window function normalization about tiler HOT 3 CLOSED

import numpy as np
from tiler import Tiler,Merger
import matplotlib.pyplot as plt

# %% Test example for online

# parameter
data_shape = [1,256,256]
tile_shape = np.array([1,64,64])
overlap=np.array([0,7,20])
tiler_mode = 'wrap'
windows_supported = ['boxcar', 'triang', 'blackman', 'hamming', 'hann', 'bartlett', 'flattop', 'parzen', 'bohman', 'blackmanharris', 'nuttall', 'barthann', 'overlap-tile']

# image
xy = np.ogrid[0:data_shape[-2],0:data_shape[-1]]
xy = np.sqrt(xy[0]*np.transpose(xy[0])+xy[1]*np.transpose(xy[1]))
im = np.cos(4*xy*np.pi/np.max(xy))[np.newaxis]
merged_images = [im,]

# tile me
tiler = Tiler(data_shape=data_shape, tile_shape=tile_shape,overlap=tuple(overlap),get_padding=True)
im_padded = tiler.pad_outer(im,tiler.pads)
weights_sums = [np.ones(im_padded.shape),]

for mwin in windows_supported:
    merger = Merger(tiler,window=mwin)
    #Merger.SUPPORTED_WINDOWS == ['boxcar', 'triang', 'blackman', 'hamming', 'hann', 'bartlett', 'flattop', 'parzen', 'bohman', 'blackmanharris', 'nuttall', 'barthann', 'overlap-tile']
    
    for tile_id, tile in tiler(im_padded):
        processed_tile = tile # lambda x: x
        merger.add(tile_id, processed_tile)

    imf = merger.merge(data_orig_shape=data_shape)
    merged_images.append(imf)
    weights_sums.append(merger.weights_sum)

# plot images
merged_images = np.array(merged_images)[:,0]
fig,ax = plt.subplots(nrows=4,ncols=4,figsize=[14,14])
plt_titles = ['reference',]+windows_supported
axm = ax.flatten()
for m,mim in enumerate(merged_images): 
    ima = axm[m].imshow(mim,interpolation='None')
    axm[m].set_title(plt_titles[m])
plt.suptitle('Resulting Images')
plt.tight_layout()
plt.show() 

# plot images
weights_sums = np.array(weights_sums)[:,0]
fig2,ax2 = plt.subplots(nrows=4,ncols=4,figsize=[14,14])
plt_titles = ['reference',]+windows_supported
axm = ax2.flatten()
inv_weights = merger.norm_by_weights((weights_sums[0])[np.newaxis], weights=weights_sums)
inv_weights/= np.max(inv_weights,axis=(-2,-1),keepdims=True)
for m,invm in enumerate(inv_weights): 
    ima = axm[m].imshow(invm**0.002,interpolation='None')
    axm[m].set_title(plt_titles[m])
plt.suptitle('Inverse Weights-maps **0.002 (for display)')
plt.tight_layout()
plt.show() 

# differences
for m,mim in enumerate(merged_images):
    print(f"All close for window_func={plt_titles[m]}?\t {np.allclose(mim,im[0])}")

All close for window_func=reference?     True
All close for window_func=boxcar?        True
All close for window_func=triang?        TrueM
All close for window_func=blackman?      True
All close for window_func=hamming?       True
All close for window_func=hann?  True
All close for window_func=bartlett?      True
All close for window_func=flattop?       False
All close for window_func=parzen?        True
All close for window_func=bohman?        True
All close for window_func=blackmanharris?        True
All close for window_func=nuttall?       True
All close for window_func=barthann?      True
All close for window_func=overlap-tile?  False