

In [1]:

    
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 *

Give the uid here



In [3]:

    
uid  = 'bbf77e12'



In [4]:

    
# unfortunately databroker has not good way of retrieving filenames
# here's a qiuck work around
# find the time
hdr = db[uid]
import time
time.ctime(hdr.start['time'])









    Out[4]:





'Thu Mar 14 21:48:53 2019'



In [10]:

    
sud = get_sid_filenames(db[uid])
print(sud)









    



(6308, 'bbf77e12-2cf1-43a2-b2f0-cb1e6497f441', [])

Here you have to manually give the fullapath of the raw data



In [11]:

    
# once the time is found, search directory to find the filename
# once filename found, open the handler with the correct filename
 
DDIR = "/XF11ID/data/2019/03/14"
prefix = '9f15f53c-ea6b-4551-ac91_6764_master.h5'

filename = DDIR + "/"



In [12]:

    
# finally use EigerImages to get the images
from eiger_io.fs_handler import EigerImages
imgs = EigerImages(DDIR + "/" + prefix, images_per_file=100)
len(imgs)









    Out[12]:





3833



In [13]:

    
imgs









    Out[13]:





<Frames>
Length: 3833 frames
Frame Shape: 2167 x 2070
Pixel Datatype: uint32

Show one image



In [21]:

    
img_index = 2000
show_img( imgs[ img_index ],  vmin= 1e-4, vmax= 100, logs=True, aspect=1,
   image_name= uid + '_img_%s'%img_index,  save=False, path='',  cmap = cmap_albula )



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