scratch_paper.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phosphogypsum Rare Earth Element Recovery System (PREERS): leaching demo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Packages\n",
    "Packages contain predefined frameworks/tools for creating the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qsdsan as qs\n",
    "import os, sys, pickle, pandas as pd, qsdsan as qs, brightway2 as bw\n",
    "from scipy import interpolate\n",
    "import numpy as np\n",
    "from chaospy import distributions \n",
    "import matplotlib.pyplot as plt\n",
    "import brightway2 as bw\n",
    "import collections.abc\n",
    "import eidl as eidl\n",
    "#hyper needs the four following aliases to be done manually.\n",
    "collections.Iterable = collections.abc.Iterable\n",
    "collections.Mapping = collections.abc.Mapping\n",
    "collections.MutableSet = collections.abc.MutableSet\n",
    "collections.MutableMapping = collections.abc.MutableMapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Biosphere database already present!!! No setup is needed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logging in to ecoinvent homepage...\n",
      "login successful!\n",
      "\n",
      " available versions:\n",
      "a 3.9.1\n",
      "b 3.9\n",
      "c 3.8\n",
      "d 3.7.1\n",
      "e 3.7\n",
      "f 3.6\n",
      "g 3.5\n",
      "h 3.4\n",
      "i 3.3\n",
      "j 3.2\n",
      "k 3.10\n",
      "l 3.1\n",
      "m 3.01\n",
      "\n",
      " system models:\n",
      "a apos\n",
      "b consequential\n",
      "c cutoff\n",
      "downloading cutoff 3.9.1 ...\n",
      "download finished!: C:\\Users\\adsme\\AppData\\Local\\EcoInventDownLoader\\EcoInventDownLoader\\cutoff391.7z\n",
      "\n",
      "Extracting XML data from 21238 datasets\n",
      "Extracted 21238 datasets in 35.57 seconds\n",
      "Applying strategy: normalize_units\n",
      "Applying strategy: update_ecoinvent_locations\n",
      "Applying strategy: remove_zero_amount_coproducts\n",
      "Applying strategy: remove_zero_amount_inputs_with_no_activity\n",
      "Applying strategy: remove_unnamed_parameters\n",
      "Applying strategy: es2_assign_only_product_with_amount_as_reference_product\n",
      "Applying strategy: assign_single_product_as_activity\n",
      "Applying strategy: create_composite_code\n",
      "Applying strategy: drop_unspecified_subcategories\n",
      "Applying strategy: fix_ecoinvent_flows_pre35\n",
      "Applying strategy: drop_temporary_outdated_biosphere_flows\n",
      "Applying strategy: link_biosphere_by_flow_uuid\n",
      "Applying strategy: link_internal_technosphere_by_composite_code\n",
      "Applying strategy: delete_exchanges_missing_activity\n",
      "Applying strategy: delete_ghost_exchanges\n",
      "Applying strategy: remove_uncertainty_from_negative_loss_exchanges\n",
      "Applying strategy: fix_unreasonably_high_lognormal_uncertainties\n",
      "Applying strategy: set_lognormal_loc_value\n",
      "Applying strategy: convert_activity_parameters_to_list\n",
      "Applying strategy: add_cpc_classification_from_single_reference_product\n",
      "Applying strategy: delete_none_synonyms\n",
      "Applied 21 strategies in 7.18 seconds\n",
      "21238 datasets\n",
      "674593 exchanges\n",
      "0 unlinked exchanges\n",
      "  \n",
      "\n",
      "Write database cutoff391 in project REEPSv1?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing activities to SQLite3 database:\n",
      "0% [##############################] 100% | ETA: 00:00:00\n",
      "Total time elapsed: 00:01:09\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: Writing activities to SQLite3 database:\n",
      "  Started: 01/26/2024 12:19:20\n",
      "  Finished: 01/26/2024 12:20:30\n",
      "  Total time elapsed: 00:01:09\n",
      "  CPU %: 5.00\n",
      "  Memory %: 23.74\n",
      "Created database: cutoff391\n"
     ]
    }
   ],
   "source": [
    "bw.projects.set_current('REEPSv1')\n",
    "\n",
    "bw.bw2setup()\n",
    "eidl.get_ecoinvent()\n",
    "# username: Shi_Research_Group\n",
    "# password: ShiGroup!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Databases dictionary with 2 object(s):\n",
      "\tbiosphere3\n",
      "\tcutoff391\n",
      "The imported ecoinvent database is of type <class 'bw2data.backends.peewee.database.SQLiteBackend'> and has a length of 4709.\n",
      "The imported ecoinvent database is of type <class 'bw2data.backends.peewee.database.SQLiteBackend'> and has a length of 21242.\n"
     ]
    }
   ],
   "source": [
    "print(bw.databases)\n",
    "bsdb = bw.Database('biosphere3') # biosphere database for ecoinvent 3.9.1\n",
    "eidb = bw.Database('cutoff391')\n",
    "print(\"The imported ecoinvent database is of type {} and has a length of {}.\".format(type(bsdb), len(bsdb)))\n",
    "print(\"The imported ecoinvent database is of type {} and has a length of {}.\".format(type(eidb), len(eidb)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PG Stack Tsioka' (t, GLO, None)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in eidb if 'PG Stack Tsioka' in i['name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['market for sulfuric acid' (kilogram, RoW, None)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in eidb if 'market for sulfuric acid' in i['name'] and 'RoW' in i['location']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[m for m in bw.methods if 'AWARE' in str(m) ] # ReCiPe 2016 v1.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'acidification: terrestrial',\n",
       "  'terrestrial acidification potential (TAP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'climate change',\n",
       "  'global warming potential (GWP1000)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'ecotoxicity: freshwater',\n",
       "  'freshwater ecotoxicity potential (FETP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'ecotoxicity: marine',\n",
       "  'marine ecotoxicity potential (METP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'ecotoxicity: terrestrial',\n",
       "  'terrestrial ecotoxicity potential (TETP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'energy resources: non-renewable, fossil',\n",
       "  'fossil fuel potential (FFP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'eutrophication: freshwater',\n",
       "  'freshwater eutrophication potential (FEP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'eutrophication: marine',\n",
       "  'marine eutrophication potential (MEP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'human toxicity: carcinogenic',\n",
       "  'human toxicity potential (HTPc)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'human toxicity: non-carcinogenic',\n",
       "  'human toxicity potential (HTPnc)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'ionising radiation',\n",
       "  'ionising radiation potential (IRP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'land use',\n",
       "  'agricultural land occupation (LOP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'material resources: metals/minerals',\n",
       "  'surplus ore potential (SOP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'ozone depletion',\n",
       "  'ozone depletion potential (ODPinfinite)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'particulate matter formation',\n",
       "  'particulate matter formation potential (PMFP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'photochemical oxidant formation: human health',\n",
       "  'photochemical oxidant formation potential: humans (HOFP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'photochemical oxidant formation: terrestrial ecosystems',\n",
       "  'photochemical oxidant formation potential: ecosystems (EOFP)'),\n",
       " ('ReCiPe 2016 v1.03, midpoint (H)',\n",
       "  'water use',\n",
       "  'water consumption potential (WCP)')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[m for m in bw.methods if 'ReCiPe' in str(m) and 'midpoint' in str(m) and '(H)' in str(m) and not 'no LT' in str(m)] # ReCiPe 2016 v1.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ReCiPe Midpoint (H) V1.13', 'agricultural land occupation', 'ALOP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'ozone depletion', 'ODPinf'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'climate change', 'GWP100'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'terrestrial acidification', 'TAP100'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'photochemical oxidant formation', 'POFP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'metal depletion', 'MDP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'water depletion', 'WDP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'freshwater ecotoxicity', 'FETPinf'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'human toxicity', 'HTPinf'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'urban land occupation', 'ULOP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'particulate matter formation', 'PMFP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'freshwater eutrophication', 'FEP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'marine eutrophication', 'MEP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'terrestrial ecotoxicity', 'TETPinf'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'fossil depletion', 'FDP'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'ionising radiation', 'IRP_HE'),\n",
       " ('ReCiPe Midpoint (H) V1.13', 'marine ecotoxicity', 'METPinf'),\n",
       " ('ReCiPe Midpoint (H)', 'terrestrial ecotoxicity', 'TETPinf'),\n",
       " ('ReCiPe Midpoint (H)', 'photochemical oxidant formation', 'POFP'),\n",
       " ('ReCiPe Midpoint (H)', 'human toxicity', 'HTPinf'),\n",
       " ('ReCiPe Midpoint (H)', 'marine eutrophication', 'MEP'),\n",
       " ('ReCiPe Midpoint (H)', 'climate change', 'GWP100'),\n",
       " ('ReCiPe Midpoint (H)', 'particulate matter formation', 'PMFP'),\n",
       " ('ReCiPe Midpoint (H)', 'agricultural land occupation', 'ALOP'),\n",
       " ('ReCiPe Midpoint (H)', 'freshwater eutrophication', 'FEP'),\n",
       " ('ReCiPe Midpoint (H)', 'metal depletion', 'MDP'),\n",
       " ('ReCiPe Midpoint (H)', 'terrestrial acidification', 'TAP100'),\n",
       " ('ReCiPe Midpoint (H)', 'water depletion', 'WDP'),\n",
       " ('ReCiPe Midpoint (H)', 'urban land occupation', 'ULOP'),\n",
       " ('ReCiPe Midpoint (H)', 'ionising radiation', 'IRP_HE'),\n",
       " ('ReCiPe Midpoint (H)', 'fossil depletion', 'FDP'),\n",
       " ('ReCiPe Midpoint (H)', 'freshwater ecotoxicity', 'FETPinf'),\n",
       " ('ReCiPe Midpoint (H)', 'marine ecotoxicity', 'METPinf'),\n",
       " ('ReCiPe Midpoint (H)', 'ozone depletion', 'ODPinf')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[m for m in bw.methods if 'ReCiPe Midpoint (H) V1.13' in str(m) and not 'LT' in str(m)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'categories': ('water', 'surface water'),\n",
       " 'code': 'db4566b1-bd88-427d-92da-2d25879063b9',\n",
       " 'CAS number': '007732-18-5',\n",
       " 'name': 'Water',\n",
       " 'database': 'biosphere3',\n",
       " 'unit': 'cubic meter',\n",
       " 'type': 'emission'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eidb = bw.Database('cutoff38')\n",
    "bsdb = bw.Database('biosphere3')\n",
    "# [i for i in eidb if 'market for neodymium' in i['name'] and 'GLO' in i['location']][0].as_dict()\n",
    "[i for i in bsdb if 'Water' in i['name'] and 'water' in i['categories'] and 'surface water' in i['categories'] and 'emission' in i['type']][0].as_dict()\n",
    "\n",
    "# [i for i in bsdb if 'Carbon monoxide, fossil' in i['name'] and 'low population density, long-term' in i['categories']][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comment': \"Transport from producers to consumers of this product in the geography covered by the market is included.\\nThis is a market activity, representing a treatment mix. In the case of products needing treatment, market mixes are supplied by the activities treating the product in the geography defined by the market, and they supply the activities needing to treat the product, as they have generated it as a by-product in the Undefined processes (present as a negative input in system models). Transport to the treating facility or losses are also accounted in this type of markets, when relevant.\\nThis is the market for  'wastewater, average', in the Global geography.\",\n",
       " 'classifications': [('EcoSpold01Categories',\n",
       "   'waste management/wastewater treatment'),\n",
       "  ('ISIC rev.4 ecoinvent', '3700:Sewerage'),\n",
       "  ('CPC', '39920: Sewage sludge')],\n",
       " 'activity type': 'market activity',\n",
       " 'activity': '6e5aa015-da83-46eb-b89b-f3ca4d9253bc',\n",
       " 'database': 'cutoff38',\n",
       " 'filename': '6e5aa015-da83-46eb-b89b-f3ca4d9253bc_e8a51ec1-60cb-4ead-a948-ad1142c61918.spold',\n",
       " 'location': 'RoW',\n",
       " 'name': 'market for wastewater, average',\n",
       " 'synonyms': [],\n",
       " 'parameters': [],\n",
       " 'authors': {'data entry': {'name': '[System]',\n",
       "   'email': 'support@ecoinvent.org'},\n",
       "  'data generator': {'name': '[System]', 'email': 'support@ecoinvent.org'}},\n",
       " 'type': 'process',\n",
       " 'reference product': 'wastewater, average',\n",
       " 'flow': 'e8a51ec1-60cb-4ead-a948-ad1142c61918',\n",
       " 'unit': 'cubic meter',\n",
       " 'production amount': -1.0,\n",
       " 'code': '0cae5efb87b2b46ae957ba6d7313657e'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in eidb if 'market for wastewater, average' in i['name'] and 'RoW' in i['location']][0].as_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile Components\n",
    "Obtain physical properties for streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CompiledComponents([S_H2, S_CH4, S_CH3OH, S_Ac, S_Prop, S_F, S_U_Inf, S_U_E, C_B_Subst, C_B_BAP, C_B_UAP, C_U_Inf, X_B_Subst, X_OHO_PHA, X_GAO_PHA, X_PAO_PHA, X_GAO_Gly, X_PAO_Gly, X_OHO, X_AOO, X_NOO, X_AMO, X_PAO, X_MEOLO, X_FO, X_ACO, X_HMO, X_PRO, X_U_Inf, X_U_OHO_E, X_U_PAO_E, X_Ig_ISS, X_MgCO3, X_CaCO3, X_MAP, X_HAP, X_HDP, X_FePO4, X_AlPO4, X_AlOH, X_FeOH, X_PAO_PP_Lo, X_PAO_PP_Hi, S_NH4, S_NO2, S_NO3, S_PO4, S_K, S_Ca, S_Mg, S_CO3, S_N2, S_O2, S_CAT, S_AN, H2O, Nd, Gypsum, H2SO4, HNO3])\n"
     ]
    }
   ],
   "source": [
    "#  Setup inlet flows\n",
    "Calcium_sulfate_dihydrate = qs.Chemical(ID='Calcium_sulfate_dihydrate', \n",
    "CAS=10101-41-4, \n",
    "search_db=True, \n",
    "phase='s', \n",
    "rho=2320, #         https://pubchem.ncbi.nlm.nih.gov/compound/24928\n",
    "Cp=186.2/172.171, #           http://chemister.ru/Database/properties-en.php?dbid=1&id=552\n",
    "Hf=-2021.1*1000) #          http://chemister.ru/Database/properties-en.php?dbid=1&id=552\n",
    "\n",
    "cmps_default = qs.Components.load_default()\n",
    "Neodymium = qs.Component('Nd', search_ID='7440-00-8', particle_size = 'Soluble', degradability='Undegradable', organic=False)\n",
    "Gypsum = qs.Component.from_chemical('Gypsum', chemical=Calcium_sulfate_dihydrate, particle_size = 'Particulate', degradability='Undegradable', organic=False)\n",
    "H2SO4 = qs.Component('H2SO4', search_ID='7664-93-9', particle_size = 'Soluble', degradability='Undegradable', organic=False)\n",
    "HNO3 = qs.Component('HNO3', search_ID='7697-37-2', particle_size = 'Soluble', degradability='Undegradable', organic=False)\n",
    "cmps = qs.Components([*cmps_default, Neodymium, Gypsum, H2SO4, HNO3])\n",
    "qs.set_thermo(cmps)\n",
    "cmps\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gather LCA Emission Factors\n",
    "Emission factors are the total environmental impact of an activity. This impact can be linked to process streams to calculate total impact of a stream"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the EcoInvent LCI Database and set directory to store data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Commented code accesses the EcoInvent website and downloads the requested database to be used in Python\n",
    "# import eidl \n",
    "# eidl.get_ecoinvent() # ei cutoff 3.8\n",
    "\n",
    "# Set the directory to store the LCA data\n",
    "c_path = os.path.abspath(r'C:\\Users\\ajs8911\\OneDrive - The Pennsylvania State University\\Research\\System Analysis\\Python Scripts\\PG_REE_base\\PG_REE')\n",
    "data_path = os.path.join(c_path, 'data_scratch')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather the LCIA indicators desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_indicators(replace=True):\n",
    "    from bw2qsd import CFgetter\n",
    "    from bw2qsd.utils import format_name\n",
    "    cutoff38 = CFgetter('cutoff38')\n",
    "    biosphere = CFgetter('biosphere3')\n",
    "    # ecoinvent version 3.8, cutoff\n",
    "    cutoff38.load_database('cutoff38')\n",
    "\n",
    "    # Only include categorical total (not including total of categories)\n",
    "    cutoff38.load_indicators(add=True,\n",
    "                            method='recipe endpoint', method_exclude='LT',\n",
    "                            category_exclude='total', indicator='total')\n",
    "    # Include TRACI model\n",
    "    cutoff38.load_indicators(add=True, method='TRACI', indicator='global warming')\n",
    "\n",
    "    ind_df_raw = cutoff38.export_indicators(show=False, path='')\n",
    "    ind_df_processed = ind_df_raw.copy()\n",
    "\n",
    "    for num, ind in ind_df_raw.iterrows():\n",
    "        old_name = ind['indicator']\n",
    "        if 'TRACI' in ind['method']:\n",
    "            new_name = old_name\n",
    "        else:\n",
    "            kind = ind['method'].split(',A)')[0][-1] # egalitarian, hierarchist, individualist\n",
    "            new_name = f'{kind}_{format_name(ind[\"category\"])}_Total' # categorical total\n",
    "        ind_df_processed.iloc[num]['indicator'] = new_name\n",
    "\n",
    "    ind_df_processed.sort_values(by=['method', 'category', 'indicator'], inplace=True)\n",
    "\n",
    "    if replace:\n",
    "        for ind_ID in ind_df_processed.indicator:\n",
    "            ind = qs.ImpactIndicator.get_indicator(ind_ID)\n",
    "            if ind:\n",
    "                #stdout = sys.stdout\n",
    "                #sys.stdout = open(os.devnull, 'w')\n",
    "                ind.deregister()\n",
    "                #sys.stdout = stdout\n",
    "\n",
    "    qs.ImpactIndicator.load_from_file(ind_df_processed)\n",
    "    indicators = qs.ImpactIndicator.get_all_indicators()\n",
    "\n",
    "    return cutoff38, ind_df_processed, indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database Brightway2 SQLiteBackend: cutoff38 with 19568 inventories has been loaded.\n",
      "\n",
      "9 indicator loaded/updated for cutoff38.\n",
      "\n",
      "1 indicators loaded/updated for cutoff38.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indicator</th>\n",
       "      <th>alias</th>\n",
       "      <th>unit</th>\n",
       "      <th>method</th>\n",
       "      <th>category</th>\n",
       "      <th>description</th>\n",
       "      <th>full_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I_HumanHealth_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (I,A)</td>\n",
       "      <td>human health</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (I,A), human h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H_HumanHealth_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (H,A)</td>\n",
       "      <td>human health</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (H,A), human h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GlobalWarming</td>\n",
       "      <td></td>\n",
       "      <td>kg CO2-Eq</td>\n",
       "      <td>TRACI</td>\n",
       "      <td>environmental impact</td>\n",
       "      <td></td>\n",
       "      <td>(TRACI, environmental impact, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H_Resources_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (H,A)</td>\n",
       "      <td>resources</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (H,A), resourc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I_EcosystemQuality_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (I,A)</td>\n",
       "      <td>ecosystem quality</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (I,A), ecosyst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E_EcosystemQuality_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (E,A)</td>\n",
       "      <td>ecosystem quality</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (E,A), ecosyst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I_Resources_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (I,A)</td>\n",
       "      <td>resources</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (I,A), resourc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E_HumanHealth_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (E,A)</td>\n",
       "      <td>human health</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (E,A), human h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>H_EcosystemQuality_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (H,A)</td>\n",
       "      <td>ecosystem quality</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (H,A), ecosyst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>E_Resources_Total</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>ReCiPe Endpoint (E,A)</td>\n",
       "      <td>resources</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Endpoint (E,A), resourc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  indicator alias       unit                 method  \\\n",
       "0       I_HumanHealth_Total           points  ReCiPe Endpoint (I,A)   \n",
       "1       H_HumanHealth_Total           points  ReCiPe Endpoint (H,A)   \n",
       "2             GlobalWarming        kg CO2-Eq                  TRACI   \n",
       "3         H_Resources_Total           points  ReCiPe Endpoint (H,A)   \n",
       "4  I_EcosystemQuality_Total           points  ReCiPe Endpoint (I,A)   \n",
       "5  E_EcosystemQuality_Total           points  ReCiPe Endpoint (E,A)   \n",
       "6         I_Resources_Total           points  ReCiPe Endpoint (I,A)   \n",
       "7       E_HumanHealth_Total           points  ReCiPe Endpoint (E,A)   \n",
       "8  H_EcosystemQuality_Total           points  ReCiPe Endpoint (H,A)   \n",
       "9         E_Resources_Total           points  ReCiPe Endpoint (E,A)   \n",
       "\n",
       "               category description                           full_name  \n",
       "0          human health              (ReCiPe Endpoint (I,A), human h...  \n",
       "1          human health              (ReCiPe Endpoint (H,A), human h...  \n",
       "2  environmental impact              (TRACI, environmental impact, g...  \n",
       "3             resources              (ReCiPe Endpoint (H,A), resourc...  \n",
       "4     ecosystem quality              (ReCiPe Endpoint (I,A), ecosyst...  \n",
       "5     ecosystem quality              (ReCiPe Endpoint (E,A), ecosyst...  \n",
       "6             resources              (ReCiPe Endpoint (I,A), resourc...  \n",
       "7          human health              (ReCiPe Endpoint (E,A), human h...  \n",
       "8     ecosystem quality              (ReCiPe Endpoint (H,A), ecosyst...  \n",
       "9             resources              (ReCiPe Endpoint (E,A), resourc...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bw2qsd import CFgetter\n",
    "from bw2qsd.utils import format_name\n",
    "cutoff38 = CFgetter('cutoff38')\n",
    "biosphere = CFgetter('biosphere3')\n",
    "# ecoinvent version 3.8, cutoff\n",
    "cutoff38.load_database('cutoff38')\n",
    "\n",
    "# Only include categorical total (not including total of categories)\n",
    "cutoff38.load_indicators(add=True,\n",
    "                        method='recipe endpoint', method_exclude='LT',\n",
    "                        category_exclude='total', indicator='total')\n",
    "# Include TRACI model\n",
    "cutoff38.load_indicators(add=True, method='TRACI', indicator='global warming')\n",
    "\n",
    "ind_df_raw = cutoff38.export_indicators(show=False, path='')\n",
    "ind_df_processed = ind_df_raw.copy()\n",
    "\n",
    "for num, ind in ind_df_raw.iterrows():\n",
    "    old_name = ind['indicator']\n",
    "    if 'TRACI' in ind['method']:\n",
    "        new_name = old_name\n",
    "    else:\n",
    "        kind = ind['method'].split(',A)')[0][-1] # egalitarian, hierarchist, individualist\n",
    "        new_name = f'{kind}_{format_name(ind[\"category\"])}_Total' # categorical total\n",
    "    ind_df_processed.iloc[num]['indicator'] = new_name\n",
    "\n",
    "ind_df_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database Brightway2 SQLiteBackend: cutoff38 with 19568 inventories has been loaded.\n",
      "\n",
      "1 indicators loaded/updated for cutoff38.\n",
      "\n",
      "18 indicator loaded/updated for cutoff38.\n"
     ]
    }
   ],
   "source": [
    "from bw2qsd import CFgetter\n",
    "from bw2qsd.utils import format_name\n",
    "cutoff38 = CFgetter('cutoff38')\n",
    "biosphere = CFgetter('biosphere3')\n",
    "# ecoinvent version 3.8, cutoff\n",
    "cutoff38.load_database('cutoff38')\n",
    "\n",
    "cutoff38.load_indicators(add=True, method='eco-indicator', method_exclude=('LT','E,E','I,I'), category_exclude='total', indicator='climate change')\n",
    "\n",
    "# Include TRACI model\n",
    "# cutoff38.load_indicators(add=True, method='TRACI')\n",
    "\n",
    "# Include ReCiPe v1.13 (Hierarchist) LCIA method \n",
    "cutoff38.load_indicators(add=True, method='ReCiPe Midpoint (H) V1.13', method_exclude=('LT'))\n",
    "\n",
    "ind_df_raw = cutoff38.export_indicators(show=False, path='')\n",
    "ind_df_processed = ind_df_raw.copy()\n",
    "\n",
    "for num, ind in ind_df_raw.iterrows():\n",
    "    old_name = ind['indicator']\n",
    "    if 'ReCiPe' in ind['method']:\n",
    "        new_name = old_name\n",
    "    else:\n",
    "        kind = ind['method'].split(') (')[0][-1] # egalitarian, hierarchist, individualist\n",
    "        new_name = f'{kind}_{format_name(ind[\"indicator\"])}'.translate({ord('&'):'And'}) # categorical total\n",
    "    ind_df_processed.iloc[num]['indicator'] = new_name\n",
    "\n",
    "ind_df_processed.sort_values(by=['method', 'category', 'indicator'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indicator</th>\n",
       "      <th>alias</th>\n",
       "      <th>unit</th>\n",
       "      <th>method</th>\n",
       "      <th>category</th>\n",
       "      <th>description</th>\n",
       "      <th>full_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ALOP</td>\n",
       "      <td></td>\n",
       "      <td>square meter-year</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>agricultural land occupation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, agr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GWP100</td>\n",
       "      <td></td>\n",
       "      <td>kg CO2-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>climate change</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, cli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDP</td>\n",
       "      <td></td>\n",
       "      <td>kg oil-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>fossil depletion</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, fos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FETPinf</td>\n",
       "      <td></td>\n",
       "      <td>kg 1,4-DCB-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>freshwater ecotoxicity</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, fre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FEP</td>\n",
       "      <td></td>\n",
       "      <td>kg P-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>freshwater eutrophication</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, fre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HTPinf</td>\n",
       "      <td></td>\n",
       "      <td>kg 1,4-DCB-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>human toxicity</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, hum...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>IRP_HE</td>\n",
       "      <td></td>\n",
       "      <td>kg U235-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>ionising radiation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, ion...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>METPinf</td>\n",
       "      <td></td>\n",
       "      <td>kg 1,4-DB-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>marine ecotoxicity</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, mar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEP</td>\n",
       "      <td></td>\n",
       "      <td>kg N-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>marine eutrophication</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, mar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MDP</td>\n",
       "      <td></td>\n",
       "      <td>kg Fe-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>metal depletion</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, met...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NLTP</td>\n",
       "      <td></td>\n",
       "      <td>square meter</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>natural land transformation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ODPinf</td>\n",
       "      <td></td>\n",
       "      <td>kg CFC-11-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>ozone depletion</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, ozo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMFP</td>\n",
       "      <td></td>\n",
       "      <td>kg PM10-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>particulate matter formation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, par...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POFP</td>\n",
       "      <td></td>\n",
       "      <td>kg NMVOC-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>photochemical oxidant formation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, pho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TAP100</td>\n",
       "      <td></td>\n",
       "      <td>kg SO2-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>terrestrial acidification</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, ter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TETPinf</td>\n",
       "      <td></td>\n",
       "      <td>kg 1,4-DCB-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>terrestrial ecotoxicity</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, ter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ULOP</td>\n",
       "      <td></td>\n",
       "      <td>square meter-year</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>urban land occupation</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, urb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>WDP</td>\n",
       "      <td></td>\n",
       "      <td>m3 water-Eq</td>\n",
       "      <td>ReCiPe Midpoint (H) V1.13</td>\n",
       "      <td>water depletion</td>\n",
       "      <td></td>\n",
       "      <td>(ReCiPe Midpoint (H) V1.13, wat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A_ClimateChange</td>\n",
       "      <td></td>\n",
       "      <td>points</td>\n",
       "      <td>eco-indicator 99, (H,A) (supers...</td>\n",
       "      <td>human health</td>\n",
       "      <td></td>\n",
       "      <td>(eco-indicator 99, (H,A) (super...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          indicator alias               unit  \\\n",
       "13             ALOP        square meter-year   \n",
       "12           GWP100                kg CO2-Eq   \n",
       "4               FDP                kg oil-Eq   \n",
       "2           FETPinf            kg 1,4-DCB-Eq   \n",
       "9               FEP                  kg P-Eq   \n",
       "18           HTPinf            kg 1,4-DCB-Eq   \n",
       "17           IRP_HE               kg U235-Eq   \n",
       "6           METPinf             kg 1,4-DB-Eq   \n",
       "3               MEP                  kg N-Eq   \n",
       "16              MDP                 kg Fe-Eq   \n",
       "10             NLTP             square meter   \n",
       "8            ODPinf             kg CFC-11-Eq   \n",
       "1              PMFP               kg PM10-Eq   \n",
       "5              POFP              kg NMVOC-Eq   \n",
       "14           TAP100                kg SO2-Eq   \n",
       "7           TETPinf            kg 1,4-DCB-Eq   \n",
       "15             ULOP        square meter-year   \n",
       "11              WDP              m3 water-Eq   \n",
       "0   A_ClimateChange                   points   \n",
       "\n",
       "                                method                         category  \\\n",
       "13           ReCiPe Midpoint (H) V1.13     agricultural land occupation   \n",
       "12           ReCiPe Midpoint (H) V1.13                   climate change   \n",
       "4            ReCiPe Midpoint (H) V1.13                 fossil depletion   \n",
       "2            ReCiPe Midpoint (H) V1.13           freshwater ecotoxicity   \n",
       "9            ReCiPe Midpoint (H) V1.13        freshwater eutrophication   \n",
       "18           ReCiPe Midpoint (H) V1.13                   human toxicity   \n",
       "17           ReCiPe Midpoint (H) V1.13               ionising radiation   \n",
       "6            ReCiPe Midpoint (H) V1.13               marine ecotoxicity   \n",
       "3            ReCiPe Midpoint (H) V1.13            marine eutrophication   \n",
       "16           ReCiPe Midpoint (H) V1.13                  metal depletion   \n",
       "10           ReCiPe Midpoint (H) V1.13      natural land transformation   \n",
       "8            ReCiPe Midpoint (H) V1.13                  ozone depletion   \n",
       "1            ReCiPe Midpoint (H) V1.13     particulate matter formation   \n",
       "5            ReCiPe Midpoint (H) V1.13  photochemical oxidant formation   \n",
       "14           ReCiPe Midpoint (H) V1.13        terrestrial acidification   \n",
       "7            ReCiPe Midpoint (H) V1.13          terrestrial ecotoxicity   \n",
       "15           ReCiPe Midpoint (H) V1.13            urban land occupation   \n",
       "11           ReCiPe Midpoint (H) V1.13                  water depletion   \n",
       "0   eco-indicator 99, (H,A) (supers...                     human health   \n",
       "\n",
       "   description                           full_name  \n",
       "13              (ReCiPe Midpoint (H) V1.13, agr...  \n",
       "12              (ReCiPe Midpoint (H) V1.13, cli...  \n",
       "4               (ReCiPe Midpoint (H) V1.13, fos...  \n",
       "2               (ReCiPe Midpoint (H) V1.13, fre...  \n",
       "9               (ReCiPe Midpoint (H) V1.13, fre...  \n",
       "18              (ReCiPe Midpoint (H) V1.13, hum...  \n",
       "17              (ReCiPe Midpoint (H) V1.13, ion...  \n",
       "6               (ReCiPe Midpoint (H) V1.13, mar...  \n",
       "3               (ReCiPe Midpoint (H) V1.13, mar...  \n",
       "16              (ReCiPe Midpoint (H) V1.13, met...  \n",
       "10              (ReCiPe Midpoint (H) V1.13, nat...  \n",
       "8               (ReCiPe Midpoint (H) V1.13, ozo...  \n",
       "1               (ReCiPe Midpoint (H) V1.13, par...  \n",
       "5               (ReCiPe Midpoint (H) V1.13, pho...  \n",
       "14              (ReCiPe Midpoint (H) V1.13, ter...  \n",
       "7               (ReCiPe Midpoint (H) V1.13, ter...  \n",
       "15              (ReCiPe Midpoint (H) V1.13, urb...  \n",
       "11              (ReCiPe Midpoint (H) V1.13, wat...  \n",
       "0               (eco-indicator 99, (H,A) (super...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_df_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use to remove indicators\n",
    "# cutoff38.remove(kind='indicator', keys=cutoff38.export_indicators(show=False, path='')['full_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_indicators(replace=True):\n",
    "    from bw2qsd import CFgetter\n",
    "    from bw2qsd.utils import format_name\n",
    "    cutoff38 = CFgetter('cutoff38')\n",
    "    # ecoinvent version 3.8, cutoff\n",
    "    cutoff38.load_database('cutoff38')\n",
    "\n",
    "    # Only include categorical total (not including total of categories)\n",
    "    cutoff38.load_indicators(add=True, method='eco-indicator', method_exclude=('LT','E,E','I,I'), category_exclude='total', indicator='climate change')\n",
    "    # Include TRACI model\n",
    "    cutoff38.load_indicators(add=True, method='TRACI')\n",
    "\n",
    "    ind_df_raw = cutoff38.export_indicators(show=False, path='')\n",
    "    ind_df_processed = ind_df_raw.copy()\n",
    "\n",
    "    for num, ind in ind_df_raw.iterrows():\n",
    "        old_name = ind['indicator']\n",
    "        if 'TRACI' in ind['method']:\n",
    "            new_name = old_name\n",
    "        else:\n",
    "            kind = ind['method'].split(',A)')[0][-1] # egalitarian, hierarchist, individualist\n",
    "            new_name = f'{kind}_{format_name(ind[\"category\"])}_Total' # categorical total\n",
    "        ind_df_processed.iloc[num]['indicator'] = new_name\n",
    "\n",
    "    ind_df_processed.sort_values(by=['method', 'category', 'indicator'], inplace=True)\n",
    "\n",
    "    if replace:\n",
    "        for ind_ID in ind_df_processed.indicator:\n",
    "            ind = qs.ImpactIndicator.get_indicator(ind_ID)\n",
    "            if ind:\n",
    "                #stdout = sys.stdout\n",
    "                #sys.stdout = open(os.devnull, 'w')\n",
    "                ind.deregister()\n",
    "                #sys.stdout = stdout\n",
    "\n",
    "    qs.ImpactIndicator.load_from_file(ind_df_processed)\n",
    "    indicators = qs.ImpactIndicator.get_all_indicators()\n",
    "\n",
    "    return cutoff38, ind_df_processed, indicators"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to get \"emission\" factors for elementary (biosphere) flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "IntegrityError",
     "evalue": "UNIQUE constraint failed: activitydataset.database, activitydataset.code",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIntegrityError\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:3197\u001b[0m, in \u001b[0;36mDatabase.execute_sql\u001b[1;34m(self, sql, params, commit)\u001b[0m\n\u001b[0;32m   3196\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3197\u001b[0m     cursor\u001b[39m.\u001b[39;49mexecute(sql, params \u001b[39mor\u001b[39;49;00m ())\n\u001b[0;32m   3198\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n",
      "\u001b[1;31mIntegrityError\u001b[0m: UNIQUE constraint failed: activitydataset.database, activitydataset.code",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mIntegrityError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\adsme\\OneDrive - The Pennsylvania State University\\Research\\System Analysis\\Python Scripts\\PG_REE_base\\PG_REE\\scratch_paper.ipynb Cell 20\u001b[0m line \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/adsme/OneDrive%20-%20The%20Pennsylvania%20State%20University/Research/System%20Analysis/Python%20Scripts/PG_REE_base/PG_REE/scratch_paper.ipynb#X26sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m eidb \u001b[39m=\u001b[39m bw\u001b[39m.\u001b[39mDatabase(\u001b[39m'\u001b[39m\u001b[39mcutoff38\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/adsme/OneDrive%20-%20The%20Pennsylvania%20State%20University/Research/System%20Analysis/Python%20Scripts/PG_REE_base/PG_REE/scratch_paper.ipynb#X26sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m CO2_emission \u001b[39m=\u001b[39m eidb\u001b[39m.\u001b[39mnew_activity(code \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mOne CO2 stream=1\u001b[39m\u001b[39m'\u001b[39m, name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mCO2_emission\u001b[39m\u001b[39m\"\u001b[39m, unit \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mkg\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/adsme/OneDrive%20-%20The%20Pennsylvania%20State%20University/Research/System%20Analysis/Python%20Scripts/PG_REE_base/PG_REE/scratch_paper.ipynb#X26sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m CO2_emission\u001b[39m.\u001b[39;49msave()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/adsme/OneDrive%20-%20The%20Pennsylvania%20State%20University/Research/System%20Analysis/Python%20Scripts/PG_REE_base/PG_REE/scratch_paper.ipynb#X26sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m CO2_fossil \u001b[39m=\u001b[39m [i \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m bsdb \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mCarbon dioxide, fossil\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m i[\u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m] \u001b[39mand\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mlow population density, long-term\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m i[\u001b[39m'\u001b[39m\u001b[39mcategories\u001b[39m\u001b[39m'\u001b[39m]][\u001b[39m0\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/adsme/OneDrive%20-%20The%20Pennsylvania%20State%20University/Research/System%20Analysis/Python%20Scripts/PG_REE_base/PG_REE/scratch_paper.ipynb#X26sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m CO2_emission\u001b[39m.\u001b[39mnew_exchange(\u001b[39minput\u001b[39m\u001b[39m=\u001b[39mCO2_fossil\u001b[39m.\u001b[39mkey,amount\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\u001b[39mtype\u001b[39m\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbiosphere\u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39msave()\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\bw2data\\project.py:358\u001b[0m, in \u001b[0;36mwritable_project\u001b[1;34m(wrapped, instance, args, kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m \u001b[39mif\u001b[39;00m projects\u001b[39m.\u001b[39mread_only:\n\u001b[0;32m    357\u001b[0m     \u001b[39mraise\u001b[39;00m ReadOnlyProject(READ_ONLY_PROJECT)\n\u001b[1;32m--> 358\u001b[0m \u001b[39mreturn\u001b[39;00m wrapped(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\bw2data\\backends\\peewee\\proxies.py:169\u001b[0m, in \u001b[0;36mActivity.save\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    167\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m dict_as_activitydataset(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_data)\u001b[39m.\u001b[39mitems():\n\u001b[0;32m    168\u001b[0m     \u001b[39msetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_document, key, value)\n\u001b[1;32m--> 169\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_document\u001b[39m.\u001b[39;49msave()\n\u001b[0;32m    171\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mapping:\n\u001b[0;32m    172\u001b[0m     mapping\u001b[39m.\u001b[39madd([\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey])\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:6694\u001b[0m, in \u001b[0;36mModel.save\u001b[1;34m(self, force_insert, only)\u001b[0m\n\u001b[0;32m   6692\u001b[0m     rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mupdate(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfield_dict)\u001b[39m.\u001b[39mwhere(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pk_expr())\u001b[39m.\u001b[39mexecute()\n\u001b[0;32m   6693\u001b[0m \u001b[39melif\u001b[39;00m pk_field \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 6694\u001b[0m     pk \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minsert(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfield_dict)\u001b[39m.\u001b[39;49mexecute()\n\u001b[0;32m   6695\u001b[0m     \u001b[39mif\u001b[39;00m pk \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_meta\u001b[39m.\u001b[39mauto_increment \u001b[39mor\u001b[39;00m\n\u001b[0;32m   6696\u001b[0m                            pk_value \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m):\n\u001b[0;32m   6697\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pk \u001b[39m=\u001b[39m pk\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:1927\u001b[0m, in \u001b[0;36mdatabase_required.<locals>.inner\u001b[1;34m(self, database, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1924\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m database:\n\u001b[0;32m   1925\u001b[0m     \u001b[39mraise\u001b[39;00m InterfaceError(\u001b[39m'\u001b[39m\u001b[39mQuery must be bound to a database in order \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1926\u001b[0m                          \u001b[39m'\u001b[39m\u001b[39mto call \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m method\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m)\n\u001b[1;32m-> 1927\u001b[0m \u001b[39mreturn\u001b[39;00m method(\u001b[39mself\u001b[39m, database, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:1998\u001b[0m, in \u001b[0;36mBaseQuery.execute\u001b[1;34m(self, database)\u001b[0m\n\u001b[0;32m   1996\u001b[0m \u001b[39m@database_required\u001b[39m\n\u001b[0;32m   1997\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexecute\u001b[39m(\u001b[39mself\u001b[39m, database):\n\u001b[1;32m-> 1998\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute(database)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:2798\u001b[0m, in \u001b[0;36mInsert._execute\u001b[1;34m(self, database)\u001b[0m\n\u001b[0;32m   2796\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_returning \u001b[39m=\u001b[39m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtable\u001b[39m.\u001b[39m_primary_key,)\n\u001b[0;32m   2797\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 2798\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m(Insert, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m_execute(database)\n\u001b[0;32m   2799\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mDefaultValuesException:\n\u001b[0;32m   2800\u001b[0m     \u001b[39mpass\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:2511\u001b[0m, in \u001b[0;36m_WriteQuery._execute\u001b[1;34m(self, database)\u001b[0m\n\u001b[0;32m   2509\u001b[0m     cursor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexecute_returning(database)\n\u001b[0;32m   2510\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 2511\u001b[0m     cursor \u001b[39m=\u001b[39m database\u001b[39m.\u001b[39;49mexecute(\u001b[39mself\u001b[39;49m)\n\u001b[0;32m   2512\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_result(database, cursor)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:3210\u001b[0m, in \u001b[0;36mDatabase.execute\u001b[1;34m(self, query, commit, **context_options)\u001b[0m\n\u001b[0;32m   3208\u001b[0m ctx \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_sql_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcontext_options)\n\u001b[0;32m   3209\u001b[0m sql, params \u001b[39m=\u001b[39m ctx\u001b[39m.\u001b[39msql(query)\u001b[39m.\u001b[39mquery()\n\u001b[1;32m-> 3210\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mexecute_sql(sql, params, commit\u001b[39m=\u001b[39;49mcommit)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:3194\u001b[0m, in \u001b[0;36mDatabase.execute_sql\u001b[1;34m(self, sql, params, commit)\u001b[0m\n\u001b[0;32m   3191\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   3192\u001b[0m         commit \u001b[39m=\u001b[39m \u001b[39mnot\u001b[39;00m sql[:\u001b[39m6\u001b[39m]\u001b[39m.\u001b[39mlower()\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mselect\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m-> 3194\u001b[0m \u001b[39mwith\u001b[39;00m __exception_wrapper__:\n\u001b[0;32m   3195\u001b[0m     cursor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcursor(commit)\n\u001b[0;32m   3196\u001b[0m     \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:2970\u001b[0m, in \u001b[0;36mExceptionWrapper.__exit__\u001b[1;34m(self, exc_type, exc_value, traceback)\u001b[0m\n\u001b[0;32m   2968\u001b[0m new_type \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexceptions[exc_type\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m]\n\u001b[0;32m   2969\u001b[0m exc_args \u001b[39m=\u001b[39m exc_value\u001b[39m.\u001b[39margs\n\u001b[1;32m-> 2970\u001b[0m reraise(new_type, new_type(exc_value, \u001b[39m*\u001b[39;49mexc_args), traceback)\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:191\u001b[0m, in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mreraise\u001b[39m(tp, value, tb\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    190\u001b[0m     \u001b[39mif\u001b[39;00m value\u001b[39m.\u001b[39m__traceback__ \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m tb:\n\u001b[1;32m--> 191\u001b[0m         \u001b[39mraise\u001b[39;00m value\u001b[39m.\u001b[39mwith_traceback(tb)\n\u001b[0;32m    192\u001b[0m     \u001b[39mraise\u001b[39;00m value\n",
      "File \u001b[1;32mc:\\Users\\adsme\\miniconda3\\envs\\bw2\\lib\\site-packages\\peewee.py:3197\u001b[0m, in \u001b[0;36mDatabase.execute_sql\u001b[1;34m(self, sql, params, commit)\u001b[0m\n\u001b[0;32m   3195\u001b[0m cursor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcursor(commit)\n\u001b[0;32m   3196\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3197\u001b[0m     cursor\u001b[39m.\u001b[39;49mexecute(sql, params \u001b[39mor\u001b[39;49;00m ())\n\u001b[0;32m   3198\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m   3199\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mautorollback \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39min_transaction():\n",
      "\u001b[1;31mIntegrityError\u001b[0m: UNIQUE constraint failed: activitydataset.database, activitydataset.code"
     ]
    }
   ],
   "source": [
    "# In order to use Biosphere3 database flows, they must be added to the Ecoinvent database via the creation of a new activity with one exchange (the bsdb flow of interest)\n",
    "eidb = bw.Database('cutoff38')\n",
    "CO2_emission = eidb.new_activity(code = 'One CO2 stream=1', name = \"CO2_emission\", unit = \"kg\")\n",
    "CO2_emission.save()\n",
    "\n",
    "CO2_fossil = [i for i in bsdb if 'Carbon dioxide, fossil' in i['name'] and 'low population density, long-term' in i['categories']][0]\n",
    "CO2_emission.new_exchange(input=CO2_fossil.key,amount=1,type='biosphere').save()\n",
    "\n",
    "[i for i in eidb if 'CO2_emission' in i['name']][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database Brightway2 SQLiteBackend: cutoff38 with 19568 inventories has been loaded.\n",
      "\n",
      "1 indicators loaded/updated for cutoff38.\n",
      "\n",
      "9 indicator loaded/updated for cutoff38.\n",
      "1 activity loaded/updated for cutoff38.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'CO2_emission': 'CO2_emission' (kg, GLO, None)}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File \"setups.pickle\" successfully removed from directory \"C:\\Users\\adsme\\AppData\\Local\\pylca\\Brightway3\\default.c21f969b5f03d33d43e04f8f136e7682\".\n"
     ]
    },
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"8\" halign=\"left\">TRACI</th>\n",
       "      <th>eco-indicator 99, (H,A) (superseded)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"5\" halign=\"left\">environmental impact</th>\n",
       "      <th colspan=\"3\" halign=\"left\">human health</th>\n",
       "      <th>human health</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>activity name</th>\n",
       "      <th>functional unit</th>\n",
       "      <th>acidification</th>\n",
       "      <th>ecotoxicity</th>\n",
       "      <th>eutrophication</th>\n",
       "      <th>...</th>\n",
       "      <th>photochemical oxidation</th>\n",
       "      <th>carcinogenics</th>\n",
       "      <th>non-carcinogenics</th>\n",
       "      <th>respiratory effects, average</th>\n",
       "      <th>climate change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>moles of H+-Eq</td>\n",
       "      <td>kg 2,4-D-Eq</td>\n",
       "      <td>kg N</td>\n",
       "      <td>...</td>\n",
       "      <td>kg NOx-Eq</td>\n",
       "      <td>kg benzene-Eq</td>\n",
       "      <td>kg toluene-Eq</td>\n",
       "      <td>kg PM2.5-Eq</td>\n",
       "      <td>points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CO2_emission</td>\n",
       "      <td>kg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              -                                TRACI               \\\n",
       "              -                 environmental impact                \n",
       "  activity name functional unit        acidification  ecotoxicity   \n",
       "0           NaN             NaN       moles of H+-Eq  kg 2,4-D-Eq   \n",
       "1  CO2_emission              kg                    0            0   \n",
       "\n",
       "                  ...                                         \\\n",
       "                  ...                           human health   \n",
       "  eutrophication  ... photochemical oxidation  carcinogenics   \n",
       "0           kg N  ...               kg NOx-Eq  kg benzene-Eq   \n",
       "1              0  ...                       0              0   \n",
       "\n",
       "                                                  \\\n",
       "                                                   \n",
       "  non-carcinogenics respiratory effects, average   \n",
       "0     kg toluene-Eq                  kg PM2.5-Eq   \n",
       "1                 0                            0   \n",
       "\n",
       "  eco-indicator 99, (H,A) (superseded)  \n",
       "                          human health  \n",
       "                        climate change  \n",
       "0                              points   \n",
       "1                             0.00545   \n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading activities to bw2qsd in \"select_items()\" is good after doing the above step. This is the guts of the emission factor calculation \"organize_cfs()\" (CFgetter from bw2qsd). \n",
    "from bw2qsd import CFgetter\n",
    "import sys, os\n",
    "from bw2qsd import remove_setups_pickle\n",
    "\n",
    "cutoff38, ind_df_processed, indicators = create_indicators()\n",
    "cutoff38.load_activities(string='CO2_emission', add=True, limit=10)\n",
    "activities = cutoff38.activities\n",
    "indicators = cutoff38.indicators\n",
    "display(activities)\n",
    "\n",
    "if not indicators:\n",
    "    raise ValueError('No loaded indicators.')\n",
    "elif not activities:\n",
    "    raise ValueError('No loaded activities.')\n",
    "\n",
    "if not set(indicators).issubset(indicators):\n",
    "    raise ValueError('Provided indicator(s) not all loaded.')\n",
    "\n",
    "\n",
    "inds = indicators if indicators else indicators\n",
    "ind_units = [bw.methods.get(i)['unit'] for i in inds] + ['-', '-']\n",
    "acts = [activities[k] for k in activities] if activities \\\n",
    "    else [v for v in activities.values()]\n",
    "{'inv': [{a:1} for a in acts],'ia': inds}\n",
    "bw.calculation_setups['multiLCA'] = {'inv': [{a:1} for a in acts],\n",
    "                                        'ia': inds}\n",
    "bw.calculation_setups['multiLCA'] \n",
    "# This is to prevent bw2 from printing in the console\n",
    "stdout = sys.stdout\n",
    "sys.stdout = open(os.devnull, 'w')\n",
    "mlca = bw.MultiLCA('multiLCA')\n",
    "sys.stdout = stdout\n",
    "\n",
    "# pd_indices = [a['name'] for a in acts]\n",
    "pd_cols = pd.MultiIndex.from_tuples(inds, names=('method', 'category', 'indicator'))\n",
    "cf_df = pd.DataFrame(data=mlca.results, columns=pd_cols)\n",
    "unit_df = pd.DataFrame(data={k: v for k, v in zip(cf_df.columns, ind_units)},\n",
    "                        index=[-1,], dtype='object')\n",
    "# cf_df = pd.DataFrame(data=mlca.results, index=pd_indices, columns=pd_cols)\n",
    "cf_df[('-', '-', 'activity name')] = [a['name'] for a in acts]\n",
    "cf_df[('-', '-', 'functional unit')] = [a['unit'] for a in acts]\n",
    "\n",
    "df = pd.concat((unit_df, cf_df))\n",
    "df.sort_index(axis=1, inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "remove_setups_pickle()\n",
    "\n",
    "display(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully removed 47 indicators from cutoff38.\n"
     ]
    }
   ],
   "source": [
    "# use to remove indicators\n",
    "# cutoff38.remove(kind='indicator', keys=cutoff38.export_indicators(show=False, path='')['full_name'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Get activities from the Ecoinvent database and calculate emission factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database Brightway2 SQLiteBackend: cutoff38 with 19568 inventories has been loaded.\n",
      "\n",
      "1 indicators loaded/updated for cutoff38.\n",
      "\n",
      "9 indicator loaded/updated for cutoff38.\n",
      "The impact indicator \"Acidification\" has been removed from the registry.\n",
      "The impact indicator \"Ecotoxicity\" has been removed from the registry.\n",
      "The impact indicator \"Eutrophication\" has been removed from the registry.\n",
      "The impact indicator \"GlobalWarming\" has been removed from the registry.\n",
      "The impact indicator \"OzoneDepletion\" has been removed from the registry.\n",
      "The impact indicator \"PhotochemicalOxidation\" has been removed from the registry.\n",
      "The impact indicator \"Carcinogenics\" has been removed from the registry.\n",
      "The impact indicator \"NonCarcinogenics\" has been removed from the registry.\n",
      "The impact indicator \"RespiratoryEffectsAverage\" has been removed from the registry.\n",
      "The impact indicator \"H_HumanHealth_Total\" has been removed from the registry.\n",
      "Excluding 7 filtered results\n",
      "3 activities loaded/updated for H2SO4_item.\n",
      "\n",
      "Successfully removed 2 activities from H2SO4_item.\n",
      "nd item\n",
      "MultiIndex([(                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            (                               'TRACI', ...),\n",
      "            ('eco-indicator 99, (H,A) (superseded)', ...)],\n",
      "           )\n",
      "                 TRACI                                            \\\n",
      "  environmental impact                                             \n",
      "         acidification ecotoxicity eutrophication global warming   \n",
      "0                -18.7       -88.3          -2.33          -52.8   \n",
      "1                -18.7       -88.3          -2.33          -52.8   \n",
      "2                -18.7       -88.3          -2.33          -52.8   \n",
      "\n",
      "                                                                           \\\n",
      "                                           human health                     \n",
      "  ozone depletion photochemical oxidation carcinogenics non-carcinogenics   \n",
      "0        -6.2e-06                  -0.166         -0.62         -1.11e+03   \n",
      "1        -6.2e-06                  -0.166         -0.62         -1.11e+03   \n",
      "2        -6.2e-06                  -0.166         -0.62         -1.11e+03   \n",
      "\n",
      "                               eco-indicator 99, (H,A) (superseded)  \n",
      "                                                       human health  \n",
      "  respiratory effects, average                       climate change  \n",
      "0                      -0.0915                              -0.287   \n",
      "1                      -0.0915                              -0.287   \n",
      "2                      -0.0915                              -0.287   \n",
      "                            -                                TRACI  \\\n",
      "                            -                 environmental impact   \n",
      "                activity name functional unit        acidification   \n",
      "0                         NaN             NaN       moles of H+-Eq   \n",
      "1  market for neodymium oxide        kilogram                 18.7   \n",
      "\n",
      "                               ...                                         \\\n",
      "                               ...                           human health   \n",
      "   ecotoxicity eutrophication  ... photochemical oxidation  carcinogenics   \n",
      "0  kg 2,4-D-Eq           kg N  ...               kg NOx-Eq  kg benzene-Eq   \n",
      "1         88.3           2.33  ...                   0.166           0.62   \n",
      "\n",
      "                                                  \\\n",
      "                                                   \n",
      "  non-carcinogenics respiratory effects, average   \n",
      "0     kg toluene-Eq                  kg PM2.5-Eq   \n",
      "1          1.11e+03                       0.0915   \n",
      "\n",
      "  eco-indicator 99, (H,A) (superseded)  \n",
      "                          human health  \n",
      "                        climate change  \n",
      "0                              points   \n",
      "1                               0.287   \n",
      "\n",
      "[2 rows x 12 columns]\n",
      "co2\n"
     ]
    },
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.00545</td>\n",
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      ],
      "text/plain": [
       "              -                                TRACI              \\\n",
       "              -                 environmental impact               \n",
       "  activity name functional unit        acidification ecotoxicity   \n",
       "0           min              kg                  123           0   \n",
       "1          mean              kg                  123           0   \n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"8\" halign=\"left\">TRACI</th>\n",
       "      <th>eco-indicator 99, (H,A) (superseded)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"5\" halign=\"left\">environmental impact</th>\n",
       "      <th colspan=\"3\" halign=\"left\">human health</th>\n",
       "      <th>human health</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>activity name</th>\n",
       "      <th>functional unit</th>\n",
       "      <th>acidification</th>\n",
       "      <th>ecotoxicity</th>\n",
       "      <th>eutrophication</th>\n",
       "      <th>...</th>\n",
       "      <th>photochemical oxidation</th>\n",
       "      <th>carcinogenics</th>\n",
       "      <th>non-carcinogenics</th>\n",
       "      <th>respiratory effects, average</th>\n",
       "      <th>climate change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>moles of H+-Eq</td>\n",
       "      <td>kg 2,4-D-Eq</td>\n",
       "      <td>kg N</td>\n",
       "      <td>...</td>\n",
       "      <td>kg NOx-Eq</td>\n",
       "      <td>kg benzene-Eq</td>\n",
       "      <td>kg toluene-Eq</td>\n",
       "      <td>kg PM2.5-Eq</td>\n",
       "      <td>points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CO2_emission</td>\n",
       "      <td>kg</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              -                                TRACI               \\\n",
       "              -                 environmental impact                \n",
       "  activity name functional unit        acidification  ecotoxicity   \n",
       "0           NaN             NaN       moles of H+-Eq  kg 2,4-D-Eq   \n",
       "1  CO2_emission              kg                  123            0   \n",
       "\n",
       "                  ...                                         \\\n",
       "                  ...                           human health   \n",
       "  eutrophication  ... photochemical oxidation  carcinogenics   \n",
       "0           kg N  ...               kg NOx-Eq  kg benzene-Eq   \n",
       "1              0  ...                       0              0   \n",
       "\n",
       "                                                  \\\n",
       "                                                   \n",
       "  non-carcinogenics respiratory effects, average   \n",
       "0     kg toluene-Eq                  kg PM2.5-Eq   \n",
       "1                 0                            0   \n",
       "\n",
       "  eco-indicator 99, (H,A) (superseded)  \n",
       "                          human health  \n",
       "                        climate change  \n",
       "0                              points   \n",
       "1                             0.00545   \n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def select_items(database):\n",
    "    all_acts = {}\n",
    "    eidb = bw.Database('cutoff38')\n",
    "\n",
    "    def new_act(database, all_acts, name):\n",
    "        act = database.copy(name)\n",
    "        all_acts[name] = act\n",
    "        return act\n",
    "\n",
    "    # Catch all printouts (i.e., don't show them in the console)\n",
    "    # stdout = sys.stdout\n",
    "    # sys.stdout = open(os.devnull, 'w')\n",
    "\n",
    "    # Sulfuric Acid\n",
    "    H2SO4_item = new_act(database, all_acts, 'H2SO4_item')\n",
    "    H2SO4_item.load_activities('market for sulfuric acid', add=True, filter={'location': 'RoW'}, limit=None)\n",
    "    to_remove = []\n",
    "    for act in H2SO4_item.activities.keys():\n",
    "        if ('bromine production' in act) or ('sodium chloride production, powder' in act):\n",
    "            to_remove.append(act)\n",
    "    H2SO4_item.remove('activity', to_remove)\n",
    "\n",
    "    # Oxalic Acid\n",
    "    OA_item = new_act(database, all_acts, 'OA_item')\n",
    "    dct = [i for i in eidb if 'market for oxalic acid' in i['name'] and 'GLO' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    OA_item._activities.update(dct2)\n",
    "\n",
    "    # Process Water\n",
    "    pwater_item = new_act(database, all_acts, 'pwater_item')\n",
    "    dct = [i for i in eidb if 'market for water, decarbonised' in i['name'] and 'US' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    pwater_item._activities.update(dct2)\n",
    "\n",
    "    # Generic Wastewater Treatment (represents a negative input, treatment of a waste)\n",
    "    wastewater_item = new_act(database, all_acts, 'wastewater_item')\n",
    "    dct = [i for i in eidb if 'market for wastewater, average' in i['name'] and 'RoW' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    wastewater_item._activities.update(dct2)\n",
    "\n",
    "    # Gypsum\n",
    "    gypsum_item = new_act(database, all_acts, 'gypsum_item')\n",
    "    dct = [i for i in eidb if 'market for gypsum, mineral' in i['name'] and 'RoW' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    gypsum_item._activities.update(dct2)\n",
    "\n",
    "    # Neodymium\n",
    "    neodymium_item = new_act(database, all_acts, 'neodymium_item')\n",
    "    dct = [i for i in eidb if 'market for neodymium' in i['name'] and 'GLO' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    neodymium_item._activities.update(dct2)\n",
    "\n",
    "    # Electricity\n",
    "    electricity_item = new_act(database, all_acts, 'electricity_item')\n",
    "    dct = [i for i in eidb if 'market for electricity, medium voltage' in i['name'] and 'US-SERC' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    electricity_item._activities.update(dct2)\n",
    "\n",
    "    # Steel\n",
    "    steel_item = new_act(database, all_acts, 'steel_item')\n",
    "    dct = [i for i in eidb if 'market for steel, chromium steel 18/8'==i['name']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    steel_item._activities.update(dct2)\n",
    "\n",
    "    # Heat Production, Natural Gas\n",
    "    heatNG_item = new_act(database, all_acts, 'heatNG_item')\n",
    "    dct = [i for i in eidb if 'heat production, natural gas, at industrial furnace >100kW' in i['name'] and 'CA-QC' in i['location']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    heatNG_item._activities.update(dct2)\n",
    "\n",
    "    # Carbon Dioxide\n",
    "    CO2_item = new_act(database, all_acts, 'CO2_item')\n",
    "    dct = [i for i in eidb if 'CO2_emission' in i['name']][0]\n",
    "    dct2 = {dct['name']: dct}\n",
    "    CO2_item._activities.update(dct2)\n",
    "\n",
    "\n",
    "    # Restore printouts\n",
    "    # sys.stdout = stdout\n",
    "\n",
    "    return all_acts \n",
    "\n",
    "def get_stats(df, keep_raw_data=False):\n",
    "    df2 = pd.DataFrame(columns=df.columns, dtype='float64') if not keep_raw_data else df.copy()\n",
    "    df2 = df2.append(df[1:].min(), ignore_index=True)\n",
    "    df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
    "    df2 = df2.append(df[1:].max(), ignore_index=True)\n",
    "    df2.loc[-3:, ('-', '-', 'activity name')] = ('min', 'mean', 'max')\n",
    "    functional_unit = df.loc[1, ('-', '-', 'functional unit')]\n",
    "    df2.loc[1:, ('-', '-', 'functional unit')] = functional_unit\n",
    "    return df2\n",
    "\n",
    "def organize_cfs(all_acts):\n",
    "    cf_dct = {}\n",
    "    cf_dct['H2SO4_item'] = get_stats(all_acts['H2SO4_item'].CFs)\n",
    "\n",
    "    cf_dct['OA_item'] = get_stats(all_acts['OA_item'].CFs)\n",
    "\n",
    "    cf_dct['steel_item'] = get_stats(all_acts['steel_item'].CFs)\n",
    "\n",
    "    cf_dct['pwater_item'] = get_stats(all_acts['pwater_item'].CFs)\n",
    "\n",
    "    cf_dct['gypsum_item'] = get_stats(all_acts['gypsum_item'].CFs)\n",
    "    cols = all_acts['gypsum_item'].CFs.columns[2:]\n",
    "    cf_dct['gypsum_item'][cols] *= -1 # credit\n",
    "\n",
    "    cf_dct['wastewater_item'] = get_stats(all_acts['wastewater_item'].CFs)\n",
    "    \n",
    "    cf_dct['neodymium_item'] = get_stats(all_acts['neodymium_item'].CFs)\n",
    "    cols = all_acts['neodymium_item'].CFs.columns[2:]\n",
    "    cf_dct['neodymium_item'][cols] *= -1 # credit\n",
    "\n",
    "    cf_dct['electricity_item'] = get_stats(all_acts['electricity_item'].CFs)\n",
    "\n",
    "    cf_dct['heatNG_item'] = get_stats(all_acts['heatNG_item'].CFs)\n",
    "\n",
    "    cf_dct['CO2_item'] = get_stats(all_acts['CO2_item'].CFs)\n",
    "\n",
    "    return cf_dct\n",
    "\n",
    "def create_items(ind_df_processed, cf_dct, replace=True):\n",
    "    items = []\n",
    "    for item_ID, df in cf_dct.items():\n",
    "        item = qs.ImpactItem.get_item(item_ID)\n",
    "        if not (replace and item):\n",
    "            if not ('item' in item_ID and item_ID!='electricity_item'):\n",
    "                item = qs.ImpactItem(ID=item_ID,\n",
    "                                     functional_unit=df.loc[1, ('-', '-', 'functional unit')])\n",
    "            else:\n",
    "                item = qs.StreamImpactItem(ID=item_ID)\n",
    "\n",
    "        for num in ind_df_processed.index:\n",
    "            ind_ID = ind_df_processed.iloc[num]['indicator']\n",
    "            ind_str = ind_df_processed.iloc[num]['full_name']\n",
    "            ind_col = tuple(i for i in ind_str.split(\"'\") if len(i)>2)\n",
    "            item.add_indicator(ind_ID, CF_value=df[df.values=='mean'][ind_col].item())\n",
    "\n",
    "        items.append(item)\n",
    "    return items\n",
    "\n",
    "def get_cf_data():\n",
    "    from bw2qsd import remove_setups_pickle\n",
    "    cutoff38, ind_df_processed, indicators = create_indicators()\n",
    "    all_acts = select_items(cutoff38)\n",
    "    cf_dct = organize_cfs(all_acts)\n",
    "\n",
    "    # Only run this at the very end to remove the outdated setup.pickle file\n",
    "    remove_setups_pickle()\n",
    "\n",
    "    return ind_df_processed, all_acts, cf_dct\n",
    "\n",
    "\n",
    "def save_cf_data():\n",
    "    ind_df_processed, all_acts, cf_dct = get_cf_data()\n",
    "\n",
    "    ind_file = 'indicators_new.tsv'\n",
    "    raw_path = os.path.join(data_path, 'CFs_new')\n",
    "\n",
    "    # Impact indicators\n",
    "    ind_df_processed.to_csv(os.path.join(data_path, ind_file), sep='\\t')\n",
    "\n",
    "    # Raw data\n",
    "    if not os.path.isdir(raw_path):\n",
    "        os.mkdir(raw_path)\n",
    "    for k, v in all_acts.items():\n",
    "        v.CFs.to_csv(os.path.join(raw_path, f'CFs_{k}.tsv'), sep='\\t')\n",
    "\n",
    "    # Organized data\n",
    "    f = open(os.path.join(data_path, 'cf_dct.pckl'), 'wb')\n",
    "    pickle.dump(cf_dct, f)\n",
    "    f.close()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "ind_df_processed, all_acts, cf_dct = get_cf_data()\n",
    "warnings.resetwarnings()\n",
    "for k, v in all_acts.items():\n",
    "    display(pd.DataFrame(v.CFs))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the LCA data and create LCA items for use in the process simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _load_lca_data():\n",
    "    '''\n",
    "    Load impact indicator and impact item data.\n",
    "    '''\n",
    "    indicator_path = os.path.join(data_path, 'indicators_new.tsv')\n",
    "    indel_col = 0\n",
    "    ind_df_processed = pd.read_csv(indicator_path, sep='\\t', index_col=indel_col)\n",
    "    qs.ImpactIndicator.load_from_file(indicator_path)\n",
    "\n",
    "    item_path = os.path.join(data_path, 'cf_dct.pckl')\n",
    "    f = open(item_path, 'rb')\n",
    "    cf_dct = pickle.load(f)\n",
    "    f.close()\n",
    "    create_items(ind_df_processed, cf_dct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\bw2qsd\\_cf_getter.py:393: ResourceWarning: unclosed file <_io.TextIOWrapper name='nul' mode='w' encoding='cp1252'>\n",
      "  sys.stdout = stdout\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:85: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].min(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:86: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].mean(), ignore_index=True)\n",
      "C:\\Users\\ajs8911\\AppData\\Local\\Temp\\ipykernel_6720\\757783383.py:87: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append(df[1:].max(), ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# run only the first time after adding indicators or activities\n",
    "# save_cf_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Unit Operation to Simulate"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's look at leaching using sulfuric acid\n",
    "This unit uses experimental data to determine leaching efficiency and complete a mass balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeachingSulfuric(qs.SanUnit):\n",
    "    def __init__(self, ID='U1', ins=None, outs=(), thermo=None, init_with='SanStream',\n",
    "    acidConc = 5, # acid concentration in wt %\n",
    "    temp = 50, # temperature in degrees celsius\n",
    "    time = 120, # leaching time in minutes\n",
    "    solventSolidRatio = 4, # default solvent to solid ratio\n",
    "    LOverV = 0.2, # default underflow to overflow mass flow ratio\n",
    "    feedPG = 1000000,\n",
    "    REEinPG = 0.005 # default underflow to overflow mass flow ratio\n",
    "    ):\n",
    "        # Some Assumed Process Parameters\n",
    "        self.Y_np1 = 0 #amount of REE remaining in recycled solvent feed (mass REE/mass solvent)\n",
    "        self.X_N = 0.00001 #amount of REE leaving in underflow with solids (mass REE/mass solvent)\n",
    "\n",
    "        # Some standard codes you need to include for all subclasses of `SanUnit`\n",
    "        qs.SanUnit.__init__(self, ID, ins, outs, thermo, init_with)\n",
    "\n",
    "        # These are the unique attributes of `LeachingSulfuric`\n",
    "        self.acidConc = acidConc\n",
    "        self.temp = temp\n",
    "        self.time = time\n",
    "        self.solventSolidRatio = solventSolidRatio\n",
    "        self.LOverV = LOverV\n",
    "        self.feedPG = feedPG\n",
    "        self.REEinPG = REEinPG\n",
    "\n",
    "        self.F_BM = {'Thickener': 3.5} # from Perry's Chemical Engineers' Handbook\n",
    "        \n",
    "    _N_ins = 3\n",
    "    _N_outs = 5\n",
    "\n",
    "    def _run(self):\n",
    "        rawPG, lixiviant_water, lixiviant_acid = self.ins\n",
    "        leachate, leachate_Ln, underflow, underflow_gypsum, leachate_gypsum = self.outs\n",
    "\n",
    "        # Add in Experimental Data to Calculate leaching efficiency (LE)\n",
    "        # data from Liang et al – 2017 – Rare earths recovery and gypsum upgrade from Florida phosphogypsum\n",
    "        LEbase = 0.43 # base case leaching efficiency\n",
    "        \n",
    "        LEbase_temp = 0.43 # leaching efficiency for optimal temperature\n",
    "        xtemp = [20, 30, 40, 50, 60, 70]\n",
    "        ytemp = [0.33, 0.4, 0.42, 0.43, 0.39, 0.34]\n",
    "        tck_temp = interpolate.splrep(xtemp, ytemp, s=0)\n",
    "\n",
    "        LEbase_conc = 0.4 # leaching efficiency for optimal acid concentration\n",
    "        xconc = [0, 1, 2.5, 5, 7.5, 10]\n",
    "        yconc = [0.01, 0.18, 0.36, 0.4, 0.39, 0.35]\n",
    "        tck_conc = interpolate.splrep(xconc, yconc, s=0)\n",
    "\n",
    "        LEbase_time = 0.43 # leaching efficiency for Liang's chosen optimal leaching time\n",
    "        xtime = [10,20,30,45,63,76,90,103,116,135,150,165,180,190,210]\n",
    "        ytime = [0.19,0.3,0.35,0.38,0.4,0.41,0.42,0.425,0.43,0.4325,0.435,0.435,0.435,0.4375,0.44]\n",
    "        tck_time = interpolate.splrep(xtime, ytime, s=0)\n",
    "\n",
    "        if not 20 <= self.temp <= 70:\n",
    "            raise AttributeError('`temp` must be within [20, 70], '\n",
    "                                f'the provided value {self.temp} is outside this range.')\n",
    "        if not 0 <= self.acidConc <= 10:\n",
    "            raise AttributeError('`acidConc` must be within [0, 10], '\n",
    "                                f'the provided value {self.acidConc} is outside this range.')\n",
    "        if not 10 <= self.time <= 210:\n",
    "            raise AttributeError('`time` must be within [10, 210], '\n",
    "                                f'the provided value {self.time} is outside this range.')\n",
    "\n",
    "        LEtemp = LEbase_temp - interpolate.splev(self.temp, tck_temp, der=0).tolist()\n",
    "        LEconc = LEbase_conc - interpolate.splev(self.acidConc, tck_conc, der=0).tolist()\n",
    "        LEtime = LEbase_time - interpolate.splev(self.time, tck_time, der=0).tolist()\n",
    "        self.leachingEFF = LEbase - (LEtemp + LEconc + LEtime)\n",
    "\n",
    "        # Calculate the need lixiviant and the mass of REE that will be leached\n",
    "        lixNeeded = self.feedPG*self.solventSolidRatio # total mass of lixiviant being used\n",
    "        fracSulfuric = self.acidConc/100 # fraction of lixiviant that is sulfuric acid\n",
    "        lixiviant_acid.imass['H2SO4'] = fracSulfuric*lixNeeded #total sulfuric acid entering the process `imass` in kg/hr\n",
    "        lixiviant_water.imass['H2O'] = (1-fracSulfuric)*lixNeeded #total water entering the process `imass` in kg/hr\n",
    "        \n",
    "        # Calculate the amount of REE leached\n",
    "        REEtotal = self.feedPG*self.REEinPG\n",
    "        gypsum = self.feedPG - REEtotal\n",
    "        REEleached = REEtotal*self.leachingEFF #total REE being leached out of the PG in the leaching stage\n",
    "        rawPG.imass['Nd'] = REEtotal\n",
    "        rawPG.imass['Gypsum'] = gypsum\n",
    "        \n",
    "\n",
    "\n",
    "        # Complete the Mass Balance\n",
    "        V_1 = lixNeeded\n",
    "        V_L = V_1 - self.LOverV*V_1\n",
    "        L_L = V_1 - V_L\n",
    "        Y_L = (REEleached - L_L*self.X_N)/V_L\n",
    "        Y_1 = (V_L*Y_L + L_L*Y_L - REEleached)/V_1\n",
    "\n",
    "        self.Y_L = Y_L\n",
    "        self.Y_1 = Y_1\n",
    "\n",
    "        # Set outlet flows to the values derived in the mass balance\n",
    "        leachate_Ln.imass['Nd'] = Y_L*V_L\n",
    "        leachate.imass['H2SO4'] = fracSulfuric*V_L\n",
    "        leachate.imass['H2O'] = (1-fracSulfuric)*V_L\n",
    "        leachate_gypsum.imass['Gypsum'] = rawPG.imass['Gypsum']*0.3 # assuming no PG dissolves in acid\n",
    "        \n",
    "        underflow.imass['Nd'] = self.X_N*L_L + (REEtotal-REEleached)\n",
    "        underflow.imass['H2SO4'] = fracSulfuric*L_L\n",
    "        underflow.imass['H2O'] = (1-fracSulfuric)*L_L\n",
    "        underflow_gypsum.imass['Gypsum'] = rawPG.imass['Gypsum']*0.7 # assuming no PG dissolves in acid\n",
    "\n",
    "    _units = {\n",
    "        'Washing Stages': '#',\n",
    "        'Total Stages': '#',\n",
    "        'Volume': 'm3',\n",
    "        'Settling Area': 'm2',\n",
    "        'Diameter': 'm',\n",
    "        'Height': 'm',\n",
    "        'Thickness': 'm',\n",
    "        'Steel': 'kg',\n",
    "        'Heat Duty': 'kJ/h',\n",
    "        'Leaching Efficiency': '%'\n",
    "    }\n",
    "\n",
    "    def _design(self):\n",
    "        D = self.design_results\n",
    "        stage_eff = 0.9\n",
    "        self.nStages = np.log10(((self.X_N - self.Y_np1)/(self.Y_L - self.Y_1)))/np.log10(self.LOverV)/stage_eff # Calculate number of actual washing stages based on McCabe-Smith algebraic method\n",
    "        HRT = self.time/60 # hydraulic residence time based on leaching time in hours\n",
    "        self.outlet_flowrate = qs.SanStream('inlet_flowrate')\n",
    "        self.outlet_flowrate.mix_from(self.outs)\n",
    "        w_vol = (self.outlet_flowrate.F_vol)*HRT/(self.nStages + 1) # volume of each leaching/washing stage (thickener)\n",
    "        tot_vol = w_vol / 0.8 # assume 80% working volume\n",
    "        dia = (24*tot_vol/3.14)**(1/3)\n",
    "        D['Leaching Efficiency'] = self.leachingEFF*100\n",
    "        D['Washing Stages'] = self.nStages\n",
    "        D['Total Stages'] = (self.nStages + 1)\n",
    "        D['Volume'] = tot_vol\n",
    "        D['Settling Area'] = 3.14*(dia/2)**2\n",
    "        D['Diameter'] = dia\n",
    "        D['Height'] = H = dia/6\n",
    "\n",
    "        # Calculate wall thickness based on pressure vessel formuala in Seider, Seader\n",
    "        thickness = (24.7*dia*39.37/(2*1/3*14800 - 1.2*24.7))*0.0254 # thickness in meters\n",
    "        steel_V = 3.14*H*((thickness + dia)**2 - dia**2)/4 + 3.14*thickness*dia**2/4 # volume of material necessary\n",
    "        D['Thickness'] = thickness\n",
    "        D['Steel'] = steel_V * 8080 # density of Carpenter 20CB-3 is 8080 kg/m3\n",
    "\n",
    "        # Heat Utility\n",
    "        self.inlet_flowrate = qs.SanStream('inlet_flowrate')\n",
    "        self.inlet_flowrate.mix_from(self.ins)\n",
    "        self.heat_duty = self.inlet_flowrate.Cp/1000*1000*self.inlet_flowrate.F_mass*(self.temp-20) # heat duty in kJ/hr\n",
    "        D['Heat Duty'] = self.heat_duty\n",
    "\n",
    "        \n",
    "\n",
    "    def _cost(self):\n",
    "        self.baseline_purchase_costs['Thickener'] = \\\n",
    "            3360*self.design_results['Settling Area']**0.58*(self.nStages+1)*3.2 # Purchase price with material factor of 3.2 from Seider and Seader pg 576\n",
    "        \n",
    "        # Scale assuming the electricity usage is proportional to the volumetric flow rate\n",
    "        self.power_utility.consumption = 12*(self.design_results['Diameter']/60) # 12 kW for a thickener w/ dia = 60m from Perry's chemical engineers' handbook\n",
    "\n",
    "        # Heat Utility Cost\n",
    "        self.add_OPEX = {'Natural Gas': 7.534*10**(-6)*self.heat_duty} # 7.534E-6 $/kJ * heat duty kJ/hr = $/hr. NG price for industry July 2022 from EIA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create System to Simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the destination to find the LCA data and store the figures \n",
    "bw_path = os.path.abspath(r'C:\\Users\\ajs8911\\OneDrive - The Pennsylvania State University\\Research\\System Analysis\\Python Scripts\\PG_REE_base\\PG_REE')\n",
    "data_path = os.path.join(bw_path, 'data_scratch')\n",
    "figures_path = os.path.join(c_path, 'figures_scratch')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define feedstock, plant capacity, and link streams to emission factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StreamImpactItem: H2SO4_item [per kg]\n",
      "Linked to       : lixiviant_acid\n",
      "Price           : 0.03 USD\n",
      "ImpactIndicators:\n",
      "                                   Characterization factors\n",
      "I_HumanHealth_Total (points)                         0.0144\n",
      "E_HumanHealth_Total (points)                          0.216\n",
      "E_EcosystemQuality_Total (points)                    0.0241\n",
      "I_Resources_Total (points)                           0.0385\n",
      "H_Resources_Total (points)                           0.0243\n",
      "GlobalWarming (kg CO2-Eq)                             0.159\n",
      "E_Resources_Total (points)                           0.0243\n",
      "I_EcosystemQuality_Total (points)                   0.00425\n",
      "H_HumanHealth_Total (points)                          0.031\n",
      "H_EcosystemQuality_Total (points)                   0.00445\n"
     ]
    }
   ],
   "source": [
    "# Define the PG composition and throughput\n",
    "feedPG = 1000000 #amount of PG being leached\n",
    "REEinPG = 0.005 #REE content in PG (wt%)\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Get the LCA emission factors\n",
    "_load_lca_data()\n",
    "\n",
    "# define LCA items and link them to the names of streams in the system\n",
    "H2SO4_item = qs.ImpactItem.get_item('H2SO4_item')\n",
    "qs.SanStream('lixiviant_acid', stream_impact_item=H2SO4_item, price=0.0302)\n",
    "\n",
    "pwater_item = qs.ImpactItem.get_item('pwater_item')\n",
    "qs.SanStream('lixiviant_water', stream_impact_item=pwater_item, price=0.000334)\n",
    "\n",
    "electricity_item = qs.ImpactItem.get_item('electrictiy_item')\n",
    "\n",
    "heatNG_item = qs.ImpactItem.get_item('heatNG_item')\n",
    "\n",
    "steel_item = qs.ImpactItem.get_item('steel_item')\n",
    "\n",
    "\n",
    "H2SO4_item.show()\n",
    "warnings.resetwarnings()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate the leaching unit and process system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\thermosteam\\_stream.py:1752: RuntimeWarning: <SanStream: rawPG> has been replaced in registry\n",
      "  new.ID = ID\n",
      "c:\\Users\\ajs8911\\Miniconda3\\envs\\bw2\\lib\\site-packages\\qsdsan\\_sanstream.py:59: RuntimeWarning: <SanStream: inlet_flowrate> has been replaced in registry\n",
      "  super().__init__(ID=ID, flow=flow, phase=phase, T=T, P=P,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and simulate the system with TEA and LCA\n",
    "\n",
    "#use qs.Flowsheet to connect LCA impact items to system streams\n",
    "flowsheet = qs.Flowsheet.flowsheet.default\n",
    "fs_stream = flowsheet.stream\n",
    "\n",
    "# Set up the unit\n",
    "U1 = LeachingSulfuric(ins=('rawPG', fs_stream.lixiviant_water, fs_stream.lixiviant_acid), outs=('leachate', 'leachate_Ln', 'underflow', 'underflow_gypsum', 'leachate_gypsum'), feedPG=feedPG, REEinPG=REEinPG)\n",
    "S1 = qs.sanunits.FakeSplitter(ID='S1', ins=U1.ins[0].copy(new_ID='rawPG'), outs=U1.ins[0]) # Fake Splitter\n",
    "U1.simulate()\n",
    "\n",
    "# Set Construction Material cost and impact\n",
    "default_U1_steel = qs.Construction('steel', item=steel_item, linked_unit=U1, quantity=U1.design_results['Steel'])\n",
    "U1.construction = default_U1_steel\n",
    "\n",
    "# Initialize the System\n",
    "sys = qs.System('sys', path=(S1,U1))\n",
    "\n",
    "# Calculate LCA and TEA\n",
    "lca = qs.LCA(system=sys, lifetime=1, lifetime_unit='year', electricity_item=lambda: U1.power_utility.rate*(365*24), heatNG_item=lambda:U1.heat_duty/1000*(365*24))\n",
    "tea = qs.SimpleTEA(sys, discount_rate=0.15, lifetime=30)\n",
    "\n",
    "sys.simulate()\n",
    "sys.diagram()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leaching Unit Design Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Leaching sulfuric</th>\n",
       "      <th>Units</th>\n",
       "      <th>U1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Power</th>\n",
       "      <th>Rate</th>\n",
       "      <td>kW</td>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>USD/hr</td>\n",
       "      <td>0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Design</th>\n",
       "      <th>Leaching Efficiency</th>\n",
       "      <td>%</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washing Stages</th>\n",
       "      <td>#</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Stages</th>\n",
       "      <td>#</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>m3</td>\n",
       "      <td>2.9e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Settling Area</th>\n",
       "      <td>m2</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diameter</th>\n",
       "      <td>m</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>m</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thickness</th>\n",
       "      <td>m</td>\n",
       "      <td>0.0705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel</th>\n",
       "      <td>kg</td>\n",
       "      <td>4.71e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heat Duty</th>\n",
       "      <td>kJ/h</td>\n",
       "      <td>5.14e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase cost</th>\n",
       "      <th>Thickener</th>\n",
       "      <td>USD</td>\n",
       "      <td>1.68e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total purchase cost</th>\n",
       "      <th></th>\n",
       "      <td>USD</td>\n",
       "      <td>1.68e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Installed equipment cost</th>\n",
       "      <th></th>\n",
       "      <td>USD</td>\n",
       "      <td>5.88e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utility cost</th>\n",
       "      <th></th>\n",
       "      <td>USD/hr</td>\n",
       "      <td>0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural Gas</th>\n",
       "      <th></th>\n",
       "      <td>USD/hr</td>\n",
       "      <td>3.88e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Leaching sulfuric                              Units       U1\n",
       "Power                    Rate                     kW     5.62\n",
       "                         Cost                 USD/hr    0.439\n",
       "Design                   Leaching Efficiency       %     43.1\n",
       "                         Washing Stages            #     2.75\n",
       "                         Total Stages              #     3.75\n",
       "                         Volume                   m3  2.9e+03\n",
       "                         Settling Area            m2      619\n",
       "                         Diameter                  m     28.1\n",
       "                         Height                    m     4.68\n",
       "                         Thickness                 m   0.0705\n",
       "                         Steel                    kg 4.71e+05\n",
       "                         Heat Duty              kJ/h 5.14e+08\n",
       "Purchase cost            Thickener               USD 1.68e+06\n",
       "Total purchase cost                              USD 1.68e+06\n",
       "Installed equipment cost                         USD 5.88e+06\n",
       "Utility cost                                  USD/hr    0.439\n",
       "Natural Gas                                   USD/hr 3.88e+03"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U1.results(include_installed_cost=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Life Cycle Assessment indicator results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LCA: sys (lifetime 1 yr)\n",
      "Impacts:\n",
      "                                   Construction  Transportation   Stream   Others    Total\n",
      "E_Resources_Total (points)             3.43e+05               0 4.26e+07 1.86e+07 6.16e+07\n",
      "E_EcosystemQuality_Total (points)      1.66e+05               0 4.24e+07 1.05e+07 5.31e+07\n",
      "I_HumanHealth_Total (points)           1.16e+05               0 2.53e+07  1.1e+07 3.64e+07\n",
      "H_HumanHealth_Total (points)           1.42e+05               0 5.45e+07 1.15e+07 6.61e+07\n",
      "E_HumanHealth_Total (points)           4.14e+05               0 3.79e+08 1.59e+07 3.95e+08\n",
      "I_EcosystemQuality_Total (points)         5e+04               0  7.5e+06 7.55e+06 1.51e+07\n",
      "I_Resources_Total (points)             4.85e+05               0 6.76e+07 1.83e+07 8.64e+07\n",
      "H_EcosystemQuality_Total (points)       4.9e+04               0 7.84e+06 6.67e+06 1.46e+07\n",
      "GlobalWarming (kg CO2-Eq)              2.26e+06               0 2.81e+08 3.64e+08 6.47e+08\n",
      "H_Resources_Total (points)             3.43e+05               0 4.26e+07 1.86e+07 6.16e+07\n"
     ]
    }
   ],
   "source": [
    "lca.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contributors to LCA impacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "constructionImpact = lca.get_impact_table('Construction')\n",
    "streamImpact = lca.get_impact_table('Stream')\n",
    "otherImpact = lca.get_impact_table('Other')\n",
    "print('')\n",
    "print(constructionImpact.iloc[:,10])\n",
    "print('')\n",
    "print(streamImpact.iloc[:,9])\n",
    "print('')\n",
    "print(otherImpact.iloc[:,9])\n",
    "print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Technoeconomic Analysis Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = qs.currency\n",
    "for attr in ('NPV', 'EAC', 'CAPEX', 'AOC', 'sales', 'net_earnings'):\n",
    "    uom = c if attr in ('NPV', 'CAPEX') else (c+('/yr'))\n",
    "    print(f'{attr} is {getattr(tea, attr):,.0f} {uom}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model to Describe Uncertainty/Sensitivity"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define distribution of parameters in the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(sys, fununit):\n",
    "    model = qs.Model(sys)\n",
    "    param = model.parameter\n",
    "    metric = model.metric\n",
    "\n",
    "    baseline = U1.time\n",
    "    dist = distributions.Uniform(lower=baseline-105, upper=baseline+85)\n",
    "    @param(name='Leaching Time', element='U1', kind='coupled', units='mins', baseline=baseline, distribution=dist)\n",
    "    def set_leachingTime(i):\n",
    "        U1.time = i\n",
    "\n",
    "    baseline = U1.temp\n",
    "    dist = distributions.Uniform(lower=baseline-20, upper=baseline+15)\n",
    "    @param(name='Leaching Temperature', element='U1', kind='coupled', units='deg C', baseline=baseline, distribution=dist)\n",
    "    def set_leachingTemp(i):\n",
    "        U1.temp = i\n",
    "\n",
    "    baseline = U1.acidConc\n",
    "    dist = distributions.Uniform(lower=baseline-4, upper=baseline+4.5)\n",
    "    @param(name='Acid Concentration', element='U1', kind='coupled', units='wt pcnt acid', baseline=baseline, distribution=dist)\n",
    "    def set_acidConc(i):\n",
    "        U1.acidConc = i\n",
    "    \n",
    "    @metric(name='Equivalent Annual Cost', units=f'USD/kg {fununit}', element='TEA')\n",
    "    def get_EAC():\n",
    "        if fununit == 'Ln':\n",
    "            return sys.TEA.EAC/(fs_stream.leachate_Ln.F_mass*8760)\n",
    "        elif fununit == 'PG':\n",
    "            return sys.TEA.EAC/(U1.feedPG*8760)\n",
    "        else:\n",
    "            raise NameError(f'For model metric EAC, {fununit} is not \"Ln\" or \"PG\"')\n",
    "\n",
    "\n",
    "    @metric(name='GWP', units=f'kg CO2/kg {fununit}', element='LCA')\n",
    "    def get_annual_GWP():\n",
    "        if fununit == 'Ln':\n",
    "            return sys.LCA.total_impacts['GlobalWarming']/(sys.LCA.lifetime*fs_stream.leachate_Ln.F_mass*8760)\n",
    "        elif fununit == 'PG':\n",
    "            return sys.LCA.total_impacts['GlobalWarming']/(sys.LCA.lifetime*U1.feedPG*8760)\n",
    "        else:\n",
    "            raise NameError(f'For model metric GwP, {fununit} is not \"Ln\" or \"PG\"')\n",
    "\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate process system as defined by model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the commented code below if running more than once. Reset the unit baseline\n",
    "# U1.time = 120\n",
    "# U1.temp = 50\n",
    "# U1.acidConc = 5 \n",
    "\n",
    "fununit = 'Ln'\n",
    "model = create_model(sys, fununit) # set system and functional unit\n",
    "\n",
    "np.random.seed(3221) # setting the seed ensures you get the same sample\n",
    "\n",
    "samples = model.sample(N=100, rule='L')\n",
    "model.load_samples(samples)\n",
    "model.evaluate()\n",
    "\n",
    "pd.DataFrame(model.table)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainty shown by box and whisker plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = qs.stats.plot_uncertainties(model)\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainty using kernel-density plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = qs.stats.plot_uncertainties(model, x_axis=model.metrics[0], y_axis=model.metrics[1], kind='kde-kde', center_kws={'fill': True}) #cneter_kws={'color': 'blue'}\n",
    "ax0, ax1, ax2 = fig.axes # KDE, top box, right box\n",
    "ax0.set(xlabel=f'Equivalent Annual Cost (USD/kg {fununit})', ylabel=f'Global Warming Potential (kg CO2 eq/kg {fununit})')\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Complete a sensitivity analysis on the model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_df, p_df = qs.stats.get_correlations(model, kind='Spearman')\n",
    "display(pd.DataFrame(r_df), pd.DataFrame(p_df))\n",
    "\n",
    "# Filter out parameters that only meet a certain threshold\n",
    "def filter_parameters(model, df, threshold):\n",
    "    new_df = pd.concat((df[df>=threshold], df[df<=-threshold]))\n",
    "    filtered = new_df.dropna(how='all')\n",
    "    param_dct = {p.name_with_units:p for p in model.get_parameters()}\n",
    "    parameters = set(param_dct[i[1]] for i in filtered.index)\n",
    "    return list(parameters)\n",
    "\n",
    "# Filter out parameters with low row (sensitivity)\n",
    "model.parameters = key_parameters = \\\n",
    "filter_parameters(model, r_df, threshold=0.01)\n",
    "\n",
    "fig, ax = qs.stats.plot_correlations(r_df, parameters=key_parameters, metrics=model.metrics, legend='brief') # plot from seaborn.relplot. Use attributes from relplot in plot_correlations() #https://stackoverflow.com/questions/66404883/seaborn-scatterplot-set-hollow-markers-instead-of-filled-markers\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the optimal conditions for the leaching process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaching_conc_temp_GWP(sys, fununit):\n",
    "    model = create_model(sys, fununit)\n",
    "\n",
    "    y = np.linspace(25, 70, 50)\n",
    "    x = np.linspace(0.5, 9.5, num=len(y))\n",
    "    z = [120]*len(y)\n",
    "    result = []\n",
    "    i = 0\n",
    "\n",
    "    while i < len(y):\n",
    "        xi = [x[i] for n in range(len(y))]\n",
    "        samples = pd.DataFrame(columns = ['x', 'y', 'z'])\n",
    "        samples['x'], samples['y'], samples['z'] = z, y, xi\n",
    "        model.load_samples(samples.to_numpy())\n",
    "        model.evaluate()\n",
    "        result.extend(model.table.iloc[:,4].values.tolist())\n",
    "        i += 1\n",
    "\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    Z = np.array(result).reshape(len(y),len(y)).T\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    cp = plt.contourf(X,Y,Z, 20, cmap='inferno_r')\n",
    "    fig.colorbar(cp, label=f'Global Warming Potential (kg CO2 eq/kg {fununit})')\n",
    "    ax.set(xlabel='Acid Concentration (wt %)', ylabel='Leaching Temperature (deg C)')\n",
    "    fig\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "leaching_conc_temp_GWP(sys, fununit)\n",
    "warnings.resetwarnings()"
   ]
  }
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