text:
System Test: Deployment

Initial Migration

[ ] Migrate all contracts and verify gas and ether used.

Note down used gas value and calculate ETH value for it on mainnet. Let team know
how it looks.

Upgrades

[ ] Upgrade BondedECDSAKeepVendor

requests are delegated to the new implementation contract

[ ] Upgrade BondedECDSAKeepFactory

requests are delegated to the new implementation contract

Deployment cost: 13,230,733 gas

# Migrations dry-run (simulation)

Network name: 'ropsten-fork'
Network id: 3
Block gas limit: 0x7a1200

1_initial_migration.js
Deploying 'Migrations'

block number: 7684574
block timestamp: 1586446766
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.947066740976362429
gas used: 210237
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.000420474 ETH

Total cost: 0.000420474 ETH

2_deploy_contracts.js
Browserslist: caniuse-lite is outdated. Please run next command npm update
Deploying 'Branch'

block number: 7684576
block timestamp: 1586446777
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.946868586976362429
gas used: 71714
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.000143428 ETH

Deploying 'Position'

block number: 7684577
block timestamp: 1586446780
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.946725158976362429
gas used: 71714
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.000143428 ETH

Deploying 'StackLib'

block number: 7684578
block timestamp: 1586446783
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.946523076976362429
gas used: 101041
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.000202082 ETH

Deploying 'Leaf'

block number: 7684579
block timestamp: 1586446785
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.946379648976362429
gas used: 71714
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.000143428 ETH

Linking

Contract: BondedSortitionPoolFactory <--> Library: StackLib

Deploying 'BondedSortitionPoolFactory'

block number: 7684580
block timestamp: 1586446788
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.941257382976362429
gas used: 2561133
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.005122266 ETH

Deploying 'KeepBonding'

block number: 7684581
block timestamp: 1586446795
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.937748596976362429
gas used: 1754393
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.003508786 ETH

Deploying 'BondedECDSAKeep'

block number: 7684582
block timestamp: 1586446800
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.930252988976362429
gas used: 3747804
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.007495608 ETH

Deploying 'BondedECDSAKeepFactory'

block number: 7684583
block timestamp: 1586446816
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.924732158976362429
gas used: 2760415
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.00552083 ETH

Deploying 'BondedECDSAKeepVendorImplV1'

block number: 7684584
block timestamp: 1586446829
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.922939790976362429
gas used: 896184
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.001792368 ETH

Deploying 'BondedECDSAKeepVendor'

block number: 7684585
block timestamp: 1586446848
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.921030548976362429
gas used: 954621
gas price: 2 gwei
value sent: 0 ETH
total cost: 0.001909242 ETH

Total cost: 0.025981466 ETH

3_initialize.js
current factory address: [0x128Fe4c78Bd23D33d30EBc5a42bC49A1D3659786]
approved operator contract [0x128Fe4c78Bd23D33d30EBc5a42bC49A1D3659786] in registry
set operator [0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D] as [0x9b873013CC55Ab1dF094B19d46EB1DaE608b7D81] contract upgrader

Total cost: 0 ETH

Summary

Total deployments: 11
Final cost: 0.02640194 ETH

Starting migrations...

Network name: 'ropsten'
Network id: 3
Block gas limit: 0x7a121d

1_initial_migration.js
Deploying 'Migrations'

transaction hash: 0x976e4ba852510de42cec32a39e78d02538c091f0df6c31c6c0395f8037fa6da8
Blocks: 2 Seconds: 44
contract address: 0xE78e9f947328E28cD3c86e229b8Fc176Ac5e7A7f
block number: 7684583
block timestamp: 1586446885
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.942982474976362429
gas used: 225237
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.00450474 ETH

Saving migration to chain.
Saving artifacts

Total cost: 0.00450474 ETH

2_deploy_contracts.js
Deploying 'Branch'

transaction hash: 0xc913fa483bc7743a8d5d5256d82e73f700e465175add9e57739d1ea96d8db9da
Blocks: 2 Seconds: 12
contract address: 0xd11699f7CF0a88BEa8696DDD94F827E6007256c0
block number: 7684587
block timestamp: 1586446974
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.940700934976362429
gas used: 71714
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.00143428 ETH

Deploying 'Position'

transaction hash: 0xad56c4419284fccc1b95735c11f71c75a8359b6e701e8676629ed6aa03d17a8a
Blocks: 1 Seconds: 16
contract address: 0x3c3225d8ef761e66519F353254a60e380D52D303
block number: 7684590
block timestamp: 1586446986
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.947668568170806873
gas used: 71714
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.00143428 ETH

Deploying 'StackLib'

transaction hash: 0xbfda945edd7271764289aa2c0cdbbda346a9a7efeda64ab82a9d713040b011d1
Blocks: 1 Seconds: 12
contract address: 0x685a1552921DefEe39A23d9Fef9167D490442AeF
block number: 7684592
block timestamp: 1586447011
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.945647748170806873
gas used: 101041
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.00202082 ETH

Deploying 'Leaf'

transaction hash: 0x3ecbb5399b02865e1bf2856df5531de51827844a4c41732559c12c45b34bd67b
Blocks: 1 Seconds: 60
contract address: 0xe80CF483070348eD6DAc2743C04E97dc0Bc68FAa
block number: 7684595
block timestamp: 1586447065
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.944213468170806873
gas used: 71714
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.00143428 ETH

Linking

Contract: BondedSortitionPoolFactory <--> Library: StackLib (at address: 0x685a1552921DefEe39A23d9Fef9167D490442AeF)

Deploying 'BondedSortitionPoolFactory'

transaction hash: 0x5d39170d0eb3a70d39ae33caf49e88a46ef2fa41d758d3eb22af1cb5b0abd666
Blocks: 1 Seconds: 52
contract address: 0x1E9cd46DE58b2e29009b40B6f490a0E6edAd590B
block number: 7684597
block timestamp: 1586447124
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.892990808170806873
gas used: 2561133
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.05122266 ETH

Deploying 'KeepBonding'

transaction hash: 0xf92e2d9df5a63e0eb77473879a2d21538c1d6d15c3e3ceb0567f97e5e4c0954a
Blocks: 1 Seconds: 24
contract address: 0x33AD3A082871943161B89cADEf69a6729542F77C
block number: 7684599
block timestamp: 1586447156
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.857302948170806873
gas used: 1784393
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.03568786 ETH

Deploying 'BondedECDSAKeep'

transaction hash: 0x60c096648c321b780a233e8eb45820800c9a41505ca3c8bf4a9e6799072b8a86
Blocks: 2 Seconds: 68
contract address: 0xeA40eBf57f615684129B0a7eefe56FF27393635e
block number: 7684603
block timestamp: 1586447210
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.782346868170806873
gas used: 3747804
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.07495608 ETH

Deploying 'BondedECDSAKeepFactory'

transaction hash: 0x163550cc6a59a99cd1967c33e5c369a87d5d63cfa3f6dbf7b09845a4f486c228
Blocks: 0 Seconds: 4
contract address: 0x128Fe4c78Bd23D33d30EBc5a42bC49A1D3659786
block number: 7684605
block timestamp: 1586447261
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.725338568170806873
gas used: 2850415
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.0570083 ETH

Deploying 'BondedECDSAKeepVendorImplV1'

transaction hash: 0xc4e2115440147443ef2649b73a1ee5d85d427a47e1590e335dd2abde8d5c93b8
Blocks: 1 Seconds: 16
contract address: 0x0e6626189d11E1eED23De18e525Bf8B414B8Bd4e
block number: 7684607
block timestamp: 1586447280
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.707114888170806873
gas used: 911184
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.01822368 ETH

Deploying 'BondedECDSAKeepVendor'

transaction hash: 0x1099122bfd0ff5bed9e10ec687e205ccf45b4b65d9db3536f3d8e43aa1aa7608
Blocks: 1 Seconds: 12
contract address: 0x9b873013CC55Ab1dF094B19d46EB1DaE608b7D81
block number: 7684610
block timestamp: 1586447302
account: 0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D
balance: 261.685922468170806873
gas used: 1059621
gas price: 20 gwei
value sent: 0 ETH
total cost: 0.02119242 ETH

Saving migration to chain.
Saving artifacts

Total cost: 0.26461466 ETH

3_initialize.js
current factory address: [0x128Fe4c78Bd23D33d30EBc5a42bC49A1D3659786]
approved operator contract [0x128Fe4c78Bd23D33d30EBc5a42bC49A1D3659786] in registry
set operator [0x923C5Dbf353e99394A21Aa7B67F3327Ca111C67D] as [0x9b873013CC55Ab1dF094B19d46EB1DaE608b7D81] contract upgrader

Saving migration to chain.

Total cost: 0 ETH

Summary

Total deployments: 11
Final cost: 0.2691194 ETH

repo: keep-network/keep-ecdsa


text:
test config

Thank you for your outstanding work. I wanted to modify the network and test it, but I did not find the test config. Could you please provide test config.

Thank you for your interest in our research.
The test config file is same as the train config.
Thank you.

hello author,I found your work done a great  job,and I  want to test the model, but I don't konw where the file“network_simplegate” is, I only find the network.py and the network_blind.py    .Could you please tell me where the file is ?

Thank you for your interest in our research.
I forgot to revert what I rewrote during the ablation study with the model using simplegate.
The correct code in L27 of generate_images_davis.py is network_module = importlib.import_module('models.network')

source: gharchive/issue

authors: clelevo kei-shibasaki w571112

repo: kei-shibasaki/PTFN

license_type: permissive

license_source: github-api


text:
init- & test-connection container image registries cannot be configured

Some environment (like ours) do not allow for pulling remote images, and thus the install tends to break on the busybox:latest container used in both the init-container from CKAN and the test-connection container from the Helm template.
It'd be great if both occurrences of the busybox image can be configured.
Proposal:
Add some fields to the values.yaml which allows for configuring the busybox registries:
...
image:
  repository: keitaro/ckan
  tag: 2.9.4
  pullPolicy: IfNotPresent
  testConnection:
    repository: harbor.containers.wurnet.nl/library/busybox
    tag: latest
    pullPolicy: IfNotPresent
  initContainer:
    repository: harbor.containers.wurnet.nl/library/busybox
    tag: latest
    pullPolicy: IfNotPresent
...

@JoooostB I will take a look at this.

repo: keitaroinc/ckan-helm


text:
Solr connection refused

Hi I got the following error:
Check_solr_connection...
HTTPConnectionPool(host='solr-headless', port=8983): Max retries exceeded with url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f2964c964f0>: Failed to establish a new connection: [Errno 111] Connection refused'))
Unable to connect to solr...retrying.
Thanks

solr 14:46:13.12 ERROR ==> Not enough nodes for the replicas and shards indicated

Lower the solr replicas and shards, im guessing you are running on less nodes so that's why solr cant start up. @pribadihcr


text:
How do I use this thing?

Seems cool. But how do I use it exactly?
I opened my downloaded project in VS Code, after unzipping it.
Where do I go from here?

If you download the extension in the VS Code marketplace first, not the zip,
https://marketplace.visualstudio.com/items?itemName=Keith-gamble.ignition-flint
Then you should able to open your project in a VS Code workspace, and it should attempt to auto-identify the gateway, enabling each of the various features.

Yes I installed the extension via the marketplace.
I was referring to the project backup file that I downloaded from the gateway.
I unzipped it into a folder then opened that folder in VS Code.
Is that the right way?

I would like to add that I am not sure how to even start using this tool. A more extensive/detailed walkthrough would be greatly appreciated. Perhaps you could give an example use in a video.

I tried to follow the Installation and Usage instructions, but the Ctrl+. command did not produce the desired effect you described. Do I create a new file, save it as .json and that should enable me to work with your tool?

Exploring Project Scripts: Open the "Ignition Project Scripts" view in the VS Code explorer sidebar to navigate and manage your project's script resources.

Also, in the instruction above, what exactly are you referencing as "Ignition Project Scripts"? I do not see this in the sidebar, even though I have your extension installed and enabled.

I think I did not understand the instructions as first, nor did I provide sufficient information in my previous request for help.
I see the "Ignition Project Scripts" workspace, but I when I expand it, I see "No Ignition project scripts found in the current workspace." What steps do I take to set up scripts in this workspace?
I am also receiving the following error when booting VS Code:

Ignition Flint failed to activate: TypeError: Cannot read properties of undefined (reading 'uri')

Note: I have Ignition installed in C:\Program Files\Inductive Automation\Ignition. Does it need to be installed in the AppData\Roaming folder?
I appreciate your assistance. I would love to use this tool as it seems quite powerful.

@GilRomero4 - a few questions for you:

are you using project inheritance, and if so how many levels does it go?
do you have any Sepasoft modules installed, and if so do you have a project called MESGateway?
what version of the module is loading in VSCode?

In my case my answers were Yes/3 and Yes/Yes.  When I used version 0.0.28 I did not see any of my projects listed. The 0.0.29 update now shows my projects (I have ~70).

@acorneliusatqs thanks for the response.

are you using project inheritance, and if so how many levels does it go?

No.

do you have any Sepasoft modules installed, and if so do you have a project called MESGateway?

No.

what version of the module is loading in VSCode?

Version 0.0.29

In my case my answers were Yes/3 and Yes/Yes. When I used version 0.0.28 I did not see any of my projects listed. The 0.0.29 update now shows my projects (I have ~70).

I see no projects listed and receive the error upon starting up VSCode:

Ignition Flint failed to activate: TypeError: Cannot read properties of undefined (reading 'uri')

Do I need to configure the gateway in the settings.json?
{
    "github.copilot.editor.enableAutoCompletions": true,
    "workbench.colorTheme": "Copilot Theme",
    "workbench.activityBar.location": "top",
    "ignitionFlint.ignitionGateways": [

    ]
}

I do know that the extension is looking for the project.json files that are present in every Ignition project.  From there is parses those folders for their content.
In my system I have all of the projects cloned locally to a folder.  Are you trying to connect to a remote gateway?  On my system I just opened the folder containing my projects.  I did not edit my config file.
Also I noticed in the current version that the project scan only happens on startup.  There is a refresh button in the Ignition Project Scripts explorer but it's not working for me.   V0.0.29.
As for Kindling, I get the impression the extension simply provides right-click capability to launch Kindling for the selected backup.  I don't think there's any additional functionality there.

Thanks @acorneliusatqs . I created a local clone and now the Project Scripts view is showing my project scripts. I am now facing other less significant obstacles, but at least I can experiment with the extension for now.
Thanks for the additional information.

If you download the extension in the VS Code marketplace first, not the zip,
https://marketplace.visualstudio.com/items?itemName=Keith-gamble.ignition-flint
Then you should able to open your project in a VS Code workspace, and it should attempt to auto-identify the gateway, enabling each of the various features.

I just installed this extension today and I'm trying to figure out how to use it. I think I've opened my project correctly (opening a project folder in VSCode located at C:\Program Files\Inductive Automation\Ignition\data\projects). I can see the scripts down in the "Ignition Project Scripts" Explorer view. However, when I try to do something like add a new script module via the Explorer view, I get an error like this:

I went to the extension settings and opened the settings.json file to try and update "ignitionFlint.ignitionGateways" with localhost:8088 and http://localhost:8088 but neither seemed to do the trick. Am I missing something in the process or do you have any ideas what might be causing this error? The fact that you mentioned auto-identifying the Gateway makes me think that there is either something on my machine preventing this from happening or I've got a PEBKAC error.
Please advise how to resolve/troubleshoot this. I'm very excited about using this extension!

authors: GilRomero4 Jeff-Krol acorneliusatqs jacksonlancaster keith-gamble

repo: keith-gamble/ignition-flint

license: MIT


text:
Jmanga Source request

Source name
Jmanga
Source link
https://jmanga.run
Source language
japanese
Other details
No response
Acknowledgements

[X] I have checked that the extension does not already exist by searching the extension listing and verified it does not appear there.
[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open or closed issue.
[X] I have written a meaningful title with the source name.
[ ] I have admitted that I am a clown by having checked this box, as I have not read these acknowledgements.
[X] I will fill out all of the requested information in this form.

Looks like WPComics

repo: keiyoushi/extensions-source


text:
The Brazilian Portuguese sources are duplicated.

Source information
Portuguese Brazilian
Source language
Portuguese
Steps to reproduce
One of the font options is with one font only and the rest are in the other option.

Expected behavior
All fonts in Brazilian Portuguese should be together.
Actual behavior
One font stands alone in the options.
Mihon/Tachiyomi version
0.16.5
Android version
Android 12
Other details
No response
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open or closed issue.
[X] I have written a short but informative title.
[X] I have updated the app to version 0.15.3.
[X] I have updated all installed extensions.
[X] I have tried the troubleshooting guide.
[X] If this is an issue with the app itself, I should be opening an issue in the app repository.
[X] I will fill out all of the requested information in this form.

App issue, report to them

turns out it was a little oopsie on our end


text:
chapter list not updating

Source information
templescans v1.4.41
Source language
English
Steps to reproduce

add any comic to library from this source.
Wait for new chapter to be added on source
Open added comic tile in library
Manually refresh chapter list
Latest chapter should be added in the list of chapters

Expected behavior
On manually updating chapter list of existing comic in library from this source, new chapter should be added in the list of chapters.
Actual behavior
Chapter list remains same as old instead of adding new chapter to the list as show in webview
Mihon/Tachiyomi version
0.16.5
Android version
Android 13
Other details
Comic URL is same and on opening source in mihon it shows comic already added in Library
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open or closed issue.
[X] I have written a short but informative title.
[X] I have updated the app to version 0.15.3.
[X] I have updated all installed extensions.
[X] I have tried the troubleshooting guide.
[X] If this is an issue with the app itself, I should be opening an issue in the app repository.
[X] I will fill out all of the requested information in this form.

Bus still exists after extension updated to v1.4.42

paid chapters get filtered out, if you want them disable the filtering in extension setting

Updated extension setting to display paid chapters, still list not updated to latest chapters

make sure to restart the app, for further help come to our discord

Now On manual refreshing it shows http 404 error, but web view is working fine. No change in URL.

Same here.
HTTP error 404 with or without displaying the paid chapters


text:
Allow conf.d and templates directory to be symlinks

filepath.Walk() ignores symlinks, so evaluate any symlinks as we recurse the root path.
Closes #741

Is there a process for getting code reviewed? Can't seem to add any reviewers or labels.
@carmstrong @gabrtv @kelseyhightower @bacongobbler @okushchenko

The last time any code was merged to master was 11 months ago, and 0.16.0 was released over a year ago. Pretty sure the project isn't seeing any recent activity, and I haven't worked on confd since 2016. If you're interested in getting this merged, I'd suggest forking. :)

@bacongobbler thanks for the response. It would probably manage a lot of expectations if the top level README indicated that this project is no longer maintained.

source: gharchive/pull-request

authors: bacongobbler jsok

repo: kelseyhightower/confd

license: mit

license_source: bigquery


text:
Deploy it on bare metal

Hey ! Nice project!
But how can I use it on baremetal k8s cluster!
i got pods via state "pending" and error:
pod has unbound immediate PersistentVolumeClaims (repeated 7 times)
Plz help

Its ok ! i resolve it with this fix https://github.com/kelseyhightower/consul-on-kubernetes/issues/20

repo: kelseyhightower/consul-on-kubernetes


text:
kube-dns behaves strangely; only responds to DNS queries from one worker

Thanks for great tutorial again!
We have setup few kubernetes clusters using your guide, and there is one thing which is really a pain in the neck. The DNS add-on (kubedns/skydns).  In past two lab setups, the DNS addons only seem to work from one worker node. We tried this on kvm/libvirt, AWS, and most recently on VirtualBox. On all instances the base OS has been Fedora 24. Here is little about the setup:

3 x etcd nodes
2 x controller nodes
2 x worker nodes

SELinux is disabled on all nodes. Firewalld package is removed from all nodes. Routes are added to default router, and  pods from different workers / PodCIDRs ping each other.
Our work (based on your guide) documented here.
Our latest setup is on virtualbox. When we test our kubedns deployment, we notice that it cycles through two correct responses, and then times out on third one.  It is not consistent. We have tried to look into logs, on controllers and on workers, but we could not put a finger on anything specific.
Here is what we see:
First we get the deployment and service files from your repo. Then we set them up (not shown).
[root@controller1 tmp]# kubectl get deployment --all-namespaces
NAMESPACE     NAME           DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
default       my-nginx       2         2         2            2           1h
kube-system   kube-dns-v20   2         2         2            2           58s

[root@controller1 tmp]# kubectl get pods
NAME                        READY     STATUS    RESTARTS   AGE
my-nginx-2494149703-8y52n   1/1       Running   0          1h
my-nginx-2494149703-hvr13   1/1       Running   0          1h
[root@controller1 tmp]#

Here are two DNS responses, and the third one is failing:
[root@controller1 tmp]# kubectl exec -it my-nginx-2494149703-8y52n bash
root@my-nginx-2494149703-8y52n:/# dig yahoo.com

;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 19492
;; flags: qr rd ra; QUERY: 1, ANSWER: 3, AUTHORITY: 6, ADDITIONAL: 11

;; QUESTION SECTION:
;yahoo.com.			IN	A

;; ANSWER SECTION:
yahoo.com.		991	IN	A	98.139.183.24
yahoo.com.		991	IN	A	98.138.253.109
yahoo.com.		991	IN	A	206.190.36.45

;; AUTHORITY SECTION:
yahoo.com.		68309	IN	NS	ns1.yahoo.com.
yahoo.com.		68309	IN	NS	ns4.yahoo.com.

;; ADDITIONAL SECTION:
ns4.yahoo.com.		167225	IN	A	98.138.11.157
ns3.yahoo.com.		165446	IN	AAAA	2406:8600:b8:fe03::1003

;; Query time: 11 msec
;; SERVER: 10.32.0.10#53(10.32.0.10)
;; WHEN: Tue Nov 22 13:06:17 UTC 2016
;; MSG SIZE  rcvd: 402

root@my-nginx-2494149703-8y52n:/# dig yahoo.com

;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 32122
;; flags: qr rd ra; QUERY: 1, ANSWER: 3, AUTHORITY: 0, ADDITIONAL: 1

;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 4096
;; QUESTION SECTION:
;yahoo.com.			IN	A

;; ANSWER SECTION:
yahoo.com.		990	IN	A	206.190.36.45
yahoo.com.		990	IN	A	98.138.253.109
yahoo.com.		990	IN	A	98.139.183.24

;; Query time: 2 msec
;; SERVER: 10.32.0.10#53(10.32.0.10)
;; WHEN: Tue Nov 22 13:06:18 UTC 2016
;; MSG SIZE  rcvd: 86

root@my-nginx-2494149703-8y52n:/# dig yahoo.com

; <<>> DiG 9.9.5-9+deb8u8-Debian <<>> yahoo.com
;; global options: +cmd
;; connection timed out; no servers could be reached
root@my-nginx-2494149703-8y52n:/#

And, this keeps repeating. Logs, etc, did not help. (May be I don't know where to look for, or may be I am blind :)
Any help is appreciated. Thanks!

This should be working fine on the master branch.

authors: KamranAzeem kelseyhightower

repo: kelseyhightower/kubernetes-the-hard-way


text:
[suggest] struct or args majorization

https://github.com/kelvins-io/g2cache/blob/0098a51433a6b1ccd9947375db8f2d504bd9f3fe/grpool.go#L89-L100
对于 pool 对象，他本身存在 jobQueue  chan. 可以去掉jobQueue  变量。
在 newWorker(int64(i), pool, jobQueue) 里面，可以从pool 中取出 jobQueue 变量。

新版本已经去掉

authors: kelvins-io ywanbing


text:
🛑 bigdata is down

In 1f63ae7, bigdata (https://bigdata.ministep.cn/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: bigdata is back up in 0880e76.

repo: kemistep/upptime


text:
Doctrine2

Please, add this ORM.

:+1:

:+1:

Order by does not work:
https://github.com/kenjis/php-orm-benchmark/blob/master/fuel/app/classes/model/doctrine/Blog/Entity/Post.php#L49
Can anybody fix it?
Controller: https://github.com/kenjis/php-orm-benchmark/blob/master/fuel/app/classes/controller/orm/doctrine.php#L37

Added: https://github.com/kenjis/php-orm-benchmark#results

authors: TheCelavi garrettw kenjis seyfer

repo: kenjis/php-orm-benchmark


text:
Add log subscriber

Add log subscriber to prefix activerecord log messages with current database name

just rebased against your master..

I see several issues with this PR:

alias_method_chain is deprecated in Rails 5 and that was the primary reason I had to release v2. Now we use the bare-metal alias_method instead (Module#prepend approach would create an infinite loop, so I had to resort to it). See slavery/active_record/base.rb
When we patch the core library, it goes to slavery/active_record and directly reopen the core class and alias them, rather than send(:include). If you do that in the same file (not lazy loading, etc.), there's no point in isolating them. Also see slavery/active_record/base.rb

Other than that, 👍

Thanks @kenn, I appreciate the feedback! I'll try another approach..

Thank you! 🎉 🎉

authors: citrus kenn

repo: kenn/slavery


text:
Obstacle course third

My code tells the robot to turn 90 degrees to the left, go forward to ram into the wood block, then back up to the initial position to then align itself for the parking position.
1 - Turn from start point 90 degrees to the left (from -10, 55, 90°)
2 - Forward to point (48, 55, 0°)
3 - Back up to start point (-10, 55, 0°)

Looks good, great job!


text:
Despite installing it, it doesn't install

I click to install this plugin.
From the console message It looks like it is installing
But when I open the settings it does not show
and the plugins section shows it wasn't installed.

@madmax2 can you copy/paste the console message when you tried to install this plugin?
Also if you can ssh to your Raspberry Pi, can you run pip list | grep OctoPrint-Anywhere and let me know the output of it?

@kennethjiang
Console message
https://pastebin.com/VfDAAhPY
I am not using raspberry pi, I installed octoprint on windows.
If the command you posted is to check if the folders of octoprint-anywhere exists, then it does show in explorer.
I can see there is 2 folders exists in C:\OctoPrint\venv\Lib\site-packages
-octoprint_anywhere
-OctoPrint_Anywhere-0.2.1-py2.7.egg-info

I have not tested my plugin on Windows and I guess that's why you ran into issues.
From the log file it seems that my plugin was indeed installed successfully. I'm 99% sure the reason why it doesn't show up in OctoPrint because of some errors during OctoPrint startup.
If you know how to get OctoPrint log file, please do so and send it to me  at kenneth dot jiang at gmail dot com . Otherwise I'll love to do a quick skype/google hangout chat with you to figure out what went wrong.

Closing it now due to lack of activities. Let me know if you still experience problems @madmax2

repo: kennethjiang/OctoPrint-Anywhere


text:
autoenv still fires off if you try and cd into a "non-configured device", or on a folder called .env

Hi, I sort of have 2 things I've discovered about autoenv. First one is pretty straightforward, it does not seem to check if .env is a dir or file:
autoenv % pwd -P
/private/tmp/autoenv

autoenv % mkdir -p hello/.env

autoenv % cd hello
shasum: /tmp/autoenv/hello/.env:
..........
autoenv:     --- (begin contents) ---------------------------------------
sed: read error on /tmp/autoenv/hello/.env: Is a directory
autoenv:     --- (end contents) -----------------------------------------
autoenv:
autoenv: Are you sure you want to allow this? (y/N) y
shasum: /tmp/autoenv/hello/.env:

I made a 10MB ext4 filesystem on a Linux computer, then copied it to my OS X laptop (10.11), mounted it and tried to cd into it. I was trying to test out the OS X fuse ext4 thingo. I believe I am running the newest version of autoenv (as it should be updating with Homebrew).
The ext4 filesystem didn't work correctly when I mounted it (ie, trying to ls into it gives ls: cannot open directory /mnt: Device not configured)
~ % ext4fuse ext4 /mnt

~ % cd /mnt
shasum: /mnt/.env:
.........
autoenv:     --- (begin contents) ---------------------------------------
sed: can't read /mnt/.env: Device not configured
autoenv:     --- (end contents) -----------------------------------------
..........

although sometimes, on that broken ext4 filesystem, it will give me "Is a directory" (but no .env directory exists?)
~ % umount /mnt
Unmount successful for /mnt

~ % ext4fuse ext4 /mnt

~ % cd /mnt
shasum: /mnt/.env:
......
autoenv:     --- (begin contents) ---------------------------------------
sed: read error on /mnt/.env: Is a directory
autoenv:     --- (end contents) -----------------------------------------
autoenv:
autoenv: Are you sure you want to allow this? (y/N) yes

/mnt % ll
ls: cannot access sha512.sums: Input/output error
ls: reading directory .: Device not configured
4.0K drwxr-xr-x 43 root root 4.0K Dec 16 19:54 ../
 12K drwx------  2 root root  12K Dec 22 18:43 lost+found/
3.0K drwxr-xr-x 63 root root 3.0K Dec 22 18:44 etc/
1.0K drwxr-xr-x  4 root root 1.0K Dec 22 18:44 ./
307K -rw-r--r--  1 root root 307K Dec 22 18:44 sha512.sums

Anyway, I'm going to end this here, I don't want this issue report to look like I'm asking for help with fuse ext4, which is probably does. Just observing that it likes to activate at some odd times.

This is fixed by now, and there is a test to verify this :+1:

authors: dasJ raincoats

repo: kennethreitz/autoenv


text:
pipenv install mkdocs --system failing inside Dockerfile

Failing to install mkdocs when passing --system flag. Works without --system flag.
NOTE!: This does work if you create a symlink from /usr/local/bin/python to /bin/python before running the pipenv install mkdocs --system command.
Describe you environment

OSX: 10.12.6 (16G29)
Docker version: 17.09.0-ce, build afdb6d4
Python version: Python 3.6.3
Pipenv version: pipenv, version 8.2.7

Expected result
Docker image would be build successfully.
Actual result
Docker image failed to build.
Steps to replicate
When using a Dockerfile with the contents:
FROM python:3.6-slim

WORKDIR /usr/src/app

RUN pip install pip --upgrade && pip install pipenv

# Fails
RUN pipenv install mkdocs --system

And running docker image build -t pipenv:latest .
Step 1/5 : FROM python:3.6-slim
 ---> 45454af642e0
Step 2/5 : WORKDIR /usr/src/app
 ---> Using cache
 ---> 994c7ad276b3
Step 3/5 : RUN pip install pip --upgrade && pip install pipenv
 ---> Using cache
 ---> 57dd47271ba9
Step 4/5 : RUN pip install setuptools --upgrade
 ---> Running in 525c7bb9f3e7
Collecting setuptools
  Downloading setuptools-36.6.0-py2.py3-none-any.whl (481kB)
Installing collected packages: setuptools
  Found existing installation: setuptools 36.5.0
    Uninstalling setuptools-36.5.0:
      Successfully uninstalled setuptools-36.5.0
Successfully installed setuptools-36.6.0
 ---> 0fd7f53cd65c
Removing intermediate container 525c7bb9f3e7
Step 5/5 : RUN pipenv install mkdocs --system
 ---> Running in b591e1587884
Creating a Pipfile for this project…
Installing mkdocs…
Collecting mkdocs
  Downloading mkdocs-0.17.0-py2.py3-none-any.whl (1.2MB)
Collecting Markdown>=2.3.1 (from mkdocs)
  Downloading Markdown-2.6.9.tar.gz (271kB)
Collecting tornado>=4.1 (from mkdocs)
  Downloading tornado-4.5.2.tar.gz (483kB)
Collecting livereload>=2.5.1 (from mkdocs)
  Downloading livereload-2.5.1.tar.gz
Collecting Jinja2>=2.7.1 (from mkdocs)
  Downloading Jinja2-2.9.6-py2.py3-none-any.whl (340kB)
Collecting click>=3.3 (from mkdocs)
  Downloading click-6.7-py2.py3-none-any.whl (71kB)
Collecting PyYAML>=3.10 (from mkdocs)
  Downloading PyYAML-3.12.tar.gz (253kB)
Collecting six (from livereload>=2.5.1->mkdocs)
  Downloading six-1.11.0-py2.py3-none-any.whl
Collecting MarkupSafe>=0.23 (from Jinja2>=2.7.1->mkdocs)
  Downloading MarkupSafe-1.0.tar.gz
Building wheels for collected packages: Markdown, tornado, livereload, PyYAML, MarkupSafe
  Running setup.py bdist_wheel for Markdown: started
  Running setup.py bdist_wheel for Markdown: finished with status 'done'
  Stored in directory: /root/.cache/pip/wheels/bf/46/10/c93e17ae86ae3b3a919c7b39dad3b5ccf09aeb066419e5c1e5
  Running setup.py bdist_wheel for tornado: started
  Running setup.py bdist_wheel for tornado: finished with status 'done'
  Stored in directory: /root/.cache/pip/wheels/b3/47/3a/96e12476829cb196adabc879fedb72f1bb2c8613b6961e78e7
  Running setup.py bdist_wheel for livereload: started
  Running setup.py bdist_wheel for livereload: finished with status 'done'
  Stored in directory: /root/.cache/pip/wheels/71/95/bf/6ccaa70b36701dd3fa337296d7ec6614bf14f51d58bc3a4a7a
  Running setup.py bdist_wheel for PyYAML: started
  Running setup.py bdist_wheel for PyYAML: finished with status 'done'
  Stored in directory: /root/.cache/pip/wheels/2c/f7/79/13f3a12cd723892437c0cfbde1230ab4d82947ff7b3839a4fc
  Running setup.py bdist_wheel for MarkupSafe: started
  Running setup.py bdist_wheel for MarkupSafe: finished with status 'done'
  Stored in directory: /root/.cache/pip/wheels/88/a7/30/e39a54a87bcbe25308fa3ca64e8ddc75d9b3e5afa21ee32d57
Successfully built Markdown tornado livereload PyYAML MarkupSafe
Installing collected packages: Markdown, tornado, six, livereload, MarkupSafe, Jinja2, click, PyYAML, mkdocs
Successfully installed Jinja2-2.9.6 Markdown-2.6.9 MarkupSafe-1.0 PyYAML-3.12 click-6.7 livereload-2.5.1 mkdocs-0.17.0 six-1.11.0 tornado-4.5.2

Adding mkdocs to Pipfile's [packages]…
Locking [dev-packages] dependencies…
Locking [packages] dependencies…
CRITICAL:pip.utils:Error [Errno 2] No such file or directory: '/bin/python': '/bin/python' while executing command python setup.py egg_info
Traceback (most recent call last):
  File "/usr/local/bin/pipenv", line 11, in <module>
    sys.exit(cli())
  File "/usr/local/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 722, in __call__
    return self.main(*args, **kwargs)
  File "/usr/local/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 697, in main
    rv = self.invoke(ctx)
  File "/usr/local/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 1066, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/local/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 895, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/local/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 535, in invoke
    return callback(*args, **kwargs)
  File "/usr/local/lib/python3.6/site-packages/pipenv/cli.py", line 1847, in install
    do_lock(system=system, pre=pre)
  File "/usr/local/lib/python3.6/site-packages/pipenv/cli.py", line 1082, in do_lock
    pre=pre
  File "/usr/local/lib/python3.6/site-packages/pipenv/utils.py", line 452, in resolve_deps
    resolved_tree.update(resolver.resolve(max_rounds=PIPENV_MAX_ROUNDS))
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/piptools/resolver.py", line 102, in resolve
    has_changed, best_matches = self._resolve_one_round()
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/piptools/resolver.py", line 200, in _resolve_one_round
    for dep in self._iter_dependencies(best_match):
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/piptools/resolver.py", line 296, in _iter_dependencies
    dependencies = self.repository.get_dependencies(ireq)
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/piptools/repositories/pypi.py", line 153, in get_dependencies
    result = reqset._prepare_file(self.finder, ireq)
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/pip/req/req_set.py", line 639, in _prepare_file
    abstract_dist.prep_for_dist()
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/pip/req/req_set.py", line 134, in prep_for_dist
    self.req_to_install.run_egg_info()
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/pip/req/req_install.py", line 438, in run_egg_info
    command_desc='python setup.py egg_info')
  File "/usr/local/lib/python3.6/site-packages/pipenv/patched/pip/utils/__init__.py", line 667, in call_subprocess
    cwd=cwd, env=env)
  File "/usr/local/lib/python3.6/subprocess.py", line 709, in __init__
    restore_signals, start_new_session)
  File "/usr/local/lib/python3.6/subprocess.py", line 1344, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: '/bin/python': '/bin/python'
The command '/bin/sh -c pipenv install mkdocs --system' returned a non-zero code: 1

I think this is related to another bug.  I'll have to look it up.  Let me try to reproduce.

Can you try with the following dockerfile?
FROM python:3.6-slim
WORKDIR /usr/src/app
RUN set -ex && pip install pipenv --upgrade
ONBUILD RUN set -ex && pipenv install --deploy --system mkdocs

I was able to pull from our example dockerfile to get this to work.

authors: erinxocon ryan-blunden

repo: kennethreitz/pipenv


text:
Dependency could not be resolved when I used asterisk.

I am using requests and pyrebase as dependency to my project.
My Pipfile looks like this: (non-relevant are removed)
[[source]]

url = "https://pypi.python.org/simple"
verify_ssl = true
name = "pypi"

[packages]

requests = "*"
pyrebase = "*"

[dev-packages]

Describe you environment

OS Type: Ubuntu 17.10 artful
Python version: Python 3.6.2
Pipenv version: pipenv, version 8.3.1

Expected result
Install requests 2.11.1 and goes well.
Actual result
Pipenv installs requests 2.18.4(which is latest version) and conflict with pyrebase(which requires 2.11.1)
Steps to replicate
pipenv install requests pyrebase

This is how our locking mechanism works.  The pipfile specifies the most recent version of requests, where as pyrebase specifically requests an older version.  Since pyrebase specifically requests an older version of requests, you get a conflict when you are trying to lock while also installing the most current version as it can't match them up.
If you just install pyrebase, you will have access to requests as it will be in your site packages.  So you don't have to install requests again.  Everything actually installs ok, the way you are doing it, it just can't create a lock file for a deterministic build.  You can use the --skip-lock command to ignore this error message if you really need the most recent version of requests installed.

So * means most recent instead of wildcard?

@Kjwon15 Indeed!

This seems like a bug to me. Shouldn't pip-tools be resolving the correct version of requests to install alongside pyrebase?
Is this a dupe of one of the many "our patched version of pip-tools is broken" tickets?

@nateprewitt i think it’s an order of operations thing. I think if you specifically ask for requests = “*” it is technically pinning the most recent version of requests and then moving on. This is confusing I agree, considering I believe we advertise the syntax as ‘unpinned’ when converting from requirements.txt. If the intention is to strictly pin the most current version of everything I would imagine this could cause problems for people who think these are not pinned at all and assume we will resolve dependencies and land on versions with no conflicts (which seems preferable)
If you want strict pins that goes in the lockfile, I would think?

I do think that they should be resolved along side pyrebase.  Also I think like @Kjwon15 the * means wild card more than most recent version.  Ideally, IMO, * should resolve to whatever version is needed by other packages, or the most recent if nothing requires it.  Unpinned implies wildcard not most recent version.

Not sure but I think this is related to https://github.com/pypa/pip/issues/988.

@tuukkamustonen excellent thank you for the ref

I'm can confirm this is related to the "we pin our selected candidate right away" issue I was talking about in https://github.com/kennethreitz/pipenv/issues/875#issuecomment-337717817 .
pipenv lock --verbose output on which I confirmed this:
$ pipenv lock --verbose
Locking [dev-packages] dependencies…
Using pip: -i https://pypi.python.org/simple

                          ROUND 1
Current constraints:

Finding the best candidates:

Finding secondary dependencies:
------------------------------------------------------------
Result of round 1: stable, done
Locking [packages] dependencies…
Using pip: -i https://pypi.python.org/simple

                          ROUND 1
Current constraints:
  pyrebase
  requests

Finding the best candidates:
  found candidate pyrebase==3.0.27 (constraint was <any>)
  found candidate requests==2.18.4 (constraint was <any>)

Finding secondary dependencies:
  requests==2.18.4          requires certifi>=2017.4.17, chardet<3.1.0,>=3.0.2, idna<2.7,>=2.5, requests==2.18.4, urllib3<1.23,>=1.21.1
  pyrebase==3.0.27          requires gcloud==0.17.0, oauth2client==3.0.0, pycryptodome==3.4.3, pyrebase==3.0.27, python_jwt==2.0.1, requests==2.11.1, requests_toolbelt==0.7.0

New dependencies found in this round:
  adding [u'certifi', '>=2017.4.17', '[]']
  adding [u'chardet', '<3.1.0,>=3.0.2', '[]']
  adding [u'gcloud', '==0.17.0', '[]']
  adding [u'idna', '<2.7,>=2.5', '[]']
  adding [u'oauth2client', '==3.0.0', '[]']
  adding [u'pycryptodome', '==3.4.3', '[]']
  adding [u'pyrebase', '==3.0.27', '[]']
  adding [u'python-jwt', '==2.0.1', '[]']
  adding [u'requests', '==2.11.1,==2.18.4', '[]']
  adding [u'requests-toolbelt', '==0.7.0', '[]']
  adding [u'urllib3', '<1.23,>=1.21.1', '[]']
Removed dependencies in this round:
Unsafe dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable

                          ROUND 2
Current constraints:
  certifi>=2017.4.17
  chardet<3.1.0,>=3.0.2
  gcloud==0.17.0
  idna<2.7,>=2.5
  oauth2client==3.0.0
  pycryptodome==3.4.3
  pyrebase==3.0.27
  python_jwt==2.0.1
  requests==2.11.1,==2.18.4
  requests_toolbelt==0.7.0
  urllib3<1.23,>=1.21.1

Finding the best candidates:
  found candidate certifi==2017.7.27.1 (constraint was >=2017.4.17)
  found candidate chardet==3.0.4 (constraint was >=3.0.2,<3.1.0)
  found candidate gcloud==0.17.0 (constraint was ==0.17.0)
  found candidate idna==2.6 (constraint was >=2.5,<2.7)
  found candidate oauth2client==3.0.0 (constraint was ==3.0.0)
  found candidate pycryptodome==3.4.3 (constraint was ==3.4.3)
  found candidate pyrebase==3.0.27 (constraint was ==3.0.27)
  found candidate python_jwt==2.0.1 (constraint was ==2.0.1)
Warning: Your dependencies could not be resolved. You likely have a mismatch in your sub-dependencies.
  You can use $ pipenv install --skip-lock to bypass this mechanism, then run $ pipenv graph to inspect the situation.
Could not find a version that matches requests==2.11.1,==2.18.4
Tried: 0.2.0, 0.2.1, 0.2.2, 0.2.3, 0.2.4, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.3.4, 0.4.0, 0.4.1, 0.5.0, 0.5.1, 0.6.0, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.6.5, 0.6.6, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.7.4, 0.7.5, 0.7.6, 0.8.0, 0.8.1, 0.8.2, 0.8.3, 0.8.4, 0.8.5, 0.8.6, 0.8.7, 0.8.8, 0.8.9, 0.9.0, 0.9.1, 0.9.2, 0.9.3, 0.10.0, 0.10.1, 0.10.2, 0.10.3, 0.10.4, 0.10.6, 0.10.7, 0.10.8, 0.11.1, 0.11.2, 0.12.0, 0.12.1, 0.13.0, 0.13.1, 0.13.2, 0.13.3, 0.13.4, 0.13.5, 0.13.6, 0.13.7, 0.13.8, 0.13.9, 0.14.0, 0.14.1, 0.14.2, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.1.0, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 2.0.0, 2.0.0, 2.0.1, 2.0.1, 2.1.0, 2.1.0, 2.2.0, 2.2.0, 2.2.1, 2.2.1, 2.3.0, 2.3.0, 2.4.0, 2.4.0, 2.4.1, 2.4.1, 2.4.2, 2.4.2, 2.4.3, 2.4.3, 2.5.0, 2.5.0, 2.5.1, 2.5.1, 2.5.2, 2.5.2, 2.5.3, 2.5.3, 2.6.0, 2.6.0, 2.6.1, 2.6.1, 2.6.2, 2.6.2, 2.7.0, 2.7.0, 2.8.0, 2.8.0, 2.8.1, 2.8.1, 2.9.0, 2.9.0, 2.9.1, 2.9.1, 2.9.2, 2.9.2, 2.10.0, 2.10.0, 2.11.0, 2.11.0, 2.11.1, 2.11.1, 2.12.0, 2.12.0, 2.12.1, 2.12.1, 2.12.2, 2.12.2, 2.12.3, 2.12.3, 2.12.4, 2.12.4, 2.12.5, 2.12.5, 2.13.0, 2.13.0, 2.14.0, 2.14.0, 2.14.1, 2.14.1, 2.14.2, 2.14.2, 2.15.1, 2.15.1, 2.16.0, 2.16.0, 2.16.1, 2.16.1, 2.16.2, 2.16.2, 2.16.3, 2.16.3, 2.16.4, 2.16.4, 2.16.5, 2.16.5, 2.17.0, 2.17.0, 2.17.1, 2.17.1, 2.17.2, 2.17.2, 2.17.3, 2.17.3, 2.18.0, 2.18.0, 2.18.1, 2.18.1, 2.18.2, 2.18.2, 2.18.3, 2.18.3, 2.18.4, 2.18.4

Perhaps I've had a related issue.
A new project with Pipfile like this:
[[source]]

url = "https://pypi.python.org/simple"
verify_ssl = true
name = "pypi"

[packages]

scrapy = "*"
pymongo = "*"

[dev-packages]

And simply typing $ pipenv install will end up with errors and not install anything:
$ pipenv install
Creating a virtualenv for this project…
⠋Using base prefix '/usr'
New python executable in /home/mike/.local/share/virtualenvs/crawler_center-fPr_NAho/bin/python
Installing setuptools, pip, wheel...done.

Virtualenv location: /home/mike/.local/share/virtualenvs/crawler_center-fPr_NAho
Pipfile.lock not found, creating…
Locking [dev-packages] dependencies…
Locking [packages] dependencies…
Warning: Your dependencies could not be resolved. You likely have a mismatch in your sub-dependencies.
  You can use $ pipenv install --skip-lock to bypass this mechanism, then run $ pipenv graph to inspect the situation.
Could not find a version that matches pymongo
Tried: (no version found at all)

@foodtooth you haven’t provided enough information to determine if your issue has anything to do with this one. Please open a new issue and provide context about your environment and we can go from there

authors: Kjwon15 erinxocon foodtooth nateprewitt techalchemy tuukkamustonen vphilippon


text:
AuthManager isn't threadsafe for requests with same URL but different credentials

When you send multiple concurrent requests (using gevent, in my case) to the same URL but with different auth parameters, every request will use the same auth parameters.
Reproduced with this code: https://gist.github.com/1300341
The sample code is using the regular api, but the bug is reproducible using requests.async as well.

Sorry for the bug. Luckily, auth manager will be removed soon. It's my least favorite part of the codebase.

This bug is fixed in 0.7.3. Thanks kenneth :)

I was so happy to see it die in a fire :)

:sparkles: :cake: :sparkles:

authors: chadselph derferman kennethreitz

repo: kennethreitz/requests


text:
about train result

May I ask why all the detection results are zero after I switched the backbone and trained on COCO? Could you please share the training logs from your training session?

May I ask why all the detection results are zero after I switched the backbone and trained on COCO? Could you please share the training logs from your training session?

If you change the batch size, you should change the lr. (reducing the lr )

May I ask why all the detection results are zero after I switched the backbone and trained on COCO? Could you please share the training logs from your training session?

If you change the batch size, you should change the lr. (reducing the lr )
”You need to download HRNet-W32 imagenet pretrained model (see above) and change the checkpoint path by modifying MODEL.PRETRAINED in .yaml, and run following commands“

repo: kennethwdk/CID


text:
stop-words-file fix

The option --stop-words-file  is ignored when using the command line interface. Here is a proposition of patch that fix it:
stop-words-file-CLI.patch.txt

i also met this problem

I'll take care of this shortly.

authors: KrallBigFan cmj1999 kennycason

repo: kennycason/kumo


text:
GRPC Reflection Usage

Hi,
I've tried to run the reflection client as described in the README, but it fails with the following error:
Traceback (most recent call last):
  File "eagr_demo.py", line 9, in <module>
    generic_myservice_client = grpc_reflection_interface.make_json_grpc_client(host, service)
TypeError: make_json_grpc_client() missing 1 required positional argument: 'service_name'

I see that the method make_json_grpc_client has a self, but I'm not sure how it's supposed to be bound. Could you please advise?
Thanks!

Hi @aarnone, thank you for reporting this. This is a bug--it's a remnant of some migrated code, and one of the only spots with no unit tests yet written.
Here is a draft PR, and I tested it against some of my own services:
https://github.com/kensho-technologies/eagr/pull/9
I plan on writing unit tests before merging, and then I'll release the next version as soon as it is merged. If you'd like to pull the branch and test it yourself to verify in the meantime, you are welcome to do so.

Hi @ljades, thanks for the quick response!
I've tried locally and it works great 👍

v0.1.1 has been published with the fix. Thanks again for reporting the issue.

authors: aarnone ljades

repo: kensho-technologies/eagr


text:
feat(vars): Convert environment vars

Converts environment vars used in npm run to account for windows/linux respectively
Add tests for command
Update npm test to look for all .test.js files

Essentially it makes cross-env PORT=3000 $npm_package_main work, otherwise on Windows you get Error: Cannot find module 'C:\Repo\$npm_package_main' since Windows expects env variables to be in the format of %npm_package_main%
As a side note, the linebreak-style rule makes it a frustrating experience committing against the repo as it fails all commits despite the fact that Git converts CRLF to LF automatically on commit (this is a default setting for Git on Windows)

Sorry about the linebreak-style issue. But without it, sometimes things can get kinda crazy. I need to add my .gitattributes file so it's more seamless. I actually need to do a few things to this project to improve it. For example, the testing stuff is sub-optimal and actually the breaking build is related to that. I'm going to make a few updates right after I merge your PR to fix things up a little. It may take a bit before I get a chance to do that though. Soon hopefully.

Current coverage is 100% (diff: 100%)

Merging #32 into master will not change coverage

@@           master   #32   diff @@
===================================
  Files           1     2     +1
  Lines          34    49    +15
  Methods         0     0
  Messages        0     0
  Branches        0     0
===================================
+ Hits           34    49    +15
  Misses          0     0
  Partials        0     0

Powered by Codecov. Last update f2275d5...e2f7acd

I can understand about the line endings, though I've personally never seen it as an issue in any repo I've worked in, both personally and professionally
Either way, I've fixed the lodash.assign bit to use Object.assign, should be good now

FYI this broke my build
https://travis-ci.org/redux-observable/redux-observable/builds/165012081
https://github.com/redux-observable/redux-observable/pull/125
> cross-env NODE_ENV=development webpack src/index.js dist/redux-observable.js
module.js:457
    throw err;
    ^
Error: Cannot find module './command'
    at Function.Module._resolveFilename (module.js:455:15)
    at Function.Module._load (module.js:403:25)
    at Module.require (module.js:483:17)
    at require (internal/module.js:20:19)
    at Object.<anonymous> (/home/travis/build/redux-observable/redux-observable/node_modules/cross-env/dist/index.js:13:16)
    at Module._compile (module.js:556:32)
    at Object.Module._extensions..js (module.js:565:10)
    at Module.load (module.js:473:32)
    at tryModuleLoad (module.js:432:12)
    at Function.Module._load (module.js:424:3)

How about bumping the version number?

Issue was fixed in https://github.com/kentcdodds/cross-env/commit/e847b1859fabccc89e97622966d6d72cb386fbfd v3.1.1

Ah, I just posted an issue as this broke our build just like yours did @jayphelps
Will update again and try to delete the issue if it's fixed now.

Oh snap, @elijahmanor, I just remembered this is a thing. I don't know whether you need to use cross-var and cross-env together. cross-env can do both by itself! (Ref https://github.com/elijahmanor/cross-var/issues/3)

authors: EnzoMartin codecov-io jayphelps kentcdodds skalfyfan triddle01

repo: kentcdodds/cross-env


text:
Centermode without infinite leaves gap left of first slide

short description of the bug / issue, provide more detail below.

When using centermode and setting infinite to false. The first slide is also centered (inside the slider) leaving an empty gap to the left. I'm assuming this is the expected behaviour but I'd suggest that the first (and last actually) slide is not centered. So that we don't get empty gaps.
====================================================================
[  paste your jsfiddle link here  ]
http://jsfiddle.net/4e8u0gcj/
====================================================================
Steps to reproduce the problem
Create a slider with centermode:true and infinite:false
====================================================================

Center mode is intended to be used with infinite, and I'm not sure there's a sensible use case for non-infinite, but perhaps you can shed some light -- consider that if part of a "previous" slide was visible, users would have an expectation that it could be navigated to. Let me know if there's something I'm overlooking in my thought process.

I've been looking for several hours to find a 'fix' for this. When I set the slider to infinite it's unclear to the (lazy) users that there are only 6 (or 5 or 4) items. They keep swiping and realize they've seen the same items when they've already seen them three or four times. So I'd like to disable infinite without wasting so much space on the left side of the first slide. I've tried to accomplish this with CSS and/or JavaScript btu without any luck due to breakpoint problems.
I'm not sure if more people are interested in a setting to move the first slide to the left, but if someone knows how to accomplish this I'm a happy man.

Here are two examples (Airbnb and Spotify) of what I'm trying to accomplish, both sliders are non-infinite. The Spotify slider has no 'stops' (number of slides to scroll), so the behavior of the Airbnb slider is more like that of Slick.

Center mode is intended to be used with infinite, and I'm not sure there's a sensible use case for non-infinite, but perhaps you can shed some light -- consider that if part of a "previous" slide was visible, users would have an expectation that it could be navigated to. Let me know if there's something I'm overlooking in my thought process.

The first slide would have no previous. I guess you could call it peeking into the next slide if there is one. This is ideal for the user because no arrows are required for touch due to the peek and also as @mgntrn was saying, it enables the user to understand when all content has finished being viewed rather than an annoying realisation for the user that you have just doubled back on yourself.

Slick is a huge asset, but the lack of this behaviour is indeed a dealbreaker for modern projects.

I has same issue and trying with this
https://codesandbox.io/p/sandbox/jn8dmw

authors: ZNS d-packs dfusionTownsend leggomuhgreggo mgntrn vuletiki

repo: kenwheeler/slick


text:
goto jquery/DOM element, or selector string

Pass a jQuery Object, DOMElement or selector string to the slickGoTo function
$slickElement.slick("slickGoTo", "#element-id");

fiddle it out

Ningi, please can you rebase this, and make a few minor tweaks (as I've commented) ? I'll mergeafter that's done! :)

Oh, I didn't even remember this... I'll look into it monday.

:+1: :ok:

authors: Ninigi simeydotme


text:
Implement progress line for downloads using LuaSocket

It looks like this:
Downloading https://luarocks.org/luacheck-0.15.0-1.src.rock
  Got 1.03 MiB (100%) | 20.77 KiB/s

Current downloaded size, percentage and download speed are updated on each luasocket step. Redirections are handled. Progress line is shown only for sufficiently large files or slow downloads, thresholds may need some tweaking.
The last commit turns this on by default and removes any mentions of cfg.show_downloads from tests.

I feel like this is overly hacky for luasocket.
This sort of thing would be much nicer with #446

Updated: simplified thresholds: instead of using speed and size simply show the progress line if download isn't complete in some time windows/

authors: daurnimator mpeterv

repo: keplerproject/luarocks


text:
CLI: The  argument of the "keptn onboard" command is useless

The keptn onboard command expects the service name to be passed as a command argument, e.g., keptn onboard my-service --chart=./carts-db --project=myproject. However, the service name is also specified in the chart and needs to be the same as defined there. If it's not you get an error message:
Provided Keptn service name "my-service" does not match Kubernetes service name "carts-db"
Error: The provided Helm chart is invalid. Please checkout the requirements

Hence, the service name as an argument is redundant and useless.
As a first step, to maintain backwards compatibility, it shall be ignored and a warning message shall indicate that the service name command argument will be removed in future releases.

Hence, the service name as an argument is redundant and useless.

Kind of agree on the redundancy. But I disagree on it being useless and on removing the argument.
Pre-requesit: The service-name is also used as the directory name in the git config repo of the project.
In that sense, consider my arguments:

You wouldn't want to create a service without specifying a name, hence keptn create service requires the name, and so should keptn onboard service.
The restriction of the service name comes from helm-service, not Keptn itself. If we replace helm-service with something else, the onboard service option might need the argument again.
Thinking on step ahead, what if we ever want to onboard a service of an existing git config repo?

In the end, keptn onboard service is really just a convenience wrapper for keptn create service and "onboard the service using helm". I would actually go as far and say that we should not use it as a wrapper, and Keptn itself should not distinguish between onboarded or created services. This should be controlled via Uniform and Shipyard. Hence, eventually, we will not need keptn onboard service.
Let's hear some more opinions on that though.

I would actually go as far and say that we should not use it as a wrapper, and Keptn itself should not distinguish between onboarded or created services. This should be controlled via Uniform and Shipyard. Hence, eventually, we will not need keptn onboard service.

:+1: totally agree on this :)

authors: christian-kreuzberger-dtx warber

repo: keptn/keptn


text:
Make the CI based on Docker.

Currently, all the instructions to run the tests are on a file called .travis.yml.
It's not very practical. We should offload as many instructions as possible to shell scripts and dockerfiles, to make the CI build easily reproducible locally.
I would also allow us to switch our CI system easily if we want to or even take avantage of the docker cache to speed up our build.
It would make it much easier for users to tests things locally. Executing a bash script should be enough to trigger the whole docker infrastructure and testing.

https://docs.travis-ci.com/user/docker/ this will come in handy.

Will take care of this

Thanks a lot @RaphaelMeudec I started doing it for keras-team keras if that can come in handy. You can take a look at https://github.com/gabrieldemarmiesse/keras/tree/docker_testing In the directory .travis you can find a dockerfile.

@gabrieldemarmiesse It seems there are some options to save the docker layers instead of pushing the image to a specific docker account : https://stackoverflow.com/questions/35305492/cache-docker-images-on-travis-ci
Also, from the same SO thread, it seems that Circleci automatically cache the docker layers. Is there a specific reason for using Travis ? I've seen a .circleci folder on your fork

One of the points of using docker for testing is also being able to easily switch CI providers (maybe using multiple ones at then same time for more cores???). Circle ci does not automatically cache docker layers, it's a paid option. Because I would prefer incremental modifications and small PRs, let's make the pull requests in this order:

Use docker on travis without any layer caching.
Implement the cache (by using the native travis cache or using dockerhub)
switch to circle ci for a single build
(optional) switch more jobs to circle ci.

Having worked with circle ci, the big advantage over travis is being able to start your vm immediatly if you choose to work within a docker container, travis jobs often take 30s to allocate the VM.

CircleCI 2.0 automatically caches docker images. They cache it on each build host, so at the beginning, one might think, there is no caching. The more builds, the more often the cache will be hit

To be clear, this is a paid feature.

@RaphaelMeudec would you need any help with this?

@gabrieldemarmiesse I saw that the Dockerfile has been merged on Keras, just didn't have time to tackle this yet. Should be able to look into this week

It's not necessary for the 1.0. so it can be postponed if you want. Btw the dockerfile in the keras master branch isn't the one used for testing, it's just a way to have a quick gpu environment with keras installed.

authors: RaphaelMeudec gabrieldemarmiesse

repo: keras-team/keras-contrib


text:
Found a broken link at "Image classification via fine-tuning with EfficientNet"

Here the text location of broken link
efficientnet_weight_update_util.py
Could not find the official link to make a PR .
Unofficial link -
https://github.com/MarkusRosen/keras-efficientnet-regression/blob/master/efficientnet_weight_update_util.py

I believe the reason for this is that TF Keras was moved out of the main TensorFlow repository. The link you're looking for is https://github.com/keras-team/keras/blob/master/keras/applications/efficientnet_weight_update_util.py

Hi @TylerADavis !
Thanks for pointing me towards the correct source. created PR #1014 to fix this.
Thank you!

Closing this issue as concerned PR has been merged.
Thank you!

authors: TylerADavis mohantym

repo: keras-team/keras-io


text:
Model does not converge (Loss does not decrease, accuracy does not change)

I am doing text classification and have made a dummy dataset to aid in debugging. The problem is that the model does not learn/converge. I am trying to predict one out of 20 labels and have tried most fixes on the net to no avail.
train_text = [['a'],['a'],['a']]
val_text = [['a'],['a'],['a']]
train_label = np.asarray([5,5,5])
val_label = np.asarray([5,5,5])

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

#Implementing a Transformer block as a layer

class TransformerBlock(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = keras.Sequential(
            [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)

class TokenAndPositionEmbedding(layers.Layer):
    def __init__(self, maxlen, vocab_size, embed_dim):
        super(TokenAndPositionEmbedding, self).__init__()
        self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)

    def call(self, x):
        maxlen = tf.shape(x)[-1]
        positions = tf.range(start=0, limit=maxlen, delta=1)
        positions = self.pos_emb(positions)
        x = self.token_emb(x)
        return x + positions

vocab_size = 40000  # Only consider the top 20k words :)
maxlen = 10  # for my task, ideally 500

train_label = tf.keras.utils.to_categorical(train_label, 20)
val_label = tf.keras.utils.to_categorical(val_label, 20)

tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=20000, lower=True, oov_token='<OOV>')
tokenizer.fit_on_texts(train_text)
train_sequences = tokenizer.texts_to_sequences(train_text)

val_sequences = tokenizer.texts_to_sequences(val_text)

train_text = tf.keras.preprocessing.sequence.pad_sequences(train_sequences, maxlen=maxlen, dtype='int32',
                                           padding='post', truncating='post')

val_text = tf.keras.preprocessing.sequence.pad_sequences(val_sequences, maxlen=maxlen, dtype='int32',
                                           padding='post', truncating='post')

from sklearn.preprocessing import normalize

train_text_n = normalize(train_text)
val_text_n = normalize(val_text)

data_set = tf.data.Dataset.from_tensor_slices((train_text_n , train_label))
data_set

from tensorflow.keras.regularizers import l2

embed_dim = 128  # Embedding size for each token
num_heads = 8  # Number of attention heads
ff_dim = 256  # Hidden layer size in feed forward network inside transformer
batch_size = 32
factor = 0.0001

inputs = layers.Input(shape=(maxlen,))

embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
x = embedding_layer(inputs)
transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)

x = transformer_block(x)

x = layers.GlobalAveragePooling1D()(x)

x = layers.Dense(160, activation="relu", kernel_initializer='glorot_uniform')(x)
x = layers.Dropout(0.15)(x)
x = layers.BatchNormalization()(x)

x = layers.Dense(90, activation="relu", kernel_initializer='glorot_uniform')(x)
x = layers.Dropout(0.1)(x)
x = layers.BatchNormalization()(x)

x = layers.Dense(40, activation="relu", kernel_initializer='glorot_uniform')(x)
x = layers.Dropout(0.1)(x)

outputs = layers.Dense(20)(x)

model = keras.Model(inputs=inputs, outputs=outputs)

adamopt = tf.keras.optimizers.Adam(learning_rate=1e-4)

model.compile(optimizer='adamopt', loss="categorical_crossentropy", metrics=["acc"])

model.summary()

history = model.fit(
    train_text_n, train_label, batch_size=batch_size, epochs=20, validation_data=(val_text_n, val_label), verbose=1)

Any Idea on how to do I fix this network? I am totally stumped after trying to fix it for 4 days :(

@neel04 Is this still an issue?
I tried to replicate this issue on colab using TF v2.8.0 and faced different error, please find the gist here and confirm the same?
Thanks!

repo: keras-team/keras


text:
model.fit() infinite loop or exits application

Issue:
When calling model.fit() on a LSTM network, the training starts, but after the 4th epoch it stops in progress for infinity. Sometimes the code breaks up and throws an exception.
Reproduce:
The "S" model is called and gets feed with [501x4] double values as input, and [1x2] double value (true or false) as expected output
import sys
import array
from decimal import Decimal
import os.path
from os import path
import pathlib
import numpy as np
import math
from keras.models import Sequential
from keras.layers import Dense, LSTM, GRU
import keras

import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

# Get Arguments
obj = sys.argv[1]
frame = sys.argv[2]
model_type = sys.argv[3]

#Trainingsfile
current_dir = str(pathlib.Path(__file__).parent.absolute())
network_dir = current_dir + "\\Data\\" + obj + "\\" + frame + "\\Networks\\" + model_type + "\\"
if(model_type[0] == "L"):
    trainings_file_path = current_dir + "\\Data\\" + obj + "\\" + frame + "\\Dataset\\LongSignalData.txt"
elif(model_type[0] == "S"):
    trainings_file_path = current_dir + "\\Data\\" + obj + "\\" + frame + "\\Dataset\\ShortSignalData.txt"
trainings_file = open(trainings_file_path, "r")
trainings_file_size = path.getsize(trainings_file_path)

#Detect training lines at the end of file
trainings_file.seek(trainings_file_size - 9, 0)
trainings_file_lines = int(trainings_file.read(9))
print("Filesize: " + str(trainings_file_size))
print("Lines: " + str(trainings_file_lines) + "\n")

#Reset seek position
trainings_file.seek(0, 0)

#Create Numpy-Memmap for Trainingsdata
print("Initialize Memmap...")
if(model_type[1] == "S"):
    input_data_all = np.require(np.memmap("input_map.memmap", dtype='double', mode='r+', shape=(trainings_file_lines, 501, 4)),requirements=['O'])
    output_data_all = np.require(np.memmap("output_map.memmap", dtype='double', mode='r+', shape=(trainings_file_lines, 1, 2)),requirements=['O'])
elif(model_type[1] == "P"):
    input_data_all = np.require(np.memmap("input_map.memmap", dtype='double', mode='r+', shape=(1, 501, 4)),requirements=['O'])
    output_data_all = np.require(np.memmap("output_map.memmap", dtype='double', mode='r+', shape=(1, 1, 3000)),requirements=['O'])
elif(model_type[1] == "D"):
    input_data_all = np.require(np.memmap("input_map.memmap", dtype='double', mode='r+', shape=(1, 502, 4)),requirements=['O'])
    output_data_all = np.require(np.memmap("output_map.memmap", dtype='double', mode='r+', shape=(1, 1, 240)),requirements=['O'])
print("...Done!\n")

#Read File byte by byte
position = 0
index = 0
while(index < trainings_file_lines):
    #Bool for Profit and Distance network to grow array
    bool_P = False
    bool_D = False

    line = "A"
    while(not line[len(line)-1] == "#"):
        line = line + trainings_file.read(1)
        position += 1
    line = line[:-1]
    line = line[1:]
    splitted_line = line.split(";")

    #Get all input candles
    i_line = 0
    if(model_type[1] == "S"):
        while(i_line < 501):
            entry = splitted_line[i_line]
            input_data_all[index][i_line][0] = Decimal(entry.split(",")[0])
            input_data_all[index][i_line][1] = Decimal(entry.split(",")[1])
            input_data_all[index][i_line][2] = Decimal(entry.split(",")[2])
            input_data_all[index][i_line][3] = Decimal(entry.split(",")[3])
            i_line += 1
    elif(model_type[1] == "P"):
        if(int(splitted_line[501]) == 1):
            while(i_line < 501):
                entry = splitted_line[i_line]
                input_data_all[len(input_data_all) - 1][i_line][0] = Decimal(entry.split(",")[0])
                input_data_all[len(input_data_all) - 1][i_line][1] = Decimal(entry.split(",")[1])
                input_data_all[len(input_data_all) - 1][i_line][2] = Decimal(entry.split(",")[2])
                input_data_all[len(input_data_all) - 1][i_line][3] = Decimal(entry.split(",")[3])
                i_line += 1
            bool_P = True

    elif(model_type[1] == "D"):
        if(int(splitted_line[501]) == 1):
            while(i_line < 501):
                entry = splitted_line[i_line]
                input_data_all[len(input_data_all) - 1][i_line][0] = Decimal(entry.split(",")[0])
                input_data_all[len(input_data_all) - 1][i_line][1] = Decimal(entry.split(",")[1])
                input_data_all[len(input_data_all) - 1][i_line][2] = Decimal(entry.split(",")[2])
                input_data_all[len(input_data_all) - 1][i_line][3] = Decimal(entry.split(",")[3])
                i_line += 1
            input_data_all[len(input_data_all) - 1][501][0] = Decimal((Decimal(splitted_line[502]) + 1) / 100)
            input_data_all[len(input_data_all) - 1][501][1] = 0
            input_data_all[len(input_data_all) - 1][501][2] = 0
            input_data_all[len(input_data_all) - 1][501][3] = 0
            bool_D = True

    #Get output
    if(model_type[1] == "S"):
        output_data_all[index][0][0] = 0
        output_data_all[index][0][1] = 0
        output_data_all[index][0][int(splitted_line[501])] = 1

    elif(model_type[1] == "P"):
        if(bool_P == True):
            p_fill = 0
            while(p_fill < 3000):
                output_data_all[len(input_data_all) - 1][0][p_fill] = 0
                p_fill += 1
            output_data_all[len(output_data_all) - 1][0][int(splitted_line[502])] = 1

            #Resize Arrays
            input_data_all.resize((len(input_data_all) + 1, 501, 4))
            output_data_all.resize((len(output_data_all) + 1, 1, 3000))

    elif(model_type[1] == "D"):
        if(bool_D == True):
            d_fill = 0
            while(d_fill < 240):
                output_data_all[len(input_data_all) - 1][0][d_fill] = 0
                d_fill += 1
            output_data_all[len(output_data_all) - 1][0][int(splitted_line[503])] = 1

            #Resize Arrays
            input_data_all.resize((len(input_data_all) + 1, 502, 4))
            output_data_all.resize((len(output_data_all) + 1, 1, 240))

    if(index > 0 and index % math.floor(trainings_file_lines/100) == 0):
        prozent = (index / math.floor(trainings_file_lines/100))
        print("Parsed " + str(prozent) + "%...")
    index += 1

print("End...")

#Delete Array last entry for Profit and Distance
if(model_type[1] == "P"):
    input_data_all.resize((len(input_data_all) - 1, 501, 4))
    output_data_all.resize((len(output_data_all) - 1, 1, 3000))
elif(model_type[1] == "D"):
    input_data_all.resize((len(input_data_all) - 1, 502, 4))
    output_data_all.resize((len(output_data_all) - 1, 1, 240))

#Trainingsdata is ready!
print("\nModel: " + model_type)
print("Input array size: " + str(len(input_data_all)))
print("Output array size: " + str(len(output_data_all)))

print(input_data_all[0])
print(output_data_all[0])
print(output_data_all[1])
print(output_data_all[2])
print(output_data_all[3])
print(output_data_all[4])

#Start Network training
if(path.exists(network_dir + "saved_model.pb")):
    model = keras.models.load_model(network_dir)
else:
    if(model_type[1] == "S"):
        print("- Signal Network -")
        model = Sequential()
        model.add(LSTM(1500, input_shape=(501, 4), return_sequences=True))
        model.add(LSTM(1500))
        model.add(Dense(1,activation="sigmoid"))
        model.compile(loss='mae', optimizer='adam')
    elif(model_type[1] == "P"):
        print("- Profit Network -")
        model = Sequential()
        model.add(LSTM(1500, input_shape=(501, 4), return_sequences=True))
        model.add(LSTM(1500))
        model.add(Dense(3000))
        model.compile(loss='mae', optimizer='adam')
    elif(model_type[1] == "D"):
        print("- Distance Network -")
        model = Sequential()
        model.add(LSTM(1500, input_shape=(502, 4), return_sequences=True))
        model.add(LSTM(1500))
        model.add(Dense(240))
        model.compile(loss='mae', optimizer='adam')

print("Start training...")
model.fit(np.asarray(input_data_all).astype(np.double), np.asarray(output_data_all).astype(np.double), batch_size=1, epochs=100, verbose=1, steps_per_epoch=len(input_data_all))

input("Close Program...")

Details:
Here is the exception it throws
4/920 [..............................] - ETA: 2:23 - loss: 0.50002021-03-12 20:23:07.575427: E tensorflow/stream_executor/dnn.cc:616] CUDNN_STATUS_EXECUTION_FAILED
in tensorflow/stream_executor/cuda/cuda_dnn.cc(1972): 'cudnnRNNBackwardData( cudnn.handle(), rnn_desc.handle(), model_dims.max_seq_length, output_desc.handles(), output_data.opaque(), output_desc.handles(), output_backprop_data.opaque(), output_h_desc.handle(), output_h_backprop_data.opaque(), output_c_desc.handle(), output_c_backprop_data.opaque(), rnn_desc.params_handle(), params.opaque(), input_h_desc.handle(), input_h_data.opaque(), input_c_desc.handle(), input_c_data.opaque(), input_desc.handles(), input_backprop_data->opaque(), input_h_desc.handle(), input_h_backprop_data->opaque(), input_c_desc.handle(), input_c_backprop_data->opaque(), workspace.opaque(), workspace.size(), reserve_space_data->opaque(), reserve_space_data->size())'
2021-03-12 20:23:07.575618: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at cudnn_rnn_ops.cc:1926 : Internal: Failed to call ThenRnnBackward with model config: [rnn_mode, rnn_input_mode, rnn_direction_mode]: 2, 0, 0 , [num_layers, input_size, num_units, dir_count, max_seq_length, batch_size, cell_num_units]: [1, 1500, 1500, 1, 501, 1, 1500]
Traceback (most recent call last):
  File "E:\Obj\AI\NetworkTrainer.py", line 187, in <module>
    model.fit(np.asarray(input_data_all).astype(np.double), np.asarray(output_data_all).astype(np.double), batch_size=1, epochs=100, verbose=1, steps_per_epoch=len(input_data_all))
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 855, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 2942, in __call__
    return graph_function._call_flat(
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 1918, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 555, in call
    outputs = execute.execute(
  File "C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError:    Failed to call ThenRnnBackward with model config: [rnn_mode, rnn_input_mode, rnn_direction_mode]: 2, 0, 0 , [num_layers, input_size, num_units, dir_count, max_seq_length, batch_size, cell_num_units]: [1, 1500, 1500, 1, 501, 1, 1500]
         [[{{node gradients/CudnnRNN_grad/CudnnRNNBackprop}}]]
         [[Adam/gradients/PartitionedCall]] [Op:__inference_train_function_4508]

Function call stack:
train_function -> train_function -> train_function

The first four epochs it predicts the output [1, 0]. But on the 5th epoch, where it breaks up, it should predict [0, 1].
Side not, I renamed my cusolver64_11.dll in cusolver64_10.dll for RTX 3090 support.

From what I see the model "S" outputs only one value:
model.add(Dense(1,activation="sigmoid"))

@MovEaxEax Please share a simple standalone code to reproduce the issue.
As mentioned by @bguetarni , shape mismatch or loss selection or broadcasting may be affecting the training.
Please check a tutorial https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/ that may help you. Thanks!

authors: MovEaxEax bguetarni jvishnuvardhan


text:
Embedding layer masking value (proposal)

Currently in Embedding layer, the value for masking is 0 which is a bit clunky as you need to shift all your indexes up by 1, which can also cause error prone code.
Proposal to introduce field to masking_value which will let you specify what value is used for masking.
For example
Embedding(..., masking_value=-1)
Embedding(..., masking_value=np.nan)
Embedding(..., masking_value=0)  # for backwards compatibility

This, please. If not this, then at least add support for the pre-existing masking layer.

@nehz Any updates on this?


text:
NEW ISSUE - Wonderwall Flatpak on Linux Mint Error Setting Wallpaper

Getting same error as previous poster on Deepin Linux using Flatpak.
Failed to set Wallpaper using- BackEnd: /home/kaiju/.var/app/com.ktechpit.wonderwall/data/ktechpit/WonderWall/backends/deepin BackTrace:
Please Open an issue in the github repo here.
Also, 'restore purchase' does not work, it simply points you to payment. This means you can't restore license if necessary. Please help.

Gnome 43.4, Arch-based.  The Flatpack version is unusable. The window's size is wrong (I'm using 100% desktop scaling with font scaling 1.2, Wayland), and I cannot even resize the window. Clicking an item does not respond. I removed the Flatpack version and downloaded the AppImage version instead, and now the window is normal.

repo: keshavbhatt/WonderWall-Packaging


text:
Comiited changes

What type of PR is this? (check all applicable)

[1 ] 🚀 Added Name
[ ] ✨ Feature
[ 1] 🌟 stared the repo
[ ] 🐛 Grammatical Error
[ ] 📝 Documentation Update
[ ] 🚩 Other

Description
Add Link of GitHub Profile

Is my pr merged??
It's not showing me!


text:
throwaway branch with all applied patches

Summary by CodeRabbit

New Features

Introduced a new .editorconfig file to enforce coding style guidelines.
Added a .gitignore file to streamline version control by ignoring unnecessary files.
Enhanced the PATCHES.md file to categorize and document available patches.
Added functionality for managing multiple monitors, including focusing and tagging specific monitors.
Implemented a fullscreen toggle for focused windows.
Introduced padding options for the status bar and improved window centering capabilities.
Enhanced window management with new keybindings for adjusting gaps and borders.
Added support for EWMH desktop management.
Introduced a static status monitor feature for dedicated status display.
Enhanced client management to preserve window tags across restarts.
Added keybindings for multimedia controls and application launching.

Bug Fixes

Improved error handling and client management in the window manager.
Resolved overflow issues in window name rendering.

Documentation

Updated the dwm.1 manual page to reflect new keyboard shortcuts and functionalities.

Chores

Normalized whitespace throughout the codebase for improved readability.

@coderabbitai review

@coderabbitai review

@coderabbitai review

@coderabbit review

@CodeRabbit review

repo: kesor/dwm


text:
git.Push doesn't look up at the _flow folder

Feature description
The new git.Push task is very nice to push scripts written in the Editor. However we can't push Flows (stored in the _flow directory). Hence users can't push their flow modification alongside their scripts.
-> add the possibility to push Flows with the git.Push task

Note: the git.Reconcile task is able to sync flows on its side

Thanks for adding this. did you talk to @brian-mulier-p? I agree this would be a nice addition
If I remember correctly, there were some technical challenges in getting access to all the flows

We didn't add it because we thought of adding a dedicated fetch flows task that would give out such infos so idk

Good idea Brian, let's talk next week how we can create that fetch flows task and integrate with git.Push. We can keep this issue open until then 👍

authors: Ben8t anna-geller brian-mulier-p

repo: kestra-io/plugin-git


text:
🛑 Frisk Luft is down

In 06d4807, Frisk Luft (https://friskluft.ketilmo.no/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Frisk Luft is back up in b79b470 after 5 minutes.

repo: ketilmo/upptime


text:
Route Service endpoints [kong 0.13.0]

Hi,
I add some API endpoint for kong 0.13.0:

Service
Route

I updated breaking change on the Upstream API.
The objective is to update the kong terraform provider (I'm working on that)

@kevholditch

@nm-harry not too much most of the work is in the gokong client. Just waiting for the readme to be updated as part of this PR

@kevholditch Sorry I haven't time to do that, now the readme is updated

authors: SublimeDamien kevholditch

repo: kevholditch/gokong


text:
allow_playback_repeats functionality

VCR's allow_playback_repeats option provides the ability to basically de-duplicate multiple identical requests made in the same session.
https://relishapp.com/vcr/vcr/docs/request-matching/playback-repeats
I have been unable to turn up similar functionality in vcrpy (though I did find it was the original behavior prior to version 0.4)
I agree with the default behavior being what it is, but it would be nice to have the option to allow this matching. Does this functionality already exist (and I just missed it) or are their plans to add it?
---Alex

I'd like to use this functionality as well.

