"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 [1]:
from chxanalys.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 chxanalys.chx_xpcs_xsvs_jupyter_V1 import *
In [2]:
Javascript( '''
var nb = IPython.notebook;
var kernel = IPython.notebook.kernel;
var command = "NFP = '" + nb.base_url + nb.notebook_path + "'";
kernel.execute(command);
''' )
Out[2]:
In [3]:
#print( 'The current running pipeline is: %s' %NFP)
In [4]:
#%reset -f -s dhist in out array
In [5]:
#scat_geometry = 'saxs' #suport 'saxs', 'gi_saxs', 'ang_saxs' (for anisotropics saxs or flow-xpcs)
scat_geometry = 'saxs'
qphi_analysis = False
#scat_geometry = 'ang_saxs' #suport 'saxs', 'gi_saxs', 'ang_saxs' (for anisotropics saxs or flow-xpcs)
#scat_geometry = 'gi_waxs' #suport 'saxs', 'gi_saxs', 'ang_saxs' (for anisotropics saxs or flow-xpcs)
# gi_waxs define a simple box-shaped ROI
#scat_geometry = 'gi_saxs'
force_compress = False #True #force to compress data
bin_frame = False #generally make bin_frame as False
para_compress = True #parallel compress
run_fit_form = False #run fit form factor
run_waterfall = False #run waterfall analysis
run_profile_plot = False #run prolfile plot for gi-saxs
run_t_ROI_Inten = True #run ROI intensity as a function of time
run_get_mass_center = False # Analysis for mass center of reflective beam center
run_invariant_analysis = False
run_one_time = True #run one-time
#run_fit_g2 = True #run fit one-time, the default function is "stretched exponential"
fit_g2_func = 'stretched'
run_two_time = True #run two-time
run_four_time = True #True #False #run four-time
run_xsvs= False #False #run visibility analysis
att_pdf_report = True #attach the pdf report to CHX olog
qth_interest = 1 #the intested single qth
use_sqnorm = True #if True, use sq to normalize intensity
use_SG = False #if True, use the Sawitzky-Golay filter for <I(pix)>
use_imgsum_norm= True #if True use imgsum to normalize intensity for one-time calculatoin
pdf_version='_%s'%get_today_date() #for pdf report name
run_dose = True #run dose_depend analysis
if scat_geometry == 'gi_saxs':run_xsvs= False;use_sqnorm=False
if scat_geometry == 'gi_waxs':use_sqnorm = False
if scat_geometry != 'saxs':qphi_analysis = False;scat_geometry_ = scat_geometry
else:scat_geometry_ = ['','ang_'][qphi_analysis]+ scat_geometry
if scat_geometry != 'gi_saxs':run_profile_plot = False
In [6]:
#%run ~/chxanalys_link/chxanalys/chx_generic_functions.py
In [7]:
scat_geometry
Out[7]:
In [8]:
taus=None;g2=None;tausb=None;g2b=None;g12b=None;taus4=None;g4=None;times_xsv=None;contrast_factorL=None; lag_steps = None
In [9]:
CYCLE= '2017_3' #change clycle here
#CYCLE= '2017_2' #change clycle here
path = '/XF11ID/analysis/%s/masks/'%CYCLE
username = getpass.getuser()
#username = 'rmhanna'
username = 'hkoerner'
#username = 'rheadric'
data_dir0 = create_user_folder(CYCLE, username)
print( data_dir0 )
In [10]:
# dynamic mask
fp = '/XF11ID/analysis/2017_3/masks/roi_mask_Nov17_Rings.pkl'
roi_mask,qval_dict = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data
print(fp)
# q map file
if scat_geometry =='gi_saxs':
# static mask
fp = data_dir0 + 'June_2017_Sam3_Graphene_no1_C60_200C_roi_static.pkl'
roi_masks,qval_dicts = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data
print(fp)
fp = data_dir0 + 'June_2017_Sam3_Graphene_no1_C60_200C_gisaxs_qmap.pkl'
print(fp)
qr_map, qz_map, ticks, Qrs, Qzs, Qr, Qz, inc_x0,refl_x0, refl_y0 = cpk.load( open(fp, 'rb' ) )
In [11]:
#%run chxanalys_link/chxanalys/chx_generic_functions.py
In [12]:
uid = '76f314' # (scan num: 9401) (Measurement: 750Hz 1k frames mbs =.05x.4 CoralPor )
uid = 'ec4b0c' # (scan num: 9402) (Measurement: 750Hz 1k frames mbs =.1x.4 CoralPor )
uid = '6d0761' #(scan num: 9403 (Measurement: 750Hz 5k frames 1000A "
uid = 'c298d2' #(scan num: 9404 (Measurement: 100Hz 5k T=.2 1000A
uid = 'f03425' #(scan num: 9405 (Measurement: 10Hz 5k T=.2 1000A
uid = 'b3ea84' #(scan num: 9406 (Measurement: 10Hz 5k T=.036 1000A "
uid = 'be6e4c' #(scan num: 9407 (Measurement: 10Hz 5k T=.036 1000E "
In [13]:
get_last_uids( -1 )
Out[13]:
In [14]:
sud = get_sid_filenames(db[uid])
print ('scan_id, full-uid, data path are: %s--%s--%s'%(sud[0], sud[1], sud[2][0] ))
#start_time, stop_time = '2017-2-24 12:23:00', '2017-2-24 13:42:00'
#sids, uids, fuids = find_uids(start_time, stop_time)
In [15]:
data_dir = os.path.join(data_dir0, '%s/'%uid)
os.makedirs(data_dir, exist_ok=True)
print('Results from this analysis will be stashed in the directory %s' % data_dir)
uidstr = 'uid=%s'%uid
In [39]:
def get_meta_data( uid,*argv,**kwargs ):
'''
Y.G. Dev Dec 8, 2016
Get metadata from a uid
Parameters:
uid: the unique data acquisition id
kwargs: overwrite the meta data, for example
get_meta_data( uid = uid, sample = 'test') --> will overwrtie the meta's sample to test
return:
meta data of the uid: a dictionay
with keys:
detector
suid: the simple given uid
uid: full uid
filename: the full path of the data
start_time: the data acquisition starting time in a human readable manner
And all the input metadata
'''
import time
md ={}
md['detector'] = get_detector( db[uid ] )
md['suid'] = uid #short uid
md['filename'] = get_sid_filenames(db[uid])[2][0]
#print( md )
ev, = get_events(db[uid], [md['detector']], fill= False)
dec = list( ev['descriptor']['configuration'].keys() )[0]
for k,v in ev['descriptor']['configuration'][dec]['data'].items():
md[ k[len(dec)+1:] ]= v
print(k)
for k,v in ev['descriptor']['run_start'].items():
if k!= 'plan_args':
md[k]= v
print(k)
md['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(md['time']))
md['stop_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime( ev['time'] ))
md['img_shape'] = ev['descriptor']['data_keys'][md['detector']]['shape'][:2][::-1]
for k,v in kwargs.items():
md[k] =v
#print(k)
return md
In [42]:
md={}
In [ ]:
In [43]:
md = get_meta_data( uid )
In [25]:
get_meta_data??
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In [44]:
md
Out[44]:
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In [17]:
get_meta_data?
In [18]:
uid
Out[18]:
In [19]:
ev, = get_events(db[uid], [md['detector']], fill= False)
ev
Out[19]:
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get_meta_data??
In [ ]:
ev['descriptor']['configuration']['eiger4m_single']['data']
In [ ]:
print(db[uid].stop)
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uid
In [ ]:
md
In [21]:
imgs = load_data( uid, md['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 )
md['acquire period' ] = md['cam_acquire_period']
md['exposure time'] = md['cam_acquire_time']
In [ ]:
In [ ]:
print_dict( md, ['suid', 'number of images', 'uid', 'scan_id', 'start_time', 'stop_time', 'sample', 'Measurement',
'acquire period', 'exposure time',
'det_distance', 'beam_center_x', 'beam_center_y', ] )
In [ ]:
if scat_geometry =='gi_saxs':
inc_x0 = md['beam_center_x']
inc_y0 = imgs[0].shape[0] - md['beam_center_y']
refl_x0 = 1541 #md['beam_center_x']
refl_y0 = 960 #imgs[0].shape[0] - 1758
print( "inc_x0, inc_y0, ref_x0,ref_y0 are: %s %s %s %s."%(inc_x0, inc_y0, refl_x0, refl_y0) )
else:
inc_x0 = imgs[0].shape[0] - md['beam_center_y']
inc_y0= md['beam_center_x']
In [ ]:
dpix, lambda_, Ldet, exposuretime, timeperframe, center = check_lost_metadata(
md, Nimg, inc_x0 = inc_x0, inc_y0= inc_y0, pixelsize = 7.5*10*(-5) )
if scat_geometry =='gi_saxs':center=center[::-1]
setup_pargs=dict(uid=uidstr, dpix= dpix, Ldet=Ldet, lambda_= lambda_, exposuretime=exposuretime,
timeperframe=timeperframe, center=center, path= data_dir)
print_dict( setup_pargs )
In [ ]:
setup_pargs
In [ ]:
if scat_geometry == 'gi_saxs':
mask_path = '/XF11ID/analysis/2017_2/masks/'
mask_name = 'Jun4_2_GiSAXS.npy'
elif scat_geometry == 'saxs':
mask_path = '/XF11ID/analysis/2017_3/masks/'
mask_name = 'Nov17_SAXS.npy'
#mask_path = '/XF11ID/analysis/2017_2/masks/'
#mask_name = 'Jul26_SAXS.npy'
In [ ]:
mask = load_mask(mask_path, mask_name, plot_ = False, image_name = uidstr + '_mask', reverse= True )
mask *= pixel_mask
show_img(mask,image_name = uidstr + '_mask', save=True, path=data_dir, aspect=1, center=center[::-1])
mask_load=mask.copy()
imgsa = apply_mask( imgs, mask )
In [ ]:
img_choice_N = 3
img_samp_index = random.sample( range(len(imgs)), img_choice_N)
avg_img = get_avg_img( imgsa, img_samp_index, plot_ = False, uid =uidstr)
if avg_img.max() == 0:
print('There are no photons recorded for this uid: %s'%uid)
print('The data analysis should be terminated! Please try another uid.')
In [ ]:
#show_img( imgsa[1000], vmin=.1, vmax= 1e1, logs=True, aspect=1,
# image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap = cmap_albula )
In [ ]:
show_img( imgs[10], vmin=.0, vmax= 1e1, logs=False, aspect=1, #save_format='tif',
image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap=cmap_albula,center=center[::-1] )
In [ ]:
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.")
In [ ]:
good_start = 5 #5 #make the good_start at least 0
In [ ]:
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
In [ ]:
import time
t0= time.time()
if bin_frame_number==1:
filename = '/XF11ID/analysis/Compressed_Data' +'/uid_%s.cmp'%md['uid']
else:
filename = '/XF11ID/analysis/Compressed_Data' +'/uid_%s_bined--%s.cmp'%(md['uid'],bin_frame_number)
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,
bins=bin_frame_number, num_sub= 100, num_max_para_process= 500, with_pickle=True )
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)//bin_frame_number)
#FD = Multifile(filename, good_start, 100)
uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end)
print( uid_ )
plot1D( y = imgsum[ np.array( [i for i in np.arange(good_start, len(imgsum)) if i not in bad_frame_list])],
title =uidstr + '_imgsum', xlabel='Frame', ylabel='Total_Intensity', legend='imgsum' )
Nimg = Nimg/bin_frame_number
run_time(t0)
In [ ]:
show_img( avg_img, vmin=.0001, vmax= 5e4, logs=True, aspect=1, #save_format='tif',
image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap = cmap_albula, center=center[::-1] )
In [ ]:
good_end= None # 2000
if good_end is not None:
FD = Multifile(filename, good_start, min( len(imgs)//bin_frame_number, good_end) )
uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end)
print( uid_ )
In [ ]:
re_define_good_start =False
if re_define_good_start:
good_start = 10
good_end = 19700
FD = Multifile(filename, good_start, good_end)
uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end)
print( FD.beg, FD.end)
In [ ]:
bad_frame_list = get_bad_frame_list( imgsum, fit='both', plot=True,polyfit_order = 30,
scale= 3.5, good_start = good_start, good_end=good_end, uid= uidstr, path=data_dir)
print( 'The bad frame list length is: %s'%len(bad_frame_list) )
In [ ]:
In [ ]:
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=uidstr + '_img_sum_t', path= data_dir )
In [ ]:
plot1D( y = imgsum_y, title = uidstr + '_img_sum_t', xlabel='Frame', c='b',
ylabel='Total_Intensity', legend='imgsum', save=True, path=data_dir)
In [ ]:
if scat_geometry =='saxs':
## Get circular average| * Do plot and save q~iq
hmask = create_hot_pixel_mask( avg_img, threshold = 1e2, center=center, center_radius= 100)
mask = mask * hmask
qp_saxs, iq_saxs, q_saxs = get_circular_average( avg_img, mask * hmask, pargs=setup_pargs )
plot_circular_average( qp_saxs, iq_saxs, q_saxs, pargs=setup_pargs,
xlim=[q_saxs.min(), q_saxs.max()*1.0], ylim = [iq_saxs.min(), iq_saxs.max()] )
#mask =np.array( mask * hmask, dtype=bool)
In [ ]:
#%run ~/chxanalys_link/chxanalys/chx_compress_analysis.py
In [ ]:
if scat_geometry =='saxs':
if run_fit_form:
form_res = fit_form_factor( q_saxs,iq_saxs, 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])
qr = np.array( [qval_dict[k][0] for k in sorted( qval_dict.keys())] )
print(len(qr))
show_ROI_on_image( avg_img, roi_mask, center, label_on = False, rwidth = 840, alpha=.9,
save=True, path=data_dir, uid=uidstr, vmin= 1e-3,
vmax= 1e3, #np.max(avg_img),
aspect=1,
show_roi_edge=True,
show_ang_cor = True)
plot_qIq_with_ROI( q_saxs, iq_saxs, np.unique(qr), logs=True, uid=uidstr, xlim=[0.0001,0.08],
ylim = [iq_saxs.min(), iq_saxs.max()*2], save=True, path=data_dir)
In [ ]:
if scat_geometry =='saxs':
Nimg = FD.end - FD.beg
time_edge = create_time_slice( Nimg, slice_num= 4, slice_width= 1, edges = None )
time_edge = np.array( time_edge ) + good_start
#print( time_edge )
qpt, iqst, qt = get_t_iqc( FD, time_edge, mask, pargs=setup_pargs, nx=1500, show_progress= False )
plot_t_iqc( qt, iqst, time_edge, pargs=setup_pargs, xlim=[qt.min(), qt.max()],
ylim = [iqst.min(), iqst.max()], save=True )
In [ ]:
if run_invariant_analysis:
if scat_geometry =='saxs':
invariant = get_iq_invariant( qt, iqst )
time_stamp = time_edge[:,0] * timeperframe
if scat_geometry =='saxs':
plot_q2_iq( qt, iqst, time_stamp,pargs=setup_pargs,ylim=[ -0.001, 0.01] ,
xlim=[0.007,0.2],legend_size= 6 )
if scat_geometry =='saxs':
plot_time_iq_invariant( time_stamp, invariant, pargs=setup_pargs, )
if False:
iq_int = np.zeros( len(iqst) )
fig, ax = plt.subplots()
q = qt
for i in range(iqst.shape[0]):
yi = iqst[i] * q**2
iq_int[i] = yi.sum()
time_labeli = 'time_%s s'%( round( time_edge[i][0] * timeperframe, 3) )
plot1D( x = q, y = yi, legend= time_labeli, xlabel='Q (A-1)', ylabel='I(q)*Q^2', title='I(q)*Q^2 ~ time',
m=markers[i], c = colors[i], ax=ax, ylim=[ -0.001, 0.01] , xlim=[0.007,0.2],
legend_size=4)
#print( iq_int )
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In [ ]:
In [ ]:
if scat_geometry =='gi_saxs':
plot_qzr_map( qr_map, qz_map, inc_x0, ticks = ticks, data= avg_img, uid= uidstr, path = data_dir )
In [ ]:
if scat_geometry =='gi_saxs':
#roi_masks, qval_dicts = get_gisaxs_roi( Qrs, Qzs, qr_map, qz_map, mask= mask )
show_qzr_roi( avg_img, roi_masks, inc_x0, ticks[:4], alpha=0.5, save=True, path=data_dir, uid=uidstr )
In [ ]:
if scat_geometry =='gi_saxs':
Nimg = FD.end - FD.beg
time_edge = create_time_slice( N= Nimg, slice_num= 2, slice_width= 2, edges = None )
time_edge = np.array( time_edge ) + good_start
print( time_edge )
qrt_pds = get_t_qrc( FD, time_edge, Qrs, Qzs, qr_map, qz_map, mask=mask, path=data_dir, uid = uidstr )
plot_qrt_pds( qrt_pds, time_edge, qz_index = 0, uid = uidstr, path = data_dir )
In [ ]:
if scat_geometry =='gi_saxs':
if run_profile_plot:
xcorners= [ 1100, 1250, 1250, 1100 ]
ycorners= [ 850, 850, 950, 950 ]
waterfall_roi_size = [ xcorners[1] - xcorners[0], ycorners[2] - ycorners[1] ]
waterfall_roi = create_rectangle_mask( avg_img, xcorners, ycorners )
#show_img( waterfall_roi * avg_img, aspect=1,vmin=.001, vmax=1, logs=True, )
wat = cal_waterfallc( FD, waterfall_roi, qindex= 1, bin_waterfall=True,
waterfall_roi_size = waterfall_roi_size,save =True, path=data_dir, uid=uidstr)
In [ ]:
if scat_geometry =='gi_saxs':
if run_profile_plot:
plot_waterfallc( wat, qindex=1, aspect=None, vmin=1, vmax= np.max( wat), uid=uidstr, save =True,
path=data_dir, beg= FD.beg)
In [ ]:
if scat_geometry =='gi_saxs':
show_qzr_roi( avg_img, roi_mask, inc_x0, ticks[:4], alpha=0.5, save=True, path=data_dir, uid=uidstr )
## Get 1D Curve (Q||-intensity¶)
qr_1d_pds = cal_1d_qr( avg_img, Qr, Qz, qr_map, qz_map, inc_x0= None, mask=mask, setup_pargs=setup_pargs )
plot_qr_1d_with_ROI( qr_1d_pds, qr_center=np.unique( np.array(list( qval_dict.values() ) )[:,0] ),
loglog=False, save=True, uid=uidstr, path = data_dir)
In [ ]:
if scat_geometry =='gi_waxs':
badpixel = np.where( avg_img[:600,:] >=300 )
roi_mask[badpixel] = 0
show_ROI_on_image( avg_img, roi_mask, label_on = True, alpha=.5,
save=True, path=data_dir, uid=uidstr, vmin=0.1, vmax=5)
In [ ]:
qind, pixelist = roi.extract_label_indices(roi_mask)
noqs = len(np.unique(qind))
In [ ]:
nopr = np.bincount(qind, minlength=(noqs+1))[1:]
nopr
In [ ]:
roi_inten = check_ROI_intensity( avg_img, roi_mask, ring_number= 2, uid =uidstr ) #roi starting from 1
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#run_waterfall = False
In [ ]:
qth_interest = 5 #the second ring. #qth_interest starting from 1
if scat_geometry =='saxs' or scat_geometry =='gi_waxs':
if run_waterfall:
wat = cal_waterfallc( FD, roi_mask, qindex= qth_interest, save =True, path=data_dir, uid=uidstr)
plot_waterfallc( wat, qth_interest, aspect= None, vmin=1e-1, vmax= wat.max(), uid=uidstr, save =True,
path=data_dir, beg= FD.beg, cmap = cmap_vge )
In [ ]:
ring_avg = None
if run_t_ROI_Inten:
times_roi, mean_int_sets = cal_each_ring_mean_intensityc(FD, roi_mask, timeperframe = None, multi_cor=True )
plot_each_ring_mean_intensityc( times_roi, mean_int_sets, uid = uidstr, save=True, path=data_dir )
roi_avg = np.average( mean_int_sets, axis=0)
In [ ]:
if run_get_mass_center:
cx, cy = get_mass_center_one_roi(FD, roi_mask, roi_ind=25)
In [ ]:
if run_get_mass_center:
fig,ax=plt.subplots(2)
plot1D( cx, m='o', c='b',ax=ax[0], legend='mass center-refl_X',
ylim=[940, 960], ylabel='posX (pixel)')
plot1D( cy, m='s', c='r',ax=ax[1], legend='mass center-refl_Y',
ylim=[1540, 1544], xlabel='frames',ylabel='posY (pixel)')
In [ ]:
In [ ]:
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 [ ]:
define_good_series = False
#define_good_series = True
if define_good_series:
good_start = 200
FD = Multifile(filename, beg = good_start, end = 800) #end=1000)
uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end)
print( uid_ )
In [ ]:
#%run /home/yuzhang/chxanalys_link/chxanalys/chx_generic_functions.py
In [ ]:
if use_sqnorm:#for transmision SAXS
norm = get_pixelist_interp_iq( qp_saxs, iq_saxs, roi_mask, center)
print('Using circular average in the normalization of G2 for SAXS scattering.')
elif use_SG:#for Gi-SAXS or WAXS
avg_imgf = sgolay2d( avg_img, window_size= 11, order= 5) * mask
norm=np.ravel(avg_imgf)[pixelist]
print('Using smoothed image by SavitzkyGolay filter in the normalization of G2.')
else:
norm= None
print('Using simple (average) normalization of G2.')
if use_imgsum_norm:
imgsum_ = imgsum
else:
imgsum_ = None
import time
In [ ]:
#show_img( FD.rdframe(10), label_array=roi_mask, aspect=1, center=center )
In [ ]:
if run_one_time:
t0 = time.time()
g2, lag_steps = cal_g2p( FD, roi_mask, bad_frame_list,good_start, num_buf = 8, num_lev= None,
imgsum= imgsum_, norm=norm )
run_time(t0)
In [ ]:
lag_steps = lag_steps[:g2.shape[0]]
In [ ]:
if run_one_time:
taus = lag_steps * timeperframe
try:
g2_pds = save_g2_general( g2, taus=taus,qr= np.array( list( qval_dict.values() ) )[:,0],
qz = np.array( list( qval_dict.values() ) )[:,1],
uid=uid_+'_g2.csv', path= data_dir, return_res=True )
except:
g2_pds = save_g2_general( g2, taus=taus,qr= np.array( list( qval_dict.values() ) )[:,0],
uid=uid_+'_g2.csv', path= data_dir, return_res=True )
In [ ]:
#g2.shape
In [ ]:
if run_one_time:
g2_fit_result, taus_fit, g2_fit = get_g2_fit_general( g2, taus,
function = fit_g2_func, vlim=[0.95, 1.05], fit_range= None,
fit_variables={'baseline':True, 'beta': True, 'alpha':True,'relaxation_rate':True,},
guess_values={'baseline':1.0,'beta': 0.1,'alpha':1.0,'relaxation_rate':0.0100,},
guess_limits = dict( baseline =[1, 1.8], alpha=[0, 2],
beta = [0, 1], relaxation_rate= [0.00001, 5000]) ,)
g2_fit_paras = save_g2_fit_para_tocsv(g2_fit_result, filename= uid_ +'_g2_fit_paras.csv', path=data_dir )
In [ ]:
print(scat_geometry_)
In [ ]:
fit_g2_func
In [ ]:
if run_one_time:
plot_g2_general( g2_dict={1:g2, 2:g2_fit}, taus_dict={1:taus, 2:taus_fit}, vlim=[0.95, 1.05],
qval_dict = qval_dict, fit_res= g2_fit_result, geometry= scat_geometry_,filename= uid_+'_g2',
path= data_dir, function= fit_g2_func, ylabel='g2', append_name= '_fit')
In [ ]:
if run_one_time:
if False:
fs, fe = 0, 9
fs,fe=0, 12
qval_dict_ = {k:qval_dict[k] for k in list(qval_dict.keys())[fs:fe] }
D0, qrate_fit_res = get_q_rate_fit_general( qval_dict_, g2_fit_paras['relaxation_rate'][fs:fe],
geometry= scat_geometry_ )
plot_q_rate_fit_general( qval_dict_, g2_fit_paras['relaxation_rate'][fs:fe], qrate_fit_res,
geometry= scat_geometry_,uid=uid_ , path= data_dir )
else:
D0, qrate_fit_res = get_q_rate_fit_general( qval_dict, g2_fit_paras['relaxation_rate'],
fit_range=[0, 26], geometry= scat_geometry_ )
plot_q_rate_fit_general( qval_dict, g2_fit_paras['relaxation_rate'], qrate_fit_res,
geometry= scat_geometry_,uid=uid_ ,
show_fit=False, path= data_dir, plot_all_range=False)
In [ ]:
#plot1D( x= qr, y=g2_fit_paras['beta'], ls='-', m = 'o', c='b', ylabel=r'$\beta$', xlabel=r'$Q( \AA^{-1} ) $' )
In [ ]:
define_good_series = False
#define_good_series = True
if define_good_series:
good_start = 5
FD = Multifile(filename, beg = good_start, end = 1000)
uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end)
print( uid_ )
In [ ]:
#%run chxanalys_link/chxanalys/chx_generic_functions.py
In [ ]:
data_pixel = None
if run_two_time:
data_pixel = Get_Pixel_Arrayc( FD, pixelist, norm= norm ).get_data()
In [ ]:
import time
t0=time.time()
g12b=None
if run_two_time:
g12b = auto_two_Arrayc( data_pixel, roi_mask, index = None )
if run_dose:
np.save( data_dir + 'uid=%s_g12b'%uid, g12b)
run_time( t0 )
In [ ]:
#%run chxanalys_link/chxanalys/Two_Time_Correlation_Function.py
In [ ]:
if run_two_time:
show_C12(g12b, q_ind=3, qlabel=qval_dict,N1= FD.beg,logs=False, N2=min( FD.end,10000), vmin= 1.01, vmax=1.12,
timeperframe=timeperframe,save=True, path= data_dir, uid = uid_ ,cmap=cmap_albula)
In [ ]:
multi_tau_steps = True
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()
max_taus = Nimg
t0=time.time()
#tausb = np.arange( g2b.shape[0])[:max_taus] *timeperframe
if multi_tau_steps:
lag_steps_ = lag_steps[ lag_steps <= g12b.shape[0] ]
g2b = get_one_time_from_two_time(g12b)[lag_steps_]
tausb = lag_steps_ *timeperframe
else:
tausb = (np.arange( g12b.shape[0]) *timeperframe)[:-200]
g2b = (get_one_time_from_two_time(g12b))[:-200]
run_time(t0)
g2b_pds = save_g2_general( g2b, taus=tausb, qr= np.array( list( qval_dict.values() ) )[:,0],
qz=None, uid=uid_ +'_g2b.csv', path= data_dir, return_res=True )
In [ ]:
if run_two_time:
g2b_fit_result, tausb_fit, g2b_fit = get_g2_fit_general( g2b, tausb,
function = fit_g2_func, vlim=[0.95, 1.05], fit_range= None,
fit_variables={'baseline':False, 'beta': True, 'alpha':False,'relaxation_rate':True},
guess_values={'baseline':1.0,'beta': 0.15,'alpha':1.0,'relaxation_rate':1,},
guess_limits = dict( baseline =[1, 1.8], alpha=[0, 2],
beta = [0, 1], relaxation_rate= [0.000001, 5000]) )
g2b_fit_paras = save_g2_fit_para_tocsv(g2b_fit_result, filename= uid_ +'_g2b_fit_paras.csv', path=data_dir )
In [ ]:
#plot1D( x = tausb[1:], y =g2b[1:,0], ylim=[0.95, 1.46], xlim = [0.0001, 10], m='', c='r', ls = '-',
# logx=True, title='one_time_corelation', xlabel = r"$\tau $ $(s)$", )
In [ ]:
if run_two_time:
plot_g2_general( g2_dict={1:g2b, 2:g2b_fit}, taus_dict={1:tausb, 2:tausb_fit}, vlim=[0.95, 1.05],
qval_dict=qval_dict, fit_res= g2b_fit_result, geometry=scat_geometry_,filename=uid_+'_g2',
path= data_dir, function= fit_g2_func, ylabel='g2', append_name= '_b_fit')
In [ ]:
if run_two_time:
if False:
fs, fe = 0,9
fs, fe = 0,12
qval_dict_ = {k:qval_dict[k] for k in list(qval_dict.keys())[fs:fe] }
D0b, qrate_fit_resb = get_q_rate_fit_general( qval_dict_, g2b_fit_paras['relaxation_rate'][fs:fe], geometry= scat_geometry_ )
plot_q_rate_fit_general( qval_dict_, g2b_fit_paras['relaxation_rate'][fs:fe], qrate_fit_resb,
geometry= scat_geometry_,uid=uid_ +'_two_time' , path= data_dir )
else:
D0b, qrate_fit_resb = get_q_rate_fit_general( qval_dict, g2b_fit_paras['relaxation_rate'],
fit_range=[0, 10], geometry= scat_geometry_ )
plot_q_rate_fit_general( qval_dict, g2b_fit_paras['relaxation_rate'], qrate_fit_resb,
geometry= scat_geometry_,uid=uid_ +'_two_time', show_fit=False,path= data_dir, plot_all_range= True )
In [ ]:
if run_two_time and run_one_time:
plot_g2_general( g2_dict={1:g2, 2:g2b}, taus_dict={1:taus, 2:tausb},vlim=[0.99, 1.007],
qval_dict=qval_dict, g2_labels=['from_one_time', 'from_two_time'],
geometry=scat_geometry_,filename=uid_+'_g2_two_g2', path= data_dir, ylabel='g2', )
In [ ]:
if run_dose:
get_two_time_mulit_uids( [uid], roi_mask, norm= norm, bin_frame_number=1,
path= data_dir0, force_generate=False )
In [ ]:
try:
print( md['transmission'] )
except:
md['transmission'] =1
In [ ]:
exposuretime
In [ ]:
if run_dose:
N = len(imgs)
print(N)
#exposure_dose = md['transmission'] * exposuretime* np.int_([ N/32, N/16, N/8, N/4 ,N/2, 3*N/4, N*0.99 ])
exposure_dose = md['transmission'] * exposuretime* np.int_([ N/8, N/4 ,N/2, 3*N/4, N*0.99 ])
print( exposure_dose )
In [ ]:
if run_dose:
taus_uids, g2_uids = get_series_one_time_mulit_uids( [ uid ], qval_dict, good_start=good_start,
path= data_dir0, exposure_dose = exposure_dose, num_bufs =8, save_g2= False,
dead_time = 0, trans = [ md['transmission'] ] )
In [ ]:
In [ ]:
if run_dose:
plot_dose_g2( taus_uids, g2_uids, ylim=[0.98, 1.2], vshift= 0.00,
qval_dict = qval_dict, fit_res= None, geometry= scat_geometry_,
filename= '%s_dose_analysis'%uid_,
path= data_dir, function= None, ylabel='g2_Dose', g2_labels= None, append_name= '' )
In [ ]:
In [ ]:
if run_dose:
qth_interest = 2
plot_dose_g2( taus_uids, g2_uids, qth_interest= qth_interest, ylim=[0.98, 1.25], vshift= 0.00,
qval_dict = qval_dict, fit_res= None, geometry= scat_geometry_,
filename= '%s_dose_analysis'%uidstr,
path= data_dir, function= None, ylabel='g2_Dose', g2_labels= None, append_name= '' )
In [ ]:
if run_four_time:
t0=time.time()
g4 = get_four_time_from_two_time(g12b, g2=g2b)[:max_taus]
run_time(t0)
In [ ]:
if run_four_time:
taus4 = np.arange( g4.shape[0])*timeperframe
g4_pds = save_g2_general( g4, taus=taus4, qr=np.array( list( qval_dict.values() ) )[:,0],
qz=None, uid=uid_ +'_g4.csv', path= data_dir, return_res=True )
In [ ]:
if run_four_time:
plot_g2_general( g2_dict={1:g4}, taus_dict={1:taus4},vlim=[0.95, 1.05], qval_dict=qval_dict, fit_res= None,
geometry=scat_geometry_,filename=uid_+'_g4',path= data_dir, ylabel='g4')
In [ ]:
#run_xsvs =True
In [ ]:
if run_xsvs:
max_cts = get_max_countc(FD, roi_mask )
#max_cts = 15 #for eiger 500 K
qind, pixelist = roi.extract_label_indices( roi_mask )
noqs = len( np.unique(qind) )
nopr = np.bincount(qind, minlength=(noqs+1))[1:]
#time_steps = np.array( utils.geometric_series(2, len(imgs) ) )
time_steps = [0,1] #only run the first two levels
num_times = len(time_steps)
times_xsvs = exposuretime + (2**( np.arange( len(time_steps) ) ) -1 ) * timeperframe
print( 'The max counts are: %s'%max_cts )
In [ ]:
if run_xsvs:
if roi_avg is None:
times_roi, mean_int_sets = cal_each_ring_mean_intensityc(FD, roi_mask, timeperframe = None, )
roi_avg = np.average( mean_int_sets, axis=0)
t0=time.time()
spec_bins, spec_his, spec_std, spec_sum = xsvsp( FD, np.int_(roi_mask), norm=None,
max_cts=int(max_cts+2), bad_images=bad_frame_list, only_two_levels=True )
spec_kmean = np.array( [roi_avg * 2**j for j in range( spec_his.shape[0] )] )
run_time(t0)
spec_pds = save_bin_his_std( spec_bins, spec_his, spec_std, filename=uid_+'_spec_res.csv', path=data_dir )
In [ ]:
if run_xsvs:
ML_val, KL_val,K_ = get_xsvs_fit( spec_his, spec_sum, spec_kmean,
spec_std, max_bins=2, fit_range=[1,60], varyK= False )
#print( 'The observed average photon counts are: %s'%np.round(K_mean,4))
#print( 'The fitted average photon counts are: %s'%np.round(K_,4))
print( 'The difference sum of average photon counts between fit and data are: %s'%np.round(
abs(np.sum( spec_kmean[0,:] - K_ )),4))
print( '#'*30)
qth= 0
print( 'The fitted M for Qth= %s are: %s'%(qth, ML_val[qth]) )
print( K_[qth])
print( '#'*30)
In [ ]:
In [ ]:
if run_xsvs:
qr = [qval_dict[k][0] for k in list(qval_dict.keys()) ]
plot_xsvs_fit( spec_his, ML_val, KL_val, K_mean = spec_kmean, spec_std=spec_std,
xlim = [0,10], vlim =[.9, 1.1],
uid=uid_, qth= qth_interest, logy= True, times= times_xsvs, q_ring_center=qr, path=data_dir)
plot_xsvs_fit( spec_his, ML_val, KL_val, K_mean = spec_kmean, spec_std = spec_std,
xlim = [0,15], vlim =[.9, 1.1],
uid=uid_, qth= None, logy= True, times= times_xsvs, q_ring_center=qr, path=data_dir )
In [ ]:
if run_xsvs:
contrast_factorL = get_contrast( ML_val)
spec_km_pds = save_KM( spec_kmean, KL_val, ML_val, qs=qr, level_time=times_xsvs, uid=uid_, path = data_dir )
#spec_km_pds
In [ ]:
if run_xsvs:
plot_g2_contrast( contrast_factorL, g2b, times_xsvs, tausb, qr,
vlim=[0.8,1.2], qth = qth_interest, uid=uid_,path = data_dir, legend_size=14)
plot_g2_contrast( contrast_factorL, g2b, times_xsvs, tausb, qr,
vlim=[0.8,1.2], qth = None, uid=uid_,path = data_dir, legend_size=4)
In [ ]:
#from chxanalys.chx_libs import cmap_vge, cmap_albula, Javascript
In [ ]:
In [ ]:
md['mask_file']= mask_path + mask_name
md['roi_mask_file']= fp
md['mask'] = mask
md['NOTEBOOK_FULL_PATH'] = data_dir + get_current_pipeline_fullpath(NFP).split('/')[-1]
md['good_start'] = good_start
md['bad_frame_list'] = bad_frame_list
md['avg_img'] = avg_img
md['roi_mask'] = roi_mask
md['setup_pargs'] = setup_pargs
if scat_geometry == 'gi_saxs':
md['Qr'] = Qr
md['Qz'] = Qz
md['qval_dict'] = qval_dict
md['beam_center_x'] = inc_x0
md['beam_center_y']= inc_y0
md['beam_refl_center_x'] = refl_x0
md['beam_refl_center_y'] = refl_y0
elif scat_geometry == 'gi_waxs':
md['beam_center_x'] = center[1]
md['beam_center_y']= center[0]
else:
md['qr']= qr
#md['qr_edge'] = qr_edge
md['qval_dict'] = qval_dict
md['beam_center_x'] = center[1]
md['beam_center_y']= center[0]
md['beg'] = FD.beg
md['end'] = FD.end
md['qth_interest'] = qth_interest
md['metadata_file'] = data_dir + 'uid=%s_md.pkl'%uid
psave_obj( md, data_dir + 'uid=%s_md.pkl'%uid ) #save the setup parameters
save_dict_csv( md, data_dir + 'uid=%s_md.csv'%uid, 'w')
Exdt = {}
if scat_geometry == 'gi_saxs':
for k,v in zip( ['md', 'roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list', 'qr_1d_pds'],
[md, roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list, qr_1d_pds] ):
Exdt[ k ] = v
elif scat_geometry == 'saxs':
for k,v in zip( ['md', 'q_saxs', 'iq_saxs','iqst','qt','roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list'],
[md, q_saxs, iq_saxs, iqst, qt,roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list] ):
Exdt[ k ] = v
elif scat_geometry == 'gi_waxs':
for k,v in zip( ['md', 'roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list'],
[md, roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list] ):
Exdt[ k ] = v
if run_waterfall:Exdt['wat'] = wat
if run_t_ROI_Inten:Exdt['times_roi'] = times_roi;Exdt['mean_int_sets']=mean_int_sets
if run_one_time:
if run_invariant_analysis:
for k,v in zip( ['taus','g2','g2_fit_paras', 'time_stamp','invariant'], [taus,g2,g2_fit_paras,time_stamp,invariant] ):Exdt[ k ] = v
else:
for k,v in zip( ['taus','g2','g2_fit_paras' ], [taus,g2,g2_fit_paras ] ):Exdt[ k ] = v
if run_two_time:
for k,v in zip( ['tausb','g2b','g2b_fit_paras', 'g12b'], [tausb,g2b,g2b_fit_paras,g12b] ):Exdt[ k ] = v
#for k,v in zip( ['tausb','g2b','g2b_fit_paras', ], [tausb,g2b,g2b_fit_paras] ):Exdt[ k ] = v
if run_dose:
for k,v in zip( [ 'taus_uids', 'g2_uids' ], [taus_uids, g2_uids] ):Exdt[ k ] = v
if run_four_time:
for k,v in zip( ['taus4','g4'], [taus4,g4] ):Exdt[ k ] = v
if run_xsvs:
for k,v in zip( ['spec_kmean','spec_pds','times_xsvs','spec_km_pds','contrast_factorL'],
[ spec_kmean,spec_pds,times_xsvs,spec_km_pds,contrast_factorL] ):Exdt[ k ] = v
In [ ]:
#%run chxanalys_link/chxanalys/Create_Report.py
In [ ]:
export_xpcs_results_to_h5( 'uid=%s_Isotropic_Res.h5'%md['uid'], data_dir, export_dict = Exdt )
#extract_dict = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%md['uid'], import_dir = data_dir )
In [ ]:
#extract_dict = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%md['uid'], import_dir = data_dir )
In [ ]:
uid
In [ ]:
pdf_out_dir = os.path.join('/XF11ID/analysis/', CYCLE, username, 'Results/')
pdf_filename = "XPCS_Analysis_Report2_for_uid=%s%s.pdf"%(uid,pdf_version)
if run_xsvs:
pdf_filename = "XPCS_XSVS_Analysis_Report_for_uid=%s%s.pdf"%(uid,pdf_version)
In [ ]:
%run /home/yuzhang/chxanalys_link/chxanalys/Create_Report.py
In [ ]:
#md['detector_distance'] = 4.8884902
In [ ]:
make_pdf_report( data_dir, uid, pdf_out_dir, pdf_filename, username,
run_fit_form,run_one_time, run_two_time, run_four_time, run_xsvs, run_dose,
report_type= scat_geometry, report_invariant= run_invariant_analysis,
md = md )
In [ ]:
#%run /home/yuzhang/chxanalys_link/chxanalys/chx_olog.py
In [ ]:
if att_pdf_report:
os.environ['HTTPS_PROXY'] = 'https://proxy:8888'
os.environ['no_proxy'] = 'cs.nsls2.local,localhost,127.0.0.1'
update_olog_uid_with_file( uid[:6], text='Add XPCS Analysis PDF Report',
filename=pdf_out_dir + pdf_filename, append_name='_r1' )
In [ ]:
uid
In [ ]:
save_current_pipeline( NFP, data_dir)
In [ ]:
get_current_pipeline_fullpath(NFP)
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]: