| Name | perlin-noise JSON |
| Version |
1.13
JSON |
| download |
| home_page | None |
| Summary | Python implementation for Perlin Noise with unlimited coordinates space |
| upload_time | 2024-07-26 08:17:47 |
| maintainer | None |
| docs_url | None |
| author | salaxieb |
| requires_python | None |
| license | None |
| keywords |
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| requirements |
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|
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|
Smooth random noise generator
read more https://en.wikipedia.org/wiki/Perlin_noise
source code: https://github.com/salaxieb/perlin_noise
noise = PerlinNoise(octaves=3.5, seed=777)
octaves : number of sub rectangles in each [0, 1] range
seed : specific seed with which you want to initialize random generator
tile_sizes : tuple of ints of you want to noise seamlessly repeat itself
```python
from perlin_noise import PerlinNoise
noise = PerlinNoise()
# accepts as argument float and/or list[float]
noise(0.5) == noise([0.5])
# --> True
# noise not limited in space dimension and seamless in any space size
noise([0.5, 0.5]) == noise([0.5, 0.5, 0, 0, 0])
# --> True
# noise can seamlessly repeat isself
noise([0.5, 0.5], tile_sizes=[2, 5]) == noise([2.5, 5.5], tile_sizes=[2, 5])
# --> True
```
Usage examples:
```python
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=10, seed=1)
xpix, ypix = 100, 100
pic = [[noise([i/xpix, j/ypix]) for j in range(xpix)] for i in range(ypix)]
plt.imshow(pic, cmap='gray')
plt.show()
```
```python
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise1 = PerlinNoise(octaves=3)
noise2 = PerlinNoise(octaves=6)
noise3 = PerlinNoise(octaves=12)
noise4 = PerlinNoise(octaves=24)
xpix, ypix = 100, 100
pic = []
for i in range(xpix):
row = []
for j in range(ypix):
noise_val = noise1([i/xpix, j/ypix])
noise_val += 0.5 * noise2([i/xpix, j/ypix])
noise_val += 0.25 * noise3([i/xpix, j/ypix])
noise_val += 0.125 * noise4([i/xpix, j/ypix])
row.append(noise_val)
pic.append(row)
plt.imshow(pic, cmap='gray')
plt.show()
```
Library has a possibility to generate repetative random noise with custom tile sizes:
```python
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=2, seed=42)
xpix, ypix = 800, 1200
lim_x, lim_y = 6, 9
pic = [
[
noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=[2, 3])
for j in range(xpix)
]
for i in range(ypix)
]
plt.imshow(pic, cmap="gray")
plt.show()
```
```python
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise1 = PerlinNoise(octaves=1)
noise2 = PerlinNoise(octaves=3)
noise3 = PerlinNoise(octaves=6)
noise4 = PerlinNoise(octaves=12)
xpix, ypix = 800, 1200
lim_x, lim_y = 4, 6
tile_sizes = (2, 3)
pic = []
for i in range(ypix):
row = []
for j in range(xpix):
noise_val = noise1([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.5 * noise2([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.25 * noise3([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.125 * noise4([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
row.append(noise_val)
pic.append(row)
plt.imshow(pic, cmap="gray")
plt.savefig("pics/multy_noise_tiled.png", transparent=True)
plt.show()
```
for tiles to work correctly, number of octaves **MUST** be integer
```python
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=2.5, seed=42)
xpix, ypix = 800, 1200
lim_x, lim_y = 6, 9
pic = [
[
noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=(2, 3))
for j in range(xpix)
]
for i in range(ypix)
]
plt.imshow(pic, cmap="gray")
plt.savefig('pics/tiled_with_step.png', transparent=True)
plt.show()
```
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"description": "Smooth random noise generator \nread more https://en.wikipedia.org/wiki/Perlin_noise \n\nsource code: https://github.com/salaxieb/perlin_noise\n\n\nnoise = PerlinNoise(octaves=3.5, seed=777) \n octaves : number of sub rectangles in each [0, 1] range \n seed : specific seed with which you want to initialize random generator\n tile_sizes : tuple of ints of you want to noise seamlessly repeat itself \n\n\n```python\nfrom perlin_noise import PerlinNoise\n\n\nnoise = PerlinNoise()\n# accepts as argument float and/or list[float]\nnoise(0.5) == noise([0.5])\n# --> True\n# noise not limited in space dimension and seamless in any space size\nnoise([0.5, 0.5]) == noise([0.5, 0.5, 0, 0, 0])\n# --> True\n# noise can seamlessly repeat isself\nnoise([0.5, 0.5], tile_sizes=[2, 5]) == noise([2.5, 5.5], tile_sizes=[2, 5])\n# --> True\n```\n\nUsage examples:\n```python\nimport matplotlib.pyplot as plt\nfrom perlin_noise import PerlinNoise\n\nnoise = PerlinNoise(octaves=10, seed=1)\nxpix, ypix = 100, 100\npic = [[noise([i/xpix, j/ypix]) for j in range(xpix)] for i in range(ypix)]\n\nplt.imshow(pic, cmap='gray')\nplt.show()\n```\n\n\n```python\nimport matplotlib.pyplot as plt\nfrom perlin_noise import PerlinNoise\n\nnoise1 = PerlinNoise(octaves=3)\nnoise2 = PerlinNoise(octaves=6)\nnoise3 = PerlinNoise(octaves=12)\nnoise4 = PerlinNoise(octaves=24)\n\nxpix, ypix = 100, 100\npic = []\nfor i in range(xpix):\n row = []\n for j in range(ypix):\n noise_val = noise1([i/xpix, j/ypix])\n noise_val += 0.5 * noise2([i/xpix, j/ypix])\n noise_val += 0.25 * noise3([i/xpix, j/ypix])\n noise_val += 0.125 * noise4([i/xpix, j/ypix])\n\n row.append(noise_val)\n pic.append(row)\n\nplt.imshow(pic, cmap='gray')\nplt.show()\n```\n\n\n\n\nLibrary has a possibility to generate repetative random noise with custom tile sizes:\n\n```python\nimport matplotlib.pyplot as plt\nfrom perlin_noise import PerlinNoise\n\nnoise = PerlinNoise(octaves=2, seed=42)\nxpix, ypix = 800, 1200\nlim_x, lim_y = 6, 9\npic = [\n [\n noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=[2, 3])\n for j in range(xpix)\n ]\n for i in range(ypix)\n]\n\nplt.imshow(pic, cmap=\"gray\")\nplt.show()\n```\n\n\n\n```python\nimport matplotlib.pyplot as plt\nfrom perlin_noise import PerlinNoise\n\nnoise1 = PerlinNoise(octaves=1)\nnoise2 = PerlinNoise(octaves=3)\nnoise3 = PerlinNoise(octaves=6)\nnoise4 = PerlinNoise(octaves=12)\n\nxpix, ypix = 800, 1200\nlim_x, lim_y = 4, 6\ntile_sizes = (2, 3)\npic = []\nfor i in range(ypix):\n row = []\n for j in range(xpix):\n noise_val = noise1([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)\n noise_val += 0.5 * noise2([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)\n noise_val += 0.25 * noise3([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)\n noise_val += 0.125 * noise4([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)\n\n row.append(noise_val)\n pic.append(row)\n\nplt.imshow(pic, cmap=\"gray\")\nplt.savefig(\"pics/multy_noise_tiled.png\", transparent=True)\nplt.show()\n```\n\n\nfor tiles to work correctly, number of octaves **MUST** be integer\n```python\nimport matplotlib.pyplot as plt\nfrom perlin_noise import PerlinNoise\n\nnoise = PerlinNoise(octaves=2.5, seed=42)\nxpix, ypix = 800, 1200\nlim_x, lim_y = 6, 9\npic = [\n [\n noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=(2, 3))\n for j in range(xpix)\n ]\n for i in range(ypix)\n]\n\nplt.imshow(pic, cmap=\"gray\")\nplt.savefig('pics/tiled_with_step.png', transparent=True)\nplt.show()\n```\n\n",
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