Me three! This would be a nice addition to this great library, thank you

This should be an easy check, but I thought it was already doing this in v2.1.0. I'll check today at work.
I agree it'd be handy in keeping cassettes small in deduplicating identically matched requests.

Any update on this? Haven't been able to find any documentation on how to accomplish this in vcrpy yet

I was reading through the changelog and there is in fact no equivalent of allow_playback_repeats.
It treats every request, even duplicates, as unique when recording and replaying. As the same request may respond slightly differently each time, for example a timestamp.
Closing this for now but PRs are welcome to introduce this behavior if it is desired.

authors: adamJLev aqw matthewarmand neozenith syst3mw0rm


text:
Stage 6 RuntimeWarning: invalid value encountered

Instrument
Light curve fitting (Stages 4-6)
What happened?
I am running Eureka from S3, but when I get to Stage 6, I get this error:
2022-08-14 15:54:41,519 - stpipe - WARNING - /mmfs1/home/kcrasmus/src/eureka/src/eureka/S6_planet_spectra/plots_s6.py:96: RuntimeWarning: invalid value encountered in sqrt
return (r/y_scalar)**(1/expFactor)/scaleHeight - H_0
I guess this is implying that y_scalar is zero. I don't have a model yet, which may be the problem? I do not think it is scaleHeight because I have that set to 2.3 in the S5 epf.
Error traceback output
slurm-5868303.txt
S6_TRAPPIST-1c.txt
S5_TRAPPIST-1c.txt
What operating system are you using?
Linux
What version of Python are you running?
3.9
What Python packages do you have installed?
No response
Code of Conduct

[X] I agree to follow this project's Code of Conduct

Not having a model to plot at the same time won't be an issue at all as you have correctly set model_spectrum to None for that case. The S5 EPF file is also not relevant for this issue - none of those settings impact Stage 6 directly. I would mention that a mean molecular weight (planet_mu in your S6 ECF) of 2.3 implies an H2 and He dominated atmosphere which is not likely to be the correct value for such a small planet.
The main issue though is likely a bug caused by you setting the planet_Rp manually but not the planet_R0 manually - I know that leaving both set to None works well, but it appears that only setting one of them might cause an issue. I'll look into this more shortly, but as a short-term fix you are likely fine setting planet_Rp to None which should avoid a crash (but do consider using a mean molecular weight more appropriate to the atmosphere you'd expect).
And note that you are able to run just Stage 6 on it's own, so you won't need to re-run everything from scratch or anything.

also, perhaps this is a second issue, but the spectra I'm getting look like
this:
[image: fig6101_transmission.png]
thanks,
kaitlin
On Tue, Aug 16, 2022 at 2:18 PM Kaitlin Rasmussen @.***> wrote:

Hello,
 Which file is planet_Rp in? I see a planet_Rad in S6.ecf but not a

planet_Rp. If I set both planet_Rad and planet_R0 to None, I get the same
error.
Thanks,
Kaitlin
On Tue, Aug 16, 2022 at 2:05 PM Taylor James Bell <
@.***> wrote:

Not having a model to plot at the same time won't be an issue at all as
you have correctly set model_spectrum to None for that case. The S5 EPF
file is also not relevant for this issue - none of those settings impact
Stage 6 directly. I would mention that a mean molecular weight (planet_mu
in your S6 ECF) of 2.3 implies an H2 and He dominated atmosphere which is
not likely to be the correct value for such a small planet.
The main issue though is likely a bug caused by you setting the planet_Rp
manually but not the planet_R0 manually - I know that leaving both set to
None works well, but it appears that only setting one of them might cause
an issue. I'll look into this more shortly, but as a short-term fix you are
likely fine setting planet_Rp to None which should avoid a crash (but do
consider using a mean molecular weight more appropriate to the atmosphere
you'd expect).
And note that you are able to run just Stage 6 on it's own, so you won't
need to re-run everything from scratch or anything.
—
Reply to this email directly, view it on GitHub
https://github.com/kevin218/Eureka/issues/452#issuecomment-1217164574,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A2E4CUEHCTG52VV7DHIAV5LVZP7C5ANCNFSM56TGW42Q
.
You are receiving this because you authored the thread.Message ID:
@.***>

I believe you're replying by email rather than on GitHub, and your attached image doesn't seem to be able to make it through that way. Can you send it on GitHub instead of email?

gotcha. here it is:

thanks,
kaitlin

I'll also note that you should look at your outputs from Stage 5 - the lsq fitter failed to converge with the default value for lsq_maxiter. If you set lsq_maxiter to something ~10x the number of evaluations it stopped at (2001 according to the log you sent me, so try 20000) then lsq should converge.
As for the plot, I'm not sure what your concern is. If you want y error bars, you'll need to use one of the methods that gives uncertainties on fitted parameters (emcee of dynesty). If you're concerned by the near-zero transit depth, then you should look at what happened during Stage 5 (see again my comments above for lsq convergence).

This is NIRSpec spectra, so shouldn't there be ~432 data points in the spectrum? Also, the lsq fitter failed to converge even with 30,000 iterations. Not sure what could be causing that.
slurm-5873588.txt
S5_TRAPPIST-1c.ecf.txt
S5_TRAPPIST-1c.epf.txt
S6_TRAPPIST-1c.ecf.txt

In your stage 4 you'll have set the number of spectral bins you want to two. You can set that to None to have unbinned spectra (very time consuming) or whatever coarser wavelength resolution you want. Not sure why lsq isn't converging - you should try troubleshooting that yourself using the plots and logs

ah, gotcha. okay, I'll see if I can figure out the non-convergence problem and report back if I figure it out. Thanks for all your help!

Ohh, I finally understand the issue you encountered with Stage 6 now - it was that you had gotten a point so close to zero that the primary y-axis has values below Rp/R* = 0, and when you then try to take the square-root of that to convert it to a scale height you end up with an imaginary number. I'll have to think a bit on how to handle that properly

Hello,
 Which file is planet_Rp in? I see a planet_Rad in S6.ecf but not a

planet_Rp. If I set both planet_Rad and planet_R0 to None, I get the same
error.
Thanks,
Kaitlin
On Tue, Aug 16, 2022 at 2:05 PM Taylor James Bell @.***>
wrote:

Not having a model to plot at the same time won't be an issue at all as
you have correctly set model_spectrum to None for that case. The S5 EPF
file is also not relevant for this issue - none of those settings impact
Stage 6 directly. I would mention that a mean molecular weight (planet_mu
in your S6 ECF) of 2.3 implies an H2 and He dominated atmosphere which is
not likely to be the correct value for such a small planet.
The main issue though is likely a bug caused by you setting the planet_Rp
manually but not the planet_R0 manually - I know that leaving both set to
None works well, but it appears that only setting one of them might cause
an issue. I'll look into this more shortly, but as a short-term fix you are
likely fine setting planet_Rp to None which should avoid a crash (but do
consider using a mean molecular weight more appropriate to the atmosphere
you'd expect).
And note that you are able to run just Stage 6 on it's own, so you won't
need to re-run everything from scratch or anything.
—
Reply to this email directly, view it on GitHub
https://github.com/kevin218/Eureka/issues/452#issuecomment-1217164574,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A2E4CUEHCTG52VV7DHIAV5LVZP7C5ANCNFSM56TGW42Q
.
You are receiving this because you authored the thread.Message ID:
@.***>


text:
Validation failed with invalid MIME type

Hello and nice work on this gem !
However, I'm trying to parse swagger file outputted with this kind of MIME types defined:
            "application/*+json": {
              "schema": {
                "$ref": "#/components/schemas/SomeDataTypeHere"
              }
            }

This gem will crash (validation failed raised) when trying to access the content of requestBody because this MIME type doesn't fit with the regexp defined in: lib/openapi3_parser/validators/media_type.rb
I've patched it like this:
    ::Openapi3Parser::Validators::MediaType::REGEX = %r{
      \A
      (\w+|\*)+ # word or asterisk
      / # separating slash
      ([-+.\w]+|\*)+ # word (with +, - & .) or asterisk
      \Z
    }x.freeze

I'm not sure if that a problem with the gem OR if the swagger file provided is not OpenApi compliant.
Tell me if you want me to create a PR :+1:

Hello @anykeyh - thanks for reporting this. It looks an interesting problem, I've not seen application/*+json as a mime type before. I'm finding it tricky to find out much about it and it's prevalence. I've done some digging to try determine if it's valid or not.
Looking at the OpenAPI Specification they reference the field to be a media type or a media range, with media range being rather cryptically defined as media-range = ( "*/*" / ( type "/*" ) / ( type "/" subtype ) ) *( OWS    ";" OWS parameter ) which I think rules out application/*+json as being valid since it is neither "/*" or a subtype. I also checked the schemas that ship with OpenAPI Specification but they seem to lack any validation on whether request bodies have a valid media type or not: https://github.com/OAI/OpenAPI-Specification/blob/f8449d1a893cc6a811c6f3d87e88b05761dc5397/schemas/v3.0/schema.json#L1260-L1265.
Where are you seeing this come from? I saw this Stack Overflow post that indicates it's provided by the Spring Framework which might indicate this approach is in the wild 🤔
Thanks for sharing your approach to resolving it 👍 I can see how that fixed it, I think that might be a bit over forgiving as it'd probably accept wrong inputs like ***/*** or application/text*json.

The schema I get come from a supplier for my current professional project, I can't tell you more as I'm bound with strict confidential policies.
I'm not 100% sure about the technology they use for their API server, but I would put my guess over .NET or Java framework.
One thing I've noticed is they provide multiple MIME type for their endpoints as example below:
          "content": {
            "application/json-patch+json": {
              "schema": {
                "$ref": "#/components/schemas/xxx"
              }
            },
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/xxx"
              }
            },
            "text/json": {
              "schema": {
                "$ref": "#/components/schemas/xxx"
              }
            },
            "application/*+json": {
              "schema": {
                "$ref": "#/components/schemas/xxx"
              }
            }
          }

In case I try to access the key application/json, validation code is called and it raises the exception. Another solution that would work in my case might be to validate over the key you're trying to access instead of the whole content block.
And, indeed my solution is a quickfix, probably not strict enough as it would validate invalid data type.

Great, thanks for sharing those details. I think I'm going to consider that to be an invalid mime type and thus not meeting the specification. If you're able to establish otherwise (perhaps raising an issue on https://github.com/OAI/OpenAPI-Specification) I'm quite happy to consider changing this.
This seems like evidence for a feature I've long considered where it allows a lenient mode for parsing where it only blows up if types are incorrect (for example any string would be accepted here but not a number). Though I've got a few more changes to make to this before that so that might not be until after OpenAPI 3.1 is out.
It might be best that you carry on using your quickfix in the meantime, particularly if you aren't able to persuade the author of the openapi file to change the media-type definition (which, in my opinion, would be the ideal approach).
Hope this helps.

authors: anykeyh kevindew