{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backcasting Demo Notebook\n",
    "\n",
    "_Loren Champlin_\n",
    "\n",
    "Adapted from _Adarsh Pyarelal_'s WM 12 Month Evaluation Notebook \n",
    "\n",
    "As always, we begin with imports, and print out the commit hash for a rendered\n",
    "version of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('retina')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from delphi.db import engine\n",
    "import random as rm\n",
    "import delphi.evaluation_port as EN\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.CRITICAL)\n",
    "from delphi.cpp.DelphiPython import AnalysisGraph as AG, InitialBeta as IB, RNG\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from delphi.jupyter_tools import display\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will set random seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(87)\n",
    "rm.seed(87)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "EN.get_data_value('Net migration',year=2012,month=2, use_heuristic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1,2,2],[4,4,4],[8,3,4],[7,2,3]])\n",
    "y = np.mean(x,axis=1)\n",
    "\n",
    "ax = sns.lineplot(x=range(5),y=range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(mpatches.Patch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "query = \" \".join(\n",
    "        [\n",
    "            f\"select * from indicator\",\n",
    "        ]\n",
    "    )\n",
    "\n",
    "results = engine.execute(query)\n",
    "\n",
    "list(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \" \".join(\n",
    "        [\n",
    "            f\"select * from indicator\",\n",
    "            f\"where `Variable` is 'Historical Production (Maize)'\",\n",
    "        ]\n",
    "    )\n",
    "\n",
    "results = engine.execute(query)\n",
    "\n",
    "results_df2 = pd.DataFrame(results,columns=results.keys())\n",
    "\n",
    "\n",
    "results_df2"
   ]
  }
 ],
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