"This notebook corresponds to version {{ version }} of the pipeline tool: https://github.com/NSLS-II/pipelines"
This notebook begins with a raw time-series of images and ends with $g_2(t)$ for a range of $q$, fit to an exponential or stretched exponential, and a two-time correlation functoin.
CHX Olog (https://logbook.nsls2.bnl.gov/11-ID/)
Import packages for I/O, visualization, and analysis.
In [441]:
import chxanalys
In [442]:
chxanalys.__file__
Out[442]:
In [365]:
from chxanalys.chx_libs import (np, roi, time, datetime, os, get_events,
getpass, db, get_images,LogNorm, plt,tqdm, utils, Model,
multi_tau_lags)
from chxanalys.chx_generic_functions import (get_detector, get_fields, get_sid_filenames,
load_data, load_mask,get_fields, reverse_updown, ring_edges,get_avg_img,check_shutter_open,
apply_mask, show_img,check_ROI_intensity,run_time, plot1D, get_each_frame_intensity,
create_hot_pixel_mask,show_ROI_on_image,create_time_slice,save_lists,
save_arrays, psave_obj,pload_obj, get_non_uniform_edges )
from chxanalys.XPCS_SAXS import (get_circular_average,save_lists,get_ring_mask, get_each_ring_mean_intensity,
plot_qIq_with_ROI,save_saxs_g2,plot_saxs_g2,fit_saxs_g2,cal_g2,
create_hot_pixel_mask,get_circular_average,get_t_iq,save_saxs_g2,
plot_saxs_g2,fit_saxs_g2,fit_q2_rate,plot_saxs_two_g2,fit_q_rate,
circular_average,plot_saxs_g4, get_t_iqc,multi_uids_saxs_xpcs_analysis,
save_g2)
from chxanalys.Two_Time_Correlation_Function import (show_C12, get_one_time_from_two_time,
get_four_time_from_two_time,rotate_g12q_to_rectangle)
from chxanalys.chx_compress import (combine_binary_files,
segment_compress_eigerdata, create_compress_header,
para_segment_compress_eigerdata,para_compress_eigerdata)
from chxanalys.chx_compress_analysis import ( compress_eigerdata, read_compressed_eigerdata,
Multifile,get_avg_imgc, get_each_frame_intensityc,
get_each_ring_mean_intensityc, mean_intensityc,cal_waterfallc,plot_waterfallc,
)
from chxanalys.SAXS import fit_form_factor
from chxanalys.chx_correlationc import ( cal_g2c,Get_Pixel_Arrayc,auto_two_Arrayc,get_pixelist_interp_iq,)
from chxanalys.chx_correlationp import (cal_g2p, auto_two_Arrayp)
from chxanalys.Create_Report import (create_pdf_report,
create_multi_pdf_reports_for_uids,create_one_pdf_reports_for_uids)
from chxanalys.XPCS_GiSAXS import (get_qedge,get_qmap_label,get_qr_tick_label, get_reflected_angles,
convert_gisaxs_pixel_to_q, show_qzr_map, get_1d_qr, get_qzrmap, show_qzr_roi,get_each_box_mean_intensity,
save_gisaxs_g2,plot_gisaxs_g2, fit_gisaxs_g2,plot_gisaxs_two_g2,plot_qr_1d_with_ROI,fit_qr_qz_rate,
multi_uids_gisaxs_xpcs_analysis,plot_gisaxs_g4,
get_t_qrc, plot_t_qrc)
#%matplotlib notebook
%matplotlib inline
In [366]:
#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/XPCS_SAXS.py
#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/Create_Report.py
#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/chx_compress.py
In [367]:
plt.rcParams.update({'figure.max_open_warning': 0})
In [368]:
#%reset
In [369]:
#%%javascript
#var nb = IPython.notebook;
#var kernel = IPython.notebook.kernel;
#var command = "NOTEBOOK_FULL_PATH = '" + nb.base_url + nb.notebook_path + "'";
#kernel.execute(command);
In [370]:
#print("NOTEBOOK_FULL_PATH:\n", NOTEBOOK_FULL_PATH)
In [371]:
CYCLE = '2016_3'
username = getpass.getuser()
#username = "kyager" #provide the username to force the results to save in that username folder
username = "zhangz"
date_path = datetime.now().strftime('%Y/%m/%d') # e.g., '2016/03/01'
data_dir = os.path.join('/XF11ID/analysis/', CYCLE, username, 'Results/')
##Or define data_dir here, e.g.,#data_dir = '/XF11ID/analysis/2016_2/rheadric/test/'
os.makedirs(data_dir, exist_ok=True)
print('Results from this analysis will be stashed in the directory %s' % data_dir)
In [454]:
uid = 'cc1bcd'
uid = 'a9f937fe'
uid = '232d01'
uid = 'd5cf86e3'
# uid = '0b093518'
# uid = '2829b7ca'
# uid= 'f997d8b3-cf77-44cd-b343-59be1ea9a756'
uid ='dffb1c81'
uid='ab4dac04'
uid='beef48c3-72d5-40c9-b04b-763d8b2866e4'
In [455]:
data_dir = os.path.join('/XF11ID/analysis/', CYCLE, username, 'Results/%s/'%uid)
os.makedirs(data_dir, exist_ok=True)
print('Results from this analysis will be stashed in the directory %s' % data_dir)
In [456]:
detector = get_detector( db[uid ] )
print ('Detector is: %s'%detector )
sud = get_sid_filenames(db[uid])
full_uid = sud[1]
print ('scan_id, full-uid, data path are: %s--%s--%s'%(sud[0], sud[1], sud[2][0] ))
In [457]:
imgs = load_data( uid, detector, reverse= True )
Nimg = len(imgs)
md = imgs.md
In [458]:
imgs
Out[458]:
In [459]:
try:
md['Measurement']= db[uid]['start']['Measurement']
md['sample']=db[uid]['start']['sample']
#md['sample']= 'SiO2 Colloidal' #change the sample name if the md['sample'] is wrong
print( 'The sample is %s' %md['sample'])
except:
md['Measurement']= 'Measurement'
md['sample']='sample'
In [460]:
print( 'The data are: %s' %imgs )
In [461]:
print( 'The Metadata are: \n%s' %md )
In [462]:
# The physical size of the pixels
dpix = md['x_pixel_size'] * 1000. #in mm, eiger 4m is 0.075 mm
lambda_ =md['incident_wavelength'] # wavelegth of the X-rays in Angstroms
Ldet = md['detector_distance'] *1000 # detector to sample distance (mm)
exposuretime= md['count_time']
acquisition_period = md['frame_time']
print( 'The sample is %s'%( md['sample'] ))
print( 'Exposuretime=%s sec, Acquisition_period=%s sec'%( exposuretime, acquisition_period ))
timeperframe = acquisition_period#for g2
#timeperframe = exposuretime#for visiblitly
#timeperframe = 2 ## manual overwrite!!!! we apparently writing the wrong metadata....
center = [ md['beam_center_x'],2167-md['beam_center_y'] ] #for 4M
# center of the speckle pattern
#assuming it was correctly entered in the eiger css screen
#center = [ md['beam_center_x'],1065-md['beam_center_y'] ] #for 4M
#center = [1341,1381]
#center = [ 507, 758]
center = [ md['beam_center_x'], md['beam_center_y'] ] #for 4M
#center = [1475-4, 2167-1381-0]
center=[center[1], center[0]]
print ('Beam center=', center)
In [463]:
#Ldet = 4890 #in mm, in this experiments, we forget to change Ldet and kept the old 3990 mm
In [ ]:
In [464]:
setup_pargs=dict(uid=uid, dpix= dpix, Ldet=Ldet, lambda_= lambda_,
timeperframe=timeperframe, center=center, path= data_dir)
In [465]:
setup_pargs
Out[465]:
In [466]:
mask_path = '/XF11ID/analysis/2016_3/masks/'
mask_name = 'Nov10_4M_mask.npy'
In [467]:
mask = load_mask(mask_path, mask_name, plot_ = True, reverse=True, image_name = 'uid=%s-mask'%uid )
In [468]:
md['mask'] = mask
md['mask_file']= mask_path + mask_name
md['NOTEBOOK_FULL_PATH'] = None #NOTEBOOK_FULL_PATH
psave_obj( md, data_dir + 'uid=%s-md'%uid ) #save the setup parameters
md = pload_obj(data_dir + 'uid=%s-md'%uid )
In [469]:
imgsa = apply_mask( imgs, mask )
In [470]:
show_img( imgsa[0], vmin= .01, vmax=50000, logs= True, image_name= 'uid= %s'%uid)
In [473]:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.imshow(avg_img, origin='lower', vmin=0, vmax=3 )
Out[473]:
In [390]:
avg_img = get_avg_img( imgsa, sampling = 1, plot_ = True, uid =uid)
In [391]:
#avg_img = get_avg_img( imgsa, sampling = int(Nimg/3), plot_ = True, uid =uid)
In [392]:
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 " + ['NOT', 'DO'][compress] + " apply compress process.")
In [393]:
good_start = 5 #make the good_start at least 0
In [394]:
#imgs
In [395]:
#compress = True
In [396]:
if True:
if compress:
filename = '/XF11ID/analysis/Compressed_Data' +'/uid_%s.cmp'%sud[1]
mask, avg_img, imgsum, bad_frame_list = compress_eigerdata(imgs, mask, md, filename,
force_compress= False, bad_pixel_threshold= 1e14,nobytes=4,
para_compress=True, num_sub= 100)
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)
FD = Multifile(filename, good_start, len(imgs))
#FD = Multifile(filename, 10,100)
plot1D( y = imgsum[ np.array( [i for i in np.arange( len(imgsum)) if i not in bad_frame_list])],
title ='Uid= %s--imgsum'%uid, xlabel='Frame', ylabel='Total_Intensity', legend='imgsum' )
In [397]:
#FD = Multifile(filename, 0, 100 )
In [398]:
#%system ls -lh {sud[2][0]+"*"}|tail -2 ; ls -lh {filename}
In [399]:
bad_pixel_threshold= 2.5*10**15 # 2.4*10**15 #if re-define a bad pixel threshold
bad_pixel_low_threshold= 0#1.8*10**15 #if re-define a bad pixel threshold
In [400]:
if bad_pixel_threshold<1e14:
mask, avg_img, imgsum, bad_frame_list = compress_eigerdata(imgs, mask, md, filename,
force_compress=False, bad_pixel_threshold= bad_pixel_threshold,
bad_pixel_low_threshold= bad_pixel_low_threshold, nobytes=4)
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)
In [401]:
if False:
good_start = 0 #0
good_end = len(imgs)
filename = '/XF11ID/analysis/Compressed_Data' +'/uid_%s.cmp'%sud[1]
FD = Multifile(filename, good_start, good_end)
avg_img= get_avg_imgc( FD, beg= None,end=None, plot_=False )
imgsum,bad_frame_list = get_each_frame_intensityc( FD, bad_pixel_threshold= 1e14, plot_=False )
In [402]:
if not compress:
#sampling = 1 #sampling should be one
sampling = 1000 #sampling should be one
good_start = check_shutter_open( imgsa, min_inten=5, time_edge = [0,10], plot_ = False )
print ('The good_start frame number is: %s '%good_start)
good_series = apply_mask( imgsa[good_start:], mask )
avg_img = get_avg_img( good_series, sampling = sampling, plot_ = False, uid =uid)
imgsum, bad_frame_list = get_each_frame_intensity(good_series ,sampling = sampling,
bad_pixel_threshold= 1e14, plot_ = False, uid=uid)
In [403]:
#print ('The bad frame list is: %s'% bad_frame_list)
print ('The number of bad frames is : %s '%len(bad_frame_list))
print ('The good_start frame number is: %s '%good_start)
md['good_start'] = good_start
md['bad_frame_list'] = bad_frame_list
In [404]:
imgsum_y = imgsum[ np.array( [i for i in np.arange( len(imgsum)) if i not in bad_frame_list])]
imgsum_x = np.arange( len( imgsum_y))
save_lists( [imgsum_x, imgsum_y], label=['Frame', 'Total_Intensity'], filename='uid=%s-imgsum'%uid, path= data_dir )
In [405]:
plot1D( y = imgsum_y, title ='uid=%s--img-sum-t'%uid, xlabel='Frame',
ylabel='Total_Intensity', legend='imgsum', save=True, path=data_dir)
In [406]:
#avg_img = get_avg_imgc( FD, beg=0,end=10000,sampling = 1, plot_ = False )
show_img( avg_img, vmin=.1, vmax=20, logs= True, image_name= 'uid=%s--img-avg-'%uid,
save=True, path=data_dir)
md['avg_img'] = avg_img
In [407]:
hmask = create_hot_pixel_mask( avg_img, 2**15 )
mask = mask * hmask
In [408]:
hmask = create_hot_pixel_mask( avg_img, 1e8)
qp, iq, q = get_circular_average( avg_img, mask * hmask, pargs=setup_pargs, nx=None,
plot_ = True, show_pixel= False, xlim=[0.0001,.15], ylim = [0.00009, 1e2], save=True)
In [409]:
fit_form = False
In [410]:
if fit_form:
form_res = fit_form_factor( q,iq, guess_values={'radius': 2500, 'sigma':0.05,
'delta_rho':1E-10 }, fit_range=[0.0001, 0.015], fit_variables={'radius': T, 'sigma':T,
'delta_rho':T}, res_pargs=setup_pargs, xlim=[0.0001, 0.015])
In [411]:
uniform = True #False
In [412]:
if not uniform:
#width = 4 # in pixel
width = 0.001
number_rings=1
#centers = [ 31, 50, 67, 84, 102, 119] #in pixel
centers = [ 0.00235,0.00379,0.00508,0.00636,0.00773, 0.00902] #in A-1
centers = [ 0.0065,0.0117,0.021,0.0336,0.044, 0.057] #in A-1
edges = get_non_uniform_edges( centers, width, number_rings )
inner_radius= None
outer_radius = None
width = None
num_rings = None
In [413]:
if uniform:
inner_radius= 0.0115
outer_radius = 0.069
width = 0.0035
num_rings = 10
edges = None
In [414]:
ring_mask, q_ring_center, q_ring_val = get_ring_mask( mask, inner_radius=inner_radius,
outer_radius = outer_radius , width = width, num_rings = num_rings, edges=edges,
unit='A', pargs=setup_pargs )
qind, pixelist = roi.extract_label_indices( ring_mask )
In [415]:
noqs = len( np.unique(qind) )
nopr = np.bincount(qind, minlength=(noqs+1))[1:]
print( nopr )
In [ ]:
In [416]:
md['ring_mask'] = ring_mask
md['q_ring_center']= q_ring_center
md['q_ring_val'] = q_ring_val
md['beam_center_x'] = center[1]
md['beam_center_y']= center[0]
psave_obj( md, data_dir + 'uid=%s-md'%uid ) #save the setup parameters
In [417]:
show_ROI_on_image( avg_img, ring_mask, center, label_on = False, rwidth=800, alpha=.9,
save=True, path=data_dir, uid=uid, vmin=.09, vmax=1e2)
In [418]:
plot_qIq_with_ROI( q, iq, q_ring_center, logs=True, uid=uid, xlim=[0.001,.12],
ylim = [1e-5, 1e2], save=True, path=data_dir)
In [419]:
roi_inten = check_ROI_intensity( avg_img, ring_mask, ring_number= 2, uid =uid, save=True, path=data_dir )
In [420]:
#FD = Multifile(filename, good_start, len(imgs))
In [421]:
if compress:
Nimg = FD.end - FD.beg
else:
Nimg = len(imgsa )
In [422]:
time_edge = create_time_slice( N= Nimg, slice_num= 3, slice_width= 10, edges = None )
time_edge = np.array( time_edge ) + good_start
In [423]:
if compress:
qpt, iqst, qt = get_t_iqc( FD, time_edge, mask, pargs=setup_pargs, nx=1500,
plot_ = True, xlim=[0.0001,.12], ylim = [0.000001, 100], save=True, path=data_dir )
else:
qpt, iqst, qt = get_t_iq( good_series, time_edge, mask*hmask, pargs=setup_pargs, nx=1500,
plot_ = True, ylim = [0.00091, 1e3] ,xlim=[0.0001,.12], save=True)
In [424]:
if False:
if compress:
qindex = 3
wat = cal_waterfallc( FD, ring_mask, qindex= qindex, save =True, path=data_dir, uid=uid)
In [425]:
if False:
if compress:
plot_waterfallc( wat, qindex, aspect=None,
vmax= 10, uid=uid, save =True,
path=data_dir, beg= FD.beg)
In [ ]:
In [426]:
if True:
if compress:
times, mean_int_sets = get_each_ring_mean_intensityc(FD, ring_mask,
timeperframe = None, plot_ = True, uid = uid, save=True, path=data_dir )
ring_avg = np.average( mean_int_sets, axis=0)
else:
mean_int_sets = get_each_ring_mean_intensity(good_series, ring_mask, sampling = sampling,
timeperframe = md['frame_time']*sampling,
plot_ = True, uid = uid, save=True, path=data_dir )
Note : Enter the number of buffers for Muliti tau one time correlation number of buffers has to be even. More details in https://github.com/scikit-beam/scikit-beam/blob/master/skbeam/core/correlation.py
In [427]:
if False:
good_start = 0
good_end = 2000
good_series = apply_mask( imgs[good_start:good_end-1], mask )
In [428]:
lag_steps = None
In [429]:
#bad_frame_list
In [430]:
para_cal = True #if True to use the parallel calculation
In [431]:
#good_start = 10
#good_end=500
#FD = Multifile(filename, good_start, good_end )
In [432]:
norm = get_pixelist_interp_iq( qp, iq, ring_mask, center)
In [433]:
t0 = time.time()
if compress:
if para_cal:
g2, lag_steps =cal_g2p( FD, ring_mask, bad_frame_list,good_start, num_buf = 8,
imgsum= None, norm=norm )
else:
g2, lag_steps =cal_g2c( FD, ring_mask, bad_frame_list,good_start, num_buf = 8,
imgsum= None, norm=norm )
else:
bad_image_process = False
if len(bad_frame_list):
bad_image_process = True
print( bad_image_process )
g2, lag_steps =cal_g2( good_series, ring_mask, bad_image_process,
bad_frame_list,good_start, num_buf = 8 )
run_time(t0)
In [ ]:
lag_steps
In [ ]:
len( lag_steps)
In [ ]:
In [ ]:
taus = lag_steps * timeperframe
res_pargs = dict(taus=taus, q_ring_center=q_ring_center, path=data_dir, uid=uid )
In [ ]:
#taus
In [ ]:
save_saxs_g2( g2, res_pargs )
In [ ]:
plot_saxs_g2( g2, taus, vlim=[0.95, 1.05], res_pargs=res_pargs)
In [ ]:
fit= True
In [ ]:
if fit:
fit_result = fit_saxs_g2( g2, res_pargs, function = 'stretched', vlim=[0.95, 1.05],
fit_variables={'baseline':True, 'beta':True, 'alpha':False,'relaxation_rate':True},
guess_values={'baseline':1.0,'beta':0.05,'alpha':1.0,'relaxation_rate':0.01})
In [ ]:
psave_obj( fit_result, data_dir + 'uid=%s-g2-fit-para'%uid )
In [ ]:
#np.arctan2(3, 1470) *180/np.pi
In [ ]:
In [ ]:
#fig,ax=plt.subplots()
#q_nums = [10,15,20]
#for q_num in q_nums:
# plot1D(ax=ax, x=taus[1:], y= np.array( g2 )[1:,q_num ], logx=True)
In [ ]:
#fit_q_rate( q_ring_center[6:12], result['rate'][6:12], power_variable=False,uid=uid, path= data_dir )
fit_q_rate( q_ring_center[:], fit_result['rate'][:], power_variable= False,
uid=uid, path= data_dir )
In [ ]:
run_two_time = True
In [ ]:
para_cal = False
In [ ]:
if run_two_time:
if compress:
#norm = None
data_pixel = Get_Pixel_Arrayc( FD, pixelist, norm=norm ).get_data()
if para_cal:
g12b = auto_two_Arrayp( data_pixel, ring_mask, index = None )
else:
g12b = auto_two_Arrayc( data_pixel, ring_mask, index = None )
if lag_steps is None:
num_bufs=8
noframes = FD.end - FD.beg
num_levels = int(np.log( noframes/(num_bufs-1))/np.log(2) +1) +1
tot_channels, lag_steps, dict_lag = multi_tau_lags(num_levels, num_bufs)
max_taus= lag_steps.max()
else:
qind, pixelist = roi.extract_label_indices( ring_mask )
#good_start = 10
#good_end = 300 #len( imgs )
#good_series = apply_mask( imgsr[good_start:good_end-1], maskr )
t0 = time.time()
data_pixel = Get_Pixel_Array( good_series , pixelist).get_data()
run_time(t0)
g12b = auto_two_Array( good_series,ring_mask, data_pixel = data_pixel )
In [ ]:
#if run_two_time:np.save( data_dir + 'uid=%s-Two_time'%uid, g12b)
In [ ]:
if run_two_time:
show_C12(g12b, q_ind= 0, N1=2, N2=1000, vmin=.95, vmax=1.2,
timeperframe=timeperframe,save=True, path= data_dir, uid = uid )
In [ ]:
if run_two_time:
if lag_steps is None:
num_bufs=8
noframes = FD.end - FD.beg
num_levels = int(np.log( noframes/(num_bufs-1))/np.log(2) +1) +1
tot_channels, lag_steps, dict_lag = multi_tau_lags(num_levels, num_bufs)
max_taus= lag_steps.max()
max_taus= lag_steps.max()
t0=time.time()
g2b = get_one_time_from_two_time(g12b)[:max_taus]
run_time(t0)
taus2 = np.arange( g2b.shape[0])[:max_taus] *timeperframe
res_pargs2 = dict(taus=taus2, q_ring_center=q_ring_center, path=data_dir, uid=uid )
save_saxs_g2( g2b, res_pargs2, taus=np.arange( g2b.shape[0]) *timeperframe,
filename='g2_from_two-time')
In [ ]:
if run_two_time:
plot_saxs_g2( g2b, taus2, vlim=[0.95, 1.05], res_pargs=res_pargs2)
In [ ]:
if run_two_time:
result2 = fit_saxs_g2( g2b, res_pargs2, function = 'simple')#, fit_range= [0, 2000 ])
fit_q_rate( q_ring_center, result2['rate'], uid=uid, path= data_dir )
save_lists( [q_ring_center**2,result2['rate']], ['q2','rate'], filename= 'Q2-rate-twoT-uid=%s'%uid, path= data_dir)
In [ ]:
if run_two_time:
plot_saxs_two_g2( g2, taus,
g2b, taus2,
res_pargs=res_pargs, vlim=[.95, 1.05], uid= uid )
In [ ]:
g12b=0
data_pixel =0
In [ ]:
run_four_time = False
In [ ]:
if run_four_time:
t0=time.time()
g4 = get_four_time_from_two_time(g12b, g2=g2b)[:max_taus]
run_time(t0)
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if run_four_time:
taus4 = np.arange( g4.shape[0])*timeperframe
res_pargs4 = dict(taus=taus4, q_ring_center=q_ring_center, path=data_dir, uid=uid )
save_saxs_g2( g4, res_pargs4, taus=taus4, filename='uid=%s--g4.csv' % (uid) )
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if run_four_time:
plot_saxs_g4( g4, taus4, vlim=[0.95, 1.05], logx=True, res_pargs=res_pargs4)
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save_res_h5( full_uid, data_dir, save_two_time=False )
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#meta_data, avg_h5, mask_h5,roi_h5, g2_h5, taus_h5, g2b_h5, taus2_h5 = load_res_h5(full_uid, data_dir )
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create_report = True
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username
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pdf_out_dir = os.path.join('/XF11ID/analysis/', CYCLE, username, 'Results/')
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if create_report:
c= create_pdf_report( data_dir, uid, pdf_out_dir,
filename= "XPCS_Analysis_Report_for_uid=%s.pdf"%uid)
#Page one: Meta-data/Iq-Q/ROI
c.report_header(page=1)
c.report_meta( top=730)
c.report_static( top=560, iq_fit =fit_form )
c.report_ROI( top= 300)
#Page Two: img~t/iq~t/waterfall/mean~t/g2/rate~q
c.new_page()
c.report_header(page=2)
c.report_time_analysis( top= 720)
c.report_one_time( top= 350)
#Page Three: two-time/two g2
if run_two_time:
c.new_page()
c.report_header(page=3)
c.report_two_time( top= 720 )
if run_four_time:
c.new_page()
c.report_header(page=4)
c.report_four_time( top= 720 )
c.save_page()
c.done()
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c.filename
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data_dir
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from chxanalys.chx_olog import LogEntry,Attachment, update_olog_uid, update_olog_id
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os.environ['HTTPS_PROXY'] = 'https://proxy:8888'
os.environ['no_proxy'] = 'cs.nsls2.local,localhost,127.0.0.1'
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c.filename
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filename = c.filename
atch=[ Attachment(open(filename, 'rb')) ]
update_olog_uid( uid=full_uid, text='Add XPCS Analysis PDF Report', attachments= atch )
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if False:
filename = '/XF11ID/analysis'
for s in NOTEBOOK_FULL_PATH.split("/")[4:]:
filename += '/'+ s
atch=[ Attachment(open(filename, 'rb')) ]
update_olog_uid( uid=uid, text='Add XPCS Analysis notebook', attachments= atch )
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