experiments/overlap/augmentations/additive_noise.py [81:111]:
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        return { 'offset' : offset,
                 'freq' : freq,
                 'amplitude' : amplitude,
                 'ring_width' : ring_width,
                 'intensity' : intensity
                }

    def transform(self, image, offset, freq, amplitude, ring_width, intensity):

        def calc_intensity(x, y, x0, y0, freq, amplitude, ring_width):
            angle = np.arctan2(x-x0, y-y0) * freq
            distance = ((np.sqrt((x-x0)**2 + (y-y0)**2) + np.sin(angle) * amplitude) % ring_width) / ring_width
            distance -= 1/2
            return distance

        noise = np.array([[calc_intensity(x, y, offset[0], offset[1], freq, amplitude, ring_width)\
                    for x in range(self.im_size)] for y in range(self.im_size)])
        noise = np.stack((intensity[0] * noise, intensity[1] * noise, intensity[2] * noise), axis=2)

        return np.clip(image + noise, 0, 255).astype(np.uint8)

    def convert_to_numpy(self, params):
        return np.array(params['offset'].tolist() + [params['freq'], params['amplitude'], params['ring_width']] + params['intensity'])

    def convert_from_numpy(self, numpy_record):
        return {'offset' : numpy_record[0:2].tolist(),
                'freq' : numpy_record[2],
                'amplitude' : numpy_record[3],
                'ring_width' : numpy_record[4],
                'intensity' : numpy_record[4:7].tolist()
                }
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imagenet_c_bar/corrupt.py [66:96]:
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        return { 'offset' : offset,
                 'freq' : freq,
                 'amplitude' : amplitude,
                 'ring_width' : ring_width,
                 'intensity' : intensity
                }

    def transform(self, image, offset, freq, amplitude, ring_width, intensity):

        def calc_intensity(x, y, x0, y0, freq, amplitude, ring_width):
            angle = np.arctan2(x-x0, y-y0) * freq
            distance = ((np.sqrt((x-x0)**2 + (y-y0)**2) + np.sin(angle) * amplitude) % ring_width) / ring_width
            distance -= 1/2
            return distance

        noise = np.array([[calc_intensity(x, y, offset[0], offset[1], freq, amplitude, ring_width)\
                    for x in range(self.im_size)] for y in range(self.im_size)])
        noise = np.stack((intensity[0] * noise, intensity[1] * noise, intensity[2] * noise), axis=2)

        return np.clip(image + noise, 0, 255).astype(np.uint8)

    def convert_to_numpy(self, params):
        return np.array(params['offset'].tolist() + [params['freq'], params['amplitude'], params['ring_width']] + params['intensity'])

    def convert_from_numpy(self, numpy_record):
        return {'offset' : numpy_record[0:2].tolist(),
                'freq' : numpy_record[2],
                'amplitude' : numpy_record[3],
                'ring_width' : numpy_record[4],
                'intensity' : numpy_record[4:7].tolist()
                }
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