Create bar graphs for distance, duration and number of runs

Describe briefly your feature:
Create bar graphs for for displaying a distance graph representing distances traveled each month from January to December[ The distances are measured in kilometers] and displaying a graph for duration and number of runs.
Describe the solution you'd like:

Develop a dedicated screen or section within the application interface for the distance graph.
Implement functionality to generate the distance graph dynamically based on user data.
Display the graph with months along the x-axis and distances (in kilometers) along the y-axis.
Allow users to interact with the graph, such as zooming in/out, panning, or viewing specific data points.
Ensure the graph is visually appealing and easy to understand, with clear labels and axis markers.
Provide filter options for users to select different time periods:
Additionally, created bar graphs for duration and number of runs to complement the distance graph, providing comprehensive insights.

Duration Graph:
Display a duration graph representing time taken to travel each month from January to December. The duration is measured in hours. The graph is displayed with months along  the x-axis and duration (in hours) along the y-axis.

Number of Runs Graph
Display a bar graph representing number of runs covered while travelling along the y-axis and month from January to December along the x-axis.
Describe alternatives you've considered:

Incorporating additional features such as customizable time periods or filtering options for the graph.
Offering different visualization types (bar graph) based on user preferences.
Providing tooltips or legends to enhance user understanding of the graph data.

Additional context:
Consider optimizing the graph for responsiveness across various devices and screen sizes. Test the functionality thoroughly to ensure accurate data representation and smooth user experience. Similary create the graphs for Duration and number of runs.

Implemented the following functionality:

Develop a dedicated screen or section within the application interface for the distance, duration and number of runs graph.
Implement functionality to generate the graphs dynamically based on random generated data.
Display the graph with time along the x-axis and distances (in kilometers) along the y-axis.
Ensure the graph is visually appealing and easy to understand, with clear labels and axis markers.

The further functionality will be implemented: #66


Some metrics supported by Microsoft.Network/azurefirewalls are not listed

Azure Firewall has supported metrics other than "network rules hit count" and "application rules hit count" : there are "Data processed", "Firewall health state", "SNAT port utilization" as the following document.
It would be nice if you could add the other metrics supported by Azure Firewall to this document.
-Azure Firewall logs and metrics
https://docs.microsoft.com/en-us/azure/firewall/logs-and-metrics#metrics

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: c40aefaa-322f-734a-b246-138a71be379f
Version Independent ID: 48980577-42a9-559e-00bd-2d5969200925
Content: Azure Monitor supported metrics by resource type
Content Source: articles/azure-monitor/platform/metrics-supported.md
Service: azure-monitor
Sub-service: metrics
GitHub Login: @anirudhcavale
Microsoft Alias: ancav

@KosukeHosoda Thanks for the feedback! We are currently investigating and will update you shortly.

Thanks for feedback. We have made changes to the document and it will be reflected in docs within 24 hours. As there are no further changes, we will close the issue now. Please revert in case of any questions.

I'm glad to hear that. Thanks for investigating and reflecting in docs this time.


Avoid synchronous loading on client if options.ssr is false

Summary
Fixes #345. See description there for more details.
tl;dr: in cases where ssr: false is set, this PR fixes the way we render the component on the client to match what the server is doing, avoiding problematic SSR hydration mismatches (https://reactjs.org/docs/react-dom.html#hydrate).
Test plan
Updated the SSR example to add a new letter, G, which is rendered on the page with both ssr: true and ssr: false. Without the change applied, the DOM looks like this (incorrect):

with the change applied, the DOM now correctly looks like this:

As mentioned in #345, I can't see any existing integration tests for SSR: if I missed some or if you think it's worth adding some, I'm happy to help set that up. Thanks in advance for reviewing!

I think it makes sense, but we should add a test on it. There are actually some tests, could you just add one to cover this case?

Hello @jdb8, any test?

@gregberge sorry, this got lost somehow.

There are actually some tests, could you just add one to cover this case?

Would you mind pointing me to a good place to add this ssr integration test? The issue I had was that this is a change that you'd ideally want to validate across the server/component boundary, to verify that SSR hydration works fully. Apart from the examples folder (which I added to), I couldn't see anywhere suitable for such an end-to-end test without setting up a new suite.

Thanks!


Can't find Sql Management Studio 2017 download

As usual MS won't allow to find anything besides the latest and greatest version 18. The problem with it is that it doesn't support Reporting project created in VS 2019 using reporting tools based on previous versions. So I have on my desktop VS solution working but on my laptop with newest software it won't load reporting project. How do I upgrade it?

@igor63132 Hello, your Issue #3040 has been recognized, and processing has begun.
@markingmyname Hi Mark, Can you please process this Issue #3040. Does our documentation provide links to install somewhat older versions of SSMS?

@igor63132 Have you looked at the Release Notes to download older versions?
https://docs.microsoft.com/en-us/sql/ssms/release-notes-ssms?view=sql-server-2017#download-ssms-1791

I just realized I have to click on a link for the particular language to get it.Not really obvious it's a link to download.For the latest version it shows as :
SSMS 18.3
Download: Download SSMS 18.3
 It's obvious, right? I expected similar for everythnig else as it used to be on product download pages before.Well, at the end my issue wasn't really with version of SSRS but rather  the way how someone gets Reporting Serviced for VS 2019 now. It's not included  in Data Services package as before but rather hidden in the extensions (in pretty "obvious" place for that matter - roaming extensions).I had a dream I still have msdn subscriptions page with all products clearly labeled and properly grouped, my old complaints how I couldn't find something there sometimes seems petty right now...
Thank you, Mark!
On Friday, September 27, 2019, 08:47:14 AM CDT, Mark Ghanayem<EMAIL_ADDRESS>wrote:
@igor63132 Have you looked at the Release Notes to download older versions?
https://docs.microsoft.com/en-us/sql/ssms/release-notes-ssms?view=sql-server-2017#download-ssms-1791
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

@igor63132,
Glad I could help.
We will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion, and we will reopen the issue. To see what the community is wanting, please visit the UserVoice site.


Converging community docs

Describe the bug

There are now multiple community documentations (https://github.com/oam-dev/kubevela/blob/master/community.md, https://github.com/oam-dev/spec/blob/master/governance.md) across projects, please consider converging them into a centralized community repo.
To Reproduce

Expected behavior

Screenshots

KubeVela Version

Cluster information

Additional context

/assign BinaryHB0916

It's done in https://github.com/kubevela/community . Thanks @BinaryHB0916


Bizarre platypus Token Mint - change 9 decimal places to 0 decimal places

I'm submitting a ...
[x] bug report
[ ] feature request
[ ] question about the decisions made in the repository
[ ] question about how to use this project

Summary

the token says 9 decimals and in the json that I uploaded I put it with 0 decimals since it is a token for NFT mint now it cannot be burned and it does not do the expected functionality, please change it to 0 decimals

https://solscan.io/token/GvRPncmFzJ7pAb3y2PjnKR6zuNkZSxdqS2cgZ6R9TeBd


Rule reorder does not get applied to Access Policy

Community Note

Please vote on this issue by adding a 👍 reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or other comments that do not add relevant new information or questions, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

Terraform Version

Terraform version - 1.7.0
zscaler/zpa v3.2.1

Affected Resource(s)

zpa_policy_access_rule_reorder

Terraform Configuration Files

locals {
  policies = { for index, policy in var.policy_config.policies :
    policy.name => merge(policy, { rule_number = index + 2 })
  }
}

resource "zpa_policy_access_rule_reorder" "access_policy_reorder" {
  policy_type = "ACCESS_POLICY"

  dynamic "rules" {
    for_each = local.policies
    content {
      id    = zpa_policy_access_rule.rules[rules.key].id
      order = rules.value.rule_number
    }
  }
}

Expected Behavior
When adding new access policy rules, they should have been created and placed in correct rule order, based on order from yaml config file
Actual Behavior
Terraform plan and apply shows correct rule order. Terraform apply is successful. However when checking Zscaler portal, access policies are just put last on the rule order list.
Steps to Reproduce
Had to separate

Reran terraform plan
Output shows update to rule order
run terraform apply
Says successful, but no changes actually made when confirming in Zscaler portal

References

#192 (similar behavior)

Hi any update here? Noticed this was labeled as "help wanted"
Let me know if more information is needed to for this issue . Thanks

@znegash-goto Where is your zpa_policy_access_rule configuration? It's hard to infer how you're iterating over the rules itself without that part of your code.
Either way, for what I can picture, your have a problem in the actual zpa_policy-access_rule resource itself specifically in the for_each loop which must be configured in this fashion:
resource "zpa_policy_access_rule" "rules" {
  for_each      = { for rule in local.policies : rule.name => rule }
  name          = each.value.name
  description   = each.value.description
  action        = "ALLOW"
  operator      = "AND"
}

Using the above code snippet along with what you've provided the policy reorder works without any issues.
If you have any additional questions please follow our General Support Statement for further assistance or reach out to Zscaler support, and ask them to escalate the question/issue to the maintainers.
Regards,

