo-obj.cfg darknet19_448.conv.23
yolo-obj
layer     filters    size              input                output
0 conv     32  3 x 3 / 1  2976 x2976 x   3   ->  2976 x2976 x  32
1 max          2 x 2 / 2  2976 x2976 x  32   ->  1488 x1488 x  32
2 conv     64  3 x 3 / 1  1488 x1488 x  32   ->  1488 x1488 x  64
3 max          2 x 2 / 2  1488 x1488 x  64   ->   744 x 744 x  64
4 conv    128  3 x 3 / 1   744 x 744 x  64   ->   744 x 744 x 128
5 conv     64  1 x 1 / 1   744 x 744 x 128   ->   744 x 744 x  64
6 conv    128  3 x 3 / 1   744 x 744 x  64   ->   744 x 744 x 128
7 max          2 x 2 / 2   744 x 744 x 128   ->   372 x 372 x 128
8 conv    256  3 x 3 / 1   372 x 372 x 128   ->   372 x 372 x 256
9 conv    128  1 x 1 / 1   372 x 372 x 256   ->   372 x 372 x 128
10 conv    256  3 x 3 / 1   372 x 372 x 128   ->   372 x 372 x 256
11 max          2 x 2 / 2   372 x 372 x 256   ->   186 x 186 x 256
12 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
13 conv    256  1 x 1 / 1   186 x 186 x 512   ->   186 x 186 x 256
14 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
15 conv    256  1 x 1 / 1   186 x 186 x 512   ->   186 x 186 x 256
16 conv    512  3 x 3 / 1   186 x 186 x 256   ->   186 x 186 x 512
17 max          2 x 2 / 2   186 x 186 x 512   ->    93 x  93 x 512
18 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
19 conv    512  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x 512
20 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
21 conv    512  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x 512
22 conv   1024  3 x 3 / 1    93 x  93 x 512   ->    93 x  93 x1024
23 conv   1024  3 x 3 / 1    93 x  93 x1024   ->    93 x  93 x1024
24 conv   1024  3 x 3 / 1    93 x  93 x1024   ->    93 x  93 x1024
25 route  16
26 conv     64  1 x 1 / 1   186 x 186 x 512   ->   186 x 186 x  64
27 reorg              / 2   186 x 186 x  64   ->    93 x  93 x 256
28 route  27 24
29 conv   1024  3 x 3 / 1    93 x  93 x1280   ->    93 x  93 x1024
30 conv     30  1 x 1 / 1    93 x  93 x1024   ->    93 x  93 x  30
31 detection
mask_scale: Using default '1.000000'
Loading weights from darknet19_448.conv.23...Done!
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Resizing
448
Loaded: 4.002774 seconds
Region Avg IOU: nan, Class: nan, Obj: nan, No Obj: 0.492963, Avg Recall: nan,  count: 0
Region Avg IOU: 0.008950, Class: 1.000000, Obj: 0.693369, No Obj: 0.494811, Avg Recall: 0.000000,  count: 123
Region Avg IOU: 0.042687, Class: 1.000000, Obj: 0.667997, No Obj: 0.492357, Avg Recall: 0.000000,  count: 14
Region Avg IOU: nan, Class: nan, Obj: nan, No Obj: 0.494897, Avg Recall: nan,  count: 0
Region Avg IOU: 0.017823, Class: 1.000000, Obj: 0.639639, No Obj: 0.492845, Avg Recall: 0.000000,  count: 125

No I have c# program which find me the objects and I changed to Yolo format using this tutorial
https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/
there is code for convert, it works well, I fed it to network and the results as above.

Hi,
I am using yolo-obj.cfg which is as follow.

Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Resizing
576 x 576
Loaded: 0.783732 seconds
Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.517770, Avg Recall: -nan,  count: 0
Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.517663, Avg Recall: -nan,  count: 0
Please suggest me some solution if anyone has faced the same .

@kpreeti096 This is normal.
https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects

Note: If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.

I am getting this while training. Is that a problem??
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.227234, .5R: -nan(ind), .75R: -nan(ind),  count: 0
Region 23 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.181644, .5R: -nan(ind), .75R: -nan(ind),  count: 0
class_id = 0, name = table,      ap = 0.00 %
for thresh = 0.25, precision = 0.00, recall = -nan(ind), F1-score = -nan(ind)
for thresh = 0.25, TP = 0, FP = 24, FN = 0, average IoU = 0.00 %
mean average precision (mAP) = 0.000000, or 0.00 %

i am training 5 classes on cpu(intel core i7-5500 2.4Ghz) 8gb ram. how many pictures should i train per classes to have good result? and how long will it take to finish?

i am training 5 classes on cpu(intel core i7-5500 2.4Ghz) 8gb ram. how many pictures should i train per classes to have good result? and how long will it take to finish?

Do you how to stop the training and where the output is obtained?

@AlexeyAB: I am trying to train a custom object detection model using yolov3.cfg(file making the required changes) and darknet53.conv.74 pre trained weights. But I am getting the following output:
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 82 Avg (IOU: 0.390918), count: 5, class_loss = 293.062775, iou_loss = 1.763977, total_loss = 294.826752
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 94 Avg (IOU: 0.358087), count: 2, class_loss = 1011.123230, iou_loss = 0.429443, total_loss = 1011.552673
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 106 Avg (IOU: 0.000000), count: 1, class_loss = 4316.066406, iou_loss = 0.000000, total_loss = 4316.066406
total_bbox = 7, rewritten_bbox = 0.000000 %
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 82 Avg (IOU: 0.376261), count: 7, class_loss = 293.145996, iou_loss = 1.990112, total_loss = 295.136108
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 94 Avg (IOU: 0.078115), count: 1, class_loss = 1010.538513, iou_loss = 1.803406, total_loss = 1012.341919
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 106 Avg (IOU: 0.000000), count: 1, class_loss = 4321.592773, iou_loss = 0.000000, total_loss = 4321.592773
total_bbox = 15, rewritten_bbox = 0.000000 %

Could you please explain me the output? Which KPI explains the number of iteration here.
Also, my weights are not getting saved into backup folder once I stop training even after 12hrs training consisting of 1 class with 150 training samples.
Just to confirm could you please tell me the steps to stop the model training.
Thanks!

@AlexeyAB: I am trying to train a custom object detection model using yolov3.cfg(file making the required changes) and darknet53.conv.74 pre trained weights. But I am getting the following output:
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 82 Avg (IOU: 0.390918), count: 5, class_loss = 293.062775, iou_loss = 1.763977, total_loss = 294.826752
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 94 Avg (IOU: 0.358087), count: 2, class_loss = 1011.123230, iou_loss = 0.429443, total_loss = 1011.552673
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 106 Avg (IOU: 0.000000), count: 1, class_loss = 4316.066406, iou_loss = 0.000000, total_loss = 4316.066406
total_bbox = 7, rewritten_bbox = 0.000000 %
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 82 Avg (IOU: 0.376261), count: 7, class_loss = 293.145996, iou_loss = 1.990112, total_loss = 295.136108
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 94 Avg (IOU: 0.078115), count: 1, class_loss = 1010.538513, iou_loss = 1.803406, total_loss = 1012.341919
v3 (mse loss, Normalizer: (iou: 0.75, obj: 1.00, cls: 1.00) Region 106 Avg (IOU: 0.000000), count: 1, class_loss = 4321.592773, iou_loss = 0.000000, total_loss = 4321.592773
total_bbox = 15, rewritten_bbox = 0.000000 %

Could you please explain me the output? Which KPI explains the number of iteration here.
Also, my weights are not getting saved into backup folder once I stop training even after 12hrs training consisting of 1 class with 150 training samples.
Just to confirm could you please tell me the steps to stop the model training.
Thanks!

@tunesh did you get the reason for it and were you able to solve this? I am also having the same issue.


How Can I add custom row dynamic before render cell

I want to insert custom row when to render rows for grouping rows.Can I add program rows the conditional

At best you can hack this feature together to get partial functionality. At worst it isn't possible. I have an idea of how to make this work but you either have to nest grids inside of detail rows or risk losing most of the grid functionality as a result.
I believe this is a planned feature on the road map; not sure on any ETA.

Closing as dupe to row grouping.


Fix Security Issues for sdk generation pipeline

update simple-git from 3.7.1 to 3.16.0
update class-validator from 0.13.2 to 0.14.0
to fix security issues https://github.com/Azure/azure-sdk-tools/issues/5322
The update has been test by trigger sdk generation pipeline. And it run successfully.
https://dev.azure.com/azure-sdk/internal/_build/results?buildId=2158185&view=results

/check-enforcer override


Add unit tests to CI

Run tests on pull requests. The main decision point is whether to include the tests that compare to PHP. In any case, a database needs to be started (most likely through spinning up the docker image). If a comparison to PHP is included, it also requires the setup of Elastic Search and the PHP API. Easiest will be to do all this through the docker-compose, but I am not sure if we would potentially be running into the limits for free Github actions (I don't think so).

Closed by #104 and #101.


[Compute Module 4] RuntimeError: ws2811_init failed with code -9 (Failed to create mailbox device)

Hello,
First of all, thanks for the great library! It has helped me a lot during my developments :) I have used this library on a standard Raspberry Pi 4B, without any issues for about a year now. I am now trying to convert my application to a custom OS on a Raspberry Pi Compute Module 4 (8GB eMMC, 4GB RAM, No Wifi)
I have installed the Python package (version 4.3.3) on my custom OS image, based off Poky distro (from the Yocto project) with this kernel: Linux raspberrypi4-64 5.10.81-v8 #1 SMP PREEMPT Mon Nov 22 14:00:03 UTC 2021 aarch64 aarch64 aarch64 GNU/Linux (so 64-bits). My OS image on the standard Pi was 32-bits Raspberry Pi OS (which seems like the biggest difference between the two images in my opinion.)
I've kept the rest of the application exactly the same. When I run my application I get the following errors:
File "/usr/bin/digital-display-v1/classes/controller/led_controller.py", line 41, in __init__
    self.strip.begin()
  File "/usr/lib/python3.9/site-packages/rpi_ws281x/rpi_ws281x.py", line 131, in begin
    raise RuntimeError('ws2811_init failed with code {0} ({1})'.format(resp, str_resp))
RuntimeError: ws2811_init failed with code -9 (Failed to create mailbox device)

To be completly honest, I have no clue what a mailbox device means in this context. After a bit of googling, I found out that it has something to do with low-level hardware (maybe the SPI bus that is used by the library?) I have no clue on how I can start to resolve this problem, or even if this problem lies with the library, or with a mistake I made when creating my OS.
The kernel does not seem to give any errors regarding SPI or mailbox devices when checked with the dmesg command.
Any guidance would be greatly appreciated.
With kind regards,
Mats de Waard
EDIT1: Readability changes

Issues with the underlying rpi_ws281x (https://github.com/jgarff/rpi_ws281x/) are outside the scope of this wrapper library and cannot be fixed here.


Doesn't map after first table

Running the tool with prisma-case-format --file prisma/schema.prisma --pluralize it doesn't map after the first table. Here's the source prisma schema:
generator client {
  provider        = "prisma-client-js"
  output          = "../node_modules/@generated/read"
  previewFeatures = ["views"]
}

generator typegraphql {
  provider = "typegraphql-prisma"
}

datasource db {
  provider = "postgres"
  url      = env("DB_URL")
}

model goose_db_version {
  id         Int       @id @default(autoincrement())
  version_id BigInt
  is_applied Boolean
  tstamp     DateTime? @default(now()) @db.Timestamp(6)
}

view accounts {
  id         String    @unique @db.Uuid
  created_at DateTime? @db.Timestamp(6)
  name       String?
  updated_at DateTime? @db.Timestamp(6)
  owned_by   String?   @db.Uuid
  users      users?
}

And the result of a dry run (which also matches running normally)
generator client {
  provider        = "prisma-client-js"
  output          = "../node_modules/@generated/read"
  previewFeatures = ["views"]
}

generator typegraphql {
  provider = "typegraphql-prisma"
}

datasource db {
  provider = "postgres"
  url      = env("DB_URL")
}

model GooseDbVersion {
  id        Int       @id @default(autoincrement())
  versionId BigInt    @map("version_id")
  isApplied Boolean   @map("is_applied")
  tstamp    DateTime? @default(now()) @db.Timestamp(6)

  @@map("goose_db_version")
}

view accounts {
id         String    @unique @db.Uuid
created_at DateTime? @db.Timestamp(6)
name       String?
updated_at DateTime? @db.Timestamp(6)
owned_by   String?   @db.Uuid
users      users?
}

You can see from the output above that after goose_db_version the tool ceases to convert.

Hey @shellscape. IIRC views are a relatively new feature in Prisma, so this tool doesn't support them yet. It looks like there are mounting feature requests so I'm trying to take care of them this weekend. Will keep you posted.

Thanks for the quick reply. If you don't have the opportunity I'll probably be able to get add that capability this next week.

@shellscape, if you take a look at https://github.com/iiian/prisma-case-format/pull/20/files#diff-390b3178f6c849ab73dd0194ed9c880eb7073d0f00ea0f872cd0f3c2af6513f8R592, does this jest snapshot match your expectations for the output based on the example schema you provided?

I think the only thing of concern would be the usersId being created randomly by the schema formatter. Although technically the example schema that I plugged in was invalid because users on view accounts doesn't have an associated id to build an fk. I'm sure if we plugged one in, everything would be fine.

Also, I think Accounts Accounts[] on the Users is getting dropped their by the formatSchema operation as well. If those pieces were in place in the source schema, as required, the tool would behave properly. Probably not a great idea to test on a schema w/ errors, I'll get that fixed.

Yeah that looks about right to me.

I've released v1.4.0-beta.0 https://www.npmjs.com/package/prisma-case-format/v/1.4.0-beta.0?activeTab=readme. Please give it a shot and let me know how it works out for you!

Much appreciated. Will do my best to try it tonight

Just gave it a try and it looks beautiful. Thanks for getting that in.


Commit dry run works on 0.18.1 release, not after recent commits

on release 0.18.1 release code, I don't get this error on mainnet trying to dry run an etch commit.
when I sync to your newest commits AFTER 0.18.1, I get this error message. Is there a new limitation that we can't kick off a commit earlier than 840k to resume later?

@raphjaph should we expect to see a fix for this today or should I go figure out how to make the change myself locally?

Can you also add the PSBT into the wallet resume & resume --dry-run? 🙏
That is null too
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@raphjaph https://github.com/raphjaph should we expect to see a fix for
this today or should I go figure out how to make the change myself locally?
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This should be fixed now


fix: corrected assertions of the scan verb test

- What I did
Changed the incorrect assertions
- How I did it
Removed the update: public command in the scan result assertion
- How to verify it
scan_verb_test.dart should pass
- Description for the changelog

@purnimavenkatasubbu I'm pushing the fix for #1216 to this PR. I've updated the scan_verb_test also. Once the checks have passed, I will publish new at_persistence_secondary_server, remove the dependency override from at_secondary_server's pubspec, push the branch again, wait for checks to pass and then merge to trunk.

@purnimavenkatasubbu I'm pushing the fix for #1216 to this PR. I've updated the scan_verb_test also. Once the checks have passed, I will publish new at_persistence_secondary_server, remove the dependency override from at_secondary_server's pubspec, push the branch again, wait for checks to pass and then merge to trunk.

Thanks


Failed contributions show up in the ceremony transcript

Expected Behavior
The hashes for the contribution that was not valid should not show up
Current Behavior
All hashes show up

Any examples? I'm not seeing this in the current ceremonies/


getAddress return the first address available even if not found the device

I prepared 3 sensors but only 1 is connected. A call to getAddress is false but the last address found is written to the variable given.
There should be no explicit address returned if the device is not found.
Reproduction: Call a device number that is not available with at least one device connected.
<EMAIL_ADDRESS>

Can you post a minimal sketch that shows the problem?
The image just does not give information about the content of the variables before your call.

Attached a simple sketch.
Output with one DS18B20 connected:
ets Jun  8 2016 00:22:57
rst:0x1 (POWERON_RESET),boot:0x13 (SPI_FAST_FLASH_BOOT)
flash read err, 1000
ets_main.c 371
ets Jun  8 2016 00:22:57
rst:0x10 (RTCWDT_RTC_RESET),boot:0x13 (SPI_FAST_FLASH_BOOT)
configsip: 0, SPIWP:0xee
clk_drv:0x00,q_drv:0x00,d_drv:0x00,cs0_drv:0x00,hd_drv:0x00,wp_drv:0x00
mode:DIO, clock div:1
load:0x3fff0030,len:1344
load:0x40078000,len:13864
load:0x40080400,len:3608
entry 0x400805f0
289D8F16A8013C7A
289D8F16A8013C7A
Output with now DS18B20 connected:
rst:0x10 (RTCWDT_RTC_RESET),boot:0x13 (SPI_FAST_FLASH_BOOT)
configsip: 0, SPIWP:0xee
clk_drv:0x00,q_drv:0x00,d_drv:0x00,cs0_drv:0x00,hd_drv:0x00,wp_drv:0x00
mode:DIO, clock div:1
load:0x3fff0030,len:1344
load:0x40078000,len:13864
load:0x40080400,len:3608
entry 0x400805f0
0000000000000000
0000000000000000
Expected result in first example would be:
289D8F16A8013C7A
0000000000000000
Dalles_Fehler.zip


train data Mohamed Elmoudni

Hi Prof,
I just loaded csv file and 500 images starting Richard_Gere_0003.jpg to Rudolph_Giuliani_0021.jpg
Also my csv file user.id is: 1029
Thank you
Mohamed

No white space in the CSV. Also, please update the legend.csv so it is free of conflicts.

Hi Prof,
I took out the white spaces. The conflict i found is the 4 initial files which they are not there anymore.  Please let me know if it is ok now.
Thank you
Mohamed


VHDL2008 selection

Hello,
It would be nice to be able to select as a global option to use VHDL2008 or pass arguments to GHDL.

