{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Angular Correlations of Amorphous Materials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates caclulating the angular correlation of diffraction patterns recorded from an amorphous (or crystalline) material.\n", "\n", "The dataset used for this demonstration is a 4-D STEM dataset of a PdNiP deposited thin film glass aquired using a DE-16 Camera and a 200keV FEI-Titan electron microscopt at 100 fps. The probe size was ~2-nm and step size was .365 nm so there there is singificant probe overlap in the probe positions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This functionality has been checked to run with pyxem-0.15.0 (April 2023). Bugs are always possible, do not trust the code blindly, and if you experience any issues please report them here: https://github.com/pyxem/pyxem-demos/issues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Angular Correlations are a very natural extension to variance type studies. They offer more insight into the symmetry of the strucutures being studied as well as offering the ability to be studied spatially. \n", "\n", "Mathmatically, the Angular correlation is the angular-autocorrelation of some polar unwrapped diffraction pattern I(k). \n", "\n", "
\n", "$ C(k,\\phi) = \\frac{_\\theta - ^2_\\theta }{^2_\\theta} $\n", "
\n", "\n", "This is simlar to the radial (\"r\") variance often calculated in Fluctuation Electron Microscopy.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Importing & Visualization\n", "2. Polar Reprojection\n", "3. Angular Correlation\n", "4. Power Spectrum and Correlation Maps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Importing and Visualization\n", "\n", "This section goes over loading the data from the data folder and visualizing the data for further use. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_path = \"data/09/PdNiP_test.hspy\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:silx.opencl.common:Unable to import pyOpenCl. Please install it from: https://pypi.org/project/pyopencl\n" ] } ], "source": [ "%matplotlib inline\n", "import pyxem as pxm\n", "import hyperspy.api as hs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.15.1'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pxm.__version__" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/carterfrancis/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/misc/utils.py:471: VisibleDeprecationWarning: Use of the `binned` attribute in metadata is going to be deprecated in v2.0. Set the `axis.is_binned` attribute instead. \n", " warnings.warn(\n", "/Users/carterfrancis/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/io.py:572: VisibleDeprecationWarning: Loading old file version. The binned attribute has been moved from metadata.Signal to axis.is_binned. Setting this attribute for all signal axes instead.\n", " warnings.warn('Loading old file version. The binned attribute '\n" ] } ], "source": [ "data = hs.load(\"./data/09/PdNiP_test.hspy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 - Polar Reprojection\n", " \n", "This section deals with converting the signal to a polar signal. This is probably the most important and difficult part of the analysis. Even small distortions in the pattern or misinterpertation of the center of the diffraction pattern will negitively affect the ability to determine correlations.\n", "\n", "There is still some ongoing development on methods for identifying and correcting for these distortions but a good check is always to perform the correct and make sure that the first amorphous ring is a line after the polar reprojection. In general your eye should be very good at identifying that. Another thing to notice is that after the correlation if you have small splititing in all of your peaks(especially the self correlation) then most likely your center isn't completely correct." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.set_signal_type(\"electron_diffraction\")\n", "data.beam_energy=200\n", "data.unit = \"k_nm^-1\"\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask =data.get_direct_beam_mask(20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Affine correction from fitting an ellipse\n", "import numpy as np\n", "center=(31.2,31.7)\n", "affine=np.array([[ 1.03725511, -0.02662789, 0. ],\n", " [-0.02662789, 1.01903215, 0. ],\n", " [ 0. , 0. , 1. ]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:pyFAI.geometry.core:No fast path for space: k\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ ] | 0% Completed | 105.78 ms" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/carterfrancis/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/misc/utils.py:471: VisibleDeprecationWarning: Use of the `binned` attribute in metadata is going to be deprecated in v2.0. Set the `axis.is_binned` attribute instead. \n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 3.36 ss\n" ] } ], "source": [ "data.set_ai(center=center)\n", "rad = data.get_azimuthal_integral2d(npt=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** This isn't perfect, as you can see there is still some distortion that an affine transformation could fix, but for the purposes of this demo this it will suffice" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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