Extend ssm_1d_reduce for the batch>32

Ticket
This is a follow-on work upon this PR
Problem description
In previous implementation of ssm_1d_sum_reduce, we assumed batch_size/users will always be 32 (Tile Height).
However, in prefill model we are utilizing this batch dimension with sequence length and would like have support for seq_len = [32, 64, 128 or larger].
What's changed
We used to same design of the reader, compute, and writer kernel. Although now since B>32, there is an extra dimension getting added along the input height. We compute this new data by looping along the input/output height blocks.
In future, the design of the kernel can be simplified by sharding the data.
Checklist

[ ] Post commit CI passes
[ ] Model regression CI testing passes (if applicable)
[x] New/Existing tests provide coverage for changes

PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype0-in_mem_config1-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype0-in_mem_config1-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype1-in_mem_config0-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype1-in_mem_config0-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype1-in_mem_config1-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[128-1024-32-dtype1-in_mem_config1-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype0-in_mem_config0-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype0-in_mem_config0-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype0-in_mem_config1-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype0-in_mem_config1-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype1-in_mem_config0-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype1-in_mem_config0-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype1-in_mem_config1-out_mem_config0]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_reduce[64-163840-32-dtype1-in_mem_config1-out_mem_config1]
PASSED tests/tt_eager/python_api_testing/unit_testing/misc/test_ssm_1d_sum_reduce.py::test_ssm_1d_sum_reduce_with_program_cache
=============================================================================== 33 passed in 70.56s (0:01:10) ===============================================================================


openstack neutron ports for calico

Documentation mentions to update Openstack Neutron ports for authorizing Calico address but the two subnet (pods and services) should be added in one command instead of two because the last command erase the previous:
# allow kube_service_addresses network
neutron port-update 5662a4e0-e646-47f0-bf88-d80fbd2d99ef --allowed_address_pairs list=true type=dict ip_address=<IP_ADDRESS>/18

# allow kube_pods_subnet network
neutron port-update 5662a4e0-e646-47f0-bf88-d80fbd2d99ef --allowed_address_pairs list=true type=dict ip_address=<IP_ADDRESS>/18

Should become
# allow kube_service_addresses and kueb_pods_subnet networks
neutron port-update 5662a4e0-e646-47f0-bf88-d80fbd2d99ef --allowed_address_pairs list=true type=dict ip_address=<IP_ADDRESS>/18 ip_address=<IP_ADDRESS>/18

i can confirm this.


Use boost::iostreams for compression streams

Boost iostreams already supports gzip and bzip2 and there exists an implementation for lz4.
So this would solve most of #4.
This would mean getting rid of the CompressorClass and instead teamplating the stream (mixins pattern)
into the ChunkIo.
However, I couldn't find a blsoc stream, so I would probably need to write this myself...
http://www.boost.org/doc/libs/1_65_1/libs/iostreams/doc/index.html
https://github.com/zed-0xff/boost_lz4_filter/blob/master/lz4_filter.cpp

Not that this also supports platform independent memory mapping :).


bower.json file is referencing incorrect build file

The main key within bower.json references the incorrect build file. The reference must be 'proton-2.1.0.min.js'

thank you very much
thanks for your attention


Threading issue

Fatal Python error: PyEval_RestoreThread: the function must be called with the GIL held, but the GIL is released (the current Python thread state is NULL)
Python runtime state: initialized
Current thread 0x00001324 (most recent call first):
File "d:\snake-ai-pytorch\game.py", line 67 in play_step
File "d:\snake-ai-pytorch\agent.py", line 118 in train
File "d:\snake-ai-pytorch\agent.py", line 147 in

Hey, did you find the solution?


Question regarding PartNet Mobility Dataset

Hi, I had a question regarding using PartNet Mobility dataset with the Sapiens simulator.
PartNet Mobility dataset provides mobility.urdf and mobility_v2.json files.
The json contains articulation parameters that are missing in the .urdf file used in sapiens mobility.
How do I get a mapping of joints in the .urdf to .json file to access all the articulation parameters available in the json?

I do not think there is much useful information in mobility_v2.json. I would consider the json file is really a dirty version of the urdf file.
One exception is the screw joint (e.g. nut-and-bolt like), which is annotated in the json file but not exported to the urdf file. However, both the urdf format and the simulator do not support screw joint, getting this information would also not be that helpful.


Translatable of localized fields

There is one frequently used case. The field has localized values in the form of an object with different locales. And at the output of the serialize method, you need to get one localized value.

Localization should be the responsibility of the UI. On the back-end, you simply retrieve/handle records based on some custom property (e.g. locale) stored in the database.


Color Management setting QT API update

2023-02-17 23:48:58.709: root: ERROR: There was an error while loading the script!
| Traceback (most recent call last):
|   File "C:\Users\light\AppData\Local\Programs\Python\Python311\Lib\site-packages\vspreview\main\window.py", line 277, in load_script
|     self.load_storage()
|   File "C:\Users\light\AppData\Local\Programs\Python\Python311\Lib\site-packages\vspreview\utils\utils.py", line 67, in _wrapped
|     ret = func(*args, **kwargs)
|           ^^^^^^^^^^^^^^^^^^^^^
|   File "C:\Users\light\AppData\Local\Programs\Python\Python311\Lib\site-packages\vspreview\main\window.py", line 374, in load_storage
|     self.current_screen = self.app.desktop().screenNumber(self)
|                           ^^^^^^^^^^^^^^^^
| AttributeError: 'QApplication' object has no attribute 'desktop'

Traceback (most recent call last):
  File "C:\Users\light\AppData\Local\Programs\Python\Python311\Lib\site-packages\vspreview\main\window.py", line 710, in moveEvent
    screen_number = self.app.desktop().screenNumber(self)
                    ^^^^^^^^^^^^^^^^
AttributeError: 'QApplication' object has no attribute 'desktop'```

Isolated it to Color Management being set to true.

I think 21af425 has Qt side fixed, but device context creation fails on my devices with vague error info
pywintypes.error: (0, 'CreateDC', 'No error message is available')

I think 21af425 has Qt side fixed,

Exactly.

but device context creation fails on my devices with vague error info pywintypes.error: (0, 'CreateDC', 'No error message is available')

Unfortunately, I can't test it :/

Apparently I had an idea on how to fix it.


Vega VT-Link II (formerly known as Essemi ES-Link II) Support

Description
pyOCD does not support the CMSIS compliant debugger VT-Link II. The debugger is CMSIS compliant but does not contain CMSIS in the product string
Justification
Expanding the compatibility of pyOCD to work with more debuggers especially in this time of chip shortages where go-to MCUs are not available

Interesting, I had not heard of this probe. (To be pedantic, if the string "CMSIS-DAP" isn't in either the USB product name or interface name strings, then it's technically not CMSIS-DAP compliant. 😉)
Which version of CMSIS-DAP does it support? More specifically, v1 with a HID interface or v2 with vendor-specific bulk interface?
Can you create a pull request to add support? Otherwise it will have to be low priority since I can't test and there are plenty of other issues affecting more users.
You'll need to modify the code in the pyocd.probe.pydapaccess.interface package to support matching against USB VID/PID pairs. Which backends depends on HID vs bulk.
There is already a function is_known_cmsis_dap_vid_pid(): https://github.com/pyocd/pyOCD/blob/6a6fa2834d3aed120125bbef04e69f9f6118dda2/pyocd/probe/pydapaccess/interface/common.py#L86-L88
The different backends currently use this to decide whether to log more detailed error logs, mostly for Linux dealing with udev permissions. The KNOWN_CMSIS_DAP_IDS table should be updated as appropriate, and the function integrated into the pyusb match callbacks.

I've made a hot fix in the Find_DAP function to make it compatible. You can look at it here: https://github.com/Entropy98/pyOCD/commit/6b6e1a66b0566b86b086ca509da2db64fe6f7320 but I'm going to clean it up before making a formal pull request. Do you have a suggestion on the best way to implement this? I can show test data of it working.

I tend to prefer generic changes if possible, so I would modify the match callback to also accept a device if is_known_cmsis_dap_vid_pid() returns True. Also, need to implement the same change in hidapi_backend.py and pywinusb_backend.py to support all OSes. Thanks!


Dashboard failing to load due to 'could not find an available backend based on hash.'

I suspect this issue is transient, but filing just in case...

This is coincident with the daily cocoon deployment and version switch over. We passed the flag --no-stop-previous-version, but it doesn't seem to work as expected:
https://github.com/flutter/cocoon/blob/main/cloud_build/deploy_app_dart.sh#L11.
We need to take a look at the flag, or the following split traffic step. Somehow the old version is stopped whereas the new version is not ready.

I'm going to close this due to being unable to reproduce over the last week during the deployment time, but if this occurs again please reopen!


[Feature Request]Firebird Support

Issue Type

[* ] Feature Request
[ ] Bugfix/Errors
[ ] Questions
[ ] Other

Would you please support Firebird Database? Thanks.

Hey @LiPengfei19820619, could you please submit a pull request of this feature?
As you are a user that understands the needs for Firebird, you could follow this commit 6cc9774 and use this lib hgourvest/node-firebird to accomplish the same result that I've got with Postgres. Is it ok?
As you can see at the commit, it's not difficult, it's almost to define the queries it needs to run.
Thanks!

I did the Firebird Interface implementation.
As I have no experience with extensions and do not know how to test.
@mtxr It would be possible to test and upgrade the master.
Tanks
https://github.com/Level0r0s/vscode-sqltools

@LiPengfei19820619 can you test the open PR?
I believe it's already working, I've updated the references to the new code.
Thanks!

Closing. Needs users to test it.
If someone want to move forward with it, here is the PR #123 and the branch with the code is feature/firebird.


Version Bump for New Keb Repo

Description
Changes proposed in this pull request:

Updated KEB image to the newest version,
Changed KEB path to point to new repo's images.

Related issue(s)

Closed in favour of https://github.com/kyma-project/control-plane/pull/3068.


Is it possible to merge two different input layers into one?

For example,
How to concatenate image embedding and word embeddings together as one sequence which is then fed to LSTM just like what is done in Google's image caption generation paper http://arxiv.org/pdf/1411.4555v2.pdf ?

Non-sequential models are not supported at this time. But caption generation can be implemented with a sequential model; for instance see: http://keras.io/examples/#architecture-for-learning-image-captions-with-a-convnet-and-a-gated-recurrent-unit

Thanks @fchollet. But I think jointly learning image embedding, word embedding and the neural language model is more natural.

I wrote a short code to try that.
Here I want to concatenate two layers' output, lets say A (n by m) and B (n by t by m), A is 2d array and B is 3d array, and I want to combine them together by first reshapeing A to (n by 1 by m) and then concatenating them to get C (n by (t+1) by m)
But there is a problem. Could you please give me some hints on this?
import theano
import theano.tensor as T
import numpy as np
from keras.layers.core import Layer
class MultiIncomingLayer(Layer):
# incomings : a list of :class:Layer instances
def init(self, incoming_layers, axis, **kwargs):
self.incoming_layers = [None if isinstance(incoming_layer, tuple)
else incoming_layer
for incoming_layer in incoming_layers]
self.axis = axis
self.params = [p for p in incoming_layer.params for incoming_layer in incoming_layers]
def _concatenate(self,train):
    layer_inputs = [incoming_layer.output(train=train) for
                    incoming_layer in self.incoming_layers]
    #print layer_inputs[0].ndim, layer_inputs[1].ndim
    layer_inputs[0] = T.reshape(layer_inputs[0], (layer_inputs[0].shape[0], 1, layer_inputs[0].shape[1]))
    new_input = T.concatenate(layer_inputs, axis=self.axis)
    #print new_input.ndim

def output(self, train):
    output = self._concatenate(train)
    return output

Preface: I have not read all of your code through, or tried to replicate it in my instance. But, the following is still applicable.
The Sequential model compiles layers in sequentially, thus the name. Therefore, if you have something along the lines of the below pseudocode:
model = Sequential
model.add(ImageEmbedding)
model.add(WordEmbedding)
model.add(Concatenate)

Then the output of the ImageEmbedding layer is input for the WordEmbedding layer, etc. As such, your Concatenate layer would get the wrong dimensionality. Unless you modified the Sequential model (and did not provide that code) or created a new model type, we can't pinpoint the "problem"; not to mention the fact that you did not provide any info as to what the problem was.

Thanks @patyork Sorry that I forgot to provide the code that how I use this layer.
model = Sequential()
image embedding
img_emb_layer = Dense(feat_size, 256)
word embedding
word_emb_layer = Embedding(vocab_size, 256)
concatenate image embedding and word embeddings
img_sent_input_layer = MultiIncomingLayer([img_emb_layer, word_emb_layer], axis=1)
model.add(img_sent_input_layer)
LSTM
model.add(LSTM(256, 256, return_sequences=True)) #
model.add(Dropout(0.5))
model.add(TimeDistributedDense(256, vocab_size, activation='softmax')) #
Besides, I also change some places in Sequential class, where I use both cnn feature and a sequence of words as two kinds of inputs
self.X = self.layers[0].incoming_layers[0].input # input of model
self.Y = self.layers[0].incoming_layers[1].input
self._train = theano.function([self.X, self.Y], train_loss,
updates=updates, allow_input_downcast=True)

The problem is that there is an error with the first Dropout layer,
'an AttributeError: 'NoneType' object has no attribute 'shape' in dropout layer'
I donot know how this error comes, or maybe my idea about this implementation is not correct as you said. (I do not know  much about how to debug the code when using a theano-based package)

Unless I missed a line of code, you did not call model.compile

@patyork I did call model.compile after adding TimeDistributedDense layer.
model.compile(loss='categorical_crossentropy_3d', optimizer = rmsprop)
where I extend the categorical_crossentropy to 3d case, but I think I can use mean_squared_error loss instead.

@patyork I notice that even with only the code
#image embedding
img_emb_layer = Dense(feat_size, 256)
#word embedding
word_emb_layer = Embedding(vocab_size, 256)
#concatenate image embedding and word embeddings
img_sent_input_layer = MultiIncomingLayer([img_emb_layer, word_emb_layer], axis=1)
model.add(img_sent_input_layer)
I can not get what I want. I think that is because just as you said, I have to change the code for layer class and change the way that connect them together.
Sorry that I did not make my problem clear and bring you so much trouble

Now possible through the use of the Merge layer; see: http://keras.io/layers/core/#merge

@fchollet Great, man. That is very helpful. Thanks!

I had met the same problem. I want to merge two different input layers into one,and I defined the way how they merged. the problem is that I can't put the layer I defined to the model (Sequential or Graph) as the first layer. this occasion is really common in practical applications. I found that the only solution is to define your model with Keras functional API. It is rather complicated and I am waiting for some improvements in keras to solve the problem.

hello, I am new todeep learning and keras. I don't know if this is the correct place to post my question. I am trying to build an auto encoder with two inputs as defined in https://blog.keras.io/building-autoencoders-in-keras.html.
The idea is build two seprate encoders , merge them and then use the output for the decoder part. But i am not sure how to use the fit function, for multiple inputs and single output. Any suggestions or references  would be very helpful.

I built a project using (Merge, merge) layer once, and use share layer in another. The problem I can't find any documentation that explains how merge or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me that helps me to understand how these layers work ???

Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer?  And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer in
another. The problem I can't find any documentation that explains how merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
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Hi,
Thanks for your kind reply. I build two convolution neural network to combine two sequences one with size (batch size, None, 23) and the other is (batch size, None, 3) note that batch size and None in both sequences are equal in each batch. This is the architecture
Convolution 1D layer (size=17,depth=256, padding=SAME,activation=prelu,input (batch size, None, 23))
Convolution 1D layer (size=16,depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Convolution 1D layer (size=17,depth=256, padding=SAME,activation=preluinput (batch size, None, 3))
Convolution 1D layer (size=16,depth=128, padding=SAME,activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Merge layer()
Dropout layer (p=0.5)
fully connected layer (units=4, activation=prelu)
for Share layer
Main Input X [batch, None, 23]
Convolution 1D layer (size=24 depth=256, padding=SAME, activation=prelu)
Auxiliary input Y [batch, None,3]
Merge [input X, input Y]
Convolution 1D layer (size=17 depth=256, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=64, padding=SAME, activation=prelu)
Fully connected layer (units=4, activation=prelu)

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 5:20:32 AM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input layers into one? (#95)
Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer? And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer in
another. The problem I can't find any documentation that explains how merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
—
You are receiving this because you commented.
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https://github.com/notifications/unsubscribe-auth/AJE8Rb9W9y-OBs6QSVw8SwSG5pQOXCSoks5s13WigaJpZM4ENyHq
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The basic Rule for Merge is that the outputs of the layers you want to
merge should be of same dimensions( assuming you already know it)And then
you specify the mode(https://keras.io/layers/merge/) based on the mode you
set the axis.
An example would be
Output= Merge( input1, input2, mode=‘concat’, axis=-1)
Hope this helps.
Regards-
Deepa
On Mon 13. Nov 2017 at 18:08, shay86<EMAIL_ADDRESS>wrote:

Hi,
Thanks for your kind reply. I build two convolution neural network to
combine two sequences one with size (batch size, None, 23) and the other is
(batch size, None, 3) note that batch size and None in both sequences are
equal in each batch. This is the architecture
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=prelu,input (batch size, None, 23))
Convolution 1D layer (size=16,depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=preluinput (batch size, None, 3))
Convolution 1D layer (size=16,depth=128, padding=SAME,activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Merge layer()
Dropout layer (p=0.5)
fully connected layer (units=4, activation=prelu)
for Share layer
Main Input X [batch, None, 23]
Convolution 1D layer (size=24 depth=256, padding=SAME, activation=prelu)
Auxiliary input Y [batch, None,3]
Merge [input X, input Y]
Convolution 1D layer (size=17 depth=256, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=64, padding=SAME, activation=prelu)
Fully connected layer (units=4, activation=prelu)

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 5:20:32 AM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer? And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer in
another. The problem I can't find any documentation that explains how
merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/fchollet/keras/issues/95#issuecomment-343774963,
or mute
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<
https://github.com/notifications/unsubscribe-auth/AJE8Rb9W9y-OBs6QSVw8SwSG5pQOXCSoks5s13WigaJpZM4ENyHq
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Hi,
your answer helps a lot but can you provide me with any source, document, or paper explains marge layer more. As far as I know, there are (Merge and merge) one of them combine tensors and the other combine weights .How they do that what is their architecture look like or what is the mathematic formula for them. I search a lot of papers none of them gives me an answer. So, I will be grateful if you direct me to some sources that specifically related to merge layers.
my best regards

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 1:31:10 PM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input layers into one? (#95)
The basic Rule for Merge is that the outputs of the layers you want to
merge should be of same dimensions( assuming you already know it)And then
you specify the mode(https://keras.io/layers/merge/) based on the mode you
set the axis.
An example would be
Output= Merge( input1, input2, mode=‘concat’, axis=-1)
Hope this helps.
Regards-
Deepa
On Mon 13. Nov 2017 at 18:08, shay86<EMAIL_ADDRESS>wrote:

Hi,
Thanks for your kind reply. I build two convolution neural network to
combine two sequences one with size (batch size, None, 23) and the other is
(batch size, None, 3) note that batch size and None in both sequences are
equal in each batch. This is the architecture
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=prelu,input (batch size, None, 23))
Convolution 1D layer (size=16,depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=preluinput (batch size, None, 3))
Convolution 1D layer (size=16,depth=128, padding=SAME,activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Merge layer()
Dropout layer (p=0.5)
fully connected layer (units=4, activation=prelu)
for Share layer
Main Input X [batch, None, 23]
Convolution 1D layer (size=24 depth=256, padding=SAME, activation=prelu)
Auxiliary input Y [batch, None,3]
Merge [input X, input Y]
Convolution 1D layer (size=17 depth=256, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=64, padding=SAME, activation=prelu)
Fully connected layer (units=4, activation=prelu)

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 5:20:32 AM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer? And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer in
another. The problem I can't find any documentation that explains how
merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
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Hi,
I haven’t come across  any paper decribing the mathematical or architecture
details on how the merge layer works. You can figure it out by looking at
the source code and by printing the output shape of your network. (This
helped me a lot)
The Merge layer is used to combine the  the outputs of two sequential
models. ( this worked when i tried to combine the output of two sequential
CNN’s For implementing a GAN)
And the merge layer is used merge the output of two layers in a functional
API (i used this two combine the output of maxpool layer with the
upsampling layer within the same network ref: unet architecture)
A better example and clear explanation Can be found here
https://github.com/fchollet/keras/issues/3921
PS: I worked on the older version of keras and the documentation had very
good examples. Things seem to have changed a lot now. You can try looking
for the older documentations. You can look at the inception architecture
from the keras documentation and the unet architecture
https://github.com/orobix/retina-unet for examples on how to implement it.
Hope this helps.
Regards-
Deepa
On Tue 14. Nov 2017 at 18:13, shay86<EMAIL_ADDRESS>wrote:

Hi,
your answer helps a lot but can you provide me with any source, document,
or paper explains marge layer more. As far as I know, there are (Merge and
merge) one of them combine tensors and the other combine weights .How they
do that what is their architecture look like or what is the mathematic
formula for them. I search a lot of papers none of them gives me an answer.
So, I will be grateful if you direct me to some sources that specifically
related to merge layers.
my best regards

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 1:31:10 PM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
The basic Rule for Merge is that the outputs of the layers you want to
merge should be of same dimensions( assuming you already know it)And then
you specify the mode(https://keras.io/layers/merge/) based on the mode you
set the axis.
An example would be
Output= Merge( input1, input2, mode=‘concat’, axis=-1)
Hope this helps.
Regards-
Deepa
On Mon 13. Nov 2017 at 18:08, shay86<EMAIL_ADDRESS>wrote:

Hi,
Thanks for your kind reply. I build two convolution neural network to
combine two sequences one with size (batch size, None, 23) and the other
is
(batch size, None, 3) note that batch size and None in both sequences are
equal in each batch. This is the architecture
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=prelu,input (batch size, None, 23))
Convolution 1D layer (size=16,depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=preluinput (batch size, None, 3))
Convolution 1D layer (size=16,depth=128, padding=SAME,activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Merge layer()
Dropout layer (p=0.5)
fully connected layer (units=4, activation=prelu)
for Share layer
Main Input X [batch, None, 23]
Convolution 1D layer (size=24 depth=256, padding=SAME, activation=prelu)
Auxiliary input Y [batch, None,3]
Merge [input X, input Y]
Convolution 1D layer (size=17 depth=256, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=64, padding=SAME, activation=prelu)
Fully connected layer (units=4, activation=prelu)

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 5:20:32 AM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer? And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer
in
another. The problem I can't find any documentation that explains how
merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
—
You are receiving this because you commented.
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https://github.com/fchollet/keras/issues/95#issuecomment-343774963,
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Hi,
that is good I will try to do as you say and I will study the links you sent
thanks a lot

From: gunashekar<EMAIL_ADDRESS>Sent: Tuesday, November 14, 2017 1:59 PM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input layers into one? (#95)
Hi,
I haven’t come across any paper decribing the mathematical or architecture
details on how the merge layer works. You can figure it out by looking at
the source code and by printing the output shape of your network. (This
helped me a lot)
The Merge layer is used to combine the the outputs of two sequential
models. ( this worked when i tried to combine the output of two sequential
CNN’s For implementing a GAN)
And the merge layer is used merge the output of two layers in a functional
API (i used this two combine the output of maxpool layer with the
upsampling layer within the same network ref: unet architecture)
A better example and clear explanation Can be found here
https://github.com/fchollet/keras/issues/3921
PS: I worked on the older version of keras and the documentation had very
good examples. Things seem to have changed a lot now. You can try looking
for the older documentations. You can look at the inception architecture
from the keras documentation and the unet architecture
https://github.com/orobix/retina-unet for examples on how to implement it.
Hope this helps.
Regards-
Deepa
On Tue 14. Nov 2017 at 18:13, shay86<EMAIL_ADDRESS>wrote:

Hi,
your answer helps a lot but can you provide me with any source, document,
or paper explains marge layer more. As far as I know, there are (Merge and
merge) one of them combine tensors and the other combine weights .How they
do that what is their architecture look like or what is the mathematic
formula for them. I search a lot of papers none of them gives me an answer.
So, I will be grateful if you direct me to some sources that specifically
related to merge layers.
my best regards

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 1:31:10 PM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
The basic Rule for Merge is that the outputs of the layers you want to
merge should be of same dimensions( assuming you already know it)And then
you specify the mode(https://keras.io/layers/merge/) based on the mode you
set the axis.
An example would be
Output= Merge( input1, input2, mode=‘concat’, axis=-1)
Hope this helps.
Regards-
Deepa
On Mon 13. Nov 2017 at 18:08, shay86<EMAIL_ADDRESS>wrote:

Hi,
Thanks for your kind reply. I build two convolution neural network to
combine two sequences one with size (batch size, None, 23) and the other
is
(batch size, None, 3) note that batch size and None in both sequences are
equal in each batch. This is the architecture
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=prelu,input (batch size, None, 23))
Convolution 1D layer (size=16,depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Convolution 1D layer (size=17,depth=256,
padding=SAME,activation=preluinput (batch size, None, 3))
Convolution 1D layer (size=16,depth=128, padding=SAME,activation=prelu)
Convolution 1D layer (size=15 depth=64,padding=SAME, activation=prelu)
Merge layer()
Dropout layer (p=0.5)
fully connected layer (units=4, activation=prelu)
for Share layer
Main Input X [batch, None, 23]
Convolution 1D layer (size=24 depth=256, padding=SAME, activation=prelu)
Auxiliary input Y [batch, None,3]
Merge [input X, input Y]
Convolution 1D layer (size=17 depth=256, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=128, padding=SAME, activation=prelu)
Convolution 1D layer (size=16 depth=64, padding=SAME, activation=prelu)
Fully connected layer (units=4, activation=prelu)

From: gunashekar<EMAIL_ADDRESS>Sent: Monday, November 13, 2017 5:20:32 AM
To: fchollet/keras
Cc: Shayan Ihsan Jalal; Comment
Subject: Re: [fchollet/keras] Is it possible to merge two different input
layers into one? (#95)
Hi, can you be a bit clear and tell me which layer you want to merge? And
where are you using the shared layer? And what exactly are you trying to
do? May be i can help.
Regards-
Deepa
On Sun 12. Nov 2017 at 23:48, shay86<EMAIL_ADDRESS>wrote:

I built a project using (Merge, merge) layer once, and use share layer
in
another. The problem I can't find any documentation that explains how
merge
or share layer works like convolution or max-pooling layers.
I will be grateful if anyone can direct me or suggest some paper to me
that helps me to understand how these layers work ???
—
You are receiving this because you commented.
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https://github.com/fchollet/keras/issues/95#issuecomment-343774963,
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I am looking for a recommendation to merge vector information with CNN output into a regression model.


Check all constants: move to configuration where applicable

For some of the constants (timeouts, sizes) it might make sense to have them specified by the integrator in the runtime configuration. Scan the code for the constants and move to configuration (with a proper doc comment) where applicable.

Decide whether to move to configuration or not:

[ ] api::client::BUFFER_SIZE Install stream buffer size in client wrapper
[ ] name::MAX_LENGTH Max name length allowed
[ ] npk::dm_verity::(SHA256|BLOCK)SIZE...
[ ] npk::npk: MKSQUASHFS, UNSQUASHFS
[ ] manifest: Reserved env variables NORTHSTAR_NAME, NORTHSTAR_VERSION...
[ ] manifest::XATTR_SIZE_MAX Max xattr size
[ ] manifest::MAX_ARG_INDEX|MAX_ARG_VALUES BPF constants
[ ] cgroups::OOM_CONTROL|EVENT_CONTROL Kernel constants
[ ] console::MAX_LINE_LENGTH Max length for a json line
[ ] console::MAX_NPK_SIZE Max npk stream length
[ ] console::NPK_STREAM_TIMEOUT Timeout for npk stream reception
[ ] EVENT_BUFFER_SIZE: Channel size of main loop
[ ] NOTIFICATION_BUFFER_SIZE Channel size for notifications
[ ] ENV_NAME, ENV_VERSION, ENV_CONTAINER, ENV_CONSOLE
[ ] runtime::mount::DEVICE_MAPPER_DEV Device mapper control file (platform specific)
[ ] runtime::mount::DM_DEVICE_TIMEOUT Device mapper timeout
[ ] runtime::mount::LOOP_DEVICE_TIMEOUT losetup find free timeout
[ ] runtime::token::TOKEN_EXPIRED_THRESHOLD Token timeout


Docker Grossery

Problem description

Problem location

I saw a problem on the following URL:

I couldn't find the information I wanted. I expected to find it near the following URL:

Other:

Project version(s) affected

Suggestions for a fix

Closing for now. If you have additional information, please open a new issue.


Cannot delete subproject

Hi,
I have added a subproject to test, what this is about. Now I can't delete it anymore.
This url: https://readthedocs.org/dashboard/<prj>/subprojects/delete/<subprj>/ does not exist.

That seems odd. Which project is this on? Could you please give the link that you are actually trying to click and get a 404 back for?

This is the link that gives me a 404: https://readthedocs.org/dashboard/fvf/subprojects/delete/manual/

Thanks, I will have a look at this.

Thanks. Since I'm not an admin of the "manual" project. Could you delete it for me?

The changes are not deployed yet. You will be able to remove the the subproject without beeing an admin of it once it's in production. Will probably happen early in the week.


Merges Master to Live. See commits for details.

Docs Build status updates of commit d85ab98:
:clock10: Full build: the average full build time is 14 min(s) 17 sec(s), this is based on the last 101 build(s) for this repository.
:warning: This is a FULL build due to the incremental build being disabled in configuration file (.openpublishing.publish.config.json in 'live' branch). For more detailed info, please click here.

Docs Build status updates of commit d85ab98:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

repo_docs/NEW_CMDLETS.md
:white_check_mark:Succeeded

sharepoint/docs-conceptual/sharepoint-pnp/sharepoint-pnp-cmdlets.md
:white_check_mark:Succeeded
View (sharepoint-ps)

sharepoint/sharepoint-ps/sharepoint-server/Move-SPUser.md
:white_check_mark:Succeeded
View (sharepoint-ps)

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


New package location

I was unable to install the package with the instruction in the Readme until I changed it to github.com/move-ton/ever-client-go

Yes, it is migration from move-ton to my personal account.

fix it in v1.43.0


Update BTHandshaker To Export std::net::TcpStream Instead Of mio::tcp::TcpStream

Currently BTHandshaker exports mio::tcp::TcpStream because internally that is the TcpStream implementation we are using to make the handshake. We should instead convert completed handshake TcpStreams into std::net::TcpStream which would involve going through net2 to re-bind the socket as a blocking socket.
When mio adds support to access the SOCKET handle on windows we can move the conversion code into bip_util and have implementations for both windows and unix conversions.

This may be a bit hard considering carllerche/mio#362 was submitted recently. Because of this, I will be closing this issue.
We will (probably) go ahead and tie our exported network primitives to mio.


Not working

/usr/local/lib/python2.7/dist-packages/fluxgui/fluxapp.py:75: GtkWarning: Cannot transform xsetting Net/IconThemeName of type gchararray to type GdkColor
gtk.gdk.screen_get_default().get_setting('gtk-icon-theme-name')
/usr/local/lib/python2.7/dist-packages/fluxgui/fluxapp.py:75: GtkWarning: Cannot transform xsetting Net/IconThemeName of type gchararray to type gint
gtk.gdk.screen_get_default().get_setting('gtk-icon-theme-name')

Please describe your system and the exact problem you are experiencing.
Is #27 by any chance related?

Same here.  New kernel.  Ubuntu.  Same as #27

Sounds like this is a duplicate of #27.


GD-112: Running tests in editor does not reveal compilation errors

Version 1.0.0
The used Godot version: v3.3.2.stable.arch_linux
OS including version: Manjaro, kernel 5.20.49-1 (64 bit)
Describe the bug
When the code under test has a compilation error, running the tests normally (not in debug mode) will return green. This means that these test results are actually lying to us, because non-compiling code can by definition not complete the test :).
Running the tests in debug mode does break at the compilation error, so currently I only run tests this way.
The runtest.sh -a command does also break at the error and opens a debug prompt. However running with the -c option wont continue the testing (probably another bug report).
Steps to Reproduce

Open the test_gdunit project.
Run the tests in the test folder.
Note how the tests return green.
Run the tests in debug mode.
Note how line 6 in Main.gd does not actually compile. Meaning that the green test results from the non-debug run were actually lying to us.

Minimal reproduction project:
test_gdunit.zip

Thanks for reporting.
Yes compile errors should end up in test errors. ;)

This is a problem with Godot and error reporting.
Calling new in a script that contains parse errors is only reported as an error internally, and I don't see any way to catch this.
The only workaround would be to provide a custom load/instance function in GdUnit. This function could then catch this and generate an error based on this information.
However, this would read wonky and deviate from the standard how to load or instantiate a script.
I have experimented a bit.
func script_instance(resource_path :String) -> Object:
	var script = load(resource_path)
	if script.can_instance():
		return script.new()
	if script.reload() == ERR_PARSE_ERROR:
		return GdUnitAssertImpl.new(self, null).override_failure_message("The script `%s` can't be instanciate, possible parse errors!" % resource_path).test_fail()
	return GdUnitAssertImpl.new(self, null).override_failure_message("The script `%s` can't be instanciate, unknown error!" % resource_path).test_fail()

func test_add() -> void:
	var instance = script_instance("res://error_case/new_script.gd")

	assert_int(instance.add(1,2)).is_equal(3)

Hmm, that does indeed complicate things. The wrapper works as expected, but I agree it's less than ideal... I tried defining a load function in the hope that it would call that instead of the default, but as suspected, that does not work :P. What might be an option is creating a _load() function or something similar that wraps around the standard load() and returns an object that acts like Script in all respects, except that it does the checks when it gets instantiated. That way the only difference would be an added underscore (though still less than ideal).
In case of this wrapper, you could even supply nice errors when (for example) the file is missing 😃:
func script_instance(resource_path :String) -> Object:
	var script: Script = load(resource_path)
	if script == null:
		return GdUnitAssertImpl.new(self, null).override_failure_message("The script `%s` can't be loaded, does the file exist?" % resource_path).test_fail()
	elif script.can_instance():
		return script.new()
	elif script.reload() == ERR_PARSE_ERROR:
		return GdUnitAssertImpl.new(self, null).override_failure_message("The script `%s` can't be instantiated, possible parse errors!" % resource_path).test_fail()
	else:
		return GdUnitAssertImpl.new(self, null).override_failure_message("The script `%s` can't be instantiated, unknown error!" % resource_path).test_fail()

Here is my attempt with a custom load.
The _load function will fail the test case when the resource can't be found.
Calling new on the wrapped script will fail the test case when there is an error in the loaded script. For the rest I think it should act exactly like a normal script.
extends GdUnitTestSuite

class GdUnitGDScriptWrapper:
	extends GDScript

	var _caller: Object

	func set_caller(caller: Object):
		_caller = caller

	func new():
		if can_instance():
			return .new()
		elif reload() == ERR_PARSE_ERROR:
			return GdUnitAssertImpl.new(_caller, null).override_failure_message("The script `%s` can't be instantiated, possible parse errors!" % resource_path).test_fail()
		else:
			return GdUnitAssertImpl.new(_caller, null).override_failure_message("The script `%s` can't be instantiated, unknown error!" % resource_path).test_fail()

func _load(resource_path: String) -> Object:
	var resource := load(resource_path)
	if resource == null:
		return GdUnitAssertImpl.new(self, null).override_failure_message("The resource `%s` can't be loaded, does the file exist?" % resource_path).test_fail()

	if resource.get_class() == "GDScript":
		resource.set_script(GdUnitGDScriptWrapper)
		resource.set_caller(self)

	return resource

func test_add() -> void:
	var script = _load("res://error_case/new_script.gd")
	var instance = script.new()

	assert_int(instance.add(1,2)).is_equal(3)

Good Idea, but it will not solve finally all problems.
e.g. a class with class_name where not need to load will also fail when calling new
class_name MyClass
extends Node
..

func test_foo():
   var my_class := MyClass.new()

I'm current researching how to access error reporting from Godot, i had this in my mind some months ago and experimenting with GdUnitPushErrorHandler.
But this need time to find a good and reliable solution.

Good point, I hadn't thought of that. The ScriptEditorDebugger GdUnitPushErrorHandler uses looks interesting, I didn't know that one existed 😃 .

@Wcubed sorry but there is current now way to solve this!
This topic is a long term discusion on Godot
https://github.com/godotengine/godot/issues/3516
https://github.com/godotengine/godot/issues/7643
The Godot main developers stubbornly refuse to include a reasonable error handling.
So i close as won't fix

How do other unit-testing frameworks (e.g. GUT) handle this?
I know GUT uses a test-scene as the test-runner, but I recall many times, running tests and seeing Godot intercept and break on compilation errors.
Maybe you can look into it or ask for some help? https://github.com/bitwes/Gut

@nightblade9 GUT UI runs allways in debug mode.

@nightblade9 GUT UI runs allways in debug mode.

So you're saying, that gdUnit3 is only useful if run in debug mode? :)

@nightblade9 no ;)
I implemented "Run" (without debug) to run tests agains your code like do it in runtime (release mode)
The "Run Debug" is usefull to test and debug in realtime your code. (debug mode)
I'm also not happy that Godot not supports any kind of error handlers to check for errors.
I tryed tp grap errors from the debug console but this is very hacky,
So i suggest to run your tests during developing on Debug mode.
The "run" version should at least run faster than the debug version, but I haven't noticed much difference.

So i suggest to run your tests during developing on Debug mode

Yeah, this seems to be the most reliable way to see real errors as errors (and not as passing tests). I refactor code a lot, and that makes things break.
Thanks for looking into this.


Add history support for proxy models

Somewhat based on https://github.com/emergence/django-simple-history/commit/9bb54005fd35ce75d887b232abb6fb44cbd13cb0.

[ ] Test different proxy configurations (historical concrete with proxy, concrete with historical proxy, etc)

Is this still on the "roadmap" ?

Hi @Alex131089. I pulled in #112 first, as there was quite a bit more interest in allowing tracking to be inherited. From a data perspective, this support is a little odd though. Proxy models reuse the table the parent model used. If I find some time to think back through what should be happening here you'll see the progress on this branch.
Feel free to take a look at it as well of course. Pull requests are very welcome.

I don't know if it can help, but for my usage, I'm using this very ugly "hack"/workaround which seems to be working :
admin.py:
class HistoTrackedModel(TrackedModel):
	# /!\ Very ugly hack
	try:
		import sys
		if sys.argv[1] not in ('makemigrations',):
			raise Exception()
	except:
		history = HistoricalRecords(table_name='project_historicalTrackedModel')

	class Meta:
		proxy = True
		default_permissions = ('change',) # So the object appear

class HistoTrackedModelAdmin(ModelAdmin):
	readonly_fields = tuple((f.name for f in TrackedModel._meta.fields))

The idea here is to set an history object pointing to the historical table of the proxyfied model, except when running makemigrations because it would try to create another table for this proxy (which will not succed). No creation/modification/deletion are allowed for this proxy model, It's a readonly view of history (revert is possible & working though).
Sorry that I cannot help more, your plugin is giving a very easy way to offer history to a model :)

hello @macro1, I need this feature.
I tried to base to 1.8.1, but without success. it seems that the code is no longer compatible or maybe I did something wrong.
https://travis-ci.org/luzfcb/django-simple-history/builds/117542656

Guys, no offense, but do you even know what a proxy model is for? I suggest reading the official Django documentation: https://docs.djangoproject.com/en/1.10/topics/db/models/#proxy-models
Also if you want this "feature" because you want to track built-in models or third party models, you can already do that: https://django-simple-history.readthedocs.io/en/latest/advanced.html#history-for-a-third-party-model
This ticket should be closed IMO.

I've been somewhat on the fence myself about adding support for proxy models. Closing, both for simplicity of the existing registry logic, and because I can't imagine a good use for it.

I wanted to bring up a potential use case for this. I currently have an Account Data model that has several proxy models drawn from it in which all have various querysets/permissions restricting and changing various views for the django-admin. After a while of playing around with django-simple-history, everything seemed great -- but I realized later that on all of the proxy models, the saves aren't being tracked -- and the history gives an  "'Options' object has no attribute 'simple_history_manager_attribute'" error.
Perhaps I'm using Proxy models incorrectly, but it sure would of been nice for it all to work out of the box.

Actually, I used:
register(workAccounts,table_name='web_historicalaccount')
for all the proxy models and referenced the original table and it seems to work.

@ThunderTrent: Would something like this solve it?
https://github.com/treyhunner/django-simple-history/compare/fix-proxy-signals
I think the original ask was the ability to register proxy models... But what you're looking for is to preserve  tracking when the proxy interface is used.

@macro1 : that's exactly what we need for our use case. Basically, make sure history is saved even when using proxy models. If there's any way I can help to get this merged, please let me know.


Perform aws partition lookup for arn

what

Instead of requiring the user to define arn_format for gov or china regions, lookup the partition in this module

why

Makes using this module easier

Fixes https://github.com/cloudposse/terraform-aws-cloudtrail-s3-bucket/issues/44

Looks like auto_format failed, but I'm not sure why.

@woz5999

/test all

@aknysh can you please review this?

/rebuild-readme

/test all

/test all

@aknysh @Nuru can someone please review/merge before more conflicts get introduced?

thanks @Gowiem !


1 add feed

The changes that were added were the following:

method was added to activate the fee by the validators <nd also modify the wallet from which the fee is sent
A fee is charged at the time the user transfers the tokens to the contract and at the time of claiming it.
Methods were added so that the interfaces know the current fee and where the fees are sent

close this PR because it should go to BridgeKoin not the main repository


Legend curve marker type temporary turns into filled square during mouse left click pan

Hello,
I just found that when you show Legend, and start to pan with keeping Mouse left button, all curves with their marker types in the Legend changes to "ScottPlot.MarkerShape.filledSquare" type from the previously set types. Stop panning and all markers reverts to original.
Edit: actually it looks like this bug happens only, when we create a curve via PlotScatter() method not explicitly specifying marker type. In this case the default marker type is "filledCircle". During mouse panning these default filledCircle markers turn into filledSquare ones.

It is rendered correct.  After PR #89 antialiasing off during pan by default. If you want to avoid this effect you can set at startup:
scottPlotUC1.plt.mouseTracker.lowQualityWhileDragging = false;

You can also toggle this behavior on and off with the right-click menu (which hopefully will be improving soon, #91)
When lowQualityWhileDragging is enabled, perhaps we should render the frame and legend in high quality, and only render the data in low quality? I guess then that causes a problem because circles would render well in the legend put poorly on the graph itself...

Thanks, clear now!

To make this clearer in the future should we display a little message in the corner when a frame is rendered low quality? Maybe something that looks kind of like the benchmark? ... or turn this back off by default?
I like how low quality rendering while dragging makes the user interaction faster, but I'm concerned this behavior might be misinterpreted as a glitch...

@swharden it looks like legend render in low resulution and then upscaled with some default method.
Maybe render legend in high resolution initialy help to resolve this problem.

Posible solutions:

Make new bitmap layer (like figure, and data)  for legend, with AA always on, and just copy it on end render end.
render each legend element on separate small bitmap with AA disabled, and copy it on dataBitmap.
disable lowQualityWhileDragging by default

Making a standalone legend bitmap is an interesting idea and one I considered before. This would make it easy to render the legend with independent settings from the rest of the graph, but there may be another advantage:
There may be an advantage to doing this - consider applications which want may desire to display the legend outside the graph as as not to cover-up any of the data. It might be nice if they can do something like GetLegendBitmap() to get just the legend as a standalone image. It also wouldn't need to be re-rendered every time the data is rendered. If this is desirable, making the legend its own bitmap could be a new and separate issue.

Legend must be rerender on each Render call, or you should check and call UpdateLegend on each posible point that can change legend, or force user to make it manually.

It also wouldn't need to be re-rendered every time the data is rendered.

Legend must be rerender on each Render call, or you should check and call UpdateLegend on each posible point that can change legend, or force user to make it manually.

My thinking is that if users Legend(false) so no legend is drawn on the graph, then use GetLegendBitmap() to get the legend Bitmap and place it somewhere in their application manually, they can be expected to call GetLegendBitmap() again when their application does something that would change how the legend looks.

@StendProg has improved this behavior (#93) and I just added quality settings to the menu (pictured). This issue is probably good for now, and we can revisit exporting legend as its own bitmap in the future (there's a lot of other stuff going on currently)


fix: add missing type definitions

Summary

I've run into some Typescript errors while using the Manual Initialization in a separate .js files, for example missing property definitions on the CmsCollection type. I've added the ones I used and the ones I have found described in the docs.
Test plan

Ran yarn type-check and yarn test without any errors.

Thanks again @andrewszucs!


No way to mock different response for the same request

I have a scenario where there is a page which loads a default table which has two rows (I am able to mock this request - Lets say request1).So the request1 is going to return with 2 rows. I can add more rows to this table by clicking on "Add data to table" button and when i do that the default table reloads with the newly added rows (lets say i added 2 new rows). In this case the same request (request 1) is called and it should return with a total of 4 rows (2 default and 2 newly added). There is no way to mock this behviour as of now.

