Template for .ipynb, things to import or load at the beginning

templates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "# Specify renderer\n",
    "# matplotlib.use('Agg')\n",
    "\n",
    "# Boiler-plate settings for producing pub-quality figures\n",
    "# 1 point = 1/72 inch\n",
    "from cycler import cycler\n",
    "matplotlib.rcParams['axes.prop_cycle'] = cycler(color='bgrcmyk')\n",
    "matplotlib.rcParams.update({'figure.figsize': (8, 5)    # inches\n",
    "                            , 'font.size': 22      # points\n",
    "                            , 'legend.fontsize': 16      # points\n",
    "                            , 'lines.linewidth': 1.5       # points\n",
    "                            , 'axes.linewidth': 1.5       # points\n",
    "                            , 'text.usetex': True    # Use LaTeX to layout text\n",
    "                            , 'font.family': \"serif\"  # Use serifed fonts\n",
    "                            , 'xtick.major.size': 10     # length, points\n",
    "                            , 'xtick.major.width': 1.5     # points\n",
    "                            , 'xtick.minor.size': 6     # length, points\n",
    "                            , 'xtick.minor.width': 1     # points\n",
    "                            , 'ytick.major.size': 10     # length, points\n",
    "                            , 'ytick.major.width': 1.5     # points\n",
    "                            , 'ytick.minor.size': 6     # length, points\n",
    "                            , \"xtick.minor.visible\": True\n",
    "                            , \"ytick.minor.visible\": True\n",
    "                            , 'font.weight': 'bold'\n",
    "                            , 'ytick.minor.width': 1     # points\n",
    "                            , 'font.serif': (\"Times\", \"Palatino\", \"Computer Modern Roman\", \"New Century Schoolbook\", \"Bookman\"), 'font.sans-serif': (\"Helvetica\", \"Avant Garde\", \"Computer Modern Sans serif\"), 'font.monospace': (\"Courier\", \"Computer Modern Typewriter\"), 'font.cursive': \"Zapf Chancery\"\n",
    "                            })\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context(\"poster\")\n",
    "sns.set(rc={'figure.figsize': (16, 9.)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO, stream=sys.stdout)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "# ML\n",
    "import sklearn\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", 120)\n",
    "pd.set_option(\"display.max_columns\", 120)\n",
    "\n",
    "#Set the display format to be scientific for ease of analysis\n",
    "# pd.options.display.float_format = '{:,.2g}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No repo found\n"
     ]
    }
   ],
   "source": [
    "try: \n",
    "    import subprocess\n",
    "    gitd = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir))\n",
    "    githash = subprocess.check_output('git --git-dir={0:s} --work-tree={1:s} '\\\n",
    "              'rev-parse HEAD'.format(gitd+'/.git',gitd),shell=True).rstrip()\n",
    "except:\n",
    "    githash = 'No repo found'\n",
    "\n",
    "print(githash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Mon Apr 20 2020 \n",
      "\n",
      "CPython 3.6.2\n",
      "IPython 6.1.0\n",
      "\n",
      "jupyerlab not installed\n",
      "numpy 1.14.5\n",
      "scipy 0.19.1\n",
      "sklearn 0.21.3\n",
      "pandas 0.24.2\n",
      "matplotlib 2.0.2\n",
      "\n",
      "compiler   : GCC 5.4.0\n",
      "system     : Linux\n",
      "release    : 3.10.0-1062.9.1.el7.x86_64\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 40\n",
      "interpreter: 64bit\n",
      "host name  : worker2082\n",
      "Git hash   :\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -h -n -g -m -p jupyerlab,numpy,scipy,sklearn,pandas,matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (python3/3.6.2)",
   "language": "python",
   "name": "module-python3-3.6.2-python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}