Contract Viewer

lord why does this build on my machine but fails on vercel
haven't I suffered enough?

Note to reviewers: I created https://github.com/smlxl/evm.codes/pull/317 to take over from this branch.
I took care of as much PR feedback as I could there, put comments where needed above to have context.
For the rest + new feedback let's continue on #317
This can be closed soon.

Note to reviewers: I created #317 to take over from this branch. I took care of as much PR feedback as I could there, put comments where needed above to have context.
For the rest + new feedback let's continue on #317
This can be closed soon.

Why did you not just push commits into the fork? Now we have yet another PR opened

Note to reviewers: I created #317 to take over from this branch. I took care of as much PR feedback as I could there, put comments where needed above to have context.
For the rest + new feedback let's continue on #317
This can be closed soon.

Why did you not just push commits into the fork? Now we have yet another PR opened

If you prefer a fork, feel free to take #317 and use it here. I will keep my work on #317 to have separation of concerns. More than one people are pushing here which makes an already confusing situation even more confusing for me.


Show Relationships with a Force Directed Graph broken after deprecated Camper news API.

Challenge Show Relationships with a Force Directed Graph has an issue.
User Agent is: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36.
Please describe how to reproduce this issue, and include links to screenshots if possible.

This was broken in #8690. cc/ @FreeCodeCamp/wiki-mods

Thanks for letting me know :-) Sorry to send a duplicate.
On Thu, May 19, 2016 at 9:49 AM, Jonathan<EMAIL_ADDRESS>wrote:

This was broken in #8690
https://github.com/FreeCodeCamp/FreeCodeCamp/pull/8690. cc/
@FreeCodeCamp/wiki-mods
https://github.com/orgs/FreeCodeCamp/teams/wiki-mods
—
You are receiving this because you authored the thread.
Reply to this email directly or view it on GitHub
https://github.com/FreeCodeCamp/FreeCodeCamp/issues/8722#issuecomment-220383012

@kristenmthayer Np :smiley:, It's not a duplicate -- this issue hasn't been brought up yet.

Oh, I see :-)

Should we add the API again or should we immediately start work on the new dataset?

The later. We need a new static data set that can be fetched, could be JSON (data hard coded).

I did this challenge recently (http://codepen.io/DealPete/pen/GZPgpG), and I used country contiguity data I got from Correlates of War. We could use that as an example.

@DealPete your example is really cool! But I did mine with the FCC data, so mine is broken :-(

Thanks @kristenmthayer, I started mine after they had discontinued Camper News, so I knew I'd have to use new data.

@DealPete Thanks a lot for the pen, and the alternate data set. Do you think you can make a PR to update the challenge.
I have forked your pen under FCC and tested it with https and it works, if you do update the challenge, then use the below as the link in the challenge:
https://codepen.io/FreeCodeCamp/pen/xVopBo

I will take care of updating the Pen's title once PR is ready and merged.

@raisedadead @theflametrooper @DealPete any chance one of you has a copy of the old data set somewhere? If the challenge gets changed, I would still want my graph to work, and it would be easier to copy an existing set rather than reverse engineering it. Thanks :-)

#7082

Ok ok good luck.com

Ok ok good luck.com

#8772

Okay, hard-coded some fake data in my graph so it works again, if you want to borrow/use it, feel free: http://codepen.io/KMT/pen/dMqgrO. Have a good day!

@kristenmthayer I think I have a copy of the data somewhere if you would prefer. I'll try to remember to look for it when I get home.

@sludge256 thanks! That would be great, if you have it, mine is pretty obviously invented.

@kristenmthayer Sorry for the delay. I don't know how much data this file contains but it's what I have.
campernews_archive_04052016.txt

@sludge256  thanks! I really appreciate that!!


fix: had url of deploy wrong

👷 Deploy Preview for darling-douhua-5dba96 processing.

Name
Link

🔨 Latest commit
3a6e34637a9009252572e8f6e2848025005269b1

🔍 Latest deploy log
https://app.netlify.com/sites/darling-douhua-5dba96/deploys/6355abc456f5710008fdf2b0


Update README.md - fix one small grammar mistake and one enhancement.

I have noticed one small grammar mistake in README inside "Introduction" section.
" preferably half-body photos and .." should be " preferably half-body photos, and .."
one enhacement ->
"If you meet some problems in the.." "If you encounter any problems in the.."
Screenshot

Please review my PR.


"Show TeX" is a disaster for large expressions

Example expression: sqrt(x+1) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x) - sqrt(x)
Show TeX:

Don't let the element become super large. Add scroll or other restriction to box size to prevent super long expression from manipulating page spacing.
Fix after #158

Adding note that, if this fix doesn't address it, should make issue for big expressions causing the error plot half of screen to be really small. I looked around and didn't see another issue for this, though I may have missed one.
Options to fix: make center divide draggable (may be a valuable feature regardless), so plot/expression table portions can be expanded/shrunk, or put limit on size of expressions table
Otherwise, haven't made any progress yet.

You can go ahead and make that issue, we will close it if it gets fixed!

FPBench server seems to do the same unnecessary-()-ing issue, most noticeably ugly for long expressions

I think I fixed this


[5.6] Cherry pick "Split Symbol.swift to reduce complexity of the file"

The Symbol.swift currently has 1400 lines.
This PR is intended to split the Symbol.swift file into a file system hierarchy.
So that the Symbol.swift only have SymbolGraph.Symbol definition and also makes it easier for writing test case.

Cherry picked from #8

Since the other 2 diff commit only changed the docs, I think they are low-risk too.
@ethan-kusters Could you please help confirm this?

cc @sara-radi, who is the interim 5.6 branch manager while @franklinsch is on vacation.
This looks like it's porting the whole of main over? That means it also includes #6 and #9 as well, not just #8. The others do seem like low-risk changes, but i was under the assumption that they weren't meant to be pushed ahead like this. I'll let Sara make the call, though.


Error: nothing can be uploaded, please use --recursive to upload directories

Storage Explorer Version:
Build Number:
Platform/OS:
Architecture:
Regression From:
Bug Description

Steps to Reproduce

Expected Experience

Actual Experience

Additional Context
{   "message": "\"failed to perform copy command due to error: cannot start job due to error: nothing can be uploaded, please use --recursive to upload directories.\\n\"" }

Hi @Sripalreddy09. It is unclear from your initial comments what you are trying to accomplish. In order to investigate your issue further, please edit your comment by adding the following to the appropriate sections:

The version of Storage Explorer you are using.
A description in your words of the problem you're encountering. Include the context and a description of what you are trying to accomplish.
A list of steps that we can follow to encounter the same issue.

@Sripalreddy09 , it looks like you're using AzCopy from the error message. Can you try running the upload manually on the command line? You can use the "Copy AzCopy Command to Clipboard" action to get a copy of the command, and then if you download AzCopy v10 you can run the command against it. Let us know if you need help with that!

Closing due to lack of response.


INFRA-25666 Adds a note about CODEOWNERS file

Refs INFRA-25666

Thank you for your PR, unfortunately we'll not be moving forward with this.


[time-scale separation] Adaptive step size for different variables?

In some scenarios, the different variables in a differential equation can have vastly different time-scales at which dynamics occurs.
This is why often, it can be useful to select different integration steps for the variables, such that the variable with fast dynamics has a smaller time-step than the variable with the slow dynamics. I am wondering if such a method is in principle possible using the AbstractAdaptiveStepSizeController class.

So I think these kinds of multiple-step-size approaches often involves:

picking a step size that is suited to the 'slow' timescale
picking your favourite solver for the slow dynamics using that step size
defining a solver for the fast dynamics that, for every one of those large steps:
3a. freezes the value of the slow dynamics
3b. runs a differential equation solver for the fast dynamics over the interval defined by the large step, and which will internally use smaller step sizes.

You should be able to do the same thing here: subclass AbstractSolver to implement a solver that exhibits the above behaviour. You will find that your .step method will make a call to diffeqsolve in order to solve the fast dynamics over that interval.
I hope that helps!


Feat: Possibility to show more than 20 lines of logs

Hey, many thanks for Colmena! It is helping me a lot :+1:
However, if a NixOS Configuration does not evaluate, only the last 20 lines of the error are shown. I added the CLI option --show-trace, but this did not increase it. How can I get the more or full output of the nixos-rebuild command that Colmena executes?
If this is not yet possible, I think that this would be a great feature that is hopefully not too complex to add ;)
Here is an example output where the last 20 lines are not enough to display any of the trace output from nixos-rebuild:
$ colmena build --show-trace
[INFO ] Using configuration: /home/yoda/Downloads/git/nix-git/hive.nix
[INFO ] Enumerating nodes...
[INFO ] Selected all 3 nodes.
         ❌ 25s Failed: Child process exited with error code: 1
   (...) ❌ 25s Evaluation failed: Child process exited with error code: 1
[ERROR] Failed to complete requested operation - Last 1 lines of logs:
[ERROR]  failure) Child process exited with error code: 1
[ERROR] Failed to evaluate yodaTab, yodaTux, and 1 other nodes - Last 20 lines of logs:
[ERROR]   stderr)
[ERROR]   stderr)        error: Package ‘r8168-6.1.53-8.048.03’ in /nix/store/3iyx3gjj3hvpd7a0bmwbp4c3jbqksd4b-nixpkgs-src/pkgs/os-specific/linux/r8168/default.nix:48 is marked as broken, refusing to evaluate.
[ERROR]   stderr)
[ERROR]   stderr)        a) To temporarily allow broken packages, you can use an environment variable
[ERROR]   stderr)           for a single invocation of the nix tools.
[ERROR]   stderr)
[ERROR]   stderr)             $ export NIXPKGS_ALLOW_BROKEN=1
[ERROR]   stderr)
[ERROR]   stderr)         Note: For `nix shell`, `nix build`, `nix develop` or any other Nix 2.4+
[ERROR]   stderr)         (Flake) command, `--impure` must be passed in order to read this
[ERROR]   stderr)         environment variable.
[ERROR]   stderr)
[ERROR]   stderr)        b) For `nixos-rebuild` you can set
[ERROR]   stderr)          { nixpkgs.config.allowBroken = true; }
[ERROR]   stderr)        in configuration.nix to override this.
[ERROR]   stderr)
[ERROR]   stderr)        c) For `nix-env`, `nix-build`, `nix-shell` or any other Nix command you can add
[ERROR]   stderr)          { allowBroken = true; }
[ERROR]   stderr)        to ~/.config/nixpkgs/config.nix.
[ERROR]  failure) Child process exited with error code: 1
[ERROR] -----
[ERROR] Operation failed with error: Child process exited with error code: 1
Hint: Backtrace available - Use `RUST_BACKTRACE=1` environment variable to display a backtrace

For comparison, here is the full output when running nixos-rebuild directly:
sudo nixos-rebuild -I nixos-config=hosts/$(hostname)/configuration.nix build --show-trace
building Nix...
building the system configuration...
error:
       … while evaluating the attribute 'buildCommand' of the derivation 'nixos-system-yodaTux-23.05.3427.e5f018cf150e'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/make-derivation.nix:303:7:

          302|     // (lib.optionalAttrs (attrs ? name || (attrs ? pname && attrs ? version)) {
          303|       name =
             |       ^
          304|         let

       … while evaluating call site

       at /nix/var/nix/profiles/per-user/root/channels/nixos/nixos/modules/system/activation/top-level.nix:17:9:

           16|       # directly into stage 2.
           17|       ${optionalString config.boot.kernel.enable ''
             |         ^
           18|         if [ ! -f ${kernelPath} ]; then

       … while calling 'optionalString'

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/strings.nix:242:5:

          241|     # String to return if condition is true
          242|     string: if cond then string else "";
             |     ^
          243|

       … while evaluating the attribute 'passAsFile' of the derivation 'kernel-modules'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/make-derivation.nix:303:7:

          302|     // (lib.optionalAttrs (attrs ? name || (attrs ? pname && attrs ? version)) {
          303|       name =
             |       ^
          304|         let

       … while evaluating the attribute 'passAsFile'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/build-support/buildenv/default.nix:77:5:

           76|     # XXX: The size is somewhat arbitrary
           77|     passAsFile = if builtins.stringLength pkgs >= 128*1024 then [ "pkgs" ] else [ ];
             |     ^
           78|   }

       … while evaluating call site

       at «none»:0: (source not available)

       … while calling anonymous lambda

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/types.nix:509:14:

          508|       merge = loc: defs:
          509|         map (x: x.value) (filter (x: x ? value) (concatLists (imap1 (n: def:
             |              ^
          510|           imap1 (m: def':

       … while evaluating the attribute 'value'

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/modules.nix:807:27:

          806|     optionalValue =
          807|       if isDefined then { value = mergedValue; }
             |                           ^
          808|       else {};

       … while evaluating call site

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/modules.nix:796:12:

          795|       if isDefined then
          796|         if all (def: type.check def.value) defsFinal then type.merge loc defsFinal
             |            ^
          797|         else let allInvalid = filter (def: ! type.check def.value) defsFinal;

       … while calling anonymous lambda

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/modules.nix:796:17:

          795|       if isDefined then
          796|         if all (def: type.check def.value) defsFinal then type.merge loc defsFinal
             |                 ^
          797|         else let allInvalid = filter (def: ! type.check def.value) defsFinal;

       … while evaluating call site

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/modules.nix:796:22:

          795|       if isDefined then
          796|         if all (def: type.check def.value) defsFinal then type.merge loc defsFinal
             |                      ^
          797|         else let allInvalid = filter (def: ! type.check def.value) defsFinal;

       … while calling 'check'

       at /nix/var/nix/profiles/per-user/root/channels/nixos/lib/types.nix:491:15:

          490|       descriptionClass = "noun";
          491|       check = x: isStringLike x && builtins.substring 0 1 (toString x) == "/";
             |               ^
          492|       merge = mergeEqualOption;

       … while evaluating the attribute 'handled'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/check-meta.nix:447:7:

          446|       # or, alternatively, just output a warning message.
          447|       handled =
             |       ^
          448|         {

       … while evaluating the attribute 'no'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/check-meta.nix:449:11:

          448|         {
          449|           no = handleEvalIssue { inherit meta attrs; } { inherit (validity) reason errormsg; };
             |           ^
          450|           warn = handleEvalWarning { inherit meta attrs; } { inherit (validity) reason errormsg; };

       … while evaluating call site

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/check-meta.nix:449:16:

          448|         {
          449|           no = handleEvalIssue { inherit meta attrs; } { inherit (validity) reason errormsg; };
             |                ^
          450|           warn = handleEvalWarning { inherit meta attrs; } { inherit (validity) reason errormsg; };

       … while calling 'handleEvalIssue'

       at /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/stdenv/generic/check-meta.nix:226:38:

          225|
          226|   handleEvalIssue = { meta, attrs }: { reason , errormsg ? "" }:
             |                                      ^
          227|     let

       error: Package ‘r8168-6.1.52-8.048.03’ in /nix/store/8qdza24r7dvwdk4b84kwc92zycf5vr65-nixos-23.05/nixos/pkgs/os-specific/linux/r8168/default.nix:48 is marked as broken, refusing to evaluate.

       a) To temporarily allow broken packages, you can use an environment variable
          for a single invocation of the nix tools.

            $ export NIXPKGS_ALLOW_BROKEN=1

        Note: For `nix shell`, `nix build`, `nix develop` or any other Nix 2.4+
        (Flake) command, `--impure` must be passed in order to read this
        environment variable.

       b) For `nixos-rebuild` you can set
         { nixpkgs.config.allowBroken = true; }
       in configuration.nix to override this.

       c) For `nix-env`, `nix-build`, `nix-shell` or any other Nix command you can add
         { allowBroken = true; }
       to ~/.config/nixpkgs/config.nix.

How can I get the more or full output of the nixos-rebuild command that Colmena executes?

Add the --verbose or -v flag when invoking colmena.

Thanks for the quick reply! I had not tried to apply this obvious argument :sweat_smile:. With this it works perfectly.

Same, I just realized that today!


Automatically service registration and discovery support in cloud enviroment

when use armeria server in cloud environment, like mesos . we should provide a easy way to let every server node automatically regist itself  in a  distribute coordinate service like ZooKeeper. Currently, i am planning to implement a ZooKeeper ServerListener, so that users could easily add this listener to achieve a service registration.
if the client is also armeria client, we could provide unique service alias to make the service discovery extremely easily. for example, when the server is deployed in a cloud ,it automatically register itself under a ZooKeeper zNode(we needn't let the user set the node name ,just chose a random name for it.) and identified this service as a alias like com.abc.mobiledep.foo_http_service, the client could simply specify " i want to use com.abc.mobledep.foo_http_servie " to connect to the server.
By this way , when the user deploy the server, he just need to add a server listener to specify ZooKeeper service url etc., and specify a unique "service name(com.abc.mobiledep.foo_http_service)", the client just need to create a ZooKeeperEndGroup, specify  the corresponding ZooKeeper connection info and specify the unique "service name(com.abc.mobiledep.foo_http_service)"

Adding a ServerListener implementation sounds like a good idea.
To map a Service into a zNode path, how about defining the following interface?
public interface ZooKeeperNodeMapper extends Function<ServiceConfig, Optional<String>> {
    ZooKeeperNodeMapper DEFAULT = ...;

    @Override
    public Optional<String> apply(ServiceConfig cfg); // Returns Optional.empty() for the excluded services
}

When creating the ServerListener implementation, a user could specify what ZooKeeperNodeMapper to use. What do you think?

/cc @imasahiro @anuraaga

@trustin sorry for this late reply, it is a good idea, i will make a draft implementation first.

@jonefeewang Gentle ping. How's it going? :-)

Sorry for this delayed feature, I am currently trapped by company project.
Hope complete this feature in two weeks.
Thanks for your patients!
From:  Trustin Lee<EMAIL_ADDRESS>Reply-To:  line/armeria
<reply+002e461760d3e3493d811d2a5fe44502b72b2b5b75bab4eb92cf0000000114672f959
<EMAIL_ADDRESS>Date:  Tuesday, December 13, 2016 at 11:40
To:  line/armeria<EMAIL_ADDRESS>Cc:  王 军飞<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject:  Re: [line/armeria] Automatically service registration and
discovery support in cloud enviroment (#331)

@jonefeewang https://github.com/jonefeewang  Gentle ping. How's it going?
:-)
―
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/line/armeria/issues/331#issuecomment-266632031 , or mute
the thread
<https://github.com/notifications/unsubscribe-auth/AC5GF1OSw_mANlgMg3mDSNIypXj
fzzP6ks5rHhOVgaJpZM4Kwk-L> .
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GitHub","url":"https://github.com/line/armeria"}},"updates":{"snippets":[{"ico
n":"PERSON","message":"@trustin in #331: @jonefeewang Gentle ping. How's it
going? :-)"}],"action":{"name":"View
Issue","url":"https://github.com/line/armeria/issues/331#issuecomment-26663203
1"}}}

Thanks for your patience!

No worries. Please take your time. :-)


Streamlined Event and Distribution Imports

Codecov Report

Merging #66 into master will not change coverage.
The diff coverage is 100%.

@@          Coverage Diff          @@
##           master    #66   +/-   ##
=====================================
  Coverage     100%   100%
=====================================
  Files           8      9    +1
  Lines         651    656    +5
=====================================
+ Hits          651    656    +5

Impacted Files
Coverage Δ

leabra7/__init__.py
100% <100%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 4756c54...fc4e35c. Read the comment docs.

Now that tests/test_xcal.py is in master, I think we can move it to the net module as well.

Hey Noam, sorry I made a really bad typo when I said to move the tests to the net import. I actually meant to use the new-style imports! My bad.


config files + relative paths / subdirectories

Actual, the config.txt is loaded from the actual style guide directory.
$.getJSON('./config.txt', function (config) {
The categories config file is loaded absolute with the hostname:
$.getJSON('//' + location.host + '/' + config.categories, function (categories) {
When you run a web server with multiple projects in different sub-directories, you get an error, that the categories.txt cannot be found (of course).
The styleguide should not be bound to the root-directory.

Hello,
yeah I can see the problem here. In fact everything works for me except that I get this error in the Terminal:
Error: ENOENT: no such file or directory, open '/app/themes/nucreative-theme/styleguide/db/categories.txt'
I specified the path in my config file and all other files inside the /db/ folder are found. This one is not found (even if the path is exactly the same) and the server goes down.
Any quick fix for this?

Hi
I have the same issue.
The structure of my projects is as follows

dist

resources
styleguide
-*.html

src
resources
*.html

So i'm using browsersync task to load my site from the dist folder. so my path in the config.text for categories is
"categories": "/styleguide/db/categories.txt",
Which loads fine but then i get the same error as michelecocuccio
Error: ENOENT, no such file or directory 'C:\styleguide\db\categories.txt'
Anyway to fix this?

I am having the same issue too, would be great if this can be fixed.

I think I am having the same issue. I have included mine as project/wwwRoot/styleguide and I'm having issues with styleguide finding the files it needs when the server is using wwwRoot as the root folder.

It's a mess with the paths in general.
I made it work by running the commands as the description says (via CLI aside the node_modules folder). You don't need to create neither the config.txt (why txt!?) file nor any file. The styleguide folder is created automatically. And the db files are created automatically.
Then, although variables files are relative to the root, projectLogo is relative to the index.html (generated).


Feature Request: Support dynamic vectors and matrices

The input signature for the solvers enforces use of vectors and matrices which are known at compile time rather than runtime, which limits use cases.  It would be nice if the solvers would accept both S and D style matrices and vectors.
use eqsolver::multivariable::MultiVarNewton;
use nalgebra::{DVector, DMatrix};

// function
let f = |v: DVector<f64>| DVector::from_vec(vec![v[0].powi(2) + v[1] - 11., v[0] + v[1].powi(2) - 7.]);

// jacobian
let j = |v: DVector<f64>| DMatrix::from_vec(2, 2, vec![2. * v[0], 1., 1., 2. * v[1]]);

let initial_guess = DVector::from_vec(vec![1., 1.]);

// this does not work
let solution = MultiVarNewton::new(f, j)
    .with_tol(1e-5)
    .with_itermax(50)
    .solve(initial_guess)
    .unwrap();

Hello @dzimmanck! I have been busy the last few weeks so I just had to time implement this!
After looking at and researching the Nalgebra library I implemented support for the DVector and DMatrix. It can be found on the dvector-support branch.
(There still are small things that need fine-tuning and cleaning to be done, but the code in the dvector-support branch worked for all tests including one similar to the one you provided)
Please do tell me if something seems off or not working as intended 😃

I have a use case that requires this feature. @AzeezDa could you merge this into master and release (or release just the branch)? Thanks!

@alissapajer The dvector-support branch has been tested, merged into master and updated on Crates.io with version 0.1.2.
Please let me know if any issues arise!


Prevent a possible RecursionError

@singhald I didn't realize this had never been merged. I just made another commit to refresh all of the files to reflect the new authentication scheme. Please re-review and merge when you have a chance (assuming you don't have any issues).


App outputs non-exiting MMIF version

Bug Description
v4.0 outputs 1.1.0 version of MMIF, but that version does not exist. This is due to a bug in mmif-python==1.0.9 (and clams-python==1.1.2) (root cause was https://github.com/clamsproject/mmif/issues/216). I will yank those two versions of packages and release next versions asap, then we can update this app to use the next version of the SDK.
Reproduction steps
$ pip install mmif-python==1.0.9
$ python -c "import mmif; print(mmif.__specver__)"
1.1.0

Expected behavior
No response
Screenshots
No response
Additional context
No response

fixed via bf41416a71d4ef0290c8758175da7411405fb604


Unable to set rotate order

Maybe I'm doing something wrong, but cannot find anything to set the rotate order in this great script.
For example; I have 4 camera's (frontleft frontmiddle front right back)
The rotation now goes from frontleft -> frontmiddle -> back -> frontright -> frontleft and so on.
How can I change that to frontleft -> frontmiddle -> frontright -> back -> frontleft...?
Hope that there is an answer for this question :-)
Thanks in advance!

They are displayed in an order that is basically a cascade in the order
your windows are configured. Changing the window configuration order would
change the sequence.
On Fri, Oct 23, 2020 at 2:04 PM MelvinRC<EMAIL_ADDRESS>wrote:

Maybe I'm doing something wrong, but cannot find anything to set the
rotate order in this great script.
For example; I have 4 camera's (frontleft frontmiddle front right back)
The rotation now goes from frontleft -> frontmiddle -> back -> frontright
-> frontleft and so on.
How can I change that to frontleft -> frontmiddle -> frontright -> back ->
frontleft...?
Hope that there is an answer for this question :-)
Thanks in advance!
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/Anonymousdog/displaycameras/issues/80, or unsubscribe
https://github.com/notifications/unsubscribe-auth/AHIYIKNMUMTNIO7GQU3L55DSMHATPANCNFSM4S43YUOQ
.


Correct info about returned types for the extract_faces method

What has been done
The 2 extract_faces methods have documentation which explain the types that are returned. This documentation doesn't seem to be correct (anymore). This PR just updates the method documentation.
How to test
No actual code has been changed.

That is my bad 😅 Thank you for your contribution 🙏


Tab indentation on editing messages

When editing chat messages, the user is not able to use the Tab key for indenting the text.

Closing this issue.
It's much easier to edit complex messages using the external text editor.


Scheduler not saved into metadata

Hey there,
the scheduler is not saved into the metadata as far as I can see, checking with exiftool as well as on CivitAI upload.

Test image + workflow in .zip as Github does not pick up on .webp files
metadata_scheduler_test.zip
The recent update that introduced clip-skip saving does also neither appear in exiftool's metadata nor on CivitAI.

Civitai doesn't support a full sampler + scheduler combo, but rather a short list of combinations: https://github.com/civitai/civitai/blob/main/src/server/common/constants.ts#L139-L165
So this node only attempts to pick one of those valid combinations, while others are ignored:
https://github.com/alexopus/ComfyUI-Image-Saver/blob/main/nodes.py#L333-L369
As for clip skip, that is filled only if it is non-zero.

I see, what a weird system for them to use then.
Can confirm the clip-skip does get picked up on when non-zero 👍
Thanks for the quick answer and continuous support!

It is historical. In the past a1111 supported only those combinations. Hopefully this will be updated in the future.


Evaluation failed when training with multiple work gpus

I set worker_gpu to 2 and use 2 gpus in the same node. training is completely fine. But evaluation fails with this error
"
tensorflow.python.framework.errors_impl.InvalidArgumentError: Number of ways to split should evenly divide the split dimension, but got split_dim 0 (size = 47) and num_split 2
[[Node: split_2 = Split[T=DT_INT32, num_split=2, _device="/job:localhost/replica:0/task:0/cpu:0"](split_2/split_dim, input_reader/ExpandDims_3/_1823)]]
[[Node: split_2/_1825 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:1", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_1113_split_2", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:1"]]
Caused by op u'split_2', defined at:
File "/raid/skyw/venv/tensorflow-pip-py27/bin/t2t-trainer", line 5, in
pkg_resources.run_script('tensor2tensor==1.2.1', 't2t-trainer')
"
It wasn't very clear what the error is. But single GPU training/evaluation is fine, the problem comes with 2 worker GPUs.

I see exactly the same problem, with T2T 1.2.1 and TensorFlow 1.3. With multiple GPUs I can train with --local_eval_frequency=0, which disables the evaluation completely.
With batch_size 2048, 1024, 512 and 256, it fails just at the beginning of the evaluation (after "Starting evaluation at..." and "Restoring parameters from...").
With batch_size=128, it fails after "Evaluation [18/100]", ie. 18 batches out of 100 of the dev set are translated OK, but the 19th fails.
With batch_size=100, it fails after "Evaluation [77/100]".
With batch_size=64, the whole evaluation goes OK and the model continues to train (but quite slowly due to the low batch size, so there is no advantage in multi-gpu).

Also having this issue.  Have you made any progress on this?

Could you guys just try --eval_steps=5 or sth like this? I mean: is it only failing because it has evaluated the whole set, or sth else? Could you try --eval_steps=5 and report back if it works? Thanks!

@lukaszkaiser My dev set has about 110k subwords (BTW: is there an easy way to compute it exactly?). When training on a single GPU with batch_size=2048, I need 55 eval steps to cover it whole. If I use more eval_steps, I see "Out of range: End of sequence" warnings, but the training continues and approx_bleu is computed correctly.
As I wrote in my last post, when training on two GPUs with batch_size=128, the evaluation fails after 18 batches, but the whole dev set has 110k/128=860 batches. So the failing is not  because of evaluating the whole set (even if I multiply 128 by 2 because of the two GPUs, BTW: is the evaluation is parallelized?).
I don't have access to two GPUs on the same machine at the moment. Once I have, I will try --eval_steps=5 and report back. Based on my previous experiments, I guess that batch_size bigger than 256 will fail anyway (it had failed before finishing the first eval batch).

I tried eval_steps=1/5/8/16 with --worker_gpu=2, none of them worked.

I tried eval_step=5 with worker_gpu=2 and it failed for batch_size 100 and more (128, 256, 512, 1024, 2048). Usually, it failed within the first evaluation (at different steps 1-4), just once it failed within the second evaluation.
With batch_size 80 or less, it worked OK (at least for 18 evaluations, when I stopped the experiment).
However, when I increased eval_step to cover the whole dev set, it failed anyway (at [289/1051]), even with tiny batch_size.
I guess this is the relevant portion of the long stacktrace:
  File "/home/popel/venv/tf1.3gpu/lib/python3.4/site-packages/tensor2tensor/utils/input_fn_builder.py", line 200, in cond_on_index
    return fn(cur_idx)
  File "/home/popel/venv/tf1.3gpu/lib/python3.4/site-packages/tensor2tensor/utils/model_builder.py", line 242, in nth_model
    features, skip=(skipping_is_on and skip_this_one))
  File "/home/popel/venv/tf1.3gpu/lib/python3.4/site-packages/tensor2tensor/utils/t2t_model.py", line 430, in model_fn
    sharded_features = self._shard_features(features)
  File "/home/popel/venv/tf1.3gpu/lib/python3.4/site-packages/tensor2tensor/utils/t2t_model.py", line 411, in _shard_features
    0))
  File "/home/popel/venv/tf1.3gpu/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py", line 1232, in split
    split_dim=axis, num_split=num_or_size_splits, value=value, name=name)

Update: I checked 1.2.2 and the problem is still there.
The problem is that here:
The feature value v is not divisible by self._num_datashards.
Is there a way in TF to split a tensor, while possibly keeping the last returned value with less items if not divisible?
Or using something like:
shard_size = (v_size // self._num_datashards) + 1
list_of_v = tf.split(v, [shard_size] * (self._num_datashards - 1) + [-1])

(but shard_size is not known at graph creation time, so this does not work).

Until the problem is fixed, is it possible to switch off multi-GPU decoding for evaluation without switching it off for training?

@ehasler: With --local_eval_frequency=0 you disable the internal evaluation completely. You can run the evaluation separately (manually, from the stored checkpoints), but I don't know how to get this into Tensorboard.

I made it possible to execute the above code, and evaluation was done halfway.
v_size = tf.shape(v)[0]
condition = tf.constant((v_size % self._num_datashards) == 0)
list_of_v = tf.cond(condition,
        lambda: tf.split(v, self._num_datashards, 0),
        lambda: tf.split(v, [(v_size // self._num_datashards) + 1] \
                * (self._num_datashards - 1) + [-1]))
sharded_features[k] = self._data_parallelism(tf.identity, list_of_v)

But I encountered another error.
INFO:tensorflow:Starting evaluation at 2017-09-13-01:18:11
2017-09-13 10:18:12.222136: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:$
0.0)
2017-09-13 10:18:12.222159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080, pci bus id: 0000:02:$
0.0)
INFO:tensorflow:Restoring parameters from /home/ryo/t2t_train/translate_ende_wmt32k/transformer-transformer_base/model.ckpt-1105
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
2017-09-13 10:18:13.764736: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Reshape cannot infer the missing input size for an empty tensor unless all specified in$
ut sizes are non-zero

......

InvalidArgumentError (see above for traceback): Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
         [[Node: body/model/parallel_1/body/encoder/layer_0/self_attention/multihead_attention/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:
0/gpu:1"](body/model/parallel_1/body/encoder/layer_0/self_attention/multihead_attention/split, body/model/parallel_1/body/encoder/layer_0/self_attention/multihead_attention/concat)]
]
         [[Node: mean_3/broadcast_weights/assert_broadcastable/AssertGuard/switch_f/_2118 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_
device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_7461_mean_3/broadcast_weights/assert_broadcastable/AssertGuard/switch_f", tensor_type=DT
_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

TL;DR: I still don't know how to workaround this problem (except for disabling the internal evaluation completely).
@ryonakamura Yes, I did something similar and also got the "Reshape cannot infer" error and I was not able to workaround this error. Apparently, the problem is that one of the list_of_v is zero-sized. I've also tried to use just one datashard (list_of_v = [v]) for the evaluation as @ehasler suggested, but self._data_parallelism is built with n fixed to the number of GPUs and there are assertions (and even if disabled, we would need to put also the logits into one datashard).

Update: I checked 1.2.3 and the problem is still there.
@lukaszkaiser, @rsepassi: Can you please comment on this issue? Can you train on multiple GPUs with evaluation? Is a fix planned (e.g. waiting for TF 1.4 as in other issues)? Or should we try to fix this ourselves and is any of the above-mentioned workarounds promising?

Until we address the underlying issue of getting eval to work on multiple gpus, the workaround would be to have your training job separate from your eval job. For training, set --schedule=train and for the eval job (with 1 GPU), set --schedule=continuous_eval. The eval job will look for new checkpoints from the training job in --output_dir and run eval on the single gpu.

Training a new model with T2T 1.2.5 and TF 1.4 allows me to evaluate using multiple gpus.

@mehmedes are you sure ?
I just started a training with TF 1.4.0-rc1 and t2t 1.2.6, it still throws an error with 2 worker gpu when it comes to evaluation.

I confirm the error (Number of ways to split...) is still there even with TF 1.4.0rc1 and T2T 1.2.6.
In some setups (just two GPUs, just ten eval steps, high local_eval_frequency) it is less likely, but it may still occur after several hours of training.
When I increase the amount of test data with --eval_steps=60, the error occurs usually within the first evaluation.
Thanks for the workaround with two jobs (one with --schedule=train and one with --schedule=continuous_eval). It has the advantage that the continuous_eval job may run on a different machine (and thus not slowing down the training).
According to the documentation, train_and_evaluate takes double resources relative to train (and continuous_train_and_eval) because it needs extra memory for loading the checkpoint to be evaluated (while keeping in memory the current model being trained). So this could be another advantage of this workaround and of not using train_and_evaluate. However, in practice I was not able to use e.g. a bigger batch size with --schedule=train (the out-of-memory error starts appearing with the same batch size).

@vince62s : I was testing on a setup with two 1080s, where it works. Just tried on four 1080 TIs and evaluation still fails.

@mehmedes  I failed to with the same configuration. Four gpus failed and two gpus worked. Really puzzling.

Please keep reporting your experiences here, it might helps us figure this out too. And thanks for checking and pushing it!

OK, keeping reporting. The problem is still here with tensorflow-gpu 1.4.0, tensor2tensor 1.2.9.
I encounter the problem even with two GPUs, after setting --eval_steps=68 (it actually crashes always in the 21st evaluation step during the very first evaluation, this is on translate_ende_wmt32k with batch_size=1500).
With the default --eval_steps=10 it does not crash (during the first few evaluations at least - I have not tried more).

@lukaszkaiser  I tried it again , when using 2 GPUs and transformer_base_single_gpu , it works well . but it failed when using  transformer_base . My configuration is t2t 1.3.2 and tf  1.4.0 .

@martinpopel have u solve the multi-devices problem recently ? I used 4 GPUs and transformer_base_single_gpu , it fails again with the same error . Really PUZZLING .

@rsepassi Good idea . But one GPU maybe wasted for evaluation . Anyway , that is a proper way to solve it .

My solution is #436 (which was merged, but then the t2t-bleu script was deleted from the git master). I also don't want to waste one GPU for evaluation (I want to run multiple training experiments in parallel, so that would mean one extra GPU per experiment), so:

I wrote another script which translates (using t2t-decoder) the test set with all checkpoints in a given directory. All translations are done on CPU and one takes about 75 minutes (3000 sentence with 32 CPU cores), but we have a CPU cluster, so all the translations are submitted there (via qsub) and translated in parallel (if there are free machines).
I adapted t2t-bleu, so it doesn't try to actively translate anything, it just waits until the translation files are created in a given directory. So it can also run on CPU, doing only the BLEU computation, which is very fast.

If you are interested I may provide my scripts (after cleaning them a bit).

@martinpopel Wow, that would be nice ! If the script is easily to use , please send it to me ~ Much THANKS.

@martinpopel  We don't have CPU cluster environment ...

Ok, so I think the only difference between training and evaluation when it comes to the input pipeline is whether long sequences are skipped. Try setting --hparams=eval_drop_long_sequences=True. Does that fix the issue?
If so, then I believe what's happening is that the batch size for those long sequences are not divisible by the number of GPUs.
Not entirely sure what the fix would be yet, but I think we could check to see if the batch is small and pad it out if it is.

I am not following, why don't you just eval on a single GPU then ?
Generally speaking, I think martin's idea to just put the training in standby, do the full evaluation with actual BLEU on some sets (on single GPU if needed) and return to multi gpu training would be a better solution.

@rsepassi: Even after adding --hparams=eval_drop_long_sequences=True the problem is still there. This is with version 1.2.9 and --eval_steps high enough to cover the whole dev set.

Just tried pull request #484 (v1.4.1), multi eval works perfectly with the preset --eval_steps=1000.
Thank you!

Great. Yes, this is fixed in 1.4.1, which will be merged and pushed to PyPI shortly.

I tried 1.4.1, but it crashed with:
INFO:tensorflow:global_step/sec: 9.23761
INFO:tensorflow:loss = 5.81556, step = 1901 (10.825 sec)
INFO:tensorflow:Reading data files from data/translate_encs_wmt32k-dev*
WARNING:tensorflow:Padding the batch to ensure that remainder eval batches have a batch size divisible by the number of data shards. This may lead to incorrect metrics for non-zero-padded features, e.g. images. U
se a single datashard (i.e. 1 GPU) in that case.
Traceback (most recent call last):
  File "/home/popel/work/tensor2tensor/new/tensor2tensor/bin/t2t-trainer", line 190, in <module>
    tf.app.run()
  ...
  File "/home/popel/work/tensor2tensor/new/tensor2tensor/data_generators/problem.py", line 588, in _pad_batch
    return pad_batch(features, config.data_parallelism.n)
  File "/home/popel/work/tensor2tensor/new/tensor2tensor/data_generators/problem.py", line 950, in pad_batch
    feature = features.items()[0][1]
TypeError: 'dict_items' object does not support indexing

Thanks! Will fix.

I confirm it is fixed (I tested with 2 GPUs so far). Thanks.

Are you sure this is fixed? So to use 8 GPUs instead of 1, we should only add --worker_gpu=8  and it should work out of the box? Because for me the evaluation step failed...

For me the internal evaluation works out of the box on 8 GPUs in T2T v1.4.2. There are 8 times more warnings "W tensorflow/core/framework/op_kernel.cc:1192] Out of range: End of sequence", but these are just warnings (and OK in this case), not errors. I can see approx_bleu, rouge etc. in Tensorboard.
So technically the internal evaluation works, although there are some related issues:

As discussed in #444, scaling optimal hyperparameters to more GPUs (and thus higher effective batch size) is not "out of the box".
As discussed in #495, with the default schedule=continuous_train_and_eval two checkpoints are saved instead of one (e.g. 2000 and 2001).
As discussed in #407, approx_bleu used to be wrong/not reliable. I am not sure if this has been improved (I guess no), I am using t2t-bleu instead.

@martinpopel I solved it by upgrading to TF 1.5.0 (cuda 9.0, cuDNN 7.0). Before that I had TF 1.4.1. Now the regular continuous_train_and_eval process with --worker_gpu=8 works out of the box.

I'm using TF 1.8.0 and I run into the same problem. I'm using 8 GPU's and have approximately 3e6 images for evaluation.
tensorflow.python.framework.errors_impl.InvalidArgumentError: Number of ways to split should evenly divide the split dimension, but got split_dim 0 (size = 250) and num_split 8
         [[Node: split_inputs/split_1 = Split[T=DT_INT32, num_split=8, _device="/job:localhost/replica:0/task:0/device:CPU:0"](split_inputs/split/split_dim, IteratorGetNext:1)]]
         [[Node: split_inputs/split_1/_4329 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:7", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_5015_split_inputs/split_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:7"]()]]


add a annotation for spec.selecter

/assign @chenopis

@zhangxiaoyu-zidif

seems to be easy to understand for newbee.
how do you think, @tengqm

Tough call, but I agree with @tengqm. I would rather call out specific aspects of the yaml file in the surrounding text. That way a yaml file can be shared by multiple topics . Each topic could then call out the relevant fields of the yaml file in the context of the topic. I recommend closing this PR. But I'm open to further discussion if others want to continue pursuing this.

I just want the pure newbie can get more infomation from a  emaple  yaml file. If you think that is not necessary, I will close the PR.


why pin cryptography==2.1.4?

I can appreciate requiring a specific version or newer, e.g. cryptography>=2.1.4, but why prevent more recent versions from being used? The pin causes my project to downgrade cryptography when I add noiseprotocol, because the current release still has a pin on 2.1.3 . Making a new noiseprotocol release will help, but it'll probably run into the same problem again in a month or two when they make a new cryptography release.
Would you accept a PR to make that >= ?
thanks!

Oh, I should mention it also prevents use in any project that uses e.g. pyopenssl, because that requires 2.1.4 or newer, and pip can't satisfy both cryptography>=2.1.4 and cryptography==2.1.3 at the same time.

I'll need to review my usage of Cryptography, but I suppose that crypto primitives that are in use won't be removed. Hence - I'll probably agree to that.

Version 0.2.2 containing your changes is live in PyPI. Thanks for your contributions!


remove Table name check

Package Name: azure-data-tables
Package Version: 12.2.0
Operating System: any
Python Version: 3.10.1

Describe the bug
SDK behavior should consider the difference while facing both Cosmos DB Table API, and Storage Account Table Service, and this issue is reporting for their naming rule difference then leads exception.
When SDK (azure-data-tables) deal with a table named "a-test-table" in Cosmos DB -Table API, it will receive exception below.

ValueError: Table names must be alphanumeric, cannot begin with a number, and must be between 3-63 characters long.

This is caused by the naming rule checking in here: /sdk/tables/azure-data-tables/azure/data/tables/_error.py
But the checking rule is only feasible for Storage Account - Table services, not Cosmos DB Table API, please reference the naming rule below.
To Reproduce
CosmosDBConnectionString = "DefaultEndpointsProtocol=https;AccountName=account;AccountKey=key;TableEndpoint=https://account.table.cosmos.azure.com:443/;"   CosmosDBTableServiceClient = TableServiceClient.from_connection_string(conn_str = CosmosDBConnectionString)   CosmosDBTableClient = CosmosDBTableServiceClient.get_table_client(table_name = "a-test-table")
Expected behavior
Suggestion 1. remove table name checking, fall back to backend service and return error message accordingly.
Suggestion 2. check which the connected service is, then applying different naming rule checking accordingly.
Screenshots

Additional context
Cosmos DB - Resource name rules
No specified for container/table level rule in doc.
But in other doc, explain the limitation of container/table name should be:
names must contain from 1 through 255 characters, and they cannot contain /, \, #, ?, or a trailing space
Storage Account -Resource name rules
table / 3-63 / Alphanumerics + Start with letter
note:
In the deprecated SDK azure-cosmosdb-table 1.0.6, it’s just simply checked the input shouldn't be empty only.

Hi @chshihMSFT
This should now be resolved in the latest Tables release :)


update cluster folder fixture

#1081  👈
main

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Join @jlewitt1 and the rest of your teammates on  Graphite


Strange behavior of the loss depending on the learning rate

I'm trying to train my own neural net using dlib, so I defined my loss class and now I'm trying to test it. But I get some strange behavior: at first, I need to set initial learning rate to 1e-8 instead of 1e-1(suggested by you), because if I set some bigger learning rate, then I will get nan loss after a few iterations, is this normal?
Next, sometimes when learning rate changing e.g. from 1e-10 to 1e-11, then my loss increases sharply, and after that does not decrease to the previous level of learning rate(also, it can even increase), what does it mean and how can I fix this?
Regards!

I don't know.  Maybe your network definition is weird and hard to optimize, maybe your data is funny.  Getting DNNs to optimize can be hard.  You should refer to the academic literature to get some ideas about what works and what doesn't.

@davisking ok, but I want to note that loss decreases before learning rate decreasing. But after learning rate decreasing to the some step(e.g. from 1e-10 to 1e-11), then my loss does not decrease.
So, I think it can be due to insufficient accuracy of the some C++ types(e.g. double): gradient multiplied by too small step size(learning rate), leads to the very small, inaccurate and distorted numbers that cannot do a gradient descent in the correct direction, so the loss decreasing is also stopped.
What do you think about this and what should I do if this is true?


style main page

divs in main page for info and map
fix header and footer
background color

header/footer pictures - 100% of page width
fix header so stuff is centered
background color
crop banner image
picture border?
need image for red box
radius on pictures/boxes?


Add edmf to perf

This PR adds edmf to the performance target

Depends on #916.

Is that PR still relevant? Asking, because some of the flags in the added jobs seem inconsistent:
turbconv_case Soares and  --forcing held_suarez
or
--moist dry and --precip_model 0M

Nope! Superseded by #1352


fix: Fix filter for requests and pinned requests (Kong#6288)

Fix the filter for requests and pinned requests, where once the user type something to filter, the lists doesn't change to show only the matches.
Closes #6288

Thanks for the help @fernandosmace but closing since this was merged in #6323


Detached windows not reachable by shortcut

From<EMAIL_ADDRESS>on 2013-04-11T22:16:07Z
Windows that have been detached are not reachable via the keyboard shortcuts.
What steps will reproduce the problem?

Detach, for example, console to a floating window
Set focus to editor
Press Shift+Ctrl+C (default switch to console shortcut)
Observe cursor still in editor

This occurs with the online help too; I presume all detached windows are affected. What is the expected output? What do you see instead? Focus should change but it does not. What version of the product are you using? On what operating system? Spyder 2.1.9 / Xubuntu 12.04 x64 / Python 2.7.3
Please provide any additional information below
.
Original issue: http://code.google.com/p/spyderlib/issues/detail?id=1351

From ccordoba12 on 2013-04-23T14:42:27Z
I can reproduce the behavior in 2.2. We'll see what we can do about it.
Thanks for your interest in Spyder
Status: Accepted
Labels: Cat-SpyderGUI