Hey,
You can already do this. https://github.com/graphman65/linter-vhdl#custom-ghdl-arguments
Let me know if you cannot get it working.

Hey,
it would be good add custom arguments in linter-vhdl options. With comment on the top I need modify all my files in the project (42) :(

Hi,
I totally agree with @qarlosalberto . It would be great to use VHDL-2018 without changing every file in the project.

Hi @graphman65 , thanks for the quickly reply!
I think that in my case the "vhdl arguments" can do the job.

Yes! "vhdl arguments"
Thank you :)

I also vote for VHDL arguments in atom! Thanks for the package it's very convenient.

Hi, I've created a pull request for passing arguments to the compiler. Please check it out and leave a comment if necessary. https://github.com/graphman65/linter-vhdl/pull/12

+1 I work on a multi-user project and don't want to hassle with user-specific code in all of my project files.

Hi, sorry for being so late, I just merged it ! Thanks for your help and your support.


Support local batch memory metadata store.

Fixes #15900
Motivation
Support local batch memory metadata store.
Need to update docs?

[x] doc-not-needed
(Please explain why)

@horizonzy Can you explain what is the advantage for batching in the in-memory implementation and why do we need 2 different implementations for in-memory metadata store?r

Hanle #15931 firstly, after it merged. This pr will rebase it.


Screen freezes when I open mirroring

When I open scrcpy to mirror, it freezes and does not show on the PC what is appearing on the cell phone,
I've tried to find some solutions but I haven't found any.

[server] INFO: Device: [Xiaomi] Redmi 2201116SG (Android 12)
[server] INFO: Retrying with -m1920...
[server] INFO: Retrying...
[server] ERROR: Encoding error: java.lang.IllegalArgumentException: null
INFO: Renderer: direct3d
INFO: Texture: 1080x2400
INFO: Texture: 864x1920

Try with another encoder: https://github.com/Genymobile/scrcpy/blob/master/doc/video.md#encoder

Try with another encoder: https://github.com/Genymobile/scrcpy/blob/master/doc/video.md#encoder

any don´t work

Remove the quotes: https://github.com/Genymobile/scrcpy/commit/ec602a0334357982d75b374f7ac753c5bef1216a

Remove the quotes: ec602a0

Dude, I managed to move the cell phone screen through my PC, but the image remains frozen, what could it be?


Improve design system for form groups

Need to improve this component on \<Component name>\src\<File Name.html>
Improvements required :
Any reference image?
Any external reference?
You can checkout this one for better understanding.

@Tushar98644 go ahead


Don't start

On a clean game with .ASI Loader (dinput8.dll) the game doesn't start. The same behaviour with CLEO 4 (Latest version)
Game version: 1.0 US (14,383,616 bytes) Just appear a CMD and the game closes on its own.

I think you're missing the bass.dll file.

Likewise, he does not want to open the game and I already tried with anyway and nothing.  Like it's compatibility issues

Negative, the version .935 removed the cmd but the game does not start, if I remove the .asi the game starts

When is the next release?

don't start i get this error

it doesn't run for me too :(

Only, if i do not rely on V HUD next release.

WAIT! i found the solution! it requires latest version of GInput an Widescreen Fix installed.


"Pesceana" , "Cermegești " , "Lupoaia " , "Negraia " , "Roești " , "Ursoaia" are listed in the drop-down menu

The villages are listed in the drop-down menu alongside its corresponding county and city, according to the law
Preconditions : the  website is up and runing. After every step delete the text you typed from the search bar.

Step 1
Write in the search bar "Pesceana"

Expected results
The location appears in the drop-down menu as "Sat Pesceana, VÂLCEA (Pesceana)"

Step 2
Write in the search bar "Cermegești "

Expected results
The location appears in the drop-down menu as "Sat Cermegești , VÂLCEA (Pesceana)"

Step 3
Write in the search bar "Lupoaia "

Expected results
The location appears in the drop-down menu as "Sat Lupoaia , VÂLCEA (Pesceana)"

Step 4
Write in the search bar "Negraia "

Expected results
The location appears in the drop-down menu as "Sat Negraia , VÂLCEA (Pesceana)"

Step 5
Write in the search bar "Roești "

Expected results
The location appears in the drop-down menu as "Sat Roești , VÂLCEA (Pesceana)"

Step 6
Write in the search bar "Ursoaia"

Expected results
The location appears in the drop-down menu as "Sat Ursoaia, VÂLCEA (Pesceana)"