{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-explicit LCA of an electric vehicle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains the code for the exemplary case study of out paper on time-explicit LCA. Here, we do a time-explicit LCA of the life cycle of an electric vehicle (EV) and compare the results to the results from static and dynamic LCAs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bw2data as bd\n",
    "\n",
    "bd.projects.set_current(\"timex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study setup\n",
    "\n",
    "### Database setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we set up the databases we need, starting with a new empty foreground database:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"foreground\" in bd.databases:\n",
    "    del bd.databases[\"foreground\"]  # to make sure we create the foreground from scratch\n",
    "foreground = bd.Database(\"foreground\")\n",
    "foreground.register()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we load our prospective background databases. In this study, we use data from [ecoinvent v3.10](https://ecoinvent.org/), and create a set of prospective databases with [`premise`](https://github.com/polca/premise). We applied projections for the future electricity sectors using the SSP2-RCP19 pathway from the IAM IMAGE.\n",
    "In the [premise documentation](https://premise.readthedocs.io/en/latest/) you can find instructions for the creation of prospective background databases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_2020 = bd.Database(\"ei310_IMAGE_SSP2_RCP19_2020_electricity\")\n",
    "db_2030 = bd.Database(\"ei310_IMAGE_SSP2_RCP19_2030_electricity\")\n",
    "db_2040 = bd.Database(\"ei310_IMAGE_SSP2_RCP19_2040_electricity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling the production system\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this study, we consider the following production system for the EV. Purple boxes are foreground, cyan boxes are background (i.e., ecoinvent/premise).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{mermaid}\n",
    "flowchart LR\n",
    "    glider_production(glider production):::ei-->ev_production\n",
    "    powertrain_production(powertrain production):::ei-->ev_production\n",
    "    battery_production(battery production):::ei-->ev_production\n",
    "    ev_production(ev production):::fg-->driving\n",
    "    electricity_generation(electricity generation):::ei-->driving\n",
    "    driving(driving):::fg-->used_ev\n",
    "    used_ev(used ev):::fg-->glider_eol(glider eol):::ei\n",
    "    used_ev-->powertrain_eol(powertrain eol):::ei\n",
    "    used_ev-->battery_eol(battery eol):::ei\n",
    "\n",
    "    classDef ei color:#222832, fill:#3fb1c5, stroke:none;\n",
    "    classDef fg color:#222832, fill:#9c5ffd, stroke:none;\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our EV model, we make the following assumptions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIFETIME = 16  # years\n",
    "MILEAGE = 150_000  # km\n",
    "ELECTRICITY_CONSUMPTION = 0.2  # kWh/km\n",
    "\n",
    "# Overall mass: 1200 kg\n",
    "MASS_GLIDER = 840  # kg\n",
    "MASS_POWERTRAIN = 80  # kg\n",
    "MASS_BATTERY = 280  # kg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we create the foreground processes:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev_production = foreground.new_node(\n",
    "    \"ev_production\", name=\"production of an electric vehicle\", unit=\"unit\"\n",
    ")\n",
    "ev_production[\"reference product\"] = \"electric vehicle\"\n",
    "ev_production.save()\n",
    "\n",
    "driving = foreground.new_node(\n",
    "    \"driving\", name=\"driving an electric vehicle\", unit=\"transport over an ev lifetime\"\n",
    ")\n",
    "driving[\"reference product\"] = \"transport\"\n",
    "driving.save()\n",
    "\n",
    "used_ev = foreground.new_node(\"used_ev\", name=\"used electric vehicle\", unit=\"unit\")\n",
    "used_ev[\"reference product\"] = \"used electric vehicle\"\n",
    "used_ev.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the actual process data from ecoinvent. However, the ecoinvent processes for the EV part production contain intermediate flows for the end of life treatment in the production processes already, which we want to separate. We fix this first by creating new processes without the EOL:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for db in [db_2020, db_2030, db_2040]:\n",
    "    for code in [\n",
    "        \"glider_production_without_eol\",\n",
    "        \"powertrain_production_without_eol\",\n",
    "        \"battery_production_without_eol\",\n",
    "    ]:\n",
    "        try:\n",
    "            act = db.get(code=code)\n",
    "            act.delete()\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    glider_production = db.get(name=\"glider production, passenger car\")\n",
    "    glider_production_without_eol = glider_production.copy(\n",
    "        code=\"glider_production_without_eol\", database=db.name\n",
    "    )\n",
    "    glider_production_without_eol[\"name\"] = (\n",
    "        \"glider production, passenger car, without EOL\"\n",
    "    )\n",
    "    glider_production_without_eol.save()\n",
    "    for exc in glider_production_without_eol.exchanges():\n",
    "        if exc.input[\"name\"] == \"market for used glider, passenger car\":\n",
    "            exc.delete()\n",
    "\n",
    "    powertrain_production = db.get(\n",
    "        name=\"powertrain production, for electric passenger car\"\n",
    "    )\n",
    "    powertrain_production_without_eol = powertrain_production.copy(\n",
    "        code=\"powertrain_production_without_eol\", database=db.name\n",
    "    )\n",
    "    powertrain_production_without_eol[\"name\"] = (\n",
    "        \"powertrain production, for electric passenger car, without EOL\"\n",
    "    )\n",
    "    powertrain_production_without_eol.save()\n",
    "    for exc in powertrain_production_without_eol.exchanges():\n",
    "        if (\n",
    "            exc.input[\"name\"]\n",
    "            == \"market for used powertrain from electric passenger car, manual dismantling\"\n",
    "        ):\n",
    "            exc.delete()\n",
    "\n",
    "    battery_production = db.get(\n",
    "        name=\"battery production, Li-ion, LiMn2O4, rechargeable, prismatic\"\n",
    "    )\n",
    "    battery_production_without_eol = battery_production.copy(\n",
    "        code=\"battery_production_without_eol\", database=db.name\n",
    "    )\n",
    "    battery_production_without_eol[\"name\"] = (\n",
    "        \"battery production, Li-ion, LiMn2O4, rechargeable, prismatic, without EOL\"\n",
    "    )\n",
    "    battery_production_without_eol.save()\n",
    "    # For the battery, some waste treatment is buried in the process \"battery cell production, Li-ion,\n",
    "    # LiMn2O4\" - but not for the whole mass of the battery(?). For simplicity, we just leave it in there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we add the intermediate flows, starting with the EV production:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "glider_production = db_2020.get(code=\"glider_production_without_eol\")\n",
    "powertrain_production = db_2020.get(code=\"powertrain_production_without_eol\")\n",
    "battery_production = db_2020.get(code=\"battery_production_without_eol\")\n",
    "\n",
    "ev_production.new_edge(input=ev_production, amount=1, type=\"production\").save()\n",
    "\n",
    "glider_to_ev = ev_production.new_edge(\n",
    "    input=glider_production, amount=MASS_GLIDER, type=\"technosphere\"\n",
    ")\n",
    "powertrain_to_ev = ev_production.new_edge(\n",
    "    input=powertrain_production, amount=MASS_POWERTRAIN, type=\"technosphere\"\n",
    ")\n",
    "battery_to_ev = ev_production.new_edge(\n",
    "    input=battery_production, amount=MASS_BATTERY, type=\"technosphere\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... the EOL:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "glider_eol = db_2020.get(name=\"treatment of used glider, passenger car, shredding\")\n",
    "powertrain_eol = db_2020.get(\n",
    "    name=\"treatment of used powertrain for electric passenger car, manual dismantling\"\n",
    ")\n",
    "battery_eol = db_2020.get(name=\"market for used Li-ion battery\")\n",
    "\n",
    "used_ev.new_edge(\n",
    "    input=used_ev, amount=-1, type=\"production\"\n",
    ").save()  # -1 as this gets rid of a used car\n",
    "\n",
    "used_ev_to_glider_eol = used_ev.new_edge(\n",
    "    input=glider_eol,\n",
    "    amount=-MASS_GLIDER,\n",
    "    type=\"technosphere\",\n",
    ")\n",
    "used_ev_to_powertrain_eol = used_ev.new_edge(\n",
    "    input=powertrain_eol,\n",
    "    amount=-MASS_POWERTRAIN,\n",
    "    type=\"technosphere\",\n",
    ")\n",
    "used_ev_to_battery_eol = used_ev.new_edge(\n",
