GISAXS (Grazing-incidence small angle X-ray scattering geometry)

More details skbeam.core.recip module: gisaxs function.

(https://github.com/scikit-beam/scikit-beam/blob/master/skbeam/core/recip.py)



In [1]:

    
import skbeam.core.recip as recip
import skbeam.core.utils as utils

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline

get the inputs



In [2]:

    
inc_x0 = 1871   # incident beam x component in pixels
inc_y0 = 339    # incident beam y component in pixels
refl_x0 = 1871   # refelcted beam x component in pixels
refl_y0 = 811    # refelcted beam y component in pixels    
dist_sample = 4.81  # distance to the sample in mm
wavelength = 1.3776 #  wavelength of the X-ray beam in um

dpix = 7.5000004e-02*1000  # pixel size



In [3]:

    
pixel_size = (dpix, dpix)
incident_beam = (inc_x0, inc_y0)
reflected_beam = (refl_x0, refl_y0)
detector_size = (2167, 2070)



In [4]:

    
g_output = recip.gisaxs(incident_beam, reflected_beam, pixel_size,
                        detector_size, dist_sample, wavelength,
                        theta_i=0.0)

Incident Angle



In [5]:

    
g_output.alpha_i









    Out[5]:





0.0036797674392063757

Tilt Angle



In [6]:

    
g_output.tilt_angle









    Out[6]:





0.0



In [7]:

    
x_ticks = []
y_ticks = []
for i in range(0, 2250, 250):
    x_ticks.append(round(g_output.alpha_f[0, i], 4))
    y_ticks.append(round(g_output.alpha_f[i, 0], 5))
    
fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(g_output.alpha_f, cmap="viridis", origin="lower")
ax.set_title("Exit angle map")
ax.set_xlabel('radians')
ax.set_ylabel('radians')
ax.set_xticks(np.arange(0, 2250, 250))
ax.set_xticklabels(x_ticks)
ax.set_yticks(np.arange(0, 2250, 250))
ax.set_yticklabels(y_ticks)
ax.grid()
fig.colorbar(im)
plt.show()

Qz map ( z component of scattering wave vector)



In [8]:

    
x_ticks = []
y_ticks = []
for i in range(0, 2250, 250):
    x_ticks.append(round(g_output.qz[0, i], 4))
    y_ticks.append(round(g_output.qz[i, 0], 5))
    
fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(g_output.qz, cmap="viridis", origin="lower")
ax.set_title("Qz map")
ax.set_xlabel('q 'r'$\AA^{-1}$')
ax.set_ylabel('q 'r'$\AA^{-1}$')
ax.set_xticks(np.arange(0, 2250, 250))
ax.set_xticklabels(x_ticks)
ax.set_yticks(np.arange(0, 2250, 250))
ax.set_yticklabels(y_ticks)
ax.grid()
fig.colorbar(im)
plt.show()

Qr (parrell component of the scattering wave vector)



In [9]:

    
xr_ticks = []
yr_ticks = []
for i in range(0, 2250, 250):
    xr_ticks.append(round(g_output.qr[0, i], 4))
    yr_ticks.append(round(g_output.qr[i, 0], 5))

fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(g_output.qr, cmap="viridis", origin="lower")
ax.set_title("Qr map")
ax.set_xlabel('q 'r'$\AA^{-1}$')
ax.set_ylabel('q 'r'$\AA^{-1}$')
ax.set_xticks(np.arange(0, 2250, 250))
ax.set_xticklabels(xr_ticks)
ax.set_yticks(np.arange(0, 2250, 250))
ax.set_yticklabels(yr_ticks)
ax.grid()
fig.colorbar(im)
plt.show()



In [10]:

    
import skbeam
skbeam.__version__









    Out[10]:





'0.0.7+36.gfafc487'



In [ ]: