docs/Main_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eppy Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors: Santosh Philip, Leora Tanjuatco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eppy is a scripting language for E+ idf files, and E+ output files. Eppy is written in the programming language Python. As a result it takes full advantage of the rich data structure and idioms that are avaliable in python. You can programmatically navigate, search, and modify E+ idf files using eppy. The power of using a scripting language allows you to do the following:\n",
    "\n",
    "- Make a large number of changes in an idf file with a few lines of eppy code.\n",
    "- Use conditions and filters when making changes to an idf file\n",
    "- Make changes to multiple idf files.\n",
    "- Read data from the output files of a E+ simulation run.\n",
    "- Based to the results of a E+ simulation run, generate the input file for the next simulation run.\n",
    "\n",
    "So what does this matter? Here are some of the things you can do with eppy:\n",
    "\n",
    "- Change construction for all north facing walls.\n",
    "- Change the glass type for all windows larger than 2 square meters.\n",
    "- Change the number of people in all the interior zones.\n",
    "- Change the lighting power in all south facing zones. \n",
    "- Change the efficiency and fan power of all rooftop units.\n",
    "- Find the energy use of all the models in a folder (or of models that were run after a certain date) \n",
    "- If a model is using more energy than expected, keep increasing the R-value of the roof until you get to the expected energy use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a short IDF file that I’ll be using as an example to start us off ::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    VERSION,\n",
    "        7.2;                     !- Version Identifier\n",
    "    \n",
    "    SIMULATIONCONTROL,\n",
    "        Yes,                     !- Do Zone Sizing Calculation\n",
    "        Yes,                     !- Do System Sizing Calculation\n",
    "        Yes,                     !- Do Plant Sizing Calculation\n",
    "        No,                      !- Run Simulation for Sizing Periods\n",
    "        Yes;                     !- Run Simulation for Weather File Run Periods\n",
    "    \n",
    "    BUILDING,\n",
    "        White House,             !- Name\n",
    "        30.,                     !- North Axis {deg}\n",
    "        City,                    !- Terrain\n",
    "        0.04,                    !- Loads Convergence Tolerance Value\n",
    "        0.4,                     !- Temperature Convergence Tolerance Value {deltaC}\n",
    "        FullExterior,            !- Solar Distribution\n",
    "        25,                      !- Maximum Number of Warmup Days\n",
    "        6;                       !- Minimum Number of Warmup Days\n",
    "    \n",
    "    SITE:LOCATION,\n",
    "        CHICAGO_IL_USA TMY2-94846,  !- Name\n",
    "        41.78,                   !- Latitude {deg}\n",
    "        -87.75,                  !- Longitude {deg}\n",
    "        -6.00,                   !- Time Zone {hr}\n",
    "        190.00;                  !- Elevation {m}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use eppy to look at this model, we have to run a little code first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you would normaly install eppy by doing\n",
    "# python setup.py install\n",
    "# or\n",
    "# pip install eppy\n",
    "# or\n",
    "# easy_install eppy\n",
    "\n",
    "# if you have not done so, uncomment the following three lines\n",
    "import sys\n",
    "# pathnameto_eppy = 'c:/eppy'\n",
    "pathnameto_eppy = '../'\n",
    "sys.path.append(pathnameto_eppy) \n",
    "\n",
    "from eppy import modeleditor \n",
    "from eppy.modeleditor import IDF\n",
    "iddfile = \"../eppy/resources/iddfiles/Energy+V7_2_0.idd\"\n",
    "fname1 = \"../eppy/resources/idffiles/V_7_2/smallfile.idf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDF.setiddname(iddfile)\n",
    "idf1 = IDF(fname1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "idf1 now holds all the data to your in you idf file.  \n",
    "\n",
    "Now that the behind-the-scenes work is done, we can print this file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "VERSION,\n",
      "    7.3;                      !- Version Identifier\n",
      "\n",
      "SIMULATIONCONTROL,\n",
      "    Yes,                      !- Do Zone Sizing Calculation\n",
      "    Yes,                      !- Do System Sizing Calculation\n",
      "    Yes,                      !- Do Plant Sizing Calculation\n",
      "    No,                       !- Run Simulation for Sizing Periods\n",
      "    Yes;                      !- Run Simulation for Weather File Run Periods\n",
      "\n",
      "BUILDING,\n",
      "    Empire State Building,    !- Name\n",
      "    30,                       !- North Axis\n",
      "    City,                     !- Terrain\n",
      "    0.04,                     !- Loads Convergence Tolerance Value\n",
      "    0.4,                      !- Temperature Convergence Tolerance Value\n",
      "    FullExterior,             !- Solar Distribution\n",
      "    25,                       !- Maximum Number of Warmup Days\n",
      "    6;                        !- Minimum Number of Warmup Days\n",
      "\n",
      "SITE:LOCATION,\n",
      "    CHICAGO_IL_USA TMY2-94846,    !- Name\n",
      "    41.78,                    !- Latitude\n",
      "    -87.75,                   !- Longitude\n",
      "    -6,                       !- Time Zone\n",
      "    190;                      !- Elevation\n",
      "\n"
     ]
    }
   ],
   "source": [
    "idf1.printidf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the same file as before, except that all the comments are slightly different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this file has four objects:\n",
    "\n",
    "- VERSION\n",
    "- SIMULATIONCONTROL\n",
    "- BUILDING\n",
    "- SITE:LOCATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let us look take a closer look at the BUILDING object.\n",
    "We can do this using this command::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    print(filename.idfobjects['OBJECTNAME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "BUILDING,\n",
      "    Empire State Building,    !- Name\n",
      "    30,                       !- North Axis\n",
      "    City,                     !- Terrain\n",
      "    0.04,                     !- Loads Convergence Tolerance Value\n",
      "    0.4,                      !- Temperature Convergence Tolerance Value\n",
      "    FullExterior,             !- Solar Distribution\n",
      "    25,                       !- Maximum Number of Warmup Days\n",
      "    6;                        !- Minimum Number of Warmup Days\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(idf1.idfobjects['BUILDING']) # put the name of the object you'd like to look at in brackets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also zoom in on the object and look just at its individual parts.\n",
    "\n",
    "For example, let us look at the name of the building.\n",
    "\n",
    "To do this, we have to do some more behind-the-scenes work, which we'll explain later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "building = idf1.idfobjects['BUILDING'][0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empire State Building\n"
     ]
    }
   ],
   "source": [
    "print(building.Name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've isolated the building name, we can change it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "building.Name = \"Empire State Building\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empire State Building\n"
     ]
    }
   ],
   "source": [
    "print(building.Name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did this actually change the name in the model ? Let us print the entire model and see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "VERSION,\n",
      "    7.3;                      !- Version Identifier\n",
      "\n",
      "SIMULATIONCONTROL,\n",
      "    Yes,                      !- Do Zone Sizing Calculation\n",
      "    Yes,                      !- Do System Sizing Calculation\n",
      "    Yes,                      !- Do Plant Sizing Calculation\n",
      "    No,                       !- Run Simulation for Sizing Periods\n",
      "    Yes;                      !- Run Simulation for Weather File Run Periods\n",
      "\n",
      "BUILDING,\n",
      "    Empire State Building,    !- Name\n",
      "    30,                       !- North Axis\n",
      "    City,                     !- Terrain\n",
      "    0.04,                     !- Loads Convergence Tolerance Value\n",
      "    0.4,                      !- Temperature Convergence Tolerance Value\n",
      "    FullExterior,             !- Solar Distribution\n",
      "    25,                       !- Maximum Number of Warmup Days\n",
      "    6;                        !- Minimum Number of Warmup Days\n",
      "\n",
      "SITE:LOCATION,\n",
      "    CHICAGO_IL_USA TMY2-94846,    !- Name\n",
      "    41.78,                    !- Latitude\n",
      "    -87.75,                   !- Longitude\n",
      "    -6,                       !- Time Zone\n",
      "    190;                      !- Elevation\n",
      "\n"
     ]
    }
   ],
   "source": [
    "idf1.printidf()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes! It did. So now you have a taste of what eppy can do. Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying IDF Fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was just a quick example -- we were showing off. Let's look a little closer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you might have guessed, changing an IDF field follows this structure:: "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    object.fieldname = \"New Field Name\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plugging the object name (building), the field name (Name) and our new field name (\"Empire State Building\") into this command gave us this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "building.Name = \"Empire State Building\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import eppy\n",
    "# import eppy.ex_inits\n",
    "# reload(eppy.ex_inits)\n",
    "import ex_inits\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But how did we know that \"Name\" is one of the fields in the object \"building\"?\n",
    "\n",
    "Are there other fields?\n",
    "\n",
    "What are they called?\n",
    "\n",
    "Let's take a look at the IDF editor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ex_inits #no need to know this code, it just shows the image below\n",
    "for_images = ex_inits\n",
    "for_images.display_png(for_images.idfeditor) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the IDF Editor, the building object is selected.\n",
    "\n",
    "We can see all the fields of the object \"BUILDING\".\n",
    "\n",
    "They are:\n",
    "\n",
    "- Name\n",
    "- North Axis\n",
    "- Terrain\n",
    "- Loads Convergence Tolerance Value\n",
    "- Temperature Convergence Tolerance Value\n",
    "- Solar Distribution\n",
    "- Maximum Number of Warmup Days\n",
    "- Minimum Number of Warmup Days\n",
    "\n",
    "Let us try to access the other fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City\n"
     ]
    }
   ],
   "source": [
    "print(building.Terrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the field \"North Axis\" ?\n",
    "\n",
    "It is not a single word, but two words.\n",
    "\n",
    "In a programming language, a variable has to be a single word without any spaces.\n",
    "\n",
    "To solve this problem, put an underscore where there is a space.\n",
    "\n",
    "So \"North Axis\" becomes \"North_Axis\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.0\n"
     ]
    }
   ],
   "source": [
    "print(building.North_Axis)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empire State Building\n",
      "30.0\n",
      "City\n",
      "0.04\n",
      "0.4\n",
      "FullExterior\n",
      "25\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(building.Name)                                                                  \n",
    "print(building.North_Axis)                                                            \n",
    "print(building.Terrain)                                                               \n",
    "print(building.Loads_Convergence_Tolerance_Value)                                     \n",
    "print(building.Temperature_Convergence_Tolerance_Value)                               \n",
    "print(building.Solar_Distribution)                                                    \n",
    "print(building.Maximum_Number_of_Warmup_Days)                                         \n",
    "print(building.Minimum_Number_of_Warmup_Days)                                         \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where else can we find the field names?\n",
    "\n",
    "The IDF Editor saves the idf file with the field name commented next to field.\n",
    "\n",
    "Eppy also does this.\n",
    "\n",
    "Let us take a look at the \"BUILDING\" object in the text file that the IDF Editor saves ::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    BUILDING,\n",
    "        White House,             !- Name\n",
    "        30.,                     !- North Axis {deg}\n",
    "        City,                    !- Terrain\n",
    "        0.04,                    !- Loads Convergence Tolerance Value\n",
    "        0.4,                     !- Temperature Convergence Tolerance Value {deltaC}\n",
    "        FullExterior,            !- Solar Distribution\n",
    "        25,                      !- Maximum Number of Warmup Days\n",
    "        6;                       !- Minimum Number of Warmup Days\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This a good place to find the field names too.\n",
    "\n",
    "It is easy to copy and paste from here. You can't do that from the IDF Editor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that in an E+ model, there will be only ONE \"BUILDING\" object. This will be the first and only item in the list \"buildings\".\n",
    "\n",
    "But E+ models are made up of objects such as \"BUILDING\", \"SITE:LOCATION\", \"ZONE\", \"PEOPLE\", \"LIGHTS\".   There can be a number of \"ZONE\" objects, a number of \"PEOPLE\" objects and a number of \"LIGHTS\" objects.\n",
    "\n",
    "So how do you know if you're looking at the first \"ZONE\" object or the second one? Or the tenth one?  To answer this, we need to learn about how lists work in python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python lesson 1: lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eppy holds these objects in a python structure called list. Let us take a look at how lists work in python. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruits = [\"apple\", \"orange\", \"bannana\"] \n",
    "# fruits is a list with three items in it.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the first item in fruits we say: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits[0]  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why \"0\" ?\n",
    "\n",
    "Because, unlike us, python starts counting from zero in a list. So, to get the third item in the list we'd need to input 2, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bannana\n"
     ]
    }
   ],
   "source": [
    "print(fruits[2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But calling the first fruit \"fruit[0]\" is rather cumbersome. Why don't we call it firstfruit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n"
     ]
    }
   ],
   "source": [
    "firstfruit = fruits[0]\n",
    "print(firstfruit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also can say"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "apple\n",
      "apple\n",
      "apple\n"
     ]
    }
   ],
   "source": [
    "goodfruit = fruits[0]\n",
    "redfruit = fruits[0]\n",
    "\n",
    "print(firstfruit)                                                                     \n",
    "print(goodfruit)\n",
    "print(redfruit)\n",
    "print(fruits[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " As you see, we can call that item in the list whatever we want.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many items in the list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To know how many items are in a list, we ask for the length of the list.\n",
    "\n",
    "The function 'len' will do this for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(fruits))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3 fruits in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving an idf file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "idf1.save()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you'd like to do a \"Save as...\" use this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "idf1.saveas('something.idf')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with E+ objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us open a small idf file that has only \"CONSTRUCTION\" and \"MATERIAL\" objects in it. You can go into \"../idffiles/V_7_2/constructions.idf\" and take a look at the file. We are not printing it here because it is too big.  \n",
    "\n",
    "So let us open it using the idfreader -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eppy import modeleditor\n",
    "from eppy.modeleditor import IDF\n",
    "\n",
    "iddfile = \"../eppy/resources/iddfiles/Energy+V7_2_0.idd\"\n",
    "try:\n",
    "    IDF.setiddname(iddfile)\n",
    "except modeleditor.IDDAlreadySetError as e:\n",
    "    pass\n",
    "\n",
    "fname1 = \"../eppy/resources/idffiles/V_7_2/constructions.idf\"\n",
    "idf1 = IDF(fname1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us print all the \"MATERIAL\" objects in this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "Material,\n",
      "    F08 Metal surface,        !- Name\n",
      "    Smooth,                   !- Roughness\n",
      "    0.0008,                   !- Thickness\n",
      "    45.28,                    !- Conductivity\n",
      "    7824,                     !- Density\n",
      "    500;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    I01 25mm insulation board,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.03,                     !- Conductivity\n",
      "    43,                       !- Density\n",
      "    1210;                     !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    I02 50mm insulation board,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.0508,                   !- Thickness\n",
      "    0.03,                     !- Conductivity\n",
      "    43,                       !- Density\n",
      "    1210;                     !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    G01a 19mm gypsum board,    !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.019,                    !- Thickness\n",
      "    0.16,                     !- Conductivity\n",
      "    800,                      !- Density\n",
      "    1090;                     !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    M11 100mm lightweight concrete,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.1016,                   !- Thickness\n",
      "    0.53,                     !- Conductivity\n",
      "    1280,                     !- Density\n",
      "    840;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    F16 Acoustic tile,        !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0191,                   !- Thickness\n",
      "    0.06,                     !- Conductivity\n",
      "    368,                      !- Density\n",
      "    590;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    M01 100mm brick,          !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.1016,                   !- Thickness\n",
      "    0.89,                     !- Conductivity\n",
      "    1920,                     !- Density\n",
      "    790;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    M15 200mm heavyweight concrete,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.2032,                   !- Thickness\n",
      "    1.95,                     !- Conductivity\n",
      "    2240,                     !- Density\n",
      "    900;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    M05 200mm concrete block,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.2032,                   !- Thickness\n",
      "    1.11,                     !- Conductivity\n",
      "    800,                      !- Density\n",
      "    920;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    G05 25mm wood,            !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.15,                     !- Conductivity\n",
      "    608,                      !- Density\n",
      "    1630;                     !- Specific Heat\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "materials = idf1.idfobjects[\"MATERIAL\"]\n",
    "print(materials)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there are many material objects in this idf file.\n",
    "\n",
    "The variable \"materials\" now contains a list of \"MATERIAL\" objects.\n",
    "\n",
    "You already know a little about lists, so let us take a look at the items in this list.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "firstmaterial = materials[0]\n",
    "secondmaterial = materials[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    F08 Metal surface,        !- Name\n",
      "    Smooth,                   !- Roughness\n",
      "    0.0008,                   !- Thickness\n",
      "    45.28,                    !- Conductivity\n",
      "    7824,                     !- Density\n",
      "    500;                      !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(firstmaterial)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us print secondmaterial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    I01 25mm insulation board,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.03,                     !- Conductivity\n",
      "    43,                       !- Density\n",
      "    1210;                     !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(secondmaterial)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is awesome!! Why?\n",
    "\n",
    "To understand what you can do with your objects organized as lists, you'll have to learn a little more about lists."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python lesson 2: more about lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More ways to access items in a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should remember that you can access any item in a list by passing in its index.\n",
    "\n",
    "The tricky part is that python starts counting at 0, so you need to input 0 in order to get the first item in a list.\n",
    "\n",
    "Following the same logic, you need to input 3 in order to get the fourth item on the list. Like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lady Gaga\n"
     ]
    }
   ],
   "source": [
    "bad_architects = [\"Donald Trump\", \"Mick Jagger\", \n",
    "        \"Steve Jobs\", \"Lady Gaga\", \"Santa Clause\"]\n",
    "print(bad_architects[3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there's another way to access items in a list. If you input -1, it will return the last item. -2 will give you the second-to-last item, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Santa Clause\n",
      "Lady Gaga\n"
     ]
    }
   ],
   "source": [
    "print(bad_architects[-1])\n",
    "print(bad_architects[-2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get more than one item in a list:\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "bad_architects[first_slice:second_slice]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mick Jagger', 'Steve Jobs']\n"
     ]
    }
   ],
   "source": [
    "print(bad_architects[1:3]) # slices at 1 and 3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do I make sense of this?\n",
    "\n",
    "To understand this you need to see the list in the following manner::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    [ \"Donald Trump\", \"Mick Jagger\", \"Steve Jobs\", \"Lady Gaga\", \"Santa Clause\" ]\n",
    "     ^               ^              ^             ^            ^              ^\n",
    "     0               1              2             3            4              5\n",
    "    -5              -4             -3            -2           -1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slice operation bad_architects[1:3] slices right where the numbers are.\n",
    "\n",
    "Does that make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try a few other slices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Steve Jobs', 'Lady Gaga']\n",
      "['Steve Jobs', 'Lady Gaga']\n"
     ]
    }
   ],
   "source": [
    "print(bad_architects[2:-1]) # slices at 2 and -1\n",
    "print(bad_architects[-3:-1]) # slices at -3 and -1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also slice in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Lady Gaga', 'Santa Clause']\n",
      "['Donald Trump', 'Mick Jagger']\n",
      "['Steve Jobs', 'Lady Gaga', 'Santa Clause']\n",
      "['Donald Trump', 'Mick Jagger', 'Steve Jobs']\n"
     ]
    }
   ],
   "source": [
    "print(bad_architects[3:] )\n",
    "print(bad_architects[:2] )\n",
    "print(bad_architects[-3:] )\n",
    "print(bad_architects[:-2] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll let you figure that out on your own."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding to a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is simple: the append function adds an item to the end of the list.\n",
    "\n",
    "The following command will add 'something' to the end of the list called listname::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    listname.append(something)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause', 'First-year students']\n"
     ]
    }
   ],
   "source": [
    "bad_architects.append(\"First-year students\")\n",
    "print(bad_architects)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deleting from a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two ways to do this, based on the information you have.  If you have the value of the object, you'll want to use the remove function.  It looks like this:  \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "listname.remove(value) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause']\n"
     ]
    }
   ],
   "source": [
    "bad_architects.remove(\"First-year students\")\n",
    "print(bad_architects)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if you know the index of the item you want to remove?\n",
    "\n",
    "What if you appended an item by mistake and just want to remove the last item in the list?\n",
    "\n",
    "You should use the pop function. It looks like this:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "listname.pop(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee', 'bacon', 'eggs']\n"
     ]
    }
   ],
   "source": [
    "what_i_ate_today = [\"coffee\", \"bacon\", \"eggs\"]\n",
    "print(what_i_ate_today)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee', 'bacon', 'eggs', 'vegetables']\n"
     ]
    }
   ],
   "source": [
    "what_i_ate_today.append(\"vegetables\") # adds vegetables to the end of the list\n",
    "# but I don't like vegetables\n",
    "print(what_i_ate_today)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee', 'bacon', 'eggs']\n"
     ]
    }
   ],
   "source": [
    "# since I don't like vegetables\n",
    "what_i_ate_today.pop(-1) # use index of -1, since vegetables is the last item in the list\n",
    "print(what_i_ate_today)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also remove the second item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bacon'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "what_i_ate_today.pop(1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the 'bacon' in the line above.\n",
    "\n",
    "pop actually 'pops' the value (the one you just removed from the list) back to you.  \n",
    "\n",
    "Let us pop the first item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was_first_item = coffee\n",
      "what_i_ate_today =  ['eggs']\n"
     ]
    }
   ],
   "source": [
    "was_first_item = what_i_ate_today.pop(0)\n",
    "print('was_first_item =', was_first_item)\n",
    "print('what_i_ate_today = ', what_i_ate_today)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what_i_ate_today is just 'eggs'?\n",
    "\n",
    "That is not much of a breakfast!  \n",
    "\n",
    "Let us get back to eppy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuing to work with E+ objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get those \"MATERIAL\" objects again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "materials = idf1.idfobjects[\"MATERIAL\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our newfound knowledge of lists, we can do a lot of things.\n",
    "\n",
    "Let us get the last material:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    G05 25mm wood,            !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.15,                     !- Conductivity\n",
      "    608,                      !- Density\n",
      "    1630;                     !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(materials[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the last two?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "Material,\n",
      "    M05 200mm concrete block,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.2032,                   !- Thickness\n",
      "    1.11,                     !- Conductivity\n",
      "    800,                      !- Density\n",
      "    920;                      !- Specific Heat\n",
      ", \n",
      "Material,\n",
      "    G05 25mm wood,            !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.15,                     !- Conductivity\n",
      "    608,                      !- Density\n",
      "    1630;                     !- Specific Heat\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(materials[-2:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting all the materials ( or counting all objects )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many materials are in this model ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(len(materials))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing a material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us remove the last material in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "was_last_material = materials.pop(-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(len(materials))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success! We have only 9 materials now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last material used to be:\n",
