"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "aligned.plot()\n",
- "aligned.axes_manager"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Template Matching\n",
- "\n",
- "A common way to find spots in a diffraction pattern is through template matching. Template matching involves creating a template of one of the diffraction spots and convolving it with the entire dataset. Usually this is done using a flat disk or a summed image of the zero beam. Often times reducing the noise in the dataset is useful as it \n",
- "\n",
- "There are a couple of ways to acomplish this is pyxem. \n",
- "\n",
- "The first is to use the `template_match_disk` function which uses the normalized_cross_correlation to find shere some diffraction spot is. We can think of this as the template being slid across the image and multiplied at every point. The result is that objects similar to the template are very visable in the filtered dataset. \n",
- "\n",
- "There are multiple ways to do this:\n",
- "\n",
- "1. Use the vaccum probe as a template and convolve the vaccum probe with the image\n",
- "2. Use the "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[########################################] | 100% Completed | 310.78 ms\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "matched = aligned.template_match_disk(disk_r=5) # the disk_r should be equal to the size of the diffraction spots. \n",
- "matched.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Peak Finding\n",
- "We can then find the position of the peaks using the `hyperspy.Signal2D.find_peaks()` method. There is an interactive form of this method as well which makes defining the proper settings a little bit easier. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "ename": "ImportError",
- "evalue": "No toolkit registered. Install hyperspy_gui_ipywidgets or hyperspy_gui_traitsui GUI elements.",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnotebook\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmatched\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_peaks\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/_signals/signal2d.py:1023\u001b[0m, in \u001b[0;36mSignal2D.find_peaks\u001b[0;34m(self, method, interactive, current_index, show_progressbar, parallel, max_workers, display, toolkit, **kwargs)\u001b[0m\n\u001b[1;32m 1020\u001b[0m peaks \u001b[38;5;241m=\u001b[39m BaseSignal(np\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes_manager\u001b[38;5;241m.\u001b[39mnavigation_shape),\n\u001b[1;32m 1021\u001b[0m axes\u001b[38;5;241m=\u001b[39maxes_dict)\n\u001b[1;32m 1022\u001b[0m pf2D \u001b[38;5;241m=\u001b[39m PeaksFinder2D(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39mmethod, peaks\u001b[38;5;241m=\u001b[39mpeaks, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1023\u001b[0m \u001b[43mpf2D\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisplay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoolkit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoolkit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1024\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m current_index:\n\u001b[1;32m 1025\u001b[0m peaks \u001b[38;5;241m=\u001b[39m method_func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
- "File \u001b[0;32m~/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/ui_registry.py:162\u001b[0m, in \u001b[0;36mget_partial_gui..pg\u001b[0;34m(self, display, toolkit, **kwargs)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpg\u001b[39m(\u001b[38;5;28mself\u001b[39m, display\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, toolkit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_gui\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoolkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoolkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisplay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 163\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoolkit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoolkit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/mambaforge/envs/pyxem-demos/lib/python3.11/site-packages/hyperspy/ui_registry.py:69\u001b[0m, in \u001b[0;36mget_gui\u001b[0;34m(self, toolkey, display, toolkit, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_gui\u001b[39m(\u001b[38;5;28mself\u001b[39m, toolkey, display\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, toolkit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m TOOLKIT_REGISTRY:\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo toolkit registered. Install hyperspy_gui_ipywidgets or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhyperspy_gui_traitsui GUI elements.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhyperspy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdefaults_parser\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m preferences\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(toolkit, \u001b[38;5;28mstr\u001b[39m):\n",
- "\u001b[0;31mImportError\u001b[0m: No toolkit registered. Install hyperspy_gui_ipywidgets or hyperspy_gui_traitsui GUI elements."
