

In [26]:

    
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import h5py

Get elemental map from txt file



In [2]:

    
# here we focus on element Ca



In [3]:

    
fpath_ca = 'output_txt_scan2D_48816/detsum_Ca_K_48816.txt'



In [4]:

    
d_ca = np.loadtxt(fpath_ca)



In [6]:

    
# path for ion chamber
fpath_ic = 'output_txt_scan2D_48816/sclr1_ch4_48816.txt'



In [7]:

    
d_ic = np.loadtxt(fpath_ic)



In [8]:

    
# plot ic map
fig, ax = plt.subplots()
ax.imshow(d_ic)









    














    











    Out[8]:





<matplotlib.image.AxesImage at 0x111f17f28>



In [9]:

    
# normalized to ic
d_ca_norm = d_ca / d_ic



In [10]:

    
# normalize to ic and plot
fig, ax = plt.subplots()
im = ax.imshow(d_ca_norm)
fig.colorbar(im)









    














    











    Out[10]:





<matplotlib.colorbar.Colorbar at 0x10dd129e8>

Get elemental map from reference sample



In [11]:

    
# get elemental map from reference sample, also focus on Ca
fpath_ref_ca = 'output_txt_scan2D_48706/detsum_Ca_K_48706.txt'

# ion chamber value for reference
fpath_ref_ic = 'output_txt_scan2D_48706/sclr1_ch4_48706.txt'



In [12]:

    
ca_ref = np.loadtxt(fpath_ref_ca)
ic_ref = np.loadtxt(fpath_ref_ic)



In [13]:

    
# normalize to ic
ca_ref_norm = ca_ref / ic_ref



In [14]:

    
# plot of reference sample
fig, ax = plt.subplots()
im = ax.imshow(ca_ref_norm)
fig.colorbar(im)









    














    











    Out[14]:





<matplotlib.colorbar.Colorbar at 0x121f11828>



In [15]:

    
# get mean
ca_ref_mean = np.mean(ca_ref_norm)



In [16]:

    
ca_ref_mean









    Out[16]:





3.8229401154059037e-05

Calculate concentration



In [18]:

    
# normalize to reference sample to get concentration
concen_val = 16.6 # ug/cm2  for Ca
ca_concen = d_ca_norm / ca_ref_mean * concen_val



In [19]:

    
np.mean(ca_concen)









    Out[19]:





15.128551099855615



In [20]:

    
# plot of sample concentration
fig, ax = plt.subplots()
im = ax.imshow(ca_concen)
fig.colorbar(im)









    














    











    Out[20]:





<matplotlib.colorbar.Colorbar at 0x1230f7320>

We can also repeat the same process by using h5 file instead of txt file. This is probably much easier, as all the data can be saved to dictionary.

Get elemental map from h5 file



In [21]:

    
fname = 'scan2D_48816.h5'
with h5py.File(fname, 'r') as f:
    d = f['xrfmap/detsum/counts'][:]
    fit_v = f['xrfmap/detsum/xrf_fit'][:]
    fit_name = f['xrfmap/detsum/xrf_fit_name'][:]
    scaler_v = f['xrfmap/scalers/val'][:]
    scaler_name = f['xrfmap/scalers/name'][:]



In [22]:

    
fit_name = [v.decode() for v in fit_name]
scaler_name = [v.decode() for v in scaler_name]



In [23]:

    
d_fit = {}
for i, v in enumerate(fit_name):
    d_fit[str(v)] = fit_v[i, : ,:]
    
d_scaler = {}
for i, v in enumerate(scaler_name):
    d_scaler[str(v)] = scaler_v[: ,:, i]



In [24]:

    
# dictionary

d_fit.keys(), d_scaler.keys()









    Out[24]:





(dict_keys(['W_L', 'Si_K', 'Mn_K', 'Cr_K', 'Ga_K', 'Br_L', 'Co_K', 'Ni_K', 'Cu_K', 'Mg_K', 'Ce_L', 'Ca_K', 'P_K', 'Fe_K', 'compton', 'elastic', 'Pt_L', 'Ar_K', 'K_K', 'Ti_K', 'Er_L', 'snip_bkg', 'Zn_K', 'Na_K', 'r2_adjust', 'Al_K', 'S_K', 'Cl_K', 'V_K', 'Au_L']),
 dict_keys(['sclr1_ch2', 'sclr1_ch3', 'sclr1_ch4', 'sclr1_ch5', 'sclr1_ch6', 'sclr1_ch7', 'sclr1_ch8']))



In [25]:

    
# plot normalized data
fig, ax = plt.subplots()
ax.imshow(d_fit['Ca_K']/d_scaler['sclr1_ch4'])









    














    











    Out[25]:





<matplotlib.image.AxesImage at 0x1231350f0>



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