CHX Olog (https://logbook.nsls2.bnl.gov/11-ID/)
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#!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 inline
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#%matplotlib notebook
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from pyCHX.chx_generic_functions import RemoveHot,create_multi_rotated_rectangle_mask
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CYCLE= '2019_3' #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 = '09c84e03' #(scan num: 23307) (Measurement: single image 4M )
uid = 'ff77f1fb' #(scan num: 24345) (Measurement: 4M single image )
uid = '13618743' #(scan num: 24403) (Measurement: Single image - offset detector TM_gel #2 )
#Maria 4 Nov 2019
uid = 'b3260e2c' #(scan num: 25143) (Measurement: image: for mask 4M )
uid = '7d93c742' #(scan num: 25142) (Measurement: images: for mask 500k )
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#get_last_uids( -2 )
#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] ))
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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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#%matplotlib notebook
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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 )
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pixel_mask = mask_badpixels( pixel_mask, md['detector'])
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#%run /nsls2/xf11id1/analysis/Analysis_Pipelines/Develop/pyCHX/pyCHX/chx_generic_functions.py
#mask_badpixels??
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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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#pixel_mask[750:788,1137]
#avg_img = get_avg_img( imgs, sampling = 10, plot_ = False, uid =uid)
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mask_rh = RemoveHot( avg_img, 2**22-1, plot_=True)
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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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md['beam_center_x'], md['beam_center_y']
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#md['beam_center_x'] - 960, md['beam_center_y']-2167, md['beam_center_y'] - 1400
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imgs[0].shape
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rmask = create_multi_rotated_rectangle_mask( imgs[0],
center= [ md['beam_center_x'], md['beam_center_y'] ], length=0, width=0, angles=[0] )
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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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#creat the right part mask
if '4m' in md['detector']:
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 )
##for GiSAXS
#partial_mask = create_cross_mask( avg_img, center=[md['beam_center_x']- 200,
# md['beam_center_y'] ], #center=[509,185],
# wy_left= 0, wy_right= 79,
# wx_up= 0, wx_down= 0,center_radius= 0 )
elif '500K' in md['detector']:
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 )
else:
print('Please check the detectors.')
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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= [ md['beam_center_x'], md['beam_center_y'] ],
wy_left= 8, wy_right= 8,
wx_up= 8, wx_down= 8,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
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show_img( partial_mask, show_colorbar=False )
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#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
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#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
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#from chxanalys.chx_generic_functions import create_multi_rotated_rectangle_mask
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#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 )
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show_img( partial_mask , show_colorbar=False )
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#creat the left/right/up/down part mask
#partial_mask *= create_cross_mask( avg_img, center=[ md['beam_center_x'], md['beam_center_y']-700],
# wy_left= 0, wy_right= 0,
# wx_up= 40, wx_down= 0,center_radius= 0 )
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#np.save( '/XF11ID/analysis/2017_1/masks/Bad_4M', partial_mask )
#Bad_4M = np.load( '/XF11ID/analysis/2017_1/masks/Bad_4M.npy' )
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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
#full_mask = Ver_Beamstop * Hor_Beamstop *Bad_4M *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 * rmask , 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= 1e5), 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=.1e-3, vmax=1e2 ),
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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# adding the hot pixels
#mask[1217:1237,1150:1170] = 0
# extra hot pixel corrections
show_img(mask, show_colorbar=False )
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np.save( path + uid +"_mask", mask)
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path + uid +"_mask"
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#if True:
if False:
date='Nov4'
if '4m' in md['detector']:
meaningful_name = '%s_2019_4M_SAXS'%date#_centered'
elif '500K' in md['detector']:
meaningful_name = '%s_2019_500K_SAXS'%date#_centered'
np.save( path + meaningful_name, mask)
print( path + meaningful_name )
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path + meaningful_name
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uid
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