Hi @willguibr,  thanks for the response . I added the zpa_policy_access_rule (sorry long due to conditions).
The terraform plan we get provides the correct order, so it does not seem like it is an issue with the access rule and how it iterates over the rules.
When I apply the access policy it places any new rule at the bottom. Upon next build it noticed incorrect order but doesn't actually change it. If I manually correct it in UI, on next run it returns with "No changes"
resource "zpa_policy_access_rule" "rules" {
  for_each = local.policies

  name          = each.value.name
  action        = each.value.action
  custom_msg    = try(each.value.custom_msg, null)
  description   = each.value.description
  operator      = try(each.value.operator, "AND")
  policy_set_id = data.zpa_policy_type.access_policy.id
  policy_type   = try(each.value.policy_type, null)
  dynamic "conditions" {
    for_each = length(lookup(each.value, "segment_groups", [])) > 0 ? [true] : []
    content {
      operator = "OR"
      dynamic "operands" {
        for_each = each.value.segment_groups
        content {
          lhs         = "id"
          name        = operands.value
          object_type = "APP_GROUP"
          rhs         = var.segment_groups[operands.value].id
        }
      }
    }
  }
  dynamic "conditions" {
    for_each = length(lookup(each.value, "segments", [])) > 0 ? [true] : []
    content {
      operator = "OR"
      dynamic "operands" {
        for_each = each.value.segments
        content {
          lhs         = "id"
          name        = operands.value
          object_type = "APP"
          rhs         = var.segments[operands.value].id
        }
      }
    }
  }
  dynamic "conditions" {
    for_each = length(lookup(each.value, "user_groups", [])) > 0 ? [true] : []
    content {
      operator = "OR"
      dynamic "operands" {
        for_each = each.value.user_groups
        content {
          lhs         = data.zpa_idp_controller.idp_name.id
          object_type = "SCIM_GROUP"
          rhs         = data.zpa_scim_groups.this[operands.value].id
        }
      }
    }
  }
  dynamic "conditions" {
    for_each = lookup(each.value, "conditions", [])
    content {
      operator = lookup(conditions.value, "operator", "OR")
      dynamic "operands" {
        for_each = try(conditions.value.operands, [])
        content {
          lhs         = operands.value.lhs
          name        = operands.value.name
          object_type = operands.value.object_type
          rhs         = operands.value.rhs
        }
      }
    }
  }
}

Rule configurations are in a yaml file ordered like this. Rules placement in file will determine order
policies:
  - name: "Example 1"
    action: "Allow"
    description: "Example 1"
  - name: "Example 2"
    action: "DENY"
    description: "Example 2"

Ok you're not providing me the entire picture, but I'll try to make some inferences here. If it still does not work, please contact Zscaler support as previously indicated.

In your last message you mentioned you're using a yaml file as the input; however in the original code your locals does not make any reference of it.
Either way, the inference I am making here is that your yaml code is structure as you posted in your last message:
Below is how the code was tested:

Instead of using the automatic index provided by Terraform's loop, I am using index() function.
index () is used to find the index of each policy within the local.policy_config.policies. It then returns the position of the given item within a list.
The rule number calculation is performed as index(local.policy_config.policies, policy) + 1` This effectively aligns with the traditional 1-based indexing. The first item has an index of 0, so 0+1 = 1, making the first rule number 1, the second 2, and so forth.
Any way I hope this helps.

locals {
  policy_config = yamldecode(file("${path.module}/policies.yaml"))
  policies = { for policy in local.policy_config.policies : policy.name => merge(policy, { rule_number = index(local.policy_config.policies, policy) + 1 }) }
}

resource "zpa_policy_access_rule" "rules" {
  for_each      = local.policies
  name          = each.value.name
  action        = each.value.action
  description   = each.value.description
  custom_msg    = try(each.value.custom_msg, null)
  operator      = try(each.value.operator, "AND")
}

resource "zpa_policy_access_rule_reorder" "access_policy_reorder" {
  policy_type = "ACCESS_POLICY"

  dynamic "rules" {
    for_each = local.policies
    content {
      id    = zpa_policy_access_rule.rules[rules.key].id
      order = rules.value.rule_number
    }
  }
}

Since we haven't received any feedback we'll be closing the issue. If there are any further questions, please contact Zscaler support for further assistance or refer to our support statement documentation here

Sorry for late reply. I have had a Zscaler support ticket as you mentioned in previous comment.
You inferred correctly on yaml structure. We have it as a variable within the access policy module. The yaml files are retrieved in our locals.tf in the root module
I tried the below based on your post. But same result. Correct re-order in terraform plan. But after applying, access policy is just placed at the bottom

#Before
locals {
  policies = { for index, policy in var.policy_config.policies :
    # Adding 2 to index as zscaler automatically creates Zscaler Deception access policy as #1 in rule order
    policy.name => merge(policy, { rule_number = index + 2 })
  }
}

#After
locals {
  # Adding 2 to index as zscaler automatically creates Zscaler Deception access policy as #1 in rule order
  policies = { for policy in var.policy_config.policies : policy.name => merge(policy, { rule_number = index(var.policy_config.policies, policy) + 2 }) }
}

Let me know of any other information that is needed

Why are you adding + 2 to the index? This is not the configuration example I've provided, hence the reason you're having problems.
locals {
  policy_config = yamldecode(file("${path.module}/policies.yaml"))
  policies = { for policy in local.policy_config.policies : policy.name => merge(policy, { rule_number = index(local.policy_config.policies, policy) + 1 }) }
}

Hi, a couple months ago, "Zscaler Deception" access policy appeared in the rule order at position 1 (mentioned in comment line). It is automatically created by Zscaler. Because of that every terraform plan was showing a change in rule order, so we added 2, which would accurately represent the rule order in ZPA
Why would this cause issues if the rule order is correct? Also in my case I am adding a new access policy
I have submitted a support request. And they said to reply here


Mast: Watch buyucoin.com

Mast requests the watch of the watch_keyword buyucoin\.com. See the MS search here and the Stack Exchange search in text, in URLs, and in code.

buyucoin\.com has been seen in 1 true positive, 0 false positives, and 0 NAAs.

Approved by Makyen in Charcoal HQ


REGEX mis-matches

foo(\.json\.(\d+)|\.|-\d+).* this pattern is matching for foo-bar-18.random string also for foo-bar-18.json
according PCRE format it's not supposed to.
Nice project btw! Really needed something like this.

Thanks for the bug report. I pushed a change for a missing check for an escaped character. Please try that and tell me how that turns out (updated latest package to v.2.2.3)

Thanks for the bug report. I pushed a change for a missing check for an escaped character. Please try that and tell me how that turns out (updated latest package to v.2.2.3). According to my tests, it now doesn't match either in accordance with prce syntax.

Awesome thanks for the quick fix!


PocketCoffea CLI not printing version

The --version argument of the CLI is not behaving as expected: the pocket_coffea version is not printed and instead a missing argument error is thrown.
(pocket-coffea) $ pocket-coffea --version
Error: Option '--version' requires an argument.

Expected behaviour: the pocket_coffea version is printed.

Addressed in #140.


use circle ci

 Tariff: Convert Tariff Apps to use ENV variables instead of Constants set at deploy time


Improve "Run and debug" button support

In order to solve this issue: https://github.com/microsoft/vscode-python-debugger/issues/146

Create a setting in order to store the different config names, that we want to show whit the editor run and debug button.
Update names (https://github.com/microsoft/vscode-python-debugger/issues/144)

MVP for user studying is done. Closing in favor of https://github.com/microsoft/vscode-python-internalbacklog/issues/468


WIP: Initial work on the update of the dashboard

Signed-off-by: Miquel Sabaté Solà<EMAIL_ADDRESS>

To do:

[ ] Tests
[ ] Polish the TODO note
[ ] UI (@vitoravelino)

For now, if you don't want to have this other process, we can do the same Rails.cache mechanism and do some crunching afterwards

I forgot to mention, this PR also assumes that the dashboard itself has authenticated as a Salt minion as well. How this is achieved will be submitted in following PRs (right now I've got some rough code spread among repos... 😅 )

Now all nodes will have the update_status field. This is not stored in the DB yet, as explained in the NOTE I've left.

Closing in favor of the PRs mentioned in #187.


Seamless Vue

Adds a class factory VueWrapper which wraps a Vue component into a Backbone view, which allows for using them as RootViews, ModalViews, or CocoViews (subviews).
Still may have edges to iron out, but good enough to use in the places I tested it.
I removed some of the examples I had put together as they're not necessary for this to be merged, just served to get things working. Those are on the seamless-vue-examples branch.

Coverage remained the same at 57.382% when pulling 4a4cce135314d3c11d252f2f3ecdd626d0ff8eff on seamless-vue into c3d5f8dd8f206c71387e769d6f81f2548baefa85 on master.

Coverage remained the same at 57.382% when pulling b19cde7cad41850cc655f4145871bf27cc640299 on seamless-vue into c3d5f8dd8f206c71387e769d6f81f2548baefa85 on master.

Coverage remained the same at 57.382% when pulling 7d163943018d891fc7049a898403d4c7ca1f1dc6 on seamless-vue into c3d5f8dd8f206c71387e769d6f81f2548baefa85 on master.


  Since you had retrolambda available I also updated to use lambda syntax consistently across the board.

Hi thanks for your pull request. For now I can't merge it because first there is a conflict with ChipsAdapter.java, and second I didn't have time to see all the changes you have made.
When I'll have time I'll see it more precisely

The merge conflict is now fixed.


Fix indentation in jck build file

Use tab instead of space to indent the if block for z/OS

Signed-off-by<EMAIL_ADDRESS><EMAIL_ADDRESS>

Test on Linux: Test_grinder/job/Grinder/6219/console
Test on z/OS: Test_grinder/job/Grinder/6218/console

What's the purpose of this?  Using space fails on z/OS?

It doesn't fail, but it's inconsistent to the rest of the file. You can see it if you open the file in Eclipse.
The block which contains indentation done by as series of single character spaces was added later to get JCK working on z/OS. However, the rest of the lines in the file contains indentation done by tab only.
This PR resolves this minor issue.


Review trading signals code, for all indicators.

Tasks:

Review trading signals code against Trading Analysis theory and modify whenever necessary.
Run trading simulation for validation.
Execute regression testing after each modification.

