plot_template.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 356.972x267.729 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "\n",
    "\n",
    "IMG_DIRECTORY = \"./Assets/\"\n",
    "\n",
    "\n",
    "def save_plot(fig, name, formats=[\"pdf\",\"jpg\"], date=True):\n",
    "    for f in formats:\n",
    "        fig.savefig(\n",
    "            os.path.join(IMG_DIRECTORY, \"{}\".format(name) + \".\" + f),\n",
    "            format=f,\n",
    "        )\n",
    "\n",
    "\n",
    "def set_size(width, fraction=1, subplots=(1, 1)):\n",
    "    if width == \"thesis\":\n",
    "        width_pt = 426.79135\n",
    "    elif width == \"beamer\":\n",
    "        width_pt = 307.28987\n",
    "    else:\n",
    "        width_pt = width\n",
    "\n",
    "    fig_width_pt = width_pt * fraction\n",
    "    inches_per_pt = 1 / 72.27\n",
    "\n",
    "    golden_ratio = (5**0.5 - 1) / 2\n",
    "\n",
    "    fig_width_in = fig_width_pt * inches_per_pt\n",
    "    fig_height_in = fig_width_in * (golden_ratio) * (subplots[0] / subplots[1])\n",
    "\n",
    "    return (fig_width_in, fig_height_in)\n",
    "\n",
    "\n",
    "save = True\n",
    "width = 1.25 * 458.63788\n",
    "\n",
    "plt.style.use(\"./latex_ready.mplstyle\")\n",
    "\n",
    "tuple_size = set_size(width, fraction=0.50)\n",
    "\n",
    "multiplier = 0.9\n",
    "second_multiplier = 0.7\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    nrows=1,\n",
    "    ncols=1,\n",
    "    sharex=True,\n",
    "    figsize=(multiplier * tuple_size[0], 0.75 * multiplier * tuple_size[0]),\n",
    "    gridspec_kw={\"hspace\": 0},\n",
    ")\n",
    "\n",
    "\n",
    "fig.subplots_adjust(left=0.16)\n",
    "fig.subplots_adjust(bottom=0.16)\n",
    "fig.subplots_adjust(top=0.97)\n",
    "fig.subplots_adjust(right=0.97)\n",
    "\n",
    "x = np.linspace(0.0, 1.0, 1000)\n",
    "y = np.exp(-3 * x)\n",
    "yerr = 0.1 * y\n",
    "\n",
    "axs.errorbar(\n",
    "    x,\n",
    "    y,\n",
    "    yerr=yerr,\n",
    "    marker=\".\",\n",
    "    markersize=0.5,\n",
    "    linewidth=0.5,\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "\n",
    "axs.set_xscale(\"log\")\n",
    "axs.set_ylabel(r\"$E_{\\mathrm{estim}}$\", labelpad=2.0)\n",
    "axs.set_xlabel(r\"$\\alpha$\", labelpad=2.0)\n",
    "axs.grid(which=\"both\", axis=\"both\", alpha=0.5)\n",
    "axs.legend()\n",
    "\n",
    "if save:\n",
    "    save_plot(\n",
    "        fig,\n",
    "        \"alpha_sweep\",\n",
    "    )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# If one needs a bit more space one can put a multiplier in the size but not more than 1.7 I should say"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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