Structure Factor + Additional Profiling

XRD

X-ray diffraction calculations.

Much of this functionality has been merged into PyXtal (see this tutorial notebook for more information).

The VXRD web app has also been merged into PyXtal (see this page for details).

In order to run and view VXRD locally, run the following shell commands:

$ cd XRD
$ pip install -r requirements.txt
$ cd ./flask
$ flask run

It's important to rerun pip install -r requirements.txt if this repository has been updated in case there are any new dependencies.

If everything is setup correctly, you should see the following output:

 * Serving Flask app "vxrd.py"
 * ...
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Then, open your web browser and enter the following URL: http://localhost:5000/

When finished, press CTRL+C in your terminal to shutdown the web-app.

In order to see the 3D structure visualized with JSmol, you'll need to unzip jsmol.zip into the following directory:

$ unzip jsmol.zip ./flask/app/static/

	@nb.njit(nb.f8[:](nb.f8[:], nb.f8, nb.f8, nb.f8, nb.f8, nb.i8), cache = True)
	def mod_pseudo_voigt(x, fwhm, A, eta_h, eta_l, N):

	"""
	A modified split-type pseudo-Voigt function for profiling peaks
	- Izumi, F., & Ikeda, T. (2000).
	"""

	tmp = np.zeros((N))
	for xi, dx in enumerate(x):
	if dx < 0:
	A = A
	eta_l = eta_l
	eta_h = eta_h
	else:
	A = 1/A
	eta_l = eta_h
	eta_h = eta_l

	tmp[xi] = ((1+A)(eta_h + np.sqrt(np.pinp.log(2))*(1-eta_h))) /\
	(eta_l + np.sqrt(np.pinp.log(2)) (1-eta_l) + A*(eta_h +\
	np.sqrt(np.pinp.log(2))(1-eta_h))) * (eta_l2/(np.pifwhm) *\
	(1+((1+A)/A)*2 (dx/fwhm)2)(-1) + (1-eta_l)np.sqrt(np.log(2)/np.pi) \
	2/fwhm np.exp(-np.log(2) ((1+A)/A)*2 (dx/fwhm)**2))
	return tmp

	@nb.njit(nb.f8[:](nb.f8, nb.f8[:], nb.f8), cache = True)
	def gaussian(theta2, alpha, fwhm):

	"""
	Gaussian function for profiling peaks
	"""

	tmp = ((alpha - theta2)/fwhm)**2
	return np.exp(-4np.log(2)tmp)

	@nb.njit(nb.f8[:](nb.f8, nb.f8[:], nb.f8), cache = True)
	def lorentzian(theta2, alpha, fwhm):

	"""
	Lorentzian function for profiling peaks
	"""

	tmp = 1 + 4((alpha - theta2)/fwhm)*2
	return 1/tmp

	def pseudo_voigt(theta2, alpha, fwhm, eta):

	"""
	Original Pseudo-Voigt function for profiling peaks
	- Thompson, D. E. Cox & J. B. Hastings (1986).
	"""

	L = lorentzian(theta2, alpha, fwhm)
	G = gaussian(theta2, alpha, fwhm)
	return eta * L + (1 - eta) * G