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finally got to the population simulation part and the first thing i add is dying of starvation. countless nameless people dying of starvation for the sake of progress 🫡

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improved the terrain generation animation

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preparing a presentation so have this gif i made

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Hello humanity, I am Hazem, speaking to you from Gaza 🇵🇸🍉. We are living in extremely difficult conditions due to the war and the blockade. I’m reaching out to ask for your support in saving my brother Samer’s life. His health has severely deteriorated due to a lack of treatment. He urgently needs essential medications, which cost around 70 euros, and even a donation of 20 euros can make a big difference. https://gofund.me/917ecb89 Campaign Verified! We are grateful for any support you can provide.

i cannot donate as of right now but i hope the signal boost helps

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quick drawing i made today

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Jokes on you with this climate it will never rain again

istockphoto

your days are numbered reversedumbrella

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Is it a big number?

istockphoto

your days are numbered reversedumbrella

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i had to redo it from scratch but here it is, a working dijkstra algorithm that creates the shortest path between two points on a height map, all this while trying to keep itself at same height (map height goes from black to white, dark to bright color).

dijkstra is an algorithm with the purpose to find the smallest path between two points and it works by dividing the map on analysed and not analysed groups of tiles, with the analysed group initially consisting of only 1 tile - the origin tile - and across many rounds the group expands until it reaches the final destination. each analysed tile possesses a distance to the origin tile and each round the algorithm picks the tile that both has the smallest distance and still has unchecked neighboors. it then analyses those neighboors, adding them to the group

because my map was so large i perform the algorithm twice

in the first wave (red and yellow) the map is divided in 8x8 chunks and the algorithm is applied to these chunks, with red tiles being analysed tiles and the yellow tiles being the chunk path found

int the second wave (green and blue) the algorithm only searches inside the yellow tiles, with green tiles being analysed tiles and blue tiles being the path found

its important to notice that this two wave approach doesnt result in the best path once we consider that the first wave treats each chunk as the average of all 64 tiles that exist within it so some nuance is lost and there is no garantee that the best path is inside the best chunk path

this becomes a trade off between precision and speed as the dijkstra algorithm tends to wander around the map, as we can see with the amount and displacement of red chunks. if i were to only perform the second wave, without the chunk path's restriction, each red chunk would be 64 rounds wasted

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everyday my hatred of the computer grows larger

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Might update it to an A* algorithm so that it takes in consideration the distance to the destination

have been working on a dijkstra algorithm that connects two dots on a height map while trying to stay on the same color.

red are areas searched by the algorithm while yellow is the path already found

because the map was so large i had to divide it into chunks that were searched first. next step is to code finding the path inside the yellow chunks

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have been working on a dijkstra algorithm that connects two dots on a height map while trying to stay on the same color.

red are areas searched by the algorithm while yellow is the path already found

because the map was so large i had to divide it into chunks that were searched first. next step is to code finding the path inside the yellow chunks

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Rate my setup

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So... redeeming Thaddeus Thawne aka Inertia...

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This video showcases my Blender model of the planet that the Scud aliens call home, the fourth and final world I've mapped out for @jayrockin's "Runaway to the Stars" project. A *lot* of maps were created in service of this final render, and also in service of presenting the special qualities of this planet. I intend to show you as many of these as I can under the cut, and also in subsequent posts focusing on some of the more interstitial, ancillary maps and figures that played a part in producing the primary maps you'll see in this main post.

Before I show the first maps I made for this project, what you see below are the satellite-style maps for the Equinoxes and Solstices, in order of (Northern) Spring, Summer, Fall, and Winter, the latter serving as the texture for the Blender object you saw in the video.

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With that matter covered, our next focus is this project's foundation: Geology. While I didn't spin as elaborate a tectonic history for this planet as I did for the Ayrum commission, I did work out as much detail as I could for the more recent geological activity, to set the stage for the elevation data - including a narrower focus on the coastal shallows that host the Scud populations.

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Once I could move on to climate, my first step was finding this planet's relative Insolation, which I managed thanks to @reversedumbrella's code and coaching. With an obliquity of only 16 degrees, this planet's yearly maximum Insolation levels stick close to the equator, compared to pole-to-pole oscillation we see on Earth

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Having a rough sense of where heat would concentrate seasonally and how the landmasses would deflect water in light of the planet's retrograde spin, I was able to set down the bi-annual ocean currents (Northern Summer above and Northern Winter below), then the monthly water temperatures pushed around by said currents, and finally -after factoring in many other considerations- the monthly land temperatures as well (combined in the second gif)

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Next came the seasonal air pressure maps and subsequent wind patterns (my first time creating those from scratch), which later factored into the precipitation maps. The incredible temperatures at the largest continent's interior make a desert of most of it, and the other interiors are fairly dry too, but all that heat on the equatorial ocean generates a *lot* of evaporation which ends up coming down elsewhere.

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With temperatures and precipitation mapped out for each month, I was able to find how the accumulation and melt of ice and snow played out, too. Given such a hot equator it's surprising to see freezing temperatures hold out in some places, but low obliquity and high elevation shield what areas they can, it seems.

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All this monthly data was then painstakingly combined and compared and plugged into equations to produce maps of discrete climate zones, using both the Köppen (left) and Trewartha (right) classification systems. The higher latitudes see some overlap with Earth's conditions, but the Tropics...

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I never really finished the map I wanted to make with my own loosely customized classification system, but I *did* get as far as this breakdown of the areas that sometimes surpass 56.7 degrees Celsius, Earth's record for highest surface temperature ever directly measured. And as you can see, that earthly record is broken by a *significant* fraction of this planet's surface, and far exceeded by the equatorial continent's deep interior

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The final phase of this project dealt with creating satellite maps of this planet's surface (which you saw at the top of this post), which started with a map of dry and submerged substrate, then a density map of the vegetation that sits atop it, then the colors of that vegetation under annual average conditions (demonstrating how they would appear in-person, rather than the area's appearance from orbit), and finally plant colors under seasonal conditions (same conceit as previous). In concert with the seasonal ice and snow maps, it was the four maps in the last sequence which were overlaid on the Substrate map, using the plant density map as raster masks, to produce the final Satellite-Style maps.

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This planet's sophonts being a marine species, it was then worth focusing on the conditions underwater, which included monthly seafloor temperatures (first gif), annual discharge of sediment from rivers (magenta in the 2nd gif), and seasonal upwelling of nutrients from deeper water (blue in the 2nd gif).

The creation of all my maps seen in this post was possible thanks to Photopea, which has been my go-to for several years now. The resolution kinda got crunched when I uploaded these here, so when I share them on Reddit later I'll add those links under this. These have also already been posted on Twitter, which you can see here if you like. Thanks for scrolling all the way down here!

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the work continues :3

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city generation + names

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sorry for dropping so much crap all at once i dont know how to update people on this 👍