XPCS Pipeline

"This notebook corresponds to version {{ version }} of the pipeline tool: https://github.com/NSLS-II/pipelines"

Version--Dec 20,2015

NSLS2 data retrieval imports



In [1]:

    
from databroker import DataBroker as db, get_images, get_table, get_events
from filestore.api import register_handler, deregister_handler
from filestore.retrieve import _h_registry, _HANDLER_CACHE



In [2]:

    
#hdr = db[{{ uid }}]



In [3]:

    
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import time
from ipywidgets import interact



In [4]:

    
cd /XF11ID/analysis/Analysis_Pipelines/Develop/









    



/XF11ID/analysis/Analysis_Pipelines/Develop



In [6]:

    
%run develop.py
%run Time_Correlation_Functions.py
%run speckle.py



In [7]:

    
%matplotlib notebook
#%matplotlib inline

Users put

uid for Bluesky Scan
filename for acquiring data directly by EigerSofteare



In [8]:

    
BlueScan = True
DirectAcq = False
detector = 'eiger_4M_cam_img_image_lightfield'  #for 4M

Users put uid here



In [9]:

    
if BlueScan:
    uid = '54614d43'
    #uid = '95782687'
    uid = '95782687'
    uid= 'ff9f20c0'
    uid='71720966'
    uid='1663d34a'
    uid = 'f505e052-3baa-47d4-bdc4-61c2eb1bcc7a'  #sid= 551, 1%PEG, 
    uid='ee6975a1-9161'   #1% wt PEG
    
else:
    uid = '/XF11ID/data/2015/11/23/d01ab510-3cf3-4719-bee3_795_master.h5'

Get data



In [12]:

    
if BlueScan:
    hdr = db[uid]
    ev, = get_events(  hdr, [detector] )
    imgs = ev['data'][detector]
else:    
    imgs =  Images(uid)
print (imgs)
Nimg=len(imgs)









    



hdf5 path = /XF11ID/data/2015/11/23/5f7b93e2-71ef-41b0-9c2c_138_master.h5
<Frames>
Length: 10000 frames
Frame Shape: 2167 x 2070
Pixel Datatype: uint16

Get data path



In [13]:

    
if BlueScan:
    from datetime import datetime
    dt = datetime.fromtimestamp(hdr['start'].time)
    path ='/XF11ID/analysis' + '/%s/%s/%s/' % (dt.year, dt.month, dt.day)
else:
    path ='/XF11ID/analysis/2015/11/23/' 
path









    Out[13]:





'/XF11ID/analysis/2015/11/23/'

Note: experiment information

The physical size of the pixels
Wavelegth of the X-rays - (units in Angstroms)
Detector to sample distance
Exposure time - (units in seconds)
acqusition period - (units in seconds)
dead time - (units in seconds)
time per frame = (exposure time + dead_time or acqusition period) - (units in seconds)



In [14]:

    
imgs.md









    Out[14]:





{'beam_center_x': 852.0,
 'beam_center_y': 1830.0,
 'count_time': 0.0049899998,
 'detector_distance': 4.8400002,
 'frame_time': 0.0049999999,
 'framerate': 200.00000447034847,
 'incident_wavelength': 1.3776,
 'pixel_mask': array([[1, 1, 1, ..., 1, 1, 0],
        [1, 1, 1, ..., 1, 1, 1],
        [1, 1, 1, ..., 1, 1, 1],
        ..., 
        [1, 1, 1, ..., 1, 1, 1],
        [1, 1, 1, ..., 1, 1, 1],
        [1, 1, 1, ..., 1, 1, 1]], dtype=uint32),
 'x_pixel_size': 7.5000004e-05,
 'y_pixel_size': 7.5000004e-05}



In [15]:

    
# The physical size of the pixels
dpix = imgs.md['x_pixel_size'] * 1000.  
lambda_ = imgs.md['incident_wavelength']    # wavelegth of the X-rays in Angstroms
Ldet = 4812.        # detector to sample distance (mm)

exposuretime= imgs.md['count_time']
acquisition_period = imgs.md['frame_time']

# deadtime= 0   # 60e-6 
# timeperframe = exposuretime + deadtime
timeperframe = acquisition_period  

timeperframe, exposuretime









    Out[15]:





(0.0049999999, 0.0049899998)

load a mask

load the mask saved in the mask_pipeline



In [16]:

    
mask = np.load( path +  str(uid)+ "_mask.npy")

Reverse the mask in y-direction due to the coordination difference between python and Eiger software



In [17]:

    
maskr = mask[::-1,:]

Plot the mask



In [18]:

    
fig, ax = plt.subplots()
im=ax.imshow(maskr, origin='lower' ,vmin=0,vmax=1,cmap='viridis')
fig.colorbar(im)
plt.show()

Interactive way to browse through images.

Note : Provide the number of images that you want to browse



In [19]:

    
def view_image(i):    
    fig, ax = plt.subplots()
    ax.imshow(imgs[i]*mask, interpolation='nearest', cmap='viridis',
                  origin='lower', norm= LogNorm(vmin=0.001, vmax=1e1) )
    ax.set_title("Browse the Image Stack")
    plt.show()



In [20]:

    
#interact(view_image, i=(0, Nimg-1))

Movie



In [21]:

    
def view_image(sleeps=1, ims=0, ime = 1):    
    fig, ax = plt.subplots()  
    for i in range( ims, ime  ):
        im=ax.imshow(imgs[i]*mask,  interpolation='nearest', cmap='viridis',
                  origin='lower', norm= LogNorm( vmin=0.01, vmax=10 ) )
        ax.set_title("images_%s"%i)
        
        time.sleep( sleeps )
        plt.draw()
    #fig.colorbar(im)
        
#view_image(.2, 0, 2)

hey, let's see if any images are bad!

load the image intensity (kymograph) saved in the mask_pipeline



In [22]:

    
kymo_sum = np.load( path +  str(uid)+"_kymo_sum.npy" )



In [23]:

    
bad_frames = np.where( kymo_sum > 1e5)[0]
bad_frames









    Out[23]:





array([7629])



In [24]:

    
fig, axes = plt.subplots(  )
axes.plot( kymo_sum, '-go'  ) 
ax.set_ylabel('Intensity')
ax.set_xlabel('Frame')
ax.set_title('Kymograph_sum') 
plt.show()

Get the Averaged Image Data

load the average intensity saved in the mask_pipeline



In [25]:

    
avg_img = np.load( path + str(uid)+"_avg_img.npy" )
avg_imgm =  avg_img * mask

Reverse the image in y-direction due to the coordination difference between python and Eiger software



In [26]:

    
avg_imgr  = avg_img[::-1,:] 
avg_imgmr  = avg_imgm[::-1,:]

Plot the averged image with the mask



In [27]:

    
fig, ax = plt.subplots()
im = ax.imshow(avg_imgmr, cmap='viridis',origin='lower',
               norm= LogNorm(vmin=0.001, vmax=1e1))
ax.set_title("Masked Averaged Image_Reversed")
fig.colorbar(im)
plt.show()

Import all the required packages for Data Analysis

scikit-xray - data analysis tools for X-ray science
- https://github.com/scikit-xray/scikit-xray
xray-vision - plotting helper functions for X-ray science
- https://github.com/Nikea/xray-vision

Get the approximate center and see the statistic to make sure



In [28]:

    
imgs.md['beam_center_x'], imgs.md['beam_center_y']









    Out[28]:





(852.0, 1830.0)



In [29]:

    
#center = (imgs.md['beam_center_x'], imgs.md['beam_center_y'])
center = [ 2167 - 336, 849]  #for not reversed
center = [ 336, 849]  #for reversed
center = [ 2167- 1830, 846]
 
center









    Out[29]:





[337, 846]

check the center with a ring

to be done-->> a better beam center finding algorithm



In [31]:

    
fig, ax = plt.subplots()
im = ax.imshow(avg_imgr, cmap='viridis',origin='lower', norm= LogNorm(vmin=0.001, vmax=1e1))

radius = 54
circle=plt.Circle( [center[1], center[0]], radius, color='b', alpha=1.0, lw=2, edgecolor='r',fill=False)
plt.gcf().gca().add_artist(circle)


ax.set_title("Masked Averaged Image_Reversed")
fig.colorbar(im)


rwidth = 100 
x1,x2 = [center[1] - rwidth, center[1] + rwidth]
y1,y2 = [center[0] - rwidth, center[0] + rwidth]
ax.set_xlim( [x1,x2])
ax.set_ylim( [y1,y2])

plt.show()









    














    











    



/home/yuzhang/.conda/envs/user_analysis/lib/python3.4/site-packages/matplotlib/patches.py:107: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties. 
  warnings.warn("Setting the 'color' property will override"

Circular Average : compute the radial integartion from the center of the speckle pattern



In [32]:

    
bin_centers, ring_averages= circular_average(avg_imgr,  center, pixel_size=(dpix, dpix), mask= maskr)

#  convert to q (reciprocal space)
two_theta = utils.radius_to_twotheta(Ldet, bin_centers)
q_val = utils.twotheta_to_q(two_theta, lambda_)



In [33]:

    
fig,axes = plt.subplots(figsize=(8, 6))
axes.semilogy(q_val, ring_averages, '-o')
axes.set_title('Circular Average')
axes.set_ylabel('Ring Avearge')
axes.set_xlabel('Q ('r'$\AA^{-1}$ )')
axes.set_xlim(0.001, 0.02)
axes.set_ylim(0.001, 10.0)
plt.show()



In [34]:

    
fig,axes = plt.subplots(figsize=(8, 6))
axes.semilogy(bin_centers/dpix, ring_averages, '-o')
axes.set_title('Circular Average')
axes.set_ylabel('Ring Avearge')
axes.set_xlabel('Bin Centers, (pixel)')
axes.set_xlim(30,  250)
axes.set_ylim(0.001, 10.0)
plt.show()

Create label array (Q rings)



In [35]:

    
inner_radius = 58 # radius of the first ring
width = 2       # width of each ring
spacing =  (166 - 58)/9 - 2    # spacing between rings
num_rings = 6   # number of rings

#  find the edges of the required rings
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
edges









    Out[35]:





array([[  58.,   60.],
       [  70.,   72.],
       [  82.,   84.],
       [  94.,   96.],
       [ 106.,  108.],
       [ 118.,  120.]])



In [36]:

    
two_theta = utils.radius_to_twotheta(Ldet, edges*dpix)
q_ring_val = utils.twotheta_to_q(two_theta, lambda_)

q_ring_center = np.average(q_ring_val, axis=1)
q_ring_center









    Out[36]:





array([ 0.00419415,  0.0050472 ,  0.00590025,  0.00675329,  0.00760634,
        0.00845939])



In [37]:

    
rings = roi.rings(edges, center, avg_imgmr.shape)

ring_mask = rings*maskr

Extract the labeled array



In [38]:

    
ring_mask









    Out[38]:





array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ..., 
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]])



In [39]:

    
qind, pixelist = roi.extract_label_indices(   ring_mask  )
noqs = len( np.unique(qind) )
nopr = np.bincount(qind, minlength=(noqs+1))[1:]



In [40]:

    
qind









    Out[40]:





array([6, 6, 6, ..., 6, 6, 6])



In [41]:

    
nopr









    Out[41]:





array([ 128,  312,  459,  583,  848, 1068])

Number of pixels in each q ring

check center



In [42]:

    
pixel = roi.roi_pixel_values(avg_imgmr, ring_mask, [2] )
fig,ax=plt.subplots()
ax.plot( pixel[0][0] ,'bo', ls='-' )









    














    











    Out[42]:





[<matplotlib.lines.Line2D at 0x7f6f465526a0>]



In [43]:

    
fig, axes = plt.subplots( figsize=(8, 6))
#axes.semilogy(q_val, ring_averages, '-o')
axes.plot(q_val, ring_averages, '-o')
axes.set_title('Circular Average with the Q ring values')
axes.set_ylabel('Ring Avearge')
axes.set_xlabel('Bin Centers 'r'$\AA^{-1}$')
axes.set_xlim(0.00, 0.02)
axes.set_ylim(0, 6)
for i in range(num_rings):
    axes.axvline(q_ring_center[i])
plt.show()



In [47]:

    
# plot the figure
fig, axes = plt.subplots(figsize=(8,8))
axes.set_title("Labeled Array on Averaged Data")
im,im_label = show_label_array_on_image(axes, avg_imgmr, ring_mask, imshow_cmap='viridis',
                        cmap='Paired',
                         vmin=0.01, vmax=5,  origin="lower")
#rwidth = 200 
#x1,x2 = [center[1] - rwidth, center[1] + rwidth]
#y1,y2 = [center[0] - rwidth, center[0] + rwidth]
#axes.set_xlim( [x1,x2])
#axes.set_ylim( [y1,y2])

#fig.colorbar(im)
rwidth = 200 

x1,x2 = [center[1] - rwidth, center[1] + rwidth]
y1,y2 = [center[0] - rwidth, center[0] + rwidth]
axes.set_xlim( [x1,x2])
axes.set_ylim( [y1,y2])
fig.colorbar(im_label)
plt.show()

Kymograph(waterfall plot) of the max-intensity ring



In [48]:

    
imgs_ =imgs
imgsr = Reverse_Coordinate(imgs_, mask)

users put the number of ring with max intensity here



In [49]:

    
max_inten_ring =2



In [44]:

    
#plt.close('all')



In [49]:

    
#kymo = roi.kymograph(imgsr, ring_mask, num = max_inten_ring)



In [52]:

    
t0 = time.time()
data_pixel =   Get_Pixel_Array( imgsr, pixelist).get_data()
run_time(t0)









    



Total time: 3.94 min



In [53]:

    
data_pixel.shape









    Out[53]:





(10000, 3398)



In [54]:

    
pixelist.shape









    Out[54]:





(3398,)



In [50]:

    
#np.save( path + 'uid_%s_data_pixel'%uid, data_pixel )
#data_pixel = np.load( path + 'uid_%s_data_pixel.npy'%(uid) )



In [51]:

    
pixelist_qi =  np.where( qind == max_inten_ring)[0]         
data_pixel_qi = data_pixel[:,pixelist_qi]



In [52]:

    
fig, ax = plt.subplots(figsize=(8,6))
ax.set_ylabel('Pixel')
ax.set_xlabel('Frame')
ax.set_title('Kymograph')

im = ax.imshow(data_pixel_qi.T, cmap='viridis', vmax=5.0)
fig.colorbar( im   )
ax.set_aspect(30.)
plt.show()

Mean intensities for each ring



In [53]:

    
mean_inten = get_mean_intensity( data_pixel, qind)



In [54]:

    
times = np.arange(  mean_inten[1].shape[0]   )  #*timeperframe  # get the time for each frame

fig, ax = plt.subplots(figsize=(8, 6))
ax.set_title("Mean intensity of each ring")
for i in range(num_rings):
    ax.plot(times, mean_inten[i+1], '--o', label="Ring "+str(i+1))
    ax.set_xlabel("Frame")
    ax.set_ylabel("Mean Intensity")
ax.legend(loc='best') 
plt.show()

One time Correlation

Note : Enter the number of levels and number of buffers for Muliti tau one time correlation number of buffers has to be even. More details in https://github.com/scikit-xray/scikit-xray/blob/master/skbeam/core/correlation.py



In [55]:

    
nopr









    Out[55]:





array([ 128,  312,  459,  583,  848, 1068])



In [56]:

    
edges









    Out[56]:





array([[  58.,   60.],
       [  70.,   72.],
       [  82.,   84.],
       [  94.,   96.],
       [ 106.,  108.],
       [ 118.,  120.]])



