{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce9349ae",
   "metadata": {},
   "source": [
    "# Use of `shrecc`\n",
    "\n",
    "This notebook guides you through the various steps needed to create spatially- and regionally-specific consumption electricity mixes. These mixes are written directly in a dedicated `brightway` database, ready to be used in your own LCA project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddd00229-e983-4012-9e79-6288efe9f5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bw2data as bd\n",
    "import bw2io as bi\n",
    "import pandas as pd\n",
    "from shrecc.database import create_database, filt_cutoff\n",
    "from shrecc.download import get_data\n",
    "from shrecc.treatment import data_processing\n",
    "\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55583e0f-db5c-4ee6-a451-3de520757d88",
   "metadata": {},
   "source": [
    "## Prepare your data\n",
    "\n",
    "`shrecc` needs as inputs the data from entso-e. It needs the raw data, and also needs to process it.\n",
    "There are 2 options to have the data \"ready\":\n",
    "\n",
    "\n",
    "A. download and process it programmatically with `shrecc`'s `get_data` and `data_processing` functions\n",
    "\n",
    "B. directly download the pre-processed data from the [shrecc_data](https://git.list.lu/shrecc_project/shrecc_data) repository\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc5c5ba-736e-46c0-bb62-447a08a0787d",
   "metadata": {},
   "source": [
    "#### The `shrecc_data` repository\n",
    "\n",
    "The [shrecc_data](https://git.list.lu/shrecc_project/shrecc_data) repository has pre-processed data for years 2022, 2023, and 2024.\n",
    "If you want to use data for other years (like 2021 in Example 2 of this notebook), you must use `shrecc`'s `get_data` and `data_processing` as illustrated in Option A) below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cfdfe5b-546a-4007-aaa3-60683302a40a",
   "metadata": {},
   "source": [
    "### Option A: download the data programmatically with `shrecc`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5b169a-6736-4a68-a2a0-521c35a2dedd",
   "metadata": {},
   "source": [
    "Do this in case you want to download data for any other year than the default year in the `shrecc_data` [repository](https://git.list.lu/shrecc_project/shrecc_data), or if you simply want to recalculate it on your own."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52637e07-d6f1-478e-96a2-82c1bf2b3f87",
   "metadata": {},
   "source": [
    "First, run function `get_data`. This will download all the electricity data for all countries for a selected year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6384fe-e258-43db-b61d-9f376995d90f",
   "metadata": {},
   "source": [
    "You have the option to download the data to \n",
    "+ a specific location you want, by specifying it at the `path_to_data` argument of the `get_data` function.\n",
    "\n",
    "+ provide no `path_to_data`, and shrecc will download it to the package location (for example `c:\\users\\jovyan\\miniconda3\\envs\\shrecc_env\\lib\\python3.13\\shrecc`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9eac15e4-3893-4418-bc76-ca0476066340",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define here the path to data, and re-use it as necessary if you want to store the data in a specific location\n",
    "PATH_TO_DATA = \"data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfc4181-5c41-433d-90c5-8a7abf19d9cf",
   "metadata": {},
   "source": [
    "#### Step 1: Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a0e6b01-405e-4cba-9a83-961ed525dc20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API data loaded successfully.\n"
     ]
    }
   ],
   "source": [
    "data = get_data(year=2024, path_to_data=PATH_TO_DATA)\n",
    "# if no path given, shrecc will create a folder inside of shrecc/data and save the data there"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04297813-32ea-4327-9b4c-d6704637b7e4",
   "metadata": {},
   "source": [
    "#### Step 2: process the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94892f16-d0d8-4e71-8608-0b51d361e528",
   "metadata": {},
   "source": [
    "Next, it's time to treat the data. This part is very heavy due to matrix inversion, and is recommended to run on a server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fb0a74b-e7d7-4f9e-ae65-81839f859b75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing data...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.12/site-packages/shrecc/treatment.py:48: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  if col in data_df.T.index\n",
      "/opt/conda/lib/python3.12/site-packages/shrecc/treatment.py:48: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  if col in data_df.T.index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treating data:\n",
      "Solving exchange network graph\n",
      "Results loaded from cons_results_2024.pkl\n",
      "Exchange network graph solved\n",
      "Applying load losses\n",
      "Load losses applied and saved\n",
      "Light results loaded\n",
      "..all done!\n"
     ]
    }
