32 lines
972 B
Python
32 lines
972 B
Python
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import torch
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import torchvision.transforms.functional as T
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import math
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torch.set_grad_enabled(False)
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device = torch.device("cuda:0")
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size = 6144
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steps = 2048
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xs = torch.linspace(-1, 1, size, dtype=torch.cfloat, device=device).tile(size, 1)
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ys = torch.linspace(-1, 1, size, dtype=torch.cfloat, device=device).tile(size, 1).t() * 1j
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zs = xs + ys
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ws = zs.clone()
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aws = abs(ws)
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dead = torch.zeros_like(xs, dtype=torch.bool, device=device)
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counts = torch.zeros_like(xs, dtype=torch.float, device=device)
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for i in range(steps):
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zs *= zs
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zs += ws
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dead |= abs(zs) > 4
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counts += torch.where(dead, 1, 0)
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zero = torch.zeros((size, size, 3), dtype=torch.float, device=device)
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blue = torch.zeros((size, size, 3), dtype=torch.float, device=device)
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blue[..., 2] = 1
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itr = torch.log((steps - counts) / steps)
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itr /= math.log(steps)
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m = itr.reshape((size, size, 1)).repeat_interleave(3, -1)
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z = m * blue
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i = T.to_pil_image(z.permute(2, 0, 1))
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i.save("/tmp/mandel.png")
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