The task is targeting planned release: 0.2 stable-release (01-01-2021)
Task Status:

[x] Accumulation Distribution Line
[ ] Average True Range
[ ] Bollinger Bands
[ ] Chaikin Money Flow
[ ] Chaikin Oscillator
[ ] Chande Momentum Oscillator
[ ] Commodity Channel Index
[ ] Detrended Price Oscillator
[ ] Directional Movement Index
[ ] Double Exponential Moving Average
[ ] Ease Of Movement
[ ] Envelopes
[ ] Fibonacci Retracement
[ ] Forecast Oscillator
[ ] Ichimoku Cloud
[ ] Intraday Movement Index
[ ] Klinger Oscillator
[ ] Linear Regression Indicator
[ ] Linear Regression Slope
[ ] Market Facilitation Index
[ ] Mass Index
[ ] Median Price
[ ] Momentum
[ ] Exponential Moving Average
[ ] Simple Moving Average
[ ] Time-Series Moving Average
[ ] Triangular Moving Average
[ ] Variable Moving Average
[ ] Moving Average Convergence Divergence
[ ] Negative Volume Index
[ ] On Balance Volume
[ ] Parabolic SAR
[ ] Performance
[ ] Positive Volume Index
[ ] Price And Volume Trend
[ ] Price Channel
[ ] Price Oscillator
[ ] Price Rate Of Change
[ ] Projection Bands
[ ] Projection Oscillator
[ ] Qstick
[ ] Range Indicator
[ ] Relative Momentum Index
[ ] Relative Strength Index
[ ] Relative Volatility Index
[ ] Standard Deviation
[ ] Stochastic Momentum Index
[ ] Fast Stochastic Oscillator
[ ] Slow Stochastic Oscillator
[ ] Swing Index
[ ] Time Series Forecast
[ ] Triple Exponential Moving Average
[ ] Typical Price
[ ] Ultimate Oscillator
[ ] Vertical Horizontal Filter
[ ] Volatility Chaikins
[ ] Volume Oscillator
[ ] Volume Rate Of Change
[ ] Weighted Close
[ ] Wilders Smoothing
[ ] Williams Accumulation Distribution
[ ] Williams %R

AccumulationDistributionLine indicator reviewed:
Trading Simulation for the <class 'tti.indicators._accumulation_distribution_line.AccumulationDistributionLine'> with arguments {}

Trading Simulation for technical indicator:  AccumulationDistributionLine

- Simulation executed in : 8.98 seconds.

- Simulation Statistics:
	number_of_trading_days: 5651
	number_of_buy_signals: 4767
	number_of_ignored_buy_signals: 0
	number_of_sell_signals: 601
	number_of_ignored_sell_signals: 0
	last_stock_value: 475.5
	last_exposure: 22340.73
	last_open_long_positions: 40
	last_open_short_positions: 0
	last_portfolio_value: 19020.0
	last_earnings: 19817.21
	final_balance: 38837.21

AverageTrueRange reviewed:"
Trading Simulation for the <class 'tti.indicators._average_true_range.AverageTrueRange'> with arguments {}

Trading Simulation for technical indicator:  AverageTrueRange

- Simulation executed in : 16.07 seconds.

- Simulation Statistics:
	number_of_trading_days: 5651
	number_of_buy_signals: 4690
	number_of_ignored_buy_signals: 0
	number_of_sell_signals: 929
	number_of_ignored_sell_signals: 0
	last_stock_value: 475.5
	last_exposure: 26276.79
	last_open_long_positions: 36
	last_open_short_positions: 11
	last_portfolio_value: 11887.5
	last_earnings: 20898.22
	final_balance: 32785.72

BollingerBands reviewed:
Trading Simulation for the <class 'tti.indicators._bollinger_bands.BollingerBands'> with arguments {'period': 50, 'std_number': 2.5}

Trading Simulation for technical indicator:  BollingerBands

- Simulation executed in : 9.34 seconds.

- Simulation Statistics:
	number_of_trading_days: 5651
	number_of_buy_signals: 114
	number_of_ignored_buy_signals: 0
	number_of_sell_signals: 124
	number_of_ignored_sell_signals: 0
	last_stock_value: 475.5
	last_exposure: 0.0
	last_open_long_positions: 0
	last_open_short_positions: 0
	last_portfolio_value: 0.0
	last_earnings: 1190.42
	final_balance: 1190.42

ChaikinMoneyFlow reviewed

Trading Simulation for technical indicator:  ChaikinMoneyFlow

- Simulation executed in : 10.56 seconds.

- Simulation Statistics:
	number_of_trading_days: 5651
	number_of_buy_signals: 582
	number_of_ignored_buy_signals: 0
	number_of_sell_signals: 616
	number_of_ignored_sell_signals: 0
	last_stock_value: 475.5
	last_exposure: 4414.7
	last_open_long_positions: 5
	last_open_short_positions: 3
	last_portfolio_value: 951.0
	last_earnings: 4266.14
	final_balance: 5217.14

ChaikinOscillator reviewed.

Trading Simulation for technical indicator:  ChaikinOscillator

- Simulation executed in : 11.27 seconds.

- Simulation Statistics:
	number_of_trading_days: 5651
	number_of_buy_signals: 374
	number_of_ignored_buy_signals: 0
	number_of_sell_signals: 464
	number_of_ignored_sell_signals: 0
	last_stock_value: 475.5
	last_exposure: 7723.62
	last_open_long_positions: 8
	last_open_short_positions: 5
	last_portfolio_value: 1426.5
	last_earnings: 2922.97
	final_balance: 4349.47


Unnecessary (inferred)  DOM elements (and also uses unnecessary  elements)

https://github.com/KaTeX/KaTeX/issues/3333
I wrote this issue on KaTex, because was not sure Temml had the same issues, and I am not yet using Temml with my Markdown processor.
However, I forgot that I could check quickly with the demo on your homepage.
It appears Temml does also suffer from these same issues:

Unnecessary explicit <mrows> that could have been inferred.
Use of <mstyle> tags, whereas style could be applied to either mrows or other elements directly.
Content of the <semantics> tag often wrapped in an unnecessary mrow.

I'm happy to be challenged, if there is something I am not considering.
I hope I am coming across in the constructive manner that I hope I am.  I'm really motivated to help math-on-the-web becomes slick, fast, with minimal DOM elements and where authors no longer have to consider performance or their lighthouse score when writing it!
Reading the discussions and issues on the Katex repository it appears that webpage payload size and DOM size really is an issue that is affecting people writing math in the real world.

In version 0.2.2, <mstyle> elements no longer contain a unecessary <mrow> child.


 See list of commits

I see a lot of changes here in the final style, and it is not just a refactor, so I need to test that deeper.

Sure, branch it, test it. See how you like it. 👍
I am better at CSS than i am at gulp. So I'm sure you will find some nice upgrades. 🙂
There is also a possibility to use CSS variables today var(--trumbowyg-this-that) if you would like them defined on the client side.
https://caniuse.com/?search=css variables

Partially merged, thank you :)


[Sort] Transcluded content is showing T instead of the arrow

Bug, feature request, or proposal:
Bug
What is the expected behavior?
I would expect the table header sort icon to paint properly.  (No icon instead of a T)
What is the current behavior?
The table paints a T instead of an arrow, and the T is visible immediately.
What are the steps to reproduce?
It is recreated at this stackblitz:
https://stackblitz.com/edit/angular-vcmq2p
If you remove either the setTimeout from app.component.ts OR you get rid of the transclusion it works fine.
My investigation seems to point to some of the animations at startup to rotate the angles of the arrow and set opacity are not firing when transclusion is mixed with a delayed *ngIf.  You can see a working version without transclusion at this stackblitz:
https://stackblitz.com/edit/angular-vcmq2p-8dpzaa
What is the use-case or motivation for changing an existing behavior?
It is a bug and causes our sorted tables to all have Ts on them.
Which versions of Angular, Material, OS, TypeScript, browsers are affected?
We are using Angular 5.2.10, and material 5.2.4.  All browsers.
Is there anything else we should know?

I have the same problem in my project

As a temporary work around I added this to the app's styles.scss:
.mat-sort-header-arrow {
  opacity: 0;
}

It prevents the T from always showing when the transcluded mat-table is first displayed. It still is there, of course, if the cursor hovers over the sort, but only for the initial state. Once the sort is used, it seems okay.

I believe this has been resolved


Info

I'm trying to load a .torrent file from my desktop in a app make with electron, but when I add the .torrent file to appp y Only have the option to save the file, and I don't know how to fix it.
Also the dragDrop function give me an error on start that say:
Uncaught TypeError: Cannot read property 'addEventListener' of null
Can anyone hel me?¿

It sounds like you're using Electron. If you don't set up drop handlers correctly, then the default browser behavior is to just save the file, which is what you're seeing.
I recommend reading the drag-drop docs more closely. It looks like you're not passing a real DOM node.

Where I can found more help to set up drop handlers? I can't find much help in docs.

Have you tried the code in the "Creating a new torrent and seed it (in the browser)" example on this page: https://webtorrent.io/intro

yes but didn't work. Myn actual code look like:
dragDrop(document.getElementById("draganddrop"), function(file){     console.log(file);     torrentID = file;     addTorrent(torrentID); })
before I pass only 'draganddrop' as object, it's my div to drag files, but never show me other option more that save the file draged, and I want to add the torrent to the client via addTorrent function.

I'm sorry, I don't understand what you're asking.


bug: cannot install complexipy

Hi, complexipy seems super cool/useful, but I'm struggling to get it installed. Running pip install complexipy gives me the following error:
➜  pip install complexipy
Collecting complexipy
  Using cached complexipy-0.3.2.tar.gz (22 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... error
  error: subprocess-exited-with-error

  × Preparing metadata (pyproject.toml) did not run successfully.
  │ exit code: 1
  ╰─> [15 lines of output]
      error: failed to select a version for `env_logger`.
          ... required by package `complexipy v0.3.2 (/private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-install-fa1nou7v/complexipy_5a3f8d99f5f74c59b7863bfc1472c7bf)`
      versions that meet the requirements `^0.11.1` (locked to 0.11.1) are: 0.11.1

      the package `complexipy` depends on `env_logger`, with features: `anstream` but `env_logger` does not have these features.
       It has an optional dependency with that name, but that dependency uses the "dep:" syntax in the features table, so it does not have an implicit feature with that name.

      failed to select a version for `env_logger` which could resolve this conflict
      💥 maturin failed
        Caused by: Cargo metadata failed. Does your crate compile with `cargo build`?
        Caused by: `cargo metadata` exited with an error:
      Error running maturin: Command '['maturin', 'pep517', 'write-dist-info', '--metadata-directory', '/private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-modern-metadata-tap0eiff', '--interpreter', '/Users/cameronbrill/.pyenv/versions/3.8.5/bin/python3.8']' returned non-zero exit status 1.
      Checking for Rust toolchain....
      Running `maturin pep517 write-dist-info --metadata-directory /private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-modern-metadata-tap0eiff --interpreter /Users/cameronbrill/.pyenv/versions/3.8.5/bin/python3.8`
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

I am running on an Apple M1 Max Macbook pro on MacOS 14.4.1. My python version is 3.8.5. Am I missing some setup step?

hey @cameronbrill thank you for using complexipy, I'm gonna check this error and try to fix it, thank you for all the details, I also use a M1 Pro so I think I could reproduce this error!

@cameronbrill I was testing locally, and it seems that complexipy isn't working in python 3.8 due to the type hints that I'm using in the project, I couldn't install python 3.8.5 locally, but I installed complexipy in the version 3.8.19 and this is the version that fails during the execution of complexipy due to the type hints as I previously mentioned.
Already tested it in the version 3.9 and is working. Used the version 3.9.19.

I think that due to this issue, I will update the minimum python version to 3.9. Thank you so much for your contribution!
If you want to be a contributor and be listed at the contributor section, feel free to create a PR updating the minimum version to 3.9, pls relate that PR to this issue. ex: fix(build): #41 ...

Hi, thanks for the response! I just updated to python 3.9.18 but am still unable to install complexipy with the same error. fwiw this seems like a rust error, but I don't have any experience developing with rust so don't really know what to try next. Here's the important-looking part of the pip install complexipy error message:
      error: failed to select a version for `env_logger`.
          ... required by package `complexipy v0.3.2 (/private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-install-i3h3smea/complexipy_353eb0ffe4ea4becb344a01a09558e31)`
      versions that meet the requirements `^0.11.1` (locked to 0.11.1) are: 0.11.1

      the package `complexipy` depends on `env_logger`, with features: `anstream` but `env_logger` does not have these features.
       It has an optional dependency with that name, but that dependency uses the "dep:" syntax in the features table, so it does not have an implicit feature with that name.

      failed to select a version for `env_logger` which could resolve this conflict
      💥 maturin failed
        Caused by: Cargo metadata failed. Does your crate compile with `cargo build`?
        Caused by: `cargo metadata` exited with an error:
      Error running maturin: Command '['maturin', 'pep517', 'write-dist-info', '--metadata-directory', '/private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-modern-metadata-3oimthw3', '--interpreter', '/Users/cameronbrill/.pyenv/versions/3.9.18/bin/python3.9']' returned non-zero exit status 1.
      Checking for Rust toolchain....
      Running `maturin pep517 write-dist-info --metadata-directory /private/var/folders/wx/0d9m1sq12992h4fxxwf4fpf00000gn/T/pip-modern-metadata-3oimthw3 --interpreter /Users/cameronbrill/.pyenv/versions/3.9.18/bin/python3.9`

@cameronbrill I'm still trying to figure out why it's giving you that error message 🤔

@cameronbrill found out that env_logger was an unused dependency, I removed the unused dependencies and published a new complexipy version, I think that you can try again to install complexipy.


Documentation improvement

-Fixing the doxygen version, not to get unexpected changes
-Added automatic build of command dictionary
-Added quick install with docker
-Added Astrobee guide
-Changes the hyperlinks to be blue
-Separated indexes
Changes are in https://marinagmoreira.github.io/astrobee/html/index.html

The review was mainly made here: https://github.com/marinagmoreira/astrobee/pull/7


Disable plugin if solr isnt part of the project being tested

On a project unrelated to solr I am getting an error because the plugin doesnt deactive if it doesnt detect solr is configured.   I'm surprised it gets loaded when it isn't even in INSTALLED_APPS, but maybe pytest loads things differently than django does.

Ya, pytest entry points are all loaded at the beginning, and need to delay any serious work until a pytest hook has called the plugin to configure itself, and then it needs to be sure the plugin is wanted.

Part of this may be because I have the plugin defined with autouse=True — but that still seems reasonable to me, once I add a check that it's in INSTALLED_APPs as you suggest.
I just pushed a hotfix branch hotfix/0.5.4   — do you want to see if it solves your problem?

Fixed in version 0.5.4.


Rtsp

Hello, does this player support RTSP streams?
Thanks

No, it's limited to the sources supported by the native video controls.


floatingIP not working

Hi all,
I was experimenting with the floating IPs but I didn't manage to have it working.
I did follow the guide but I cannot ping the pod, neither access to the service it is providing (a basic nginx just for testing)
I'm pretty sure I'm doing something wrong, but I was unable to debug further from the container/host.
Any help would be more than appreciated.
Expected Behavior
I'm able to access the service exposed from outside the cluster
Current Behavior
I cannot access the service provided by the pool
Possible Solution

Steps to Reproduce (for bugs)

Enable floating IPs

cni_network_config:
----
{
  "name": "k8s-pod-network",
  "cniVersion": "0.3.1",
  "plugins": [
    {
      "type": "calico",
      "log_level": "info",
      "log_file_path": "/var/log/calico/cni/cni.log",
      "datastore_type": "kubernetes",
      "nodename": "__KUBERNETES_NODE_NAME__",
      "mtu": __CNI_MTU__,
      "ipam": {
          "type": "calico-ipam"
      },
      "policy": {
          "type": "k8s"
      },
      "kubernetes": {
          "kubeconfig": "__KUBECONFIG_FILEPATH__"
      },
      "feature_control": {
          "floating_ips": true
      }
    },
    {
      "type": "portmap",
      "snat": true,
      "capabilities": {"portMappings": true}
    },
    {
      "type": "bandwidth",
      "capabilities": {"bandwidth": true}
    }
  ]
}

Create IPv4 pool:

kubectl calico get ippools my.ippool-1 -o yaml
apiVersion: projectcalico.org/v3
kind: IPPool
metadata:
  creationTimestamp: "2023-07-31T15:08:16Z"
  name: my.ippool-1
  resourceVersion: "4982"
  uid: 52436049-4c25-4bd8-9efe-a64df2b42363
spec:
  allowedUses:
  - Workload
  - Tunnel
  blockSize: 26
  cidr: <IP_ADDRESS>/24
  ipipMode: Always
  natOutgoing: true
  nodeSelector: all()
  vxlanMode: Never

create a deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
      annotations:
        "cni.projectcalico.org/floatingIPs": "[\"<IP_ADDRESS>\"]"
        # "cni.projectcalico.org/ipAddrs": "[\"<IP_ADDRESS>\"]" tried with and without, no changes
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

from another machine: curl http://<IP_ADDRESS>

Context
I was trying to understand if calico and floating IP are a solution to my problem of exposing a service outside of K8s cluster
Your Environment

Calico version : v.3.26.1
Orchestrator version: v1.27.3+k3s1
Operating System and version: Ubuntu 22.04.2
k3s node ip is <IP_ADDRESS>, connected to the <IP_ADDRESS>/24 subnet, the machine trying to ping is attached to the same subnet.

kubectl get nodes -o wide
NAME         STATUS   ROLES                  AGE    VERSION        INTERNAL-IP     EXTERNAL-IP   OS-IMAGE             KERNEL-VERSION      CONTAINER-RUNTIME
k8s-master   Ready    control-plane,master   138m   v1.27.3+k3s1   <IP_ADDRESS>   <none>        Ubuntu 22.04.2 LTS   5.15.0-78-generic   containerd://1.7.1-k3s1

Hi @gabrik, it looks like you added the floatingIPs annotation to a deployment, and that won't work. In order to get it to work, you need to annotate a specific pod.
BTW, since you tried to add floating IP to a deployment, may I ask why not use a kubernetes service? There is support for making services accessible outside the cluster (though I might be missing some extra requirement/issue you may be having with it)...


Add Poison 3.0 as possible dependency

Unlocks Poison to be able to use 3.0

Coverage remained the same at 84.444% when pulling 1fce33d3dc92c2fe58c08fcac9b88c1b1513a67e on cs-victor-nascimento:vn/add_poison_3.0 into 1a96ce2fb745bcecff7026b4c6018828704d7031 on bryanjos:master.

👍


Databricks Jobs failing to import mmlspark. ModuleNotFoundError: No module named 'mmlspark'

Hi, I'm able to run fine mmlspark on my notebook in Databricks. However, when I try to run this same notebook in a scheduled job, it fails and retrieves the message: ModuleNotFoundError: No module named 'mmlspark' .
The version currently of installed on my cluster is "Azure:mmlspark:0.17"
Do you have any recommendation on how to set up a Job to run mmlspark correctly?
Thanks

Hey @nataliabernardo consider using our spark package install on databricks described in this section
https://github.com/Azure/mmlspark#databricks
This link gives a detailed step by step of the databricks UI
https://docs.microsoft.com/en-us/azure/cognitive-services/big-data/getting-started#azure-databricks
Please feel free to comment or re-open if this doesn't work!

Hi @mhamilton723,
I followed the steps and installed "com.microsoft.ml.spark:mmlspark_2.11:1.0.0-rc3" and attached to my cluster. Also uninstalled the other version ("Azure:mmlspark:0.17").
After that, both notebook and job are failing to run

When try running the Job: ModuleNotFoundError: No module named 'mmlspark'
When try running the Notebook: ImportError: cannot import name 'LightGBMRegressor' from 'mmlspark' (/local_disk0/spark-2d6934aa-583e-441f-a43b-05885373d248/userFiles-1aa6ffb5-d69b-479a-b75e-9640f9a7fbdf/addedFile7238710938107874708mmlspark_2_11_1_0_0_rc3-86e1e.jar/mmlspark/init.py)

Spark version: 6.6 (includes Apache Spark 2.4.5, Scala 2.11)


Add debian branch in Vcs-Git field, use secure URI.

