{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "\n\n# Plotting topographic arrowmaps of evoked data\n\nLoad evoked data and plot arrowmaps along with the topomap for selected time\npoints. An arrowmap is based upon the Hosaka-Cohen transformation and\nrepresents an estimation of the current flow underneath the MEG sensors.\nThey are a poor man's MNE.\n\nSee :footcite:`CohenHosaka1976` for details.\n\n## References\n.. footbibliography::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Sheraz Khan <sheraz@khansheraz.com>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n\nimport mne\nfrom mne import read_evokeds\nfrom mne.datasets import sample\nfrom mne.datasets.brainstorm import bst_raw\nfrom mne.viz import plot_arrowmap\n\nprint(__doc__)\n\npath = sample.data_path()\nfname = path / \"MEG\" / \"sample\" / \"sample_audvis-ave.fif\"\n\n# load evoked data\ncondition = \"Left Auditory\"\nevoked = read_evokeds(fname, condition=condition, baseline=(None, 0))\nevoked_mag = evoked.copy().pick(picks=\"mag\", exclude=\"bads\")\nevoked_grad = evoked.copy().pick(picks=\"grad\", exclude=\"bads\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot magnetometer data as an arrowmap along with the topoplot at the time\nof the maximum sensor space activity:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "max_time_idx = np.abs(evoked_mag.data).mean(axis=0).argmax()\nplot_arrowmap(evoked_mag.data[:, max_time_idx], evoked_mag.info)\n\n# Since planar gradiometers takes gradients along latitude and longitude,\n# they need to be projected to the flatten manifold span by magnetometer\n# or radial gradiometers before taking the gradients in the 2D Cartesian\n# coordinate system for visualization on the 2D topoplot. You can use the\n# ``info_from`` and ``info_to`` parameters to interpolate from\n# gradiometer data to magnetometer data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot gradiometer data as an arrowmap along with the topoplot at the time\nof the maximum sensor space activity:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_arrowmap(\n    evoked_grad.data[:, max_time_idx],\n    info_from=evoked_grad.info,\n    info_to=evoked_mag.info,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Since Vectorview 102 system perform sparse spatial sampling of the magnetic\nfield, data from the Vectorview (info_from) can be projected to the high\ndensity CTF 272 system (info_to) for visualization\n\nPlot gradiometer data as an arrowmap along with the topoplot at the time\nof the maximum sensor space activity:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "path = bst_raw.data_path()\nraw_fname = path / \"MEG\" / \"bst_raw\" / \"subj001_somatosensory_20111109_01_AUX-f.ds\"\nraw_ctf = mne.io.read_raw_ctf(raw_fname)\nraw_ctf_info = mne.pick_info(\n    raw_ctf.info, mne.pick_types(raw_ctf.info, meg=True, ref_meg=False)\n)\nplot_arrowmap(\n    evoked_grad.data[:, max_time_idx],\n    info_from=evoked_grad.info,\n    info_to=raw_ctf_info,\n    scale=6e-10,\n)"
      ]
    }
  ],
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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