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25 changes: 0 additions & 25 deletions doc/examples/Ni_calculation.py

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151 changes: 151 additions & 0 deletions doc/source/examples.rst
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.. _examples:

Examples
########

Welcome! This guide offers several examples to help you effectively utilize this package.

Files needed:

1. :download:`Ni-xray.gr <examples/Ni-xray.gr>` - experimental X-ray PDF data
2. :download:`Ni.stru <examples/Ni.stru>` - Ni f.c.c. structure in PDFfit format

======================================
Example 1: Calculate PDF of FCC nickel
======================================

The first example shows how to calculates the PDF for FCC nickel and saves the resulting data to a file and plot it using matplotlib.

1. Imports the PdfFit class from the diffpy.pdffit2 module::

from diffpy.pdffit2 import PdfFit

2. Create a PDF calculator object and assigned to the variable ``P``. Make sure the ``Ni.stru`` file is in the same directory as the script and you've cd to the directory, load structure file. Then allocate and configure PDF calculation and run the calculation::

# create new PDF calculator object
P = PdfFit()

# load structure file in PDFFIT or DISCUS format
P.read_struct("Ni.stru")

radiation_type = "X" # x-rays
qmax = 30.0 # Q-cutoff used in PDF calculation in 1/A
qdamp = 0.01 # instrument Q-resolution factor, responsible for PDF decay
rmin = 0.01 # minimum r-value
rmax = 30.0 # maximum r-value
npts = 3000 # number of points in the r-grid

# allocate and configure PDF calculation
P.alloc(radiation_type, qmax, qdamp, rmin, rmax, npts)
P.calc()

3. Save the refined result::

P.save_pdf(1, "Ni_calculation.cgr")

4. We can also plot it using matplotlib::

import matplotlib.pyplot as plt

# obtain list of r-points and corresponding G values
r = P.getR()
G = P.getpdf_fit()

# matplotlib.pyplot is an matplotlib interface with an MATLAB-like way of plotting.
plt.plot(r, G)
pylab.xlabel("r (Å)")
pylab.ylabel("G (Å$^{-2}$)")
pylab.title("x-ray PDF of nickel simulated at Qmax = %g" % qmax)

# display plot window, this must be the last command in the script
pylab.show()

The scripts can be downloaded :download:`here <examples/Ni_calculation.py>`.

=======================================
Example 2: Performing simple refinement
=======================================

The second example shows how to perform simple refinement of Ni structure to the experimental x-ray PDF. The example uses the same data files as the first example.

1. Imports the PdfFit class from the diffpy.pdffit2 module::

from diffpy.pdffit2 import PdfFit

2. Load experimental x-ray PDF data and nickel structure file::

# Load experimental x-ray PDF data
qmax = 30.0 # Q-cutoff used in PDF calculation in 1/A
qdamp = 0.01 # instrument Q-resolution factor, responsible for PDF decay
pf.read_data("Ni-xray.gr", "X", qmax, qdamp)
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# Load nickel structure, must be in PDFFIT or DISCUS format
pf.read_struct("Ni.stru")

3. Configure refinement and refine::

# Refine lattice parameters a, b, c.
# Make them all equal to parameter @1.
pf.constrain(pf.lat(1), "@1")
pf.constrain(pf.lat(2), "@1")
pf.constrain(pf.lat(3), "@1")
# set initial value of parameter @1
pf.setpar(1, pf.lat(1))

# Refine phase scale factor. Right side can have formulas.
pf.constrain("pscale", "@20 * 2")
pf.setpar(20, pf.getvar(pf.pscale) / 2.0)

# Refine PDF damping due to instrument Q-resolution.
# Left side can be also passed as a reference to PdfFit object
pf.constrain(pf.qdamp, "@21")
pf.setpar(21, 0.03)

# Refine sharpening factor for correlated motion of close atoms.
pf.constrain(pf.delta2, 22)
pf.setpar(22, 0.0003)

# Set all temperature factors isotropic and equal to @4
for idx in range(1, 5):
pf.constrain(pf.u11(idx), "@4")
pf.constrain(pf.u22(idx), "@4")
pf.constrain(pf.u33(idx), "@4")
pf.setpar(4, pf.u11(1))

# Refine all parameters
pf.pdfrange(1, 1.5, 19.99)
pf.refine()

4. Save the refined result::

pf.save_pdf(1, "Ni_refinement.fgr")
pf.save_struct(1, "Ni_refinement.rstr")
pf.save_res("Ni_refinement.res")

5. We can also plot it using matplotlib::

import matplotlib.pyplot as plt
import numpy

# matplotlib.pyplot is an matplotlib interface with an MATLAB-like way of plotting.
# obtain data from PdfFit calculator object
r = pf.getR()
Gobs = pf.getpdf_obs()
Gfit = pf.getpdf_fit()

# calculate difference curve
Gdiff = numpy.array(Gobs) - numpy.array(Gfit)
Gdiff_baseline = -10

plt.plot(r, Gobs, "ko")
plt.plot(r, Gfit, "b-")
plt.plot(r, Gdiff + Gdiff_baseline, "r-")

plt.xlabel("r (Å)")
plt.ylabel("G (Å$^{-2}$)")
plt.title("Fit of nickel to x-ray experimental PDF")

# display plot window, this must be the last command in the script
plt.show()

The scripts can be downloaded :download:`here <examples/Ni_refinement.py>`.
File renamed without changes.
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#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Calculate PDF of FCC nickel and plot it using matplotlib.
"""Calculate PDF of FCC nickel. Save data to Ni_calculation.cgr and plot it using matplotlib.
"""

import pylab
import matplotlib.pyplot as plt

from diffpy.pdffit2 import PdfFit

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P.alloc(radiation_type, qmax, qdamp, rmin, rmax, npts)
P.calc()

P.save_pdf(1, "Ni_calculation.cgr")

# obtain list of r-points and corresponding G values
r = P.getR()
G = P.getpdf_fit()

# pylab is matplotlib interface with MATLAB-like plotting commands
pylab.plot(r, G)
pylab.xlabel("r (Å)")
pylab.ylabel("G (Å$^{-2}$)")
pylab.title("x-ray PDF of nickel simulated at Qmax = %g" % qmax)
# matplotlib.pyplot is an matplotlib interface with an MATLAB-like way of plotting.
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plt.plot(r, G)
plt.xlabel("r (Å)")
plt.ylabel("G (Å$^{-2}$)")
plt.title("x-ray PDF of nickel simulated at Qmax = %g" % qmax)

# display plot window, this must be the last command in the script
pylab.show()
plt.show()
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Save fitted curve, refined structure and results summary.
"""

import pylab
import matplotlib.pyplot as plt
import numpy

from diffpy.pdffit2 import PdfFit

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# Plot results ---------------------------------------------------------------

# pylab is matplotlib interface with MATLAB-like plotting commands
# matplotlib.pyplot is an matplotlib interface with an MATLAB-like way of plotting.
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# obtain data from PdfFit calculator object
r = pf.getR()
Gobs = pf.getpdf_obs()
Gfit = pf.getpdf_fit()

# calculate difference curve, with pylab arrays it can be done
# without for loop
Gdiff = pylab.array(Gobs) - pylab.array(Gfit)
# calculate difference curve
Gdiff = numpy.array(Gobs) - numpy.array(Gfit)
Gdiff_baseline = -10

pylab.plot(r, Gobs, "ko")
pylab.plot(r, Gfit, "b-")
pylab.plot(r, Gdiff + Gdiff_baseline, "r-")
plt.plot(r, Gobs, "ko")
plt.plot(r, Gfit, "b-")
plt.plot(r, Gdiff + Gdiff_baseline, "r-")

pylab.xlabel("r (Å)")
pylab.ylabel("G (Å$^{-2}$)")
pylab.title("Fit of nickel to x-ray experimental PDF")
plt.xlabel("r (Å)")
plt.ylabel("G (Å$^{-2}$)")
plt.title("Fit of nickel to x-ray experimental PDF")

# display plot window, this must be the last command in the script
pylab.show()
plt.show()
46 changes: 43 additions & 3 deletions doc/source/index.rst
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.. |title| replace:: diffpy.pdffit2 documentation

diffpy.pdffit2 - PDFfit2 - real space structure refinement program..
diffpy.pdffit2 - PDFfit2 - real space structure refinement program.

| Software version |release|.
| Last updated |today|.

The diffpy.pdffit2 package provides functions for calculation and
refinement of atomic Pair Distribution Function (PDF) from crystal
structure model. It is used as a computational engine by PDFgui. All
refinements possible in PDFgui can be done with diffpy.pdffit2,
although less conveniently and with a fair knowledge of Python.
The package includes an extension for the interactive `IPython
<http://ipython.org>`_ shell, which tries to mimic the old PDFFIT
program. To start IPython with this extension and also with plotting
functions enabled, use ::

ipython --ext=diffpy.pdffit2.ipy_ext --pylab

The IPython extension is suitable for interactive use, however
refinement scripts should be preferably written as a standard
Python code. This is more reliable and needs only a few extra
statements.

=======
Authors
=======

diffpy.pdffit2 is developed by Billinge Group
and its community contributors.
This code was derived from the first PDFFIT program by Thomas Proffen.
The sources were converted to C++ by Jacques Bloch and then extensively hacked,
extended and purged from most glaring bugs by Chris Farrow and Pavol Juhas.
This code is currently maintained as part of the DiffPy project to create
python modules for structure investigations from diffraction data.

The DiffPy team is located in the Billinge-group at the Applied Physics
and Applied Mathematics Department of the Columbia University in New York.
Previous significant contributors to this code were

Pavol Juhas, Chris Farrow, Jacques Bloch, Wenduo Zhou

For a detailed list of contributors see
https://github.com/diffpy/diffpy.pdffit2/graphs/contributors.


=========
Reference
=========

If you use this program for a scientific research that leads to publication,
we ask that you acknowledge use of the program by citing the following paper
in your publication:

C. L. Farrow, P. Juhás, J. W. Liu, D. Bryndin, E. S. Božin, J. Bloch, Th. Proffen
and S. J. L. Billinge, PDFfit2 and PDFgui: computer programs for studying nanostructure
in crystals (https://stacks.iop.org/0953-8984/19/335219), *J. Phys.: Condens. Matter*, 19, 335219 (2007)

============
Installation
============
Expand All @@ -34,6 +73,7 @@ Table of contents

license
release
examples
Package API <api/diffpy.pdffit2>

=======
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