- ]
- }
- ],
- "source": [
- "%matplotlib notebook\n",
- "matched.find_peaks()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "peaks = matched.find_peaks(threshold=1.0, distance=5, interactive=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Converting Peaks to Diffraction Vectors\n",
- "\n",
- "We then need to take the peaks identified and transform them into diffraction Vectors. We can construct the signal using the following code. Note that the center of the diffraction pattern and the calibration need to be passed once again. We can take these from the diffraction signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "center = [a.offset/a.scale*-1 for a in matched.axes_manager.signal_axes] # should be the center of the pattern now...\n",
- "\n",
- "calibration = matched.axes_manager.signal_axes[0].scale\n",
- "\n",
- "vectors = pxm.signals.DiffractionVectors.from_peaks(peaks,center = center, calibration=calibration)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Filter the Diffraction Vectors\n",
- "\n",
- "We want to only return the diffraction vectors which are within some distance of a basis set of diffraction vectors. This basis set can be just be a single set of diffraction spots but ideally more than 2 spots will be used. This allows for the least squares fitting to also return a residual. In many cases this is prefered as it allows outliers to be identified as points with high residuals. Note that in some cases, using too many spots can also be probalematic as low intensity spots are harder to fit. In those cases a weight parameter is also allowed to help refine the fitting. \n",
- "\n",
- "Defining the basis set of vectors can be done in multiple different ways. \n",
- "\n",
- "1. Define a basis from a known basis set of vectors. Using the known lattice parameter for the material it is possible to define the proper basis set for the material.\n",
- "\n",
- "2. Define the basis from an unstrained part of the material. \n",
- "\n",
- "\n",
- "In most cases the secound way is easier and prefered over the first. It is less dependent on the calibration and things like elliptical distortion in the diffraction pattern will show up as strains in the first case and a very high quality of experiment is necessary.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get the basis vectors from an unstrained part of the material. \n",
- "basis = vectors.inav[1:2,1:2]\n",
- "basis.filter_magnitude(min_magnitude =0.1, max_magnitude=1)# remove the zero_beam\n",
- "basis_array = basis.data[0,0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "filtered_vectors = vectors.filter_basis(basis_array, distance =.2, inplace= False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pyxem.generators.displacement_gradient_tensor_generator import get_DisplacementGradientMap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "strain_map = get_DisplacementGradientMap(filtered_vectors, basis_array)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "maps = strain_map.get_strain_maps()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "f= plt.figure(figsize=(7,7))\n",
- "hs.plot.plot_images(maps,per_row=2,fig=f,\n",
- " label=[\"e11\",\"e22\", \"e12\", \"theta\"],tight_layout=True)\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "pyxem-demos",
- "language": "python",
- "name": "pyxem-demos"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3"
- },
- "widgets": {
- "state": {
- "280e931f7b274209a009d92f04098e5c": {
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- {
- "cell_index": 54
- }
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- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/05 Strain Mapping.ipynb b/05 Strain Mapping.ipynb
new file mode 100644
index 0000000..305f716
--- /dev/null
+++ b/05 Strain Mapping.ipynb
@@ -0,0 +1,1467 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Strain Mapping \n",
+ "\n",
+ "This notebook goes through the steps to calculate the strain using pyxem. This data was taken from the paper:\n",
+ "\n",
+ "```\n",
+ "Microstructure and microchemistry study of irradiation-induced precipitates in proton irradiated ZrNb alloys\n",
+ "Yu, Zefeng; Zhang, Chenyu; Voyles, Paul M.; He, Lingfeng; Liu, Xiang; Nygren, Kelly; Couet, Adrien\n",
+ "10.18126/2nj3-gyd8 \n",
+ "```\n",
+ "\n",
+ "It shows a percipitate which arises from irradiation in the ZrNb sample. The dataset shows the strain for one of these precipitates. The results in this notebook are slightly different than those published as the paper only uses two diffraction spots to calculate strain. Here we define a `basis` set of diffraction spots from an unstrained region of the sample and then use that basis set of spots to refine the diffraction spots found in the rest of the dataset.\n",
+ "\n",
+ "Then a gradient tensor which maps each set of found points at (x,y) is calculated such that the tensor maps the points onto the basis.\n",
+ "\n",
+ "Transforming that gradient tensor we can plot the percent strain in the E11 E22 and E33 directions as well as a Theta displacement. \n",
+ "\n",
+ "In this sample you can see that there is mostly compressive stress on the percipite as well as shear stress. Hot spots on the edge of the theta map suggest the presence of dislocations as well. \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This functionaility has been checked to run in pyxem-0.19.0 (May 2024). 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": [
+ "# Contents"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Visualizing and Loading Data\n",
+ "2. Pattern Averaging the Data\n",
+ "3. Filtering with a Disk Template Matching\n",
+ "4. Peak Finding\n",
+ "5. Determining Strain\n",
+ "6. Visualizing Strain"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Visualizing, Loading and Centering Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import pyxem and other required libraries"
+ ]
+ },
+ {
+ "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"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.19.dev0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pyxem as pxm\n",
+ "import hyperspy.api as hs\n",
+ "print(pxm.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Load the data. This is an example dataset downloaded from a Zenodo Repository and cached on your computer.\n",
+ "s = pxm.data.zrnb_precipitate(allow_download=True, lazy=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
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+ "
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+ "\n",
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+ "
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+ "
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+ "
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+ "
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+ "\n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ ],
+ "text/plain": [
+ "\n",
+ " Name | size | index | offset | scale | units \n",
+ "================ | ====== | ====== | ======= | ======= | ====== \n",
+ " | 60 | 0 | 0.5 | 0.9 | nm \n",
+ " | 40 | 0 | 0.5 | 0.9 | nm \n",
+ "---------------- | ------ | ------ | ------- | ------- | ------ \n",
+ " | 256 | 0 | -6.6 | 0.051 | nm^-1 \n",
+ " | 256 | 0 | -6.6 | 0.051 | nm^-1 "
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.axes_manager"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set the scale \n",
+ "s.calibration.scale= 0.051"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set axis labels\n",
+ "s.axes_manager.signal_axes[0].name=\"kx\"\n",
+ "s.axes_manager.signal_axes[1].name=\"ky\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Set the figure dpi so that things show up nicely side by side (This is different for every monitor. \n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.rcParams['figure.dpi'] = 60"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the dataset\n",
+ "\n",
+ "We can use the `%matplotlib ipympl` jupyter magic comand to make it interactive"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "57ec82e9761a46339879bd77b5a5cf05",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Ba…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib ipympl\n",
+ "s.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Pattern Averaging the Data\n",
+ "\n",
+ "\n",
+ "Sometimes we see some varible intensity in the disks. We can actually correct some of this by just gaussian filtering the data in real space. \n",
+ "For strain mapping we can make this very local (sigma = 0.5,0.5,0.0,0.0) and we don't lose much spatial resolution (its equivlent to having a slightly larger probe size in real-space). Of course this also greatly increses your Signal to noise ratio which is more of what we are interested in :). \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from dask_image.ndfilters import gaussian_filter # For lazy signals\n",
+ "#from scipy.ndimage import gaussian_filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s = pxm.data.zrnb_precipitate(allow_download=True, lazy=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# filter the dataset\n",
+ "filtered = s.filter(gaussian_filter, sigma=(1,1,0,0)) # in pixels "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ ""
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+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "filtered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[########################################] | 100% Completed | 2.42 sms\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d358f8c2adf445339666d5f989cf2bad",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Ba…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c4b1b1b9e96245a7b208b954f08d4a3e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Ba…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the filtered dataset (Lets compare the two!)\n",
+ "%matplotlib ipympl\n",
+ "\n",
+ "filtered.plot(vmax=\"99th\")\n",
+ "s.plot(axes_manager=filtered.axes_manager, vmax=\"99th\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 3. Filtering with a Disk Template Matching\n",
+ "\n",
+ "\n",
+ "Then we can use template matching before finding the diffraction vectors in the dataset. I like to do this lazily and then adjust the parameters. The disk_r can be read from the size of the direct beam but it is also good to view the template result to make sure that things worked correctly. If your disk_r is too small you might end up with a valley at the center of the disk and if your radius is too large you end up with a platau at the center.\n",
+ "\n",
+ "This is shown below where the ideal radius is around ~11 pixels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "51f0ad5038264c03ae165a18e9332b74",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "image/png": 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+ ],
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# lets just try to see what the effect of different disk radii is:\n",
+ "import matplotlib.pyplot as plt\n",
+ "fig = plt.figure(figsize=(15, 5))\n",
+ "one_pattern=filtered.inav[5,5]\n",
+ "hs.plot.plot_images([one_pattern.template_match_disk(r) for r in [5,10,15]], \n",
+ " axes_decor=\"off\",\n",
+ " scalebar=\"all\",\n",
+ " label=[\"Radius=5pix\",\"Radius=10pix\",\"Radius=15pix\"], fig=fig)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# template matching using a disk. \n",
+ "temp = filtered.template_match_disk(disk_r=11, subtract_min=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[########################################] | 100% Completed | 6.14 sms\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "07bd31a732bc4a2d9c761dae6eac0bbc",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Ba…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "temp.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Peak Finding\n",
+ "\n",
+ "Now we can see what a good value for peak finding is. We can either use the interactive peak finding in hyperspy but I tend to just play with the vmin value with plotting until I get a reasonable min value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;20mWARNING | Hyperspy | Estimated number of bins using `bins='fd'` is too large (366). Capping the number of bins at `max_num_bins=250`. Consider using an alternative method for calculating the bins such as `bins='scott'`, or increasing the value of the `max_num_bins` keyword argument. (hyperspy.misc.hist_tools:178)\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:hyperspy.misc.hist_tools:Estimated number of bins using `bins='fd'` is too large (366). Capping the number of bins at `max_num_bins=250`. Consider using an alternative method for calculating the bins such as `bins='scott'`, or increasing the value of the `max_num_bins` keyword argument.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[########################################] | 100% Completed | 103.08 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "82515eb7e6014e7c972cde92bc327529",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "image/png": 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",
+ "text/html": [
+ "\n",
+ "
\n",
+ "
\n",
+ " Figure histogram Signal\n",
+ "
\n",
+ " \n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# lets look at the histgram for just a couple of points\n",
+ "temp.inav[3:10, 3:10].get_histogram().plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "79f989b02bea49bf81266c2e666083a4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Ba…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Lets plot the data with an adjusted vmin \n",
+ "temp.plot(vmin=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;20mWARNING | Hyperspy | The function you applied does not take into account the difference of units and of scales in-between axes. (hyperspy.signal:5320)\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:hyperspy.signal:The function you applied does not take into account the difference of units and of scales in-between axes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get the diffraction vectors\n",
+ "vect = temp.get_diffraction_vectors(threshold_abs=0.4,\n",
+ " distance=10, get_intensity=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "