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from chxanalys.chx_packages import *
%matplotlib notebook
plt.rcParams.update({'figure.max_open_warning': 0})
import pandas as pds
#%reset -f  #for clean up things in the memory









    



/opt/conda_envs/analysis/lib/python3.5/site-packages/filestore/retrieve.py:15: UserWarning: Do not import filestore.retrieve, import filestore.api instead
  warnings.warn("Do not import filestore.retrieve, "
/opt/conda_envs/analysis/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
  "`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)

Define Result Path here



In [2]:

    
data_dir = '/XF11ID/analysis/2017_2/sutton//Results/'



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uid='56ba9a'#(scan num: 23830) (Measurement: T3 SBA15 in cryostat xpcs -1000frames acq=0.00134s exp=0.00134s att 1 )
uid='9eebab'#(scan num: 23829) (Measurement: T3 SBA15 in cryostat xpcs -2000frames acq=0.5s exp=0.5s att .002 )
uid='c72a69'#(scan num: 23828) (Measurement: T3 SBA15 in cryostat xpcs -1000frames acq=0.00134s exp=0.00134s att 1 )
uid='4254d4'#(scan num: 23827) (Measurement: T3 SBA15 in cryostat xpcs -2000frames acq=0.5s exp=0.5s att .002 )
uid='3b1e79'#(scan num: 23826) (Measurement: T3 SBA15 in cryostat xpcs -1000frames acq=0.00134s exp=0.00134s att 1 )
uid='ce5107'#(scan num: 23825) (Measurement: T3 SBA15 in cryostat xpcs -2000frames acq=0.5s exp=0.5s att .002 )

Build a uid dictionary here



In [7]:

    
suid_dict = {'m1' : '56ba9a', 'm2': '9eebab', 'm3': 'c72a69', 
             'm4': '4254d4', 'm5': '3b1e79', 'm6': 'ce5107'
             }



In [8]:

    
uid_dict = {}
for u in list( suid_dict.keys() ):
    uid_dict[u] =  get_meta_data( suid_dict[u] )['uid']
print( uid_dict )









    



{'m1': '56ba9aeb-df22-40db-b950-4bb059de925a', 'm6': 'ce510798-e808-4c4f-8cef-435e09050c5b', 'm4': '4254d43f-0d90-4e52-a5e2-b8606d85c1d8', 'm3': 'c72a69a7-e128-4f6f-99cf-2c3d8f991c41', 'm5': '3b1e798d-6668-479e-8e50-cf2810c6d334', 'm2': '9eebab25-5a75-403f-98a2-925c95d5e1c8'}

Extract result here



In [9]:

    
total_res = {}



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for u in list( uid_dict.keys() ):
    inDir =  data_dir + suid_dict[u] + '/'
    total_res[u] = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%uid_dict[u], import_dir = inDir )



In [11]:

    
print( total_res[list( uid_dict.keys() )[0]].keys()  )









    



dict_keys(['taus', 'mask', 'qval_dict_v', 'tausb', 'imgsum', 'g12b', 'iq_saxs', 'taus_uids', 'pixel_mask', 'g2b_fit_paras', 'mean_int_sets', 'roi_mask', 'q_saxs', 'g2_uids', 'qval_dict_p', 'avg_img', 'iqst', 'g2', 'qt', 'times_roi', 'bad_frame_list', 'md', 'g2_fit_paras', 'qval_dict', 'g2b'])



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Do some analysis here



In [29]:

    
qr = np.array( [total_res['m1']['qval_dict'][k][0] for k in list(total_res['m1']['qval_dict'].keys())] )



In [30]:

    
qr









    Out[30]:





array([ 0.00368,  0.00874,  0.0138 ,  0.01885,  0.02391,  0.02897,
        0.03403,  0.03909,  0.04415,  0.047  ,  0.0538 ,  0.05932])



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qinds=[0,1,3,9]
fig,ax=plt.subplots(2,2)
ax=ax.ravel()
for k in sorted( list(total_res.keys())):
    #tt=np.load(filename)
    taus = total_res[k]['taus']
    g2  =  total_res[k]['g2']
    pos  = 0 
    for qind in qinds:
            ax[pos].semilogx(taus[1:],g2[1:,qind])
            pos += 1
fig.suptitle("T=380K")
pos=0
for qind in qinds:
    #print(qind)    
    ax[pos].set_title('qr=%s A-1'%qr[int(qind)])    
    #ax[pos].set_title('qind={:d}'.format(   qind  ) )
    pos += 1
    
fig.tight_layout()



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1) plot g4



In [341]:

    
qth_interest = 6 #3      #the intested single qth



In [342]:

    
fig,ax=plt.subplots()
for u in sorted(list( uid_dict.keys() )):
    print(u)
    plot1D( x =  total_res[u]['taus4'], y= total_res[u]['g4'][:,qth_interest],
                   ax=ax, legend= u, ylim=[0, 0.0001], legend_size = 8, xlabel='t (sec)', ylabel='g4',
           title='four_order_time_correlation-->q=%s'%total_res[u]['qval_dict'][qth_interest][0], logx=True )
    
plt.savefig( data_dir +'g4_101k44pc_190_180_175_25_new_1.png' )









    














    











    



Temp=175C_c_1
Temp=175C_c_2
Temp=175C_c_3
Temp=175C_c_4
Temp=180C_c_2
Temp=180C_c_3
Temp=180C_c_4
Temp=190C_h_3
Temp=190C_h_4
Temp=25C_1
Temp=25C_2
Temp=25C_3
Temp=25C_4

2) Load g2 and re-do fit



In [353]:

    
fit_g2_func = 'stretched'

for u in sorted(list( uid_dict.keys() )):
    g2 = total_res[u]['g2']
    taus = total_res[u]['taus']    
    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':False, 'beta':True, 'alpha':False,'relaxation_rate':True},                                  
            guess_values={'baseline':1.0,'beta':0.05,'alpha':1.0,'relaxation_rate':0.01,})     
    g2_fit_paras = save_g2_fit_para_tocsv(g2_fit_result,  filename= u  +'_g2_fit_paras.csv', path=data_dir )    
    #print( g2.shape)









    



The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=175C_c_1_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=175C_c_2_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=175C_c_3_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=175C_c_4_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=180C_c_2_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=180C_c_3_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=180C_c_4_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=190C_h_3_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=190C_h_4_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=25C_1_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=25C_2_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=25C_3_g2_fit_paras.csv
The g2 fitting parameters are saved in /XF11ID/analysis/2017_1/manisen/Results/Temp=25C_4_g2_fit_paras.csv

3) Plot g2



In [361]:

    
fig,ax=plt.subplots()
for u in sorted(list( uid_dict.keys() )):
    y= total_res[u]['g2'][1:,qth_interest]
    #ym = (y-y.min())/(y.max()-y.min())    
    
    #fits = total_res[u]['g2_fit_paras']  #here load from XPCS_Single Pipeline fit results
    fits = pds.read_csv( data_dir + u + '_g2_fit_paras.csv' )  #load from re-fit results
    
    ym = ( y -  fits['baseline'][qth_interest] )/ fits['beta'][qth_interest]    
    plot1D( x =  total_res[u]['taus'][1:], y= ym,
                   ax=ax, legend= u, ylim=[-0.1, 1.1], legend_size = 10, xlabel='tau (sec)', ylabel='g1',
           title='normalized' +'one_time_correlation-->q=%s'%total_res[u]['qval_dict'][qth_interest][0], logx=True )
plt.savefig( data_dir +'normalized'+'g__101k44pc_190_180_175_25_new_.png' )



In [362]:

    
fig,ax=plt.subplots()
for u in sorted(list( uid_dict.keys() )):
    plot1D( x =  total_res[u]['taus'], y= total_res[u]['g2'][:,qth_interest],
                   ax=ax, legend= u, ylim=[0.99, 1.08], legend_size = 8, xlabel='tau (sec)', ylabel='g2',
           title='one_time_correlation-->q=%s'%total_res[u]['qval_dict'][qth_interest][0], logx=True )
plt.savefig( data_dir +'g2_101k44pc_190_180_175_25_new.png' )

4) Plot q~relaxation time



In [333]:

    
qs = np.array([total_res[u]['qval_dict'][i][0] for i in range(   len(list(total_res[u]['qval_dict'].keys()))   ) ])



In [365]:

    
fig,ax=plt.subplots()
for u in sorted(list( uid_dict.keys() )):
    x= qs
    
    #y= 1/total_res[u]['g2_fit_paras']['relaxation_rate']  #here load from XPCS_Single Pipeline fit results
    y =  1/ ( pds.read_csv( data_dir + u + '_g2_fit_paras.csv' )['relaxation_rate'] ) #load from re-fit results
    
    #print(y)
    plot1D( x =  qs, y= y, ax=ax, ylim=[0,200], legend= u, legend_size = 8, ls = '--',
           xlabel='q, (A-1)', ylabel='relaxation_time, (s)',
           title='q~relaxation_time', logx=True, logy=True )
plt.savefig( data_dir +'q~relaxation_time.png' )



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