CHX Olog (https://logbook.nsls2.bnl.gov/11-ID/)
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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
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CYCLE= '2017_2' #change clycle here
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path = '/XF11ID/analysis/%s/masks/'%CYCLE
print ("The analysis results will be saved in : %s"%path)
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uid = '8bd04b' # (scan num: 22932) (Measurement: single frame 0.3 deg for mask )
uid = 'acac04' # (scan num: 23612) (Measurement: CoralPor for mask )
uid = 'c2afee' # (scan num: 23614) (Measurement: CoralPor for mask_new )
uid = 'e75486' # ] (scan num: 23675) (Measurement: 5 CoralPor images - make mask )
uid = 'ebb2b9' #ct : 1 ['ebb2b9'] (scan num: 23708) (Measurement: Single frame for masking )
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#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/chx_generic_functions.py
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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] ))
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print(md['beam_center_y'], md['beam_center_x'])
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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 )
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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)
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show_img( avg_img*pixel_mask , vmin=.001, vmax=1e3, logs=True,
image_name ='uid=%s'%uid, aspect=1, cmap= cmap_albula )
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pixel_mask = mask_badpixels( pixel_mask, md['detector'])
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show_img( pixel_mask, vmin=0, vmax=1, image_name ='pixel_mask--uid=%s'%uid ,aspect=1 )
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#avg_img = get_avg_img( imgs, sampling = 1000, plot_ = False, uid =uid)
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mask_rh = RemoveHot( avg_img, 2**16-1, plot_=True)
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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 )
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#md['beam_center_x']= 1422
#md['beam_center_y']= 1439
#md['beam_center_x'],2167-md['beam_center_y']
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md['beam_center_x'], md['beam_center_y']
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#md['beam_center_x'] = 1080
#md['beam_center_y'] = 1127
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#creat the right part mask
partial_mask = create_cross_mask( avg_img, center=[1290,1469],
wy_left= 0, wy_right= 30,
wx_up= 0, wx_down= 0,center_radius= 0 )
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show_img( partial_mask )
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avg_img.shape
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#creat the left/right/up/down part mask
partial_mask *= create_cross_mask( avg_img, center=[ 1422,1439],
wy_left= 0, wy_right= 0,
wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False
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show_img( partial_mask )
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#creat the left/right/up/down part mask
partial_mask *= create_cross_mask( avg_img, center=[ 982, 2100],
wy_left= 0, wy_right= 0,
wx_up= 0, wx_down= 0,center_radius= 0 )
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#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
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full_mask = partial_mask *window_shadow
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show_img( full_mask, aspect = 1 )
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#mask = np.array ( full_mask * pixel_mask*mask_rh , dtype = bool )
mask = np.array ( full_mask * pixel_mask , dtype = bool )
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fig, ax = plt.subplots()
#new_mask =
im=ax.imshow( (~mask) * avg_img,origin='lower' ,
norm= LogNorm( vmin=0.001, vmax= 1e2 ), cmap= cmap_albula)
#im = ax.imshow(avg_img, cmap='viridis',origin='lower', norm= LogNorm( vmin=0.001, vmax=100 ) )
plt.show()
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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()
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#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()
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np.save( path + uid +"_mask", mask)
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path + uid +"_mask"
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meaningful_name = 'Jun16_SAXS_10m'
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#np.save( path + meaningful_name, mask)
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path + meaningful_name
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uid
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