WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Electron version: 1.8.1

Operating system: Windows 7

Expected behavior

I create a BrowserWindow with webPreferences.zoomFactor=0.5. Everything looks small (which is expected). Now I close the application and then change zoomFactor=1.0, it should look larger. But no. I checked the Preferences file in %appdir%, there is one section per_host_zoom_levels, which I think Electron does not update the values correctly. Please check.

Issue 2:
Electron version: 1.7.6

Operating system: MacOS 10.11.6

Expected behavior

When a custom zoomFactor is defined in the BrowserWindow initialization (say 1.5), the window is displayed in that specific zoomFactor.

If you close the app, set a new zoomFactor in the code (say 1.2), and start the app again, the new zoomFactor should take in effect.

Actual behavior

The new zoomFactor does not take into effect, and the old one is still being used by the browser window.

The only way I know of to reset it is to manually do a zoom-default keyboard command in the window (Cmd+0 in OSX), then restart the app.

How to reproduce

Set zoomFactor to something other than the default of 1.0:

mainWindow = new electron.BrowserWindow({
width: 800,
height: 600,
useContentSize: true,
webPreferences: {
zoomFactor: 1.5
}
})

Close the window.

Set zoomFactor: 1.2.

Start the app again.

Notice the page is still zoomed in at 1.5. This will persist even with an app restart, and one way to get it to read the new value is to set browser zoom to 0 manually (Cmd+0 on OSX). Also, sometimes, the page will have the proper zoomFactor visually, but if you check with require('electron').webFrame.getZoomFactor() in the renderer side, you'll still get the old/wrong value.
Issue 1:
Uncaught Error: EACCES, open '/home/alex/.atom/compile-cache/cson/5af7724b023a6274b520f79f04fb798a67319ca7.json'

Atom Version: 0.154.0

System: linux 3.13.0-40-generic

Thrown From: Atom Core

Steps To Reproduce

...

...

Stack Trace

At fs.js:75

Error: EACCES, open '/home/alex/.atom/compile-cache/cson/5af7724b023a6274b520f79f04fb798a67319ca7.json'
at Error (native)

Issue 2:
I'm not sure if this is already possible in some way, or if it's desirable beyond a very specific use case I have run into.

I would like to be able to write require "react-atom-fork" in a package and get access to the version of React used by Atom itself. This works around a React bug where having multiple copies of React on the same page leads to duplicate reactids.

If the bug is not worked around, React will give the following error when events fire:

Invariant Violation: ReactMount: Two valid but unequal nodes with the same `data-reactid`

This is admittedly a very niche use case, and obviously the right fix is inside React, but I thought I'd open an issue since I think anyone trying to use React in a package will run into this.
Issue 1:
Mauricio Noda opened SPR-6743 and commented

Affects: 3.0 GA

Issue Links:

#11410 Make HttpServletRequest and HttpServletResponse available for injection ("duplicates")

Issue 2:
Mauricio Noda opened SPR-6744 and commented

While creating a custom Spring Security AuthenticationProvider I stumbled on the need for instances of both HttpServletRequest and HttpServletResponse. There´s no way to easily access them. Modifying org.springframework.web.context.request.RequestContextListener to register both instances in Spring request scope would be ideal.

I´m trying to integrate Spring Security with Weblogic Servlet Authentication. (http://download.oracle.com/docs/cd/E15051_01/wls/docs103/javadocs/weblogic/servlet/security/ServletAuthentication.html)

I see it becoming a major issue for any web container as JSR-196, which has the same problem, made its way into JEE 1.6 specification.

Affects: 3.0 GA

Issue Links:

#11409 Make HttpServlertRequest and HttpServletResponse available for injection ("is duplicated by")
Issue 1:
Hi, I try to add shared layer in Graph, but facing this problem:

from keras.models import Graph
from keras.layers import containers

block = containers.Sequential()
block.add(Dense(8, input_dim=20, activation='relu'))
block.add(Dense(1))

graph = Graph()
graph.add_input(name='input1', input_shape=(2,))
graph.add_input(name='input2', input_shape=(2,))

graph.add_shared_node(block, name='blocks', inputs=['input1', 'input2'], merge_mode='sum')
graph.add_node(Activation('sigmoid'), name='sigm', input='blocks')

graph.add_output(name='output', input='sigm')
graph.compile('rmsprop', {'output':'binary_crossentropy'})

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-501bebb9a4df> in <module>()
8 graph.add_input(name='input2', input_shape=(2,))
9
---> 10 graph.add_shared_node(block, name='blocks', inputs=['input1', 'input2'], merge_mode='sum')
11 graph.add_node(Activation('sigmoid'), name='sigm', input='blocks')
12

/usr/local/lib/python2.7/site-packages/Keras-0.3.0-py2.7.egg/keras/layers/containers.pyc in add_shared_node(self, layer, name, inputs, merge_mode, concat_axis, dot_axes, outputs, create_output)
327 raise Exception('Unknown identifier: ' + input)
328 s = Siamese(layer, layers, merge_mode, concat_axis=concat_axis, dot_axes=dot_axes)
--> 329 s.set_name(name)
330 self.namespace.add(name)
331 self.nodes[name] = s

AttributeError: 'Siamese' object has no attribute 'set_name'

Is it my misunderstanding of using pattern or problem in code?

Issue 2:
Hi,

I have been going through the latest the LSTM model for the text generation example from Andrej Kaparthy. It's pretty cool and works pretty well. Now, I am wondering if the LSTM model by Junhua Mao (http://arxiv.org/abs/1412.6632) can be supported easily. The reason i am asking this is the duplication of an embedding layer before the RNN (figure2). I guess one can use the RepeatVector, but I havent seen enough examples to be sure. So some clarification will be helpful.

Thanks! Keras rocks!
Issue 1:
Electron Version: 2.0.3

Operating System (Platform and Version): Windows_NT x64 10.0.17134

Last known working Electron version: None

Expected Behavior

When a window is maximized, the edges of the window should be in the same position as the edges of the screen it is maximized in

Actual behavior

Every other time a window is maximized, the window if offset downwards by 17 pixels for seemingly no reason.

To Reproduce

Demonstration repository

To start the application:

$ git clone https://github.com/TomSputz/electron-frameless-demo
$ cd electron-frameless-demo
$ start.bat

Screenshots

(Using the demo application)

Normal:

Issue:

Additional Information

This is quite similar to an issue that was close last year in #8728 , I don't know if they're related but looking at the changes made could be of use. As I understand it there is also a good chance that this is an upstream issue, if so is there any way I might generate support for that being fixed?

Issue 2:
Electron Version: 2.0.1

Operating System (Platform and Version): Windows10 1803

Last known working Electron version: 2.0.1

Expected Behavior

Maximized normal and restore normal when i click button in no frame window

Actual behavior

The first click is normal, but on the second click, there is a white edge on the window, and the window is also embedded under the taskbar.

To Reproduce

$ git clone https://github.com/muxiaozi/electron-quick-start.git
$ cd electron-quick-start
$ npm install
$ npm start || electron .

Screenshots

Additional Information

(┬＿┬)
Issue 1:
It would be so cool to have a power toy that recorded your screen, with microphone and camera. Windows already has this, but it's limited to 2 minutes, and you can only record screen in one app at a time.

Issue 2:
ℹ Computer information

PowerToys version: 0.20.1

PowerToy Utility: Settings

Running PowerToys as Admin: no

Windows build number: [run "winver"] 18362 (happens only on laptop screen but not on attached monitor).

📝 Provide detailed reproduction steps (if any)

Try to navigate the left navigation pane using arrow keys.

❌ Actual result

What is the actual result of the above steps?

Navigation Issue: Navigation view control is expected to navigate using the up and down arrow keys, but it does not happen. The weird thing is that when I try to do the same on the monitor attached to my laptop it works fine but just not on my laptop screen. Seems like it has something to do with the hardware configuration of my laptop screen as it works fine on the attached monitor. We are expected to navigate to the selected index but we always end up navigating to the first element.

Narrator Issue: The narrator is expected to say General selected 1 of 9... and similar things for all the items but it only does so on my monitor and not on my laptop screen. On my laptop screen it does so for the first tab but for the others it just reads out the first item in content of that page.

Steps to reproduce the bug

Steps to reproduce the behavior:

Try to tab into a navigation view control

✔️ Expected result

As you can see in the gif. The smaller recording is on my monitor screen and the arrow based navigation works as expected. The larger one is on my laptop screen and it does not work. The monitor is just connected to my laptop by a HDMI cable.

Screenshots

📷 Screenshots

Are there any useful screenshots? WinKey+Shift+S and then just paste them directly into the form

Corresponding issue in the microsoft-ui-xaml repo - microsoft/microsoft-ui-xaml#3179
Issue 1:
Problem description

When clicking a menu item that is disabled onTouchTap is fired anyways.

alert("what")} />

Versions

Material-UI: 0.18.X (latest)

React: 15.5

Browser: chrome latest

Issue 2:
Hi,

if a menu item is disabled and clicked the onTouchTap function is still called. Code example below. Happens in latest 0.18 release. It has correct disabled styling.

what i would expect: disabled icons do not trigger onTouchTap function.

Regards

<IconMenu>
<MenuItem
onTouchTap={this._clickable}
leftIcon={<SomeIcon />}
disabled={true}
/>
</IconMenu>
Issue 1:
I was testing a few different ways to calculate a quantity over an array, and noticed for loops slowed down significantly from commit 317a4d1* (16 days old master). @timholy suggested I file an issue here.

The test code is here: https://github.com/JaredCrean2/julia_tests/tree/master/array_speed2. It does the calculating using a manually inlined double for loop, ArrayViews, and SubArrays.

Using Julia Version 0.4.0-dev+5149 (2015-06-01 18:58 UTC) Commit 317a4d1* (16 days old master)

592.426 milliseconds (14232 allocations: 586 KB)
double loop @time printed above
495.138 milliseconds (14422 allocations: 599 KB)
ArrayView @time printed above
15.753 seconds (300 M allocations: 8240 MB, 3.39% gc time)
slice @time printed above

Using Julia Version 0.4.0-dev+5439 (2015-06-18 13:48 UTC) Commit a23caa0* (0 days old master):

1.720 seconds (102 k allocations: 3600 KB)
double loop @time printed above
566.122 milliseconds (16237 allocations: 692 KB)
ArrayView @time printed above
4.137 seconds (240 M allocations: 8240 MB, 12.96% gc time)
slice @time printed above

I looked at @code_warntye getEulerFlux(q,F), and the a23caa0 produced a huge amount of code (>1100 lines) compared to the 317a4d1 (~70 lines), and included many lines that looked like

(top(throw_boundserror))(q::Array{Float64,3},GenSym(45))::Union{}

that were not present in 317a4d1.

Issue 2:
It would be great to get suggestions from the REPL on methods for a given type. For example, I enter a DataFrame df on the REPL, and it provides me with a list of methods that I can use with a DataFrame. Amongst others, this would suggest head for the DataFrame. I could then select and autocomplete with [TAB] in the same way as currenly be done with autocompletion of fields etc. Finally, the REPL prepends the selection to create the resulting method call, e.g. head(df). This can be based on methodswith.

With this feature, we replicate the benefit that the Python user enjoys when she types df.[TAB] to get a list of names (including functions) associated with an object.

One way to evoke this list may be to type (df)[TAB] to get the list list of methodswith(typeof(df)), but any better suggestion from a REPL expert is welcome.

An alphabetically ordered list would be a good start. As a refinement, the list can have clever prioritization/filtering, e.g. prioritize methods where the type is the only argument, the first argument, methods that are unique to this type (as opposed to e.g. + which is defined for many types), ... .
Issue 1:
Reproducing code example:

There should be a default serializer for the type of float32 defined. Flask/jsonify() can serilize the type of float64 but not float32.

The following code throws error: TypeError: Object of type float32 is not JSON serializable

x = {'value': numpy.float32(0.1)}
return jsonify(x)

This one works:

x = {'value': numpy.float64(0.1)}
return jsonify(x)

Error message:

TypeError: Object of type float32 is not JSON serializable

NumPy/Python version information:

Issue 2:
It seems that the np.geomspace() functions produces wrong numbers

Reproducing code example:

import numpy as np

a = np.geomspace(1, 16, 5, dtype=int)
print(a)

[ 1 2 4 7 16]

The correct answer should be

[ 1 2 4 8 16]

NumPy/Python version information:

1.19.1 3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)]
Issue 1:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04

TensorFlow installed from (source or binary): Binary, tf-nightly

TensorFlow version (use command below): 1.7.0.dev20180214, git version b2a0f1c

Python version: 2.7.12

Bazel version (if compiling from source): N/A

GCC/Compiler version (if compiling from source): N/A

CUDA/cuDNN version: N/A

GPU model and memory: N/A

Exact command to reproduce:

import tensorflow as tf
from tensorflow.contrib.image.ops import gen_distort_image_ops
from tensorflow.python.framework import tensor_util
tensor_util.constant_value(tf.convert_to_tensor([1., 1.]))

Describe the problem

When I run the program above, I get the warning

/home/reedwm/venvs/tfnightlycpu/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py:560: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
return np.fromstring(tensor.tensor_content, dtype=dtype).reshape(shape)

This may not seem so bad, but when running tf_cnn_benchmarks, I get hundreds of such warnings.

What's very strange is that if I comment the line from tensorflow.contrib.image.ops import gen_distort_image_ops, I don't get the warning.

This is the same issue as tensorpack/tensorpack#641. @yaroslavvb, did you file a TensorFlow bug for this? If so, this can be marked as a duplicate.

Not really sure who to triage this to. /CC @mrry, can you address this or retriage?

Issue 2:
Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):

Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:

TensorFlow installed from (source or binary):

TensorFlow version (use command below):

Python version:

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version:

GPU model and memory:

You can collect some of this information using our environment capture

script

You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior

Describe the expected behavior

Code to reproduce the issue

Provide a reproducible test case that is the bare minimum necessary to generate the problem.

Other info / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
Issue 1:
Version

2.3.3

Reproduction link

https://github.com/Lacroute/webpack-simple-bug

Steps to reproduce

Just create a minimal project from this template and add some logs inside the hooks, you should obtain this in the console.

What is expected?

The previous instance should be destroyed before the new one is created.

What is actually happening?

The next one is created, so there is several init and then, the old one is destroyed so the init hooks are useless.

I don't know if this issue is related to the template or to the framework so I created also here > vuejs-templates/webpack-simple#123 (unfortunately no response)

Issue 2:
Version

2.6.12

Reproduction link

https://jsbin.com/tusikubevu/edit?html,js,console,output

Steps to reproduce

If you move between 100 and 125000 there is no problem, but if you choose 130000 there is a max stack trace error in chrome 85.0.4183.102 (stable), this is a VERY minimal reproduction, what really happens to me is with bootstrap-vue table with scopes and several chained components my app stop working and have to capture the error and reload the page, and my client is complaining a lot about it.

What is expected?

Don't know if you can solve it or if it's a chrome problem, because in FF it doesn't happens

What is actually happening?

I got this kind of error when using lots of components.

RangeError: Maximum call stack size exceeded

at normalizeArrayChildren (vue.js:2393)

at normalizeChildren (vue.js:2368)

at _createElement (vue.js:3406)

at createElement (vue.js:3360)

at Proxy.vm._c (vue.js:3497)

at Proxy.eval (eval at createFunction (vue.js:11649), :3:19)

at Vue._render (vue.js:3551)

at Vue.updateComponent (vue.js:4067)

at Watcher.get (vue.js:4478)

at Watcher.run (vue.js:4553)

logError @ vue.js:1897

globalHandleError @ vue.js:1888

handleError @ vue.js:1848

Vue._render @ vue.js:3553

updateComponent @ vue.js:4067

get @ vue.js:4478

run @ vue.js:4553

flushSchedulerQueue @ vue.js:4311

(anonymous) @ vue.js:1989

flushCallbacks @ vue.js:1915

Promise.then (async)

timerFunc @ vue.js:1942

nextTick @ vue.js:1999

queueWatcher @ vue.js:4403

update @ vue.js:4543

notify @ vue.js:745

reactiveSetter @ vue.js:1070

proxySetter @ vue.js:4630

change @ VM33:3

invokeWithErrorHandling @ vue.js:1863

invoker @ vue.js:2188

original._wrapper @ vue.js:7547

ListPicker._handleMouseUp

As I said before this happens on Chome, in FF there is no error, and I use 2.6.12, not 2.6.11, but can't choose it.
Issue 1:
When any one of the independent variables are bound to a single number (by setting the upper bound equal to the lower bound), the SLSQP optimization fails. However, setting the bounds to (lower_bound, upper_bound + 1e-8) works fine. This wasn't an issue in 1.4.1 and seems to have started happening in 1.5.0.

Reproducing code example:

import scipy.optimize as opt
import numpy as np

def f(x: np.ndarray):
return 0.4 * x[0]**2 + 0.6 * x[0] * x[1] + 0.85 * x[1]**2 - 5 * x[1]

x0 = np.array([0, 0])
bounds = [(0, 0), (-np.inf, np.inf)]
output1 = opt.minimize(f, x0, method='SLSQP', bounds=bounds)

bounds = [(0, 1e-8), (-np.inf, np.inf)]
output2 = opt.minimize(f, x0, method='SLSQP', bounds=bounds)

print(output1)
print(output2)

Results:

J_transposed[i] = df / dx
fun: 0.0
jac: array([ nan, -4.99999999])
message: 'Inequality constraints incompatible'
nfev: 3
nit: 1
njev: 1
status: 4
success: False
x: array([0., 0.])

fun: -7.352941176470589
jac: array([1.76470589, 0. ])
message: 'Optimization terminated successfully'
nfev: 9
nit: 3
njev: 3
status: 0
success: True
x: array([0. , 2.94117646])

Scipy/Numpy/Python version information:

1.5.0 1.19.1 sys.version_info(major=3, minor=8, micro=5, releaselevel='final', serial=0)

Issue 2:
Hello,

It seems that in _numdiff.py module the _dense_difference function does not take into account situation of an optimisation with equal bounds constraints and where initial solution respect this constraint for some .

In that case line 519 in _numdiff.py module dx and df are equal to zero and trigger nan in the jacobian.

It then triggers an exit n° 4 "Inequality constraints incompatible"

Scipy/Numpy/Python version information:

1.5.1 1.16.5 sys.version_info(major=3, minor=7, micro=4, releaselevel='final', serial=0)

my script does not raise error with following setup

1.3.1 1.16.5 sys.version_info(major=3, minor=7, micro=4, releaselevel='final', serial=0)
Issue 1:
by mazdak@chango.com:

On OS X 10.9 Mavericks, when building the following code snippet, I get the following
error:

# command-line-arguments
xxxx.go:190:12: struct size calculation error off=40 bytesize=32

The same go file (which is actually a cgo file) works on older versions of OS X and
Linux.

To assist, here is a snippet that reproduces the above problem:

----------

package main

/*

#include <stdlib.h>

enum ct { A = 0x00, B = 0x01};
typedef enum ct ct;

typedef struct cobject_s {
enum ct type;
size_t sz;
union {
char *str; // note for str: sz is strlen (not strlen+1
void *blob;
int64_t i64; // easiest to have one large int type
} u;

void *free; // if this is set, this must be freed on destructuion

} cobject;

typedef struct cbin_s {
char bin_name[32];
cobject object;
} cbin;

*/
import "C"

func main() {
var a C.cbin
}

Issue 2:
http://play.golang.org/p/FYbJS-dtlY

If one gets variable names confused and accidentally take the index of an integer type,
the error returned is "index of type int". This error makes it sound like the
index variable is of integer type, which is correct code. I think it would be much
better if it read "taking index of type int". This makes it clear that the
error refers to the type having its index taken, and not the type of the index variable
itself.
Issue 1:
OK. The glow text example I gave that's currently inside the code (but unused) isn't great.

I've received feedback from @MilesMSCohen that it has a fundamental flaw:

It loses all font hinting which makes it hard to read, especially at small sizes.

Ouch. I want hinting. The recommended alternative is to render the text to a command list and use the command list as input to an effect that can cause a glow.

@DHowett-MSFT already had a sample lump of code to do something like this that he was playing around with a few weeks ago because glow text is cool. It's around here somewhere.

Also, I got the warning from @MilesMSCohen that this might have worse performance, but we'd have to decide if that's worth it.

Issue 2:
Microsoft Windows [Version 10.0.18362.86]

Issue: Text rendering for CustomTextRenderer::_DrawGlowGlyphRun seems off.

I did the exact modifications as the dev (Mike) at the build conference to enable glowing text, which required uncommenting the line to call the function (and commenting the default text render call). I'm sorry if this is out of the scope of this issue tracker since it is not the default code, but I thought it should be included as glowing text makes the blurred background many times more usable, and well it seems off on my system.

The first image is the dev's system from the video on top and mine right beneath it, the second image is when I modified the function to draw random colors, to demonstrate that some colors are off (they look bolded and blurred) and others seem correct. Ideally I'm certain all of the colors should look something like Mike's did. I realize Mike was running regular conhost, but even in my pure open terminal window, you can see for instance the WinStore.App looks much better than most the others.