{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install hyperspy and ipympl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "%pip install ipywidgets==8.1.3\n",
        "%pip install ipympl hyperspy[gui-jupyter]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "trusted": true
      },
      "source": [
        "# Activate interactive matplotlib plotting (`widget` backend from ipympl library)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%matplotlib widget"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Try hyperspy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import hyperspy.api as hs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "dir(hs.data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "s = hs.data.two_gaussians()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "s.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m = s.create_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "g1 = hs.model.components1D.GaussianHF()\n",
        "g2 = hs.model.components1D.GaussianHF()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "g1.name = \"wide\"\n",
        "g2.name = \"narrow\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.extend([g1, g2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.print_current_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.axes_manager.indices = (0, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.components.narrow.centre.value = 60\n",
        "m.components.wide.centre.value = 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.fit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.multifit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.red_chisq.get_histogram().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.multifit(iterpath=\"serpentine\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "m.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python (Pyodide)",
      "language": "python",
      "name": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}