ワード進行の具体例

please make picture of drone

A detailed image of a drone resembling the DJI Phantom. The drone has a sleek, white body with four rotors evenly spaced on each corner. The design is streamlined for aerodynamic efficiency. There is a prominent camera mounted underneath the drone, capable of high-resolution imaging. Each rotor is equipped with black propellers, and LED lights are visible on the arms. The drone's compact and sturdy structure makes it suitable for both recreational and professional use. The focus is on the intricate design elements and the technological sophistication of the drone

Create an image of a Japanese male professor and a diverse group of students engaging in a drone flying activity outdoors. The professor, with a round face, very short hair, and no beard, is wearing round glasses and a light blue scarf. He has a joyful expression as he instructs the students. The students are attentively operating a drone, with a few looking on with interest. The drone resembles a DJI Phantom, floating steadily in the air against a backdrop of a clear sky. The setting is a grassy field on a university campus, hinting at an educational and exploratory atmosphere. Everyone is dressed casually, appropriate for an outdoor academic activity.
教授が学生と一緒にドローンを飛ばしている絵のpromptを作成してください
1番シンプルな方法

Create an image of a joyful Japanese professor with a round face and very short hair, no beard, wearing round glasses and a light blue scarf. He is accompanied by students of varying appearances, all focused on flying a creatively designed drone, reminiscent of fantastical flying machines often seen in animated adventure films. They are in a lush, green open field with hints of an expansive, whimsical forest in the background, evoking a sense of magical realism and academic wonder. The atmosphere is bright, with a soft color palette and a touch of whimsy, suitable for an educational and exploratory scene in an animated world.
宮崎駿風の、教授が学生と一緒にドローンを飛ばしている絵のpromptを作成してください
世界観の一致をした場合

Create an image of a joyful Japanese professor with a round face and very short hair, no beard, wearing round glasses and a light blue scarf. He is accompanied by students of varying appearances, all focused on flying a creatively designed drone, reminiscent of fantastical flying machines often seen in animated adventure films. They are in a lush, green open field with hints of an expansive, whimsical forest in the background, evoking a sense of magical realism and academic wonder. The atmosphere is bright, with a soft color palette and a touch of whimsy, suitable for an educational and exploratory scene in an animated world.
detail of drone
resembling the DJI Phantom. The drone has a sleek, white body with four rotors evenly spaced on each corner. The design is streamlined for aerodynamic efficiency. There is a prominent camera mounted underneath the drone, capable of high-resolution imaging. Each rotor is equipped with black propellers, and LED lights are visible on the arms. The drone's compact and sturdy structure makes it suitable for both recreational and professional use. The focus is on the intricate design elements and the technological sophistication of the drone
detail of professor
an image of a congenial Japanese male professor with a round face, very short hair, and no beard. He is wearing round glasses and exudes a warm smile. Adorning his neck is a light blue scarf. His hands are holding a MacBook, which features an assortment of subtle stickers, conveying his personality and interests.

"Imagine a scene inspired by the enchanting and whimsical style of Hayao Miyazaki. In a vast, open field under a bright blue sky dotted with fluffy white clouds, a diverse group is gathered. In the center, an East Asian male professor, wearing a light-colored lab coat and glasses, is holding a remote control, intently teaching his students about drone technology. Surrounding him are students of various descents, including Caucasian, Black, Hispanic, and South Asian, showing a mix of awe and curiosity. They are all focused on a small, intricate drone buzzing in the air above them, capturing the essence of learning and innovation in a natural setting, reminiscent of Miyazaki's magical worlds."

Imagine a scene inspired by the enchanting and whimsical style of Hayao Miyazaki. In a vast, open field under a bright blue sky dotted with fluffy white clouds, a diverse group is gathered. In the center,a congenial Japanese male professor with a round face, very short hair, and no beard. He is wearing round glasses and exudes a warm smile. Adorning his neck is a light blue scarf. His hands are holding a MacBook, which features an assortment of subtle stickers, conveying his personality and interests., is holding a remote control, intently teaching his students about drone that  the DJI Phantom. The drone has a sleek, white body with four rotors evenly spaced on each corner. The design is streamlined for aerodynamic efficiency. There is a prominent camera mounted underneath the drone, capable of high-resolution imaging. Each rotor is equipped with black propellers, and LED lights are visible on the arms. The drone's compact and sturdy structure makes it suitable for both recreational and professional use. The focus is on the intricate design elements and the technological sophistication of the drone. Surrounding him are students of various descents, including Caucasian, Black, Hispanic, and South Asian, showing a mix of awe and curiosity. They are all focused on a small, intricate drone buzzing in the air above them, capturing the essence of learning and innovation in a natural setting, reminiscent of Miyazaki's magical worlds.

Imagine a scene inspired by the enchanting and whimsical style of Hayao Miyazaki. In a vast, open field under a bright blue sky dotted with fluffy white clouds, a diverse group is gathered. In the center, an East Asian male professor, wearing a light-colored lab coat and glasses, is holding a remote control, intently teaching his students about drone technology. Surrounding him are students of various descents, including Caucasian, Black, Hispanic, and South Asian, showing a mix of awe and curiosity. They are all focused on a small, intricate drone buzzing in the air above them, capturing the essence of learning and innovation in a natural setting, reminiscent of Miyazaki's magical worlds.

Imagine a scene inspired by the enchanting and whimsical style of Hayao Miyazaki. In a vast, open field under a bright blue sky dotted with fluffy white clouds, a diverse group is gathered. In the center, a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf is holding a remote control, intently teaching his students about drone. that has a sleek, white body with four rotors evenly spaced on each corner. Surrounding him are students of various descents, including Caucasian, Black, Hispanic, and South Asian, showing a mix of awe and curiosity. They are all focused on a small, intricate drone buzzing in the air above them, capturing the essence of learning and innovation in a natural setting, reminiscent of Miyazaki's magical worlds

Painting, a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf teach about the way of drone that has a sleek, white body with four rotors evenly spaced on each corner to 5 students.

Painting, a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, teach about the way of drone that has white body with four rotors evenly spaced on each corner to 5 university students.

Create a Splatoon-inspired, vibrant and colorful painting featuring a congenial Japanese male professor and five university students.  He is teaching his students about a drone with a white body and four rotors evenly spaced on each corner. The students, diverse in appearance and style, are attentively listening and engaging. The scene is lively and animated, with bold, vivid colors and imaginative elements typical of the Splatoon aesthetic. The background is playful and futuristic, echoing the energetic and youthful vibe of the game, while maintaining a focus on the educational aspect of the drone demonstration.

Imagine a scene inspired by style of 1980's Japan amime. In a bright blue sky dotted with fluffy white clouds, a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, teach to 5 university students about the way of drone that has white body with four rotors evenly spaced on each corner to 5 university students, Miyazaki Hayao style.

Painting, some tents and tree houses is in forest , and a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, teach to 5 university students about the way of drone that has white body with four rotors evenly spaced on each corner to 5 university students, Miyazaki Hayao style

Painting, a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, is cutting a tree with 5 university students

Create an artistic, illustrative image of a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, and three university students in a stylized mountain forest in Japan. The professor, with a distinct, slightly exaggerated character design, short hair, and wearing colorful, artistic outdoor attire, is teaching woodcutting. The students, each with unique, vibrant character designs from various ethnic backgrounds, are using exaggerated, oversized tools like cartoonish saws and axes. The forest is rendered in a whimsical, colorful style, with exaggerated shapes and vibrant colors, giving it a fantastical, illustration-like quality. The scene should look like it's from a storybook, capturing the whimsy and charm of an educational adventure in nature

Create an artistic, illustrative image of a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, and three university students in a stylized mountain forest in Japan is teaching woodcutting. The forest is rendered in a whimsical, colorful style, with exaggerated shapes and vibrant colors, giving it a fantastical, illustration-like quality. The scene should look like it's from a storybook, capturing the whimsy and charm of an educational adventure in nature

Create an artistic, illustrative image of a congenial Japanese male professor with a round face, very short hair, and no beard, wearing light blue scarf, 40 years olds, and three university students are climbing the vivid mountain.

Create a Ghibli style image of a 40-year-old Japanese male professor and seven university students having a barbecue inside a tent. The image should have a more illustrated, less photorealistic style, akin to a painting or a vibrant drawing. The professor and students, diverse in appearance, are engaged in a lively barbecue scene, with exaggerated expressions of joy and camaraderie. The tent is depicted in a colorful and whimsical manner, with an open view to a stylized natural landscape in the background. The overall scene should be warm, inviting, and full of character, capturing the essence of a joyful group activity in a creative, illustrative style.

Create a vibrant, animated-style image of a 20-year-old Japanese university student, engaged in researching map-related information on a Mac. The student, depicted with exaggerated anime features such as large expressive eyes and a youthful demeanor, is sitting at a colorful and creatively organized desk. The Mac screen should display a stylized, cartoonish map with bright colors and simplified geographical details. The environment around the student is lively and whimsical, with elements like exaggerated-sized books, colorful notes, and a whimsically designed cup of coffee, all contributing to a playful and energetic academic setting. The overall image should capture the charm and enthusiasm of a young student immersed in their studies, in a lively anime-inspired world

Create a dynamic, anime-style image of a 20-year-old Japanese university student operating a distinctly recognizable 3D printer. The student, with typical anime aesthetics like large, detailed eyes and a spirited expression, is actively engaged with a clearly identifiable 3D printer. The printer, designed in a vibrant, exaggerated anime style, is in the process of creating an intricate, fantastical object, demonstrating its complex capabilities. The lab setting is bright and futuristic, filled with stylized technological paraphernalia and colorful decor. The student's attire is trendy and casual, fitting the anime theme. This image should vividly portray the fusion of cutting-edge technology with the imaginative, energetic world of anime, highlighting the student's interaction with a distinctly anime-styled 3D printer in a creative academic environment

Create a vibrant, anime-style image of a typical Japanese university student with black hair, around 20 years old, operating a 3D printer. The student, depicted with characteristic anime aesthetics such as detailed eyes and an energetic demeanor, is focused on the 3D printer. The printer itself is designed in an anime style, clearly recognizable as a 3D printer, and is printing an intricate object. The setting is a colorful and futuristic university lab, with other technological devices and gadgets around. The student's attire is casual, reflecting a typical university student's style in Japan. This image should capture the blend of anime artistry and modern technology, emphasizing the student's engagement with the 3D printer in an imaginative and lively academic environment

Create an American comic book style image of a 20-year-old Japanese university student presenting about maps at a large academic conference.  They are confidently illustrating points about maps on a large screen, with bold colors and dramatic designs. The audience is captivated, depicted with various expressions of intrigue and admiration, characteristic of comic book art. The setting is a grand conference hall, rendered with vibrant colors and dramatic lighting, emphasizing the importance and excitement of the academic presentation

Envision an American comic book style illustration of a 20-year-old Japanese university student giving a presentation on maps at an international conference. The student, drawn with bold and exaggerated comic book features, is standing confidently at a podium in front of a large audience. They are passionately explaining and gesturing towards a vividly illustrated map displayed on a large screen behind them. The audience, composed of a diverse group of people from different countries, is depicted with various expressions of intrigue and engagement, all in comic book art style. The conference hall is illustrated with dramatic, vibrant colors and dynamic perspectives, capturing the excitement and grandeur of the event in a true American comic book fashion

Visualize a scene in the enchanting style of Studio Ghibli, where a 20-year-old Japanese university student is delicately painting small, detailed images. In this whimsical world, the student is portrayed with the distinctive charm and detail that Studio Ghibli is known for. They are seated in a cozy, imaginative corner of a room, surrounded by elements that blend nature and fantasy, such as indoor plants, gentle lighting, and whimsical ornaments. The focus is on the student's meticulous brushwork as they create tiny canvases filled with scenes of miniature landscapes, fantasy elements, or beautiful, dream-like imagery. This image should capture the serene and magical atmosphere of a Ghibli film, emphasizing the student's deep concentration and the artistry of creating small-scale paintings in a world filled with wonder and creativity.

Picture a charming scene in the distinctive style of Studio Ghibli, where a 20-year-old Japanese university student is intently drawing small, intricate pictures in a cozy research lab. This lab, brimming with the magical and detailed aesthetic of Ghibli, is a small space shared with several other students, each absorbed in their own creative or academic pursuits. The room is adorned with whimsical decorations, shelves filled with books, plants, and other fascinating gadgets that spark curiosity. A Mac computer is placed within this enchanting setting, seamlessly integrating modern technology into the whimsical Ghibli environment. The focus is on the student's precise and delicate artistry, while the presence of their peers adds a sense of collaboration and lively academic interaction. This image should capture the spirit of artistic creativity and teamwork, all set in a quaint, technology-enhanced, Ghibli-inspired lab.

Visualize a Studio Ghibli-style scene where a 20-year-old Japanese university student is drawing small, detailed pictures in a quaint, concrete-structured research lab. The lab, although modern with its concrete design, is transformed into an enchanting Ghibli-like space filled with warmth and charm. The student, surrounded by other students engaged in various academic activities, is focused on their miniature artwork. A Mac computer is subtly included in the setting, adding a touch of contemporary technology amidst whimsical decorations, soft lighting, and indoor plants. This blending of a modern, concrete lab with the imaginative and cozy atmosphere typical of Studio Ghibli creates a unique and captivating environment. The image should capture the essence of creativity and academic collaboration in a magical, Ghibli-inspired world.

https://media.discordapp.net/attachments/990816819654852648/1186474418017411143/Imagine_a_Splatoon-inspired_image_featuring_three_20-year-ol_69164657-34fa-40fe-8ef9-b0a217d86771.png?ex=65936168&is=6580ec68&hm=0c6d11fa2a2e2b67cec665f6e29924fc12dadd6efe9c4f18d5b15e9359425e44&=&format=webp&quality=lossless&width=686&height=686

Imagine a Splatoon-inspired image featuring three 20-year-old Japanese university students enjoying a location-based game. The students, portrayed with vibrant colors and dynamic styles characteristic of Splatoon, are engaged in playful and energetic interactions. They are depicted outdoors, possibly in a park or an urban setting, with their smartphones in hand, actively participating in the game. The environment is colorful and lively, mirroring the vivid, playful aesthetics of Splatoon, with imaginative elements like colorful ink splashes and whimsical urban landscapes. The image should capture the excitement and fun of playing a location-based game, with a youthful and energetic vibe typical of the Splatoon universe.


downgraded chardet

PR Checklist

[ ] A description of the changes is added to the description of this PR.
[x] If there is a related issue, make sure it is linked to this PR.
[ ] If you've fixed a bug or added code that should be tested, add tests!
[ ] Documentation in docs is updated

Description of changes

Codecov Report

Merging #205 (e77f7c4) into main (ad5f767) will not change coverage.
The diff coverage is n/a.

:exclamation: Current head e77f7c4 differs from pull request most recent head 3c159b7. Consider uploading reports for the commit 3c159b7 to get more accurate results

@@          Coverage Diff          @@
##            main    #205   +/-   ##
=====================================
  Coverage   95.5%   95.5%
=====================================
  Files         29      29
  Lines        920     920
  Branches     207     207
=====================================
  Hits         879     879
  Misses        27      27
  Partials      14      14

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.


fix-rst-subcommands

This pull requests reinstates support for reStructuredText formatting in subcommand descriptions.
It looks like it was accidentally removed when the ext module was refactored: the map_nested_definitions function was storing each node using astext(), which stripped away all markup.

Hmm, looks like the Travis build is failing for code that I didn't touch, so I guess that's an issue with the development branch.
In any case, this PR fixes https://github.com/ribozz/sphinx-argparse/issues/75.

Oh man, it's been a while, I didn't even remember making this! I think it can be merged as-is? 🤔

Yup, it can be merged as is. Travis is still showing issues, but that's not your fault. I'll try to push out a new release tomorrow.


fix(@schematics/angular): use TS 3.0+ for experimentalAngularNext

Sets the TypeScript version to ~3.0.3 if the option experimentalAngularNext (or experimentalIvy) is set.
Currently the generated app uses TS ~2.9.2 and throws if the next version of Angular is used:
Error: The Angular Compiler requires TypeScript >=3.0.1 and <3.1.0 but 2.9.2 was found instead.

Note: on master this is superseded by https://github.com/angular/angular-cli/commit/7421d63d4ae51bd3862df55c830d4c00874671ad#diff-e86faf3f22e7942a51391830c102eee9

Closing this as it's already in master for everyone (not just experimental).


CLI developers can use CLI commands instead of swagger concepts to customize in readme file

CLI developers can use CLI commands instead of swagger concepts to customize in readme file so that they can customize more easily as they don't need to understand swagger.

solution provided.


OS/2 fsSelection (italic) check should apply in all correct profiles

Should be triggered if "Italic" is part of the file name, or NameID2, or NameID17.
The ticked cases indicates where you can already find it.
Style linking must be set in OS/2 table:
🔥 FAIL: Checking OS/2 fsSelection value.
* [com.google.fonts/check/fsselection](https://font-bakery.readthedocs.io/en/latest/fontbakery/profiles/googlefonts.html#com.google.fonts/check/fsselection)

Profile

[x] in google-font profile
[ ] in universal profile
[ ] in opentype profile

Format

[ ] in VF check
[x] in static check

Profiles where it should be included

[x] in google-font profile
[x] in universal profile
[ ] in opentype profile

The only part that should be in OpenType profile is italic angle being zero for italic fonts and vice versa, the other checks are GF-specifc. For example, and Arabic “italic” can lean from right to left and this has a positive italic angle, FAILing a font for +ve italic angle is a mistake. The same goes for the other arbitrary limit on the italic angle value.


Rename "empty" iLoci to intergenic iLoci (iiLoci)

[x] Usage statement
[x] Code
[x] Documentation

Closed by 11b0bb6.


OpenVMS rand_vms.c dependency

Hello again Richard (I'm guessing).
Building on VMS V8.4 introduces a dependency on SYS$GETTIM_PREC that precludes an executable from running on say V8.3 (and there are lots of VMS systems about that have not moved to V8.4 for various reasons).  To address this dependency I suggest adopting the approach used for SYS$GET_ENTROPY, that is, dynamically loading the SYS$GETTIM_PREC address at runtime.  If it cannot be found (e.g. on V8.3) then just setting the address to be SYS$GETTIM (which is always available).  This will extend the availability of OpenSSL builds provided by say VSI, to a significantly broader range of VMS systems.
#define PUBLIC_VECTORS "SYS$LIBRARY:SYS$PUBLIC_VECTORS.EXE"
#define GET_GETTIM_PREC "SYS$GETTIM_PREC"

static void (*get_gettim_prec_address)(void*) = NULL;
static void init_get_gettim_prec_address(void)
{
    get_gettim_prec_address = dlsym(dlopen(PUBLIC_VECTORS, 0), GET_GETTIM_PREC);
    if (get_gettim_prec_address == NULL) get_gettim_prec_address = (void*)sys$gettim;
}

#undef PUBLIC_VECTORS

Then for rand_pool_add_nonce_data()
data.pid = getpid();
data.tid = CRYPTO_THREAD_get_current_id();
if (get_gettim_prec_address == NULL) init_get_gettim_prec_address();
get_gettim_prec_address((void*)&data.time);

and rand_pool_add_additional_data()
data.tid = CRYPTO_THREAD_get_current_id();
if (get_gettim_prec_address == NULL) init_get_gettim_prec_address();
get_gettim_prec_address((void*)&data.time);

This has been tested under OpenSSL 1.1.1l on VMS V8.4 and V8.3.
The full modified OpenSSL 1.1.1l rand_vms.c for comparison purposes can be found at
https://wasd.vsm.com.au/wasd_root/src/openssl-1_1_1l/crypto/rand/rand_vms.c

Hi Daniel,
Please try the patch from #18730.  Comment there if you find trouble.

I assume the same rand_vms.c can/will be applied to OpenSSL 1.1.1[n] and 3.0.0[n].

There are two PRs, #18730 which covers 1.1.1[n] and #18731 which covers 3.0[n]...  this was necessary because rand_vms.c has moved for 3.0

Thanks for testing!


Add IBM Swift Sandbox in Tools

Project URL
https://swiftlang.ng.bluemix.net/
Description
The IBM Swift Sandbox is an interactive website that lets you write Swift code and execute it in a server environment – on top of Linux!
Checklist

[x] Only one project/change is in this pull request
[x] Addition in chronological order (bottom of category)
[x] Supports Swift 3

          1 Error

:no_entry_sign:
Found 3 link issues

Link issues by awesome_bot

Line
Status
Link

170
301
http://www.codeproject.com/Articles/88929/Getting-Started-with-iPhone-and-iOS-Development redirects tohttps://www.codeproject.com/articles/88929/getting-started-with-iphone-and-ios-development

987
301
https://github.com/alvinvarghese/EasyIAPs redirects tohttps://github.com/aaalveee/EasyIAPs

2073
302
https://swiftlang.ng.bluemix.net redirects tohttps://swiftlang.ng.bluemix.net/verify

  Generated by :no_entry_sign: danger

Hey @naeemshaikh90. the link you added there is redirecting to https://swiftlang.ng.bluemix.net/#/repl

@lfarah  yes I have tried with that link too, but it was redirecting to https://swiftlang.ng.bluemix.net/verify


[BUG]  XmlException: Reference to undeclared entity when loading SVG file

Maybe this is somehow the wrong repo? but...
I installed the SkiaSharp.SVG 1.60.0 nuget package into a dotnet6 wpf project and trying to load a specific SVG file causes this exception

Since it seems to be an XML exception I tried running the SVG through some online XML parserrs and they are all happy. The SVG also renders fine in browsers, and another library is able to load it. Furthermore the Entity that is being reported as missing is clearly defined in the XML on line 63.

Code
var    svgSrc    ="C:/Folder/badcake.svg";
var svg  = new SkiaSharp.Extended.Svg.SKSvg();
var pict = svg.Load(svgSrc);
Since the problem seems to be the call into System.Xml I tried callling that directly like this:
var byteArray = File.ReadAllBytes("C:/Folder/badcake.svg");
using (var stream = new MemoryStream(byteArray))
{
System.Xml.Linq.XDocument.Load(stream);
}
Which does not produce the exception

Oh I take it this belongs in https://github.com/mono/SkiaSharp.Extended

I solved it using this:
            XmlReaderSettings settings = new();
            settings.DtdProcessing = DtdProcessing.Parse;
            XmlReader reader = XmlReader.Create(stream, settings);
            var       svg    = new SKSvg();
            var       pict   = svg.Load(reader);


"recently Traded" dropdown doesnt work on Market List pg

This does not update automatically....I need to toggle to another drop down option and then go back for it to update

closing


Add a feature to temporarily suspend a foreground task with a shortcut key

Description of the new feature / enhancement
Press, for example ctrl + escape to suspend a task that is currently in foreground, press again to unfreeze it.
It can be also coded in a way, that if it has some suspended app, pressing the key once again resumes it, so there will be no need to take care of what is in the foreground (especially when windows likes to add "non responding" message in front of these programs)
Scenario when this would be used?
When using some resource heavy apps / games, that cannot be paused, they can be minimized and paused, so they have a saved state (no need to load it to RAM again), but they don't consume any CPU / GPU power
Supporting information
Windows resource monitor has the exact feature I want to implement, but the process needs to be found manually and then manually suspended, instead of simple shortcut action.

I just made a program that does the exact thing! Check it out!
https://github.com/Matszwe02/PauseProgram


client is busy with a request

Set data to view using DeviceEventEmitter.addListener by binding native mic volume level.
<Text style={styles.instructions}>
          Mic Level {this.state.micLevel}
        </Text>

DeviceEventEmitter.addListener('onMicLevelChange', (data) => {
            this.setState({micLevel:data});
        });

but I get error as follows,

How to solve this?

Not sure if this is react-native issue or a third-party library.
It also seems disrespectful to reopen your closed issue https://github.com/facebook/react-native/issues/14098 line by line, even though @hramos had closed it with a reasonable comment.

It looks like your description is missing some necessary information. Can you please add all the details specified in the template? This is necessary for people to be able to understand and reproduce the issue being reported.

Looks like this error message comes from code that lives outside React Native: https://github.com/aws/aws-sdk-android/blob/master/aws-android-sdk-lex/src/main/java/com/amazonaws/mobileconnectors/lex/interactionkit/InteractionClient.java#L818
Closing this issue for that reason, if you find that this is a bug with React Native itself then please open another issue.


CASSANDRA-17999: Bug fix for WriteTimeoutException when using Paxos v2 in LWT performance test

A bug fix for WriteTimeoutException when using Paxos v2 in an LWT performance test that only has a single datacenter due to Paxos still waiting for a response from another datacenter during the Commit/Acknowledge phase even though we are running with LOCAL_SERIAL.
patch by @marianne-manaog ; reviewed by @blambov for CASSANDRA-17999
The Cassandra Jira

Closing this PR as per this comment on Jira from Branimir Lambov, and I will raise a new PR with both the bug fix and the in-jvm dtest.


Update mk

Description
update cilium pkg
Dependencies

n/a
Checklist
If you are not familiar with our processes or don't know what to answer in the list below, let us know in a comment: the maintainers will take care of that.

[ ] Will this change affect NetObserv / Network Observability operator? If not, you can ignore the rest of this checklist.
[ ] Is this PR backed with a JIRA ticket? If so, make sure it is written as a title prefix (in general, PRs affecting the NetObserv/Network Observability product should be backed with a JIRA ticket - especially if they bring user facing changes).
[ ] Does this PR require product documentation?

[ ] If so, make sure the JIRA epic is labelled with "documentation" and provides a description relevant for doc writers, such as use cases or scenarios. Any required step to activate or configure the feature should be documented there, such as new CRD knobs.

[ ] Does this PR require a product release notes entry?

[ ] If so, fill in "Release Note Text" in the JIRA.

[ ] Is there anything else the QE team should know before testing? E.g: configuration changes, environment setup, etc.

[ ] If so, make sure it is described in the JIRA ticket.

QE requirements (check 1 from the list):

[ ] Standard QE validation, with pre-merge tests unless stated otherwise.
[ ] Regression tests only (e.g. refactoring with no user-facing change).
[ ] No QE (e.g. trivial change with high reviewer's confidence, or per agreement with the QE team).

/lgtm
/approve

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 33.41%. Comparing base (40c4f8b) to head (139b470).

Additional details and impacted files
@@           Coverage Diff           @@
##             main     #325   +/-   ##
=======================================
  Coverage   33.41%   33.41%
=======================================
  Files          48       48
  Lines        3477     3477
=======================================
  Hits         1162     1162
  Misses       2218     2218
  Partials       97       97

Flag
Coverage Δ

unittests
33.41% <ø> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

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

/approve


refactor: pagination update

This pull request standardized the pagination by implementing a single, reusable component across various pages. This change aims to enhance code maintainability and ensure a consistent user experience.
Steps to check:

open preview blog page, events page, labs page
make sure that everything looks and feels as expected

Hi @xmulligan, i hope you're doing well! When you have a moment, could you please take a look at it?


using put_file method instead of python shutil.copyfileobj

Signed-off-by: Rachana Patel<EMAIL_ADDRESS>

LGTM, I think you got the dest path correct.

@zmc @djgalloway FYI

I guess you can close this PR and cherry-pick it from upstream or rebase 'rh' to master?


GH Action - compat matrix

Set up a GH action to run the yet-to-be-written unit tests (#9) on the following test matrix:

Linux, MacOS
Python 3.7-3.11

also dependent on pip installable tiledbsoma: single-cell-data/TileDB-SOMA#494

Implemented in the GH workflows unittests.yml and formatting.yml


Change how gold stars are awarded at end of game

See https://boardgamearena.com/bug?id=67958 and https://boardgamearena.com/bug?id=28575 for more details. Basically we want to give all winners 1 gold star and all other players 0 gold stars.
I'm lumping this into the Echoes release so that we can get some extra playtesting on it before it hits production.

The logic that needs to be updated is in stJustBeforeGameEnd.

I would make sure that team games don't end in a tie, but a joint win for the winning team (i.e. like werewolf or pandemic).

In a team game, both players on the winning team would get 1 gold star and both players on the losing team would get 0 gold stars.
We don't really have a way to tell BGA the difference between two players winning together vs two players being tied. Presumably BGA takes care of the difference automatically since it knows which players are on which teams?

that's not how it works at the moment.  a win is considered a tie for the winning team and not a team win.


uuid support for reset password

When using uuids as primary keys, an exception gets thrown when trying to parse user_id as an integer.  This fix checks that the user id is an integer prior to parsing it.

Thanks for this 👍


feat(ui): remove Apple ProSF font and replace it with google provided Inter

Breaking Changes
N/A
Changes

feat: replace Apple SF Pro font with Inter
fix:
chore:

Associated Issues
closes #1510
Screenshots
Rendered font in gallery

You can merge this one if approved by Diane

@tomiir Are we still waiting for the approval for this? Just checking our PRs which can be merged

This has been approved and I have updatred with base branch. Seem to have los the approves somehow though.


Removing print statements in EventAlign

Hello,
I am working in Google Colab and trying to use nanopolish with r9.4.1-generated data, but I run into the following message when running the eventalign command:
IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
--NotebookApp.iopub_data_rate_limit.

Current values:
NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
NotebookApp.rate_limit_window=3.0 (secs)

Based on what I found online about this issue, I have concluded that nanopolish is trying to print a lot more (or faster?) than the Colab notebook allows. Suggestions elsewhere have told OPs to just change their script to no longer print everything.
So here is my question:
Is there a way I can change the eventalign script to not print everything? Can it just be written to a file, or not displayed so that google colab's data rate limit is not continuously triggered? Thanks in advance for any help/insight you may have!
best,
S

I have found that adding %%capture at the start of my code will hide the output and resolves that data rate issue, but now am wondering if that will have any effect on the command/further analysis? Is everything that is printed during EventAlign just for the user to see what is happening? Sorry if that is a super basic question about how print works!

Hi,
The prints to stdout is the primary output of eventalign and expected to be consumed by some downstream tool. What are you trying to do with eventalign?
Jared

On Aug 1, 2022, at 4:05 PM, najohink @.***> wrote:

I have found that adding %%capture at the start of my code will hide the output and resolves that data rate issue, but now am wondering if that will have any effect on the command/further analysis? Is everything that is printed during EventAlign just for the user to see what is happening? Sorry if that is a super basic question about how print works!
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you are subscribed to this thread.

Thanks for clearing that up. I was just trying to align squiggles to a reference so I can look and see what is happening at certain positions (eventually). I assumed eventalign would save something that I can that use for the next step in my pipeline. I should have known considering there is no --output flag.
I appreciate you taking the time to answer my (dumb) question!
best,
S

No problem, glad to help.
Jared

On Aug 1, 2022, at 8:06 PM, najohink @.***> wrote:

Closed #1024 as completed.
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.


Adding support for loading datasets and visualizing model predictions via FiftyOne

What does this PR do?
Integrates Lightning Flash with FiftyOne, the open source dataset and model analysis library!
Loading FiftyOne data into Flash
This PR adds FiftyOneDataSources for image/video classification, object detection, semantic segmentation, and image embedding tasks that load FiftyOne Datasets into Flash.
Loading Flash predictions into FiftyOne
This PR adds Serializer implementations that can convert classification/detection/segmentation model outputs into the appropriate FiftyOne label types so that they can be added to FiftyOne datasets and visualized.
Note
This PR requires a source install of FiftyOne on this branch https://github.com/voxel51/fiftyone/pull/1059 in order to function.
git clone https://github.com/voxel51/fiftyone
cd fiftyone
git checkout --track origin/flash-video
bash install.bash

The above branch also contains a parallel integration that enables FiftyOne users to add predictions from any Flash model to their datasets 😄
Points of discussion

It'd be great if these examples could be integrated into the Flash documentation/README in the appropriate places 😄

The new FiftyoneDataSource classes introduced in this PR require a label_field argument to specify which field of the FiftyOne dataset should be used as the label field. To enable this, we added **data_source_kwargs to Flash's processor interface. Perhaps there's a better way to support this?

When serializing object detections, Flash models seem to return bounding boxes in absolute coordinates, but FiftyOne expects bounding boxes in relative coordinates. Is it possible for FiftyOneDetectionLabels to access the dimensions of the current image when serialize() is called? Perhaps using set_state() as is done for class labels? The current implementation requires fiftyone.utils.flash.normalize_detections() to be manually called to convert to relative coordinates for import into FiftyOne, but it would be much cleaner if this could be done natively within FiftyOneDetectionLabels...

Basic patterns
The following subsections show the basic patterns enabled by this integration. See the next section for concrete examples of each task type.
Loading data from FiftyOne into Flash
FiftyOne users can load their datasets into Flash Data Sources via the pattern below:
from flash.image import ImageClassificationData

import fiftyone as fo

train_dataset = fo.Dataset.from_dir(
    "/path/to/train",
    fo.types.ImageClassificationDirectoryTree,
    label_field="ground_truth",
)

val_dataset = fo.Dataset.from_dir(
    "/path/to/val",
    fo.types.ImageClassificationDirectoryTree,
    label_field="ground_truth",
)

datamodule = ImageClassificationData.from_fiftyone(
    train_dataset=train_dataset,
    val_dataset=val_dataset,
    label_field="ground_truth",
)

Visualizing Flash predictions in FiftyOne
Flash users can swap out the serializer on their model with the corresponding FiftyOne serializer for the task type, and then visualize their predictions in the FiftyOne App via the pattern below:
from flash.core.classification import FiftyOneLabels
from flash.video import VideoClassifier

import fiftyone as fo

classifier = VideoClassifier.load_from_checkpoint(...)
classifier.serializer = FiftyOneLabels()

filepaths = ["list", "of", "filepaths"]
predictions = classifier.predict(filepaths)

dataset = fo.Dataset()
dataset.add_samples([fo.Sample(filepath=f, predictions=p) for f, p in zip(filepaths, predictions)])

session = fo.launch_app(dataset)

Applying Flash models to FiftyOne datasets
In addition to this PR, https://github.com/voxel51/fiftyone/pull/1059 adds a parallel integration in the FiftyOne library that enables FiftyOne users to add predictions from any Flash model to their datasets via the pattern below:
from flash.image import ObjectDetector

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart", max_samples=10)

model = ObjectDetector.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/object_detection_model.pt")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

Task examples
The subsections below demonstrate both (a) FiftyOne dataset -> Flash, and (b) Flash predictions -> FiftyOne for each task type.
Video classification
from torch.utils.data.sampler import RandomSampler

import flash
from flash.core.classification import FiftyOneLabels
from flash.core.data.utils import download_data
from flash.video import VideoClassificationData, VideoClassifier

import fiftyone as fo

# 1 Download data

download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip")

# 2 Load data into FiftyOne

train_dataset = fo.Dataset.from_dir(
    "data/kinetics/train",
    fo.types.VideoClassificationDirectoryTree,
    label_field="ground_truth",
    max_samples=5,
)

val_dataset = fo.Dataset.from_dir(
    "data/kinetics/val",
    fo.types.VideoClassificationDirectoryTree,
    label_field="ground_truth",
    max_samples=5,
)

predict_dataset = fo.Dataset.from_dir(
    "data/kinetics/predict",
    fo.types.VideoDirectory,
    max_samples=5,
)

# 3 Fine tune a model

classifier = VideoClassifier.load_from_checkpoint(
    "https://flash-weights.s3.amazonaws.com/video_classification.pt",
    pretrained=False,
)

datamodule = VideoClassificationData.from_fiftyone(
    train_dataset=train_dataset,
    val_dataset=val_dataset,
    predict_dataset=predict_dataset,
    label_field="ground_truth",
    batch_size=8,
    clip_sampler="uniform",
    clip_duration=1,
    video_sampler=RandomSampler,
    decode_audio=False,
    num_workers=8,
)

trainer = flash.Trainer(max_epochs=1, fast_dev_run=1)
trainer.finetune(classifier, datamodule=datamodule)
trainer.save_checkpoint("video_classification.pt")

# 4 Predict from checkpoint

classifier = VideoClassifier.load_from_checkpoint(
    "https://flash-weights.s3.amazonaws.com/video_classification.pt",
    pretrained=False,
)

classifier.serializer = FiftyOneLabels()

filepaths = predict_dataset.values("filepath")
predictions = classifier.predict(filepaths)

predict_dataset.set_values("predictions", predictions)

# 5 Visualize in FiftyOne App

session = fo.launch_app(predict_dataset)

Image classification
import itertools

import torchvision
from torch import nn

import flash
from flash.core.classification import FiftyOneLabels, Labels, Probabilities
from flash.core.data.utils import download_data
from flash.core.finetuning import FreezeUnfreeze
from flash.image import ImageClassificationData, ImageClassifier

import fiftyone as fo

# 1 Download data

download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip")

# 2 Load data into FiftyOne

train_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/train/",
    fo.types.ImageClassificationDirectoryTree,
    max_samples=10,
)

val_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/val/",
    fo.types.ImageClassificationDirectoryTree,
    max_samples=10,
)

test_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/test/",
    fo.types.ImageClassificationDirectoryTree,
    max_samples=10,
)

# 3 Load data into Flash

datamodule = ImageClassificationData.from_fiftyone(
    train_dataset=train_dataset,
    val_dataset=val_dataset,
    test_dataset=test_dataset,
)

# 4 Fine tune a model

@ImageClassifier.backbones(name="resnet18")
def fn_resnet(pretrained: bool = True):
    model = torchvision.models.resnet18(pretrained)
    backbone = nn.Sequential(*list(model.children())[:-2])
    num_features = model.fc.in_features
    return backbone, num_features

model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes, serializer=Labels())

trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
trainer.save_checkpoint("image_classification_model.pt")

# 5a Predict from checkpoint

model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
model.serializer = FiftyOneLabels()

predict_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/predict/",
    fo.types.ImageDirectory,
    max_samples=5,
)

filepaths = predict_dataset.values("filepath")
predictions = model.predict(filepaths)

predict_dataset.set_values("predictions", predictions)

# 5b Predict from trainer

datamodule = ImageClassificationData.from_fiftyone(predict_dataset=predict_dataset)

predictions = flash.Trainer().predict(model, datamodule=datamodule)
predictions = list(itertools.chain.from_iterable(predictions))

predict_dataset.set_values("predictions", predictions)

# 6 Visualize in FiftyOne App

session = fo.launch_app(predict_dataset)

Object detection
import flash
from flash.core.data.utils import download_data
from flash.image import ObjectDetectionData, ObjectDetector
from flash.image.detection.serialization import FiftyOneDetectionLabels

import fiftyone as fo
import fiftyone.zoo as foz
import fiftyone.utils.flash as fouf

# 1 Download data

download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip")

# 2 Load data into FiftyOne

dataset = foz.load_zoo_dataset("quickstart", max_samples=15, shuffle=True)
val_dataset = dataset.take(5).clone()
train_dataset = dataset.exclude(val_dataset).clone()

# 3 Load data into Flash

datamodule = ObjectDetectionData.from_fiftyone(
    train_dataset=train_dataset,
    val_dataset=val_dataset,
    batch_size=4,
    num_workers=4,
)

# 3 Fine tune a model

model = ObjectDetector(model="retinanet", num_classes=datamodule.num_classes)

trainer = flash.Trainer(max_epochs=1, fast_dev_run=1)
trainer.finetune(model, datamodule=datamodule)
trainer.save_checkpoint("object_detection_model.pt")

# 4 Predict

model = ObjectDetector.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/object_detection_model.pt")
model.serializer = FiftyOneDetectionLabels()

predict_dataset = fo.Dataset.from_dir(
    "data/coco128/images/train2017",
    fo.types.ImageDirectory,
    max_samples=10,
)

filepaths = predict_dataset.values("filepath")
predictions = model.predict(filepaths)

# TODO: this should happen natively in serializer
fouf.normalize_detections(filepaths, predictions)

predict_dataset.set_values("predictions", predictions)

# 5 Visualize in FiftyOne App

session = fo.launch_app(predict_dataset)

Semantic segmentation
import flash
from flash.core.data.utils import download_data
from flash.image import SemanticSegmentation, SemanticSegmentationData
from flash.image.segmentation.serialization import SegmentationLabels, FiftyOneSegmentationLabels

import fiftyone as fo

# 1 Download data

download_data("https://github.com/ongchinkiat/LyftPerceptionChallenge/releases/download/v0.1/carla-capture-20180513A.zip")

# 2 Load data into FiftyOne

dataset = fo.Dataset.from_dir(
    "data/",
    fo.types.ImageSegmentationDirectory,
    data_path="CameraRGB/",
    labels_path="CameraSeg/",
    force_grayscale=True,
    max_samples=20,
)
train_dataset = dataset.take(10).clone()
predict_dataset = dataset.exclude(train_dataset).clone()

# 3 Load data into Flash

datamodule = SemanticSegmentationData.from_fiftyone(
    train_dataset=train_dataset,
    batch_size=4,
    val_split=0.3,
    image_size=(200, 200),  # (600, 800)
    num_classes=21,
)

# 4 Fine tune a model

model = SemanticSegmentation(
    backbone="torchvision/fcn_resnet50",
    num_classes=datamodule.num_classes,
    serializer=SegmentationLabels(visualize=True),
)

trainer = flash.Trainer(max_epochs=1, fast_dev_run=1)
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
trainer.save_checkpoint("semantic_segmentation_model.pt")

# 5 Prediction

model = SemanticSegmentation.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/semantic_segmentation_model.pt")
model.serializer = FiftyOneSegmentationLabels()

filepaths = predict_dataset.values("filepath")
predictions = model.predict(filepaths)
predict_dataset.set_values("predictions", predictions)

# 6 Visualize in FiftyOne App

session = fo.launch_app(predict_dataset)

Image embeddings
import numpy as np
import torch

from flash.core.data.utils import download_data
from flash.image import ImageEmbedder

import fiftyone as fo
import fiftyone.brain as fob

# 1 Download data

download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip")

# 2 Load data into FiftyOne

predict_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/predict/",
    fo.types.ImageDirectory,
    max_samples=10,
)

# 3 Load model

embedder = ImageEmbedder(backbone="swav-imagenet", embedding_dim=128)

# 4 Generate embeddings

filepaths = predict_dataset.values("filepath")
embeddings = np.stack(embedder.predict(filepaths))

# 5 Visualize in FiftyOne App

results = fob.compute_visualization(predict_dataset, embeddings=embeddings)

session = fo.launch_app(predict_dataset)

plot = results.visualize()
plot.show()

Before submitting

[X] (This PR was discussed face-to-face) Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
[X] Did you read the contributor guideline, Pull Request section?
[X] Did you make sure your PR does only one thing, instead of bundling different changes together?
[ ] Did you make sure to update the documentation with your changes?
[X] Did you write any new necessary tests? [not needed for typos/docs]
[X] Did you verify new and existing tests pass locally with your changes?
[X] If you made a notable change (that affects users), did you update the CHANGELOG?

PR review

[X] Is this pull request ready for review? (if not, please submit in draft mode)

Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Did you have fun?

I have created this other PR in the meanwhile and here is the new API: https://github.com/PyTorchLightning/lightning-flash/pull/365/commits/5278af2fe24c3ee5b9b181cde74c9855489a71d9
import flash
from flash.core.classification import FiftyOneLabels, Labels, Probabilities
from flash.core.data.utils import download_data
from flash.core.finetuning import FreezeUnfreeze
from flash.core.integrations.fiftyone import fiftyone_launch_app
from flash.image import ImageClassificationData, ImageClassifier

# 1 Download data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip")

# 2 Load data using fiftyone
datamodule = ImageClassificationData.fiftyone_from_dir(
    train_dir="data/hymenoptera_data/train/",
    val_dir="data/hymenoptera_data/val/",
    test_dir="data/hymenoptera_data/test/",
    predict_dir="data/hymenoptera_data/predict/",
)

# 3 Fine tune a model
model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes, serializer=Labels())
trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
trainer.save_checkpoint("image_classification_model.pt")

# 4 Predict from checkpoint
model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
model.serializer = FiftyOneLabels()
predictions = trainer.predict(model, datamodule=datamodule)

# 5. Visualize predictions in FiftyOne for 2 minutes.
session = fiftyone_launch_app(predictions)

Could you use this schema for all tasks ? Same for ImageEmbedder.
Best,
T.C

Would you mind allowing me to commit to this branch: https://docs.github.com/en/github/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork.

Hi @tchaton,
It looks like it's currently an open issue you can't "Allow edits from maintainers" for organization owned forks: https://github.com/isaacs/github/issues/1681
It would probably be easiest for you to just make a PR with your changes into this PR that I can then merge in.

Hey @edenlightning ,
Can you try this:

Best,
T.C

I think this API would be the cleanest.
import flash
from flash.core.classification import FiftyOneLabels
from flash.core.data.utils import download_data
from flash.core.finetuning import FreezeUnfreeze
from flash.core.integrations.fiftyone import fiftyone_launch_app
from flash.image import ImageClassificationData, ImageClassifier

# 1 Download data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip")

# 2 Load data using fiftyone
datamodule = ImageClassificationData.fiftyone_from_dir(
    train_dir="data/hymenoptera_data/train/",
    val_dir="data/hymenoptera_data/val/",
    test_dir="data/hymenoptera_data/test/",
)

# 3 Fine tune a model
model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes, serializer=FiftyOneLabels())
trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
trainer.save_checkpoint("image_classification_model.pt")

# 4 Predict from checkpoint
model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")

predict_dataset = fo.Dataset.from_dir(
    "data/hymenoptera_data/predict/",
    fo.types.ImageDirectory,
)

predictions = trainer.predict(model, dataloader=DataLoader(predict_dataset))

predict_dataset.set_values("predictions", predictions)

session = fiftyone_launch_app(predict_dataset)

Hey @tchaton I do think it could be interesting to keep a fiftyone_from_dir() method, but it's primary utility would be in cases where a dataset_type argument is explicitly required to select the dataset format, like in object detection:
datamodule = ObjectDetectionData.fiftyone_from_dir(
    train_dir="/path/to/train",
    val_dir="/path/to/val",
    test_dir="/path/to/test",
   dataset_type=fo.types.VOCDetectionDataset,  # VOCDetectionDataset, COCODetectionDataset, YOLODataset, etc...
)

as this would be shorthand for:
train_dataset = fo.Dataset.from_dir("/path/to/train", fo.types.VOCDetectionDataset)
val_dataset = fo.Dataset.from_dir("/path/to/val", fo.types.VOCDetectionDataset)
test_dataset = fo.Dataset.from_dir("/path/to/train", fo.types.VOCDetectionDataset)

datamodule = ObjectDetectionData.fiftyone_from_dataset(
    train_dataset=train_dataset,
    val_dataset=val_dataset,
    test_dataset=test_dataset,
    ...
)

In fact for classification, we support multiple formats too (directory trees, TF records, etc), so Flash users would get access to other classification formats via fiftyone_from_dir() under this pattern.
For FiftyOne users that have already constructed FiftyOne datasets, we'd still like to maintain the fiftyone_from_dataset() syntax too, of course.
I might recommend from_fiftyone_dirs() and from_fiftyone_datasets() for these two syntaxes, to mimic your other from_*() methods.
We'll do some tinkering over the weekend on this and have something for your consideration.

I think this syntax is cool:
predict_dataset = fo.Dataset.from_dir("data/hymenoptera_data/predict/", fo.types.ImageDirectory)
predictions = trainer.predict(model, dataloader=DataLoader(predict_dataset))
predict_dataset.set_values("predictions", predictions)
session = fiftyone_launch_app(predict_dataset)

but if a user is already onboard with predict_dataset being a FiftyOne dataset, then fiftyone_launch_app() could just be replaced with fo.launch_app().
In the case where
from flash.core.integrations.fiftyone import visualize

model = ImageClassifier(..., serializer=FiftyOneLabels())

predictions = model.predict(filepaths)

session = visualize(filepaths, predictions)  # launches FiftyOne App

It'd be really awesome if we could find a way to let Flash users use flash.core.integrations.fiftyone.visualize() to visualize predictions where FiftyOne-format data was not used at any point:
from flash.core.integrations.fiftyone import visualize

# use FiftyOneLabels bc we want to use `visualize` later
model = ImageClassifier(..., serializer=FiftyOneLabels())

# dataloader is any data loader folks might use
predictions = trainer.predict(model, dataloader=...)

session = visualize(filepaths, predictions)  # launches FiftyOne App

Hmm, answering my own question, I guess that's why you wanted the FiftyOne serializers to return fo.Sample objects...

Hey @brimoor,
For the first part.
I am good with the function names and and yes, dataset_type and predict_type should be provided. For predict_type, only if predict folder is.
Yes, we thought about visualize running on file paths and predictions directly.
session = visualize(predict_dataset.values("filepath"), predictions)  # launches FiftyOne App

I would let you both do the changes accordingly :)
Best,
T.C

Hi @ehofesmann,
My 2c on your message:

Yeah, that's a bit of a pain. I'm not opposed to just doing datamodule.predict_dataset for now at least. One option is to add a filepaths attribute to the dataset. We'd have to set dataset.filepaths inside the load_data method, but that could work. @tchaton any thoughts on this?
I think Trainer().predict should do everything you want here as it is a lightning method so it supports lightnings accelerators and should have a progress bar.
Yeah, I think that makes sense. I vote to just go with a from_fiftyone for now (that takes datasets) to unblock, we can always come back and add utility methods later.

Hope that helps :smiley:

We are trying to find the best way to generate predictions inside of the fo_dataset.apply_model() function.

Yes, this API won't work for people who don't use FiftyOne datasets but still want to use the app.

And this won't work if the dataset isn't as expected and filepaths can't be extracted.
session = visualize(datamodule.predict_dataset, predictions)

The only clean solution I see is to package both predictions and filepaths together within the predictions.

Trainer().predict() actually perform distributed training. Model.predict is just a utils to make sure it works fine or for EndPoint deployement.

I am fine removing from_fiftyone_dir and keep only Fiftyone datasets support, but it means people with other datasets won't be able to properly use the App.

Best,
T.C

Hello @ehofesmann! Thanks for updating this PR.

In the file flash/core/classification.py:

Line 216:121: E501 line too long (136 > 120 characters)

In the file flash/core/integrations/fiftyone/utils.py:

Line 30:5: E121 continuation line under-indented for hanging indent
Line 30:5: E125 continuation line with same indent as next logical line
Line 74:24: E251 unexpected spaces around keyword / parameter equals
Line 74:26: E251 unexpected spaces around keyword / parameter equals

In the file flash/image/data.py:

Line 43:46: E231 missing whitespace after ','
Line 53:46: E231 missing whitespace after ','
Line 63:46: E231 missing whitespace after ','
Line 73:46: E231 missing whitespace after ','

In the file flash/image/detection/data.py:

Line 98:46: E231 missing whitespace after ','
Line 166:46: E231 missing whitespace after ','

In the file flash/image/detection/serialization.py:

Line 52:9: E121 continuation line under-indented for hanging indent
Line 52:9: E125 continuation line with same indent as next logical line

In the file flash/image/segmentation/serialization.py:

Line 106:9: E121 continuation line under-indented for hanging indent
Line 106:9: E125 continuation line with same indent as next logical line

@tchaton and @ethanwharris
I have updated the FiftyOne serializers with a return_filepaths (False) flag that allows the serializers to either return a list of FiftyOne labels, or a list of (filepath, fo_label) tuples. fiftyone_app_launch() now also accepts either this list of tuples directly, or the list of FiftyOne labels along with a filepaths argument separately:
# Predicting with the model directly
filepaths = [...]
model.serializer = FiftyOneLabels()
predictions = model.predict(filepaths)  # List of FO labels
fiftyone_app_launch(predictions, filepaths = filepaths)

# Predicting with a trainer
datamodule = ....
model.serializer = FiftyOneLabels(return_filepaths = True)
predictions = Trainer().predict(model, datamodule=datamodule)  # List of tuples
fiftyone_app_launch(predictions)

I will likely still update the name of fiftyone_app_launch
Where I need your input is that in order for the serializer to have access to filepaths no matter the data_source used to construct the datamodule, I needed to update the PathsDataSources to include filepaths somewhere in the samples when predict_load_data is called.  I initially wanted to store it in sample[DefaultDataKeys.METADATA], but that is already used to store image dimensions and when I tried to turn it into a dict, the collate functions no longer worked when calling trainer.predict().
My solution to this was to create a DefaultDataKeys.FILEPATH that would be stored on all prediction samples loaded by a PathsDataSource or by a FiftyOneDataSource.
Please let me know if there is another way that you would prefer this done
These changes also allow you to provide a datamodule to fiftyone_launch_app() and the filepaths will be read from the FILEPATH key in the samples in datamodule.predict_dataset.data since all relevant data sources now store filepaths:
# Predicting with a trainer
model.serializer = FiftyOneLabels()
predictions = Trainer().predict(model, datamodule=datamodule)  # List of FO labels
fiftyone_app_launch(predictions, datamodule=datamodule)

Tensor and numpy data
This datamodule syntax will likely be necessary to extend visualization capabilities to the Tensor and Numpy data sources. In that case, we could access the raw tensors from datamodule.predict_dataset.data and use the FiftyOne dataset ingestors to write the images to disk when creating the FiftyOne Dataset.
The alternatives for visualizing tensor or numpy data would be to either:

Return tuples of (tensor, fo_label) from the FiftyOne serializers that will then be written to disk in fiftyone_launch_app
Write the images to disk within the serialize() call of the FiftyOne serializers

Hi @ehofesmann thanks for your work on this!

My solution to this was to create a DefaultDataKeys.FILEPATH that would be stored on all prediction samples loaded by a
PathsDataSource or by a FiftyOneDataSource.
Please let me know if there is another way that you would prefer this done

This seems like a bug with metadata. I've openened issue #384. Let'suse FILEPATH as a temporary solution for now but with a view to replacing it with meta data once #384 is resolved.

Tensor and numpy data

I would personally opt for saving to disk inside the fiftyone launch app method as it seems like a reasonable assumption that it would have side effects (whereas serailizers with side effects sounds a bit weird to me, even though we do it in some places haha...). For now, let's just leave tensor / numpy as unsupported and can come back and add it in a later PR as it sounds like it won't require any change to what is already here.

Hi @ehofesmann - just an FYI, if you update to your branch you should now be able to use a dictionary for the metadata, rather than needing the FILEPATH key :smiley:

Hi @ehofesmann - just an FYI, if you update to your branch you should now be able to use a dictionary for the metadata, rather than needing the FILEPATH key

Awesome! Thanks @ethanwharris, I'll update it

Alright! This PR is ready for you guys to take a final pass over @ethanwharris @tchaton
There is one thing I need your assistance with. In the integration docs I added a video and an image. They would likely be best hosted by you, but I am just unsure what your process is for hosting documentation media.
They currently link here:
https://user-images.githubusercontent.com/21222883/121972505-45114b00-cd49-11eb-9ef5-9a69fd90bf59.png
https://voxel51.com/images/fiftyone_long_sizzle.mp4

Hey @ehofesmann, just an FYI, you're getting some test failures due to an unrelated dependency issue. I'm working on a fix, I'll let you know when it's in master :smiley:

Hey @ehofesmann, just an FYI, you're getting some test failures due to an unrelated dependency issue. I'm working on a fix, I'll let you know when it's in master

Gotcha! I was racking my brain trying to figure out what I broke. I'll wait until it's in master :+1:

@ehofesmann If you update to master the CI should be working properly again :smiley:

Tests are passing, LGTM

Awesome! We're just taking a final pass over everything. I'll let you know once we're done and then it's all yours.

@ethanwharris and @tchaton
It would be great to make it easier for users to find these visualization capabilities. What do you guys think about me merging in this section to the README?
https://github.com/voxel51/lightning-flash/pull/4
The proposed content:
Visualization
Predictions from image and video tasks can be visualized through our integration with FiftyOne
allowing you to better understand and analyze how your model is performing.
from flash.core.data.utils import download_data
from flash.core.integrations.fiftyone import visualize
from flash.image import ObjectDetector
from flash.image.detection.serialization import FiftyOneDetectionLabels

# 1. Download the data
# Dataset Credit: https://www.kaggle.com/ultralytics/coco128
download_data(
    "https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip",
    "data/",
)

# 2. Load the model from a checkpoint and use the FiftyOne serializer
model = ObjectDetector.load_from_checkpoint(
    "https://flash-weights.s3.amazonaws.com/object_detection_model.pt"
)
model.serializer = FiftyOneDetectionLabels()

# 3. Detect the object on the images
filepaths = [
    "data/coco128/images/train2017/000000000025.jpg",
    "data/coco128/images/train2017/000000000520.jpg",
    "data/coco128/images/train2017/000000000532.jpg",
]
predictions = model.predict(filepaths)

# 4. Visualize predictions
session = visualize(predictions, filepaths=filepaths)

Hey @ehofesmann , awesome work with this integration.


Bug: Focus is lost when using search

What I did:
I pressed on a key, and then the keycode, this opened a search dialogue and I typed in "space". I then press Enter.
What I expect:
The best match of the search term should be focused, and when I press Enter, the focused item should be picked.
What happened:
The results show the space key, but it doesn't have focus, so nothing happens when I press enter. If there are more results, one of them will have focus, and when there are a lot of results it seems to just pick one in the middle.
Browser: Firefox 95.0.1 (64-bit)

Yeah, this is a problem, thanks for the report!
I think it might be maintaining the highlight on the position that was previously highlighted, even if it's not the current "best" match or the results are filtered down to a set that doesn't include that position. The goal was to show the current selection when that panel opens up but it would probably be worth clearing that once you start typing.

The goal was to show the current selection when that panel opens up but it would probably be worth clearing that once you start typing.

That makes sense. But yeah, once you start typing you are implicitly going for a change, and if you need to press enter anyway to have it select it, it can just revert to the current selection when the search field is empty.
When searching, focus should in my opinion always be on the top element, and the search results should be ordered by closeness of match, which I think they are? I can see you can change focus by using arrow up/down, which is neat! But it highlights both the current bound option and the current selected option with the same color, which is a bit confusing.

This should be fixed now, let me know if there's still any lingering problem.

It is certainly fixed! Great stuff!
But it highlighted another small ux issue, in that when you select behaviour for a key and press enter, the dialogue box closes, and in most cases you want to set the key straight after, so it would be nice to be able to press tab, to reach the next empty binding. But thats definitely not related to this issue.


disable preload in HLS

Hello,
If I use HLS plugin, after I init the video, it will preload about 30 .ts files.
There is a "preload" option in the player, but it does not work on HLS videos.
Is it possible to disable preload function in HLS videos?
Thank you.

同求

repeat with #258


DOC: Clarified the meaning of optional arguments in optimize.leastsq

https://stackoverflow.com/q/75278309/2988730 and my own experience indicate that the docs are not quite clear on this point
[ci skip]

Thanks! When gh-17878 merges, I'll go ahead and merge this.
@madphysicist as a follow-up, would you consider doing something like gh-17837 for curve_fit?  The first two outputs can be unpacked (for backward compatibility), but the remaining return values would still be accessible as attributes.


feat: add VSCode dev container config and update Rocket to 0.4.5

This PR:

updates Rocket as it was broken with the latest nightly build. The error mentioned in this issue kept appearing with the older version.
adds a config file to auto-setup Rust and relevant extensions out-of-the-box using VSCode's dev container setup (and in the future Codespaces). It is based on the official config file provided by Microsoft for Rust dev containers. See the new README section for more information.

English isn't my first language, but I tried my best to simplify the new doc section and add links for more information. I'd really appreciate your feedback :)

This is fantastic. All looks great. Thanks so much for adding that as an option @madyanalj ! 😄 We really appreciate it!

This is fantastic. All looks great. Thanks so much for adding that as an option @madyanalj ! 😄 We really appreciate it!

No worries man! Thanks for reviewing and merging 🙇


Create draft report

Let's summarize our findings! Following ticket #118, this ticket documents building out each segment with draft content.
Acceptance Criteria:

[x] Add draft content to each segment (can be rough)
[x] Pass draft to project team for feedback
[x] Iterate on feedback

Hi @jaimiebutton - are we in a place to close off the draft report? The finalize report ticket may now be in the backlog or icebox, but we can pull out to finish up next sprint.

Hi @jaimiebutton - just confirming if it is ok to close this draft report ticket?

Yes, let's close this one. Thanks @LindsayMacfarlane!


[WIP] Add Metadata Nav Item to SharePage

This PR resolves #1679.
This PR adds a new Nav Item to the Share Page titled "METADATA" which on hover, reveals a window showing the brew's authors, tags, systems, and description.
To do list:

[x] Add METADATA nav item
[x] Show window on hover

[x] Description
[x] Tags
[x] Systems
[x] Authors

[ ] Apply styling to match mock up

Some adjustments from this morning's code shuffling:

I've said it before, I'll say it again: display: flex is black magic.

I wonder if the labels should be right-aligned?  What are they in the metadata editor?

Though that would look bad when very skinny

Current state of the PR:

The transparent full width element that allows the window to be centered was sitting over the drop down menus, causing them to immediately close when the metadata window was open. I had to modify the z-index of the drop down menus to have it sit over the new invisible window.

Also, change Last Updated to just Updated
Is there a way we can have the (i) icon in a subdued hue which goes full brightness when the title area is hovered?

Should it only display fields that have values?

Is there a way we can have the (i) icon in a subdued hue which goes full brightness when the title area is hovered?

Whatever the behavior, it should be identical to all the other nav bar buttons.

Is there a way we can have the (i) icon in a subdued hue which goes full brightness when the title area is hovered?

Whatever the behavior, it should be identical to all the other nav bar buttons.

Hmm .. whole nav segment changes bgcolor and any associated drop-down auto appears?
OK, with that going on don't bother with any special visual treatment of the (i) icon.

Should it only display fields that have values?

I think this might be a bad idea, instead we could have a There is no X text.

Converting this to a checklist for actions:

[ ] The box is slightly overlapping the nav bar itself, but behind the "title" button. This is more noticeable on a narrow screen where the nav bar becomes two lines tall.
[ ] Can we style the "tags" with a round pill shape as we do everywhere else?
[ ] Could we perhaps unify the style with the "do you want to send this to Google" popup box (from now on, "the Popup")? Add a bit of a border, etc.? I also propose alternating background color of the rows instead of the divider lines (mockup examples below).
[ ] Some of the lines will be mostly empty. Could we narrow the max width (~440px?)
[ ] Can the dropdown be centered under the title button instead of centered on the page? This would also simplify this without needing a separate "window wrapper" class. See the Popup for how to achieve this.
[ ] Can we add a transition animation similar to the Popup?

I think this might be a bad idea, instead we could have a There is no X text.

The error checking was MEANT to display "No authors" or "No systems" or "No tags". Clearly that wasn't working correctly; I've added that check to the To Do list.

Right, fixed the "no data" error messages:

This is most of the checklist completed:

Another point, to help users learn about this new feature, i believe this menu should be open by default, at least a few weeks

I've completed most of the checklist now, but I'm breaking for the night.
The last thing I've added at this point is the min and max widths; the only issue as a result of this is that the window is now no longer centered. I don't expect that this will be particularly difficult to resolve once I'm able to return to the issue.

Full screen width (hovering over title):

Skinny (not hovering over title):

Brew with no tags, no systems, and no description:

Mouse over the author "test":

Unless there's something else that needs to be added, corrected, or otherwise changed, I think this one is done!

Dammit, there's a bug with the UserPage as a result of this; I assume it's probably due to z-index changes in some of the stuff I was experimenting with earlier. I'll dig into it and resolve prior to closing this out.

Ok, I'm not aware of any further bugs induced by this PR; it should be ready for review/merging.


Use gzweb2 from npm

Signed-off-by: Nate Koenig<EMAIL_ADDRESS>Removes the built-in gz3d.min.js and replaces it with the upstream gzweb2 library.
I've also removed the admin/cloudsim visualizer. We'll use the main visualizer going forward.

I'm going to merge this so that we are down to one PR that updates to angular 14.

I'm going to merge this so that we are down to one PR that updates to angular 14.

I haven't tested nor taken a look into this PR so I don't think merging it is a good idea. We can deploy the Angular 14 one without this change.


Add per RLP metrics & RL headers by default

Change the deployment of Limitador to enable the exhaustive metrics and the rateLimitHeaders as our default

Kuadrant operator needs to import the Limitador type that expose those settings. As of today, only [v0.6.0

Superseded by #301


events support?

Any plans on events support? Like on update?

Fixed in 120af09.
Oui already supports updates via a callback. This has now been added to datoui. Syntax is:
datoui( opts, callback )

See the api


Update pg-theorem.mdx

#438

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.


See if Crowdin fixes the PR issue

Description of Work
Crowdin is creating phantom PRs.  Something is out of sync so creating a PR to try to resolve the string issues.
Notes to Test (Optional)
N/A

Closing out.


Running Commands of CASPI(MinTL),M_soft(act), End-to-End

Hi,
Really amazing paper! When I read the ACL 2022 version of the paper, I find that there is an end-to-end version of CASPI(MinTL),M_soft(act), which achieves the best performance in Table 1, but I can not find the exact commands of that in your readme file. I wanna know if you could add  the exact hyperparameters of that part so we can reproduce the amazing results.
Best.

Sorry, my mistake. It is already the end-to-end version for CASPI(MinTL),M_soft(act).


Error while provisioning content type and define a list with said item

I have declared a XML-provisioning schema that I try to provision out content types with site columns attached to it. Content types are defined in the content type hub and so are the site columns (which are attached to the content types).
I have looked at examples etc, but no mater what I try, it doesnt work. See attached file for the code.
The error message I get while I tyr to upload it via PowerShell is.
`
Apply-PnPProvisioningTemplate : The provided template is not valid!
At line:1 char:1
+ Apply-PnPProvisioningTemplate -Path C:\xml\xmlSchema.xml

  + CategoryInfo          : WriteError: (:) [Apply-PnPProvisioningTemplate], ApplicationException
  + FullyQualifiedErrorId : EXCEPTION,SharePointPnP.PowerShell.Commands.Provisioning.Site.ApplyProvisioningTemplate`

example.zip

Hi,
Thanks for your feedback. Unfortunately, we don't have support for the Content Type Hub at the engine level.


Fix a bug in StringBuilderEncoder.encode()

Headroom for char sequence encoded data is overestimated.

And, well, it doesn't fix the problem entirely, because getByteBuffer() is not called in the context of synchronized on the ByteBufferDestination, and after this call the remaining space may be reduced. I'm working on a fix.

The intended fix is to add the following method to the ByteBufferDestination interface:
    /**
     * Writes the given data to this ByteBufferDestination entirely. Call of this method should *not* be protected
     * with synchronized on this ByteBufferDestination instance.
     */
    public void write(ByteBuffer data);

Implementations of this method should synchronize internally, if needed.
Then StringBuilderEncoder.encode() only estimates if the data fits the thread-local byte buffer, without querying destination.getByteBuffer().remaining(), and then calls destination.write(temp) with the buffer with encoded data.

Roman, what problem are you trying to solve? Is it related to this twitter thread?

@remkop there is a race condition that could lead to tearing encoded events apart of writing a truncated event close to the boundaries of Manager's ByteBuffer. I. e. in case of MemoryMappedFileManager, close to 32 MB boundaries.
It is related to that Twitter thread in the sense that I spotted this problem while working on more concurrency-friendly implementation of MemoryMappedFileManager. A pull-request will follow after this PR.
I've pushed the full solution to the race condition in StringBuilderEncoder.encode() now; please review. It adds ByteBufferDestination.write(ByteBuffer) and ByteBufferDestination.write(byte[], int, int) methods which should care about synchronization themselves. This is also essential for lock-free MemoryMappedFileManager, because exactly those methods are going to be implemented lock-free. getByteBuffer() + drain() are designed so that synchronization couldn't be avoided. But the expectation is that most of encoded events fit the thread-local buffers, and on of write() methods could be called instead of writing to destination.getByteBuffer().

Also this PR alone improves concurrency, because it delays locking on the ByteBufferDestination object as far as possible. The encoding work should now mostly be done outside of synchronized block on the ByteBufferDestination.

OK I will try to look at this on the weekend because I'm swamped at work this week. Can I ask you to raise a Jira for this? (The text of your last 2 comments are fine as description.)

@remkop I reviewed the existing code again and found that there is actually no race condition or bugs, if only one assumption is true: ByteBufferDestination.drain() returns ByteBuffer of capacity which is the same as the former ByteBufferDestination.getByteBuffer()'s capacity.
However this refactoring is still needed, because it allows lock-free MemoryMappedFileManager. I've "rebraded" this PR as #71. The Jira issue with description is https://issues.apache.org/jira/browse/LOG4J2-1874.


INSERT in "double" column with fixed precision

Hi I have a table that contain double columns that I'm inserting into.
I see that the doubles are serialized in scientific format, loosing significant digits in the process.
What's the recommended way of forcing a fixed precision serialization of the sqlpp11 text sql statement output (w/o redefining the columns).
E.g
INSERT INTO tbl (id,x,y,z,effective) VALUES(162235429305716905,20.2556,2.05634e+07,3.82616e+07,DATE '2023-06-07')

Just saw that a patch was released the other day, closing this issue.


Azure SDK Review - [Introduction to IC3 ACS Chat]

New SDK Review meeting has been requested.
Service Name: IC3 ACS Chat
Review Created By: Jim Chou
Review Date: 09/21/2023 02:05 PM PT
Hero Scenarios Link: Not Provided
Architecture Diagram Link: Not Provided
Core Concepts Doc Link: Not Provided
APIView Links:
Description: Chat JS SDK API to support image upload and send image with message
Detailed meeting information and documents provided can be accessed here

Meeting updated by Jim Chou
Service Name: IC3 ACS Chat
Review Created By: Jim Chou
Review Date: 09/21/2023 02:05 PM PT
Hero Scenarios Link: Not Provided
Architecture Diagram Link: Not Provided
Core Concepts Doc Link: Not Provided
APIView Links: Javascript,
Description: Chat JS SDK API to support image upload and send image with message
Detailed meeting information and documents provided can be accessed here

Meeting updated by Jim Chou
Service Name: IC3 ACS Chat
Review Created By: Jim Chou
Review Date: 09/27/2023 02:05 PM PT
Hero Scenarios Link: here
Architecture Diagram Link: Not Provided
Core Concepts Doc Link: Not Provided
APIView Links: Javascript,
Description: Chat JS SDK API to support image upload and send image with message
Detailed meeting information and documents provided can be accessed here

Cancelled by: Jim Chou


Error when a class defined via $.Class() extends an ES6 class

This is the line that errors: https://github.com/LeaVerou/bliss/blob/master/bliss.shy.js#L214
This can easily happen when base code refactors to use ES6 classes, but plugins and libraries based on it do not.

It looks like there might not be a way to work around this, it doesn't look possible to call ES6 constructors as a regular function. Perhaps our best bet is to detect these cases and provide a more meaningful error.

Closing for now, since there doesn't seem to be anything better we can do.


[OOA] Lock on goron mountain

Hello,
I encountered a small problem on my first try with this randomiser : I'm currently stuck. Made everything I was able to do according to EmoTracker, except in one part of the world that I cannot reach yet because I don't have the correct songs on the Harp.
Is it me who missed something or is it a non-handled case ?
My modified rom has name ooarando_3.4.1_3547215a, if it may be useful.
Every items I currently have, except rings, have been marked there.

I saw you got help for this in the Discord, so I'll close.

Yeah, I never thought of that, I wanted to remove that issue by myself when I got the answer, but I didn't find any way to do it. Thank you anyway.


Configs WAXS_liquid and WAXSNEXAFS get saxs_det

Currently dry run assigns any config that isn't "WAXS" the saxs detector.
https://github.com/NSLS-II-SST/rsoxs_scans/blob/a73efae8322681da7bb4c1b4d0b9f25e617a8085/rsoxs_scans/acquisition.py#L49-L52

My solution: (will be adopted if you accept the pull request late tonight)
https://github.com/NSLS-II-SST/rsoxs_scans/blob/5908c2b7d6b91ad87027396d680d9d7aebb894f3/rsoxs_scans/acquisition.py#L59-L65
Only question is whether you normally update the RSoXS_Main_Det for NEXAFS plans too or want to leave it as SAXS.

I think for NEXAFS, an error (or warning?) should be thrown if a rsoxs or spirals are taken…

added error checking in the notebook.  lists of valid entries are added to defaults.py


Add YouTubers to the website

Add Youtubers on the website on the "/addYoutuber" route and update README to mark the contribution. (https://youtubers-birthdays.kendalldoescoding.tech/addYouTuber)
Note: It may take a while to load the first time as it has to load the database code, etc.
Check Contribution Guidelines and Code of Conduct for more info.
You must state their:

Category (Eg: Gaming, Tech)
Channel Name (not real name)
Birthday
Total Views (Approx)

NOTE - THE FINAL DECISION ON WHETHER THE YOUTUBER IS ADDED OR NOT, IS UP TO @KendallDoesCoding. I WILL NOT ADD CONTROVERSIAL YOUTUBERS TO A EXTENT, ETC.
This issue is open to all contributors, so anyone can contribute to the project.

@KendallDoesCoding can i work on it?

Hey @vr-varad, this issue is open to all. You can read the contributing guidelines to get started.

Hey @MayankBansal12 this site "https://youtubers-birthdays.kendalldoescoding.tech/addYouTuber" is down right now. Is there any other way to contribute?

Hey @andrito9755, you can check other url for the site. To add youtubers, you can read the contributing guidelines and follow the steps mentioned.


Game#place_computer_submarine could be re-factored...

the local variable of valid_sub_positions adds a lot of clutter.... might be a good chance to reduce clutter, re-factor and practice with some Enumerable methods or Hash methods....

valid_sub_positions looks like it's reporting some state about the board, yeah? Specifically the computers board.
valid_sub_positions is a two-dimensional array....they're coordinates.
The easiest thing to do is to just pull the thing out of the current method, and place it in it's own method.

done


Make possible other variants of identicons

Nowadays is very common the “bubble” format of user's avatars, for example the contact list on mobile devices. Square shaped avatars tend to look not centered (vertically) and some corners could be cut off.
An interesting approach I've found is using 4 levels of circles starting at different angles, with variable lengths for segments. An example of this is done by the project: https://github.com/bitverseio/identicon#examples . The code implementing this algorithm is in there (but in PHP).
Perhaps Pydenticon could include this feature too having a library for rings.

Hm... Would be certainly interesting to do that, although I guess I'd need to refactor the library a bit to accommodate for support for different identicon types. Probably should not increase complexity too much with sane defaults.
As a curiosity, do you think that the current implementation would be interesting for such a use-case in case the corners of generated identicon were rounded a bit?


add th1124zb-g2 fingerprint

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

Hello @aguilbault-Sinope Can you please sign the CLA? This necessary before the code is merged.
Thank you


validating the maps search for side street

[Enter feedback here]
Hi
I am learning the maps API and the query : https://atlas.microsoft.com/search/address/reverse/crossstreet/json?&api-version=1.0&subscription-key=3CW1zxJDw0VLLOmSWV5Thz-v-Ez3HPPvweBNIM2LKHA&language=en-US&query=47.591180,-122.332700 should give a different side-street according to the docs. It should give "Occidental Avenue South." or something like that. It instead gives:
{
"summary": {
"numResults": 1,
"queryTime": 156
},
"addresses": [
{
"address": {
"streetName": "South Atlantic Street & Edgar Martinez Drive South",
"crossStreet": "South Atlantic Street",
"municipalitySubdivision": "Downtown Seattle",
"municipality": "Seattle",
"countrySecondarySubdivision": "King",
"countrySubdivision": "WA",
"countrySubdivisionName": "Washington",
"postalCode": "98134",
"countryCode": "US",
"country": "United States",
"countryCodeISO3": "USA",
"freeformAddress": "South Atlantic Street & Edgar Martinez Drive South, Seattle, WA 98134",
"localName": "Seattle",
"street": "Edgar Martinez Drive South"
},
"position": "47.59029,-122.33325"
}
]
}
I am following this
Here is my map if you want to test against it.
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 2578f489-79b3-4bb0-c620-609ca7b78e47
Version Independent ID: f088832f-3327-02df-ec5f-c6e08e809115
Content: Search for a location using Azure Maps Search services
Content Source: articles/azure-maps/how-to-search-for-address.md
Service: azure-maps
GitHub Login: @anastasia-ms
Microsoft Alias: v-stharr

@bitflame we'll review your feedback and get back to you shortly. Thanks.

@bitflame we'll review your feedback and get back to you shortly. Thanks.

Adding @rbrundritt for further help.
@rbrundritt Could you please look into this issue and provide your suggestions?

Adding @rbrundritt for further help.
@rbrundritt Could you please look into this issue and provide your suggestions?

Docs are wrong. Fixing

Docs are wrong. Fixing

@anastasia-ms Thanks for fixing the doc..
@bitflame We hope that address your issue. 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.

@anastasia-ms Thanks for fixing the doc..
@bitflame We hope that address your issue. 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.

Thank you for looking into it.

Thank you for looking into it.

@anaharris-ms  May I what is the exact fix. We are using the same service, but it gives different coordinates with 49 meter range difference.
For Example
https://atlas.microsoft.com/search/address/reverse/json?subscription-key={APIKEY}&api-version=1.0&query=21.52665,39.16763&language=en-US
{     "summary": {         "queryTime": 19,         "numResults": 1     },     "addresses": [         {             "address": {                 "buildingNumber": "3590",                 "building": "JCHA3590",                 "streetNumber": "3590",                 "routeNumbers": [],                 "street": "Palasteen Road",                 "streetName": "Palasteen Road",                 "streetNameAndNumber": "3590 Palasteen Road -",                 "countryCode": "SA",                 "countrySubdivision": "Makkah Al Mukarramah Province",                 "municipality": "Jeddah",                 "postalCode": "23323",                 "municipalitySubdivision": "Al Hamra",                 "country": "Saudi Arabia",                 "countryCodeISO3": "SAU",                 "freeformAddress": "JCHA3590 - , 3590 Palasteen Road - , Al Hamra - , Jeddah 23323",                 "boundingBox": {                     "northEast": "21.525074,39.167716",                     "southWest": "21.524753,39.167576",                     "entity": "position"                 },                 "countrySubdivisionName": "Makkah Al Mukarramah Province",                 "countrySubdivisionCode": "2",                 "localName": "Jeddah"             },             "position": "21.524935,39.168163",             "id": "lWAcXojrsvI7du3k7NGpbQ"         }     ] }
In the Json Response You can see Position as "21.524935,39.168163", in my request you can see the position as 21.52500,39.16769
Originally this location name is Pointed at one hospital Dr.Soliman Fakeeh Hospital. Azure API give gives the position at the middle of the road which is close to the hospital.
Let me know if you have any resolution

#reassign @eriklindeman

This is by design. Reverse geocoding/address services general (e.g. pretty much every map platform out there) have a goal to determine the approximate street address for a coordinate, not to find points of interest at a specific location. To do this, they look for the closest road segment within a limit of 150 meters of the provided coordinate. This is why you are seeing a different coordinate in the response.
If you are looking for the hospital, a point of interest, using a coordinate, you can use one of the point of interest services in Azure Maps: https://learn.microsoft.com/en-us/rest/api/maps/search?view=rest-maps-1.0 For your scenario, the Search Nearby service is like most likely what you are looking for: https://learn.microsoft.com/en-us/rest/api/maps/search/get-search-nearby?view=rest-maps-1.0&tabs=HTTP For example the following takes your coordinate and looks for all points of interest within 150 meters: https://atlas.microsoft.com/search/nearby/json?api-version=1.0&lat=21.52665&lon=39.16763&radius=150&subscription-key=Your_Azure_Maps_Key
Note that no place is a single coordinate. A place of any kind generally covers an area (an address has a property with an area), and there is an infinite number of possible coordinates that could be represented within the range of that area (different spots within that area, and different number of decimal places). No place in the world is represented by a single exact coordinate (it would have to be infinitely thin).
Side note, it looks like you are using the V1 search service for reverse geocoding, there is a new reverse geocoding service version available here: https://learn.microsoft.com/en-us/rest/api/maps/search/get-reverse-geocoding?view=rest-maps-2024-04-01&tabs=HTTP It still operates in a similar way as I described but has better global data coverage.


Genji js to use indexdb

Proposal

Motivation

https://github.com/JohnStarich/go-wasm/tree/master/internal/indexeddb
Will allow genji.js to have an indexdb store , like the currently badger dB store.
wonder if golang leveldb  is a good indexdb emulator ? I read somewhere that there is a relationship between leveldb and indexdb.
The indexdb btw is used as a file system abstraction so that golang wasm has a file system to talk to. So it essentially can be used as a blob store for golang wasm apps too.
It does not work with tinygo . But I think it’s just a matter of time and help from others .

This is indeed part of a long term plan for genji.js. I'm closing this issue as this is not directly related to Genji, please follow the genjidb/genji.js repo instead 👍🏼

Hey @asdine
Code is now here:
https://github.com/hack-pad/go-indexeddb
He refectored it out to make it reusable for all. and so can be a driver for Genji if need be.
I am playing with it  with GIOUI. Its quite fun to be pure go...


feat: point to --verbose when command fails

Summary:
Our command error handler eats error stack and leaves the user with only a message. Example: https://github.com/react-native-community/react-native-cli/issues/273
I don't think we need to put stacks in every error out there, but we should enable them in --verbose mode.
Test Plan:

So good! I actually just had this thought while we were chatting!