    "    input=battery_eol,\n",
    "    amount=-MASS_BATTERY,\n",
    "    type=\"technosphere\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and, finally, driving:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_production = db_2020.get(\n",
    "    name=\"market group for electricity, low voltage\", location=\"WEU\"\n",
    ")\n",
    "\n",
    "driving.new_edge(input=driving, amount=1, type=\"production\").save()\n",
    "\n",
    "driving_to_used_ev = driving.new_edge(input=used_ev, amount=-1, type=\"technosphere\")\n",
    "ev_to_driving = driving.new_edge(input=ev_production, amount=1, type=\"technosphere\")\n",
    "electricity_to_driving = driving.new_edge(\n",
    "    input=electricity_production,\n",
    "    amount=ELECTRICITY_CONSUMPTION * MILEAGE,\n",
    "    type=\"technosphere\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "glider_to_ev.save()\n",
    "powertrain_to_ev.save()\n",
    "battery_to_ev.save()\n",
    "ev_to_driving.save()\n",
    "electricity_to_driving.save()\n",
    "driving_to_used_ev.save()\n",
    "used_ev_to_glider_eol.save()\n",
    "used_ev_to_powertrain_eol.save()\n",
    "used_ev_to_battery_eol.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To allow a comparison with a static LCA later, we calculate the radiative forcing results at this point, before temporalization:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.13e+13)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "\u001b[32m2025-02-07 10:18:19.799\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timex_lca\u001b[0m:\u001b[36mbuild_timeline\u001b[0m:\u001b[36m216\u001b[0m - \u001b[1mNo edge filter function provided. Skipping all edges in background databases.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting graph traversal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-07 10:18:23.932\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.933\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.933\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.933\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:23.935\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation count: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/bw2calc/lca_base.py:127: SparseEfficiencyWarning: splu converted its input to CSC format\n",
      "  self.solver = factorized(self.technosphere_matrix)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.86e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.86e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/dynamic_characterization/dynamic_characterization.py:81: UserWarning: No custom dynamic characterization functions provided. Using default dynamic characterization functions.                The flows that are characterized are based on the selection of the initially chosen impact category.                You can look up the mapping in the bw_timex.dynamic_characterizer.characterization_functions.\n",
      "  warnings.warn(\n"
     ]
    },
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       "      <td>9.472615e-13</td>\n",
       "      <td>1034</td>\n",
       "      <td>109673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37206</th>\n",
       "      <td>2123-01-01 00:10:48</td>\n",
       "      <td>1.051160e-12</td>\n",
       "      <td>1031</td>\n",
       "      <td>109675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37207</th>\n",
       "      <td>2123-01-01 00:10:48</td>\n",
       "      <td>1.773673e-12</td>\n",
       "      <td>1031</td>\n",
       "      <td>109673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37208</th>\n",
       "      <td>2123-01-01 00:10:48</td>\n",
       "      <td>6.377169e-12</td>\n",
       "      <td>1031</td>\n",
       "      <td>109669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date        amount  flow  activity\n",
       "0     2024-12-31 05:49:12 -4.771888e-17  3269    109673\n",
       "1     2024-12-31 05:49:12 -1.887051e-17  3269    109674\n",
       "2     2024-12-31 05:49:12 -6.949656e-18  3211    109669\n",
       "3     2024-12-31 05:49:12 -4.256251e-18  3269    109675\n",
       "4     2024-12-31 05:49:12 -4.007917e-18  3211    109673\n",
       "...                   ...           ...   ...       ...\n",
       "37204 2123-01-01 00:10:48  5.735388e-13   107    109669\n",
       "37205 2123-01-01 00:10:48  9.472615e-13  1034    109673\n",
       "37206 2123-01-01 00:10:48  1.051160e-12  1031    109675\n",
       "37207 2123-01-01 00:10:48  1.773673e-12  1031    109673\n",
       "37208 2123-01-01 00:10:48  6.377169e-12  1031    109669\n",
       "\n",
       "[37209 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from bw_timex import TimexLCA\n",
    "\n",
    "method = (\"EF v3.1\", \"climate change\", \"global warming potential (GWP100)\")\n",
    "\n",
    "database_dates_dlca = {\n",
    "    db_2020.name: datetime.strptime(\"2020\", \"%Y\"),\n",
    "    \"foreground\": \"dynamic\",  # flag databases that should be temporally distributed with \"dynamic\"\n",
    "}\n",
    "dlca_no_tds = TimexLCA({driving: 1}, method, database_dates_dlca)\n",
    "dlca_no_tds.build_timeline(starting_datetime=\"2024-01-01\", temporal_grouping=\"month\")\n",
    "dlca_no_tds.lci()\n",
    "dlca_no_tds.dynamic_lcia(metric=\"radiative_forcing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding temporal distributions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the production system is modelled, we can add temporal distributions at the intermediate flow level. The temporal information we want to embed in our product system looks like this:\n",
    "\n",
    "```{mermaid}\n",
    "flowchart LR\n",
    "    glider_production(glider production):::ei-->|0-2 years prior|ev_production\n",
    "    powertrain_production(powertrain production):::ei-->|1 year prior|ev_production\n",
    "    battery_production(battery production):::ei-->|1 year prior|ev_production\n",
    "    ev_production(ev production):::fg-->|0-3 months prior|driving\n",
    "    electricity_generation(electricity generation):::ei-->|uniformly distributed \\n over lifetime|driving\n",
    "    driving(driving):::fg-->|after ev lifetime|used_ev\n",
    "    used_ev(used ev):::fg-->|3 months after \\n ev lifetime|glider_eol(glider eol):::ei\n",
    "    used_ev-->|3 months after \\n ev lifetime|powertrain_eol(powertrain eol):::ei\n",
    "    used_ev-->|3 months after \\n ev lifetime|battery_eol(battery eol):::ei\n",
    "\n",
    "    classDef ei color:#222832, fill:#3fb1c5, stroke:none;\n",
    "    classDef fg color:#222832, fill:#9c5ffd, stroke:none;\n",
    "```\n",
    "\n",
    "To include this temopral information, we use the `TemporalDistribution` class from `bw_temporalis`. For more info, take a look at the [bw_temporalis documentation](https://github.com/brightway-lca/bw_temporalis).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create the relative `TemporalDistribution` objects:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bw_temporalis import TemporalDistribution, easy_timedelta_distribution\n",
    "import numpy as np\n",
    "\n",
    "td_assembly_and_delivery = TemporalDistribution(\n",
    "    date=np.array([-3, -2], dtype=\"timedelta64[M]\"), amount=np.array([0.2, 0.8])\n",
    ")\n",
    "\n",
    "td_glider_production = TemporalDistribution(\n",
    "    date=np.array([-2, -1, 0], dtype=\"timedelta64[Y]\"), amount=np.array([0.7, 0.1, 0.2])\n",
    ")\n",
    "\n",
    "td_produce_powertrain_and_battery = TemporalDistribution(\n",
    "    date=np.array([-1], dtype=\"timedelta64[Y]\"), amount=np.array([1])\n",
    ")\n",
    "\n",
    "td_use_phase = easy_timedelta_distribution(\n",
    "    start=0,\n",
    "    end=LIFETIME - 1,  # boundaries are inclusive\n",
    "    resolution=\"Y\",\n",
    "    steps=LIFETIME,\n",
    "    kind=\"uniform\",  # you can also do \"normal\" or \"triangular\" distributions\n",
    ")\n",
    "\n",
    "td_disassemble_used_ev = TemporalDistribution(\n",
    "    date=np.array([LIFETIME], dtype=\"timedelta64[Y]\"), amount=np.array([1])\n",
    ")\n",
    "\n",
    "td_treating_waste = TemporalDistribution(\n",
    "    date=np.array([3], dtype=\"timedelta64[M]\"), amount=np.array([1])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now add the rTDs to the intermediate flows of our EV system.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "glider_to_ev[\"temporal_distribution\"] = td_glider_production\n",
    "glider_to_ev.save()\n",
    "\n",
    "powertrain_to_ev[\"temporal_distribution\"] = td_produce_powertrain_and_battery\n",
    "powertrain_to_ev.save()\n",
    "\n",
    "battery_to_ev[\"temporal_distribution\"] = td_produce_powertrain_and_battery\n",
    "battery_to_ev.save()\n",
    "\n",
    "ev_to_driving[\"temporal_distribution\"] = td_assembly_and_delivery\n",
    "ev_to_driving.save()\n",
    "\n",
    "electricity_to_driving[\"temporal_distribution\"] = td_use_phase\n",
    "electricity_to_driving.save()\n",
    "\n",
    "driving_to_used_ev[\"temporal_distribution\"] = td_disassemble_used_ev\n",
    "driving_to_used_ev.save()\n",
    "\n",
    "used_ev_to_glider_eol[\"temporal_distribution\"] = td_treating_waste\n",
    "used_ev_to_glider_eol.save()\n",
    "\n",
    "used_ev_to_powertrain_eol[\"temporal_distribution\"] = td_treating_waste\n",
    "used_ev_to_powertrain_eol.save()\n",
    "\n",
    "used_ev_to_battery_eol[\"temporal_distribution\"] = td_treating_waste\n",
    "used_ev_to_battery_eol.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the temporal distributions, we can calculate the dynamic LCA for later comparison.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.13e+13)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "\u001b[32m2025-02-07 10:18:28.827\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timex_lca\u001b[0m:\u001b[36mbuild_timeline\u001b[0m:\u001b[36m216\u001b[0m - \u001b[1mNo edge filter function provided. Skipping all edges in background databases.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting graph traversal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-07 10:18:33.034\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2021-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.035\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2021-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.035\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.035\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.036\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.036\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.036\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.036\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2022-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.036\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2023-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2023-10-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2023-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2023-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.038\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2025-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.038\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2026-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.038\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2027-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.038\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2028-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.039\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2029-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.039\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2030-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.039\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2031-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.039\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2032-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.039\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2033-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2034-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2035-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2036-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2037-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2038-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.041\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2039-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.041\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.041\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.041\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:33.042\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation count: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/bw2calc/lca_base.py:127: SparseEfficiencyWarning: splu converted its input to CSC format\n",
      "  self.solver = factorized(self.technosphere_matrix)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.86e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.86e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/dynamic_characterization/dynamic_characterization.py:81: UserWarning: No custom dynamic characterization functions provided. Using default dynamic characterization functions.                The flows that are characterized are based on the selection of the initially chosen impact category.                You can look up the mapping in the bw_timex.dynamic_characterizer.characterization_functions.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "      <td>2023-01-01 05:49:12</td>\n",
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       "      <td>1034</td>\n",
       "      <td>109696</td>\n",
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       "      <td>2139-01-01 00:10:48</td>\n",
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       "<p>154154 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      date        amount  flow  activity\n",
       "0      2023-01-01 05:49:12 -2.672257e-17  3269    109667\n",
       "1      2023-01-01 05:49:12 -6.680643e-18  3269    109666\n",
       "2      2023-01-01 05:49:12 -2.244433e-18  3211    109667\n",
       "3      2023-01-01 05:49:12 -5.611083e-19  3211    109666\n",
       "4      2023-01-01 05:49:12  2.799755e-41  1152    109666\n",
       "...                    ...           ...   ...       ...\n",
       "154149 2139-01-01 00:10:48  1.727540e-14  1034    109696\n",
       "154150 2139-01-01 00:10:48  2.581032e-14   107    109696\n",
       "154151 2139-01-01 00:10:48  4.551497e-14  1031    109698\n",
       "154152 2139-01-01 00:10:48  1.596721e-13  1031    109696\n",
       "154153 2139-01-01 00:10:48  2.789117e-13   107    109698\n",
       "\n",
       "[154154 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlca = TimexLCA({driving: 1}, method, database_dates_dlca)\n",
    "dlca.build_timeline(starting_datetime=\"2024-01-01\", temporal_grouping=\"month\")\n",
    "dlca.lci()\n",
    "dlca.dynamic_lcia(metric=\"radiative_forcing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-explicit LCA calculations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that everything is set up, we can calculate a Time-explicit LCA, first setting up a new `TimexLCA` object and building the timeline:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "\u001b[32m2025-02-07 10:18:41.537\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timex_lca\u001b[0m:\u001b[36mbuild_timeline\u001b[0m:\u001b[36m216\u001b[0m - \u001b[1mNo edge filter function provided. Skipping all edges in background databases.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting graph traversal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-07 10:18:54.722\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:54.722\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n",
      "\u001b[32m2025-02-07 10:18:54.722\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mbw_timex.timeline_builder\u001b[0m:\u001b[36mget_weights_for_interpolation_between_nearest_years\u001b[0m:\u001b[36m522\u001b[0m - \u001b[1mReference date 2040-04-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation count: 9\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_producer</th>\n",
       "      <th>producer_name</th>\n",
       "      <th>date_consumer</th>\n",
       "      <th>consumer_name</th>\n",
       "      <th>amount</th>\n",
       "      <th>temporal_market_shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>588.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>588.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>84.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>powertrain production, for electric passenger ...</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>80.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>battery production, Li-ion, LiMn2O4, rechargea...</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>280.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>84.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>powertrain production, for electric passenger ...</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>80.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>battery production, Li-ion, LiMn2O4, rechargea...</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>280.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>168.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>0.2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>glider production, passenger car, without EOL</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>168.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>production of an electric vehicle</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>0.8</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2027-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2028-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2029-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2030-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2031-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2032-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2033-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2034-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2035-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2036-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2037-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2038-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2039-01-01</td>\n",
