Atari testing

Was this ever tested on hard exploration games on Atari like Montezuma's Revenge or Pitfall? ?, If so, I'm curious to know how it performed.

Thank you for your interest in our work!
We preliminarily tested DEIR on Montezuma's Revenge and found it capable of exploring more efficiently there as well (cumulatively visits more distinct states, see definition in appendix A.7 of our ArXiv preprint).
We're working on tuning hyperparameters for Atari games and will provide more results and discussions once complete.

A.7 Detailed Experimental Results in MiniGrid.
I don't see anything relating to Montezuma's Revenge. Maybe you can provide a link to this paper.

Sorry for my unclear explanation.
In Appendix A.7 of our ArXiv preprint, we defined two metrics for evaluating the exploration efficiency for an exploration method, using the number of distinct states visited per timestep by the agent.
At present, we have preliminarily tested and observed better exploration efficiency (based on the above two metrics) for DEIR in Montezuma's Revenge. We'll provide those results after our Atari experiments are complete.

Okay, well thank you for taking the time to respond.
I have questions about the preliminary testing you did on Montezuma's revenge. What score did it reach and at what time step did it reach these values? Did you also test it on dense reward games and compared them to NGU and RND? Can you provide any graphs for them?

Thanks for your patience. We preliminarily trained DEIR, RND, NGU, and PPO agents in Montezuma's Revenge with 8 runs for each method. As shown in the below figure, DEIR agents approached an average return of 2500 using about 10 million frames. To get a higher score, the agent must learn to pass a level where only limited observational novelty is available and the agent's life can be easily lost. In such levels, novelty-driven methods (including RND, NGU, and DEIR) still require a considerable number of samples and a well-tuned training scheme.

For your reference, we also attached a sample video of a DEIR agent that explores in the game after 10M training frames. The above-mentioned hard game level can be found in the last five seconds of the sample video. We also demonstrate the intrinsic reward generated at each step in the video. Following RND's practice, we only generate non-negative intrinsic rewards in this experiment to encourage exploration and prevent the agent from losing its life and terminating the episode too shortly.
We did not deliberately test on games with dense rewards, since it is not the primary goal of this work. However, compared with MiniGrid games and Montezuma's Revenge, ProcGen games generally have much denser extrinsic rewards. You may refer to the ProcGen results reported in our paper (Figure 4). According to my experience, intrinsic exploration rewards are usually unnecessary when the environment is already densely rewarded. However, when rewards given by the environment are almost irrelevant to the novelty of observation, novelty-driven exploration methods may still help the agent to learn a better policy faster to some extent.

Thanks for your patience. We preliminarily trained DEIR, RND, NGU, and PPO agents in Montezuma's Revenge with 8 runs for each method. As shown in the below figure, DEIR agents approached an average return of 2500 using about 10 million frames. To get a higher score, the agent must learn to pass a level where only limited observational novelty is available and the agent's life can be easily lost. In such levels, novelty-driven methods (including RND, NGU, and DEIR) still require a considerable number of samples and a well-tuned training scheme.

For your reference, we also attached a sample video of a DEIR agent that explores in the game after 10M training frames. The above-mentioned hard game level can be found in the last five seconds of the sample video. We also demonstrate the intrinsic reward generated at each step in the video. Following RND's practice, we only generate non-negative intrinsic rewards in this experiment to encourage exploration and prevent the agent from losing its life and terminating the episode too shortly.
We did not deliberately test on games with dense rewards, since it is not the primary goal of this work. However, compared with MiniGrid games and Montezuma's Revenge, ProcGen games generally have much denser extrinsic rewards. You may refer to the ProcGen results reported in our paper (Figure 4). According to my experience, intrinsic exploration rewards are usually unnecessary when the environment is already densely rewarded. However, when rewards given by the environment are almost irrelevant to the novelty of observation, novelty-driven exploration methods may still help the agent to learn a better policy faster to some extent.

First of all, thank you for taking the time to respond and the effort you took for the visuals. A couple of things on this graph, How did NovelID perform on this task?
Also, it's a bit strange to me that PPO is doing not so bad considering the original PPO paper, it got a low score on Montezuma's Revenge.
Also from your code ngu.py, line 149 (ngu_lifelong_rewards = ngu_rnd_error + 1), I didn't get why you added 1 and not just leave it as "ngu_rnd_error", but I do understand this part "lifelong_reward = min(max(ngu_lifelong_rewards[env_id], 1.0), L)"
I am trying to implement r2d2, and I will certainly try to include your great work in it hopefully, it performs better in these environments.

Thanks for the questions. I am very glad to provide more information for your reference. We haven't tested NovelD and ICM on Montezuma's Revenge, but we'll schedule a quick test for them and update the results here in several days.
Our PPO implementation is based on Stable Baselines 3, which introduced several techniques to help PPO perform better while being more stable, including fine-tuned hyperparameters (e.g., using 1e-5 for the optimizer's epsilon), generalized advantage estimation (GAE) and per-batch advantage normalization (code). Also, we applied Layer Normalization to all CNN layers, and used GRU for all agents in our Atari experiments (see more discussions in A.4 Hyperparameters of our arXiv preprint). All of the above may collectively cause PPO agents to give different results, and our purpose here is to use an efficient but easy-to-implement baseline to save computing and implementation costs for experiments as much as possible.
We added a constant 1 to normalized ngu_rnd_error (ngu.py, L149) following the original definition given in the paper of NGU (see paragraph "Integrating life-long curiosity", page 4). It was said that:

We then define the modulator α_t as a normalized mean squared error, as done in Burda et al. (2018b): α_t = 1 + (err(x_t) - μ_e) / σ_e, where σ_e and μ_e are running standard deviation and mean for err(x_t).

The exact reason for adding 1 was not explicitly explained in NGU's paper. According to our understanding, ngu_rnd_error (i.e.,x_t) after normalization (ngu.py, L139-L147) shall follow a normal distribution with a mean value of 0, and then the mean of 1 + ngu_rnd_error is roughly 1. Given that, lifelong_reward = min(max(ngu_lifelong_rewards[env_id], 1.0), L) can be seen as mapping all positive ngu_rnd_errors to rewards greater than 1 but smaller than L=5 while mapping all non-positive errors to exact 1.

Again, thanks for your patience. We quickly ran 8 runs for NovelD and ICM in Montezuma's Revenge, using the same hyperparameters used in our previous preliminary experiments. Their results are merged with those of the other methods and shown in the figure below.

Thank you again for these charts, I know they aren't easy to produce since training can be quite time-consuming.
I tried to replicate some of your results on minigrid and pretty much got the same results. Any reason why  NGU or DEIR only goes up to 2500 and not beyond it?? Is this mostly because this is an on-policy method without a  replay buffer?

Thank you for taking time to replicate our experiments on MiniGrid, and we are happy to know it worked!
With sufficient training samples and an appropriate training scheme, we believe that both NGU and DEIR can collect more than 2500 scores in Montezuma's Revenge. An on-policy method without a replay buffer may be an important reason why more samples are required, but hard to say it is the only one. Considering that the original implementations of RND and NovelD are all based on PPO, fine-tuning other hyperparameters and schemes for training should also help (e.g., learning rates, number of frames per roll-out, normalization schemes of intrinsic rewards and advantages, etc.). Although we tried to make each method perform as well as possible in our preliminary experiments, please note that we currently don't have enough time to ensure our hyperparameters and training schemes are the best.

Thank you for taking time to replicate our experiments on MiniGrid, and we are happy to know it worked!
With sufficient training samples and an appropriate training scheme, we believe that both NGU and DEIR can collect more than 2500 scores in Montezuma's Revenge. An on-policy method without a replay buffer may be an important reason why more samples are required, but hard to say it is the only one. Considering that the original implementations of RND and NovelD are all based on PPO and have reached 10000+ scores, fine-tuning other hyperparameters and schemes for training should also help (e.g., learning rates, number of frames per roll-out, normalization schemes of intrinsic rewards and advantages, etc.). Although we tried to make each method perform as well as possible in our preliminary experiments, please note that we currently don't have enough time to ensure our hyperparameters and training schemes are the best.

Thank you for your response, I had to change line 181 (base_model.py) inside the "_get_rnd_embeddings" function.
from
                    gru_mems = self._get_rnn_embeddings(mems, cnn_embs, self.policy_rnns)
to
                    gru_mems = self._get_rnn_embeddings(mems, cnn_embs, self.model_rnns)
Can you please verify if this is correct?
Also, if able, can you share the atari training portion as well?

If you changed L181, you may also need to change L178-179.
In our current implementation, line 181 of base_model.py shall only be reached when options rnd_use_policy_emb (L177) and use_model_rnn (L180) are enabled.

When rnd_use_policy_emb is on, RND reuses (but does not update) the embeddings learned by the policy network's CNN as the inputs of its RNN/MLP.
When  use_model_rnn is on, depending on the status of rnd_use_policy_emb, RND either learns a separate RNN or reuses the policy's RNN.

In case you wish to train separate CNN and RNN for RND agents, you can simply specify --rnd_use_policy_emb=0 --use_model_rnn=1 in your command. Please let me know if I misunderstood your purpose.
As for the Atari training portion, we implemented and tested it in a private repository. We'll try to resolve conflicts and release it sooner than later, but it may take some time due to other priorities. Thanks for your understanding.

Hi, it is a very interesting work. I have attempted Noveld on env MultiRoom-N6. When rnd_use_policy_emb and use_model_rnn are both 1, there shows: AttributeError: 'NovelDModel' object has no attribute 'policy_rnns'. When I tried --rnd_use_policy_emb=0 --use_model_rnn=1, the obtained return is only about 0.2. Can you give some instructions about reproduction for NovelD?

@AnneZhu1020 Thank you for your interest and for letting us know the error message! We are sorry that we unexpectedly removed policy_rnns passed into intrinsic reward models (including NovelD) during code refactoring. It has been fixed so you may now reuse the policy's RNN to train RND-based models.
I tested with the following command to train NovelD on my local. NovelD agents reached an average episodic return of 0.606925 using about 0.5 million frames in MultiRoom-N6, with both the policy's CNN and RNN reused. (P.S. Please note that the results may still vary on different devices to some extent.)
PYTHONPATH=./ python3 src/train.py
  --int_rew_source=NovelD  --env_source=minigrid  --game_name=MultiRoom-N6 \
  --rnd_use_policy_emb=1  --use_model_rnn=1 \
  --features_dim=128  --model_features_dim=128  --model_latents_dim=128 \
  --int_rew_coef=3e-2  --rnd_err_norm=0

In the above command,  --rnd_use_policy_emb=1 --use_model_rnn=1 is to reuse the policy's CNN and RNN for NovelD. With those two options enabled, it also means that the number of features input into NovelD's MLP must be equal to the number of features output by the policy's CNN & RNN (otherwise, additional FC layers are needed). Thus, we explicitly specify --features_dim=128 --model_features_dim=128 --model_latents_dim=128 to define the dimension of features.
As for --int_rew_coef=3e-2 --rnd_err_norm=0, they are hyperparameter values taken from Table A1 (in the appendix of our arXiv preprint). Since the default value of each option in train.py is optimized for DEIR, when training other methods, please find and use the corresponding hyperparameter values specified in Table A1. We are sorry for the inconvenience, and will try to create pre-defined config files for all methods later.

Can you provide some of your parameters for Montezuma's Revenge?
Also for a harder exploration game, what parameters should one try for Pitfall!?
It seems like your batch-sizes are quite large, 1024 and n-step; any reason as to why? How did this play in exploration?

@hlsafin Thanks for your questions. For your reference, the following are major options we used for testing DEIR's performance in Montezuma's Revenge:

--game_name=MontezumaRevenge-v5
--atari_max_steps=4500 --atari_gray_scale=1
--atari_img_size=64 --atari_stack_num=1
--episodic_life=0 --frame_skip=8 --use_inter_area_resize=0
--repeat_act_prob=0.0 --atari_clip_rew=1
--num_processes=128 --n_steps=128 --batch_size=512
--learning_rate=3e-5 --model_learning_rate=1e-3
--gamma=0.999 --gae_lambda=0.95
--pg_coef=1.0 --vf_coef=0.5 --ent_coef=1e-2
--clip_range=0.1 --clip_range_vf=0.1
--n_epochs=3 --model_n_epochs=3 --ext_rew_coef=10
--int_rew_coef=1e-3 --int_rew_norm=3 --int_rew_momentum=-1
--int_rew_source=DEIR --use_model_rnn=1
--adv_norm=1 --adv_eps=1e-5 --adv_momentum=0.9
--features_dim=512 --latents_dim=512
--policy_cnn_type=1 --model_cnn_type=1
--model_features_dim=512 --model_latents_dim=512
--policy_cnn_norm=LayerNorm --policy_mlp_norm=NoNorm --policy_gru_norm=NoNorm
--model_cnn_norm=LayerNorm --model_mlp_norm=NoNorm --model_gru_norm=NoNorm
--policy_mlp_layers=1 --model_mlp_layers=1 --gru_layers=1

Since we haven't experimented with Pitfall!, we are sorry that we cannot provide constructive suggestions for you at this moment.
We decided to use a larger mini-batch size when training ProcGen agents mainly because it was suggested by previous studies [Cobbe et al., 2020 and 2021], and we also observed non-trivial performance improvements during training when using a larger mini-batch size (e.g., 2048 sample per batch).

[Cobbe et al., 2020] Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman. Leveraging procedural generation to benchmark reinforcement learning. In International Conference on Machine Learning, pages 2048–2056. PMLR, 2020.
[Cobbe et al., 2021] Karl W. Cobbe, Jacob Hilton, Oleg Klimov, and John Schulman. Phasic policy gradient. In International Conference on Machine Learning, pages 2020–2027. PMLR, 2021.

Thank you, there seems to be a lot of parameter/hyperparameters to choose from. How does one go about picking one over the other for different types of environments? What is your thought process for choosing them? What are the key parameters that play a significant role or a detrimental role in the success of an agent in a given environment?

Thank you for your elaboration, I will take note of this when making further experiments.
I've been trying to do experiments on Solaris and Pitfall! environments with your hyperparameters, however, what I've found to be the most important indicator of learning is the max-step-size. I tried Solaris with 4500 steps, and I learned very good policies in a short amount of time. However, when I took off max_step to default value, which I believe is 108,000, or roughly 30 mins of play. It failed to learn anything! It was even weaker than a regular PPO+GRU. I haven't experimented with NovelID or NGU, but I presume I would get similar results. Exploration of the long-term environment is still a hard problem, is this assessment correct? Care to give any advice to get decent results on this? Or provide some better parameters?


Expected null for Long Delay Invocation?

Hi, thanks for these nice exercises. I'm not sure if I'm misreading the task description for the Long Delay Invocation, but as you write for the no-argument-case: "in which case it should return sum of all arguments passed", I was surprised to get: "Expected result: null". Return result var in solution, line 6?

Hi iiiw,
Thank you for pointing out this issue. I have fixed it in 1.0.10 version. feel free to reach me if you face any issue or you have any suggestion to make it more better.
Thanks & Regards,
Brij Kishor


Fix error message typo.

Fixes #4396 .

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Tests: List of ObjC tests

Purpose: to avoid pollution on the iOS specs spreadsheet and keep record of tests to be removed or rewritten. Current state of the tests.
Legend:

remove ❌
spec don't exist but seems ok ⚠
rewrite ‼
fix ⭕
seems ok ✅

Some of them I couldn't relate to a specific spec because most of them are testing multi aspects. Ex: Checking Channel#subscribe and Channel#unsubscribe on the same test method.

Class ARTFallbackTest

testAllHostsIncludedOnce: ❌ (doesn't make sense)

Class ARTHttpTest

testPingGoogle: ⚠
testNonExistantPath: ⚠ (I think the idea of the test is checking if the 404 code is well handled)

Class ARTLogTest

testLogLevelToError: ❌
testLogLevel: ❌
testLogLevelNone: ❌
testNoCrashWithoutCustomLogger: ❌

The logging tests have been implemented on Swift.
Class ARTRealtimeAttachTest

testAttachOnce RTL4a: ⭕ (incomplete, doesn't make sense, it has a flag but it never changes)
testSkipsFromDetachingToAttaching: ⚠ (invoke channel.attach when it is trying to DETACHING)
testAttachMultipleChannels: ⭕⚠ (doesn't raise any XCTFail)
testDetach RTL5d: ⭕ (doesn't raise any XCTFail)
testDetaching: ✅ (checking state order)
testSkipsFromAttachingToDetaching: ✅ (checking state order)
testDetachingIgnoresDetach RTL5a: ⭕ (incomplete)
testAttachFailsOnFailedConnection RTL4b: ⭕ (incomplete, doesn't force the failed state)
testAttachRestricted RTL4d: ✅
testAttachingChannelFails:  ❌ (the testAttachRestricted already check this)
testAttachedChannelFails:  ❌ (the testAttachRestricted already check this)
testChannelClosesOnClose: ⚠ (there is no Channel#close)
testPresenceEnterRestricted: ‼

Class ARTRealtimeChannelHistoryTest

testHistory RTL10c: ✅
testHistoryBothChannels RTL10d: ⭕ (should have 2 instances of RealtimeClient)
testHistoryForward: ⚠
testHistoryForwardPagination: ⚠
testHistoryBackwardPagination: ⚠
testTimeBackwards: ⚠ (could be better)
testTimeForwards: ⚠ (could be better)
testHistoryFromAttach: ❌ (last tests already covers what this test is covering)

Class ARTRealtimeChannelTest

testAttach: ❌ (already tested with ARTRealtimeAttachTest)
testAttachBeforeConnect: ⭕ (doesn't avoid to attach after it is connected)
testAttachDetach: ⚠
testAttachDetachAttach: ⚠
testSubscribeUnsubscribe: ⚠ (testing 2 specs at the same time)
testSuspendingDetachesChannel: ⚠
testFailingFailsChannel: ⭕ (it doesn't check the SUSPENDED state)
testGetChannels RTS3a: ✅
testGetSameChannelWithParams: ⭕ (has forced strings)
testAttachFails: ⚠ (forced failure)
testDetachFails: ⚠ (forced failure)
testPublishTooManyInArray: ⭕ (have some doubts about [ARTPayload getPayloadArraySizeLimit:2 modify:true])
testClientIdPreserved: ❌ (already implemented in Swift)

Class ARTRealtimeConnectFailTest
❌ Remove all of it because it does nothing.
Class ARTRealtimeConnectTest
❌ Remove all of it because it already exists in Swift.
Class ARTRealtimeCryptoMessageTest

testCBCParser RSL5b: ⚠
testCBCParser256: ✅ (same parser check of testCBCParser)
testCaseByFileContents: ⚠

Class ARTRealtimeCryptoTest

testSendEncodedMessage: ⭕ (cipherParams is now part of ChannelOptions)
testSendEncodedMessageOnExistingChannel: ⭕ (cipherParams is now part of ChannelOptions)

Class ARTRealtimeInitTest
❌ Remove all of it because it already exists in Swift.
Class ARTRealtimeMessageTest

multipleSendName RTL6b: ⭕ (incomplete, add error check)
testSingleSendText: ⚠ (separate subscribe and publish test)
testPublish_10_1000: ⚠
testPublish_20_200: ⚠
testMultipleText_1000_10: ⚠
testEchoMessagesDefault, RTL7f: ✅
testEchoMessagesFalse: ✅ (variation of testEchoMessagesDefault)
testSubscribeAttaches: ⭕ (assign subscribeToStateChanges callback before the subscribe, ensures the channel gets attached when invoking subscribe)
testMessageQueue RTL6c2 (kind of): ⭕ (have some doubts with connectingHappened flag)
testConnectionIdsInMessage RTL6f (kind of): ⭕ (have some doubts with the history call)
testPublishImmediate: ⭕ (assign subscribeToStateChanges and subscribe before eventEmitter.on)
testPublishArray RTL6i: ⭕ (assign subscribe before call publish)
testPublishWithName RTL6i1: ⭕ (assign subscribe before call publish)
testSubscribeToName: ⭕ (have some doubts: multiple tests in one spec) RTL7b

Class ARTRealtimePresenceHistoryTest

testPresenceHistory RSP4a: ✅
testForward RSP4b2: ✅
testSecondChannel: ⚠
testWaitTextBackward: ⚠
testLimitForward: ⚠
testLimitBackward: ⚠
testFromAttach: ⚠ (callback hell)
testPresenceHistoryMultipleClients: ⚠

Class ARTRealtimePresenceTest

testTwoConnections: ⚠ (the 2 instances should receive the message published on the same channel name)
testEnterSimple: ⚠
testEnterAttachesTheChannel RTP8d: ✅
testSubscribeConnects RTP6c: ✅
testUpdateConnects: ⚠
testEnterBeforeConnect: ⚠
testEnterLeaveSimple: ⚠
testEnterEnter: ⚠ (Presence#enter twice, first enter, second update)
testEnterUpdateSimple: ⚠ (Presence#enter and then Presence#update)
testUpdateNull RTP9a: ⭕ (incomplete, check adding data for the present member)
testEnterLeaveWithoutData RTP10a: ✅ (data argument omitted)
testUpdateNoEnter RTP9b: ✅
testEnterAndGet: ⭕ (2 client instances, same channel, both entered to the channel, get presence of channel but order of clients is random)
testEnterNoClientId RTP8c: ⭕ (is checking for an exception but should verify the clientId)
testEnterOnDetached RTP8g: ⭕ (should check for exception)
testEnterOnFailed RTP8g: ⭕ (should check for exception)
testLeaveAndGet: ⚠ (check if the client has been removed when a leave occurs)
testLeaveNoData RTP10a: ✅ (data argument omitted)
testLeaveNoMessage: ⚠
testLeaveWithMessage: ⚠
testLeaveOnDetached RTP10d: ✅
testLeaveOnFailed RTP10d: ✅
testEnterFailsOnError: ⚠
testGetFailsOnDetachedOrFailed: ⚠ (forcing fail)
testEnterClient RTP14d: ✅
testEnterClientIdFailsOnError: ⚠
testWithNoClientIdUpdateLeaveEnterAnotherClient RTP15b: ⭕ (incomplete)
test250ClientsEnter: ⭕ (change limit to 250)
testPresenceMap RTP2: ✅
testLeaveBeforeEnterThrows RTP10d: ✅
testSubscribeToAction: ⭕ (used unsubscribe instead using subscribe)
testSyncResumes: ⚠ (need to run it)
testPresenceNoSideEffects RTP15: ✅
testPresenceWithData: ⚠
testPresenceWithDataOnLeave: ⚠

Class ARTRealtimeRecoverTest

testRecoverDisconnected RTN15d: ‼ (recovering the connection with 2 instances of RealtimeClient but using options.recover = nil. Confusing)
testRecoverFails: ⚠

Class ARTRealtimeResumeTest

testSimple: ⚠ (2 connections, each connected to the same channel. Disconnect and reconnect one of the connections, then use that channel to send and receive the message)
testSimpleDisconnected: ⚠

Class ARTRealtimeTokenTest
❌ Remove all of it because it does nothing.
Class ARTRestAppStatsTest
❌ Remove all of it because it already exists in Swift.
Class ARTRestCapabilityTest

testPublishRestricted: ⭕ (fixed capability)

Class ARTRestChannelHistoryTest

testTimeBackwards: ⚠
testTimeForwards: ⚠
testHistoryForwardPagination: ⚠
testHistoryBackwardPagination: ⚠
testHistoryBackwardDefault: ⚠
testHistoryTwoClients: ⚠
testHistoryLimit RSL2b3: ✅ (check exception for invalid 1001 items)

Class ARTRestChannelPublishTest

testTypesByText RSL1b: ✅ (Channels#publish, the name is implicit)
testPublishArray RSL1a: ⭕ (array of messages, currently returns a single message back instead of 3)
testPublishTooManyInArray: ❌ (don't see any limit for the array of messages)
testPublishUnJsonableType: ⚠ (makes sense for Objective-C)

Class ARTRestCryptoTest

testSendBinary: ⚠ (checking NSData and NSString)
testSendEncodedMessage: ⚠ (fixed string)

Class ARTRestInitTest

testInternetIsUp: ⚠
testInitWithKey: ❌ (already implemented in Swift)
testInitWithNoKey: ❌
testInitWithKeyBad: ❌
testInitWithOptions: ❌
testInitWithOptionsEnvironment: ❌
testGetAuth: ❌
testInitWithOptionsBad: ❌
testRestTimeNoFallbackHost: ⭕ (it doesn't check the fallback host)
testRestTime: ⭕ (Expect local clock and server clock to be synced within 10 seconds - don't expect)
testDefaultAuthType: ❌

Class ARTRestPresenceTest

testPresence: ❌ (incomplete)
testHistory RSP4a: ✅ (using channel persisted:presence_fixtures)
testHistoryDefaultBackwards RSP4b2: ✅ (using test-app-setup.json)
testHistoryDirection RSP4b2: ✅ (using test-app-setup.json)
testPresenceLimit RSP4b3: ✅ (checks exception for up max limit)

Class ARTRestTokenTest

testTokenSimple: ❌ (already implemented in Swift)
testInitWithBadToken: ⚠
testInitWithBorrowedToken: ⚠
testInitWithBorrowedTokenParam: ⚠
testUseTokenAuthForcesToken RSA4: ✅
testClientIdForcesToken RSA4: ✅
testAuthURLForcesToken RSA4: ✅ (doesn't ensures if the transport connects with Token auth)
testTTLDefaultOneHour RSA5: ✅
testTTL RSA5: ✅
testTokenExpiresGetsReissued: ⚠
testSameTokenIsUsed: ⚠
testToken401GetsReissued: ⚠
testReissuedTokenFailReturnsError: ❌ (it modifies the keySecret, forced change)
testUseTokenAuthThrowsWithNoMeansToCreateToken: ❌ (already implemented in Swift)
testExpiredBorrowedTokenErrors: ⚠
testInitWithBorrowedAuthCb: ❌ (doesn't make sense)

Started at 2015-11-11 12:30:20.086
Ended at 2015-11-11 13:47:39.556
Executed 165 tests, with 161 failures (0 unexpected) in 4639.331 (4639.466) seconds

It's failing on parsing test-app-setup.json.

I t's failing on parsing test-app-setup.json
Why  I know that our https://github.com/ably/ably-common/blob/master/test-resources/test-app-setup.json used to have duplicates, see https://github.com/ably/ably-common/commit/873505632e6b21a778e02d19481b66eb67cfaf3a.  Are you sure the client lib is using the latest (if at all of course)?

In response to your issue and analysis of the tests, this is very good, thanks.  I do question your allocation of ⚠ in some cases though. For example, testHistoryForwardPagination does actually demonstrate that paging works, that the API for history requests is available, etc.  I realise it may not map 1-1 with an exact spec, however it is fine to complete the spreadsheet spec with references to more than one test to demonstrate that the functionality is implemented

@mattheworiordan Yes, you right. The ⚠ in some points is dubious. Initially, I tried to map 1-1 but the tests wasn't created that way so I decided to check the aggregation of specs. It is not easy because in some of them I needed to check a lot of specs.

Running the tests
Started at 2015-11-13 07:07:57.727
Ended at 2015-11-13 08:11:39.016
Executed 155 tests, with 58 failures (16 unexpected) in 3821.059 (3821.287) seconds

After #51.


"bfloat16.enabled" needed be specified when training T5

I met following situation when training T5. "ValueError: bfloat16.enabled not found in kwargs. Please specify bfloat16.enabled without auto(set to correct value) in the DeepSpeed config file or pass it in kwargs."
I use transfromers==4.28.0 and accelerate===0.20.3
the variable trainer has type "transformers.trainer_seq2seq.Seq2SeqTrainer". I don't get how to pass in configurations about bfloat16 in trainer.train method. Could anyone helps?
The information are listed below:
File "./ds_train.py", line 378, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/transformers/trainer.py", line 1659, in _inner_training_loop
model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/accelerate/accelerator.py", line 1178, in prepare
result = self._prepare_deepspeed(*args)
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/accelerate/accelerator.py", line 1486, in _prepare_deepspeed
deepspeed_plugin.deepspeed_config_process(must_match=False, **config_kwargs)
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/accelerate/utils/dataclasses.py", line 624, in deepspeed_config_process
self.deepspeed_config_process(
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/accelerate/utils/dataclasses.py", line 628, in deepspeed_config_process
self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs)
File "/home/hechenghua/anaconda3/envs/swiftsage/lib/python3.8/site-packages/accelerate/utils/dataclasses.py", line 603, in fill_match
raise ValueError(
ValueError: bfloat16.enabled not found in kwargs. Please specify bfloat16.enabled without auto(set to correct value) in the DeepSpeed config file or pass it in kwargs.

cc @pacman100


[Bug] Json path not saving per request

Insomnia Version: 5.14.7
Operating System: Ubuntu 17.10 - artful

JsonPath is saving globally for me not per request as was before, therefore if I set it to $.class it will show that across all the requests/environments. Before it used to save them per request.

Related to #766

Forgot to close this. Was fixed in the last release.


I like consistancy...

This is intentionally not capitalized—all other entries in this list are specific coins (proper nouns) whereas this is a category that encompasses a few different ones.


Clear default account

Hy,
would it be possible to get the possibility to clear the default account so that a different account can be signed in without having to clear app data?

1.android
Setting - Account - Google - Logout with previous google account and login with other google account
2.ios
Setting - Game Center - Logout with previous ios account and login with other ios account
Thanks

What I ment by this is that if I have multiple Google accounts and want only the game to use a different one, i cant change this without going into google play games and logging out my account there from the game. In other games if i logout and then log back in I can specify again which account I want to use


Create Logo

We need a logo for Marketplace.

logomakr is a great website to create logos, tagging as good first issue.

hey please assign this issue to me ,
i am skilled at graphic designing and logo designing
@aitikgupta

Hey @KanishkKhurana, sure have a shot!

hey! which folder should i make my PR to ? or should it be the main directory only ?

A part of this is figuring out if we can update the logo without shipping a release, which is under @YuanSamuel
Anyway, the logo can exist in root directory 👍🏼

cool i'll make a PR.
there are a lot of rules to how a logo should look and i did my best but let me know if there is any change.

@aitikgupta please check


[olm-cmd] Package SDK with a default version of OLM

Description of the change:
This PR uses go-bindata to embed a default version of OLM, when
user does not have any preference, so that SDK need not fetch
the manifests from github everytime.
The OLM manifests in this PR have the version "v0.15.1". With every
release SDK the olm-bindata-vX.X.X.go intends to be updated
with the most successful released version of OLM manifests.
Motivation for the change:
Packaging the most successful released version of OLM along with SDK,
so that for every install the manifests need not be fetched from GitHub. This
will also help in avoiding errors when the "latest" tagged version of OLM has some
problems after its release.

@estroz @camilamacedo86 @joelanford
This PR also addresses the issue #3095 . If we intend to follow this approach, these are few other suggestions:

Can we have a script during release process, which updates olm-bindata with the most successfully released version (tagged "latest") of OLM. If we decide to do so, when do we plan to update the default OLM version - only with major releases of SDK, or even minor release (if a new OLM release falls in-between).
Could we include in every release note of SDK the particular version of OLM being included? (ie here)
The release process may also need to be updated to include an additional step that the person performing the release should check whether the "latest" release of OLM was successful. For example, something like 0.15.0 should not be shipped with SDK.

@joelanford @camilamacedo86 @estroz Made modifications as suggested and added relevant documentation. Please have a look.

Closing this PR in favor of https://github.com/operator-framework/operator-sdk/pull/3906


Issue with occupancy state

It looks like after an update (unfortunately I have no idea when) the sensor is not turning back to "clear" status after the given time, however, there is a refreshment every 5 seconds (can see it in the lux values) so partly it is working. (HACS 1.29.1; AppDaemon 0.11.0) I tried restarting it, removing and adding again, but none of those helped. Is there any idea what can I try to make it work again? Thanks!

I have problem too with the occupancy state. There is the log in the appdaemon:
2024-05-16 13:47:09.011716 INFO AppDaemon: Loading App Module: /config/apps/appdaemon_aqara_motion_sensors/aqara.py
2024-05-16 13:47:09.030116 INFO AppDaemon: Loading app aqara using class Aqara from module aqara
2024-05-16 13:47:09.030986 INFO AppDaemon: Calling initialize() for aqara
2024-05-16 13:47:09.035602 WARNING aqara: aqara: Entity . not found in namespace default
2024-05-16 13:47:09.050808 INFO AppDaemon: App initialization complete
2024-05-16 13:48:05.754690 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:07.047005 WARNING HASS: Error getting services - retrying
Task exception was never retrieved
future: <Task finished name='Task-93' coro=<HassPlugin.run_hass_service_check() done, defined at /usr/lib/python3.11/site-packages/appdaemon/plugins/hass/hassplugin.py:763> exception=ClientResponseError(RequestInfo(url=URL('http://supervisor/core/api/services'), method='GET', headers=<CIMultiDictProxy('Host': 'supervisor', 'x-ha-access': '8fb93c02e2e57207070bdd57bd5febca06837c9f3f1c49582716b4eb1cdc9d7cd0773da8724c6425c915ceccc92a5a2046497bd543edcfff', 'Accept': '*/*', 'Accept-Encoding': 'gzip, deflate', 'User-Agent': 'Python/3.11 aiohttp/3.8.6')>, real_url=URL('http://supervisor/core/api/services')), (), status=502, message='Bad Gateway', headers=<CIMultiDictProxy('Content-Type': 'text/plain; charset=utf-8', 'Content-Length': '16', 'Date': 'Thu, 16 May 2024 11:48:07 GMT', 'Server': 'Python/3.12 aiohttp/3.9.5')>)>
Traceback (most recent call last):
  File "/usr/lib/python3.11/site-packages/appdaemon/plugins/hass/hassplugin.py", line 772, in run_hass_service_check
    hass_services = await self.get_hass_services()
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/site-packages/appdaemon/plugins/hass/hassplugin.py", line 732, in get_hass_services
    r.raise_for_status()
  File "/usr/lib/python3.11/site-packages/aiohttp/client_reqrep.py", line 1011, in raise_for_status
    raise ClientResponseError(
aiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('http://supervisor/core/api/services')
2024-05-16 13:48:10.758357 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:15.762089 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:20.765386 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:25.810649 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:36.634301 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:41.639217 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:46.645459 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:51.650545 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:48:56.655944 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:49:01.661310 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:49:06.666842 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:49:11.671582 WARNING HASS: Disconnected from Home Assistant, retrying in 5 seconds
2024-05-16 13:49:16.677827 INFO HASS: Connected to Home Assistant 2024.5.3
2024-05-16 13:49:16.701225 INFO HASS: Evaluating startup conditions
2024-05-16 13:49:16.704956 INFO HASS: Startup condition met: hass state=RUNNING
2024-05-16 13:49:16.705065 INFO HASS: All startup conditions met
2024-05-16 13:49:16.725926 INFO AppDaemon: Processing restart for HASS
2024-05-16 13:49:16.726102 INFO AppDaemon: Terminating aqara
2024-05-16 13:49:16.727717 INFO AppDaemon: Reloading Module: /config/apps/appdaemon_aqara_motion_sensors/aqara.py
2024-05-16 13:49:16.728509 INFO AppDaemon: Loading app aqara using class Aqara from module aqara
2024-05-16 13:49:16.729178 INFO AppDaemon: Calling initialize() for aqara
2024-05-16 13:49:16.732568 INFO aqara: Previous message repeated 1 times
2024-05-16 13:49:16.732673 WARNING aqara: aqara: Entity . not found in namespace default


Error when trying to construct an index with NNDescent

Hello,
When I try to construct a NNDescent (pynndescent v0.4.3) index using correlation or euclidean distance I get a numba error (Numba version 0.47.0).
Here is the code I used that triggers the error and X is a NumPy float64 array with shape (1624, 2000).:
from pynndescent import NNDescent

def _KNN(X, random_state, n_neighbors = 30, metric='correlation'):
    index = NNDescent(X, n_neighbors=n_neighbors, metric=metric, random_state=random_state)
    knn_indices, knn_dists = index.query(X, n_neighbors=n_neighbors)

Here is the error I get:
~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py in __init__(self, data, metric, metric_kwds, n_neighbors, n_trees, leaf_size, pruning_degree_multiplier, diversify_epsilon, n_search_trees, tree_init, random_state, algorithm, low_memory, max_candidates, n_iters, delta, n_jobs, compressed, seed_per_row, verbose)
    977                     leaf_array=leaf_array,
    978                     verbose=verbose,
--> 979                     seed_per_row=seed_per_row,
    980                 )
    981         else:

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/dispatcher.py in _compile_for_args(self, *args, **kws)
    399                 e.patch_message(msg)
    400
--> 401             error_rewrite(e, 'typing')
    402         except errors.UnsupportedError as e:
    403             # Something unsupported is present in the user code, add help info

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/dispatcher.py in error_rewrite(e, issue_type)
    342                 raise e
    343             else:
--> 344                 reraise(type(e), e, None)
    345
    346         argtypes = []

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/six.py in reraise(tp, value, tb)
    666             value = tp()
    667         if value.__traceback__ is not tb:
--> 668             raise value.with_traceback(tb)
    669         raise value
    670

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Internal error at <numba.typeinfer.CallConstraint object at 0x122cd8eb8>.
Failed in nopython mode pipeline (step: nopython mode backend)
scalar type Tuple() given for non scalar argument #5

File "../../../.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py", line 232:
def generate_leaf_updates(leaf_block, dist_thresholds, data, dist, dist_args):
    <source elided>

    for n in numba.prange(leaf_block.shape[0]):
    ^

[1] During: lowering "id=9[LoopNest(index_variable = parfor_index.1714, range = (0, $28.6, 1))]{186: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (241)>, 136: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (238)>, 138: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (238)>, 88: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (232)>, 718: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (245)>, 174: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (238)>, 720: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (245)>, 208: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (243)>, 210: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (244)>, 112: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (233)>, 212: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (245)>, 114: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (236)>, 214: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (238)>, 216: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (245)>, 90: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (233)>, 219: <ir.Block at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (245)>}Var(parfor_index.1714, pynndescent_.py:232)" at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (232)
[2] During: resolving callee type: type(CPUDispatcher(<function generate_leaf_updates at 0x11d87c950>))
[3] During: typing of call at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (265)

Enable logging at debug level for details.

File "../../../.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py", line 265:
def init_rp_tree(data, dist, dist_args, current_graph, leaf_array):
    <source elided>
        updates = generate_leaf_updates(
            leaf_block, dist_thresholds, data, dist, dist_args
            ^

[1] During: resolving callee type: type(CPUDispatcher(<function init_rp_tree at 0x11d87cbf8>))
[2] During: typing of call at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (464)

[3] During: resolving callee type: type(CPUDispatcher(<function init_rp_tree at 0x11d87cbf8>))
[4] During: typing of call at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (464)

File "../../../.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py", line 464:
def nn_descent(
    <source elided>
    if rp_tree_init:
        init_rp_tree(data, dist, dist_args, current_graph, leaf_array)
        ^

This is not usually a problem with Numba itself but instead often caused by
the use of unsupported features or an issue in resolving types.

To see Python/NumPy features supported by the latest release of Numba visit:
http://numba.pydata.org/numba-doc/latest/reference/pysupported.html
and
http://numba.pydata.org/numba-doc/latest/reference/numpysupported.html

For more information about typing errors and how to debug them visit:
http://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#my-code-doesn-t-compile

If you think your code should work with Numba, please report the error message
and traceback, along with a minimal reproducer at:
https://github.com/numba/numba/issues/new

I don't know what is going on here, so just a side remark: you don't need to run query() after NNDescent() if you used the desired number of neighbors for index construction. The kNN graph is stored as the neighbor_graph attribute of the index. Source: https://twitter.com/leland_mcinnes/status/1213946195162390528

This cropped up very recently. I believe it may be related to some combination of numba and llvmlite versions. If you can try falling back to numba v0.46 and see if that corrects the issue. I'm still trying to work out the causes here.

Thanks for the remark regarding the query function @dkobak  that will be really helpful.
@lmcinnes Thanks so much for the quick reply. I tried degrading to v0.46 and reinstalling pynndescent and umap.
When I run pynndescent now I get the error below I also get a similar error when I import umap.
TypingError                               Traceback (most recent call last)
<ipython-input-10-7175d2ad6206> in <module>
----> 1 sapi.KNN(vg, 30, metric='euclidean')

~/projects/scapi/scapi/grph/__init__.py in KNN(sample, n_neighbors, metric, random_state, use_pca, use_pcs, verbose, metric_kwds)
     98         X = sample.norm.T
     99
--> 100     knni, knnd = _KNN(X, random_state, n_neighbors, metric=metric, metric_kwds=metric_kwds, verbose=verbose)
    101     sample.cm.knn = knni
    102     sample.cm.knnd = knnd

~/projects/scapi/scapi/grph/__init__.py in _KNN(X, random_state, n_neighbors, metric, metric_kwds, verbose)
     77
     78 def _KNN(X, random_state, n_neighbors = 20, metric='euclidean', metric_kwds=None, verbose=False):
---> 79     index = NNDescent(X, n_neighbors=n_neighbors, metric=metric, random_state=random_state)
     80     knn_indices, knn_dists = index.query(X, n_neighbors=20)
     81     sample.cm.distances, sample.cm.connectivities = _compute_connectivities_umap(knn_indices, knn_dists, X.shape[0], n_neighbors)

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py in __init__(self, data, metric, metric_kwds, n_neighbors, n_trees, leaf_size, pruning_degree_multiplier, diversify_epsilon, n_search_trees, tree_init, random_state, algorithm, low_memory, max_candidates, n_iters, delta, n_jobs, compressed, seed_per_row, verbose)
    977                     leaf_array=leaf_array,
    978                     verbose=verbose,
--> 979                     seed_per_row=seed_per_row,
    980                 )
    981         else:

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/dispatcher.py in _compile_for_args(self, *args, **kws)
    399                 e.patch_message(msg)
    400
--> 401             error_rewrite(e, 'typing')
    402         except errors.UnsupportedError as e:
    403             # Something unsupported is present in the user code, add help info

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/dispatcher.py in error_rewrite(e, issue_type)
    342                 raise e
    343             else:
--> 344                 reraise(type(e), e, None)
    345
    346         argtypes = []

~/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/numba/six.py in reraise(tp, value, tb)
    666             value = tp()
    667         if value.__traceback__ is not tb:
--> 668             raise value.with_traceback(tb)
    669         raise value
    670

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Internal error at <numba.typeinfer.CallConstraint object at 0x127361da0>.
Failed in nopython mode pipeline (step: nopython mode backend)
Type of #4 arg mismatch: i1 != i32

File "../../../.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/utils.py", line 138:
def make_heap(n_points, size):
    <source elided>
    """
    result = np.zeros((3, int(n_points), int(size)), dtype=np.float32)
    ^

[1] During: lowering "$0.14 = call $0.2($0.10, func=$0.2, args=[Var($0.10, /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/utils.py (138))], kws=[('dtype', Var($0.12, /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/utils.py (138)))], vararg=None)" at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/utils.py (138)
[2] During: resolving callee type: type(CPUDispatcher(<function make_heap at 0x1274c5378>))
[3] During: typing of call at /Users/gww/.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py (461)

Enable logging at debug level for details.

File "../../../.pyenv/versions/3.6.6/Python.framework/Versions/3.6/lib/python3.6/site-packages/pynndescent/pynndescent_.py", line 461:
def nn_descent(
    <source elided>

    current_graph = make_heap(data.shape[0], n_neighbors)
    ^

This is not usually a problem with Numba itself but instead often caused by
the use of unsupported features or an issue in resolving types.

To see Python/NumPy features supported by the latest release of Numba visit:
http://numba.pydata.org/numba-doc/latest/reference/pysupported.html
and
http://numba.pydata.org/numba-doc/latest/reference/numpysupported.html

For more information about typing errors and how to debug them visit:
http://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#my-code-doesn-t-compile

If you think your code should work with Numba, please report the error message
and traceback, along with a minimal reproducer at:
https://github.com/numba/numba/issues/new

This seems very odd -- everything was working very recently, but has been broken with some of the numba updates. The llvmlite version may also matter. I'm really not sure what is going on here. I'm working with the numba people to try and determine what is up.

I think you are correct that the problem is llvmlite.  I downgraded to numba==0.46 (0.47 requires llvmlite 0.31)  and llvmlite==0.30 and pynndescent is working and umap imports fine.

I'm glad that works for you -- sorry for the issues. Hopefully I'll have
something sorted soon that can work around this.
On Mon, Jan 6, 2020 at 2:03 PM GWW<EMAIL_ADDRESS>wrote:

I think you are correct that the problem is llvmlite. I downgraded to
numba==0.46 (0.47 requires llvmlite 0.31) and llvmlite==0.30 and
pynndescent is working and umap imports fine.
—
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Reply to this email directly, view it on GitHub
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No need to apologize.  Both umap and pynndescent have been immensely useful for my research projects so thank you for creating them and making them so easy to use!


Delete key does not work in command mode for Vim

In Vim, if you press the Delete key it should delete the current character, like how the 'x' command behaves.

Possibly the fix is as simple as adding a mapping in command mode from  to 'x' ?


Raise error when a newly created dialog dropdown has no entries

https://bugzilla.redhat.com/show_bug.cgi?id=1246693

@skateman Would you please add a spec that reproduces the BZ issue before your fix and addressed after.

@chessbyte done!


NAS for TensorFlow

I will re-generate the commit history to make this patch clear. Expected commit order is shown as follow.
The items in itatic font are not yet ready.
Some features may be moved to future PRs.

[x] mutable interface
[x] mutator interface
[x] ENAS
[ ] ENAS-specific static graph
[ ] DARTS
[ ] DARTS-specific static graph
[ ] docstring
[ ] Proxyless NAS (under evaluation)
[ ] General static graph interface
[ ] Port static graph interface to model compression
[ ] Other NAS algorithms

better to add docstring

These codes look almost identical to those of PyTorch. Can we have some diffchecker that can diff tensorflow directory with pytorch directory? To me, another draft PR with overwriting would be great.

If no further improvement is planned for this PR, I'm strongly against merging it into master. Merging into a dev branch might be more acceptable.


Added more tuple sizes for web::Path for OperationModifier impl

Hi there,
This PR adds more tuple sizes (8 more), to support more tuple sizes.
Reasoning
Quite simple, I ran into the limit of 5, and expect to be using a bit more soon too :)
Backwards compatibility
This PR does not affect backwards compatibility
Thanks.

Before you merge, something weird is going on. The following signature:
#[api_v2_operation]
pub async fn update(data: web::Data<Arc<AppData>>,
    web::Path((organization_id, location_id, room_id, controller_id, sensor_id, alert_id)): web::Path<(i32, i32, i32, i32, i32, i32)>,
    payload: web::Json<UpdateAlert>,
    session: Session
) -> Result<Empty>

generates the following YAML (Converted by editor.swagger.io from the original JSON):
      parameters:
        - in: path
          name: ''
          required: true
          type: integer
          format: int32
        - in: path
          name: organization_id
          required: true
          type: integer
          format: int32
        - in: path
          name: location_id
          required: true
          type: integer
          format: int32
        - in: path
          name: room_id
          required: true
          type: integer
          format: int32
        - in: path
          name: controller_id
          required: true
          type: integer
          format: int32
        - in: path
          name: sensor_id
          required: true
          type: integer
          format: int32

What happened to 'alert_id` here (and why?), and why is the first param empty?


NTUEEPLUS-210. revert pr #161

What is this PR for?

pr#161 push to main,which is wrong, this revert it
What type of PR is it?
[Bug Fix | Improvement | Feature | Documentation | Hot Fix | Refactoring]
hot fix
Todos

[ ] - no

What is the Jira issue?

https://ntueeplus.atlassian.net/browse/NTUEEPLUS-210
Screenshots (if appropriate)

wrong one


Disabling sourceMap doesn't seem to be working

Hi there, and thanks for this cool extension. Weird that it appears to be the only sass compiler available for Brackets. I'm quite happy with it, but want to be able to disable .css.map files. As I understand it, adding this to one's brackets.json preference file should do the trick:
"sass.options": {
    "sourceMap": false,
}

However, it doesn't stop .css.map files from being compiled. Is this a bug or am I doing something wrong?

This has been bugging me, as well. Looking at the source code, it seems that the boolean alternative to the sourceMap option isn't even being considered. I don't know what the appropriate resolution to this problem is, but I did find a place where you can make a tweak to make it behave correctly. If you find where Brackets is storing the extension's files (for me it's C:\Users\username\AppData\Roaming\Brackets\extensions\user\jasonsanjose.brackets-sass), you can open the main.js file, search for "(map)", and change it to "(map && prefs.options.sourceMap)", and that'll do it.

Same issue

I changed the main.js like @codestothestars said, and ("sourceMap": false) works now.
On MacOS this file is stored here (/Users/username/Library/Application Support/Brackets/extensions/user/jasonsanjose.brackets-sass/)
Maybe @jasonsanjose can update this file on next version :)


[BUG] Connect-PnPOnline - Use 'Update-Module -Name PnP.PowerShell'

I have not used PnP.Powershell for a couple of years so when I returned to use it today using Connect-PnPOnline I received this message
------ start -----
WARNING:
A newer version of PnP PowerShell is available: 1.12.0.
Use 'Update-Module -Name PnP.PowerShell' to update.
Use 'Get-PnPChangeLog -Release 1.12.0' to list changes.
You can turn this check off by setting the 'PNPPOWERSHELL_UPDATECHECK' environment variable
to 'Off'.
------- end ------
So I ran 'Update-Module -Name PnP.PowerShell' to update then closed PowerShell_Ise and tried again only to see the same message again.
Tried several other things like re-installing then updating but it continues to suggest it is out of date.
Running 'Get-PnPChangeLog -Release 1.12.0' does not appear to do anything. (just returns back to prompt, presumably because I do not have that version)
What is the version of the Cmdlet module you are running?
Get-Module -Name "PnP.PowerShell" -ListAvailable
ModuleType Version

Manifest   1.9.0
Manifest   1.8.0
Manifest   1.7.0
Manifest   1.6.0
Which operating system/environment are you running PnP PowerShell on?

[ ] Windows

@GTEM19889 - As you mention the PowerShell ISE, I believe you're still on PS5. Can you try run Install-Module PnP.PowerShell -RequiredVersion 1.12.0 -Force.


alpine version

I'm a great fan of your project and a great fan of docker.
Now I was a great fan of Alpine, while it ran all of my apps from a way smaller image.
But I found hectane not working in this environment alpine:3.4 or alpine:edge.
Do you which library is missing in Alpine Linux?
Maybe we can make it run after all..

I was hoping to eventually move the container over to Alpine, so this is definitely something I'll look into. This may be related to the way Go links the executable. I'm not sure.

A bit of digging reveals that the CGO_ENABLED=0 environment variable needs to be set when building the binary and when this is set, the resulting binary runs in the container. I'll look into what would be needed in order to get the application to be built with this set.

Great! I would like to help you, but I am not a Go specialist.
But if there is anything else please let me know..
2016-09-19 19:28 GMT+02:00 Nathan Osman<EMAIL_ADDRESS>
A bit of digging reveals that the CGO_ENABLED=0 environment variable
needs to be set when building the binary and when this is set, the
resulting binary runs in the container. I'll look into what would be needed
in order to get the application to be built with this set.
—
You are receiving this because you authored the thread.
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https://github.com/hectane/hectane/issues/27#issuecomment-248061087, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AJG6XSWx_wmEIcm4ifxiBV9Y5LdctQN5ks5qrsYsgaJpZM4J-tk0
.

--

Finally managed to get this to work and the difference is huge! The entire image is 5 MB now. I've made some minor adjustments to the way the container is launched (flags instead of passing environment variables) and I've added experimental SMTP support too.
Here's the new image: https://hub.docker.com/r/hectane/hectane/
(Sidenote: the image gets rebuilt automatically once per week too now thanks to a Jenkins task I set up.)

@nathan-osman  That's great news, I am going to try it right away


SelectPanel2: 3 tiny bug fixes

Fix 1
In #3758, I forgot that we would need to add Suspense support to useSlots first before making footer optional. Default header is fine because it can't be wrapped in suspense, but footer totally can be.
Haven't tried to update useSlots yet, will explore later.

Before
After

Fix 2
The menu was not closing on it's own because I made a silly mistake. We will be able to catch these silly things once I write some tests 😅
- if (props.open === 'undefined') setInternalOpen(false)
+ if (props.open === undefined) setInternalOpen(false)

Fix 3
Added a warning to the ActionMenu story because it's implementation will most definitely change once we implement SelectPanel as a modal

we would need to add Suspense support to useSlots first before making footer optional

Just wanted to ask, is it possible for useSlots to be Suspense-compatible? I would have thought that React.Children and Suspense would be incompatible and was curious how this will work 👀


Add Instructions for customising the config

It would be useful if there was information to help users to customise the scripts, for example using our own configtx.yaml and crypto.yaml files - how would we pass these into the pods?
Other general information about customising the scripts would be good (as well as some links to specific pages in the kubernetes docs to serve as a pre-requisite to getting stated modifying them).

The only way that the user will be able to change the configs is by making a new image. So for eg. if someone wants to change configtx.yaml they can do something like:
Dockerfile:
FROM ibmblockchain/fabric-tools:1.0.1
COPY configtx-custom.yaml /sampleconfig/configtx.yaml

and then build the image
docker build -f Dockerfile -t custom-tag .
push the image to personal dockerhub
docker login
docker push custom-tag
docker logout
change the tools image in the kubeconfig/*.yaml from ibmblockchain/fabric-tools:1.0.1 to custom-tag.

@pastadin did the above mentioned solution work for you? Can we close this?


Use size to determine if resource is a collection

This PR fixes #229.

The fix works well for AR and Sequel but is not future-proof for others, I suggest to add ability for specific DB toolkits with fallback to some generic solution

And, of course, additional option to force smth like is_collection: true via options is required

Please, see https://github.com/Netflix/fast_jsonapi/pull/239 related to this

#239 is more flexible


need spinner when publishing a notebook

Add an upload spinner

there may be a spinner, but it's in the window behind.  it needs to be front and center, in the dialog where you are posting from.

@scottdraves I reused the standard beaker spinner...didn't put it in the center because it didn't look good there, chose a different spot. Let me know what you think...
implemented in https://github.com/twosigma/beaker-notebook/pull/2218 /  a4818da

:+1:

merged


Cobertura fails to execute properly with Java 8

See also: https://github.com/cobertura/cobertura/issues/176
Just logging the issue now and linking to some related issues. Hopefully they get fixed anytime soon. :smile:

Link to #93


[hotfix][EMB-466] Don't add ERPC to registries providers

Purpose
As part of prereg winddown, inactive registration schemas will be returned by the API by default. This is good, and makes an old hack on the registries discover page unnecessary.

Summary of Changes
When the enable_inactive_schemas flag is enabled, don't manually append ERPC to the "OSF Registration Type" facet at /registries/discover. However, since ERPC should be included in the API response, there's no visible change.

Side Effects
n/a

Feature Flags
enable_inactive_schemas

QA Notes
When the flag is enabled, check ERPC isn't in there twice.

Ticket

https://openscience.atlassian.net/browse/EMB-466
Reviewer Checklist

[ ] meets requirements
[ ] easy to understand
[ ] DRY
[ ] testable and includes test(s)
[ ] changes described in CHANGELOG.md

Pull Request Test Coverage Report for Build 2118

7 of 7 (100.0%)  changed or added relevant lines in 1 file are covered.
3 unchanged lines in 1 file lost coverage.
Overall coverage increased (+0.01%) to 63.233%

Files with Coverage Reduction
New Missed Lines
%

lib/registries/addon/components/registries-registration-type-facet/component.ts
3
84.51%

Totals

Change from base Build 2115:
0.01%

Covered Lines:
3792

Relevant Lines:
5681

💛  - Coveralls


get full company data with job

So in my React Native App, i will show the user some jobs. For the preview, it would be nice to have the company logo in the job data too. So at the moment there is only the DisplayName of the company?!
Is there a option to  get the company data with the search query of a job_search?

https://issuetracker.google.com/issues/145387894


Application Error for Live Demos

Hi,
I am trying to access the PyGoat Application live demo using the below three links.
http://pygoat-web.herokuapp.com/
http://pygoat.herokuapp.com/
http://pygoat-dep.herokuapp.com/
But getting application error

hi @gcdeshpande i suggest you install pygoat in your local machines. we don't maintance these heroku live demo
you can watch pygoat in here
https://youtu.be/RReAi3FJXDk

Hi @adeyosemanputra  Thanks for the update.


Add npmjs package for azure-maps-html-marker-layer

Please provide us with the following information:

This issue is for a:

[ ] bug report -> please search issues before submitting
[x] feature request
[ ] documentation issue or request
[ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Attempt to use this in an npm application

Expected/desired behavior

I would like to cleanly use this along with the azure-maps-control npm package in a react application.

Versions

"0.0.1"

Mention any other details that might be useful

I'm not sure if this is the best place for this, but it would be awesome if we could use npm to install this as a package.  Adding to our dist folder is a little brittle for us, might cause confusion for future developers, and does not effectively handle versioning for a feature like this.  Currently, we have to lift and shift this code to our own repository, which isn't ideal either.  Having an installable package in npmjs would clarify this dependency and make future maintenance easier.

Yes, it would be nice if you can add this to a npm registry so it can be install with the map control package

@rbrundritt Could we please get this one rolling?
Thanks.

Hey @mdelrossi1, @rbnrs folks,
I'm wondering how are things for you with this PBI, a need to have NPM package for azure-maps-html-marker-layer.
I'm struggling with bringing both azure-maps-control, storybook and HtmlMarker layer; was wondering if any of you ever made it thru?
Thanks,
Nik

I'm no longer on the Azure Maps team and there is an internal process for rolling out packages like this at Microsoft where this should be done by the Azure Maps dev team. This was handed off to that team and was planned a while back, but not sure where they are with this now. I'll ping the team internally to see if they have any updates on this.


Why is block.Index always 0?

Hi,
I'm using the Ledger smart contract to retrieve a block:
	block := ledger.GetBlock(blockhash)

When I check the properties, the block.Index is always zero. The block timestamp is not zero and contains a valid value.
Is this a bug, or am I doing something wrong?

It shouldn't be. Ledger does return index properly.

A very quick crude test:
diff --git a/cli/vm/cli_test.go b/cli/vm/cli_test.go
index 555c66fc9..02e8bbc53 100644
--- a/cli/vm/cli_test.go
+++ b/cli/vm/cli_test.go
@@ -1135,6 +1135,21 @@ func TestEvents(t *testing.T) {
        e.checkEvents(t, false, expectedEvent) // printed after `events` command
 }

+func TestSpecial(t *testing.T) {
+       e := newTestVMClIWithState(t)
+
+       script := io.NewBufBinWriter()
+       h, err := e.cli.chain.GetContractScriptHash(2) // examples/runtime/runtime.go
+       require.NoError(t, err)
+       emit.AppCall(script.BinWriter, h, "blockIndex", callflag.All, 1)
+       e.runProg(t,
+               "loadhex "+hex.EncodeToString(script.Bytes()),
+               "run",
+               "exit")
+       e.checkNextLine(t, "READY: loaded 43 instructions")
+       e.checkStack(t, stackitem.Make(1))
+}
+
 func TestEnv(t *testing.T) {
        t.Run("default setup", func(t *testing.T) {
                e := newTestVMCLI(t)
diff --git a/examples/runtime/runtime.go b/examples/runtime/runtime.go
index a98d39d29..9ad329f7d 100644
--- a/examples/runtime/runtime.go
+++ b/examples/runtime/runtime.go
@@ -2,6 +2,7 @@ package runtimecontract

 import (
        "github.com/nspcc-dev/neo-go/pkg/interop/lib/address"
+       "github.com/nspcc-dev/neo-go/pkg/interop/native/ledger"
        "github.com/nspcc-dev/neo-go/pkg/interop/native/management"
        "github.com/nspcc-dev/neo-go/pkg/interop/runtime"
 )
@@ -79,3 +80,9 @@ func Update(nef, manifest []byte) {
        }
        management.Update(nef, manifest)
 }
+
+// BlockIndex returns the index of the block received from the Ledger.
+func BlockIndex(i int) int {
+       block := ledger.GetBlock(i)
+       return block.Index
+}

Works fine.

Hello,
I found the issue: my app sent an empty byte array as block hash and the vm/compiler understood it as 0.
Could this be improved? IMO, if we call getBlock passing an empty block hash, it should return nil instead of the genesis block.

getBlock is made to accept both hashes and indexes. So if you pass 0 to it --- you get the genesis block, that's just how Ledger works.


Cannot run anything as admin

When the hypervisor is running. (PART 8) I cannot run any process as admin. it gives error like this:
https://cdn.discordapp.com/attachments/713358396346073122/810214626565947432/unknown.png
which is STATUS_ILLEGAL_INSTRUCTION. nothing in the vmexit handler is called when this happens (related to it)

It's weird, if you can provide more information on why this happens, we might find the solution.
Btw, please test HyperDbg (https://github.com/HyperDbg/HyperDbg), it's a newer version of Hypervisor From Scratch, and check whether you experience the same error or not.

I tried to figure it out but it doesn't make sense i thought its maybe with capturing / restoring the guest registers but im not even sure. can you try it yourself. run the driver open the usermode application so the HV is running. then try running notepad as admin

also idk if i read this write but "(HyperDbg is NOT in a WORKING STATE - YOU SHOULD NOT USE IT, but you can observe codes, please wait for first release in late March 2021)"

I ran Hypervisor From Scratch on my Laptop (Skylake processor), and it works perfectly (I can open the Notepad.exe as administrator).
HyperDbg is almost done; we just not test it yet, but it works perfectly for the simple features. You can use it too.

i ran the hv from scratch part 8 again on my main pc and laptop so not just my VM. made sure hv is launched (usermode application is running and called DrvCreate to launch hv) and i get the error still when i try running anything as admin. can you add me on discord. flushin#9408 i hope we can fix this issue.

i tried using the vmcs setup from hyperdbg and still have the same issue.

nothing is different in vmcs of HyperDbg and hypervisor from scratch, they're almost the same.
Run HyperDbg using ".connect local" and "load vmm" commands.

i compiled HyperDbg but when i try run it it says path not found????? with sc create/sc start

You can load it automatically without any external command. you should run :   ".connect local" and "load vmm" commands.

I'm hitting the same issue does that have to do something for the NtCreateFile hook implemented?
I do keep hitting first chance exceptions like this Unhandled exception 0xc000001d, hit in C:\Windows\System32\svchost.exe -k NetworkService -p -s DoSvc:
but I  don't think it's related

So, what's your processor model? and your Windows version? and have you tested it with the "Debug" version of HyperDbg?
Errors like this are really hard to investigate.


Feat/extend supported datasets

This PR

This PR introduces additional support for new time formats.
This PR resolves #1799

Description
Our functions only allowed for DD.MM.YY supported dates on datasets causing issue's w/ the dataset mentioned in #1799 . This PR introduces 10 new supported Time formats increasing supported time formats from 5 to 15. Additionally a lot of components where not formatDate resulting everything to be stacked on top of each other or not visible.

How to test

Since this Dataset is only available on Int or ssl development to Int you can only test this PR once deployed
Go to this link
Select Column-Line Chart See how we have a bunch of NaN.NaN.NaN dates
Select they are gone now view image

@noahonyejese dataset selection generally doesn't depend on environment, except we limit data sources to INT and PROD on INT, and to PROD on PROD – every other environment (local, Vercel, TEST) has access to every cube. The one from the issue can be checked with this PR via https://visualization-tool-git-feat-extend-supported-datasets-ixt1.vercel.app/en/browse?previous={"order"%3A"SCORE"%2C"search"%3A"moderhinke"%2C"includeDrafts"%3Atrue}&dataset=https%3A%2F%2Fhealth.ld.admin.ch%2Ffsvo%2Fmoderhinke_entwicklung%2F1&dataSource=Int (you need to enable draft datasets and change data source to INT to find it) :)

@noahonyejese dataset selection generally doesn't depend on environment, except we limit data sources to INT and PROD on INT, and to PROD on PROD – every other environment (local, Vercel, TEST) has access to every cube. The one from the issue can be checked with this PR via https://visualization-tool-git-feat-extend-supported-datasets-ixt1.vercel.app/en/browse?previous={"order"%3A"SCORE"%2C"search"%3A"moderhinke"%2C"includeDrafts"%3Atrue}&dataset=https%3A%2F%2Fhealth.ld.admin.ch%2Ffsvo%2Fmoderhinke_entwicklung%2F1&dataSource=Int (you need to enable draft datasets and change data source to INT to find it) :)

Ah okay thanks @bprusinowski didn't know that!

@noahonyejese are you sure it was expecting MM.DD.YY? I think the issue was that we mapped formatDate over xDomain, and afterwards didn't format the date (thus the issue with dots and dashes), but I'd be surprised if there was a US-date format that was expected 😱

@noahonyejese are you sure it was expecting MM.DD.YY? I think the issue was that we mapped formatDate over xDomain, and afterwards didn't format the date (thus the issue with dots and dashes), but I'd be surprised if there was a US-date format that was expected 😱

@bprusinowski  I mean I logged everything and I saw it there. Pick Combo-line Chart here and you'll see some dates are working and some are NaN.NaN.NaN if you look closely you don't even have to use console.log, the last working date is 10.12.2024 and from 10.13.2024 it says NaN.NaN.NaN so I assumed it was having trouble because there is no 13th month, also
It works now (view the picture above)

@noahonyejese just a small FYI, you can't share /create links, you'd need to publish the chart so I can access it (you can try to go to this link in a private browser) 👀
I hope I'm not too picky, but it looks like it's correct in the data, see a SPARQL query with the full dataset: https://s.zazuko.com/22nfxhH, that's why I doubt that the US-date format can appear here. Did you console.log the observations to see how it's formatted? Maybe we do something on the server side that makes it fail. It's just that I've never seen a problem like that in Visualize, and here also it looks like the date format is correct (EU) on the data side, so maybe it's another bug we need to find?
PS. It seems to work correctly with the current logic in other places (see the image below), that's why I doubt it's a new formatting issue 🤔

I think I'd try to see why column chart X axis works correctly (without new date formats), and then see what's different in combo charts, to narrow down the issue 👀

@bprusinowski Thanks for your insights, maybe that's true I will try to compare it. Maybe it's another issue, but that's still quite a weird because I console.log(x) the output was 10.10.2024, 10.11.2024
etc
Then I console.logged xScaled which was undefined
 const x = getX(d);
    const xScaled =
      (xScale(formatDate(x)) as number) + xScale.bandwidth() * 0.5;

also xScaled was only undefined for values above 10.12.2024 I will further investigate this tho.

Thanks @noahonyejese, yeah, but 10.10.2024 and 10.11.2024 are still valid dates with our current formatting, right? I think it would be super weird if this bug was related to US-date formatting, but only for combo charts, that why I was a bit pushing against adding a new date format options 😅
I think with the comment to add more formats, I was thinking about more formats like "decade", "microsecond", etc, but not about introducing MM.DD.YYYY ones, unless we really see that the data has it defined in such way.

Thanks @noahonyejese, yeah, but 10.10.2024 and 10.11.2024 are still valid dates with our current formatting, right? I think it would be super weird if this bug was related to US-date formatting, but only for combo charts, that's why I was a bit pushing against adding a new date format options 😅
I think with the comment to add more formats, I was thinking about more formats like "decade", "microsecond", etc, but not about introducing MM.DD.YYYY ones, unless we really see that the data has it defined in such way.

I see, I will investigate this


Add plots as an example study

My code should be clean enough. How about I make a clean version of the plotting example and we include it as an example study?

That is a very good idea indeed. This issue could be about you doing a
plotting example study then.
On Wed, Oct 21, 2015 at 11:59 AM, elisafilevich<EMAIL_ADDRESS>wrote:

My code should be clean enough. How about I make a clean version of the
plotting example and we include it as an example study?
—
Reply to this email directly or view it on GitHub
https://github.com/JATOS/JATOS/issues/119.

It's in the wiki. :)


chat: virtual scroller woes part 3

We have a few remaining issues with the virtual scroller/chat window centered around sending messages. It seems like after I send a message the window jumps when hearing the fact back from the poke. Similarly it seems that we are rerendering way too high in the tree after sending a message because if messages are sent in quick succession there's a chance one will get lost.

#1404


Wazuh Dashboard first steps

Wazuh dashboard
This repository forks the OpenSearch Dashboards repository to introduce changes that will ease its usage by Wazuh users and integrate itself with the Wazuh development process.
We want to accommodate our UX changes within the upstream code, minimizing conflicts. We want to contribute bug fixes and any component not tied to Wazuh upstream.
Also, we need to check all standard (vanilla) plugins are compatible with our builds, or we will need to build the infrastructure to build them ourselves too.
To do
Our release branch will be based on OpenSearch Dashboards 2.3.x.

Repository maintenance

[x] #2
[x] Import release branch from upstream
[x] Update README with project information
[x] #10

CI pipelines

[x] #11
[ ] Create release branch pipelines in CI
[ ] #21

Introduce Wazuh changes

[ ] #15
[x] #16
[ ] #17
[ ] #18
[ ] #19
[ ] Add ability to manage the Wazuh app using the plugin manager (bin/opensearch-plugins)
[ ] Create default config files for Wazuh
[ ] Generate production packages with Wazuh changes and config files

Test the development cycle

[ ] Bring recent changes from upstream
[ ] Generate new package and docker environment

To keep this repository up-to-date with the original OpenSearch Repository, we need to periodically sync the fork bringing here (fork) the new changes made in the original repository (upstream).
For that, we'll follow the strategy defined in this article. To minimize the conflicts between the repos, we'll use the rebase strategy, replaying our work on top of OpenSearch Dashboards features.

Hello Wazuh Team!
I want to ask whether the version of Wazuh dashboard at this docker image wazuh/wazuh-dashboard:4.3.11-dev is using code from this repo or using Kibana with plugin (Wazuh-kibana-app)[https://github.com/wazuh/wazuh-kibana-app]?
Thanks!

@hieu-ht it uses the code from wazuh-kibana-app and a standard binary distribution of opensearch. Our plan is to generate our own version of opensearch-dashboards from this repository in the coming weeks.
Initially, the changes we're going to introduce here are the ones we're applying to the standard distribution in our packaging pipeline.

URL of the OpenSearch Security plugin: https://repo1.maven.org/maven2/org/opensearch/plugin/opensearch-security/<IP_ADDRESS>/opensearch-security-<IP_ADDRESS>.zip


[FEAT] Please include the solution of TCL error: ERR_NOT_SUPPORTED after upgrade to version 14.1.0 or later

It would be a great inclusion if you automate the iRule issues when you are upgrading up to 14.1.0 or later and the iRules show the TCL error: ERR_NOT_SUPPORTED.
https://support.f5.com/csp/article/K23237429

Filed internal Jira SOLUTIONS-5114.

We'll be supporting iRules migration in another solution.


Signal code actions in initialize response

Following on from the conversation in #147 ...

This resolves the problem of missing code actions from the perspective of Emacs/lsp-ui (https://github.com/emacs-lsp/lsp-ui/issues/319), so I am confident that this change had some positive effect, as I had something to test against beyond the internal tests in #147.
But there are lots of other blanks in those tests.  Can you think of any external verification mechanism for any of them?

The other ones look fine to me but I might be missing something. Not sure how to verify them.


High-level model interface to get objects via iterators

It could be useful to have a number of commonly-used classes accessibly via iterators from the main Model object: e.g. model.grids(). This interface removes the need for a user to have to deal with uuids/parts/nodes, and so could make a nice high-level interface.
E.g.
for grid in model.grids():
    print(grid.uuid)

In cases where there is usually only one of the object present, we could perhaps have a method that fetches it directly but throws and exception if there is not exactly one present, e..g.
model.get_crs()   # return resqpy.crs.Crs

model.get_grid()  # return resqpy.grid.Grid

This has already been implemented for:

wells
trajectories

It would perhaps be a design choice whether to add these methods for all classes or a subset; and which subset. Perhaps the 6 or so most commonly-used objects.

for well in model.iter_objs(cls=reqpy.well.WellboreFeature):
    pass

Could be implemented with a class method along these lines?

class WellboreFeature

    @classmethod
    def _iter_objs(cls, model):
        parts = parts_of_type('WellboreFeatureInterpretation')
        return (cls(model, p) for p in parts)


fix: little typo in section number

I was going through the notebook for timing estimates and found this little typo (section 2.4 after section 2.2, instead of section 2.3), so fixed the typo.
Thanks

Thanks @khalidsaifullaah. Our policy is to not fix small changes through PRs though to avoid chaos so could you note this in the feedback doc for the timing estimations?

Thanks @khalidsaifullaah. Our policy is to not fix small changes through PRs though to avoid chaos so could you note this in the feedback doc for the timing estimations?

Oh I see, will do that. Thanks!🙂


Time format is not consistent

I live in Sweden we use a 24 H clock, and it seems like some parts of the GUI adapt to it correctly.
Here the time format is correct:

But if i hover over a value it uses a super weird format 12 H with am and pm translated to fm and em (i have never in my life seen fm and em before).

This is a frontend issue reported here:
https://github.com/home-assistant/home-assistant-polymer/issues/196


Allow customisation of icons.

Is your feature request related to a problem? Please describe.
VSCode icons, the icons this extension this uses, don't look great to me.
Describe the solution you'd like
Be able to change the icons of the solution explorer. Use the same file icons as the file explorer.
Describe alternatives you've considered
You could switch to some other icon pack like Studio Icons. If you don't like that, stick with your preference.
Additional context
Here is the problem:

Here is a quick mockup:

You can already do it. In the Visual Studio Code settings panel you can look for "Solution Explorer Icons":

And them select:

solution-explorer: custom icons from vscode-solution-explorer extension.
mix: file and folder icons from the installed icons theme.
current-theme: all icons are from the installed icons theme.

Once you have changed it, remeber reload the Visual Studio Code window:

I'll just say that the default setting for this should be current-theme.


Loading obj file doesn't work for objects which do not only consist of triangles

When I attempt to load a obj file, which has e.g. rectangles as facet pyntcloud crashes with an AssertionError within pandas:
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-36-170af64711ff> in <module>()
----> 1 obj.read_obj("/Users/johannes/test.obj")

~/anaconda3/envs/suction/lib/python3.6/site-packages/pyntcloud/io/obj.py in read_obj(filename)
     50     f = [re.split(r'\D+', x) for x in f]
     51
---> 52     mesh = pd.DataFrame(f, dtype='i4', columns=mesh_columns)
     53     # start index at 0
     54     mesh -= 1

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    367                     if is_named_tuple(data[0]) and columns is None:
    368                         columns = data[0]._fields
--> 369                     arrays, columns = _to_arrays(data, columns, dtype=dtype)
    370                     columns = _ensure_index(columns)
    371

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _to_arrays(data,     columns, coerce_float, dtype)
   6282     if isinstance(data[0], (list, tuple)):
   6283         return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 6284                                dtype=dtype)
   6285     elif isinstance(data[0], collections.Mapping):
   6286         return _list_of_dict_to_arrays(data, columns,

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
   6361         content = list(lib.to_object_array(data).T)
   6362     return _convert_object_array(content, columns, dtype=dtype,
-> 6363                                  coerce_float=coerce_float)
   6364
   6365

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _convert_object_array(content, columns, coerce_float, dtype)
   6418             # caller's responsibility to check for this...
   6419             raise AssertionError('%d columns passed, passed data had %s '
-> 6420                                  'columns' % (len(columns), len(content)))
   6421
   6422     # provide soft conversion of object dtypes

AssertionError: 9 columns passed, passed data had 12 columns

Hola!
Thanks for pointing this out!
I'm aware that the current .obj parser does not cover the entire file format capabilities.
Could you share the file that is causing the error??

Unfortunately, no. But I can see, if I can generate a simple example generating the same defect.

After #209 This should be fixed. A utility for converting quadrilateral faces to triangular has also been added. In summary, the following code should work as intended:
from pyntcloud import PyntCloud
from pyntcloud.utils.mesh import quadrilateral_to_triangular
cloud = PyntCloud.from_file("test.obj")
cloud.mesh = quadrilateral_to_triangular(cloud.mesh)

I am having similar error. The thing is that the error occurs when we are reading the file (in PyntCloud.from_file('..') line) so it doesn't reach the line where quadrilateral faces are converted.

I have the same issue. @daavoo your workaround does not work as pointed in previous comment. Could you provide another workaround please?


apply new tui-doc and update readme

🚀✈

[x] apply  tui-doc
[x] add link(react-wrapper)

https://github.com/nhnent/toast-ui.react-chart

It will be fixed v3.6.0.


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