Hi @arunbcodes . That's exactly right. It's not a common pattern to have API calls return different values with exactly the same parameters. Do you have other options? Perhaps, moving some of the state to the client.
Good luck!


Observable.forkJoin is not calling subscribe when using ReplaySubject

RxJS version:
5.0.0-beta.6
Code to reproduce:
var subject = new Rx.ReplaySubject(1);
subject.onNext('c');

/* Using observables and Promises */
var source = Rx.Observable.forkJoin(
    Rx.Observable.return(42),
    subject,
);

var subscription = source.subscribe(
  x => console.log(`onNext: ${x}`),
  e => console.log(`onError: ${e}`),
  () => console.log('onCompleted'));

Expected behavior:
Output on console
Actual behavior:
Nothing happen
Additional information:

It's working, I just forgot to call subjet.complete(); sorry for the noise


Left mouse button input delay on macOS 14.1

Bevy version
https://github.com/bevyengine/bevy/commit/83a358bf3396368a1778bab5e34ec828aa261168
What you did
Running the mouse_grab example in this repository shows a noticeable delay (a few 100ms or so) between when I click the LMB (using an external, Logitech bluetooth mouse) and when the cursor is hidden (as per the example code).
When I change the example to use MouseButton::Right (or anything other than Left), the delay is gone.
I initially reported this in the bevy_mod_pickings plugin, as that's where I initially encountered the delay, but while digging deeper, I realized this happened in stock Bevy systems as well, and then noticed there was actually an existing example that shows the same behavior.

Do you have a macOS plugin called Magnet?
If so, try quitting it and see if the problem goes away.

 I do, and it is indeed the cause of this issue. I would have never connected the dots (and I shouldn't have to, this is clearly a bug on their end).
I'll let them know, and I apologize for causing any unnecessary work here.

For future reference, here are related reports, also pointing to Magnet.app (and also BetterTouchTool.app):
https://github.com/bevyengine/bevy/issues/5778
https://github.com/bevyengine/bevy/issues/6075

@JeanMertz glad to help. I was tearing my hair out for two days over same issue.


#3877 Update insert_couplet and destroy_couplet to deal with ordering…

… issues
This seems to work, and I've added more tests, including testing positions after insert_couplet and destroy_couplet.
Once again, as was the case when has_closure_tree was setting positions, many attempts that seemed perfectly reasonable (to me) failed when reparenting children with acts_as_list, meaning one or more child-pairs involved wound up with what seemed to me like incorrect positions given the operations performed.
For insert_couplet the closest I was able to get using 'natural' expected-to-work calls was to have the reparented children with correct positions but the inserted nodes with the same position (or vice versa I think). I could use builtin calls like lead.move_higher to fix things up in that situation without explicitly setting position values, but it would have still seemed strange since you wouldn't expect that call to be needed in the first place. So instead I went with explictly setting positions of both the inserted nodes and their new children. It's more calls than necessary (given the way things currently work), but it seems to work and I don't think it's too many extra database calls for a function that won't see much use (but would welcome other thoughts on that). It may also be more future proof, maybe.
That said, at some point I'd like to dig into why the expected calls are not working the way I'd expect and then maybe revisit this solution. If you'd rather figure things out that way now instead of using the direct position-setting calls, that's fine with me, just let me know.
New-rails-person lessons learned for me so far:

there are more database position-setting calls going on than what appears in my docker compose up output; presumably this is acts_as_list making direct sql calls in addition to using builtin rails methods: that those direct sql calls don't show up in the output (I'd welcome corrections here). Lesson being you can't always just watch that output to figure out what's going on.
you may need to reload your ActiveRecord objects after behind-the-scenes updates; otherwise things like updating position may fail if the actual position is different than what your outdated object thinks it is.
(Don't expect the docs to always be accurate (I already knew that :).)

@mjy, since there are more tests now and it's been a number of years, I went ahead and disregarded your warning from mx this time:
https://github.com/mx3/mx/blob/e63b72db1e3cfd3f448430fc2bc07214288ab925/app/models/clave.rb#L110
I added grandkids to one of the destroy_couplet tests to try to test that warning a little more deeply and things are looking good, but if you remember any explicit things worth adding for that scenario, or you think it's wiser to do that destroy the mx way, please let me know!

Codecov Report
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app/models/lead.rb
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Additional details and impacted files
@@               Coverage Diff                @@
##           development    #3879       +/-   ##
================================================
- Coverage        84.65%   58.05%   -26.61%
================================================
  Files             2117     1634      -483
  Lines            78344    50943    -27401
================================================
- Hits             66326    29577    -36749
- Misses           12018    21366     +9348

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Here's a version that works as expected without setting any explicit positions:
c, d = Lead.transaction do
  if children_present
    a.update!(parent: nil)
    b.update!(parent: nil)
  end
  c = children.create!(text: left_text)
  d = children.create!(text: right_text)
  if children_present
    new_parent = current_node_is_left_child ? c : d
    a.update!(parent: new_parent)
    b.update!(parent: new_parent)
  end
  [c, d]
end

Still interested in looking into why more direct versions fail.

Better, maybe optimal:
c, d = Lead.transaction do
  c = children.create!(text: left_text)
  d = children.create!(text: right_text)
  if children_present
    new_parent = current_node_is_left_child ? c : d
    a.update!(parent: new_parent)
    b.reload
    b.update!(parent: new_parent)
  end
  [c, d]
end

The b.reload is necessary here.
Before c and d are added to the same parent as a and b, a and b have positions 1 and 2 respectively. When c and d are added then a, b, c, d have positions 1, 2, 3, 4 respectively. When a gets reparented it's given position 1 (because it's the first child of new_parent) and b, c, d are shifted down to positions 1, 2, 3. That all sounds good.
A theory: if b doesn't get reloaded it still thinks it has position 2, so when you do the update to reparent it as the second child of new_parent, the part of the code that decides if it needs to be given a new position thinks it's already set with position 2, when in reality in the database it actually has position 1, which is wrong.


Crash when placing AE2s Vibrant Quartz Glass around the infusion alter.

crash-2018-09-04_22.17.22-client.txt
crash-2018-09-04_22.43.25-client.txt
Possible do to the checks for instability correction items around it.

The first crash report is useless. The client crashed when it's already crashing, yielding gibberish.

The seconds crash report indicate this happens on client side not server side, thus completed not related to TC6. As a side note, the stack trace indicates an extremely rare situation that involve like a billion mod rendering something inside something inside something inside something inside ... Try remove CTM or FoamFix to see if it works. If not, sorry, I have no good idea.

Please try and use gist or hastebin for crashes as it will be easier for others to view the crash logs then to download the file and have to delete is after.


Adding ASP.NET Core 2.0 Web API Simple Sample

Adding Microsoft.Bot.Sample.AspNetCore.Simple project and update the Microsoft.Bot.Connector.AspNetCore for ASP.NET Core 2.0. This sample is using ASP.NET Core 2.0 Web API and similar to the BotApplication template.
The sample is working with channels and I made the blog 3 days ago:
https://www.rizamarhaban.com/2017/11/27/creating-chat-bot-using-asp-net-core-2-web-api-microsoft-bot-framework/

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.:x: rizamarhaban sign nowYou have signed the CLA already but the status is still pending? Let us recheck it.

@rizamarhaban Thank you for your contribution.  We have already merged in samples that include one that is almost identical to yours.  We have also just released new .net packages that make the bot framework compatible with .NET core.


Bump cookstyle and foodcritic deps

We need a release of ChefDK in current that has the latest cookstyle and foodcritic. Right now if someone does the right thing with amazon for Chef 13, then travis goes red since old Foodcritic blows up.
Signed-off-by: Tim Smith<EMAIL_ADDRESS>

This will need to get rebased once #1226 is merged which should fix the foodcritic failure


BUG: scipy.optimize.ridder() does not obey absolute tolerance

Describe your issue.
Hi,
I would like to use scipy.optimize.ridder() for a function of me.
I set the absolute tolerance to 1. and expect that the resulting function call would be in the range [-1.:+1.].
When looking at the second call to ridder(), I can see 5.000065000084806 as a result.
Did I make a mistake when calling ridder() or is something wrong with the result of it?
Thanks in advance!
Reproducing Code Example
import scipy.optimize as sco
import numpy as np
# import scipy

import sys, scipy, numpy;

print(scipy.__version__, numpy.__version__, sys.version_info)

amountOfCalls = 0

def myFunc(x):
    global amountOfCalls
    amountOfCalls += 1
    y = x ** 3 + x - 5
    return y

for xtol in [0.00001, 1., ]:
    amountOfCalls = 0
    xValue, root1_result = sco.ridder(myFunc, -1000, 1000, xtol=xtol, full_output=True, disp=True, )
    print('atol:', xtol, 'nullvalue:', myFunc(xValue), 'xValue:', xValue, 'amountOfCalls:', amountOfCalls)  # , ' '.join(str(root1_result).split('\n')))

Error message
1.2.2 1.17.0 sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)
atol: 1e-05 nullvalue: 3.9394811365944804e-05 xValue: 1.515985217781899 amountOfCalls: 31
atol: 1.0 nullvalue: 5.000065000084806 xValue: 2.000004999994985 amountOfCalls: 13

SciPy/NumPy/Python version information
1.2.2 1.17.0 sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)

The ridder doc says xtol is

The computed root x0 will satisfy np.allclose(x, x0, atol=xtol, rtol=rtol), where x is the exact root. The parameter must be nonnegative.

So, xtol means the tolerance for x, not for f(x).
In your example, the root is 1.5, the range is [0.5, 2.5]. The solution 2.0 is included in the range.

I believe we can close the issue since it's more of a user question than a bug then.

Hi,
I know that ridder is really an old, robust, and commonly used algorithm.
But I wonder why the users do not need what I assumed to be case.
From a user perspective: doesn't it make sense to look for the max absolute deviation from f(x) instead  from x?

AFAIK, for all our optimizers, the tolerance refers to the optimization variable, x.

Actually some minimize solvers have fatol option to set absolute error in f(x) between iterations that is acceptable for convergence.
But I'm not sure it is reasonable for root-finding solvers or not.

Actually some minimize solvers have fatol option to set absolute error in f(x) between iterations that is acceptable for convergence.
But I'm not sure it is reasonable for root-finding solvers or not.

Indeed. In general it would be good to uniformize the way we want stopping criterion to function. We have per-function and per-iteration criteria and under this we have criteria on the input and solution. We can think about how to design this in a sensible way. I would not just add the feature without discussion.

I would not just add the feature without discussion.
I agree. I updated the issue title. Let's leave it and hear other opinions.

In gh-6379, I've suggested adding relative and absolute tolerances on x and f(x) for all scalar root finders. For other possibilities, we offer a callback interface.

Let's close this to keep track of a single issue: gh-6379 which has this info.


Added db close call upon exit

Codecov Report

Merging #14 into master will decrease coverage by 0.5%.
The diff coverage is 0%.

@@            Coverage Diff             @@
##           master      #14      +/-   ##
==========================================
- Coverage   38.53%   38.03%   -0.51%
==========================================
  Files           5        5
  Lines         301      305       +4
==========================================
  Hits          116      116
- Misses        181      185       +4
  Partials        4        4

Impacted Files
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Add $transaction_settlement_enable property

Description
Fix deprecation warning.
Deprecated: Creation of dynamic property Paytrail\WooCommercePaymentGateway\Gateway::$transaction_settlement_enable is deprecated in paytrail-for-woocommerce/src/Gateway.php on line 180

Please start using PHP 8.3 for testing your code.

This was fixed in 2.2.0


Updated StructureMap to version <IP_ADDRESS>

Installing the prerelease packages from NuGet automatically pulls in the
latest stable version of StructureMap. However, the ShortBus.StructureMap
package is built against an old version of StructureMap (2.6.3). This
commit updates to the current latest stable release of StructureMap and
updates the tests and examples by removing deprecated features.
Signed-off-by: Jos van der Til<EMAIL_ADDRESS>

@jvandertil thanks for that! :thumbsup:
I'm trying to set up the StructureMap for ShorBus in Web API 2 application and can't get this working :(
Do you know any examples I could investigate?
Thanks,
Marek

I'd suggest you take a look at MediatR from jbogard. Is a bit more up to date.
Op 17 feb. 2015 om 17:20 heeft Marek Stachura <notifications<EMAIL_ADDRESS>het volgende geschreven:
@jvandertilhttps://github.com/jvandertil thanks for that! [:thumbsup:]
I'm trying to set up the StructureMap for ShorBus in Web API 2 application and can't get this working :(
Do you know any examples I could investigate?
Thanks,
Marek

Reply to this email directly or view it on GitHubhttps://github.com/mhinze/ShortBus/pull/35#issuecomment-74695802.


Dead link in https://tfhub.dev/deepmind/mmt

Hi
I think there is a dead link in https://tfhub.dev/deepmind/mmt/architecture_heads6-64/1 and all similar link https://tfhub.dev/deepmind/mmt/* for colab link

_Overview
Code given something is wrong with newline?

and variable name  is it m or model?

DeepMind folks are still in the process to publish a colab at this location.
On Sat, Nov 27, 2021 at 12:16 PM LaurentBerger @.***>
wrote:

Hi
I think there is a dead link in
https://tfhub.dev/deepmind/mmt/architecture_heads6-64/1 and all similar
link https://tfhub.dev/deepmind/mmt/* for colab link
[image: image]
https://user-images.githubusercontent.com/3591626/143720954-9ad93a73-ce9d-481d-b057-5ec39dece9e6.png
_Overview
Code given something is wrong with newline?
[image: image]
https://user-images.githubusercontent.com/3591626/143720984-ebe3e2ff-88d4-4c22-ab8d-fb2d7c8ddf2f.png
and variable name is it m or model?
[image: image]
https://user-images.githubusercontent.com/3591626/143721003-41fe606e-1da1-4eef-8a4f-d4cee5ab99f5.png
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https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675
or Android
https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

FYI the repo is now live :) https://github.com/deepmind/multimodal_transformers


Add template lint script

This will catch templates like this:
{% load core_tags core_tags %}

And also this:
{% load core_tags %}

core_tags not needed here

@alexmuller do you think we could pop template_lint.py in molo.core so that it's more reusable?

That is technically a much better idea but will need a couple of tweaks, please hold the line.
I'm kind of surprised I can't find a better one on PyPI that someone else has written, ideally I'd like to publish it there but can't be bothered / too much work right now.

Can we get this merged in? There are quite a few template errors on the develop branch which I've fixed in #711, for example:

templates/springster/core/tags/load_tags_for_homepage.html contains endblock but no block
templates/springster/core/tags/nav_tags_section_listing_homepage.html contains image tag which isn't loaded
templates/springster/patterns/components/article-page/sp_variations/main-article-page.html contains bbm_share_url tag which isn't loaded
templates/springster/registration/password_reset_done.html contains pageurl tag which isn't loaded

👍 wtih


IDL mismatches spec for query()

The IDL says that query() returns a FontIterator. However the spec algorithm for query() says that it returns a Promise<FontIterator>.

Hmm, the code examples seem to treat it as if it returns a FontIterator... but I'm not sure how you'd do the algorithm that way. I guess you could move all the asynchronous permission-checking stuff into the first invocation of "get the next iterator result"?

This also confused me. Is there any reason this has to be an async iterator to begin with? I realize we don't want to load the individual fonts, but it seems gathering all the relevant font names + pointers to data can be done in a single request?

I'd also like to see justification for this being an async iterator.
However, if there's good reason, I don't see an issue with the permission checking stuff happening on iteration. It's kinda nice that it can happen per iteration, as it can reject if permission is revoked.
https://github.com/WICG/local-font-access/issues/27 suggests we might want options to query(), or a method like matchAll(), so it might still make sense as a method rather than a property.

Did some thinking around the async iterator stuff in https://github.com/WICG/local-font-access/issues/51


Adding delay

FIX previous PR: int to float (my bad)
Sometime, servers where we can do requests have limit of requests per second. So adding a delay avoids this problem.
I saw in the package that there is a :param delay. Maybe you're already working on it.
This addition of delay is due to the fact that I use an API where we have an easily reachable limit. Usually 0.25 seconds is enough

Hey, does this have any advantages over adding a delay to the actual search function or adding some other logic to limit requests? Adding the delay at this level would cause a delayed load of the whole component and the page aswell.


feat(katana): L1 initialization command

Initial implementation of the katana init subcommand for doing L1 (settlement layer) initialization. It sets up the settlement layer with the necessary contracts and configurations to properly enable a rollup.
The idea is that these configurations are stored in a file that can later be used by katana. Something like katana --chain path/to/config/file. I'm aware that katana already has a similarly named CLI argument, --chain-id. Perhaps, the argument can be replaced with the new one while still preserving its original behaviour. Meaning the later renamed argument, --chain, will accept either a path to the config file, or, the chain id value (currently accepted by --chain-id).
Currently, this is the only thing that are being initialized:

Settlement contract ( https://github.com/cartridge-gg/piltover )

Declare and deploy
Initialize with correct program and config hash

We don't yet initialize a bridge contract, for bridging L1 assets to the new chain. That is a necessary component, but we shall ignore that for now. So, some of the values in the generated config file are mocked.

UPDATE: added a gif for usage demo


Add a simple example of using the InitRelay into the installation instructions.

Also need to add a step four to the instructions that talks about setting up the sandbox with a port number.


ciscoPacketTracer: move to by-name

Description of changes

Follow the new standard of using pkgs/by-name for ciscoPacketTracer
8.2.1 is still the latest version

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
23.11 Release Notes (or backporting 23.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

This pull request has been mentioned on NixOS Discourse. There might be relevant details there:
https://discourse.nixos.org/t/prs-ready-for-review/3032/2796

I am going to close this and migrate all my packages at once in another PR


TreeItem is too slow

VSCode Version: 1.25.1
OS Version: 10.13.5

I've added the following view to Todo+:

I've been benchmarking it against magento2, which contains about 30k files and 2M lines of code. If the user has ag installed in his system the process of finding those todos is quite fast, its takes about 4s to do that on my laptop.
Quite surprisingly though the bulk of the time is spent on these lines, which are executed about 1k times, since the extension creates about 1k TreeItems under these circumstances:

Adding the tooltips requires about 500 extra milliseconds, I would expect that number to be near 0ms, I see basically nothing going on here that can justify that number.

Adding the commands requires about 4 extra seconds. 4 seconds for doing what? Creating those objects should be almost free, and until those TreeItems get clicked they don't even change anything as far as the user is concerned, I think.

Adding the icons requires about 14 extra seconds.  There are less than 10 different images loaded, but for some reason loading them a few hundred times is that slow.

@fabiospampinato Is it possible to share the performance profile you created to come up with above numbers?

I don't know what you mean exactly by "performance profile", but I got the numbers this way:

Have Todo+ installed, I think it was v4.0.5, but any more recent release should have basically identical performance.
Download magento2 and open it in the VSC window that gets created for debugging extensions.
Wrap this line in console.time('foo') and console.timeEnd('foo'), this is where the 4s figure for searching the files comes from
Put a if ( !item ) debugger; here, this gets hit right before creating any TreeItem object.
Start a stopwatch and continue debugging at the same time.
Stop the stopwatch when you see the view populated.

Now:

If these lines are commented out the whole thing takes an amount of time that I don't remember, let's call it X, to render the view.
If only this line is not commented out it takes about X + 500ms to do the work.
If only these lines are not commented out it takes about X + 4s to do the work.
If only these lines are not commented out it takes about X + 14s to do the work.

You might think that the problem here is actually the code that runs inside Item#setTypeIcon, but that's actually optimized enough to not really matter, and while benchmarking this thing I've replaced the body of the function with something like return '/path/to/my/icon.svg', so that's not the problem.

I've taken these measurements multiple times just to be sure. All those numbers aren't super precise of course. I've run the code on an early-2015 MBP.
I hope this answers your question, let me know if you need anything else.

Thanks for detailed explanation. I got it now.
Will investigate.

I can see a small slowdown when a command and tooltip are included, but it's 10s of milliseconds, and I don't see an obvious way to bring this down. Since this issue was filed, we've swapped out the internal tree that the extension tree is built on top of. I expect this new tree is faster.


Library is breaking safari-features (e.g. web-push)

In this line the variable safari is set, which will also override window.safari :
https://github.com/marrio-h/universal-parallax/blob/257276d064c2150958af696c47c27286a593e6d3/dist/universal-parallax.js#L13
However, this is already set by safari itself and contains important objects like window.safari.pushNotification.
This prevents/breaks web-push functionality on safari.
These checks should be scoped locally anyways to prevent polluting the global namespace.

I opened the test/demo files on my Mac, and on Mac every browser I have fully worked, except Safari  show any images, it did show the shaded containers with the info and that was it....
The Safari code line in all .JS files needs to be updated.

UPDATE!
For me this issue has been resolved... I decided to do further testing, so I uploaded the demo and dist files to a test folder on one of my websites. Guess what loaded in Safari? - yeah the demo index file. I even tested the link on an iOS device and it loaded. The Parallax effect was fully functional.  With my test I even created a video at the top and that was fully working for the most part, though I have to redo the code for the video to make sure it takes full screen even for mobile and to remove padding below it.
With Safari on Mac OS, if you do not have the Developer option turned on you will not see the demo live or function.. With my test the server version was fully functional before turning the Developer mode on, once it was activated then the local demo files were also working.  On my Mac I opened the local files with VS Code and then used LiveServer to view the content in Safari to see if the issue was still there and it was not; this prompted me to do an actually test from a live website and it worked..

This issue is not about the functionality of the library, it's working fine as it is.
It's about the fact that it's breaking other functions that the browser provides (like webpush).
Which can be prevented by not polluting the safari property.


Parameter dataset is not always required

The parameter "dataset" should not always be required. It is required in linking mode, but in spot or classify mode it is not required. Right now it is implemented as always required.

fixed.


fix: nonce correctly determined

Description

Refresh signer's access key  when getting new nonce

Type of change

[ ] 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] 🏗 Build configuration (CI configuration, scaffolding etc.)
[x] 🐛 Bug fix (non-breaking change which fixes an issue)
[ ] 📝 Documentation update(s)
[ ] 📦 Dependency update(s)
[ ] 👩🏽💻 Improve developer experience
[ ] ⚡ Improve performance
[ ] ✨ New feature (non-breaking change which adds functionality)
[ ] ♻ Refactor
[ ] ⏪ Revert changes
[ ] 🧪 New tests or updates to existing tests

:tada: This PR is included in version 1.0.1-beta.2 :tada:
The release is available on:

npm package (@beta dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

:tada: This PR is included in version 1.1.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


EN_getpatternid() valid range of index

Hey folks, I've got a question. The issue of an ID for pattern index 0 (which is the default pattern always, I believe) is driving me just a little crazy at the moment. Should EN_getpatternid() return an empty character string if called with pattern index 0? Right now in that case it returns a 205 error code.
I know that pattern index 0 does not really have an ID, being the "default pattern" (right?). But programmatically this can create awkward edge cases if a pattern ID doesn't exist.
Also, related, note that EN_adddemand() will currently set the base demand for pattern index 0, if it is called with a null or empty demand pattern ID. So it seems like a null or empty demand pattern ID should be allowed both ways for pattern index 0.
If someone can confirm that this sounds right, I'll submit a fix.

The "default pattern" feature only appears as an option in an INP file, which the parser reads and then applies to all nodal demands with no assigned pattern after the file has been processed. After that it doesn't exist as a parameter that can be retrieved or assigned via a toolkit function. That means that it won't show up in the [OPTIONS] section of an INP file written by EN_saveinpfile(). As with other objects, Pattern[0] remains empty because of EPANET's 1's indexing convention.
The use of the default pattern feature in the INP file has some quirks. If a default pattern was named but doesn't exist then no error or warning message is issued and no action in setting demand patterns is taken. If no default pattern ID is specified but the INP defines a pattern named "1" then that pattern is applied as the default after the file is read (to maintain compatibility with older EPANET versions).

Thank you Lew. So, to confirm my understanding, a node that appears twice in [DEMANDS] as follows:
node1 1.0
node1 10.0 P1
will have a demand with two indices [1,2] where pattern P1 will be applied to base demand 10.0 and this will be added to base demand 1.0 multiplied by the default demand pattern if one is specified in [OPTIONS], else pattern ID "1" if that pattern is specified in [PATTERNS], else by a constant multiplier equal to the global demand multiplier.
Given this, what should the following return for the pattern index?
EN_getnodeindex(p, "node1", &index)
EN_getdemandpattern(p, index, 1, &patIndex)
Currently this returns 0 for the pattern index in the case where there is no default pattern. Maybe this should only return a pattern index in the case where a default pattern is defined, and return an error otherwise? This is the source of my various errors, leading me to mistakenly use a zero pattern index. Or maybe we should always define a default pattern ID "1" (to be overwritten if one is specified in the input) with a single value of 1.0? If we did the latter, it wouldn't have to affect any computations, but it could make doing math with patterns a little more reliable for this edge case.
Interested in your thoughts. Thanks!

I have to retract my statement about there not being a pattern 0. There actually is one used as a "dummy" pattern with a single
multiplier of 1.0. It gets created from calling the netsize() function in both EN_open() (if project data is read from an INP file) and EN_init() (if a project is built using API calls (see lines 73 - 81 in input2.c). When an input file is used, as demands are read in from either the [JUNCTIONS] or [DEMANDS] sections they are assigned pattern 0 if no pattern ID is supplied in the input line. As stated previously, if a Default Pattern was supplied in the file (either explicitly or implicitly by defining a pattern named "1") then all of these demands with pattern index of 0 will be assigned the index of the default pattern. When the function EN_addnode() is used to create a junction node, the function adddemand() is called that adds a single demand of base flow 0 and pattern index of 0.
So bottom line, when EN_getdemandpattern() returns a pattern index of 0 it just means that the demand in question has no user-supplied pattern assigned to it. In other words pattern 0 is just a flag for "no assigned pattern". Also note that:

none of the pattern-related API functions are allowed to retrieve or modify patterns whose index is 0
EN_setdemandpattern() will accept a pattern index of 0 as an argument thus allowing you to remove a demand's pattern
if you need to involve an object's pattern in some calculation you need to first check if its index is 0 and if so treat it as a pattern with a single multiplier of 1.

Very clear Lew - thanks again. I think we'd both like this to be simpler (so you wouldn't have to explain this to me over a weekend), but we're bound by historical precedent.
Based on this, I should maybe revert my previous one-character bug fix, which let getaveragepatternvalue() return a value for pattern index 0. It's not wrong (it returns 1.0), because it's an exception to the idea that the programmer should know about pattern index 0 and deal with it, outside of the toolkit. It's super minor, but if you have an opinion, let me know.

If you think that having a usable pattern 0 is too confusing then we can do away with it by:

removing line 59 and lines 72 - 81 in input2.c and changing line 82 to parser->MaxPats = 0 .
changing lines 486 - 489 in hydraul.c to:

  double f = 1.0;
  j = demand->Pat;
  if (j > 0) then
  {
      k = p % (long)net->Pattern[j].Length;
      f = net->Pattern[j].F[k];
  }
  djunc = (demand->Base) * f * hyd->Dmult;

Now a pattern index of 0 would be consistent with how the 0-index is used by Curves to signify that no curve has been assigned to some object (e.g., a pump's efficiency curve). If this were implemented then we'd have to retract your bug fix.

Lew, just to close this out, I guess I'm perfectly fine with having index=0 being a usable pattern.