{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# freud.locality.PeriodicBuffer: Unit Cell RDF\n", "The `PeriodicBuffer` class is meant to replicate points beyond a single image while respecting box periodicity. This example demonstrates how we can use this to compute the radial distribution function from a sample crystal's unit cell." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import freud\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we create a box to represent the unit cell and put two points inside. We plot the box and points below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "box = freud.box.Box(Lx=2, Ly=2, xy=np.sqrt(1 / 3), is2D=True)\n", "points = np.array([[-0.5, -0.5, 0], [0.5, 0.5, 0]])\n", "system = freud.AABBQuery(box, points)\n", "system.plot(ax=plt.gca())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we create a `PeriodicBuffer` instance and have it compute the \"buffer\" points that lie outside the first periodicity. These positions are stored in the `buffer_points` attribute. The corresponding `buffer_ids` array gives a mapping from the index of the buffer particle to the index of the particle it was replicated from, in the original array of `points`. Finally, the `buffer_box` attribute returns a larger box, expanded from the original box to contain the replicated points." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.65470022 1.5 0. ]\n", " [ 1.80940032 3.5 0. ]\n", " [ 2.96410179 5.5 0. ]\n", " [-3.96410131 -6.5 0. ]\n", " [-2.80940104 -4.49999952 0. ]\n", " [-1.65470016 -2.50000048 0. ]\n", " [ 1.50000024 -0.5 0. ]\n", " [ 2.65470076 1.5 0. ]\n", " [ 3.80940032 3.5 0. ]\n", " [ 4.96410179 5.5 0. ]] ...\n" ] } ], "source": [ "pbuff = freud.locality.PeriodicBuffer()\n", "pbuff.compute(system=(box, points), buffer=6, images=True)\n", "print(pbuff.buffer_points[:10], \"...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we plot the original unit cell and the replicated buffer points and buffer box." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "system.plot(ax=plt.gca())\n", "plt.scatter(pbuff.buffer_points[:, 0], pbuff.buffer_points[:, 1])\n", "pbuff.buffer_box.plot(ax=plt.gca(), linestyle=\"dashed\", color=\"gray\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can plot the radial distribution function (RDF) of this replicated system, using a value of `r_max` that is larger than the size of the original box. This allows us to see the interaction of the original points with their replicated neighbors from the buffer." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rdf = freud.density.RDF(bins=250, r_max=5)\n", "rdf.compute(system=(pbuff.buffer_box, pbuff.buffer_points), query_points=points)\n", "rdf.plot(ax=plt.gca())\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }