{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two classes for analysis: Alternative which provides methods for a [Cost Benefit Analysis](https://en.wikipedia.org/wiki/Cost–benefit_analysis) and Decision which provides methods for a [Multiple Criteria Analysis](https://en.wikipedia.org/wiki/Multiple-criteria_decision_analysis). The Timeline class generates a [Gantt Chart](https://en.wikipedia.org/wiki/Gantt_chart)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from dpd.analysis import Activity, Alternative, Currency\n",
    "\n",
    "USD = Currency(\"USD\", 2020, 0.07).discount()\n",
    "\n",
    "a1 = Alternative(name=\"Alternative One\")\n",
    "planning = Activity(\n",
    "    \"Planning\", datetime(2005, 1, 1), datetime(2009, 12, 31), 100 * USD, 0 * USD\n",
    ")\n",
    "construction = Activity(\n",
    "    \"Construction\", datetime(2010, 1, 1), datetime(2014, 12, 31), 100 * USD, 0 * USD\n",
    ")\n",
    "operations = Activity(\n",
    "    \"Operations\",\n",
    "    construction.End,\n",
    "    construction.End + timedelta(days=365 * 20 + 4),\n",
    "    0 * USD,\n",
    "    400 * USD,\n",
    ")\n",
    "\n",
    "for activity in [planning, construction, operations]:\n",
    "    a1.add_activity(activity)\n",
    "\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1.benefit_cost_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1.period_range_pivot().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "a1.cash_flow_diagram(ax=ax)\n",
    "ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dpd.analysis import Timeline\n",
    "\n",
    "t = Timeline()\n",
    "for activity in [planning, construction, operations]:\n",
    "    t.add_activity(activity)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "t.plot_gantt(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "a1.timeline.plot_gantt(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from random import randint\n",
    "\n",
    "from astropy import units\n",
    "\n",
    "from dpd.analysis import Alternative, Currency, Decision\n",
    "from dpd.analysis.criteria import AttributeCriterion, CostCriterion, Criterion\n",
    "\n",
    "USD = Currency(\"USD\", 2020, 0.07).discount()\n",
    "decision = Decision()\n",
    "\n",
    "no_build_alternative = Alternative(\"No Build Alternative\")\n",
    "no_build_alternative.some_cost = 0 * USD\n",
    "alternative_one = Alternative(\"Alternative 1\")\n",
    "alternative_one.some_cost = 100 * USD\n",
    "alternative_two = Alternative(\"Alternative 2\")\n",
    "alternative_two.some_cost = 200 * USD\n",
    "\n",
    "\n",
    "@property\n",
    "def travel_time(self):\n",
    "    return randint(100, 200) * units.second\n",
    "\n",
    "\n",
    "setattr(Alternative, \"travel_time\", travel_time)\n",
    "\n",
    "decision.add_criterion(AttributeCriterion(\"Travel Time\", weight=1 / units.second))\n",
    "decision.add_criterion(\n",
    "    Criterion(\"Distance\", lambda x: 200 * units.meter, weight=1 / units.meter)\n",
    ")\n",
    "decision.add_criterion(AttributeCriterion(\"Some Cost\", weight=1 / USD))\n",
    "\n",
    "for alternative in [no_build_alternative, alternative_one, alternative_two]:\n",
    "    decision.add_alternative(alternative)\n",
    "\n",
    "decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision.multiple_criteria_decision_analysis().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dpd.analysis.values import (\n",
    "    Value_of_Reduced_Fatalities_and_Injuries,\n",
    "    Value_of_Travel_Time_Savings,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Value_of_Reduced_Fatalities_and_Injuries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Value_of_Travel_Time_Savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.units import day, minute, year\n",
    "from dpd.analysis.units import person, usd2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "    5\n",
    "    * minute\n",
    "    * 40000\n",
    "    * person\n",
    "    / day\n",
    "    * Value_of_Travel_Time_Savings[\n",
    "        \"Walking Cycling, Waiting, Standing, and Transfer Time\"\n",
    "    ]\n",
    ").decompose().to(usd2020 / year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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