


from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    _lowercase :							Dict   	=	k_size // 2
    _lowercase :							str   	=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _lowercase :							Optional[Any]   	=	1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase				) + square(_lowerCamelCase				)) / (2 * square(_lowerCamelCase				))				)
    return g



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    _lowercase :							Optional[Any]   	=	image.shape[0], image.shape[1]
    # dst image height and width
    _lowercase :							int   	=	height - k_size + 1
    _lowercase :							Dict   	=	width - k_size + 1

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    _lowercase :							Any   	=	zeros((dst_height * dst_width, k_size * k_size)				)
    _lowercase :							List[str]   	=	0
    for i, j in product(range(_lowerCamelCase				)							,				range(_lowerCamelCase				)				):
        _lowercase :							Optional[int]   	=	ravel(image[i : i + k_size, j : j + k_size]				)
        _lowercase :							Union[str, Any]   	=	window
        row += 1

    #  turn the kernel into shape(k*k, 1)
    _lowercase :							List[Any]   	=	gen_gaussian_kernel(_lowerCamelCase							,				_lowerCamelCase				)
    _lowercase :							List[str]   	=	ravel(_lowerCamelCase				)

    # reshape and get the dst image
    _lowercase :							Optional[Any]   	=	dot(_lowerCamelCase							,				_lowerCamelCase				).reshape(_lowerCamelCase							,				_lowerCamelCase				).astype(_lowerCamelCase				)

    return dst


if __name__ == "__main__":
      # read original image
      SCREAMING_SNAKE_CASE :   Dict							      =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      SCREAMING_SNAKE_CASE :   int							      =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      SCREAMING_SNAKE_CASE :   List[str]							      =				gaussian_filter(gray, 3, sigma=1)
      SCREAMING_SNAKE_CASE :   Optional[int]							      =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()






from __future__ import annotations

import requests



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict:
    _lowercase :							Dict   	=	F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
    return requests.get(lowerCamelCase_				).json()



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		list[dict]:
    _lowercase :							Dict   	=	'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
    _lowercase :							Dict   	=	requests.get(lowerCamelCase_				).json()[:max_stories]
    return [get_hackernews_story(lowerCamelCase_				) for story_id in story_ids]



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		str:
    _lowercase :							Optional[int]   	=	hackernews_top_stories(lowerCamelCase_				)
    return "\n".join('* [{title}]({url})'.format(**lowerCamelCase_				) for story in stories				)


if __name__ == "__main__":
 print(hackernews_top_stories_as_markdown())