Add debian branch in Vcs-Git field, use secure URI.

Hi @jelmer, thanks for your PR, but note that flickrbackup is marked for removal (#904925) and will no longer be updated in Debian.

Ah, thanks. Sorry for the noise.


x/website: Go latest release version info oh home page

What version of Go are you using (go version)?
NA
Does this issue reproduce with the latest release?
NA
What operating system and processor architecture are you using (go env)?
NA
What did you do?
Visit home page https://golang.org/
What did you expect to see?
This is new feature.  As user of Go language, it would be great if I can see latest release version of Go on home page.
The Usecase is, as Go lang user, I visit home page to see latest updates like new blog, new go release etc.  If I can see latest release version of Go then I can instantly download and start using it.
I just expect Go release number somewhere on home page.
What did you see instead?
Currently, I need to first visit Home page https://golang.org/ and then click "Download Go" which takes me to https://golang.org/dl/ where I can see latest release as well as download link.

Thanks for the request.
This is essentially a dup of #33083 (show current Go version next to download button on homepage). Andybons is working on this, so I think we can close this issue in favour of the older one. Feel free to subscribe to that thread for updated.

Sorry for the confusion here. OP had opened #33145 which I closed citing the same reason. And I asked him to open a new issue for this.
As far as I understand, #33083 is just about showing the existing string which was shown at the footer to be shown next to the download page. But that string just shows the version with which x/website is being built.
OP wants the latest released Go version to be displayed on the home page. I understand both are eventually equivalent, but not exactly same. If that is what @andybons meant in #33083, then we can close this.
So just to be clear, I will reopen this and leave it to Andy for a final call.
Apologies for the to-and-fro.

Wait, I don't get it. If we end up showing something on the DOWNLOAD button, it'll be the latest released version, right? Which is what the OP is asking here. So #33083 will solve the issues this OP has.
I don't think the version near the download button will show the version the website was built with (it wouldn't make much sense for that information to be on the download button).
Also the OP in that old issues wrote:

I used to check the website to see if a new version was released

it seems he's talking about the latest released version, I don't think he cares about the version the website was build for. So my understanding is that 1) OP here and OP in #33083 are asking exactly for the same thing 2) andybons work will fix #33083 (and this one) and satisfy both OPs

@ALTree yes, I don't care about the version the website was build for.  I am requesting to show Go latest release version on home page. (anywhere on homepage).

@ALTree  Yeah, I guess you are right. I just wanted to clarify that the initial assumption of OP in #33083 was wrong. It's not just a matter of showing the exact same string in a different location now. We will be showing something completely different.
I guess we can close this since the end result of #33083 is the right one in any case.
@nurali-techie - My apologies for asking you to open a new issue. It seems #33083 is what you should be tracking.


Update epoch node configuration

change configuration format to YAML
enable persistance
autostart miner

Because of bug in the chain.db_path it's always set to /.chain.

Please remove chain db path workaround.

@dincho I understand that as long as PR https://github.com/aeternity/epoch/pull/376 is merged before this, it is safe to remove workaround.
I understand PR https://github.com/aeternity/epoch/pull/377 is not needed for this PR.
Cc @velzevur @uwiger

@dincho PR #376 was merged so this PR can be amended removing workaround.

@dincho I understand that as long as PR #376 is merged before this, it is safe to remove workaround.

Yes, I'll do. Is the daemon creates the configured directory automatically or we need to do it prior configuration?

Yes, I'll do. Is the daemon creates the configured directory automatically or we need to do it prior configuration?

The deamon creates the configured directory automatically.

I see one of the two Travis CI builds passed.
I see the other Travis CI build had all required jobs passing except package job on OSX:
Will mine till block 50
[node1] height=20
[node2] height=2
[node3] height=20

[node1] height=20
[node2] height=2
[node3] height=20

[node1] height=20
[node2] height=2
[node3] height=20

[node1] height=21
[node2] height=2
[node3] height=21

[node1] height=21
[node2] height=2
[node3] height=21

[node1] height=21
[node2] height=2
[node3] height=21

[node1] height=23
[node2] height=2
[node3] height=23

[node1] height=23
[node2] height=2
[node3] height=23

[node1] height=24
[node2] height=2
[node3] height=24

[node1] height=25
[node2] height=2
[node3] height=25

[node1] height=25
[node2] height=2
[node3] height=25

...

@dincho This looks like intermittent test failure independent from this PR.
Cc @velzevur @sennui @wagerlabs


BT-111 added WIH of storing CommRequest

Adds a WIH for storing a FHIR4 CommunicationRequest based on SMS data

The content will be moved elsewhere


Generation of our own dataset

How can we generate the label file for each image? Can someone help me.

I think: You have a set of images and a set of labels for those images. The labels are just the masks of those images. The area of the image which you feel the model should be trained on must be in white and other areas black. I trained my model with this approach, but in the end, when the masks were generated by the model for the test data, it was full black. I don't know where I am going wrong. Any help from someone who was successful would be great.

Hey did you got any solution for this, I'm also facing same issue.

I have obtained the solution. All you have to do is to add the BatchNormalization after every Conv2D layer except for the last Conv2D layer. Ex:
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = BatchNormalization()(conv1)

Go on till 9th layer. Don't add for 10th Conv2D layer. And also make sure that the resolution of your training set and test set images are divided by 32. Ex. 512x512 or 640x960. Otherwise, you can expect the model to output a blank image.

Thanks a lot. Still I'm facing same issue. Code s available in the link below
https://github.com/sudhasubramaniam/segmentation

Thanks a lot. Still I'm facing same issue. Code s available in the link below
https://github.com/sudhasubramaniam/segmentation

You need to add the BatchNormalization to the up-sampling layers as well. For example:
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
up6 = BatchNormalization()(up6)

Do this for up6, up7, up8 and up9.

No change. Still facing same issue.

No change. Still facing same issue.

Try the code from my repo. Link here.

Thankyou, It working now. If you see my github repository my training dataset has training image and label. Please let me know whether I can extract boundary information as in label dataset.

Try to keep only the boundary information on the labels. Blackout all areas except for the boundary region so that the network focuses on the specific region. Ex :
 without those red and yellow lines and then train the network.

Thank you. Let me try as such.

Hi sai, I tried training by retaining only the boundary information on labels but haven't got proper output. (epoch-300)
/home/ksr-ece/Downloads/U-Net-master/data/results/3.jpg

Hi Sai, I trained with boundary information but still my perdiction is not good. (epoch-300).
Please comment on causes
https://github.com/sudhasubramaniam/segmentation
I used your code for training.

Hi,
The possible reasons why your model has failed are:

The image set has extra information. Can you remove all those? Like
Length, LCCA, + Markers and other such information.

The masks are somewhat not clear. There are shades of the surface here
and there. The algorithm can make mistakes while learning. Have you done
the masks manually? I would suggest you define the Region of Interest (RoI)
as a formula or something and then create masks programmatically using
OpenCV. In my case, the RoI was a set of points. So, I would create a
polygon out of those points and then prepare the masks accordingly.

Most importantly: Training Data size. After taking a look at your
repository, I found that you were having just 12 images which are very
insufficient for training a complicated algorithm like U-Net or any other
such algorithm. I had a total of 120 images and out of which 100 were given
as training set and the rest 20 were given as test set. I ran the algorithm
first for 20 epochs and then for 50 epochs. The accuracy improved. I am not
talking about the accuracy that is getting produced as output after every
epoch. By accuracy, I mean manually comparing the original test masks with
the generated test masks. The generated ones were a little better. Now I
might train my U-Net with a set of 1000 images. I also advise you to do the
same. A large training set is important.

Also, I feel that there might be a case of overfitting the model when
you run for 300 epochs. Start with 20 or 25. Then go to 50 and so on.

Try to change the metric to IoU (Intersection over Union) in the U-Net
code. Might perform a little better.

On Thu, Aug 29, 2019 at 3:50 PM sudhasubramaniam<EMAIL_ADDRESS>wrote:

Hi Sai, I trained with boundary information but still my perdiction is not
good. (epoch-300).
Please comment on causes
https://github.com/sudhasubramaniam/segmentation
I used your code for training.
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Thank you for your comments.

Try to keep only the boundary information on the labels. Blackout all areas except for the boundary region so that the network focuses on the specific region. Ex :
 without those red and yellow lines and then train the network.

HI, i'm wondering how to make the label or exactly the mask of the image.
With labelme I get  the json file that description different area with different labels, so if I want to export the label as Image, shall I use  GIMPS2 to set background as BLACK?
By the way,  even if the background can be picked out, how should I notate  other area with more than 2 categories (in other words, there are background area, target area, and some fault area).
How can the image be colored by different area and same category should be the same color,  like the video shows:

@BriFuture I will tell you the simple steps to create the masks. Assuming you have 2 classes:

Open the JSON file in python and process image by image.
Read the corresponding image from the disk.
Create a mask having the same size as the image using np.zeros()
I have not used labelme, but I have used VGG Annotation tool. Here you need to read the coordinates one by one for the corresponding image and then use cv2.drawContours() to fill that polygon with (255, 255, 255) on the mask. Do this for all the polygons.
Save the masks separately in a masks folder using cv2.imwrite().
Do this for all the images.

Thank you.
But should different  classes be filled with different color? Em, I have tried to train the network with custom datasets (build with GIMP but poorly distinguished), here is a sample:
origin:

label:

In the origin image, the white area is one class, while red area is another one, the gray is background. I just colored background with black color. Others are left unchanged. Is that OK?
Do I need leave contours only without any other color information ?

I searched google and find this repo which contains 4 classes.

One color for one class. The regions that are not of interest make it black. And also make sure you change the loss function from binary_crossentropy if you are dealing with more than 2 classes.

I searched google and find this repo which contains 4 classes.
Ok, so you can also color your labels(masks) in this way (1, 1, 1) or (2, 2, 2) instead of red, blue, green white etc.

thanks for the details.
I'll give it a try.

@BriFuture I will tell you the simple steps to create the masks. Assuming you have 2 classes:

Open the JSON file in python and process image by image.
Read the corresponding image from the disk.
Create a mask having the same size as the image using np.zeros()
I have not used labelme, but I have used VGG Annotation tool. Here you need to read the coordinates one by one for the corresponding image and then use cv2.drawContours() to fill that polygon with (255, 255, 255) on the mask. Do this for all the polygons.
Save the masks separately in a masks folder using cv2.imwrite().
Do this for all the images.

Could you please release the code which use to generate masks(labels) with the annotated json file?

@synioe That completely depends on the json file that you have. Different annotation tool generates jsons in a different way.

@sainatarajan I use VGG image annotator to aquire the json file.

@synioe
As @sainatarajan  said, different tools generates json may differ from eachother. The json format you currently used may be different from mine.
Here is a sample code I write to process with labelme:
for ld in sorted(labeldir.iterdir()):
    if ld.suffix == ".json":
        name = str(ld)[:-5]
        print("Processing", name)
        img = cv2.imread(f"{name}.bmp")

        contoursImg = np.zeros(img.shape)
        with open(ld, "r") as f:
            jsoncontent = json.load(f)
            shapes = jsoncontent["shapes"][0]["points"]

            for i, s in enumerate(shapes):
                 shapes[i] = np.array([ np.array(s).astype(int) ])
            contour = np.array(shapes)

            contouredImg = cv2.drawContours(contoursImg, [contour], -1, (255, 255, 0), 2)
            plt.figure()
            plt.imshow(contouredImg)
            plt.show()
            slashpos = name.find('/')
            cv2.imwrite(f'label/{name[slashpos+1:]}.png', contouredImg)

just be aware of that, contours should be colored, while other areas not.
By the way, the param contour of drawContours method is hard to understand and construct, I tried several times to get it work.

@synioe Even I use the VGG tool. So in that case I would be able to help you.

@BriFuture Thank you for your sharing. @sainatarajan Thank you all the same.

@synioe This might look a bit complicated, but it might help.
"""
my format of json

{
"image1.jpg":{
        "filename":"image1.jpg",
        "size":123456,
        "regions":[
            {
                "shape_attributes":{
                    "all_points_x":[
                        675,
                        808,
                        957,
                        967,
                        929,
                        791,
                        678,
                        703
                    ],
                    "all_points_y":[
                        543,
                        518,
                        492,
                        722,
                        760,
                        760,
                        760,
                        647
                    ],
                    "name":"polygon"
                },
                "region_attributes":{
                    "Object":"class1",
                }
            },...all other regions..{}]
    }, ..all other images..., {}
}

"""

import glob, os, cv2
import numpy as np
import multiprocessing as mp
from PIL import Image
from delaunay2D import Delaunay2D

def calculateDelauneyPoints(points):
    points= np.array(points).astype(int)
    points= [list(item) for item in points]
    dt = Delaunay2D()
    for s in points:
        dt.addPoint(s)
    coord, tris= dt.exportDT()
    return np.array(coord)

def maskImage(filename, polygons, o_list):
    root_path= '../data/'
    image = cv.imread(root_path+"all_images/"+filename)
    mask = np.zeros(shape = image.shape, dtype = "uint8")
    i= 0
    for points in polygons:
        points= [(int(x), int(y)) for x,y in points]
        try:
            rect= calculateDelauneyPoints(points)
        except:
            continue
        if o_list[i] in ['class1']:
            cv.drawContours(mask,[rect], -1,(1, 1, 1),cv.FILLED)
        elif o_list[i] in ['class2']:
            cv.drawContours(mask,[rect], -1,(2, 2, 2),cv.FILLED)
        elif o_list[i] in ['class3']:
            cv.drawContours(mask,[rect], -1,(3, 3, 3),cv.FILLED)
        i+= 1

    cv.imwrite(root_path+"dataset/images/"+os.path.splitext(filename)[0]+".png", image)
    cv.imwrite(root_path+"dataset/masks/"+os.path.splitext(filename)[0]+".png", mask)

def getRegionProperties(region):
    shape_attributes= region["shape_attributes"]
    region_attributes= region["region_attributes"]
    objects= region_attributes["Object"]
    regions= ['class1', 'class2', 'class3']
    all_points_x= shape_attributes["all_points_x"]
    all_points_y= shape_attributes["all_points_y"]
    coordinates= []
    for i in range(0, len(all_points_x)):
        coordinates.append((all_points_x[i], all_points_y[i]))

    return (objects, coordinates)

def parallelizePlotting(data, region_mappings, json_data):

    polygon_coordinates= {}
    polygons= []
    img_json_data= json_data[data]

    filename= img_json_data["filename"]

    # Open the original image here
    image_matrix= np.array(Image.open('../data/all_images/'+filename), dtype= np.uint8)
    region_data= img_json_data["regions"]
    objects_list= []

    for region in region_data:
        objects, coordinates= getRegionProperties(region)
        if coordinates is not None:
            polygons.append(coordinates)
            polygon_coordinates[objects]= coordinates
            objects_list.append(objects)

    # Masking the images
    maskImage(filename, polygons, objects_list)
    return (filename, polygon_coordinates)

def main():

    root_path= 'path'

    json_file= open(root_path+'filename.json')
    json_data= json.load(json_file)

    # for multiprocessing
    pool = mp.Pool(mp.cpu_count()-1)

    def resultCallback(item):
        return

    for data in json_data:
        pool.apply_async(parallelizePlotting, args=(data, json_data), callback= resultCallback)

    pool.close()
    pool.join()

if __name__ == "__main__":
    main()

@synioe This might look a bit complicated, but it might help. Take the delaunay2D file from here
"""
my format of json

{
"image1.jpg":{
        "filename":"image1.jpg",
        "size":123456,
        "regions":[
            {
                "shape_attributes":{
                    "all_points_x":[
                        675,
                        808,
                        957,
                        967,
                        929,
                        791,
                        678,
                        703
                    ],
                    "all_points_y":[
                        543,
                        518,
                        492,
                        722,
                        760,
                        760,
                        760,
                        647
                    ],
                    "name":"polygon"
                },
                "region_attributes":{
                    "Object":"class1",
                }
            },...all other regions..{}]
    }, ..all other images..., {}
}

"""

import glob, os, cv2
import numpy as np
import multiprocessing as mp
from PIL import Image
from delaunay2D import Delaunay2D

def calculateDelauneyPoints(points):
    points= np.array(points).astype(int)
    points= [list(item) for item in points]
    dt = Delaunay2D()
    for s in points:
        dt.addPoint(s)
    coord, tris= dt.exportDT()
    return np.array(coord)

def maskImage(filename, polygons, o_list):
    root_path= '../data/'
    image = cv.imread(root_path+"all_images/"+filename)
    mask = np.zeros(shape = image.shape, dtype = "uint8")
    i= 0
    for points in polygons:
        points= [(int(x), int(y)) for x,y in points]
        try:
            rect= calculateDelauneyPoints(points)
        except:
            continue
        if o_list[i] in ['class1']:
            cv.drawContours(mask,[rect], -1,(1, 1, 1),cv.FILLED)
        elif o_list[i] in ['class2']:
            cv.drawContours(mask,[rect], -1,(2, 2, 2),cv.FILLED)
        elif o_list[i] in ['class3']:
            cv.drawContours(mask,[rect], -1,(3, 3, 3),cv.FILLED)
        i+= 1

    cv.imwrite(root_path+"dataset/images/"+os.path.splitext(filename)[0]+".png", image)
    cv.imwrite(root_path+"dataset/masks/"+os.path.splitext(filename)[0]+".png", mask)

def getRegionProperties(region):
    shape_attributes= region["shape_attributes"]
    region_attributes= region["region_attributes"]
    objects= region_attributes["Object"]
    regions= ['class1', 'class2', 'class3']
    all_points_x= shape_attributes["all_points_x"]
    all_points_y= shape_attributes["all_points_y"]
    coordinates= []
    for i in range(0, len(all_points_x)):
        coordinates.append((all_points_x[i], all_points_y[i]))

    return (objects, coordinates)

def parallelizePlotting(data, region_mappings, json_data):

    polygon_coordinates= {}
    polygons= []
    img_json_data= json_data[data]

    filename= img_json_data["filename"]

    # Open the original image here
    image_matrix= np.array(Image.open('../data/all_images/'+filename), dtype= np.uint8)
    region_data= img_json_data["regions"]
    objects_list= []

    for region in region_data:
        objects, coordinates= getRegionProperties(region)
        if coordinates is not None:
            polygons.append(coordinates)
            polygon_coordinates[objects]= coordinates
            objects_list.append(objects)

    # Masking the images
    maskImage(filename, polygons, objects_list)
    return (filename, polygon_coordinates)

def main():

    root_path= 'path'

    json_file= open(root_path+'filename.json')
    json_data= json.load(json_file)

    # for multiprocessing
    pool = mp.Pool(mp.cpu_count()-1)

    def resultCallback(item):
        return

    for data in json_data:
        pool.apply_async(parallelizePlotting, args=(data, json_data), callback= resultCallback)

    pool.close()
    pool.join()

if __name__ == "__main__":
    main()

very userful,thanks a lot.

i m facing the same issue. My predicted image is a gray box. Can anyone help

@BriFuture I will tell you the simple steps to create the masks. Assuming you have 2 classes:

Open the JSON file in python and process image by image.
Read the corresponding image from the disk.
Create a mask having the same size as the image using np.zeros()
I have not used labelme, but I have used VGG Annotation tool. Here you need to read the coordinates one by one for the corresponding image and then use cv2.drawContours() to fill that polygon with (255, 255, 255) on the mask. Do this for all the polygons.
Save the masks separately in a masks folder using cv2.imwrite().
Do this for all the images.

can you share me the code for this. I have multiple classes in a single image

@Cipherpy Take a look here

@Cipherpy Take a look here

Thank you

@Cipherpy Take a look here

what's the data in the def parallelizePlotting(data, region_mappings, json_data):?
get confused

@sainatarajan
do you still remember where the data came from in the function of "def parallelizePlotting(data, region_mappings, json_data):"


⚠ Jenkins Plugins Artifactory has degraded performance

In b969cf2, Jenkins Plugins Artifactory (https://get.jenkins.io/plugins/artifactory/) experienced degraded performance:

HTTP code: 200
Response time: 2852 ms

Resolved: Jenkins Plugins Artifactory performance has improved in 346a640 after 2 hours, 4 minutes.


Sort printed tables by name

The output generated by diesel print-schema is not stable across database environments.
I guess it's sensible to sort the tables alphabetically to be able to diff schemas.

Can we do the sorting in the actual queries that we execute instead?

Sure, that makes sense and is consistent with the code that does this ordering on Postgres/MySQL already.
I've added the same behavior to the SQLite variant. Should I keep the tests and move them to diesel_tests?

Yeah, that's way better.
@Mrmaxmeier, I'd keep the cli test and add a small unit test to diesel_infer_schema/src/sqlite.rs as well. (Do the others have tests for that? If not, maybe you can add them for PG and MySQL too).

I've added unit tests for SQLite, PG and MySQL and Travis seems happy with them.

Thanks, great work!


Different results between evaluation during training and testing.

Thanks to open source. I have a question, why in Pointcept, the results of the best model evaluation during training are not consistent with the results of the test script when loading best_model.pth?

Hi, the description here might be helpful. (https://github.com/Pointcept/Pointcept#testing)

If my own model does not utilize the data augmentation from Pointcept (including voxelization), can it be considered as having no difference?

Yes, don't forget to disable TTA.

Thanks for your reply!

May I ask what's the definition of "different evaluation processes of training".

For example, I load the same checkpoint, and learning rate is set to 0 (means that model weights doesn't changed, and it's been proven), but I got different result in different evaluation process during traning.

If it is not the design of the Pointcept framework, then I will check my own code.

For example, I load the same checkpoint, and learning rate is set to 0 (means that model weights doesn't changed, and it's been proven), but I got different result in different evaluation process during traning.

It should be fixed unless random augmentation exists in the evaluation transform list. Kindly remind that some operators like scatter in PyG can not be fixed by seed, and my suggestion is that there is no need to care much about how results are fixed by seed. (By results should can be reproduced in the final test process)

Sincerely thanks for your advice!


[1.12.2] Water and air

fml-junk-earlystartup.log
fml-server-latest.log
There are tons of air bubbles throughout ocean biomes;

Plays havoc sounds and sounds like there's a river everywhere in the ocean, not to mention lots of splashing water particle effects.
Water does not flow down into crevices, nooks, and crannies as it should.
Randomly the squids are getting trapped in these air bubbles and suffocating to death, leaving their drops lurking around.
Doesn't make for a pretty map render.
I also have concerns over performance issues, both server side and client side that arise from this type of behaviour, especially if randomly the server decides to start flowing water through these air blocks.
Is this something OTG needs to fix or is there a setting somewhere that I can tweak to fix it?

Fixed in Biome Bundle v6.1. If continuing an existing v6 world make sure you delete the Biome Bundle folder from \mods\OpenTerrainGenerator\worlds as the v6.1 jar will not automatically overwrite your v6 configs.


feat: Add support for creating KMS key for cluster secret encryption

Description

Add support for creating KMS key for cluster secret encryption

Motivation and Context

Adding the cluster IAM role to a KMS key policy creates a cyclical dependency when the key/policy are created externally unless users also create the role externally. Provisioning a cluster with secrets encrypted using KMS requires the KMS key to be created first. Creating the KMS key first with a key policy causes a conflict because the cluster IAM role has not been created yet and cannot be referenced. The only resolution currently is to either create the key, key policy, and role externally to ensure proper ordering and name resolution, or apply secret encryption AFTER the cluster and cluster IAM role have been created (poor user experience). Embedding the key and key policy creation into the module resolves this scenario while allowing users to extend the policy as well

Breaking Changes

No

How Has This Been Tested?

[x] I have updated at least one of the examples/* to demonstrate and validate my change(s)
[x] I have tested and validated these changes using one or more of the provided examples/* projects

[x] I have executed pre-commit run -a on my pull request

Lgtm. I was thinking for some time while reading this if we should prefix variables with 'key_' or 'kms_key_' to avoid potential confusion with "private key". Current naming is good regardless :)

thats a good point, I've updated to include kms_ to make it more clear

This PR is included in version 18.25.0 :tada:


Add code-of-conduct

http://geekfeminism.wikia.com/wiki/Community_anti-harassment/Policy

Actually, I think I'm gonna go with Contributor Covenant


[Bug] Follow the Quick guide to run LLaVA, but a TypeError

Checklist

[x] 1. I have searched related issues but cannot get the expected help.
[x] 2. The bug has not been fixed in the latest version.
[x] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/lmdeploy/vl/model/llava.py", line 176, in forward
image_features = self.encode_images(concat_images)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/lmdeploy/vl/model/llava.py", line 143, in encode_images
image_features = self.mm_projector(image_features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/fsx/wenhao/software/miniconda3/lib/python3.12/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: linear(): argument 'input' (position 1) must be Tensor, not tuple
Reproduction
from lmdeploy import pipeline
from lmdeploy.vl import load_image
pipe = pipeline('liuhaotian/llava-v1.6-vicuna-7b')
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
response = pipe(('describe this image', image))
print(response)
Environment
sys.platform: linux
Python: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed:<PHONE_NUMBER>
GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-40GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.1, V12.1.105
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.2.2+cu121
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.3.2 (Git Hash 2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.9.5  (built against CUDA 12.2)
    - Built with CuDNN 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.2.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

TorchVision: 0.17.2+cu121
LMDeploy: 0.5.1+
transformers: 4.40.0
gradio: 4.38.1
fastapi: 0.111.1
pydantic: 2.8.2
triton: 2.2.0
NVIDIA Topology:
	GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV12	NV12	NV12	NV12	NV12	NV12	NV12	0-23,48-71	0		N/A
GPU1	NV12	 X 	NV12	NV12	NV12	NV12	NV12	NV12	0-23,48-71	0		N/A
GPU2	NV12	NV12	 X 	NV12	NV12	NV12	NV12	NV12	0-23,48-71	0		N/A
GPU3	NV12	NV12	NV12	 X 	NV12	NV12	NV12	NV12	0-23,48-71	0		N/A
GPU4	NV12	NV12	NV12	NV12	 X 	NV12	NV12	NV12	24-47,72-95	1		N/A
GPU5	NV12	NV12	NV12	NV12	NV12	 X 	NV12	NV12	24-47,72-95	1		N/A
GPU6	NV12	NV12	NV12	NV12	NV12	NV12	 X 	NV12	24-47,72-95	1		N/A
GPU7	NV12	NV12	NV12	NV12	NV12	NV12	NV12	 X 	24-47,72-95	1		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

Error traceback
No response

I didn't reproduce the issue. Here are my steps:
pip install lmdeploy
pip install git+https://github.com/haotian-liu/LLaVA.git --no-deps

test code:
from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('liuhaotian/llava-v1.6-vicuna-7b')

image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
response = pipe(('describe this image', image))
print(response)

Can you create a new environment and follow the above steps to reproduce it again?


Facing Error in Azure cognitiveservices speech

Package Name: azure-cognitiveservices-speech
Package Version: 1.21.0
Operating System:Ubuntu(with KDE) 22.04
Python Version: 3.10.6

Description
It is working fine on every machine I have setup before but it is not working on new ones, it throws an error which is mentioned below.
Error
./tts-media/audios/test/[00:00:00,00:00:08] .mp3
Traceback (most recent call last):
  File "/home/shaheer/Desktop/azure-tts-tool/src/new_main_app.py", line 86, in <module>
    subtitleToClips(folderName) # text to video clips
  File "/home/shaheer/Desktop/azure-tts-tool/src/new_main_app.py", line 78, in subtitleToClips
    lineToClip(line, audioOutputFolder, videOutputFolder)
  File "/home/shaheer/Desktop/azure-tts-tool/src/new_main_app.py", line 39, in lineToClip
    textToMp3(text, audioFilePath)
  File "/home/shaheer/Desktop/azure-tts-tool/src/textToAudio.py", line 16, in textToMp3
    speech_synthesizer = speechsdk.SpeechSynthesizer(
  File "/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/speech.py", line 1563, in __init__
    self._impl = self._get_impl(impl.SpeechSynthesizer, speech_config, audio_config, auto_detect_source_language_config)
  File "/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/speech.py", line 1667, in _get_impl
    _impl = synth_type._from_config(speech_config._impl, None if audio_config is None else audio_config._impl)
RuntimeError: Runtime error: Failed to initialize platform (azure-c-shared). Error: 2153
[CALL STACK BEGIN]

/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1a7253) [0x7fb3b43a7253]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1c108f) [0x7fb3b43c108f]
/lib/x86_64-linux-gnu/libc.so.6(+0x99f68) [0x7fb3b66d1f68]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1c21dc) [0x7fb3b43c21dc]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x19f12f) [0x7fb3b439f12f]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x19d039) [0x7fb3b439d039]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1abb5f) [0x7fb3b43abb5f]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x189c46) [0x7fb3b4389c46]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1843cc) [0x7fb3b43843cc]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0xc3bfd) [0x7fb3b42c3bfd]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1abb5f) [0x7fb3b43abb5f]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x17c3dd) [0x7fb3b437c3dd]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(+0x1e0840) [0x7fb3b43e0840]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/libMicrosoft.CognitiveServices.Speech.core.so(synthesizer_create_speech_synthesizer_from_config+0xf3) [0x7fb3b42ab6ac]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/_speech_py_impl.so(+0xde414) [0x7fb3b4ade414]
/home/shaheer/Desktop/azure-tts-tool/venv/lib/python3.10/site-packages/azure/cognitiveservices/speech/_speech_py_impl.so(+0xd2573) [0x7fb3b4ad2573]
python(+0x15c9c8) [0x56300cccc9c8]
[CALL STACK END]

The issue was resolved by installing "libssl1.1"
I was facing an issue in installing it and Ubuntu 22 has the latest version of it so I skipped it and then faced this issue.
I followed the answer to this question.
https://askubuntu.com/questions/1403619/mongodb-install-fails-on-ubuntu-22-04-depends-on-libssl1-1-but-it-is-not-insta

@shaheer-haider I cannot express enough gratitude for your solution that helped me get past a difficult issue that I was stuck on. Your efforts and expertise saved me a lot of time and enabled me to work with ease and confidence. I am immensely thankful to you for your contribution, and your kindness and generosity are truly appreciated. Thank you once again for your invaluable help!❤ @shaheer-haider 😊


python setup.py egg_info did not run successfully

Describe the bug
"python setup.py egg_info did not run successfully"
could not install on a fresh 3.9 python env
Version
Running install on Macbook Pro M1
Mac Os Ventura Version 13.2.1 (22D68)
To Reproduce
Steps to reproduce the behavior:
pip install langflow
Additional context
There's a good chance i did something quite wrong. i'm a total noobie.
 Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [25 lines of output]
      /Users/alimuhareb/opt/miniconda3/envs/langflow/lib/python3.9/site-packages/setuptools/config/setupcfg.py:508: SetuptoolsDeprecationWarning: The license_file parameter is deprecated, use license_files instead.
        warnings.warn(msg, warning_class)
      running egg_info
      creating /private/var/folders/13/7t6g66w167n8ss7rsxv8bz6r0000gn/T/pip-pip-egg-info-0gkrbfqr/psycopg2.egg-info
      writing /private/var/folders/13/7t6g66w167n8ss7rsxv8bz6r0000gn/T/pip-pip-egg-info-0gkrbfqr/psycopg2.egg-info/PKG-INFO
      writing dependency_links to /private/var/folders/13/7t6g66w167n8ss7rsxv8bz6r0000gn/T/pip-pip-egg-info-0gkrbfqr/psycopg2.egg-info/dependency_links.txt
      writing top-level names to /private/var/folders/13/7t6g66w167n8ss7rsxv8bz6r0000gn/T/pip-pip-egg-info-0gkrbfqr/psycopg2.egg-info/top_level.txt
      writing manifest file '/private/var/folders/13/7t6g66w167n8ss7rsxv8bz6r0000gn/T/pip-pip-egg-info-0gkrbfqr/psycopg2.egg-info/SOURCES.txt'

      Error: pg_config executable not found.

      pg_config is required to build psycopg2 from source.  Please add the directory
      containing pg_config to the $PATH or specify the full executable path with the
      option:

          python setup.py build_ext --pg-config /path/to/pg_config build ...

      or with the pg_config option in 'setup.cfg'.

      If you prefer to avoid building psycopg2 from source, please install the PyPI
      'psycopg2-binary' package instead.

      For further information please check the 'doc/src/install.rst' file (also at
      <https://www.psycopg.org/docs/install.html>).

      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.

Issue #167 is where we are tracking this. I'll push the fix in a few minutes.


Handle undefined and null

ms(undefined) returns "undefinedms" and ms(null) returns "nullms". I've changed it such that it just returns the value unchanged if you happen to pass in either of those two values.

+1

Bump.

You've missed the January 22nd this year - you've had an anniversary! :)

Heh! Just cleaning up old PR's I had open.

Doesn't look like this is wanted. Hence closing it. Please re-open it if we want it after all.


Length of an email validation

The email validation introduced with #795 has some problems.
According to the rfc3696 section 3. the maximum length of an email address is A@B A(local part)=64 characters and B(domain part)=255 characters.
The minimal length is three because a valid email address has the form "a@b" and the smallest length for a and b is one.
The email user (the local part) and the host (the domain part) can also have non ASCII UTF-8 characters. These regex expressions will make them invalid.
Do we want to enable email addresses which have UTF-8 characters as well?
@mainflux/contributors any thoughts on this?

@anovakovic01 @drasko What do you think ^?

According to the rfc3696 section 3. the maximum length of an email address is A@B A(local part)=64 characters and B(domain part)=255 characters.
The minimal length is three because a valid email address has the form "a@b" and the smallest length for a and b is one.

I agree that these boundaries should be fixed.
Regarding email addresses that have UTF-8, I think that we should support these too, but it doesn't have high priority.

Thanks, working on it.

@rudder-free any updates on this?

@rudder-free any updates on this?

Apologies for the delays, I have done some ground work for this, understanding how utf-8 and emails relates to each other. I am about to approach coding it. But if someone wants to take a stab at it, please feel free.

@rudder-free any updates on this?

@rudder-free  would you like to re-take this one?

Sorry I can't wait.

@filinvadim thank you!

Despite the fact that my PR was not accepted for third-party reasons feel free to copy-paste my code and make another PR. Cheers!

@filinvadim Your PR was not accepted simply because you didn't want to resolve our remarks and decided that it was the moment to merge while the work left was doable in 5min.

@manuio i've just wanted emphasize difference between "contribution" and "working for free".

@filinvadim I understand the difference and we always appreciate any type of contribution, including yours.  However, we can't review depending on any external criteria. Otherwise it would be a mess to maintain this project.
I think that the work left can be done pretty fast. It would be a pity that you don't finish this PR and become contributor. There is no pressure, you can do it when you want and it would be normal if it takes some days.
We would also understand that you don't want to continue and in any case we thank you for your code!

You take my heart away @manuio! It's already done.

@filinvadim Glad to ear that and thank you for your understanding :) I'll reopen the issue right now.
I'll do a last review but keep in mind that at least one more maintainer must review this PR. Good luck and thank you!


[🐛 Bug]: Marquee breaks when a custom mode is no longer available

VSCode Environment
Version: 1.67.2
Commit: c3511e6c69bb39013c4a4b7b9566ec1ca73fc4d5
Date: 2022-05-17T18:20:57.384Z
Electron: 17.4.1
Chromium: 98.0.4758.141
Node.js: 16.13.0
V8: <IP_ADDRESS>-electron.0
OS: Darwin x64 21.5.0
Marquee Version
v3.0.2
Workspace Type
Local Workspace
What happened?

Create a custom mode.
Switch to this new mode.
Close the Marquee dash and either remove the mode from settings.json or reimport a previous state without the mode.
On reopen of Marquee dash it will break.

What is your expected behavior?
Revert back to default when a mode is no longer available?
I'm suspecting the currently active mode is saved in persistent state versus config. The dash loading code does not seem to handle this scenario gracefully.
Relevant Log Output
main.js:130835 Uncaught TypeError: Cannot read properties of undefined (reading 'icon')
    at ModeSelector (main.js:56808:12)
    at renderWithHooks (main.js:122550:18)
    at mountIndeterminateComponent (main.js:125376:13)
    at beginWork (main.js:126614:16)
    at HTMLUnknownElement.callCallback (main.js:111510:14)
    at HTMLUnknownElement.sentryWrapped (main.js:43439:23)
    at Object.invokeGuardedCallbackDev (main.js:111559:16)
    at invokeGuardedCallback (main.js:111621:31)
    at beginWork$1 (main.js:131524:7)
    at performUnitOfWork (main.js:130339:12)
    at workLoopSync (main.js:130267:5)
    at renderRootSync (main.js:130230:7)
    at performSyncWorkOnRoot (main.js:129853:18)
    at scheduleUpdateOnFiber (main.js:129441:7)
    at updateContainer (main.js:133042:3)
    at main.js:133581:7
    at unbatchedUpdates (main.js:129991:12)
    at legacyRenderSubtreeIntoContainer (main.js:133580:5)
    at Object.render (main.js:133663:10)
    at main.js:183968:42
    at main.js:183977:3
    at main.js:183979:12

Code of Conduct

[X] I agree to follow this project's Code of Conduct

Is there an existing issue for this?

[X] I have searched the existing issues

@sourishkrout this patch will switch back to the default mode if it detects that the current mode is not available anymore.


Properly add Polish language support

If there is any other thing i missed, please let me know. Also sorry for the previous commit-fail.

Thank you! I was about to add that code.

Glad to help :)


Update README - wrong Docker Hub reference

Description

Expected Behavior
Should be a link to (https://hub.docker.com/r/layer5/meshery-nsx-sm)
Contributor Resources
The meshery.io website uses Jekyll and GitHub Pages. Site content is found under the master branch.

See the Contributing to Meshery.io Website section of the readme.md and other contributing instructions, too.
See Meshery site designs in this Figma designfile. Fill-in the Layer5 Community Member Form and join the Layer5 Community Slack for access.

Hello, I'd like to work on this issue.

Sure @fazer1929 🚀


AnimalLabel model class added

Summary Change Description
Added AnimalLabel.

Reset the affected migrations.
Moved tracer_labeled_elements into Infusate.
Moved serum_tracers_enrichment_fractions into AnimalLabel and edited to work on only 1 element.
Improved exceptions for PeakGroupLabel.normalized_labeling.
Added AnimalLabel loading to the sample_table_loader.

Affected Issue Numbers

Resolves #477

Code Review Notes
AnimalLabel is the new home of serum_tracers_enrichment_fraction so that it doesn't return a dict.  I was going to create InfusateLabel, but I realized that the data relationship was wrong for that.
Note, the TODO comments were removed in another PR I will submit momentarily.
Checklist

[x] All issue requirements satisfied (or no linked issues)
[x] Linting passes.

Everything passes locally but the errors on github are unrelated to this code change

[x] Migrations created & committed (or no model changes)
[x] Tests implemented (or no code changes)

[x] New test classes/functions tagged with @tag("multi_working")

[x] All (ci-run) tests pass

[x] Test function tag @tag("multi_working") added and test class/function tags @tag("multi_broken"), @tag("multi_unknown"), or @tag("multi_mixed") removed or changed.

I would like to go ahead and merge this if there are not any objections.