In [57]:

    
num_buf =8



In [58]:

    
ring_mask = np.array( ring_mask, dtype=int)



In [59]:

    
good_start = 0
good_end = Nimg   #all the images

#good_start = 7600
#good_end = 7650    

imgs_ =imgs[good_start: good_end-1]
imgsr = Reverse_Coordinate(imgs_, mask)



In [67]:

    
g2_t1, lag_steps_t1 = autocor_one_time( num_buf,  ring_mask, imgsr, num_lev=None,
                                       bad_images=None, threshold= 65500 )









    



The lev number is 12
Doing g2 caculation of 9999 frames---
###########Total time: 2.55 min



In [68]:

    
g2, lag_steps = g2_t1, lag_steps_t1
lags = lag_steps*timeperframe



In [69]:

    
tg2 = np.hstack( [ lags.reshape( len(lags),1), g2] )
#np.save( path + 'g2_%s-%s--%s'%(uid,good_start, good_end), tg2)
np.savetxt( path + 'g2_%s-%s--%s.txt'%(uid,good_start, good_end), tg2)



In [62]:

    
#tg2 = np.loadtxt( path + 'g2_%s-%s--%s.txt'%(uid,good_start, good_end)  )
#lags=tg2[:,0]
#g2=tg2[:,1:]



In [103]:

    
#plt.close('all')

Plot the one time correlation functions



In [63]:

    
sx = int(round(np.sqrt(num_rings)) )
if num_rings%sx == 0: 
    sy = int(num_rings/sx)
else:
    sy=int(num_rings/sx+1)
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid,fontsize=20, y =1.08)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx,sy,sn+1 )
    ax.set_ylabel("g2") 
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')
    y=g2[:, sn]
    ax.semilogx(lags, y, '-o', markersize=6) 
    #ax.set_ylim([min(y)*.95, max(y[1:])*1.05 ])
    ax.set_ylim([    min(y) , max(y[1:]) ])
    ax.set_xlim([   min(lags)+0*1e-6, max(lags)])
plt.show()
fig.tight_layout()

Fit g2



In [64]:

    
from lmfit import  Model
mod = Model(corr.auto_corr_scat_factor)



In [65]:

    
rate = []

sx = int( round (np.sqrt(num_rings)) )
if num_rings%sx==0:
    sy = int(num_rings/sx)
else:
    sy = int(num_rings/sx+1)
    
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid, fontsize=20, y =1.08)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx, sy, sn+1 )
    y=g2[1:, sn]
    result1 = mod.fit(y, lags=lags[1:], beta=.1,
                      relaxation_rate =.5, baseline=1.0)
    rate.append(result1.best_values['relaxation_rate'])
    
    ax.semilogx(lags[1:], y, 'ro')
    ax.semilogx(lags[1:], result1.best_fit, '-b')
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')  
    ax.set_ylim([min(y)*.95, max(y[1:]) *1.05])
    txts = r'$\gamma$' + r'$ = %.3f$'%(rate[sn]) +  r'$ s^{-1}$'
    ax.text(x =0.015, y=.55, s=txts, fontsize=14, transform=ax.transAxes)              
 
fig.tight_layout()

Plot the relaxation rates vs (q_ring_center)**2



In [66]:

    
fig, ax=plt.subplots()
ax.plot(q_ring_center**2, rate, 'ro', ls='--')
ax.set_ylabel('Relaxation rate 'r'$\gamma$'"($s^{-1}$)")
ax.set_xlabel("$q^2$"r'($\AA^{-2}$)')
plt.show()

Fitted the Diffusion Coefficinet D0



In [67]:

    
D0 = np.polyfit(q_ring_center**2, rate, 1)
gmfit = np.poly1d(D0)
print ('The fitted diffusion coefficient D0 is:  %.2E   A^2S-1'%D0[0])









    



The fitted diffusion coefficient D0 is:  6.41E+03   A^2S-1



In [68]:

    
fig,ax = plt.subplots()
ax.plot(q_ring_center**2, rate, 'ro', ls='')
ax.plot(q_ring_center**2,  gmfit(q_ring_center**2),  ls='-')
ax.set_ylabel('Relaxation rate 'r'$\gamma$'"($s^{-1}$)")
ax.set_xlabel("$q^2$"r'($\AA^{-2}$)')
plt.show()

Two_time Correlation



In [69]:

    
good_start= 0
good_end = 10000
imgs_ =imgs[good_start: good_end]
imgsr = Reverse_Coordinate(imgs_, mask)



In [70]:

    
g12b = auto_two_Array( imgsr, ring_mask, data_pixel = data_pixel )









    



Total time: 0.34 min



In [71]:

    
g12_num = 0  #0: the firs ring
data = g12b[:,:,g12_num]
fig, ax = plt.subplots()
im=ax.imshow( data, origin='lower' , cmap='viridis', 
             norm= LogNorm( vmin= 1, vmax= 1.6 ), 
        extent=[0, data.shape[0]*timeperframe, 0, data.shape[0]*timeperframe ] )
ax.set_title('0-%s frames--Qth= %s'%(Nimg,g12_num))
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
plt.show()

find the bad frames and mask it in two-time correlation



In [79]:

    
#np.where( g12b[:,:,0] == g12b[:,:,0].min() )



In [72]:

    
g12b_mask = make_g12_mask(bad_frames, g12b[:,:,0].shape)



In [73]:

    
g12bm  =  masked_g12( g12b, bad_frames)

get one time correlation



In [74]:

    
g2b = get_one_time_from_two_time(g12bm)



In [75]:

    
sx = int(round(np.sqrt(num_rings)) )
if num_rings%sx == 0: 
    sy = int(num_rings/sx)
else:
    sy=int(num_rings/sx+1)
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid,fontsize=20, y =1.08)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx,sy,sn+1 )
    ax.set_ylabel("g2") 
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')     
    y=g2b[:, sn]
    ax.semilogx( np.arange(len(y))*timeperframe, y, '-o', markersize=6) 
    #ax.semilogx(lags, y, '-o', markersize=6) 
    #ax.set_ylim([min(y)*.95, max(y[1:])*1.05 ])
    ax.set_ylim( [1.0, max(y[1:])*1.05 ] )
    
plt.show()
fig.tight_layout()

plot g2 by multi-tau and g2 from two-time



In [76]:

    
sx = int(round(np.sqrt(num_rings)) )
if num_rings%sx == 0: 
    sy = int(num_rings/sx)
else:
    sy=int(num_rings/sx+1)
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid,fontsize=20, y =1.06)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx,sy,sn+1 )
    ax.set_ylabel("g2") 
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')     
    y=g2b[:, sn]
    ax.semilogx( np.arange(len(y))*timeperframe, y, '--r', markersize=6) 
    
    y2=g2[:, sn]
    ax.semilogx(lags, y2, 'o', markersize=6) 
    
    #ax.semilogx(lags, y, '-o', markersize=6) 
    #ax.set_ylim([min(y)*.95, max(y[1:])*1.05 ])
    ax.set_ylim( [1.0, max(y2[1:])*1.05 ] )
plt.show()
fig.tight_layout()

Another defination of two-time correlation (different normalization)



In [77]:

    
g12b_norm, g12b_not_norm, norms = auto_two_Array_g1_norm( imgsr, ring_mask, data_pixel = data_pixel )









    



Total time: 0.44 min



In [78]:

    
g12b_not_norm_m  =  masked_g12( g12b_not_norm, bad_frames )



In [79]:

    
g2b_norm =  g2b



In [80]:

    
g2b_not_norm = get_one_time_from_two_time(g12b_not_norm_m, norms=norms, nopr=nopr  )



In [81]:

    
#num_rings =6


sx = int(round(np.sqrt(num_rings)) )
if num_rings%sx == 0: 
    sy = int(num_rings/sx)
else:
    sy=int(num_rings/sx+1)
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid,fontsize=20, y =1.06)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx,sy,sn+1 )
    ax.set_ylabel("g2") 
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')    
    
    y=g2b_norm[:, sn]
    #ax.semilogx( np.arange(len(y))*timeperframe, y, '--r', markersize=3) 
    
    y3=g2b_not_norm[:, sn]
    ax.semilogx( np.arange(len(y))*timeperframe, y3, '-g', lw=4, markersize=3)     
    
    y2=g2[:, sn]
    #ax.semilogx(lags, y2, '-ko', markersize= 3)
    ax.semilogx(lags, y2, 'o', markersize=6) 
    
    #ax.semilogx(lags, y, '-o', markersize=6) 
    #ax.set_ylim([min(y)*.95, max(y[1:])*1.05 ])
    ax.set_ylim( [1.0, max(y2[1:])*1.05 ] )
plt.show()
fig.tight_layout()

get one time correlation @different age



In [89]:

    
#plt.close('all')



In [82]:

    
g2_aged = get_aged_g2_from_g12q( g12bm[:,:,0], slice_num =3, slice_width= 500, 
                slice_start=4000, slice_end= 20000-4000  )



In [83]:

    
show_g12q_aged_g2( g12bm[:,:,0], g2_aged,slice_width= 500, timeperframe=1,vmin= 1, vmax= 1.22 )









    



the cut age centers are: [4000, 10000, 16000]

get taus histgram



In [85]:

    
taus = get_tau_from_g12q( g12b_norm[:,:,0], slice_num = 5, slice_width=1, 
                  slice_start=3, slice_end= 5000-1  )



In [108]:

    
show_g12q_taus( g12b_norm[:,:,0], taus,  slice_width=50, timeperframe=1,vmin=1.01,vmax=1.55 )









    



the cut tau centers are: [3, 1252, 2501, 3750, 4999]



In [109]:

    
his = histogram_taus(taus, hisbin=30, plot=True, timeperframe=timeperframe)

four-time correlation



In [110]:

    
bad_frames









    Out[110]:





array([7629])



In [89]:

    
s1,s2 = 0,2000



In [90]:

    
g4 = get_four_time_from_two_time( g12bm, g2b, rois=[s1,s2,s1,s2] )

plot four-time correlation function



In [111]:

    
sx = int(round(np.sqrt(num_rings)) )
if num_rings%sx == 0: 
    sy = int(num_rings/sx)
else:
    sy=int(num_rings/sx+1)
#fig = plt.figure(figsize=(14, 10))
fig = plt.figure()
plt.title('uid= %s'%uid,fontsize=20, y =1.08)  
plt.axis('off')
for sn in range(num_rings):
    ax = fig.add_subplot(sx,sy,sn+1 )
    ax.set_ylabel("g4") 
    ax.set_title(" Q= " + '%.5f  '%(q_ring_center[sn]) + r'$\AA^{-1}$')     
    y=g4[:, sn]
    ax.semilogx( np.arange(len(y))*timeperframe, y, '-o', markersize=6) 
    #ax.semilogx(lags, y, '-o', markersize=6) 
    ax.set_ylim([min(y)*.95, max(y[1:])*1.05 ])
    #ax.set_ylim( [1.0, max(y[1:])*1.05 ] )
    
plt.show()
fig.tight_layout()