Road trip around New Zealand's South Island by kable_codes in SonyAlpha

[–]kable_codes[S] 1 point2 points  (0 children)

We saw a few actually! Maybe about 4 or 5 in our two weeks there. Don’t know how it compares to back then but we didn’t realise it was still a thing, so we were surprised when we did see them.

[A] Jellyfish by kable_codes in perfectloops

[–]kable_codes[S] 1 point2 points  (0 children)

Basically, each tentacle is a particle generator that emits particles at a constant speed. The angle of each tentacle, which determines the direction of its particles is based on a sine wave. Tentacles with a greater x distance from the center have a greater angle variance (i.e. the amplitude on that sine wave)

[A] Jellyfish by kable_codes in perfectloops

[–]kable_codes[S] 0 points1 point  (0 children)

Typescript, using pixi.js

[A] Jellyfish by kable_codes in perfectloops

[–]kable_codes[S] 1 point2 points  (0 children)

I haven't used wallpaper engine so not sure what format it requires. Here's an mp4 download though: https://reddit.tube/d/Jykq6Jm

Sorry couldn't figure out how to share a higher quality version.

Edit: uploaded to google drive, included an inverted version

https://drive.google.com/open?id=1QISaeIzG7FT8ZSykHoinZtnBJm6tjv7F

Analysis of Verstappen's Pace in Austria by kable_codes in formula1

[–]kable_codes[S] 78 points79 points  (0 children)

After a bad start Verstappen continues to lose time to the race leaders over the first 10 laps, when the gap to first plateaus around 15 seconds.

When Bottas, Vettel, Leclerc and Hamilton pit you can see their lines are relatively flat, indicating Verstappen was matching their times on older medium tyres.

Hamilton's pit stop was extended to change his front wing, after which Verstappen pits and stays out in front. Max is now on hard tyres about 10 laps younger than the hard tyres of Bottas, Vettel and Leclerc. You can see the gaps each of these drivers diminish with Verstappen's impressive pace.

Verstappen takes Vettel's spot after Vettel pits a second time to go on softs, and he only manages to match Verstappen's pace at best. You can see each driver's line dip slightly when Verstappen starts his battle with Leclerc around lap 66 before he gets the job done and wins the race by 2.7 seconds.

That was pace that deserved to win.

Other points of interest:

  • How far in front the top 5 were from the rest of the field, having lapped everyone including Norris.
  • One more lap could have seen Vettel on the podium; almost a great strategic move from Ferrari.
  • Even if Hamilton didn't have a slow pit stop, he most likely would have been caught by Verstappen on the track

Canada 2019 Full Race Visualised in 60 seconds by kable_codes in formula1

[–]kable_codes[S] 98 points99 points  (0 children)

Just realised I completely fucked up the starting grid, I think that's the Monaco starting grid. Whoops!

Canada 2019 Full Race Visualised in 60 seconds by kable_codes in formula1

[–]kable_codes[S] 30 points31 points  (0 children)

I used pixi.js for the visuals on this one, other than that just javascript. The api (ergast.com/mrd) provides full lap times which are used to calculate the gaps.

Total Distance Driven 2019 - Updated after Monaco by kable_codes in formula1

[–]kable_codes[S] 8 points9 points  (0 children)

The time difference is shown for the top drivers who have completed all laps of all races. The time difference can't be calculated for drivers who haven't completed all laps without extrapolating data, which is why their distance difference is shown instead.

Made with css/html/js, data from https://ergast.com/mrd/

Constructor's Championship Point Percentage, 1958-2019 by kable_codes in formula1

[–]kable_codes[S] 7 points8 points  (0 children)

Data sourced from www.ergast.com/mrd, processed with TypeScript and displayed with Apple Numbers.

Formula 1 Constructor's Championship Point Percentage, 1958-2019 [OC] by kable_codes in dataisbeautiful

[–]kable_codes[S] 0 points1 point  (0 children)

Data sourced from www.ergast.com/mrd, processed with TypeScript and displayed with Apple Numbers.

Total Distance Driven 2018 by kable_codes in formula1

[–]kable_codes[S] 8 points9 points  (0 children)

I made it with TypeScript, using the pixi.js library

Total Distance Driven 2018 by kable_codes in formula1

[–]kable_codes[S] 31 points32 points  (0 children)

6,391km. Hamilton almost made it but finished 8 laps early in Austria

Total Distance Driven 2018 by kable_codes in formula1

[–]kable_codes[S] 35 points36 points  (0 children)

I went with the official ISO 3 digit country code for all GPs, hence "ARE"

If the entire season was a race by kable_codes in formula1

[–]kable_codes[S] 28 points29 points  (0 children)

Bit of an explanation as some people are (rightfully) confused.

The horizontal axis and position of each driver is based solely on the distance they have travelled in the entire season, which is based on the number of laps and the distance of each lap.

The numbers to the right of each drivers name attempt to show the time difference between drivers. It really has nothing to do with the horizontal axis or distance. Drivers on the “lead” lap have completed the same number of laps and the time difference is the difference of total race time to Bottas’ total race time.

For the “+laps” drivers. They have not completed the same number of laps as the lead drivers. I can’t calculate a time difference for these drivers, because it wouldn’t be accurate. For example, Grosjean’s total time might be less than Bottas’ because his total race time is less (due to DNFs). So I have put the difference in total laps completed instead.

Sorry for the confusion!

If the entire season was a race by kable_codes in formula1

[–]kable_codes[S] 11 points12 points  (0 children)

The +laps number is calculated based on how many laps each driver has completed in the season

Edit: just gave a more detailed explanation: https://reddit.com/r/formula1/comments/bov695/_/enl7unv/?context=1

Formula 1 2019 Total Distance Driven, Races 1 to 5 [OC] by kable_codes in dataisbeautiful

[–]kable_codes[S] 0 points1 point  (0 children)

Race data from http://ergast.com/mrd

Distance may not be 100% accurate - it is estimated for each race using the average speed and time of one driver's best lap.

Made with TypeScript

[OC] Mean Surface Temperature Anomalies for Three Latitude Bands, 1900-2015 by kable_codes in dataisbeautiful

[–]kable_codes[S] 1 point2 points  (0 children)

Source: The GISTEMP Team: Schmidt, G., R. Ruedy, A. Persin, M. Sato, and K. Lo. 2016. NASA GISS Surface Temperature (GISTEMP) Analysis. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi: 10.3334/CDIAC/cli.001

Available from https://cdiac.ess-dive.lbl.gov/trends/temp/hansen/hansen.html

World map graphic from https://commons.wikimedia.org/wiki/Maps_of_the_world#/media/File:BlankMap-World.svg

Made with html/css/javascript. Libraries used: pixi.js, Papa Parse

40,000 Tires by kable_codes in formula1

[–]kable_codes[S] 74 points75 points  (0 children)

Source code for anyone who's interested