Masking Pipeline

CHX Olog NoteBook

CHX Olog (https://logbook.nsls2.bnl.gov/11-ID/)



In [89]:

    
#!env



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from pyCHX.chx_packages import *
%matplotlib notebook
plt.rcParams.update({'figure.max_open_warning': 0})
plt.rcParams.update({ 'image.origin': 'lower'   })
plt.rcParams.update({ 'image.interpolation': 'none'   })
import pickle as cpk
from pyCHX.chx_xpcs_xsvs_jupyter_V1 import *



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%matplotlib notebook



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from pyCHX.chx_generic_functions import RemoveHot,create_multi_rotated_rectangle_mask

Path for Saving Results



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CYCLE= '2019_1'  #change clycle here



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path = '/XF11ID/analysis/%s/masks/'%CYCLE
print ("The analysis results will be saved in : %s"%path)









    



The analysis results will be saved in : /XF11ID/analysis/2019_1/masks/

Get the image series and metadata from the uid



In [53]:

    
uid = '11ad9f1b' # (scan num: 3514) (Measurement: CoralPorfor masking )
uid = '1640938c' #(scan num: 3548 (Measurement: SiO2_Dia250nm_Tween=1:2single image for masking 
uid = '37fc521f' # (scan num: 3566) (Measurement: single image for 500k mask CoralPor )
uid = '764cf803' #(scan num: 3568) (Measurement: single image for 4M mask CoralPor )



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#get_last_uids( -3 )
#path



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md = get_meta_data( uid )
detector = get_detector( db[uid ] )
print ('Detector is:  %s'%detector  )
sud = get_sid_filenames(db[uid])

print ('scan_id, full-uid, data path are:  %s--%s--%s'%(sud[0], sud[1], sud[2][0] ))









    



Detector is:  eiger4m_single_image
scan_id, full-uid, data path are:  3568--764cf803-58b0-470e-8605-7bd8f8f3e01e--/nsls2/xf11id1/data/2019/02/06/b8074ad5-6b90-4c0f-8e9d_2067_master.h5



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print(md['beam_center_y'], md['beam_center_x'])









    



1230.0 1124.0



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imgs = load_data( uid, detector, reverse= False  )
#imgs = load_data( uid, detector, reverse= True  )
md.update( imgs.md );Nimg = len(imgs);
#if 'number of images'  not in list(md.keys()):
md['number of images']  = Nimg
pixel_mask =  1- np.int_( np.array( imgs.md['pixel_mask'], dtype= bool)  )
print( 'The data are: %s' %imgs )









    



The data are: EigerImages processed through proc_func. Original repr:
    <Frames>
    Length: 1 frames
    Frame Shape: 2167 x 2070
    Pixel Datatype: uint32



In [59]:

    
pixel_mask =  1- np.int_( np.array( md['pixel_mask'], dtype= bool)  )



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img_choice_N = 1  #can change this number to select more frames for average
img_samp_index = random.sample( range(len(imgs)), img_choice_N) 
avg_img =  get_avg_img( imgs, img_samp_index, plot_ = False, uid = uid)

show image and the pixel mask

show image



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show_img(  avg_img*pixel_mask , vmin=.001, vmax=1e6, logs=True, 
         image_name ='uid=%s'%uid, aspect=1, cmap= cmap_albula  )

pixel mask

Update Pixel Mask due to bad pixel of Eiger Detector



In [62]:

    
pixel_mask = mask_badpixels( pixel_mask, md['detector'])



In [63]:

    
show_img( pixel_mask, vmin=0, vmax=1, image_name ='pixel_mask--uid=%s'%uid ,aspect=1 )

Remove hotspots in the image



In [64]:

    
#avg_img =  get_avg_img( imgs, sampling = 10, plot_ = False, uid =uid)



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mask_rh  = RemoveHot( avg_img, 2**20-1, plot_=True)



In [66]:

    
show_img(avg_img*pixel_mask,vmin=0.1e-2,vmax=1e5, logs=True, 
         image_name= 'uid= %s with pixel mask'%uid , aspect=1, cmap= cmap_albula  )



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Create a polygon mask

check beam center



In [67]:

    
md['beam_center_x'], md['beam_center_y']









    Out[67]:





(1124.0, 1230.0)



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In [68]:

    
imgs[0].shape









    Out[68]:





(2167, 2070)

Create a rotated region



In [69]:

    
rmask = create_multi_rotated_rectangle_mask( imgs[0], 
                                            center= [ md['beam_center_x'], md['beam_center_y']   ], length=0, width=0, angles=[0] )



In [70]:

    
show_img(avg_img*pixel_mask*rmask,vmin=0.1e-2,vmax=1e5, logs=True, 
         image_name= 'uid= %s with pixel mask'%uid , aspect=1, cmap= cmap_albula  )



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To create multi-rectangle masks, for each sub-mask



In [72]:

    
#creat the right part mask

partial_mask = create_cross_mask(  avg_img, center=[md['beam_center_x']- 60, md['beam_center_y']  ], #center=[509,185],
                            wy_left= 0, wy_right= 40, 
                              wx_up= 0, wx_down= 0,center_radius= 0 )

#partial_mask = create_cross_mask(  avg_img, center=[md['beam_center_x']+2, md['beam_center_y']+45  ], #center=[509,185],
#                            wy_left= 0, wy_right= 0, 
#                              wx_up= 0, wx_down= 25,center_radius= 0 )



#partial_mask = create_cross_mask(  avg_img, center=[md['beam_center_x'], md['beam_center_y']+45  ], #center=[509,185],
#                            wy_left= 0, wy_right= 0, 
#                            wx_up= 0, wx_down= 25,center_radius= 0 )



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show_img(  partial_mask, show_colorbar=False   )



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#np.save( '/XF11ID/analysis/2017_1/masks/Ver_Beamstop', partial_mask )
#Ver_Beamstop = np.load( '/XF11ID/analysis/2017_1/masks/Ver_Beamstop.npy' )
#Ver_Beamstop = move_beamstop( Vertical_Beamstop, xshift=0, yshift=0 )



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#parasitic scattering from slits
#partial_mask *= create_cross_mask(  avg_img,center=[1300,1469],
#                            wy_left= 0, wy_right= 40, 
#                            wx_up= 0, wx_down= 0,center_radius= 0 )
#parasitic scattering from slits
#partial_mask *= create_cross_mask(  avg_img,center=[946,1700],
#                            wy_left= 0, wy_right= 0, 
#                            wx_up= 0, wx_down= 0,center_radius= 0 )
#parasitic scattering from slits
#partial_mask *= create_cross_mask(  avg_img,center=[ 946,2167-60],
#                            wy_left= 0, wy_right= 0, 
#                            wx_up= 0, wx_down= 0,center_radius= 0 )



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#Some bad lines between chips - should be masked elswhere
#partial_mask[1909:1910,:] = False
#partial_mask[257:259,:] = False



In [74]:

    
show_img(  partial_mask, show_colorbar=False   )



In [75]:

    
#creat the left/right/up/down part mask
partial_mask *=  create_cross_mask(  avg_img, center=[ md['beam_center_x'], md['beam_center_y'] ],
                            wy_left= 10, wy_right= 10, 
                              wx_up= 10, wx_down=10,center_radius=0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [76]:

    
#creat the left/right/up/down part mask
#partial_mask *=  create_cross_mask(  avg_img, center=[ 1600,1800+25 ],
#                            wy_left= 0, wy_right= 0, 
#                              wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [77]:

    
#from chxanalys.chx_generic_functions import create_multi_rotated_rectangle_mask



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In [78]:

    
#np.save( '/XF11ID/analysis/2017_1/masks/Hor_Beamstop', partial_mask )
#Hor_Beamstop = np.load( '/XF11ID/analysis/2017_1/masks/Hor_Beamstop.npy' )
#Hor_Beamstop = move_beamstop( Hor_Beamstop, xshift=0, yshift=0 )



In [79]:

    
show_img(  partial_mask , show_colorbar=False   )



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In [80]:

    
#creat the left/right/up/down part mask
#partial_mask *=  create_cross_mask(  avg_img, center=[ 1030,395],
#                            wy_left= 145, wy_right= 0, 
#                              wx_up= 0, wx_down= 0,center_radius= 0 )



In [81]:

    
#np.save( '/XF11ID/analysis/2017_1/masks/Bad_4M', partial_mask )
#Bad_4M = np.load( '/XF11ID/analysis/2017_1/masks/Bad_4M.npy' )



In [82]:

    
#create a circle mask for windows
if False: #make it True to make window mask
    window_shadow  = ~create_cross_mask(  avg_img, center=[ 911,997],
                           wy_left= 0, wy_right= 0, 
                             wx_up= 0, wx_down= 0,center_circle=True, center_radius= 680)
else:
    window_shadow = 1



In [83]:

    
full_mask = partial_mask *window_shadow
#full_mask = Ver_Beamstop * Hor_Beamstop *Bad_4M *window_shadow



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In [84]:

    
#show_img( full_mask, aspect = 1 )



In [85]:

    
mask = np.array ( full_mask * pixel_mask*mask_rh * rmask , dtype = bool )
#mask = np.array ( full_mask * pixel_mask , dtype = bool )



In [86]:

    
fig, ax = plt.subplots()
#new_mask = 
im=ax.imshow(   (~mask) * avg_img,origin='lower' , 
      norm= LogNorm( vmin=0.001, vmax= 1e5), cmap= cmap_albula)
#im = ax.imshow(avg_img,  cmap='viridis',origin='lower', norm= LogNorm( vmin=0.001, vmax=100 ) )
plt.show()



In [87]:

    
fig, ax = plt.subplots()
im = ax.imshow((mask)*avg_img,  cmap= cmap_albula,origin='lower',
               norm= LogNorm( vmin=.1e-3, vmax=1e0 ),
               interpolation='none')
plt.show()

Combine the hand-drawn/polygon mask and the pixel mask and hot pixel mask



In [88]:

    
#mask = np.array ( ~new_mask* ~plgon_mask * md['pixel_mask']*mask_rh, dtype = bool )

fig, ax = plt.subplots()
im=ax.imshow(mask, origin='lower' ,vmin=0, vmax=1,cmap='viridis')
fig.colorbar(im)
plt.show()

Save the combined mask to use in further data analysis



In [89]:

    
np.save(  path +   uid +"_mask", mask)



In [94]:

    
path +   uid +"_mask"









    Out[94]:





'/XF11ID/analysis/2019_1/masks/764cf803_mask'

save with a meaningful filename, make False after excute to avoid overwrite



In [91]:

    
#if True:
if False:    
    #meaningful_name = 'Feb6_2019_500K_SAXS'#_centered' 
    meaningful_name = 'Feb6_2019_4M_SAXS'#_centered'
    np.save(  path +   meaningful_name, mask)
    print( path +   meaningful_name )









    



/XF11ID/analysis/2019_1/masks/Feb6_2019_4M_SAXS



In [92]:

    
path +   meaningful_name









    Out[92]:





'/XF11ID/analysis/2019_1/masks/Feb6_2019_4M_SAXS'



In [93]:

    
uid









    Out[93]:





'764cf803'



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