{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5c8aa3fd", "metadata": {}, "outputs": [], "source": [ "from scipy.io import loadmat as scipy_loadmat\n", "from mat73 import loadmat as mat73_loadmat\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "id": "4a419040", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ADU', 'alpha0', 'cbed', 'df', 'rbf', 'rot_ang', 'scanstep', 'voltage']\n" ] } ], "source": [ "# fold_slice output\n", "f = \"/home/swim/data/mose2-chen-natcomm/rawdata_1x_crop.mat\"\n", "contents = scipy_loadmat(f)\n", "print([key for key in contents.keys() if not key.startswith('__')])" ] }, { "cell_type": "code", "execution_count": 9, "id": "59ec3de4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__header__\n", "b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Sat Feb 22 01:27:35 2020'\n", "\n", "__version__\n", "1.0\n", "\n", "__globals__\n", "[]\n", 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0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]]\n", "\n", " [[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " ...\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00]]]]\n", "\n", "df\n", "[[-500]]\n", "\n", "rbf\n", "[[26]]\n", "\n", "rot_ang\n", "[[-30]]\n", "\n", "scanstep\n", "[[0.85]]\n", "\n", "voltage\n", "[[80]]\n", "\n" ] } ], "source": [ "for key in contents.keys():\n", " print(key)\n", " print(contents[key])\n", " print()" ] }, { "cell_type": "code", "execution_count": 10, "id": "1f24ed6c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(124, 124, 60, 60)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contents['cbed'].shape" ] }, { "cell_type": "code", "execution_count": 11, "id": "cba406f1", "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 37, "id": "d5a95408", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float32(-36.020027)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contents['cbed'].min()" ] }, { "cell_type": "code", "execution_count": 40, "id": "22fb3775", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(\n", " np.log1p((contents['cbed'] - contents['cbed'].min()) / contents['ADU'] + 1e-10).sum(axis=(0, 1))\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "lemon", "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }