<jupyter_start><jupyter_text>Smooth Algorithm 1<jupyter_code># Smooth Algorithm 1
w, h = im.shape # Get Width and Height
imtot = np.zeros((w,h)) # Initialize New Image
for i in range(w-1): # Smooth Algoritm
    for j in range(h-1):
        if i > 0 and i < w-1 and j > 0 and j < h-1:
            imtot[i][j] =  ( im[i-1][j-1]*1 + im[i][j-1]*2 + im[i+1][j-1]*1 + \
                            im[i-1][j]*2 + im[i][j]*4 + im[i+1][j]*2 + \
                            im[i-1][j+1]*1 + im[i][j+1]*2 + im[i+1][j+1]*1 ) / 16.0
            
matplotlib.pyplot.imshow(imtot) # Show Results of Smooth<jupyter_output><empty_output><jupyter_text>Smooth Algorithm 2<jupyter_code># Smooth Algorithm 2
size = 9
kernel = np.ones((size,size)) # Smooth Kernel
imconv = np.zeros((w,h))
for a in range(w-size+1):
    for b in range(h-size+1):
        imconv[a][b] = np.sum( im[a:a+size, b:b+size]*kernel ) / (size**2)
matplotlib.pyplot.imshow(imconv) # Show Results of Smooth<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18375634517766498, 'fraction_numerical': 0.03857868020304569, 'mean_word_length': 2.9282868525896415, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '351778', 'license': 'MIT', 'n_tokens_mistral': 435, 'n_tokens_neox': 417, 'n_words': 87, 'path': 'cv/spatial/numpy-smooth.ipynb', 'repo_name': 'Kinvert/resume', 'size': '985'}