In [2]:
from chxanalys.chx_packages import *
%matplotlib notebook
plt.rcParams.update({'figure.max_open_warning': 0})
from chxanalys.chx_libs import markers, colors, cmap_vge, cmap_albula
import pandas as pds
#%reset -f #for clean up things in the memory
In [3]:
data_dir = '/XF11ID/analysis/2017_1/yuzhang/Results/Protein_sample_MIT/'
data_dir_tif = '/XF11ID/analysis/2017_1/yuzhang/Results/Protein_sample_MIT/epxort_tif/'
In [4]:
uid = 'uid=Protein_sample_MIT'
In [5]:
extract_dict = extract_xpcs_results_from_h5( filename = uid + '_Res.h5', import_dir = data_dir )
In [6]:
extract_dict.keys()
Out[6]:
In [9]:
g2 = extract_dict['g2']
taus = extract_dict['taus']
qval_dict = extract_dict['qval_dict']
In [10]:
fit_g2_func = 'stretched'
scat_geometry = 'saxs'
In [11]:
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':0.08,'alpha':1.0,'relaxation_rate':0.01,},
guess_limits = dict( baseline =[0.5, 2.5], alpha=[0, 2],
beta = [0, 1], relaxation_rate= [0.001, 100])
)
g2_fit_paras = save_g2_fit_para_tocsv(g2_fit_result, filename= uid +'_g2_fit_paras.csv', path=data_dir )
In [12]:
g2_fit_paras
Out[12]:
In [210]:
#g2_fit_paras
In [13]:
from chxanalys.chx_libs import markers, colors
In [14]:
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 [15]:
list( range( g2.shape[1] - 6 ))
Out[15]:
In [16]:
i=0
fig,ax=plt.subplots( figsize=(8, 8) )
for qth in list( range( g2.shape[1] - 6 )) + [ 7 ] :
#for qth in list( range( g2.shape[1] - 0 )) :
i +=1
x = taus[1:]
delta = g2[1:,qth] - g2_fit_paras['baseline'][qth]
g2i = np.sqrt( np.abs( delta ) / g2_fit_paras['beta'][qth] ) * np.sign( delta )
g1_fit = np.exp( - (g2_fit_paras['relaxation_rate'][qth] * x )** g2_fit_paras['alpha'][qth] )
plot1D( x = x, y= g1_fit, marker = '', color= colors[i], ls = '-', lw= 2 ,
ax=ax, logx=True, legend= None, )
plot1D( x = x, y= g2i, marker = markers[qth], color= colors[i], ls = '',
ax=ax, logx=True,
legend= r'$Q_r= $'+'%.3f '%( qval_dict[qth][0] ) + r'$\AA^{-1}$', legend_size = 14,
ylim=[-0.1, 1.1],
xlabel=r"$t $ $(s)$",
ylabel = r"$S(q,t)$" )
ax.set_title('')
ax.yaxis.label.set_size(26)
ax.xaxis.label.set_size(26)
plt.tick_params(axis='both', labelsize=20)
print(i)
#ax.set_title ('normalized' +'one_time_correlation-->q=%s')
plt.savefig( data_dir_tif + 'G1_plot.tif', dpi= 300)
#fig.tight_layout()
In [17]:
data_dir
Out[17]:
In [ ]:
In [18]:
#qvc = qval_dict.copy
qval_dict_ = {}
g2_fit_paras_ = []
for k in list( range( g2.shape[1] - 6 )) + [ 7 ]:
qval_dict_[k] = qval_dict[k]
g2_fit_paras_.append( g2_fit_paras['relaxation_rate'][k] )
In [ ]:
In [19]:
D0, qrate_fit_res = get_q_rate_fit_general( qval_dict_,
g2_fit_paras_,
geometry= scat_geometry )
plot_q_rate_fit_general( qval_dict_, g2_fit_paras_, qrate_fit_res,
geometry= scat_geometry,uid=uid, path= data_dir )
In [ ]:
In [20]:
uid = '77f73345'
In [21]:
md = get_meta_data( uid )
imgs = load_data( uid, md['detector'], reverse= True )
In [22]:
mask_path = '/XF11ID/analysis/2016_3/masks/'
mask_name = 'Nov3_4M_mask.npy'
mask = load_mask(mask_path, mask_name, plot_ = False, image_name = '_mask', reverse=True )
mask[:,2069] =0 # False #Concluded from the previous results
mask_load=mask.copy()
imgsa = apply_mask( imgs, mask )
In [23]:
filename = '/XF11ID/analysis/Compressed_Data' +'/uid_%s.cmp'%md['uid']
mask, avg_img, imgsum, bad_frame_list = compress_eigerdata(imgs, mask, md, filename,
force_compress= False, para_compress= True, bad_pixel_threshold= 1e14,
bins=1, num_sub= 100, num_max_para_process= 500, with_pickle=True )
In [24]:
#show_img( imgsa[100], vmin= 0.00001, vmax= .1, logs=True, cmap = cmap_albula,
# image_name= '', save=True, path=data_dir, show_ticks = False, aspect=1.0, )
In [25]:
#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/chx_generic_functions.py
In [33]:
unit = 1 #1.333*10**(-3)
ax = plt.subplots( )
show_img( avg_img/unit, vmin= 0.001/unit, vmax= 2/unit, logs=True, cmap = cmap_albula,
image_name= '', save=False, path=data_dir, show_ticks = True, ax=ax,
xlabel=r'$pixel$ $x$',ylabel=r'$pixel$ $y$', save_format='tif', dpi=300, file_name ='img_avg_plot0',
)
fig, ax1 = ax
ax1.yaxis.label.set_size(26)
ax1.xaxis.label.set_size(26)
plt.tick_params(axis='both', labelsize= 14)
plt.savefig( data_dir_tif + 'img_avg_plot0.tif', dpi= 300)
#fig.tight_layout()
In [27]:
#ax=plt.subplots( figsize=(8, 6) )
#show_img( avg_img, vmin= 0.001, vmax= 2, logs=True, cmap = cmap_albula, aspect=1.0,
# image_name= '', save=True, path=data_dir, show_ticks = False, ax = ax, )
In [28]:
data_dir
Out[28]:
In [31]:
ax = plt.subplots( )
show_img( avg_img/unit, vmin= 0.001/unit, vmax= 2/unit, logs=True, cmap = cmap_albula,
image_name= '', save=False, path=data_dir_tif, show_ticks = True, ax=ax,
xlabel=r'$pixel$ $x$',ylabel=r'$pixel$ $y$', save_format='tif', dpi=300, file_name ='img_avg_plot1',
)
fig, ax1 = ax
ax1.yaxis.label.set_size(26)
ax1.xaxis.label.set_size(26)
plt.tick_params(axis='both', labelsize= 14)
plt.savefig( data_dir_tif + 'img_avg_plot1.tif', dpi= 300)
#fig.tight_layout()
In [ ]:
In [34]:
data_dir
Out[34]:
In [35]:
q_saxs = extract_dict['q_saxs']
iq_saxs = extract_dict['iq_saxs']
In [36]:
fig, ax=plt.subplots( figsize=(8,6) )
ax.semilogy(q_saxs, iq_saxs , '-o')
ax.set_xlabel(r'$q $ ('r'$\AA^{-1}$)', fontsize= 34 )
ax.set_ylabel(r'$I(q)$', fontsize= 34 )
plt.xticks( fontsize = 26 )
plt.yticks( fontsize = 26 )
ax.set_xlim( 0.001, 0.1)
ax.set_ylim( 0.0005, 1)
fig.tight_layout()
#title = ax1.set_title('%s_Circular Average'%uid)
plt.savefig( data_dir_tif + 'Iq_plot.tif', dpi= 300)
In [ ]:
In [ ]:
In [ ]: