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add tutorial for momentum correction of already binned datasets
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "8ad4167a-e4e7-498d-909a-c04da9f177ed", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "source": [ | ||
| "# Momentum correction and alignment of already binned data\n", | ||
| "This example shows how to use the momentum correction and alignment features of `sed-processor` with data that come already in a binned format - e.g. a tiff file stored from a momentum microscope. For demonstration purposes, we will use the WSe2 data from [tutorial 2](2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb), and bin these data into the raw coordinates X/Y/TOF, similar as they would come from a momentum microscope tiff file." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "fb045e17-fa89-4c11-9d51-7f06e80d96d5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import sed\n", | ||
| "from sed.dataset import dataset\n", | ||
| "\n", | ||
| "%matplotlib widget" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "42a6afaa-17dd-4637-ba75-a28c4ead1adf", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Load Data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "34f46d54", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dataset.get(\"WSe2\") # Put in Path to a storage of at least 20 GByte free space.\n", | ||
| "data_path = dataset.dir # This is the path to the data\n", | ||
| "scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f1f82054", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# create sed processor using the config file:\n", | ||
| "sp = sed.SedProcessor(folder=scandir, config=\"../src/sed/config/mpes_example_config.yaml\", user_config={}, system_config={}, verbose=True)\n", | ||
| "sp.add_jitter()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2af4e36d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Generate binned dataset" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5b6564f7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "binned_data = sp.pre_binning()" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "70aa4343", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Distortion correction and alignment for binned data\n", | ||
| "### 1. step: load data into momentum corrector class instance\n", | ||
| "If data come as xarray with defined axes ranges, it can just directly be passed. If the data are just a plain numpy array, also provide the detector coordinate ranges of the binned data. The interactive tool allows you to select a slice for correction." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "345ab949", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# sp.mc.load_data(binned_data) # as xarray DataArray\n", | ||
| "bin_ranges = ((binned_data.X[0], binned_data.X[-1]), (binned_data.Y[0], binned_data.Y[-1]), (binned_data.t[0], binned_data.t[-1]))\n", | ||
| "sp.mc.load_data(binned_data.data, bin_ranges=bin_ranges) # as np.array\n", | ||
| "sp.mc.select_slicer(plane=33, width=10, apply=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "fee3ca76", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### 2. Step:\n", | ||
| "Next, we select a number of features corresponding to the rotational symmetry of the material, plus the center. These can either be auto-detected (for well-isolated points), or provided as a list (these can be read-off the graph in the cell above).\n", | ||
| "These are then symmetrized according to the rotational symmetry, and a spline-warping correction for the x/y coordinates is calculated, which corrects for any geometric distortions from the perfect n-fold rotational symmetry." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "fd9666c5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#features = np.array([[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]])\n", | ||
| "#sp.mc.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)\n", | ||
| "# Manual selection: Use a GUI tool to select peaks:\n", | ||
| "#sp.mc.feature_select(rotation_symmetry=6, include_center=True)\n", | ||
| "# Autodetect: Uses the DAOStarFinder routine to locate maxima.\n", | ||
| "# Parameters are:\n", | ||
| "# fwhm: Full-width at half maximum of peaks.\n", | ||
| "# sigma: Number of standard deviations above the mean value of the image peaks must have.\n", | ||
| "# sigma_radius: number of standard deviations around a peak that peaks are fitted\n", | ||
| "sp.define_features(rotation_symmetry=6, auto_detect=True, include_center=True, fwhm=10, sigma=12, sigma_radius=4, apply=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "f7519ff8", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### 3. Step: \n", | ||
| "Generate nonlinear correction using splinewarp algorithm. If no landmarks have been defined in previous step, default parameters from the config are used" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d27cd7a4", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Option whether a central point shall be fixed in the determination fo the correction\n", | ||
| "sp.mc.spline_warp_estimate(include_center=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "7f32988f", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "sp.mc.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a85b6bcc", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### 4. Step: Apply correction to binned dataset\n", | ||
| "This function uses interpolation to apply the generated displacement field to the whole binned dataset. There might be some loss of data quality due to this process." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "37233997", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "corrected_data = binned_data.copy()\n", | ||
| "corrected_data.data = sp.mc.apply_correction(binned_data.data, axis=2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "2eb55ff0", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "plt.figure()\n", | ||
| "corrected_data.isel(t=40).T.plot(cmap=\"terrain_r\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "05488944", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "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": "3.12.8" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |