WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
The new theme seems to have introduced a duplicate reference in the URL, e.g.,

Before: https://docs.scipy.org/doc/scipy/reference/linalg.interpolative.html

After: https://docs.scipy.org/doc/scipy/reference/reference/linalg.interpolative.html

The effect was that today all SciPy search engine results I found were broken.

The search engines will update eventually. But the double /reference/reference/ in the url is unnecessary, so it might be better removed?

Issue 2:
<<Please describe the issue in detail here, and for bug reports fill in the fields below.>>

I am using scipy.odr to fit an implicitly defined function. It seems that I have to assign directly to ifixx when creating an odr.ODR object instead of relying on my earlier assignment to fix when creating the odr.Data object that I pass to odr.ODR. Perhaps defining fix in odr.Data objects should simply be removed, because this duplicated functionality is somewhat confusing and doesn't seem to work as expected with odr.ODR. Otherwise, if this is the intended behavior, then perhaps this requirement to specify both fix and ifixx should be better documented.

Reproducing code example:

Input:

import scipy.odr as odr

# Some noisy data solving x2 = x1**2
x1 = [-2.01, -0.99, -0.001, 1.02, 1.98]
x2 = [3.98, 1.01, 0.001, 0.998, 4.01]
fix = np.vstack((np.zeros_like(x1, dtype=int), np.ones_like(x2, dtype=int)))

# Fixing x1 via the odr.Data object
odr_data = odr.Data(np.vstack((x1, x2)), y=1, fix=fix)

# Define the odr.Model object with an implicit model
odr_model = odr.Model(lambda beta, x: x[1, :] - beta[0] * x[0, :]**2., implicit=True)

# Prep and run the model WITHOUT specifying ifixx in odr.ODR object construction
odr_odr = odr.ODR(odr_data, odr_model, beta0=np.array([1.]))
odr_sol = odr_odr.run()
print(odr_sol.beta)

# Prep and run the model WITH specifying ifixx in odr.ODR object construction
odr_odr = odr.ODR(odr_data, odr_model, beta0=np.array([1.]), ifixx=fix)
odr_sol = odr_odr.run()
print(odr_sol.beta)

Output:

[1.00205273]
[1.00277605]

(I think the output should be the second output in both cases.)

Error message:

N/A

Scipy/Numpy/Python version information:

Python version: 3.6.5
Numpy version: 1.14.5
Scipy version: 1.1.0
Issue 1:
When an installation of a plugin with the plugin script fails, it returns an incorrect exit code: 0. Example:

vagrant@precise64:~$ sudo /usr/local/bin/plugin --install FOOBAR
-> Installing FOOBAR...
Failed to install FOOBAR, reason: failed to download out of all possible locations..., use -verbose to get detailed information
vagrant@precise64:~$ echo $?
0

This makes it cumbersome in automated scenarios; related: sous-chefs/elasticsearch#124

Issue 2:
Steps to reproduce:

$ sudo /usr/share/elasticsearch/bin/plugin -install foo
-> Installing foo...
Failed to install foo, reason: failed to download out of all possible locations..., use -verbose to get detailed information
$ echo $?
0

Expected result: The exit code should be != 0

Actual result: The exit code is 0
Issue 1:

Issue 2:
Adding required files for a challenge or a challange block requires adding the 'required' key to the challenge or challenge block.

See:

https://github.com/FreeCodeCamp/FreeCodeCamp/blob/staging/seed/challenges/01-front-end-development-certification/bootstrap.json#L6

https://github.com/FreeCodeCamp/FreeCodeCamp/blob/staging/common/models/challenge.json#L110

This is an example of how to add it to a whole block.

{
"name": "Responsive Design with Bootstrap",
"order": 3,
"time": "5 hours",
"helpRoom": "Help",
"required": [
{ "link": "https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.css" }
],
"challenges": [
{
"id": "bad87fee1348bd9acde08712",
"title": "Use Responsive Design with Bootstrap Fluid Containers",
"description": [
"In the HTML5 and CSS section of FreeCodeCamp we built a Cat Photo App. Now let's go back to it. This time, we'll style it using the popular Bootstrap responsive CSS framework.",
"Bootstrap will figure out how wide your screen is and respond by resizing your HTML elements - hence the name <code>Responsive Design</code>.",
"With responsive design, there is no need to design a mobile version of your website. It will look good on devices with screens of any width.",
"You can add Bootstrap to any app by adding the following code to the top of your HTML:",
"<code>&#60;link rel=\"stylesheet\" href=\"//maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css\"/&#62;</code>",
"In this case, we've already added it for you to this page behind the scenes.",
"To get started, we should nest all of our HTML in a <code>div</code> element with the class <code>container-fluid</code>."
],
Issue 1:
Lets say I define a tuple type with two members a,b

immutable TupleTest{A,B}
a::A
b::B
end

tv = TupleTest(rand(3,3),rand(4,2))

next, I also define a simple matrix

tc = rand(4,2)

such that typeof(tc) and typeof(tv.b) give the same result.

typeof(tc) == typeof(tv.b)
> true

Now benchmarking them I get the following results:

@time for i in 1:10^6
tv.b+tv.b*2.0;
end
0.215183 seconds (3.00 M allocations: 274.658 MB, 10.95% gc time)

@time for i in 1:10^6
tc+tc*2.0;
end
0.179003 seconds (3.00 M allocations: 274.658 MB, 11.28% gc time)

The tuple type version is 15%-20% slower. Is this due to the overhead of using tuples and is that constant or does it grow with problem size ? I don't understand how tuple types work internally, but I read somewhere that there is no overhead if you are using the same concrete types in the computation. Does that statement apply here as-well ? Pardon me if this questions sounds naive, am trying to understand the reason for performance difference.

Assuming that the overhead of using tuple types is constant, then if my computation within the loop is complex, the relative performance different should decrease, which will be nice...

I am on Julia 0.4

thanks,

Nitin

Issue 2:
This keeps coming up, and rather than direct people to the performance tips, I feel like we should really just try to fix the issue. One relatively non-disruptive approach might be to emit code that accesses globals with a fast path that assumes that they have the same type that they currently have and a slow path that handles arbitrary type. A harder fix would be the compile-time version of that: generate code that assumes that globals retain their current type and invalidate it if the type changes. One might want to let compilation happen a couple of times before giving up and generating pessimistic general code.

Related: #265, #524, #964
Issue 1:
ℹ Computer information

PowerToys version: v0.20.1

PowerToy module: Keyboard Manager

There is no visual difference between some numpad and non-numpad keys in the dropdown menu.

📷 Screenshots

Edited screenshot of what is shown for Numpad . and .

Screenshot of numeric NumPad keys:

Issue 2:
An example of this is /. There are two keys which display / on the UI now (on the keyboard one is near RShift and the other is on the numpad). We should try adding some identifier to distinguish them like NumPad / for example. The issue with hardcoding that in is that it may not translate well to other languages.
Issue 1:
🐛 Bug

CPU usage tops at 100% when running models on macOS

To Reproduce

Steps to reproduce the behavior:

>>> import torch
>>> torch.get_num_threads()
1
>>> torch.torch.set_num_threads(4)
>>> torch.get_num_threads()
1
>>> import mkl
>>> mkl.get_max_threads()
4

Intel MKL allows 4 threads but cannot make torch go multithreaded.

Environment variables are set as

export MKL_NUM_THREADS=4
export OMP_NUM_THREADS=4

Expected behavior

CPU usage > 100% and torch.get_num_threads()>1

Environment

- PyTorch Version (e.g., 1.0): 1.2.0
- OS (e.g., Linux): macOS
- How you installed PyTorch (`conda`, `pip`, source): pip
- Build command you used (if compiling from source):
- Python version: 3.7.0
- CUDA/cuDNN version: NA
- GPU models and configuration: NA
- Any other relevant information: Quad core CPU MacBook pro

Additional context

Also tried to manually reinstall intel MKL with no avail.

Issue 2:
🐛 Bug

The pip package does not ship MKL with it and MKL is required for fft support on cpu.

To Reproduce

Steps to reproduce the behavior:

Run the following code with Pytorch installed with pip

import torch

from kymatio import Scattering2D

scattering = Scattering2D(J=2, shape=(32, 32))

x = torch.randn(1, 1, 32, 32)

Sx = scattering(x)

print(Sx.size())

Expected behavior

Environment

PyTorch Version (e.g., 1.0): 1.0.1.post2

OS (e.g., Linux): Mac OS Mojave version 10.14.3

How you installed PyTorch (conda, pip, source): pip

Build command you used (if compiling from source): None

Python version: 3.6.1

CUDA/cuDNN version: None

GPU models and configuration: None

Any other relevant information: None

cc @ezyang @gchanan @zou3519
Issue 1:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.3

Operating System version: windows7

Java version: 1.8

Steps to reproduce this issue

1.<dubbo:registry file="/dubbo/cache/demo.cache">/

2. Startup application

3. No files were generated in the corresponding directory

Pls. provide [GitHub address] to reproduce this issue.

Expected Result

I want to generate cache files in the corresponding directory after the program starts

Actual Result

No files were generated in the corresponding directory

Issue 2:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.6

Operating System version: MACOS 10.15.X

Java version: 1.8.0_131

Steps to reproduce this issue

实现ExporterListener接口，并重载unexport方法

实现dubbo协议的接口

优雅停机，发现unexport方法并未被调用

Pls. provide [GitHub address] to reproduce this issue.

Expected Result

What do you expected from the above steps？

Actual Result

What actually happens?

If there is an exception, please attach the exception trace:

Just put your stack trace here!

考虑的解决方式：

发现代码存在疑点，如图：org..apache.dubbo.registry.integration.RegistryProtocol.java:743

删除sleep代码或者减少sleep时间，应该能避免这个问题

并且org.apache.dubbo.common.config.ConfigurationUtils#getServerShutdownTimeout标记了//FIXME，说明这是遗留问题？
Issue 1:
Challenge Waypoint: Local Scope and Functions has an issue.

User Agent is: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36.

Please describe how to reproduce this issue, and include links to screenshots if possible.

My code:

function myFunction() {
var myVar = 10;
console.log(myVar);
}
myFunction();

// run and check the console
// myVar is not defined outside of myFunction

// now remove the console.log line to pass the test

This is returning undefined with error Remove the second console log but it seems the code is correct

Issue 2:
Challenge Waypoint: Local Scope and Functions has an issue.

User Agent is: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.48 Safari/537.36.

The test asks you to remove the second console.log() to pass. When you remove the second console.log() it still fails. You have to remove both console.log() calls to pass(including the one inside myFunction().)

Reproducible every time by removing the console.log() outside of myFunction(), as asked by test. I'm assuming this is a bug in the test and not the text as there's no reason to remove the console.log() call within myFunction().
Issue 1:
Short:

using the customizer, glyphicons are jumbled or missing.

Long:

After using the customizer with default settings, I attempted to use glyphicon-wrench and it came up with a box (no icon), switching to glyphicon-cog, the email icon was displayed. I reverted to the font files in the direct BS3 download zip and all is well. Customizer messes something up in glyphicons.

Issue 2:
When using a customized version of Bootstrap 3.0 the Glyph icons don't work. The font file sizes differ from the "normal" version.
Issue 1:
D3 Challenges Dataset identifier dataset has already been declared error.

Essentially the D3 challenges won't run after 1 failed attempt.

Challenge work-with-dynamic-data-in-d3 has an issue.

User Agent is: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36.

Please describe how to reproduce this issue, and include links to screenshots if possible.

<body>
<script>
let dataset = [12, 31, 22, 17, 25, 18, 29, 14, 9];

d3.select("body").selectAll("h2")
.data(dataset)
.enter()
.append("h2")
// Add your code below this line

.text((d) => d + ' USD');

// Add your code above this line
</script>
</body>

Issue 2:
Issue 1:
From examples:

{

"id": "redis-master-2",

"desiredState": {

"manifest": {

"id": "redis-master-2"

This stems from the fact that Kubelet takes ContainerManifest structs and needs them to have an ID, but none of the frontends massage the info from the Pod struct into the Manifest struct.

We should think hard about whether using the same structs for the user API and the Kubelet API makes sense. Or maybe we can restructure them to nest better, so the kubelet API looks more like:

{

id

manifest

}

Issue 2:
Is this a request for help? (If yes, you should use our troubleshooting guide and community support channels, see http://kubernetes.io/docs/troubleshooting/.): No

What keywords did you search in Kubernetes issues before filing this one? (If you have found any duplicates, you should instead reply there.): kube-dns stale/wrong

Is this a BUG REPORT or FEATURE REQUEST? (choose one): BUG REPORT

Kubernetes version (use kubectl version): 1.3.5

Environment:

Cloud provider or hardware configuration: AWS

OS (e.g. from /etc/os-release): Ubuntu Xenial

Kernel (e.g. uname -a): Linux 4.4.0-21-generic #37-Ubuntu SMP Mon Apr 18 18:33:37 UTC 2016 x86_64 GNU/Linux

Install tools: Custom install (fluentd + etcd via ELB + kube-master via ELB)

Others: The custom install isn't terribly "custom" - it's based on an older version of Kelsey's Kubernetes the hard way. The major difference is that both etcd and the kube-master go through an ELB that terminates idle connections after 60s.

What happened: kube-dns didn't refresh the DNS entry of a pod that got recreated.

What you expected to happen: kube-dns to be up to date

How to reproduce it (as minimally and precisely as possible):

I don't have solid reproduction steps since this is very intermittent, but here is how this particular instance happened:

Create a PetSet (say petset) and Headless service (say disco)

Start a different pod with nslookup

nslookup petset-0.disco

Delete the PetSet pod. It should be recreated with the same name.

nslookup petset-0.disco See the same IP as before (rather than the new IP).

Anything else do we need to know:

Here's the reality I'm seeing now:

# nslookup kafka-0.kafka-discovery.default.svc.cluster.local
Server: 10.254.0.10
Address: 10.254.0.10#53

Name: kafka-0.kafka-discovery.default.svc.cluster.local
Address: 10.9.13.13

But really:

$ kubectl get pod kafka-0 -o yaml | grep podIP
podIP: 10.9.13.5

Interestingly, both instances of kube-dns are wrong on this one. It looks like the endpoints are correct though:

$ kubectl get --context prod endpoints kafka-discovery
NAME ENDPOINTS AGE
kafka-discovery 10.9.13.5:9092,10.9.37.5:9092,10.9.73.5:9092 68d

Restarting both kube-dns pods fixed the issue. Is it perhaps a refresh triggering an OnUpdate rather than an OnDelete+OnAdd & the handler doesn't expect the pod ip to change?

@justinsb
Issue 1:
Migrated issue, originally created by Andrey Semenov

I need to construct a query like

SELECT (jsonb_each('{"a": 1, "b": 2}')).key

jsonb_each() returns a RECORD type which in turn has it's own fields named key and value. Yes, in PostgreSQL it MUST be also wrapped into braces, because of extraction and executing precedence (or it will try to select a key column from the jsonb_each() column - that leads to SQL engine error raised). It's not obvious how to query those fields while constructing a query (or make a dot-notated reference to a custom sub-field when this capability is provided by an engine).

Also, dialects.util.dottedgetter does not work with func.* (returns None if called against)

Issue 2:
Migrated issue, originally created by Michael Bayer (@zzzeek)

this would expand upon _load_enums() in postgresql/base.py, so that any kind of custom type is recognized during reflection. We'd likely have to create some kind of "ad-hoc" type that is declared like this:

from sqlalchemy.dialects.postgresql import TYPE
Column("mycolumn", TYPE("MY_CUSTOM_TYPE"))
Issue 1:
Issue Type:

Bug Report

Ansible Version:

ansible 1.7

Environment:

client: Arch Linux

server: Ubuntu 14.04, ssh as root user without sudo.

Summary:

Before upgrading from 1.6.6, unarchive would extract files in a second. After the upgrade, the client takes a full CPU core for 7 minutes to extract 7k+ files.

Steps To Reproduce:

The tasks file is pretty basic:

# run as ssh user root, without sudo
- name: copy and extract app tarball
unarchive: src={{app_tarball}}
dest={{tarball_dest_dirname}}

Expected Results:

Instant execution in a few seconds.

Actual Results:

It takes 7 minutes while hogging a full CPU core. A gdb backtrace showed a trace where the output of tar --diff gets concatenated.

Issue 2:
ISSUE TYPE

Bug Report

COMPONENT NAME

rpm_key

yum_repository

ANSIBLE VERSION

ansible 2.2.0.0
config file =
configured module search path = Default w/o overrides

CONFIGURATION

No changes

OS / ENVIRONMENT

Running from MacOS, installing in a Centos 7 VM.

SUMMARY

After I run yum_repository and rpm_key to add a yum repository and (presumably) import its GPG key, I still can't install package from the repository because yum complains that it can't verify its respond.xml: Gpg Keys not imported, cannot verify repomd.xml for repo {...}.

STEPS TO REPRODUCE

- name: add Grafana repository
yum_repository:
name: grafana
description: Grafana YUM repo
baseurl: https://packagecloud.io/grafana/stable/el/$releasever/$basearch/
gpgcheck: yes
repo_gpgcheck: yes
gpgkey: https://packagecloud.io/gpg.key https://grafanarel.s3.amazonaws.com/RPM-GPG-KEY-grafana
sslverify: yes
sslcacert: /etc/pki/tls/certs/ca-bundle.crt

- name: add the repository keys
rpm_key:
key: "{{ item }}"
state: present
with_items:
- https://packagecloud.io/gpg.key
- https://grafanarel.s3.amazonaws.com/RPM-GPG-KEY-grafana

- name: install Grafana package
yum: name=grafana

EXPECTED RESULTS

Package grafana is installed.

ACTUAL RESULTS

TASK [add Grafana repository] **************************************************
ok: [node0.vagrant.test] => {"changed": false, "repo": "grafana", "state": "present"}

TASK [add the repository keys] *************************************************
ok: [node0.vagrant.test] => (item=https://packagecloud.io/gpg.key) => {"changed": false, "item": "https://packagecloud.io/gpg.key"}
ok: [node0.vagrant.test] => (item=https://grafanarel.s3.amazonaws.com/RPM-GPG-KEY-grafana) => {"changed": false, "item": "https://grafanarel.s3.amazonaws.com/RPM-GPG-KEY-grafana"}

TASK [install Grafana package] *************************************************
fatal: [node0.vagrant.test]: FAILED! => {"changed": false, "failed": true, "msg": "Failure talking to yum: failure: repodata/repomd.xml from grafana: [Errno 256] No more mirrors to try.\nhttps://packagecloud.io/grafana/stable/el/7/x86_64/repodata/repomd.xml: [Errno -1] repomd.xml signature could not be verified for grafana"}
Issue 1:
VSCode Version:1.1.1

OS Version:Windows 10

Steps to Reproduce:

Create a JS or TS file.

Type a nested JS or TS ternary condition containing a string containing a link.

Ex:

var a = true ? 0 : ('http://mySite.com' ? 1 : 0);

Issue:

Breaks the syntax highlight for everything following the line.

Issue 2:
Under vscode master (0.10.9) with language mode JavaScript.

'amd64' has classes: token block decl expr function meta single string ts var var-single-variable js

'i386' has classes: token annotation block decl expr function meta single string ts type var var-single-variable js

annotation scope is causing the following theme style to be applied:

.monaco-editor.vs-dark.vscode-theme-colorful-defaults-themes-dark_plus-tmTheme .token.var.annotation.type.js {
color: rgba(78, 201, 176, 1);
}
Issue 1:
VSCode Version:

Version 0.10.11

Commit f291f4a

Date 2016-03-11T17:10:35.304Z

Shell 0.35.6

Renderer 45.0.2454.85

Node 4.1.1

OS Version: Windows 7 Enterprise Version 6.1.7601

Steps to Reproduce:

Start VSCode.

Press Control + a few times because you're getting older and your monitor is very large. :)

Right click on some text.

Expected result: The context menu opens at the mouse pointer.

Actual result: The context menu opens at what would be the right location if you hadn't increased the font size.

Notes:

I could not for the life of me find a way to search these issues, apologies in advance if this is a dupe.

This is a new bug for this update - this didn't used to happen.

Issue 2:
VSCode Version:1.2.0

OS Version:10.0.10586

Steps to Reproduce:

[View] - [Toggle Integrated Terminal]

run some command

Multi-byte characters (ex. Japanese) are corrupted
Issue 1:
I am building my model and storing it at a location using

joblib.dump(model, modelStoreLocation, compress=9)

Then in a separate execution I am trying to read the model from the location as show below :

# loading the model stored on disk model = joblib.load(modelStoreLocation)

I get the following error :

File "/Users/venuktangirala/PycharmProjects/nFlate/compute/prediction/categories/logisticRegression.py", line 84, in <module>x
prediction = predictDataFrame(dataFrame.ix[600:623, :-1].values, modelStoreLocation)
File "/Users/venuktangirala/PycharmProjects/nFlate/compute/prediction/categories/logisticRegression.py", line 58, in predictDataFrame
prediction = model.predict(dataToPredict)
File "/anaconda/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 223, in predict
scores = self.decision_function(X)
File "/anaconda/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 207, in decision_function
dense_output=True) + self.intercept_
File "/anaconda/lib/python2.7/site-packages/sklearn/utils/extmath.py", line 83, in safe_sparse_dot
return np.dot(a, b)
TypeError: can't multiply sequence by non-int of type 'float'`

The same works with out any problem when its in the same execution i.e. build the model , write to dsik and read from disk in the same execution.

What is the fix for this ?

Issue 2:
I am building my model and storing it at a location using

joblib.dump(model, modelStoreLocation, compress=9)

Then in a separate execution I am trying to read the model from the location as show below :

# loading the model stored on disk model = joblib.load(modelStoreLocation)

I get the following error :

Traceback (most recent call last):
File "/Users/venuktangirala/PycharmProjects/nFlate/compute/prediction/categories/logisticRegression.py", line 84, in <module>x
prediction = predictDataFrame(dataFrame.ix[600:623, :-1].values, modelStoreLocation)
File "/Users/venuktangirala/PycharmProjects/nFlate/compute/prediction/categories/logisticRegression.py", line 58, in predictDataFrame
prediction = model.predict(dataToPredict)
File "/anaconda/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 223, in predict
scores = self.decision_function(X)
File "/anaconda/lib/python2.7/site-packages/sklearn/linear_model/base.py", line 207, in decision_function
dense_output=True) + self.intercept_
File "/anaconda/lib/python2.7/site-packages/sklearn/utils/extmath.py", line 83, in safe_sparse_dot
return np.dot(a, b)
TypeError: can't multiply sequence by non-int of type 'float'

The same works with out any problem when its in the same execution i.e. build the model , write to dsik and read from disk in the same execution.

What is the fix for this ?