docs/tutorials/1 - Beginner Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Create Two Scenarios with Two Materials\n",
    "\n",
    "\n",
    "This journal shows how to load the baselines and run the dynamic mas flow analysis, plotting the results for two scenarios and two materials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Set Working Folder and Import PV ICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\PV_ICE\\PV_ICE\\TEMP\\Tutorial1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'Tutorial1')\n",
    "\n",
    "if not os.path.exists(testfolder):\n",
    "    os.makedirs(testfolder)\n",
    "\n",
    "print (\"Your simulation will be stored in %s\" % testfolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PV_ICE\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on a  Windows 10\n",
      "Python version  3.11.4 | packaged by Anaconda, Inc. | (main, Jul  5 2023, 13:38:37) [MSC v.1916 64 bit (AMD64)]\n",
      "Pandas version  2.1.0\n",
      "PV_ICE version  v0.2.0+551.g3772975.dirty\n"
     ]
    }
   ],
   "source": [
    "# This information helps with debugging and getting support :)\n",
    "import sys, platform\n",
    "print(\"Working on a \", platform.system(), platform.release())\n",
    "print(\"Python version \", sys.version)\n",
    "print(\"Pandas version \", pd.__version__)\n",
    "print(\"PV_ICE version \", PV_ICE.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Add Scenarios and Materials\n",
    "\n",
    "``silicon`` and ``glass`` materials are added to the two simulations, along with a scenario, in this case the ``baseline_modules_US``. The baseline files for decadence scenario will be modified.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path = C:\\Users\\sayala\\Documents\\GitHub\\PV_ICE\\PV_ICE\\TEMP\\Tutorial1\n",
      "Baseline folder directed to default:  C:\\Users\\sayala\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\n",
      "No energy module file passed. If desired, pass one of the following options:  ['baseline_modules_energy.csv']\n",
      "No energy module file passed. If desired, pass one of the following options:  ['baseline_modules_energy.csv']\n"
     ]
    }
   ],
   "source": [
    "r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder)\n",
    "r1.createScenario(name='standard', massmodulefile=r'..\\..\\baselines\\baseline_modules_mass_US.csv')\n",
    "r1.scenario['standard'].addMaterial('glass', massmatfile=r'..\\..\\baselines\\baseline_material_mass_glass.csv' )\n",
    "r1.scenario['standard'].addMaterial('silicon', massmatfile=r'..\\..\\baselines\\baseline_material_mass_silicon.csv' )\n",
    "\n",
    "r1.createScenario('low_quality', massmodulefile=r'..\\..\\baselines\\baseline_modules_mass_US.csv')\n",
    "r1.scenario['low_quality'].addMaterial('glass', massmatfile=r'..\\..\\baselines\\baseline_material_mass_glass.csv')\n",
    "r1.scenario['low_quality'].addMaterial('silicon', massmatfile=r'..\\..\\baselines\\baseline_material_mass_silicon.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring that the data got loaded properly, we can look at each scenario object, and material object saved dataframe and properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>new_Installed_Capacity_[MW]</th>\n",
       "      <th>mod_eff</th>\n",
       "      <th>mod_reliability_t50</th>\n",
       "      <th>mod_reliability_t90</th>\n",
       "      <th>mod_degradation</th>\n",
       "      <th>mod_lifetime</th>\n",
       "      <th>mod_MFG_eff</th>\n",
       "      <th>mod_Repair</th>\n",
       "      <th>mod_MerchantTail</th>\n",
       "      <th>...</th>\n",
       "      <th>mod_EOL_pg1_landfill</th>\n",
       "      <th>mod_EOL_pg2_stored</th>\n",
       "      <th>mod_EOL_pg3_reMFG</th>\n",
       "      <th>mod_EOL_pg4_recycled</th>\n",
       "      <th>mod_EOL_reMFG_yield</th>\n",
       "      <th>mod_EOL_sp_reMFG_recycle</th>\n",
       "      <th>mod_EOL_pb1_landfill</th>\n",
       "      <th>mod_EOL_pb2_stored</th>\n",
       "      <th>mod_EOL_pb3_reMFG</th>\n",
       "      <th>mod_EOL_pb4_recycled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>12.5</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>0.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996</td>\n",
       "      <td>9.667667</td>\n",
       "      <td>12.7</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>0.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  new_Installed_Capacity_[MW]  mod_eff  mod_reliability_t50  \\\n",
       "0  1995                    12.500000     12.5                   16   \n",
       "1  1996                     9.667667     12.7                   16   \n",
       "\n",
       "   mod_reliability_t90  mod_degradation  mod_lifetime  mod_MFG_eff  \\\n",
       "0                   21             0.75          10.0           98   \n",
       "1                   21             0.75          10.0           98   \n",
       "\n",
       "   mod_Repair  mod_MerchantTail  ...  mod_EOL_pg1_landfill  \\\n",
       "0           0                 0  ...                    60   \n",
       "1           0                 0  ...                    60   \n",
       "\n",
       "   mod_EOL_pg2_stored  mod_EOL_pg3_reMFG  mod_EOL_pg4_recycled  \\\n",
       "0                   0                  0                    40   \n",
       "1                   0                  0                    40   \n",
       "\n",
       "   mod_EOL_reMFG_yield  mod_EOL_sp_reMFG_recycle  mod_EOL_pb1_landfill  \\\n",
       "0                    0                       100                    60   \n",
       "1                    0                       100                    60   \n",
       "\n",
       "   mod_EOL_pb2_stored  mod_EOL_pb3_reMFG  mod_EOL_pb4_recycled  \n",
       "0                   0                  0                    40  \n",
       "1                   0                  0                    40  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1.scenario['standard'].dataIn_m.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>mat_virgin_eff</th>\n",
       "      <th>mat_massperm2</th>\n",
       "      <th>mat_MFG_eff</th>\n",
       "      <th>mat_MFG_scrap_Recycled</th>\n",
       "      <th>mat_MFG_scrap_Recycling_eff</th>\n",
       "      <th>mat_MFG_scrap_Recycled_into_HQ</th>\n",
       "      <th>mat_MFG_scrap_Recycled_into_HQ_Reused4MFG</th>\n",
       "      <th>mat_PG3_ReMFG_target</th>\n",
       "      <th>mat_ReMFG_yield</th>\n",
       "      <th>mat_PG4_Recycling_target</th>\n",
       "      <th>mat_Recycling_yield</th>\n",
       "      <th>mat_EOL_Recycled_into_HQ</th>\n",
       "      <th>mat_EOL_RecycledHQ_Reused4MFG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>20.0</td>\n",
       "      <td>848.421429</td>\n",
       "      <td>45.0</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996</td>\n",
       "      <td>20.4</td>\n",
       "      <td>831.785714</td>\n",
       "      <td>45.0</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  mat_virgin_eff  mat_massperm2  mat_MFG_eff  mat_MFG_scrap_Recycled  \\\n",
       "0  1995            20.0     848.421429         45.0                     100   \n",
       "1  1996            20.4     831.785714         45.0                     100   \n",
       "\n",
       "   mat_MFG_scrap_Recycling_eff  mat_MFG_scrap_Recycled_into_HQ  \\\n",
       "0                           20                               0   \n",
       "1                           20                               0   \n",
       "\n",
       "   mat_MFG_scrap_Recycled_into_HQ_Reused4MFG  mat_PG3_ReMFG_target  \\\n",
       "0                                        100                     0   \n",
       "1                                        100                     0   \n",
       "\n",
       "   mat_ReMFG_yield  mat_PG4_Recycling_target  mat_Recycling_yield  \\\n",
       "0                0                        20                   80   \n",
       "1                0                        20                   80   \n",
       "\n",
       "   mat_EOL_Recycled_into_HQ  mat_EOL_RecycledHQ_Reused4MFG  \n",
       "0                         0                              0  \n",
       "1                         0                              0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1.scenario['standard'].material['silicon'].matdataIn_m.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Modify values\n",
    "    \n",
    "PV_ICE has some dedicated functions that create changes based on improvements, but for this example we'll just be modifying values for the full column and comparing effects. To modify these columns, we specify the scenario or material parameter we want to modify, and assign the new value. In this case we are updating:\n",
    "* module lifetime\n",
    "* module degradation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1.scenario['low_quality'].dataIn_m['mod_lifetime'] = 15 \n",
    "r1.scenario['low_quality'].dataIn_m['mod_degradation'] = 1.4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Run the Mass Flow Calculations on All Scenarios and Materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>> Calculating Material Flows <<<<\n",
      "\n",
      "Working on Scenario:  standard\n",
      "********************\n",
      "Finished Area+Power Generation Calculations\n",
      "==> Working on Material :  glass\n",
      "==> Working on Material :  silicon\n",
      "Working on Scenario:  low_quality\n",
      "********************\n",
      "Finished Area+Power Generation Calculations\n",
      "==> Working on Material :  glass\n",
      "==> Working on Material :  silicon\n"
     ]
    }
   ],
   "source": [
    "r1.calculateMassFlow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have results on the mass layer that we can access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0',\n",
       "       'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife',\n",
       "       'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored',\n",
       "       'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG',\n",
       "       'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill',\n",
       "       'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield',\n",
       "       'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]',\n",
       "       'Resold_Area', 'Resold_[W]', 'WeibullParams',\n",
       "       'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure',\n",
       "       'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad',\n",
       "       'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL',\n",
       "       'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure',\n",
       "       'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad',\n",
       "       'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL',\n",
       "       'irradiance_stc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1.scenario['standard'].dataOut_m.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What can aggregate results from dataOut_m and matdataOut_m and compile the data so we can use it more easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "USyearly, UScum = r1.aggregateResults()\n",
    "# r1.USyearly # another way of accessing after running aggregateResults()\n",
    "# r1.UScum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VirginStock_glass_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_Module_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'VirginStock_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'VirginStock_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'newInstalledCapacity_Simulation1_standard_[MW]',\n",
       "       'newInstalledCapacity_Simulation1_low_quality_[MW]',\n",
       "       'ActiveCapacity_Simulation1_standard_[MW]',\n",
       "       'DecommisionedCapacity_Simulation1_standard_[MW]',\n",
       "       'ActiveCapacity_Simulation1_low_quality_[MW]',\n",
       "       'DecommisionedCapacity_Simulation1_low_quality_[MW]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USyearly.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VirginStock_glass_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteAll_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteEOL_Module_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_glass_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_silicon_Simulation1_standard_[Tonnes]',\n",
       "       'WasteMFG_Module_Simulation1_standard_[Tonnes]',\n",
       "       'VirginStock_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'VirginStock_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'VirginStock_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteAll_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteEOL_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_glass_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_silicon_Simulation1_low_quality_[Tonnes]',\n",
       "       'WasteMFG_Module_Simulation1_low_quality_[Tonnes]',\n",
       "       'newInstalledCapacity_Simulation1_standard_[MW]',\n",
       "       'newInstalledCapacity_Simulation1_low_quality_[MW]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UScum.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ":) Saved Input and Output Dataframes\n"
     ]
    }
   ],
   "source": [
    "r1.saveSimulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Use internal plotting functions to plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this list, select the one that fits your study and select the type of plotting method. There are various plotting options:\n",
    "* `plotScenariosComparison`\n",
    "* `plotMaterialComparisonAcrossScenarios`\n",
    "* `plotMetricResults`: You can select the following keyword options: 'VirginStock', 'WasteALL', 'WasteEOL', 'WasteMFG'\n",
    "* `plotMaterialResults`\n",
    "* `plotInstalledCapacityResults`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also view all the keywords you can use by calling the function without argumnets, or by printing the keys to the module data or the material data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please pass a keyword:\n",
      "\n",
      " ** Scenario Data In Mass  ['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50', 'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff', 'mod_Repair', 'mod_MerchantTail', 'mod_EOL_collection_eff', 'mod_EOL_pg0_resell', 'mod_EOL_pg1_landfill', 'mod_EOL_pg2_stored', 'mod_EOL_pg3_reMFG', 'mod_EOL_pg4_recycled', 'mod_EOL_reMFG_yield', 'mod_EOL_sp_reMFG_recycle', 'mod_EOL_pb1_landfill', 'mod_EOL_pb2_stored', 'mod_EOL_pb3_reMFG', 'mod_EOL_pb4_recycled']\n",
      "\n",
      " ** Scenario Data Out Mass  ['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0', 'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife', 'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored', 'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG', 'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill', 'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield', 'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]', 'Resold_Area', 'Resold_[W]', 'WeibullParams', 'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure', 'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad', 'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL', 'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure', 'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad', 'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL', 'irradiance_stc']\n"
     ]
    }
   ],
   "source": [
    "r1.plotScenariosComparison()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r1.plotScenariosComparison('Effective_Capacity_[W]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Glass simulation1 standard",
         "legendgroup": "VirginStock_glass_Simulation1_standard_[Tonnes]",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "Glass simulation1 standard",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1995,
          1996,
          1997,
          1998,
          1999,
          2000,
          2001,
          2002,
          2003,
          2004,
          2005,
          2006,
          2007,
          2008,
          2009,
          2010,
          2011,
          2012,
          2013,
          2014,
          2015,
          2016,
          2017,
          2018,
          2019,
          2020,
          2021,
          2022,
          2023,
          2024,
          2025,
          2026,
          2027,
          2028,
          2029,
          2030,
          2031,
          2032,
          2033,
          2034,
          2035,
          2036,
          2037,
          2038,
          2039,
          2040,
          2041,
          2042,
          2043,
          2044,
          2045,
          2046,
          2047,
          2048,
          2049,
          2050
         ],
         "xaxis": "x",
         "y": [
          858.8167561761546,
          653.7601667659601,
          774.9354155024645,
          774.7104976491182,
          1095.2289205225634,
          1339.2073441033754,
          1767.2034717137917,
          2658.3933497739663,
          3706.1758811591108,
          5826.337165327294,
          5887.100720050186,
          5935.136810131715,
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