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Merge pull request #182 from ahoust17/main
fixed atom_tools
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Basics of reading an MRC file with 4D STEM data from the Spectra300 at UTK\n", | ||
| "## By Austin Houston\n", | ||
| "### Last updated 2024-09-14" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "import sys\n", | ||
| "\n", | ||
| "%matplotlib ipympl\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "\n", | ||
| "sys.path.insert(0, '/Users/austin/Documents/GitHub/SciFiReaders/')\n", | ||
| "import SciFiReaders\n", | ||
| "\n", | ||
| "sys.path.insert(0, '/Users/austin/Documents/GitHub/pyTEMlib/')\n", | ||
| "import pyTEMlib\n", | ||
| "import pyTEMlib.file_tools as ft\n", | ||
| "\n", | ||
| "print(\"SciFiReaders version: \", SciFiReaders.__version__)\n", | ||
| "print(\"pyTEMlib version: \", pyTEMlib.__version__)\n", | ||
| "\n", | ||
| "# for beginning analysis\n", | ||
| "from sklearn.cluster import KMeans\n", | ||
| "from sklearn.decomposition import PCA\n", | ||
| "from sklearn.cluster import KMeans\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "mrc_filepath = '/Users/austin/Dropbox/GaTech_colabs/SnSe_MgO/2024_06_19_data/4D_STEM/'\n", | ||
| "\n", | ||
| "files = os.listdir(mrc_filepath)\n", | ||
| "files = [f for f in files if f.endswith('.mrc')]\n", | ||
| "\n", | ||
| "# Load the first file\n", | ||
| "dset = ft.open_file(mrc_filepath + files[1])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "data = dset['Channel_000']\n", | ||
| "\n", | ||
| "view = data.plot()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "mrc_array = np.array(data)\n", | ||
| "N, M, height, width = data.shape\n", | ||
| "datacube_flat = mrc_array.reshape(N * M, -1)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Perform KMeans clustering\n", | ||
| "clusters = 3 \n", | ||
| "kmeans = KMeans(n_clusters=clusters, random_state=0).fit(datacube_flat)\n", | ||
| "labels = kmeans.labels_\n", | ||
| "cluster_centers = kmeans.cluster_centers_\n", | ||
| "\n", | ||
| "# Reduce the data to 3D using PCA\n", | ||
| "pca = PCA(n_components=3)\n", | ||
| "datacube_reduced = pca.fit_transform(datacube_flat)\n", | ||
| "cluster_centers_reduced = pca.transform(cluster_centers)\n", | ||
| "\n", | ||
| "# Create a 3D plot\n", | ||
| "fig = plt.figure()\n", | ||
| "ax = fig.add_subplot(111, projection='3d')\n", | ||
| "scatter = ax.scatter(datacube_reduced[:, 0], datacube_reduced[:, 1], datacube_reduced[:, 2], c=labels, cmap='viridis', marker='o')\n", | ||
| "ax.set_xlabel('PCA Component 1')\n", | ||
| "ax.set_ylabel('PCA Component 2')\n", | ||
| "ax.set_zlabel('PCA Component 3')\n", | ||
| "ax.set_xticks([])\n", | ||
| "ax.set_yticks([])\n", | ||
| "ax.set_zticks([])\n", | ||
| "plt.show()\n", | ||
| "\n", | ||
| "\n", | ||
| "label_image = labels.reshape((M, N))\n", | ||
| "\n", | ||
| "plt.figure()\n", | ||
| "plt.imshow(label_image, cmap='viridis')\n", | ||
| "plt.colorbar()\n", | ||
| "plt.show()\n", | ||
| "\n", | ||
| "# Reshape cluster centers back to original image dimensions\n", | ||
| "cluster_center_images = cluster_centers.reshape((kmeans.n_clusters, height, width))\n", | ||
| "\n", | ||
| "# Plot the average images\n", | ||
| "fig, axes = plt.subplots(1, kmeans.n_clusters, figsize=(15, 5))\n", | ||
| "\n", | ||
| "for i, ax in enumerate(axes):\n", | ||
| " ax.imshow(cluster_center_images[i], cmap='viridis')\n", | ||
| " ax.set_title(f'Cluster Center {i+1}')\n", | ||
| " ax.axis('off')\n", | ||
| "\n", | ||
| "plt.show()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pytemlib", | ||
| "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": "3.11.0" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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