Metadata-Version: 2.1
Name: perlin_noise
Version: 1.3
Summary: Python implementation for Perlin Noise with unlimited coordinates space
Home-page: UNKNOWN
Author: salaxieb
Author-email: salaxieb.ildar@gmail.com
License: UNKNOWN
Description: Smooth random noise generator  
        read more https://en.wikipedia.org/wiki/Perlin_noise  
        
        
        noise = PerlinNoise(n_dims=2, octaves=3.5, seed=777)  
        &nbsp;&nbsp;&nbsp;&nbsp;n_dims : positive int, optional, default = 1  
             &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;space dimension  
         &nbsp;&nbsp;&nbsp;&nbsp;octaves : positive float, optional, default = 1  
             &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;positive number of sub rectangles in each [0, 1] range  
         &nbsp;&nbsp;&nbsp;&nbsp;seed : positive int, optional, default = None  
             &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;specific seed with which you want to initialize random generator  
        
        Usage examples:
        ```python
        import matplotlib.pyplot as plt
        from perlin_noise import PerlinNoise
        
        noise = PerlinNoise(n_dims=2, octaves=2)
        xpix = 100
        ypix = 100
        pic = []
        for i in range(xpix):
            row = []
            for j in range(ypix):
                row.append(noise([i/xpix, j/ypix]))
            pic.append(row)
        
        plt.imshow(arr, cmap='gray')
        plt.show()
        ```
        ![png](https://raw.githubusercontent.com/salaxieb/perlin_noise/master/pics/output_4_0.png)
        
        ```python
        import matplotlib.pyplot as plt
        from perlin_noise import PerlinNoise
        
        noise1 = PerlinNoise(n_dims=2, octaves=0.5)
        noise2 = PerlinNoise(n_dims=2, octaves=1)
        noise3 = PerlinNoise(n_dims=2, octaves=2)
        noise4 = PerlinNoise(n_dims=2, octaves=4)
        
        xpix = 100
        ypix = 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()
        ```
        
        ![png](https://raw.githubusercontent.com/salaxieb/perlin_noise/master/pics/output_5_0.png)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
