GO:0000289 nuclear-transcribed mRNA poly(A) tail shortening

Please provide as much information as you can:

GO term ID and Label

GO:0000289 nuclear-transcribed mRNA poly(A) tail shortening
Shortening of the poly(A) tail of a nuclear-transcribed mRNA from full length to an oligo(A) length.

Reason for deprecation

This term is describing MF (exonuclease) + BP

"Replace by" term (ID and label)
If all annotations can safely be moved to that term

we need a term to represent
"cotranscriptional 3’ processing of RNA polymerase II transcripts, termination coupled"
"CPF (cleavage and polyadenylation specificity factor) -coupled termination"  seem to mean the same thing"
I need to check with experts

is CPF always termination-coupled
is cotranscriptional 3’ processing of RNA polymerase II transcripts always CPF dependent
transcription can be regulated via this mechanism, but this is probably 'affecting' rather than regulating

(this would subsume some of the cleavage and polyadenylation terms which should all be represented by the distinct location specific pathways)

"Consider" term(s) (ID and label)
Suggestions for reannotation

Are there annotations to this term?

How many EXP:
80 (GO) 65 (AMiGO)

S. pombe 16 (removing)
S. cerevisiae 17
Drosophila (11)
Mouse 6
Arabidopsis 5
Human 3
Zfin 3
C. elegans 3
T. brucei 1

Are there mappings and cross references to this term? (InterPro, Keywords; check QuickGO cross-references section)

lots of inferred

Is this term in a subset? (check the AmiGO page for that term)
NO

Any other information

I'm not sure about this. Need to look at the pathways in more detail.

Check this but can go ahead with obsoletion of
GO:0000289 nuclear-transcribed mRNA poly(A) tail shortening


Giving parameters with proper format

Issue #7 :
Description of changes:
I corrected the format of the parameterTemplate argument.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Looks good. Thanks for the fix!


pyactr installs, but fails to run w/Python 3.10

When trying to run a model on pyactr with Python 3.10:

python package 'pyactr' not found. Please ensure it is installed with pip or is in your PYTHONPATH env variable

The problem here is that pyactr is using a deprecated import for collections.Sequence in chunks.py (& possibly elsewhere?). What was a deprecation warning in Python <= 3.9 is now an error in 3.10.
See: Stack Overflow question

Fixes proposed for pyactr. We will need a pyactr pip release so people can run with Python 3.10.

pyactr 0.3.1 has been released as a pip package with fixes for this.
Need to update pyactr in requirements.txt to 0.3.1.

Looks like it's still a problem.
When I ran the command that checks this on the command line it worked, so I'm not sure what else is going on here.

Cannot reproduce using homebrew python 3.10 on macOS 12.4 or macOS 12.14.6...
If I install everything from scratch it works.


meta.yaml formatting

Hi @zhengp0 and @jiaweih,
I'm writing a script to prep the meta.yaml file so that I don't have to do that by-hand when we change the dataset.
I have a small request -- the following format is difficult or impossible for me to write from R.
time_start: [2010, 1]
time_end_0: [2020, 8]
time_end_1: [2020, 36]

Can you please modify the code to accept this format instead?
time_start:
    year: 2020
    detailed: 1
time_end_0:
    year: 2020
    detailed: 8
time_end_1:
    year: 2020
    detailed: 36

This is really easy for me to do on my own on the meta.yaml side, but less easy for me to modify the data manager code to work with this format. Thanks!!

Hi @krpaulson! Please check PR (#24) I think that will do the trick :)

Hi @krpaulson! Please check PR (#24) I think that will do the trick :)

Looks great! Thanks for doing that.

Looks great! Thanks for doing that.


Emotes Use Localhost URL

My emotes are all failing after a default install. They appear to be using the localhost URL as opposed to a local redirect or non host name URL.
Please fix or advise of patch. Thank you!

Fixed. Was a config.json issue with NodeBB.


YARN-10822. Containers going from New to Scheduled transition for kil…

…led container on recovery

Description of PR
How was this patch tested?
For code changes:

[ ] Does the title or this PR starts with the corresponding JIRA issue id (e.g. 'HADOOP-17799. Your PR title ...')?
[ ] Object storage: have the integration tests been executed and the endpoint declared according to the connector-specific documentation?
[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?
[ ] If applicable, have you updated the LICENSE, LICENSE-binary, NOTICE-binary files?

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HDF5 problem

Hello, I am trying to run nanopolish in Google Colab Notebooks and running into a HDF5 issue similar to those previously reported but slightly different. I'm guessing because I'm in Google Colab's virtual environment and others were not? Anyways, when I run:
git clone --recursive https://github.com/jts/nanopolish.git
cd /content/nanopolish
make

I get something that looks like this:
nanopolish_make_log.txt
It starts out seemingly ok, then I get a super long list of errors that looks like this:

In file included from H5public.h:37:0,
from H5private.h:29,
from H5.c:24:
/usr/include/features.h:184:3: warning: #warning "_BSD_SOURCE and _SVID_SOURCE are deprecated, use _DEFAULT_SOURCE" [-Wcpp]
warning "_BSD_SOURCE and _SVID_SOURCE are deprecated, use _DEFAULT_SOURCE"
^~~~~~~
In file included from H5.c:27:0:
H5Eprivate.h:35:32: warning: anonymous variadic macros were introduced in C99 [-Wvariadic-macros]
#define HERROR(maj_id, min_id, ...) H5E_printf_stack(NULL, FILE, FUNC, LINE, H5E_ERR_CLS_g, maj_id, min_id, VA_ARGS)
^~~
In file included from H5.c:27:0:
H5Eprivate.h:41:33: warning: anonymous variadic macros were introduced in C99 [-Wvariadic-macros]
#define HCOMMON_ERROR(maj, min, ...)
^~~
H5Eprivate.h:55:40: warning: anonymous variadic macros were introduced in C99 [-Wvariadic-macros]
#define HDONE_ERROR(maj, min, ret_val, ...) {
^~~
H5Eprivate.h:67:40: warning: anonymous variadic macros were introduced in C99 [-Wvariadic-macros]
#define HGOTO_ERROR(maj, min, ret_val, ...) {
^~~

Things I tired:

I tried it on Ubuntu 20.04.4 LTS, still got errors/warning like those above.
I tried to install HDF5 myself and then use HDF5=noinstall during the make command, but that did not work either.
I thought maybe the above warnings aren't actually errors, and that I should just go for it and see if nanopolish will do anything to my data... so I went for it and got the same "vbz plugin not found" as others did in the past. Even with trying the different export commands, the only way I was able to get nanopolish to run was if i ran it like this:

HDF5_PLUGIN_PATH=/content/ont-vbz-hdf-plugin-1.0.1-Linux/usr/local/hdf5/lib/plugin/ /content/nanopolish/nanopolish index -s supoutdir/sequencing_summary.txt -d /fast5dir supoutdir/all_D1.fastq
In the end, from nanopolish eventalign I got:

[post-run summary] total reads: 138255, unparseable: 0, qc fail: 0, could not calibrate: 0, no alignment: 138255, bad fast5: 0

Questions I have:

Any suggestions for installing on Google Colab? Or ubuntu?
I noticed in the nanocompore pipeline, they do the minimap2 and samtools steps before running nanopolish index... whereas the nanopolish manual does the nanopolish index step first, does that matter?
I used a 10.4 flowcell, how will that effect my ability to use nanopolish? or how would it affect my data?

Thank you in advance for reading over my issues and questions.
best,
S

Hi,
The normal HDF5 build produces a large number of warnings that you can ignore. I noticed the end of the make log contains this message:
make: *** [lib/libhdf5.a] Interrupt

Did you press ctrl+c to interrupt the build?

No problem, good luck!

Hi Jared,
I usually use Nanopolish, it's a great tool, thanks for this!
Today, I obtain some surprising results of type :
[post-run summary] total reads: 8135, unparseable: 0, qc fail: 0, could not calibrate: 0, no alignment: 8135, bad fast5: 1941
[post-run summary] total reads: 7845, unparseable: 0, qc fail: 0, could not calibrate: 0, no alignment: 7845, bad fast5: 2011
...
For the first time, the sequencing has been done on 10.4 flowcell, is it the source of problem ?
Do you have any idea when Nanopolish will be able to work with ? Or maybe, you think it's useless since 10.4 flowcell is supposed to be of good quality ?
Thanks for your reply

Hi @DelphIONe,
Currently nanopolish does not support R10.4 data, so you will have to use another approach (medaka/clair3/guppy+remora) depending on your application.
Jared

ok but... why ? (It's for polishing an assembly)
It's not planified ?

ok, it seem's logical :-)
Thanks again !

Hi,
Any recommendation about the "good tools available for R10.4"? Are you referring to Megalodon? Thank you!


Create subscription sample is not working

The sample for V2 and higher for creating a subscription via the CLI is not working anymore. If the command is used, the cli return following error "az eventgrid: 'resource' is not in the 'az eventgrid' command group. See 'az eventgrid --help'."

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

ID: f79bffab-0af8-75fc-f800-73eb979611ad
Version Independent ID: ca382131-4030-d1ba-44d4-35309bb91f94
Content: Azure Event Grid trigger for Azure Functions
Content Source: articles/azure-functions/functions-bindings-event-grid-trigger.md
Service: azure-functions
GitHub Login: @craigshoemaker
Microsoft Alias: cshoe

@mregni Thank you for your feedback! We will review and update as appropriate.

@mregni Thanks again for your feedback! It looks like the Portal experience has changed quite a bit. As such, we are proceeding to assign this issue to the content author to review further and take the right course of action.

Creating an eventgrid subscription to a deployment slot doesn't work in the portal!

Hi - I agree this is an issue - however I would like to extend it to say that there are different issues between the azure cli, the azure portal and the azure powershell sdk. In the Azure portal i can successfully subscribe my even topic to my azure function - this then shows in the portal as an AzureFunction endpoint - when i use powershell i can successfully subscribe to my event grid using New-AzEventGridSubscription but can not create an AzureFunction endpoint; only one that will show up as a webhook. Then finally with the azure cli despite following the above examples to get the various different keys etc, I am still unable to get the function to respond with the validation result required for it to think it was successful and create the subscription. Both powershell and azure cli seem to be missing the ability to create the AureFunction endpoint unless this is automatically assigned somehow. At the minute the only way i can successfully bind an event topic to my azure function is in the portal, which is not so great for automatic deployment from something like AzureDevOps.

Hi,
I had the same issue. I think az cli version could be the issue (I have upgraded to 2.3.1) and it does not recognise the "resource" subcommand. Here is how I managed to create via az. Key is the _master for Host Keys:
sourceResourceId="/subscriptions/${subscriptionID}/resourceGroups/${resourceGroupName}/providers/Microsoft.EventGrid/topics/${topicName}"
endpoint="https://${funcAppName}.azurewebsites.net/runtime/webhooks/eventgrid?functionName=${funcName}&code="${key}"
az eventgrid event-subscription create --endpoint $endPoint \
    --source-resource-id $sourceResourceId \
    --name $eventSubscriptionName ```


Allow duplicate entries with different case

Add configuration to allow entering the same values in different cases.

Sounds useful. I'll add this to the backlog.

Would pull request #339 fix this?

This issue was selected to be spring-cleaned by a script. More information can be found here.
If anyone thinks this was a mistake and this issue should be reopened, please leave a message below explaining why. Before doing so, please consider reading the CONTRIBUTING file.

was there any fix given for this issue?


Streamline ParseType Logic

The ParseType code has a lot of jumps and incongruity in respect to the code's behavior despite there being functions that have identical behavior. For readability, and to highlight this, I refactored this file to better reflect that.

@swift-ci Please smoke test

Reran the tests and I realized I made a typo. Now this compiles and works.

Done!

@swift-ci Please smoke test

@swift-ci Please smoke test

The last PR wasn't closed by me, which is why I opened this new one.

@xwu Done!

@swift-ci please smoke test

@swift-ci Please smoke test

@swift-ci Please smoke test

@pi1024e Please test it locally before pushing the changes 🙏

@rintaro done

Closing because I messed up the git tree


Support for different kinds of feature source files

I was thinking about the appropriate way to manage different kinds of inputs like we discussed in Copenhagen:

gherkin
excel
markdown

And come up with idea, that we could probably have converters:

excel to gherkin
markdown to gherkin

Then running the features would support same interface - gherkin. For example a simple markdown like:
Cunicorn formatter
In order to be able to support different sources
As a cunicorn user
I need to able to convert source to gherkin
Convert markdown

Given a markdown feature:

some markdown

When I run it through cunicorn event stream
Then I should see my feature passed

Would transform to:
Feature: Cunicorn formatter
  In order to be able to support different sources
  As a cunicorn user
  I need to able to convert source to gherkin

  Scenario: Convert markdown
    Given a markdown feature:
      ```
        some markdown
      ```
    When I run it through cunicorn event stream
    Then I should see my feature passed

IIRC the discussion about support different source formats, has focused on having the pickle as the common format, so each source format should supply its own parser and compiler. The consequence for cunicorn would in that case be that the TestSource event would need a sourceType attribute, the then the cunicorn itself would need a plugin of the different source types supported.


Show all build statuses with ci-status

If a repository has multiple CI's setup, ci-status -v will only show the first one in the list. I would expect it to list all CI's
Tested against version 2.2.1

Good idea. ci-status was designed before GitHub had multiple CI status support. Now we have to rethink it.

:+1:

Is this something we that is being considered?

I definitely want to solve this soon.

This morning I noticed that this is working now. Thank you so much!


Fix duplicate co-author in squashed merge commit messages

Fix: #31980

The function you removed was done intentionally: Improve squash merge commit author and co-author with private emails (#22977)
I think you need to write some tests to clarify the logic.


As a EDP User, I want to have capability to deploy Sonar as a part of add-ons approach

Use Upstream helm chart deploy sonar as a shared component

Use the Upstream Sonar helm chart;
Use the Latest LTS version;
Use Add-ons approach (put the result in add-ons repo);
Integrate EDP instances with External Sonar;
Ensure EDP can be deployed without Sonar-Operator;

From now on there is an opportunity to install and use the latest upstream SonarQube  LTS version using our add-ons Helm chart approach. This provides a simple and convenient way to integrate SonarQube both during a clean installation of EDP and when connecting to an existing installed version of EDP.
By using the add-ons Helm chart approach, we can easily and seamlessly deploy the SonarQube LTS along with EDP. Alternatively, if you already have an installed version of EDP, you can install the SonarQube add-ons Helm chart and connect it instead of the edp-sonar-operator. For more detailed integration information, you can refer to the article on our EDP website.


Low read throughput when queue has retained a lot of messages.

We've been doing some nice SQS tests thanks to elasticMQ and were planning to use it for some load testing to understand throughput on our code, and while write results were very nice and definitely are on par to what is described on the README, read throughput was very low at an high message count, and it seemed to degrade the more messages were retained in the queue.
Here's our scenario for more context.
Queue configuration:
queues {
  queue-test {
    defaultVisibilityTimeout = 10 seconds
    delay = 0 seconds
    receiveMessageWait = 0 seconds
    fifo = true
    contentBasedDeduplication = false
    tags {
      tag1 = "tagged1"
      tag2 = "tagged2"
    }
  }
}

We wrote around 216000 messages of varying size (don't have an exact number, but not bigger than a few kb)  without consuming it, to this queue without any discernable delay, once they were all within the queue, we started to consume them with spring-cloud-aws, and we were only receiving around 3~ messages/second, testing different implementation and approacches (e.g, multiple consumers, other libraries) yielded the same rate.
After making sure the bottleneck was not in the implementation side, we did tests with different messsage sizes and it seems that the more messages there is, the slower it would become, at around 80k messages the rate was 15 messages/second, at 150k it was 5 messages/second, for comparison sake, writing and reading at the same time so the queue would not have any messages retained gave us rates of 100-150 messages/second,
As far as I tested fifo vs standard queues reacted the same, docker or the jar standalone (v1.3.3) made no difference, and we didn't see any obvious problem monitoring the jar through JMX
Is this a known limitation and out of the scope of elasticMQ or a legitimate bug? it has been very useful for our automatic and manual tests so it was sad to see it would choke while doing load tests.
Thanks for the help!

Hi, it may be related to how we store and use the data in in-memory collections. We'll investigate and let you know.

Fixed in https://github.com/softwaremill/elasticmq/releases/tag/v1.3.5


Help

I cant find the programes

https://github.com/libertyernie/BrawlManagers/releases/tag/v3.8
https://github.com/libertyernie/BrawlManagers/releases/download/v3.8/BrawlManagers-3.8.zip

Thnx


Find 3D models/assets/sprites

We need to find some graphics for the game including player models and the world

This is done


chore(Version): Bump to 0.2.2

@knrz Lemme know if you want to full transfer this or if you are fine with maintaining ownership of hex.pm package

Down to transfer it, let’s do it 💯


dont need captureNewlyAddedTables when isRemainingTablesCheckpointed …

dont need captureNewlyAddedTables when isRemainingTablesCheckpointed is false and mysqlsnapshot split assign isnt finished on MySqlSnapshotSplitAssigner

try again


Update grunt-browserify to 3.8.0

Update grunt-browserify to 3.8.0 and removes redundant browserify dependency.
Removals

Removes browserify module from package.json, because browserify is downloaded via grunt-browserify already.

Changes

Fixes typo and order of grunt-browserify module to minimize conflicts with https://github.com/cfpb/cfgov-refresh/pull/527
Update grunt-browserify to 3.8.0 from 3.7.0, which updates Browserify to 10.0.

Testing

Throw out node_modules and re-install with npm update. Run grunt vendor and grunt jsdev to regenerate assets and see that the site doesn't break.

Review

@KimberlyMunoz
@sebworks
@jimmynotjim

Seems to be working locally :+1:


pt file showed a chaos picture on complex model

Netron v5.7.3 + .pt + MacOs show blow picture, I don't know why but maybe more comprehensible  is better:

[pytorch_mobile.pt.zip](https://github.com/lutzroeder/netron/files/8637166/pytorch_mobile.pt.zip)

I checked the web version, It is just fine.

Sorry, macOS v5.7.6 is fine too, gr8 work. so I'll close this post.


fix: Allow filtering for nominees who have not declined

Fixes #6380

I created #6393 for the spurious CI failure.


Update CI

Changes

Only run CI on stable Rust
update to actions/checkout@v3
use rustup instead of actions-rs/toolchain
fix deprecated chrono functions
drop paths-ignore

Future work: Run the CI against nightly Rust in scheduled runs, but not on PRs. They should not be blocked by nightly problems.

bors r+


test: fix test flakiness in vm and resource

[x] The PR has modified HISTORY.rst describing any customer-facing, functional changes. Note that this does not include changes only to help content. (see Modifying change log).

[x] I adhere to the Command Guidelines.

@tjprescott, can you take another look?


Illegal character SPACE

Hi, this is perfect for what I need. However is it possible to allow %20 spaces in the parameters?
Bad Message 400
reason: Illegal character SPACE=' '
https://httpsproxy2http.novalagung.com/reverse/<IP_ADDRESS>:8045/translate?q=space here&source=en&target=tdt

it should be possible. I'll try to take a look tonight

@popstick try again. it should be working now

Thank you novalagung. Very much appreciated.


Image noise problem

when I use the Bayesian convolution neural network to process image problems, the output image has noise. Do you know why this is?

Hi @GGyan,
Can you please provide more information? Like what file you ran on what dataset and where was the noise?

I am very happy with your reply,When I use Bayesian convolution neural networks, I define a convolution layer and then input the dataset for training. After finishing the training, I input a picture and test it. The result showed that the picture produced noise. I don't know what caused the noise and hope to remove the noise. My dataset: https://sites.google.com/site/boyilics/website-builder/project-page. Because I am studying image defogging, my dataset is divided into two parts: artificial synthetic haze image and the original no-haze image. The results of my test are shown below:
On 5/15/2019 15:36，Kumar<EMAIL_ADDRESS>wrote：
Hi @GGyan,
Can you please provide more information? Like what file you ran on what dataset and where was the noise?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

http://chuantu.xyz/t6/702/1557922583x2362407012.png
http://chuantu.xyz/t6/702/1557922656x2362407012.png

Did you use the super resolution code for this? I think the result is interesting and maybe due to the latest changes in the Bayesian distribution layer, the noise is added due to an inefficient sampling method use. The results should be fixed with the code update that I will push soon (in 10 days). Thank you for addressing the issue.

Thank you for your timely answer.

Hi @GGyan,
can you try to run your code now and see if anything changes.


Security Slam: Kubernetes Lightning Round

Description
Open tasks for Kubernetes Security Slam 2023
Release to Community Infrastructure
The following two tasks are not relevant, because images are not part of the release process for Karpenter

[x] Task 1: Releases are being made via CI (https://github.com/kubernetes-sigs/karpenter/issues/868)
[x] Task 2: Check that Images are Staged and Promoted Using Community Infra (https://github.com/kubernetes-sigs/karpenter/issues/874)

Automate Security Documentation

[ ] Task 3: Ensure SBOMs are Generated by Kubernetes BOM (https://github.com/kubernetes-sigs/karpenter/pull/871)
[ ] Task 4: Ensure SLSA Attestations are Generated when Possible (work started in this fork here, but didn’t want to open PR until the GitHub action run was succeeding)
[ ] Task 5: Ensure the Project has a VEX Feed (https://github.com/kubernetes-sigs/karpenter/pull/869 and https://github.com/kubernetes-sigs/karpenter/pull/870)

CLOMonitor: Secure Development Practices

[x] Task 7: Set up CLOMonitor Tracking (https://github.com/cncf/clomonitor/pull/1385)
[x] Task 8: Check for Binary Artifacts (no binaries found in the repo)
[x] Task 9: Review the Code Review (all changesets reviewed)
[x] Task 10: Dangerous Workflow (no dangerous workflow patterns detected)
[ ] Task 11: Security Insights (https://github.com/kubernetes-sigs/karpenter/issues/877 and https://github.com/kubernetes-sigs/karpenter/pull/878)
[ ] Task 12: Dependencies Policy
[x] Task 13: Dependency update tool (update tool detected, dependabot)
[x] Task 15: Token Permissions (https://github.com/kubernetes-sigs/karpenter/pull/884)

This checklist was developed by @eddie-knight and @puerco to help guide contributions. When the project tracking kicks in on https://clomonitor.io/projects/cncf/karpenter then more of the security hygiene state will be visible as well.

@ricardoapl we need to update this

Thank you @coder12git, should be updated now
Please let me know if I missed something
/cc @YashPimple @sreeram-venkitesh

Ensure SBOMs are Generated by Kubernetes BOM

How common are SBOMs when you are just shipping a library? I'm familiar with pushing them as part of an image build, but since we aren't building an image here, where would these get stored?

How common are SBOMs when you are just shipping a library? I'm familiar with pushing them as part of an image build, but since we aren't building an image here, where would these get stored?

That's a good point, I'm also not familiar with such use case. Go's go.sum already provides a detailed list of direct and transitive dependencies, but maybe generating an SBOM for Karpenter could be useful for supplying clear and accurate information about dependencies (and transitive dependencies) downstream in a consistent format. Perhaps the biggest benefit here might come from including VEX (Vulnerability Exploitability eXchange) information later on to the SBOM for preemptively assessing vulnerabilities and communicating likely false positives downstream. Not really sure where these would get stored though, but perhaps @puerco can help us out here?

but I think they would be placed in GitHub Release

Agree that this is best put next to a Release in the Github Releases tab. I'll wait for @puerco to comment here before we decide what we should do with the SBOM generation.

@ricardoapl task- 11, 12 is also completed.

@coder12git I think this project doesn't pass the dependencies policy check yet, maybe we are missing something?
Perhaps the Google Docs for Security Slam: Kubernetes Lightning Round could be useful in figuring out what's missing

Ensure SBOMs are Generated by Kubernetes BOM

How common are SBOMs when you are just shipping a library? I'm familiar with pushing them as part of an image build, but since we aren't building an image here, where would these get stored?

Library SBOMs are not particularly useful unless that library is a distributable artifact along with its resolved dependencies.

#889 had referenced this which caused this issue to close by accident

@ricardoapl task-4 done

Library SBOMs are not particularly useful unless that library is a distributable artifact along with its resolved dependencies

So IIUC, since the library is getting distributed through the go management system and not as a separate artifact, we can close task 3 as completed/not needed?

So IIUC, since the library is getting distributed through the go management system and not as a separate artifact, we can close task 3 as completed/not needed?

Yes..

With #870 merged, task 5 is also completed 🎉 Huge thanks to @jonathan-innis!

So IIUC, since the library is getting distributed through the go management system and not as a separate artifact, we can close task 3 as completed/not needed?

Yes, let's close Task 3

@ricardoapl task - 12 done

With this, ig we have completed all our tasks, huge thanks to @jonathan-innis for all your reviews 🎉

Great! All CLOMonitor Security checks should be passing now, except for SBOM and signed releases (as expected) :rocket:
https://clomonitor.io/projects/cncf/karpenter#karpenter_security
Thank you everyone, I believe we can close this issue now


lua moonjit support

Support building with moonjit which is a fork of luajit:
https://groups.google.com/forum/#!topic/vim_use/O0vek60WuTk .

Hmm, that gives confusing output.  I'll re-order that a bit.  Please check when the patch is out.


[main] Upgrade to latest dependencies

Cron -knative-prow-robot
/cc we don't use Prow
/assign we don't use Prow
Produced by: knative-sandbox/knobots/actions/update-deps
Details:
bumping knative.dev/eventing 609741f...2c059b2:
  > 2c059b2 Validate DeliverySpec in Channels & Subscriptions (# 5777)
  > 2e917e1 Forward prefer: reply header for brokers (# 5773)
  > c39c5a6 upgrade to latest dependencies (# 5770)
bumping knative.dev/pkg 2267a4c...74ac82a:
  > 74ac82a Check Knative CustomResourceDefinitions stored version (# 2272)

Codecov Report

Merging #311 (cec2468) into main (3a65905) will decrease coverage by 0.52%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##             main     #311      +/-   ##
==========================================
- Coverage   80.65%   80.13%   -0.53%
==========================================
  Files          20       20
  Lines         765      765
==========================================
- Hits          617      613       -4
- Misses        134      137       +3
- Partials       14       15       +1

Impacted Files
Coverage Δ

pkg/vsphere/adapter.go
61.24% <0.00%> (-3.11%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
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[PostgreSQL] Support ENUM object

Environment
Liquibase Version: 4.1
Liquibase Integration & Version:  CLI
Liquibase Extension(s) & Version: N/A
Database Vendor & Version: PostgreSQL 12
Operating System Type & Version: Docker
Description
https://www.postgresql.org/docs/12/datatype-enum.html
CREATE TYPE mood AS ENUM ('sad', 'ok', 'happy');

this is a dupe of https://github.com/liquibase/liquibase/issues/1126, sort off. used ENUM as an example for type, but we should do both as part of 1126.


estimator: allow export_outputs to be a Tensor

When someone wants to use tf.estimator.Estimator.export_savedmodel to save the predictions of a model and tf.contrib.predictor.from_saved_model to load the prediction function, it is currently necessary to define boilerplate code. This PR reduces the amount of code.
Currently:
...
ret['export_outputs'] = {
        'predictions': tf.estimator.export.PredictOutput(
            {'predictions': ret['predictions']}
        )
    }
tf.estimator.EstimatorSpec(**ret)

With this PR the following is enough:
ret['export_outputs'] = {'predictions': ret['predictions']}
ret['export_outputs'] = ret['predictions']

The predictor repr for the first example is equal to the original code
SavedModelPredictor with feed tensors {'x': <tf.Tensor 'Placeholder:0' shape=(1, 10) dtype=float32>} and fetch_tensors {'predictions': <tf.Tensor 'dense/Relu:0' shape=(1, 10) dtype=float32>}

and for the second the output name changes
SavedModelPredictor with feed tensors {'x': <tf.Tensor 'Placeholder:0' shape=(1, 10) dtype=float32>} and fetch_tensors {'output': <tf.Tensor 'dense/Relu:0' shape=(1, 10) dtype=float32>}

When someone wants to verify the code, here a full example:
from types import SimpleNamespace
import numpy as np
import tensorflow as tf
from tensorflow.contrib import predictor

def model_fn(features, labels, mode):
    assert labels is None, labels
    x = features['x']
    y = features['y']

    y_hat = tf.layers.Dense(10, tf.nn.relu)(x)
    loss = tf.losses.mean_squared_error(y, y_hat)
    train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

    ret = dict(
        mode=mode,
        loss=loss,
        predictions=y_hat,
        train_op=train_op,
    )

    # Boilderplate:
    ret['export_outputs'] = {  # <------------------- old
        'predictions': tf.estimator.export.PredictOutput(
            {'predictions': ret['predictions']}
        )
    }
    ret['export_outputs'] = {'predictions': ret['predictions']}  # <------------------- new
    ret['export_outputs'] = ret['predictions']  # <------------------- new alternate

    return tf.estimator.EstimatorSpec(**ret)

estimator = tf.estimator.Estimator(
    model_fn=model_fn
)

def generator():
    x = np.random.randn(1, 10).astype(np.float32)
    y = np.random.randn(1, 10).astype(np.float32)
    yield {'x': x, 'y': y}

def input_fn():
    ds = tf.data.Dataset.from_generator(generator, {'x': tf.float32, 'y': tf.float32}, {'x': [1, 10], 'y': [1, 10]})
    it = ds.make_one_shot_iterator()
    element = it.get_next()
    return element

estimator.train(input_fn)

def serving_input_receiver_fn():
    x = tf.placeholder(tf.float32, [1, 10])
    y = tf.placeholder(tf.float32, [1, 10])

    ret = SimpleNamespace()
    ret.features = {'x': x, 'y': y}  # for graph def
    ret.receiver_tensors = {'x': x}  # for serving
    ret.receiver_tensors_alternatives = None
    return ret

model_path = estimator.export_savedmodel('tmp', serving_input_receiver_fn)

predict_fn = predictor.from_saved_model(model_path)
# predict_fn({'x': np.random.randn(1, 10)})
predict_fn

Can one of the admins verify this patch?

@davidsoergel WDYT?

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@boeddeker Thanks for your contribution and apologies for the delay in reverting. Can you please sync with latest TensorFlow branch and test again? @martinwicke Can you please take a look? Thanks!

@ymodak and @martinwicke: I can not rebase to the current master, because the code is moved to tensorflow_estimator.
Furthermore, I can not run the tests in tensorflow_estimator, so it is difficult for me to write tests.
The reason for that may be that tensorflow 1.12 does not use tensorflow_estimator (I use pip to install tensorflow).

@case540 is tensorflow_estimator installed separately, or bundled using bazel? I thought it was the former, so installing tensorflow, you should have tensorflow_estimator.
Either way, it doesn't matter much, since you created this patch as well, without the tensorflow source being installed via pip -- you probably cloned the tensorflow repo, and copied your changes into the cloned tree, right?
Simply do the same thing with tensorflow_estimator.
As to tests: most tests are runnable by just running the script. For final verification, we can trigger the tests on the PR you submit. While it is ideal if you can test in the same environment (using bazel and all), this isn't necessary.

Closing this PR as the code is being moved to Estimator repo.


OkHttp3 - sll exception on reading response data

Trace is here :
javax.net.ssl.SSLException: Read error: ssl=0x8797a780: I/O error during system call, Connection reset by peer
at com.android.org.conscrypt.NativeCrypto.SSL_read(Native Method)
at com.android.org.conscrypt.OpenSSLSocketImpl$SSLInputStream.read(OpenSSLSocketImpl.java:758)
at okio.Okio$2.read(Okio.java:139)
at okio.AsyncTimeout$2.read(AsyncTimeout.java:237)
at okio.RealBufferedSource.read(RealBufferedSource.java:46)
at okhttp3.internal.http1.Http1Codec$FixedLengthSource.read(Http1Codec.java:384)
at okio.Buffer.writeAll(Buffer.java:1005)
at okio.RealBufferedSource.readByteArray(RealBufferedSource.java:107)
at okhttp3.ResponseBody.bytes(ResponseBody.java:136)
The library triggers onResponse callback with 304 status code. It is true for my case. I can get content length of body, headers of response. But I couldn't get bytes or string of response body.
            ResponseBody body = response.body();
            int statusCode = response.code();
            byte[] bodyBytes = body.bytes();

The error is thrown on body.bytes() method. Okhttp version is 3.8.1.

Executable test case?

My body content is here
Full code is below:
`int timeout = 200000;
OkHttpClient.Builder clientBuilder, OkHttpClient client;
clientBuilder = new OkHttpClient.Builder();
clientBuilder.connectTimeout(timeout, TimeUnit.MILLISECONDS);
clientBuilder.readTimeout(timeout, TimeUnit.MILLISECONDS);
clientBuilder.writeTimeout(timeout, TimeUnit.MILLISECONDS);
client = clientBuilder.build();
client.retryOnConnectionFailure();
const String URL = "https://portalapi.smartface.io/api/v1/rau/check?v=" + Math.floor(Math.random() * 1000);
final Request request = new Request.Builder().url(URL)
.post(RequestBody.create(MediaType.parse("application/json; charset=utf-8"), fileContentText)).build();
Call call = client.newCall(request);
call.enqueue(new Callback() {
@Override
public void onFailure(Call call, IOException e) {
Log.i("@@TAG", " Failure " + e.getMessage());
}
@Override
public void onResponse(Call call, Response response) throws IOException {
    String message = response.message();

    Headers headers = response.headers();
    for(int i = 0; i < headers.size(); i++) {
        Log.d("@@Headers ", i + " ] " + headers.name(i) + " : " + headers.value(i));
    }

    ResponseBody body = response.body();
    int statusCode = response.code();

    Log.i("@@LOG " , " statusCode: " + statusCode + " contentLength: " + body.contentLength());
    byte[] bodyBytes = body.bytes();
    Log.i("@@LOG " , " bodyLength: " + bodyBytes.length);
}

}
`

Are you reproducing this error? If yes, is there any solution to fix it?

I can reproduce the hang and then timeout, seems like a problem with the response from the server, Content-Length is 30, but I don't see those bytes come back.
$ oksocial -i -H 'Content-Type: application/json; charset=utf-8' -d @Downloads/body.txt 'https://portalapi.smartface.io/api/v1/rau/check?v=0'

HTTP/1.1 304 Not Modified
Cache-Control: no-cache
Pragma: no-cache
Content-Length: 30
Content-Type: application/json; charset=utf-8

Curl seems to handle it more gracefully by giving up.
$ curl -s -X POST -H "User-Agent:oksocial/1.0.53" -H "Content-Type:application/json; charset=utf-8" -H "Content-Length:495803" -H "Host:portalapi.smartface.io" -H "Connection:Keep-Alive" -H "Accept-Encoding:gzip" -d @Downloads/body.txt https://portalapi.smartface.io/api/v1/rau/check?v=0 | wc -c
       0

Yeah, the response body is empty for this request. It is an expected behaviour. But okhttp doesn't return null when response.body() is called. So, I don't know the body is empty or not in programmatically if the return value isn't null.
But it throws error response.body().string() or response.body().source() etc. I expect that response.body() returns null if the response body is empty.

