WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Any hope??? after keen monitoring it crushes . 12 - 24 hrs

BrokenPipe: Broken pipe (os error 32)

at unwrapResponse (deno:cli/rt/10_dispatch_minimal.js:59:13)

at sendAsync (deno:cli/rt/10_dispatch_minimal.js:98:12)

at async write (deno:cli/rt/12_io.js:117:20)

at async Object.writeAll (deno:cli/rt/13_buffer.js:223:19)

at async BufWriter.flush (bufio.ts:468:7)

at async writeResponse (_io.ts:288:3)

at async Server.iterateHttpRequests (server.ts:154:11)

at async Server.acceptConnAndIterateHttpRequests (server.ts:227:5)

at async MuxAsyncIterator.callIteratorNext (mux_async_iterator.ts:30:31)

Issue 2:
Hello,

I'm compiling my module with:

deno compile --unstable --allow-net module.js
Check file:///chop/chop/module.js
Bundle file:///chop/chop/module.js
Compile file:///chop/chop/module.js
Emit module

Now when I try to run it I get ReferenceError:

./module
error: ReferenceError: Cannot access 'Error' before initialization
at file://$deno$/bundle.js:325:30

I'm not referencing Error explicitly in my code, but I'm using std/node to get crypto working, so I guess that's where it's coming from? I can share source code if that would be helpful, it is only ~150 lines of code at this stage.

It works fine when I simply run it with:

deno run --unstable --allow-net module.js

My environment:

Linux 5.13.0-7614-generic on x86-64

deno 1.15.1 (release, x86_64-unknown-linux-gnu), v8 9.5.172.19, typescript 4.4.2

Any ideas what am I doing wrong or where the problem is? Thank you in advance.
Issue 1:
The layout of fancyzones depends on the number of programs active

When only one program is active, win + up maximizes

Two programs, user preselects between split-screen or emphasis on either program

Three, preselection between all the configurations for three programs

If a new program opens, the rest auto-resize to the next fancy zones layout. This is intended for users that have a dynamic quantity of programs open and do not wish to manually adjust fancy zones settings as it is faster to manually drag

Issue 2:
ℹ Computer information

Windows build number: 18363.959

PowerToys version: 0.20

PowerToy module: Color Picker

📝 Provide detailed reproduction steps (if any)

Open the color picker

Hover over a link in chrome

The link's tooltip shows over top of the color picker

✔️ Expected result

For the color picker to remain on top

❌ Actual result

The color picker is hidden by the tooltip

📷 Screenshots
Issue 1:
I had a series containing strings like these:

"November 1, 2013"

Series length was about 500,000

A)

running pd.to_datetime(s) takes just over a minute.

B)

running pd.to_datetime(s, format="%B %d, %Y") takes about 7 seconds!

My suggestion is a way to make case A (where user doesn't specify the format type) take about as long as case B (user does specify).

Basically it looks like the code is always using date_util parser for case A.

My suggestion is based upon the idea that it's highly likely that the date strings are all in a consistent format (it's highly unlikely in this case that they would be in 500K separate formats!).

In a nutshell:

figure out the date format of the first entry.

try to use that against the entire series, using the speedy code in tslib.array_strptime

if that works, we've saved heaps of time, if not fall back to the current slower behaviour of using dateutil parse each time.

Here's some pseudo-code::

datestr1 = s[0]
# I'm assuming dateutil has something like this, that can tell you what the format is for a given date string.
date_format = figure_out_datetime_format(datestr1)

try:
# use the super speed code that pandas uses when you tell it what the format is.
dt_series = tslib.array_strptime(s, format=datestr1, *, ...)
except:
# date strings aren't consistent after all. Let's do it the old slow way.
dt_series = tslib.array_to_datetime(s, format=None)

return dt_series

Issue 2:
Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame({'a': [1, 10, 8, 11, 8],
'b': list('abdce'),
'c': [1.0, 2.0, np.nan, 3.0, 4.0]})
print('_________')
print(df.nlargest(10,['a','b']))

Problem description

DataFrame的nlargest在遇到rank相同的情况时，结果错误。如下，第二行和第四行反复出现了。

Expected Output

a b c
3 11 c 3.0
1 10 b 2.0
2 8 d NaN
4 8 e 4.0
2 8 d NaN
4 8 e 4.0
0 1 a 1.0
[Finished in 0.6s]

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
Issue 1:
From @kabo on 2015-12-18T13:49:27Z

ISSUE TYPE

Feature Idea

COMPONENT NAME

new module

ANSIBLE VERSION

N/A

SUMMARY

AWS has released support for Managed NAT Gateways. It would be nice if one could set them up with Ansible.

Copied from original issue: ansible/ansible-modules-extras#1401

Issue 2:
From @KpaBap on 2016-09-18T23:05:31Z

ISSUE TYPE

Bug Report

COMPONENT NAME

ec2_eni

ANSIBLE VERSION

ansible 2.2.0 (devel fa5f8a7543) last updated 2016/09/18 02:10:05 (GMT -700)
lib/ansible/modules/core: (detached HEAD 488f082761) last updated 2016/09/18 02:10:48 (GMT -700)
lib/ansible/modules/extras: (detached HEAD 24da3602c6) last updated 2016/09/18 02:10:57 (GMT -700)
config file = /etc/ansible/ansible.cfg
configured module search path = ['/usr/share/ansible']

CONFIGURATION

Default

OS / ENVIRONMENT

N\A

SUMMARY

I'm trying to create a new ENI in AWS with secondary private IPs.

Instead of creating new ENIs, Ansible just fails because it seems to be finding an existing ENI and refusing to change it.

STEPS TO REPRODUCE

- name: Create ENI for eth0 on Edge instances
ec2_eni:
state: present
secondary_private_ip_address_count: 3
subnet_id: subnet-72870d2a
security_groups:
- sg-5f4c6f27

EXPECTED RESULTS

A new ENI to be created with 3 secondary private IPs.

ACTUAL RESULTS

The module finds some existing ENI and just returns "changed":"false". This breaks creating ENIs completely.

TASK [Create ENI for eth0 on Edge instances] ***********************************
ok: [localhost] => {"changed": false, "interface": {"attachment": {"attach_time": "2016-04-05T21:04:51.000Z", "attachment_id": "eni-attach-476e1abb", "delete_on_termination": false, "device_index": 1, "instance_id": "i-f3a7d668", "status": "attached"}, "description": "", "groups": {"sg-5f4c6f27": "GWAAS-SG-Iavor-Edge"}, "id": "eni-df31ee8f", "mac_address": "0e:e6:a9:ff:d4:4b", "owner_id": "953738397088", "private_ip_address": "172.16.2.151", "private_ip_addresses": [{"primary_address": true, "private_ip_address": "172.16.2.151"}, {"primary_address": false, "private_ip_address": "172.16.2.230"}, {"primary_address": false, "private_ip_address": "172.16.2.61"}, {"primary_address": false, "private_ip_address": "172.16.2.62"}], "source_dest_check": true, "status": "in-use", "subnet_id": "subnet-72870d2a", "vpc_id": "vpc-617a6b05"}}

Copied from original issue: ansible/ansible-modules-extras#2956
Issue 1:
Deleting files from the right-click menu in the sidebar fails to actually delete the file. After clicking "delete" in the menu, the dialog pops up asking me if I want to move the file/dir to the trash. So I click "Move to trash" and then nothing happens. The file is still visible in the sidebar, and it still exists on my filesystem.

I'm on Linux (Debian) x64, Atom version 0.126, but I had the same problem with 0.124 and 0.123

Issue 2:
I'm trying to remove files/dirs using sidebar, Atom asks if I want to move them to Trash and then nothing happens (files stay in the sidebar and on the disk).

This is valid only for files on a network partition, everything is ok with local ones

Version: 0.123
Issue 1:
[ ] bug report => search github for a similar issue or PR before submitting

[ ] feature request

[x ] support request => Please do not submit support request here, instead see https://github.com/angular/angular/blob/master/CONTRIBUTING.md#question

Current behavior

docs: https://angular.io/docs/ts/latest/cookbook/i18n.html

show only how to work with template via

<p i18n>foo</p>

would like the recommended way to work with strings when eval in code:

var foo = 'Hello world'

Expected behavior

so maybe

var foo = '<i18n>Hello world' ?

Language: [all | TypeScript X.X | ES6/7 | ES5]

all

Angular 2 Kitchen sink: http://ng2.javascriptninja.io

and source@ https://github.com/born2net/Angular-kitchen-sink

Regards,

Sean

Issue 2:
Steps to reproduce and a minimal demo of the problem

View hash_location_strategy:

void onPopState(UrlChangeListener fn) {
this._platformLocation.onPopState(fn);
this._platformLocation.onHashChange(fn);
}

Internal clients rely on manually dispatching window.hashchange events in order to have other libraries that rely on listening to hashchange manually know what the route is. Unfortunately, this seems to cause duplicate routing now.

A workaround is to override HashLocationStrategy and replace:

@override
void onPopState(UrlChangeListener fn) {
// Instead of listening to hashChange + popState, just listen to hashChange.
//
// We manually invoke hashChange events which can cause duplicate routing.
_platformLocation.onPopState(fn);
}

I'd prefer not to keep it like that long-term.

Current behavior

Listens to both type of events.

Expected/desired behavior

I'd imagine listen to just one?
Issue 1:
E.g. http://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.sparse.csr_matrix.html

In the Attributes section, one has:

nnz Get the count of explicitly-stored values (nonzeros) :Parameters: axis : None, 0, or 1 Select

between the number of values across the whole matrix, in each column, or in each row.

First, it seems to need the full stop after (nonzeros) to render correctly (same for getnnz). Second, the list of Parameters seems to duplicate the one for getnnz.

Issue 2:
See #10238 (comment)

scipy.fft currently lacks any plan caching. For repeated transforms, this does a significant amount of duplicate work and makes scipy.fft slower than scipy.fftpack for repeated regular sized ffts. (For one off ffts, pocketfft is still much faster)

Things to look into/discuss:

How much faster is pocketfft after adding a cache?

Can we have users pass in the plans instead of caching?

Should the user have control over the cache itself?
Issue 1:
So. I want to add a button on the label of RadioButton.

For example, 'remove the option' button.

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

Expected Behavior

Expecting to be able to add MORE THAN JUST TEXT in the label of a radio...

Current Behavior

Currently I replace the RadioButton with just a div, and cant add component inside the label.

Getting this message:

input is a void element tag and must neither have `children` nor use `dangerouslySetInnerHTML`

Steps to Reproduce (for bugs)

create RadioButtonGroup

create RadioButton inside it

add RaisedButton to the label of the RadioButton.

See '[object Object]' instead of the actual results.

Context

Your Environment

Tech
Version

Material-UI
0.18.5

React
15.6.1

browser
Google Chrome 63.0.3239.132

etc

Issue 2:
A weird yellow highlight appears when opening a temporary drawer.

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

Expected Behavior

There should not be a yellow highlight

Current Behavior

A weird yellow border appears around the drawer.

Steps to Reproduce (for bugs)

Navigate to https://material-ui.com/

Make use of chrome dev tools to enable the response device layout

Make viewport width smaller untill the responsive menu icon appears in AppBar

Press the menu icon to open the drawer

Context

I suspect this issue has to do with :focus, because the border dissappears when clicking somewhere else besides the drawer.

Your Environment

Tech
Version

Material-UI
v1.0.0-beta.21

React
16.1.1

browser
Chrome 62.0.3202.94

os
Windows 10
Issue 1:
Hello,

I use dbscan with a custom distance metric(average between levenshtein and manhatan).

Two questions:

Is there an implementation for an incremental version of dbscan for frequent updates ? or lets say incremental updates ?

Since I'm using a custom distance metric, then I use the trick of precomputed distance, so:

if an incremental version of dbscan exist, how to deal with the precomputed distance for the updates ? should I increment each time this precomputed distance for the frequent updates or ... ?

Thanks in advance for your help/discussion

Issue 2:
is there any plan to add incremental clustering for sklearn.cluster.DBSCAN ?
Issue 1:
Q
A

Bug report?
yes

Feature request?
no

Symfony version
3.3.0

I've just updated to the current symfony version and seen the new exception design - why I can't select the error message with my mouse? That's not nice...

Issue 2:
Q
A

Bug report?
no, annoying though

Feature request?
yes

Symfony version
3.3.0

Copy-pasing the exception message is a common thing, at least for specific keywords. I think CSS or so blocks it 😕
Issue 1:
run latest Windows 10

pin either electron or atom to the taskbar

click it to open the app

=> you end up having 2 icons

=> somehow the app when started does not get associated to the pinned entry in the taskbar

=> it does not reproduce on Windows 8.x

I wonder if your change in fb6c80d could have an impact here. Imho it is used to find out if an application belongs to the same process group or not: https://msdn.microsoft.com/en-us/library/windows/desktop/dd378422(v=vs.85).aspx

Issue 2:
Preflight Checklist

I have read the Contributing Guidelines for this project.

I agree to follow the Code of Conduct that this project adheres to.

I have searched the issue tracker for an issue that matches the one I want to file, without success.

Issue Details

Electron Version:

v8.0.0-beta.3 and later (including v8.0.1)

Operating System:

OS-independent (tested on macOS 10.15.3 / Windows 10 (1909))

Last Known Working Electron version:

v8.0.0-beta.2

Expected Behavior

Should be able to open chrome://webrtc-internals under Electron.

Actual Behavior

Blank page is displayed.

Console error says: "Not allowed to load local resource: chrome://webrtc-internals/" and "No resource with given URL found"

To Reproduce

Simply open chrome://webrtc-internals in some BrowserWindow under Electron, e.g.:

https://gist.github.com/kubaau/20accd9a83080d9e0a402b9c1c469825

Screenshots

Additional Information

Many other chrome:// pages have stopped working as well, e.g. chrome://about.
Issue 1:
Julia is thwarting LLVM's CSE when the subexpression occurs in a loop test and loop body. Here is an example:

function foo(a, b,c)
while (a*b+c)==1
a /= (a*b+c)
end
a
end

code_llvm (foo,(Float64,Float64,Float64))

The output is:

L: ; preds = %L.preheader, %L
%a.0 = phi double [ %8, %L ], [ %0, %L.preheader ]
%6 = fmul double %a.0, %1, !dbg !10
%7 = fadd double %6, %2, !dbg !10
%8 = fdiv double %a.0, %7, !dbg !10
%9 = fmul double %8, %1, !dbg !10
%10 = fadd double %9, %2, !dbg !10
%11 = fcmp oeq double %10, 1.000000e+00, !dbg !10
br i1 %11, label %L, label %L3.loopexit, !dbg !10

Note how a*b+c is evaluated twice in the loop. A partial iteration has been peeled from the loop, which obscures the fact that a*b+c is being redundantly evaluated.

From first glance, it appears that the partial peeling has already happened when LLVM sees the IR. I'm guessing that it's being done early to improve type inference for the loop test (i.e. poor man's context-sensitivity by cloning code). It's conceivable that a CSE-like pass could recognize this sort of thing. Or maybe try to unpeel after type inferencing when types match?

See my comments here for another example.

Issue 2:
Consider the following code:

julia> num_dict = Dict(1 => IntSet(1:2), 2 => IntSet(1:3))
Dict{Int64,IntSet} with 2 entries:
2 => IntSet([1, 2, 3])
1 => IntSet([1, 2])

julia> length(values(num_dict))
2

julia> length.(values(num_dict))
2

julia> versioninfo()
Julia Version 0.6.0
Commit 903644385b (2017-06-19 13:05 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin13.4.0)
CPU: Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz
WORD_SIZE: 64
BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
LAPACK: libopenblas64_
LIBM: libopenlibm
LLVM: libLLVM-3.9.1 (ORCJIT, haswell)

It seems erroneous to me that in this case length.(values(num_dict)) returns 2 instead of a vector with 3 and 2 as it's elements.
Issue 1:
I am using Tensorflow for poet guide for train own model. I have create retrained_graph.pb and retrained_labels.txt. While I use it in application then I get error that

Caused by: java.lang.UnsupportedOperationException: Op BatchNormWithGlobalNormalization is not available in GraphDef version 21. It has been removed in version 9. Use tf.nn.batch_normalization(). at org.tensorflow.Graph.importGraphDef(Native Method) at org.tensorflow.Graph.importGraphDef(Graph.java:118)

After That further train model for application use Tensorflow for mobile blog and create optimized_graph.pb, rounded_graph.pb, mmapped_graph.pb files.

optimized_graph.pb and rounded_graph.pb file work in android application without any error.

While use mmapped_graph.pb I get error that Failed to initialize: java.io.IOException: Not a valid TensorFlow Graph serialization: Invalid GraphDef

Performance of application is not good while use optimized_graph.pb and rounded_graph.pb file.While application camera screen not contain any flower photos otherwise random flower name show with high confidence rate. Any way to detect only flower and remain blank when not flowers.

Issue 2:
I am following Tensorflow for poet instruction for retrain model. I have successfully create retrained_graph.pb and retrained_labels.txt. While I use imagenet_comp_graph_label_strings.txt and tensorflow_inception_graph.pb then application run without any error. But use my created file then I get error that:

Caused by: java.lang.UnsupportedOperationException: Op BatchNormWithGlobalNormalization is not available in GraphDef version 21. It has been removed in version 9. Use tf.nn.batch_normalization(). at org.tensorflow.Graph.importGraphDef(Native Method) at org.tensorflow.Graph.importGraphDef(Graph.java:118) at org.tensorflow.Graph.importGraphDef(Graph.java:102) at org.tensorflow.contrib.android.TensorFlowInferenceInterface.load(TensorFlowInferenceInterface.java:402) at org.tensorflow.contrib.android.TensorFlowInferenceInterface.initializeTensorFlow(TensorFlowInferenceInterface.java:91) at org.tensorflow.demo.TensorFlowImageClass
Issue 1:
After the upgrade to beta 2 I suddenly see the following errors:

Error:(4, 5) TS2304: Cannot find name 'it'.

Error:(13, 9) TS2304: Cannot find name 'expect'.

Error:(39, 36) TS2339: Property 'toBeNull' does not exist on type 'NgMatchers'.

Issue 2:
I'm submitting a ... (check one with "x")

[ ] bug report
[x] feature request
[ ] support request => Please do not submit support request here, instead see https://github.com/angular/angular/blob/master/CONTRIBUTING.md#question

Current behavior

Right now you cannot import two directives of the same module

Expected/desired behavior

You can use same directives/components in multiple modules without errors.

What is the motivation / use case for changing the behavior?

Concept of the shared module with its shared resources is good for components that are used everywhere. But if you have a complex application (ours contain more then 50 modules and 1500 components, services and pipes all together) multiple components can be reused different way. For example QuestionListPage uses UserActiveListPanel from User module. Also PhotoListPage uses same UserActiveListPanel, and VideoListPage uses UserActiveListPanel. How do I reuse this panel? Best way is see is to provide it in directives of the component as it was before rc.5. But right now I can't do it. But I can create lets say UserShareModule and put it there... its good, but if I put everything that is used by other modules from user module, i'll LOT of things there unnecessary in most of times. Then what to do? Create UserActiveListPanelShareModule? It will create lot of such trash files. So what to do? Maybe we simply can provide same directives multiple times without having this annoying error Type X is part of the declarations of 2 modules?

If you tell me that its by design, then my answer is: in my opinion its a bad design.

Please tell us about your environment:

Angular version: 2.0.0-rc.5

Browser: [all ]

Language: [all]
Issue 1:
I'm facing issues with visible class implementation specifically

visible-xs visible-sm

in version 3.1.0

Example 3.1.0 Not working

http://jsfiddle.net/7AD9z/embedded/result/

Example 3.0.3 Working

http://jsfiddle.net/DwzbH/embedded/result/

Issue 2:
https://dl.dropboxusercontent.com/u/37682778/error/index.html
Issue 1:
The following looks like a bug to me as DataFrame.duplicated() gives different results on what should be identical inputs. To me it looks like the problem is with the datetime64 values because if you look at the output of dates.values it's clear that the last 4 values are duplicates.

Please see the code below that reproduces the problem:

In [701]: dates = date_range('2010-07-01', end='2010-08-05')

In [702]: tst = DataFrame({'symbol': 'AAA', 'date': dates})

In [703]: tst.tail()
Out[703]:
date symbol
31 2010-08-01 00:00:00 AAA
32 2010-08-02 00:00:00 AAA
33 2010-08-03 00:00:00 AAA
34 2010-08-04 00:00:00 AAA
35 2010-08-05 00:00:00 AAA

In [704]: tst.duplicated().tail()
Out[704]:
31 False
32 False
33 False
34 False
35 False

In [705]: tst.duplicated(['date', 'symbol']).tail()
Out[705]:
31 False
32 True
33 True
34 True
35 True

In [706]: dates.values
Out[706]:
array([1970-01-15 40:00:00, 1970-01-15 64:00:00, 1970-01-15 88:00:00,
1970-01-15 112:00:00, 1970-01-15 136:00:00, 1970-01-15 160:00:00,
1970-01-15 184:00:00, 1970-01-15 208:00:00, 1970-01-15 232:00:00,
1970-01-15 00:00:00, 1970-01-15 24:00:00, 1970-01-15 48:00:00,
1970-01-15 72:00:00, 1970-01-15 96:00:00, 1970-01-15 120:00:00,
1970-01-15 144:00:00, 1970-01-15 168:00:00, 1970-01-15 192:00:00,
1970-01-15 216:00:00, 1970-01-15 240:00:00, 1970-01-15 08:00:00,
1970-01-15 32:00:00, 1970-01-15 56:00:00, 1970-01-15 80:00:00,
1970-01-15 104:00:00, 1970-01-15 128:00:00, 1970-01-15 152:00:00,
1970-01-15 176:00:00, 1970-01-15 200:00:00, 1970-01-15 224:00:00,
1970-01-15 248:00:00, 1970-01-15 16:00:00, 1970-01-15 40:00:00,
1970-01-15 64:00:00, 1970-01-15 88:00:00, 1970-01-15 112:00:00], dtype=datetime64[ns])

In [707]: dates
Out[707]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-07-01 00:00:00, ..., 2010-08-05 00:00:00]
Length: 36, Freq: D, Timezone: None

In [708]: dates.dtype
Out[708]: dtype('datetime64[ns]')

In [709]: dates.values.dtype
Out[709]: dtype('datetime64[ns]')

In [710]: sys.version
Out[710]: '2.7.3 (default, Aug 1 2012, 05:14:39) \n[GCC 4.6.3]'

In [711]: np.version.version
Out[711]: '1.6.1'

In [713]: pd.version.version
Out[713]: '0.8.1'

In [714]:

I remember reading somewhere that there are problems with datetime64 in numpy 1.6 but I don't understand what coercions are taking place behind the scenes. Also, if someone could please explain to me why the dates in dates.values above are wrong and how to avoid this, I would appreciate it.

Issue 2:
Hello the Pandas team,

I ran this morning into some nasty bug with Pandas 18.1.

I was not able to find whether you are aware of it or not. Here it is

idx=[pd.to_datetime('2012-02-27 14:23:00') , pd.to_datetime('2012-08-27 14:33:00'), pd.to_datetime('2012-02-27 14:23:00')]
test=pd.DataFrame({'A':['one','two', 'three']},index=idx)
test['alt']=test.index.tz_localize('UTC').tz_convert('US/Eastern').tz_localize(None)

Out[9]:
A alt
2012-02-27 14:23:00 one 2012-02-27 09:23:00
2012-08-27 14:33:00 two 2012-08-27 10:33:00
2012-02-27 14:23:00 three 2012-02-27 10:23:00

where the third column is obviously wrong. What have I done wrong here? Are indices not meant to work with duplicate values?

Thanks!