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Updated colormap
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‎figures/.ipynb_checkpoints/Fig1_Data_preprocessing-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Stages of data preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings as wn\n",
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"wn.filterwarnings(\"ignore\")\n",
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"\n",
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"import numpy as np\n",
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"from fuller.mrfRec import MrfRec\n",
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"from fuller.generator import rotosymmetrize\n",
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"from fuller.utils import saveHDF\n",
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"from mpes import analysis as aly, fprocessing as fp\n",
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"\n",
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"import os\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib as mpl\n",
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"from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
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"%matplotlib inline\n",
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"\n",
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"# mpl.rcParams['font.family'] = 'sans-serif'\n",
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"# mpl.rcParams['font.sans-serif'] = 'Arial'\n",
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"mpl.rcParams['axes.linewidth'] = 2\n",
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"mpl.rcParams['pdf.fonttype'] = 42\n",
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"mpl.rcParams['ps.fonttype'] = 42"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before we start with the preprocessing, we determine the location of the high symmetry points which we need for plotting the data. For simplicity and linearity of the code, we do this using the symmetrized data from file but of course we could also do the preprocessing first before plotting the resulting data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fdata = fp.readBinnedhdf5('../data/pes/1_sym.h5')\n",
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"mc = aly.MomentumCorrector(fdata['V'])\n",
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"\n",
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"mc.selectSlice2D(selector=slice(30, 32), axis=2)\n",
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"mc.featureExtract(mc.slice, method='daofind', sigma=6, fwhm=20, symscores=False)\n",
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"\n",
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"# False detection filter, if needed\n",
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"try:\n",
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" mc.pouter_ord = mc.pouter_ord[[0,1,3,5,6,9],:]\n",
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"except:\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"mc.view(image=mc.slice, annotated=True, points=mc.features)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define high-symmetry points\n",
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"G = mc.pcent # Gamma point\n",
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"K = mc.pouter_ord[0,:] # K point\n",
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"K1 = mc.pouter_ord[1,:] # K' point\n",
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"M = (K + K1) / 2 # M point\n",
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"\n",
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"# Define cutting path\n",
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"pathPoints = np.asarray([G, M, K, G])\n",
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"nGM, nMK, nKG = 70, 39, 79\n",
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"segPoints = [nGM, nMK, nKG]\n",
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"rowInds, colInds, pathInds = aly.points2path(pathPoints[:,0], pathPoints[:,1], npoints=segPoints)\n",
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"nSegPoints = len(rowInds)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define plotting function\n",
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"\n",
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"def plot_path(mrf, vmax, save_path):\n",
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" # Normalize data\n",
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" imNorm = mrf.I / mrf.I.max()\n",
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"\n",
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" # Sample the data along high-symmetry lines (k-path) connecting the corresponding high-symmetry points\n",
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" pathDiagram = aly.bandpath_map(imNorm, pathr=rowInds, pathc=colInds, eaxis=2)\n",
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"\n",
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" Evals = mrf.E\n",
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" ehi, elo = Evals[0], Evals[449]\n",
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"\n",
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" f, ax = plt.subplots(figsize=(10, 6))\n",
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" colornames = ['#646464', '#666666', '#6a6a6a', '#6f6f6f', '#737373', '#787878', '#7d7d7d', '#828282', '#878787', '#8d8d8d', '#929292', '#989898', '#9e9e9e', '#a4a4a4', '#aaaaaa', '#b0b0b0', '#b6b6b6', '#bcbcbc', '#c2c2c2', '#c9c9c9', '#cfcfcf', '#d6d6d6', '#dcdcdc', '#e3e3e3', '#eaeaea', '#efefee', '#efeee5', '#efeddc', '#efecd3', '#eeebca', '#eeeac0', '#eee9b7', '#eee8ad', '#ede7a4', '#ede69a', '#ede590', '#ede487', '#ece37d', '#ece273', '#ece069', '#ecdf5f', '#ebde55', '#ebdd4b', '#ebdc41', '#ebdb37', '#ebd333', '#ebc933', '#ecbe32', '#ecb432', '#eda931', '#ee9e31', '#ee9330', '#ef8830', '#ef7d2f', '#f0722f', '#f0672e', '#f15c2e', '#f2512d', '#f2462d', '#f33b2c', '#f3302c', '#f4252b', '#f4192b', '#ef182f', '#e81834', '#e21939', '#db1a3e', '#d51a43', '#ce1b48', '#c71b4d', '#c11c52', '#ba1c58', '#b31d5d', '#ac1d62', '#a61e67', '#9f1e6c', '#981f72', '#911f77', '#8a207c', '#842182']\n",
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" custom_cmap = mpl.colors.LinearSegmentedColormap.from_list('custom', colornames, N=256)\n",
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" plt.imshow(pathDiagram[:450, :], cmap=custom_cmap, aspect=10.9, extent=[0, nSegPoints, elo, ehi], vmin=0, vmax=vmax)\n",
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" ax.set_xticks(pathInds)\n",
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" ax.set_xticklabels(['$\\overline{\\Gamma}$', '$\\overline{\\mathrm{M}}$',\n",
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" '$\\overline{\\mathrm{K}}$', '$\\overline{\\Gamma}$'], fontsize=15)\n",
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" for p in pathInds[:-1]:\n",
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" ax.axvline(x=p, c='r', ls='--', lw=2, dashes=[4, 2])\n",
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" # ax.axhline(y=0, ls='--', color='r', lw=2)\n",
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" ax.yaxis.set_major_locator(MultipleLocator(2))\n",
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" ax.yaxis.set_minor_locator(MultipleLocator(1))\n",
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" ax.yaxis.set_label_position(\"right\")\n",
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" ax.yaxis.tick_right()\n",
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" ax.set_ylabel('Energy (eV)', fontsize=15, rotation=-90, labelpad=20)\n",
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" ax.tick_params(axis='x', length=0, pad=6)\n",
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" ax.tick_params(which='both', axis='y', length=8, width=2, labelsize=15)\n",
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" \n",
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" plt.savefig(save_path, dpi=200)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load data\n",
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"data = fp.readBinnedhdf5('../data/pes/0_binned.h5')\n",
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"I = data['V']\n",
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"E = data['E']\n",
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"kx = data['kx']\n",
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"ky = data['ky']\n",
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"\n",
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"# Create reconstruction object from data file\n",
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"mrf = MrfRec(E=E, kx=kx, ky=ky, I=I)\n",
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"\n",
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"# Create plot folder if needed\n",
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"if not os.path.exists('../results/figures'):\n",
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" os.mkdir('../results/figures')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Main Figure 1c: photoemission band mapping data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"plot_path(mrf, 0.5, '../results/figures/fig_1c.png')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Main Figure 1d: Pattern symmetrization in $(k_x, k_y)$ plane (rotation and reflection)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"mrf.symmetrizeI()\n",
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"plot_path(mrf, 0.5, '../results/figures/fig_1d.png')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Main Figure 1e: Normalization and contrast enhancement (MCLAHE)\n",
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"MCLAHE stands for multidimensional contrast limited adaptive histogram equalization (see publication [here](https://ieeexplore.ieee.org/document/8895993))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"mrf.normalizeI(kernel_size=(20, 20, 25), n_bins=256, clip_limit=0.15, use_gpu=True)\n",
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"plot_path(mrf, 1, '../results/figures/fig_1e.png')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Main Figure 1f: Multidimensional smoothing using Gaussian filter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"mrf.smoothenI(sigma=(.8, .8, 1.))\n",
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"plot_path(mrf, 1, '../results/figures/fig_1f.png')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# # Save data to disc if needed\n",
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"# data_save = [['axes', {'E': mrf.E, 'kx': mrf.kx, 'ky': mrf.ky}], ['binned', {'V': mrf.I}]]\n",
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"# saveHDF(*data_save, save_addr='../data/preprocessed.h5')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.13"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"metadata": {
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"collapsed": false
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},
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"source": ""
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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