{
"cells": [
{
"cell_type": "markdown",
"id": "91549fbc-6b78-4b80-9e5b-8c13432cef78",
"metadata": {
"tags": []
},
"source": [
"# Conventions and interoperability"
]
},
{
"cell_type": "markdown",
"id": "f1ee29e5-4160-4123-a6ff-a74237272aeb",
"metadata": {
"tags": []
},
"source": [
"## Conventions for template matching\n",
"Template matching in pyxem is typically used to determine the orientation of the crystal that formed the diffraction pattern. To describe the orientation of a crystal, two coordinate systems are needed: the lab reference frame (sometimes called the sample reference frame) and the crystal reference frame. The crystal reference frame ($X_c, Y_c, Z_c$) is fixed to the unit cell of the crystal, while the lab reference frame ($X_L, Y_L, Z_L$) is in pyxem fixed to the detector. The orientation of the crystal is the rotation needed to bring the lab reference frame into coincidence with the crystal reference frame [[Rowenhorst *et al.*, 2015](http://dx.doi.org/10.1088/0965-0393/23/8/083501)].\n",
"\n",
"In addition, there are grid coordinate systems associated with the acquisition of scanning electron diffraction data ($X_\\text{scan}, Y_\\text{scan}$), and the layout of the diffraction pattern array in computer memory ($X_\\text{Signal2D}, Y_\\text{Signal2D}$). These reference frames are illustrated in the figure below."
]
},
{
"cell_type": "markdown",
"id": "ff4225ee-24a3-4aec-a797-5850c994c777",
"metadata": {},
"source": [
"\n",
"\n",
"**Figure 1** The different reference frames used by pyxem. Note the TEM convention of viewing the diffraction pattern from above (i.e., looking from the electron source to the detector). (a) Perspective view (b) The diffraction pattern on the detector"
]
},
{
"cell_type": "markdown",
"id": "70ac0845-087b-4ebe-b029-4e6acfd4f122",
"metadata": {},
"source": [
"### Rotation conventions\n",
"Rotations and orientations in three dimensions can be described using Euler angles. Pyxem uses the following conventions for Euler angles:\n",
"1. Rotations and coordinate systems are right-handed.\n",
"2. Rotations are passive (also known as intrinsic), meaning that it is the coordinate axes that rotate.\n",
"3. Euler angles ($\\phi_1$, $\\Phi$, $\\phi_2$) are given in the form ZX'Z''.\n",
"4. Euler angles rotate the sample reference frame to the crystal reference frame (\"lab2crystal\")."
]
},
{
"cell_type": "markdown",
"id": "1ba523cf-8793-4d06-900b-8cd07f0cda53",
"metadata": {},
"source": [
"The sample reference frame is first rotated $\\phi_1$ around $Z_L$ (the lab z-axis), followed by $\\Phi$ around the new $X_L$, followed by $\\phi_2$ around the updated $Z_L$. If $\\phi_1=\\Phi=\\phi_2=0$, then $X_L || X_c, Y_L || Y_c$ and $Z_L || Z_c$."
]
},
{
"cell_type": "markdown",
"id": "d023c2d5-2811-417f-aaea-b17181021420",
"metadata": {
"tags": []
},
"source": [
"### Cartesian crystal reference frame\n",
"For convenience, it is standard to always use a Cartesian crystal reference frame even when the crystal lattice is not cubic. The choice of Cartesian reference frame is relatively straightforward for crystal systems with orthogonal lattice vectors, but for the other crystal systems (triclinic, monoclinic, trigonal, hexagonal) there is no obvious choice. Since the diffraction pattern templates are created by diffsims (which uses the `diffpy.structure.Structure` class) the convention used by diffsims determines the convention of pyxem:\n",
"\n",
"$\\mathbf{X_c} || \\mathbf{a^*}$, $\\mathbf{Y_c} || (\\mathbf{Z_c} \\times \\mathbf{X_c})$, $\\mathbf{Z_c} || \\mathbf{c}$."
]
},
{
"cell_type": "markdown",
"id": "6c607ce9-3158-4836-a648-9314771d1ab9",
"metadata": {
"tags": []
},
"source": [
"### Experimental diffraction patterns\n",
"We see in Figure 1 that in the reference frame of the detector, the lab reference frame is oriented with $X_L$ pointing to the right, $Y_L$ pointing up, and $Z_L$ pointing outwards. This is also the reference frame used when simulating diffraction patterns with diffsims (see below). However, in Matplotlib, Hyperspy and pyxem, images (including diffraction patterns) are typically plotted with the origin in the top-left corner and the y-axis pointing *down*. We therefore have to be careful when comparing a diffraction pattern stored as a numpy/dask array with simulated diffraction patterns during template matching. In pyxem, we assume that the pyxem/hyperspy [`Signal2D`](https://hyperspy.org/hyperspy-doc/current/api/hyperspy._signals.signal2d.html) or numpy array to be template matched is oriented correctly when plotted with the x-axis ($X_\\text{Signal2D}$) pointing to the right and the y-axis ($Y_\\text{Signal2D}$) pointing *down*. In other words, $Y_L || -Y_\\text{Signal2D}$."
]
},
{
"cell_type": "markdown",
"id": "6028e340-ae5b-4433-ad8f-facbc7d48498",
"metadata": {
"tags": []
},
"source": [
"### Scanning electron diffraction\n",
"The lab reference frame is fixed to the detector that records the diffraction patterns. However, the sides of the detector are often *not* parallel to the scan directions in a scanning electron diffraction (i.e., 4D) dataset. If the in-plane orientation of the diffraction patterns is of interest, care must be taken to modify the in-plane rotation ($\\phi_1$) to compensate for this. Pyxem uses the same convention as hyperspy for the navigation axes: x-axis ($X_\\text{scan}$) to the right and the y-axis ($Y_\\text{scan}$) down."
]
},
{
"cell_type": "markdown",
"id": "73842529-c2a4-4019-8840-d128d9435407",
"metadata": {},
"source": [
"### Examples"
]
},
{
"cell_type": "markdown",
"id": "42dd9f02-7023-4b5f-8fec-dd8dbf79a39f",
"metadata": {},
"source": [
"We can illustrate these convention using diffsims (the package used to simulate diffraction patterns used for template matching) for some simple orientations. First we create the structure (using cubic aluminium as an example):"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c594ad09-976b-4773-a53e-2d7bca399447",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import diffpy\n",
"from diffsims.generators.diffraction_generator import DiffractionGenerator\n",
"\n",
"latt = diffpy.structure.lattice.Lattice(4,4,4,90,90,90)\n",
"atoms = [diffpy.structure.atom.Atom(atype='Al',xyz=[0.0,0.0,0.0],lattice=latt),\n",
" diffpy.structure.atom.Atom(atype='Al',xyz=[0.5,0.5,0.0],lattice=latt),\n",
" diffpy.structure.atom.Atom(atype='Al',xyz=[0.5,0.0,0.5],lattice=latt),\n",
" diffpy.structure.atom.Atom(atype='Al',xyz=[0.0,0.5,0.5],lattice=latt)]\n",
"structure_matrix = diffpy.structure.Structure(atoms=atoms,lattice=latt)"
]
},
{
"cell_type": "markdown",
"id": "8f060b53-de73-4669-968d-0250d3610ad8",
"metadata": {},
"source": [
"We can then simulate a diffraction pattern for the orientation (10°, 0°, 0°) in Euler angles:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93c64048-35a4-4930-a300-003a7050cebd",
"metadata": {},
"outputs": [],
"source": [
"scale = 0.027; half_shape = 64; rec_rad = scale * half_shape; phi1 = 10\n",
"diff_gen = DiffractionGenerator(accelerating_voltage=200,\n",
" precession_angle=0,\n",
" scattering_params='xtables',\n",
" shape_factor_model=\"linear\",\n",
" minimum_intensity=0.01,\n",
" )\n",
"sim = diff_gen.calculate_ed_data(structure_matrix, reciprocal_radius=rec_rad,\n",
" rotation=(phi1, 0, 0), with_direct_beam=True,\n",
" max_excitation_error=0.04)"
]
},
{
"cell_type": "markdown",
"id": "c3020233-35df-4090-9b4e-c3793b585072",
"metadata": {},
"source": [
"and plot the diffsims simulation with labeled diffraction spots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8cfaa46-a4f5-4535-887c-73cf79401352",
"metadata": {},
"outputs": [
{
"data": {
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",
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