.. include:: ../references.txt .. _visualization_tutorial: ************* Visualization ************* Polycrystalline diffraction patterns may be hard to decrypt without some visualization tools. This tutorial goes over some of the tools available in `scikit-ued` that help solve this problem. .. _patterson: Patterson pair-pair distribution function ========================================= The calculation of the Patterson pair-pair distribution requires knowledge of the investigated material. You must also determine what range of real-space radii over which you want to calculate the distribution. As an example, let's use a simulated diffraction pattern of monoclinic VO$_2$: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from crystals import Crystal >>> from skued import patterson, powdersim >>> >>> # Simulation of polycrystalline diffraction pattern >>> # for monoclinic VO2 >>> cryst = Crystal.from_database('vo2-m1') >>> q = np.linspace(0.2, 10, 1024) >>> I = powdersim(cryst, q) >>> >>> # Determination of the pair-pair distribution function >>> rr = np.linspace(1, 5, 256) >>> pairdist = patterson(q = q, I = I, crystal = cryst, radii = rr) >>> >>> fig, ax = plt.subplots(1,1) >>> ax.plot(rr, pairdist, '.k') # doctest: +SKIP .. plot:: import numpy as np import matplotlib.pyplot as plt from crystals import Crystal from skued import patterson from skued import powdersim # To simulate experimental data # Simulation of polycrystalline diffraction pattern # for monoclinic VO2 cryst = Crystal.from_database('vo2-m1') q = np.linspace(0.2, 10, 1024) I = powdersim(cryst, q) # Determination of the pair-pair distribution function rr = np.linspace(1, 5, 256) pairdist = patterson(q = q, I = I, crystal = cryst, radii = rr) fig, ax = plt.subplots(1,1) ax.plot(rr, pairdist, '.k') ax.set_xlabel('Radial distance $\AA$') ax.set_ylabel('Pair-pair correlation [a.u.]') :ref:`Return to Top `