experiments/overlap/augmentations/additive_noise.py [303:341]:
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        amount = 100 - amount
        color = np.array([255, 255, 255])
        randomness = 25
        seed = np.random.randint(low=0, high=2**32)
        rays = np.random.randint(low=50, high=200)

        return {'center' : center, 'radius' : radius, 'color' : color, 'randomness' : randomness,
                'seed' : seed, 'rays' : rays, 'amount' : amount
                }

    def transform(self, image, center, radius, rays, amount, color, randomness, seed):

        def kernel(point, value, center, radius, ray_lengths, amount, color):
            rays = len(ray_lengths)
            dp = point - center
            dist = np.linalg.norm(dp)
            angle = np.arctan2(dp[1], dp[0])
            d = (angle + np.pi) / (2 * np.pi) * rays
            i = int(d)
            f = d - i 

            if radius != 0:
                length = ray_lengths[i % rays] + f * (ray_lengths[(i+1) % rays] - ray_lengths[i % rays])
                g = length**2 / (dist**2 + 1e-4)
                g = g ** ((100 - amount) / 50.0)
                f -= 0.5
                f = 1 - f**2
                f *= g
            f = np.clip(f, 0, 1)
            return color + f * (value - color)

        random_state = np.random.RandomState(seed=seed)
        ray_lengths = [radius + randomness / 100.0 * radius * random_state.randn()\
                for i in range(rays)]

        out = np.array([[kernel(np.array([y,x]), image[y,x,:].astype(np.float32), center, radius, ray_lengths, amount, color)\
                for x in range(self.im_size)] for y in range(self.im_size)])

        return np.clip(out, 0, 255).astype(np.uint8)
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imagenet_c_bar/corrupt.py [280:318]:
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        amount = 100 - amount
        color = np.array([255, 255, 255])
        randomness = 25
        seed = np.random.randint(low=0, high=2**32)
        rays = np.random.randint(low=50, high=200)

        return {'center' : center, 'radius' : radius, 'color' : color, 'randomness' : randomness,
                'seed' : seed, 'rays' : rays, 'amount' : amount
                }

    def transform(self, image, center, radius, rays, amount, color, randomness, seed):

        def kernel(point, value, center, radius, ray_lengths, amount, color):
            rays = len(ray_lengths)
            dp = point - center
            dist = np.linalg.norm(dp)
            angle = np.arctan2(dp[1], dp[0])
            d = (angle + np.pi) / (2 * np.pi) * rays
            i = int(d)
            f = d - i 

            if radius != 0:
                length = ray_lengths[i % rays] + f * (ray_lengths[(i+1) % rays] - ray_lengths[i % rays])
                g = length**2 / (dist**2 + 1e-4)
                g = g ** ((100 - amount) / 50.0)
                f -= 0.5
                f = 1 - f**2
                f *= g
            f = np.clip(f, 0, 1)
            return color + f * (value - color)

        random_state = np.random.RandomState(seed=seed)
        ray_lengths = [radius + randomness / 100.0 * radius * random_state.randn()\
                for i in range(rays)]

        out = np.array([[kernel(np.array([y,x]), image[y,x,:].astype(np.float32), center, radius, ray_lengths, amount, color)\
                for x in range(self.im_size)] for y in range(self.im_size)])

        return np.clip(out, 0, 255).astype(np.uint8)
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