{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallel Plots using JSON files \n",
    "</html>\n",
    "<div class=\"alert alert-info\" role=\"alert\" style=\"margin-top: 10px\">\n",
    "The goal of this exercise is to take the json files saved by the optimizer in OPAL,\n",
    "and use pyOPALTools to produce a parallel plot. \n",
    "</div>\n",
    "</html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "import numpy as np\n",
    "from opal.parser.OptimizerParser import OptimizerParser\n",
    "\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 99 json files.\n"
     ]
    }
   ],
   "source": [
    "files = './json-reader/data/'\n",
    "\n",
    "#Find all .json files in a directory\n",
    "optjson = OptimizerParser(files)\n",
    "optjson.readGeneration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load names of design variables and objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design variables:  ['benergy', 'phiinit', 'prinit', 'rinit']\n",
      "number of design variables =  4\n"
     ]
    }
   ],
   "source": [
    "dvars = optjson.getDesignVariables()\n",
    "print ( \"Design variables: \", dvars)\n",
    "print ( \"number of design variables = \", np.size(dvars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objectives:  ['dpeak1', 'dpeak2', 'dpeak3_5']\n",
      "number of objectives 3\n"
     ]
    }
   ],
   "source": [
    "objs = optjson.getObjectives()\n",
    "print ( \"Objectives: \", objs)\n",
    "print ( \"number of objectives\", np.size(objs))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find number of generations run by optimizer\n",
    "This should match number of files found by json reader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of generations =  99\n"
     ]
    }
   ],
   "source": [
    "n = optjson.getNumOfGenerations()\n",
    "print('Number of generations = ', n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load all data in first generation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optjson.readGeneration(1)\n",
    "dvarsall = optjson.getAllInput()\n",
    "objsall  = optjson.getAllOutput()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assign labels, data, and plot paralllel coord\n",
    "I will try to add a function (in visualize script) to do this without explicitly making Parcoord obj.  \n",
    "\n",
    "You can adjust data range by changing min and max: range = [1.5,10]  \n",
    "You can adjust tick marks as well: tickvals = [1.5,3,4.5],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pardata = [go.Parcoords(\n",
    "                line = dict(color = 'blue'),\n",
    "                dimensions = list([\n",
    "                    dict(#range = [1.5,10],\n",
    "                         label = dvars[0], values = dvarsall[:,0]),\n",
    "\n",
    "                    dict(#range = [-30,0],\n",
    "                         label = dvars[1], values = dvarsall[:,1]),\n",
    "\n",
    "                    dict(#range = [200,500],\n",
    "                         #tickvals = [1.5,3,4.5],\n",
    "                         label = dvars[2], values = dvarsall[:,2]),\n",
    "\n",
    "                    dict(#range = [170,260],\n",
    "                         #tickvals = [1,2,4,5],\n",
    "                         label = objs[0], values = objsall[:,0]),\n",
    "                         #ticktext = ['text 1', 'text 2', 'text 3', 'text 4']),\n",
    "\n",
    "                    dict(#range = [-8.0, 8.0],\n",
    "                         label = objs[1], values = objsall[:,1]),\n",
    "\n",
    "                    dict(#range = [-8.0,8.0],\n",
    "                         label = objs[2], values = objsall[:,2])\n",
    "\n",
    "                ]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot parallel coord plot and open in new tab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/matthias/Documents/projects/pyOPALTools/tests/testParallel.html'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotly.offline.plot(pardata, filename = 'testParallel.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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