From 7f2e36e956aeb17154af0883cf8044572cff4543 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marek=20Ne=C4=8Dada?= Date: Tue, 23 Jun 2020 05:48:28 +0300 Subject: [PATCH] Square lattice example mode plot Former-commit-id: a07106cf57e2d2d7c2c9c19712fc4affbdad3419 --- .../rectangular/modes/04_collect_modes.ipynb | 1992 +++++++++++++++++ 1 file changed, 1992 insertions(+) create mode 100644 examples/rectangular/modes/04_collect_modes.ipynb diff --git a/examples/rectangular/modes/04_collect_modes.ipynb b/examples/rectangular/modes/04_collect_modes.ipynb new file mode 100644 index 0000000..82dcf1f --- /dev/null +++ b/examples/rectangular/modes/04_collect_modes.ipynb @@ -0,0 +1,1992 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import re\n", + "import numpy as np\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "from scipy.constants import hbar, e as eV, c\n", + "eh = eV/hbar\n", + "import glob\n", + "def ri(z): return (z.real, z.imag)\n", + "#m = re.compile(r\"([^_]+)_r([0-9.]+)nm_\")\n", + "#removek = re.compile(r\"(k\\([^)]+\\)um-1_)\")\n", + "#remover = re.compile(r\"r[0-9.]+nm_\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "allfiles=glob.glob('*cyl*k(0_0)*.npz')\n", + "allgraphs=dict()\n", + "for f in allfiles:\n", + " base = remover.sub('', f)\n", + " if base in allgraphs.keys():\n", + " allgraphs[base] += 1\n", + " else:\n", + " allgraphs[base] = 1\n", + "for k in sorted(allgraphs.keys()):\n", + " print(k, allgraphs[k])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "filenames=glob.glob('modes_basis*.npz')\n", + "projectors = np.load('projectors_D4h_lMax3.npz')\n", + "irlabels = sorted(projectors.keys())\n", + "projectors = {f: projectors[f] for f in irlabels}\n", + "projectors_list = [projectors[irlabels[i]] for i in range(len(projectors))]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "plotdata = []\n", + "for file in filenames:\n", + " dat = np.load(file, allow_pickle=True)\n", + " kx = dat['meta'][()]['k'][0]\n", + " b = dat['meta'][()]['band_index']\n", + " eigvals = dat['eigval']\n", + " lMax = dat['meta'][()]['lMax'][None]\n", + " residuals = dat['residuals']\n", + " #print(kx, residuals)\n", + " ef =dat['empty_freqs']\n", + " eigvecs = dat['eigvec']\n", + " if(kx==0): \n", + " irweights = np.array([np.linalg.norm(np.tensordot(proj, eigvecs, axes=(-1, -1)), axis=0,ord=2) for proj in projectors_list]).T\n", + " irclass = np.argmax(irweights, axis=-1)\n", + " else:\n", + " irweights = None\n", + " irclass = [None] * len(eigvals)\n", + " plotdata.append((kx, eigvals, residuals, b, ef, irclass))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "markerdict = {\n", + " 4: \"3\",\n", + " -4: \"4\",\n", + " 3: \"^\",\n", + " -3: \"v\",\n", + " -2: 'x',\n", + " 2: '+',\n", + " 1: 's',\n", + " -1: 'd',\n", + "}\n", + "\n", + "prop_cycle = plt.rcParams['axes.prop_cycle']\n", + "colors = prop_cycle.by_key()['color']\n", + "colordict = {i: colors[(i+1)] for i in range(-4,8)}\n", + "\n", + "def markerfun(b):\n", + " if b in markerdict.keys():\n", + " return markerdict[b]\n", + " else: return 'X'\n", + "\n", + "def colorfun(b):\n", + " if (b+1) in colordict.keys():\n", + " return colordict[b+1]\n", + " else: return colordict[0]\n", + " \n", + "labeldict = {\n", + " \"E'\" : \"$E'$\",\n", + " \"A2''\" : \"$A_2''$\",\n", + "}\n", + "def label2latex(label):\n", + " return labeldict[label] if label in labeldict.keys() else label\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, None}\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(6,8))\n", + "ax = fig.add_subplot(211)\n", + "\n", + "res_thr = 0.1\n", + "xlim = [40,200]\n", + "plot_irreps = None #{\"E''\"}\n", + "\n", + "\n", + "plot_iris=set(range(len(irlabels))) if plot_irreps is None else {iri for iri in range(len(irlabels)) if irlabels[iri] in plot_irreps}\n", + "plot_iris |= {None}\n", + "print(plot_iris)\n", + "\n", + "ir_labeled=set()\n", + "for (kx, eigvals, residuals, b, ef, irclass) in plotdata:\n", + " for i, (e, res, iri) in enumerate(zip(eigvals, residuals, irclass)):\n", + " #if i == 0:\n", + " if iri in plot_iris and res < res_thr:# and e.real < 2.14e15:\n", + " if iri in ir_labeled: \n", + " label=None\n", + " else:\n", + " ir_labeled.add(iri)\n", + " label=irlabels[iri] if iri is not None else None\n", + " ax.plot(kx/1e6, e.real/eh,\n", + " marker=markerfun(b) if iri is None else \"D\",\n", + " ms=4, #c=colorfun(b)\n", + " c=matplotlib.cm.hsv(iri/9 if iri is not None else 0),\n", + " #c = colorfun(iri),\n", + " label=label2latex(label),\n", + " )\n", + " #print(kx, abs(e.imag/e.real), res, b)\n", + "#for eff in ef:\n", + "# ax.axhline(y= eff/eh)\n", + "ax.set_xlabel(\"$k_x/\\mathrm{\\mu m^{-1}}$\")\n", + "ax.set_ylabel('$\\hbar \\Re \\omega / \\mathrm{eV}$')\n", + "ax.legend(title=\"Irrep at $\\\\Gamma$\")\n", + "#ax.set_ylim([2.075e15,2.085e15])\n", + "#ax.set_ylim([1.30, 1.42])\n", + "#ax.set_xlim(xlim)\n", + "\n", + "#ax.set_xlim([40,100])\n", + "#ax.legend()\n", + "\n", + "ax = fig.add_subplot(212)\n", + "ir_labeled=set()\n", + "for (kx, eigvals, residuals, b, ef, irclass) in plotdata:\n", + " for i, (e, res, iri) in enumerate(zip(eigvals, residuals, irclass)):\n", + " #if i==0:\n", + " if iri in plot_iris and res < res_thr:# and e.real < 2.14e15:\n", + " if iri in ir_labeled: \n", + " label=None\n", + " else:\n", + " ir_labeled.add(iri)\n", + " label=irlabels[iri] if iri is not None else None,\n", + " ax.plot(kx/1e6, e.imag/eh*1000, \n", + " #marker='x', \n", + " #c=colorfun(b), \n", + " c=matplotlib.cm.hsv(iri/9 if iri is not None else 0),#colorfun(iri),\n", + " marker=markerfun(b) if iri is None else \"D\", ms=4,\n", + " label=label2latex(label)\n", + " )\n", + " #print(kx, abs(e.imag/e.real), res, b)\n", + "#ax.set_ylim([-15,0])\n", + "#ax.set_xlim(xlim)\n", + "ax.set_xlabel(\"$k_x/\\mathrm{\\mu m^{-1}}$\")\n", + "ax.set_ylabel('$\\hbar \\Im \\omega / \\mathrm{meV}$')\n", + "#fig.suptitle('$l_\\mathrm{max}=%d$, residual threshold = %g' % (lMax, res_thr) )\n", + "#fig.savefig(plotfilename)\n", + "#fig.savefig(plotfilename.replace('pdf', 'png'))\n", + "fig.savefig(\"sqlat_beyn_dispersion.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(-266666.6666666667, array([3.35664512e+15-8.70857302e+11j]), array([0.00326631]), -4, array([5.25951681e+13, 3.25205670e+15, 3.30507039e+15, 3.35724704e+15,\n", + " 4.63644226e+15, 4.71082070e+15, 6.55670858e+15, 6.60951301e+15,\n", + " 6.66189891e+15, 7.34242136e+15, 7.36605517e+15, 7.41309677e+15,\n", + " 7.43650598e+15, 9.30985088e+15, 9.38423109e+15, 9.86136045e+15,\n", + " 9.91409513e+15, 9.96655079e+15, 1.04003439e+16, 1.04337140e+16,\n", + " 1.04669778e+16, 1.05001361e+16, 1.31660123e+16, 1.32187121e+16,\n", + " 1.32712027e+16]), [None]), (400000.0, array([3.30014096e+15-2.51999947e+12j]), array([5.91025308e-05]), -3, array([7.88927521e+13, 3.22575912e+15, 3.30559345e+15, 3.38354462e+15,\n", + " 4.61803485e+15, 4.72959810e+15, 6.53041099e+15, 6.60977458e+15,\n", + " 6.68819650e+15, 7.31894745e+15, 7.35448284e+15, 7.42504345e+15,\n", + " 7.46007348e+15, 9.29134886e+15, 9.40291808e+15, 9.83506287e+15,\n", + " 9.91426952e+15, 9.99284837e+15, 1.03754125e+16, 1.04255474e+16,\n", + " 1.04754422e+16, 1.05251006e+16, 1.31397147e+16, 1.32188429e+16]), [None]), 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4.74844689e+15, 6.50411341e+15, 6.61014077e+15,\n", + " 6.71449408e+15, 7.29549280e+15, 7.34298646e+15, 7.43706393e+15,\n", + " 7.48365918e+15, 9.27288451e+15, 9.42164140e+15, 9.80876528e+15,\n", + " 9.91451365e+15]), [None]), (133333.33333333334, array([3.27835479e+15+7.00997131e+09j]), array([0.16601648]), 2, array([2.62975840e+13, 3.27835429e+15, 3.30475651e+15, 3.33094946e+15,\n", + " 4.65492544e+15, 4.69211554e+15, 6.58300616e+15, 6.60935606e+15,\n", + " 6.63560133e+15, 7.36591434e+15, 7.37770309e+15, 7.40122424e+15,\n", + " 7.41295683e+15, 9.32839033e+15, 9.36558065e+15, 9.88765803e+15,\n", + " 9.91399050e+15]), [None]), (533333.3333333334, array([3.18994344e+15-5.57748807e+12j]), array([0.00536213]), -2, array([1.05190336e+14, 3.19946154e+15, 3.30632560e+15, 3.40984221e+15,\n", + " 4.59970413e+15, 4.74844689e+15, 6.50411341e+15, 6.61014077e+15,\n", + " 6.71449408e+15, 7.29549280e+15, 7.34298646e+15, 7.43706393e+15,\n", + " 7.48365918e+15, 9.27288451e+15, 9.42164140e+15, 9.80876528e+15,\n", + " 9.91451365e+15]), [None]), (-373333.3333333333, array([], dtype=complex128), array([], dtype=float64), 1, array([7.36332353e+13, 3.23101864e+15, 3.30547211e+15, 3.37828511e+15,\n", + " 4.62171023e+15, 4.72583689e+15, 6.53567051e+15, 6.60971390e+15]), []), (133333.33333333334, array([], dtype=complex128), array([], dtype=float64), 1, array([2.62975840e+13, 3.27835429e+15, 3.30475651e+15, 3.33094946e+15,\n", + " 4.65492544e+15, 4.69211554e+15, 6.58300616e+15, 6.60935606e+15]), []), (-293333.3333333333, array([3.30517599e+15-1112660.64916992j]), array([0.14959926]), 3, array([5.78546849e+13, 3.24679719e+15, 3.30515827e+15, 3.36250656e+15,\n", + " 4.63275469e+15, 4.71457043e+15, 6.55144906e+15, 6.60955696e+15,\n", + " 6.66715843e+15, 7.33772504e+15, 7.36373465e+15, 7.41548018e+15,\n", + " 7.44121801e+15, 9.30614747e+15, 9.38796557e+15, 9.85610093e+15,\n", + " 9.91412443e+15, 9.97181030e+15, 1.03953571e+16, 1.04320759e+16,\n", + " 1.04686659e+16, 1.05051285e+16, 1.31607528e+16, 1.32187341e+16]), [None]), (-186666.66666666666, array([3.30485388e+15+4.66629553e+09j, 3.30484930e+15-7.25032353e+08j,\n", + " 3.30485218e+15-5.60888939e+08j]), array([0.37812244, 0.30679969, 0.30683937]), -3, array([3.68166176e+13, 3.26783525e+15, 3.30485695e+15, 3.34146849e+15,\n", + " 4.64752313e+15, 4.69958888e+15, 6.57248713e+15, 6.60940629e+15,\n", + " 6.64612036e+15, 7.35651487e+15, 7.37303487e+15, 7.40596433e+15,\n", + " 7.42237427e+15, 9.32097007e+15, 9.37303643e+15, 9.87713900e+15,\n", + " 9.91402398e+15, 9.95077224e+15, 1.04153060e+16, 1.04386428e+16,\n", + " 1.04619275e+16, 1.04851606e+16, 1.31817909e+16, 1.32186588e+16]), [None, None, None])]\n" + ] + } + ], + "source": [ + "print (plotdata)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}