Masking Pipeline

CHX Olog NoteBook

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



In [2]:

    
from chxanalys.chx_libs import (np, roi, time, datetime, os,  getpass, db, get_images,LogNorm, plt,ManualMask)
from chxanalys.chx_libs import cmap_albula, cmap_vge, random
from chxanalys.chx_generic_functions import (get_detector, get_meta_data,create_user_folder,
                                             get_fields,  get_sid_filenames,load_data,
                                             RemoveHot, show_img,get_avg_img, 
                                             reverse_updown,create_cross_mask,mask_badpixels )
from skimage.draw import line_aa, line, polygon, circle
 
%matplotlib notebook

Path for Saving Results



In [3]:

    
CYCLE= '2017_2'  #change clycle here



In [4]:

    
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/2017_2/masks/

Get the image series and metadata from the uid



In [8]:

    
uid = 'af84e5'# (scan num: 3218) (Measurement: scattering for mask )


#uid = 'ea5492' # (scan num: 3216) (Measurement: direct beam position )

uid = '76cab3' # (scan num: 3265) (Measurement: Single image )
uid = '3d08d0' # (scan num: 4010) (Measurement: for GISAXS mask )



In [9]:

    
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:  4010--3d08d0c2-551b-4b57-8be7-4685c53a5939--/XF11ID/data/2017/07/15/5330577f-2032-4662-b728_12008



In [10]:

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









    



1701000.0 1373.0



In [11]:

    
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: <Frames>
Length: 1 frames
Frame Shape: 2167 x 2070
Pixel Datatype: uint32



In [12]:

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



In [13]:

    
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



In [14]:

    
show_img(  avg_img*pixel_mask , vmin=.001, vmax=1e4, 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 [15]:

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



In [16]:

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

Remove hotspots in the image



In [17]:

    
#avg_img =  get_avg_img( imgs, sampling = 1000, plot_ = False, uid =uid)



In [19]:

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



In [20]:

    
show_img(avg_img*pixel_mask,vmin=0.1,vmax=1e3, logs=True, 
         image_name= 'uid= %s with pixel mask'%uid , aspect=1, cmap= cmap_albula  )



In [ ]:

Create a polygon mask

check beam center



In [21]:

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









    Out[21]:





(1373.0, 1701000.0)

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



In [25]:

    
path









    Out[25]:





'/XF11ID/analysis/2017_2/masks/'



In [105]:

    
#creat the right part mask
partial_mask = create_cross_mask(  avg_img,center=[1377.0,1193.0],
                            wy_left= 0, wy_right= 0, 
                              wx_up= 20, wx_down= 0,center_radius= 0 )



In [106]:

    
show_img(  partial_mask   )



In [123]:

    
#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 )



In [107]:

    
#creat the left/right/up/down part mask
partial_mask *=  create_cross_mask(  avg_img, center=[ 1205,1838],
                            wy_left= 0, wy_right= 65, 
                              wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [108]:

    
#creat the left/right/up/down part mask
partial_mask *=  create_cross_mask(  avg_img, center=[ 1370,1925],
                            wy_left= 0, wy_right= 50, 
                              wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [109]:

    
#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 [110]:

    
show_img(  partial_mask   )



In [111]:

    
#creat the left/right/up/down part mask
#partial_mask *=  create_cross_mask(  avg_img, center=[ 1615,2000],
#                            wy_left= 0, wy_right= 100, 
#                              wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [112]:

    
#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 [113]:

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



In [114]:

    
#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 [126]:

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



In [ ]:



In [127]:

    
show_img( full_mask, aspect = 1 )



In [118]:

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



In [119]:

    
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 [120]:

    
fig, ax = plt.subplots()
im = ax.imshow((mask)*avg_img,  cmap= cmap_albula,origin='lower',norm= LogNorm( vmin=.1, vmax=1e5 ),
               interpolation='none')
plt.show()

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



In [156]:

    
#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 [157]:

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



In [158]:

    
path +   uid +"_mask"









    Out[158]:





'/XF11ID/analysis/2017_2/masks/3d08d0_mask'

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



In [128]:

    
if False:
    meaningful_name = 'Jul15_GISAXS' 
    np.save(  path +   meaningful_name, mask)
    print( path +   meaningful_name )



In [ ]:



In [164]:

    
path +   meaningful_name









    Out[164]:





'/XF11ID/analysis/2017_2/masks/Jul15_GISAXS'



In [165]:

    
uid









    Out[165]:





'3d08d0'