       "      <td>market group for electricity, low voltage</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>used electric vehicle</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>driving an electric vehicle</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2040-04-01</td>\n",
       "      <td>market for used Li-ion battery</td>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>used electric vehicle</td>\n",
       "      <td>-280.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2040-04-01</td>\n",
       "      <td>treatment of used powertrain for electric pass...</td>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>used electric vehicle</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2040-04-01</td>\n",
       "      <td>treatment of used glider, passenger car, shred...</td>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>used electric vehicle</td>\n",
       "      <td>-840.0</td>\n",
       "      <td>{'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_producer                                      producer_name  \\\n",
       "0     2021-10-01      glider production, passenger car, without EOL   \n",
       "1     2021-11-01      glider production, passenger car, without EOL   \n",
       "2     2022-10-01      glider production, passenger car, without EOL   \n",
       "3     2022-10-01  powertrain production, for electric passenger ...   \n",
       "4     2022-10-01  battery production, Li-ion, LiMn2O4, rechargea...   \n",
       "5     2022-11-01      glider production, passenger car, without EOL   \n",
       "6     2022-11-01  powertrain production, for electric passenger ...   \n",
       "7     2022-11-01  battery production, Li-ion, LiMn2O4, rechargea...   \n",
       "8     2023-10-01      glider production, passenger car, without EOL   \n",
       "9     2023-10-01                  production of an electric vehicle   \n",
       "10    2023-11-01      glider production, passenger car, without EOL   \n",
       "11    2023-11-01                  production of an electric vehicle   \n",
       "12    2024-01-01          market group for electricity, low voltage   \n",
       "13    2024-01-01                        driving an electric vehicle   \n",
       "14    2025-01-01          market group for electricity, low voltage   \n",
       "15    2026-01-01          market group for electricity, low voltage   \n",
       "16    2027-01-01          market group for electricity, low voltage   \n",
       "17    2028-01-01          market group for electricity, low voltage   \n",
       "18    2029-01-01          market group for electricity, low voltage   \n",
       "19    2030-01-01          market group for electricity, low voltage   \n",
       "20    2031-01-01          market group for electricity, low voltage   \n",
       "21    2032-01-01          market group for electricity, low voltage   \n",
       "22    2033-01-01          market group for electricity, low voltage   \n",
       "23    2034-01-01          market group for electricity, low voltage   \n",
       "24    2035-01-01          market group for electricity, low voltage   \n",
       "25    2036-01-01          market group for electricity, low voltage   \n",
       "26    2037-01-01          market group for electricity, low voltage   \n",
       "27    2038-01-01          market group for electricity, low voltage   \n",
       "28    2039-01-01          market group for electricity, low voltage   \n",
       "29    2040-01-01                              used electric vehicle   \n",
       "30    2040-04-01                     market for used Li-ion battery   \n",
       "31    2040-04-01  treatment of used powertrain for electric pass...   \n",
       "32    2040-04-01  treatment of used glider, passenger car, shred...   \n",
       "\n",
       "   date_consumer                      consumer_name  amount  \\\n",
       "0     2023-10-01  production of an electric vehicle   588.0   \n",
       "1     2023-11-01  production of an electric vehicle   588.0   \n",
       "2     2023-10-01  production of an electric vehicle    84.0   \n",
       "3     2023-10-01  production of an electric vehicle    80.0   \n",
       "4     2023-10-01  production of an electric vehicle   280.0   \n",
       "5     2023-11-01  production of an electric vehicle    84.0   \n",
       "6     2023-11-01  production of an electric vehicle    80.0   \n",
       "7     2023-11-01  production of an electric vehicle   280.0   \n",
       "8     2023-10-01  production of an electric vehicle   168.0   \n",
       "9     2024-01-01        driving an electric vehicle     0.2   \n",
       "10    2023-11-01  production of an electric vehicle   168.0   \n",
       "11    2024-01-01        driving an electric vehicle     0.8   \n",
       "12    2024-01-01        driving an electric vehicle  1875.0   \n",
       "13    2024-01-01                                 -1     1.0   \n",
       "14    2024-01-01        driving an electric vehicle  1875.0   \n",
       "15    2024-01-01        driving an electric vehicle  1875.0   \n",
       "16    2024-01-01        driving an electric vehicle  1875.0   \n",
       "17    2024-01-01        driving an electric vehicle  1875.0   \n",
       "18    2024-01-01        driving an electric vehicle  1875.0   \n",
       "19    2024-01-01        driving an electric vehicle  1875.0   \n",
       "20    2024-01-01        driving an electric vehicle  1875.0   \n",
       "21    2024-01-01        driving an electric vehicle  1875.0   \n",
       "22    2024-01-01        driving an electric vehicle  1875.0   \n",
       "23    2024-01-01        driving an electric vehicle  1875.0   \n",
       "24    2024-01-01        driving an electric vehicle  1875.0   \n",
       "25    2024-01-01        driving an electric vehicle  1875.0   \n",
       "26    2024-01-01        driving an electric vehicle  1875.0   \n",
       "27    2024-01-01        driving an electric vehicle  1875.0   \n",
       "28    2024-01-01        driving an electric vehicle  1875.0   \n",
       "29    2024-01-01        driving an electric vehicle    -1.0   \n",
       "30    2040-01-01              used electric vehicle  -280.0   \n",
       "31    2040-01-01              used electric vehicle   -80.0   \n",
       "32    2040-01-01              used electric vehicle  -840.0   \n",
       "\n",
       "                               temporal_market_shares  \n",
       "0   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "1   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "2   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "3   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "4   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "5   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "6   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "7   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "8   {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "9                                                None  \n",
       "10  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "11                                               None  \n",
       "12  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "13                                               None  \n",
       "14  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "15  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "16  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "17  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "18  {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....  \n",
       "19     {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 1}  \n",
       "20  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "21  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "22  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "23  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "24  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "25  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "26  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "27  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "28  {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....  \n",
       "29                                               None  \n",
       "30     {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}  \n",
       "31     {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}  \n",
       "32     {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database_dates = {\n",
    "    db_2020.name: datetime.strptime(\"2020\", \"%Y\"),\n",
    "    db_2030.name: datetime.strptime(\"2030\", \"%Y\"),\n",
    "    db_2040.name: datetime.strptime(\"2040\", \"%Y\"),\n",
    "    \"foreground\": \"dynamic\",  # flag databases that should be temporally distributed with \"dynamic\"\n",
    "}\n",
    "\n",
    "tlca = TimexLCA({driving: 1}, method, database_dates)\n",
    "tlca.build_timeline(starting_datetime=\"2024-01-01\", temporal_grouping=\"month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can expand the matrices:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/bw2calc/lca_base.py:127: SparseEfficiencyWarning: splu converted its input to CSC format\n",
      "  self.solver = factorized(self.technosphere_matrix)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 5.78e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 5.78e+12)\n",
      "  warnings.warn(msg, UmfpackWarning)\n"
     ]
    }
   ],
   "source": [
    "tlca.lci()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GWI via GWP100\n",
    "\n",
    "Now we can calculate the GWI over the EV life cycle. We characterize the time-explicit inventory using GWP100 with a time horizon of 100 years counting from the time of each emissions. We use the implementations from the [`dynamic_characterization` library](https://dynamic-characterization.readthedocs.io/en/latest/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/dynamic_characterization/dynamic_characterization.py:81: UserWarning: No custom dynamic characterization functions provided. Using default dynamic characterization functions.                The flows that are characterized are based on the selection of the initially chosen impact category.                You can look up the mapping in the bw_timex.dynamic_characterizer.characterization_functions.\n",
      "  warnings.warn(\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>date</th>\n",
       "      <th>amount</th>\n",
       "      <th>flow</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>-1.621040e-02</td>\n",
       "      <td>3269</td>\n",
       "      <td>109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>-4.052738e-03</td>\n",
       "      <td>3269</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>-1.374486e-03</td>\n",
       "      <td>3211</td>\n",
       "      <td>109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>-3.434914e-04</td>\n",
       "      <td>3211</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>1.394444e-26</td>\n",
       "      <td>1152</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>1.465837e+01</td>\n",
       "      <td>1390</td>\n",
       "      <td>109698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>2.631662e+01</td>\n",
       "      <td>107</td>\n",
       "      <td>109696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>3.588365e+01</td>\n",
       "      <td>1031</td>\n",
       "      <td>109698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>5.361418e+01</td>\n",
       "      <td>1031</td>\n",
       "      <td>109696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>2040-01-01</td>\n",
       "      <td>3.962367e+02</td>\n",
       "      <td>107</td>\n",
       "      <td>109698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2291 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date        amount  flow  activity\n",
       "0    2022-01-01 -1.621040e-02  3269    109667\n",
       "1    2022-01-01 -4.052738e-03  3269    109666\n",
       "2    2022-01-01 -1.374486e-03  3211    109667\n",
       "3    2022-01-01 -3.434914e-04  3211    109666\n",
       "4    2022-01-01  1.394444e-26  1152    109666\n",
       "...         ...           ...   ...       ...\n",
       "2286 2040-01-01  1.465837e+01  1390    109698\n",
       "2287 2040-01-01  2.631662e+01   107    109696\n",
       "2288 2040-01-01  3.588365e+01  1031    109698\n",
       "2289 2040-01-01  5.361418e+01  1031    109696\n",
       "2290 2040-01-01  3.962367e+02   107    109698\n",
       "\n",
       "[2291 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tlca.dynamic_lcia(metric=\"GWP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare the time-explicit results to prospective LCA results, we do additional calculations for cases where the entire supply chain comes from the years 2020, 2030 and 2040.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.13e+13)\n",
      "  warnings.warn(msg, UmfpackWarning)\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n",
      "  warnings.warn(msg, UmfpackWarning)\n"
     ]
    }
   ],
   "source": [
    "import bw2calc as bc\n",
    "from collections import defaultdict\n",
    "\n",
    "prospective_scores = defaultdict(dict)\n",
    "\n",
    "for year, db in zip([2020, 2030, 2040], [db_2020, db_2030, db_2040]):\n",
    "    try:\n",
    "        prospective_driving = driving.copy(\n",
    "            code=f\"prospective_driving_{year}\",\n",
    "            name=f\"driving an electric vehicle in {year}\",\n",
    "        )\n",
    "    except:\n",
    "        foreground.get(code=f\"prospective_driving_{year}\").delete()\n",
    "        prospective_driving = driving.copy(\n",
    "            code=f\"prospective_driving_{year}\",\n",
    "            name=f\"driving an electric vehicle in {year}\",\n",
    "        )\n",
    "\n",
    "    for exc in prospective_driving.technosphere():\n",
    "        if exc.input == ev_production:\n",
    "            prospective_ev_production = ev_production.copy(\n",
    "                name=f\"production of an electric vehicle in {year}\"\n",
    "            )\n",
    "            prospective_ev_production.save()\n",
    "            exc.input = prospective_ev_production\n",
    "            exc.save()\n",
    "            for subexc in prospective_ev_production.technosphere():\n",
    "                subexc.input = bd.get_node(\n",
    "                    database=db.name,\n",
    "                    name=subexc.input[\"name\"],\n",
    "                    product=subexc.input[\"reference product\"],\n",
    "                    location=subexc.input[\"location\"],\n",
    "                )\n",
    "                subexc.save()\n",
    "        elif exc.input == used_ev:\n",
    "            prospective_used_ev = used_ev.copy(name=f\"used electric vehicle in {year}\")\n",
    "            exc.input = prospective_used_ev\n",
    "            exc.save()\n",
    "            for subexc in prospective_used_ev.technosphere():\n",
    "                subexc.input = bd.get_node(\n",
    "                    database=db.name,\n",
    "                    name=subexc.input[\"name\"],\n",
    "                    product=subexc.input[\"reference product\"],\n",
    "                    location=subexc.input[\"location\"],\n",
    "                )\n",
    "                subexc.save()\n",
    "        else:\n",
    "            exc.input = bd.get_node(\n",
    "                database=db.name,\n",
    "                name=exc.input[\"name\"],\n",
    "                product=exc.input[\"reference product\"],\n",
    "                location=exc.input[\"location\"],\n",
    "            )\n",
    "        exc.save()\n",
    "\n",
    "    lca = bc.LCA({prospective_driving.key: 1}, method)\n",
    "    lca.lci()\n",
    "    for exc in prospective_driving.technosphere():\n",
    "        if exc.input[\"name\"] in (\n",
    "            prospective_ev_production[\"name\"],\n",
    "            prospective_used_ev[\"name\"],\n",
    "        ):\n",
    "            for subexc in exc.input.technosphere():\n",
    "                lca.lcia(\n",
    "                    demand={\n",
    "                        subexc.input.id: exc.amount\n",
    "                        * subexc.amount\n",
    "                        * subexc.input.rp_exchange().amount\n",
    "                    }\n",
    "                )\n",
    "                prospective_scores[year][subexc.input[\"name\"]] = lca.score\n",
    "        else:\n",
    "            lca.lcia(demand={exc.input.id: exc.amount})\n",
    "            prospective_scores[year][exc.input[\"name\"]] = lca.score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the overall scores:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score 2020 20858.470012031674\n",
      "Score 2030:  9137.99912057819\n",
      "Score 2040:  6522.389036408176\n",
      "Time-explicit score:  12076.393848996586\n"
     ]
    }
   ],
   "source": [
    "print(\"Score 2020\", sum(prospective_scores[2020].values()))\n",
    "print(\"Score 2030: \", sum(prospective_scores[2030].values()))\n",
    "print(\"Score 2040: \", sum(prospective_scores[2040].values()))\n",
    "print(\"Time-explicit score: \", tlca.dynamic_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot this as a waterfall chart, comparing the different approaches. The function below, that directly produces the Figure used in the paper, is a slightly customized version of the bw_timex utility function `bw_timex.utils.plot_characterized_inventory_as_waterfall()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from bw_timex.utils import resolve_temporalized_node_name\n",
    "\n",
    "plt.rcParams[\"text.usetex\"] = False\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "colors = [\n",
    "    \"#00549F\",\n",
    "    \"#F6A800\",\n",
    "    \"#57AB27\",\n",
    "    \"#CC071E\",\n",
    "    \"#7A6FAC\",\n",
    "    \"#0098A1\",\n",
    "    \"#BDCD00\",\n",
    "    \"#006165\",\n",
    "]\n",
    "\n",
    "\n",
    "def plot_characterized_inventory_as_waterfall(\n",
    "    lca_obj,\n",
    "    prospective_scores=None,\n",
    "    order_stacked_activities=None,\n",
    "):\n",
    "    time_res_dict = {\n",
    "        \"year\": \"%Y\",\n",
    "        \"month\": \"%Y-%m\",\n",
    "        \"day\": \"%Y-%m-%d\",\n",
    "        \"hour\": \"%Y-%m-%d %H\",\n",
    "    }\n",
    "    plot_data = lca_obj.characterized_inventory.copy()\n",
    "\n",
    "    plot_data[\"year\"] = plot_data[\"date\"].dt.strftime(time_res_dict[\"year\"])\n",
    "\n",
    "    # Optimized activity label fetching\n",
    "    unique_activities = plot_data[\"activity\"].unique()\n",
    "    activity_labels = {\n",
    "        idx: resolve_temporalized_node_name(\n",
    "            lca_obj.activity_time_mapping.reversed[idx][0][1]\n",
    "        )\n",
    "        for idx in unique_activities\n",
    "    }\n",
    "    plot_data[\"activity_label\"] = plot_data[\"activity\"].map(activity_labels)\n",
    "\n",
    "    plot_data = plot_data.groupby([\"year\", \"activity_label\"], as_index=False)[\n",
    "        \"amount\"\n",
    "    ].sum()\n",
    "    pivoted_data = plot_data.pivot(\n",
    "        index=\"year\", columns=\"activity_label\", values=\"amount\"\n",
    "    )\n",
    "\n",
    "    combined_data = []\n",
    "    combined_data.append(pivoted_data)  # making sure the order is correct\n",
    "\n",
    "    # Adding exchange_scores as a prospective column\n",
    "    aggregated_row = pd.DataFrame(\n",
    "        {col: [pivoted_data[col].sum()] for col in pivoted_data.columns}, index=[\"Sum\"]\n",
    "    )\n",
    "    combined_data.append(aggregated_row)\n",
    "\n",
    "    total_timex_score = aggregated_row.T.sum() / 1e3\n",
    "\n",
    "    spacer_row = pd.DataFrame(\n",
    "        {col: [np.nan] for col in combined_data[-1].columns}, index=[\" \"]\n",
    "    )  # Create a spacer row with NaN values\n",
    "    combined_data.append(spacer_row)  # Add the spacer row before prospective data\n",
    "\n",
    "    for scores, yr in zip(prospective_scores, [2020, 2030, 2040]):\n",
    "        prospective_data = pd.DataFrame(\n",
    "            scores.items(), columns=[\"activity_label\", \"amount\"]\n",
    "        )\n",
    "        prospective_data[\"year\"] = f\"Static ({yr})\"\n",
    "        pivoted_prospective_data = prospective_data.pivot(\n",
    "            index=\"year\", columns=\"activity_label\", values=\"amount\"\n",
    "        )\n",
    "        combined_data.append(pivoted_prospective_data)\n",
    "\n",
    "    combined_df = pd.concat(combined_data, axis=0)\n",
    "\n",
    "    if order_stacked_activities:\n",
    "        combined_df = combined_df[\n",
    "            order_stacked_activities\n",
    "        ]  # change order of activities in the stacked bars of the waterfall\n",
    "\n",
    "    # Calculate the bottom for only the dynamic data\n",
    "    dynamic_bottom = pivoted_data.sum(axis=1).cumsum().shift(1).fillna(0)\n",
    "\n",
    "    # Add the spacer row to dynamic_bottom\n",
    "    dynamic_bottom = pd.concat([dynamic_bottom, pd.Series([np.nan], index=[\" \"])])\n",
    "\n",
    "    bottom = pd.concat(\n",
    "        [dynamic_bottom, pd.Series([0]), pd.Series([0]), pd.Series([0]), pd.Series([0])]\n",
    "    )\n",
    "\n",
    "    # Reset NaN values in the spacer row to 0 for the bottom array\n",
    "    bottom = bottom.fillna(0)\n",
    "\n",
    "    bottom = bottom / 1e3\n",
    "\n",
    "    activity_labels_simplified = {\n",
    "        \"treatment of used powertrain for electric passenger car, manual dismantling\": \"Powertrain EOL\",\n",
    "        \"market for used Li-ion battery\": \"Battery EOL\",\n",
    "        \"treatment of used glider, passenger car, shredding\": \"Glider EOL\",\n",
    "        \"market group for electricity, low voltage\": \"Electricity Generation\",\n",
    "        \"powertrain production, for electric passenger car, without EOL\": \"Powertrain Production\",\n",
    "        \"battery production, Li-ion, LiMn2O4, rechargeable, prismatic, without EOL\": \"Battery Production\",\n",
    "        \"glider production, passenger car, without EOL\": \"Glider Production\",\n",
    "    }\n",
    "    combined_df.columns = combined_df.columns.map(activity_labels_simplified)\n",
    "\n",
    "    combined_df = combined_df / 1e3\n",
    "\n",
    "    # Plotting\n",
    "    ax = combined_df.plot(\n",
    "        kind=\"bar\",\n",
    "        stacked=True,\n",
    "        bottom=bottom,\n",
    "        figsize=(7, 5),\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.5,\n",
    "        color=colors,\n",
    "        width=0.65,\n",
    "    )\n",
    "    ax.set_ylim((0, 23))\n",
    "    ax.set_ylabel(\n",
    "        \"Global Warming Impact \\n (fixed 100 year time horizon) \\n [10$^{3}$ kg CO$_2$-eq]\"\n",
    "    )\n",
    "    ax.set_xlabel(\"\")\n",
    "    plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "    # Add horizontal lines for waterfall structure\n",
    "    cumulative_totals = combined_df.sum(axis=1).cumsum()\n",
    "    for i in range(len(cumulative_totals) - 5):\n",
    "        ax.hlines(\n",
    "            y=cumulative_totals.iloc[i],\n",
    "            xmin=i,\n",
    "            xmax=i + 1,\n",
    "            colors=\"gray\",\n",
    "            # linestyles=\"dashed\",\n",
    "            linewidth=1,\n",
    "            zorder=0,\n",
    "        )\n",
    "\n",
    "    # vertical line separating static results\n",
    "    vertical_line_x = len(combined_df) - 4\n",
    "    ax.axvline(x=vertical_line_x, color=\"white\", lw=2)\n",
    "    ax.axvline(x=vertical_line_x, color=\"black\", linestyle=\"--\", lw=1)\n",
    "\n",
    "    ax.axhline(\n",
    "        y=float(total_timex_score), color=\"black\", linestyle=\"dotted\", lw=1, zorder=0\n",
    "    )\n",
    "    ax.text(\n",
    "        -0.25,\n",
    "        total_timex_score * 1.02,\n",
    "        \"Time-explicit score\",\n",
    "        va=\"bottom\",\n",
    "        style=\"italic\",\n",
    "        backgroundcolor=\"white\",\n",
    "        bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    "    )\n",
    "\n",
    "    ax.text(\n",
    "        9,\n",
    "        -3000 / 1e3,\n",
    "        \"Time-explicit\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "    )\n",
    "\n",
    "    total_2020 = sum(prospective_scores[0].values()) / 1e3\n",
    "    ax.annotate(\n",
    "        \"\",\n",
    "        xy=(len(combined_df) - 2, total_timex_score),\n",
    "        xytext=(len(combined_df) - 2, total_2020),\n",
    "        arrowprops=dict(arrowstyle=\"->\", color=\"black\"),\n",
    "    )\n",
    "    ax.text(\n",
    "        x=len(combined_df) - 2,\n",
    "        y=(total_2020 - total_timex_score) / 2 + total_timex_score,\n",
    "        s=f\"$\\\\times${round(float(total_timex_score)/total_2020, ndigits=1)}\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        rotation=90,\n",
    "        bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    "    )\n",
    "\n",
    "    total_2030 = sum(prospective_scores[1].values()) / 1e3\n",
    "    ax.annotate(\n",
    "        \"\",\n",
    "        xy=(len(combined_df) - 2, total_timex_score),\n",
    "        xytext=(len(combined_df) - 2, total_2030),\n",
    "        arrowprops=dict(arrowstyle=\"->\", color=\"black\"),\n",
    "    )\n",
    "    ax.text(\n",
    "        x=len(combined_df) - 2,\n",
    "        y=((total_2030 - total_timex_score) / 2 + total_timex_score) * 0.99,\n",
    "        s=f\"$\\\\times${round(float(total_timex_score)/total_2030, ndigits=1)}\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        rotation=90,\n",
    "        bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    "    )\n",
    "\n",
    "    total_2040 = sum(prospective_scores[2].values()) / 1e3\n",
    "    ax.annotate(\n",
    "        \"\",\n",
    "        xy=(len(combined_df) - 1, total_timex_score),\n",
    "        xytext=(len(combined_df) - 1, total_2040),\n",
    "        arrowprops=dict(arrowstyle=\"->\", color=\"black\"),\n",
    "    )\n",
    "    ax.text(\n",
    "        x=len(combined_df) - 1,\n",
    "        y=((total_2040 - total_timex_score) / 2 + total_timex_score),\n",
    "        s=f\"$\\\\times${round(float(total_timex_score)/total_2040, ndigits=1)}\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        rotation=90,\n",
    "        bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    "    )\n",
    "\n",
    "    ax.hlines(\n",
    "        y=total_2020,\n",
    "        xmin=len(combined_df) - 3,\n",
    "        xmax=len(combined_df) - 2,\n",
    "        colors=\"black\",\n",
    "        linestyles=\"dotted\",\n",
    "        linewidth=1,\n",
    "        zorder=0,\n",
    "    )\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(\n",
    "        handles[::-1], labels[::-1], loc=\"upper left\"\n",
    "    )  # Reversing the order for the legend\n",
    "\n",
    "    # remove tick at vertical line\n",
    "    ticks, labels = ax.get_xticks(), ax.get_xticklabels()\n",
    "    filtered_ticks_labels = [\n",
    "        (tick, label.get_text())\n",
    "        for tick, label in zip(ticks, labels)\n",
    "        if label.get_text() != \" \"\n",
    "    ]\n",
    "    filtered_ticks, filtered_labels = zip(*filtered_ticks_labels)\n",
    "    ax.set_xticks(filtered_ticks)\n",
    "    ax.set_xticklabels(filtered_labels)\n",
    "    ax.set_yticks(np.arange(0, 23, 2.5))\n",
    "\n",
    "    ax.set_axisbelow(True)\n",
    "\n",
    "    ax.grid(which=\"major\", linestyle=\"-\", linewidth=0.5, alpha=0.7)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig(\"waterfall.svg\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l1/k90rhb0j0ns58y35ymznsd700000gn/T/ipykernel_86123/2575960787.py:137: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  ax.axhline(y=float(total_timex_score), color=\"black\", linestyle=\"dotted\", lw=1, zorder=0)\n",
      "/var/folders/l1/k90rhb0j0ns58y35ymznsd700000gn/T/ipykernel_86123/2575960787.py:151: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  ax.text(x=len(combined_df)-2, y=(total_2020-total_timex_score)/2+total_timex_score, s=f\"$\\\\times${round(float(total_timex_score)/total_2020, ndigits=1)}\", ha=\"center\", va=\"center\", rotation=90, bbox=dict(boxstyle='square,pad=0.1', fc=\"white\", ec=\"none\"))\n",
      "/var/folders/l1/k90rhb0j0ns58y35ymznsd700000gn/T/ipykernel_86123/2575960787.py:156: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  ax.text(x=len(combined_df)-2, y=((total_2030-total_timex_score)/2+total_timex_score)*0.99, s=f\"$\\\\times${round(float(total_timex_score)/total_2030, ndigits=1)}\", ha=\"center\", va=\"center\", rotation=90, bbox=dict(boxstyle='square,pad=0.1', fc=\"white\", ec=\"none\"))\n",
      "/var/folders/l1/k90rhb0j0ns58y35ymznsd700000gn/T/ipykernel_86123/2575960787.py:161: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  ax.text(x=len(combined_df)-1, y=((total_2040-total_timex_score)/2+total_timex_score), s=f\"$\\\\times${round(float(total_timex_score)/total_2040, ndigits=1)}\", ha=\"center\", va=\"center\", rotation=90, bbox=dict(boxstyle='square,pad=0.1', fc=\"white\", ec=\"none\"))\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/matplotlib/text.py:906: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/matplotlib/text.py:1477: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/matplotlib/text.py:763: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  posy = float(self.convert_yunits(y))\n",
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/matplotlib/text.py:568: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  posy = float(self.convert_yunits(self._y))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order_stacked_activities = [\n",
    "    glider_production_without_eol[\"name\"],\n",
    "    battery_production_without_eol[\"name\"],\n",
    "    powertrain_production_without_eol[\"name\"],\n",
    "    electricity_production[\"name\"],\n",
    "    glider_eol[\"name\"],\n",
    "    battery_eol[\"name\"],\n",
    "    powertrain_eol[\"name\"],\n",
    "]\n",
    "\n",
    "plot_characterized_inventory_as_waterfall(\n",
    "    tlca,\n",
    "    prospective_scores=[\n",
    "        prospective_scores[2020],\n",
    "        prospective_scores[2030],\n",
    "        prospective_scores[2040],\n",
    "    ],\n",
    "    order_stacked_activities=order_stacked_activities,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Radiative forcing\n",
    "\n",
    "Next, we calculate the radiative forcing over the EV life cycle via dynamic characterization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timodiepers/anaconda3/envs/timex/lib/python3.11/site-packages/dynamic_characterization/dynamic_characterization.py:81: UserWarning: No custom dynamic characterization functions provided. Using default dynamic characterization functions.                The flows that are characterized are based on the selection of the initially chosen impact category.                You can look up the mapping in the bw_timex.dynamic_characterizer.characterization_functions.\n",
      "  warnings.warn(\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>date</th>\n",
       "      <th>amount</th>\n",
       "      <th>flow</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01 05:49:12</td>\n",
       "      <td>-2.670181e-17</td>\n",
       "      <td>3269</td>\n",
       "      <td>109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-01 05:49:12</td>\n",
       "      <td>-6.675680e-18</td>\n",
       "      <td>3269</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-01 05:49:12</td>\n",
       "      <td>-2.264057e-18</td>\n",
       "      <td>3211</td>\n",
       "      <td>109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-01 05:49:12</td>\n",
       "      <td>-5.657999e-19</td>\n",
       "      <td>3211</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-01 05:49:12</td>\n",
       "      <td>2.799797e-41</td>\n",
       "      <td>1152</td>\n",
       "      <td>109666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154146</th>\n",
       "      <td>2139-01-01 00:10:48</td>\n",
       "      <td>4.878320e-15</td>\n",
       "      <td>1034</td>\n",
       "      <td>109696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154147</th>\n",
       "      <td>2139-01-01 00:10:48</td>\n",
       "      <td>1.843442e-14</td>\n",
       "      <td>107</td>\n",
       "      <td>109696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154148</th>\n",
       "      <td>2139-01-01 00:10:48</td>\n",
       "      <td>2.513599e-14</td>\n",
       "      <td>1031</td>\n",
       "      <td>109698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154149</th>\n",
       "      <td>2139-01-01 00:10:48</td>\n",
       "      <td>3.755597e-14</td>\n",
       "      <td>1031</td>\n",
       "      <td>109696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154150</th>\n",
       "      <td>2139-01-01 00:10:48</td>\n",
       "      <td>2.775582e-13</td>\n",
       "      <td>107</td>\n",
       "      <td>109698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154151 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      date        amount  flow  activity\n",
       "0      2023-01-01 05:49:12 -2.670181e-17  3269    109667\n",
       "1      2023-01-01 05:49:12 -6.675680e-18  3269    109666\n",
       "2      2023-01-01 05:49:12 -2.264057e-18  3211    109667\n",
       "3      2023-01-01 05:49:12 -5.657999e-19  3211    109666\n",
       "4      2023-01-01 05:49:12  2.799797e-41  1152    109666\n",
       "...                    ...           ...   ...       ...\n",
       "154146 2139-01-01 00:10:48  4.878320e-15  1034    109696\n",
       "154147 2139-01-01 00:10:48  1.843442e-14   107    109696\n",
       "154148 2139-01-01 00:10:48  2.513599e-14  1031    109698\n",
       "154149 2139-01-01 00:10:48  3.755597e-14  1031    109696\n",
       "154150 2139-01-01 00:10:48  2.775582e-13   107    109698\n",
       "\n",
       "[154151 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tlca.dynamic_lcia(metric=\"radiative_forcing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to compare the time-explicit results to the ones from dynamic LCA with and without having defined temporal distributions. These were calculated in the beginning. To format the dynamic inventories correctly, we introduce a helper function here:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from bw_timex.utils import round_datetime\n",
    "\n",
    "\n",
    "def create_plot_df(lca):\n",
    "    activity_name_cache = {\n",
    "        activity: resolve_temporalized_node_name(\n",
    "            lca.activity_time_mapping.reversed[activity][0][1]\n",
    "        )\n",
    "        for activity in lca.characterized_inventory[\"activity\"].unique()\n",
    "    }\n",
    "\n",
    "    life_cycle_stage_mapping = {\n",
    "        \"battery production, Li-ion, LiMn2O4, rechargeable, prismatic, without EOL\": \"Production\",\n",
    "        \"glider production, passenger car, without EOL\": \"Production\",\n",
    "        \"market for used Li-ion battery\": \"EOL\",\n",
    "        \"market group for electricity, low voltage\": \"Use\",\n",
    "        \"powertrain production, for electric passenger car, without EOL\": \"Production\",\n",
    "        \"treatment of used glider, passenger car, shredding\": \"EOL\",\n",
    "        \"treatment of used powertrain for electric passenger car, manual dismantling\": \"EOL\",\n",
    "    }\n",
    "\n",
    "    plot_data = (\n",
    "        lca.characterized_inventory.assign(\n",
    "            activity_label=lambda df: df[\"activity\"].map(activity_name_cache)\n",
    "        )\n",
    "        .groupby([\"date\", \"activity_label\"], as_index=False)\n",
    "        .sum()\n",
    "    )\n",
    "\n",
    "    plot_data[\"date\"] = plot_data[\"date\"].apply(\n",
    "        partial(round_datetime, resolution=\"year\")\n",
    "    )\n",
    "    plot_data[\"life_cycle_stage\"] = plot_data[\"activity_label\"].map(\n",
    "        life_cycle_stage_mapping\n",
    "    )\n",
    "\n",
    "    final_data = (\n",
    "        plot_data.groupby([\"date\", \"life_cycle_stage\"], as_index=False)[\"amount\"]\n",
    "        .sum()\n",
    "        .pivot(index=\"date\", columns=\"life_cycle_stage\", values=\"amount\")\n",
    "        .reindex(columns=[\"Production\", \"Use\", \"EOL\"])\n",
    "    )\n",
    "\n",
    "    return final_data / 1e-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tlca = create_plot_df(tlca)\n",
    "df_dlca = create_plot_df(dlca)\n",
    "df_dlca_no_tds = create_plot_df(dlca_no_tds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Providing initial zero value for cumulative plots:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tlca = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame({col: [0] for col in df_tlca.columns}, index=[min(df_tlca.index)]),\n",
    "        df_tlca,\n",
    "    ]\n",
    ")\n",
    "df_dlca = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame({col: [0] for col in df_dlca.columns}, index=[min(df_dlca.index)]),\n",
    "        df_dlca,\n",
    "    ]\n",
    ")\n",
    "df_dlca_no_tds = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(\n",
    "            {col: [0] for col in df_dlca_no_tds.columns},\n",
    "            index=[min(df_dlca_no_tds.index)],\n",
    "        ),\n",
    "        df_dlca_no_tds,\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x650 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from matplotlib.ticker import NullLocator\n",
    "\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "fig, axes = plt.subplots(3, 3, figsize=(7, 6.5), sharex=True)\n",
    "colors = [\"#00549F\", \"#F6A800\", \"#57AB27\", \"#AAAAAA\"]\n",
    "labels = [\"Production\", \"Use\", \"EOL\", \"Cum. sum\"]\n",
    "\n",
    "all_data = pd.concat([df_tlca.fillna(0), df_dlca.fillna(0)])\n",
    "global_ylim = (0, 4.3)\n",
    "\n",
    "df_tlca.fillna(0).plot(\n",
    "    ax=axes[0, 2],\n",
    "    linewidth=1,\n",
    "    xlim=(datetime(2018, 1, 1), datetime(2102, 1, 1)),\n",
    "    ylim=(0, 2.5),\n",
    "    color=colors,\n",
    "    legend=False,\n",
    ")\n",
    "df_dlca.fillna(0).plot(\n",
    "    ax=axes[0, 1],\n",
    "    linewidth=1,\n",
    "    xlim=(datetime(2018, 1, 1), datetime(2102, 1, 1)),\n",
    "    ylim=(0, 2.5),\n",
    "    color=colors,\n",
    "    legend=False,\n",
    ")\n",
    "df_dlca_no_tds.fillna(0).plot(\n",
    "    ax=axes[0, 0],\n",
    "    linewidth=1,\n",
    "    xlim=(datetime(2018, 1, 1), datetime(2102, 1, 1)),\n",
    "    ylim=(0, 2.5),\n",
    "    color=colors,\n",
    "    legend=False,\n",
    ")\n",
    "\n",
    "axes[1, 2].stackplot(\n",
    "    df_tlca.index,\n",
    "    df_tlca[\"Production\"].fillna(0),\n",
    "    df_tlca[\"Use\"].fillna(0),\n",
    "    df_tlca[\"EOL\"].fillna(0),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "axes[1, 1].stackplot(\n",
    "    df_dlca.index,\n",
    "    df_dlca[\"Production\"].fillna(0),\n",
    "    df_dlca[\"Use\"].fillna(0),\n",
    "    df_dlca[\"EOL\"].fillna(0),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "axes[1, 0].stackplot(\n",
    "    df_dlca_no_tds.index,\n",
    "    df_dlca_no_tds[\"Production\"].fillna(0),\n",
    "    df_dlca_no_tds[\"Use\"].fillna(0),\n",
    "    df_dlca_no_tds[\"EOL\"].fillna(0),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "axes[2, 2].stackplot(\n",
    "    df_tlca.index,\n",
    "    df_tlca[\"Production\"].fillna(0).cumsum(),\n",
    "    df_tlca[\"Use\"].fillna(0).cumsum(),\n",
    "    df_tlca[\"EOL\"].fillna(0).cumsum(),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "axes[2, 1].stackplot(\n",
    "    df_dlca.index,\n",
    "    df_dlca[\"Production\"].fillna(0).cumsum(),\n",
    "    df_dlca[\"Use\"].fillna(0).cumsum(),\n",
    "    df_dlca[\"EOL\"].fillna(0).cumsum(),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "axes[2, 0].stackplot(\n",
    "    df_dlca_no_tds.index,\n",
    "    df_dlca_no_tds[\"Production\"].fillna(0).cumsum(),\n",
    "    df_dlca_no_tds[\"Use\"].fillna(0).cumsum(),\n",
    "    df_dlca_no_tds[\"EOL\"].fillna(0).cumsum(),\n",
    "    labels=labels,\n",
    "    colors=colors,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "axes[2, 2].set_ylim((0, 175))\n",
    "axes[2, 1].set_ylim((0, 175))\n",
    "axes[2, 0].set_ylim((0, 175))\n",
    "\n",
    "fig.text(0.265, 0.99, \"Static\", ha=\"center\")\n",
    "fig.text(0.555, 0.99, \"Dynamic\", ha=\"center\")\n",
    "fig.text(0.85, 0.99, \"Time-explicit\", ha=\"center\")\n",
    "\n",
    "fig.text(\n",
    "    0.381,\n",
    "    0.955,\n",
    "    \"(a)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.669,\n",
    "    0.955,\n",
    "    \"(b)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.96,\n",
    "    0.955,\n",
    "    \"(c)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "\n",
    "fig.text(\n",
    "    0.381,\n",
    "    0.644,\n",
    "    \"(d)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.669,\n",
    "    0.644,\n",
    "    \"(e)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.96,\n",
    "    0.644,\n",
    "    \"(f)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "\n",
    "fig.text(\n",
    "    0.381,\n",
    "    0.336,\n",
    "    \"(g)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.669,\n",
    "    0.336,\n",
    "    \"(h)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "fig.text(\n",
    "    0.96,\n",
    "    0.336,\n",
    "    \"(i)\",\n",
    "    ha=\"center\",\n",
    "    backgroundcolor=\"white\",\n",
    "    bbox=dict(boxstyle=\"square,pad=0.1\", fc=\"white\", ec=\"none\"),\n",
    ")\n",
    "\n",
    "axes[0, 0].set_ylabel(\n",
    "    \"Instantaneous radiative forcing \\n (individual life cycle stages) \\n [10$^{-11}$ W m$^{-2}$]\"\n",
    ")\n",
    "axes[1, 0].set_ylabel(\n",
    "    \"Instantaneous radiative forcing \\n (stacked life cycle stages) \\n [10$^{-11}$ W m$^{-2}$]\"\n",
    ")\n",
    "axes[2, 0].set_ylabel(\"Cumulative radiative forcing \\n [10$^{-11}$ W m$^{-2}$]\")\n",
    "\n",
    "handles, labels = axes[1, 1].get_legend_handles_labels()\n",
    "fig.legend(\n",
    "    handles,\n",
    "    labels,\n",
    "    loc=\"upper center\",\n",
    "    ncol=len(labels),\n",
    "    bbox_to_anchor=(0.525, 0.025),\n",
    "    frameon=False,\n",
    "    markerscale=2,\n",
    ")\n",
    "\n",
    "major_locator = mdates.YearLocator(10)\n",
    "minor_locator = mdates.YearLocator(10)\n",
    "\n",
    "for ax_rows in axes:\n",
    "    for ax in ax_rows:\n",
    "        ax.xaxis.set_major_locator(major_locator)\n",
    "        ax.xaxis.set_minor_locator(NullLocator())\n",
    "        ax.grid(which=\"major\", linestyle=\"-\", linewidth=0.5, alpha=0.7)\n",
    "\n",
    "        ax.set_axisbelow(True)\n",
    "\n",
    "        for label in ax.get_xticklabels():\n",
    "            label.set_rotation(45)\n",
    "            label.set_ha(\"right\")\n",
    "\n",
    "for ax in axes[0]:\n",
    "    ax.set_ylim((0, 2.5))\n",
    "\n",
    "for ax in axes[1]:\n",
    "    ax.set_ylim((0, 4.5))\n",
    "\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "\n",
    "def format_func(value, _):\n",
    "    return f\"{value:.1f}\"\n",
    "\n",
    "\n",
    "formatter = FuncFormatter(format_func)\n",
    "for ax in axes[1]:\n",
    "    ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "axes[0, 1].set_yticklabels([])\n",
    "axes[0, 2].set_yticklabels([])\n",
    "axes[1, 1].set_yticklabels([])\n",
    "axes[1, 2].set_yticklabels([])\n",
    "axes[2, 1].set_yticklabels([])\n",
    "axes[2, 2].set_yticklabels([])\n",
    "\n",
    "plt.tight_layout(w_pad=-0.4)\n",
    "# plt.savefig(\"radiative_forcing.svg\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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