{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Interpolation on Graphene deflector scan\n\nSimple workflow for the interpolation of data along a generic path in the\nk-space from its isoenergy cuts.\nThe data are a deflector scan on graphene as simulated from a third nearest\nneighbor tight binding model. The same workflow can be applied to any\ntilt-, polar-, deflector- or hv-scan.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import the \"fundamental\" python libraries for a generic data analysis:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Instead of loading the file as for example:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# from navarp.utils import navfile\n# file_name = r\"nxarpes_simulated_cone.nxs\"\n# entry = navfile.load(file_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here we build the simulated graphene signal with a dedicated function defined\njust for this purpose:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from navarp.extras.simulation import get_tbgraphene_deflector\n\nentry = get_tbgraphene_deflector(\n    scans=np.linspace(-5., 20., 91),\n    angles=np.linspace(-25, 6, 400),\n    ebins=np.linspace(-13, 0.4, 700),\n    tht_an=-18,\n    phi_an=0,\n    hv=120,\n    gamma=0.05\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fermi level autoset\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.autoset_efermi(scan_range=[-5, 5], energy_range=[115.2, 115.8])\nprint(\"Energy of the Fermi level = {:.0f} eV\".format(entry.efermi))\nprint(\"Energy resolution = {:.0f} meV\".format(entry.efermi_fwhm*1000))\n\nentry.plt_efermi_fit()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Check for the Fermi level alignment\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.isoscan(scan=0, dscan=0).show(yname='eef')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting iso-energetic cut at ekin = efermi\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.isoenergy(0, 0.02).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set the k-space for the transformation\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.set_kspace(\n    tht_p=0.1,\n    k_along_slit_p=1.7,\n    scan_p=0,\n    ks_p=0,\n    e_kin_p=114.3,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "and check the isoenergy at the Fermi level:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.isoenergy(0, 0.02).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Define the interpolation path points\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "kbins = 900\nk_GK = 1.702\nk_pts_xy = np.array([\n    [0, 0],\n    [k_GK, 0],\n    [k_GK*np.cos(np.pi/3), k_GK*np.sin(np.pi/3)+0.05],\n    [0, 0]\n])\nkx_pts = k_pts_xy[:, 0]\nky_pts = k_pts_xy[:, 1]\n\nklabels = [\n    r'$\\Gamma$',\n    r'$\\mathrm{K}$',\n    r'$\\mathrm{K}^{\\prime}$',\n    r'$\\Gamma$'\n]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "and show them on the isoenergy at the Dirac point energy:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "entry.isoenergy(-1.1, 0.02).show()\nplt.plot(kx_pts, ky_pts, '-+')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the interpolation defining an isok:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "isok = entry.isok(kx_pts, ky_pts, klabels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show the final results with the executed path on the isoenergy:\nsphinx_gallery_thumbnail_number = 6\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, axs = plt.subplots(1, 2)\n\nentry.isoenergy(0, 0.02).show(ax=axs[0])\n\nisok.path_show(axs[0], 'k', 'k', xytext=(8, 8))\n\nqmesh = isok.show(ax=axs[1])\n\nfig.tight_layout()\nfig.colorbar(qmesh)"
      ]
    }
  ],
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      "display_name": "Python 3",
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    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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