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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 *
import itertools


    

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#%matplotlib notebook
#%matplotlib inline


    

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sud = get_sid_filenames(db[uid])
for pa in sud[2]:
    if 'master.h5' in pa:
        data_fullpath = pa
print ('scan_id, full-uid, data path are:  %s--%s--%s'%(sud[0], sud[1], data_fullpath ))

md = get_meta_data( uid )
md_blue = md.copy()
detectors = sorted(get_detectors(db[uid]))
print('The detectors are:%s'%detectors)
if len(detectors) >1:
    md['detector'] = detectors[1]
    print( md['detector'])
    
if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image':    
    reverse= True
    rot90= False
elif md['detector'] =='eiger500K_single_image':    
    reverse= True
    rot90=True
elif md['detector'] =='eiger1m_single_image':    
    reverse= True
    rot90=False
print('Image reverse: %s\nImage rotate 90: %s'%(reverse, rot90))        
    

imgs = load_data( uid, md['detector'], reverse= reverse, rot90=rot90  )
md.update( imgs.md );Nimg = len(imgs);
#md['beam_center_x'], md['beam_center_y']  = cx, cy
#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 )

#md['acquire period' ] = md['cam_acquire_period']
#md['exposure time'] =  md['cam_acquire_time']
mdn = md.copy()


#### Load Chip mask depeding on detector

if md['detector'] =='eiger1m_single_image':
    Chip_Mask=np.load( '/XF11ID/analysis/2017_1/masks/Eiger1M_Chip_Mask.npy')
elif md['detector'] =='eiger4m_single_image' or md['detector'] == 'image':    
    Chip_Mask= np.array(np.load( '/XF11ID/analysis/2017_1/masks/Eiger4M_chip_mask.npy'), dtype=bool)
    BadPix =     np.load('/XF11ID/analysis/2018_1/BadPix_4M.npy'  )  
    Chip_Mask.ravel()[BadPix] = 0
elif md['detector'] =='eiger500K_single_image':
    #print('here')
    Chip_Mask=  np.load( '/XF11ID/analysis/2017_1/masks/Eiger500K_Chip_Mask.npy')  #to be defined the chip mask
    Chip_Mask = np.rot90(Chip_Mask)
    pixel_mask = np.rot90(  1- np.int_( np.array( imgs.md['pixel_mask'], dtype= bool))   )
    
else:
    Chip_Mask = 1
    
#show_img(Chip_Mask)
#print(Chip_Mask.shape, pixel_mask.shape)

use_local_disk = True
import shutil,glob

if len(detectors)==2:
    if '_image' in md['detector']:
        pref = md['detector'][:-5]
    else:
        pref=md['detector']
    for k in [ 'beam_center_x', 'beam_center_y','cam_acquire_time','cam_acquire_period','cam_num_images',
             'wavelength', 'det_distance', 'photon_energy']:
        md[k] =  md[ pref + '%s'%k]    
    
 
mask =    pixel_mask * Chip_Mask


    

    




filepath : /nsls2/xf11id1/data/2018/09/27/3ffee007-5891-4e77-a0d6
got images_per_file
scan_id, full-uid, data path are:  24810--2555d366-37ed-4768-9334-e5b5acc1cb5e--/nsls2/xf11id1/data/2018/09/27/3ffee007-5891-4e77-a0d6_598_master.h5
More than one device. This would have unintented consequences.Currently, only use the first device.
The detectors are:['OAV_image', 'eiger4m_single_image']
eiger4m_single_image
Image reverse: True
Image rotate 90: False
The data are: Pipeline processed through proc_func. Original repr:
    EigerImages processed through proc_func. Original repr:
        <Frames>
        Length: 100 frames
        Frame Shape: 2167 x 2070
        Pixel Datatype: uint32

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para_compress = True    #parallel compress

#force_compress = True

compress=True
#photon_occ = len( np.where(avg_img)[0] ) / ( imgsa[0].size)
#compress =  photon_occ < .4  #if the photon ocupation < 0.5, do compress
#print ("The non-zeros photon occupation is %s."%( photon_occ))
print("Will " + 'Always ' + ['NOT', 'DO'][compress]  + " apply compress process.")

if  md['detector'] =='eiger4m_single_image' or md['detector'] == 'image':    
    good_start =    0  #make the good_start at least 0
elif md['detector'] =='eiger500K_single_image': 
    good_start = 0  #5  #make the good_start at least 0
    
elif  md['detector'] =='eiger1m_single_image' or md['detector'] == 'image':    
    good_start =   0    

bin_frame =  False # True  #generally make bin_frame as False
if bin_frame:
    bin_frame_number=4
    acquisition_period = md['acquire period']
    timeperframe = acquisition_period * bin_frame_number
else:
    bin_frame_number =1



import time
t0= time.time()

if not use_local_disk:
    cmp_path = '/nsls2/xf11id1/analysis/Compressed_Data'
else:
    cmp_path = '/tmp_data/compressed'
    
    
cmp_path = '/nsls2/xf11id1/analysis/Compressed_Data'    
if bin_frame_number==1:   
    cmp_file = '/uid_%s.cmp'%md['uid']
else:
    cmp_file = '/uid_%s_bined--%s.cmp'%(md['uid'],bin_frame_number)
    
filename = cmp_path + cmp_file  
mask, avg_img, imgsum, bad_frame_list = compress_eigerdata(imgs, mask, md, filename, 
         force_compress= force_compress,  para_compress= para_compress,  bad_pixel_threshold = 1e14,
                                    reverse=reverse, rot90=rot90, nobytes=nobytes,
                        bins=bin_frame_number, num_sub= 100, num_max_para_process= 500, with_pickle=True,
                        direct_load_data =use_local_disk, data_path = data_fullpath, )                                  
                                                         
min_inten = 10    
good_start = max(good_start, np.where( np.array(imgsum) > min_inten )[0][0] )    
print ('The good_start frame number is: %s '%good_start)
 
run_time(t0)


    

    




Will Always DO apply compress process.
Using already created compressed file with filename as :/nsls2/xf11id1/analysis/Compressed_Data/uid_2555d366-37ed-4768-9334-e5b5acc1cb5e.cmp.
The good_start frame number is: 0 
Total time: 0.173 sec

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