I believe the server is sending an incorrect header.  You can send "Connection: close" as a header to make it clearer what is going on, but OkHttp is doing the right thing here.
$ oksocial -i -H 'Content-Type: application/json; charset=utf-8' -H 'Connection: close'  -d @Downloads/body.txt 'https://portalapi.smartface.io/api/v1/rau/check?v=0'

java.net.ProtocolException: unexpected end of stream
	at okhttp3.internal.http1.Http1Codec$FixedLengthSource.read(Http1Codec.java:387)
	at okio.RealBufferedSource.exhausted(RealBufferedSource.java:55)
	at com.baulsupp.oksocial.output.DownloadHandler.writeToSink(DownloadHandler.java:55)
	at com.baulsupp.oksocial.output.ConsoleHandler.showOutput(ConsoleHandler.java:79)
	at com.baulsupp.oksocial.Main.showOutput(Main.java:592)
	at com.baulsupp.oksocial.Main.processResponses(Main.java:570)
	at com.baulsupp.oksocial.Main.executeRequests(Main.java:558)
	at com.baulsupp.oksocial.Main.run(Main.java:341)
	at com.baulsupp.oksocial.Main.main(Main.java:120)

I am not sure. Volley works properly. Also, curl doesn't throw any error.

curl -X POST -H "User-Agent:oksocial/1.0.53" -H "Content-Type:application/json; charset=utf-8" -H "Content-Length:495803" -H "Host:portalapi.smartface.io" -H "Connection:Keep-Alive" -H "Accept-Encoding:gzip"  -d @/users/auser/Downloads/body.txt https://portalapi.smartface.io/api/v1/rau/check?v=11 | wc -c
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
Dload  Upload   Total   Spent    Left  Speed
100  484k    0     0  100  484k      0   159k  0:00:03  0:00:03 --:--:--  159k
0

curl -X POST -H "User-Agent:oksocial/1.0.53" -H "Content-Type:application/json; charset=utf-8" -H "Content-Length:495803" -H "Host:portalapi.smartface.io" -H "Connection:close" -H "Accept-Encoding:gzip"  -d @/users/auser/Downloads/body.txt https://portalapi.smartface.io/api/v1/rau/check?v=11 | wc -c
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
Dload  Upload   Total   Spent    Left  Speed
100  484k    0     0  100  484k      0   153k  0:00:03  0:00:03 --:--:--  153k
0

ok. how about you check the status before reading the body?

Look at here.  It says that "The 304 response MUST NOT contain a message-body". If the status code is 304, we shouldn't read response body content.

@swankjesse what is expected behaviour for 304, when server indicates there is a response, but it doesn't come?

We handle 304s properly in our cache code. When we get a 304 when the cache isn’t involved we might be broken. We should confirm that we expect no response body.

    // If the Content-Length or Transfer-Encoding headers disagree with the response code, the
    // response is malformed. For best compatibility, we honor the headers.
    if (contentLength(response) != -1
        || "chunked".equalsIgnoreCase(response.header("Transfer-Encoding"))) {
      return true;
    }

Going to close this as won't fix.  We risk breaking other clients by changing this behaviour, and ultimately the server should be fixed.


Handle JSON/JSONB scalar types

This PR fixes issue https://github.com/vietj/vertx-pg-client/issues/26

Fixed https://github.com/vietj/vertx-pg-client/pull/29


Vulnerability (CVE-2022-42969) introduced by using 'retry' package

Describe the bug
The introduction of the 'retry' package in 14.0 introduced the vulnerability with CVE code 'CVE-2022-42969' into the repo. Retry depends on the 'py' library which contains a 'high' vulnerability: https://www.cve.org/CVERecord?id=CVE-2022-42969
To Reproduce
Scan the package with any vulnerability scan tool like for instance Mend. This will yield this vulnerability from 14.0 and higher but not for 13.2 and lower.
Expected behavior
No active vulnerabilities in the repo when using the latest version.
Screenshots
N.A.
Environment (please complete the following information):

edr Version: 0.14.1
dbt package Version: 0.14.1

Additional context
The github repos we use are scanned automatically on vulnerabilities using Mend.io . Any vulnerability will raise a flag that needs to be dealt with. As such, having this new vulnerability prevents us from using elementary.

@thijs-nijhuis-shell thank you for informing us. The fix will be included in our next release.


Record the VPN process crashes as GLOBAL

Task/Issue URL: https://app.asana.com/0/414730916066338/1202000697555267/f
Description
The crashes coming from the vpn process do not currently appear in SAFE, but now that it's more mainstream we do want to have them in there. See this task for more context.
Steps to test this PR
Just code review, we're basically copy-pasting the logic from the AlertingUncaughtExceptionHandler

Current dependencies on/for this PR:

develop

PR #1818   (this PR)

This comment was auto-generated by Graphite.


Fix code scanning alert - Constructor Deserialization Remote Code Execution

Tracking issue for:

[x] https://github.com/bcgov/nr-forest-client/security/code-scanning/56

Closing as remediation was taken to prevent problems


Is it possible to add scroll bar to the items?

I also noticed some possible improvements in typing. If you think this is a good idea, I could make a pull request.

Yeah I think that would be good idea to be improvements! I really appreciate your suggestion but I'm thinking to renew this library from scratch like rebuilding structure of library and add more features.

It's impossible add scroll bars in items

I add marquee option in resultOptions and dropdownItemOptions.


nvidia-inspector: Add version <IP_ADDRESS>

Cannot currently be PR-ed due to an ssl cert error

[x] I have read the Contributing Guide.

/verify


Unify T1 and RB guesses through the guess library

Summary
Both models of T1 and RB are exponential decays, however their default guesses for parameter values are different. They will benefit from sharing the code that generates default guesses. This is the idea behind what I call in this issue "the guess library", which is the recent file guess.py, consisting of methods for guess generation for various fit functions. The guess library already contains a method for exponential decays (with one parameter, which is the exponent; without amplitude and offset).
For simplicity, from now on I'll assume that RB is a 1-qubit RB.
Default guess for the offset
When there are no SPAM errors, the T1 curve converges to 0 when x tends to infinity, and the RB curve converges to 0.5. This is why the default guess for the offset of RB is currently 0.5.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L48
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L116
For T1, on the other hand, the current guess for the offset takes into account SPAM errors by setting the offset guess to y[-1].
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L48
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L103-L104
The T1 approach makes more sense. In the shared code, I suggest to set the offset guess to y[-1] (unless specified otherwise by the user).
Default guess for the exponent
Here the parameter is alpha for RB and T1 for T1, with the relation alpha = e^(-1/T1). We can still guess one of them, say alpha, in a shared location, and this implies a guess for T1.
The current guess of T1 is just the mean of the delays.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L49
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L99-L100
RB extracts the guess for alpha from the two first data points, using the already existing guess of the offset.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L49-L50
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L118-L121
By the way we could do this trick with any pair of data points, not necessarily the first two. So in fact this RB approach consists of many sub-options, because it requires to choose the pair of data points to be used.
There is a third approach, which is to use the guess method from the guess library.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/curve_analysis/guess.py#L131-L141
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/curve_analysis/guess.py#L152-L154
We can do it if we take new_y = (y-B)/A, where B is the guess of the offset, which we already have, and A is the guess of the amplitude, which we don't have yet. In the next section we'll see two ways to guess the amplitude. In one of them the guess does not depend on the guess for the exponent; in this case, we can first guess the amplitude, and then use this approach to guess the exponent. Similarly, this approach is applicable if the user provides a guess for the amplitude.
Yet a fourth option to guess the exponent: this option, like the previous one, assumes that we already have a guess for the amplitude. Then we can choose a data point x[i], y[i] for some i and extract alpha from the fit function.
Overall, out of the four options described here, I'm not in favor of the first option. The T1 approach is too heuristic.
Default guess for the amplitude
When x is equal to 0, y is equal to the sum of the amplitude and the offset. This is why, for T1, the guess of the amplitude is y[0] minus the guess of the offset.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L47
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/characterization/t1_analysis.py#L105-L106
RB doesn't assume that the first x is 0. Actually, since we have x[0], and if we already have a guess for the exponent (either because the user provides a guess for the exponent, or because we've applied one of the first two options in the previous section), we can use any data point to extract the amplitude, similarly to the fourth option in the previous section. RB chooses to do it with the first data point.
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L46-L47
https://github.com/Qiskit/qiskit-experiments/blob/c3458fbb5e994e7152d27c092b4459b00ddeef00/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py#L126-L127
Here I think the RB approach is what we should adapt in the shared code.
Refactor the guess handling in CurveAnalysis
In RB, the generation of default guesses is done in the method _initial_guess. It is called by _setup_fitting, which overrides these default guesses if the user provides her own guesses. There is a problem here, when the default guess of one parameter depends on the guess of another parameter. Suppose that the user provides a guess for the offset, but not for the exponent. Then, when calculating the default guess of the exponent, we use the default guess of the offset, instead of the user's guess.
Following the same logic, every method in the guess library should receive the user's guess as an input parameter. In general, we have to reconsider the relations between these three: _setup_fitting, _initial_guess, and the guess library. Do we still need _initial_guess, now that we have the guess library, or can we remove it from the CurveAnalysis class?
Sanity checks
RB ensures that the generated guesses are reasonable. For example, it verifies that the guess of the exponent is smaller than 1. If not then it overrides it with 0.99. It does so only for guesses not coming from the user; if the user provides a guess for the exponent which is greater than 1, it is not overridden.
So, in our shared code, we'll have to make the following decisions:

Do we want to override unphysical guesses?
If yes, do we want to do it also for user guesses?

Does it really matter?
As far as I know, fitting exponential decays is not sensitive to the initial point.

Stale, closing


make sure service::nix is completed before starting the service

With include it was failing. So had to move it to contain which will make sure that its setup the licences thing.
Error: Could not start Service[splunk]: Execution of '/sbin/service splunk start' returned 2: Starting Splunk...
License not yet accepted, but executed with no-prompt flag.  Exiting.
Error: /Stage[main]/Splunk::Forwarder::Service/Service[splunk]/ensure: change from 'stopped' to 'running' failed: Could not start Service[splunk]: Execution of '/sbin/service splunk start' returned 2: Starting Splunk...
License not yet accepted, but executed with no-prompt flag.  Exiting. (corrective)

It was failing because of the stale init.d/splunk file


D-Kit : Revert the NewtonM2 GPS device driver

D-Kit : Revert the NewtonM2 GPS device driver

D-Kit : Revert the NewtonM2 GPS device driver

Please tell the reason why revert without git revert.

D-Kit : Revert the NewtonM2 GPS device driver

Please tell the reason why revert without git revert.

git revert failed


function check_equality() wanted

function that compares two variables or two whole datasets

same valuemap?
same codes?
same type?
-same questionlabel?

DataSet.compare() is done

in branch for #629.

Was not merged. My error. Now in branch for #646


Update ud_linux.go

Added import os package to linux userdir

lousy mistake isn't it, thanks. fixed it

lousy mistake isn't it, thanks. fixed it


Takeoff mode in indoor environment

I am working to make my pixhawk based multicopters (hexacopter and coaxial octacopter) operate in indoor environment (devoid of GPS) using offboard with ROS. My plan is to automate the entire operation through offboard script, including arming, takeoff, waypoint navigation, and landing.
The arming and waypoint navigation through the offboard script is done, but I also wish to use the Takeoff and Land mode indoors, by calling the takeoff and land services of MAVROS. The problem, however, is that it requires the use of GPS coordinates.
Is there any workaround to use the takeoff and land services without using GPS coordinates?
My reason for using the takeoff service instead of directly giving the takeoff waypoints as local_position_setpoint in local coordinate, as mentioned in MAVROS Offboard example, is that when directly giving local waypoints, the takeoff is not smooth. I have tried several experiments and the takeoff service gives better performance.

Here is a flying example: https://www.youtube.com/watch?v=wrqZFTiHyTU
It does not need gps, but need to enable VIO.
https://docs.px4.io/master/en/advanced_config/tuning_the_ecl_ekf.html#external-vision-system


Help installing, how to run npm setup

Hi, currently stuck at step #2
1.- cloned directory, opened terminal in fig magic folder
2.- then ran npm setup
Output:
Usage: npm <command>

where <command> is one of:
    access, adduser, audit, bin, bugs, c, cache, ci, cit,
    clean-install, clean-install-test, completion, config,
    create, ddp, dedupe, deprecate, dist-tag, docs, doctor,
    edit, explore, get, help, help-search, hook, i, init,
    install, install-ci-test, install-test, it, link, list, ln,
    login, logout, ls, org, outdated, owner, pack, ping, prefix,
    profile, prune, publish, rb, rebuild, repo, restart, root,
    run, run-script, s, se, search, set, shrinkwrap, star,
    stars, start, stop, t, team, test, token, tst, un,
    uninstall, unpublish, unstar, up, update, v, version, view,
    whoami

npm <command> -h  quick help on <command>
npm -l            display full usage info
npm help <term>   search for help on <term>
npm help npm      involved overview

Specify configs in the ini-formatted file:
    /Users/myusername/.npmrc
or on the command line via: npm <command> --key value
Config info can be viewed via: npm help config

<EMAIL_ADDRESS>/Users/myusername/.nvm/versions/node/v12.6.0/lib/node_modules/npm

Did you mean one of these?
    set
    stop

Hi Sogen,
Sorry for very late reply. The command was a sloppy one on my part: it should of course be npm run setup as usual with NPM. This is now also amended in the readme file.
Closing this now.

thanks!


Create an app that uses React Context for theming

[x] Use React.Context to avoid prop drilling
[x] Create a light and dark theme toggle
[x] Consider using Styled Components

Finished with two apps:

https://github.com/1Copenut/react-bytes/tree/main/context-theming
https://github.com/1Copenut/react-bytes/tree/main/context-styled-components


Usage in React v16

Is it possible to make use of this package in a React v16.8+ environment?

Yes


BadAccess, BadShmSeg

Please describe the issue in as much detail as possible, including any errors and traces
I get this when i run the test command
X Error: BadAccess (attempt to access private resource denied) 10
Extension:    130 (MIT-SHM)
Minor opcode: 1 (X_ShmAttach)
Resource id:  0x2e00003
X Error: BadShmSeg (invalid shared segment parameter) 128
Extension:    130 (MIT-SHM)
Minor opcode: 5 (X_ShmCreatePixmap)
Resource id:  0x2e0000d

Linux distribution (if applicable): Ubuntu 18.04.1
Howdy version:

Could you try running QT_X11_NO_MITSHM=1, and then trying the test command again?

Hi there, thanks for putting this together!
I had the same issue upon trying to run sudo howdy test and the viewer window was blank.
I tried running sudo QT_X11_NO_MITSHM=1 howdy test and it worked, though the image it returned was basically blank (see attached) and not dynamic (the image didn't really change even as I moved my face closer to the camera/emitters or waved my hand in front of them).
When I try using howdy normally it just times out and I have to enter the password. I assume it's because the image is not actually dynamic (i.e. not even recognizing something that resembles a face).
I'm running Ubuntu 18.04 LTS on a Thinkpad T480s.

This is a known decoding error in some cameras, could you try running sudo howdy config and setting force_mjpeg to true?

OK, did that and the test procedure gives an actual image -- great!
I just added two versions of my face (with and without glasses) to my profile then tried a few tests. Unfortunately I still get the timeout problem, so I still have to enter my password manually.

Try upping the certainty value, howdy might be recognizing you with too little match certainty.

I upped the certainty value to 9 and lowered the dark_threshold to 1 but still got the timeout error.
I noticed that when I run the test procedure, the frame rate and exposure consistency seems to depend heavily on the distance between my face and the emitters/camera. Still, even when I added a new face based on the distance and position I felt was most consistent in the test procedure, I still get the timeout errors.
Any other suggestions?

Following up on issues i forgot to reply to, are you still having this problem or did you fix it/uninstall Howdy?

Hi, thanks for checking in. I disabled (but didn't uninstall) Howdy because
I could never get it to recognize me, no matter how much I tweaked the
parameters. I had written earlier about how the output was just greenish
static when I used the "sudo howdy test" command, so perhaps the
authentication routine is getting the same data every time it turns on the
camera and of course it in no way resembles a face.
On Thu, Oct 4, 2018 at 9:48 AM boltgolt<EMAIL_ADDRESS>wrote:

Following up on issues i forgot to reply to, are you still having this
problem or did you fix it/uninstall Howdy?
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Greenish static is a new symptom for me, does this static also show up when you open Cheese (search for Cheese in the dash)? You might need to try out different webcams/video sizes in the Preferences menu.

Indeed it does show up initially in Cheese because the default camera is
the one associated with the IR emitters. I always have to select the normal
webcam in the Preferences menu to get a clear picture.
On Thu, Oct 4, 2018 at 8:41 PM boltgolt<EMAIL_ADDRESS>wrote:

Greenish static is a new symptom for me, does this static also show up
when you open Cheese (search for Cheese in the dash)? You might need to try
out different webcams/video sizes in the Preferences menu.
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So Cheese is also just getting the garbage data? Does switching resolutions in the preferences change anything?

Yep, the cheese default is the IR camera at 480x400 and it's the green
garbage. Upon switching resolutions to 400x400 it looks as expected,
however.
On Fri, Oct 5, 2018 at 11:53 AM boltgolt<EMAIL_ADDRESS>wrote:

So Cheese is also just getting the garbage data? Does switching
resolutions in the preferences change anything?
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Great! Could you set the frame size in sudo howdy config to that format:
frame_width = 400
frame_height = 400

This should fix the problem.

Had the same problem, running it with sudo QT_X11_NO_MITSHM=1 howdy test finally worked.

I had exactly the same issue.
I was able so see my image in test, but it never had been recognized. Also, is test, using IR camera, i never shown the red circle around my face (testing with the normal camera, everything worked fine). I completely solved the issue with the steps bellow:
1 - To fix green image:
In howdy config, simply set ( no need to set force_mjpeg or anything else):
frame_width = 400
frame_height = 400
2 - To fix testing interface:
add the line to /etc/environment
QT_X11_NO_MITSHM=1
3 - Finally to get the correct detection, including the red circle in howdy test, I had to set bright and contrast  the parameters in v4l:
v4l2-ctl -d /dev/video0 --set-ctrl=contrast=0
and:
v4l2-ctl -d /dev/video0 --set-ctrl=brightness=255
check with:
$ v4l2-ctl -d /dev/video0 --list-ctrls
                    brightness 0x00980900 (int)    : min=0 max=255 step=1 default=128 value=255
                       contrast 0x00980901 (int)    : min=0 max=255 step=1 default=32 value=0
Doing this, I had no more problem with detection with IR camera.


Fixing go formatting issues with go version 1.17.8

Description: Fixing go formatting issues with go version 1.17.8
Changes Proposed in PR: Fixing go formatting issues with go version 1.17.8

/azp run


Cache: improves threshold management in FScache

Adds cache element to maintain element count, to improve speed
with very large caches.

Supports disabling threshold by setting to zero.

@ThiefMaster Is the Travis CI failing anything other than a random failure? It seems unrelated to my work at least.

The cache tests randomly fail on Travis for some reason. Don't worry if those are the only failures. I'm re-running that job.


Scene Manager: V5.0.0-A33

Bullet Physics Update - BtSmoothTriangleMesh Support

@CedricGuillemet FYI

I ll merge after the build


Minor changes

Removed the testimonial and blog part and added the team page

Can you provide some description on why do we need these changes?

Removed the testimonial and blog part and added the team page

Firstly, we need the blog and testimonials to be there on the front page. Though there aren't any as of now, let's keep the empty sections for now.
Coming to the team page, it needs to be a seperate html page.
With this, I don't see there is any need for this PR. I want to hear your justifications for this PR

The blog and testimonial are only commented out, we can have them included when we have any of those.
The team section would provide a brief intro to the team and would redirect to the main team page from there.

That's perfectly fine.
In my view, it is always to have, though they are empty sections, so that people who see the website understand what we are going to add to the website. if you really want to make it more lively, we can write a few blogs and collect testimonials from our previous workshop attendees and add them. I would suggest not to comment them out.
Coming to the team page, let's not keep anything on the main for now. Once the organization team stabilizes well enough, we can think of it.

Okay. I am closing this PR


Handle syscall in RadecoIL

What I am wondering is to make a fake function to handle syscall,  which is lacked in RadecoIL.

I think the same applies to TODO opcode, so radeco would take original instructions and insert it as a function-intrinsics. Same applies for imported functions, may be some builtins, etc. So it has the benefits for the readability by the user, and also an ability to reason about.


Generate provider-azure from the Azure Service Operator codegen pipeline

What problem are you facing?

We would like to build on the great work in https://github.com/azure/k8s-infra/ to generate experimental crossplane controllers using the same codegen pipeline as ASO. Doing so would allow us to keep functional parity with ASO as additional resources are supported by the project.
How could Crossplane help solve your problem?

There are likely to be many steps towards reconciling different design decisions and constraints between the 2 projects, so as a first discrete step we want to only attempt to generate crossplane types via an ASO subcommand. These types should be checked into a new crossplane provider template repo for further iteration towards a full Reconciler implementation.

I did! My mistake.


Multicomponent systems stop prematurely

Hi Brandon,
I am trying to use pycalphad to simulate solidification behaviour of alloys 625 and Monel.
I noticed that simulation ends at some temperature dependent on composition and temperature step, even if solidification is far from complete.
I attach an extreme example for Monel, comparing temperature steps of 5K and 2K. The simulation with 2K stops prematurely.
I noted a similar behavior with alloys 625, even if not so dramatic. Also changes in composition affects the end point.

I attach the code and the database I am using, derived from mc_ni_v2.034.tdb
Thank you very much for your help and all you did with pycalphad!
MonelScheil.txt
ARNi.txt

Hi @AndRicci, thanks for reporting this and especially for providing a script. I am able to reproduce something similar to these two plots with pycalphad 0.8.5. Can you share which version of pycalphad you are using?
The expected behavior is that:

For Scheil simulations at the same composition, different step sizes produce qualitatively similar results. In practice, the the different step sizes means that the fraction that solidifies at each step is different and this can lead to some minor discrepancies. The script and images here seem anomalous.
For Scheil simulations at different compositions, the endpoint may change if the eutectic changes (or if the stopping liquid phase fraction is reached before reaching the eutectic). I don't know enough about this system or the compositions you are testing to be able to tell whether this is happening.

If I use the verbose flag to simulate_scheil_solidification, I get the output:
building callables... done
T=1650.000, X_CU=0.28, X_FE=0.03, X_C=0.01, X_SI=0.02, X_MN=0.01, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1645.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1640.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1635.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1630.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1625.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1620.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
No liquid phase found at T=1615.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1620.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=4.167, NL: 1.000, NP(LIQUID)=1.000
No liquid phase found at T=1615.833, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1620.000, X_C=0.01, X_CU=0.28, X_FE=0.03, X_MN=0.01, X_SI=0.02, ΔT=3.472, NL: 1.000, NP(LIQUID)=1.000
New phases seen: {'FCC_A1'}.  (Found set()) (Convergence failure) Stepping back and reducing step size.
No liquid phase found at T=1505.417, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
No liquid phase found at T=1508.310, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1510.721, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02, ΔT=2.009, NL: 0.123, NP(LIQUID)=1.000
T=1508.712, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02, ΔT=2.009, NL: 0.123, NP(LIQUID)=1.000
No liquid phase found at T=1506.703, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1508.712, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02, ΔT=1.674, NL: 0.123, NP(LIQUID)=1.000
No liquid phase found at T=1507.038, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1508.712, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02, ΔT=1.395, NL: 0.123, NP(LIQUID)=1.000
No liquid phase found at T=1507.317, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
T=1508.712, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02, ΔT=1.163, NL: 0.123, NP(LIQUID)=1.000
No liquid phase found at T=1507.549, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Stepping back and reducing step size.
No liquid phase found at T=1508.712, X_C=0.00, X_CU=0.55, X_FE=0.01, X_MN=0.02, X_SI=0.02. (Found set()) (Convergence failure) Maximum step size reduction exceeded. Stopping.

So it looks like convergence failures are probably causing the early termination because every failure it tries to step backward and reduce the step size up to a minimum allowed step size.
Maybe the correct behavior here should be that the step size gets reset if an equilibrium is successfully found?

Hi Brandon,
I am using pycalphad 0.8.5 too.
I agree that maybe resetting the step size could avoid some early terminations caused by convergence failures. In my case there should be no need to continue the simulation with such small step size.
I share another example with alloy 625, where a change in composition makes the simulation stop earlier. The first two graphs are obtained with the code below and the same database already shared. The third one setting Si and Mn at 1w%.
This system should reach an eutectic (NbC carbide), as opposed to the Monel one. Is this the reason for the stop?

import matplotlib.pyplot as plt
from pycalphad import Database, equilibrium
import pycalphad.variables as v
from pycalphad.plot.utils import phase_legend
import numpy as np

# Load database
dbf = Database('mc_ni_v2.034.pycalphad.tdb')
# Define the components and phases
comps = ['NI', 'CR', 'MO', 'NB', 'FE', 'SI', 'MN', 'C', 'VA']
phases = ['LIQUID','BCC_A2','FCC_A1', 'LAVES']
# Create the dictionary of conditions
mass_fracs = {v.W('CR'): 0.21,
              v.W('MO'): 0.08,
              v.W('NB'): 0.035,
              v.W('FE'): 0.04,
              v.W('SI'): 0.5/100,
              v.W('MN'):0.5/100,
              v.W('C'): 0.03/100}
conds = v.get_mole_fractions(mass_fracs, 'NI', dbf)
conds[v.P] = 1e5
conds[v.N] = 1

phase_handles, phasemap = phase_legend(phases)

from scheil import simulate_scheil_solidification

# setup the simulation parameters
initial_composition = v.get_mole_fractions(mass_fracs, 'NI', dbf)
liquid_phase_name = 'LIQUID'
start_temperature = 1650

# perform the simulation
sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5)

# plot the result
plt.figure(figsize=(9,6))
for phase_name, amounts in sol_res.cum_phase_amounts.items():
    plt.plot(sol_res.temperatures, amounts, label=phase_name)
plt.plot(sol_res.temperatures, sol_res.fraction_liquid, label='LIQUID')
plt.ylabel('Phase Fraction')
plt.xlabel('Temperature (K)')
plt.title('Alloy 625 Scheil simulation, phase fractions')
plt.legend(loc='best')
plt.show()

How do I use the verbose flag?
Thank you
Andrea

This system should reach an eutectic (NbC carbide), as opposed to the Monel one. Is this the reason for the stop?

Using the verbose flag should tell you the reason why it stops, so you'll be able to check that way. To enable it, replace the line:
sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5)
with
sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5, verbose=True)

One other thing: the if the carbide is a new phase that should precipitate, it should also be present in the list of phases: phases = ['LIQUID','BCC_A2','FCC_A1', 'LAVES']

It seems that both compositions of alloy 625 end the simulation with a "Maximum step size reduction exceeded." I attach two scripts with their verbose results.
The NbC phase is described in the database as FCC_A1, so I cannot explicitly add it. pycalphad should detect the presence of a second FCC_A1 phase of different composition. Actually when I run an equilibrium calculation setting C=0.1w%, the NbC FCC_A1 phase is correctly accounted for. But when I run a Scheil simulation with the same composition it is not.
625Scheil1.txt
625Scheil2.txt

It seems that simulate_scheil_solidification gets confused when there are phases with order/disorder forms. For example, if I simulate a Ni-Si binary alloy with 0.1 Si using the nisi_tok.tdb database available from NIMS, the simulation goes on but the liquid fraction remains 1, until it finally stops with "Maximum step size reduction exceeded". The simulation runs okay if the ordered fcc BETA1 phase is deactivated.
By the way, I upgraded to pycalphad 0.9.0.

@AndRicci, I just merged #16 which fixes the case in the Ni-Si binary where the disordered configurations that become stable when the ordered phase is entered weren't being accounted for. Thanks for reporting this case and for upgrading.
I will also note that stopping with Maximum step size reduction exceeded isn't necessarily a failure mode. There are two ways to that the Scheil simulation can terminate:

When the phase fraction of the liquid becomes lower than what is specified by the stop parameter of simulate_scheil_solidification (defaults to 1e-4).
When the simulation finds steps down in temperature and finds no liquid, it goes back and takes a smaller step. This helps get past peritectic points if the liquid composition at the step above the peritectic reaction doesn't have any liquid phase below the peritectic reaction. Smaller steps will get it closer to the composition where it can continue to make progress. This means that when the simulation reaches an actual end-point, i.e. a eutectic, it will step back and reduce the step size to make sure it can't continue to make progress. At a eutectic, it will eventually reach the maximum step size reduction and exit successfully.

The converged attribute of the SolidificationResult object returned by simulate_scheil_solidificaition programmatically indicates type (1) success. The simulate_scheil_solidification cannot yet distinguish whether a type (2) termination was a successful simulation (exiting at a eutectic) or unsuccessful (exiting for some other reason, such as repeated convergence failures or failing to proceed past a eutectic point), so the converged attribute will be alwaysFalse for this termination mode. With the new minimizer in pycalphad 0.9, we hope to expose the ability to compute invariant reactions directly, so in the future scheil can more robustly handle these cases without relying on step size reduction at all.

Here's the script and verbose output for the Ni-Si case:
from pycalphad import Database
import pycalphad.variables as v
from scheil import simulate_scheil_solidification

dbf = Database('nisi_Tok.tdb')
comps = ['NI', 'SI', 'VA']
phases = list(dbf.phases.keys())

initial_composition = {v.X('SI'): 0.1}
liquid_phase_name = 'LIQUID'
start_temperature = 1650

sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5, verbose=True)

building callables... done
T=1650.000, X_SI=0.10, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1645.000, X_SI=0.10, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
T=1640.000, X_SI=0.10, ΔT=5.000, NL: 1.000, NP(LIQUID)=1.000
New phases seen: {'FCC_A1'}.  T=1415.000, X_SI=0.20, ΔT=5.000, NL: 0.066, NP(LIQUID)=0.962 NP(BETA3)=0.003 NP(FCC_A1)=0.034
No liquid phase found at T=1410.000, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=4.167, NL: 0.066, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1410.833, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=3.472, NL: 0.065, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1411.528, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=2.894, NL: 0.065, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1412.106, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=2.411, NL: 0.064, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1412.589, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=2.009, NL: 0.064, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1412.991, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=1.674, NL: 0.064, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1413.326, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=1.395, NL: 0.063, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1413.605, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=1.163, NL: 0.063, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1413.837, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Stepping back and reducing step size.
T=1415.000, X_SI=0.20, ΔT=0.969, NL: 0.062, NP(LIQUID)=0.994 NP(BETA3)=0.003 NP(FCC_A1)=0.003
No liquid phase found at T=1414.031, X_SI=0.20. (Found {'FCC_A1', 'BETA3'}) Maximum step size reduction exceeded. Stopping.

It stopped around 1414 K, which corresponds to the eutectic reaction for LIQUID -> FCC_A1 (disordered BETA1) + BETA3.

With the changes in pycalphad/solve-advance branch that is in the pull request here: https://github.com/pycalphad/pycalphad/pull/373, I re-ran the Inconel 625 example with adaptive sampling enabled in the following script:
import matplotlib.pyplot as plt
from pycalphad import Database, equilibrium
import pycalphad.variables as v
from pycalphad.plot.utils import phase_legend
import numpy as np

# Load database
dbf = Database('scratch/mc_ni_v2.034.pycalphad.tdb')
# Define the components and phases
comps = ['NI', 'CR', 'MO', 'NB', 'FE', 'SI', 'MN', 'C', 'VA']
phases = ['LIQUID','BCC_A2','FCC_A1', 'LAVES']
# Create the dictionary of conditions
mass_fracs = {v.W('CR'): 0.21,
              v.W('MO'): 0.08,
              v.W('NB'): 0.035,
              v.W('FE'): 0.04,
              v.W('SI'): 0.5/100,
              v.W('MN'):0.5/100,
              v.W('C'): 0.03/100}
conds = v.get_mole_fractions(mass_fracs, 'NI', dbf)
conds[v.P] = 1e5
conds[v.N] = 1

phase_handles, phasemap = phase_legend(phases)

from scheil import simulate_scheil_solidification

# setup the simulation parameters
initial_composition = v.get_mole_fractions(mass_fracs, 'NI', dbf)
liquid_phase_name = 'LIQUID'
start_temperature = 1650

# sample points for adaptive mode
from pycalphad import Model
from pycalphad.core.calculate import _sample_phase_constitution
from pycalphad.core.utils import point_sample
points_dict = {}
for phase_name in phases:
    mod = Model(dbf, comps, phase_name)
    points_dict[phase_name] = _sample_phase_constitution(mod, point_sample, True, pdens=50)

# perform the simulation
sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5, verbose=True, eq_kwargs=dict(calc_opts=dict(points=points_dict)))

# plot the result
plt.figure(figsize=(9,6))
for phase_name, amounts in sol_res.cum_phase_amounts.items():
    plt.plot(sol_res.temperatures, amounts, label=phase_name)
plt.plot(sol_res.temperatures, sol_res.fraction_liquid, label='LIQUID')
plt.ylabel('Phase Fraction')
plt.xlabel('Temperature (K)')
plt.title('Alloy 625 Scheil simulation, phase fractions')
plt.legend(loc='best')
plt.savefig('scheil-iss14.png')

I verified that there is a miscibility gap where the NbC carbide begins to precipitate as a second FCC_A1 phase. The verbose output is a little misleading because it combines the phase fractions of the two FCC_A1 phases as one unified phase fraction. The simulation is correct, but the combined phase amounts in the printed output and the SolidificationResult object are a little difficult to use in the case of miscibility gaps.

After #22 was merged, adaptive sampling is now on by default and the above plot can be produced by the following script when on the pycalphad:develop branch:
import matplotlib.pyplot as plt
from pycalphad import Database, equilibrium, variables as v
from scheil import simulate_scheil_solidification

# Load database
dbf = Database('scratch/mc_ni_v2.034.pycalphad.tdb')
# Define the components and phases
comps = ['NI', 'CR', 'MO', 'NB', 'FE', 'SI', 'MN', 'C', 'VA']
phases = ['LIQUID','BCC_A2','FCC_A1', 'LAVES']
# Create the dictionary of conditions
mass_fracs = {v.W('CR'): 0.21,
              v.W('MO'): 0.08,
              v.W('NB'): 0.035,
              v.W('FE'): 0.04,
              v.W('SI'): 0.5/100,
              v.W('MN'):0.5/100,
              v.W('C'): 0.03/100}
conds = v.get_mole_fractions(mass_fracs, 'NI', dbf)
conds[v.P] = 1e5
conds[v.N] = 1

# setup the simulation parameters
initial_composition = v.get_mole_fractions(mass_fracs, 'NI', dbf)
liquid_phase_name = 'LIQUID'
start_temperature = 1650

# perform the simulation
sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=5, verbose=True)

# plot the result
plt.figure(figsize=(9,6))
for phase_name, amounts in sol_res.cum_phase_amounts.items():
    plt.plot(sol_res.temperatures, amounts, label=phase_name)
plt.plot(sol_res.temperatures, sol_res.fraction_liquid, label='LIQUID')
plt.ylabel('Phase Fraction')
plt.xlabel('Temperature (K)')
plt.title('Alloy 625 Scheil simulation, phase fractions')
plt.legend(loc='best')
plt.savefig('scheil-iss14.png')

Closing this issue since it is resolved pending new releases of pycalphad and scheil.


Update "Important Mods" for Masterstroke

This was very rushed, I may have missed a few things. I recommend merging this and then doing a new writeup

I forgot to update the prompt when SexLab installs itself, this could be integrated as well.