In [2]:

    

# Import a bunch of things
from ase import Atoms
from pyiid.experiments.elasticscatter import ElasticScatter
import matplotlib.pyplot as plt


    

In [3]:

    

# Create the atoms, use the "setup" -> "Nanoparticle" tool in the GUI
atoms = Atoms()
atoms.edit()
print(atoms)


    

    




/home/christopher/mc/envs/sim_dev2/lib/python2.7/site-packages/gtk-2.0/gtk/__init__.py:127: RuntimeWarning: PyOS_InputHook is not available for interactive use of PyGTK
  set_interactive(1)






    




Atoms(symbols='Ni675', positions=..., magmoms=..., tags=..., cell=[21.12, 21.12, 21.12], pbc=[False, False, False])

In [4]:

    

# Now to simulate the x-ray scattering
scat = ElasticScatter()
pdf = scat.get_pdf(atoms)
r = scat.get_r()


    

In [5]:

    

# Plot the data
%matplotlib inline
plt.plot(r, pdf)    
plt.show()


    

In [6]:

    

# Note that we can change the experimental parameters
print(scat.exp)
scat.update_experiment({'qmin':2, 'sampling':'ns'})
print(scat.exp)


    

    




{'rmin': 0.0, 'qbin': 0.1, 'rstep': 0.01, 'rmax': 40.0, 'qmax': 25, 'sampling': 'full', 'qmin': 0.0}
{'rmin': 0.0, 'qbin': 0.1, 'rstep': 0.12566370614359174, 'rmax': 40.0, 'qmax': 25, 'sampling': 'ns', 'qmin': 2}

In [7]:

    

# now regenerate the pdf and r
pdf = scat.get_pdf(atoms)
r = scat.get_r()


    

In [8]:

    

# Plot the data
%matplotlib inline
plt.plot(r, pdf)    
plt.show()


    

In [10]:

    

# We can do this for the other data in the PDF creation stack
q = scat.get_scatter_vector()
fq = scat.get_fq(atoms)


    

In [11]:

    

plt.plot(q, fq)
plt.show()


    

In [12]:

    

# we can even simulate noisy data
nfq = scat.get_fq(atoms, noise=.1)
plt.plot(q, nfq)
plt.plot(q, fq)
plt.show()


    

In [21]:

    

# and noisy pdfs
npdf = scat.get_pdf(atoms, noise=1)
plt.plot(r, pdf)
plt.plot(r, npdf)
plt.show()


    

In [29]:

    

# noise can be an array
npdf = scat.get_pdf(atoms, noise=scat.get_scatter_vector(pdf=True)*.1)
fig, axs = plt.subplots(1, 2)
axs[0].plot(r, pdf, label='noise free')
axs[0].plot(r, npdf, label='noisy')
axs[0].set_title('PDF')
axs[0].legend()
axs[1].plot(scat.get_scatter_vector(pdf=True), scat.get_scatter_vector(pdf=True) * .1)
axs[1].set_title('Noise function')
plt.show()