    "\n",
    "'G05 25mm wood'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    M05 200mm concrete block,    !- Name\n",
      "    MediumRough,              !- Roughness\n",
      "    0.2032,                   !- Thickness\n",
      "    1.11,                     !- Conductivity\n",
      "    800,                      !- Density\n",
      "    920;                      !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(materials[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the last material in the list is:\n",
    "\n",
    "'M15 200mm heavyweight concrete'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a material to the list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have the old last material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    G05 25mm wood,            !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.15,                     !- Conductivity\n",
      "    608,                      !- Density\n",
      "    1630;                     !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(was_last_material)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us add it back to the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "materials.append(was_last_material)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(len(materials))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again we have 10 materials and the last material is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Material,\n",
      "    G05 25mm wood,            !- Name\n",
      "    MediumSmooth,             !- Roughness\n",
      "    0.0254,                   !- Thickness\n",
      "    0.15,                     !- Conductivity\n",
      "    608,                      !- Density\n",
      "    1630;                     !- Specific Heat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(materials[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add a new material to the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have been working only with materials that were already in the list.\n",
    "\n",
    "What if we want to make new material?\n",
    "\n",
    "Obviously we would use the function 'newidfobject'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "MATERIAL,\n",
       "    ,                         !- Name\n",
       "    ,                         !- Roughness\n",
       "    ,                         !- Thickness\n",
       "    ,                         !- Conductivity\n",
       "    ,                         !- Density\n",
       "    ,                         !- Specific Heat\n",
       "    0.9,                      !- Thermal Absorptance\n",
       "    0.7,                      !- Solar Absorptance\n",
       "    0.7;                      !- Visible Absorptance"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf1.newidfobject(\"MATERIAL\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(materials)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 11 items in the materials list.\n",
    "\n",
    "Let us take a look at the last material in the list, where this fancy new material was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MATERIAL,\n",
      "    ,                         !- Name\n",
      "    ,                         !- Roughness\n",
      "    ,                         !- Thickness\n",
      "    ,                         !- Conductivity\n",
      "    ,                         !- Density\n",
      "    ,                         !- Specific Heat\n",
      "    0.9,                      !- Thermal Absorptance\n",
      "    0.7,                      !- Solar Absorptance\n",
      "    0.7;                      !- Visible Absorptance\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(materials[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks a little different from the other materials. It does have the name we gave it. \n",
    "\n",
    "Why do some fields have values and others are blank ?  \n",
    "\n",
    "\"addobject\" puts in all the default values, and leaves the others blank. It is up to us to put values in the the new fields. \n",
    "\n",
    "Let's do it now.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "materials[-1].Name = 'Peanut Butter'\n",
    "materials[-1].Roughness = 'MediumSmooth'\n",
    "materials[-1].Thickness = 0.03\n",
    "materials[-1].Conductivity = 0.16\n",
    "materials[-1].Density = 600\n",
    "materials[-1].Specific_Heat = 1500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print'. Did you mean print(materials[-1])? (<ipython-input-137-dc200e5378be>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-137-dc200e5378be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print materials[-1]\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(materials[-1])?\n"
     ]
    }
   ],
   "source": [
    "print materials[-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Copy an existing material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "MATERIAL,\n",
       "    Peanut Butter,            !- Name\n",
       "    MediumSmooth,             !- Roughness\n",
       "    0.03,                     !- Thickness\n",
       "    0.16,                     !- Conductivity\n",
       "    600,                      !- Density\n",
       "    1500,                     !- Specific Heat\n",
       "    0.9,                      !- Thermal Absorptance\n",
       "    0.7,                      !- Solar Absorptance\n",
       "    0.7;                      !- Visible Absorptance"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Peanutbuttermaterial = materials[-1]\n",
    "idf1.copyidfobject(Peanutbuttermaterial)\n",
    "materials = idf1.idfobjects[\"MATERIAL\"]\n",
    "len(materials)\n",
    "materials[-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python lesson 3: indentation and looping through lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm tired of doing all this work, it's time to make python do some heavy lifting for us!  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python can go through each item in a list and perform an operation on any (or every) item in the list.\n",
    "\n",
    "This is called looping through the list.\n",
    "\n",
    "Here's how to tell python to step through each item in a list, and then do something to every single item.\n",
    "\n",
    "We'll use a 'for' loop to do this. ::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    for <variable> in <listname>:\n",
    "        <do something>\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick note about the second line. Notice that it's indented? There are 4 blank spaces before the code starts::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    in python, indentations are used    \n",
    "    to determine the grouping of statements  \n",
    "           some languages use symbols to mark \n",
    "           where the function code starts and stops   \n",
    "           but python uses indentation to tell you this  \n",
    "                    i'm using indentation to\n",
    "                    show the beginning and end of a sentence\n",
    "           this is a very simple explanation\n",
    "           of indentation in python\n",
    "     if you'd like to know more, there is plenty of information\n",
    "     about indentation in python on the web\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's elegant, but it means that the indentation of the code holds meaning.\n",
    "\n",
    "So make sure to indent the second (and third and forth) lines of your loops!\n",
    "\n",
    "Now let's make some fruit loops.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruits = [\"apple\", \"orange\", \"bannana\"] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the syntax I gave you before I started rambling about indentation, we can easily print every item in the fruits list by using a 'for' loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "orange\n",
      "bannana\n"
     ]
    }
   ],
   "source": [
    "for fruit in fruits:\n",
    "   print(fruit)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was easy! But it can get complicated pretty quickly... \n",
    "\n",
    "Let's make it do something more complicated than just print the fruits.  \n",
    "\n",
    "Let's have python add some words to each fruit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am a fruit said the apple\n",
      "I am a fruit said the orange\n",
      "I am a fruit said the bannana\n"
     ]
    }
   ],
   "source": [
    "for fruit in fruits:\n",
    "    print(\"I am a fruit said the\", fruit)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll try to confuse you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "rottenfruits = [] # makes a blank list called rottenfruits\n",
    "for fruit in fruits: # steps through every item in fruits\n",
    "    rottenfruit = \"rotten \" + fruit # changes each item to \"rotten _____\"\n",
    "    rottenfruits.append(rottenfruit) # adds each changed item to the formerly empty list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rotten apple', 'rotten orange', 'rotten bannana']\n"
     ]
    }
   ],
   "source": [
    "print(rottenfruits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here's a shorter way of writing it\n",
    "rottenfruits = [\"rotten \" + fruit for fruit in fruits]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did you follow all that??\n",
    "\n",
    "Just in case you didn't, let's review that last one::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    [\"rotten \" + fruit for fruit in fruits]\n",
    "                       -------------------\n",
    "                       This is the \"for loop\"\n",
    "                       it steps through each fruit in fruits\n",
    "    \n",
    "    [\"rotten \" + fruit for fruit in fruits]\n",
    "     -----------------\n",
    "     add \"rotten \" to the fruit at each step\n",
    "     this is your \"do something\"\n",
    "     \n",
    "    [\"rotten \" + fruit for fruit in fruits]\n",
    "    ---------------------------------------\n",
    "    give a new list that is a result of the \"do something\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rotten apple', 'rotten orange', 'rotten bannana']\n"
     ]
    }
   ],
   "source": [
    "print(rottenfruits)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering in a loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what if you don't want to change *every* item in a list?\n",
    "\n",
    "We can use an 'if' statement to operate on only some items in the list.  \n",
    "\n",
    "Indentation is also important in 'if' statements, as you'll see::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    if <someconstraint>:\n",
    "        <if the first line is true, do this>\n",
    "    <but if it's false, do this>\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruits = [\"apple\", \"orange\", \"pear\", \"berry\", \"mango\", \"plum\", \"peach\", \"melon\", \"bannana\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orange\n",
      "bannana\n"
     ]
    }
   ],
   "source": [
    "for fruit in fruits:               # steps through every fruit in fruits\n",
    "    if len(fruit) > 5:             # checks to see if the length of the word is more than 5\n",
    "        print(fruit)                # if true, print the fruit\n",
    "                                   # if false, python goes back to the 'for' loop \n",
    "                                      # and checks the next item in the list\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to pick only the fruits that start with the letter 'p'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_fruits = []                      # creates an empty list called p_fruits\n",
    "for fruit in fruits:               # steps through every fruit in fruits\n",
    "    if fruit.startswith(\"p\"):      # checks to see if the first letter is 'p', using a built-in function\n",
    "        p_fruits.append(fruit)     # if the first letter is 'p', the item is added to p_fruits\n",
    "                                   # if the first letter is not 'p', python goes back to the 'for' loop\n",
    "                                      # and checks the next item in the list\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pear', 'plum', 'peach']\n"
     ]
    }
   ],
   "source": [
    "print(p_fruits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here's a shorter way to write it\n",
    "p_fruits = [fruit for fruit in fruits if fruit.startswith(\"p\")]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    [fruit for fruit in fruits if fruit.startswith(\"p\")]\n",
    "           -------------------\n",
    "           for loop\n",
    "    \n",
    "    [fruit for fruit in fruits if fruit.startswith(\"p\")]\n",
    "                               ------------------------\n",
    "                               pick only some of the fruits\n",
    "    \n",
    "    [fruit for fruit in fruits if fruit.startswith(\"p\")]\n",
    "     -----\n",
    "     give me the variable fruit as it appears in the list, don't change it\n",
    "     \n",
    "    [fruit for fruit in fruits if fruit.startswith(\"p\")]\n",
    "    ----------------------------------------------------\n",
    "    a fresh new list with those fruits\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pear', 'plum', 'peach']\n"
     ]
    }
   ],
   "source": [
    "print(p_fruits)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting through loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not really needed, but it is nice to know. You can safely skip this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's built-in function range() makes a list of numbers within a range that you specify.\n",
    "\n",
    "This is useful because you can use these lists inside of loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 4)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(4) # makes a list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    print(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p_fruits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print'. Did you mean print(i)? (<ipython-input-155-78336a17d9ea>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-155-78336a17d9ea>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print i\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(i)?\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(p_fruits)):\n",
    "    print i\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pear\n",
      "plum\n",
      "peach\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(p_fruits)):\n",
    "    print(p_fruits[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 pear\n",
      "1 plum\n",
      "2 peach\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(p_fruits)):\n",
    "    print(i,  p_fruits[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'pear')\n",
      "(1, 'plum')\n",
      "(2, 'peach')\n"
     ]
    }
   ],
   "source": [
    "for item_from_enumerate in enumerate(p_fruits):\n",
    "    print(item_from_enumerate)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 pear\n",
      "1 plum\n",
      "2 peach\n"
     ]
    }
   ],
   "source": [
    "for i, fruit in enumerate(p_fruits):\n",
    "    print(i, fruit)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looping through E+ objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have read the python explanation of loops, you are now masters of using loops.\n",
    "\n",
    "Let us use the loops with E+ objects.\n",
    "\n",
    "We'll continue to work with the materials list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F08 Metal surface\n",
      "I01 25mm insulation board\n",
      "I02 50mm insulation board\n",
      "G01a 19mm gypsum board\n",
      "M11 100mm lightweight concrete\n",
      "F16 Acoustic tile\n",
      "M01 100mm brick\n",
      "M15 200mm heavyweight concrete\n",
      "M05 200mm concrete block\n",
      "G05 25mm wood\n",
      "Peanut Butter\n",
      "Peanut Butter\n"
     ]
    }
   ],
   "source": [
    "for material in materials:\n",
    "    print(material.Name )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F08 Metal surface',\n",
       " 'I01 25mm insulation board',\n",
       " 'I02 50mm insulation board',\n",
       " 'G01a 19mm gypsum board',\n",
       " 'M11 100mm lightweight concrete',\n",
       " 'F16 Acoustic tile',\n",
       " 'M01 100mm brick',\n",
       " 'M15 200mm heavyweight concrete',\n",
       " 'M05 200mm concrete block',\n",
       " 'G05 25mm wood',\n",
       " 'Peanut Butter',\n",
       " 'Peanut Butter']"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[material.Name for material in materials] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Smooth',\n",
       " 'MediumRough',\n",
       " 'MediumRough',\n",
       " 'MediumSmooth',\n",
       " 'MediumRough',\n",
       " 'MediumSmooth',\n",
       " 'MediumRough',\n",
       " 'MediumRough',\n",
       " 'MediumRough',\n",
       " 'MediumSmooth',\n",
       " 'MediumSmooth',\n",
       " 'MediumSmooth']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[material.Roughness for material in materials]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0008,\n",
       " 0.0254,\n",
       " 0.0508,\n",
       " 0.019,\n",
       " 0.1016,\n",
       " 0.0191,\n",
       " 0.1016,\n",
       " 0.2032,\n",
       " 0.2032,\n",
       " 0.0254,\n",
       " 0.03,\n",
       " 0.03]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[material.Thickness for material in materials]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1016, 0.1016, 0.2032, 0.2032]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[material.Thickness for material in materials if material.Thickness > 0.1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['M11 100mm lightweight concrete',\n",
       " 'M01 100mm brick',\n",
       " 'M15 200mm heavyweight concrete',\n",
       " 'M05 200mm concrete block']"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[material.Name for material in materials if material.Thickness > 0.1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "thick_materials = [material for material in materials if material.Thickness > 0.1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       " Material,\n",
       "     M11 100mm lightweight concrete,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.1016,                   !- Thickness\n",
       "     0.53,                     !- Conductivity\n",
       "     1280,                     !- Density\n",
       "     840;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     M01 100mm brick,          !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.1016,                   !- Thickness\n",
       "     0.89,                     !- Conductivity\n",
       "     1920,                     !- Density\n",
       "     790;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     M15 200mm heavyweight concrete,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.2032,                   !- Thickness\n",
       "     1.95,                     !- Conductivity\n",
       "     2240,                     !- Density\n",
       "     900;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     M05 200mm concrete block,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.2032,                   !- Thickness\n",
       "     1.11,                     !- Conductivity\n",
       "     800,                      !- Density\n",
       "     920;                      !- Specific Heat]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thick_materials\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the names of the thick materials\n",
    "for material in thick_materials:\n",
    "    material.Name = \"THICK \" + material.Name\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       " Material,\n",
       "     THICK M11 100mm lightweight concrete,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.1016,                   !- Thickness\n",
       "     0.53,                     !- Conductivity\n",
       "     1280,                     !- Density\n",
       "     840;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     THICK M01 100mm brick,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.1016,                   !- Thickness\n",
       "     0.89,                     !- Conductivity\n",
       "     1920,                     !- Density\n",
       "     790;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     THICK M15 200mm heavyweight concrete,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.2032,                   !- Thickness\n",
       "     1.95,                     !- Conductivity\n",
       "     2240,                     !- Density\n",
       "     900;                      !- Specific Heat,\n",
       " \n",
       " Material,\n",
       "     THICK M05 200mm concrete block,    !- Name\n",
       "     MediumRough,              !- Roughness\n",
       "     0.2032,                   !- Thickness\n",
       "     1.11,                     !- Conductivity\n",
       "     800,                      !- Density\n",
       "     920;                      !- Specific Heat]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thick_materials\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we're working with two different lists: materials and thick_materials.\n",
    "\n",
    "But even though the items can be separated into two lists, we're still working with the same items.\n",
    "\n",
    "Here's a helpful illustration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for_images.display_png(for_images.material_lists) # display the image below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F08 Metal surface',\n",
       " 'I01 25mm insulation board',\n",
       " 'I02 50mm insulation board',\n",
       " 'G01a 19mm gypsum board',\n",
       " 'THICK M11 100mm lightweight concrete',\n",
       " 'F16 Acoustic tile',\n",
       " 'THICK M01 100mm brick',\n",
       " 'THICK M15 200mm heavyweight concrete',\n",
       " 'THICK M05 200mm concrete block',\n",
       " 'G05 25mm wood',\n",
       " 'Peanut Butter',\n",
       " 'Peanut Butter']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here's the same concept, demonstrated with code\n",
    "# remember, we changed the names of the items in the list thick_materials\n",
    "# these changes are visible when we print the materials list; the thick materials are also in the materials list\n",
    "[material.Name for material in materials]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometry functions in eppy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, we want information about the E+ object that is not in the fields. For example, it would be useful to know the areas and orientations of the surfaces. These attributes of the surfaces are not in the fields of surfaces, but surface objects *do* have fields that have the coordinates of the surface. The areas and orientations can be calculated from these coordinates.  \n",
    "\n",
    "Pyeplus has some functions that will do the calculations.  \n",
    "\n",
    "In the present version, pyeplus will calculate:\n",
    "\n",
    "- surface azimuth\n",
    "- surface tilt\n",
    "- surface area\n",
    "\n",
    "Let us explore these functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OLD CODE, SHOULD BE DELETED\n",
    "# from idfreader import idfreader\n",
    "\n",
    "# iddfile = \"../iddfiles/Energy+V7_0_0_036.idd\"\n",
    "# fname = \"../idffiles/V_7_0/5ZoneSupRetPlenRAB.idf\"\n",
    " \n",
    "# model, to_print, idd_info = idfreader(fname, iddfile)\n",
    "# surfaces = model['BUILDINGSURFACE:DETAILED'] # all the surfaces\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eppy import modeleditor\n",
    "from eppy.modeleditor import IDF\n",
    "\n",
    "iddfile = \"../eppy/resources/iddfiles/Energy+V7_2_0.idd\"\n",
    "try:\n",
    "    IDF.setiddname(iddfile)\n",
    "except modeleditor.IDDAlreadySetError as e:\n",
    "    pass\n",
    "\n",
    "\n",
    "fname1 = \"../eppy/resources/idffiles/V_7_0/5ZoneSupRetPlenRAB.idf\"\n",
    "idf1 = IDF(fname1)\n",
    "surfaces = idf1.idfobjects['BUILDINGSURFACE:DETAILED']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surface azimuth = 180.0 degrees\n",
      "surface tilt = 90.0 degrees\n",
      "surface area = 18.299999999999997 m2\n"
     ]
    }
   ],
   "source": [
    "# Let us look at the first surface\n",
    "asurface = surfaces[0]\n",
    "print(\"surface azimuth =\",  asurface.azimuth, \"degrees\")\n",
    "print(\"surface tilt =\", asurface.tilt, \"degrees\")\n",
    "print(\"surface area =\", asurface.area, \"m2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WALL-1PF', 'WALL-1PR', 'WALL-1PB', 'WALL-1PL', 'TOP-1']\n"
     ]
    }
   ],
   "source": [
    "# all the surface names\n",
    "s_names = [surface.Name for surface in surfaces]\n",
    "print(s_names[:5]) # print five of them\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('WALL-1PF', 180.0), ('WALL-1PR', 90.0), ('WALL-1PB', 0.0), ('WALL-1PL', 270.0), ('TOP-1', 0.0)]\n"
     ]
    }
   ],
   "source": [
    "# surface names and azimuths\n",
    "s_names_azm = [(sf.Name, sf.azimuth) for sf in surfaces]\n",
    "print(s_names_azm[:5]) # print five of them\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PF 180.0\n",
      "WALL-1PR 90.0\n",
      "WALL-1PB 0.0\n",
      "WALL-1PL 270.0\n",
      "TOP-1 0.0\n"
     ]
    }
   ],
   "source": [
    "# or to do that in pretty printing\n",
    "for name, azimuth in s_names_azm[:5]: # just five of them\n",
    "    print(name, azimuth)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PF 90.0\n",
      "WALL-1PR 90.0\n",
      "WALL-1PB 90.0\n",
      "WALL-1PL 90.0\n",
      "TOP-1 0.0\n"
     ]
    }
   ],
   "source": [
    "# surface names and tilt\n",
    "s_names_tilt = [(sf.Name, sf.tilt) for sf in surfaces]\n",
    "for name, tilt in s_names_tilt[:5]: # just five of them\n",
    "    print(name, tilt)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PF 18.299999999999997 m2\n",
      "WALL-1PR 9.119999999999997 m2\n",
      "WALL-1PB 18.299999999999997 m2\n",
      "WALL-1PL 9.119999999999997 m2\n",
      "TOP-1 463.59999999999997 m2\n"
     ]
    }
   ],
   "source": [
    "# surface names and areas\n",
    "s_names_area = [(sf.Name, sf.area) for sf in surfaces]\n",
    "for name, area in s_names_area[:5]: # just five of them\n",
    "    print(name, area, \"m2\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try to isolate the exterior north facing walls and change their construnctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WALL-1PF', 'WALL-1PR', 'WALL-1PB', 'WALL-1PL', 'FRONT-1', 'SB12', 'SB14', 'SB15', 'RIGHT-1', 'SB21', 'SB23', 'BACK-1', 'SB32', 'SB35', 'LEFT-1', 'SB41', 'SB43', 'SB45', 'SB51', 'SB53', 'SB54', 'WALL-1SF', 'WALL-1SR', 'WALL-1SB', 'WALL-1SL']\n"
     ]
    }
   ],
   "source": [
    "# just vertical walls\n",
    "vertical_walls = [sf for sf in surfaces if sf.tilt == 90.0]\n",
    "print([sf.Name for sf in vertical_walls])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WALL-1PB', 'SB15', 'BACK-1', 'SB53', 'WALL-1SB']\n"
     ]
    }
   ],
   "source": [
    "# north facing walls\n",
    "north_walls = [sf for sf in vertical_walls if sf.azimuth == 0.0]\n",
    "print([sf.Name for sf in north_walls])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WALL-1PB', 'BACK-1', 'WALL-1SB']\n"
     ]
    }
   ],
   "source": [
    "# north facing exterior walls\n",
    "exterior_nwall = [sf for sf in north_walls if sf.Outside_Boundary_Condition == \"Outdoors\"]\n",
    "print([sf.Name for sf in exterior_nwall])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PB 0.0 WALL-1\n",
      "BACK-1 0.0 WALL-1\n",
      "WALL-1SB 0.0 WALL-1\n"
     ]
    }
   ],
   "source": [
    "# print out some more details of the north wall\n",
    "north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall]\n",
    "for name, azimuth, construction in north_wall_info:\n",
    "    print(name, azimuth, construction)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the construction in the exterior north walls\n",
    "for wall in exterior_nwall:\n",
    "    wall.Construction_Name = \"NORTHERN-WALL\" # make sure such a construction exists in the model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PB 0.0 NORTHERN-WALL\n",
      "BACK-1 0.0 NORTHERN-WALL\n",
      "WALL-1SB 0.0 NORTHERN-WALL\n"
     ]
    }
   ],
   "source": [
    "# see the change\n",
    "north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall]\n",
    "for name, azimuth, construction in north_wall_info:\n",
    "    print(name, azimuth, construction)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-1PF 180.0 WALL-1\n",
      "WALL-1PR 90.0 WALL-1\n",
      "WALL-1PB 0.0 NORTHERN-WALL\n",
      "WALL-1PL 270.0 WALL-1\n",
      "TOP-1 0.0 ROOF-1\n",
      "C1-1P 0.0 CLNG-1\n",
      "C2-1P 0.0 CLNG-1\n",
      "C3-1P 0.0 CLNG-1\n",
      "C4-1P 0.0 CLNG-1\n",
      "C5-1P 0.0 CLNG-1\n",
      "FRONT-1 180.0 WALL-1\n",
      "C1-1 180.0 CLNG-1\n",
      "F1-1 0.0 CLNG-1\n",
      "SB12 45.000000000000014 INT-WALL-1\n",
      "SB14 315.0 INT-WALL-1\n",
      "SB15 0.0 INT-WALL-1\n",
      "RIGHT-1 90.0 WALL-1\n",
      "C2-1 0.0 CLNG-1\n",
      "F2-1 0.0 CLNG-1\n",
      "SB21 225.0 INT-WALL-1\n",
      "SB23 315.7848246029919 INT-WALL-1\n",
      "SB25 270.0 INT-WALL-1\n",
      "BACK-1 0.0 NORTHERN-WALL\n",
      "C3-1 0.0 CLNG-1\n",
      "F3-1 0.0 CLNG-1\n",
      "SB32 135.78482460299188 INT-WALL-1\n",
      "SB34 224.21517539700812 INT-WALL-1\n",
      "SB35 180.0 INT-WALL-1\n",
      "LEFT-1 270.0 WALL-1\n",
      "C4-1 0.0 CLNG-1\n",
      "F4-1 0.0 CLNG-1\n",
      "SB41 135.0 INT-WALL-1\n",
      "SB43 44.21517539700812 INT-WALL-1\n",
      "SB45 90.0 INT-WALL-1\n",
      "C5-1 0.0 CLNG-1\n",
      "F5-1 0.0 CLNG-1\n",
      "SB51 180.0 INT-WALL-1\n",
      "SB52 89.99999999999997 INT-WALL-1\n",
      "SB53 0.0 INT-WALL-1\n",
      "SB54 270.0 INT-WALL-1\n",
      "WALL-1SF 180.0 WALL-1\n",
      "WALL-1SR 90.0 WALL-1\n",
      "WALL-1SB 0.0 NORTHERN-WALL\n",
      "WALL-1SL 270.0 WALL-1\n",
      "BOTTOM-1 0.0 FLOOR-SLAB-1\n",
      "F1-1S 0.0 CLNG-1\n",
      "F2-1S 0.0 CLNG-1\n",
      "F3-1S 0.0 CLNG-1\n",
      "F4-1S 0.0 CLNG-1\n",
      "F5-1S 0.0 CLNG-1\n"
     ]
    }
   ],
   "source": [
    "# see this in all surfaces\n",
    "for sf in surfaces:\n",
    "    print(sf.Name, sf.azimuth, sf.Construction_Name)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the \"NORTHERN-WALL\" in the print out above.\n",
    "\n",
    "This shows that very sophisticated modification can be made to the model rather quickly. "
   ]
  }
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