{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "flex_source_code = \"https://github.com/danielfrg/jupyter-flex/blob/master/examples/widgets/mpl-histogram.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import clear_output, display\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import set_matplotlib_formats\n",
    "%matplotlib inline\n",
    "set_matplotlib_formats('svg')\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "# fig_size[0] = 6 * 3\n",
    "# fig_size[1] = 4 * 3\n",
    "# plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "size=250"
    ]
   },
   "source": [
    "## Sidebar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "inputs"
    ]
   },
   "outputs": [],
   "source": [
    "mu_label = widgets.Label(value=\"Mean:\")\n",
    "mu_var = widgets.BoundedIntText(value=100, min=10, max=300)\n",
    "sigma_label = widgets.Label(value=\"Sigma:\")\n",
    "sigma_var = widgets.BoundedIntText(value=15, min=10, max=50)\n",
    "bins_label = widgets.Label(value=\"Bins:\")\n",
    "bins_var = widgets.IntSlider(value=50, min=1, max=100, step=1)\n",
    "\n",
    "widgets.VBox([mu_label, mu_var, sigma_label, sigma_var, bins_label, bins_var])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "size=750"
    ]
   },
   "source": [
    "## Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = widgets.Output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_value_change(change):\n",
    "    mu = mu_var.value\n",
    "    sigma = sigma_var.value\n",
    "    num_bins = bins_var.value\n",
    "\n",
    "    with out:\n",
    "        fig, ax = plt.subplots()\n",
    "\n",
    "        # the histogram of the data\n",
    "        x = mu + sigma * np.random.randn(437)\n",
    "        n, bins, patches = ax.hist(x, num_bins, density=1)\n",
    "\n",
    "        # add a 'best fit' line\n",
    "        y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "        ax.plot(bins, y, '--')\n",
    "        \n",
    "        ax.set_xlabel('X')\n",
    "        ax.set_ylabel('Probability density')\n",
    "        ax.set_title(f'Histogram with: mu={mu}, sigma={sigma}, bins={num_bins}')\n",
    "\n",
    "        clear_output(wait=True)\n",
    "        plt.show(fig)\n",
    "\n",
    "mu_var.observe(on_value_change, names=\"value\")\n",
    "sigma_var.observe(on_value_change, names=\"value\")\n",
    "bins_var.observe(on_value_change, names=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "chart"
    ]
   },
   "outputs": [],
   "source": [
    "on_value_change(None)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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