   ],
   "source": [
    "data_processing(data_df=data, year=2024, path_to_data=PATH_TO_DATA)\n",
    "# if no path given, shrecc will create a folder inside of shrecc/data and save the data there"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "09cc0333-bb4b-449a-855c-61ed233b76ed",
   "metadata": {},
   "source": [
    "### Option B: download directly the data from `shrecc_data` repository\n",
    "\n",
    "1. Go to the [shrecc_data repository](https://git.list.lu/shrecc_project/shrecc_data/)\n",
    "2. Locate the \"Code\" blue button, and click on it.\n",
    "3. Use one of the options of the \"Download source code\"\n",
    "\n",
    "![image.png](attachment:8d86c40a-d0e5-4a6a-9044-396939b70fe6.png)\n",
    "\n",
    "After the download is finished, extract it, and make sure the parent directory is called \"data\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a0cb39-ee35-4786-b333-0d00fda42328",
   "metadata": {},
   "source": [
    "And that's literally it! Now it's time to select which countries and times you are interested in and create your database."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512022d8-6f6e-403e-a1f5-da1622f7a8d0",
   "metadata": {},
   "source": [
    "## Get your project ready\n",
    "In order to execute the next cells of this notebook, you need to have a `brightway` (2 or 2.5) project with ecoinvent installed. Matching of electricity sources is valid for ecoinvent 3.9.1, but should be compatible with 3.10 and 3.11. You also need to have a `pandas` dataframe with all the electricity data. Either you created it on your own with the above steps, or you downloaded it from [the shrecc git repository](https://git.list.lu/shrecc_project/shrecc_data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbca7c61-6b39-4ab1-9bdf-fe6779db6d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_NAME = \"ecoinvent311\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bfe1c52-0664-4add-ba66-834a44f94505",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd.projects.set_current(PROJECT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f9f340c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Databases dictionary with 0 objects"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd.databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f52435a-47ce-4d8b-9c22-cec81905ee73",
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"ecoinvent-3.11-cutoff\" not in bd.databases:\n",
    "    bi.import_ecoinvent_release(\n",
    "        version=\"3.11\",\n",
    "        system_model=\"cutoff\",\n",
    "        username=\"XX\",\n",
    "        password=\"YY\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcc2ad5-9fdf-49a1-92fd-b6a73cd43810",
   "metadata": {},
   "source": [
    "## Example 1: all days in June around noon\n",
    "In this example, we model the consumption mix of five countries (1), around noon (2) on all days in June 2024 (3).\n",
    "\n",
    "### Filter the data\n",
    "For this, we use the `filt_cutoff` function, specifying the following:\n",
    "\n",
    "1. The list of countries (a list of strings with the code for each country) ➡️ `countries`\n",
    "2. The start and end dates ➡️ `general_range`\n",
    "3. a list of start and end hours ➡️ `refined_range` = [10, 14]\n",
    "4. a frequency by hour ➡️ `freq` = \"h\"\n",
    "5. a cutoff value ➡️ `cutoff` = 1e-3\n",
    "6. Wether or not to include the cutoff ➡️ `include_cutoff` = True\n",
    "7. The path where the entso-e data is located ➡️ `path_to_data` PATH_TO_DATA (defined above)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7031b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = [\"FI\", \"SE\", \"LU\", \"AT\", \"FR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c75160-977d-4d61-9492-8aa175075ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = filt_cutoff(\n",
    "    countries=countries,\n",
    "    general_range=[\"2024-06-01 01:00:00\", \"2024-06-30 23:00:00\"],\n",
    "    refined_range=[10, 14],\n",
    "    freq=\"h\",\n",
    "    cutoff=1e-3,\n",
    "    include_cutoff=True,\n",
    "    path_to_data=PATH_TO_DATA,\n",
    ")\n",
    "# if no path given, shrecc will look for the data inside of shrecc/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c916de-e7f5-4e93-843c-12ce93daaa54",
   "metadata": {},
   "source": [
    "### Create a database based on our filtered and cut off dataframe with selected days/times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a7daa44",
   "metadata": {},
   "outputs": [],
   "source": [
    "example.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7563d9f9-55b4-4c61-b4eb-5afc3aa31430",
   "metadata": {},
   "outputs": [],
   "source": [
    "create_database(\n",
    "    dataframe_filt=example,\n",
    "    project_name=PROJECT_NAME,\n",
    "    db_name=\"elec_june_2024_noon\",\n",
    "    eidb_name=\"ecoinvent-3.11-cutoff\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71303dfb-1de3-4eb1-b1bc-891f0980008c",
   "metadata": {},
   "source": [
    "## Example 2: all days on February evenings\n",
    "Another example of dataframe, this time on February evenings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f28e163-3c7b-4fd5-b66a-053929af7ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "example2 = filt_cutoff(\n",
    "    countries=countries,\n",
    "    general_range=[\"2024-02-01 01:00:00\", \"2024-02-28 23:00:00\"],\n",
    "    refined_range=[19, 23],\n",
    "    freq=\"h\",\n",
    "    cutoff=1e-3,\n",
    "    include_cutoff=True,\n",
    "    path_to_data=PATH_TO_DATA,\n",
    ")\n",
    "# if no path given, shrecc will look for the data in shrecc/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd635013-6ddc-4140-a57d-6be63ea4c7a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "create_database(\n",
    "    dataframe_filt=example2,\n",
    "    project_name=PROJECT_NAME,\n",
    "    db_name=\"elec_february_2024_evening\",\n",
    "    eidb_name=\"ecoinvent-3.11-cutoff\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c36ce32-4d7e-4eb5-ae7a-44a9bb5c4083",
   "metadata": {},
   "source": [
    "## Show that shrecc results are similar enough to ecoinvent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c98318-8d0f-4cff-b9d5-eb238dc71ac9",
   "metadata": {},
   "source": [
    "We compare 2021 Energy-Charts data with ecoinvent 3.11 cutoff, which uses 2021 data for most European countries (except of Switzerland).\n",
    "You can download our data from shrecc_project/shrecc_data/2021. Supply the path to were you saved the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "333d480c-95c9-4305-a8f9-2639f0167974",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_data(year=2021, path_to_data=PATH_TO_DATA)\n",
    "# if no path given, shrecc will create a folder inside of shrecc/data, download the data and save it there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69401466-1668-4491-ae86-6f4441c0622a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processing(data_df=data, year=2021, path_to_data=PATH_TO_DATA)\n",
    "# if no path given, shrecc will create a folder inside of shrecc/data, download the data and save it there"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c96190-4992-40b0-8d16-c9f8405a7f3d",
   "metadata": {},
   "source": [
    "Selecting all the countries that do not contain missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90b63a71-a92c-428e-9417-8e03b5dd2336",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = [\n",
    "    \"AT\",\n",
    "    \"BE\",\n",
    "    \"CZ\",\n",
    "    \"DE\",\n",
    "    \"DK\",\n",
    "    \"ES\",\n",
    "    \"FI\",\n",
    "    \"FR\",\n",
    "    \"GR\",\n",
    "    \"HU\",\n",
    "    \"IE\",\n",
    "    \"IT\",\n",
    "    \"LU\",\n",
    "    \"NL\",\n",
    "    \"NO\",\n",
    "    \"PL\",\n",
    "    \"PT\",\n",
    "    \"RO\",\n",
    "    \"SE\",\n",
    "    \"SK\",\n",
    "    \"SI\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d406614b-49c0-4b96-9cd6-93f17bb53034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using mapping root: data/\n",
      "Mapping technologies...\n",
      "Technologies mapped.\n",
      "Filtering dataframe...\n",
      "Dataframe filtered.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app/shrecc/database.py:332: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  df_filt.loc[\n"
     ]
    }
   ],
   "source": [
    "year2021 = filt_cutoff(\n",
    "    countries=countries,\n",
    "    general_range=[\"2021-01-01 00:00:00\", \"2021-12-31 23:00:00\"],\n",
    "    freq=\"h\",\n",
    "    cutoff=1e-8,\n",
    "    include_cutoff=True,\n",
    "    path_to_data=PATH_TO_DATA,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32291e9d-8021-4c7e-bede-8e185267742d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldnt find activity:electricity, high voltage, biofuels, import from Germany, CH\n",
      "Couldnt find activity:electricity, high voltage, hydro, reservoir, import from France, CH\n",
      "Couldnt find activity:electricity, high voltage, hydro, run-of-river, import from France, CH\n",
      "Couldnt find activity:electricity, high voltage, natural gas, import from Germany, CH\n",
      "Couldnt find activity:electricity, high voltage, nuclear, import from France, CH\n",
      "Couldnt find activity:electricity, high voltage, wind power, import from Germany, CH\n",
      "Couldnt find activity:electricity, low voltage, photovoltaic, import from Germany, CH\n",
      "Couldnt find activity:electricity, medium voltage, municipal waste incineration, import from Germany, CH\n",
      "Couldnt find activity:treatment of digester sludge, municipal incineration, future, CH\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 119.47it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m15:26:21\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVacuuming database            \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "create_database(\n",
    "    dataframe_filt=year2021,\n",
    "    project_name=PROJECT_NAME,\n",
    "    db_name=\"full year 2021\",\n",
    "    eidb_name=\"ecoinvent-3.11-cutoff\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7d62ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bw2calc as bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2650043",
   "metadata": {},
   "outputs": [],
   "source": [
    "ef_methods = [m for m in bd.methods if m[1] == \"EF v3.0\"]\n",
    "config = {\"impact_categories\": ef_methods}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4aa935dc-2e28-4ea3-a695-1d7ad7a2c47f",
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_year = {\n",
    "    f\"{a['name']}, full year\": {a[\"id\"]: 1} for a in bd.Database(\"full year 2021\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "548d0c5f-642c-4fbe-9052-87dd3be6ee5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "acts_ei = [\n",
    "    bd.get_node(\n",
    "        database=\"ecoinvent-3.11-cutoff\",\n",
    "        location=country,\n",
    "        name=\"market for electricity, low voltage\",\n",
    "    )\n",
    "    for country in sorted(countries)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "816a75e9-3bb9-4f1b-9eb7-e8678f4ec753",
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_year.update({f\"{a['name']}, {a['location']}\": {a[\"id\"]: 1} for a in acts_ei})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01acf631-141a-42fd-897a-f92885d84f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_objs = bd.get_multilca_data_objs(\n",
    "    functional_units=demands_year, method_config=config\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2965b3be-1f8f-45e3-a563-de2f492f1dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca = bc.MultiLCA(demands=demands_year, method_config=config, data_objs=data_objs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a293598c-8541-4001-96ac-9fbc4ea02625",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.lci()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a14453f-df9b-4c02-85e4-38ebf0dfeaba",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.lcia()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "38643b9b-6a30-4590-ae8d-fe741904f3c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for (method, fu), score in m_lca.scores.items():\n",
    "    demand = m_lca.demands[fu]\n",
    "    a_id = list(demand).pop()\n",
    "    a = bd.get_activity(a_id)\n",
    "    results.append(\n",
    "        {\n",
    "            \"activity\": a[\"name\"],\n",
    "            \"database\": a[\"database\"],\n",
    "            \"location\": a[\"location\"],\n",
    "            \"production amount\": demand[a_id],\n",
    "            \"activity unit\": a[\"unit\"],\n",
    "            \"reference product\": a[\"reference product\"],\n",
    "            \"methodology\": method[1],\n",
    "            \"category\": method[2],\n",
    "            \"indicator\": method[3],\n",
    "            \"score\": score,\n",
    "            \"unit\": bd.Method(method).metadata[\"unit\"],\n",
    "        },\n",
    "    )\n",
    "res_df = pd.DataFrame(results)\n",
    "res_df = res_df.set_index([c for c in res_df.columns if c != \"score\"]).unstack(\n",
    "    [\"category\", \"indicator\", \"unit\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "50292ad0-b99b-4c12-9908-8caff0f7fa64",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df.sort_index(level=\"location\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4107c5d9-2977-4b2d-a441-bf8b0f526b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = res_df[\n",
    "    (\"score\", \"climate change\", \"global warming potential (GWP100)\", \"kg CO2-Eq\")\n",
    "]\n",
    "data = data.droplevel(\"activity\")\n",
    "data = data.loc[data.index.get_level_values(\"location\").isin(countries)]\n",
    "data = data.unstack(\"location\").T\n",
    "\n",
    "ax = data.plot.bar(figsize=(10, 6))\n",
    "ax.set_ylabel(\"kg CO₂ eq./kWh\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2e4b30-acb2-42de-9ac2-f0f036119426",
   "metadata": {},
   "source": [
    "There are differences in some countries, namely Norway and The Netherlands.In Norway, we recommend setting the cut off to 0, as small imports are summed together and European mix is used as the source. For most countries, that is fine, but in this specific case, Norwegian electricity is very low impact, so using the European average skews it a lot. In case of The Netherlands, there are some issues in solar power reporting, and this will be fixed in the future versions of shrecc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17344161",
   "metadata": {},
   "source": [
    "# Test\n",
    "We run an LCA for the 4 mix samples, as well as their ecoinvent 3.11 equivalents. It is fair to say that here, we are using 2024 data (shrecc) and ecoinvent 3.11 (2021 data), so the results also differ due to changes in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "deaa956a-03b8-4c49-8678-022ba7571194",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = [\"AT\", \"FR\", \"LU\", \"SE\", \"FI\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6bea58a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_summer_noon = {\n",
    "    f\"{a['name']}, summer, noon\": {a[\"id\"]: 1}\n",
    "    for a in bd.Database(\"elec_june_2024_noon\")\n",
    "}\n",
    "demands_winter_evening = {\n",
    "    f\"{a['name']}, winter, evening\": {a[\"id\"]: 1}\n",
    "    for a in bd.Database(\"elec_february_2024_evening\")\n",
    "}\n",
    "demands = {**demands_summer_noon, **demands_winter_evening}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d390a0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['market for electricity, low voltage' (kilowatt hour, AT, None),\n",
       " 'market for electricity, low voltage' (kilowatt hour, FI, None),\n",
       " 'market for electricity, low voltage' (kilowatt hour, FR, None),\n",
       " 'market for electricity, low voltage' (kilowatt hour, LU, None),\n",
       " 'market for electricity, low voltage' (kilowatt hour, SE, None)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acts_ei = [\n",
    "    bd.get_node(\n",
    "        database=\"ecoinvent-3.11-cutoff\",\n",
    "        location=country,\n",
    "        name=\"market for electricity, low voltage\",\n",
    "    )\n",
    "    for country in sorted(countries)\n",
    "]\n",
    "acts_ei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5871b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Electricity mix in LU, summer, noon': {231017788359184384: 1},\n",
       " 'Electricity mix in SE, summer, noon': {231017788359184385: 1},\n",
       " 'Electricity mix in FR, summer, noon': {231017788329824258: 1},\n",
       " 'Electricity mix in FI, summer, noon': {231017788329824257: 1},\n",
       " 'Electricity mix in AT, summer, noon': {231017788329824256: 1},\n",
       " 'Electricity mix in FR, winter, evening': {231018132996755458: 1},\n",
       " 'Electricity mix in AT, winter, evening': {231018132996755456: 1},\n",
       " 'Electricity mix in SE, winter, evening': {231018133026115585: 1},\n",
       " 'Electricity mix in LU, winter, evening': {231018133026115584: 1},\n",
       " 'Electricity mix in FI, winter, evening': {231018132996755457: 1},\n",
       " 'market for electricity, low voltage, AT': {139717708668215296: 1},\n",
       " 'market for electricity, low voltage, FI': {139717795129597953: 1},\n",
       " 'market for electricity, low voltage, FR': {139717687646363648: 1},\n",
       " 'market for electricity, low voltage, LU': {139717592968339457: 1},\n",
       " 'market for electricity, low voltage, SE': {139717799219044352: 1}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demands.update({f\"{a['name']}, {a['location']}\": {a[\"id\"]: 1} for a in acts_ei})\n",
    "demands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d0017e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_objs = bd.get_multilca_data_objs(functional_units=demands, method_config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "214c8aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca = bc.MultiLCA(demands=demands, method_config=config, data_objs=data_objs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a1100a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.lci()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "70244608",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.lcia()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "961b8cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for (method, fu), score in m_lca.scores.items():\n",
    "    demand = m_lca.demands[fu]\n",
    "    a_id = list(demand).pop()\n",
    "    a = bd.get_activity(a_id)\n",
    "    # prod_e = list(a.production()).pop()\n",
    "    results.append(\n",
    "        {\n",
    "            \"activity\": a[\"name\"],\n",
    "            \"database\": a[\"database\"],\n",
    "            \"location\": a[\"location\"],\n",
    "            \"production amount\": demand[a_id],\n",
    "            \"activity unit\": a[\"unit\"],\n",
    "            \"reference product\": a[\"reference product\"],\n",
    "            \"methodology\": method[0],\n",
    "            \"category\": method[1],\n",
    "            \"indicator\": method[2],\n",
    "            \"score\": score,\n",
    "            \"unit\": bd.Method(method).metadata[\"unit\"],\n",
    "        },\n",
    "    )\n",
    "res_df = pd.DataFrame(results)\n",
    "res_df = res_df.set_index([c for c in res_df.columns if c != \"score\"]).unstack(\n",
    "    [\"category\", \"indicator\", \"unit\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "232cfb5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df.sort_index(level=\"location\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "92fdd24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kg CO2 eq./kWh')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (\n",
    "    res_df[(\"score\", \"EF v3.0\", \"climate change\", \"kg CO2-Eq\")]\n",
    "    .droplevel([\"activity\"])\n",
    "    .unstack([\"location\"])\n",
    "    .T.plot.bar(figsize=(10, 6))\n",
    ")\n",
    "ax.set_ylabel(\"kg CO2 eq./kWh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd05b99",
   "metadata": {},
   "source": [
    "# Further analysis and comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c86096-ebc8-4e4c-9edd-e3bf576c3a0b",
   "metadata": {},
   "source": [
    "The following code uses the `invert_technosphere_matrix` method of the base bw2calc LCA class.\n",
    "For this method to work correctly, the version of **bw2calc must be**: `>= 2.1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c81a81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.lci()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d54b78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.invert_technosphere_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0eb968",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.demands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd28247b",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_indices = [\n",
    "    {k: m_lca.activity_dict[vk] for vk in v.keys()} for k, v in m_lca.demands.items()\n",
    "]\n",
    "matrix_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11b772c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_lca.inverted_technosphere_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db32fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_inverted = {\n",
    "    k: m_lca.inverted_technosphere_matrix[:, v]\n",
    "    for d in matrix_indices\n",
    "    for k, v in d.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6ffc3c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "reversed_activity_dict = {v: k for k, v in m_lca.activity_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e91958",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_index = [\n",
    "    bd.get_activity(reversed_activity_dict[i])\n",
    "    for i in range(m_lca.inverted_technosphere_matrix.shape[0])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc84d376",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties = [\n",
    "    \"name\",\n",
    "    \"unit\",\n",
    "    \"code\",\n",
    "    \"location\",\n",
    "    \"reference product\",\n",
    "    \"type\",\n",
    "    \"database\",\n",
    "    \"id\",\n",
    "]\n",
    "df_index = pd.MultiIndex.from_tuples(\n",
    "    [[m[prop] for prop in properties] for m in matrix_index], names=properties\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936533a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_inverted_df = pd.DataFrame(demands_inverted, index=df_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f769be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "process_contribution = demands_inverted_df.sort_values(\n",
    "    by=demands_inverted_df.columns[0], ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24699522",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_elec = process_contribution.index.get_level_values(\"name\").str.startswith(\n",
    "    \"electricity production\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dff3309c",
   "metadata": {},
   "outputs": [],
   "source": [
    "process_contribution_elec = (\n",
    "    process_contribution.loc[idx_elec]\n",
    "    .sort_index(axis=1)\n",
    "    .droplevel([\"unit\", \"code\", \"reference product\", \"type\", \"database\", \"id\"])\n",
    ")\n",
    "process_contribution_elec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e0cb423-2245-4b27-ac81-63b11488213b",
   "metadata": {},
   "outputs": [],
   "source": [
    "country = \"lu\"\n",
    "threshold = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64c0c714",
   "metadata": {},
   "outputs": [],
   "source": [
    "for country in countries:\n",
    "    process_contribution_elec_c = process_contribution_elec[\n",
    "        [c for c in process_contribution_elec if country in c]\n",
    "    ]\n",
    "\n",
    "    idx_filt = (process_contribution_elec_c >= threshold).any(axis=1)\n",
    "    mix_to_plot = pd.concat(\n",
    "        [\n",
    "            process_contribution_elec_c.loc[idx_filt],\n",
    "            pd.DataFrame(\n",
    "                process_contribution_elec_c[~idx_filt].sum(), columns=[(\"REST\", \"REST\")]\n",
    "            ).T,\n",
    "        ],\n",
    "        axis=0,\n",
    "    ).T  # .droplevel(['code','unit'], axis=0)\n",
    "\n",
    "    ax = mix_to_plot.plot.bar(stacked=True)\n",
    "    h, l = ax.get_legend_handles_labels()\n",
    "    ax.legend(\n",
    "        loc=\"center left\", bbox_to_anchor=(1.05, 0.5), handles=h[::-1], labels=l[::-1]\n",
    "    )\n",
    "    ax.set_title(\n",
    "        f\"Comparison of background process contribution for electricity-producing activities\\nbetween shrecc and ecoinvent, for location {country}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457256a2",
   "metadata": {},
   "source": [
    "Some remarks about the mix analysis.\n",
    "\n",
    "1. From the comparison of background processes in Sweden, you can see that the electricity mix done by shrecc contains some aggregated data for hydro electricity. On the Energy Chart API, there is no distinction between the types, and so all hydro (run-off-river, reservoir, hydro pumped storage) is together in one category. Ecoinvent has the different types in the mix.\n",
    "2. Not all background processes sum to one; we performed our search based on \"electricity production\", and there might be some activities with a different name, still contributing to the electricity mix."
   ]
  }
 ],
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