224 lines (223 with data), 4.6 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show Matplotlib plots inline with the content in the notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enable auto-reloading of python scripts for faster development:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Python-R interface, enabling `%R` and `%%R` magics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And load the frequently used R packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%R require(ggplot2)\n",
    "%R require(patchwork)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And set seed (for reproducible graphics):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R set.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add top directory to Python path, make all paths relative to root:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caching:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.magic import register_cell_magic\n",
    "from pathlib import Path\n",
    "from datetime import datetime\n",
    "import pickle\n",
    "\n",
    "\n",
    "@register_cell_magic\n",
    "def cache(line, cell):\n",
    "    assert line\n",
    "    path, variable = line.split(' ')\n",
    "    path = Path('cache/' + path + '.pickle')\n",
    "    path.parent.mkdir(parents=True, exist_ok=True)\n",
    "    if path.exists():\n",
    "        date = datetime.fromtimestamp(path.stat().st_mtime)\n",
    "        print(\n",
    "            f'Reusing the results from {path}'\n",
    "            f' (last modified on {date:%Y-%m-%d %H:%M%z})'\n",
    "        )\n",
    "        with open(path, 'rb') as f:\n",
    "            result = pickle.load(f)\n",
    "    else:\n",
    "        exec(cell)\n",
    "        result = locals()[variable]\n",
    "        print(f'Storing results in {path}')\n",
    "        with open(path, 'wb') as f:\n",
    "            pickle.dump(result, f)\n",
    "\n",
    "    globals()[variable] = result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data archive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_vault\n",
    "%open_vault --path data/storage.zip --secure False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add custom pandas methods for more efficient coding and more reproducible diffs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "from helpers.frames import value_counts\n",
    "\n",
    "# if used instead of value_counts() returns value_counts()\n",
    "# with equal counts predictably sorted which reduces diffs\n",
    "Series.sorted_value_counts = value_counts"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "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": "3.8.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}