From 737be1628b9651b165bf21f4ad2add27301f6ae0 Mon Sep 17 00:00:00 2001 From: Timo Diepers <90762029+TimoDiepers@users.noreply.github.com> Date: Fri, 3 May 2024 14:13:12 +0200 Subject: [PATCH] Delete archive directory --- archive/12 - Edge extractor.ipynb | 628 --- ...1150 scenario foreground integration.ipynb | 3125 -------------- ...efinitive TD with SSP2-1150 scenario.ipynb | 2569 ------------ archive/Case study/Premise_DB.ipynb | 866 ---- archive/Case study/wind-example_LCI_TD_.ipynb | 540 --- .../wind-example_LCI_TD_more TD.ipynb | 3646 ---------------- archive/Edge replacer.ipynb | 345 -- .../Fly ash usage model.ipynb | 544 --- archive/Mini-Wind-Example/utilities.py | 229 - .../wind-example-clean.ipynb | 1338 ------ archive/Mini-Wind-Example/wind-example.ipynb | 1290 ------ ...biosphere3_positive_and_negative_CO2.ipynb | 1066 ----- archive/example_databases.py | 3708 ----------------- archive/notebooks/bioflows_advanced.ipynb | 623 --- archive/notebooks/example_29032024.ipynb | 2902 ------------- archive/notebooks/example_EV_setac.ipynb | 1075 ----- archive/notebooks/example_EV_timo.ipynb | 2259 ---------- .../notebooks/example_EV_timo_incl_car.ipynb | 1074 ----- .../example_premise_background.ipynb | 1272 ------ .../example_premise_dummy_background.ipynb | 817 ---- archive/notebooks/medusa_abc_example.ipynb | 796 ---- .../notebooks/medusa_hydrogen_example.ipynb | 624 --- .../notebooks/multibranch_timeline_fix.ipynb | 2656 ------------ archive/notebooks/premise-example.ipynb | 1169 ------ archive/notebooks/project-adeline.ipynb | 2791 ------------- archive/notebooks/simple_loop.ipynb | 2479 ----------- ...test_TD_at_two_consecutive_processes.ipynb | 1527 ------- archive/notebooks/test_amounts.ipynb | 1516 ------- archive/notebooks/test_background_dbs_1.ipynb | 845 ---- archive/notebooks/test_background_dbs_2.ipynb | 2776 ------------ archive/notebooks/test_issue6.ipynb | 1312 ------ archive/test.ipynb | 1096 ----- 32 files changed, 49503 deletions(-) delete mode 100644 archive/12 - Edge extractor.ipynb delete mode 100644 archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario foreground integration.ipynb delete mode 100644 archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario.ipynb delete mode 100644 archive/Case study/Premise_DB.ipynb delete mode 100644 archive/Case study/wind-example_LCI_TD_.ipynb delete mode 100644 archive/Case study/wind-example_LCI_TD_more TD.ipynb delete mode 100644 archive/Edge replacer.ipynb delete mode 100644 archive/Mini-Wind-Example/Fly ash usage model.ipynb delete mode 100644 archive/Mini-Wind-Example/utilities.py delete mode 100644 archive/Mini-Wind-Example/wind-example-clean.ipynb delete mode 100644 archive/Mini-Wind-Example/wind-example.ipynb delete mode 100644 archive/example_biosphere3_positive_and_negative_CO2.ipynb delete mode 100644 archive/example_databases.py delete mode 100644 archive/notebooks/bioflows_advanced.ipynb delete mode 100644 archive/notebooks/example_29032024.ipynb delete mode 100644 archive/notebooks/example_EV_setac.ipynb delete mode 100644 archive/notebooks/example_EV_timo.ipynb delete mode 100644 archive/notebooks/example_EV_timo_incl_car.ipynb delete mode 100644 archive/notebooks/example_premise_background.ipynb delete mode 100644 archive/notebooks/example_premise_dummy_background.ipynb delete mode 100644 archive/notebooks/medusa_abc_example.ipynb delete mode 100644 archive/notebooks/medusa_hydrogen_example.ipynb delete mode 100644 archive/notebooks/multibranch_timeline_fix.ipynb delete mode 100644 archive/notebooks/premise-example.ipynb delete mode 100644 archive/notebooks/project-adeline.ipynb delete mode 100644 archive/notebooks/simple_loop.ipynb delete mode 100644 archive/notebooks/test_TD_at_two_consecutive_processes.ipynb delete mode 100644 archive/notebooks/test_amounts.ipynb delete mode 100644 archive/notebooks/test_background_dbs_1.ipynb delete mode 100644 archive/notebooks/test_background_dbs_2.ipynb delete mode 100644 archive/notebooks/test_issue6.ipynb delete mode 100644 archive/test.ipynb diff --git a/archive/12 - Edge extractor.ipynb b/archive/12 - Edge extractor.ipynb deleted file mode 100644 index 3ffb8a9..0000000 --- a/archive/12 - Edge extractor.ipynb +++ /dev/null @@ -1,628 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, easy_datetime_distribution, TemporalisLCA, Timeline, TemporalDistribution\n", - "from bw_temporalis.lcia import characterize_methane, characterize_co2\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw_graph_tools as graph\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d00df98a-fcae-4160-a30f-54aed29c1f19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current(\"📊📈💎🕤🗓️\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "79a523bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:00 List:\n", - " heap = []\n", - " timeline = []\n", - "\n", - " for edge in self.edge_mapping[self.unique_id]:\n", - " node = self.nodes[edge.producer_unique_id]\n", - " heappush(\n", - " heap,\n", - " (\n", - " 1 / node.cumulative_score,\n", - " self.t0 * edge.amount,\n", - " node,\n", - " ),\n", - " )\n", - " timeline.append(\n", - " Edge(\n", - " distribution=self.t0 * edge.amount,\n", - " leaf=False,\n", - " consumer=self.unique_id,\n", - " producer=node.activity_datapackage_id,\n", - " )\n", - " )\n", - "\n", - " while heap:\n", - " _, td, node = heappop(heap)\n", - "\n", - " for edge in self.edge_mapping[node.unique_id]:\n", - " row_id = self.nodes[edge.producer_unique_id].activity_datapackage_id\n", - " col_id = node.activity_datapackage_id\n", - " exchange = self.get_technosphere_exchange(\n", - " input_id=row_id,\n", - " output_id=col_id,\n", - " )\n", - " value = (\n", - " self._exchange_value(\n", - " exchange=exchange,\n", - " row_id=row_id,\n", - " col_id=col_id,\n", - " matrix_label=\"technosphere_matrix\",\n", - " )\n", - " / node.reference_product_production_amount\n", - " )\n", - " producer = self.nodes[edge.producer_unique_id]\n", - " leaf = self.edge_ff(row_id)\n", - "\n", - " distribution = (td * value).simplify()\n", - " timeline.append(\n", - " Edge(\n", - " distribution=distribution,\n", - " leaf=leaf,\n", - " consumer=node.activity_datapackage_id,\n", - " producer=producer.activity_datapackage_id,\n", - " )\n", - " )\n", - " if not leaf:\n", - " heappush(\n", - " heap,\n", - " (\n", - " 1 / node.cumulative_score,\n", - " distribution,\n", - " producer,\n", - " ),\n", - " )\n", - " return timeline" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [\n", - " bd.get_node(code=code).id \n", - " for code in ('Avoided impact - waste', 'Landfill', 'Thinning')\n", - "]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 10\n" - ] - } - ], - "source": [ - "eelca = EdgeExtracter(lca, edge_filter_function=filter_function)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "71a4206c-130e-4e91-b189-6b3c9cd11eeb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3798b53b-7834-444c-bce5-edb1dbf6b95e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=-1, producer=4),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=4, producer=5),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 0.2, leaf=True, consumer=4, producer=11),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 0.8, leaf=False, consumer=4, producer=12),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 0.8, leaf=True, consumer=12, producer=13),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=5, producer=6),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=6, producer=7),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=7, producer=8),\n", - " Edge(distribution=TemporalDistribution instance with 1 values and total: 1.2, leaf=False, consumer=8, producer=9),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 1.8, leaf=True, consumer=9, producer=10)]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tl" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "2cd6661e-dbb5-4148-b779-5d238df376cc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " year producer consumer amount leaf\n", - "0 2019 10 9 0.6 1\n", - "1 2022 9 8 1.2 0\n", - "2 2022 10 9 0.6 1\n", - "3 2023 4 -1 1.0 0\n", - "4 2023 5 4 1.0 0\n", - "5 2023 6 5 1.0 0\n", - "6 2023 7 6 1.0 0\n", - "7 2023 8 7 1.0 0\n", - "8 2023 10 9 0.6 1\n", - "9 2023 11 4 0.2 1\n", - "10 2023 12 4 0.8 0\n", - "11 2023 13 12 0.8 1\n" - ] - } - ], - "source": [ - "#group flows from same producer to consumer in the same year\n", - "\n", - "edges_dict_list = [{\"datetime\": edge.distribution.date, 'amount': edge.distribution.amount, 'producer': edge.producer, 'consumer': edge.consumer, \"leaf\": edge.leaf} for edge in tl]\n", - "edges_dataframe = pd.DataFrame(edges_dict_list)\n", - "edges_dataframe = edges_dataframe.explode(['datetime', \"amount\"])\n", - "edges_dataframe['year'] = edges_dataframe['datetime'].apply(lambda x: x.year)\n", - "edge_dataframe = edges_dataframe.loc[:, \"amount\":].groupby(['year', 'producer', 'consumer']).sum().reset_index()\n", - "print(edge_dataframe) \n" - ] - }, - { - "cell_type": "markdown", - "id": "8b700960-a790-439a-b381-14bf89d19e39", - "metadata": {}, - "source": [ - "Compare with basic timeline:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "37b8ab2a-550c-4923-a317-cf23d5acd151", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 10\n" - ] - }, - { - "data": { - "text/plain": [ - "[FlowTD(distribution=TemporalDistribution instance with 4 values and total: 0.02, flow=2, activity=11),\n", - " FlowTD(distribution=TemporalDistribution instance with 1 values and total: -0.32, flow=1, activity=13),\n", - " FlowTD(distribution=TemporalDistribution instance with 1 values and total: 0.1, flow=1, activity=7),\n", - " FlowTD(distribution=TemporalDistribution instance with 1 values and total: 0.1, flow=1, activity=8),\n", - " FlowTD(distribution=TemporalDistribution instance with 6 values and total: -0.72, flow=1, activity=9),\n", - " FlowTD(distribution=TemporalDistribution instance with 3 values and total: -0.54, flow=1, activity=14)]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca = TemporalisLCA(lca)\n", - "tl_original = tlca.build_timeline()\n", - "tl_original.data" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario foreground integration.ipynb b/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario foreground integration.ipynb deleted file mode 100644 index 68522dd..0000000 --- a/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario foreground integration.ipynb +++ /dev/null @@ -1,3125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "3d095fe9-f532-4866-8b77-d0be04d6e406", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2analyzer as ba\n", - "import bw_temporalis as bwt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d34bd596-7b79-442b-8e04-18a7add52503", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ad409bf8-2230-4932-bdf2-e7341bcb2c43", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from typing import Optional\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "markdown", - "id": "0ac61910-ea08-46b0-b5b7-d362e5e6b0f1", - "metadata": {}, - "source": [ - "# Setting the project to the team one on the server" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7dfabb12-9c97-4729-9ab3-b9c42a27419d", - "metadata": {}, - "outputs": [], - "source": [ - "def change_base_directory(base_dir: Path) -> None: \n", - " assert isinstance(base_dir, Path) and base_dir.is_dir() and os.access(base_dir, os.W_OK) \n", - " \n", - " bd.projects._base_data_dir = base_dir\n", - " bd.projects.db.change_path(base_dir / \"projects.db\") \n", - " bd.projects.set_current(\"default\", update=False) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c6b7af93-6cd4-45cf-a0a4-c7a5d12b3243", - "metadata": {}, - "outputs": [], - "source": [ - "tictac_team_dir = Path(\"/srv/teams/tictac_team\") \n", - "change_base_directory(tictac_team_dir) \n", - "\n", - "bd.projects.set_current(\"tictac_premise\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a3f31190-badd-44fc-8cef-664a1fe848e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tictac_premise'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.current" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "46e3ed14-c739-49e1-8429-96962e2cb310", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "markdown", - "id": "43645e58-0e00-43eb-a12a-e77d3f9e0690", - "metadata": {}, - "source": [ - "# Setting the project to one having ecoinvent, from the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ef162137-362e-4b83-88e1-898a6c86a408", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restoring project backup archive - this could take a few minutes...\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Project tictac3 already exists", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrestore_project_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/srv/data/ecoinvent-3.9-cutoff.tar.gz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtictac3\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2io/backup.py:121\u001b[0m, in \u001b[0;36mrestore_project_directory\u001b[0;34m(fp, project_name, overwrite_existing)\u001b[0m\n\u001b[1;32m 118\u001b[0m project_name \u001b[38;5;241m=\u001b[39m get_project_name(fp) \u001b[38;5;28;01mif\u001b[39;00m project_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m project_name\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m project_name \u001b[38;5;129;01min\u001b[39;00m projects \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m overwrite_existing:\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProject \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m already exists\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(project_name))\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tempfile\u001b[38;5;241m.\u001b[39mTemporaryDirectory() \u001b[38;5;28;01mas\u001b[39;00m td:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tarfile\u001b[38;5;241m.\u001b[39mopen(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr:gz\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m tar:\n", - "\u001b[0;31mValueError\u001b[0m: Project tictac3 already exists" - ] - } - ], - "source": [ - "bi.restore_project_directory(\"/srv/data/ecoinvent-3.9-cutoff.tar.gz\", project_name=\"tictac3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9a84b131-6e66-441d-858c-e20078ea6a92", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Brightway2 projects manager with 8 objects:\n", - "\tTemporalis example project\n", - "\tbw_temporalis example\n", - "\tdefault\n", - "\tecoinvent-3.9-cutoff\n", - "\tecoinvent=3.9-cutoff\n", - "\tpremise_ei39\n", - "\ttictac2\n", - "\ttictac3\n", - "Use `projects.report()` to get a report on all projects." - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1f945519-0060-49cc-87c8-456e6020fbef", - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('tictac3')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9e99f3b6-1e1e-40c9-9c8f-c808168247b0", - "metadata": {}, - "outputs": [], - "source": [ - "# bd.projects.migrate_project_25()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9c722374-00b4-4763-99bd-1345bb9c7ab9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 4 object(s):\n", - "\tbiosphere3\n", - "\tecoinvent-3.9-cutoff\n", - "\twind-example\n", - "\twind_db" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4fbf78bf-3edf-4a5f-a1e2-43bfec5e121f", - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']\n", - "ei = bd.Database('ecoinvent-3.9-cutoff')" - ] - }, - { - "cell_type": "markdown", - "id": "4c829c64-b7aa-4062-baed-091d8027644a", - "metadata": {}, - "source": [ - "# Temporal distribution for wind electricity (onshore) in `Europe`, corresponding to `remind SSP2 - 1150` IAM scenario" - ] - }, - { - "cell_type": "markdown", - "id": "58a69475-cce7-43b9-bd1d-5749182f4279", - "metadata": {}, - "source": [ - "### Values " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "be2f275b-e672-41bf-b726-eb5bed3bef15", - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([0.79, 1.57, 3.6, 6.03, 8.73, 10.66, 11.27, 11.31])\n", - "a = a/np.sum(a) # normalizing the trend in Exajoules to get an actual TD" - ] - }, - { - "cell_type": "markdown", - "id": "dc29fa94-8763-4015-96cc-e8929b4b1102", - "metadata": {}, - "source": [ - "## Absolute TD" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "a72e4fa3-7824-42b4-a531-68beff424b49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['2010-01-01' '2020-01-01' '2030-01-01' '2040-01-01' '2050-01-01'\n", - " '2060-01-01' '2070-01-01' '2080-01-01']\n" - ] - } - ], - "source": [ - "d = np.array([str(2010+k*10)+\"-01-01\" for k in range(8)])\n", - "d = np.array(d,dtype=np.datetime64)\n", - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "4c65162b-a1dd-4213-983b-eb1ff483064d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inc_wind_turbine_energy_absolute = bwt.TemporalDistribution(\n", - " date=d,\n", - " amount=a\n", - ")\n", - "inc_wind_turbine_energy_absolute.graph()" - ] - }, - { - "cell_type": "markdown", - "id": "f8ede963-2013-4d76-8cf4-73aeaec07590", - "metadata": {}, - "source": [ - "## Relative TD" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6b874e2a-c23d-4104-8a50-bd8b984d4c43", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10 20 30 40 50 60 70 80]\n" - ] - } - ], - "source": [ - "delta = np.array([np.timedelta64(10*(k+1), 'Y') for k in range(8)])\n", - "print(delta)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2aa59c16-bbf1-4d56-ae9f-e58cc314cea8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inc_wind_turbine_energy_relative = bwt.TemporalDistribution(\n", - " date=delta,\n", - " amount=a\n", - ")\n", - "inc_wind_turbine_energy_relative.graph()" - ] - }, - { - "cell_type": "markdown", - "id": "574c9ac2-b1f9-4af6-ae1d-fe436645715d", - "metadata": {}, - "source": [ - "# New background activities for the wind turbine" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8b1822a5-ed03-43ee-9ed8-7fdc96123626", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'wind turbine construction, 2MW, onshore' (unit, GLO, None)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# retrieve activity\n", - "wind_cons = bd.Database(\"ecoinvent-3.9-cutoff\").get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\")\n", - "wind_cons['code']\n", - "\n", - "del bd.databases['wind_db']\n", - "bd.Database(\"wind_db\").register()\n", - "wind_cons.copy(database=\"wind_db\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7c6ada89-25cb-44b4-af4b-d9d78e452d13", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wind=bd.Database(\"wind_db\")\n", - "len(wind)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e731d61f-71ed-4013-b6f8-cfbdde8d7989", - "metadata": {}, - "outputs": [], - "source": [ - "wind_cons = bd.Database(\"wind_db\").get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\")\n", - "ex = [e for e in wind_cons.exchanges() if \"electricity\" in e.input[\"name\"]][0]\n", - "\n", - "# change amount\n", - "ex[\"amount\"] = 0\n", - "ex.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0b7e3eb0-4046-477b-97bd-5ebeaaf5856a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Exchange: 0 kilowatt hour 'market group for electricity, medium voltage' (kilowatt hour, GLO, None) to 'wind turbine construction, 2MW, onshore' (unit, GLO, None)>]\n" - ] - } - ], - "source": [ - "wind_cons = bd.Database(\"wind_db\").get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\") #without electricity\n", - "ex = [e for e in wind_cons.exchanges() if \"electricity\" in e.input[\"name\"]]\n", - "print(ex)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "1e28e213-d4d5-4563-b60d-6d6c76861a79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'market group for electricity, medium voltage' (kilowatt hour, RER, None)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wind_elec = bd.Database(\"ecoinvent-3.9-cutoff\").get(name=\"market group for electricity, medium voltage\", location=\"RER\")\n", - "wind_elec" - ] - }, - { - "cell_type": "markdown", - "id": "ffb6f221-6576-42f0-b648-342413243c03", - "metadata": {}, - "source": [ - "# The wind turbine system " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4d043052-e4bc-422b-9a37-fbd56a34d973", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'wind-example'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabases\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwind-example\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/meta.py:107\u001b[0m, in \u001b[0;36mDatabases.__delitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 107\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mDatabases\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__delitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/serialization.py:162\u001b[0m, in \u001b[0;36mSerializedDict.__delitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__delitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[0;32m--> 162\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflush()\n", - "\u001b[0;31mKeyError\u001b[0m: 'wind-example'" - ] - } - ], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "468816c8-15a6-46d2-a661-6bfd5ec99003", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bwt.easy_timedelta_distribution(\n", - " start=10,\n", - " end=20,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 15\n", - " ).graph()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "434fc6c0-50e6-4011-87bd-aae624ef37b9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5/5 [00:00<00:00, 40721.40it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "LT = 20 # 20 years lifetime of a wind turbine\n", - "generated_electricity_over_lifetime = 0.2*(2000*365*24)*LT # Amount of electricity generated by a wind turbine over its lifetime in kWh, 20% of capacity factor\n", - "share_of_wind_in_electricity_mix = 0.50\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', 'electricity-mix+wind'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " #'temporal_distribution' : TD_constant_increase_wind_share,\n", - " },\n", - " {\n", - " 'input': (wind_elec),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : inc_wind_turbine_energy_relative, #we would prefer to use the absolute TD, but for some reason the graph reversal isn't working with it...\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=10,\n", - " end=20,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 15\n", - " )\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix+wind'),\n", - " 'amount': 0.75*1.443e7,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': wind_cons,\n", - " 'amount': 0.75,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix+wind'),\n", - " 'amount': 0.25*1.443e5,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': wind_cons,\n", - " 'amount': 0.25,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "3abffe5e-b7ec-48b8-8880-bc696ba69199", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.04689971080853378" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, ('IPCC 2021 no LT', 'climate change no LT', 'global warming potential (GWP100) no LT'))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "404401e1-af9d-4951-b5fa-dbb4cd75550c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0.0033388510224970644,\n", - " 0.0028170441659112447,\n", - " 'electricity production, lignite' (kilowatt hour, DE, None)),\n", - " (0.0020648984336291013,\n", - " 0.0024319244005687178,\n", - " 'pig iron production' (kilogram, RoW, None)),\n", - " (0.0019196896056442282,\n", - " 0.0019249281770696383,\n", - " 'heat and power co-generation, hard coal' (kilowatt hour, PL, None)),\n", - " (0.001472380886073288,\n", - " 0.0030799861063356792,\n", - " 'electricity production, natural gas, conventional power plant' (kilowatt hour, GB, None)),\n", - " (0.0013407465283681954,\n", - " 0.0011186013886293035,\n", - " 'electricity production, hard coal' (kilowatt hour, UA, None)),\n", - " (0.001266647637175243,\n", - " 7.259974299132435e-05,\n", - " 'natural gas venting from petroleum/natural gas production' (cubic meter, GLO, None)),\n", - " (0.0011703990212173318,\n", - " 0.0010444037452340198,\n", - " 'heat and power co-generation, lignite' (kilowatt hour, PL, None)),\n", - " (0.001112127453690432,\n", - " 0.00015637182678687788,\n", - " 'nylon 6-6 production, glass-filled' (kilogram, RoW, None)),\n", - " (0.0011001438541413677,\n", - " 0.0013601515881651186,\n", - " 'clinker production' (kilogram, RoW, None)),\n", - " (0.001061530237163241,\n", - " 0.0011786738007197903,\n", - " 'electricity production, hard coal' (kilowatt hour, DE, None))]" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_wind_proc = ba.ContributionAnalysis().annotated_top_processes(lca)[:10]\n", - "top_wind_proc" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "c5805c6d-3454-4d39-ab5f-7ea378e5a423", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 1074\n" - ] - } - ], - "source": [ - "tlca = bwt.TemporalisLCA(lca, starting_datetime=np.datetime64(40, 'Y'))" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "5782bfb8-c241-4fd6-a454-87333e26cdf1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "7676edf4-506d-4e0d-9dfc-9bc6160df58d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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261772244-12-31 23:42:008.774564e-13468821342DEmarket for electricity, high voltagekilowatt hourNaNNaNNaN
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261812247-01-01 11:20:248.663245e-1519996339DEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
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codedatabaseidnameunit
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3electricity-production-windwind-example25967Electricity production, windkilowatt hour
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02016-12-31 16:44:241.862012e-13199924594CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2016-12-31 16:44:241.862012e-131999CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
12016-12-31 16:44:241.862012e-13199924594CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2017-12-31 22:33:363.724024e-131999CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
22016-12-31 16:44:241.862012e-13199924594CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2019-01-01 04:22:485.586036e-131999CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
32016-12-31 16:44:241.862012e-13199924594CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2027-01-01 02:56:243.700454e-131999CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
42016-12-31 16:44:241.862012e-13199924594CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2028-01-01 08:45:367.400908e-131999CHmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
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63628252088-12-31 03:46:483.597144e-12199919389EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2077-12-31 11:45:362.389615e-121999EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
63628262088-12-31 03:46:483.597144e-12199919389EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2078-12-31 17:34:483.584422e-121999EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
63628272088-12-31 03:46:483.597144e-12199919389EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2086-12-31 16:08:241.199048e-121999EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
63628282088-12-31 03:46:483.597144e-12199919389EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN2087-12-31 21:57:362.398096e-121999EEmarket for electricity, medium voltagekilowatt hourNaNNaNNaN
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6362830 rows × 19 columns

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" - ], - "text/plain": [ - " date_x amount_x flow_x activity \\\n", - "0 2016-12-31 16:44:24 1.862012e-13 1999 24594 \n", - "1 2016-12-31 16:44:24 1.862012e-13 1999 24594 \n", - "2 2016-12-31 16:44:24 1.862012e-13 1999 24594 \n", - "3 2016-12-31 16:44:24 1.862012e-13 1999 24594 \n", - "4 2016-12-31 16:44:24 1.862012e-13 1999 24594 \n", - "... ... ... ... ... \n", - "6362825 2088-12-31 03:46:48 3.597144e-12 1999 19389 \n", - "6362826 2088-12-31 03:46:48 3.597144e-12 1999 19389 \n", - "6362827 2088-12-31 03:46:48 3.597144e-12 1999 19389 \n", - "6362828 2088-12-31 03:46:48 3.597144e-12 1999 19389 \n", - "6362829 2088-12-31 03:46:48 3.597144e-12 1999 19389 \n", - "\n", - " activity_location_x activity_name_x \\\n", - "0 CH market for electricity, medium voltage \n", - "1 CH market for electricity, medium voltage \n", - "2 CH market for electricity, medium voltage \n", - "3 CH market for electricity, medium voltage \n", - "4 CH market for electricity, medium voltage \n", - "... ... ... \n", - "6362825 EE market for electricity, medium voltage \n", - "6362826 EE market for electricity, medium voltage \n", - "6362827 EE market for electricity, medium voltage \n", - "6362828 EE market for electricity, medium voltage \n", - "6362829 EE market for electricity, medium voltage \n", - "\n", - " activity_unit_x flow_location_x flow_name_x flow_unit_x \\\n", - "0 kilowatt hour NaN NaN NaN \n", - "1 kilowatt hour NaN NaN NaN \n", - "2 kilowatt hour NaN NaN NaN \n", - "3 kilowatt hour NaN NaN NaN \n", - "4 kilowatt hour NaN NaN NaN \n", - "... ... ... ... ... \n", - "6362825 kilowatt hour NaN NaN NaN \n", - "6362826 kilowatt hour NaN NaN NaN \n", - "6362827 kilowatt hour NaN NaN NaN \n", - "6362828 kilowatt hour NaN NaN NaN \n", - "6362829 kilowatt hour NaN NaN NaN \n", - "\n", - " date_y amount_y flow_y activity_location_y \\\n", - "0 2016-12-31 16:44:24 1.862012e-13 1999 CH \n", - "1 2017-12-31 22:33:36 3.724024e-13 1999 CH \n", - "2 2019-01-01 04:22:48 5.586036e-13 1999 CH \n", - "3 2027-01-01 02:56:24 3.700454e-13 1999 CH \n", - "4 2028-01-01 08:45:36 7.400908e-13 1999 CH \n", - "... ... ... ... ... \n", - "6362825 2077-12-31 11:45:36 2.389615e-12 1999 EE \n", - "6362826 2078-12-31 17:34:48 3.584422e-12 1999 EE \n", - "6362827 2086-12-31 16:08:24 1.199048e-12 1999 EE \n", - "6362828 2087-12-31 21:57:36 2.398096e-12 1999 EE \n", - "6362829 2088-12-31 03:46:48 3.597144e-12 1999 EE \n", - "\n", - " activity_name_y activity_unit_y \\\n", - "0 market for electricity, medium voltage kilowatt hour \n", - "1 market for electricity, medium voltage kilowatt hour \n", - "2 market for electricity, medium voltage kilowatt hour \n", - "3 market for electricity, medium voltage kilowatt hour \n", - "4 market for electricity, medium voltage kilowatt hour \n", - "... ... ... \n", - "6362825 market for electricity, medium voltage kilowatt hour \n", - "6362826 market for electricity, medium voltage kilowatt hour \n", - "6362827 market for electricity, medium voltage kilowatt hour \n", - "6362828 market for electricity, medium voltage kilowatt hour \n", - "6362829 market for electricity, medium voltage kilowatt hour \n", - "\n", - " flow_location_y flow_name_y flow_unit_y \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "... ... ... ... \n", - "6362825 NaN NaN NaN \n", - "6362826 NaN NaN NaN \n", - "6362827 NaN NaN NaN \n", - "6362828 NaN NaN NaN \n", - "6362829 NaN NaN NaN \n", - "\n", - "[6362830 rows x 19 columns]" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.merge(df.rename(columns={'id' : 'activity'}), on='activity')" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "df9d774a-8d3f-4b0d-8503-07792cfba8d9", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date_x\",\n", - " y = 'amount_y',\n", - " hue = 'activity_name_x',\n", - " data = df.merge(\n", - " df.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y'), xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=0.05)\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "6f1e8767-d17a-4d8d-82cc-1992e1454151", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Time (years)')" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df[\"amount_sum\"] = df['amount'].cumsum()\n", - "axes = df.plot(x=\"date\", y=\"amount_sum\")\n", - "axes.set_ylabel(\"Cumulative $CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time (years)\")" - ] - }, - { - "cell_type": "markdown", - "id": "cd48a3c6-34fb-4246-867c-dee7e970c49f", - "metadata": {}, - "source": [ - "# Timeline of activities" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "72640c26-84af-4b10-a9ed-bce8a7e54dff", - "metadata": {}, - "outputs": [], - "source": [ - "def build_timeline_processes(tlca, db) :\n", - " ' take as input'\n", - " ' - the result of the graph reversal (given by the \"bwt.TemporalisLCA\") '\n", - " ' - the name of the database as str (static database, not prospective) '\n", - " ' return a dataframe within the following columns '\n", - " ' - date '\n", - " ' - amount '\n", - " ' - activity (?)'\n", - " ' - database '\n", - " ' - name (of activity) '\n", - " ' - type '\n", - " ' - unit '\n", - " \n", - " tl_activities= tlca.build_timeline(node_timeline=True) #creating timeline for the nodes, not the flows\n", - " df_tl_act = tl_activities.build_dataframe() # converting the data into dataframe\n", - " df_tl_act = df_tl_act.merge(bd.Database(db).nodes_to_dataframe().rename(columns={\"id\" : \"activity\"}), on='activity_x')\n", - " \n", - " del df_tl_act['flow']\n", - " \n", - " return df_tl_act\n" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "a21bbf23-0079-4c1e-84fd-fdd3480f01c7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw_temporalis/lca.py:135: UserWarning: This functionality is experimental, and will change.\n", - "You have been warned.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'activity_x'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_1691061/3565607137.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_tl_act\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_timeline_processes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtlca\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ecoinvent-3.9-cutoff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf_tl_act\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/tmp/ipykernel_1691061/2617866613.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(tlca, db)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m' - unit '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mtl_activities\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtlca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_timeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_timeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#creating timeline for the nodes, not the flows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mdf_tl_act\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtl_activities\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# converting the data into dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdf_tl_act\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_tl_act\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDatabase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes_to_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"id\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m\"activity\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'activity_x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mdf_tl_act\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flow'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[0;31m# Then we're either Hashable or a wrong-length arraylike,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;31m# the latter of which will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1268\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1269\u001b[0;31m \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1270\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1271\u001b[0m \u001b[0;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1272\u001b[0m \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mother_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_level_reference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1842\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1843\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1844\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1845\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1846\u001b[0m \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1847\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'activity_x'" - ] - } - ], - "source": [ - "df_tl_act = build_timeline_processes(tlca, 'ecoinvent-3.9-cutoff')\n", - "df_tl_act" - ] - }, - { - "cell_type": "markdown", - "id": "f8bda26e-6da9-414a-8d84-2c30c4cf9965", - "metadata": {}, - "source": [ - "### Example : exchanges timeline for energy activities" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "49d4fc48-bdfd-45c0-8e7d-bb7c268775ad", - "metadata": {}, - "outputs": [], - "source": [ - "df_tl_energy = df_tl_act[df_tl_act['unit']=='kilowatt hour']" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "ffa1088d-7fce-41a9-8166-4c29f1c09fb7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenameunit
02010-01-01 00:00:001.000000e+0025967electricity-production-windwind-exampleElectricity production, windkilowatt hour
12016-12-31 16:44:243.014584e-0425967electricity-production-windwind-exampleElectricity production, windkilowatt hour
22017-12-31 22:33:366.029168e-0425967electricity-production-windwind-exampleElectricity production, windkilowatt hour
32019-01-01 04:22:489.043752e-0425967electricity-production-windwind-exampleElectricity production, windkilowatt hour
42024-01-01 09:28:489.087717e-0825967electricity-production-windwind-exampleElectricity production, windkilowatt hour
........................
8352322-01-01 15:50:249.280586e-0825966electricity-mix+windwind-exampleElectricity mixkilowatt hour
8362323-01-01 21:39:361.353057e-0725966electricity-mix+windwind-exampleElectricity mixkilowatt hour
8372324-01-02 03:28:481.405098e-0725966electricity-mix+windwind-exampleElectricity mixkilowatt hour
8382325-01-01 09:18:009.367320e-0825966electricity-mix+windwind-exampleElectricity mixkilowatt hour
8392326-01-01 15:07:123.512745e-0825966electricity-mix+windwind-exampleElectricity mixkilowatt hour
\n", - "

840 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+00 25967 electricity-production-wind \n", - "1 2016-12-31 16:44:24 3.014584e-04 25967 electricity-production-wind \n", - "2 2017-12-31 22:33:36 6.029168e-04 25967 electricity-production-wind \n", - "3 2019-01-01 04:22:48 9.043752e-04 25967 electricity-production-wind \n", - "4 2024-01-01 09:28:48 9.087717e-08 25967 electricity-production-wind \n", - ".. ... ... ... ... \n", - "835 2322-01-01 15:50:24 9.280586e-08 25966 electricity-mix+wind \n", - "836 2323-01-01 21:39:36 1.353057e-07 25966 electricity-mix+wind \n", - "837 2324-01-02 03:28:48 1.405098e-07 25966 electricity-mix+wind \n", - "838 2325-01-01 09:18:00 9.367320e-08 25966 electricity-mix+wind \n", - "839 2326-01-01 15:07:12 3.512745e-08 25966 electricity-mix+wind \n", - "\n", - " database name unit \n", - "0 wind-example Electricity production, wind kilowatt hour \n", - "1 wind-example Electricity production, wind kilowatt hour \n", - "2 wind-example Electricity production, wind kilowatt hour \n", - "3 wind-example Electricity production, wind kilowatt hour \n", - "4 wind-example Electricity production, wind kilowatt hour \n", - ".. ... ... ... \n", - "835 wind-example Electricity mix kilowatt hour \n", - "836 wind-example Electricity mix kilowatt hour \n", - "837 wind-example Electricity mix kilowatt hour \n", - "838 wind-example Electricity mix kilowatt hour \n", - "839 wind-example Electricity mix kilowatt hour \n", - "\n", - "[840 rows x 7 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tl_energy" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "93b64cfd-3309-4ce5-b0fd-151420e287cb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1691061/508802167.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_tl_energy['amount'] = df_tl_energy['amount']*1000\n" - ] - } - ], - "source": [ - "df_tl_energy['amount'] = df_tl_energy['amount']*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "bdf8c18d-4670-4aaa-b47c-d04a5b0d087a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenameunit
02010-01-01 00:00:001000.00000025967electricity-production-windwind-exampleElectricity production, wind1e-3 kilowatt hour
12016-12-31 16:44:240.30145825967electricity-production-windwind-exampleElectricity production, wind1e-3 kilowatt hour
22017-12-31 22:33:360.60291725967electricity-production-windwind-exampleElectricity production, wind1e-3 kilowatt hour
32019-01-01 04:22:480.90437525967electricity-production-windwind-exampleElectricity production, wind1e-3 kilowatt hour
42024-01-01 09:28:480.00009125967electricity-production-windwind-exampleElectricity production, wind1e-3 kilowatt hour
........................
8352322-01-01 15:50:240.00009325966electricity-mix+windwind-exampleElectricity mix1e-3 kilowatt hour
8362323-01-01 21:39:360.00013525966electricity-mix+windwind-exampleElectricity mix1e-3 kilowatt hour
8372324-01-02 03:28:480.00014125966electricity-mix+windwind-exampleElectricity mix1e-3 kilowatt hour
8382325-01-01 09:18:000.00009425966electricity-mix+windwind-exampleElectricity mix1e-3 kilowatt hour
8392326-01-01 15:07:120.00003525966electricity-mix+windwind-exampleElectricity mix1e-3 kilowatt hour
\n", - "

840 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1000.000000 25967 electricity-production-wind \n", - "1 2016-12-31 16:44:24 0.301458 25967 electricity-production-wind \n", - "2 2017-12-31 22:33:36 0.602917 25967 electricity-production-wind \n", - "3 2019-01-01 04:22:48 0.904375 25967 electricity-production-wind \n", - "4 2024-01-01 09:28:48 0.000091 25967 electricity-production-wind \n", - ".. ... ... ... ... \n", - "835 2322-01-01 15:50:24 0.000093 25966 electricity-mix+wind \n", - "836 2323-01-01 21:39:36 0.000135 25966 electricity-mix+wind \n", - "837 2324-01-02 03:28:48 0.000141 25966 electricity-mix+wind \n", - "838 2325-01-01 09:18:00 0.000094 25966 electricity-mix+wind \n", - "839 2326-01-01 15:07:12 0.000035 25966 electricity-mix+wind \n", - "\n", - " database name unit \n", - "0 wind-example Electricity production, wind 1e-3 kilowatt hour \n", - "1 wind-example Electricity production, wind 1e-3 kilowatt hour \n", - "2 wind-example Electricity production, wind 1e-3 kilowatt hour \n", - "3 wind-example Electricity production, wind 1e-3 kilowatt hour \n", - "4 wind-example Electricity production, wind 1e-3 kilowatt hour \n", - ".. ... ... ... \n", - "835 wind-example Electricity mix 1e-3 kilowatt hour \n", - "836 wind-example Electricity mix 1e-3 kilowatt hour \n", - "837 wind-example Electricity mix 1e-3 kilowatt hour \n", - "838 wind-example Electricity mix 1e-3 kilowatt hour \n", - "839 wind-example Electricity mix 1e-3 kilowatt hour \n", - "\n", - "[840 rows x 7 columns]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df_tl_energy.replace({'unit': {'kilowatt hour' :'1e-3 kilowatt hour'}})" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "a5a353f8-eb36-41c3-ae8d-1acc86029a96", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.1, 1.0)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'name',\n", - " data = df_tl_energy,\n", - ")\n", - "axes.set_ylabel(\"$10^{-3}$ kilowatt hour\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y') ,xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=1, ymin=-0.1)\n", - "# axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "id": "e0ab0ba5-7aab-4946-8b7e-ae8cb1235758", - "metadata": {}, - "source": [ - "# Wind turbine system w/ some ei process" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "590178c6-7702-41c9-811f-3e4875eea332", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['market group for electricity, medium voltage' (kilowatt hour, RER, None),\n", - " 'market for electricity, medium voltage, aluminium industry' (kilowatt hour, IAI Area, Russia & RER w/o EU27 & EFTA, None)]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ei.search('market group electricity medium voltage RER')" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "c3b691c4-46dd-49c0-8293-3af05eefe4a8", - "metadata": {}, - "outputs": [], - "source": [ - "act = bd.get_node(name=\"market group for electricity, medium voltage\", location='RER')" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "3df55e1f-1f61-43a4-aa91-981d1497dc5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'17c9d0c2c72446bf59393f60c096a588'" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act['code']" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "5792238f-145e-4630-882e-63eb7a1dc41e", - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "850d4c7b-9d6d-4b8a-83b2-5c95b64d6e50", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 117734.85it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 / 1e3 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : inc_wind_turbine_energy_relative, #we would prefer to use the absolute TD, but for some reason the graph reversal isn't working with it...\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=-1,\n", - " end=1,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=3,\n", - " # kind = '',\n", - " # param = 0\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # CO2 emissions corresponding to maintenance\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('ecoinvent-3.9-cutoff', act['code']),\n", - " 'amount': 2e5,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input' : ('ecoinvent-3.9-cutoff', act['code']),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "e7e5573a-ee83-47de-a414-85d51323d552", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.25114154738048455" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "70632cdd-b03a-4c1c-80a4-5758c1f2a355", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 3\n" - ] - } - ], - "source": [ - "tlca = bwt.TemporalisLCA(lca, starting_datetime=np.datetime64(40, 'Y'))" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "4fc9f5f2-ba3c-49c9-ace6-7c87cfe3f950", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "6bb53309-ff79-4856-9e8d-97ff16e52d6b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4db25a08-f65e-4ea1-9c0c-5c3b9f807348", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'End-of-life, wind turbine' (unit, None, None)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_activity(id=25972)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "7a13b406-ddd9-4739-a8b1-3950e64ab3ac", - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "6798f1e6-8d2a-4eb0-9fdb-3a5bb00019e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001.000000e+0025969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
12019-01-01 04:22:481.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
22020-01-01 10:12:001.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
32020-12-31 16:01:121.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
42028-12-31 14:34:482.214279e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
...........................
1092116-01-01 16:55:121.157922e-0825972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1102116-12-31 22:44:242.583674e-0925972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1112118-01-01 04:33:362.120808e-1025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1122119-01-01 10:22:486.404287e-1225972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1132120-01-01 16:12:007.114520e-1425972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
\n", - "

114 rows × 8 columns

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" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+00 25969 electricity-production-wind \n", - "1 2019-01-01 04:22:48 1.114191e-09 25971 wind-turbine-construction \n", - "2 2020-01-01 10:12:00 1.114191e-09 25971 wind-turbine-construction \n", - "3 2020-12-31 16:01:12 1.114191e-09 25971 wind-turbine-construction \n", - "4 2028-12-31 14:34:48 2.214279e-09 25971 wind-turbine-construction \n", - ".. ... ... ... ... \n", - "109 2116-01-01 16:55:12 1.157922e-08 25972 eol-wind \n", - "110 2116-12-31 22:44:24 2.583674e-09 25972 eol-wind \n", - "111 2118-01-01 04:33:36 2.120808e-10 25972 eol-wind \n", - "112 2119-01-01 10:22:48 6.404287e-12 25972 eol-wind \n", - "113 2120-01-01 16:12:00 7.114520e-14 25972 eol-wind \n", - "\n", - " database name type unit \n", - "0 wind-example Electricity production, wind NaN kilowatt hour \n", - "1 wind-example Wind turbine construction NaN unit \n", - "2 wind-example Wind turbine construction NaN unit \n", - "3 wind-example Wind turbine construction NaN unit \n", - "4 wind-example Wind turbine construction NaN unit \n", - ".. ... ... ... ... \n", - "109 wind-example End-of-life, wind turbine NaN unit \n", - "110 wind-example End-of-life, wind turbine NaN unit \n", - "111 wind-example End-of-life, wind turbine NaN unit \n", - "112 wind-example End-of-life, wind turbine NaN unit \n", - "113 wind-example End-of-life, wind turbine NaN unit \n", - "\n", - "[114 rows x 8 columns]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tl_energy_w_ei = build_timeline_processes(tlca, 'wind-example')\n", - "df_tl_energy_w_ei" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "09e57b41-375b-4495-94c2-491b46e27faf", - "metadata": {}, - "outputs": [], - "source": [ - "df_tl_energy_w_ei['amount'] = df_tl_energy['amount']*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "97163008-b806-4be9-9306-ea1b48d7f148", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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02010-01-01 00:00:001000000.00000025969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
12019-01-01 04:22:48178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
22020-01-01 10:12:00178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
32020-12-31 16:01:12178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
42028-12-31 14:34:480.03178025971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
...........................
1092116-01-01 16:55:120.16308025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1102116-12-31 22:44:240.00086625972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1112118-01-01 04:33:360.00484625972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1122119-01-01 10:22:482405.52502225972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1132120-01-01 16:12:006.97951025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
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114 rows × 8 columns

\n", - "
" - ], - "text/plain": [ - " date amount activity \\\n", - "0 2010-01-01 00:00:00 1000000.000000 25969 \n", - "1 2019-01-01 04:22:48 178.270616 25971 \n", - "2 2020-01-01 10:12:00 178.270616 25971 \n", - "3 2020-12-31 16:01:12 178.270616 25971 \n", - "4 2028-12-31 14:34:48 0.031780 25971 \n", - ".. ... ... ... \n", - "109 2116-01-01 16:55:12 0.163080 25972 \n", - "110 2116-12-31 22:44:24 0.000866 25972 \n", - "111 2118-01-01 04:33:36 0.004846 25972 \n", - "112 2119-01-01 10:22:48 2405.525022 25972 \n", - "113 2120-01-01 16:12:00 6.979510 25972 \n", - "\n", - " code database name \\\n", - "0 electricity-production-wind wind-example Electricity production, wind \n", - "1 wind-turbine-construction wind-example Wind turbine construction \n", - "2 wind-turbine-construction wind-example Wind turbine construction \n", - "3 wind-turbine-construction wind-example Wind turbine construction \n", - "4 wind-turbine-construction wind-example Wind turbine construction \n", - ".. ... ... ... \n", - "109 eol-wind wind-example End-of-life, wind turbine \n", - "110 eol-wind wind-example End-of-life, wind turbine \n", - "111 eol-wind wind-example End-of-life, wind turbine \n", - "112 eol-wind wind-example End-of-life, wind turbine \n", - "113 eol-wind wind-example End-of-life, wind turbine \n", - "\n", - " type unit \n", - "0 NaN 1e-3 kilowatt hour \n", - "1 NaN unit \n", - "2 NaN unit \n", - "3 NaN unit \n", - "4 NaN unit \n", - ".. ... ... \n", - "109 NaN unit \n", - "110 NaN unit \n", - "111 NaN unit \n", - "112 NaN unit \n", - "113 NaN unit \n", - "\n", - "[114 rows x 8 columns]" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df_tl_energy_w_ei.replace({'unit': {'kilowatt hour' :'1e-3 kilowatt hour'}})" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "6cbbc1ef-513b-4527-b689-e30acf36633b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.1, 1.0)" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'name',\n", - " data = df_tl_energy_w_ei,\n", - ")\n", - "axes.set_ylabel(\"$10^{-3}$ kilowatt hour\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y') ,xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=1, ymin=-0.1)\n", - "# axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2634448-d33c-49fd-bacb-9d311bf3d340", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario.ipynb b/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario.ipynb deleted file mode 100644 index 732fa87..0000000 --- a/archive/Case study/CaseStudy1_definitive TD with SSP2-1150 scenario.ipynb +++ /dev/null @@ -1,2569 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "3d095fe9-f532-4866-8b77-d0be04d6e406", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2analyzer as ba\n", - "import bw_temporalis as bwt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d34bd596-7b79-442b-8e04-18a7add52503", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ad409bf8-2230-4932-bdf2-e7341bcb2c43", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from typing import Optional\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "markdown", - "id": "0ac61910-ea08-46b0-b5b7-d362e5e6b0f1", - "metadata": {}, - "source": [ - "# Setting the project to the team one on the server" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7dfabb12-9c97-4729-9ab3-b9c42a27419d", - "metadata": {}, - "outputs": [], - "source": [ - "def change_base_directory(base_dir: Path) -> None: \n", - " assert isinstance(base_dir, Path) and base_dir.is_dir() and os.access(base_dir, os.W_OK) \n", - " \n", - " bd.projects._base_data_dir = base_dir\n", - " bd.projects.db.change_path(base_dir / \"projects.db\") \n", - " bd.projects.set_current(\"default\", update=False) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c6b7af93-6cd4-45cf-a0a4-c7a5d12b3243", - "metadata": {}, - "outputs": [], - "source": [ - "tictac_team_dir = Path(\"/srv/teams/tictac_team\") \n", - "change_base_directory(tictac_team_dir) \n", - "\n", - "bd.projects.set_current(\"tictac_premise\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a3f31190-badd-44fc-8cef-664a1fe848e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tictac_premise'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.current" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "46e3ed14-c739-49e1-8429-96962e2cb310", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "78bdd020-4e65-46f8-b3c2-e007754d8147", - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current(\"default\")" - ] - }, - { - "cell_type": "markdown", - "id": "43645e58-0e00-43eb-a12a-e77d3f9e0690", - "metadata": {}, - "source": [ - "# Setting the project to one having ecoinvent, from the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ef162137-362e-4b83-88e1-898a6c86a408", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restoring project backup archive - this could take a few minutes...\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Project ecoinvent-3.9-cutoff already exists", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrestore_project_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/srv/data/ecoinvent-3.9-cutoff.tar.gz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2io/backup.py:121\u001b[0m, in \u001b[0;36mrestore_project_directory\u001b[0;34m(fp, project_name, overwrite_existing)\u001b[0m\n\u001b[1;32m 118\u001b[0m project_name \u001b[38;5;241m=\u001b[39m get_project_name(fp) \u001b[38;5;28;01mif\u001b[39;00m project_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m project_name\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m project_name \u001b[38;5;129;01min\u001b[39;00m projects \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m overwrite_existing:\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProject \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m already exists\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(project_name))\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tempfile\u001b[38;5;241m.\u001b[39mTemporaryDirectory() \u001b[38;5;28;01mas\u001b[39;00m td:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tarfile\u001b[38;5;241m.\u001b[39mopen(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr:gz\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m tar:\n", - "\u001b[0;31mValueError\u001b[0m: Project ecoinvent-3.9-cutoff already exists" - ] - } - ], - "source": [ - "bi.restore_project_directory(\"/srv/data/ecoinvent-3.9-cutoff.tar.gz\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9a84b131-6e66-441d-858c-e20078ea6a92", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Brightway2 projects manager with 16 objects:\n", - "\tTemporalis example project\n", - "\tUSEEIO-1.1\n", - "\tbw_temporalis example\n", - "\tdefault\n", - "\tdynamic distribution example\n", - "\tecoinvent-3.9-cutoff\n", - "\tecoinvent=3.9-cutoff\n", - "\tpremise_ei39\n", - "\tpremise_ei39_2\n", - "\tspreadsheet\n", - "\tsupply chain graph\n", - "\tsupply chain graph_0\n", - "\ttictac\n", - "\ttictac spring server\n", - "\ttictac2\n", - "\ttictac3\n", - "Use `projects.report()` to get a report on all projects." - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1f945519-0060-49cc-87c8-456e6020fbef", - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('premise_ei39')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9e99f3b6-1e1e-40c9-9c8f-c808168247b0", - "metadata": {}, - "outputs": [], - "source": [ - "# bd.projects.migrate_project_25()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9c722374-00b4-4763-99bd-1345bb9c7ab9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 3 object(s):\n", - "\tbiosphere3\n", - "\tecoinvent-3.9-cutoff\n", - "\twind-example" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4fbf78bf-3edf-4a5f-a1e2-43bfec5e121f", - "metadata": {}, - "outputs": [], - "source": [ - "ei = bd.Database('ecoinvent-3.9-cutoff')" - ] - }, - { - "cell_type": "markdown", - "id": "4c829c64-b7aa-4062-baed-091d8027644a", - "metadata": {}, - "source": [ - "# Temporal distribution for wind electricity (onshore) in `Europe`, corresponding to `remind SSP2 - 1150` IAM scenario" - ] - }, - { - "cell_type": "markdown", - "id": "58a69475-cce7-43b9-bd1d-5749182f4279", - "metadata": {}, - "source": [ - "### Values " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "be2f275b-e672-41bf-b726-eb5bed3bef15", - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([0.79, 1.57, 3.6, 6.03, 8.73, 10.66, 11.27, 11.31])\n", - "a = a/np.sum(a) # normalizing the trend in Exajoules to get an actual TD" - ] - }, - { - "cell_type": "markdown", - "id": "dc29fa94-8763-4015-96cc-e8929b4b1102", - "metadata": {}, - "source": [ - "## Absolute TD" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a72e4fa3-7824-42b4-a531-68beff424b49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['2010-01-01' '2020-01-01' '2030-01-01' '2040-01-01' '2050-01-01'\n", - " '2060-01-01' '2070-01-01' '2080-01-01']\n" - ] - } - ], - "source": [ - "d = np.array([str(2010+k*10)+\"-01-01\" for k in range(8)])\n", - "d = np.array(d,dtype=np.datetime64)\n", - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4c65162b-a1dd-4213-983b-eb1ff483064d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inc_wind_turbine_energy_absolute = bwt.TemporalDistribution(\n", - " date=d,\n", - " amount=a\n", - ")\n", - "inc_wind_turbine_energy_absolute.graph()" - ] - }, - { - "cell_type": "markdown", - "id": "f8ede963-2013-4d76-8cf4-73aeaec07590", - "metadata": {}, - "source": [ - "## Relative TD" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6b874e2a-c23d-4104-8a50-bd8b984d4c43", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10 20 30 40 50 60 70 80]\n" - ] - } - ], - "source": [ - "delta = np.array([np.timedelta64(10*(k+1), 'Y') for k in range(8)])\n", - "print(delta)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2aa59c16-bbf1-4d56-ae9f-e58cc314cea8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inc_wind_turbine_energy_relative = bwt.TemporalDistribution(\n", - " date=delta,\n", - " amount=a\n", - ")\n", - "inc_wind_turbine_energy_relative.graph()" - ] - }, - { - "cell_type": "markdown", - "id": "ffb6f221-6576-42f0-b648-342413243c03", - "metadata": {}, - "source": [ - "# Dummy wind turbine system " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4d043052-e4bc-422b-9a37-fbd56a34d973", - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "434fc6c0-50e6-4011-87bd-aae624ef37b9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 47127.01it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 / 1e3 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : inc_wind_turbine_energy_relative, #we would prefer to use the absolute TD, but for some reason the graph reversal isn't working with it...\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=-1,\n", - " end=1,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=3,\n", - " # kind = '',\n", - " # param = 0\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # CO2 emissions corresponding to maintenance\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input' : ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3abffe5e-b7ec-48b8-8880-bc696ba69199", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2787439554172777" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c5805c6d-3454-4d39-ab5f-7ea378e5a423", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 31\n" - ] - } - ], - "source": [ - "tlca = bwt.TemporalisLCA(lca, starting_datetime=np.datetime64(40, 'Y'))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5782bfb8-c241-4fd6-a454-87333e26cdf1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7676edf4-506d-4e0d-9dfc-9bc6160df58d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "3491272e-bda8-41a6-aada-da547dbb2c9e", - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "df9d774a-8d3f-4b0d-8503-07792cfba8d9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y'), xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=0.05)\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "id": "cd48a3c6-34fb-4246-867c-dee7e970c49f", - "metadata": {}, - "source": [ - "# Timeline of activities" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "72640c26-84af-4b10-a9ed-bce8a7e54dff", - "metadata": {}, - "outputs": [], - "source": [ - "def build_timeline_processes(tlca, db) :\n", - " ' take as input'\n", - " ' - the result of the graph reversal (given by the \"bwt.TemporalisLCA\") '\n", - " ' - the name of the database as str (static database, not prospective) '\n", - " ' return a dataframe within the following columns '\n", - " ' - date '\n", - " ' - amount '\n", - " ' - activity (?)'\n", - " ' - database '\n", - " ' - name (of activity) '\n", - " ' - type '\n", - " ' - unit '\n", - " \n", - " tl_activities= tlca.build_timeline(node_timeline=True) #creating timeline for the nodes, not the flows\"\n", - " df_tl_act = tl_activities.build_dataframe() # converting the data into dataframe\n", - "\n", - " if 'ecoinvent' in db : \n", - " df_tl_act=df_tl_act.rename(columns={\"activity\" :\"id\"}).merge(bd.Database(db).nodes_to_dataframe(), on='id')\n", - " else :\n", - " df_tl_act=df_tl_act.merge(bd.Database(db).nodes_to_dataframe().rename(columns={\"id\" : \"activity\"}), on='activity')\n", - " del df_tl_act['flow']\n", - " \n", - " return df_tl_act\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a21bbf23-0079-4c1e-84fd-fdd3480f01c7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw_temporalis/lca.py:135: UserWarning: This functionality is experimental, and will change.\n", - "You have been warned.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001.000000e+0025969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
12019-01-01 04:22:481.782706e-0425969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
22020-01-01 10:12:001.782706e-0425969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
32020-12-31 16:01:121.782706e-0425969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
42028-01-01 08:45:363.178041e-0825969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
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36262226-01-01 09:07:126.377526e-1525972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
36272227-01-01 14:56:242.467268e-1625972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
36282228-01-01 20:45:365.695356e-1825972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
36292229-01-01 02:34:487.290148e-2025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
36302230-01-01 08:24:004.049311e-2225972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
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" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+00 25969 electricity-production-wind \n", - "1 2019-01-01 04:22:48 1.782706e-04 25969 electricity-production-wind \n", - "2 2020-01-01 10:12:00 1.782706e-04 25969 electricity-production-wind \n", - "3 2020-12-31 16:01:12 1.782706e-04 25969 electricity-production-wind \n", - "4 2028-01-01 08:45:36 3.178041e-08 25969 electricity-production-wind \n", - "... ... ... ... ... \n", - "3626 2226-01-01 09:07:12 6.377526e-15 25972 eol-wind \n", - "3627 2227-01-01 14:56:24 2.467268e-16 25972 eol-wind \n", - "3628 2228-01-01 20:45:36 5.695356e-18 25972 eol-wind \n", - "3629 2229-01-01 02:34:48 7.290148e-20 25972 eol-wind \n", - "3630 2230-01-01 08:24:00 4.049311e-22 25972 eol-wind \n", - "\n", - " database name type unit \n", - "0 wind-example Electricity production, wind NaN kilowatt hour \n", - "1 wind-example Electricity production, wind NaN kilowatt hour \n", - "2 wind-example Electricity production, wind NaN kilowatt hour \n", - "3 wind-example Electricity production, wind NaN kilowatt hour \n", - "4 wind-example Electricity production, wind NaN kilowatt hour \n", - "... ... ... ... ... \n", - "3626 wind-example End-of-life, wind turbine NaN unit \n", - "3627 wind-example End-of-life, wind turbine NaN unit \n", - "3628 wind-example End-of-life, wind turbine NaN unit \n", - "3629 wind-example End-of-life, wind turbine NaN unit \n", - "3630 wind-example End-of-life, wind turbine NaN unit \n", - "\n", - "[3631 rows x 8 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tl_act = build_timeline_processes(tlca, 'wind-example')\n", - "df_tl_act" - ] - }, - { - "cell_type": "markdown", - "id": "f8bda26e-6da9-414a-8d84-2c30c4cf9965", - "metadata": {}, - "source": [ - "### Example : exchanges timeline for energy activities" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "49d4fc48-bdfd-45c0-8e7d-bb7c268775ad", - "metadata": {}, - "outputs": [], - "source": [ - "df_tl_energy = df_tl_act[df_tl_act['unit']=='kilowatt hour']" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ffa1088d-7fce-41a9-8166-4c29f1c09fb7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001.000000e+0025969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
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22020-01-01 10:12:001.782706e-0425969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
32020-12-31 16:01:121.782706e-0425969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
42028-01-01 08:45:363.178041e-0825969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
...........................
16152198-12-31 19:58:481.115600e-0825968electricity-production-coalwind-exampleElectricity production, coalNaNkilowatt hour
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16172200-01-01 01:48:003.305324e-1025968electricity-production-coalwind-exampleElectricity production, coalNaNkilowatt hour
16182201-01-01 07:37:123.631540e-1225968electricity-production-coalwind-exampleElectricity production, coalNaNkilowatt hour
16192201-01-01 07:37:123.631540e-1225968electricity-production-coalwind-exampleElectricity production, coalNaNkilowatt hour
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1620 rows × 8 columns

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" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+00 25969 electricity-production-wind \n", - "1 2019-01-01 04:22:48 1.782706e-04 25969 electricity-production-wind \n", - "2 2020-01-01 10:12:00 1.782706e-04 25969 electricity-production-wind \n", - "3 2020-12-31 16:01:12 1.782706e-04 25969 electricity-production-wind \n", - "4 2028-01-01 08:45:36 3.178041e-08 25969 electricity-production-wind \n", - "... ... ... ... ... \n", - "1615 2198-12-31 19:58:48 1.115600e-08 25968 electricity-production-coal \n", - "1616 2200-01-01 01:48:00 3.305324e-10 25968 electricity-production-coal \n", - "1617 2200-01-01 01:48:00 3.305324e-10 25968 electricity-production-coal \n", - "1618 2201-01-01 07:37:12 3.631540e-12 25968 electricity-production-coal \n", - "1619 2201-01-01 07:37:12 3.631540e-12 25968 electricity-production-coal \n", - "\n", - " database name type unit \n", - "0 wind-example Electricity production, wind NaN kilowatt hour \n", - "1 wind-example Electricity production, wind NaN kilowatt hour \n", - "2 wind-example Electricity production, wind NaN kilowatt hour \n", - "3 wind-example Electricity production, wind NaN kilowatt hour \n", - "4 wind-example Electricity production, wind NaN kilowatt hour \n", - "... ... ... ... ... \n", - "1615 wind-example Electricity production, coal NaN kilowatt hour \n", - "1616 wind-example Electricity production, coal NaN kilowatt hour \n", - "1617 wind-example Electricity production, coal NaN kilowatt hour \n", - "1618 wind-example Electricity production, coal NaN kilowatt hour \n", - "1619 wind-example Electricity production, coal NaN kilowatt hour \n", - "\n", - "[1620 rows x 8 columns]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tl_energy" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "93b64cfd-3309-4ce5-b0fd-151420e287cb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1706180/508802167.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_tl_energy['amount'] = df_tl_energy['amount']*1000\n" - ] - } - ], - "source": [ - "df_tl_energy['amount'] = df_tl_energy['amount']*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "bdf8c18d-4670-4aaa-b47c-d04a5b0d087a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001.000000e+0325969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
12019-01-01 04:22:481.782706e-0125969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
22020-01-01 10:12:001.782706e-0125969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
32020-12-31 16:01:121.782706e-0125969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
42028-01-01 08:45:363.178041e-0525969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
...........................
16152198-12-31 19:58:481.115600e-0525968electricity-production-coalwind-exampleElectricity production, coalNaN1e-3 kilowatt hour
16162200-01-01 01:48:003.305324e-0725968electricity-production-coalwind-exampleElectricity production, coalNaN1e-3 kilowatt hour
16172200-01-01 01:48:003.305324e-0725968electricity-production-coalwind-exampleElectricity production, coalNaN1e-3 kilowatt hour
16182201-01-01 07:37:123.631540e-0925968electricity-production-coalwind-exampleElectricity production, coalNaN1e-3 kilowatt hour
16192201-01-01 07:37:123.631540e-0925968electricity-production-coalwind-exampleElectricity production, coalNaN1e-3 kilowatt hour
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1620 rows × 8 columns

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" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+03 25969 electricity-production-wind \n", - "1 2019-01-01 04:22:48 1.782706e-01 25969 electricity-production-wind \n", - "2 2020-01-01 10:12:00 1.782706e-01 25969 electricity-production-wind \n", - "3 2020-12-31 16:01:12 1.782706e-01 25969 electricity-production-wind \n", - "4 2028-01-01 08:45:36 3.178041e-05 25969 electricity-production-wind \n", - "... ... ... ... ... \n", - "1615 2198-12-31 19:58:48 1.115600e-05 25968 electricity-production-coal \n", - "1616 2200-01-01 01:48:00 3.305324e-07 25968 electricity-production-coal \n", - "1617 2200-01-01 01:48:00 3.305324e-07 25968 electricity-production-coal \n", - "1618 2201-01-01 07:37:12 3.631540e-09 25968 electricity-production-coal \n", - "1619 2201-01-01 07:37:12 3.631540e-09 25968 electricity-production-coal \n", - "\n", - " database name type unit \n", - "0 wind-example Electricity production, wind NaN 1e-3 kilowatt hour \n", - "1 wind-example Electricity production, wind NaN 1e-3 kilowatt hour \n", - "2 wind-example Electricity production, wind NaN 1e-3 kilowatt hour \n", - "3 wind-example Electricity production, wind NaN 1e-3 kilowatt hour \n", - "4 wind-example Electricity production, wind NaN 1e-3 kilowatt hour \n", - "... ... ... ... ... \n", - "1615 wind-example Electricity production, coal NaN 1e-3 kilowatt hour \n", - "1616 wind-example Electricity production, coal NaN 1e-3 kilowatt hour \n", - "1617 wind-example Electricity production, coal NaN 1e-3 kilowatt hour \n", - "1618 wind-example Electricity production, coal NaN 1e-3 kilowatt hour \n", - "1619 wind-example Electricity production, coal NaN 1e-3 kilowatt hour \n", - "\n", - "[1620 rows x 8 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df_tl_energy.replace({'unit': {'kilowatt hour' :'1e-3 kilowatt hour'}})" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "a5a353f8-eb36-41c3-ae8d-1acc86029a96", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.1, 1.0)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'name',\n", - " data = df_tl_energy,\n", - ")\n", - "axes.set_ylabel(\"$10^{-3}$ kilowatt hour\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y') ,xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=1, ymin=-0.1)\n", - "# axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "id": "e0ab0ba5-7aab-4946-8b7e-ae8cb1235758", - "metadata": {}, - "source": [ - "# Wind turbine system w/ some ei process" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "590178c6-7702-41c9-811f-3e4875eea332", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['market group for electricity, medium voltage' (kilowatt hour, RER, None),\n", - " 'market for electricity, medium voltage, aluminium industry' (kilowatt hour, IAI Area, Russia & RER w/o EU27 & EFTA, None)]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ei.search('market group electricity medium voltage RER')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c3b691c4-46dd-49c0-8293-3af05eefe4a8", - "metadata": {}, - "outputs": [], - "source": [ - "act = bd.get_node(name=\"market group for electricity, medium voltage\", location='RER')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3df55e1f-1f61-43a4-aa91-981d1497dc5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'17c9d0c2c72446bf59393f60c096a588'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act['code']" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "5792238f-145e-4630-882e-63eb7a1dc41e", - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "850d4c7b-9d6d-4b8a-83b2-5c95b64d6e50", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 105186.31it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 / 1e3 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : inc_wind_turbine_energy_relative, #we would prefer to use the absolute TD, but for some reason the graph reversal isn't working with it...\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=-1,\n", - " end=1,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=3,\n", - " # kind = '',\n", - " # param = 0\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # CO2 emissions corresponding to maintenance\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('ecoinvent-3.9-cutoff', act['code']),\n", - " 'amount': 2e5,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input' : ('ecoinvent-3.9-cutoff', act['code']),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e7e5573a-ee83-47de-a414-85d51323d552", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.25114154738048455" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "70632cdd-b03a-4c1c-80a4-5758c1f2a355", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 3\n" - ] - } - ], - "source": [ - "tlca = bwt.TemporalisLCA(lca, starting_datetime=np.datetime64(40, 'Y'))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4fc9f5f2-ba3c-49c9-ace6-7c87cfe3f950", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "6bb53309-ff79-4856-9e8d-97ff16e52d6b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "# df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ac8b20f6-6b1b-4f11-897f-a1eb13e981e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountflowactivity
02019-01-01 04:22:481.114191e-032596525971
12020-01-01 10:12:001.114191e-032596525971
22020-12-31 16:01:121.114191e-032596525971
32028-12-31 14:34:482.214279e-032596525971
42029-12-31 20:24:002.214279e-032596525971
...............
1002116-01-01 16:55:121.157922e-032596525972
1012116-12-31 22:44:242.583674e-042596525972
1022118-01-01 04:33:362.120808e-052596525972
1032119-01-01 10:22:486.404287e-072596525972
1042120-01-01 16:12:007.114520e-092596525972
\n", - "

105 rows × 4 columns

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" - ], - "text/plain": [ - " date amount flow activity\n", - "0 2019-01-01 04:22:48 1.114191e-03 25965 25971\n", - "1 2020-01-01 10:12:00 1.114191e-03 25965 25971\n", - "2 2020-12-31 16:01:12 1.114191e-03 25965 25971\n", - "3 2028-12-31 14:34:48 2.214279e-03 25965 25971\n", - "4 2029-12-31 20:24:00 2.214279e-03 25965 25971\n", - ".. ... ... ... ...\n", - "100 2116-01-01 16:55:12 1.157922e-03 25965 25972\n", - "101 2116-12-31 22:44:24 2.583674e-04 25965 25972\n", - "102 2118-01-01 04:33:36 2.120808e-05 25965 25972\n", - "103 2119-01-01 10:22:48 6.404287e-07 25965 25972\n", - "104 2120-01-01 16:12:00 7.114520e-09 25965 25972\n", - "\n", - "[105 rows x 4 columns]" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "4b004620-4cc6-41bd-83ca-ce009f965c35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Wind turbine construction' (unit, None, None)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_activity(id=25971)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "7a13b406-ddd9-4739-a8b1-3950e64ab3ac", - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('ecoinvent').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "6798f1e6-8d2a-4eb0-9fdb-3a5bb00019e3", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'activity'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_1706180/2168020672.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m axes = sb.scatterplot(\n\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"date\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'amount'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'activity_name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m data = df.merge(\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'id'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m'activity'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'activity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 8\u001b[0m )\n", - "\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 10486\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mMergeValidate\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10487\u001b[0m ) -> DataFrame:\n\u001b[1;32m 10488\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10490\u001b[0;31m return merge(\n\u001b[0m\u001b[1;32m 10491\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10492\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10493\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m )\n\u001b[1;32m 168\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m op = _MergeOperation(\n\u001b[0m\u001b[1;32m 170\u001b[0m \u001b[0mleft_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0mright_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[0;31m# Then we're either Hashable or a wrong-length arraylike,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;31m# the latter of which will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1268\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1269\u001b[0;31m \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1270\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1271\u001b[0m \u001b[0;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1272\u001b[0m \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mother_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_level_reference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1842\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1843\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1844\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1845\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1846\u001b[0m \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1847\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'activity'" - ] - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y'), xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=0.05)\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "baa1b09e-80d2-4ff7-9ed7-1f332a2ac6a1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw_temporalis/lca.py:135: UserWarning: This functionality is experimental, and will change.\n", - "You have been warned.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001.000000e+0025969electricity-production-windwind-exampleElectricity production, windNaNkilowatt hour
12019-01-01 04:22:481.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
22020-01-01 10:12:001.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
32020-12-31 16:01:121.114191e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
42028-12-31 14:34:482.214279e-0925971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
...........................
1092116-01-01 16:55:121.157922e-0825972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1102116-12-31 22:44:242.583674e-0925972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1112118-01-01 04:33:362.120808e-1025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1122119-01-01 10:22:486.404287e-1225972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1132120-01-01 16:12:007.114520e-1425972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
\n", - "

114 rows × 8 columns

\n", - "
" - ], - "text/plain": [ - " date amount activity code \\\n", - "0 2010-01-01 00:00:00 1.000000e+00 25969 electricity-production-wind \n", - "1 2019-01-01 04:22:48 1.114191e-09 25971 wind-turbine-construction \n", - "2 2020-01-01 10:12:00 1.114191e-09 25971 wind-turbine-construction \n", - "3 2020-12-31 16:01:12 1.114191e-09 25971 wind-turbine-construction \n", - "4 2028-12-31 14:34:48 2.214279e-09 25971 wind-turbine-construction \n", - ".. ... ... ... ... \n", - "109 2116-01-01 16:55:12 1.157922e-08 25972 eol-wind \n", - "110 2116-12-31 22:44:24 2.583674e-09 25972 eol-wind \n", - "111 2118-01-01 04:33:36 2.120808e-10 25972 eol-wind \n", - "112 2119-01-01 10:22:48 6.404287e-12 25972 eol-wind \n", - "113 2120-01-01 16:12:00 7.114520e-14 25972 eol-wind \n", - "\n", - " database name type unit \n", - "0 wind-example Electricity production, wind NaN kilowatt hour \n", - "1 wind-example Wind turbine construction NaN unit \n", - "2 wind-example Wind turbine construction NaN unit \n", - "3 wind-example Wind turbine construction NaN unit \n", - "4 wind-example Wind turbine construction NaN unit \n", - ".. ... ... ... ... \n", - "109 wind-example End-of-life, wind turbine NaN unit \n", - "110 wind-example End-of-life, wind turbine NaN unit \n", - "111 wind-example End-of-life, wind turbine NaN unit \n", - "112 wind-example End-of-life, wind turbine NaN unit \n", - "113 wind-example End-of-life, wind turbine NaN unit \n", - "\n", - "[114 rows x 8 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_tl_energy_w_ei = build_timeline_processes(tlca, 'wind-example')\n", - "df_tl_energy_w_ei" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "09e57b41-375b-4495-94c2-491b46e27faf", - "metadata": {}, - "outputs": [], - "source": [ - "df_tl_energy_w_ei['amount'] = df_tl_energy['amount']*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "97163008-b806-4be9-9306-ea1b48d7f148", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountactivitycodedatabasenametypeunit
02010-01-01 00:00:001000000.00000025969electricity-production-windwind-exampleElectricity production, windNaN1e-3 kilowatt hour
12019-01-01 04:22:48178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
22020-01-01 10:12:00178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
32020-12-31 16:01:12178.27061625971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
42028-12-31 14:34:480.03178025971wind-turbine-constructionwind-exampleWind turbine constructionNaNunit
...........................
1092116-01-01 16:55:120.16308025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1102116-12-31 22:44:240.00086625972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1112118-01-01 04:33:360.00484625972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1122119-01-01 10:22:482405.52502225972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
1132120-01-01 16:12:006.97951025972eol-windwind-exampleEnd-of-life, wind turbineNaNunit
\n", - "

114 rows × 8 columns

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" - ], - "text/plain": [ - " date amount activity \\\n", - "0 2010-01-01 00:00:00 1000000.000000 25969 \n", - "1 2019-01-01 04:22:48 178.270616 25971 \n", - "2 2020-01-01 10:12:00 178.270616 25971 \n", - "3 2020-12-31 16:01:12 178.270616 25971 \n", - "4 2028-12-31 14:34:48 0.031780 25971 \n", - ".. ... ... ... \n", - "109 2116-01-01 16:55:12 0.163080 25972 \n", - "110 2116-12-31 22:44:24 0.000866 25972 \n", - "111 2118-01-01 04:33:36 0.004846 25972 \n", - "112 2119-01-01 10:22:48 2405.525022 25972 \n", - "113 2120-01-01 16:12:00 6.979510 25972 \n", - "\n", - " code database name \\\n", - "0 electricity-production-wind wind-example Electricity production, wind \n", - "1 wind-turbine-construction wind-example Wind turbine construction \n", - "2 wind-turbine-construction wind-example Wind turbine construction \n", - "3 wind-turbine-construction wind-example Wind turbine construction \n", - "4 wind-turbine-construction wind-example Wind turbine construction \n", - ".. ... ... ... \n", - "109 eol-wind wind-example End-of-life, wind turbine \n", - "110 eol-wind wind-example End-of-life, wind turbine \n", - "111 eol-wind wind-example End-of-life, wind turbine \n", - "112 eol-wind wind-example End-of-life, wind turbine \n", - "113 eol-wind wind-example End-of-life, wind turbine \n", - "\n", - " type unit \n", - "0 NaN 1e-3 kilowatt hour \n", - "1 NaN unit \n", - "2 NaN unit \n", - "3 NaN unit \n", - "4 NaN unit \n", - ".. ... ... \n", - "109 NaN unit \n", - "110 NaN unit \n", - "111 NaN unit \n", - "112 NaN unit \n", - "113 NaN unit \n", - "\n", - "[114 rows x 8 columns]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df_tl_energy_w_ei.replace({'unit': {'kilowatt hour' :'1e-3 kilowatt hour'}})" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "6cbbc1ef-513b-4527-b689-e30acf36633b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.1, 1.0)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'name',\n", - " data = df_tl_energy_w_ei,\n", - ")\n", - "axes.set_ylabel(\"$10^{-3}$ kilowatt hour\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_xlim(xmin=np.datetime64(40, 'Y') ,xmax=np.datetime64(110, 'Y'))\n", - "axes.set_ylim(ymax=1, ymin=-0.1)\n", - "# axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2634448-d33c-49fd-bacb-9d311bf3d340", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04a546c5-77d1-4fba-a8e3-ba7bf25bc3f9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d09b1b9c-0db2-4589-bc8f-27537aca19c8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fd13d50-b656-4f87-be5e-ba125b8ddecc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Case study/Premise_DB.ipynb b/archive/Case study/Premise_DB.ipynb deleted file mode 100644 index 0cdf458..0000000 --- a/archive/Case study/Premise_DB.ipynb +++ /dev/null @@ -1,866 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2b32b6cf-4560-496a-8ee0-91efea8060d0", - "metadata": {}, - "outputs": [], - "source": [ - "import wurst\n", - "import bw2io, bw2data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bf760a5e-4d59-4a0d-9eb4-195acd615044", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restoring project backup archive - this could take a few minutes...\n" - ] - }, - { - "data": { - "text/plain": [ - "'premise_ei391'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bw2io.restore_project_directory(\"/srv/data/ecoinvent-3.9.1-cutoff.tar.gz\", project_name=\"premise_ei391\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ea00bc9e-01c1-4a35-aa52-48e03262365a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Applying automatic update: 4.0 migrations filename change\n", - "Applying automatic update: 4.0 new processed format\n", - "Updating all LCIA methods\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "762it [00:42, 18.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updating all LCI databases\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2it [00:07, 3.86s/it]\n" - ] - } - ], - "source": [ - "bw2data.projects.set_current(\"premise_ei391\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fedd455c-9690-400b-8b1c-3461b5b28ae7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 2 object(s):\n", - "\tbiosphere3\n", - "\tecoinvent-3.9.1-cutoff" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bw2data.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a2276263-f0e2-4847-ae32-d1148785842f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Getting activity data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 21238/21238 [00:00<00:00, 95434.16it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding exchange data to activities\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 674593/674593 [00:16<00:00, 41541.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling out exchange data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 21238/21238 [00:01<00:00, 18442.81it/s]\n" - ] - } - ], - "source": [ - "database = wurst.extract_brightway2_databases(\"ecoinvent-3.9.1-cutoff\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "42682c41-21d6-4ba4-b074-5a3e27ae27dc", - "metadata": {}, - "outputs": [], - "source": [ - "from premise import *" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c2100a8c-d2df-4a3f-a911-839fb426bc37", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "premise v.(1, 7, 8)\n", - "+------------------------------------------------------------------+\n", - "| Warning |\n", - "+------------------------------------------------------------------+\n", - "| Because some of the scenarios can yield LCI databases |\n", - "| containing net negative emission technologies (NET), |\n", - "| it is advised to account for biogenic CO2 flows when calculating |\n", - "| Global Warming potential indicators. |\n", - "| `premise_gwp` provides characterization factors for such flows. |\n", - "| It also provides factors for hydrogen emissions to air. |\n", - "| |\n", - "| Within your bw2 project: |\n", - "| from premise_gwp import add_premise_gwp |\n", - "| add_premise_gwp() |\n", - "+------------------------------------------------------------------+\n", - "+--------------------------------+----------------------------------+\n", - "| Utils functions | Description |\n", - "+--------------------------------+----------------------------------+\n", - "| clear_cache() | Clears the cache folder. Useful |\n", - "| | when updating `premise`or |\n", - "| | encountering issues with |\n", - "| | inventories. |\n", - "+--------------------------------+----------------------------------+\n", - "| get_regions_definition(model) | Retrieves the list of countries |\n", - "| | for each region of the model. |\n", - "+--------------------------------+----------------------------------+\n", - "| ndb.NewDatabase(...) | Generates a summary of the most |\n", - "| ndb.generate_scenario_report() | important scenarios' variables. |\n", - "+--------------------------------+----------------------------------+\n", - "Keep uncertainty data?\n", - "NewDatabase(..., keep_uncertainty_data=True)\n", - "\n", - "Disable multiprocessing?\n", - "NewDatabase(..., use_multiprocessing=False)\n", - "\n", - "Hide these messages?\n", - "NewDatabase(..., quiet=True)\n", - "\n", - "//////////////////// EXTRACTING SOURCE DATABASE ////////////////////\n", - "Cannot find cached database. Will create one now for next time...\n", - "Getting activity data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 21238/21238 [00:00<00:00, 225889.52it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding exchange data to activities\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 674593/674593 [00:20<00:00, 33147.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling out exchange data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 21238/21238 [00:01<00:00, 18420.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set missing location of datasets to global scope.\n", - "Set missing location of production exchanges to scope of dataset.\n", - "Correct missing location of technosphere exchanges.\n", - "Correct missing flow categories for biosphere exchanges\n", - "Remove empty exchanges.\n", - "Remove uncertainty data.\n", - "Done!\n", - "\n", - "////////////////// IMPORTING DEFAULT INVENTORIES ///////////////////\n", - "Cannot find cached inventories. Will create them now for next time...\n", - "Importing default inventories...\n", - "\n", - "Extracted 1 worksheets in 0.10 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 7 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.39 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "| fluorspar production, 97% purity | fluorspar, 97% purity | GLO | lci-PV.xlsx |\n", - "| metallization paste production, back side | metallization paste, back side | RER | lci-PV.xlsx |\n", - "| metallization paste production, back side, alumini | metallization paste, back side | RER | lci-PV.xlsx |\n", - "| metallization paste production, front side | metallization paste, front sid | RER | lci-PV.xlsx |\n", - "| photovoltaic module production, building-integrate | photovoltaic module, building- | RER | lci-PV.xlsx |\n", - "| photovoltaic module production, building-integrate | photovoltaic module, building- | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for facad | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for flat- | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for slant | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic panel factory construction | photovoltaic panel factory | GLO | lci-PV.xlsx |\n", - "| polyvinylfluoride production | polyvinylfluoride | US | lci-PV.xlsx |\n", - "| polyvinylfluoride production, dispersion | polyvinylfluoride, dispersion | US | lci-PV.xlsx |\n", - "| polyvinylfluoride, film production | polyvinylfluoride, film | US | lci-PV.xlsx |\n", - "| silicon production, metallurgical grade | silicon, metallurgical grade | NO | lci-PV.xlsx |\n", - "| vinyl fluoride production | vinyl fluoride | US | lci-PV.xlsx |\n", - "| wafer factory construction | wafer factory | DE | lci-PV.xlsx |\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "Extracted 1 worksheets in 0.05 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "| carbon dioxide, captured at cement production plan | carbon dioxide, captured and r | RER | lci-synfuels-from-methanol-from-cement-plant.xlsx |\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "| methanol distillation, hydrogen from coal gasifica | methanol, purified | RER | lci-synfuels-from-methanol-from-coal.xlsx |\n", - "| methanol synthesis, hydrogen from coal gasificatio | methanol, unpurified | RER | lci-synfuels-from-methanol-from-coal.xlsx |\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.00 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 5 worksheets in 0.14 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.08 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 2 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Remove uncertainty data.\n", - "Data cached. It is advised to restart your workflow at this point.\n", - "This allows premise to use the cached data instead, which results in\n", - "a faster workflow.\n", - "Done!\n", - "\n", - "/////////////////////// EXTRACTING IAM DATA ////////////////////////\n", - "Done!\n" - ] - } - ], - "source": [ - "ndb = NewDatabase(\n", - " scenarios=[\n", - " {\"model\":\"remind\", \"pathway\":\"SSP2-PkBudg1150\", \"year\":2020},\n", - " {\"model\":\"remind\", \"pathway\":\"SSP2-PkBudg1150\", \"year\":2030},\n", - " {\"model\":\"remind\", \"pathway\":\"SSP2-PkBudg1150\", \"year\":2040},\n", - " {\"model\":\"remind\", \"pathway\":\"SSP2-PkBudg1150\", \"year\":2050},\n", - " ],\n", - " source_db=\"ecoinvent-3.9.1-cutoff\", # <-- name of the database in the BW2 project. Must be a string.\n", - " source_version=\"3.9\", # <-- version of ecoinvent. Can be \"3.5\", \"3.6\", \"3.7\" or \"3.8\". Must be a string.\n", - " key='tUePmX_S5B8ieZkkM7WUU2CnO8SmShwmAeWK9x2rTFo=' # <-- decryption key\n", - " # to be requested from the library maintainers if you want ot use default scenarios included in `premise`\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6092d163-6511-455e-8840-282839d59142", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "`update_all()` will skip the following steps:\n", - "update_two_wheelers(), update_cars(), and update_buses()\n", - "If you want to update these steps, please run them separately afterwards.\n", - "Extracted 1 worksheets in 5.21 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Extracted 1 worksheets in 5.62 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Extracted 1 worksheets in 5.27 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Extracted 1 worksheets in 4.85 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "Done!\n", - "\n", - "Write new database(s) to Brightway.\n", - "One or multiple duplicates detected. Removing them...\n", - "One or multiple duplicates detected. Removing them...\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 79%|███████▉ | 21977/27901 [00:00<00:00, 26886.08it/s]/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/backends/typos.py:110: UserWarning: Possible incorrect activity key found: Given `commnet` but `comment` is more common\n", - " warnings.warn(\n", - "100%|██████████| 27901/27901 [00:01<00:00, 26412.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: SSP2-PkBudg1150 2020\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 77%|███████▋ | 21980/28405 [00:00<00:00, 26836.10it/s]/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/backends/typos.py:110: UserWarning: Possible incorrect activity key found: Given `commnet` but `comment` is more common\n", - " warnings.warn(\n", - "100%|██████████| 28405/28405 [00:02<00:00, 10703.16it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: SSP2-PkBudg1150 2030\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 68%|██████▊ | 19615/29007 [00:00<00:00, 27911.67it/s]/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/backends/typos.py:110: UserWarning: Possible incorrect activity key found: Given `commnet` but `comment` is more common\n", - " warnings.warn(\n", - "100%|██████████| 29007/29007 [00:01<00:00, 26883.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: SSP2-PkBudg1150 2040\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 75%|███████▌ | 22006/29271 [00:00<00:00, 27256.03it/s]/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/backends/typos.py:110: UserWarning: Possible incorrect activity key found: Given `commnet` but `comment` is more common\n", - " warnings.warn(\n", - "100%|██████████| 29271/29271 [00:01<00:00, 26434.39it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: SSP2-PkBudg1150 2050\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Generate scenario report.\n", - "Report saved under /home/jupyter-as23_pingping.wang-b5811/tictac_lca/export/scenario_report.\n", - "Generate change report.\n", - "Report saved under /home/jupyter-as23_pingping.wang-b5811/tictac_lca.\n" - ] - } - ], - "source": [ - "ndb.update_all()\n", - "ndb.write_db_to_brightway(\n", - " name=[\n", - " \"SSP2-PkBudg1150 2020\", \n", - " \"SSP2-PkBudg1150 2030\", \n", - " \"SSP2-PkBudg1150 2040\", \n", - " \"SSP2-PkBudg1150 2050\", \n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0739fd8d-e07e-4729-b7e0-2fc67b266a5c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['market group for electricity, medium voltage' (kilowatt hour, RER, None),\n", - " 'market group for electricity, medium voltage' (kilowatt hour, RER, None),\n", - " 'market group for electricity, medium voltage' (kilowatt hour, RER, None),\n", - " 'market group for electricity, medium voltage' (kilowatt hour, RER, None)]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acts_elemix = [\n", - " act for db in [\n", - " \"SSP2-PkBudg1150 2020\", \n", - " \"SSP2-PkBudg1150 2030\", \n", - " \"SSP2-PkBudg1150 2040\", \n", - " \"SSP2-PkBudg1150 2050\", \n", - " ] for act in bw2data.Database(db) if \"market group for electricity, medium voltage\" in act[\"name\"]\n", - " and act[\"unit\"] == \"kilowatt hour\" \n", - " and act[\"location\"] == \"RER\"\n", - "]\n", - "acts_elemix" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2f8be8ce-dda9-49ba-a1d1-4441a1ab7444", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0c02fccd63475e8f7e6b9627f34c00bc\n", - "0c02fccd63475e8f7e6b9627f34c00bc\n", - "0c02fccd63475e8f7e6b9627f34c00bc\n", - "0c02fccd63475e8f7e6b9627f34c00bc\n" - ] - } - ], - "source": [ - "for i in range(len(acts_elemix)):\n", - " print(acts_elemix[i]['code'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e39b8749-2ce1-4a8d-8785-470365ba3c4f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Case study/wind-example_LCI_TD_.ipynb b/archive/Case study/wind-example_LCI_TD_.ipynb deleted file mode 100644 index 6fc2338..0000000 --- a/archive/Case study/wind-example_LCI_TD_.ipynb +++ /dev/null @@ -1,540 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalisLCA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current('tictac2')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'wind-example'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabases\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwind-example\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/meta.py:107\u001b[0m, in \u001b[0;36mDatabases.__delitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 107\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mDatabases\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__delitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/tljh/user/envs/autumn_school/lib/python3.11/site-packages/bw2data/serialization.py:162\u001b[0m, in \u001b[0;36mSerializedDict.__delitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__delitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[0;32m--> 162\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflush()\n", - "\u001b[0;31mKeyError\u001b[0m: 'wind-example'" - ] - } - ], - "source": [ - "# del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [] - }, - "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": 9, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 50994.58it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.5\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import bw_temporalis as bwt" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "td = easy_timedelta_distribution(\n", - " start=-20,\n", - " end=49,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=70,\n", - " kind = 'triangular',\n", - " param = 49\n", - " )\n", - "td.graph()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 1 object(s):\n", - "\twind-example" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"wind-example\")).write([\n", - " (('wind-example', \"CO2\"), 1),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0002819731177506934" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 7\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# df" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# df2" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by 1WT')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by 1WT\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/Case study/wind-example_LCI_TD_more TD.ipynb b/archive/Case study/wind-example_LCI_TD_more TD.ipynb deleted file mode 100644 index b0622ed..0000000 --- a/archive/Case study/wind-example_LCI_TD_more TD.ipynb +++ /dev/null @@ -1,3646 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalisLCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current('tictac2')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 1 object(s):\n", - "\twind-example" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 1 object(s):\n", - "\twind-example" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 88301.14it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.80\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Result for 1 WT" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 1 object(s):\n", - "\twind-example" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"wind-example\")).write([\n", - " (('wind-example', \"CO2\"), 1),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00026348474919501795" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-production-wind'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 7\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# df" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# df2" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by 1WT')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by 1WT\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Results for elec mix" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1802107857163343" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 4\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
02020-10-10 20:16:290.00003017Wind turbine constructionunitcarbon dioxidekilogram
12021-10-11 02:05:410.00006117Wind turbine constructionunitcarbon dioxidekilogram
22022-10-11 07:54:530.00009117Wind turbine constructionunitcarbon dioxidekilogram
32023-10-11 13:44:050.18000014Electricity production, coalkilowatt hourcarbon dioxidekilogram
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" - ], - "text/plain": [ - " date amount flow activity activity_name \\\n", - "0 2020-10-10 20:16:29 0.000030 1 7 Wind turbine construction \n", - "1 2021-10-11 02:05:41 0.000061 1 7 Wind turbine construction \n", - "2 2022-10-11 07:54:53 0.000091 1 7 Wind turbine construction \n", - "3 2023-10-11 13:44:05 0.180000 1 4 Electricity production, coal \n", - "\n", - " activity_unit flow_name flow_unit \n", - "0 unit carbon dioxide kilogram \n", - "1 unit carbon dioxide kilogram \n", - "2 unit carbon dioxide kilogram \n", - "3 kilowatt hour carbon dioxide kilogram " - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# df2" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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i1atXiIyMlLhtZUpKSuDq6so2QevVqxfmzZsHCwsLfPz4EWfOnMGpU6eQkpKC0aNHIzIyUmIfKcayZctw9+5dTJgwAU5OTjA2NsaHDx/g7e2Na9eu4eXLl1i3bh0OHjwosl1aWhoWLlyIgoICNG/eHLNmzULv3r3RrFkzFBQU4O3bt7hz5061OsX/+uuvbCD6zTffYPny5ejWrRsKCgpw/fp1eHl5IS8vD/PnzweXy8WwYcPYbfv27QtjY2OkpKTgzJkzFQajZ8+eZV8alA1qgZq9pn/66Sf8999/GDZsGCZPnowWLVrg06dPFb6VE7Zx40YsWbIEixYtwv3799G1a1d4eXmJpFFRUZFqX2XVxeft1q1bmDhxIoqLi6Grq4s5c+agc+fOMDY2RkZGBoKCgnD06FHcuXMHU6ZMwcWLF8W6L5DG7c8//2Rf7g0ePBjHjh2Durq6SBo7Ozt2qpaoqKhyg1GmRRHz/7Nnz/Dx40eRlxwlJSW4d+8eAKBTp04S334zbty4gZiYGLRr146t8SouLkZoaCh27dqFwsJCLFmyBPb29pUGiZVZsWIF7t+/j9GjR2PKlCkwMDBAQkICvL29ERkZicePH2Py5Mm4fPlyuS+Qc3Jy4Obmhry8PPzvf//Dt99+i6ZNm+LZs2cwMzMDUPp9NX78eBQUFEBRUREzZ87EqFGjoKWlhadPn8LLywtPnz7FhQsXoKCggCNHjogdJzY2FnPmzEFxcTFUVFSwYMECDB06FGpqaoiJicHOnTuxcuXKSp9lasqqVavYgR/19PQwe/ZsthaGx+Ph0aNHuHjxIjgcjti28fHxcHR0xJcvX6ChoYHvvvsOPXv2hKmpKbKysnDt2jUcPHgQz58/Z1/kamtry/18bN68GbGxsejXrx9mzZqFFi1a4P379zh+/DiCgoLw7t07ODk5ITIyUqQVnpubGzw9PcHn8+Hn54dVq1aVewzme7BLly5VmuqpJs5tXRwjIyMDw4YNY4NZGxsbTJ48Ge3bt4eSkhKSkpIQFRUlMrWisbExoqKiEBsbi8WLFwMoDVS6desmsm9jY2OJ+Z42bRqSk5Mxe/ZsjBw5Erq6ukhISICOjo7UZff09MRPP/0EANDU1MTMmTPRv39/NG/eHF++fMGTJ08QEhKCV69eSb3Pc+fOobCwkK29nDVrFmbNmiWShrlnDho0CCYmJkhKSsLx48crDEaPHz8OoPR5RdJzl7Q+fPiAGTNmQFtbGxs3bkT37t1RWFiICxcuYP/+/UhPT8fSpUvx+++/Y/78+bCwsMCiRYtgaWmJnJwcHD9+HKdOncK7d++wfv16ieP3VERPTw979+7F+PHj8f79eyxZskSk9pvP52P+/PnIzMyEoqIivL29oampKdMxZApGnz59yr4xKm9ABVmkpKSwP1cUTAAQeZNVthZPGpcvX8Z///0HoPRi0tLSknkfQOkw1swfYevWrZg3b57I+i5dumD8+PGYN28e/P39sWnTJowfP77cL/+YmBi4u7uL1DJ36dIF9vb26NGjB7KzszF79mxkZGTAx8cHjo6OIum6du2Kfv36oaSkBEeOHMHWrVulLsvbt2/ZN9K//fabWFmA0mYCGzZsEKvhCQgIAJ/Ph4aGBkJDQ2FgYCCyvk+fPpgyZQpyc3NlroU+cuQIG4iOHj0aR44cEdnH4MGD0bNnT3z//ffg8XhYs2ZNhfPr3blzR+zNXufOnTFkyBA4OTnh1q1bOHfuHNzd3UUe4EJCQtimwAEBAejUqZPIfplmzFu2bJGquUZZcXFx+PPPPwEAFhYWuHLlisjDna2tLUaOHAlHR0fk5uZi2bJlePjwITu9kYKCApydnbF7927cvn0biYmJ7MNXWadPn2aP0717d5F1NX1N//fff1ixYgX7hSErY2NjGBsbsw/n6urqNTbQTm1/3nJzczFv3jwUFxfD3t4efn5+YjflQYMGYdiwYXB1dcWdO3fg5+cHNze3GimfrJ4+fVrh+jZt2lCgXIMEAgE2btyIPXv2ACgdTGnv3r0Sz7Fwa6GoqCixl01lg9HWrVtDX18faWlpiIqKwujRo9m0Dx8+ZLu3VNYK6e7duxg0aBBOnDghMpVa7969YWFhgQULFiArKwunTp3CwoULZSm+mNjYWKxduxarV69ml3Xp0gWjR4/GvHnzcPr0ady5cwc+Pj6YPn26xH18+vQJ6urquHTpkshzSdeuXdmfly9fjoKCAnA4HBw9elTkc921a1c4OzvDyckJ0dHROH/+PC5fvowRI0aIHGflypUoLi6GoqIiTp8+jQEDBrDrunfvjtGjR2Pw4MF48uRJtc6JNK5cucIGol27dsXZs2fFair69++PJUuWiNWcCQQCzJ49G1++fEGHDh1w/vx5se/v/v37w8nJCQ4ODnj9+jV2796NDRs2iKSRx/mIjY3F1KlT2c8PUHq9jBgxAps2bYKHhwfevn0LDw8PbNy4kU1jYWEBe3t7RERE4MSJE/j+++8lBunR0dF4+fIlgNLmmLKqqXNbF8dYuXIle22U/QwCpdfVqFGj8Msvv7CtCZSVldGxY0dkZGSw6Vq0aCH193NcXBxOnTqFIUOGsMtkiSX+++8/bNq0CUBp7dv58+fRsmVLkTR2dnaYN29ehc3Ty2rdurXI782bNy+3TAoKCpg6dSq2bNmCsLAwJCQkoEWLFmLpcnNzce7cOQBgA++qevXqFSwsLBASEiLyjGpjYwMlJSX2GXDChAno0aMH/vnnH5GXm3379kVBQQHOnz+PCxcuiL2slMbgwYMxd+5cHDhwAEFBQThy5AhmzJgBoHQsn7CwMAClL8mqMlK7TFGCcL+0igYLkpZw38+yAxeVJbxe1j6jGRkZbJM6RUVFrFu3TqbthTHNToYOHSoxeGOOsX37dqiqqiI7O7vCkXt79OghsbmzgYEB207848ePGDdunMgXKMPS0pJtcsUEcNJKTU1lf67sAaXsmyumlsvCwkLsRihMXV293CZP5WFqKLW0tLBr1y6Jwezs2bPZfsTMG9HyODg4SGxioqCggCVLlgAobb559+5dkfVMGblcrlggKkxZWVnmt0BAaVM4prZy586dEmsZunXrxjZ9+PDhg9i1xLxtEwgE8Pf3l3ic+Ph4tkXDhAkTxNbX9DVtbm6O9evXl7tenmr78+br64sPHz5AWVm5wreDw4YNY4MF4TeMdc3W1rbCf8IvDEn1lJSUYNGiReyD9Jw5c+Dt7V1usG9qasq+hC3b9Pbdu3ciNRqM8vqNCnd3qKzvkpqaGvbt2ydxTu+JEyeyLWdqYlqgjh07Sqyl4nA4+OOPP9gXXt7e3hXuZ+nSpeU+1MbGxiImJgZAaf4lfa6ZMjPdF8pOIRcbG8u+uJ0yZYpI4MUwMjLC5s2bK8xnTWHu2aqqqjh69GiFD7plm6ReuXKFDRC9vLzK/f7u2rUrZs+eDUD8HiWv86Gnp1fuC/d169ax/ayPHDnCDozJYF74vXnzptx5hpmaLDU1tUoH5pSkJs5tXRzj1atX7Hf44MGDxQJRYSoqKtDX15cpj+WZNGmSSCAqK+Em6QcPHhQLRIVV1BS7uqZOnQoFBQUIBAKcOHFCYpqAgAB2DujqNtEFSisKJAWQwjW4GRkZ8PT0FGtlA4B9mSnpWVdav/zyCxukr1u3Di9evMCDBw/w22+/ASh9YVlRq4OKyBSMCgeBkgorq7y8PPbnyt6+C38xylILVVxcjJkzZ7K1qatXr660j2F5mGarACrtL8PlctkmfBX94ceNG1fuOuG5UJ2cnCpNJ2s7fOahAgBOnDghcZCMyrZ99uyZWH+D6vjw4QPbxn706NEVNidj3srw+Xz2rYwkkgIwhvDb87Lnjykjj8erlfmpmD6sLVu2ZANrSYRrBMr2e7W0tGRvDmfOnJG4PVMrCoifi9q6psv2Sa0vavvzxlwnvXr1KrepEoMJCmJjY8sdUIr8n8ePH4PH4zXI/qL5+fmYNm0a++CyevVq/PHHHxJrZ4QxLwlTUlJErjcmuPzmm29E3sqXF4wyv3M4nEpfPPbv37/ch08FBQV07twZgOzfN5K4urqW23JGW1ubHSjlv//+q7CZf0VN4ITvmRW1QGjZsiUbVEVHR4v0uRIetKOivnOOjo4yN7mUVWZmJu7cuQOgdPCY8lrDlIe5R5mamoo1ryyLuUe9f/9e5IWvvM7H2LFjy624UFJSwqRJkwCUPpQ/evRIZP3o0aPZl+pM0Cnsy5cvbJPUyp49ylMT57YujhESEsI+71W3dYMsqtNUVSAQsIOM9erVCz179qypbMnMxMQEgwYNAlD+szPTWs/ExATffvtttY6nra3NHq+sli1bsi+9O3XqVG6zeOEm51W9d6upqeGvv/6CmpoacnNzMXv2bMyZMwdFRUXQ0tLCgQMHoKioWKV9yxSMCs9LlpubW6UDCmvSpAn7c9m3WGUJfzFIW9MmEAiwcOFCNlBxcHCo0iA/DOHR4RYtWiQ22lbZfw8ePACACr9EyzYPECZ8E5cmnaw1xi1atGAfTPbu3Ys+ffpg8+bNUo1Y7OLiAhUVFRQUFGDYsGGYOHEi/vrrLzx58kRsQCNZCDcbrOxm06NHD4nblVVRnxXhGt+yZR45ciT7hTR16lQ4Ojpiz549NRI8FBQUsH0aKiungYEB+8AhqZxMgBkXFyf2BSxcY9qzZ0+xgQxq45quSj+bulLbnzcmsJc0AnDZf8xAUEVFRWLN4OsKE9yV909S8yMimy9fvsDZ2RmXLl1ia/yknVxcOHAUrt1kauXLjkDPPHw+efIEnz9/BlB6D2CCl3bt2lXaz7OyPn7MPbEm5mUt22WgovXl3eObNm1a4SAwcXFxAEoD6coe3pnvlIKCAra5pvCxFRQUKmxWqKysXOWX3dJ69OgR+/Bb3gwEFWHuUe/evav0HsUEd4DoPV9e50OW64XplsVQU1NjvysDAwPZWivGP//8w3bLqUoTXaBmzm1dHOPhw4cASl9O9e7du0plrQrhF76ySkhIYF9EVuW6r2nMi613797h1q1bIuvevHnD3qMnT55crQFTgdIWiBW9uGSeSaR9vqnOvbtjx47seAcPHz5kRy3etm1bhTXVlZHpDAk3BWGGlK8O4eC2omk6yq6XdrLuH374ga0V6t+/P/7+++9qXRQVjdRXkYoCd+GAvCzhvEqTripB4KFDh0QGvti+fTvGjh2Lli1bYsiQIfDy8pI4XHqbNm1w+PBh6Orqori4GCEhIfj+++9hb28Pc3NzzJgxA1evXpU5P8IP5ZW1aRdunlLRw7y057jsiIQ6Ojo4deoUTExMIBAIEBERgQ0bNmDgwIFo0aIFJk6ciHPnzlXpvAvX7kjTdp8pq6Ryjh8/ni1H2dpRpi8pILmGuDau6aq8Ua4rtfl5KyoqYgMAWdXEy726Jm1LCuF0ldUENkYPHz5kA0k3N7cKB/8qq7z5Rsv2F2VYWVmhadOm4PP5bAAaFxfHdrGRZn7Riq594P+u/5qYmqiy7j7C68ubvqqymjfmnqmpqVnpi+zyvlOE9yGp+bKwmmrSWB7h/noVdZEpT03c8+V1Pqp7vTCtjHJzc8Wm6GKasrZo0aJKA2QCtfN9WhvHYK4hTU3NSrvI1aTqPBsIl7sq131NGzFiBJuPsjXtx48fh0AgAIfDqZFRiCu7JzPfq1V91pXV/PnzRSodRo4cKfLioypkakvXsWNHKCoqoqSkhK0hqQ7hZmyVDUok3Bm5ssGOAODnn39m+x727t1bbDCGqhD+A+7bt49trlSZmmjSXFsMDQ1x+fJlhIeHIzAwEJGRkYiLi0NxcTHu3buHe/fuYdeuXfD19RWpiQRKa5r79euH8+fP49q1a4iOjkZqaip4PB7Onz+P8+fPY+jQoTh69GilHyZJ6sODa+/evfHvv/8iKCgIwcHBiI6Oxrt375CTk4OQkBCEhISge/fuOHXqVJWn6JCmnBU9+H/zzTewtbVFREQEzp49i19++UUsOFVSUpLYRLU2runqvgVsqITPpaOjo0x90ytr0lufNGnSBHl5eSLdLCoi/BBUlw8+9UWHDh1QXFyMFy9ewMfHB7a2tlI3V2vdujUMDAyQmprKBqDp6ens2+iywaiioiJ69uyJGzduICoqCkOHDpV5ftG6VNm9T5oXHtLeb6pzn2WWV/deXdOq8h3J3Kd69OiBXbt2Sb2dcCsJeZ2P6l4vHTt2RI8ePRATE4Pjx4+zXX1evHjBvryZOnVqlZ89auLc1uUx6voZq6pNOMuqD8+GSkpKcHV1xZ9//omLFy+Cx+OBy+WipKSEnbO8X79+jbJ1UXh4uEjLg4cPHyIzM1OmUZHLkikY1dLSgpWVFR48eICXL1+yIzxVlXBzIOFpXiQRnsC2smZEW7duZUcp7dy5M06fPl0jD0HCzZsEAkGNjfBZH/Tt25d9G8jj8RAeHg5fX18EBwcjNTUV06ZNw/3798XeLGtqamLatGlsB+1Xr14hODgYBw8exNu3b3HlyhX8+uuv+P3336XKh/DFXFntu/AATNX5EFRGVVUV48aNY4O5pKQkXL16lW2W/O+//2L58uUS+6GUR/gNoTStDJgmNuWVc8KECYiIiEBKSgoiIiLQr18/dv5RoHQEV0nN8xrzNV3X1NTUoKGhgZycHPB4vEZ7LnV0dJCXlyd10zLhz2l9rjWvLbq6ujh06BBGjRqFFy9eYMGCBeBwOBX2ZRdma2uLc+fO4c2bN3j//j3bX1u4D7ewPn36sMEogHodjKalpVXYtEy4NqSqo1Ey98ysrCzk5+dXWDsqfE0L32uF91FQUFDhi+2K7ufCgXNlLWrKqy0Tvmd/+PChwn2Ut31aWhrS0tKqfI+qqfMhq8ruOdJcL25uboiJiUFMTAzi4+PRvn17tn+fgoICJk+eXOX81cS5rYtjMOcmKysLOTk5DeIlYXWv+9rATBmUn58Pf39/zJ49G9euXWMr12pi4KL6hsfjYf78+eDz+dDU1ER2djaSk5OxfPlyHD16tMr7lbkKg6lyFggE2L9/f5UPDJS+qWFqBIT7w0jCjH6mqqoqMuhMWbt374a7uzuA0jfS586dq7EO9MJ9H65du1Yj+6yPuFwuRo0ahZMnT7JvDt+/f4/bt29Xui0zv9HNmzfZ5jnC81RVRvjmyoyAWB7hgZPq8sHfxMQEM2bMwPXr19njBgcHS11TBJRex8yLnMoGgEpLS2Ob2pZXzjFjxrAPWUzT9NDQULapUnkPvvX5mq4Pbz9lxZzPe/fuifVJaiyYkaWTk5OletAUbkVTnT5DDZmhoSECAwPRunVr8Pl8LFiwoNwBx8oq21SXCS779Okj8TPC1Jbev38fubm5bN8lc3NzmSc7r22V3fuE+7RX9R7PBOx8Pp/tb1dZflRVVUWCZObYfD6/wlZhxcXF7Mjlkgh3MapoIK5Pnz6JNMcVZm1tzf7dqzKiMdO8LjExUaRfrCxq6nzIqiauF2dnZ3bAl+PHj6O4uBinTp0CAAwcOFCqlnflqYlzWxfHYPr5CgQCqZ7rypLHd3OLFi3Yl5k1MZJ3TTA3N4e9vT2A/2uqy/yvra0tceTuhm758uVssH3gwAF2NN+AgACZKmTKkjkYnTp1KjvK6KFDh8Q67lYkICBApO8nh8Nhp1N4+fJluVOTJCYmsoMQDRgwoNw+o4cOHWLnlrKwsMD58+erNbdPWa1atWJvcBcuXMDr169rbN/1Vf/+/dmfy/tylITL5bJNPmXZztDQkJ3cPTAwsMI+eMxbGAUFhQpHo60tKioq7INfcXGxzMEHM8La69evK3wZc+zYMbFtytLW1sawYcMAlF6bBQUF7MOupqYmRo4cKXG7+nxNM8F1YWGhnHMiPeY8FxQUYO/evXLOTe0QvicwD3Hl4fP57HWorKxc72rm6pJwQFpSUoL58+eXOx2TsPKC0fLOZY8ePaCsrIyioiKcOXMG79+/ByBdf9G6dvLkyXJrCLOysnDhwgUApYFFVfseCt8zK3pYSkhIYEfetbGxEantE566xM/Pr9x9MM31ytOiRQv2Qb6iwFh4BPSydHR02FGTL168yL6olBbzzAX83xQxsqqp8yGrgICAcmuMS0pK2PuRrq5uuQMnaWhowNnZGUDp/evSpUts643q1mTVxLmti2MMGzaMvQ737dsn8/bCrQvq6vtZQUEBQ4cOBQC2C1lNq8ozB9MP+cGDB7h16xaCg4MBlFYAyDqtYX3n6+vLVi7NnDkTI0eOxObNm9G2bVsAwJo1a/DmzZsq7VvmYLRJkybw9vaGoqIi+Hw+XF1dxTqCl/Xx40d8//33mD59utiouQsXLmSndVm9erXYQEbFxcVYuXIl205+6dKlEo/h5+fHjpRramqKgICAWunkLDwC5tSpU9kveklKSkpw+vTpSvvDysujR4/YUdXKIzwsvnDb98DAwAq/ZDIzM9k3prK2mWcG+ODxeFi5cqXEfiCHDx9mh5d3cHCAqampTMeQxtWrVyv8++bn57MvUDQ1NSsdpbKsWbNmsc22/ve//0k8nw8ePMDOnTsBlD7MVjT9ClP7mZWVhTNnzrA3RUdHxwr77NbXa5r5/L59+7ZO+2FVx8yZM9lBNLZv384+TJfnv//+w+XLl+siazVm6tSp7AvBP/74o8JJ7bds2cKOvDlu3LhqzU9tZWXFjhLZUBkZGSEwMBAWFhYoKSnBvHnzcPbs2Qq36dixI/tSNTQ0lO2rU96Ikurq6uyLQObeAdS/JrpA6fUv6YFaIBBg1apV7D1RlkGfyurWrRs7yqqfnx87PYSwgoICLFq0iB0lvex8y927d2fPqY+Pj8SpxFJTU7Fhw4YK88IVmrPa19dX4ovap0+fVtqthZl7uqCgANOnT69wAD/h8TaA0ulgmBeQJ06cqLSF29u3b8VemtTU+ZBVWlpauSNRu7u7s925pk+fDhUVlXL3wwQQ6enp7LyIzZo1w4gRI6qVv5o4t3VxDAsLC3ae66tXr5Y7dytQGpiVbQEj/Gxd1eCjKpYuXcr2O50zZw4SEhLKTVv2upcGUy5ZyjRq1Cj2/jx37lw2kK3qiMz11du3b7FmzRoApQOYMnOLNmnSBAcPHoSKigq+fPmCuXPnVmm2iSpNBtivXz/s2bMHy5YtQ25uLr777jvs2bMHY8aMgbW1NXR0dJCTk4N3797h+vXruHTpUrlDCbdq1QrLli3D9u3b8ejRIwwZMgQrVqxA69atkZSUhL1797IP/BMnTpT4dvfixYtYvHgxBAIB1NXVsWXLFmRlZVU43YexsXGVHmrGjh2LGTNm4MiRI3j69Cn69OmDGTNmoF+/ftDT00N+fj4SExNx9+5dXLhwAR8+fEBUVFS1mn7UlsePH2PRokXo0qULhg8fjs6dO8PQ0BB8Ph9JSUk4c+YMLl68CKC0WYfwsOn79+/H3LlzMWTIEPTr1w9t27YFl8tFVlYWnjx5goMHD7I3MOFJeaUxY8YM+Pv7Izo6Gv7+/khOTsbcuXPRqlUrZGRkwN/fn+0gzuVysWXLlho6I6LOnj0LV1dX9O/fHwMHDkTHjh2ho6OD3NxcvHjxAocOHWKvMTc3N5nn1uzQoQOWL18ODw8PPHv2DH379sXy5cvRtWtXFBQU4Pr16/Dy8kJubi44HA48PT0r7JszdOhQ6Orq4tOnT1i/fj3bbLiyvmn19Zru3bs3fH19kZ6ejnXr1mHixInQ0tICUDp4gKzz69WFpk2b4vDhw3ByckJRURGmT5+OESNGwMnJCebm5lBUVER6ejoePXqE4OBg3Lt3D4sXL672Q1Bd4nK5+OOPP7BgwQJ8/vwZQ4YMwdSpUzFo0CAYGhqiqKgIz58/x8mTJxEeHg6gNAhjvrzk7fXr12KtcJiXoDk5OWITxNvY2MDc3LzGjm9kZISLFy/C0dERr169wty5c6GgoFDu3LYcDgd9+vTBpUuX2FqwJk2aVDilRp8+fRATEyMyn1x9rBnt1q0bNm/ejCdPnmDy5MnQ19dHQkICDh48yHbN6dGjR4Xzg0rD09MTgwYNQkFBASZPnoxZs2bBwcEBWlpaiIuLw+7du9l7+dixYyV+Hnfs2IHhw4ejuLgY48ePx4IFCzB06FCoqanh33//hYeHBz5+/AhLS8sKX9DMnTsXS5cuRXp6OoYPH45Vq1ahXbt2yMrKwo0bN+Dt7Q0DAwOoqKiUO3LqsGHD2Hv2/fv30atXL8yePRs2Njbgcrng8Xh4/PgxLl68CEVFRfZ7HCitYTp8+DCGDBmCrKwsrFmzBhcvXsTEiRPRvn17KCsrIzMzE0+ePMG1a9cQFhYGR0dHuLi41Mr5kEW3bt1w9OhRJCQkYNasWTA1NUVqaiqOHz+OwMBAAICZmRlWrlxZ4X66du3K5oupFZ04cWKFAaw0aurc1sUxduzYgZiYGCQnJ8Pd3R03b97ElClT0L59eygpKSE5ORm3b9/G2bNnsX79epFRYU1NTfHNN98gOTkZu3fvhrGxMdq0acMGinp6emxT6JpkaWmJ9evXY9OmTXj79i3s7e0xc+ZMfPvtt2jWrBm+fPmCuLg4XL58GS9fvpR5oNXevXsjISEBly9fxuHDh9G7d2+2dlNTU1Piy1RVVVVMmDAB+/fvZ68la2trqQeDbAhKSkowd+5cZGdnQ1lZGQcPHhQZxLJz585Yv349fvrpJ9y7dw/btm2TaQBHoIrBKFA6WXWrVq3www8/4NGjR4iNjRVpr19Ws2bNsHr1avZhUtj69euRmZnJPtxLegs6cuTIckcOCwoKYmtOc3NzpRpK2cvLq8pDLnt4eEBPTw87d+7E58+f4enpCU9PT4lpVVRU6n1V/YMHDyr80Hbs2BE+Pj5i/QTy8vJw4cKFCmt/5s2bh7lz58qUH0VFRfj5+WHKlCmIjIxEdHS0xCbcxsbGOHnyZK0GRUVFRbh69WqF09SMHTsWP/74Y5X2v3HjRuTm5mL//v149+6dxC9RNTU1eHp6ss1wy6OsrIyxY8fi77//Zps3GxoaijSrLE99vKbHjRsHDw8PvH37Fvv27RNpTmRqalqjfZFqkr29PS5cuIDZs2cjOTkZly5dwqVLl8pNXxtf2rXN1dUVxcXF+OGHH5CXl4eDBw+yo5eX1bFjR/j6+lZ5tOmaFh0djUWLFklc9+nTJ7F1Xl5eNRqMAv9XQ+ro6IjXr19jzpw54HA4GDt2rMT0tra2ItcQ0xS3PDY2NtizZw/7u4mJSb18ebNz504sXbqUHX29rE6dOsHPz6/ao3BaWlri9OnTmD59Ong8Hg4cOIADBw6IpRs9enS5NU09evTA/v37sXDhQhQUFODPP/9kB0oESl+Q7dixA7dv364w+Jo2bRquXbuGgIAAvHjxQuz70czMDCdPnpQ4+rkwDw8PaGhoYO/evUhPT2fHyihL0kuIdu3aITQ0FNOnT0d8fDwiIiLY4F8SSfeomjofstiwYQO8vLxw7do1tmWUMBMTE5w7d06q6f+mT5/O1ooCNVeTVRPnti6O0bx5c1y+fBlTpkzB48ePy33OKs///vc/rFy5EgkJCWKDPlXn+Vqa4yorK2PTpk3Izs7Grl27JMYGVWktt3jxYgQEBKCgoAArVqwQWefq6lpuk+bp06eL3DcaW63otm3b2IHz1q9fL/FF6JIlS3D16lWEh4djx44dGDRokExz2FY5GAVK377eunULoaGhCAkJwe3bt9mpPdTV1WFkZIQuXbpg2LBhGDlyZLkPsBwOBzt27ICjoyMOHz6MmJgYfPz4ETo6OrCyssLUqVPL/ZKWBwUFBfZN0ZEjR3Dr1i0kJCQgKysLampqMDIyQqdOnTBgwACMGjVK5uabdWX8+PEwMzPDrVu3EBUVhZSUFKSnp6OoqAi6urqwsrLCqFGj4OrqKvbgc+TIEdy8eRM3b97E48ePkZaWhoyMDCgrK8PExAS9e/eGm5sbevbsWaW8cblcXLx4Ef/88w9Onz6NBw8e4NOnT9DQ0EDbtm3h4OCA2bNn1+oocFu2bMHIkSNx69YtxMbGIjU1Fenp6VBUVIShoSF69OiBSZMmYdCgQVU+BofDwZYtW+Ds7IxDhw4hKioKaWlpUFJSgqmpKb799lssWLBA6hvrxIkT8ffff7O/Ozs7SzX1QX28pps2bYorV67Aw8MDN27cwLt37xrMfJw2Njb4999/cfLkSVy+fBmPHz9mm+Tp6uqiTZs26NOnDxwcHBrsG9Rp06Zh+PDhOHLkCG7cuIEXL16Ax+NBRUUFzZo1Q/fu3TFq1CiMGTOmxob0b0yMjY3ZGtLXr19j9uzZ4HA4EpviM4NkMJg+g+WxsbEBh8Nhm7fXxya6QOl9PiQkBN7e3jh79izevHmDkpISmJubY/z48Zg3b161p2Rj9O/fH7Gxsdi/fz+uXLmC169fIz8/H82bN0ePHj0wZcqUSl/4ubi4wNLSEjt37kRYWBgyMjLQvHlz9O7dG4sWLUKPHj0qHRCGw+Hg77//ho+PD3x9fREfH4/i4mKYmZlh1KhRWLx4sVStthQUFPDbb7/B1dUVR44cQXh4OFJSUlBcXAwDAwO0aNGCbZUhSbt27RAZGYlz584hMDAQsbGx+PjxI4qLi6GjowMLCwv07NkTw4cPL/f6qYnzIQtlZWWcOXMGR48excmTJ/H8+XPk5eWhRYsWGDVqFJYuXSqxwkOS8ePHY/Xq1eDz+ejevXuNDoJYE+e2Lo7BPP+dPXsW586dw4MHD/Dx40doaGjAyMgIlpaWcHJykviMM2vWLOjp6eHw4cN4/PgxeDxelZpnVsWSJUswatQoHDp0CDdu3EBiYiIKCgqgr68PExMTDBkypNKXOZJYW1vjypUr2LVrF+7cuYO0tDSp+o926NAB1tbWePToEVtT2ljcvXsX27dvB1D6Yqu8rpIKCgrYv38/7OzswOPxMHfuXISHh0v9eeTweLyG0RmLEEIIIQ2ar68vW/v88OHDRjkPH6n/bt++jeHDhwMA/vzzT3bmAEJklZeXxza1d3FxwV9//SXvLDU4X+fs9IQQQggh5KvEjFKvoaFRpVo0Qhjnzp1jZ1NojHOL1gUKRgkhhBBCyFfh3bt37CjWLi4uUjclJKSskpIS7N69GwDQunVruUwz2BhUq88oIYQQQggh9VlKSgry8vKQkJCAn3/+GQUFBVBSUsKyZcvknTXSwGRmZrL/9u7di7i4OADAihUrxAb6JNKhYJQQQuqBlJSUKk0Qr6KigtatW9d8hgghpJGYM2cOIiMjRZb973//q/GRsknjt3//frG5We3t7eHq6iqnHDV8FIwSQkg98Ouvv8LPz0/m7erzNDeEEFKfNGnSBObm5pg7d261568lXzclJSWYmJhg7NixWLlypVQzFxDJaDRdQgipBxYsWEDBKCGEEEK+KhSMEkIIIYQQQgipc1SnTAghhBBCCCGkzlEwSgghhBBCCCGkzlEwSr56+fn5eP36NfLz8+WdlVpB5Wv4GnsZqXwNW2MvH9C4y9iYy8b4GspISENFwSghKJ24uDGj8jV8jb2MVL6GrbGXD2jcZWzMZWN8DWUkpCGiYJQQQgghhBBCSJ2jYJQQQgghhBBCSJ2jYJQQQgghhBBCSJ2jYJQQQgghhBBCSJ2jYJQQQgghhBBCSJ2jYJQQQgghhBBCSJ1TkncGCKmP+Hw+srKyUFRUJO+sVBufz4eKigo+f/6M7OxseWenxjX28gGNv4xUvoaDw+FAS0sLKioq8s4KIYSQRoCCUULKKCwsBI/Hg7a2NrS1tcHhcOSdpWrh8/koLCyEiooKFBQaX2OIxl4+oPGXkcrXcPD5fGRkZEBbW5sCUkIIIdXWsL8VCakF2dnZaNasGVRVVRt8IEoIITVJQUEBzZo1Q1ZWlryzQgghpBGgmlFCyuDz+VBUVJR3NgghpF5SUFCAQCCQdzYIqVOqJUVQzv0CfEoHmqiDr6WDfHVN8Pl8eWeNkAaNglFCCCGEEELKoV6YD4VT+4FrAcD/fxGjoGcE9XU7kdvMCHx6OUNIlVEzXUIIIYQQQiRQ4nCgcD0AuHqeDUQBAOnvgR/no0kONVknpDooGCWEEEIIIUQClZws4IKP5JXZPHASX9L4EoRUAwWjhBBCCCGESMApKQJyvpSfIPktBaOEVAMFo4QQIsTKygpWVlZy3wchhBD5EyipANo65Sdo0YYG9CKkGigYJYR8VRYsWAAul4uEhIQ6Pa67uzu4XC7Cw8Pr9LiEEEKqrkBDC3CZLXllcwPwTVpSMEpINdBouoQQIuTChQv1Yh+EEELkr4TPR4nNIChm84Dzx4DCgtIVFh2AFb8hr4mm6MBGhBCZUDBKAACxsbFwd3fH3bt3UVRUhPbt22PBggUYP368VNunp6fDx8cHDx48wIMHD5CYmAgA4PF4EtP7+vpi0aJFFe6zX79+Ig/17u7u2Lp1q8S0qqqqSE1NlSqvhFSkVatW9WIfhBBC6oc8ZTUoj3SFygBHcHKyARVVFKtrokC1CdWKElJNFIwShIeHw9nZGSoqKhg3bhy0tLQQGBiIOXPmIDExEStXrqx0H/Hx8di0aRM4HA4sLCygrq6O3NzcctNbWVlh9erVEtdduHABcXFxGDRokMT1rq6uMDMzE1mmpESXcmNTWFiIw4cPIyQkBM+ePUN6ejq0tLTQp08frFq1Cp07dxbb5tKlS/jrr79w//595ObmQl9fHzY2Nli+fDk6duwIKysrvHv3DgBEtrezs0NQUBAAsH09Hz9+DADYunUr3N3dsX//fkyaNEnsmKdPn8bcuXOxbt06/PDDDxL34eDggMjISADAqFGj2G1NTU3x8OFDdOnSBdnZ2YiPj4eqqqrYMQYPHozHjx/jyZMnMDAwkPocMi9wAgMD8fHjR+zcuRPPnz+HtrY2xowZg19++QVNmjRh08t6zpmXSl5eXtDV1cX27dvx9OlTcLlcTJkyBevWrYOCggJOnz6N3bt348WLF2jevDnmzZuHJUuWiOVXIBDAx8cHx48fx9OnT1FcXIx27dph1qxZmDZtmtTlJoSQmlbEUUCRhjagof1/CykQJaTa6An+K1dcXIylS5eCw+EgKCiIfdhcvXo1hg4dCnd3d4wdOxYWFhYV7qddu3YICgqCtbU1NDU10bNnT7x48aLc9NbW1rC2thZbXlhYiIMHD0JJSQmurq4St508eTL69u0rQylJQ5SZmYm1a9fCxsYGQ4YMAZfLxdu3b3H58mVcvXoVly5dQrdu3dj0P/30E3bv3g0dHR04ODhAT08PycnJuHXrFrp06YKOHTtiwYIFOHHiBJ48eYL58+dDW7v0oaLsyw1hEydOhLu7O06fPl1uMMrhcDBhwoRy9zF58mQAQGRkpMjLFG1tbSgoKGD69On49ddfceHCBbHWCP/99x9iY2PZMlXFX3/9hatXr2LkyJGwt7fHtWvX4O3tjczMTBw8eJBNJ+s5Z1y8eBE3btyAg4MDevfujStXrmD79u1sGf/44w+MGDECtra2CAwMxMaNG2FgYCByzgQCAebNmwd/f3+0bt0aLi4uUFZWxs2bN7FkyRI8e/YMmzdvrlL5CSGEEFI/UTD6lQsLC8ObN28wZcoUkVoPTU1NrFq1Ct999x18fX3x448/VrgffX196OvrVzs/Fy9exKdPn+Dg4FAj+yMNF5fLxZMnT2BsbCyyPC4uDkOGDMGmTZtw/vx5AMDVq1exe/dudOzYERcvXoSuri6bvri4GJ8+fQIALFy4kK1hXLBgAVq0aFFpPlq2bIk+ffrg1q1bSE1NFamZTE9Px82bN9GnTx+0bNmy3H1MmTIFiYmJiIyMlPgyZerUqXB3d8exY8fEgtFjx46x+6iqGzdu4ObNm2jTpg0AIC8vD3379oW/vz82bdoEIyMjALKdc2FXr15FSEgIG6iuXbsW3bp1w969e6GpqYmwsDD2/CxZsgTdunWDp6enSDDq6+sLf39/TJs2DTt37mRbOxQWFsLNzQ179uyBi4sLunTpUuXzQAghhJD6hUbT/cpFREQAAAYOHCi2jlnGNC+sCz4+pRNLu7m5lZsmOjoanp6e2L17N0JCQlBQUFBX2SN1SFVVVSwoAoAOHTrA3t4eUVFRKCoqAgAcPnwYALBlyxaRQBQobcJd3RcbEyZMQElJCfz9/UWW+/v7o7i4uMJaUWkYGBhgxIgRiIiIwJs3b9jlBQUFOH36NExMTDBgwIAq73/+/PlsIAoATZo0gbOzMwQCAR48eMAul+WcCxs/frxIjammpiaGDRuG3NxcfPfddyKBuomJCfr06YP4+HgUFxezy//++29oaGjgjz/+EGl2r6Kigo0bNwKA2PknhBBCSMNGNaNfuVevXgGAxGa4XC4XzZo1Y9PUtsTERNy6dQvGxsYYPHhwuel+//13kd8NDQ2xb98+fPvtt1IdJz8/X+T3wsJCkf/5fD74fL4sWa/XmMEVBAJBgyvX48ePsWvXLty+fRtpaWligVB6ejoMDAxw//59qKqqwtbWVuoyVnY+hNeNHTsWa9aswenTp7FgwQJ2+alTp6CiooIxY8ZI3Jfwssr+DtOnT0dgYCCOHTvGBl8XLlxAZmYm5s6dCwUFBZn/hswxra2txbZjakN5PJ7IOmnOuaGhocj+raysxPbP1CBbWlpKXFdSUoIPHz7A2NgYOTk5iIuLg6GhITw8PMTKwQStz58/b3DXMNCwP4Pl4fP57L207D20MWrMZWzMZWM0pDKqqanJOwuE1CkKRr9yWVlZAAAtLS2J6zU1NZGSklInefH19QWfz8fkyZOhqKgott7Kygr79u2DnZ0d9PX1kZKSgrNnz8LDwwOurq4IDQ1lB46pSEpKCkpKSsSWM6PxqqioNIgvLFlJqtGqz+7duwcXFxcAQP/+/eHo6AgNDQ1wOBwEBwfjv//+w5cvX6Crq4usrCwYGhqK1LSVh/nbFxYWSvw7M4GD8Dp1dXUMHjwYly5dwn///Yc2bdrg5cuXePDgARwcHKCuri6SXtI+mOMWFRVJPK6dnR3MzMxw4sQJfP/991BUVMSxY8egoKDA1rzK+jdkjtmkSZNyr+mCggJ2nbTnnEnPnO+K9q+mpia2jsPhAAByc3NRWFiIjx8/QiAQ4P3799i2bVu55RE+dkPU0D6DFcnPz2e/Pxhfw4jmjbmMjblsjPpeRkVFRZibm8s7G4TUKQpGSb3A5/Ph6+sLDoeDqVOnSkzj6Ogo8ru5uTlWrVoFfX19LFu2DNu3b8fRo0crPVbZZoiFhYVsX0AVFRV8/vwZKioqVS9MPSMQCFBUVARlZWU2CGgIdu3ahYKCAly6dAl9+vQRWXf//n38999/UFFRgbKyMrS0tJCeng4lJSUoKFTc+4B50aGioiLx78yco7LrJk2ahEuXLuH8+fNYv349zp07xy4vm1bSPpjjKisrl3t9MQMZ3bx5Ex07dkRERAQGDx6Mli1bVulvWNExmaawSkpK7DppzzmTXtI+pDl22b+Bjo4OgNIRjm/cuCF1+RqKhvoZrIiamhpb+132HtoYNeYyNuayMb6GMhLSUFEw+pVjakTLvuFmZGdnl1trWpNu3LiBpKQk9O/fv8KBYCRxdXXFypUrcefOHanSl9cERkVFBWpqasjOzq40oGlImGaBHA6nQZXr7du30NHRga2trcjy3NxcPHr0CEBpmTgcDrp27Ypr164hKioK/fr1q3C/TCAkEAgqPB9l1w0fPhxcLhdnzpzB+vXr4e/vDx0dHQwbNqzc/QgvZwK3io47bdo0bNmyBT4+PrC0tIRAIICbmxsbwMj6N6xoO0nrpD3nTHpZ9y8pfwoKCtDS0kKbNm3w4sULZGVlgcvlSl3GhqChfgYroqCgIHYvZe6hjVljLmNjLhvjaygjIQ1N4/hWJFXG9BWV1C+Ux+MhIyOj0mldaoI0AxeVR0VFBU2bNq1wXlPS8JiamoLH4yEuLo5dVlJSgo0bN+Ljx48iaWfOnAkAWLNmDTIzM0XWFRcXIy0tjf2dqYVLTk6WKT8qKipwcnJCYmIi/vzzTyQkJMDJyUnqt+zSHFdfXx8jRoxAaGgojh49yg5sVFdkOec1bfbs2cjNzcWyZcuQk5Mjtv7t27dISEio1TwQQgghpG5RzehXzs7ODh4eHrh+/TqcnZ1F1l2/fp1NU5s+ffqES5cuQUdHR6wprjRevXoFHo8HS0vLWsgdkZe5c+fi+vXrGD58OJycnKCqqoqIiAi8f/8e9vb27EjQADB48GAsXrwYe/bsQbdu3eDo6Ag9PT2kpKQgLCwMixcvxsKFCwEA/fr1w+7du7FixQqMGTMGGhoaMDExEZtSRZKJEyfi8OHDcHd3Z3+XVt++fcHhcLB582a8ePECWlpa0NLSwqxZs0TSzZw5ExcuXEB6ejqWL18OJSWlOhv0RpZzXtPc3Nzw4MED+Pn54c6dO+jfvz+MjIyQlpaGFy9eICYmBn/99ZdU0/EQQgghpGGgmtGvHNMs1t/fn22GB5Q2z2WmWJg8eTK7PCMjA8+fP0dGRkaN5eHkyZMoLCzEhAkToKqqKjFNdnY2njx5Iracx+Nh8eLFAMAOvEIah+HDh+Po0aNo2bIlTp8+DX9/f7Rt2xbXr1+HqampWPpNmzbh2LFjsLS0REBAALy8vBAVFYW+ffuKjLTMzJfJ5/Ph6emJX375BUeOHJEqT8x8okVFRWjZsiV69+4tdXnat28PLy8vcLlc7N27F7/88gv+/PNPsXQDBgyAsbExOBxOlVoKVIes57wmcTgceHl54fDhw2jfvj1CQkLg5eWFmzdvQlVVFb/++mu1prchhBBCSP3D4fF4AnlngshXWFgYnJ2doaqqCmdnZ2hqaiIwMBAJCQnYsGEDvv/+ezatu7s7tm7ditWrV2Pt2rUi+xGe8iIoKAhZWVlwdXVll23evBnNmjUTO76trS2ePn2KyMhIdOrUSWIeExIS0LlzZ3Tt2hUdO3Zka72uXr2KT58+4dtvv2Wn2ZBVfn4+3r17B1NTU6ipqSE9PR16enoy76e+4vP5KCwshIqKSqPpryassZXv/fv3sLKygo2NDQIDAwE0vjKWReVreITvk2XvoY1RYy5jYy4b42soIyENFTXTJejXrx+Cg4Ph7u6Oc+fOoaioCO3bt8f69evZKSWk4efnV+GyNWvWiAWj//77L54+fYru3buXG4gCpf3t5syZg3v37iE4OBifP3+Guro6OnXqhAkTJsDNzU3idDCENDT79u1DcXExvvvuO3lnhRBCCCGkVlHNKPnqUc1ow9YYyvf582f8/fffePfuHY4ePYq2bdsiIiKCfcHSGMpYESpfw0M1o41HYy4b42soIyENFdWMEkKInPF4PPzyyy9o0qQJbGxssHPnTok1/YmJiRJbIJSlra3NDthECCGEEFJfUTBKCCFy1qJFC/B4vErTJSYmYuvWrZWmMzU1pWCUEEIIIfUeBaOEENJA2NvbSxW0EkIIIYQ0BI2j8wohhBBCCCGEkAaFglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCCCGEEEIIIXWOglFCSK0IDw8Hl8uFu7t7rR3D3d0durq6iIyMrLVjVFVCQgK4XC4WLFgg9TYLFiwAl8tFQkJCLeaM1Ed18XkhhBBC6hsKRgkhAIDo6GhwuVxMnDhR4vrly5eDy+Wib9++Etdv27YNXC4Xnp6etZnNKqtKcEgaHgcHB3C5XHlnQyJdXV04ODjIOxuEEEJIvaEk7wwQQuqHHj16QENDA9HR0SgpKYGioqLI+oiICHA4HDx58gSZmZnQ0dERWw+ADVa7d++Ou3fvolmzZnVTgEbgp59+wooVK2BsbCzvrJA6Rp8XQgghXyOqGSVEDnKhjOQiFTzNUUJKkQpyoSzvLEFZWRm9e/dGVlYWHj58KLLuw4cPePnyJRwdHSEQCNjAk1FYWIh79+5BS0sLnTt3BgCoq6ujbdu29HAtA0NDQ7Rt2xbKyvK/Hkjdos8LIYSQrxEFo4TUsUyBKmZcS0en429gezoBHY+/wYxr6cgUqMo7a2ytZtlgk/l98eLFaNq0qdj6mJgY5OXlwcbGhq1RLa8PnJWVFaysrJCTk4N169ahQ4cO0NfXh62tLQICAiTmKykpCbNmzULLli3xzTffYOTIkTL1E/X19WWDZD8/P3C5XPZfeHg4gIr7a7q7u4ukLVu+u3fvYty4cTAzM5PYRPTp06dwcXGBmZkZTE1NMXHiRMTHx4ulk5SH8PBw6Orq4o8//sDDhw8xbtw4mJiYwMzMDFOmTCm3f+nbt2+xZMkSWFpaQl9fH+3atcOCBQuQmJgo9XkDgPT0dGzYsAE9evSAgYEBWrZsicGDB2P37t1iaYODg+Ho6AgzMzMYGhrC3t4ee/fuRUlJiUg64SbTb9++xfTp09GuXTuYmJhgzJgxePz4sdi+X716hYULF8La2hoGBgYwNzdHv379sH79ejYNl8tlrwvhvzHTNFv4uM+fP8fUqVNhbm7OnvPKmnJzuVyJzWyzs7OxdetW2NrawtjYGGZmZujbty82b96MoqIiREREwNDQEAAQGRkpkjdfX18AFfcZjYuLw8yZM9G6dWvo6+vD2toaa9euRWZmpljaqny+CCGEEHmhZrqE1KFcKGPe9VRcScwRWX4lMQfzrqfiyCA9qKNITrn7v2A0PDwcS5cuZZeHh4dDU1MT3bt3R+/evUWCMma98PaVKS4uxrhx45CZmQlHR0fk5eXhn3/+wYwZM3D27FkMHDiQTfvhwwcMHToUKSkpGDRoEDp37oxnz57ByclJ6uNZWVlh/vz52L9/PywtLUUCCjMzM6n2UZ67d+/Cw8MDffv2xYwZM5CUlCSy/u3btxg+fDi6deuGWbNm4dWrV7h48SJu376NK1euoF27dlId5+HDh9i3bx/s7e0xY8YMPHr0CEFBQXj69Cmio6OhpqbGpo2JicG4ceOQm5uL4cOHw9zcHImJiThz5gyuXr2K0NBQtGzZstJjvnr1CqNGjUJKSgpsbGzg4OCA3NxcPH36FDt27MCSJUvYtPv27cPatWuho6MDFxcXqKurIzg4GOvWrUN0dDSOHTsGDocjsv/ExEQMGjQI7dq1w6RJk/Du3TtcunQJo0aNwt27d6Gvrw8AeP/+PQYOHIjc3FwMHToU48aNQ05ODl69egVvb2/89ttvAIDVq1fjxIkTePfuHVavXs0ex8rKSuS4b968weDBg9GhQwe4uroiMzMTKioqKCwslOpvISwjIwMODg6Ij4+HlZUVZs6cCT6fjxcvXsDT0xOLFy+GmZkZVq5ciR07dsDU1BSTJ08uN29l3blzB+PGjUNBQQHGjBkDMzMz3Lt3D/v27cOVK1cQGhoKXV1dkW1k+XwRQggh8kTBKCF1KLOIIxaIMq4k5iCzSB/qcmyh2bVrV2hqauL27dsoLi6GklLpLSIiIgK9e/eGkpIS7Ozs8OuvvyIjI4NtUli2v2hl3r9/j65duyIwMBAqKioAgPHjx2PMmDHw8vISeVj+5ZdfkJKSgg0bNuD7779nlx85cgTLly+X6njW1tbQ1tbG/v37YWVlhbVr10q1nTRu3LiB3bt3Y9q0aRLXR0dH4/vvv8eGDRvYZX5+fliwYAFWrVqFCxcuSHWcq1ev4q+//oKLiwu7bN68eTh16hSCgoLg7OwMACgqKsJ3330HgUCAGzduiAQ70dHRcHR0xOrVq3Hq1KlKjzl37lykpKTA09MT06dPF1mXnJzM/vz27Vts3LgRenp6uHHjBkxMTAAAP/74I5ycnBAYGIjTp0+LDY4VGRmJn3/+GUuXLkVhYSFUVFTw+++/Y/v27fD19cWKFSsAABcuXMDnz5+xZcsWzJ8/X2QfGRkZ7M9r165FREQE3r17V+Hf+Pbt21i1apVIrSqAKo1ivHLlSsTHx2PlypXYuHGjyLq0tDQ0bdoUWlpaWLVqFXbs2AEzMzOprz8+n4+FCxciJycHZ8+exaBBg9h1mzZtgoeHB3766SexWmpZPl+EEEKIPFEzXULq0OdCfoXrsypZX9sUFRVhY2OD7OxsPHjwAEDpg+2rV69gZ2cHALCzsxPpN1pYWIiYmBhwudxKa3mE/f777+yDMgD0798fpqamiI2NZZcVFhbi3Llz0NPTw+LFi0W2d3NzQ+vWrata1BpjbW1dbiAKlDbtZIIqxqRJk9CxY0eEhYWJ1aSWp0+fPhg3bpzIsqlTpwKAyDkLDg5GYmIili5dKvb3sLGxwciRIxEaGoqsrKwKjxcbG4t///0Xtra2YoEoAHzzzTfsz6dPn0ZxcTEWL17MBqIAoKKigp9//hkAcOLECbF9tGjRQqQGHgB7LoXLxGjSpInYsqr0sTQwMMCqVatk3q6stLQ0BAQEoFWrVlizZo3Yen19ffaFTlXcvn0br169wpAhQ0QCUaA0CNbV1YW/v7/EGl1pPl+EEEKIvFEwSkgd0lap+COnVcn6uiDcVBf4v1pPe3t7AEC3bt2grq7OLr937x7y8vJgb28PBQXp8q+trS2xmeg333yDz58/s7+/ePEC+fn56Nq1q0gzVABQUFBAr169ZCtcLejevXuF662traGhoSGyjMPhoE+fPgCAJ0+eSHUca2trsWVMQCh8zmJiYgCUnjt3d3exf2lpaeDz+Xj16lWFx/v3338BQKpatEePHgH4v2tEWM+ePdGkSROJ/UAtLS3FrhlJZRo2bBjU1dXx/fffY8aMGfDx8cHLly8rzVd5LC0tRQK1qrp//z4EAgH69u1bK4NOVXReNTQ00LVrV+Tl5YmdC2k/X4QQQoi8UTNdQuqQjrIAQ800JDbVHWqmAR1lgRxyJUp4EKMVK1YgPDycffAFSkfd7dmzJxuMytpfFAC0tLQkLldUVASf/3+1w0ztXfPmzSWmZ/oUypOenl6V1jPLK6uhZGhqaootYwaLEh4giBnU5vTp0xXuLydHcnNxBhO0GBkZVZq37OxsAOWXtXnz5nj//r3YcknXAVOTKFymli1b4sqVK9i6dSuuXr2K8+fPAwDatGmD9evXY+zYsZXmUVhlfzNpyXKOqqKy88pc/2WvIWk/X4QQQoi8yb8ahpCviDqKcGCgAYaaidaUDTXTwIGBBnIdvIhhbW0NLpfL9hsV7i/KsLe3R1xcHNLT02XuLyoL5qH648ePEtenpaXV2LGYGrqyI78CFQeMZQflKSs9Pb3C5eUFDlXFBK0nT54Ej8cr95+k2jZh2traACAxiCzvmOWV9ePHjxKDaVlYWlrCx8cHb968QWhoKH744QekpaVh5syZuH37tkz7Ku9vVtE1IKlGUZZzVBWVnVdmeXXPLSGEECIvFIwSUsd0OAU4MkgP/01thegJLfDf1FY4MkgPOpwCeWcNQOkDua2tLXJychAUFITXr1+z/UUZzO/Xr19HTEwMmjdvjg4dOtR4Xtq0aQM1NTXcv38f+fn5Iuv4fD7u3r0r9b4k1SIKY6ZkSUlJEVvHNJesikePHkmshWQCKEtLyyrvW5IePXoAKG0+XR1M8+Pr169XmpZpQlx2yh+gtLlvXl6eTP2JK8LUzK9btw5bt26FQCBASEgIu76yv3NFmOBS2muga9euUFBQQHh4OIqKKn+RpKCgIFPNZEXnNTc3F/fv30eTJk3Qpk0bqfdJCCGE1CcUjBIiB+oowjfKheigUYxvlAvrRY2oMKaWc+vWrQDE+6x1794dampq8PT0RH5+Puzt7SutIawKFRUVjB07Funp6dizZ4/IumPHjsnUb5DL5YLD4UgMNACwzZDLDrQTEBAg05ymZfF4POzcuVNkmZ+fH54+fYp+/fqJDPhTE0aOHAkTExN4eXlJzHdRURGio6Mr3U+3bt3QvXt3REVF4ejRo2Lrhc/j+PHjoaSkBC8vL5FawqKiInYAI+HpTGQVGxsrsXaQWSbcn1hHRweA6Gi/0tLS0kLr1q1x+/ZtvH79ml2enZ2NTZs2iaXX19fH6NGj8ebNG/azUjZ/xcXFInmTJV99+vRBq1atEBoaips3b4qs8/DwQEZGBpydnWuk/yshhBAiD9RnlBAihglGnz59CnV1dXTr1k1kvaqqKnr06FGrTXQZP//8M8LCwrB582bcvn0b1tbWePbsGUJDQzFw4ECpau4AoGnTpujWrRuioqKwcOFCWFhYQEFBAS4uLjA1NYWDgwNatGiBEydOIDk5GdbW1nj+/DnCwsIwdOhQXLlypUr5t7Gxgbe3N2JiYtCtWze8fPkSFy9ehJaWFv74448q7bMiqqqqOHbsGFxcXODg4ID+/fuztdZJSUmIjo6Grq6uVDWn3t7ecHR0xLJly3Dy5En06tUL+fn5iI+Px6NHj/DmzRsAQKtWrfDzzz9jw4YNsLOzg5OTE9TV1RESEoLnz59j5MiRYtO6yOL06dM4dOgQ7O3tYW5uDk1NTcTHxyM0NBTNmjVjRxUGgH79+iEgIAAzZszAkCFDoKamho4dO2LYsGFSHWvRokVYsWIFhgwZgrFjx4LP5yM0NJR9WVHWjh07EBcXh+3bt+PKlSvo168fBAIBXr58iRs3buD58+dsU+y+ffvi/PnzcHNzg7W1NRQVFTF06FB06tRJ4r4VFBSwd+9eODs7Y/z48Rg7dixMTU0RExODsLAw9rwTQgghDRUFo4QQMZ06dUKzZs2QkZGBXr16SRwp1M7Ork6CUUNDQ4SEhOCnn37CtWvXEBUVhc6dO+PcuXMICwuTOhgFgAMHDmDdunUICgpCVlYWBAIBevToAVNTUzRp0gQBAQFYt24dwsPDERMTgx49euDSpUsIDg6ucjDasmVLbN++HT/99BMOHjwIgUCAIUOG4Oeff0a7du2qtM/KdOvWDREREdi1axdCQ0Nx+/ZtqKqqwsjICA4ODuycpJWxsLDArVu34OHhgeDgYOzbtw8aGhqwsLAQmfMVABYvXgxzc3N4eXnh9OnTKCwshIWFBTZv3oz58+dXq+bcxcUFBQUFuHPnDmJjY1FYWAhjY2PMnj0bS5YsEZlmZvr06UhMTMTZs2exY8cOFBcXw9XVVepgdObMmSgqKsL+/ftx7NgxGBgYYPLkyVi1apXEgYSaNWuG0NBQ7N69GwEBATh48CBUVVXRokULLF++XGQkZXd3d3A4HISFheHixYvg8/nQ19cvNxgFSl9mhIaGYtu2bbh+/TqysrJgaGiIefPm4YcffqjS1DaEEEJIfcHh8XjyH76TEDnKz8/Hu3fvYGpqCjU1NaSnp9fYaJv1AZ/PR2FhIVRUVKSeeqUhaezlAxp/Gal8DY/wfbLsPbQxasxlbMxlY3wNZSSkoWoc34qEEEIIIYQQQhoUCkYJIYQQQgghhNQ5CkYJIYQQQgghhNQ5CkYJIYQQQgghhNQ5CkYJIYQQQgghhNQ5CkYJIYQQQgghhNQ5CkYJACA2Nhbjx49HixYtYGxsjIEDB+LMmTNSb5+eng4PDw92Mnculwsul1vhNlZWVmy6sv9WrFghcZusrCysW7cOlpaW0NfXh6WlJdatW4esrCxZiksIIYQQQgiRMyV5Z4DIX3h4OJydnaGiooJx48ZBS0sLgYGBmDNnDhITE7Fy5cpK9xEfH49NmzaBw+HAwsIC6urqyM3NrXQ7LS0tLFiwQGx5165dxZbl5OTAwcEBjx8/xrfffgsXFxc8efIEe/fuRXh4OIKDg0UmmCeEEEIIIYTUXxSMfuWKi4uxdOlScDgcBAUFoXPnzgCA1atXY+jQoXB3d8fYsWNhYWFR4X7atWuHoKAgWFtbQ1NTEz179sSLFy8qPb62tjbWrl0rVV49PT3x+PFjLFu2DL/88gu7/Pfff8e2bdvg6emJdevWSbUvQgghhBBCiHxRM92vXFhYGN68eQMXFxc2EAUATU1NrFq1CsXFxfD19a10P/r6+rCzs4Ompmat5FMgEMDHxwdNmzbFDz/8ILLuf//7H7hcLo4fPw6BQFArxyeEEEIIIYTULKoZ/cpFREQAAAYOHCi2jlkWGRlZa8cvLCzEiRMn8P79e3C5XPTq1QtWVlZi6V69eoX3799j0KBBYk1x1dTUYGtri0uXLuH169eV1uISQgghhBBC5I+C0a/cq1evAEBiAMflctGsWTM2TW1ITU3FwoULRZYNHjwYBw4cQLNmzcTyaW5uLnE/TP5fvXpVaTCan58v8nthYaHI/3w+H3w+X4ZS1G9MbbFAIGhU5WI09vIBjb+MVL6Gh8/ns/fSsvfQxqgxl7Exl43RkMqopqYm7ywQUqcoGP3KMaPQamlpSVyvqamJlJSUWjn21KlTYWdnhw4dOkBFRQXPnj3D1q1bERoaCldXV4SEhIDD4YjkU1tbu9x8CqerSEpKCkpKSsSWp6amAgBUVFQaxBeWrIqKimplvz169AAAxMTE1Mr+pSVt+aqb36VLl+L06dO4e/cuzMzMqrSPqqqtv2FNcHJyQnR0ND58+FDlfdRk+WoiPzWtPv/9EhMT0atXL0yYMAG7du2qNH1+fr7Y/Za5hzZmjbmMjblsjPpeRkVFxXJfuhPSWFEwSuRm9erVIr/36NEDp06dgoODA6Kjo3HlyhUMGzasxo9rbGws8nthYSFSU1NhYGAAFRUVfP78GSoqKjV+XHkRCAQoKiqCsrIyG9yXJzExEV26dKkwjaWlJcLCwtjfmX3W1TkbNWoUIiMj8enTJwCylQ+ofn4VFRXZ7Zl9REREYPTo0fjhhx+wZs2aKu23IrKWUR4UFEqHIKjKea1K+bZs2YJt27bhwoULsLe3r9H81LSG8PdjzpOioqJU50xNTQ0GBgYAxO+hjVFjLmNjLhvjaygjIQ0VBaNfOaZGtLwaxezs7HJrTWuDgoICJk+ejOjoaNy5c4cNRpk8fP78udx8CqerSHlNYFRUVKCmpobs7Gz2QbYxYJoFcjicSsvFPCi3atUKEyZMkJjGwMBA4n7q+pwxx5OlfABw4cIFke1l9dNPP2HFihX45ptv2H0w503aPMhK1jLKU1XyV5XyVXbO9+/fj7y8vHpxvhrC3084SJYmjwoKCmL3UuYe2pg15jI25rIxvoYyEtLQUDD6lRPua1m2RozH4yEjIwO9e/eu0zwxfUWF5yll8vn69WuJ21TU95XIztzcXOopdxqaVq1aVWt7Q0NDGBoa1lBuSG0xNTWVdxYIIYQQUon6+YqW1Bk7OzsAwPXr18XWMcuYNHXl33//BQCR/ngWFhYwMjLCnTt3kJOTI5I+Pz8fUVFRMDIyajB9LVSLC9H080c0ff8WTbMyoFrc+PqoMtPxDBs2DKampjAyMsKAAQPg4+NTbvoTJ05gxIgRMDMzg5GREbp164YVK1bg3bt3AEoH1WJGd+ZyueByudDV1cXSpUsBAAkJCeByuViwYAGeP3+OqVOnwtzcHFwuFwkJCQAAKysriSM2S3N8AFiwYIHI/tzd3TFq1CgAwNatW9l8MWnmz58PLpeL2NhYieX+8ccfweVyERgYWOk51dXVhYODA5KSkjBz5ky0atUKxsbGcHBwwJ07d8TSM3l9+/YtvLy80KdPH+jr62PBggVsmri4OMycOROtW7eGvr4+rK2tsXbtWmRmZkrMQ3R0NEaOHAljY2O0atUKM2fORFJSksS0Zc+VMHd3d3C5XISHh4uti4qKwpQpU9CmTRvo6+ujU6dOmDp1KqKjowEADg4O2Lp1K4DSZtvM+Rb+uzo4OIDL5Yrtu7i4GF5eXrCzs4OhoSHMzMzg6OiIkJAQsbS+vr7gcrnw9fXFrVu3MHz4cLbc8+fPZ5uKV1d6ejo2bNiAHj16wMDAAC1btsTgwYOxe/dusbTBwcFwdHSEmZkZDA0NYW9vj71790rsB+/j4wNXV1dYWVmx+x03bpxIM3tCCCFEnqhm9CvXv39/tGzZEv7+/pg3bx6sra0BlDZ7/eOPP6CkpITJkyez6TMyMpCRkYFmzZqJjHYrq/j4eBgaGoo9LEZHR8PLywuqqqrsAz5Q2oRs2rRp2LZtG7Zt24ZffvmFXefh4QEej4e5c+fW2/5YwjTyv4CzbzPw4Da7TLmrDZTmr0eOWlM55qzmCAQCzJ07F2fOnEHr1q3h4uICZWVl3Lx5E0uWLMGzZ8+wefNmkfSzZs3CP//8A2NjY7i4uEBTUxOJiYn4559/MGjQIJiammL16tU4ceIE3r17x/Y5FggE6NChg8jx37x5g8GDB6NDhw5wdXVFZmZmhf2EpD2+JPb29khMTISfnx/s7OxE+i9qa2tj5syZOHnyJI4ePYpu3bqJbFtUVISTJ0/CwMAAI0aMkOrc8ng8DB8+HAYGBpgxYwZSUlJw7tw5jBo1CmfPnkXfvn3Ftvnhhx9w7949DB06FMOGDYOenh4A4M6dOxg3bhwKCgowZswYmJmZ4d69e9i3bx+uXLmC0NBQ6Orqsvu5desWXFxcoKCgACcnJxgZGbFBWnmDi8nq4MGD+OGHH9CkSRM4OjrCxMQEKSkpuH37NgICAmBjY8PekyIjI+Hq6sq+uKosDwKBADNnzkRgYCBat26N2bNnIzc3F+fOncPEiROxZcsWzJ8/X2y74OBghISEYPjw4fjuu+8QFRWFkydP4u3btwgODq5WeV+9eoVRo0YhJSUFNjY2cHBwQG5uLp4+fYodO3ZgyZIlbNp9+/Zh7dq10NHRgYuLC9TV1REcHIx169YhOjoax44dE7kHrlq1CpaWlhgwYACaN2+OlJQUXLp0CWPHjoWPjw8cHByqlXdCCCGkuigY/copKSlh165dcHZ2xsiRI+Hs7AxNTU0EBgYiISEBGzZsQOvWrdn03t7e2Lp1K1avXi3WjFO4toUZsU542ebNm9kA9ty5c9i1axf69esHMzMzqKqqIi4uDtevX4eCggJ27twp9vC/bNkyXL58GZ6ennj06BG6dOmCJ0+eIDQ0FFZWVli2bFmNn5+aplpcKBaIAgDuR4Oz/zeoLv4FBUryH1zh9evXcHd3l7iuZ8+eGDx4cIXbHzt2DGfOnMG0adOwc+dOKCmV3moKCwvh5uaGPXv2wMXFhW0afujQIfzzzz/o378/Tp48iSZNmrD7ysvLY6eQWLt2LSIiIvDu3Tv2+uPz+WKjH9++fRurVq3C+vXrpSqvtMeXhAn+/Pz8YG9vL/a56N27Nzp27Ih//vkHv//+u8g8ucHBwUhLS8Py5cvZc1SZ//77DxMnTsT+/fvZwGPatGkYNWoUli1bhpiYGLE+f//99x/CwsJEPlN8Ph8LFy5ETk4Ozp49i0GDBrHrNm3aBA8PD/z0009s7Ryfz8eyZctQXFyMS5cuwcbGBoDoi4fqevr0KdasWQNDQ0MEBwejRYsW7DqBQMCOjDtlyhQkJiYiMjISkydPlhiAS3Lq1CkEBgbCzs4O586dY19QrFy5EgMGDMDGjRsxfPhwtGzZUmS7y5cv4+LFi+jTpw8AoKSkBGPGjEFERATu3buHnj17VrnMc+fORUpKCjw9PTF9+nSRdcnJyezPb9++xcaNG6Gnp4cbN27AxMQEQGnNupOTEwIDA3H69GlMnDiR3eb27dtiZfnw4QO+/fZb/PjjjxSMEkIIkTtqpkvQr18/BAcHo0+fPjh37hwOHToEXV1deHt74/vvv5d6P35+fuw/ZkAk4WVfvnxh0/bt2xfDhw/HixcvcPLkSRw4cADx8fEYN24crly5Ajc3N7H9a2ho4OLFi1i4cCFevHiBPXv2IC4uDgsXLsTFixdFHvLrK+WcLPFAlHE/unR9PfDmzRts3bpV4r+rV69Wur23tzc0NDTY2nWGiooKNm7cCADw9/dnl//1119QVFSEh4eHSCAIAE2aNIGOjo5M+TcwMMCqVaukTl/Txy9r+vTpyM7Oxj///COy3MfHBxwOR+L1Xh5FRUVs3LhRpAbM3t4eQ4cOxevXryU2112yZInYy53bt2/j1atXGDJkiEggCpQGZ7q6uvD392cD/ejoaLx9+xbDhg1jA1GgtNXCxo0b2VGGq+Pw4cMoKSnB+vXrRQJR5jhGRkbV2v+JEycAlAbbwjXl33zzDRYuXIiioiKJQbWLiwsbiAKlfwNXV1cAKLf5tTRiY2Px77//wtbWViwQZfLFOH36NIqLi7F48WI2EAVKP1M///yzSPkYZQNRoLTP86hRo/Dq1SskJiZWOe+EEEJITaCaUQIA6N69u0hwUJ61a9eWO7ANj8eT+nj29vYSp2OojLa2Nn7//Xf8/vvvMm9bL+R+qWR9DqDdvG7yUoFBgwbh7NmzVdqWaWJoZGSEnTt3iq0vLi4GALx48QIAkJOTg/j4eJibm9fYAFSWlpZSD99fG8cva+LEifj555/h4+ODadOmASid7/batWuws7OTqa+zqampSDDCsLGxQUhICB4/fiwSLAKln++yHj16BAASP4caGhro2rUrrl27hpcvX6Jjx4548uQJAMDW1lYsvZmZGb755ptqBzdMYDdw4MBq7ac8jx49QpMmTSSeD+Y8PH78WGxd586dxZYxgWJ5I3xLg+kfL015K/p79ezZE02aNBHL+9u3b+Hh4YGwsDC8f/8eBQUFIus/fPhQ53PlEkIIIcIoGK3n+Hw+8vPzoa6uLu+skJqgXkmfUPX6X7tbGR6PB4FAgJSUFHaQGUmYgaiYh/nq1noJY/pESqM2jl8Wl8vF2LFj4efnh/j4eLRv3x6+vr4oKSmRWCNWkfLKxiyXNE2TpG2Y6ZDK25++vr7I/pj/mzeX/LJEX1+/2sHo58+fweFwam204uzsbJHaRmFlyytM0pRRTE2wpIGDpCXLtVfZ36t58+Z4//49+/vr168xcOBAZGdnsy1RNDU1oaCggIiICERGRooFp4QQQkhdo2C0HikuLsbVq1cRHh6OqKgoJCQk4PPnzxAIBFBVVUXz5s3RtWtX2NvbY8iQIdWeooLUvSINLSh3tQHuR4uv7GqDIo26m9O1tmhqagIAunTpgps3b1aannnQF36Qri5ZBrKqjeNLMnPmTPj5+eHYsWP47bff4OvrCx0dHZGBuqSRnp5e4XJJgZOk88H8nSrbH5OO2e/Hjx8lpk9LSxNbxvRdlRSwSQr6tLW12b6hxsbGEo9THZqamlKXty4wAy5Jc+0J/70k1WZ+/PhRJO979+4Fj8eDt7e32JzBK1asYEelJoQQQuSJ+ozWA8nJydi0aRM6duyIyZMnY9++fXjw4AEyMzPB5/MhEAiQn5+PpKQkBAYGYs2aNejevTvGjRuHixcvyjv7RAYFSioQzF8PdBVtRomuNhDM31AvBi+qLk1NTbRr1w7Pnz+Xqul206ZN0b59eyQkJLDzxVakJmqkqnP8quapV69e6NixI06dOoXQ0FC8ffsWEyZMkHkC9nfv3kmcSoWZ9kTStDWSMCNnR0REiK3Lzc3F/fv30aRJE7Rp0wZAadNnoHTalbISExNFBtthMKNlp6SkiK1jmp0KY0YbljTVVFnMOefz+ZWmZVhbWyMvL49tHiuMCc6kPX81gWkuLE15K/p7/fvvv8jLyxPJ+5s3bwBAbJRmPp8vsV8xIYQQIg8UjMrRly9fsGnTJvTo0QM7d+5ERkYGunbtijlz5sDb2xuBgYGIiIhATEwMQkNDcfr0afz8888YOXIkmjVrhhs3bsDNzQ0DBgygt9wNSI5aUxQt/gXY5Q9sOQrs8kfR4l+Qo9bwm+gy5s2bh9zcXCxbtkxsXligtC+b8NyTs2fPRklJCVauXIm8vDyRtPn5+SJzXjKDCUkKfqpKluNLwuRJUtAlbMaMGcjIyGBHfpZl4CJGSUkJfv31VwgEAnZZREQErly5AnNzc/Tu3Vuq/fTp0wetWrVCaGioWA22h4cHMjIy4OzszPa9tbGxQYsWLRASEsIGvkDpKLe//vqrxEC8a9euAMQH1gkICJB4z5o5cyYUFRXx22+/iTX5FR5NF6jadcAMOvTLL7+gqKiIXZ6SkgIvLy8oKSmJ1SLKiplb1dfXt9K03bp1Q/fu3REVFYWjR4+KrRe+nsaPHw8lJSV4eXmJ1KQWFRWxAxgJT8PFDFh1+7bogGl//vknnj59KlOZCCGEkNpCzXTlqFu3bkhPT0ebNm0wZcoUTJgwodK+Q0OGDAFQ+nb7+vXrOHnyJAIDAzFq1Chs374d3333XV1knVRTgZIKCrSb14vBiiSpaGoXAOUOYsWYOXMm7t27Bz8/P9y5cwf9+/eHkZER0tLS8OLFC8TExOCvv/5iR0ydNWsWIiMjce7cOXTv3h0jRoyApqYmkpKScO3aNezevRuOjo4ASkd/DggIwIwZMzBkyBCoqqqiTZs21ZqmQpbjS9K2bVsYGRnhn3/+gbq6OoyNjcHhcPDdd9+JzH3JDGT0/v179OjRA506dZI5r506dUJkZCQGDx6Mfv364f379/jnn3+grKwMT09PsWldyqOgoIC9e/fC2dkZ48ePx9ixY2FqaoqYmBiEhYWhVatWbJDDpPf09GTTMvOMhoWFITU1FZ06dcJ///0ncgwHBwe0aNECJ06cQHJyMqytrfH8+XOEhYVh6NChuHLlikj6jh07wt3dHatXr2bn3DQ1NUVqaiqioqIwdOhQbNmyBUDpiNwcDgebN2/GixcvoKWlBS0tLcyaNavcMk+aNAmBgYG4dOkS7OzsMGzYMHae0U+fPmHz5s0SR6CVBVNTK+1UPd7e3nB0dMSyZctw8uRJ9OrVC/n5+YiPj8ejR4/YGk7m77FhwwbY2dnByckJ6urqCAkJwfPnzzFy5EiRaV1mzpwJX19fTJs2DU5OTtDV1UVMTAwePnyIYcOGISQkpFrlJIQQQmoCBaNypKWlhd9++w0uLi4y9XEDSh8MBw8ejMGDByMxMRHbtm2TaTRbQirCTO1SnsqCUQ6Hg3379mHo0KE4evQoQkJCkJOTAz09PZibm+PXX3/FgAEDRNL//fff+Pbbb+Hj44OTJ09CIBDAyMgITk5O7HykQOk0KYmJiTh79ix27NiB4uJiTJgwoVrBqCzHl0RRURE+Pj746aefcOrUKXawmXHjxokEo9ra2hg5ciT8/f2rVCsKlDZ9PXnyJDZu3IjDhw+joKAAPXr0wI8//igy/Yg0bGxsEBoaim3btuH69evIysqCoaEh5s2bhx9++IGdF5gxYMAABAQEYPPmzQgICICamhr69++PI0eOYP78+WL7b9KkCQICArBu3TqEh4cjJiYGPXr0wKVLlxAcHCwWjAKl82526NABe/bsQWhoKHvddO/eHU5OTmy69u3bw8vLC3v27MHevXtRUFAAU1PTCoNRDoeDY8eOYd++ffDz84O3tzdUVFRgbW2NRYsWYeTIkTKdP0ni4uKgqamJYcOGSZXewsICt27dgoeHB4KDg7Fv3z5oaGjAwsJCbGqtxYsXw9zcHF5eXjh9+jQKCwthYWGBzZs3Y/78+SLfI507d8Y///yD3377DRcvXoSCggJ69+6N4OBgXL58mYJRQggh9QKHx+MJKk9GakNJSUmNzM3H4PP5UteKkP+Tn5+Pd+/ewdTUFGpqakhPT5dpNNb6js/no7CwECoqKo3y+mho5evTpw+SkpIQHx+Ppk0rGV35/2PKaGhoCDs7OwQFBdVyLutWQ/sblicrKwstW7bE4sWLsWnTJnZ5YymfMOH7ZNl7aGPUmMvYmMvG+BrKSEhD1Ti+FRuomgxEATSahxxCGqsrV64gPj4eEydOlDoQJQ3HnTt3oKysjEWLFsk7K4QQQkiDQM10CSGklh06dAjJyck4evQomjRpgqVLl8o7S6QWDBkyBKmpqfLOBiGEENJgUDBKCCG17M8//0RKSgratGmDn3/+mR24iRBCCCHka0bBaD0jS/MuRUVFaGpqokWLFrC1tWXnASSE1C+PHz+ukf18+vSJmuMTQgghpNGgYLSeYebjEx4VUXg+QUnLmWU2Njbw8vKq9tQEhBBCCCGEEFLbKBitZ1avXo3Pnz/j0KFD4PP56NOnDywtLdG0aVN8+fIFT548we3bt6GoqIjvvvsOSkpKeP78OW7evImoqCiMHj0aYWFh4HK58i4KIYQQQgghhJSLgtF6Zv78+Rg0aBDatGmDo0ePonXr1mJpXr58CTc3N1y5cgXXrl0Dl8tFYmIiJk2ahPj4eOzduxfr1q2TQ+4JIYQQQgghRDrU+aie2bp1KxISEuDr6ysxEAWA1q1bw9fXF2/fvsWWLVsAAGZmZjhw4AAEAgGCg4PrMsuEEEIIIYQQIjMKRuuZoKAgtGvXrtJ+n61atUL79u1x6dIldpmVlRXMzMzw5s2bWs5l4yfcT5cQQsj/ofsjIYSQmkLBaD2TlpYm9WiZCgoKSEtLE1nWvHlzFBUV1UbWvhpqamrIz8+XdzYIIaReKigogLKysryzQQghpBGgYLSead68OeLj45GcnFxhuqSkJMTFxaFZs2Yiyz98+AAdHZ3azGKjp6GhgS9fviAvL49qAAghREhJSQmysrKgpaUl76wQQghpBGgAo3pm6NChOHz4MNzc3ODj4wNjY2OxNMnJyXBzcwOfz8fw4cPZ5Z8+fcL79+/Rs2fPusxyo6OgoIBmzZohJycHHz9+lHd2qo3P5yM/Px9qamqNco7Kxl4+oPGXkcrXcCgoKIDL5Tb4chBCCKkfKBitZ9asWYPLly8jNjYW3bt3x4ABA2BpaQlNTU1kZ2fjyZMnuHnzJvLz82FkZIQ1a9aw2/r5+UEgEGDAgAHyK0AjoaCgAE1NTWhqaso7K9WWn5+PrKwsGBgYQE1NTd7ZqXGNvXxA4y8jlY8QQgj5OlEwWs/o6+sjKCgIc+fOxb///ovg4GCEhISw65lmoz169IC3tzf09PTYdQ4ODujXrx/MzMzqPN+EEEIIIYQQIgsKRushc3NzXL16FREREbh69SpevHiBnJwcaGhooE2bNhg0aBD69u0rtl1lI/ASQgghhBBCSH1BwWg9w+fz2b449vb2sLe3rzD9hw8fYGhoWBdZI4QQQgghhJAaQyMQ1DPLly+XOm1qaipGjx5de5khhBBCCCGEkFpCwWg94+Pjg99++63SdB8/fsTo0aPx8uXLOsgVIYQQQgghhNQsCkbrGWNjY+zYsQOHDx8uN01GRgZGjx6N58+fo3///nWYO0IIIYQQQgipGRSM1jP+/v7Q0tLCqlWrcPHiRbH1mZmZGDt2LOLi4mBvbw8/Pz855JIQQgghhBBCqoeC0XqmQ4cO8PPzg7KyMubMmYPbt2+z63g8HpycnPDkyRP06dMHp06dojnrCCGEEEIIIQ0SBaP1kI2NDby9vVFYWIjJkyfj2bNnyMrKgpOTEx4+fIiePXvizJkzUFdXl3dWCSGEEEIIIaRKKBitp0aNGoU//vgDmZmZcHZ2hpOTEx48eICuXbvC398fTZs2lXcWCSGEEEIIIaTKaJ7Reuy7775DSkoKduzYgZSUFFhbW+PcuXPQ0tKSd9YIIYQQQgghpFooGJWjyMjIStN8++23uH79Ol6/fo2VK1fiyZMnYmns7OxqI3uEEEIIIYQQUmsoGJUjR0dHcDgcqdPPmDFDbBmHw0FGRkYN5ooQQgghhBBCah8Fo3JkYmIiUzBKCCGEEEIIIY0FBaNy9PjxY3lngRBCCCGEEELkgkbTJQCA2NhYjB8/Hi1atICxsTEGDhyIM2fOSL19eno6PDw84ObmBmtra3C5XHC53HLTp6SkYO/evXBycoKlpSX09PTQtm1bTJs2DTExMRK3cXd3Z/db9p+BgYGsRSaEEEIIIYTIEdWMEoSHh8PZ2RkqKioYN24ctLS0EBgYiDlz5iAxMRErV66sdB/x8fHYtGkTOBwOLCwsoK6ujtzc3HLTe3t7488//0SrVq0wYMAA6Onp4dWrVwgKCkJQUBAOHToEJycnidu6urrCzMxMZJmSEl3KhBBCCCGENCT0BC9HeXl5aNKkiVz3V1xcjKVLl4LD4SAoKAidO3cGAKxevRpDhw6Fu7s7xo4dCwsLiwr3065dOwQFBcHa2hqampro2bMnXrx4UW76bt264dKlS7C1tRVZHhUVhTFjxuB///sfRo4cCVVVVbFtJ0+ejL59+8pUTkIIIYQQQkj9Qs105ahLly7w9vZGUVFRtfbz+PFjTJo0Cbt375Z527CwMLx58wYuLi5sIAoAmpqaWLVqFYqLi+Hr61vpfvT19WFnZwdNTU2pjjt69GixQBQAbG1t0bdvX2RmZuLp06fSF4QQQgghhBDSoFDNqBw1adIEa9aswZ9//glXV1dMmjQJbdq0kWrbvLw8BAYGws/PD2FhYQCAMWPGyJyHiIgIAMDAgQPF1jHLpJkPtSYpKysDABQVFSWuj46ORmxsLBQUFNC2bVsMGDBAYg1qefLz80V+LywsFPm/saHyNXyNvYxUvoatsZcPaNxlbMxlYzSkMqqpqck7C4TUKQ6PxxPIOxNfq6KiIuzbtw87duxAVlYW29+yR48e6NatGwwNDaGjowM1NTVkZmbi06dPiIuLw7179/DgwQPk5eVBIBCgb9+++P3332FpaSlzHqZPn46AgADcvHkTXbp0EVtvYWEBDoeDly9fyrRfppkuj8eTabt3796hR48e4HK5ePr0qUhA6u7ujq1bt4ptY2hoiH379uHbb7+V6hivX79GSUmJTPkihBBCCKlNioqKMDc3l3c2CKlTFIzWAzweD8eOHcPhw4fx9u1bAKhw/lGBQABlZWU4Ojpi1qxZsLOzq/KxnZyccOPGDcTGxkq8AXbp0gUpKSlIS0uTab9VCUaLioowZswYREVFYf/+/Zg0aZLI+osXLyI7Oxt2dnbQ19dHSkoKzp49Cw8PDwgEAoSGhsLKyqrS40iqGU1NTYWBgQFUVFSkzm9DQeVr+Bp7Gal8DVtjLx/QuMvYmMvGaEhlpJpR8rWhZrr1AJfLxdKlS7F06VLcvXsXYWFhiIqKQkJCAj5+/Ij8/Hw0a9YMenp66NKlC+zt7fHtt9+iefPm8s56jeHz+Vi0aBGioqIwffp0sUAUABwdHUV+Nzc3x6pVq6Cvr49ly5Zh+/btOHr0aKXHKu9Gr6Ki0qi/BKh8DV9jLyOVr2Fr7OUDGncZG3PZGF9DGQlpaCgYrWd69eqFXr161dnxtLS0AABZWVkS12dnZ7NpaotAIMDSpUtx+vRpTJgwATt37pRpe1dXV6xcuRJ37typpRwSQgghhBBCahqNpvuVY6ZsefXqldg6Ho+HjIyMSqd1qQ4+n4/Fixfj+PHjcHFxwb59+6CgINtlqaKigqZNm1Y4rykhhBBCCCGkfqFg9CvH9De9fv262DpmWXX6pFaEz+djyZIl8PX1xbhx43DgwIFyR9CtyKtXr8Dj8WBmZlYLuSSEEEIIIYTUBgpGv3L9+/dHy5Yt4e/vj0ePHrHLs7Oz8ccff0BJSQmTJ09ml2dkZOD58+fIyMio1nGZGlFfX1+MHTsW3t7eFQai2dnZePLkidhyHo+HxYsXAwBcXFyqlSdCCCGEEEJI3aE+o185JSUl7Nq1C87Ozhg5ciScnZ2hqamJwMBAJCQkYMOGDWjdujWb3tvbG1u3bsXq1auxdu1akX0tWLCA/Tk1NVVs2ebNm9GsWTMAwNatW3HixAk0bdoUrVu3xh9//CGWNwcHB1hbWwMAPn36BHt7e3Tt2hUdO3aEnp4eUlJScPXqVXz69AnffvstFi5cWHMnhhBCCCGEEFKrKBgl6NevH4KDg+Hu7o5z586hqKgI7du3x/r16zFhwgSp9+Pn51fhsjVr1rDBaGJiIgDgy5cv2L59u8T9mZmZscGojo4O5syZg3v37iE4OBifP3+Guro6OnXqhAkTJsDNza1KTXwJIYQQQggh8kHBKAEAdO/eHf7+/pWmW7t2rViNKEOWOUX37duHffv2SZ1eS0tLYu0pIYQQQgghpGGiPqOEEEIIIYQQQuocBaOEEEIIIYQQQuocBaOEEEIIIYQQQuocBaMNUH5+vryzQAghhBBCCCHVQsFoPfP27Vv4+PggJiZGZDmfz8fmzZvRqlUrGBsbw9bWFnfu3JFTLgkhhBBCCCGkeigYrWf+/vtvLFu2DMnJySLLPT09sWPHDvB4PAgEAsTFxWH8+PF49+6dnHJKCCGEEEIIIVVHwWg9ExUVBWVlZYwYMYJdVlJSgr1794LD4WDLli0ICwvD2LFjkZ2dDS8vLznmlhBCCCGEEEKqhoLReiYlJQWGhoZQUVFhl927dw8fP35E//79MW/ePFhZWeHPP/+Eqqoqbty4IcfcEkIIIYQQQkjVUDBaz2RkZEBPT09k2b1798DhcDBs2DB2mba2NszNzZGUlFTXWSSEEEIIIYSQaqNgtJ5RUVEBj8cTWXb79m0AgI2NjchydXV1lJSU1FXWCCGEEEIIIaTGUDBaz7Rq1Qpv3rxhazyzs7Nx69YtNG3aFNbW1iJpU1NT0bx5c3lkkxBCCCGEEEKqhYLResbBwQF8Ph+urq44cOAApkyZgtzcXDg4OIDD4bDp0tPTkZSUBDMzMznmlhBCCCGEEEKqRkneGSCiFi1ahICAADx58gRr166FQCCArq4uVq9eLZIuMDAQAGBvby+PbBJCCCGEEEJItVAwWs80bdoUoaGh8PHxwfPnz2FiYoKpU6dCX19fJF1SUhJGjhwJBwcHOeWUEEIIIYQQQqqOgtF6SENDA/Pnz68wzY8//lhHuSGEEEIIIYSQmkd9RgkhhBBCCCGE1DkKRgkhhBBCCCGE1DlqplsP8fl8nDx5EsHBwXj9+jVycnLA5/MlpuVwOHjw4EHdZpAQQgghhBBCqomC0XomOzsbLi4uuHfvHgQCQaXphad7IYQQQgghhJCGgoLRembr1q24e/cumjRpgilTpqBPnz7Q09ODggK1qCaEEEIIIYQ0HhSM1jMXLlwAh8OBn58f+vfvL+/sEEIIIYQQQkitoOq2eiY1NRVmZmYUiBJCCCGEEEIaNQpG65lmzZpBR0dH3tkghBBCCCGEkFpFwWg9M3DgQMTHxyM7O1veWSGEEEIIIYSQWkPBaD2zZs0aqKqqYvXq1SgpKZF3dgghhBBCCCGkVtAARvVMQkIC1q5di40bN+L+/ftwc3ND69atoa6uXu42dnZ2dZhDQgghhBBCCKk+CkbrGUdHR3bu0Pj4eKxfv77C9BwOBxkZGXWRNUIIIYQQQgipMRSM1jMmJiZsMEoIIYQQQgghjRUFo/XM48eP5Z0FQgghhBBCCKl1NIARIYQQQgghhJA6R8EoIYQQQgghhJA6R81066mcnBwcP34cV65cwfPnz/Hlyxc0bdoU7dq1w7BhwzB58mRoaGjIO5uEEEIIIYQQUiUUjNZDDx48wLRp05CcnAyBQMAu5/F4SEpKwvXr17F79274+Pigc+fOcswpIYQQQgghhFQNBaP1TFpaGlxcXJCRkQFNTU1MmzYNHTt2hKGhIT58+IC4uDj4+Pjg3bt3cHFxQWRkJPT19eWdbUIIIYQQQgiRCfUZrWc8PT2RkZGB/v3749GjR/jtt98wZcoUDBo0CFOmTMHmzZvx6NEjDBgwABkZGdi9e3eNHDc2Nhbjx49HixYtYGxsjIEDB+LMmTNSb5+eng4PDw+4ubnB2toaXC4XXC63xo+blZWFdevWwdLSEvr6+rC0tMS6deuQlZUldV4JIYQQQggh8kfBaD0TGhoKFRUVHDx4sNxgTltbGwcOHICSkhJCQkKqfczw8HAMHz4c0dHRGDNmDL777jtkZGRgzpw52LFjh1T7iI+Px6ZNmxAYGAgVFRWoq6vX+HFzcnLg4OCAvXv3ok2bNli4cCHat2+PvXv3wsHBATk5OTKXnRBCCCGEECIfFIzWM0lJSejQoQP09PQqTKevr48OHTogKSmpWscrLi7G0qVLweFwEBQUhF27dmHz5s2IiIhAhw4d4O7ujlevXlW6n3bt2iEoKAiJiYmIiYnBN998U+PH9fT0xOPHj7Fs2TKcO3cOP//8M/z9/fHDDz/g8ePH8PT0rNa5IIQQQgghhNQdCkbrGSUlJRQUFEiVtrCwEEpK1ev2GxYWhjdv3sDFxUVkMCRNTU2sWrUKxcXF8PX1rXQ/+vr6sLOzg6amZq0cVyAQwMfHB02bNsUPP/wgsq///e9/4HK5OH78uMiAT4QQQgghhJD6i4LResbCwgLPnj3Ds2fPKkzHpLGwsKjW8SIiIgAAAwcOFFvHLIuMjKzWMWriuK9evcL79+/Ru3dvsSlt1NTUYGtri5SUFLx+/brG80oIIYQQQgipeTSabj0zevRodmoXb29vdOnSRSzNo0ePMHv2bADAmDFjqnU8pimspKCWy+WiWbNmUjXTre3jMj+bm5tL3B+zn1evXlUaoOfn54v8XlhYKPJ/Y0Pla/gaexmpfA1bYy8f0LjL2JjLxmhIZVRTU5N3FgipUxSM1jPz5s3DqVOn8OzZMwwcOBB2dnbo2LEjDAwMkJqairi4OEREREAgEKBDhw6YN29etY7HjEKrpaUlcb2mpiZSUlKqdYyaOC6TXltbu9z0wukqkpKSgpKSErHlqamplW7bkFH5Gr7GXkYqX8PW2MsHNO4yNuayMep7GRUVFct96U5IY0XBaD2jrq6O8+fPY/bs2YiMjERERIRIc1WmT6S9vT0OHjyIJk2ayCurDZaxsbHI74WFhUhNTYWBgQFUVFTklKvaQ+Vr+Bp7Gal8DVtjLx/QuMvYmMvG+BrKSEhDRcFoPWRoaIiLFy8iOjoaV65cwYsXL/Dlyxc0bdoUbdu2xdChQ9GnT58aORZTM1lejWJ2dna5tZd1eVzm58+fP5ebXjhdRcprAqOiotKom8dQ+Rq+xl5GKl/D1tjLBzTuMjbmsjG+hjIS0tBQMFqP2djYwMbGplaPIdzXsmz/VB6Ph4yMDPTu3Vvux2XSlzdAUUV9UAkhhBBCCCH1D42m+5Wzs7MDAFy/fl1sHbOMSSPP41pYWMDIyAh37txBTk6OSPr8/HxERUXByMiI+loQQgghhBDSQFAw+pXr378/WrZsCX9/fzx69Ihdnp2djT/++ANKSkqYPHkyuzwjIwPPnz9HRkZGnR6Xw+Fg2rRp+PLlC7Zt2yayLw8PD/B4PEybNg0cDqda+SKEEEIIIYTUDWqmK0d+fn4ASvs5Ojg4iCyThaura5XzoKSkhF27dsHZ2RkjR46Es7MzNDU1ERgYiISEBGzYsAGtW7dm03t7e2Pr1q1YvXo11q5dK7KvBQsWsD8zI9YJL9u8eTOaNWtWpeMCwLJly3D58mV4enri0aNH6NKlC548eYLQ0FBYWVlh2bJlVT4PhBBCCCGEkLpFwagcLVy4EBwOB23atGGDUWaZLKoTjAJAv379EBwcDHd3d5w7dw5FRUVo37491q9fjwkTJki9H0mBtPCyNWvWsMFoVY6roaGBixcvYuvWrbhw4QIiIiJgYGCAhQsXYvXq1dDQ0JCx5IQQQgghhBB5oWBUjmxtbcHhcGBiYiK2rK51794d/v7+laZbu3atWI0og8fj1dpxGdra2vj999/x+++/y3wsQgghhBBCSP1BwagcBQUFSbWMEEIIIYQQQhobGsCIEEIIIYQQQkido2C0AcrPz5d3FgghhBBCCCGkWigYrWfevn0LHx8fxMTEiCzn8/nYvHkzWrVqBWNjY9ja2uLOnTtyyiUhhBBCCCGEVA8Fo/XM33//jWXLliE5OVlkuaenJ3bs2AEejweBQIC4uDiMHz8e7969k1NOCSGEEEIIIaTqKBitZ6KioqCsrIwRI0awy0pKSrB3715wOBxs2bIFYWFhGDt2LLKzs+Hl5SXH3BJCCCGEEEJI1VAwWs+kpKTA0NAQKioq7LJ79+7h48eP6N+/P+bNmwcrKyv8+eefUFVVxY0bN+SYW0IIIYQQQgipGgpG65mMjAzo6emJLLt37x44HA6GDRvGLtPW1oa5uTmSkpLqOouEEEIIIYQQUm0UjNYzKioq4PF4Istu374NALCxsRFZrq6ujpKSkrrKGiGEEEIIIYTUGApG65lWrVrhzZs3bI1ndnY2bt26haZNm8La2lokbWpqKpo3by6PbBJCCCGEEEJItVAwWs84ODiAz+fD1dUVBw4cwJQpU5CbmwsHBwdwOBw2XXp6OpKSkmBmZibH3BJCCCGEEEJI1SjJOwNE1KJFixAQEIAnT55g7dq1EAgE0NXVxerVq0XSBQYGAgDs7e3lkU1CCCGEEEIIqRYKRuuZpk2bIjQ0FD4+Pnj+/DlMTEwwdepU6Ovri6RLSkrCyJEj4eDgIKecEkIIIYQQQkjVUTBaD2loaGD+/PkVpvnxxx/rKDeEEEIIIYQQUvOoz2gDlJ+fL+8sEEIIIYQQQki1UDBaz7x9+xY+Pj6IiYkRWc7n87F582a0atUKxsbGsLW1xZ07d+SUS0IIIYQQQgipHgpG65m///4by5YtQ3JysshyT09P7NixAzweDwKBAHFxcRg/fjzevXsnp5wSQgghhBBCSNVRMFrPREVFQVlZGSNGjGCXlZSUYO/eveBwONiyZQvCwsIwduxYZGdnw8vLS465JYQQQgghhJCqoWC0nklJSYGhoSFUVFTYZffu3cPHjx/Rv39/zJs3D1ZWVvjzzz+hqqqKGzduyDG3hBBCCCGEEFI1FIzWMxkZGdDT0xNZdu/ePXA4HAwbNoxdpq2tDXNzcyQlJdV1FgkhhBBCCCGk2igYrWdUVFTA4/FElt2+fRsAYGNjI7JcXV0dJSUldZU1QgghhBBCCKkxFIzWM61atcKbN2/YGs/s7GzcunULTZs2hbW1tUja1NRUNG/eXB7ZJIQQQgghhJBqoWC0nnFwcACfz4erqysOHDiAKVOmIDc3Fw4ODuBwOGy69PR0JCUlwczMTI65JYQQQgghhJCqUZJ3BoioRYsWISAgAE+ePMHatWshEAigq6uL1atXi6QLDAwEANjb28sjm4QQQgghhBBSLRSM1jNNmzZFaGgofHx88Pz5c5iYmGDq1KnQ19cXSZeUlISRI0fCwcFBTjklhBBCCCGEkKqjYLQe0tDQwPz58ytM8+OPP9ZRbgghhBBCCCGk5lGfUUIIIYQQQgghdY5qRuXIz88PAKClpcU2t2WWycLV1bVG80UIIYQQQgghtY2CUTlauHAhOBwO2rRpwwajzDJZUDBKCCGEEEIIaWgoGJUjW1tbcDgcmJiYiC0jhBBCCCGEkMaMglE5CgoKkmoZIYQQQgghhDQ2NIARIYQQQgghhJA6R8EoIYQQQgghhJA6R8EoIYQQQgghhJA6R31G66mwsDCEhITgzZs3yMnJAZ/Pl5iOw+HgwoUL1T5ebGzs/2vv3sNjvPP/jz8niQSRSIRIqEOdmjoU1W2JSurQ1KIkErbsooelrTr8UJSyW11bjZbSFsvWfksbVXRDHesQJQR12F20ddYIKemGIULkMPP7wzWzpknIcWYyXo/r6qW578987s8r95175j33iRkzZvDdd9+Rk5NDSEgIr776Kv369StyHyaTiU8++YRPP/2UM2fO4O3tTadOnZg6dSqNGze2aRsXF8drr7121/7CwsJsss2YMYPY2NgC23p5eXHp0qUij1VERERERBxLxaiTuXnzJs8//zxbtmwBwGw237V9Wdx5NzExkejoaDw9Penbty++vr6sXbuWoUOHcu7cOcaNG1ekfsaMGcOSJUsICQlh2LBhpKWlER8fT0JCAps3byYkJMTatlWrVkycOLHAfr7++mt+/PFHunbtWuD8AQMGUL9+fZtpHh7alEVEREREKhJ9gncyM2bMYPPmzXh4eNCzZ0/atm1LzZo1y+1xL7m5uYwaNQqDwcD69etp3bo1ABMnTiQiIoIZM2YQGRmZ78jmr+3cuZMlS5bQoUMHVq9ejZeXF3C7cIyMjGTs2LFs2LDB2v6RRx7hkUceyddPdnY2f//73/Hw8Cj0+akDBw6kU6dOJY0sIiIiIiJOQMWok/nnP/+Jm5sbX375JV26dCn35e3cuZOzZ8/y+9//3lqIAvj4+DB+/HhefPFF4uLi+NOf/nTXfpYuXQrAlClTrIUoQHh4OF27dmXr1q2cOnWKJk2a3LWfdevWcfnyZXr27ElgYGApkomIiIiIiDNTMepk/vvf/9KgQQO7FKIAu3btAihweZZpu3fvLlI/3t7etG/fvsB+tm7dyu7du+9ZjH722WcADB48uNA2e/bs4dChQ7i5udGsWTOeeuopmwJYREREREScn4pRJ1O3bl2qVq1qt+WdPn0aoMDTcP38/AgICLC2KUxmZiYXL16kefPmuLu755tv6fte/Zw7d44dO3ZQp04dunXrVmi7d955x+bnoKAgFixYQOfOne/av0VWVpbNz9nZ2Tb/uhrlq/hcPaPyVWyung9cO6MrZ7OoSBkrV67s6CGI2JWKUSfTp08fPvroIy5evEhQUFC5L+/atWsA+Pr6Fjjfx8eH1NTUUvdxZ7vCxMXFYTKZGDhwYIFFbatWrViwYAEdO3YkMDCQ1NRUvvrqK2bPns2AAQPYsmULrVq1uusyAFJTU8nLy8s33dXvxqt8FZ+rZ1S+is3V84FrZ3TlbBbOntHd3Z1GjRo5ehgidqVi1MmMGTOGDRs28MILL7B48WLq1Knj6CHZhclkIi4uDoPBwB/+8IcC2/Tq1cvm50aNGjF+/HgCAwMZPXo077//PkuWLLnnsn79O83OzubSpUvUrl0bT0/PkodwUspX8bl6RuWr2Fw9H7h2RlfOZnE/ZBSpqFSMOhkfHx82btzIH//4Rx577DG6du1Ko0aN7nrqbmGPSCkKy9HMwo5aZmRkFHrEszh93NmuINu3b+f8+fOEh4fTsGHDew3bxoABAxg3bhz79u0rUvvCToHx9PR06dNjlK/ic/WMylexuXo+cO2MrpzN4n7IKFLRqBh1QsuXL2f//v3cvHmT9evXF9rObDZjMBhKVYzeeT1nmzZtbOYZjUbS09N54okn7tqHt7c3QUFBJCcnk5eXl+8U27tdl2pRlBsXFcbT05Nq1apx48aNYr9WREREREQcQ8Wok/niiy+YPHkyAMHBwbRo0aJcnzPasWNHZs+eTUJCAtHR0TbzEhISrG2K0s9XX33F3r1787W/Vz+XL19mw4YN+Pv75zsVtyhOnz6N0WikZcuWxX6tiIiIiIg4hopRJzNv3jwMBgMTJkxg/PjxBd7IpyxZTotdtWoVL7/8Mo888ghw+9Ta9957Dw8PDwYOHGhtn56eTnp6OgEBAQQEBFinDxkyhK+++orp06ezZs0a6zUZO3bsYNu2bYSGhhb6WJfly5eTnZ3NCy+8UOgjWjIyMkhOTs5XcBqNRkaMGAFATExMyX8RIiIiIiJiVypGncyZM2cIDAzkjTfesMvyPDw8+PDDD4mOjqZHjx5ER0fj4+PD2rVrSU5OZsqUKTZF5KJFi4iNjWXixIlMmjTJOj0sLIzBgwezdOlSwsLCiIiIIC0tjfj4eHx8fJg9e3ahY/j888+Bu5+ie/nyZZ588knatm1L8+bNqVWrFqmpqWzdupXLly/TuXNnhg8fXga/ERERERERsQcVo07Gx8fH7nfQDQsLY9OmTcyYMYP4+HhycnIICQnhzTffpH///kXuZ86cObRo0YJPP/2UhQsX4u3tTffu3Zk6dWqhR0UPHjzIDz/8QLt27WjRokWhffv7+zN06FD279/Ppk2buHr1KlWrVqVFixb079+fwYMHl/tRZBERERERKTsqRp1Mp06d2Lx5M1lZWXa941u7du1YtWrVPdtNmjTJ5ojondzc3Hj55Zd5+eWXi7Vco9F4z3a+vr689957Re5XREREREScm5ujByC23njjDcxmM1OnTnX0UERERERERMqNjow6mUuXLjFx4kTefvtt9u7dyx/+8Id7Pme0KHe7FRERERERcSYqRp1Mr169MBgMmM1mvv/++0JPibUwGAykp6fbaXQiIiIiIiJlQ8Wok3nggQfK7ZmiIiIiIiIizkLFqJM5cuSIo4cgIiIiIiJS7nQDIxEREREREbE7FaNOzmw2k56eTkpKiqOHIiIiIiIiUmZUjDqpXbt20a9fPx544AGaNm1KmzZtbObPmTOH1157jStXrjhmgCIiIiIiIqWgYtQJzZ07lz59+rB161Zu3LiB2WzGbDbbtPHx8eGLL75g48aNDhqliIiIiIhIyakYdTKJiYm89dZbVKlShenTp3P48GGeeOKJfO2effZZzGYzmzZtcsAoRURERERESkd303Uy8+fPx2AwMHfuXKKjowEKfNRLYGAgdevW5eTJk/YeooiIiIiISKnpyKiTOXDgADVq1LAWondTu3ZtUlNT7TAqERERERGRsqVi1MlcvXqVBx54oEht8/LyyM7OLucRiYiIiIiIlD0Vo07G39+f8+fP37NdXl4eZ86cITAw0A6jEhERERERKVsqRp1M27ZtuXz5Mjt37rxru5UrV5KRkVHgzY1EREREREScnYpRJ/P8889jNpsZM2YMx44dK7DN9u3bmTBhAgaDgRdeeMHOIxQRERERESk93U3XyXTv3p1+/fqxcuVKwsPDefzxxzl79iwAb775Jvv27ePQoUOYzWZefPFFOnTo4OARi4iIiIiIFJ+KUSe0YMECgoODWbBgAbt27bKZbjab8fDwYPjw4fz5z3924ChFRERERERKTsWoE3J3d2fatGm88sorrF+/nqNHj2I0GvH29qZ58+Y8++yz1K9f39HDFBERERERKTEVo04sODiYP/7xj44ehoiIiIiISJnTDYxERERERETE7lSMioiIiIiIiN2pGBURERERERG7UzEqIiIiIiIidqdiVEREREREROxOxaiIiIiIiIjYnYpRERERERERsTsVoyIiIiIiImJ3KkZFRERERETE7lSMioiIiIiIiN2pGK0AUlJSmDZtGsOGDWPOnDkYjcZ8bY4fP86zzz5r/8GJiIiIiIiUgIejByB3l5yczFNPPcXVq1cJCAhg5cqVzJs3j08++YTw8HBru4yMDHbv3u3AkYqIiIiIiBSdjow6uXfeeYeaNWvy73//m5MnT7Jnzx6aNGlCv379WLNmjaOHJyIiIiIiUiIqRp1cUlISEyZMoH79+gCEhISwdu1a+vXrx0svvcSyZcscPEIREREREZHiUzHq5NLT06lbt67NNA8PD+bNm8dLL73EyJEj+eSTT0q9nEOHDtGvXz8aNGhAnTp16NKlCytXrixWHyaTiUWLFhEaGkpQUBCNGzfm+eef5/Tp0wW2b9WqFX5+fgX+N2bMmAJfc+3aNSZPnkzLli0JDAykZcuWTJ48mWvXrhU7s4iIiIiIOI6uGXVywcHBnDx5ktDQ0HzzYmNj8fLyYsKECfTt27fEy0hMTCQ6OhpPT0/69u2Lr68va9euZejQoZw7d45x48YVqZ8xY8awZMkSQkJCGDZsGGlpacTHx5OQkMDmzZsJCQnJ9xpfX19effXVfNPbtm2bb1pmZiY9e/bkyJEjdO7cmZiYGI4ePcr8+fNJTExk06ZNeHt7F/8XICIiIiIidqdi1Mk9/vjj/POf/2TIkCEFzn/77bfx9PRk1qxZGAyGYvefm5vLqFGjMBgMrF+/ntatWwMwceJEIiIimDFjBpGRkTRu3Piu/ezcuZMlS5bQoUMHVq9ejZeXFwADBgwgMjKSsWPHsmHDhnyvq169OpMmTSrSWOfOncuRI0cYPXo006ZNs05/5513mDlzJnPnzmXy5MlFjS4iIiIiIg6k03Sd3HPPPYe/vz/p6emFtpkyZQpvv/12gUdP72Xnzp2cPXuWmJgYayEK4OPjw/jx48nNzSUuLu6e/SxdutQ6FkshChAeHk7Xrl1JSkri1KlTxR6fhdls5rPPPqNatWpMmDDBZt7YsWPx8/Pj888/x2w2l3gZIiIiIiJiPypGnVx4eDiffvopAQEBd203cuRI1q1bV+z+d+3aBUCXLl3yzbNMK8ojY3bt2oW3tzft27cvVj/Z2dksW7aMWbNmsXjxYo4cOVJg/6dPn+bnn3/miSeeyHcqbuXKlQkNDSU1NZUzZ87cc6wiIiIiIuJ4Ok3XySQnJ/Of//wHo9FI9erVqVu3Lm3btsXd3b1clme5uVBBp+H6+fkREBBQ6A2ILDIzM7l48SLNmzcvcJyWvgvq59KlSwwfPtxmWrdu3Vi4cKFNAW55baNGjQocw53LuNcpxVlZWTY/Z2dn2/zrapSv4nP1jMpXsbl6PnDtjK6czaIiZaxcubKjhyBiVypGncSBAweYPHkyBw4cyDevWrVq9OjRg1GjRtG8efMyXa7lLrS+vr4Fzvfx8SE1NbXUfdzZzuIPf/gDHTt25OGHH8bT05Pjx48TGxvLli1bGDBgAN988431OljLa6tXr16sZRQkNTWVvLy8fNMvXbp0z9dWZMpX8bl6RuWr2Fw9H7h2RlfOZuHsGd3d3Qv90l3EVakYdQIbNmzgxRdfJDs7u8BrHjMyMlixYgUrV67kxRdfZPr06TbXZVZUEydOtPn5scce48svv6Rnz57s2bOHzZs388wzz5T5cuvUqWPzc3Z2NpcuXaJ27dp4enqW+fIcTfkqPlfPqHwVm6vnA9fO6MrZLO6HjCIVlYpRB0tLS+OVV17h1q1btGrVijFjxtC+fXtq1KhBRkYGx48fZ+fOnaxYsYKffvqJxYsX891337FmzRr8/PxKvXzL0czCjihmZGQUesSzOH3c2e5u3NzcGDhwIHv27GHfvn3WYtTy2qtXr5Z6GYWdAuPp6enSp8coX8Xn6hmVr2Jz9Xzg2hldOZvF/ZBRpKLRDYwcbOHChWRkZNC1a1cSEhKIiooiODgYLy8vatasSceOHZk0aRIHDx5k3rx5+Pr6cvjwYaKiorh+/Xqpl3+36zmNRiPp6en3vAbT29uboKAgkpOTCzz99W7XpRbEcq3ojRs38o2zsBsUFXcZIiIiIiLiWCpGHWzbtm0YDAZmz56Nh0fhB6otRwx37tzJww8/zH/+8x+bZ22WVMeOHQFISEjIN88yzdLmXv1kZmayd+/eUvUDcPDgQQDq169vnda4cWOCg4PZt28fmZmZNu2zsrJISkoiODhY11qIiIiIiFQQKkYd7OzZszRu3Nim8Lqb+vXr8+WXX1KzZk3+7//+j2PHjpVq+eHh4TRs2JBVq1Zx+PBh6/SMjAzee+89PDw8GDhwoHV6eno6J06cyPfc0yFDhgAwffp0m7vV7dixg23bthEaGkqTJk2s048dO4bRaMw3nj179jBv3jy8vLx49tlnrdMNBgODBg3i+vXrzJw50+Y1s2fPxmg0MmjQIOsNj0RERERExLnpmlEHu3XrlvVOsEVVr149xo8fz4QJE1i5ciVTp04t8fI9PDz48MMPiY6OpkePHkRHR+Pj48PatWtJTk5mypQpNkXkokWLiI2NZeLEiUyaNMk6PSwsjMGDB7N06VLCwsKIiIggLS2N+Ph4fHx8mD17ts1y4+Pj+fDDDwkLC6N+/fp4eXnx448/kpCQgJubGx988AH16tWzec3o0aPZuHEjc+fO5fDhw7Rp04ajR4+yZcsWWrVqxejRo0v8exAREREREfvSkVEHCwwM5MKFC8V+3cCBA6lUqRLbtm0r9RjCwsLYtGkT7du3Jz4+nsWLF1OjRg0WLVrE66+/XuR+5syZQ2xsLAaDgYULF7J582a6d+9OQkICISEhNm07depE9+7dOXnyJMuXL2fhwoUcO3aMvn37snnzZgYPHpyvf29vb9atW8fw4cM5efIkH3/8MT/++CPDhw9n3bp1eHt7l/p3ISIiIiIi9mEwGo35nyUidhMTE0NCQgIHDhwo9vWObdu25fr165w8ebKcRnd/yMrKIiUlhXr16rnkXfaUr+Jz9YzKV7G5ej5w7YyunM3ifsgoUlHpyKiDPfvss5jNZhYsWFDs19aoUaPQx6mIiIiIiIg4MxWjDhYVFUXNmjX5xz/+wbp164r12pSUlDJ51qiIiIiIiIi9qRh1MF9fX/76179iMpkYOnQo//jHP4r0uq1bt/LLL7/QqlWrch6hiIiIiIhI2VMx6gT69+/PmDFjyMrK4vXXXyc6Opr9+/cX2v7IkSOMGDECg8FAVFSUHUcqIiIiIiJSNvRoFyfxpz/9icqVKzNz5ky2b9/O9u3badiwIZ06daJp06ZUq1YNo9HI3r172bZtG7m5ubRu3ZrnnnvO0UMXEREREREpNhWjTmTChAmEhYUxadIk/v3vf3P27Fl++umnfO3MZjMtWrRg2bJluLu723+gIiIiIiIipaRi1Mm0b9+e7du3s2PHDtasWcOOHTtISUkhJycHLy8vWrduTUxMDIMHD8bLy8vRwxURERERESkRFaNO4PLly1SrVg1PT0/rtPDwcMLDw60/37x5kypVqjhieCIiIiIiImVONzBysLy8PHr06MEDDzzAzJkzC22nQlRERERERFyJilEHW7duHcePH6dBgwaMHj3a0cMRERERERGxCxWjDrZmzRoMBgOTJ08u8jWgy5cvJyYmhlmzZpXz6ERERERERMqHilEHO3jwIJUrV+a3v/1tkV/Tv39/zp07x1//+ldOnDhRjqMTEREREREpHypGHSwtLY0GDRpQuXLlIr/Gzc2NV155BbPZzMaNG8txdCIiIiIiIuVDxaiDeXh4lOgRLb169QIgISGhrIckIiIiIiJS7lSMOlitWrVISUkp9usCAwOpW7cup06dKodRiYiIiIiIlC8Vow7WtGlTrly5wg8//FDs19aqVYv09PRyGJWIiIiIiEj5UjHqYL/97W8xm83Mmzev2K81m80YDIZyGJWIiIiIiEj5UjHqYFFRUQQEBPDFF1+wfPnyIr/OZDJx5swZAgICynF0IiIiIiIi5UPFqINVr16dP//5z5jNZkaNGsWcOXOK9Lq1a9eSkZFBmzZtynV8IiIiIiIi5UHFqBMYNGgQI0aMICcnh7fffptnnnnmrnfJPXz4MK+//joGg4G+ffvacaQiIiIiIiJlw8PRA5Db/vKXv+Dj48PMmTPZv38/MTExBAYG0qlTJ0JCQqhevTrXr1/nu+++Y8uWLeTm5vLYY48RGRnp6KGLiIiIiIgUm4pRJzJhwgS6dOnClClT2LdvH5cuXeKrr77K185sNtOyZUs+++wz3Nx0cFtERERERCoeFaNO5rHHHmPTpk189913rF27lsTERM6dO8fVq1epVq0azZs3Jzo6msGDB+Pp6eno4YqIiIiIiJSIilEn9fjjj/P44487ehgiIiIiIiLlQud4ioiIiIiIiN2pGBURERERERG7UzEqIiIiIiIidqdiVEREREREROxOxaiIiIiIiIjYnYpRERERERERsTsVoyIiIiIiImJ3KkZFRERERETE7lSMioiIiIiIiN2pGBURERERERG7UzEqABw6dIh+/frRoEED6tSpQ5cuXVi5cmWx+jCZTCxatIjQ0FCCgoJo3Lgxzz//PKdPn87XNjU1lfnz5xMVFUXLli2pVasWzZo1Y9CgQRw4cKDA/mfMmIGfn1+B/9WuXbtEuUVERERExDE8HD0AcbzExESio6Px9PSkb9+++Pr6snbtWoYOHcq5c+cYN25ckfoZM2YMS5YsISQkhGHDhpGWlkZ8fDwJCQls3ryZkJAQa9tFixYxZ84cHnzwQZ566ilq1arF6dOnWb9+PevXr2fx4sVERUUVuJwBAwZQv359m2keHtqURUREREQqEn2Cv8/l5uYyatQoDAYD69evp3Xr1gBMnDiRiIgIZsyYQWRkJI0bN75rPzt37mTJkiV06NCB1atX4+XlBdwuHCMjIxk7diwbNmywtn/00UfZsGEDoaGhNv0kJSXRp08fxo4dS48ePaz93GngwIF06tSptNFFRERERMSBdJrufW7nzp2cPXuWmJgYayEK4OPjw/jx48nNzSUuLu6e/SxduhSAKVOm2BSQ4eHhdO3alaSkJE6dOmWd3rt373yFKEBoaCidOnXiypUr/PDDD6WJJiIiIiIiTkxHRu9zu3btAqBLly755lmm7d69u0j9eHt70759+wL72bp1K7t376ZJkyb37KtSpUoAuLu7Fzh/z549HDp0CDc3N5o1a8ZTTz1V4BFUERERERFxXipG73OWmwsVdBqun58fAQEBBd6A6E6ZmZlcvHiR5s2bF1hAWvq+Vz8AKSkpfPvtt9SuXZsWLVoU2Oadd96x+TkoKIgFCxbQuXPne/YPkJWVZfNzdna2zb+uRvkqPlfPqHwVm6vnA9fO6MrZLCpSxsqVKzt6CCJ2pWL0Pnft2jUAfH19C5zv4+NDampqqfu4s11hcnJyePnll7l16xbTpk3LV9i2atWKBQsW0LFjRwIDA0lNTeWrr75i9uzZDBgwgC1bttCqVau7LgNu38k3Ly8v3/RLly7d87UVmfJVfK6eUfkqNlfPB66d0ZWzWTh7Rnd3dxo1auToYYjYlYpRcQomk4nXXnuNpKQkhgwZwnPPPZevTa9evWx+btSoEePHjycwMJDRo0fz/vvvs2TJknsuq06dOjY/Z2dnc+nSJWrXro2np2fpgjgh5av4XD2j8lVsrp4PXDujK2ezuB8yilRUKkbvc5ajmYUdtczIyCj0iGdx+riz3a+ZzWZGjRrFihUr6N+/Px988EGRxm4xYMAAxo0bx759+4rUvrBTYDw9PV369Bjlq/hcPaPyVWyung9cO6MrZ7O4HzKKVDS6m+597m7XcxqNRtLT0+/5WBdvb2+CgoJITk4u8PTXu12XajKZGDFiBJ9//jkxMTEsWLAAN7fibZaenp5Uq1aNGzduFOt1IiIiIiLiOCpG73MdO3YEICEhId88yzRLm3v1k5mZyd69e4vcj8lkYuTIkcTFxdG3b18WLlxY6B107+b06dMYjUbq169f7NeKiIiIiIhjqBi9z4WHh9OwYUNWrVrF4cOHrdMzMjJ477338PDwYODAgdbp6enpnDhxgvT0dJt+hgwZAsD06dNt7la3Y8cOtm3bRmhoqM1jXSxHROPi4oiMjGTRokV3LUQzMjI4evRovulGo5ERI0YAEBMTU8z0IiIiIiLiKLpm9D7n4eHBhx9+SHR0ND169CA6OhofHx/Wrl1LcnIyU6ZMsSkiFy1aRGxsLBMnTmTSpEnW6WFhYQwePJilS5cSFhZGREQEaWlpxMfH4+Pjw+zZs22WGxsby7Jly6hWrRpNmjThvffeyze2nj178sgjjwBw+fJlnnzySdq2bUvz5s2pVasWqampbN26lcuXL9O5c2eGDx9eTr8lEREREREpaypGhbCwMDZt2sSMGTOIj48nJyeHkJAQ3nzzTfr371/kfubMmUOLFi349NNPWbhwId7e3nTv3p2pU6faFLQA586dA+D69eu8//77BfZXv359azHq7+/P0KFD2b9/P5s2beLq1atUrVqVFi1a0L9/fwYPHlyiU3xFRERERMQxDEaj0ezoQYg4UlZWFikpKdSrV88l77KnfBWfq2dUvorN1fOBa2d05WwW90NGkYpK14yKiIiIiIiI3akYFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxOxWjIiIiIiIiYncqRkVERERERMTuVIyKiIiIiIiI3akYFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxOxWjIiIiIiIiYncqRkVERERERMTuVIyKiIiIiIiI3akYFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxOxWjIiIiIiIiYncqRkVERERERMTuVIyKiIiIiIiI3akYFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxOxWjIiIiIiIiYncqRkVERERERMTuVIyKiIiIiIiI3akYFREREREREbtTMSoAHDp0iH79+tGgQQPq1KlDly5dWLlyZbH6MJlMLFq0iNDQUIKCgmjcuDHPP/88p0+fLrPlXrt2jcmTJ9OyZUsCAwNp2bIlkydP5tq1a8Uaq4iIiIiIOJaHowcgjpeYmEh0dDSenp707dsXX19f1q5dy9ChQzl37hzjxo0rUj9jxoxhyZIlhISEMGzYMNLS0oiPjychIYHNmzcTEhJSquVmZmbSs2dPjhw5QufOnYmJieHo0aPMnz+fxMRENm3ahLe3d5n9XorDYDBw0+xBlgk83cx4G/Iwm80OGYtIQQxubhjz3DGZDVRxN1OFXG2j4lRyDB5k5N3+jtzH3UQlc66DRyQiIuVNxeh9Ljc3l1GjRmEwGFi/fj2tW7cGYOLEiURERDBjxgwiIyNp3LjxXfvZuXMnS5YsoUOHDqxevRovLy8ABgwYQGRkJGPHjmXDhg2lWu7cuXM5cuQIo0ePZtq0adbp77zzDjNnzmTu3LlMnjy5zH43RZWFByevmXlrXxrfp2fTxK8Sf36iJi383KmKPkyJ42XgxYoT1/ngX5dJz8ojvG5V/hpai4ZV8nA3mxw9PLnPGQwGUnMqMXXvf4k/lYHBADFNfJjWvhbBlXIwmbSNioi4Kp2me5/buXMnZ8+eJSYmxloQAvj4+DB+/Hhyc3OJi4u7Zz9Lly4FYMqUKdZCFCA8PJyuXbuSlJTEqVOnSrxcs9nMZ599RrVq1ZgwYYLNsseOHYufnx+ff/65/Y/0uLmzNTWbTqvOsS3lBhdv5LIr9SZPx6ew6sxN8gzu9h2PyK9kUIlh2y8xLjGN89dzuZlrZlNyJk98+RPHr7thMBgcPUS5z6XletJx5TlWncwgzwy5Jlh+IoOOK5NJy63k6OGJiEg5UjF6n9u1axcAXbp0yTfPMm337t1F6sfb25v27dsXqZ/iLvf06dP8/PPPPPHEE/lOxa1cuTKhoaGkpqZy5syZe461LF3OdWfEt5cKnDdh1y9cyVMxKo514YaZjT9l5puea4LROy5x3awTZMSB3Nz5vx+vcjkrL9+s/97M44vj1zC4aT8qIuKq9CnkPme5uVBBp+H6+fkREBBw1xsQwe1rOS9evEjz5s1xd8//ocHS9539FHe5lv9v1KhRgWO4cxn3OqU4KyvL5ufs7Gybf4vjvyZ3jLcKPoUsK8/Mz5m5VKuUVeB8eylNvorA1fNByTNWqlSJLcm3Cp2//1IW13PMuOdpGy1Pyle4LI8qrD17vdD5q89cZ0jTynjmahstL66czaIiZaxcubKjhyBiVypG73OWu9D6+voWON/Hx4fU1NRS93Fnu5Is19K+evXqRV5GYVJTU8nLy/8t/KVLBR/hvBtzUNO7zjeYTKSkpBS73/JQknwViavng+JnrFKlCj6VahU638MNcnKySbmgbdQelC8/74Da+HkVfuTT38uN61eNZF5OK83Qyowrr0NXzmbh7Bnd3d0L/dJdxFWpGJX7Tp06dWx+zs7O5tKlS9SuXRtPT89i9XXDw4MHqnlw/nr+GxX5e7kR5O1BtXr1SjXe0ipNvorA1fNB6TJGuFfDkPhfCrqaun9THwK8DBi0jZYr5Sucm5sbY9tUIvHCjQLn/782/tT0yaaGt7bR8uLK2Szuh4wiFZWK0fuc5chkYUcUMzIyCj16WZw+7mxXkuVa/v/q1atFXkZhCjsFxtPTs9inx1R1M7Gsex2ejk/hVt7/Pu57uEFc9zrUrGQCD+c45aYk+SoSV88HJctYExPzOtdm+HbbIwINfCvxdvtaVHG7Be7O8Xtz9XWofAV7LNCTP4T48vkx2/eDF5pXp3VAJTyd6B5brrwOXTmbxf2QUaSiUTF6n7vzWss2bdrYzDMajaSnp/PEE0/ctQ9vb2+CgoJITk4mLy8v33WjBV0fWtzlWtoXdoOiu12DWp5MJhOtfN35z8CGLD12jQNpWbQK8OLF5tUJ8swDc/7TgUXsyZM8ohtWpuPAhnx27Brnr+fSp1E12tf2ooZ7NnrUqDiaD9m826EGI1v789WpDNwMBqKbVKNOFQPe5Dh6eCIiUo50N937XMeOHQFISEjIN88yzdLmXv1kZmayd+/eIvVT3OU2btyY4OBg9u3bR2am7Z1Bs7KySEpKIjg42CHXWhjMeQR5ZPNGa2+WdavJn9tWo26lbNxViIqT8CKXBl45vNXOh0/C/elZ1x1/t2z7PwpJpBDVyOGhqrlMbVuNN9t407RKrgpREZH7gIrR+1x4eDgNGzZk1apVHD582Do9IyOD9957Dw8PDwYOHGidnp6ezokTJ0hPT7fpZ8iQIQBMnz7d5m51O3bsYNu2bYSGhtKkSZMSL9dgMDBo0CCuX7/OzJkzbZY9e/ZsjEYjgwYNcugzE015eXiYcjGZVISKc8rNzSU3NxeTqeA7QIs4Wl5eXoE3mBMREdek03Tvcx4eHnz44YdER0fTo0cPoqOj8fHxYe3atSQnJzNlyhSbInLRokXExsYyceJEJk2aZJ0eFhbG4MGDWbp0KWFhYURERJCWlkZ8fDw+Pj7Mnj27VMsFGD16NBs3bmTu3LkcPnyYNm3acPToUbZs2UKrVq0YPXp0+f6yRERERESkzOjIqBAWFsamTZto37498fHxLF68mBo1arBo0SJef/31IvczZ84cYmNjMRgMLFy4kM2bN9O9e3cSEhIICQkp9XK9vb1Zt24dw4cP5+TJk3z88cf8+OOPDB8+nHXr1uHt7V2q34OIiIiIiNiPwWg06qIhua9lZWWRkpJCvXr1XPIue8pX8bl6RuWr2Fw9H7h2RlfOZnE/ZBSpqHRkVEREREREROxOxaiIiIiIiIjYnYpRERERERERsTsVoyIiIiIiImJ3KkZFRERERETE7lSMioiIiIiIiN2pGBUB3N3dHT2EcqV8FZ+rZ1S+is3V84FrZ3TlbBb3Q0aRikjPGRURERERERG705FRERERERERsTsVoyIiIiIiImJ3KkZFRERERETE7lSMioiIiIiIiN2pGBURERERERG7UzEqIiIiIiIidqdiVEREREREROxOxajYTWpqKvPnzycqKoqWLVtSq1YtmjVrxqBBgzhw4ECBr7l27RqTJ0+mZcuWBAYG0rJlSyZPnsy1a9fytT18+DDTp0+nW7duNGnShMDAQFq3bs24ceNITU0tdFynT5/m+eefp3HjxgQFBREaGsqiRYswmUzFzjdv3jx+85vfULNmTfz8/PD39+fhhx9m9erVBb7GaDTy7LPPUqtWLZv2q1atKpN8GzduZPz48TzzzDPUqVMHPz8/ZsyYUaxcpcn36aefEhoaas3n5+dH3759y2z9OTpfixYtrLl+/d+YMWOcKh/A+fPnGTRoEHXr1rWOs3bt2vTt27fAv0GTycSHH35IkyZN8Pf3x8/Pj8DAQAYMGFAm6/Dy5ct8+umnPPfcc7Ru3ZrAwEAaNWpETEwM27ZtK1Y2y/4lMjKShg0bWvMFBgbSu3dvTp8+ne81165dIzo6mrp161rz+fn58cknnzhVNovirr9WrVoVun2+9tprTpfR2fahZrOZqVOn0rNnT0JCQqhduzZNmzYlIiKCpUuXkpOTU+75ynMfWtb5wPm20fLIaDKZWLRoEaGhoQQFBdG4cWOef/75Avcxd7YPCwsjODiYevXq0aNHDzZs2FDsZYu4IoPRaDQ7ehByf3jrrbeYM2cODz74IB07dqRWrVqcPn2a9evXYzabWbx4MVFRUdb2mZmZdO/enSNHjtC5c2dat27N0aNH2bp1K61atWLTpk14e3tb23fr1o2DBw/y6KOP0q5dO7y8vDhw4AB79uwhICCAjRs30qxZM5sxHTt2jIiICG7evElUVBTBwcFs2bKFH374gSFDhjB37txi5wOoXr06TZo04dKlS5w/fx6A6dOnM2LECGv769ev07x5c65du0blypV56KGH+O9//8uFCxcKbF+SfD179mT37t34+vpSs2ZNzpw5w8SJE5k0aVKRc5U0X2ZmJg0bNiQnJ4dKlSpRuXJlMjIyAMps/TlLvoYNG1K7dm3S0tI4e/YsgYGBzJw5k8jISKfJB/Dkk09y9OhRKlWqRNOmTTGbzRw/fhyTyYSbm5vN36DZbOb3v/+99QOTpWA7fvw4OTk51KlTh/3795dqHf7jH/9g7Nix1KlTh06dOlGnTh0uXLjA2rVruXnzJn/5y18YOXJkkbJZ1p+Pjw8ZGRnUqFEDf39/zpw5g9lspkqVKmzfvp2QkBDr+rPsXwAqV64MQFZWFg888AD79u1zmmwlWX9w+8uSn3/+GZPJlG8bbdiwIbt373aqjM62D83NzaVu3bq0bduWZs2aUbNmTYxGI1u3buXcuXN07dqVlStX4uZWtO/1nW0fWtb5wPm20fLIOHr0aJYsWUJISAgRERGkpaURHx+Pl5cXmzdvtu5j4PZ+dMiQIXz99dc8+OCDdOvWjezsbDZs2MAvv/zCzJkzGTZsWJGXLeKKVIyK3Xz99dfUrFmT0NBQm+lJSUn06dOHatWqcezYMby8vAB45513mDlzJqNHj2batGnW9pbpEyZMYPLkydbpixYt4umnn+bBBx+06X/OnDm89dZbREREsGLFCpt5PXr0ICkpiRUrVhAREQFATk4OMTEx7Nixg6+//pqwsLAi5Xv33Xd599136dChA6tXr7bmmD9/PpMnT8bDw4MLFy5Ypw8ZMoQ1a9YQHBzMoUOHqFKlCgBTp07lo48+wt3dnUOHDtGgQYMS50tKSqJ27do0atSIf/7zn7z00kslLmaKm8+ynl544QU++OADPvjgA6ZNm8Zvf/tbNm7cWCbrzxny+fj4kJKSYu2nLLfPssy3c+dOevfuTYsWLUhISLDm2LFjB3369MFgMODn52f9G1yzZg1DhgwB4LXXXuOvf/0rAOnp6bRt25Zr167x8ssvExsbW+KMO3bsICsri6efftrmw+DJkyfp2rUrN2/e5PDhwwQHB98z39dff82FCxeYNGmSzTpMSkqid+/e5Obm0r59ezZt2gT8bz1FRUUxbdo06tevb91GgVKvv7LMBsVffwD169fn2rVr5bYPLeuMzrgPzcrKsn5RYZGbm0tUVBSJiYl8+eWXPPPMM+WSzx770LLM54zbaHll/PU63LFjB5GRkXTo0MHmiKdlP9q+fXvi4+Ot22h6ejpPPfUUaWlpfPfdd9ZtVOR+pNN0xW569+6drxAFCA0NpVOnTly5coUffvgBuP1t4meffUa1atWYMGGCTfuxY8fi5+fH559/jtn8v+9Shg0blu8NCmDkyJFUrVqV3bt320w/deoUSUlJdOrUyVqIAlSqVImpU6cCsHTp0iLnO3XqFABTpkyxvkEBDB8+nBo1apCbm8s333xjzbd582YAYmNjrW9QAG+++SZVqlQhLy+Pzz//vMT54PbvtnHjxhgMhiLnKKt8lvU3ffp0m36eeeaZMll/zpDPYDDg6+tr009ZbZ9lnc+yLc+cOdMmX3h4ON26dcNsNtv8Da5btw64fcTwzg+DAQEBvP766wDExcWVKmN4eDjPPPNMvqMSTZs2pW/fvuTk5PDdd98VKV/v3r05ePAgYLsOQ0NDrV8o7d27l1OnTtlsnx9//DH169e36atq1aqlXn9lmQ2Kv/7MZjOZmZkYDIZy24eWdUZn3If+uogB8PDwoGfPngCcOXOm3PLZYx9alvmccRstr4y/Xofh4eF07dqVpKQk63oGWL9+vTXPndtoQEAAw4cP59atW8TFxRV5+SKuSMWoOIVKlSoB4O7uDty+jvPnn3/miSeesDlFB26/sYSGhpKamlqkNxGDwYC7u7u1b4tdu3YB0KVLl3yvadeuHdWrVy/wja0wu3btwtvbm/bt2+ebV6tWLQDrKYGnT5/m5s2bADz00EM2bStXrkzz5s0B2LJlyz2XW1i+slbcfIWtv0qVKpXJ+itrJcnn5eVFbm4uy5YtY9asWSxevJiTJ09WuHx3/g1YxpGcnAxAmzZt8q3DJk2aALdPkyyvjL/eJxRFYRktfQHs3r37rtsn3M5XnuuvLLNBwevv9OnT5OXlUalSJdasWWPdPo8cOVJm+9C7KeuMzrQPNZlM1mtiLcspioqyDy2PfM62jZZnxjs/N6SlpQEUeOTTMi0xMbHIyxdxRR6OHoBISkoK3377LbVr16ZFixYA1hsBNGrUqMDXNG7c2NrO8v+FWbNmDRkZGTbX791rGQaDgUaNGvGvf/2LGzduULVq1bsuIzMzk4sXL9K8efN8b4YpKSnWN1PLh6c7b3SQnJyc78OUZXlFeRMuLF9ZKmm+8lx/Zamk+Tw8PLh06RLDhw+3eY3lQ0ZFyAe3r1+D29ud5W/QUkz4+fnla28pVKF8MmZkZLBmzRrrB9KiKCyjZf/i5+eH0Wjk9OnTBAUFAYVvn5bCwNmzWRS0/izbaHZ2dr7ts1u3bjRs2NDaztkzOsM+1HLjsMuXL7Njxw5OnDjBwIEDCQ8Pv3c4nH8fWp75wDm20fLOeOc6sahZsyZQ8DZq2Y/eeSRV5H6kYlQcKicnh5dffplbt24xbdo06w7ecqdAyxvYr/n4+Ni0K8z58+eZOHEiVapU4c0337SZV5xl3KsYtfT161M2Lfksd+y7fv16vnF/8MEHhIWFWU8lunz5MocPHwZuv/mVNF9ZKmm+8lx/Zamk+R555BEmT57Mww8/jKenJ8ePHyc2NtZ6NObq1at3Xa6j88HtjAsWLABunxFg+Rt86KGH2L17N0eOHLG55ury5cvW9nf2XZiSZBw7dixpaWlMnjyZGjVqFOk1BWW8c/8yYsQIZs2axbVr1+65fVpOp3PmbBaFrT/La373u9/xzjvv5Ns+69atWyEyOss+9M5row0GAyNHjuTPf/5zkbLdOV5n3YeWVz5wnm20PDNCweukW7durFq1qsBt1PI7udf7hIirUzEqDmMymXjttddISkpiyJAhPPfcc2Xa/5UrV+jfvz+//PILf/vb32jatGmp+ktMTLSe2mvRqlUrevXqVWD7O/N1797deuOUO9WrV489e/YQGhpK165dyc3NZf369Xh6egLc9Q5/FSFfaVSEfKGhoTz55JPWnx977DG+/PJLWrduTUpKCkeOHKFfv34F9u/ofPC/jEePHgWwOfrw+OOP849//IMLFy7k2z4tRw7vpSQZ3377bVauXEm3bt0YN25ciTP+ev/StWtXZs2aVaRxF4Ujs1ncbf1Z1KtXj4CAAOB/22fPnj3Zs2eP02d0pn2o0WjEZDLx888/88033zBt2jT279/PihUrrMVJRd6Hlkc+cK5ttLwy3k1MTAxxcXEkJiYWuh8t70s0RJydilFxCLPZzKhRo1ixYgX9+/fngw8+sJlveWMo7BtDy+3tC/uG0mg00qdPH3788Udmz57N7373u3xtiroMy7edu3btsvlmFWDAgAH06tXL2pflG9Ff54uKimLTpk3WdpZ/n376aQIDA1m1ahVLlizB19eXXr16kZeXx2effVaqfMVVHvnKc/05az43NzeaNWtGSkoK586dc8p8v87YqVMnEhMTbdaHv78/AI8++ijXrl2z2T5HjhxJu3btgLJdh++++y6zZ88mLCyMzz77rMDrvIuSsaD9i+Vota+v7z23T8upks6YzeJe66+wjG5ubgwcOND6Qd9ZMzrjPtTNzY26devy4osvUqNGDZ5//nlmzZplvQtsRd+HlmW+X2d0lm20rDPeqaB14uHhYT0y+utt1LIftRTiIvcrFaNidyaTiZEjRxIXF0dMTAwLFizI9+215dvTwq73sVyTUdC3rFeuXKFPnz4cPnyY999/nxdeeKHAPu62DLPZzJkzZwgODrbePGLSpEmFPlLD29uboKAgkpOTycnJ4f/9v/9nk2/hwoU2y7T8m5yczOzZs3njjTds+rPcZv6RRx4pcb7iKo985bn+isue+YxGI1DwXRwdnS8vL896ipolY5s2bUhMTLRZH5b/9/f3JyEhwaa/O2+4UVbr0PLYiyeffJLly5fb3HmyOBl/+uknXnvtNZYtW2azf7lzm7vX+vvll1+cMltJ1l9BGe/88OuMGSvCPrRz584ANkfRXGkfWpp8zrqNlnXGX3/ZUtg68fLy4o033si3jVr2o23bti322EVcie6mK3Z1ZyHat29fFi5cWOiNAIKDg9m3b1++a36ysrJISkoiODg4380d7nyDmjlzJn/84x8LHYvl9Mpff9AGOHjwIFevXqVjx45FztaxY0cyMzMZOHBgvnyWZVj6u1e+f/3rXwAMHjy4xPnKWlnly8nJKZP1V9bKcv19//33wO3Tu+7kDPmSkpLy/Q1+++231jYWd8u4fPly4PbNjcpiHc6YMYN3332Xjh07smLFinteo12Y0NBQbty4wbJly/LtX+5ch3fLBrdvKFJW22dZZSvL9bdv3z7g9hEcZ8xYEfahFy9eBG4f+bJ3PnvsQ0uTzxm30YKUJuPevXvzzfv1OryXlStXAhAdHV3k5Yu4IhWjYjcmk4kRI0YQFxdHZGQkixYtKvRaCYPBwKBBg7h+/TozZ860mTd79myMRiODBg2yef7ilStX6N27N4cPH+bdd99l2LBhdx1PkyZNCA0NJTEx0fq8Orj9Rm95rtuvP8jczaBBg4DbjxLo3bu3Nd+OHTvYtm0boaGh1kdi3C3fyy+/THZ2NkFBQfTu3bvE+cpaWeX75ptvymT9lbXi5uvRo0eB+caOHUtWVhbu7u5Otf6GDBkCwIsvvmjzN7hr1658+eB2xv79++fLuGbNGutz8YYOHVrqdfjOO+8QGxtLhw4dSlXImEwm6+l+AQEBfPzxx9b9y6/X4d22T4AbN26UyfZZVtmg+Ovv+PHj9OvXL1/GPXv28PHHHwMwcOBAp8robPvQEydOWI+S3+nGjRvWG+V069bN7vnKah9a1vmccRstr4zTp08nOzvbOr2gdWhR0Gm9a9as4fPPP+fRRx/l2WefLfLyRVyRwWg0mu/dTKT0ZsyYQWxsLNWqVeOVV14psBDt2bOn9bSqzMxMunfvzpEjR+jcuTNt2rTh6NGjbNmyhVatWrFp0yab56/17NmT3bt306xZM6Kiogocw6uvvmrzqIpjx44RERFBVlYWkZGRBAcHs3XrVr7//nsGDx7Mhx9+WOx8cPvDcOPGjcnMzOTYsWO4u7szaNAghgwZYpOvUaNG3Lp1iwceeICgoCBOnz7NlStX8PT0ZOfOnYSEhJQq37p166wP3U5OTiYpKYmWLVtaj9h16NChyAV3SfI9/vjjXLhwgaCgIEwmk/WZa35+fjz99NP06dPHemOIipZv2rRpzJkzB7PZTN26dalduzbJycmkp6cD8N577zF06FBr/47OBxAWFsbhw4dxc3OjXbt23LhxwyZfzZo1bf4GH3vsMS5evMj169epX78+JpOJ8+fPAxASEsK2bdtK9TcYFxfHa6+9hoeHB6+88kqBz/x88skn6dSp0z2zWdafh4cHubm5Ba7DTz75xFqc3Ll/CQoKwt/fn/Pnz5ORkUHVqlXp2bMnHh4e9OzZk169ejk0m0Vx1t+MGTOYO3culSpVIiMjg3r16uHm5mZ9nETdunX57rvvnGb9gfPtQ+fPn89bb73Fk08+ScOGDfH19SU1NZWtW7dy+fJl2rdvzz//+c8iF+DOtg8t63zgfNtoeWQcNWoUS5cuJSQkhIiICNLS0oiPj8fLy4vNmzfbbHNw+2ZwdevWpVmzZlSuXJmDBw+ya9cuGjZsyNdff039+vWLvGwRV6RrRsVuLDdzuX79Ou+//36BberXr299I/b29mbdunXExsby9ddfs2vXLmrXrs3w4cOZOHFivg8+lv5PnDiR7wYEFgMHDrT5sB8SEkJCQgJ/+ctf2Lp1q/XDTWxsrE0hUZx8AOnp6daiBCAvL4/Fixfz6KOP2uR79dVXWbp0KRcuXOD8+fN4eHjQrl07lixZwgMPPFDqfEeOHOGLL76waXP06FHrnQ2h6Ed/S5Kvffv2fPXVV9bToSyMRiMrV66kUaNG1g9SFS1f165dOXnyJImJiaSmpnLhwgXc3d1p0qQJs2bNyvfsOkfnA2jRogWHDx/GZDKxf//+fPnA9m8wOjqa1atXc+bMGev4fXx8+N3vfsfUqVNL/TdoaZ+bm2s9ElKQohQzd/YFBa9Dyw1G4H/7l4iICI4fP26zjd64ccN6Cl39+vXp1auXQ7NZFGf9derUiRMnTvCvf/2LrKwsUlJSgNuPrenRowezZ892qvV3Z3/gHPvQp556ikGDBrFnzx4OHTrE9evX8fX15eGHHyY6OprBgwcX6xRPZ9uHlnU+cL5ttDwyzpkzhxYtWvDpp5+ycOFCvL296d69O1OnTs13VBQgKiqKtWvXcuDAAXJycmjQoAGvv/46o0aNKvTmTCL3Ex0ZFREREREREbvTNaMiIiIiIiJidypGRURERERExO5UjIqIiIiIiIjdqRgVERERERERu1MxKiIiIiIiInanYlRERERERETsTsWoiIiIiIiI2J2KUREREREREbE7FaMiIiIiIiJidypGRUREgOTkZPz8/PDz83P0UERERO4LHo4egIiISFkpaSE5b948nnzyybIdjIiIiNyVilEREXEZ7du3L3D63r17AWjcuDG1atXKNz8wMJBKlSrRtGnTch2fiIiI/I/BaDSaHT0IERGR8mQ5Yjpv3jx+//vfO3YwIiIiAuiaUREREREREXEAFaMiIiLc/QZGPXv2xM/Pj7i4OC5evMjo0aNp3rw5QUFB/OY3v+Gjjz7CbL59olF2djZz5syhffv2BAcH07RpU0aNGsXly5cLXbbJZOLLL78kKirKeirxww8/zEsvvcR//vOf8oosIiLiUCpGRUREiiglJYXw8HCWL19OrVq1CAgI4OTJk0ydOpU33niDW7duERkZybRp0zCbzdSrV4/09HSWLl1Knz59yMnJyddnRkYGffv25eWXX2b79u14eHjw8MMPk5mZyVdffUXXrl1ZsWKFA9KKiIiULxWjIiIiRTRr1iwee+wxjh07xo4dO/j+++/56KOPAPj73//OSy+9xC+//MLevXvZt28f3333Hdu2bcPX15cjR47wxRdf5Otz1KhRfPvttzzyyCNs376d48ePs3PnTn766SdmzJiByWRi5MiRnDx50t5xRUREypWKURERkSLy9/dn4cKF+Pv7W6cNGjSIRx99FJPJxPr16/nb3/7GQw89ZJ3ftm1bhgwZAsA333xj09/BgweJj4/H39+fL7/8krZt21rnubm58eqrr/LHP/6RW7duMX/+/HJOJyIiYl8qRkVERIooOjqaatWq5Zvepk0bAFq2bEm7du3yzbcUmWfPnrWZvnr1agC6d+9OcHBwgcvs3bs3ADt37izpsEVERJySnjMqIiJSRI0aNSpwes2aNYs0PzMz02b60aNHAdi1axfdu3cv8LVZWVkApKamFn/AIiIiTkzFqIiISBFVrVq1wOkGg6FI800mk810o9EI3L4xUkpKyl2XffPmzeIMVURExOmpGBUREXEQb29vAGbMmMGrr77q4NGIiIjYl64ZFRERcZDmzZsDsG/fPgePRERExP5UjIqIiDhIVFQUAOvXr+eHH35w8GhERETsS8WoiIiIg3To0IHIyEhycnKIjo5m48aNmM1mmzbJycl8+OGHLF261EGjFBERKR+6ZlRERMSB5s+fz61bt9i4cSMDBgzA39+fBx98EJPJRGpqKmlpaQBMnDjRwSMVEREpWypGRUREHKhq1aosW7aMTZs2ERcXx8GDBzl69Cje3t4EBwcTFhbGb3/7W55++mlHD1VERKRMGYxGo/nezURERERERETKjq4ZFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxOxWjIiIiIiIiYncqRkVERERERMTuVIyKiIiIiIiI3akYFREREREREbtTMSoiIiIiIiJ2p2JURERERERE7E7FqIiIiIiIiNidilERERERERGxu/8P2pwJrhp9zSUAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's reduce the fraction of coal prod in the mix" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 65922.26it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "generated_electricity_over_lifetime = 4.38e9 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.95\n", - "LT = 25 # 25 years lifetime of a wind turbine\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.04524152774118663" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 5\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - 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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
02020-10-10 20:23:463.614916e-0517Wind turbine constructionunitcarbon dioxidekilogram
12021-10-11 02:12:587.229832e-0517Wind turbine constructionunitcarbon dioxidekilogram
22022-10-11 08:02:101.084475e-0417Wind turbine constructionunitcarbon dioxidekilogram
32023-10-11 13:51:224.500000e-0214Electricity production, coalkilowatt hourcarbon dioxidekilogram
42043-10-11 10:15:223.224620e-1118End-of-life, wind turbineunitcarbon dioxidekilogram
52044-10-10 16:04:342.902710e-0918End-of-life, wind turbineunitcarbon dioxidekilogram
62045-10-10 21:53:469.612456e-0818End-of-life, wind turbineunitcarbon dioxidekilogram
72046-10-11 03:42:581.171037e-0618End-of-life, wind turbineunitcarbon dioxidekilogram
82047-10-11 09:32:105.248223e-0618End-of-life, wind turbineunitcarbon dioxidekilogram
92048-10-10 15:21:228.652857e-0618End-of-life, wind turbineunitcarbon dioxidekilogram
102049-10-10 21:10:345.248223e-0618End-of-life, wind turbineunitcarbon dioxidekilogram
112050-10-11 02:59:461.171037e-0618End-of-life, wind turbineunitcarbon dioxidekilogram
122051-10-11 08:48:589.612456e-0818End-of-life, wind turbineunitcarbon dioxidekilogram
132052-10-10 14:38:102.902710e-0918End-of-life, wind turbineunitcarbon dioxidekilogram
142053-10-10 20:27:223.224620e-1118End-of-life, wind turbineunitcarbon dioxidekilogram
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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 2020-10-10 20:23:46 3.614916e-05 1 7 \n", - "1 2021-10-11 02:12:58 7.229832e-05 1 7 \n", - "2 2022-10-11 08:02:10 1.084475e-04 1 7 \n", - "3 2023-10-11 13:51:22 4.500000e-02 1 4 \n", - "4 2043-10-11 10:15:22 3.224620e-11 1 8 \n", - "5 2044-10-10 16:04:34 2.902710e-09 1 8 \n", - "6 2045-10-10 21:53:46 9.612456e-08 1 8 \n", - "7 2046-10-11 03:42:58 1.171037e-06 1 8 \n", - "8 2047-10-11 09:32:10 5.248223e-06 1 8 \n", - "9 2048-10-10 15:21:22 8.652857e-06 1 8 \n", - "10 2049-10-10 21:10:34 5.248223e-06 1 8 \n", - "11 2050-10-11 02:59:46 1.171037e-06 1 8 \n", - "12 2051-10-11 08:48:58 9.612456e-08 1 8 \n", - "13 2052-10-10 14:38:10 2.902710e-09 1 8 \n", - "14 2053-10-10 20:27:22 3.224620e-11 1 8 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Wind turbine construction unit carbon dioxide kilogram \n", - "2 Wind turbine construction unit carbon dioxide kilogram \n", - "3 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "4 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "5 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "6 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "7 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "8 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "9 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "10 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "11 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "12 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "13 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "14 End-of-life, wind turbine unit carbon dioxide kilogram " - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's come back to a more realistic share of coal in the mix, but reducing its energy prod over the LT" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 70197.56it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "LT = 25 # 25 years lifetime of a wind turbine\n", - "generated_electricity_over_lifetime = 2*1e6*365*24*LT/ 1e6 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'normal',\n", - " param = 0.1\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.842626329380516" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 349\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
01999-10-11 18:28:279.439022e-1017Wind turbine constructionunitcarbon dioxidekilogram
12000-10-11 00:17:391.510244e-0817Wind turbine constructionunitcarbon dioxidekilogram
22001-10-11 06:06:511.283707e-0717Wind turbine constructionunitcarbon dioxidekilogram
32002-10-11 11:56:037.400193e-0717Wind turbine constructionunitcarbon dioxidekilogram
42002-10-11 11:56:031.550359e-0817Wind turbine constructionunitcarbon dioxidekilogram
...........................
26472141-10-11 04:54:513.291841e-2218End-of-life, wind turbineunitcarbon dioxidekilogram
26482141-10-11 04:54:511.791241e-2417Wind turbine constructionunitcarbon dioxidekilogram
26492142-10-11 10:44:031.957784e-2418End-of-life, wind turbineunitcarbon dioxidekilogram
26502142-10-11 10:44:034.965530e-2717Wind turbine constructionunitcarbon dioxidekilogram
26512143-10-11 16:33:155.437255e-2718End-of-life, wind turbineunitcarbon dioxidekilogram
\n", - "

2652 rows × 8 columns

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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 1999-10-11 18:28:27 9.439022e-10 1 7 \n", - "1 2000-10-11 00:17:39 1.510244e-08 1 7 \n", - "2 2001-10-11 06:06:51 1.283707e-07 1 7 \n", - "3 2002-10-11 11:56:03 7.400193e-07 1 7 \n", - "4 2002-10-11 11:56:03 1.550359e-08 1 7 \n", - "... ... ... ... ... \n", - "2647 2141-10-11 04:54:51 3.291841e-22 1 8 \n", - "2648 2141-10-11 04:54:51 1.791241e-24 1 7 \n", - "2649 2142-10-11 10:44:03 1.957784e-24 1 8 \n", - "2650 2142-10-11 10:44:03 4.965530e-27 1 7 \n", - "2651 2143-10-11 16:33:15 5.437255e-27 1 8 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Wind turbine construction unit carbon dioxide kilogram \n", - "2 Wind turbine construction unit carbon dioxide kilogram \n", - "3 Wind turbine construction unit carbon dioxide kilogram \n", - "4 Wind turbine construction unit carbon dioxide kilogram \n", - "... ... ... ... ... \n", - "2647 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "2648 Wind turbine construction unit carbon dioxide kilogram \n", - "2649 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "2650 Wind turbine construction unit carbon dioxide kilogram \n", - "2651 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "\n", - "[2652 rows x 8 columns]" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Put triangular distrubution, just to see what it change" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 107202.66it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "LT = 25 # 25 years lifetime of a wind turbine\n", - "generated_electricity_over_lifetime = 2*1e6*365*24*LT/ 1e6 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 25\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.842626329380516" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 349\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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01999-10-11 18:31:119.439022e-1017Wind turbine constructionunitcarbon dioxidekilogram
12000-10-11 00:20:231.510244e-0817Wind turbine constructionunitcarbon dioxidekilogram
22001-10-11 06:09:351.283707e-0717Wind turbine constructionunitcarbon dioxidekilogram
32002-10-11 11:58:471.550359e-0817Wind turbine constructionunitcarbon dioxidekilogram
42002-10-11 11:58:477.400193e-0717Wind turbine constructionunitcarbon dioxidekilogram
...........................
22692137-10-11 05:40:471.025666e-0718End-of-life, wind turbineunitcarbon dioxidekilogram
22702137-10-11 05:40:472.254974e-0817Wind turbine constructionunitcarbon dioxidekilogram
22712138-10-11 11:29:592.279258e-0818End-of-life, wind turbineunitcarbon dioxidekilogram
22722138-10-11 11:29:592.601893e-0917Wind turbine constructionunitcarbon dioxidekilogram
22732139-10-11 17:19:112.849072e-0918End-of-life, wind turbineunitcarbon dioxidekilogram
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2274 rows × 8 columns

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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 1999-10-11 18:31:11 9.439022e-10 1 7 \n", - "1 2000-10-11 00:20:23 1.510244e-08 1 7 \n", - "2 2001-10-11 06:09:35 1.283707e-07 1 7 \n", - "3 2002-10-11 11:58:47 1.550359e-08 1 7 \n", - "4 2002-10-11 11:58:47 7.400193e-07 1 7 \n", - "... ... ... ... ... \n", - "2269 2137-10-11 05:40:47 1.025666e-07 1 8 \n", - "2270 2137-10-11 05:40:47 2.254974e-08 1 7 \n", - "2271 2138-10-11 11:29:59 2.279258e-08 1 8 \n", - "2272 2138-10-11 11:29:59 2.601893e-09 1 7 \n", - "2273 2139-10-11 17:19:11 2.849072e-09 1 8 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Wind turbine construction unit carbon dioxide kilogram \n", - "2 Wind turbine construction unit carbon dioxide kilogram \n", - "3 Wind turbine construction unit carbon dioxide kilogram \n", - "4 Wind turbine construction unit carbon dioxide kilogram \n", - "... ... ... ... ... \n", - "2269 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "2270 Wind turbine construction unit carbon dioxide kilogram \n", - "2271 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "2272 Wind turbine construction unit carbon dioxide kilogram \n", - "2273 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "\n", - "[2274 rows x 8 columns]" - ] - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Adding a TD for the share of wind elec in the elec mix" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import bw_temporalis as bwt" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 174, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "TD_constant_increase_wind_share = bwt.easy_timedelta_distribution(\n", - " start=-20,\n", - " end=49,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=70,\n", - " kind = 'triangular',\n", - " param = 49\n", - " )\n", - "td.graph()" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 63191.02it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "LT = 25 # 25 years lifetime of a wind turbine\n", - "generated_electricity_over_lifetime = 2*1e6*365*24*LT/ 1e6 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : TD_constant_increase_wind_share,\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 25\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.842626329380516" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 349\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - 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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
01847-10-11 22:04:078.158117e-3717Wind turbine constructionunitcarbon dioxidekilogram
11848-10-11 03:53:192.610597e-3517Wind turbine constructionunitcarbon dioxidekilogram
21849-10-11 09:42:314.307486e-3417Wind turbine constructionunitcarbon dioxidekilogram
31850-10-11 15:31:434.855711e-3317Wind turbine constructionunitcarbon dioxidekilogram
41851-10-11 21:20:554.190009e-3217Wind turbine constructionunitcarbon dioxidekilogram
...........................
372892403-10-11 17:59:194.737795e-1517Wind turbine constructionunitcarbon dioxidekilogram
372902404-10-10 23:48:311.236131e-1517Wind turbine constructionunitcarbon dioxidekilogram
372912405-10-11 05:37:432.533467e-1617Wind turbine constructionunitcarbon dioxidekilogram
372922406-10-11 11:26:553.634950e-1717Wind turbine constructionunitcarbon dioxidekilogram
372932407-10-11 17:16:072.750127e-1817Wind turbine constructionunitcarbon dioxidekilogram
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37294 rows × 8 columns

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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 1847-10-11 22:04:07 8.158117e-37 1 7 \n", - "1 1848-10-11 03:53:19 2.610597e-35 1 7 \n", - "2 1849-10-11 09:42:31 4.307486e-34 1 7 \n", - "3 1850-10-11 15:31:43 4.855711e-33 1 7 \n", - "4 1851-10-11 21:20:55 4.190009e-32 1 7 \n", - "... ... ... ... ... \n", - "37289 2403-10-11 17:59:19 4.737795e-15 1 7 \n", - "37290 2404-10-10 23:48:31 1.236131e-15 1 7 \n", - "37291 2405-10-11 05:37:43 2.533467e-16 1 7 \n", - "37292 2406-10-11 11:26:55 3.634950e-17 1 7 \n", - "37293 2407-10-11 17:16:07 2.750127e-18 1 7 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Wind turbine construction unit carbon dioxide kilogram \n", - "2 Wind turbine construction unit carbon dioxide kilogram \n", - "3 Wind turbine construction unit carbon dioxide kilogram \n", - "4 Wind turbine construction unit carbon dioxide kilogram \n", - "... ... ... ... ... \n", - "37289 Wind turbine construction unit carbon dioxide kilogram \n", - "37290 Wind turbine construction unit carbon dioxide kilogram \n", - "37291 Wind turbine construction unit carbon dioxide kilogram \n", - "37292 Wind turbine construction unit carbon dioxide kilogram \n", - "37293 Wind turbine construction unit carbon dioxide kilogram \n", - "\n", - "[37294 rows x 8 columns]" - ] - }, - "execution_count": 166, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Adding the corresponding decreasing TD to coal in the energy mix" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "TD_constant_decrease_coal = bwt.easy_timedelta_distribution(\n", - " start=-20,\n", - " end=49,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=70,\n", - " kind = 'triangular',\n", - " param = -20\n", - " )\n", - "TD_constant_decrease_coal.graph()" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['wind-example']" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 0 objects" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 8/8 [00:00<00:00, 66182.31it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "LT = 25 # 25 years lifetime of a wind turbine\n", - "generated_electricity_over_lifetime = 2*1e6*365*24*LT/ 1e6 # Amount of electricity generated by a wind turbine over its lifetime in kWh\n", - "share_of_wind_in_electricity_mix = 0.8\n", - "\n", - "bd.Database('wind-example').write({\n", - " ('wind-example', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', \"coal\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"coal\",\n", - " \"unit\": \"kilogram\",\n", - " },\n", - " ('wind-example', 'electricity-mix'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : TD_constant_increase_wind_share,\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-coal'),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': TD_constant_decrease_coal,\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-coal'): {\n", - " 'name': 'Electricity production, coal',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'coal'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " },\n", - " # maybe add maintenance, oil changes, ...\n", - " ]\n", - " },\n", - " ('wind-example', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=20,\n", - " end=30,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 25\n", - " )\n", - " },\n", - " # aggregate the rest to direct co2 emissions\n", - " # {\n", - " # 'input': ('wind-example', 'CO2'),\n", - " # 'amount': 100,\n", - " # 'type': 'biosphere',\n", - " # }\n", - " ]\n", - " },\n", - " ('wind-example', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 200000,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e6,\n", - " 'type': 'biosphere',\n", - " } #taking into account CO2 of machine and materials, not just operational energy\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'CO2'),\n", - " 'amount': 1e5,\n", - " 'type': 'biosphere',\n", - " }, #taking into account CO2 of machines and materials, not just operational energy\n", - " {\n", - " 'input': ('wind-example', 'electricity-mix'),\n", - " 'amount': 1e5,\n", - " 'type': 'technosphere',\n", - " }\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.842626329380516" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('wind-example', 'electricity-mix'): 1}, (\"GWP\", \"wind-example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 349\n" - ] - } - ], - "source": [ - "tlca = TemporalisLCA(lca)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tl = tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sb\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = tl.build_dataframe()\n", - "df = tl.add_metadata_to_dataframe(['wind-example'])" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
01847-10-11 22:13:158.158117e-3717Wind turbine constructionunitcarbon dioxidekilogram
11848-10-11 04:02:272.610597e-3517Wind turbine constructionunitcarbon dioxidekilogram
21849-10-11 09:51:394.307486e-3417Wind turbine constructionunitcarbon dioxidekilogram
31850-10-11 15:40:514.855711e-3317Wind turbine constructionunitcarbon dioxidekilogram
41851-10-11 21:30:034.190009e-3217Wind turbine constructionunitcarbon dioxidekilogram
...........................
381052403-10-11 18:08:274.737795e-1517Wind turbine constructionunitcarbon dioxidekilogram
381062404-10-10 23:57:391.236131e-1517Wind turbine constructionunitcarbon dioxidekilogram
381072405-10-11 05:46:512.533467e-1617Wind turbine constructionunitcarbon dioxidekilogram
381082406-10-11 11:36:033.634950e-1717Wind turbine constructionunitcarbon dioxidekilogram
381092407-10-11 17:25:152.750127e-1817Wind turbine constructionunitcarbon dioxidekilogram
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38110 rows × 8 columns

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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 1847-10-11 22:13:15 8.158117e-37 1 7 \n", - "1 1848-10-11 04:02:27 2.610597e-35 1 7 \n", - "2 1849-10-11 09:51:39 4.307486e-34 1 7 \n", - "3 1850-10-11 15:40:51 4.855711e-33 1 7 \n", - "4 1851-10-11 21:30:03 4.190009e-32 1 7 \n", - "... ... ... ... ... \n", - "38105 2403-10-11 18:08:27 4.737795e-15 1 7 \n", - "38106 2404-10-10 23:57:39 1.236131e-15 1 7 \n", - "38107 2405-10-11 05:46:51 2.533467e-16 1 7 \n", - "38108 2406-10-11 11:36:03 3.634950e-17 1 7 \n", - "38109 2407-10-11 17:25:15 2.750127e-18 1 7 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Wind turbine construction unit carbon dioxide kilogram \n", - "2 Wind turbine construction unit carbon dioxide kilogram \n", - "3 Wind turbine construction unit carbon dioxide kilogram \n", - "4 Wind turbine construction unit carbon dioxide kilogram \n", - "... ... ... ... ... \n", - "38105 Wind turbine construction unit carbon dioxide kilogram \n", - "38106 Wind turbine construction unit carbon dioxide kilogram \n", - "38107 Wind turbine construction unit carbon dioxide kilogram \n", - "38108 Wind turbine construction unit carbon dioxide kilogram \n", - "38109 Wind turbine construction unit carbon dioxide kilogram \n", - "\n", - "[38110 rows x 8 columns]" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = bd.Database('wind-example').nodes_to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'C02 emissions over time_ FU : 1 kWh produced by electricity mix')" - ] - }, - "execution_count": 187, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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kd+7c4fjx4yZfmxatVkufPn2ULmj16tVj5MiRlClThmfPnrF+/XrWrl1LQEAAnTp14vjx4ybHSBm8++67nDlzhl69etG1a1fc3d158uQJixcvZv/+/dy+fZtPPvmEX375xeh1gYGBjBkzhpiYGIoWLcrQoUOpX78+RYoUISYmhvv373P69OksDYr/6quvlEC0ePHiTJgwgVq1ahETE8OBAweYP38+UVFRjBo1Co1GQ5s2bZTXNm7cGHd3dwICAli/fn2qweiff/6pPDRIGtRC9r6nP//8c/7991/atGlD3759KVGiBM+fP0/1qVxin376KePGjWPs2LH8888/1KxZk/nz5xttY2lpma59JZUbn7fDhw/Tu3dv4uPjKVy4MMOHD6d69eq4u7sTHBzM9u3bWbZsGadPn6Zfv35s27Yt2fAF8Wr78ccflYd7LVu2ZPny5dja2hpt06hRI2WqlhMnTqQYjBp6FBn+v3HjBs+ePTN6yKHVajl79iwAb7zxhsmn3wYHDx7k3LlzVKhQQWnxio+PZ+/evcydO5fY2FjGjRuHj49PmkFiWiZOnMg///xDp06d6NevH66urvj5+bF48WKOHz/O5cuX6du3Lzt37kzxAXJERAQDBw4kKiqK9957jzfffBN7e3tu3LiBl5cXkPB91bNnT2JiYjAzM2PIkCF07NgRR0dHrl69yvz587l69SpbtmxBrVazdOnSZMc5f/48w4cPJz4+HktLS0aPHk3r1q2xtrbm3Llz/PDDD7z//vtp3stklw8//FBJ/Ojs7MywYcOUVpjQ0FAuXbrEtm3bUKlUyV57/fp1OnTowMuXL7Gzs+Ptt9+mbt26eHp6EhYWxv79+/nll1+4efOm8iC3UKFCeV4f06ZN4/z58zRp0oShQ4dSokQJHj9+zMqVK9m+fTsPHz6ka9euHD9+3KgX3sCBA5kzZw46nY7Vq1fz4YcfpngMw/dgjRo1MjXVU3bUbW4cIzg4mDZt2ijBrLe3N3379qVixYqYm5vj7+/PiRMnjKZWdHd358SJE5w/f5533nkHSAhUatWqZbRvd3d3k+UeMGAAjx49YtiwYbRv357ChQvj5+eHk5NTus99zpw5fP755wA4ODgwZMgQmjZtStGiRXn58iVXrlxh9+7d3LlzJ9373LhxI7GxsUrr5dChQxk6dKjRNoZrZosWLfDw8MDf35+VK1emGoyuXLkSSLhfMXXflV5Pnjxh8ODBFCpUiE8//ZTatWsTGxvLli1bWLhwIUFBQYwfP55vvvmGUaNGUaZMGcaOHUuVKlWIiIhg5cqVrF27locPHzJlyhST+XtS4+zszIIFC+jZsyePHz9m3LhxRq3fOp2OUaNGERISgpmZGYsXL8bBwSFDx8hQMHr16lXliVFKCRUyIiAgQPk5tWACMHqSlbQVLz127tzJv//+CyS8mRwdHTO8D0hIY234I8ycOZORI0cara9RowY9e/Zk5MiRbNiwgS+//JKePXum+OV/7tw5pk+fbtTKXKNGDXx8fKhTpw7h4eEMGzaM4OBgVqxYQYcOHYy2q1mzJk2aNEGr1bJ06VJmzpyZ7nO5f/++8kT666+/TnYukNBNYOrUqclaeDZv3oxOp8POzo69e/fi6upqtL5Bgwb069ePyMjIDLdCL126VAlEO3XqxNKlS4320bJlS+rWrcsHH3xAaGgoH3/8carz650+fTrZk73q1avTqlUrunbtyuHDh9m4cSPTp083uoHbvXu30hV48+bNvPHGG0b7NXRjnjFjRrq6ayR17do1fvzxRwDKlCnDnj17jG7uGjZsSPv27enQoQORkZG8++67XLx4UZneSK1W0717d+bNm8epU6d48OCBcvOV1Lp165Tj1K5d22hddr+n//33XyZOnKh8YWSUu7s77u7uys25ra1ttiXayenPW2RkJCNHjiQ+Ph4fHx9Wr16d7KLcokUL2rRpQ58+fTh9+jSrV69m4MCB2XJ+GXX16tVU15crV04C5Wyk1+v59NNP+emnn4CEZEoLFiwwWceJewudOHEi2cOmpMFo2bJlcXFxITAwkBMnTtCpUydl24sXLyrDW9LqhXTmzBlatGjBH3/8YTSVWv369SlTpgyjR48mLCyMtWvXMmbMmIycfjLnz59n8uTJTJo0SVlWo0YNOnXqxMiRI1m3bh2nT59mxYoVDBo0yOQ+nj9/jq2tLTt27DC6L6lZs6by84QJE4iJiUGlUrFs2TKjz3XNmjXp3r07Xbt25eTJk2zatImdO3fSrl07o+O8//77xMfHY2Zmxrp162jWrJmyrnbt2nTq1ImWLVty5cqVLNVJeuzZs0cJRGvWrMmff/6ZrKWiadOmjBs3LlnLmV6vZ9iwYbx8+ZJKlSqxadOmZN/fTZs2pWvXrvj6+nL37l3mzZvH1KlTjbbJi/o4f/48/fv3Vz4/kPB+adeuHV9++SWzZ8/m/v37zJ49m08//VTZpkyZMvj4+HDs2DH++OMPPvjgA5NB+smTJ7l9+zaQ0B0zo7KrbnPjGO+//77y3kj6GYSE91XHjh354osvlN4EFhYWVK5cmeDgYGW7EiVKpPv7+dq1a6xdu5ZWrVopyzISS/z77798+eWXQELr26ZNmyhZsqTRNo0aNWLkyJGpdk9PqmzZska/Fy1aNMVzUqvV9O/fnxkzZnDkyBH8/PwoUaJEsu0iIyPZuHEjgBJ4Z9adO3coU6YMu3fvNrpH9fb2xtzcXLkH7NWrF3Xq1OGvv/4yerjZuHFjYmJi2LRpE1u2bEn2sDI9WrZsyYgRI1i0aBHbt29n6dKlDB48GEjI5XPkyBEg4SFZZjK1ZyhKSDwuLbVkQemVeOxn0sRFSSVen9Exo8HBwUqXOjMzMz755JMMvT4xQ7eT1q1bmwzeDMf4/vvvsbKyIjw8PNXMvXXq1DHZ3dnV1VXpJ/7s2TO6detm9AVqUKVKFaXLlSGAS6+nT58qP6d1g5L0yZWhlatMmTLJLoSJ2draptjlKSWGFkpHR0fmzp1rMpgdNmyYMo7Y8EQ0Jb6+via7mKjVasaNGwckdN88c+aM0XrDOWo0mmSBaGIWFhYZfgoECV3hDK2VP/zwg8lWhlq1aildH548eZLsvWR42qbX69mwYYPJ41y/fl3p0dCrV69k67P7PV26dGmmTJmS4vq8lNOft1WrVvHkyRMsLCxSfTrYpk0bJVhI/IQxtzVs2DDVf4kfGIqs0Wq1jB07VrmRHj58OIsXL04x2Pf09FQewibtevvw4UOjFg2DlMaNJh7ukNbYJWtra37++WeTc3r37t1b6TmTHdMCVa5c2WQrlUql4rvvvlMeeC1evDjV/YwfPz7Fm9rz589z7tw5IKH8pj7XhnM2DF9IOoXc+fPnlQe3/fr1Mwq8DIoVK8a0adNSLWd2MVyzraysWLZsWao3ukm7pO7Zs0cJEOfPn5/i93fNmjUZNmwYkPwalVf14ezsnOID908++UQZZ7106VIlMaaB4YHfvXv3Upxn2NCSZW1tnWZiTlOyo25z4xh37txRvsNbtmyZLBBNzNLSEhcXlwyVMSVvvfWWUSCaUYm7pP/yyy/JAtHEUuuKnVX9+/dHrVaj1+v5448/TG6zefNmZQ7orHbRhYSGAlMBZOIW3ODgYObMmZOslw2gPMw0da+bXl988YUSpH/yySfcunWLCxcu8PXXXwMJDyxT63WQmgwFo4mDQFMnm1FRUVHKz2k9fU/8xZiRVqj4+HiGDBmitKZOmjQpzTGGKTF0WwXSHC+j0WiULnyp/eG7deuW4rrEc6F27do1ze0y2g/fcFMB8Mcff5hMkpHWa2/cuJFsvEFWPHnyROlj36lTp1S7kxmeyuh0OuWpjCmmAjCDxE/Pk9af4RxDQ0NzZH4qwxjWkiVLKoG1KYlbBJKOe61SpYpycVi/fr3J1xtaRSF5XeTUezrpmNT8Iqc/b4b3Sb169VLsqmRgCArOnz+fYkIp8Z/Lly8TGhpaIMeLRkdHM2DAAOXGZdKkSXz33XcmW2cSMzwkDAgIMHq/GYLL4sWLGz2VTykYNfyuUqnSfPDYtGnTFG8+1Wo11atXBzL+fWNKnz59Uuw5U6hQISVRyr///ptqN//UusAlvmam1gOhZMmSSlB18uRJozFXiZN2pDZ2rkOHDhnucplRISEhnD59GkhIHpNSb5iUGK5Rnp6eybpXJmW4Rj1+/NjogW9e1UeXLl1SbLgwNzfnrbfeAhJuyi9dumS0vlOnTspDdUPQmdjLly+VLqlp3XukJDvqNjeOsXv3buV+L6u9GzIiK11V9Xq9kmSsXr161K1bN7uKlWEeHh60aNECSPne2dBbz8PDgzfffDNLxytUqJByvKRKliypPPR+4403UuwWn7jLeWav3dbW1vz6669YW1sTGRnJsGHDGD58OHFxcTg6OrJo0SLMzMwyte8MBaOJ5yWLjIzM1AETs7GxUX5O+hQrqcRfDOltadPr9YwZM0YJVHx9fTOV5McgcXa4sWPHJsu2lfTfhQsXAFL9Ek3aPSCxxBfx9GyX0RbjEiVKKDcmCxYsoEGDBkybNi1dGYt79OiBpaUlMTExtGnTht69e/Prr79y5cqVZAmNMiJxt8G0LjZ16tQx+bqkUhuzkrjFN+k5t2/fXvlC6t+/Px06dOCnn37KluAhJiZGGdOQ1nm6uroqNxymztMQYF67di3ZF3DiFtO6desmS2SQE+/pzIyzyS05/XkzBPamMgAn/WdIBBUXF5esG3xuMQR3Kf0z1f1IZMzLly/p3r07O3bsUFr80ju5eOLAMXHrpqFVPmkGesPN55UrV3jx4gWQcA0wBC8VKlRIc5xnWmP8DNfE7JiXNemQgdTWp3SNt7e3TzUJzLVr14CEQDqtm3fDd0pMTIzSXTPxsdVqdardCi0sLDL9sDu9Ll26pNz8pjQDQWoM16iHDx+meY0yBHdgfM3Pq/rIyPvFMCzLwNraWvmu3Lp1q9JqZfDXX38pw3Iy00UXsqduc+MYFy9eBBIeTtWvXz9T55oZiR/4ZpSfn5/yIDIz7/vsZniw9fDhQw4fPmy07t69e8o1um/fvllKmAoJPRBTe3BpuCdJ7/1NVq7dlStXVvIdXLx4Ucla/O2336baUp2WDNVQ4q4ghpTyWZE4uE1tmo6k69M7WfdHH32ktAo1bdqU33//PUtvitQy9aUmtcA9cUCeVOKypme7zASBv/32m1Hii++//54uXbpQsmRJWrVqxfz5802mSy9XrhxLliyhcOHCxMfHs3v3bj744AN8fHwoXbo0gwcPZt++fRkuT+Kb8rT6tCfunpLazXx66zhpRkInJyfWrl2Lh4cHer2eY8eOMXXqVJo3b06JEiXo3bs3GzduzFS9J27dSU/ffcO5mjrPnj17KueRtHXUMJYUTLcQ58R7OjNPlHNLTn7e4uLilAAgo7Lj4V5uS29PisTbpdUS+Cq6ePGiEkgOHDgw1eRfSaU032jS8aIGVatWxd7eHp1OpwSg165dU4bYpGd+0dTe+/Df+z87piZKa7hP4vUpTV+VVsub4Zrp4OCQ5oPslL5TEu/DVPflxLKrS2NKEo/XS22ITEqy45qfV/WR1feLoZdRZGRksim6DF1ZS5QokakEmZAz36c5cQzDe8jBwSHNIXLZKSv3BonPOzPv++zWrl07pRxJW9pXrlyJXq9HpVJlSxbitK7Jhu/VzN7rZtSoUaOMGh3at29v9OAjMzLUl65y5cqYmZmh1WqVFpKsSNyNLa2kRIkHI6eV7Ajgf//7nzL2sH79+smSMWRG4j/gzz//rHRXSkt2dGnOKW5ubuzcuZOjR4+ydetWjh8/zrVr14iPj+fs2bOcPXuWuXPnsmrVKqOWSEhoaW7SpAmbNm1i//79nDx5kqdPnxIaGsqmTZvYtGkTrVu3ZtmyZWl+mEzJDzeu9evX5++//2b79u3s2rWLkydP8vDhQyIiIti9eze7d++mdu3arF27NtNTdKTnPFO78S9evDgNGzbk2LFj/Pnnn3zxxRfJglNzc3OTXVRz4j2d1aeABVXiuuzQoUOGxqan1aU3P7GxsSEqKspomEVqEt8E5eaNT35RqVIl4uPjuXXrFitWrKBhw4bp7q5WtmxZXF1defr0qRKABgUFKU+jkwajZmZm1K1bl4MHD3LixAlat26d4flFc1Na1770PPBI7/UmK9dZw/KsXquzW2a+Iw3XqTp16jB37tx0vy5xL4m8qo+svl8qV65MnTp1OHfuHCtXrlSG+ty6dUt5eNO/f/9M33tkR93m5jFy+x4rs104k8oP94bm5ub06dOHH3/8kW3bthEaGopGo0Gr1Spzljdp0uSV7F109OhRo54HFy9eJCQkJENZkZPKUDDq6OhI1apVuXDhArdv31YyPGVW4u5Aiad5MSXxBLZpdSOaOXOmkqW0evXqrFu3LltughJ3b9Lr9dmW4TM/aNy4sfI0MDQ0lKNHj7Jq1Sp27drF06dPGTBgAP/880+yJ8sODg4MGDBAGaB9584ddu3axS+//ML9+/fZs2cPX331Fd988026ypH4zZxW63viBExZ+RCkxcrKim7duinBnL+/P/v27VO6Jf/9999MmDDB5DiUlCR+QpieXgaGLjYpnWevXr04duwYAQEBHDt2jCZNmijzj0JCBldT3fNe5fd0brO2tsbOzo6IiAhCQ0Nf2bp0cnIiKioq3V3LEn9O83OreU4pXLgwv/32Gx07duTWrVuMHj0alUqV6lj2xBo2bMjGjRu5d+8ejx8/VsZrJx7DnViDBg2UYBTI18FoYGBgql3LEreGZDYbpeGaGRYWRnR0dKqto4nf04mvtYn3ERMTk+qD7dSu54kD57R61KTUWpb4mv3kyZNU95HS6wMDAwkMDMz0NSq76iOj0rrmpOf9MnDgQM6dO8e5c+e4fv06FStWVMb3qdVq+vbtm+nyZUfd5sYxDHUTFhZGREREgXhImNX3fU4wTBkUHR3Nhg0bGDZsGPv371ca17IjcVF+ExoayqhRo9DpdDg4OBAeHs6jR4+YMGECy5Yty/R+M9yEYWhy1uv1LFy4MNMHhoQnNYYWgcTjYUwxZD+zsrIySjqT1Lx585g+fTqQ8ER648aN2TaAPvHYh/3792fLPvMjjUZDx44dWbNmjfLk8PHjx5w6dSrN1xrmNzp06JDSPSfxPFVpSXxxNWRATEnixEm5eePv4eHB4MGDOXDggHLcXbt2pbulCBLex4YHOWklgAoMDFS62qZ0np07d1Zusgxd0/fu3at0VUrpxjc/v6fzw9PPjDLU59mzZ5ONSXpVGDJLP3r0KF03mol70WRlzFBB5ubmxtatWylbtiw6nY7Ro0enmHAsqaRddQ3BZYMGDUx+Rgytpf/88w+RkZHK2KXSpUtneLLznJbWtS/xmPbMXuMNAbtOp1PG26VVHisrK6Mg2XBsnU6Xaq+w+Ph4JXO5KYmHGKWWiOv58+dG3XETq1atmvJ3z0xGY0P3ugcPHhiNi82I7KqPjMqO90v37t2VhC8rV64kPj6etWvXAtC8efN09bxLSXbUbW4cwzDOV6/Xp+u+Lqm8+G4uUaKE8jAzOzJ5Z4fSpUvj4+MD/NdV1/B/oUKFTGbuLugmTJigBNuLFi1Ssvlu3rw5Qw0ySWU4GO3fv7+SZfS3335LNnA3NZs3bzYa+6lSqZTpFG7fvp3i1CQPHjxQkhA1a9YsxTGjv/32mzK3VJkyZdi0aVOW5vZJqlSpUsoFbsuWLdy9ezfb9p1fNW3aVPk5pS9HUzQajdLlMyOvc3NzUyZ337p1a6pj8AxPYdRqdarZaHOKpaWlcuMXHx+f4eDDkGHt7t27qT6MWb58ebLXJFWoUCHatGkDJLw3Y2JilJtdBwcH2rdvb/J1+fk9bQiuY2Nj87gk6Weo55iYGBYsWJDHpckZia8Jhpu4lOh0OuV9aGFhke9a5nJT4oBUq9UyatSoFKdjSiylYDSluqxTpw4WFhbExcWxfv16Hj9+DKRvvGhuW7NmTYothGFhYWzZsgVICCwyO/Yw8TUztZslPz8/JfOut7e3UWtf4qlLVq9eneI+DN31UlKiRAnlRj61wDhxBvSknJyclKzJ27ZtUx5Uppfhngv+myImo7KrPjJq8+bNKbYYa7Va5XpUuHDhFBMn2dnZ0b17dyDh+rVjxw6l90ZWW7Kyo25z4xht2rRR3oc///xzhl+fuHdBbn0/q9VqWrduDaAMIctumbnnMIxDvnDhAocPH2bXrl1AQgNARqc1zO9WrVqlNC4NGTKE9u3bM23aNMqXLw/Axx9/zL179zK17wwHozY2NixevBgzMzN0Oh19+vRJNhA8qWfPnvHBBx8waNCgZFlzx4wZo0zrMmnSpGSJjOLj43n//feVfvLjx483eYzVq1crmXI9PT3ZvHlzjgxyTpwBs3///soXvSlarZZ169alOR42r1y6dEnJqpaSxGnxE/d937p1a6pfMiEhIcoT04z2mTck+AgNDeX99983OQ5kyZIlSnp5X19fPD09M3SM9Ni3b1+qf9/o6GjlAYqDg0OaWSqTGjp0qNJt67333jNZnxcuXOCHH34AEm5mU5t+xdD6GRYWxvr165WLYocOHVIds5tf39OGz+/9+/dzdRxWVgwZMkRJovH9998rN9Mp+ffff9m5c2duFC3b9O/fX3kg+N1336U6qf2MGTOUzJvdunXL0vzUVatWVbJEFlTFihVj69atlClTBq1Wy8iRI/nzzz9TfU3lypWVh6p79+5VxuqklFHS1tZWeRBouHZA/uuiCwnvf1M31Hq9ng8//FC5JmYk6VNStWrVUrKsrl69WpkeIrGYmBjGjh2rZElPOt9y7dq1lTpdsWKFyanEnj59ytSpU1MtiybRnNWrVq0y+aD26tWraQ5rMcw9HRMTw6BBg1JN4Jc43wYkTAdjeAD5xx9/pNnD7f79+8kemmRXfWRUYGBgipmop0+frgznGjRoEJaWlinuxxBABAUFKfMiFilShHbt2mWpfNlRt7lxjDJlyijzXO/bty/FuVshITBL2gMm8b11ZoOPzBg/frwy7nT48OH4+fmluG3S9316GM4rI+fUsWNH5fo8YsQIJZDNbEbm/Or+/ft8/PHHQEICU8PcojY2Nvzyyy9YWlry8uVLRowYkanZJjI1GWCTJk346aefePfdd4mMjOTtt9/mp59+onPnzlSrVg0nJyciIiJ4+PAhBw4cYMeOHSmmEi5VqhTvvvsu33//PZcuXaJVq1ZMnDiRsmXL4u/vz4IFC5Qb/t69e5t8urtt2zbeeecd9Ho9tra2zJgxg7CwsFSn+3B3d8/UTU2XLl0YPHgwS5cu5erVqzRo0IDBgwfTpEkTnJ2diY6O5sGDB5w5c4YtW7bw5MkTTpw4kaWuHznl8uXLjB07lho1atC2bVuqV6+Om5sbOp0Of39/1q9fz7Zt24CEbh2J06YvXLiQESNG0KpVK5o0aUL58uXRaDSEhYVx5coVfvnlF+UClnhS3vQYPHgwGzZs4OTJk2zYsIFHjx4xYsQISpUqRXBwMBs2bFAGiGs0GmbMmJFNNWLszz//pE+fPjRt2pTmzZtTuXJlnJyciIyM5NatW/z222/Ke2zgwIEZnluzUqVKTJgwgdmzZ3Pjxg0aN27MhAkTqFmzJjExMRw4cID58+cTGRmJSqVizpw5qY7Nad26NYULF+b58+dMmTJF6Tac1ti0/Pqerl+/PqtWrSIoKIhPPvmE3r174+joCCQkD8jo/Hq5wd7eniVLltC1a1fi4uIYNGgQ7dq1o2vXrpQuXRozMzOCgoK4dOkSu3bt4uzZs7zzzjtZvgnKTRqNhu+++47Ro0fz4sULWrVqRf/+/WnRogVubm7ExcVx8+ZN1qxZw9GjR4GEIMzw5ZXX7t69m6wXjuEhaERERLIJ4r29vSldunS2Hb9YsWJs27aNDh06cOfOHUaMGIFarU5xbluVSkWDBg3YsWOH0gpmY2OT6pQaDRo04Ny5c0bzyeXHltFatWoxbdo0rly5Qt++fXFxccHPz49ffvlFGZpTp06dVOcHTY85c+bQokULYmJi6Nu3L0OHDsXX1xdHR0euXbvGvHnzlGt5ly5dTH4eZ82aRdu2bYmPj6dnz56MHj2a1q1bY21tzd9//83s2bN59uwZVapUSfUBzYgRIxg/fjxBQUG0bduWDz/8kAoVKhAWFsbBgwdZvHgxrq6uWFpappg5tU2bNso1+59//qFevXoMGzYMb29vNBoNoaGhXL58mW3btmFmZqZ8j0NCC9OSJUto1aoVYWFhfPzxx2zbto3evXtTsWJFLCwsCAkJ4cqVK+zfv58jR47QoUMHevTokSP1kRG1atVi2bJl+Pn5MXToUDw9PXn69CkrV65k69atAHh5efH++++nup+aNWsq5TK0ivbu3TvVADY9sqtuc+MYs2bN4ty5czx69Ijp06dz6NAh+vXrR8WKFTE3N+fRo0ecOnWKP//8kylTphhlhfX09KR48eI8evSIefPm4e7uTrly5ZRA0dnZWekKnZ2qVKnClClT+PLLL7l//z4+Pj4MGTKEN998kyJFivDy5UuuXbvGzp07uX37doYTrdavXx8/Pz927tzJkiVLqF+/vtK66eDgYPJhqpWVFb169WLhwoXKe6latWrpTgZZEGi1WkaMGEF4eDgWFhb88ssvRkksq1evzpQpU/j88885e/Ys3377bYYSOEImg1FImKy6VKlSfPTRR1y6dInz588b9ddPqkiRIkyaNEm5mUxsypQphISEKDf3pp6Ctm/fPsXMYdu3b1daTiMjI9OVSnn+/PmZTrk8e/ZsnJ2d+eGHH3jx4gVz5sxhzpw5Jre1tLTM9031Fy5cSPVDW7lyZVasWJFsnEBUVBRbtmxJtfVn5MiRjBgxIkPlMTMzY/Xq1fTr14/jx49z8uRJk1243d3dWbNmTY4GRXFxcezbty/VaWq6dOnCZ599lqn9f/rpp0RGRrJw4UIePnxo8kvU2tqaOXPmKN1wU2JhYUGXLl34/fffle7Nbm5uRt0qU5If39PdunVj9uzZ3L9/n59//tmoO5Gnp2e2jkXKTj4+PmzZsoVhw4bx6NEjduzYwY4dO1LcPie+tHNanz59iI+P56OPPiIqKopffvlFyV6eVOXKlVm1alWms01nt5MnTzJ27FiT654/f55s3fz587M1GIX/Wkg7dOjA3bt3GT58OCqVii5dupjcvmHDhkbvIUNX3JR4e3vz008/Kb97eHjky4c3P/zwA+PHj1eyryf1xhtvsHr16ixn4axSpQrr1q1j0KBBhIaGsmjRIhYtWpRsu06dOqXY0lSnTh0WLlzImDFjiImJ4ccff1QSJULCA7JZs2Zx6tSpVIOvAQMGsH//fjZv3sytW7eSfT96eXmxZs0ak9nPE5s9ezZ2dnYsWLCAoKAgJVdGUqYeQlSoUIG9e/cyaNAgrl+/zrFjx5Tg3xRT16jsqo+MmDp1KvPnz2f//v1Kz6jEPDw82LhxY7qm/xs0aJDSKgrZ15KVHXWbG8coWrQoO3fupF+/fly+fDnF+6yUvPfee7z//vv4+fklS/qUlfvr9BzXwsKCL7/8kvDwcObOnWsyNshMb7l33nmHzZs3ExMTw8SJE43W9enTJ8UuzYMGDTK6brxqraLffvutkjhvypQpJh+Ejhs3jn379nH06FFmzZpFixYtMjSHbaaDUUh4+nr48GH27t3L7t27OXXqlDK1h62tLcWKFaNGjRq0adOG9u3bp3gDq1KpmDVrFh06dGDJkiWcO3eOZ8+e4eTkRNWqVenfv3+KX9J5Qa1WK0+Kli5dyuHDh/Hz8yMsLAxra2uKFSvGG2+8QbNmzejYsWOGu2/mlp49e+Ll5cXhw4c5ceIEAQEBBAUFERcXR+HChalatSodO3akT58+yW58li5dyqFDhzh06BCXL18mMDCQ4OBgLCws8PDwoH79+gwcOJC6detmqmwajYZt27bx119/sW7dOi5cuMDz58+xs7OjfPny+Pr6MmzYsBzNAjdjxgzat2/P4cOHOX/+PE+fPiUoKAgzMzPc3NyoU6cOb731Fi1atMj0MVQqFTNmzKB79+789ttvnDhxgsDAQMzNzfH09OTNN99k9OjR6b6w9u7dm99//135vXv37uma+iA/vqft7e3Zs2cPs2fP5uDBgzx8+LDAzMfp7e3N33//zZo1a9i5cyeXL19WuuQVLlyYcuXK0aBBA3x9fQvsE9QBAwbQtm1bli5dysGDB7l16xahoaFYWlpSpEgRateuTceOHencuXO2pfR/lbi7uystpHfv3mXYsGGoVCqTXfENSTIMDGMGU+Lt7Y1KpVK6t+fHLrqQcJ3fvXs3ixcv5s8//+TevXtotVpKly5Nz549GTlyZJanZDNo2rQp58+fZ+HChezZs4e7d+8SHR1N0aJFqVOnDv369UvzgV+PHj2oUqUKP/zwA0eOHCE4OJiiRYtSv359xo4dS506ddJMCKNSqfj9999ZsWIFq1at4vr168THx+Pl5UXHjh1555130tVrS61W8/XXX9OnTx+WLl3K0aNHCQgIID4+HldXV0qUKKH0yjClQoUKHD9+nI0bN7J161bOnz/Ps2fPiI+Px8nJiTJlylC3bl3atm2b4vsnO+ojIywsLFi/fj3Lli1jzZo13Lx5k6ioKEqUKEHHjh0ZP368yQYPU3r27MmkSZPQ6XTUrl07W5MgZkfd5sYxDPd/f/75Jxs3buTChQs8e/YMOzs7ihUrRpUqVejatavJe5yhQ4fi7OzMkiVLuHz5MqGhoZnqnpkZ48aNo2PHjvz2228cPHiQBw8eEBMTg4uLCx4eHrRq1SrNhzmmVKtWjT179jB37lxOnz5NYGBgusaPVqpUiWrVqnHp0iWlpfRVcebMGb7//nsg4cFWSkMl1Wo1CxcupFGjRoSGhjJixAiOHj2a7s+jKjQ0tGAMxhJCCCFEgbZq1Sql9fnixYuv5Dx8Iv87deoUbdu2BeDHH39UZg4QIqOioqKUrvY9evTg119/zesiFTiv5+z0QgghhBDitWTIUm9nZ5epVjQhDDZu3KjMpvAqzi2aGyQYFUIIIYQQr4WHDx8qWax79OiR7q6EQiSl1WqZN28eAGXLls2TaQZfBVkaMyqEEEIIIUR+FhAQQFRUFH5+fvzvf/8jJiYGc3Nz3n333bwumihgQkJClH8LFizg2rVrAEycODFZok+RPhKMCiFEPhAQEJCpCeItLS0pW7Zs9hdICCFeEcOHD+f48eNGy957771sz5QtXn0LFy5MNjerj48Pffr0yaMSFXwSjAohRD7w1VdfsXr16gy/Lj9PcyOEEPmJjY0NpUuXZsSIEVmev1a83szNzfHw8KBLly68//776Zq5QJgm2XSFECIfGD16tASjQgghhHitSDAqhBBCCCGEECLXSZuyEEIIIYQQQohcJ8GoEEIIIYQQQohcJ8GoEAKA6Oho7t69S3R0dF4X5ZUm9Zx7pK5zh9Rz7pB6zj1S10LkHglGhRAKrVab10V4LUg95x6p69wh9Zw7pJ5zj9S1ELlDglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELlOglEhhBBCCCGEELnOPK8LIIQQ4hWnUhGitSAwWkdkvJ7idmZozLRYos3rkgkhhBAiD0kwKoQQIueo1Pwbrqbb9gc8jUwIPs1U8H6twoyvVgh7YvO4gEIIIYTIK9JNVwghRI4J0lrQauN/gSiAVg/f/v2cXQ+jMDMzy8PSCSGEECIvSTAqhBAiR6jVavY9iCAyXm9y/VdngnkeL19DQgghxOtK7gKEEELkCLVazeVnMSmu9wuLQ6tX5WKJhBBCCJGfSDAqhBAiR+h0Ouq52aS4vryTJeYq062mQgghhHj1STAqhBAiR+h0OrzdbXGyMv1V87W3MxozyagrhBBCvK4kGBWZcv78eXr27EmJEiVwd3enefPmrF+/PkP70Ol0LF68mIYNG+Lm5kaZMmUYPHgwd+7cMbl91apV0Wg0Jv9NnDgxO05LCJGNwvSWTD72lBVti1PRyVJZbm+h5rvGLvi4WaLT6fKwhEIIIYTISzK1i8iwo0eP0r17dywtLenWrRuOjo5s3bqV4cOH8+DBA95///107WfixIksW7aMihUrMmLECAIDA9m4cSMHDhxgz549VKxYMdlrHB0dGT16dLLlNWvWzPJ5CSGy1/2XWv6685JzgTFMrFWYUo6WxOkSuuVuuxtGTGk7bOSRqBBCCPHakmBUZEh8fDzjx49HpVKxfft2qlevDsCkSZNo3bo106dPp0uXLpQpUybV/Rw5coRly5bh7e3Npk2bsLKyAqBPnz506dKF9957jx07diR7XaFChZg8eXL2n5gQIlup1WrOPI0C4EF4HBMPP022zaTaRdBIMCqEEEK8tuQ2QGTIkSNHuHfvHj169FACUQAHBwc+/PBD4uPjWbVqVZr7Wb58OQBTp05VAlGApk2b0qJFC06cOMHt27ez/wSEELlCr9fjaW+R4nprMxWWMsWoEEII8VqTllGRIceOHQOgefPmydYZlh0/fjxd+7Gzs6NBgwYm97Nv3z6OHz9O2bJljdbFxsbyxx9/8PjxYzQaDfXq1aNq1aqZORUhRA7S6/XUdLHG1lxlcp7RIZUL4WSuAxkyKoQQQry2JBgVGWJILmSqG65Go6FIkSIpJiAyiIiI4MmTJ1SuXBkzs+RNI4Z9m9rP06dPGTNmjNGyli1bsmjRIooUKZKuc4iOjk7Xdq+b2NhYo/9Fznhd6jne3Iq9D1+ypLU7b+8NICLuv4C0YTEbJtV2Ii4yjLgcLMPrUtd5Teo5d0g95568rGtra+tcP6YQeUmCUZEhYWFhQEIiIVMcHBwICAjI8j4Sb2fQv39/GjVqRKVKlbC0tOTGjRvMnDmTvXv30qdPH3bv3o1KpUrzHAICAtBqZTqJlDx9mnxsn8h+r3o9W3uUZ9zhQOq52bC8TXECI+MJitJSubAVz6O1REZGEBTwMFfK8qrXdX4h9Zw7pJ5zT27XtZmZGaVLl87VYwqR1yQYFQXGpEmTjH6vU6cOa9euxdfXl5MnT7Jnzx7atGmT5n7c3d1zqogFWmxsLE+fPsXV1RVLS8u0XyAy5XWoZ5VKxYkXOnR6OPU4iu7b/HG3M6eQlRq/sDgi4/X806cEnp6eOVqO16Gu8wOp59wh9Zx7pK6FyD0SjIoMMbRmJm21NAgPD0+xxTMj+0i8XWrUajV9+/bl5MmTnD59Ol3BqHSBSZ2lpaXUUS541evZ2ty4l0JARDwBEf/9bq5WYW2ZO+f/qtd1fiH1nDuknnOP1LUQOU+y6YoMSW08Z2hoKMHBwWlO62JnZ4ebmxt+fn4mu8umNi7VFMNY0cjIyHRtL4TIeeU0llibme42X62oFU4pJ9oVQgghxGtCglGRIY0aNQLgwIEDydYZlhm2SWs/ERERnDp1Kkv7Afj7778B8PLyStf2QoicpVKrCY3RMruJK0nDUQdLNUtaFcNBHZ8nZRNCCCFE/iHBqMiQpk2bUrJkSTZs2MClS5eU5eHh4Xz33XeYm5vTt29fZXlwcDA3b94kODjYaD+DBg0CYNq0aUbZ6g4fPsz+/ftp2LCh0bQu169fJzQ0NFl5Tp48yfz587GysqJjx47ZdZpCiCx4Hm+O7xZ/Dj+KZGtnT4ZX0dC2pD2T6hThT18PXG1U6PXJp3sRQgghxOtFxoyKDDE3N2fu3Ll0796d9u3b0717dxwcHNi6dSt+fn5MnTrVKIhcvHgxM2fOZNKkSUyePFlZ3qRJEwYOHMjy5ctp0qQJrVu3JjAwkI0bN+Lg4MDs2bONjrtx40bmzp1LkyZN8PLywsrKimvXrnHgwAHUajU//PBDjidDEUKkz7MYPU8jtay9GcbGO+G0KWGHu505Jx5HMfNcMOf7lMRehmEJIYQQrz0JRkWGNWnShF27djF9+nQ2btxIXFwcFStWZMqUKfTq1Svd+/nxxx954403WLp0KYsWLcLOzo62bdvy6aefGgW0AI0bN+bmzZtcvHiREydOEB0djYuLC926dWPMmDHUrl07u09TCJFJcbr/Wj1jtXq23n2Z4nohhBBCvL5UoaGhclcghCA6OpqHDx/i6ekp2QNz0OtQz890llRZeZ9obfKvF3sLNRf7lqCIOucnk38d6jo/kHrOHVLPuUfqWojcI2NGhRBCZCtztZop9YqaXPedjwtOZsmzaAshhBDi9SPBqBBCiGwThTkjDzwhIl7Hktbu1HaxxtFSTV1Xa1a1LU7bErao9RKMCiGEEELGjAohhMhGofFqdvtFsNsvgpKOFgyurKG4vTkPw+OYfDyQde3ccbLL61IKIYQQIj+QYFQIIUS2iYjTKT/fD4vjf6eCjNYHRWupKMGoEEIIIZBuukIIIbKRxkqNjbkqxfUlHSxysTRCCCGEyM8kGBVCCJFt7MxVjK9R2OQ635J2FLGUBO5CCCGESCDBqBBCiGyhUpux8sZLPB0smFSnCI6WCV8xlmYqBlcuxLxmbtgSl8elFEIIIUR+IWNGhRBCZItn8WZ8c/YZITE6OpSyZ1GLYpipVKhVsPluOP7hcRR2zOtSCiGEECK/kGBUCCFEtojW6gmJSUhgtO3eS7bde2m0voGrNTU0luh0OlMvF0IIIcRrRrrpCiGEyBZWalWqyYtKF7JAr5cxo0IIIYRIIMGoEEKIbFHIQs/Iqk4m1znbmFFBI8GoEEIIIf4jwagQQogsU6lU3A7X413Mho6l7Y3Wedibs7urF0XN4/OodEIIIYTIj2TMqBBCiCyL0Jsz8chT/g6M4oPaRRlZ1YmnkfEUsjTjRayWmHittIoKIYQQwogEo0IIIbIsUqfi1JMoAKaffYYKcLBUExmvI14HH9YqTJVa9mi12rwtqBBCCCHyDemmK4QQIsvUgG2i5EV6ICw2IRAFKGpjliflEkIIIUT+JcGoEEKILHOy0PP2G5oU13csJa2iQgghhDAmwagQQogsex6npmNpB6oUsUq2blFzN4pYSCAqhBBCCGMyZlQIIUSW6FXmzL8Uyu9XQ5ndxA2VCo4HROJkZUYzD1tcbNRYq7RI/iIhhBBCJCbBqBBCiCx5oVWz8sYLQmN0vL03ADdbc6o5W3E1OIbZ54PpXd6RRU2c0MbL1C5CCCGE+I8Eo0IIIbJMl6jV80lkPE/84o3XSauoEEIIIZKQMaNCCCGyxNFcR5/yjimuf7tyIXQ6GTMqhBBCCGMSjAohhMiSCK2KPhULUcwueWeb1l62VNSYo5cBo0IIIYRIQrrpCiGEyDS1Ws3ZJ3FMPBrIry2LcdA/kt1+L7E1V/NWBUdaetpRSB0ryYuEEEIIkYy0jAohhMi0CJ2a784/xy8sDt/ND/knMJquZRx409OWxZdD+OREIHGY5XUxhRBCCJEPScuoEEKITIvTq3ge/d940P0PI9j/MEL5vZCVGXF6lXzZCCGEECIZaRkVQgiRafZmetp42aW4vmMpe2zVulwskRBCCCEKCglGhRBCZJpWr2doFSccLJN/nRSxNqN3OQf0kklXCCGEECZIMCqEECLTnsSYMWRPAGvbe9DKyw4VoFZBh1L2HOheAleL+DT3IYQQQojXkwSjQgghMkdtzsLLIZwPiqb/rkdULWrFOl8P1rTzoISjBZ+eDCRCJ18zQgghhDBNckoIIYTIlFg9XHkeC8DzaC2zzz8HnivrvRwsiNGCtcSjQgghhDBBbhGEEEJkipVaT20XqxTXVyliiY2ZTDAqhBBCCNMkGBVCCJE5Oh1DKjthkcI3yaf1imKFJC8SQgghhGkSjAohhMiUYK0F084EsaJtcYrb/zfqo7C1GX+0LU4Ze9DrpWVUCCGEEKbJmFEhhBAZplarOREQzdqb4Vx7HsuX3s44WZmh00OsTs+JgAiaFSuEZZLXmZsnfO1otVoJVIUQQojXnASjQgghMkytVrPjfgQAl57FMHTvY6P1VmYq3q/phK0q4fdIvQX+0XpWXQ8jMk5Pr/IOVChkgaMqNreLLoQQQoh8QoJRIYQQGabX6ynlaJHieldbc9QktHxG6M357sILfrwQoqz/5d9QmrjbsKJNMQoRk+PlFUIIIUT+I2NGhRBCZJhWq+WtCoVQpbD+g1pOOJklJC+6H6E3CkQNjgREsenOS9Rq+SoSQgghXkdyByCEECLDIrBg9/2X/NjUFbMkEWm3Mg50LW2PTqdDZWbOz5dDU9zPvEuhhGrNcrawQgghhMiXpJuuEEKIDAuI0vPBsUA6l3FgU0dPbobGEh6ro4azNUGR8ZihA0AHhMToUtxPeKwWnR5SbGIVQgghxCtLglEhhBAZYmZmxi6/hORFm++Es/lOOKULWWBjrmb62WfEaPXcGlgKWzMw0+t4q7wD2++9NLkv35L2OJjrIeV4VQghhBCvKAlGhRBCZIher8fG3Lgp8+6LOOVnMxWo/n+1TqfDp5gNZTWW3A41zpxrb6Hmw9qFMdNJRl0hhBDidSRjRoUQQmSITqejTQn7FNd3LeuAo9l/TZ2F1bHs6eLBhJpOOFqqsTRT0bOcA6d7l6CYRXxuFFkIIYQQ+ZC0jAohhMiQeNQ8j9byQe0ifP93sNE6N1tzvvYuiqX+v9ZOvV5PYVUMn9dy5N1qGgDszXRY6GP5/9lfhBBCCPEakmBUCCFEhoRozWm18T7jaxRmg68Hf90O41m0Fh93Wxq526Ixx2SQqdLF42TojyNBqBBCCPHak266Qggh0k2lUnE5OJZYrZ7v/w5m8J4AtHrwcrBgy91wWvzpR0hc2vsRQgghhJCWUSGEEBkSEfffeNCXcTrW3gwzWh+nk2ZPIYQQQqRNWkaFEEKkm16vp6aLdYrrSzla4Gghk4YKIYQQIm0SjAohhEg3lUqFpZmK/hULJV8HzH/TFSczyZArhBBCiLRJMCqEECLdQnXmdNjsj3cxG75v7Ep5J0vsLdQ09bBla2dPqhW2QKfTpb0jIYQQQrz2ZMyoEEKIdHsWDTdDYxl78AnVna0YXsWJItZmXA+JYcieAA5198LeIq9LKYQQQoiCQILRV4xOpyM6OhpbW9u8LooQ4hUUEqNVfr4YFMPFoKdG6yPjdSDBqBBCCCHSQYLRAiw+Pp59+/Zx9OhRTpw4gZ+fHy9evECv12NlZUXRokWpWbMmPj4+tGrVilKlSuV1kYUQBZy7XcpfG5ZmKjSWMvpDCCGEEOkjwWgB9OjRI3777TdWrlzJs2fPgIQMl4lFR0fj7++Pv78/27Zt4+OPP6ZZs2a8/fbbdOjQIS+KLYR4BdhaqOlc2p7Nd18mWzemqgYncx3IzC5CCCGESAcJRguQly9fMnv2bH7++Weio6NRq9XUrFmTOnXqUKdOHYoVK4aTkxPW1taEhIQQEhLC1atXOXv2LKdPn+bgwYMcOnSIatWq8fXXX9OoUaO8PiUhRAESpzJn+pln9K+kwcnajD9uhBGr1WNnoWJUVSfGVddgro/J62IKIYQQooCQYLQAqVWrFkFBQZQrV45+/frRq1cvihUrluprWrVqBSSMJT1w4ABr1qxh69atdOzYke+//5633347U2U5f/4806dP58yZM8TFxVGxYkVGjx5Nz549070PnU7Hr7/+ytKlS7l79y52dnY0btyYTz/9lDJlyqT5+jlz5vD5558DsHfvXurWrZupcxFCpM/zeDW//vuC366+oE+FQvzRtjg6PejRs+5mGBFxejQyXlQIIYQQ6STBaAHi6OjI119/TY8ePVCpMjapvFqtpmXLlrRs2ZIHDx7w7bffEhoamqlyHD16lO7du2NpaUm3bt1wdHRk69atDB8+nAcPHvD++++naz8TJ05k2bJlVKxYkREjRhAYGMjGjRs5cOAAe/bsoWLFiim+9saNG3zzzTfY2dkRERGRqfMQQmTMixgdWj2ghxXXXrDi2guj9WOqaSiefPpRIYQQQgiTJBgtQE6fPo2ZmVmW9+Pl5cVPP/2UqbkA4+PjGT9+PCqViu3bt1O9enUAJk2aROvWrZk+fTpdunRJs2XzyJEjLFu2DG9vbzZt2oSVlRUAffr0oUuXLrz33nvs2LHD5Gu1Wi2jR4+mSpUqlClThnXr1mX4PIQQGWdvkXpyoiLWZoDMMSqEEEKI9JG0hwVIdgSiianVGf/zHzlyhHv37tGjRw8lEAVwcHDgww8/JD4+nlWrVqW5n+XLlwMwdepUJRAFaNq0KS1atODEiRPcvn3b5Gt//PFHrly5wk8//ZTtdSKESJmDhYpWXnYm11UtakURy1wukBBCCCEKNAlGRYYcO3YMgObNmydbZ1h2/PjxdO3Hzs6OBg0aZGg/V69eZebMmXzwwQdUqlQpQ2UXQmSeWq3m72cxTKxVhLqu1kbrKhW2ZH374jiq4/OodEIIIYQoiKSbrsiQO3fuAJjshqvRaChSpIiyTUoiIiJ48uQJlStXNtmyadh30v3Ex8czZswYypcvz8SJEzN7CkRHR2f6ta+y2NhYo/9Fziio9RxhbsfYg095Gafj03rOTK3nzJPIeFxszXj0Mh6dTkdUVFReF9NIQa3rgkbqOXdIPeeevKxra2vrtDcS4hUiwWgBN3bs2HRva2ZmhoODAyVKlKBhw4ZUqVIlw8cLCwsDEpIpmeLg4EBAQECW95F4O4NZs2Zx5coV9u3bh4VF5lN2BgQEoNVqM/36V93Tp0/zugivhYJWzyq3sjx6mdDy+cHRp5ipwMFSzcs4HfE6WNbShRq6p/nyRrmg1XVBJfWcO6Sec09u17WZmRmlS5fO1WMKkdckGC3g/vjjDwCj7Lp6/X8zzptabljm7e3N/PnzKVmyZC6UNGsuX77M999/z7hx46hRo0aW9uXu7p49hXrFxMbG8vTpU1xdXbG0lMF/OaWg1vNzM+OyavUQGvNfsqLCNha4ObgZXX/yWkGt64JG6jl3SD3nHqlrIXKPBKMF3KRJk3jx4gW//fYbOp2OBg0aUKVKFezt7Xn58iVXrlzh1KlTmJmZ8fbbb2Nubs7Nmzc5dOgQJ06coFOnThw5cgSNRpOu4xlaM5O2WhqEh4en2OKZkX0k3g5g9OjRlCpVio8//jhd5UyNdIFJnaWlpdRRLiho9eykgjc9bDnoH5lsnY25ikqFLbHKp1kIClpdF1RSz7lD6jn3SF0LkfMkGC3gRo0aRYsWLShXrhzLli2jbNmyyba5ffs2AwcOZM+ePezfvx+NRsODBw946623uH79OgsWLOCTTz5J1/ESj+dM2kIZGhpKcHAw9evXT3UfdnZ2uLm54efnh1arTTZu1NS41CtXrgDg6upqcp+tWrUCYOXKlXTo0CFd5yKESB+VSsWLOBUf1y3K9ZAAHkf8l6jIXA3r23tQ2EwL+adRVAghhBAFgASjBdzMmTPx8/Pj3LlzKXa3LVu2LKtWraJOnTrMmDGDGTNm4OXlxaJFi2jSpAm7du1KdzDaqFEjZs+ezYEDB+jevbvRugMHDijbpGc/f/75J6dOnUq2van9DBgwwOR+Tpw4wZ07d2jXrh1FixbFy8srXechhEi/KMyZeCSQqyGxzG3mxuOIeP4JjMbd3pyGxWwoU8gctT7/jRUVQgghRP6mCg0NlWfZBVi1atVwcHBI13QqjRo1Ijw8nEuXLinLqlevzvPnz3n48GG6jhcfH0+dOnV4/Pgxe/fupVq1akBC19rWrVtz69YtTp06pbTQBgcHExwcTJEiRShSpIiynyNHjtCpUye8vb3ZvHmzMibj8OHDdOnSBW9vb3bs2JFmeUaPHs3q1avZu3cvdevWTdc5CNOio6N5+PAhnp6e0i0pBxXEeg7SWlJ++T2l4bNMIQsqOFnxLFrLmSdRzGrszPDy1uh0ulT3k9sKYl0XRFLPuUPqOfdIXQuRe/LpCB+RXoGBgajV6fszqtVqAgMDjZYVLVqUuLi4dB/P3NycuXPnotPpaN++Pe+++y5Tp07Fx8eHa9eu8fHHHxt1FV68eDH16tVj8eLFRvtp0qQJAwcO5OTJkzRp0oTPPvuMUaNG0atXLxwcHJg9e3a6yySEyHmJn1reeRHHjvsvOfMkYSqXeJ1xsjQhhBBCiPSQYLSAK1q0KNevX+fRo0epbufv78+1a9eMWicBnjx5gpOTU4aOaeja26BBAzZu3Mhvv/1G4cKFWbx4MR988EG69/Pjjz8yc+ZMVCoVixYtYs+ePbRt25YDBw5QsWLFDJVJCJFz7M10vOlhm+L6NiXsZLokIYQQQmSYdNMt4N577z2WLFlCrVq1WLFihclpSx49esSAAQO4cOECQ4YMYdasWQA8f/6csmXLUrduXXbv3p3bRRf5jHRLyh0FsZ5fqqy4Eaql09YHRMQZf2WMqFKI/9V1wpb097DILQWxrgsiqefcIfWce6Suhcg9ksCogPv444/ZuXMn58+fp3bt2jRr1owqVarg4OBAeHg4V65c4dChQ0RHR1OsWDGjqVFWr16NXq+nWbNmeXcCQoh8TaU2Y8W1cP68E85fHTxZfyuMY48iKWpjzoBKhXjTwwZbYvK6mEIIIYQogCQYLeBcXFzYvn07I0aM4O+//2bXrl1GrZyGCejr1KnD4sWLcXZ2Vtb5+vrSpEkTyUArhEhRSLwZcy6E8CQyHt/ND+hSxoG+FQsRHqvjm7PP8AtzZHJ16aYrhBBCiIyTYPQVULp0afbt28exY8fYt28ft27dIiIiAjs7O8qVK0eLFi1o3LhxstelNBWMEEIY6FUQHJ0wr2i8DjbcCmfDrXBl/aOX8ZK8SAghhBCZIsFoAafT6ZRsuj4+Pvj4+KS6/ZMnT3Bzc8uNogkhXgHWKh1Ni9uy72GkyfUdS9nnuyldhBBCCFEwSDbdAm7ChAnp3vbp06d06tQp5wojhHjlqFXwaX1nzEw0fpYuZEFtF0sJRoUQQgiRKRKMFnArVqzg66+/TnO7Z8+e0alTJ27fvp0LpRJCvApUKhVXX+iZeS6Ydb4e1HFNyCppoYa3Kjiys4sXhdX5L4uuEEIIIQoGCUYLOHd3d2bNmsWSJUtS3CY4OJhOnTpx8+ZNmjZtmoulE0IUZFF6c74684wd918y4fATOpd2YL2vByvbFsfaTMXiy89BLV8jQgghhMgcGTNawG3YsIF27drx4Ycf4uzsTIcOHYzWh4SE0KVLF65du4aPjw+rV6/Oo5IKIQqaaB3cDIkF4GF4PJ+eDDJa/6aHLbE6RyzyonBCCCGEKPDkkXYBV6lSJVavXo2FhQXDhw/n1KlTyrrQ0FC6du3KlStXaNCgAWvXrpXJm4UQ6WZjpueNIlYprq/tYo2lWp+LJRJCCCHEq0SC0VeAt7c3ixcvJjY2lr59+3Ljxg3CwsLo2rUrFy9epG7duqxfvx5bW9u8LqoQogCxUeuZUq+oyXUWanj7jULoZX5RIYQQQmSSBKOviI4dO/Ldd98REhJC9+7d6dq1KxcuXKBmzZps2LABe3v7vC6iEKKAeRJrxpobYcxr5obG6r+vC3c7c3Z08cLNQgJRIYQQQmSejBl9hbz99tsEBAQwa9YsAgICqFatGhs3bsTR0TGviyaEKGjUZiy7EsaCSyE087BlYYtimKlUmKngRayOM48jqe1kC9JLVwghhBCZJMFoAXL8+PE0t3nzzTc5cOAAd+/e5f333+fKlSvJtmnUqFFOFE8I8QqJ1KnY8yACgEP+kRzyjzRaX6mwJQMr2OOgktZRIYQQQmSOBKMFSIcOHVCpTMw8n4LBgwcnW6ZSqQgODs7GUgkhXkWWqoTuuClxtTXHQo20jAohhBAi0yQYLUA8PDwyFIwKIURmWaBlQs0ibL770uT6j2oXxoZ4iUWFEEIIkWkSjBYgly9fzusiCCFeE6E6C/4NjmRizcL88M9zo3UTahamemFz9Pq4PCqdEEIIIV4FEowKIYQwolKpuPgslncOPWVUNSe2d/bkfGA0Oj3UdrUmNFqLtUonXXSFEEIIkSUSjAohhDCiUpux9FpCa+jCSyEsvhxCBSdL1CoVX54OwsnajEa9vHBSS/IiIYQQQmSeBKMFSFRUFDY2Nvl2f0KIV4MKsDb7b3y6Tg/Xnscqv1uZqVBJs6gQQgghskid9iYiv6hRowaLFy8mLi5r47QuX77MW2+9xbx587KpZEKIV4lOp2V4FU2K64dWLoTGXJd7BRJCCCHEK0mC0QLExsaGjz/+mOrVq/PVV19x69atdL82KiqKdevW0bVrV5o1a8bevXvx9PTMwdIKIQqqSL0ZOj10K+uQbF0FJ0uGVC4EOumiK4QQQoiskW66BcjZs2f5+eefmTVrFrNnz+aHH36gTJky1KlTh1q1auHm5oaTkxPW1taEhITw/Plzrl27xtmzZ7lw4QJRUVHo9XoaN27MN998Q5UqVfL6lIQQ+dCzWBWtN/ox08eVzqUd+OtOOJFxOtqWtKeOizUas3iQhlEhhBBCZJEEowWIhYUF48ePZ+DAgSxfvpwlS5Zw+/Ztbt++zdq1a1N8nV6vx8LCgi5dujB06FAaNWqUi6UWQhQkZmZmbLrzEq0ePjj6FBdbM9qXtMfV1ozfroQy5UUsV/uXpLAkLxJCCCFEFkkwWgBpNBrGjx/P+PHjOXPmDEeOHOHEiRP4+fnx7NkzoqOjKVKkCM7OztSoUQMfHx/efPNNihYtmtdFF0IUAKGx/zV7BkZqWXr1hdF6SV0khBBCiOwgwWgBV69ePerVq5fXxRBCvCJ0Oh0dS9kz6/xzk+ube9piq5Y+ukIIIYTIOklgJIQQ4j8qFS625jQslnzaJ0szFd/5uGCrki66QgghhMg6CUaFEEIoguPNaf6XHxNrFWFy3aK425lja66ifUl79nXzoqRdwjh0IYQQQoiskmBUCCEEACqViivP43gSoaXndn9OPI5kSr2iLGhejDIaS3pu8yckTpXXxRRCCCHEK0LGjAohhAASglG/8Fjl98P+kRz2jzTaJkarl8eYQgghhMgWEowKIYQAEpIXVS9qleJ6F1szbM1SbxnV6XREREQQHR2d3cXLMJ1Oh6WlJS9evCA8PDyvi/PKknrOHVLPuScn6tra2ho7OzvUanmaJ0RiEowKIYRQFLe3oHJhK64+j0m27ssGRSliHo8uhWS6Op2O4OBg7O3tKVq0KCpV3nbp1el0xMbGYmlpKTeAOUjqOXdIPeee7K5rvV5PdHQ0wcHBFClSRP5+QiQinwYhhBAAvMSCdw8/YVYTV3xL2WMIJZ2s1Mz0ccG3hC26lCJRICIiAnt7e2xsbPI8EBVCiPxCpVJhY2ODvb09EREReV0cIfIVaRkVQggBQFAMbLsXwSH/KN5+Q8NfHT2I10GsVs+q66F0K22Hg1nKr4+OjqZo0aK5V2AhhChArK2tefbsGQ4ODnldFCHyDQlGhRBCJCQvCosD4GWcjrkXnjP3wnOjbaZ563BOJRg17EcIIURycn0UIjnppvsayA+JRIQQ+Zter8fNLuXnk2oV2JnLV4YQQgghso/cWRRw9+/fZ8WKFZw7d85ouU6nY9q0aZQqVQp3d3caNmzI6dOn86iUQoiCwNnGjNKFLEyu61zaHo1FyuNFhRBCCCEySoLRAu7333/n3Xff5dGjR0bL58yZw6xZswgNDUWv13Pt2jV69uzJw4cP86ikQoj8TKsyY8+DCBY0L5YsIPUpZsPsxi5Y6ePzqHRCCCGEeBXJmNEC7sSJE1hYWNCuXTtlmVarZcGCBahUKqZPn07Dhg2ZPXs2mzZtYv78+cyYMSMPSyyEyI9CtGZMPByIs60Zn9ZzprC1GYFR8RS3s+BWaAw6vR5kuJMQQgghspEEowVcQEAAbm5uWFpaKsvOnj3Ls2fPaNasGSNHjgTgxx9/ZOfOnRw8eDCviiqEyMeConREa/U8DI9nxP7HWJqpcLBQExqjRauHhsVKUMQur0sp8rPq1asDcPny5Uzvo2rVqlnehxBCiIJDuukWcMHBwTg7OxstO3v2LCqVijZt2ijLChUqROnSpfH398/tIgohCgArM+Nmz1itnuDohEAUwEotzaKvu9GjR6PRaPDz88vV406fPh2NRsPRo0dz9bhCCCFynrSMFnCWlpaEhoYaLTt16hQA3t7eRsttbW3RarW5VTQhRAFS2EpFCUcLZXqXxDwdzCliLcGoSN2mTZtQq7P2jHvLli3ZVBohhBAFgbSMFnClSpXi3r17SotneHg4hw8fxt7enmrVqhlt+/TpU5mQXgiRjFqt5nmMnrlN3bC3MP5asLNQsb59cQqbSfIikbpSpUpRqlSpPN+HEEKIgkOC0QLO19cXnU5Hnz59WLRoEf369SMyMhJfX1+jyZWDgoLw9/fHy8srD0srhMiPQrVmvLUrgC9PB7GmfXGmNXRmQKVCfOXtzLr2HhSzMUOnk2ldCorY2FgWLVpEt27deOONN3BxcaFs2bL079+fixcvmnzNjh076NatG6VKlcLV1ZWqVasyYsQIrl69CiSM5Vy9ejWQMDZUo9Gg0Wjw9fVV9lG9enVlzCfAzJkz0Wg0rFmzxuQx161bh0aj4dtvv1WWVa1a1Wgfvr6+zJw5E4COHTsqx61atSo6nY5q1apRqlQpYmJiTB6jefPmODs7ExQUlJ6qUyTuGrxx40aaNGmCm5sbFSpU4KOPPiIqKspo+4zW+apVq9BoNKxatYqdO3fSokULihUrRqVKlZg2bZryeVu3bh2NGzfGzc2NqlWrsmDBApPl1ev1rFixgjZt2uDp6UmxYsVo1qwZK1asyNB5CyFEbpNuugXc2LFj2bx5M1euXGHy5Mno9XoKFy7MpEmTjLbbunUrAD4+PnlRTCFEPhYaBzdDYgHosPkhNZyt8XQw58/b4Xx6MoidnT3wLpzHhRTpFhISwuTJk/H29qZVq1ZoNBru37/Pzp072bdvHzt27KBWrVrK9p999hlz587FyckJX19fnJ2defToEYcPH6ZGjRpUrlyZ0aNH88cff3DlyhVGjRpFoUKFAFJ9wNm7d2+mT5/OunXreOutt5KtX7duHSqVil69eqW4j759+wJw/Phx+vTpoxyvUKFCqNVqBg0axFdffcWWLVvo2bOn0Wv//fdfzp8/T6dOnZLlVkivX3/9lX379tG+fXt8fHzYv38/ixcvJiQkhF9++UXZLqN1brBt2zYOHjyIr68v9evXZ8+ePXz//ffKOX733Xe0a9eOhg0bsmXLFr788kuKFy9O7969lX3o9XpGjBjB+vXrKVu2LD169MDCwoJDhw4xbtw4bty4wbRp0zJ1/kIIkdMkGC3g7O3t2bt3LytWrODmzZt4eHjQv39/XFxcjLbz9/enffv2Rk+xhRACQK83/v1CUDQXEjUkafWgUqnQJ91Q5EsajYYrV67g7u5utPzatWu0atWKL7/8kk2bNgGwZ88e5s6dS+XKldm2bRuFC//31CE+Pp7nz58DMGbMGC5fvsyVK1cYPXo0JUqUULZLqdW8ZMmSNGjQgMOHD/P06VNcXV2VdUFBQRw6dIgGDRpQsmTJFM+lX79+PHjwgOPHj9O3b18aN25stL5///5Mnz6d5cuXJwtGly9fDsDAgQNT3H9aDh48yKFDhyhXrhwAUVFRNG7cmA0bNvDll19SrFgxIGN1nti+ffvYvXu3EqhOnjyZWrVqsWDBAhwcHDhy5IhSP2PHjqVOnTrMnTvXKBhdvnw569evZ8CAAfzwww+Ymyfc2sXGxjJw4EB++uknevToQY0aNTJdD0IIkVOkm+4rwM7OjlGjRjF79mzee++9ZIEoJDz5XrVqlZJ6XwghDDSWCcmLTDFXQ3mNhQSiBYiVlVWyoAigUqVK+Pj4cOLECeLiEhJV/frrrwDMmDHDKBAFMDc3N/l9khG9evVCq9WyYcMGo+UbNmwgPj4+1VbR9HB1daVdu3YcO3aMe/fuKctjYmJYt24dHh4eNG/ePNP7HzVqlBKIAtjY2NC9e3f0ej0XLlxQlmekzhPr2bOnUYupg4MDbdq0ITIykrffftsoUPfw8KBevXpcv36d+Pj/xnAvXrwYOzs7vvvuOyUQhYQEh59++ilAsvoXQoj8QlpGhRDiNWemVjPTx4V+Ox8pU7kYfNPQGY2ZZOEuaC5dusTcuXM5deoUT58+TRYIBQcH4+bmxt9//42VlVWODeHo1q0bH3/8MevWrWPs2LHK8rVr12JpaUnXrl2zfIwhQ4awdetWVqxYwWeffQYkdH8NCQlh5MiRWcrwa+oBbvHixQF48eKF0fL01nliSRMNAso2icfOGri6uqLVagkMDMTd3Z3IyEiuXr1KsWLF+OGHH5Jtbwhab926ldppCiFEnpFgVAghXmPxKnMmn3hGSIyWLZ08+f3fUC4+i8HLwYKhb2ho4GqFJaaTw4j86fTp03Tq1AmAN998k86dO2NnZ4dKpWL79u1cuXJFSfjz4sULihUrluUpWVKi0Who3bo127Zt4+bNm5QvX55bt25x4cIFJSFRVr355puUKFGCP/74gylTpmBmZsby5ctRq9X0798/S/t2dHRMtszMzAzAaKq0jNR5Yg4ODinuP7V1hkA3NDQUvV5PQECAkujJlIiIiBTXCSFEXpJg9BWg0+lYs2YNu3bt4u7du0RERKQ4hkelUhl1LRJCvN5C4tWsufmCOB0c9I+gd/lCvP2GhsDIeN478oQ5TVxp566WbroFyKxZs4iJiWHXrl00aNDAaN25c+e4cuWK8nuhQoUIDAxEp9PlWEDau3dvtm3bxrp165g6dSpr165VlmcHlUrFoEGD+PLLL9m9ezeVK1fmyJEjtGrVCg8Pj2w5RloyUufZyRCw1qhRg0OHDuXIMYQQIidJMFrAhYeH06NHD86ePZuum8XE070IIUSsTk/c/z+7iojT8/u/oUbrn0bGo1JZSTBagNy7dw8nJ6dkQVFkZGSyaUZq167Nnj17OHbsGE2aNEl1v4ZWuYxO89OmTRs0Gg3r1q3jk08+Yf369Tg5OdG6det0vT49x02cyKhKlSro9XoGDBiQoXJmRUbqPDs5ODhQoUIFbt68SWhoaLa0NAshRG6SYLSAmzlzJmfOnMHGxoZ+/frRoEEDnJ2dc+wJtxDi1WJvrqKkowX3w5InVwGoX8wGnS7e5DqRP3l6enL79m2uXbtGpUqVgIQupZ9++inPnj0z2nbYsGHs2bOHjz/+mO3bt+Pk5KSsM2TTNSQxMqx79OgRpUqVSnd5DGNDlyxZwo8//oifnx9vv/02lpaW6Xp94uOmxMXFhXbt2rF9+3b+/vtvJbFRbslInWe3kSNH8t577/Huu++yYMEC7OzsjNbfv38flUpllAFZCCHyCwlGC7gtW7agUqlYvXo1TZs2zeviCCEKGGszmNbQhf67kt/oN3a3obiN9KYoaEaMGMGBAwdo27YtXbt2xcrKimPHjvH48WN8fHw4duyYsm3r1q0ZN24c8+bNo1atWnTo0AFnZ2cCAgI4cuQI77zzDmPGjAGgSZMmzJs3j4kTJypjIj08POjevXuaZerduzdLlixh+vTpyu/p1bhxY1QqFdOmTePWrVs4Ojri6OjI0KFDjbYbMmQIW7ZsISgoiAkTJhhlls1pGanz7DZkyBDOnj3L6tWrOX36NE2bNqVYsWIEBgZy69Ytzp07x6+//irBqBAiX5LmswLu6dOneHl5SSAqhMgwtVrNzgfRHA+I5NeWxSirSWipsjVXMbyKhqWt3XFQmW4xFflX27ZtWbZsGSVLlmTdunVs2LCB8uXLc+DAATw9PZNt/9VXXyndWzdv3sz8+fM5ceIEjRs35s0331S2M8yXqdPpmDNnDl988QVLly5NV5kM84nGxcVRsmRJ6tevn+7zqVixIvPnz0ej0bBgwQK++OILfvzxx2TbNWvWDHd3d1QqVZbmFs2MjNZ5dlKpVPz8888sWbKEihUrsnv3bubPn8+hQ4ewsrLiq6++olmzZjlaBiGEyCxVaGioDAQqwCpXroyrqysHDx7M66KIAi46OpqHDx/i6emJtbV1XhfnlZWf6vmF3oImfz3CLyyON4pYMbKqE8XszInX6fnzdhjtS9jRo4RFuscIBgUF4ezsnMOlTj+dTkdsbCyWlpYydCEH5Zd6fvz4MVWrVsXb25utW7fmWTlySn6p59dBTtZ1frtOCpHX5GpWwDVv3pzr168THh6eq8c9f/48PXv2pESJEri7u9O8eXPWr1+foX3odDoWL15Mw4YNcXNzo0yZMgwePJg7d+4k2zY0NJSPPvqIVq1aUb58eVxcXKhUqRIdO3Zk8+bNklxFiEyI16vw+/+xov8GxzD+0BN6bvenz85HbLgVzj9BMXLTKwqMn3/+mfj4eN5+++28LooQQoh0kruMAu7jjz/GysqKSZMmGc15lpOOHj1K27ZtOXnyJJ07d+btt98mODiY4cOHM2vWrHTvZ+LEiXz00UfodDpGjBhBq1at2LlzJ2+++SbXr1832vb58+esWrUKW1tbfH19eeedd2jZsiXXr19n0KBBTJgwIZvPUohXn7lKTylHixTX13KxznDmVCFy04sXL/jhhx947733mD9/PpUqVVLm+xRCCJH/STfdAu748eNcuXKFTz/9lDJlyjBw4EDKli2Lra1tiq9p1KhRpo8XHx9P3bp1CQgIYM+ePVSvXh1ImGKmdevW3Lp1i9OnT1OmTJlU93PkyBE6deqEt7c3mzZtwsrKCoDDhw/TpUsXvL292bFjh7K9VqtFr9cnS0gRHh5Oq1atuH79OidPnlSyGIqMy0/dR19l+aqezSzY6BfDkL2Pk60qZKXm77dKUlQdk+7d5bfuZ9KtMXfkZT37+flRvXp1bGxsqF27Nj/88APlypUzud0ff/yR5v4KFSqkJGzKb+T9nHukm64QuUey6RZwHTp0UOYOvX79OlOmTEl1e5VKRXBwcKaPd+TIEe7du0e/fv2UQBQS5jr78MMPefvtt1m1ahWfffZZqvtZvnw5AFOnTlUCUYCmTZvSokUL9u3bx+3btylbtizw3zxzSTk4OChdle/evSvBqBAZcD8SnkRqmVSnCHP+eU60NuHZZOlCFqxt54GzWSzSA17kZyVKlCA0NDTN7R48eMDMmTPT3M7T0zPfBqNCCPEqkmC0gPPw8FCC0dxgSE/fvHnzZOsMy44fP56u/djZ2SWbINywn3379nH8+HElGE1JdHQ0R44cQaVSUbFixfScghACiFWZ88XpZ2y885Ke5RxZ3b44cVqwNFPxOCKOW6HRVLAzl/HY4pXQuHHjdAWtQgghcpcEowXc5cuXc/V4huRCprrhajQaihQpYjIBUWIRERE8efKEypUrm2zxNOw7pURGP//8MzqdjmfPnrF37178/f2ZNGlSml2DDaKjo9O13esmNjbW6H+RM/JLPUeY2XHkURQA62+Fsf5WmNH6DqXsaeVaCG1s+j8vOp0uX40xNQTSer0+X5XrVSP1nDuknnNPTta1TqdL9T4kz4dvCJHLJBgVGRIWlnDD6ujoaHK9g4MDAQEBWd5H4u0Se/HihVFXKwsLC7766iveeeedtAv//wICAnIt2VNB9PTp07wuwmshr+vZxsUDZxszgqNNfxaK25nx/FkQYS9C071PS0vLPA+yTYmLk7lSc4PUc+6Qes49OVHX0dHRJu9vIGFIUunSpbP9mELkZxKMigLFMD5Iq9Xi7+/PX3/9xVdffcXp06dZunRpsgRHpri7u+dCSQue2NhYnj59iqurK5aWlnldnFdWfqlnCwsLPqhdhGH7kicvAhhRRUMhlTmFHB3Svc8XL17kq/eOXq8nLi4OCwuLXB3O8LqRes4dUs+5Jyfr2traGldX12zdpxAFmQSjr4iIiAhWrlzJnj17uHnzJi9fvsTe3p4KFSrQpk0b+vbti52dXZaPY2jNTOmpXnh4eIotnhnZR+LtTDEzM6NEiRJMnDgRMzMzPvvsM5YtW8bQoUPTPAfpApM6S0tLqaNckNf1HKK3wsY8mq5lHNh45795ilXAj01dKW4D1mSsfOHh4fkqy6ehe51KpcpX5XrVSD3nDqnn3JOTda1Wq+U7VohEJBh9BVy4cIEBAwbw6NEjo2QjoaGh+Pv7c+DAAebNm8eKFSuMMuBmRuLxnDVq1DBaFxoaSnBwMPXr1091H3Z2dri5ueHn54dWq002bjS1cammvPnmm0BCUqT0BKNCvO5UajV/3XzJR8cC+bS+M4MrazjzNAobcxW1XGzQ6XTYmemR3uxCCCGEyEnyaK2ACwwMpEePHvj7+2Nvb8+YMWP46aef2LBhAz/99BNjx47FwcGBhw8f0qNHDwIDA7N0PMMcpQcOHEi2zrAsPfOYNmrUiIiICE6dOpWl/QA8efIEIF1ddIUQ8FJrxorrYWj18L9TQfTa4c+2e+Gsuv4C300P+OpMMC+10g1QCCGEEDlLgtECbs6cOQQHB9O0aVMuXbrE119/Tb9+/WjRogX9+vVj2rRpXLp0iWbNmhEcHMy8efOydLymTZtSsmRJNmzYwKVLl5Tl4eHhfPfdd5ibm9O3b19leXBwMDdv3kw2t+mgQYMAmDZtmlHCk8OHD7N//34aNmxoNK3LpUuXePHiRbLyhISE8OWXXwLQsmXLLJ2bEK8LlUqPRaKrf4xWz8WgGK49j0UPWKhVMiZNCCGEEDlOmpIKuL1792Jpackvv/yCRqMxuU2hQoVYtGgRVapUYffu3Xz11VeZPp65uTlz586le/futG/fnu7du+Pg4MDWrVvx8/Nj6tSpRkHk4sWLmTlzJpMmTWLy5MnK8iZNmjBw4ECWL19OkyZNaN26NYGBgWzcuBEHBwdmz55tdNw//viDFStW4OPjg5eXF7a2tjx8+JA9e/bw8uVLOnXqRM+ePTN9XkK8TuzVOkZVdeLsU9PJi8ZW02Cr0qKTKUaFEEIIkYOkZbSA8/f3p1KlSjg7O6e6nYuLC5UqVcLf3z/Lx2zSpAm7du2iQYMGbNy4kd9++43ChQuzePFiPvjgg3Tv58cff2TmzJmoVCoWLVrEnj17aNu2LQcOHKBixYpG23bu3JnOnTtz79491q5dy/z58zl69CgNGjTg119/ZdmyZZLQQYh0CtOZUaqQBfXcbJKta1rchoZuVjKPYR46evQoGo2G6dOn59gxpk+fjkaj4ejRozl2jMzy8/NDo9EwevTodL9m9OjRaDQa/Pz8crBkIj/Kjc+LECLnSMtoAWdubk5MTEy6to2Njc22cZW1a9dmw4YNaW43efJkoxbRxNRqNSNHjmTkyJFp7sfb2xtvb+8Ml1MIYUylUnErVEvHrf7Mf7MYgyoVYvPdcFRAlzIO1HS2prC5VpIXZcDJkydp164dbdq0Ye3atcnWT5gwgaVLl1K1alWTwd+3337LN998wxdffMG7776bG0XOED8/P6pXr06fPn34+eef87o4Iof4+vpy/PhxQkND87ooyWg0Gho1asT27dvzuihCiGwmwWgBV6ZMGS5evMiNGzeoUKFCitvduHGDGzduZDmbrhCiYNOpzJh/+TkRcXoG7wmgrMaSFp526NHz7blgrM1V7O7kjqNKotH0qlOnDnZ2dpw8edJkhvBjx46hUqm4cuUKISEhODk5JVsP0LhxYyDhYd+ZM2coUqRI7pzAK+Dzzz9n4sSJMo/0a0g+L0IUbNKvsYDr1KkTer2eAQMGcOHCBZPbXLp0iQEDBgAJ3V2FEK8vLRAW+18X3NuhsSy6HMLiy6HcC4sjPFYnY0UzyMLCgvr16xMWFsbFixeN1j158oTbt2/ToUMH9Hq9EngaxMbGcvbsWRwdHZWHhba2tpQvX15urjPAzc2N8uXLY2FhkddFEblMPi9CFGwSjBZwI0eOpEKFCty6dYvmzZvTsWNHJk2axOzZs5k0aRKdOnWiWbNm3Lp1i4oVK6arS6wQ4tVliY63yjmmuL5zaXsczfPveNFILHgUZ8nVCHMC4iyJJH8EH4ZWzaTBpuH3d955B3t7+2Trz507R1RUFN7e3kqLakpj4KpWrUrVqlWJiIjgk08+oVKlSri4uNCwYUM2b95sslz+/v4MHTqUkiVLUrx4cdq3b8/x48fTfV6rVq1SguTVq1ej0WiUf4Yux2PHjsXNzY0HDx4ke72psamJz+/MmTN069YNLy8vk0n4rl69So8ePfDy8sLT05PevXtz/fr1ZNuZGjOa+DgXLlygW7dueHh44OXlRb9+/VIcX3r//n3GjRtHlSpVcHFxoUKFCowePdrk+aUmKCiIqVOnUqdOHVxdXSlZsiQtW7Y0mdV+165ddOjQAS8vL9zc3PDx8WHBggVok/SXf/DgAYULF2b06NHcv3+fgQMHUqJECdzd3encuTOXL19Otu87d+4wZswYqlWrhqurK6VLl6ZJkyZMmTJF2Uaj0Sjvi8R/Y8O43cTjeG/evEn//v0pXbq0UudpjfPVaDT4+vomWx4eHs7MmTNp2LAh7u7ueHl50bhxY6ZNm0ZcXJzyNwQ4fvy4UdlWrVoFpD5m9Nq1awwZMoSyZcvi4uJCtWrVmDx5MiEhIcm2Tfz5mjJlCjVq1MDNzS3Vz5cQIuukm24BZ2try6ZNmxg2bBjHjx/n2LFjRjcaen1CE4ePjw+//PILNjbJE5YIIV4fcXqo42ZD6UIW3H0RZ7SukJWa8TUKo9albxx6bgvRWzHywFP2PIhQlrX2smNRc1ecVHlbZkMwevToUcaPH68sP3r0KA4ODtSuXZv69esnGzNq+N3w+rTEx8fTrVs3QkJC6NChA1FRUfz1118MHjyYP//8k+bNmyvbPnnyhNatWxMQEECLFi2oXr06N27coGvXruk+XtWqVRk1ahQLFy6kSpUqRgGFl5dXuvaRkjNnzjB79mwaN27M4MGDkyXYu3//Pm3btqVWrVoMHTqUO3fusG3bNk6dOsWePXtSHZqS2IULF5g3bx4+Pj4MHjyYS5cusX37dq5evcrJkyextrZWtj137hzdunUjMjKStm3bUrp0aR48eMD69evZt28fe/fupWTJkmke886dO3Ts2JGAgAC8vb3x9fUlMjKSq1evMmvWLMaNG6ds+/PPPzN58mScnJzo0aMHtra27Nq1i08++YSTJ0+yfPnyZFMtPXjwgBYtWlChQgX69+/PvXv32LFjBx07duTMmTO4uLgA8PjxY5o3b05kZCStW7emW7duREREcOfOHRYvXszXX38NwKRJk/jjjz94+PAhkyZNUo5TtWpVo+Peu3ePli1bUqlSJfr06UNISAiWlpZGU7SlV3BwML6+vly/fp2qVasyZMgQdDodt27dYs6cObzzzjt4eXkxadIkZs6ciaenp9HUcUnLltTp06fp1q0bMTExdO7cGS8vL86ePcvPP//Mnj172Lt3L4ULFzZ6jeHz9fz5c9q3b09MTAwbN240+fkSQmQPCUZfAW5ubmzbto2TJ0+yZ88ebt26xcuXL7G3t6d8+fK0bt2aBg0a5HUxhRD5QFC8OW03+vFLS3e23H3JupsviNHq6VDagY9qF8HdSos2Pq9LmVwkFskCUYA9DyIYeeApS1s4Y0tcCq/OeTVr1sTBwYFTp04RHx+vJIs7duwY9evXx9zcnEaNGvHVV18RHBysdClMOl40LY8fP6ZmzZps3boVS0tLAHr27Ennzp2ZP3++0c3yF198QUBAAFOnTjXKdL506VImTJiQruNVq1aNQoUKsXDhQqpWrZpiQrrMOHjwIPPmzVOGkSR18uRJPvjgA6ZOnaosW716NaNHj+bDDz9ky5Yt6TrO7t27+f333+nWrZuybOTIkaxdu5bt27fTvXt3AOLi4nj77bfR6/UcPHjQKNg5efIkHTp0YNKkSSaTVCU1YsQIAgICmDNnjjKvtsGjR4+Un+/fv8+nn36Ks7MzBw8exMPDA4DPPvuMrl27snXrVtatW0fv3r2N9nH8+HH+97//Gf0dp02bxvfff8+qVauYOHEiAFu2bOHFixfMmDGDUaNGGe0j8fzfkydP5tixYzx8+DDVv/GpU6f48MMPjVpVgUxlMX7//fe5fv0677//Pp9++qnRusDAQOzt7dFoNEyePJmZM2fi5eWV7vefTqdjzJgxRERE8Oeff9KiRQtl3Zdffsns2bP5/PPPk7VSGz5fhpZQS0tLevXqZfLzJYTIHtJN9xXi7e3N559/zsqVK9m0aRMrV67ks88+k0BUCAGAmZkZm+68JCBCS6ctD3kaGc/3TVxZ2KIY9hZq+u58xPM4Vdo7ygMhcapkgajBngcRhORxuc3MzPD29iY8PFwZv//48WPu3LlDo0aNAGjUqJHRuNHY2FjOnTuHRqNJs5UnsW+++UYJRAGaNm2Kp6cn58+fV5bFxsayceNGnJ2deeedd4xeP3DgQKP5oPNKtWrVUgxEIaFrpyGoMnjrrbeoXLkyR44cSfdUZQ0bNjQKRAH69+8PYFRnu3bt4sGDB4wfPz7Z38Pb25v27duzd+9ewsLCUj3e+fPn+fvvv2nYsGGyQBSgePHiys/r1q0jPj6ed955RwlEISEI+t///gckzLOdVIkSJYxa4AGlLhOfk4GpXlGZGWPp6urKhx9+mOHXJRUYGMjmzZspVaoUH3/8cbL1Li4uWcr+f+rUKe7cuUOrVq2MAlFICIILFy7Mhg0bTLbopufzJYTIPhKMCiHEa0KlUnEjJOHmS6uHTXfCGbr3MQN3B/D7v6HcfhGLVp8/g9EXsamPYw1LY31uSNxVF/5r9fTx8QGgVq1a2NraKsvPnj1LVFQUPj4+6Z4nuVChQia7iRYvXpwXL14ov9+6dYvo6Ghq1qxp1A0VEqbVqlevXsZOLgfUrl071fXVqlXDzs7OaJlKpVIesF65ciVdxzGVRd4QECaus3PnzgEJdTd9+vRk/wIDA9HpdNy5cyfV4/39998A6WpFu3TpEvDfeySxunXrYmNjY3IcaJUqVZK9Z0ydU5s2bbC1teWDDz5g8ODBrFixgtu3b6dZrpRUqVLFKFDLrH/++Qe9Xk/jxo1zJOlUavVqZ2dHzZo1iYqKSlYX6f18CSGyj3TTFUKI14ROp6NZcVuWXDV9U1XD2RpLdf5MpVvIMvVgzTGN9bkhcRKjiRMncvToUeXGFxKy7tatW1cJRjM6XhTA0dF08ikzMzN0uv8CckPrXdGiRU1ubxhTmJecnZ0ztd6wPK0WSgNTdWZIFpU4QZAhqc26detS3V9EhOkWegND0FKsWLE0yxYeHg6kfK5Fixbl8ePHyZabOidDS2LicypZsiR79uxh5syZ7Nu3j02bNgFQrlw5pkyZQpcuXdIsY2Jp/c3SKyN1lBlp1avh/Z/0PZTez5cQIvtIMFqArF69Gki4WBqSSBiWZUSfPn2ytVxCiIJBr9fTwN0WF1szAiOTzyP6bSNnHFTx6PNhPOpkoae1l53Jrrqtvexwssj7QlerVg2NRqOMG008XtTAx8eHr7/+mqCgoAyPF80Iw031s2fPTK4PDAzMtmMZkuvExycfbJxawJg0KU9SQUFBqS5PKXDILAcHBwDWrFlD27ZtM72fQoUKAZgMIlM6ZlBQkMmEUM+ePVO2yawqVaqwYsUK4uLiuHDhAnv37mXRokUMGTIENze3DA3lSelvZmilTZr9FzDZopiROsqMxPVqimF5VutWCJF1EowWIGPGjEGlUlGuXDklGDUsywgJRoV4PYXoLBi1/zHLWhfni9NBnHocBYCbrTnfNnahqpMZen3eJQFKjS1xLGrummI2XVvyPgOwWq2mYcOG7Nixg+3bt3P37l369etntI1h/OiBAwc4d+4cRYsWpVKlStlelnLlymFtbc0///xDdHS0UVddnU7HmTNn0r0vU62IiRmm3nj8+HGysaiG7pKZcenSJSIiIpJ11T116hSQEGRlpzp16gAJ3aezEowauh8fOHDAKHGUKdWqVWPbtm0cO3YsWbflv//+m6ioKOrWrZvpsiRmaJmvW7cupUuXZtSoUezevVsJRhP/nQ0/p5chuAwICEi2ztR7oGbNmqjVao4ePUpcXFyaXXXVanWGWiarVasGJPRSePfdd43WRUZG8s8//2BjY0O5cuXSvU8hRM7I+35NIt0aNmxIw4YNlS5fiZdl5J8Q4vWjUqm4ERrHQf9IBu15RJsS9mzs6MF6Xw9m+Liw/uYLIrX5c7yogZMqhqUtnPm3fylO9irBv/1LsbSFc55P65KYoZVz5syZQPIxa7Vr18ba2po5c+YQHR2Nj49Phh8opoelpSVdunQhKCiIn376yWjd8uXLMzRuUKPRoFKpTAYagPKdlDTRzubNmzM0p2lSoaGh/PDDD0bLVq9ezdWrV2nSpIlRwp/s0L59ezw8PJg/f77JcsfFxXHy5Mk091OrVi1q167NiRMnWLZsWbL1ieuxZ8+emJubM3/+fKNWwri4OCWBUeLpTDLq/PnzJlsHDcsSP6RwcnICjLP9ppejoyNly5bl1KlT3L17V1keHh7Ol19+mWx7FxcXOnXqxL1795TPStLyJW5pd3JyylC5GjRoQKlSpdi7dy+HDh0yWjd79myCg4Pp3r17tox/FUJkjbSMFiDbt29P1zIhhEhKrVZzLjChJTQwUssXp5LfoH7nQ75/RGlLHLYWUDz7c55kC0MwevXqVWxtbalVq5bReisrK+rUqZOjXXQN/ve//3HkyBGmTZvGqVOnqFatGjdu3GDv3r00b96cAwcOpGs/9vb21KpVixMnTjBmzBjKlCmDWq2mR48eeHp60r59e7y8vFi9ejUBAQFUq1aNmzdvcuTIEVq3bs2ePXsyVX5vb28WL17MuXPnqFWrFrdv32bbtm04Ojry3XffZWqfqbGysmL58uX06NEDX19fmjZtqrRa+/v7c/LkSQoXLszZs2fT3NfixYvp0KED7777LmvWrKFevXpER0dz/fp1Ll26xL179wAoVaoU//vf/5g6dSqNGjWia9eu2Nrasnv3bm7evEn79u2TTeuSEevWreO3337Dx8eH0qVL4+DgwPXr19m7dy9FihRRsgoDNGnShM2bNzN48GBatWqFtbU1lStXpk2bNuk61tixY5k4cSKtWrWiS5cu6HQ69u7da/QAPbFZs2Zx7do1vv/+e/bs2UOTJk3Q6/Xcvn2bgwcPcvPmTaXVvUmTJmzcuJGBAwdSrVo1zMzMaN26NW+88YbJfavVahYsWED37t3p2bMnXbp0wdPTk3PnznHkyBGl3oUQeU+CUSGEeA3odDrKFko5gnOwVGORzwPRguCNN96gSJEiBAcHU69ePZPdDxs1apQrwaibmxu7d+/m888/Z//+/Zw4cYLq1auzceNGjhw5ku5gFGDRokV88sknbN++nbCwMPR6PXXq1MHT0xMbGxvWr1/PF198wdGjRzl37hx16tRhx44d7Nq1K9PBaMmSJfn+++/5/PPP+eWXX9Dr9bRq1Yr//e9/VKhQIVP7TEutWrU4duwYc+fOZe/evZw6dQorKyuKFSuGr6+vMidpWsqUKcPhw4eZPXs2u3bt4ueff8bOzo4yZcok67r7zjvvULp0aebPn8+6deuIjY2lTJkyTJs2jVGjRmWp5bxHjx7ExMRw+vRpzp8/T2xsLO7u7gwbNoxx48YZTTMzaNAgHjx4wJ9//smsWbOIj4+nT58+6Q5GhwwZQlxcHAsXLmT58uW4urrSt29fPvzwQ5OJhIoUKcLevXuZN28emzdv5pdffsHKyooSJUowYcIEo+7ZM2bMAODIkSNs27YNnU6Hi4tLisEoJDzM2Lt3L99++y0HDhwgLCwMNzc3Ro4cyUcffZSpqW2EENlPFRoamvdZH0SOSjpeSAhToqOjefjwIZ6envJ+yUF5Wc+BOmtq/XGPl3HJx159UrcIH1WzA53pcYHpERQUlGa2TR0qtP//raNWgRk59xWk0+mIjY3F0tIy3VOniIyTes4dUs+5JyfrOj3XSSFeJ3I1K+Du37/PihUrlPnRDHQ6HdOmTaNUqVK4u7vTsGFDTp8+nUelFELktSjMWX0jlBVt3SlibZycpFsZB0ZXKZSlQDQ9YnQq7ryI40pwDFeCY7j7Io4YXf4epyqEEEKInCPddAu433//nZ9++oklS5YomQAB5syZw6xZs5Tfr127Rs+ePTl+/Dienp55UVQhRB56Hqfm81PBVCtqxbw33dDrISRGS0lHC648i0Gr0+fo48k4vYrrITHoEjWEvozTcT0khsqFrbBQSScdIYQQ4nUjLaMF3IkTJ7CwsKBdu3bKMq1Wy4IFC1CpVMyYMYMjR47QpUsXwsPDmT9/fh6WVgiRF1QqFZeDEzLOXnoWQ9+djxixP4AvTwfRactDPj4eyIu4nA0Gg6K0RoGogU4PQVHxOdhZVwghhBD5lQSjBVxAQABubm5G6cnPnj3Ls2fPaNq0KSNHjqRq1ar8+OOPWFlZcfDgwTwsrRAir9gnyU4UEacnMPK/ANFSnXPdZbV6FeGxKc8RGBarMxmoCiGEEOLVJsFoARccHJxsIPzZs2dRqVRGGfAKFSpE6dKl8ff3z+0iCiHymF6vp4KTJTbmpgPORsVs0FjkXDSoUulTzdRroVaRA1NtCiGEECKfk2C0gLO0tCQ0NNRo2alTp4CEtOaJ2draotXmbIISIUT+o1er8Q+PZ0HzYpglCfpcbM34paUbtqp40y/OBmrA1TblFAVutubyZSSEEEK8hiSBUQFXqlQp/v33X/z9/fHw8CA8PJzDhw9jb29PtWrVjLZ9+vQpRYsWzaOSCiHySki8OV22++HjbsvWzl4c8o/APzyOWi42VHSyxNFChV6fs/1krc1VFLMz53GEcdDrZmuOtbkKZNSoEEII8dqRYLSA8/X15fLly/Tp04f+/fuzfft2IiMj6d27t9FE2UFBQfj7+ydrLRVCvPqeROl4EaNj+72X7Lj3kobuNhS1Mee3f0O49jyWv98qib1NzpbBDD0uNmoKW1nxMk6HHnCwUGOu1qOWQFQIIYR4LUkwWsCNHTuWzZs3c+XKFSZPnoxer6dw4cJMmjTJaLutW7cC4OPjkxfFFELkIV2iVk89cDwgynh9LgWDasBSraewleFBmQShQgghxOtMgtECzt7enr1797JixQpu3ryJh4cH/fv3x8XFxWg7f39/2rdvj6+vbx6VVAiRV9xszbE1VxEZnzz4K2JtRhErGbEphBBCiNwnwegrwM7OjlGjRqW6zWeffZZLpRFC5CcqlQoV8KW3Mx8cDUy2/ufmbhQxiyeHh4wKIYQQQiQjj8NfA9HR0XldBCFEHnmpN+etXQHcfRHH2vbFaeVlRylHC9qXtGdLJ0/quVih16c8B6gQQgghRE6RYLSAu3//PitWrODcuXNGy3U6HdOmTaNUqVK4u7vTsGFDTp8+nUelFELklZBYFWefRrPgUghjDz6hUmErRlVzoqSjBQN3P+JplEz3JIQQQoi8IcFoAff777/z7rvv8ujRI6Plc+bMYdasWYSGhqLX67l27Ro9e/bk4cOHeVRSIUReiNL+1+r5LErL3AvPmXQskAWXQgiNSciyKwq+0aNHo9Fo8PPzy9Hj7Nu3j5YtW+Ll5YVGo2H06NGpbu/r64tGozFadvToUTQaDdOnT8/y/vMLU+eZ3QoXLkzXrl1z9BiZldH3n5+fX4H6+wohco4EowXciRMnsLCwoF27dsoyrVbLggULUKlUzJgxgyNHjtClSxfCw8OZP39+HpZWCJHbnCzV2JqrTK5TAZ4OkjogOxhurlP7V9Czmd+/f59+/frh7+/PgAEDmDRpUrYmxcvp/b9OcuvhhBBCZJXchRRwAQEBuLm5YWlpqSw7e/Ysz549o1mzZowcORKAH3/8kZ07d3Lw4MG8KqoQIg/YWKh4v1YRvjrzLNm6QZUK4WQumYuyU6lSpejVq5fJda6urrlcmux15MgRYmJi+Prrr+nevbuyXKfLWOt67dq1OXPmDEWKFEnX/guChQsXEhUVlfaGAgB3d3fOnDmDo6NjXhdFCJHHJBgt4IKDg6latarRsrNnz6JSqWjTpo2yrFChQpQuXVqekgrxGtGpzZl74QWFbcz4ppELc/95zpPIeDRWakZUcWJ0NQ3WSIKz7FS6dGkmT56c18XIEY8fPwZINnVYRtna2lK+fPkc239e8PT0zOsiFCgWFhYm3wNCiNePdNMt4CwtLQkNDTVadurUKQC8vb2Nltva2qLVSrISIV4Xz+PVzLuYMEZ0+71wpjV0Zr2vB/OauXEpOAa/sLi8LmKGWcXHYv/iGfaP72MfFoxVfGxeFynTNBoNvr6+PHv2jLFjx1K2bFnc3Nxo2bIlR48eNfmaa9eu0bt3bzw8PPDy8qJnz55cvXo102W4du0aQ4YMoWzZsri4uFCtWjUmT55MSEiIso2hC7JhjGfHjh2VrseZecCZdMxoevd///59xo0bR5UqVXBxcaFChQqMHj2aBw8eZPr8AT7++GM0Gg2XLl0yWt6rVy80Gg3jxo0zWr537140Gg0//vijsszUmNFVq1ah0WhYtWoVhw8fpm3btri7u1OqVClGjRrF8+fPTZZn+fLleHt74+rqyhtvvMFnn32Woaz4VatWZfXq1QBUr15dqcv/Y+/Ow6Oq7j+Ov8+9d7bMTPaEhLAviuCGiooIWBVFcENwbd2tW63W1qVad22p2lq1dak/bV2quFOrVgR3NlHcFRVkDVsgCUkmme0u5/fHkEhM2IQkhH5fz8OD3nvn3jN3hmQ+c875nsYhz5ubr7nhsT98fo0914MHD6awsLDFvF/P87jrrrsYPHgwXbp0YZ999uHee+9t0Xu+sTY0XsdxHO644w723HNPiouL2XfffXn44Ydbba/WmieeeIIjjzyS7t27U1payiGHHMITTzyxxfdMCNFxpGe0k+vduzdfffUVy5cvp1u3bsRiMd59910ikQh77rlns2MrKiooLCzsoJYKIdpbwtHEncww3JkrE8xc2XwY4YS+EfbJ9W31MMuOEk7Wox64DT59v2mbb/BQrAt/R0Mw0oEt+/Fqa2s58sgjiUajnHjiiVRWVvLiiy8yfvx43nnnHQYOHNh07Lx58xg9ejT19fUcc8wx9O3bl48++ojRo0czaNCgrb72nDlzOOGEE0ilUhx33HH06NGDDz/8kAceeICpU6cybdo08vPzycnJ4eqrr2bGjBnMnDmTU089lR49egCZUTfbakvOP3fuXE444QTi8TijR4+mT58+LFu2jOeee4433niDadOm0atXrx91/eHDh/Pggw8yffr0pt+brus2fbH7wy8GZsyY0fS4LTFlyhRef/11Ro8ezTnnnMOsWbN4+umnWbJkCVOmTGl27B133MEf/vAHiouLOeOMM/D5fLz44ot8++23W/x8LrroIp566im+/PJLLrzwwqZ72HhPt8Xpp5/Ol19+yaGHHkpeXl6Le/7b3/6WuXPnMm7cOAKBAC+//DI33HADixYtahbeN+fcc8/lo48+4vDDD8c0TSZPnswVV1yBz+fjzDPPbDpOa83555/Pc889R79+/ZgwYQI+n4933nmHX/7yl3z77bfcdttt2/y8hRBtR8JoJzd27Fi++OILTj31VH72s5/x6quvEo/HOfnkk1Hq+6Ila9euZfny5S16S4UQO6+QpciyVFMg/aH+uX48r3OMlgg46RZBFIBPZqMe/D2BS24mZflbf3A7WrRoUatVYgGGDBnC4Ycf3mzbl19+yXnnnccdd9yBYWQGKw0fPpxLL72U//u//+Mvf/lL07FXXnkldXV1PPTQQ83mpd5yyy3cddddW9VOz/O4+OKLaWho4IUXXuCwww5rcb4bb7yRv/71r+Tm5nLNNdcwceJEZs6cyWmnndYsiG3rlxmbO79t25xzzjlorXn77bebTU2ZPXs2Rx99NFdffTXPPPPMj7r+sGHDMAyD6dOn84tf/AKATz/9lLq6OkaOHMm7775LeXl501Dc6dOnE41G2Xvvvbfo/K+99hqvvPIKBx54IJAJuscddxwzZszgww8/ZMiQIUDmvXPHHXfQtWtX3n33XYqKioBMwNvw9dmciy++mC+++IIvv/ySiy66iJ49e27xYzdn1apVzJw5k7y8vFb3f/LJJ8yYMYPS0lIArrnmGo444ggeffRRTjrpJA466KAtus6KFSuYNWtW05zSCy+8kKFDh/K3v/2tWRh9/PHHee655zj99NP5y1/+gmVlPtam02nOOOMM/va3vzFhwoQtfq2EEO1Phul2cr/4xS/Ybbfd+PLLL7nmmmuYPn06eXl5XH311c2Oe/nllwE6fTVHIcSWy7Y0F++V3+q+Xtk+ekXNdm7Rj+drqGsZRBt9MjuzfwewePFibr/99lb/vPHGGy2OD4fD3HTTTU1BFOC0007Dsiw+/vjjpm3l5eXMnDmTQYMGtSiQ9Otf/3qreyjff/99Fi5cyKhRo1oEnd/85jfk5+fz/PPPk053/DDoKVOmsGzZMi699NIWNRKGDh3KmDFjmDZtGnV1P+49kJuby+67786sWbOaprJMnz4dpRS//e1vgUxxJYC6ujo+++wzhg4dimlu2b+fCRMmNAVRANM0OfXUUwGavcbPPfccjuNw8cUXNwVRgOzsbK644oof9dy2t2uuuWajQRTgggsuaAqiAJFIpOnzSOPQ4S1xww03NCtu1L9/fw444AAWLFhALBZr2v7QQw8RDoe58847m4IoZKYwXX/99QA8//zzW3xdIUT7k57RTi4SiTBt2jSeeOIJ5s+fT7du3fjZz37WogDE8uXLGTNmjJTJF+J/hGEYfFLlskdBgJ8NyOGpb2vx1neQ7lkY4Omjysg10ujOUkw3Xr+Z/Q2Q0/HTEA477DBeeOGFLT6+T58+RCLNhxhblkVxcTG1tbVN27788kugZS0AyPwe2GOPPZqGjwLU1NTwwAMPtDi2sbhS4/zI1r6gDIfDDB48mDfffJPvvvuu2VDhH3ryySdZunQprutimiZKKcaOHdtimsi2mDt3LgALFixotdd5zZo1eJ7HwoULGTx48I+6xvDhw/n888/57LPP2GeffZg+fTq7775709zN6dOn89Of/rQpsG7pEF3IzNv8obKyMoBWX+PWeg93lFFN++677yb3t9bOxm1ffPHFFl9nc/csGo0Sj8eZN28epaWlzUYQNHIcB8i8b4QQOy4JozuBcDjMhRdeuMljbrjhhnZqjRBiR1Drmvx6+iq+rk5xwR55vHRMd5Kuxm8oFtSkqYw7lG37dL/2k7WZOaFZ4fZpx3a2saUtTNNsVnCusddvY/P+f/gFZG1tLbfffnuL4xrDaGPv0oY9cK2db3O9jU899RQzZ85stq1Hjx7bNYw2FlN69tlnN3lcQ0PDj77G8OHDue+++5rmjc6ZM4czzjgDyAT2xqDfOH90a8Joa69xY6/qlr7GO0qF4c21o7X3U1FREYZhbFXPdWs9/T+8ZzU1NWitWblyZavv9Ubb8r4QQrQ9CaNCCLETSnqKr6pSADzw+Toe+Hxds/3X71/A4Lxwp6mwbYez8Q0eCp/Mbrlz8FDs8M69XmFjoKmsbLleLGR6BzfUs2fPFpXWNxSNRoFMPYHWNG5vPG5jXn31VTzPI51O4/f7mw033l4a2/D0008zevTo7X5+yPRGmqbJ9OnTGTp0KPX19U2Bc/jw4bzwwgssXryYGTNmkJ2dvV3DdqMNX+MfFhv64eu7LRpfo9b+7W/YU9uaDWtRtGbt2rX079+/xTbP87b7mqKN74u9996bd955Z7ueWwjRfiSMdiKN8y2ys7ObhttuzRyMRo1zVYQQOy9TQdinaLBbH4fbNWyhO80YXUhZfqwLf4d68PfNA+ngoegLr9shihe1pd133x3IFOz5ofr6+q0aAgk0hakZM2Zw2WWXNdsXj8f55JNPCIVCLYJFR9hvv/2AzBrabRVGGwPm+++/z1tvvYVpmk3DZUeMGAFkai988cUXHHHEEW0SunfffXdefvllZs2axT777NNsX2uv+6Y09iK2Vlyqsddx5cqVLfb9cHmbrTV79uwWw4wb2/7D+b7bKhqNsuuuuzJ//nxqampaLK0jhOgcJIx2IhdffDFKKfr3798URhu3bQ0Jo0Ls/PJ9Hj/fPY+7P2m5lqGp4NDuWXhexxen2RoNwQiBS27OFCuKN0BWGDucvdMHUYDu3btz0EEHMWvWLJ599tlmRYzuuuuuzfZo/dCBBx5I7969mTZtGu+88w6HHHJIs/NVVVXxs5/9DL+/4+/tmDFj6NatG/fddx+HHnoow4YNa7bftm3mzp3bbL7ixIkTuf3227n66qubhiZvzvDhw/nkk094+OGH2WuvvZpCW58+fSgrK2taL3NrhuhujRNPPJE77riD+++/n5NPPrlpyGtdXR1/+tOftupcjUWGVqxYQe/evZvty87Opl+/frz//vssWrSIPn36AJmh27fccss2PYe///3vnHbaaU1FjOrr65uG0J5yyinbdO7WXHDBBfz617/msssu4/777yccbj5cf8mSJSiltmtFYSHE9iVhtBM56KCDUErRrVu3FtuEEGJDa9Imh/cIM2tVgg9Wf7++qGXAs2PKyDedDmzdj5ey/KRyCneIYkWt2dTSLsAWB6PW/OlPf2L06NFceOGFvPrqq/Tt25ePP/6Yjz/+mKFDh25V75lhGNx///2MHz+eE088keOPP57u3bszd+5c3nvvPXr37s1NN930o9u6PQUCAR5//HEmTJjA2LFjGTlyJLvtthuQKc43e/Zs8vPz+fDDD5se09gjuGGF1c0ZPnw49957L5WVlfz0pz9ttu/ggw9uWjqmrcJonz59uOqqq5g4cSLDhg3j+OOPx7Is/vOf/zBo0KCtKsQzYsQI/vrXv3L55Zdz3HHHEQ6H6datGyeeeCKQqcR/+eWXM2rUKI4//ng8z2PatGk/ugBUo8GDB3PwwQdzwgkn4Pf7efnll1m2bBlnnnlmiy8Rtoezzz6bDz/8kEmTJjFnzhxGjhxJaWkpa9asYcGCBcydO5eHH35YwqgQOzAJo53Iq6++ukXbhBD/21zD4o9zqnhmQR23H9yF3+yTz0drkhQETQYVBCgKGviV23kq6XYijUu7bMy2hNGBAwcyZcoUbrrpJt58803eeustDjzwQKZMmcJf//rXrR7KOXToUKZNm8Ydd9zBW2+9RV1dHSUlJVxwwQVcddVVFBQU/Oi2bm/77LMPM2bM4N5772XatGm8//77BAIBSktLGTt2LOPHj292/Ndff41hGIwbN26LrzF06FAsy8JxnBaBc/jw4TzzzDNNy8C0lauvvprS0lLuv/9+Hn30UYqKijjhhBO49tprmy2ZsjmjRo3illtu4bHHHuOee+7Btm2GDRvWFEbPPvtsbNvmwQcf5PHHH6dLly6cdtppXHnllRstarUl/vjHPzJ58mQef/xxVq5cSVlZGTfffDOXXHLJjz7npiileOCBBzjiiCN47LHHeP3112loaKCoqIg+ffpw6623Nuv1F0LseFRNTY18HBFCkEwmmxZ2DwaDHd2cnVZ73Od1np+9Ji2lLp3pHcr2G/TP9VNve3y7Ls3pA7J5YHhe09IH28vatWu36YPs9tbWhXVExo54n/v168fBBx/Mo48+2tFN2W52xPu8s2rLe72j/ZwUoqNJz6gQQuxkFJl5oY3q0h4frUk2/b/flKH9Yuf17bffUllZyeWXX97RTRFCCLEZ8tWaEELsZHIsjzMH5m50/zkDc7Z7r6gQO4pdd92Vmpoa9tprr45uihBCiM2QntGdxHvvvcfrr7/O4sWLaWhoaLWcO2TmV/znP/9p59YJIdpTzDMY3y/KvxfGWFJnN9v30wHZ9IwYQOdYX1QIIYQQOy8Jo51cIpHgrLPOYtq0aQCbXTdQKu8KsXMzDIM3lyW5dtZa7j+0hHlVaaYuqydsGZy4SzaDiwJElS3Fi4QQQgjR4SSMdnITJ05k6tSpWJbF2LFjGTx4MIWFhRI6hfgfVeca3PPpOlY1OIx7eTn7Fgc5sDREytVcM2MNQ0qCPPqTAkwtw3SFEEII0bEkjHZyL774IoZh8Mwzz3DooYd2dHOEEB3M1YpY+vshuB+tSTYrXlSb8nA1mB3ROCGEEEKIDUgBo06usrKSnj17ShAVQgCQbWmO7RPd6P6T+0cJGTJGVwghhBAdT8JoJ1dWVkY4HO7oZgghdhBpT3PGwBzyAi1/vHePWhzVMwvXleJFQgghhOh4EkY7ueOOO45vvvmG1atXd3RThBA7gPKEwWmvrWDSmG6ctEs2QVMR8RmcPTCX147vQaElc0WFEEIIsWOQMNrJXX755fTt25ezzz6blStXdnRzhBAdyDMs7vm0mq+r04x7uZyIz+DhUV257yclNDgeV7xXQVzLj30hhBBC7BikgFEnF41Gee211zjvvPPYb7/9OOyww+jTpw9ZWVkbfczVV1/dji0UQrSXtKdYWJtZVzThaP7xVQ3/+KqmaX+/XD8pVxGUPCqEEEKIHYCE0Z3A008/zYcffkgikeDVV1/d6HFaa5RS2yWMfvzxx0ycOJEPPvgA27YZMGAAF110ESeeeOIWn8PzPB5++GEeffRRFi1aRDgcZvjw4Vx//fX07du32bErV67k3//+N9OmTWPBggVUVFSQl5fHAQccwGWXXcZ+++23zc9JiM4uaHocVBpi1qpEq/uHFAcJmxqkfpEQQgghdgASRju5SZMmce211wJQWlrKoEGD2nyd0enTpzN+/Hj8fj8nnHAC2dnZvPzyy/z85z9n2bJl/OY3v9mi81x++eU89thjDBgwgPPPP581a9YwefJk3nrrLaZOncqAAQOajn3ooYe4++676d27N4cccghFRUUsXLiQV199lVdffZVHHnmEcePGtdVTFqJTUJ7HmQNz+dtn60i6zROnqeDq/fKxtN1BrRNCCCGEaE7V1NTId+Sd2MEHH8y8efO46qqruPLKKzHNtl090HEchgwZwsqVK5k6dSp77bUXALFYjCOOOIIFCxYwZ86cFj2bP/Tee+9x7LHHMnToUP79738TCAQAePfddzn++OMZOnQo//3vf5uO/89//kNhYSEHHXRQs/PMmjWL4447jkgkwjfffNN0HrH1kskk5eXldO/enWAw2NHN2Wm15X2u9Pxc8nYF5++ZzzUz1/DtujQAvbJ9PHBoKfvng6nbrpLu2rVrKSoqarPzby3P80in0/j9fgxjxxubvMceewDwxRdfdHBLtszG2rul9/miiy5i0qRJfPbZZ/Ts2bNN29qZjB07lpkzZ1JTU7PJ49rr/byl7dmZbc29Xrp0KXvttRennnoqDzzwwGbPvaP9nBSio+14v53FVlm0aBHFxcX89re/bfMgCpkQuXjxYiZMmNAURCEzd/XKK6/EcRyefPLJzZ7n8ccfB+C6665rFiBHjhzJYYcdxqxZs/juu++ath977LEtgijAQQcdxPDhw1m3bh3z5s3blqcmRKdmGAavL40zZVmcK6dXcNGeebx4dDdeOLobvx1SyNPf1pKS4kVtZunSpeTm5m7yz8EHH9yhbRw7diy5ubkd2oYfmj59Orm5uUycOLGjm/I/Y+LEieTm5jJ9+vSObooQQsgw3c4uGo3StWvXdrvejBkzADj00ENb7GvcNnPmzC06Tzgc5sADD2z1PG+88QYzZ86kX79+mz2Xz+cDaJcwLsSOSimDN5fHAVhYa/Ordyua7Q/7FDftn0+W5NE21bt3b0466aRW93Xp0qWdW7N9/ec//9mmx994441cfvnl7fo7S2y9Bx98kESi9XnnQgixvUkY7eSGDx/O1KlTSSaT7TK0cuHChQCtDsPNzc2loKCg6ZiNaWhoYPXq1QwcOLDVANl47s2dB6C8vJx33nmHLl26MGjQoC15CiSTyS067n9NOp1u9rdoG211n/1+PwPyfBvd3z3iQ3kOyXTbvf89z8PzvDY7P0DAtDG9erTTgPJFcFWYlNv689ZaN/3d1u1qvFbv3r03WSSutXa0ddu21/Uah9b+8PFbep+Li4spLi5udo72fI12dJt7/tvrXm3uPGVlZVvUnp3Z1tzrxmNhy+6Z53mb/Bwi02TE/xoJo53cb3/7W15//XWuv/567rzzzja/Xl1dHQDZ2dmt7o9Go5td73RLzrHhcRtj2zYXXHABqVSKm2++eYt7RleuXInrtt28uc6uoqJi8weJbba977NSilN2Gcgf51bjtVIJ4Lf75FK/ehlr27DHw+/3t9mXGUopsoM29Z/fQXrtB99fs/gAIntcSV3S1+xD4YZsu+2LNjU+b631Ft+Dxvb+8HitNZMmTWLSpEl8/fXXuK5L//79OeusszjttNNaPc+zzz7LpEmTmDdvHrZtU1JSwvDhw7n00kvp1q0bJSUlTcfn5+c3/fdJJ53Evffey7Jly9h///056aST+OUvf8nEiRN5//33qa6u5oMPPqBHjx5NVcvnzp271dcHuPTSS3n22WebznfnnXfy5z//GYA77riDO+64o+mcH3zwAXfeeSfPPfccr732GoMHD27xvG+55Rbuv/9+/vGPfzBmzJhN3uuSkhKGDh3K3/72N26++WamT59OMplk77335tprr2XIkCHNjm9s65w5c5gyZQpPPvkkS5Ys4fjjj+fee+8F4JtvvuGuu+5i5syZxGIxunTpwlFHHcXll19OXl5eizbMmTOHiRMn8tlnnxEMBhk+fDg33nhjU4DZ8H3ww3u1oT/84Q/8+c9/5oUXXmDYsGHN9r3//vv8/e9/58MPP6Suro7CwkL23ntvLrjgAg444ADGjRvH7Nmzgcz0l0bdunVrel0bj1m9enWzczuOwyOPPMIzzzzDokWL8Pl87LHHHlx00UWMGjWq2bFPP/00v/rVr7j77rspKyvjzjvv5IsvviAYDHL44Ydz8803N3sf/lhr167lvvvuY9q0aSxfvpxQKESfPn045phjuOiii5odO3XqVB588EG++OILbNumb9++nHzyyZx77rktPj889dRTvP7663z11VesXbuWUCjE3nvvzSWXXNJiuH3j6+a67hb9208mkxv9fGOaJn369NmaWyBEpydhtJOrqKjg6quv5pZbbuH999/nZz/72WbXGf3hL6/OyPM8fvGLXzBr1izOPPNMTjnllC1+rAwRa106naaiooIuXbrg9/s7ujk7rba6z0lfmL9/XcffDyvl0ndWk3AyQUcBv9grj0O7hfCnC7fb9VpTW1vbZu+dgNkyiAKk18yh/os7iexxLSm3+bW11ti2jc/na9MK40DT81ZKbfE9aGzThsdrrbngggt4/vnn6devHxMmTMDv9/P222/z61//moULF3Lrrbc2O/68885j8uTJlJaWMn78eKLRKMuWLeOll15i1KhR9OnTh6uuuopJkyZRXl7OVVdd1fT4PfbYA7/f39SGpUuXMnbsWAYMGMApp5zCunXrCIfD+P3+jbZ3S64P30+laLzeyJEjWblyJZMmTWLYsGHNfjcVFRVxzjnn8NxzzzFp0iQOOOCAZvfOtm2ef/55unTpwtFHH41lbf7jTF1dHcceeyxdunThzDPPZNWqVUyePJnx48fz/PPPNwsZjW297rrrmDt3LkcccQSjR4+mqKgIv9/PnDlzmDBhAqlUimOPPZYePXowd+5cHnroId58801ef/31ZmHr3Xff5aSTTsIwDMaNG0dJSUlTIb/Gebwb3tcf3qvGe23bdtM+n8/X7DEPP/wwV199NaFQiLFjx9KtWzdWrVrF+++/z3//+1+GDx/OT3/6UwzDYObMmZx66ql0794dgJycnKZzNRbsae11fuWVV+jXrx/nnnsu8Xicf//735x++ulMnDiRCy64oOn4xtfjjTfeYOrUqRx55JEMGTKE2bNn89xzz7Fs2TJee+21zb5mm7Jw4UKOPfZYVq1axYEHHsjYsWOJx+N8/fXX3HPPPVx22WVNxz744INce+215OXlMWHCBLKysnj99de54YYb+OCDD3jsscea/Yy49tprGThwIIcccgiFhYWsWrWKV199lZNOOonHH3+82ZcfjffJNM0t+rcfDAY7/ZB9IbYnCaOd3NFHH41SCq01X331Fddcc80mj1dKUVVV9aOv19ibubFv9WKx2EZ7PLfmHBse90Na66ZvjU866ST+8pe/bFHbG8kQmE3z+/1yj9rB9r7PC2Iw8cMqDu0e5snRZdSkPBpsj765fr6sTGIZqs1f11gs1mZVPk3d0CKINkqvmUNUN2AYzXtaGnuclFJtXk238YPs4sWLuf3221s9ZsiQIRx++OEttm/Ytscee4znn3+e008/nb/85S9NH+rT6TRnnHEG9913HyeeeCJ77703kAkgkydPZuTIkTz99NOEQqGmcyUSCZLJJIZhcO211zJz5kzKy8ublgNrrf1z5szhyiuv5He/+91Gn+uG7W28/vDhw3n66acJh8OtXv+H1zIMgxEjRqCUYtKkSRx88MEtfn8NHTqUgQMHMnnyZCZOnNjs3FOnTmXNmjX86le/2uLw/9VXX3HyySfz4IMPNj3f008/nWOOOYZf/epXzJ07t0Vb582bx3vvvdcU2iDzvrrkkktoaGjghRde4LDDDmvad8stt3DXXXdx880389e//rXp+MsvvxzHcfjvf//L0KFDgczvsvPPP5/nnnuuxX394b1qPM/G9jX+/i8pKWHKlCnNqhVrrVm9ejWGYfCzn/2M8vJyZs6cyWmnncbw4cM3er82bM/TTz/NK6+8wrBhw5g8eXLTPb/iiis45JBDuOGGGzjqqKPo1atXU9sApkyZwiuvvNJUH8J1XY477jhmzJjBRx991KJHemtceOGFrFq1invuuYczzzyz2b4VK1Y0tX/JkiXccMMNFBUV8fbbbzf11N94442MGzeOV155heeff56TTz656fGzZ8+mtLS0WTXd1atX85Of/IQbb7yRo48+uunYDUPslvycMQxDfscKsQEpZdHJdevWjW7dutG9e/em/97Un8a5ID/WpuZz1tTUUFVVtdllXcLhMCUlJSxdurTV4bKbmpfa+CHgX//6FxMmTOCBBx7YIZdsEKI9mabJa0sbAHirvIETXlnOb95bzW0frGXsv5dx1Yw1xN227Rlsa9qu3/R+Z9P720tjGG3tzxtvvLHZxz/00EOEw2HuvPPOZr19fr+f66+/HoDnn3++afvDDz+MaZrcddddzYIoQCgUanW46KZ06dKFK6+8couPb7z+7bffvl2u/0NnnnkmsViMF198sdn2J554AqUUZ5xxxhafyzRNrr/++mbh4eCDD+aII45g0aJFzJkzp8VjfvnLXzYLopAZCrtw4UJGjRrVLIgC/OY3vyE/P5/nn3++acjm7NmzWbJkCUceeWRTEIVMiLn++uu3S/G9f/7zn7iuy+9+97sWy+YopSgtLd2m8z/11FNAJmxvGP7Lysq4+OKLsW27KVRvaMKECc0KFZqmyamnngrAxx9//KPb8/HHH/PRRx9x0EEHtQiije1q9Oyzz+I4DpdccklTEIXMv6mbbrqp2fNr1NrSQyUlJRxzzDEsXLiQZcuW/ei2CyGak57RTq6916cbNmwYd911F2+99Rbjx49vtu+tt95qOmZLzvPCCy/w/vvvtzh+Y+fxPI9f/vKXPPnkk5xwwgn8/e9/lwq6QqyX42/+pcy61Pe9KAFTYXTuLIryRTa939r0/vZy2GGH8cILL/yox8bjcebNm0dpaWmrIz4cxwFgwYIFQKYY3DfffEOfPn02+yXgltp99923uKdxw+u31Ty3k08+mZtuuoknnniC008/HcjM+3/zzTcZNmzYVl238UvbHxo6dCivv/46X3zxRbOwCLDvvvu2OP7zzz8HaHWpnnA4zODBg3nzzTf57rvvGDhwIF9++SVAq8uT9ejRg7Kysm0ONx999BHQeqX77eHzzz8nFAq1ej8a70Nrn0c2XAKuUWNQrK2t/dHt2Zrnu6nXa8iQIYRCoRZtX7JkCX/+85+ZOXMmq1atIpVKNdu/evXqFnN5hRA/joRRsVVGjhxJr169eP7557ngggvYc889gczwvMZv8jcssFFVVUVVVRUFBQUUFBQ0bT/zzDN54YUXuO2223jppZeaPvy8++67vPnmmxx00EHNlnVp7BF96qmnOP7443nooYckiAqxnuu6jO0d4eqZa1vdf8aAbHIsDzpxcUzPiOAvPoD0mpa9V/7iA/CMSKd+fpAZXaK1ZuXKlRsd6guZEAjff5jf1l6vDRUVFW3xsW1x/R/Kzc3l+OOPZ9KkSXzzzTcMGDCAJ598Etd1W+0R25SNPbfG7a1NHWntMY1TSTZ2vsaKwY3na/y7sLD1OdvFxcXbHEZra2tRSjUrVLU9xWKxjY6s+uHz3VBr020af3dvSyHBrXnvbe71apwT2mjRokUceuihxGIxDj74YEaPHk00GsUwDGbMmMHMmTNbhFMhxI8nYXQno7WmurqaeDzeYmjR9mBZFvfeey/jx49nzJgxTcUqXn75ZZYuXcp1113XLEQ+9NBD3H777Vx99dXN5gONGDGCM844g8cff5wRI0ZwxBFHsGbNGiZPnkw0GuWuu+5qdt3bb7+dp556ikgkQr9+/VqtHDx27NimcCzE/5I0JotrbW4+sIgb328eSPvl+rlmSAGm17k/PKU9P9l7XUXdZ3c0C6T+4gPI3utqEm7nL7rVWEl877335p133tns8Y0f9Df8IL2ttqbQU1tcvzVnn302kyZN4vHHH+f3v/89Tz75JHl5eRxzzDFbdZ61a1v/sqZxe2vBqbX70fg6be58jcc1nreysrLV49esWdNiW+P0k9YCW2uhLycnp2luaFsU6YtGo1v8fNtDTk4OsGXvvQ1fr9Z6MysrK5u1/f7776empob77ruPU089tdlUoMsvv3yL1lIXQmw5mWy3k5gxYwYnnngi3bp1o3///k3FLRrdfffd/OIXv2DdunXbfK0RI0YwZcoUDjzwQCZPnswjjzxCfn4+Dz30EFdcccUWn+fuu+/m9ttvRynF3//+d6ZOncro0aN56623GDBgQLNjG781rq+v509/+lOr87Hae8iyEDuKda7Jca8sZ0WDzcvHdufiPfM4Zdds7j+0hLtHdiHL6ORdhusl3DDhPX9HwaFPkT/iIQoOfYrwnr8j4W68enhnEo1G2XXXXZk/fz41NTWbPT4SiTBgwACWLl26Resyb48eqY1df9GiRT/qHFvSpv3335+BAwfyzDPPMG3aNJYsWcJJJ5201UVgysvLWb58eYvtjUud7LHHHlt0nsYvPWfMmNFiXzwe55NPPiEUCtG/f38gM/QZYNasWS2OX7ZsGStWrGixvbHCbmtLpbX2u65x+GzjNJdNabznW7OO6J577kkikWgaHruhxnC2pfdve9ia57up1+ujjz4ikUg0a/vixYsBOPLII5sd63leq/OKhRDbRsLoTuCee+7huOOO44033iAej6O1brHeXjQaZdKkSdtcSr3Rvvvuy/PPP8+yZctYtWoVb7/9NieddFKL46655hpqamparfJrGAYXXHABs2fPpqKigkWLFvHYY48161lt9MADD1BTU7PJPz/96U+3y3MTojNRSvFhRRJPw0Nf1DD+1eUsqEmTcjR/+qiKo18qp8bu5BNGN5D2/CTJJ2X1IEk+aa/z94hu6IILLiAej3PZZZc1Dcfd0JIlS1i6dGnT/5933nm4rstvfvMbEj9YQzaZTDb7ArKxmFBr4efHarz+b3/7281evzWNbdrc+tRnnXUWVVVVTct1bE3hokau63Lrrbc2+/04Y8YMpk6dSp8+fVosH7MxBx54IL1792batGkterDvuusuqqqqGD9+fNP0k6FDh9KzZ09ef/31puALmZFMt956a6tBvHFd1R8W1nn55Zdb7Zk7++yzMU2T3//+9y2G/Db2mDb6Me+DxqJDN998c7N1e1euXMl9992HZVmtfgbYGhdddBG5ubk8+eSTmz12n332Yd9992XWrFk89thjLfZv+H468cQTsSyL++67r1lPqm3bTQWMNpxe1Diq7IfB8+6772bevHlb9ZyEEJsnw3Q7uenTp3PTTTcRDoe55pprOPbYY/n5z3/OBx80XwLhmGOO4YorrmDKlCmtLpouhOiclFKk3e8/XKddzbRlzUOM+4Mvp0TbWbRoERMnTtzo/s0tv3X22Wfz4YcfMmnSJObMmcPIkSMpLS1lzZo1LFiwgLlz5/Lwww83Vfs899xzmTlzJpMnT2bfffflqKOOIhqNsnz5ct58803++te/Ni1DMWLECF566SXOOussRo0aRTAYZODAgS16gLbGueeey4wZM/j3v//Nfvvtx5gxYzZ6/dbssssulJaW8uKLL5KVlUXXrl1RSnHOOec0DcWE7wsZrVq1iv32249BgwZtdVsHDRrEzJkzOfzwwxkxYgSrVq3ixRdfxOfzcc8992xxZXbDMLj//vsZP348J554Iscffzzdu3dn7ty5vPfee/Tu3bsp5DQef8899zQdO27cOEpLS3nvvfeoqKhg0KBBfPXVV82uMXbsWHr27MlTTz3FihUr2HPPPfn222+ZPn06o0aNYtq0aS2e28SJE7n66qsZOnQoY8eOpXv37lRUVDBr1iyOOOII/vjHPwIwfPhwlFLcdtttLFiwgOzsbLKzszn33HM3+pxPOeUUXn75Zf773/8ybNgwjjzySOLxOJMnT6a6uprbbrutaVmXH6uxp3ZL1oyFzDSgo48+mssuu4ynn36a/fffn2QyyTfffMPnn3/e1MPZ+Hpcd911DBs2jHHjxjWtMzp//nzGjBnTbFmXs88+myeffJJzzz2XcePGkZ+fz9y5c/nss8848sgjef3117fpeQohmpMw2sndf//9KKW45557mqrbtjbHpbi4mLKysqYqjEKInYPneRxQEtro/t0LAmT72rFB/+M2tc4obD6MKqV44IEHOOKII3jsscd4/fXXaWhooKioiD59+nDrrbdyyCGHNDv+H//4Bz/5yU944oknePrpp9FaU1payrhx45pN2TjzzDNZtmwZL7zwAn/+859xHIdTTz11m8KoUopHHnmEESNG8NRTT23y+q0xTZMnnniCG2+8kWeeeaap2MwJJ5zQLIzm5OQwZswYnn/++R/VKwqZoa9PP/00119/Pf/85z9JpVLst99+3HDDDc2WH9kSQ4cOZdq0adxxxx289dZb1NXVUVJSwgUXXMBVV13VrGAfwCGHHMJLL73UVLQvGAwycuRIHn30US688MIW5w+FQrz00ktce+21TJ8+nblz57LffvsxefJk3nrrrRZhFOD8889nt912429/+xvTpk1ret/su+++jBs3rum4AQMGcN999/G3v/2N+++/n1QqRffu3TcZRpVSPP744zzwwANMmjSJhx56CL/fz5577skvfvELxowZs1X3rzVff/010Wh0i9+Pffv25d133+Wuu+5iypQpPPDAA4TDYfr27dtiytAll1xCnz59uO+++3j22WdJp9P07duX2267jQsvvLDZ56a99tqrqcDiyy+/jGmaHHDAAUyZMoXXXntNwqgQ25mqqamRr8w7sf79+6O15rvvvmvadtRRRzFnzhyqq6ubHXvYYYfx3XffNRviJUSjZDJJeXk53bt3lwW529D2vs9KKVbYfu7+pJqHvqxpts9nwNvje7JntrdV88N+rLVr125VNda25nke6XS62cL1Yvtrr/t84IEHsnz5cr755hsika1byic3N5dhw4bx6quvtlHr2t7O/H6uq6ujV69eXHLJJdxyyy0d3Zw2vdc72s9JITrazvXT7H9QbW1tq+umtcZ13aZFuIUQO4dqz8dhLy6jb66fvx9WygElIXpm+5jQP8qU43vQJ2q0SxAVoi1NnTqVb775hpNPPnmrg6jY8c2ZMwefz8cvfvGLjm6KEKKdyTDdTi4vL6/V6oA/5LouixYtaloPTAixc1gd91jV4HD1jDX0yvZx6q455AdN5lWlOOY/5Xx4Si+yZJiu6KQeeeQRVqxYwWOPPUYoFOLSSy/t6CaJNjBq1CgqKio6uhlCiA4gYbSTGzx4MFOnTuW9995jxIgRGz3uueeeIxaLMXr06HZsnRCira1LfV+Jc0mdzcQPm69lmHA8kDAqOqm7776blStX0r9/f2666aamwk1CCCF2DhJGO7mzzjqL119/ncsvv5wnn3yyxfqcAG+//TZXXXUVSinOPvvsDmilEKKt9IpuPGmGfYpcv8zGEJ3X9lo/ekvWbRVCCNH+5FNKJzd69GhOPPFEFi1axMiRIznmmGOaypn/7ne/4/DDD2f8+PHEYjHOPvtshg4d2sEtFkJsT1k+gxP7R1vd97shBeT7ZL6oEEIIIXZM0jO6E3jggQcoLS3lgQceYMaMGc22a62xLIuLL76YG2+8sQNbKYTY3pL4+PV7FZzQP4euER//+KqGWNqjS5bJb/Yp4OT+EQwv1dHNFEIIIYRolYTRnYBpmtx8881ceOGFvPrqq3z55ZfU1NQQDocZOHAgxxxzDD169OjoZgohtrN1juLFhfX8e1E9Y3pFePDQUvymIpb2+Oe8Gsb0CpMj80WFEEIIsYOSMLoTKS0t5bzzzuvoZggh2kltKjME19PwyuJ6Xllc32z/uqRLt3YOo1rrZgvICyGEyNBad3QThNjhyJxRIYTopLIDm/4Rnhcw26klGcFgkGQy2a7XFEKIziKZTBIMBju6GULsUCSMCiFEJxWxFGN6hVvdN6Isizx/+34LHw6Hqa+vJ5FISA+AEEKsp7UmkUhQX19PONz6z2wh/lfJMF0hhOiMDJOpSxq4cM980h68sayhadfwsiz+OaqUMO3bS2kYBgUFBTQ0NFBZWbn5B7Qxz/OaeiIMQ757bStyn9uH3Of20xb3OhgMUlBQIK+dED8gYVQIITqhasfkqpmVJB2PX+9TwK8G55NwNCFL8VFFkoTjQgcULzIMg2g0SjTa+nIz7SmZTFJXV0eXLl1kaFwbkvvcPuQ+tx+510K0HwmjQgjRCdWmNdVJF4DbPmjZC7lbno/upYYMlxVCCCHEDkvGCgghRCcUsjZdsTY/aEoQFUIIIcQOTcKoEEJ0Qtk+zRE9Wi+EkRsw6JMtA1+EEEIIsWOTMCqEEJ2MUoo1SfjNvgX0zm4+MTTsU7x8bHfyTaeDWieEEEIIsWXkq3MhhOhkGrTFxW9XsKjW5o7hxTgefFWVolvUone2n6KQAVrCqBBCCCF2bBJGhRCik4k5ilmrEgCc8fpKirNMemf7qUw4LKy1uXtEMef0D+B5Xge3VAghhBBi42SY7k6ovLycm2++mfPPP5+7776bmpqaFsd8++23HHPMMe3fOCHENlM/qF20Ju4yZ3WChbU2ANYPDxBCCCGE2AFJGN3JLF26lBEjRnDPPffw9ttvc/PNNzNkyBDefffdZsfFYjFmzpzZQa0UQmyLbEszaiPFiwAO7Z4lvaJCCCGE2OFJGN3J/OEPf6CwsJBPP/2UBQsWMHv2bPr168eJJ57ISy+91NHNE0JsB2kMrt6vgNxAyx/hNx5QSL5PgqgQQgghdnwyZ3QnM2vWLG644QZ69OgBwIABA3j55Ze57LLLOPfcc2loaOC0007r4FYKIX4sbZj87dMaXl3SwKSjuvFmeQOzVsYpyrI4ddds9i4MECTV0c0UQgghhNgsCaM7maqqKsrKypptsyyL++67j0gkwi9/+Uvi8Th77713xzRQCLFNal2TB7+ooS7tMebfyzi0e5j9S0LUpjwuenMVv9grj6v3DOO6bkc3VQghhBBikySM7mRKS0tZsGABBx10UIt9t99+O4FAgKuuuooTTjihA1onhNhWWkO9nRmGq4E3yxt4s7yhaX9lwkVJASMhhBBCdAIyZ3Qns//++/Piiy9udP8tt9zCr3/9a1544YV2bJUQYnsJGZrDu2+8eNG4vhHpFRVCCCFEpyBhdCdzyimnkJeXR1VV1UaPue6667jlllta7T0VQuzYtILrDijEb7bs/dynOMiAXB9a6w5omRBCCCHE1pEwupMZOXIkjz76KAUFBZs87pe//CWvvPJKO7VKCLE9KKX4cK3DDbPX8uLR3RjVI4ypIDdgcMleeTx9VBk5Kt3RzRRCCCGE2CIyZ7STW7p0KZ999hk1NTXk5ORQVlbG4MGDMU2zo5smhNjO6rXFbR9W8MHqBJ9XpjhrYA7nDMol6Wqem1/HfZ9Vc+t+UTwZpiuEEEKITkDCaCc1d+5crr32WubOndtiXyQSYcyYMVx66aUMHDiwA1onhGgLtgdL6zI9n9VJl7s+rm62/4ieYWytkK+ihBBCCNEZyDDdTui///0vRx99NHPnzkVr3eJPLBbj2WefZfjw4Vx55ZWkUrLmoBA7g5ChGVwU3Oj+4V1D+JXMFxVCCCFE5yBhtJNZs2YNF154IalUit13351//OMfzJs3j9WrV7NgwQJeeeUVrrrqKnr27InneTzyyCMcccQR1NTUdHTThRDbyG94XH9AEa0t3BL2KU7uny2VdIUQQgjRaUgY7WT+/ve/E4vFOOyww3jrrbcYN24cpaWlBAIBCgsLGTZsGNdccw0fffQR9913H9nZ2Xz++eeMGzeO+vr6jm6+EGIbrEpbPPB5NQ+PKqVr+PtZFgPzA7x5Qk+KLbsDWyeEEEIIsXUkjHYyb775Jkop7rrrLixr41N+DcPgtNNO47333mO33Xbjs88+4+abb27HlgohtifPsPjbZ+v41zd13PfZOm49qIjnx3bjxaO7cd7uubyyKIanWuszFUIIIYTYMUkBo05m8eLF9O3blx49emzR8T169OCZZ57hsMMO45///CfnnnsuAwYMaONWCiG2t4SrmLUqCcDHa5KcO21Vs/17Fga4cFCEiORRIYQQQnQS0jPayaRSKaLR6FY9pnv37lx55ZW4rstzzz3XRi0TQrQlv6HpEd3494c9oz4C8hNdCCGEEJ2IfHTpZIqLi1mxYsVWP+60007D5/Px5ptvtkGrhBBtLaA8fr1PwUb3X7lvPn6keJEQQgghOg8Jo53MLrvswtq1a1m0aNFWPS4cDlNWVvajgqwQouNVuRYzVsa54YBCzA2G4poK7hxeTP+oQmtZ1kUIIYQQnYeE0U7mmGOOQWvNAw88sNWPzc/Pp66urg1aJYRoS4ZhMGt1kutmrWV+TZqXju3OfT8p4W8/KeGlY7sT9SkChgRRIYQQQnQuUsCokxk3bhy33XYb//jHPxg5ciRHH330Fj+2vLyc3NzctmucEKJNuMrgqW9jADz9bR1Pf1tHSVbmx/fquENJlsWR3buRZ8gwXSGEEEJ0HtIz2slkZ2fz+9//Hs/z+PnPf84//vGPLXrcG2+8wdq1a9ljjz3auIVCiO3NALJ9zX9cr447rI47AIR9qtnQXSGEEEKIzkDCaCd00kkncfnll5NMJrniiisYP348H3744UaP/+KLL7jkkktQSjFu3Lh2bKkQYrvwXC7YM2+ju3+xZx65pvSKCiGEEKJzkWG6ndQNN9xAMBjkjjvu4O233+btt9+mV69eDB8+nP79+xOJRKipqeH999/nzTffxHEc9tprL0455ZSObroQYis1aIualMMZu+Xw+Ne1zfYNLQkxvm8Ez0t1UOuEEEIIIX4cCaOd2FVXXcWIESO45ppr+PTTT1m8eDFLlixpcZzWmkGDBvHUU09hmmb7N1QIsU1WJjTjXl7OjQcW8dzYbry6OEbS1RzRI0JZ2CRiuuB1dCuFEEIIIbaOhNFO7sADD+Ttt9/m3Xff5aWXXuLdd9+lvLwc27YJBALstddeTJgwgTPOOINAINDRzRVCbCXTNHl2QQwN3PT+WqJ+g590y8JnKK6btYbqpMvXp/ciT+aMCiGEEKKTkTDaCVVXVxOJRPD7/U3bRo4cyciRI5v+P5FIEAqFOqJ5QojtSClF0v1+2ZZY2uM/i+qb/t9UoDUgYVQIIYQQnYwUMOpkXNdlzJgxdOvWjTvuuGOjx0kQFWLn4LouE/pFN7r/uD4RIqaM0RVCCCFE5yNhtJN55ZVX+Pbbb+nZsyeXXXZZRzdHCNHGXBQ5AZPDe4Rb7Iv6DW4dWoRfOx3QMiGEEEKIbSNhtJN56aWXUEpx7bXXbvEc0KeffpoJEybw5z//uY1bJ4TY3qodi+HPLuX0ATncfnAxuxcE6B61OHNgDlPH9aA0oDd/EiGEEEKIHZCE0U7mo48+IhgMctRRR23xY0466SSWLVvG73//e+bPn9+GrRNCbE9KKT5ck6TO9jhz6komfVvHybtk86vBBaRczREvLqPa6dyTRU2fn7gRpIYQ1WT+rtJB6lQQ1wpIBXAhhBBiJyYFjDqZNWvW0KtXL4LB4BY/xjAMLrzwQn7zm9/w2muvscsuu7RhC4UQ24tSihX13w/B/XRtkk/XJpsdY3t0uq8VTdOkyvORdDSptMb2XFwPbE+TWF+sKeIzSLuanIBJQdBPlqExneRmziyEEEKIzqSTfYQRlmX9qCVajj76aADeeuut7d0kIUQb8TyPA0s3XoysZ7aPkNl5humapkm1DrI87WNlvct3NTbf1dosqbNZVu9QXu+wrM5m/ro0axMudWmPBTVpvqhKMa/GoVIHsU3/5i8khBBCiE5BwmgnU1RURHl5+VY/rri4mLKyMr777rvt0o6PP/6YE088kZ49e9K1a1cOPfRQnnvuua06h+d5PPTQQxx00EGUlJTQt29fzjrrLBYuXNjq8c888wy/+tWvOOSQQyguLiY3N5cnn3xyezwdIXZISilKwj4GF7U+EuJPBxeTb7rt3Kofp04FWZH2sbze5pvqFNVJF8uEgJEZZuy4GkdrgpZBz2wfPkPxbXWKyoRHXcqjPObwUUWSb2o9agihVOceniyEEEIICaOdTv/+/Vm3bh3z5s3b6scWFRVRVVW1zW2YPn06o0ePZvbs2Rx33HGcc845VFVV8fOf/3yriiRdfvnlXHXVVXiex/nnn8+oUaN47bXX+MlPfsI333zT4vjbbruNRx99lPLycrp06bLNz0OIHV2t5+NnU1Zyy9AiTts1G7+ZCWA9s338c1RXDirx43k79rIuhmFQpYOsTXgsqElRZ2s0YGtNLO1RnXKpSXnUpl1iKY+1CYfP1yb5YFUcv6XonePjg9UJgpbiy6okq+MOC2rTLLf9aMvX0U9PCCGEENtAwmgnc9RRR6G15r777tvqx2qtt7k3wXEcLr30UpRSvPrqq9x7773cdtttzJgxg912242JEydutGdzQ++99x6PPfYYQ4cO5d133+WWW27hwQcf5NlnnyUWi/HrX/+6xWP++te/8vnnn7Nw4ULOOeecbXoeQnQGqxIe769OMOHV5ZiG4snRZTw3thu/3a+AB79YR3wHX9FFGxarbD/L6x0W19m4GhKOxtOggIBpELYMwj4Dv6nI8hmUhi365/npnesn6WheWRSjd7aPuKMBxadrk9TbmeG7i+oVaRm2K4QQQnRaEkY7mXHjxlFQUMCkSZN4+umnt/hxnuexaNEiCgoKtun67733HosXL2bChAnstddeTduj0ShXXnkljuNs0dDZxx9/HIDrrruu2RzYkSNHcthhhzFr1qwWQ4oPOeQQevTosU3tF6KzUEqxNGYDkHI1T3xdy4mvLufEV5dz0VurmbM6Qb29484XtQ2L1bZJeb3N4to0MdujJuVRl3aJpV1iaY+E4+EzoSBo0i3iIzdgYClFwDQImga75gU4pFuYdWmP15fGGFaWxZI6mwXr0mhgScxmUUyTMrZ+Hr0QQgghOp6E0U4mJyeHG2+8Ea01l156KXffffcWPe7ll18mFoux9957b9P1Z8yYAcChhx7aYl/jtpkzZ27RecLhMAceeOA2nUeInZXWmu6RjQ9D9RkQ9u2Y8yZt5aMiZbC41mZhrU1VymVVvc3auEPa1WT5DPKDJgVBk6hlYhkKy4C8oEm3iIlPQbbfIO1lwvY+xUEO6x7hLx9V8dMBubxZ3sCCdWkUsKrBYWHMwzElkAohhBCdjSzt0gmdfvrpzJ8/n7/97W/ccsstvPbaa1x99dWtBkSAzz//nCuuuAKlFCeccMI2XbtxCG7fvn1b7MvNzaWgoGCzw3QbGhpYvXo1AwcObHUNwcZzb8lw3x8jmZTlIVqTTqeb/S3axtbc5+JQDrvk+Zm/ruWxZ+6WQzYpksnUdm/jtjD8AVa5ivI6h+X1NrVpj7yAQb8cH35TUZ10qU66BExFXsDAbxqYBlhK4TdAKcgOmDTYGp8JSUdTXm8TMg0u2iufO+dWcvk+Bdz5USUHlmRxQEmIyqSLqaBXUKHTiaa2yHu6fch9bh9yn9tPR97rrVm6T4idgYTRTurWW28lGo1yxx138OGHHzJhwgSKi4sZPnw4AwYMICcnh/r6ej744AOmTZuG4zjst99+HH/88dt03bq6OgCys7Nb3R+NRlm5cuU2n2PD47a3lStX4rqdowJpR6ioqOjoJvxP2Nx9zsor5JnVmnsPKeGq6RV8Xvl96Dyhb5SrB2ezfPF8tN5xhupGc3JI53ZnZYNNRdwh7DPoke2nMuHw/uo4A/IClGRZBEwozrLQWpNlKbIsg7Bhk47VYts2kUiEUCBMftBHRdxFa/i2JsXquMNV+xVyy/truO6AIu76uJovq1Ic3DWLNVkmOV1DuFUriMcbmrVL3tPtQ+5z+5D73H7a+16bpkmfPn3a9ZpCdDQJo53YVVddxaGHHsp1113HnDlzqKio4IUXXmhxnNaa3XffnSeeeALDkJHZXbt27egm7JDS6TQVFRV06dIFv1+KwrSVLb3PtVaU37+yhIKvarlq30J2yfOTcDzCPoMZK+J4yqRbt27t2PJNM02TKiPC4nU2tqcpCVtUJlxeWVTHXoUhhnQJUZlwSXuaopCFz1AUBE3yDJtUvAF3/TlM08RxHIJOLYZhYGblEPYZaDSfV6Z4u7yBG4cWc8V7FdwxvAtTltTz4nd17JafuZcju/WhMF2L1lre0+1E7nP7kPvcfuReC9F+JIx2cvvttx9Tpkzhgw8+4OWXX2b69OksW7aM2tpaIpEIAwcOZPz48Zxxxhnb5QdqY2/mxnotY7HYRns8t+YcGx63vckQmE3z+/1yj9rB5u7zwnqNq2FN3OWK6Zlv5w0F66dRclyfCF3CO87rtI4gS9bZuDpTLfe9FXHqbY/j+mbzVnkD3aJZBK3M3NCo36A4aBDRSfA2/W/ST4KsoJ+QFSDlaj5dm+Q/i2L8aUQXLnpzFb/ep4CDuoaYsyrBinqHZTGHvtnZ5PJ9T7K8p9uH3Of2Ife5/ci9FqLtSRjdSey///7sv//+bX6dDedz/rAYUk1NDVVVVRxwwAGbPEc4HKakpISlS5fium6LeaObmpcqxP+KkNWyOJGnN72/o8SNIMtqHeKOx4r6zJDcA0uy8JmZtUHzgyaOB2VhH92iJnmWxu9t+dxtn5um0LTYuyiI7cHsVXEmf1fHn0d04c6PqvAZinMH5ZLlM6hLuyyq0+yeI7/ehBBCiB2djNkUW2XYsGEAvPXWWy32NW5rPGZz52loaOD999/fpvMIsbPKCyh6Z7deTXfXPD/5O8jIMdewKG9wqUq6zK1IMHt1nPH9svm8MkGXLJMV9TalYYuwT1GcZVBgafze1hcFUZ5DvumwV2GAoaVZ1Kc93l0R59DuYcb2jvCb6RXcMbeS5+bX8XZ5AzWutc3rKgshhBCibUkYFVtl5MiR9OrVi+eff57PP/+8aXssFuPOO+/EsixOO+20pu1VVVXMnz+fqqqqZuc588wzAbjtttuaVat79913efPNNznooIPo169fGz8bIXZMhmFQXu/y15+UkBdo/mO6MGTy3Jgycgyng1r3PaUUa2yTJXU27y5vYFWDw+kDcnnoi3WM75/D+6sSlGT58BuKLlkWRT6N9SOCaCPDcyjye/TN8XFMnyhfVmZ6XZ/4upZ/jurK2N5REo6mNq1ZGrNJ+aPb8dkKIYQQYnuTcUxiq1iWxb333sv48eMZM2YM48ePJxqN8vLLL7N06VKuu+66ZiHyoYce4vbbb+fqq6/mmmuuado+YsQIzjjjDB5//HFGjBjBEUccwZo1a5g8eTLRaJS77rqrxbUff/xxZs+eDcC8efMAeOKJJ5rWPh07dixHH310Wz59IdpFtWtx2pRysv0mD4/qysp6h4W1afrn+umSZZHtUztEFd06FeCLyiSzVsZJuJpTds3htjmVXD2kgNkr45gKCoImu+b7yfVBEHubr+nzbHqEA6Q9H6fumsP9n6/j9oOLOW3KCvbvEuL03XIImgarGxz8SlEUzdkOz1QIIYQQbUHCqNhqI0aMYMqUKUycOJHJkydj2zYDBgzgd7/7HSeddNIWn+fuu+9m0KBBPProo/z9738nHA4zevRorr/++lZ7RWfPns2kSZOabXv//febhvr26NFDwqjYKaxLaZbXO4DD+FeW0yvbR9ewxYvfxVgWs5l6fHf2z+vYNjqGj4W1Dp+uTWJ7cEK/bK6btYYbDijig9UJ1iZcBuT52TXPT55fEWX7rYca9FKUhoPUpixO2iWb381awx+HFVMec7jsndWkPU2vqI9x/aKctVs3SLfNMlFCCCGE2Daqpqam479eF0J0uGQySXl5Od27d5fqgW1oS+7zkpSPvZ9astFzvDGuO/vlem3Uws1TSrGOIM/Mr2VZzOHYPlGunlHBxGFd+O+SeioabPYsCrJnQYDdCwPks+XFiraUYRgsTflYWJvmpYX1+E3F60vrufHAIkKWYnWDQ4+oj4Cp2CfXIySTUtqM/OxoH3Kf24/cayHaj/SMCiHEDibPnyletLiu5bDWoKnolW0BP37u5baqVwHeK4+zot7h6N5RrlkfRB/4rBpXw8FlWZRFLHbLD1BopPHaIDd7nkcXv8dqn8G4flEuf3c1DxxayvlvrmR1g8NPuofJDZigNf0PKibUBoFYCCGEENtGvisWQogdjMbgT8O74DdbVoP9+2El5JkdV7zIUyYrGly+rEoxqkeEO+ZWcvWQQn7z3mq6Z/s4c2Auu+T56RH1kWs5eG2RRNfza5teUR+Op7lmSCEXvbWKEWVhJh/Tnf65flxPs1+XEMvrHWxjByk/LIQQQogm0jMqhBA7kBQWv3y3ggZH859ju/P8gjo+r0zRK9vHzwbkMCjPh6m33/zLrVWrfcxcGeOA0hD/XhjjsB5hbplTya8GF1AYMtFAwFT0y7bwbcVaoj9WFytNVdjC07B3UZAhXYIc81I53aIWJ/TLpjbt8X9frON3BxTSTX7jCSGEEDsU6RkVQogdSI1r8Mriet4qb+CYl5ZRlXAZ3jULv6E49bXlfLQm2WHrZ9qGj6+r03QJ+1hQk2ZVg8O369LccXAx5fU201fE8TT0zvZt14JFm+J5Hl2zFDUpl/N2z+XK6RX8eUQXbjigiM/XJnl1cT3FYYvqpEvaaH3dViGEEEJ0DPmeWAghdiCOp2msKmd7MHlhrNn+6pSLUma7L+2ilGJN2qA2bZN2Nf/3xTquO6CI1Q0OV0yvoCTL4oCSEGVhk5KAh3bbr305yuaAkhBTlzVw7f5FzFmd4Jn5dexdFOS4vlH8huJvn1ZzwwFFdJM8KoQQQuwwJIwKIcQOJGJBnxwfi2pbX5PzgJIQnrft63VurZj2sbDWJmAqbnx/DfcdWspl76wm7DM4a2AuUb+Bp6E0y8J027dYkNaa4oAGrdmrMMCdcyv51+gyymM2T8+vJW5rRveKUJPyKAn4sDrg/gkhhBCiJRmmK4QQO5CAafCHg4pb3Teub5TiDqjDo5Sicn3x3v/7soYL9sjn3Gkr+cVe+dx4QBHdoz56Rn3s3yVIgdkxVX4DXppj+kR5Z3mcu0eW8MDn1Vwzcw1aw7h+UbIsxS1z1lBpmx3SPiGEEEK0JD2jQgixgzBMk399W8+na1M8M6aMOz+qYm5FkuIsk58PyuOcQTmEVfsvUVKj/Syvt5m5Mo6rNd+uS3HvISX886saFtXa7FkY4JzdcynNMjqk17ZRvk9TGDQwDfi8MsVTR2V6R1/8LoanNcf2ibI24dAl20R5boe1UwghhBAZEkaFEGIHUW2b3D63irUJl/dWxDMFefYtoC7t8a9vaumTY3FiT1+bLpfyQ4ZhUJnQrKx3+PfCOv48ooQ/fFDJs/PrOGmXbPbtEsJ2PXpEfGSTon1nsjZnuSlO3jWb381aywOHlvKnj6r4eE2SkiyLYV1DfLQmycuLYjx1VBmFSsKoEEII0dEkjAohxA7C0bA2kQlJy2I2N8xe22z/kOIgqlf7jtOtcn2sqrf5vy/XceW+hZz62gquGVLINUMKqUy65AdMNJpcn4v2OjKKZuT7oFfUR0Xc4evqFA8eVkrAVExdWo/PUJyybwHLYg7FuUa7hnohhBBCtCRhVAghdhB+Y9PFiw4sCbV7r2htUvPvRTF65/h5b0Wchw8v5a6Pq7lp9hr65fr52W65nNQ/SrAd1hTdEn4vzU8H5HDpuxU8Mqor931WzcyVCRRQnGXy0qIYR/WM0PugQvJUx63XKoQQQggJo0IIscMIm5pbDiziZ6+vbLGvJMtinyI/WrdfgaAq18ena5N8sDrB7Qd34Rdvr+LN8gbOHZTLpXvnU5V0Kc0yyTFd2EE6GbXWFAQUA/L8LKpNM2tlgssG53NEjzCL62wiPoOo32BV3CM/otp9iRwhhBBCfE/CqBBC7ACUUsyPab6qTvP7g4q546NKalOZhLdflyD/HNWVAjNNe2UnpRSVCY9/fVPDuYNyOWvqCu4eWcK6lMuslQnq0h6jeoTZNdePbwfpFW2Ua9icuVsOF7y1mr/+pIQPKxKMfam8aX/Yp/i/w7vSP+rDp2WZFyGEEKKjSBgVQogdQFyb/G72Wt4qjzOqR5gHDy3FMhQ+Q/F5ZZK5FXF69vC1W09eDD9vLa8n22+ScDWn7JrD6a+voHvExx6FAWK2i6kiFFrODtMr2sjzPLpk+eiT7aPB9nhsXi0D8vxcuGceZREfnta8sqiefQoLKJHfgkIIIUSHkV/DQgixA0h6Bh9WZHoYpy1rYNqyhmb7x/WNMr5nPu2R/JRSrE5oXltSz1X7FXLcf5Zx6q45/PuY7tSmPIKWIu1qdsmxwNsx510WWi4X7ZnHL95ezVkDczikW5g/fFjJ/HVpAqbipF2yqXc0ZsDEdaWyrhBCCNERjI5ugBBCCDCVpiy88e8H++f6MFT7tCVlBPj3wjoO7prFXz6uYtJR3fh4TZKjXyrn7GkrueeTarpHfRT6dtwQpzyHvrk+snyKQ7qFOWvqSuavS7Nbvp8xvSIsqbM5/82V1LjynawQQgjRUeS3sBBC7ADyfZrfDinkrKktixcp4IzdcnCcti9epJSiIqn5Zl2aM3fL5Z/zarhyegUX75VP7+xMIP66OkVJloHn7pi9oo2ytM2V+xbyxw8r6Zvj487hXVhUa/PxmgQHlIT4SbcsKpIe2SEpZCSEEEJ0BAmjQgixA1idNkm5ac4cmMNj82qbtgdMxWNHdKWL34V2yEspI8Ckb2sY2zvKxW+v4pFRXXnm2zpunbMWT8PxfaP8cu98iiwPveN2jAIQcJPsVRhhbcLlyaPKOPP1FVTEv2/0Xz6u4l+jy+gbtlCu04EtFUIIIf43SRgVQoiOZpg8+lUtv/+gissG5/Pqcd35dl2aLJ9BSZaF53kElMZr4zCqlGJVQpNyIel49Mnxc8xLyxjXL5u7R5ZgKPhwdYL8gIF2d6wKuq3xPI+gcrhkrzx+/0ElFXGXw7qHOWdQLgFTkXI1z86vZUhxMYWGhFEhhBCivUkYFUKIDqSUos4xeOG7GBq4+5Nq7v20mrKIRdrVVMRdDioNcdDoYoJtXLyoAT8vL45xSLcsTnx1OQ8eVspxfaI8Pb+Ouz6u4qheEX46ICczV3QH7hU1TRNbWaSCQcJKcWh3g9vnVvHOhB6kHE1ByCLiVyitGNIlSMBSmFYAz07LcF0hhBCiHUkYFUKIdmaaJpGAi6XXgJOm2Arx2ugsjnld8UVVCk9Deez7nrqwz8BUtOkwXcMwWNGgyQ+aTFlazyV753PW1JUMzA9wbN8IfkOxsCZNYchE7YC9oj6fjyrHxHbBcTX1tqbBdvG0Jidg8tYJPTAMhed51KY9KhMapTRRn4mrNSHLIOLzE/EZhE2Nsne85yiEEELsbCSMCiFEO1JK0b9HLl7le3ihLpltvgglhp+Pjo2yJJHHfi/WUpf+vhf00r3z8OO26ZTRas/HJ2sTlIYtrpxewfm75/Hi0d14eVE931SnOaRbFofsnkeB5e5Q64oalh+voZ5kyqaaMGsSLtb6OvEJRxOzPbIDJmG/wcdrkqDA9cDVYCrIDXjkBUxqUg6mgpBlEPYZhH0Boj5FrkrjeTvQExZCCCF2IhJGhRCiHYX8Hu6KaZiBbJQVwktV43lpDCsLrV16+BNUnh5lcrmfk6dWM6FflL3zfWjddpV0lWGwos6jOMti4oeV/HNUGWdPW8Hfv1jHqJ4RwpbivRVxju4dwfR2jB5Dw/ITrl2Dfm8KKp2CHn3JKe5KfWE3ptb4STgaywC/abA24VIYMimL+Ei5Gg0oDaYBpqFocDTlMRtTQZ8cPwtq0nTJMon5DOp8PvICiohOyRBeIYQQYjuTMCqEEO3IRxzyd8NzEth1i/DsGP6cXUC7uIkKTH8urmtzfGmQ9Hk5LEnlkWM6tGXn3BrHz+pEiiV1Nq7W3PVJFU8cWcbquENF3GVQfoCIX5FnOO1S0XdTlOUnUl+NijfgPfFXjGNPQ7/xEvrNlyAcJXrYcRw99Aierw0TMDMLs9alPcKWQimoSbmkXE3K1TiuxkWjlCLbZ9Aly6Le9lhSlyYvEOTLuhTdoz5itkFpVpCo5WK2w/I6QgghxP8KCaNCCNGOtJNEuzYAVrgMZfjx7Fqc+uWY64fteolVEMhHeTY9rSTKiJDQoTbpmdOGRXmdg6HgTx9V8sioMv70URUnvLKcsohFYdBkVUmIq/crwNQd1yuqlCJkp7Bi1eh4A3r66xhjT8Gb+GtIJSEQhGgO+pVJ+D9/n+Hn3sw7sSABC0KWwjIVOZai3jZQaQ+tNX7TwFKZIbsx22NVZZKk49E/14+robzepmvExxdrk6wJ+ygJm5SEguQq6SUVQgghtgcJo0II0Y6UL4yyg7h2LTq5NhNMrRC+nP4oM4BTvxzt2RiBAtyGFRjBApSbImSF0b4wCdu3Xduz2raIpVM8Pq+W83bP4+T/LueSvfL51eB8ko4mYCq01uQYdof1igaUwh+vRTXUo+vrIF4PoRDeK0+BUqjzrkIVFKOXLYScfFReAWWpanpm9yDlapKuZk2DQ2nER2HQJNtvkLQ1dbaHocBnKHIDJpahSDoeaxIOH1XUs1dRkLqUS4OjWR3PfIEQtzXdIwGKfDba3YFLCgshhBCdgIRRIYRoRykvRMDKwjT9eCkTrRrASWCn1qGUwvDlYEW7ou0YbrISI5CH01COFSxAuUmyfBFcI0rK3vZkGFd+lsVsvqhKsSxmM6ggwJ+Gd+HeT6u586NKdskNcOngPEZ1D2N1QK+oZVkEG+pQsXUo20HXVkFDLLOz724w+TGMK+/Ae+Ef6G8/b3qcDoYwr7idktyerEhockwDv6HIshRRn8WauIPhU+QFTerSLvb6gka2p/EZih5RP31y/Cyps5m1qp7j+mYzfUUD3SI+FtamiTseKs9PWJkEkWG7QgghxI8lYVQIIdpRMq1RVgFBox5lhVDJSrTRgKHy0J6DZ8dw130DaKxIN9AeOlWL54uik+UYwXwMf4KwL0qaMLb943rnDMOgKqWoiDtM+qaWO0eUcPqUFRSGTM4ZlEv3qI8VMZsBeQEKTLtdK+gqpYgkG6AuCbVVKK3R6yoh0QCJODqVQnUpQw0fjZ79JmwQRAFIJtB3XkXvu59DZxXiN8FQCp8Bb5TH+dc3tTxwaAkGkGVl5pWujjuELIO0q1mXdInZmSq74/vn8N7yBqJ+g/KYTcgy+GRNknVJl4EFAYqDASI61X43RwghhNiJSBgVQoh2Nn9xBQN26UXASKKUiWfWod0ECjD8EUCh3TSeHcOJLUX5clDKwHMTaKcBN12H58/BCubj90dIOP6tXn6k0vVTHkvzr69rmbBLDr96dzWPjOrK/HUpPqtMUZPy+Em3LPpGTfDaL2yFnTSmY6OrKjJVcj0XHavNDM11HIhmY5TloX0B1E+Oxrvlku8fnF8EJd2gpgpWLsNa/A0F/ffGdDPrkCoUPkMxsizMNTPXMn1FnLuGF3NQaRbRXD91aY+atIffBLceltfbLKxNM7g4iKfhxe/qOGtgLt/VpFlYm8Y0FImoj16RIOEOnE8rhBBCdFYSRoUQogM0JFwI55JQAXJDITynAZ2uBe2gPRtl+jHNAqysErSXxoktw03X4g/koj0b7Biu04D2ZxMKFqKtIPG0uUXXdg0f5bUOry+tJ+wzyA0YHNM7yqmvLadPjp9+uX4qEw5dIxbZKk171OoJKE2gIYZOJtA1lSjXg1QCbRhgWqiCLmAY6Npq9PwvoXtf6NEX7DQUdkGd8StIJWDZd1BYgirtgRtvoHJNBYWFhfh8PrTW7FcUIOI3uPH9tQA8PK+W6pRHbsBgz8IAvaI+KpMOYcvAVFCVdHl/VZyikMW5u+fx98+rOX+PfF5YUItlKCwDXK3pkx0i4iXa/kYJIYQQOxEJo0II0UGUUlw2O8XCGpc3xuQQDEdxExUow4f2XDw7hnaqAIURyMOK9kA7CZz6pViRHigziJuoQjtxDF+UcLAQlyBJe9PXrEgbfLa2gc8qU/x2SCE/fW0FP+mexb9Gl+Fp8BuKBtulwK/RXtsmUcs0CTbUYSQa0A0xiNejPA9cB3x+lPYyAbViOTqdwui7G9ofyARkraFnP4yzLse771aoXN10Xh0IYlz/N0ylml2vKAiTFiSJ+AweO7IrcyuS/OnjKurTHqN6Rvj5oFwG5vuotzVBS7GgxibsM1hQk+LlRTHOGpjHs/NrGdM7yrPzazEUKBSehn45QSI7yDqsQgghRGdgdHQDhBDif5XneYzpGeaDiiTZ/6zl8g8URrgbhj8fwwxiBvIxrCxQ4CbWkF73DU58Nb7sPhj+bNzEajy7DrSHE6/AqV+GSlcQ9iXx+VrvJY2pAJ+sTTJlaT3nDMrl0ndW86/RZfSI+rh1TiX3fVbN2oTDkC4hQl7bFueJ2Emyqisw6qrRVRWZMJlKoFMJdKwWXb4I77t50BBD9d4VVViCl0qBz4+RV4g2TNSFv8N77uFmQRSAVBL9x19TluVvtjmAS7bf4C8ju/DHDyuZ+GEl5TGHdSmPZ+fXMe6V5VSnNCVWmvyAycB8P13CFvuXhOgatnh+QS3Dumbx/qoEY3pHmbEywbzqFCvqHRbVOcRUoE3vmRBCCLEzkZ5RIYToQENKsiiLWKyod7jvizru+wKeOTKXcT0ieMl1mGYQlarCUxag0W4SO7YUtIvhy8YKFeI5DXipWgwrC6e+HDNQjy+Qh98XJuGGmuaT2oaPRXUOM1YmOLgszGdrU5y5Wy4TXilnWFkWR/WKoND0yfFR5HOgjVYuCbk2lp2G6jXgeeiaamioA8fOFB9Kp1E+C4rLMLIi6PKF6FQSsiIYkSjaF4RIFB3ORilg3ietX6i+jlCshnRBGdowMoWM0BzdO8ysVSk+rMj0Yg4vy2JMrwimgneWx/n9B5X89eA88lWSQCBA0PJRHnPYNS9AcZbFO8sbGFgQZEmdzfF9ozw3v4647dFg+wmaAcyQjyw20T0thBBCCEDCqBBCdJhKx+K0Kct5ZFRX7vmkmilL6tHAjR/EyQl14ZBiH4YbRxk+lFmL5zRg+HMxgw5oD8+ux679Ds9N44v2Ajy8dA2GL4ITW4oRyCcYzAdfkITjp96zmLGilrDPoG+Oj2fn19EtYvHE6DJcDeb6Ea39cyxwt3/RIr+hCCYa0LEaVEMsU5yophqScXQijlIKsnMxCorRiQb00u9Q/XeH/GJUMIQ2LYhkoyPZJMI5OI5DxHE2eU03FmNlUuN4HlqB0lCcZfF2eTW5AYNHj+jKJ2uSTF5Yh+fBgaUhxvaOktAmPhzCOkXQ8mFEfSgcNHBc32z+uzhGUZZFeczmZ7vl8PS3ddTbHq7WhMuyCPktlLfptgkhhBD/6ySMCiFEBzAMg08r03y8JsXJ/13OWQNzOW/3brgerGyw+f0HlQwY1YV8K0gwEMC0Aqh0PdppQHsADkYwHzNUjNYOXjpGunoJRiAP0Hh2DGWFcOoWYQQKCAXz8KVdKpMuo3pEOHfaSv40ogurGhz+8kk1ChjXL8ronmGi23mpEtM0yWqoBcdGV1agPBfSKXQ6lZkbGgpjFBSDMtBrVuLN/wJjlz2gtAcYJoQj6GguRLKpjxYQ9wwSNtjaxAxGCIWjmfVHBx+EGnkUyh9Ep1Po917D7dqDtUkPk+9Xp1EKsv0G/zm2O8/Mr+WEvtmM7hkh4jewFBgGBH0GjZ2bpmdTbLmobD9enWZd0uOEftm8sjhG2jL4aE2S83bP5T+LYny6NknQNDiwNEiZz9vqKsdCCCHE/xIJo0II0QFM0+TTysww0dqUxz2fVHPPJ9XNjklrhed5xL0QlhnGHwygvWy8RAXgR7upTBVeN4UyfPjzdgOlcBpW4qVrMYNFaDeBZ9eh7TqKAvn8cZ8o//wuzgOHlnLd7DV4Gg7vESbHbzAoP0CRz9uua4qG7SRmXQM60QCxWpTrZirgWhYqKwKBELq+Dv3dPMjOQ3XtAb718zxDYXQkGxXNJZkVZbUOkk5BbdoFDWlPs1ZH2eenv8RvKFi7Cv3wnej6OghHYczJ2P4sFlXZoFhfbAjKlc2Fe+axvN5meNcwtqexPc3KBge/oQj7FA22JicQJMtUZBsOtm1TbKbxon5cL01F3OXYPlH+s6ievKDBv76p5dxBucyrTrM0ZlOUZWLl+SlSUtBICCGE2BgJo0II0QE8z2NQ/saL3RSFTHwbFIJ1XA+HCH4LrEgAL1WNUhbaDOClXLTTgJ2sBBSGPxt/7gC0l8SpX44vdxc81waqIF3HWb1ycU2XxL4FBPwGrgcazcB8P9Z2Wp4khIfVEEPFG9ANdZmeS9PMzBHVGupqoK4GnWxAde+L2mUPdMWKTLdlVhQCIVRJN5xAiHIjm6qES9xxMBTUplySjsY0MuuGxvY5hPz//AP++/T3DWiIwXMPE6qPsf+Y81lq+7AUuOuLA3ta4zMU+UETBdQ7HnUpjwbHoyTLIsunWN3gUBAyCZiKsC9AvqEo9dnobD+uTvFNdZpj+0SY9G0dw7pm8buZa/jl4Hz65PhIOpqFNWnCBUGypMKuEEII0SoJo0II0QFc12VwcZScgEFtqmVX5NX7FVDgc/F+UEQo7UCaLIIBC8OfxkuuxQh1wUtVYygTtI2XWoebqADDhxXpjjJ8uA0rIViAMizclIth1nNC1xziKsy7a4P0zw0Q1Um2dSEXn2kSiMdQddUod/2c0Poa0GSG5jbE0K6LCoZQPfpmtlWvRUVzIJqLNn2o7r3QWVHKfbksqXNIeTY+A+rTHnW2h4Ei5FM4HriuJpCOw+vPt9oe8/Xn6Tn6VL7z8nCABtsj7a1ftqU6jVZgr1++xlSQ489UIU67UJv2SDiavKDBmjisCxgUZ/kpCgIqQE1KM2tVnFN2zebeT6u5ekgh181aw8huYX7SLYu0p/i2xmaPXB+WJwWNhBBCiB+SMCqEEB0g5cvink8q+deRZVz89irKY5liN4aCC/bI5ZT+YTx340urJB0/hhEkELJQTgOGGcBL1+DZMSx/LlprtJvATazFqVuK4Y9gWFnr1y6No3zZOG6aoFXPUcU5eGYOSVtt9Hqbo5QikmgAOwHrqjPrg9ZUQ7w+U6AonUL5/NClG0Y0G12xEm/tKoyS7ugcN7NMS1lPdHYua33ZfFbjYXtp/KYi7WrWxl3iTiY0+k0w3cyQW5+hsOprM3NPW+O5WA01BEL5mCgMoN72qGhwKAqbpNxMD6mnM3nZ8TQr6h3qbRfbg17ZPhwPqpMujqepTrnk+g26hCyGdAlSl3aZurSeywcXcNX01dx/aCkPf1nDjbPXMqQkxJAuQUrDUUotJ9MjLIQQQogmEkaFEKKdKaWosg3u+7yWqcsSXL9/EcVZFgnHI+o3eHt5A45HJm1tgud5JLwgpplFwIphGH6UGcJzGlDaQxkmZiAf0HhOHDu2FG3XYWX3R2sPL7kWFSzAdW0Mq4EsfxRH5ZJ2tm7SaNhJYdo2uqoCZafBddD1sUwQ1S7k5mNEczPblyxAp4tQBcXoZAKtwOhSBvmF1Puy+KDWIO66GApSTmYuZyztkXI1Go3jZQJjcn32NJTCCQbZ1OqeRiCE64GtPQKmosRvkXI9CoMWS2I2AVORdjNfBGRZiqgPSsMWppEJwuX1NhUNDv1y/UT9JstiDnFH0y1sMbQ0RG3K5d8L6/jTiBLOf2MV1x9QyOheYd4uj7OgxmZ+TRp/foB8mT8qhBBCNCNhVAgh2pnP5+PjtZmKtQtq0pz/5ioALAMac+CZA7LJ9W/Z+VzXI04Yvz+MZQVQbjZeci1oC924RIsyscJdUUYvPDuGXbsYM1gIGHiJ1RAsQnk2ppUk7AuT8KKbrQQbROOP16FTSXR1JUp7kEyAaWZ6QYtKQHvo6kq8FZ+hevaH7n3QrguGkVnCJTcfJxjho0SQFTUOSmkUNC01YypFbsBErS8+ZCiFIjO1VGtwtSbmyyHcvQ+UL2rZyLJeJMO5lCkLY324b7A9lDLJ8ikKgyb5QZPlDQ6epwFF3PFocFxSrsZQELIMdi8M4jcVK+ttalIeuQGTj9amOKAkyODiEOtSLv9eGOMvI7tw2weVdItYnLFbLppMO79el2a/Qj8Bb+O93UIIIcT/GgmjQgjRzjzPoyDLbLG9MYgqIGBs/ZDZtA3rjC5EjDpCkRBeYi0Yvsx6nnYtXrIO7aXBDOLL6YcyAzgNK9BOHCNYjBtfhREsBC9N0EyCFSHhBlsML7VMk2BDLUa8AR2PQUMMpQHPzVTCdZ3M9uVr0YDRZ1fwB9BKoQwTI5qLjmSjwxG+VrksqnGoTGRCmt9UKBSOp3G0zvRoeh6eBstQ5AdMcvwGIctAKfC0osKXTeEVd2LdfDHsMgg17AiUaaIdG6//HiTCORRnciZaK3L8Bp4GvwHrDI9v16Uoi/ioTXsEDEXQMlFJ0NrDdjU1SZdV9TYaCPsMemX7sD3N6gaHrypT7FEUpDYV4pXF9cxaleD4vlHSrua8N1ZwWPcIAwsC+A1Fz2iUHgFDlnsRQggh1pMwKoQQ7cxxHAbk+5uWEPmhsb0j5Po8traaUEwFWVJns7rBR3m95rx+xQRUGi9ZjWEWoTHWLwWTxK5bjFJg+KJY2X3QTgw3sQYjkIvTsBwjUIDhpQhZYbQVJWEbmXmh6TiqtgFSCXRdDcRqwTDRnpsZltsQQ6fTqMJiVP9BULECbdsQCGJEctBZYXQ4wipfHp/Xw7pkmoTtknA1aS9T5dZQ4DcUeQGT3JCJb/28UdvTRH0G2QED29NEfAZRn4GhIBHtRvbER+C/z6DvuxmdTEBOPvrE88gdMhI7GP7+fqrMH9M0sSM+zn9zFf1y/dw5vJiErVmXcimLWNTbHpUJN/PlgGmRdDVxO1MlN2Z7FIdMLNPgo4oEuxcEsD14/OsajuwZ4YWldTwzpjvPzK/jpYUxSsIWfXP9ZHUJynBdIYQQYj0Jo0II0c5C0Ry+qkrzf4d35eypK0m536fO3tk+7h5RTECntuqcNQRZ3eCysDbNV1VpqpIOM/JzmLMyzu/2KkHbMTD8kK5Bp+swfDlo7aLtOtLrvgE0ZrgMAC9Vi7IiOOlajEAuhi9Jli+C4QZhzSrQGl1TBfV14NiZAkWOgwoEUd16o3x+9LLvwLYhnI0KR9GBIISjNASivBvzYzdo0p5H2tUYhiLPMglaiojPADJriVYnXSJ+g2wrs604ZBGwIMsyCJqKfJ+Lk84Eu0AijX70bpj9xvc3pbYa4+E78KeT6MNPwPlBuHddFxOTVXGH/bqE+GJtiuIsk17ZvqbCRlGfQVXSxVQKpTRhH5RFLDydGe77VVWSdSmXHlEffXN9jO+XzT/mreO3+xVx8qvLuXCvPP4wrJgG2yPLMphfk2affAu/3kjBJSGEEOJ/iIRRIYRoZyqniNOeX8kBJSGeH9uNr6pTrKx32KsoQMRn4Fdb1yW6jiDVSZcvq1J8U51CAyfvksOt76/h+gOLuXROjJN6F3BwURLT8KOtEJ5dj1IG+MKYykC7aTy7jnT9MgxfFKUMPDeBti1cux7Dn4325WD06oqxaDnEatbPD7WgoAtGbj66rgZv6UKMXXaHgi7gD0BWGMLZeFkRPvRyWFnjosn0fEZ9JhEfpF1NXdqjPOYQMBW9c3zk+E2yLEVhyEIDvbMt/IYi3+fh2UnQ4Gww/dLXUNc8iG54v599mMDQw3GyslvsCynNsb2jHNU7wkmvrQAyc3f/dHARx/XNJu1llnupSrqYRiY8p12N62mU0vTPCxCxFPNr0gwuCtI/189xfbL5wweVPD66jEe+WIfSmpN3ySZkGSyN2dS5FkWmK9V1hRBC/M+TMCqEEO1sdQISjuad5XHeWR5nYH6AgpDJcwvqWNXg8NGpPckObv48hmGwxvVTlXT5qirF/Jo0RVkWexYG+f2ctVx/YDHPL6jD05rPayBJlEO6ZGFZQZQVwUtVow0FXhpl+DBDRVhZpWjPxqlfjpdeh5G7K3gObrIG5cTRdgTdswtGr+4Yi5ZAMoFevRyvqgKjz25QbGfK0kZz0OEoRLNZZBUwa22agOlkwpynSTiauO3h6ExV2q5hH/lBk5TrEfYZpF1NSdhPlqWIWBAhjed5tLZcp1IKVpdv/EYl46h4PbQaRh1+O6SA06as4PcHFfKzATlAZv6u50GWAaGgIuqzUEpRl/bwtCblZobwehrmr0sT8ikabI+yiMW+xUEG5PtZEUtzzyHFAHgoPK3pk+3HMhQNRhDb02RZBlEfpJNJmUsqhBDif46EUSGEaGfqB7WJ5lU3H5KrNremC6BNi5W2RVXCYUGNTXnMYVB+gJBl8KePKrn2gCJeWhgDYEhJiLKwj90L/NhOCptcQn4fpi+Ml6hAaz/aTWfWJbWrQWuMQC5WtDvaSWDHlmCFy0BbuMm1aDeB54ti9u0BcRvTH4Q1mV5FItnoYBYqJ5+6YA4vrHBJOElSbmZYbmN12tIsi+xcP7Upl7jjYRlguNA9YuE3DXLDJvl+D+wkeLCpmKa1hmhu843ZeZAVgeo1kE6h/ZnFXwKBACltUO9o0i5YKtMTOvGgYgbkB3A9j7gDaU9jKJWp6GtkgmnKdTPnMBUhyyQ/aBEwFSsb0sTSmoKAic+EPtk+DKXYLccilnYxDBNz/UtqqMzc2Lij0RpSrkfCBdv1ozVkBwwsBWFTk0gkNvs+EEIIITozCaNCCNHOSkOKLEsR/+EkRjLzEfMDmw6j9SpAVQKqkjarGxzijsfg4iDLYjYvflfHpXsX8E55A67WHFASoiRssXtBgBySTUND404WpmkSyDLRThydqkGbAdAenpPATa7FaViB4YtgRXthhgpx4qtBZ4oeuckq8ByMYB7urv1QRUVQV4cqKSPlz+KNGj816zwsQ+E3NblBk/yAH0MpVscdVscdcgImIcsgP2ASMAy655uETEWe6WDbCWilF3RjvPwijLxCyCvEmHAuOl4PtetQZT3RqRTxrFyqdBAjDQpNfTqzkKvfbAzHJmvjDj5T4TcyIdTWmeM8DdYG21MepNdX+PUbiv27hNAKUlqTsMFnZKoBYxg4jkZ54BmZ8JlZlibzGjhaE095rEtC0FKETKhLa9KeImiCzwjh6vW9p4ZLKrV184iFEEKIHZ2EUSGEaEeGYeBqi9sP7sIv31ndbJ9lwKOjSsk3HVobsWmYFus8HysbHGpSHg22R9AyKPOZvLe8AVtrjuubzaK6NLVpl32LQ5SGLQYVBMjbIIg2cl2XlBklEMxG+SLoZCXKDGF4adyGFZg5pTixxSgrjJuqwTAjmMEc3HQMM9wVhYVnxzAAnRuC3CCeEWZ1KkDAdMgPKUyliNkuaxMuq+odds0LUBQyyQ1kihDlBQyy1lfFzVUpPM/D3sLRqqZpZv5oF6WzUDc9gFq9HO+hP6BGjIURozNzVjWE7TjhdMP6bmkNev3fjgFp6KoUmAqNwlE+Ev4gMe2jVkHS0QQsg5SrM8WmtEaTCZNo6JHtI+5ovPW3N+FkjjPX94IGDM26pNe0RE0mlKqmHnJTge0ptM/EZ2hMI1Nl2cMjZChc7bHOgywzC41Hrt8EOyHDeoUQQnR6EkaFEKIdNRghJryyiv1LQvz7mO48+U0ti+vSDMwP8NMBOeySa+F5Pxi2qxS1BEjZUF6fJu5k5lwGTcXqBod3VzTwk+5hltTahH2Kz9am2bMwSLeIxcANgqhpZpZJUV4c5aVAGeDZ6IQNCpQVycy/RKPMIMqwsCI9MYOFgEZrh1T1PAJ5A3GTVVihYpThw0tVrw95JspsoEyZdM1XGMrCM/wktZ816Sy+Wpci4lekXegSscgNGIQtRZaXaZ/XSj0fy7KwlMZKJlCuC64Drrt+KLNGe976bQ4qEML7510Yv/4j2u+HVDLTQ+p5mfYpBYaJMsz1/71+mzLWD4zOnNNE4QdylKK7aaINA/CBYaAtSAfC1Jgh1sQd4g7UplxClkHS1fgNhas1rs6cx/U8SqJ+1qVSWIZBwDQImJkgmuklbVxpJtMCW2tSjofrQcLxWIcm228Q9RvEbJdM/nWxtZ8sU2EoyDE96TUVQgjRKUkYFUKIdlTjKL5Zl+abdWle+K6OE/plc2j3MItrbY75TznTJ/QgJ+v745NmgDo7UxxnZYODpyHlauptj4/XpKhJefx0QC6Tvq3h5F1y+GRNkj0LAgzpEqJPtsbvrgM80E6mRy/tgpfAc20wLLRno+060KAM//ptacxgMW6qCtOfi/YyYRU3TSB/EACmPxsvXYu2G/C8NGiNMjK/UpThQ1lZuIYPlEFQe/Qw/fQs9oHygVJoM4RHENtO42mNUgq/z8JKJFDpRKa3VXuZ5WE8LxMTHRvSSXCcTBVf0wTXRacSKA062YA67SKwU5ngqkDbaXQqhXLSmXVGDYVe/1hl+sDnawqkTVN1lYHSuimsKq3Q2s3cI8siYNXSxeejRBloZaCNEK7hZ40VpiblErEMsqzM/NAsbbAslqZLlkl10sNUmaVp4o5HytVoGueOajKdrhoPMFVmCHGWlYmpaXd9YFWampRH0FJ4BqRccDwDZQbxPEXaA43GXB9yLZU5R9DwQGts25YqvkIIIXYYEkaFEKIdpTdYU7TB1jzxdW2z/Y3zSOMqQFIrqhpc0q5meb1DytXYnmZpnU1N0mVQYYCeUR+G8vjL0DARM86oovUTLXUMnUxngod28JwEaBsMH+Ch7QTas1GGhVIWWmm0l0Kna9BeGi9ZBcrECnXBs2szx2gPPBsvHcv0oGovM9pVGSgzgMJDex6em0ZpDwwfhmGhlQnaQev1xyoDZdmYVhrTUjSOSVYYEPTwAiYeKhMI3SAqaUMingmY6TReKgmpJKDB8mP4fJnCSVUVkJOHV1ebCZ/KAMtCWT5UMCsTXrWHdhywbbx4Q+acXqYwEYaRCbmGmTnWNJsHVU2mrdoD08ILZaGCWRjxepSGruEIZZYPbfpxAyEajACOhjXxFD0jJjn+TAjVWpMbMDOFm5SmNq3JtQzqUi6OUvgVFIUs6m2PnIBJZdIh6Xq464cFK8DTCq0NTENhO5rVDQ45AaMp1GbuJ5iGwsqMQMYyFJbyo5VCkSnQZClFyIKwoTNVk11ZckYIIUT7kTAqhBDtKD+gCPsycwI3ZBnwx4MK6ZXto1r7qUxkwmdifdDIshR9c3z0CqXJ76kx0UASdCwTsLQHqcYeTxM0eHYdeDbK9IMKoLXOFCryUihloczg+pDogjJBZ0KZUmZTIHEaylHKBDOE8oXRbgrDCqA9B+0k0G4KrT2snK6Axoktzaxf6q0PwW6md9EM5KOsAE66DitYDLjodC0oE2X4QIFnp1FGaH0vrpfpcVUKAgoVNEBFQeWgMPAMA1AoTSagOQpKuqFWL0fVVmeeiz+YCbTrh/Jq14ZUChw7M6xXGZm1UAOR9b2OKhNItUZ7bqYHFtB2KvN8TDMTViFzjz0PlYxnqgdHIuh4HJ2lIA1WOkW21iitGY4HdUDjNZrqU6nv/9IKHVB4holnWDj+IHYkSF3aoyBgknDAQ2MqhQFYJhhK4TMUy+rs9T2vLglHk1y/fA6s79wlM5zX0xA0FaYBIdPANDIh1VCZbYYyMTFRTcFV4TMgx9Io7eI4jgRVIYQQ25WEUSGEaEdBn8E1+xVy3ey17FkQ4LaDitgtP1Nl1gB8piJIkpJoCgs3E6ryPTJDbUG7SXTaxVNqVIDNuAAAPgxJREFUfcCKZ45RmXUwPTeFdtOZnkvDygy71RptrwMvnRlyC2CAVvb6EGZkeiyN9QEV1gczH0r50U49XroOpQxMXwQMH9qJ4ykfBh4oC60dlHYzQ1+VLxNcU+syc1VDRWjDjx0rx5fTF+2m8NI1oAwMM4jWNp4dxwjmg+eA0nhOPNOTq8kEVn822nPAczKbdGY4sTZ8gMqsC6NMVFkudCtEKQONl9mmDLSnM9HPBWV7kEyh6mvBcVHpZCZcKgXpJNp1MgHWtKBuHcq0oFsvsHwo00JXVaCKu0LVGrRpgt+PjtVmgm08DsFg5lhN5jVKJjIBWKlM0HUcVON8Vb3+Xls+0B6G5cMwDCwgaJhETQttGBiQef1NAwwTxwjgBMOsTnpk+TLLzRRmZb6isB2NqRWp9Qvi1KY0nvZQKlPF2daA1qxLeRSsX9s1S6nMYzXYHoQsTVorApaiOukRDVigTZRShC1wNQSMTK+q3++nuLgY0zTb5d+QEEKInYeEUfGjfPzxx0ycOJEPPvgA27YZMGAAF110ESeeeOIWn8PzPB5++GEeffRRFi1aRDgcZvjw4Vx//fX07du3za4rRHszTROfz4erDKqq41y0m8GVuxehcEHXr++RzFRoVZ6H9lKAyhT08ZJoN1OcRikfWjtoux6NhzL8KDMA2s2EQyeeOc4Ksr6rkfUb1gdOE5TOhDTPRnvp79c0NSyUEUD5czB8WWjPQfmiKMPCCBZk5nm6Nph+NBrli2Ja4cx2u6HpumZAgxXFTaxe34umUb4IuEmscAl4mSBm+LMzPZMKvFQdZrAQrR1A4Saq8VJVaO2iDAtf7oBMDy9khhkbJtpzUVakaQiyYQUzZWoNP+DhaRelrEwPMaAMM3OHDdA+H4QMyI+iDCPTC6oz51baRTf1XqrMPFg3E5BR/kwbu5ZmdncpWV/8SGUCr+tlnq+n0UqjXI22k5kwGwhlgna8AeUPQDKBl6jPhNBUMhNcTRNqqzJtiWRDOpWZsxoKo0NhSMUz9zuYhekYmA219DKM/2/vzMPlqMr8/zmnqrr7rrlJyCoESNiURRRFICYKjBmULYDjyDDggo6gQmb05zIIzqAwPDguDDwg4DiDDFFBEQEjKIuyiGQMMsgSRnbjJBBNcnNzl+6uqnN+f7ynqu/lJpCLyY3A+3me6KX79Knqt05Vn+95l8NOsQcfS7ixQdympZfXQcWV+cGYGJD/JorwNpLCTMaSe8lfdV62n2nksg3OhIqlmXmsFe/sUDAHFhoZRJGlbfI0arEl9Z6qlTHVyD258yRWwoKdN+RAJQKLJfM5sbXkuYz+SmQBR2TtsKJOcl8QPLLe+xH/nv9a8d8Fw19XFEVR/vwwvb29+pRWxsRdd93FcccdR6VS4dhjj6W7u5sbb7yRZ555hrPOOotPfvKTm9XPokWL+Na3vsUee+zBggULWL16Nddddx3VapWf/vSn7LHHHlvluFsDYwwNEhpI6KBUHjUyISXMo0LOmSkmudIqTLRobTkBRIC1ppWXh5EcOhc+YUM+HWCNlRDN0EY8aDKZ9ngJ6bMGawwdkSMe7MdmqUwmTXlw+WfKqV+Zm9bCi6ChmPAhHiHblAl+6yuCNxgbJoVeiq6YYJOQ8YYJnpjyg4Tw0KLzYCd5I0yqi8+KHAoCgvA/JnyuyKvzeCLwVibl4Z/BiofNe8lrJMP7WM69aGeiUIHVgcuD3VuirdUmF+eWyzDkeCzGicfLRRbrmnIskOuSSxEd79NggwTv6iLmTBxEVgbZID5vSPhqKTaH8K4pHtAoadnD5/i8jnPimQQjAjWqhCI8ESbuwOf1ECrbjmusC6KuRpS0yXWyEWDK49ikU8QTErJqojYwyN6jURVT6cHYGJfVieIqLhsK4bYhLxOPtRXxouZDsgVMZYKIkuYGCQ0O19rldeKO14SqvDE2bsNng+LVDCJThomHqIb3OdZW8HmdtP93xB0zMaEwUjHeWjdnHDzHxXiJ8c0h+X5G9lQV2yfgUxHIoR2+Cd7KrWkM3sbSttyfxYvH1UYykH0WjtUaH+JdNniCCMS1Bn0oqGTyJj54cPHBo1t4ast702PyXMaOEQ+k8eBNGPtBfIoXOIwDX3jUnYxNH+5hY8LxDN57jAnXGC9j2YexYIOgNRYT7OqdB59jfFPuG+8gEjHbur9CXq3PJI/Yx9J3eX9Zca3aPBSncvI8caHCcRGCbWwoHGXk2vjwRPNe4osLr74vjBH6cqFNWS3ZYLK62LVcfglPEUPrWRI818W75XPOGCAX+8UdGNMGeSpVnZ1430nCgo8t7GtC8a0Mk2eQI8Wyoqj1PDIGn+eyaJQ7aUcEcdxqN7yvTCIDjHMQJa124Tt65yBrSF8+B+LRfeW5jNPMh768LIiMyJE2+DSVdmnoy75AX7nDZJkM7UoFgve+bNdsQh6udZ5KxEG4p4p2smDgIMtkTDkLUXjAD28zuKH4CSp/RUf+Vphhiw/y+1P+tBrK/PC81k6j0kHqDSme2En1axv5ELJuWF0vlwhlPHiDw5M5T7v1dNlMt1dSlC2AilFlTGRZxpvf/GZWrlzJT3/6U17/+tcDsGHDBhYsWMBjjz3G0qVLN+nZLLjzzjs56qijOPDAA/nhD39ItVoF4I477mDhwoUceOCB/PjHP97ix90a1Il5rmnFi2CCkDQGY2TikwdPg0e2rjC08rSGb3nonLRrSyRXyxrKnC4v8ywywPnWlCoi1FoJ/ebGSztf/F5LTlkbninZeuKBPpmwEDosJtXlxC0ST1GhoIt8u+DhApksmmobeU8Nlw+IUCgmgcVErij6IkpRCAJFJsdeitXYQrwnmLga+soRoQalyLRR+Fsmwt774FUkTBQtEEml1HKymZeTTPlvj6Eigi9ORBjmDo9MmKGYi1bk3JJEwjXJQ4GdkE9JOC9TxdsY78JkzUieYzHJMt7LJNxG4Bt4EDHTWIPPU2y1B+MdWbOBrXaIKEv7JAzWNcmHVofJchXXWIvtmImNarj66uDFrIjASzpFIEWdIgatkaq4JiZb/zC2OgVb7RFhmdch7sDbmpxjPgjG4tN+ySvNm+Jxjdrk9TwjH1qJiSrYpDt4/ySM1UTtGGtJ63+g0jUbn4ctVGwF1+zF1rYLQrkpE1YcJu4IItrh86Ys2Lg6Ju6Q6Z5rYON2+RwesiFM0okxbXgLPhsQL2TcLgswoUKweAAJIs2JoIqq5aKMjI8gfsuc2lxCfj2YSqfkvmKxUSWM16xV2AgngqwcsxW8d3JIG4EPoqMo6AQi3myCNwlg5Vq6JhDOGYsxoX+TALF4KUE8wj6Xfr0s5BRb5xTC2UG4N5syfj3Pu2/icPwoCMxMxrpvjXUxTgWiBGdsEENZy67FopGPROxHMc5bjKuH+ysPur942oT7KwpeWp+X1754fBhv8EglY+cI914WFtRcuQBmCB5wKwsuFEK52KYHMMRyv8cR3hXtMnnGFZceGTfyAVuKzdYzS/oz2GHPkpHPLN9YQ//j3yXre4qoYyZde3yQuHNXTGYhTTEuK/sFoNaJqVQginDNIQhizBTPXgBbwbS1QZLg8lwKcuUZxqVlE7yHtq5hfQ1CKsW3iuuI95C0YWo1iGNcvS7jJg2LU+V5RdDWgUkSEVBZBllDRCS+UN1QacNUq9JXswnNIRH5mVwDsVEM7W2YOMY5L3nYLsW4XH5Tii9Q7cBUE4gT3NAGyJ2ExTtXLhoSVTCRPG9dFhZzMochk/eL0zdgBgeLJYJwWxaLHsXgMtIXttXOu9AOeHYF/kffhdX/BzvMwRx/Cv0zd6bfVkkd5OHmNQZ6G2LfKPxIO2BD03HB/Wu5Z+UQU9sj/t9+kzl4ZpUumiiK8tJRMaqMidtvv51jjz2WE044gYsvvnjEez/4wQ/44Ac/yCc+8Qk+//nPv2A/H/rQh/j+97/PkiVLmDt37oj33v3ud3PrrbeybNkydtllly163C2OjVi2zrO+Kd5Ia6FiDW2xVLB0HlLnqedSRdUbmeok1lCLjOz5COROio50VyyxlWIisTFE1pCESZUz0Cy3gxCxGltDFIqM2LDCnztHPfOkHroqkms2K1tP26onMXHhPfJSLCaVQi7FdyFOMEkSvDLgswzfbMpqduFViSPcLjuRp2vCD76T8E3XbIkxIglNNNUwifeyhUjeoCiSgwkhkLZKVJsqkz6XiTeq8OzYSIrn2AiR+XI851JMngaxJu8bU0zULR6LtzUshaAoJsOFpzrGx4lUiHXN4OEsJvRhBuRDvmVUwZNj86w1saZoZ4A2fFIRvWIyyBtBcPlycpsN/pG4c2YpcLwVj1Le/wxR147BdoDtxBsHjT/iXYatTZJ9POtrsZUuEWgOSGpynCLkMngwRSQHr1zhVc3r0n9WD17SWundwhahnTEE7yUuJe6cJX+D5ILaClS68f0rIJmAr/+BuHs2ad8TRNXtIKoAefDANvF5HRO34QZW4fImUcdMmWRHtSAEg6BwXiadURv50B8wSZuIzagqY8XEIsbiGsZ4XHMAH1VE6EUJZP34bCjkuRqMreKNkSJLNhaPko2DWMjxLohcCq+gw8Rt4BpBSAZvm8/xuWyBYpMuudJxO+DEIzj8GmPAW1loqtREtLlU3svrrbHic5k3R1UR3DbG5ENIASlXCi6xTVWuXeEBdym4xrBFoTCR9zEuTrBxW2jTHCYkhy0gUcEnwW4+k7aF15TinkjkOsZVXJ5ifXPYuPLD2sX4uIJ4jPNwzGGeqOLeiWJ8CCs3wavZej+sEPkEH0V4b7A+hXKBqegL8MM9cYXnOSxmmbCShyykeSOeqygP96mxZBueDM+IquT7ekRkumZ41vjRz6wQeeJdRj6wgvUPfHnUo79rr9Non3W0nF99ENNstMQRyPetdUK7VHF2Q4OSm5yH61MsMngLEyaKGEtTaAwGL2MxHrwI/o4uCfWOItxQvxwvz8oFRfkKVejqFPGX1WGwLgsMpQgu+poA1YroxsZQOPdwvCK3OmmDjjZIKrj6EKYxBFnxLJXxZwjnVamKKG3WMWmz9ZtSnFccB69wgksbImxLERlsZRJIxMPsnIO0KaK1eCb/39NBlEt2mc9zfNrEZCnlPsI2wiSV1nZNzuOyJjSbmCeW46+5fNR15FPnU99vHhsyQ90ZnIPf/LFBbA3VyFCxsri6rpHzV0t+T/o8R+jf7tHN+QdMosOko/tWFGWz0JxRZUzcfffdABxyyCGj3ite+8UvfrFZ/XR0dHDAAQdstJ9bb72VX/ziF6UY3VLH3dKszSIaeUZnbMm8CM6+Zs6auvzOxtZQieRHraNiyB3lZvbrG7IFgzWUbboqkteW5tBwjrTpyTFEBqqRCNVqLOF2ObLNx2Cek4eJQSW0iSNLFcB7prZZak+uwPf1ymq4lYm8SSoiPJNE9mrMM/zQAPSFyWUUQVLBJhWohlDJLMP1bEfa/wTeDSEhVInsK2ljEUE+w7sM1+wH1yvOqRBaakwCVsQdLsNlDWylhmuuLbcYEa9fKLrjMqkOGybqIk7l/dKT6nLIUxxDGOcwlW6MMSFMLYgAUylDLK33YHNM3gQ/GCaEHpzFRTE2roYJkHimTFZvTaSdeFVNXBORnTdke5R0aKQ4MW34pIrF4vN+4o4pGFcXLyFIoR9bg/bXQNIlIWy+Aa4u3oWoIqG4eYbzMaZjpszfXQamic+D7fMhOZ5tCxVzxXMpQmgQsg1B3MTkeUZU6RGRajJMPiA5hD4DV5d+iKHSJUWBkh7IB4KAcdDoFW8jVs7bWKIJu4in0WcYInyzV74f4lWNumZhTQR5XXI2XRpClcOCRFQD24WJYxHeJoQJ5035bjZB8jQtxlpMpRvSDXKuWSMsDuS4fAgTT8BW2pBFCy+5ncEbLN52i4tqREmnhBenGzB5XY7jMxEnGIg6IO4mqoSQ8axfJuppr4wlLxNpH3VgknbxVucD2KwOIWdXijCZsHDQKSLPGGw2JOIsH8CnaTgvD9TwSQdRlOBcLiIpH4J8IEza86DxakEURxjfBJdiXQMaQ5QigQgfJZi4De8MlhTvU0w6MELweh/jkxrWVsXv45oieJsD2MLr5iJ8HEsero1E2Pg0iOhWhIUP7aK4LYQIi0g1vt66v7zFR8HzaCOsd3iTY9wwoeo93kd4a/EmxvpUIiCCUBIPsSzIOHKsDVWfDUHwIlWmg0j1uYR6u7yJTwdCBIMtIwuMrcpGrCOeWeuCxzvBRAkbln9jo8/+DY9cStvUudh+5Lycgwnb4atVkX31IdleqPePEp7qnXzPaid0duLjGNesY5pNGFgfvKI+VH82MHGS9OU9vj4o7YYGwDm5Ps6JqGxvx0fiETVpHTb0ihe2TENIoKcbX6lJWG1zUMbLUJ88L11YAGnvxLe1S7RHISo3rA9hs8H2SQ1q7fhaGz5rQiPs+zvYj3HBS+5y6OiWNnEsdmg2gvd0UPpyThYY4gSfVCTSxzl5ljTrLVsRQWTwa/+Abzagvy9EX9jw25RALPeWpDekuA1BzJe/cwkmTnA/+M+N/4hfeh7mS3vTW51I7D0bck9kDY3M0deU31iL4cIH1o4SogBXPdrHJ984iZ2rG+9eUZQXR8WoMiaeeOIJgI2Gw/b09DB58uSyzaYYGBjg2Wef5XWve91Gqy8WfQ/vZ0sct6Ber794o81krYto5p7ezNF0njSXrRfaE0s1CgVokLlYbyN4LCVml2ps6KrYsk1soL+Zk4ZV5xzxsnYlUsyj6TxDafihDCvGmZN9BNtiSyUypLkPe1E6KtYQW6meaVb9TvKC2tpDLpSEHvm0KUVSMvkhN9ZCtU08qOUk0uMH+iFtSkXQ10wPnq+O1oQUgkesITltLpcJZ9QmIrWYTBuHT/vANUUsYDHt0yT/z2ciVlwx4S/2pfRhYliTY7kUn9UlRy0U6JFCNxEmqkneqWuCG8JHndikkyiqha1IBhHLWjkH18S7GFPtxFZqRCaCbKBVcbbwoLgmrjqNuNqOjSq4tB/SIu/RSF95Ex9PwFY6JOfR5fi0N5yjwftc+sojfHs3UdxGYifiGmtGetBCXqiPezBxlSiqiEBLN5Q2w6fB1jVM+ySiuCZXobFOPg8UobC4Br42g7gzeFnTDZD2U+YGF+eedGE7Z2KjRDzdeRB9JhZb5k2wVXwUE1W65L3G+iBkkf6COPeVSdi4TYRTNiShvYUQyJuyn6mvYaIQFu6hKKiETYKABJp9+Op2SORd0hJ7Pg12z0VQkGCSNvGulOG7hQcqk+/nDMQdsmBiQvh0bkP+rogkn9fxcRfWRiIg08EwFlM593CO3rdhqm3iwcWL17kcMyE02TVx7TOJ43axe2N9uVDgS7s3wphpD0IuE/EbCjm1zt1C2ySipE3Cs5u9+GxoxL2Ha+ArPdi4GO8NyPtKId4a7xG0TZYxYyJMc30omFWMGQm59JVJ2GonUVzBZ4My/lzIRQhVmT0VTKWDqFrDG4tr9mN8IcRF4Pu8ia9MJE7asFFV7h3fKEP6iwUKH9WwtoJNauE7DVHm3/pcbOU8JFWsTYhsJWxRFL4fvozQ8KaKiRKy/hX4NBT1iqTCc+G5LI4h/QaPq42wIfy8ePa55vqNP/xdSl5fTTTUDtNm4Ws1EUmNIcyGXnmmGiueu7QJ1S78lCmtvMx1f8Q05N4xXjyBpE3Yfo4U2LIWBvoxA32tSJEshbQBPZPx3ZOkjXPSVypFz4x38kxPm/hZcyCpyvH6ejFBzBpjwvEa0DMF3z1Bfh+adcz6P4ZzN2Ff3wbEVfzkKVIl2lg5XrPe8nw2G5LTOnk6vqNTFjgH+jF9veIltUaicNKmRJt0dIqXN46kXZbKoybPSzv47h6otEG1Cg/fJ+K0UoX2DvnbS+SJbwyF3zDxkJo4gVpbyL+WhBY/2C/vb4wNvZj+Xro6uplQrfDrpwfZkDqclwrV3WFRatlzm543/GzFIDvsZMiybJNtxkKtVtsi/SjKywUVo8qY6OvrA6C7u3uj73d1dbFy5co/uY/h7bbUcQtWrlxJnucv3nAz8NN3xYZQ2aJWBiE8F0K+Z/j/YuP5EOhEUbXSmlZ/xoAtCmsUDjsnv9mRMTgpPkoUMq2kKBC4IGAjg7QpQqh8ESla5FuGwijFwUxRHCIPbRABZmiFwRlkApVJ+yLVx3gJgyv7IoTT5RaMTPgkLwuKgitF6K33Uqil9IbgpW9T5PoYyoJBnnJiKOccScimkQInQeWGiC8XUjZDQRXTCk8rCnOUYV8hnNWYUG3T+xCmOSxnrLCRsRhy+T7eh/MMF7v4fjbCZCk+cTJpt4gwtqYlnkwENngjy0lxxIgCTSYKeZZDknvnfRBQhc0KOyaS85o3JezXJDibSB6cEzFjTIQ3FYxv4GnH+Ey8j8UCAdK3iSqQpvhKLp6pQoTmDfmKJgrXLRPhkA5J6GwIYZbcRiNhgoDJh/C2FkJtE8nxzAbleFaKReEcPpUcMBtV8VEtLFqk4ZwiPA6T13FAZGLADuvLiUAlFBdxmdw3tgZRu4gZa8CFcZI3MVkT5/tETPsYH7WLt8ZIbqWxMaQDuLyJqU0M4ccGzJCIfJtgTCT5l80NOO+JkirEnSLEfUOiQ+OaaJt0COc8UaUD4vYQ5hpCIkNfuCYur4OJZP/WqCaLE8GjLZderqkP4eTGJniblWG0xoaf8maKtxnehtBrE1MIVmwkeZg+D4saHmsNzsbh+jmxr00kJ7v0fA4bfyaEQ5oYjMM4uSd8KLJmbCjaNOJejUI3OUVGd3lvl/dqJEXCLGXoqifk1JfRBhHGiEiQ51KxqGbKNuV9Xzy6TBQKPIX83iLVYPgzK9zfRfi9x2Eoike9MFI0y4p4K3PvxfOKGZZjaSMY7AO/XevDURxyK3zLdjYS8ZYkoU3IUS6qJNvwvE7z8np5Y0Q8F/mwxsjzOo+k4FVcvFaE8IeSPEVfRRjvsGtWhkuHcUNjcNh1DecewmC9KfqyIUoi9BWF3JHiMVcWbSoWOXM8UTj34hIGW9kwHor0CmtD9Ihv9eVb47rsu/SwO7GBL34wX3jLIWMjUm9w3hPbYmlD/hW/0cUpboyq9axa9ewWWeiOoojZs2f/yf0oyssJFaPKq46ZM2dusb4G45hVjZwJ1QiDJ3WQ5pL/uaEpq9lJBB5LLbZ0ViRMN809DecZzGRylVjII0t7EhEBmRcPapZ7hjKHz0LIrzXEEdTCz2XqJN80zT3rc0dkpE0SWfnbwJp6zrRZu2D++KyshOetMCcTJ9DWKb+0RahuWscMDYpAjBOgInk4tXaZkAw0sN0TxetXiFubYG0FE3dBgoTguhRciksHZCXeJkH/tbUK0BR5pC7swRjCfY0J1TxDkRkXvJoGQohdIl7MqCpzskg8YT4c1xgrhW+8h7yOS5sSImgqEFUlRNDLpF4qcKb4tClCLErwtk28cS4PbapSfKbZwLkIl1SxSTeSXtjEulRCGX0O2QbxIpkKxBW8SbBFyK+Voi4mr4tXxldxSQVbnRgmWxJ6aYq+mutw4ZyIxJNgfSqTPhzYDOMG8fUBvE9wSYUo7qbMC7Sh0InLobEW5ww+suLRMyFUMnjrTJRDs1c8uD6GpB0fd2KNCX3FIfQvh3S9VMS1ImS9rWJDASuxVw5uAN/IgArOQFTpwWdS+AbrW3mE+aBUtI0qeCzeJGJ7fCu/1Ke45hrIPc5YbLUHnw9hMC2vpc9E3Pp++e+4Kte2MiEULZJQZOMbuEYIyTVVSLpDwaNa6GtI8kebvXIOxGBrEFVDwbFMQm1dBtkGXLYefARxGybpETHjMootZnADuLqEi3rTBvEEjEulXbhO5dhqGhk3UYeMYfJWTrPPZDw4ZNxHYQy6FO/q4hUih3wAn23Ak+DjGjZqF5u7VMZLlGNcA9+o412MTxJsXOQjZ8PGn4O0F9eUEFxj2/BFhWiTh4gHh/Epvjns3jFtMvd3mXgtbVWWzrJBXFqXgrlRJSx4efBR63jkcg2thIw7LBYr48VYidosQoObdZw1WBO16tcUKQPI+Ik7dyQbelaiLVwIb/cyrqRtBZN0y3X2uTyz8hSXF8+smKh9BvngqlHPfhN3YGuTZU1nsA8zuB7yEF47YZIscNXrkNbFK1d1mDXPhvDaHmjvwHd240N4rU9TqUTbtw6zfo2EsHd34ydOwec5pjmIb6byTHaZPM+dw3RPxHf1yPGa9eBZTMUjun4dxq+Fagd0tuO368Q3JWzWpykkFQkPXrtazr2rE9/dIwK+PhgKL6VQrYm3t28d1Nqg2o6fNBXfTMO5NyHJIWti1qyWRalqgu+eKBpzsF9ChJNEzjl4eI1N8JUYX20Xo7oMEiceUO8wQ4NS2GnWHPya1ZIjOjQo3zWOQ42DCnRNoEgj8Vkq591shOdAgqlU8O2dMNg/+kd82mvIOifQzCNWDXp2n1jlmf6UNPc0w+9v7j0LduzgJ88MjB4HwME7dDApn7K50wZFUZ6HilFlTBSeyeFey+Fs2LBhk97LsfQxvN2WOm7BlgyBabeO7TtjnhuU8FrnIYkM7YmhGkfEYXU9cyF8NhcvprXQFVm2q0lOKUDuPfXUEUeyGhsZQ6ViSUIxo2I7k0buaDonNRus5Ju2J5bEGqyR7VNy72jkjswYelND18TpdPavx3R0yWSmWJEPP94mlN0nijDVHhGpYTXZ5xkuyyQPxzvoW0cydWeySERhKRpDjp4P+V02qsg2CDYpV6192SZreVx8im2bAcaGPFGZOBdbfxgTYeOKeA5Ma4XbBRErfhYLtoKNwhYdNhbPnM+wPgeTiw/FN4uIYfAJJG3BQdkqGCLCMcPkBk8CNpHcp8xjaQA5NuTXyf6SHu9qUGmXKp4uBTKZaOd1DPXgHYrwUU0m7FkDQ4ZxOTYfgjwIbSK8qUoxE59jfSaVa8khGxjmT6rg46psqeFDNU8QoVBWYA02iCPIPYYmsj2FCAMbejNUxANrYgnX9MXWCo2yJqVUeq2EgkcADTA+5H81S7eBQUSZC1WKrZHCRtYjiwk+lb6wYNvDuMmDtyQPub5F2GwMpg0vHw5bUHjJl8v7W3mNJOJps7HkuuYyJnxZ3VOKCRmSsLVIW9iSQiqvmmxAxCdJ8KLU5Dr6QgDF0kdRCAsjtjUh37qoGosP+60WURcRkEj1Yp+G8PgMsmYrYqDwCBu5NyzibZTxUEQLiKiUzLU8eE29eJ/zOsZ4jI/xUVXysV0DEyr7GteQyrdFBVTbho8SXB7amFyuXwgxNtiwiJKIFzbkFBuXA4NliL8hxsft5b0jWxvJvWN8DrnHhyq93kRSUCpUyZZ1hiyMv9DOWpwP4pxcFhlwQZCH8RdZIJL73uSyBoa4rEzx/YzBG9m6yfqM8DYYI4Wq7AQRQCDPMhcWscL1s7YCSYeI43B+3Xufzrpf/bPk0xaYiIn7/RM26sFMquDyHN8YlFDUgfUwUPhsveSIdnRLddqBfqlgmw5BbwgfxmNMBTonyH3fHBKB6jLYsE7GbhHh0tYNXT047/FDIbx1aBCG+lvRMHENuieGyrpD+EYd4xrQG0KCwzOLjglQSXDNTARqnkvl3MZgOJ4JIjYURBrog2Ym7fr7wj2F5PmH0FyXpa1zb9ZD/qcPwy+BSrvUW8rSULnZS35rnoX7IRQfqkW44rfJObFN79pQ56BShjp753BZiklbz29TrWLau1pFjPAi5k/+FP7iL9CqkI2I3n84FzNxIlOajrqHhjOlLTsTy6SaLPD+v/0m8+vVdf4wNDKq6l/nTWVy4qgmGlqrKC8VFaPKmBiez7nvvvuOeK+3t5c1a9bwlre85QX76OjoYPr06TzzzDPkeT4qb3Rj+aFb4rhbA+ccs9sMEyoJA1kIlw1Y5AfdGhGREvLjy6hTivAfU5SPF5E0vDKu8dKPDWG6BqjEttwGJqSfShsr4btRZPDe0hb7UKre84eoi2iH3Wkb6gseiyAzTAhdC+X4i338vKF1ct6IZ2NY1Ua7vkE8cQaeRpDIhUxqVZOUL2RCHLL8KyZo5Z5ywaXhXYaJJVfLWC+eyPL94l8IxQpVdW2RH1i0M6FNEVbrc4yPy3A7P3wrBFORlXWbhEJH4RSHnb+IExEjJi+26glhZuThm8TBKyZbqRjTLK7GMDtIURbZnzSRoiGEcOFQ8En6qgChCqmzpRCUrUPEBjJ6amGvvxifOxHiYfLeCvdN8IRj+kIpRkH0IKG8ACQhxC4JXuA4xKal4bxDKGMoOuPzcK1NLDYI47QoEiVhfjJBNeV4MGIzl+NNFMaE7GloXBS84gRhILPu0tMlMa9yDtYBaXHjhalwcd2laJExRia0PhXxaordIosxYXDOYeNKqMwq22j4YiwFT7mNKiGU1QShGcKUQ75kudVKnsl1wGKskxh5H1NseeJsFCb2YQsg6+X7GCv9FwsuVGRSH3IDMWHLkSJ00XlM7KV/Ww3CSLzG3odw3FB5GCrhdpPCPyK8bLCjLApZokKlgU9LG3mqEEVB+PrW4oMvqupGYYHGhPGeIVV1Y4pK09KXFNOSisFIOGthy7AfqOy7akK4aoTNsnBORchsMeblfVmI8ti4EhYAhoXgE75bFGGKsGwr94Lt3FEKjRXXkxDWWj6qhz1nyoeioQjnjZIutpt3MUP/dztZ3xPEXTvRPutIbDw5hOOa1nUrc01lscFXO6FawVgrlXJNuOeNK5+lPm6TfNMowhd7ddoiJD88Q0yEb2uHivRFo1EKb6kCHnbcrLZLjmXRVxEF4yXE2iBj3be3QxwiUEwzhPW60vYGj692hHOPcfVG6/uFbVkMyOJarVi8aJ1uuQpQPN/bZOsqE8dynCiETYfqw8aDr1TENnFYLLBWntFhEdS8Zmd8sx7ylm15DVsFslrPdx9SMlrpFEB3D/bcf8ffeRN+1QrMrnvi334EjYlTcWn4jPdYPHtMqjCQhuJFpgjTNdy0cBY/eaafO/9vkBkdMafuM5FZbZ4qWyZXVFFeragYVcbE3Llz+epXv8rtt9/OcccdN+K922+/vWyzOf1ce+213HvvvaPab6yfLXXcrYHxOdvZnKltEXmxcb28EfbUk9wnX0wsw0QbKCffDHsJCullcOVnvcyNvJEJty8mfeZ5H/JhYmJCH148BR7y6gSGunpIyLBOvA+mmFsARUGbwvdRymMDvpjslDM4+UImiohMmPCGrTLK71d8t7DKXqRrST5a0XdpGPleJmgnR7mqXeYGDbddkc9Vbsvgg+gJgqDUvza0Cd4AFzovhCBIOKehFOTSRxA5TsKFTWJb50VpMOmzqGCKka04onC+Puz5WAjlIOy8hXLfH5wcr5iAyypAEHwVMO1QLGUU+1pSmMxiYxMmUS7M/4rVCQlblYMZqUhp2mSyVXgLyn6RiZ0LfRHEahCxphzLlAsURbuN59X5Yb8soT8jG9FvovUINq+NH/Vi+Z9F7l6pt4b1aEzrO8etxZVyoh3aeBDvsPdITqx43603oQiSaQndYtP7sIBT7oNpDLZoM+wc5Pp5yr1BTXFftZ4bIOO2uAUKXFz00x5EYFgKKj5TPk86NmJFKNVC8SxqndEwQ+YyiS/WEcKzxBQ5mi5rPcvCfrUUz6lyCxEXHh0Wn5hWqnbxsBl+Xxd5fZVaKbzLXG8X7OFlIaW893Pf+m9HuGeljSnuidCPMSB5w8iCR8irLq+hGzZ6ii2HaEWiGGPwztOx+8mAx0QVTB7Eiwv3ZLUm1WOhvAayR3HRD1K5vFIR+WwM5Gl4NPjyepiwBYo3HcGWob+iUm0xNms1MG1456SdcfiiEm/4/SmPl+fhXLPw7A3X3dpw7u34GlIcKuyn7EOdKsICTHm8PC8XEqUvL89Ra+X5nST4aogsKIsehEseSZVkGUftIQpGrpHPvLxuIrmHymvjQhs/7NwdOIcrth9z4fnlnSyFFL+RFPfP8N85jz9xV7yHNKpgkEXe2HhqBjqc/ER4B74mn8mQrdjy8Ju5697dnLpnF1HY/klRlD8d3WdUGRNZlvGmN72JVatWccstt7DPPvsAEia7YMECHnvsMe69995yS5Y1a9awZs0aJk+ezOTJk8t+7rzzTo466igOPPBArr/+eioV2Wj+jjvuYOHChRx44IH8+Mc/fsnHVcZOvV5nxYoV7LDDDlrNbyuidh4/1Nbjg9p5fFA7jx9qa0UZP+yLN1GUFnEcc+GFF+Kc413veheLFi3izDPP5K1vfSvLly/ns5/97AhBePnll7P//vtz+eUjN5ueP38+J510Er/85S+ZP38+n//85znllFN4z3veQ1dXF1/96lf/pOMqiqIoiqIoivLnjYbpKmNm/vz53HzzzZx33nlcd911pGnKHnvswec+9zne8573bHY/F1xwAXvuuSdXXHEFl112GR0dHRx22GGcddZZGxWWW+q4iqIoiqIoiqJsezRMV1EUQMOSxgu18/ihth4f1M7jg9p5/FBbK8r4oWG6iqIoiqIoiqIoyrijYlRRFEVRFEVRFEUZd1SMKoqiKIqiKIqiKOOOilFFURRFURRFURRl3FExqiiKoiiKoiiKoow7KkYVRVEURVEURVGUcUfFqKIoJVEUbetTeFWgdh4/1Nbjg9p5fFA7jx9qa0UZH3SfUUVRFEVRFEVRFGXcUc+ooiiKoiiKoiiKMu6oGFUURVEURVEURVHGHRWjiqIoiqIoiqIoyrijYlRRFEVRFEVRFEUZd1SMKoqiKIqiKIqiKOOOilFFURRFURRFURRl3FExqiiKoiiKoiiKoow7KkYV5WXG1Vdfzd///d/z9re/nalTp9LT08PixYs32X7ZsmUcf/zxzJ49m6lTp7Lffvtx7rnnMjQ0tMnPfO973+OQQw5h5syZ7LjjjvzVX/0V999//ybbP/HEE7z//e9nzpw5TJ8+nYMOOojLL78c59yf9F23JStXruSSSy7hmGOOYa+99mLKlCnstttunHjiiSxbtmyjn+nr6+OMM85gr732YurUqey1116cccYZ9PX1bfI4r3Zbj9XOv/nNb/jCF77Asccey5w5c+jp6eHwww9/0eO82u0MY7N1mqZcf/31nHrqqey///7MnDmT7bffnkMPPZR///d/J8/zTR7n1W7rsY7pb33rW/z1X/81++yzDzNnzmTWrFnMnTuXc889l3Xr1m3yOGrnsT+jh/P000/zmte8hp6eHv7hH/5hk+1e7XZWlK2N6e3t9dv6JBRF2Xz23ntvVqxYweTJk2lvb2fFihVcfPHFnHDCCaPa3nDDDXzwgx8kiiKOOuoopk6dytKlS1m2bBkHHHAA119/PdVqdcRnvvKVr/DFL36R7bffnqOPPpqBgQF+8IMfUK/Xufbaa5k3b96I9o8++igLFixgaGiIY445hhkzZnDLLbfwyCOP8L73vY9/+7d/26r22Fr88z//MxdccAE777wzc+fOZcqUKTzxxBMsWbIE7z3f/OY3OeaYY8r2AwMDHHbYYTz44IMcfPDBvP71r+ehhx7i1ltvZe+99+bmm2+mo6NjxDHU1mO383nnncf5559PpVJhl1124ZFHHmHu3LksWbJkk8dQOwtjsfVvf/tb9t9/f7q6upg3bx677rorfX193HzzzaxatYrDDjuM73znOxhjRhxDbT32Mf3Od76T9evXs/feezN9+nQajQbLli1j2bJlbL/99tx2221MmzZtxDHUzmO383C89xxxxBH8z//8DwMDA3zgAx/ga1/72qh2amdF2fqoGFWUlxk///nPmT17NrNmzeJrX/saZ5999kbF6NDQEHvttRd9fX3ccsst7LvvvoD8CH/605/mG9/4Bv/0T/80YkX4iSee4C1veQs77bQTt912GxMmTABg+fLlHHrooUybNo1f/epXxHFcfuZd73oX99xzD9dccw0LFiwAxKvy7ne/mzvuuIMbbriB+fPnb2WrbHluuOEGtttuOw466KARr99zzz0cffTRdHZ28uijj5Zi/l/+5V/40pe+xKJFizj77LPL9sXrn/70pznjjDPK19XWwljtvHz5chqNBnvuuSdr165l9913f0ExqnZuMRZbr1y5kptuuonjjz+e9vb2su3AwABHHHEE999/P1dccQULFy4s31NbC2Md0/V6nVqtNqqfc845hy9/+cucdtppfPGLXyxfVzsLY7XzcC699FLOPPNMzj77bD73uc9tVIyqnRVlfNAwXUV5mfH2t7+dWbNmvWi7pUuXsmbNGg4//PBSiAIYY/jc5z4HwH/8x3/gfWs9avHixWRZxic/+cnyhxfgta99Le9973t56qmnuPPOO8vXH3/8ce655x7mzZtX/vACJEnCWWedBcCVV175kr/rtuSoo44aNckBOOigg5g3bx7r1q3jkUceAUTg/9d//RednZ18+tOfHtH+E5/4BD09PVx11VVq640wFjuD2GffffclSZLN6l/t3GIstp45cyYnn3zyCCEK0NHRwcc+9jEAfvGLX4x4T20tjHVMb0yIAqXQf/LJJ0e8rnYWxmrngieffJIvfOELLFq0iH322WeT/audFWV8UDGqKK9QVq9eDcCOO+446r2enh56enpYsWIFTz/9dPn63XffDcAhhxwy6jPFa8MnoC/Ufr/99mPChAmjJqyvBAohFEURICvoq1at4i1vecuoUNxarcZBBx3EypUrR0wq1dYvzvPt/FJQO28eY7H1ptqqrV+csdj5pz/9KSDiZzhq5xdnU3Z2zvGxj32MHXbYYdTC4fNROyvK+BC/eBNFUV6ObLfddgA888wzo95bv349vb29gKzm7rzzzoCIqs7OzlH5SQBz5swp2xQUf8+ePXtUe2MMs2fP5v7772dwcHCUh+XlyooVK/j5z3/OtGnT2HPPPYEXtgOMtN3wv9XWm2Zjdn4pqJ1fnLHa+qqrrgJGT7rV1i/Mi9l58eLF/O53v6O/v58HHniAu+++m3322YePf/zjI9qpnV+YF7LzJZdcwtKlS7n55ps3Gr47HLWzoowPKkYV5RXK/vvvT3d3N0uWLOGBBx7g9a9/ffneueeeW/69fv368u++vj6mTJmy0f66urrKNsPbAyNCmDb1mVfCj2+apnzkIx+h0Whw9tlnl6vuY7FDgdp602zKzi8FtfMLM1ZbX3HFFdxyyy3Mnz9/RCgiqK1fiM2x87e//e0RnrNDDjmEyy67jJ6enhHt1M6b5oXs/Pjjj3PuuedyyimnsP/++79oX2pnRRkfNExXUV6hdHZ2cs4555CmKQsWLODv/u7vOPPMM1mwYAFXXHEFu+22G/CnhUC+mijCu+655x7e97738d73vndbn9IrErXz+DFWW//kJz/hU5/6FDvssAOXX375OJ3ly5/NtfOSJUvo7e3liSee4Oqrr2blypW87W1v46GHHhrnM3558kJ2ds7x0Y9+lOnTp3PmmWduw7NUFOX5qBhVlFcwJ510Et/73vd485vfzI9//GO++c1vEkUR119/fRmaO3ny5LJ9d3f3JvfE3LBhQ9lmeHsY6V3d2GeKFeGXK957Tj/9dK655hre8573jKq6uLl2eL7t1NYjeTE7vxTUzhtnrLa+7bbbOOmkk5g6dSo33ngj06dPH9VGbT2alzKmJ0+ezF/+5V/y/e9/nzVr1rBo0aIR76udR/Nidr700kv51a9+xYUXXrjZXkm1s6KMDypGFeUVzjve8Q5+9KMf8fvf/55Vq1Zx0003ceCBB7J8+XKstSPCd+fMmUN/fz/PPffcqH6KfJgiV2b438+v9ggyOXjyySeZMWPGqKI+Lyecc3z84x/nqquu4t3vfjdf//rXsXbko/OF7ACbtp3ausXm2PmloHYezVhtfeutt3LCCScwefJkbrzxRnbaaaeNtlNbj+RPHdPbb789u+22G7/+9a8ZHBwsX1c7j2Rz7Pzggw/ivefII48sC/j19PRw5JFHAvCf//mf9PT08Dd/8zflZ9TOijI+qBhVlFch9957L7/73e/4i7/4ixH5LXPnzgXg9ttvH/WZ4rWiDcBb3/rWTba/7777WL9+/Yj2Lzecc5x22mksXryYY489lssuu2yjYc1z5sxhxowZLF26lIGBgRHv1et17rnnHmbMmDGisIXausXm2vmloHYeyVhtXQjRnp4ebrzxxk0W6QK19XC21Jh+7rnnMMaM+KzaucXm2nnu3LmceOKJo/4Vec+77bYbJ554IgcffPCIz4DaWVG2NipGFeUVzMZCjFatWsXpp59OHMecccYZI9474YQTiOOYr3zlKyNCjZYvX853v/tddt555xEbdu+yyy4cdNBB3HXXXeU2BCBFJM455xxAQoVfjhSr7YsXL2bhwoVcfvnlm5xMGmM48cQT6e/v50tf+tKI97761a/S29vLiSeeiDGmfF1tLYzFzi8FtXOLsdr6+UJ0uBdoY6ithbHYee3atSxfvnzU6957zjvvPFavXs28efNGVH5VOwtjsfPf/u3fctFFF436d9pppwEiKi+66CI+/OEPl59ROyvK+GB6e3v9izdTFOXPhSuvvJJf/vKXADzyyCM88MADHHDAAWUO6OGHH84RRxwBwL/+679yzTXXcMABBzBlyhR+//vfc9NNNzE4OMhFF100IiSp4Mtf/jLnnHMO22+/PUcffTSDg4Nce+21DA0Nce2114748QV49NFHWbBgAfV6nYULFzJjxgxuvfVWHn74YU466SQuvPDCrWyRrcN5553H+eefT2dnJ6eccspGJzmHH354uWn6wMAAhx12GA8++CAHH3ww++67Lw899BC33HILe++9NzfffPOo8Cy19djt/Nvf/rbMB6vX61x33XVMnTqVQw89FJB8u2LiV6B2FsZi69/+9rfMmzePRqPBcccdxy677DKq7axZszjhhBNGvKa2Hpudf/Ob3zB//nz2228/dt99d6ZNm8aaNWv45S9/yWOPPca0adP40Y9+xK677jri82rnsT87NsZdd93FkUceyQc+8IGN5vOqnRVl66NiVFFeZpx66ql85zvf2eT7n/nMZ/jHf/xHAO644w6+9rWv8fDDD9Pb28ukSZOYO3cuixYtGpEr+nyuueYavv71r/Poo4+SJAn7778/Z5xxBm984xs32v7xxx/ni1/8InfddRcDAwPMnj2b97///Xz4wx/eInl/24IXszPAxRdfPGIyvn79es4//3xuuOEGnnvuOaZNm8ZRRx3FZz7zmU2W+3+123qsdi4mj5tihx124MEHHxz1+qvdzjA2W7+YnUG8SUuWLBn1+qvd1mOxc29vLxdeeCF33303Tz31FOvWraNWqzF79mwWLFjARz/6USZNmrTRPtTOY39GP58XE6OgdlaUrY2KUUVRFEVRFEVRFGXc0SUaRVEURVEURVEUZdxRMaooiqIoiqIoiqKMOypGFUVRFEVRFEVRlHFHxaiiKIqiKIqiKIoy7qgYVRRFURRFURRFUcYdFaOKoiiKoiiKoijKuKNiVFEURVEURVEURRl3VIwqiqIoiqIoiqIo446KUUVRFEVRFEVRFGXcUTGqKIqiKMAzzzxDT08PPT092/pUFEVRFOVVQbytT0BRFEVRthQvVUhefPHFvPWtb92yJ6MoiqIoyguiYlRRFEV5xXDAAQds9PV7770XgDlz5jBlypRR70+dOpUkSdh111236vkpiqIoitLC9Pb2+m19EoqiKIqyNSk8phdffDEnnHDCtj0ZRVEURVEAzRlVFEVRFEVRFEVRtgEqRhVFURSFFy5gdPjhh9PT08PixYt59tlnWbRoEa973euYPn06b37zm7nooovwXgKNms0mF1xwAQcccAAzZsxg11135fTTT2ft2rWbPLZzjquvvppjjjmmDCV+7Wtfy8knn8wDDzywtb6yoiiKomxTVIwqiqIoymayYsUK3va2t/Hd736XKVOmMHnyZB577DHOOussPvvZz9JoNFi4cCFnn3023nt22GEH1qxZw5VXXsnRRx9Nmqaj+tywYQPHHnssH/nIR/jZz35GHMe89rWvZWBggGuvvZZDDz2Ua665Zht8W0VRFEXZuqgYVRRFUZTN5Ctf+QpvetObePTRR7njjjt4+OGHueiiiwD4xje+wcknn8wf/vAH7r33XpYuXcp///d/c9ttt9Hd3c2DDz7Id77znVF9nn766fz85z9nn3324Wc/+xn/+7//y5133snTTz/Neeedh3OO0047jccee2y8v66iKIqibFVUjCqKoijKZjJx4kQuu+wyJk6cWL524okn8sY3vhHnHEuWLOHSSy9l9913L99/wxvewPve9z4AfvKTn4zo77777uO6665j4sSJXH311bzhDW8o37PWcuqpp/KhD32IRqPBJZdcspW/naIoiqKMLypGFUVRFGUzOe644+js7Bz1+r777gvAXnvtxX777Tfq/UJkPvXUUyNe/+EPfwjAYYcdxowZMzZ6zKOOOgqAO++886WetqIoiqL8WaL7jCqKoijKZjJ79uyNvr7ddttt1vsDAwMjXn/ooYcAuPvuuznssMM2+tl6vQ7AypUrx37CiqIoivJnjIpRRVEURdlM2tvbN/q6MWaz3nfOjXi9t7cXkMJIK1aseMFjDw0NjeVUFUVRFOXPHhWjiqIoirKN6OjoAOC8887j1FNP3cZnoyiKoijji+aMKoqiKMo24nWvex0AS5cu3cZnoiiKoijjj4pRRVEURdlGHHPMMQAsWbKERx55ZBufjaIoiqKMLypGFUVRFGUbceCBB7Jw4ULSNOW4447jpptuwns/os0zzzzDhRdeyJVXXrmNzlJRFEVRtg6aM6ooiqIo25BLLrmERqPBTTfdxPHHH8/EiRPZeeedcc6xcuVKVq9eDcBnPvOZbXymiqIoirJlUTGqKIqiKNuQ9vZ2vv3tb3PzzTezePFi7rvvPh566CE6OjqYMWMG8+fP553vfCfveMc7tvWpKoqiKMoWxfT29voXb6YoiqIoiqIoiqIoWw7NGVUURVEURVEURVHGHRWjiqIoiqIoiqIoyrijYlRRFEVRFEVRFEUZd1SMKoqiKIqiKIqiKOOOilFFURRFURRFURRl3FExqiiKoiiKoiiKoow7KkYVRVEURVEURVGUcUfFqKIoiqIoiqIoijLuqBhVFEVRFEVRFEVRxh0Vo4qiKIqiKIqiKMq4o2JUURRFURRFURRFGXdUjCqKoiiKoiiKoijjjopRRVEURVEURVEUZdz5//v2Iep8l+aaAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "axes = sb.scatterplot(\n", - " x = \"date\",\n", - " y = 'amount',\n", - " hue = 'activity_name',\n", - " data = df.merge(\n", - " df2.rename(columns={'id' : 'activity'}), on='activity'\n", - " )\n", - ")\n", - "axes.set_ylabel(\"$CO_{2}$ emissions (kg)\")\n", - "axes.set_xlabel(\"Time\")\n", - "axes.set_title(\"C02 emissions over time_ FU : 1 kWh produced by electricity mix\") #right after being put in service" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/Edge replacer.ipynb b/archive/Edge replacer.ipynb deleted file mode 100644 index 785b2d3..0000000 --- a/archive/Edge replacer.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "149d78d6-5f31-489e-9774-c5523781fb2d", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Union, Tuple, Optional\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw_processing as bwp\n", - "import uuid\n", - "import logging\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "0f0d3203-2553-48b3-a79f-4859cf302314", - "metadata": {}, - "source": [ - "Change level to `logging.DEBUG` to print too much, `logging.WARNING` to print less" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "df572256-572d-4d0f-9f5d-80d70e092a67", - "metadata": {}, - "outputs": [], - "source": [ - "logging.basicConfig(level=logging.WARNING)\n", - "logger = logging.getLogger('shaving-club')\n", - "logger.setLevel(level=logging.INFO)" - ] - }, - { - "cell_type": "markdown", - "id": "f3205443-21ca-414d-b369-8a66a2a1d8aa", - "metadata": {}, - "source": [ - "## Moving an edge to another producing node safely\n", - "\n", - "We use datapackages so that the underlying database isn't modified." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c76bdade-a82e-4e38-9cbf-8d704517447b", - "metadata": {}, - "outputs": [], - "source": [ - "def safety_razor(\n", - " consumer: Union[bd.Node, Tuple[str, str], int], \n", - " previous_producer: Union[bd.Node, Tuple[str, str], int], \n", - " new_producer: Union[bd.Node, Tuple[str, str], int], \n", - " datapackage: Optional[bwp.Datapackage] = None,\n", - " amount: Optional[float] = None,\n", - " name: Optional[str] = None,\n", - " ) -> bwp.Datapackage:\n", - " \"\"\"Replace an existing edge with another edge. Zeroes out the existing edge.\n", - "\n", - " Inputs:\n", - " consumer: Union[bd.Node, Tuple[str, str], int]\n", - " The consuming node \n", - " previous_producer: Union[bd.Node, Tuple[str, str], int]\n", - " The producing node which should be replaced\n", - " new_producer: Union[bd.Node, Tuple[str, str], int]\n", - " The new producing node\n", - " datapackage: Optional[bwp.Datapackage]\n", - " Append to this datapackage, if available. Otherwise create a new datapackage.\n", - " amount: Optional[float]\n", - " Amount of the new edge. Will be the *sum of all (previous_producer, consumer) edge amounts if not provided.\n", - " name: Optional[str]\n", - " Name of this datapackage resource.\n", - " \n", - " Returns a `bw_processing.Datapackage` with the modified data.\"\"\"\n", - "\n", - " def resolve_node(node: Union[bd.Node, Tuple[str, str], int]) -> bd.Node:\n", - " \"\"\"Return a Brightway node from many different input possibilities.\n", - " \n", - " This isn't super-efficient - you could look up the `id` values ahead of time.\n", - " In production you don't need fancy logging messages.\"\"\"\n", - " if isinstance(node, tuple):\n", - " assert len(node) == 2\n", - " return bd.get_node(database=node[0], code=node[1])\n", - " elif isinstance(node, int):\n", - " return bd.get_node(id=int)\n", - " elif isinstance(node, bd.Node):\n", - " return node\n", - " else:\n", - " raise ValueError(f\"Can't understand {node}\")\n", - " \n", - " consumer = resolve_node(consumer)\n", - " previous_producer = resolve_node(previous_producer)\n", - " new_producer = resolve_node(new_producer)\n", - "\n", - " assert new_producer.get(\"type\", \"process\") == \"process\", \"Wrong type of edge source\"\n", - " # Remove if creating new edge instead of moving or replacing existing an edge\n", - " assert any(exc.input == previous_producer for exc in consumer.technosphere())\n", - "\n", - " if not name:\n", - " name = uuid.uuid4().hex\n", - " logger.info(f\"Using random name {name}\")\n", - "\n", - " if not amount:\n", - " amount = sum(\n", - " exc['amount'] \n", - " for exc in consumer.technosphere() \n", - " if exc.input == previous_producer\n", - " )\n", - " logger.info(f\"Using database net amount {amount}\")\n", - "\n", - " logger.info(f\"Zeroing exchange from {previous_producer} to {consumer}\")\n", - " logger.info(f\"Adding exchange of {amount} {new_producer} to {consumer}\")\n", - "\n", - " if datapackage is None:\n", - " datapackage = bwp.create_datapackage()\n", - "\n", - " datapackage.add_persistent_vector(\n", - " # This function would need to be adapted for biosphere edges\n", - " matrix=\"technosphere_matrix\",\n", - " name=name,\n", - " data_array=np.array([0, amount], dtype=float),\n", - " indices_array=np.array([\n", - " (previous_producer.id, consumer.id), \n", - " (new_producer.id, consumer.id)\n", - " ], dtype=bwp.INDICES_DTYPE),\n", - " flip_array=np.array([False, True], dtype=bool)\n", - " ) \n", - " return datapackage" - ] - }, - { - "cell_type": "markdown", - "id": "590e11ba-44d8-4d74-b997-716656a76e3e", - "metadata": {}, - "source": [ - "## Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a8094cc-21f2-44d6-b705-cf70d35c8c70", - "metadata": {}, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2analyzer as ba" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "359fab53-08a9-4f3a-84ee-21b3b2320740", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restoring project backup archive - this could take a few minutes...\n" - ] - }, - { - "data": { - "text/plain": [ - "'🪒'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bi.restore_project_directory(\n", - " fp=\"/srv/data/ecoinvent-3.9-cutoff.tar.gz\", \n", - " project_name=\"🪒\", # Some silliness late at night :)\n", - " overwrite_existing=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a4fcae4e-014b-419b-b8b9-ae7e5c64fe2f", - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current(\"🪒\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0e659a23-55ec-41aa-96a5-0bd405be3976", - "metadata": {}, - "outputs": [], - "source": [ - "pear_market = bd.get_node(name=\"market for pear\")\n", - "pear_china = bd.get_node(name=\"pear production\", location=\"CN\")\n", - "apple = bd.get_node(name=\"apple production\", location=\"CL\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a7e69539-dcb0-494c-8e5a-25f55fb3cb4e", - "metadata": {}, - "outputs": [], - "source": [ - "ipcc = ('IPCC 2021', 'climate change: fossil', 'global warming potential (GWP100)')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7e25083f-caac-46c7-865d-007053f3d574", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {pear_market: 1}" - ] - }, - { - "cell_type": "markdown", - "id": "ff85461a-d221-4470-a0cc-9fff05d544f1", - "metadata": {}, - "source": [ - "Get the list of `data_objs` - our new datapackage will be appended to this list." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "659b635a-3e20-4160-aa87-df36f7d2262a", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = bd.prepare_lca_inputs(demand=demand, method=ipcc)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "aed4db60-3f0c-4684-9895-95c875830366", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pear: 0.44607599419190447\n" - ] - } - ], - "source": [ - "lca = bc.LCA(fu, data_objs=data_objs, remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "print(\"Pear:\", lca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7604862a-70c8-441b-b8d9-6a57cf53363f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:shaving-club:Using random name 054616497092409c876e1c722aeb7bef\n", - "INFO:shaving-club:Using database net amount 0.693363752738162\n", - "INFO:shaving-club:Zeroing exchange from 'pear production' (kilogram, CN, None) to 'market for pear' (kilogram, GLO, None)\n", - "INFO:shaving-club:Adding exchange of 0.693363752738162 'apple production' (kilogram, CL, None) to 'market for pear' (kilogram, GLO, None)\n" - ] - } - ], - "source": [ - "dp = safety_razor(\n", - " consumer=pear_market,\n", - " previous_producer=pear_china, \n", - " new_producer=apple, \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4468e7eb-3cc0-42bb-a9f4-d38a3576c932", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Apples are not pears: 0.25399614413937016\n" - ] - } - ], - "source": [ - "lca = bc.LCA(fu, data_objs=data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "print(\"Apples are not pears:\", lca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28bf9392-9460-4549-9b9f-0c9fcf529afb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Mini-Wind-Example/Fly ash usage model.ipynb b/archive/Mini-Wind-Example/Fly ash usage model.ipynb deleted file mode 100644 index 6972c99..0000000 --- a/archive/Mini-Wind-Example/Fly ash usage model.ipynb +++ /dev/null @@ -1,544 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "41bb8e52-2de7-445c-a4c9-95e155bdf67c", - "metadata": {}, - "source": [ - "# Fly ash usage model\n", - "\n", - "\n", - "\n", - "We want to change the fraction of primary versus recycle steel production over time, and have the fraction of fly ash flowing into concrete production change at the same time.\n", - "\n", - "To accomplish this, we need to create a new steel process (in a new database), which will be a mix of different steel production types." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a60a3876-a927-4011-9efd-5022ba4815ed", - "metadata": {}, - "outputs": [], - "source": [ - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "import bw_processing as bwp\n", - "from dataclasses import dataclass\n", - "from typing import Callable\n", - "import logging\n", - "import numpy as np\n", - "import seaborn as sb\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "69e406c9-e6e1-4230-919f-ff57b9c04d60", - "metadata": {}, - "source": [ - "Change level to `logging.DEBUG` to print too much, `logging.WARNING` to print less" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9c85f037-3a08-44b4-8657-44840ee3eaea", - "metadata": {}, - "outputs": [], - "source": [ - "logging.basicConfig(level=logging.WARNING)\n", - "logger = logging.getLogger('flexible-chains')\n", - "logger.setLevel(level=logging.INFO)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2d0c18a7-cddd-4fc0-b54a-27cd3ba50615", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restoring project backup archive - this could take a few minutes...\n" - ] - }, - { - "data": { - "text/plain": [ - "'♻️💥🔥🔗'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bi.restore_project_directory(\n", - " fp=\"/srv/data/ecoinvent-3.9-cutoff.tar.gz\", \n", - " project_name=\"♻️💥🔥🔗\",\n", - " overwrite_existing=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "23eeb9f1-1d5a-477c-863a-4f906072e8d0", - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current(\"♻️💥🔥🔗\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c857d1da-2d07-4f8b-8fa9-a24cccbf0148", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclass\n", - "class Constants:\n", - " steel_market: bd.Node\n", - " cement_market: bd.Node\n", - " slag_process: bd.Node\n", - " mass_fly_ash_per_kilogram_steel: float\n", - " fraction_fly_ash_to_this_cement_market: float\n", - " converter_filter_function: Callable\n", - " electric_filter_function: Callable\n", - " clinker_filter_function: Callable" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "70d82e07-7eec-4e71-83ef-4f0f5d9284c2", - "metadata": {}, - "outputs": [], - "source": [ - "my_configuration = Constants(\n", - " steel_market = bd.get_node(\n", - " name=\"market for steel, low-alloyed\", \n", - " location=\"GLO\", \n", - " database=\"ecoinvent-3.9-cutoff\"\n", - " ),\n", - " cement_market = bd.get_node(\n", - " name=\"cement production, CEM II/A\", \n", - " location=\"CH\", \n", - " database=\"ecoinvent-3.9-cutoff\"\n", - " ),\n", - " slag_process = bd.get_node(\n", - " name=\"ground granulated blast furnace slag production\", \n", - " location=\"RoW\", \n", - " database=\"ecoinvent-3.9-cutoff\"\n", - " ),\n", - " mass_fly_ash_per_kilogram_steel = 0.0025, # Fix, total guess\n", - " fraction_fly_ash_to_this_cement_market = 0.5, # Configurable\n", - " converter_filter_function = lambda x: \"converter\" in x['name'].lower() and \"steel\" in x['name'].lower(),\n", - " electric_filter_function = lambda x: \"electric\" in x['name'].lower() and \"steel\" in x['name'].lower(),\n", - " clinker_filter_function = lambda x: 'clinker' in x['name'].lower()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5497227f-fead-4752-8545-b6fcd04d726b", - "metadata": {}, - "outputs": [], - "source": [ - "def get_exchange_production_volume(exchange: bd.Edge) -> float:\n", - " try:\n", - " return exchange.input.rp_exchange()['production volume']\n", - " except:\n", - " return 0." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4e11ee1e-0c09-42d0-8af4-710b0c36b6fe", - "metadata": {}, - "outputs": [], - "source": [ - "class BlastOff:\n", - " def __init__(self, configuration):\n", - " self.config = configuration\n", - "\n", - " def generate_datapackage(\n", - " self, \n", - " fraction_blast_oven_steel: float, \n", - " ) -> bwp.Datapackage:\n", - " \"\"\"Generate a datapackage that will change matrix values based on the given configuration.\n", - " \n", - " Inputs:\n", - " fraction_blast_oven_steel: float\n", - " The fraction of steel which is made from the blast oven (between 0 and 1)\n", - " \"\"\"\n", - " dp = bwp.create_datapackage()\n", - "\n", - " assert 0 <= fraction_blast_oven_steel <= 1\n", - " mass_fly_ash = self.calculate_mass_fly_ash(\n", - " fraction_blast_oven_steel, \n", - " )\n", - " self.adjust_steel_market(fraction_blast_oven_steel, dp)\n", - " self.adjust_cement_market(mass_fly_ash * self.config.fraction_fly_ash_to_this_cement_market, dp)\n", - " return dp\n", - "\n", - " def adjust_cement_market(self, mass_fly_ash: float, dp: bwp.Datapackage) -> None:\n", - " \"\"\"Change the relative fraction of all players in `self.config.cement_market`.\n", - "\n", - " Adds a resource group to `self.dp`.\"\"\"\n", - " exchanges = [{\n", - " 'indices': (exc.input.id, exc.output.id),\n", - " 'input': exc.input,\n", - " 'original': exc['amount'],\n", - " 'pv': get_exchange_production_volume(exc)\n", - " } for exc in self.config.cement_market.technosphere()\n", - " if self.config.clinker_filter_function(exc.input)]\n", - " total_clinker_fraction = sum(o['original'] for o in exchanges)\n", - " total_clinker_pv = sum(o['pv'] for o in exchanges)\n", - " fly_ash_fraction = mass_fly_ash / (total_clinker_pv / total_clinker_fraction)\n", - " assert 0.25 < total_clinker_fraction <= 1\n", - "\n", - " logger.info(f\"Fly ash mass coming into cement market: {mass_fly_ash}\")\n", - " logger.info(f\"Total clinker fraction before modification: {total_clinker_fraction}\")\n", - " logger.info(f\"Total clinker PV (kg) before modification: {total_clinker_pv}\")\n", - "\n", - " # Fly ash could already be present. There are also other binders that we\n", - " # don't touch. Can't just multiply, need to be careful\n", - " existing_fly_ash_fraction = sum([\n", - " (exc['amount'] if exc.input == self.config.slag_process else 0)\n", - " for exc in self.config.cement_market.technosphere()\n", - " ])\n", - "\n", - " reduction_in_clinker = fly_ash_fraction - existing_fly_ash_fraction\n", - " clinker_multiplier = (total_clinker_fraction - reduction_in_clinker) / total_clinker_fraction\n", - " if clinker_multiplier < 0:\n", - " clinker_multiplier = 0\n", - "\n", - " max_possible_fly_ash = total_clinker_fraction + existing_fly_ash_fraction\n", - " if fly_ash_fraction > max_possible_fly_ash:\n", - " fly_ash_fraction = max_possible_fly_ash\n", - "\n", - " logger.info(f\"Total fly ash fraction before modification: {existing_fly_ash_fraction}\")\n", - " logger.info(f\"New fly ash fraction: {fly_ash_fraction}\")\n", - " logger.info(f\"Clinker multiplier: {clinker_multiplier}\")\n", - "\n", - " data, indices = [fly_ash_fraction], [(self.config.slag_process.id, self.config.cement_market.id)]\n", - " for exc in exchanges:\n", - " data.append(exc['original'] * clinker_multiplier)\n", - " logger.info(f\"Changing input of {exc['input']} from {exc['original']} to {exc['original'] * clinker_multiplier}\")\n", - " indices.append(exc['indices'])\n", - "\n", - " dp.add_persistent_vector(\n", - " matrix=\"technosphere_matrix\",\n", - " data_array=np.array(data),\n", - " name=\"cement market clinker substitution\",\n", - " indices_array=np.array(indices, dtype=bwp.INDICES_DTYPE),\n", - " flip_array=np.ones(len(data), dtype=bool)\n", - " ) \n", - " \n", - " def adjust_steel_market(self, fraction_blast_oven_steel: float, dp: bwp.Datapackage) -> None:\n", - " \"\"\"Change the relative fraction of all players in `self.config.steel_market`.\n", - " \n", - " Adds a resource group to `self.dp`.\"\"\"\n", - " exchanges = [{\n", - " 'indices': (exc.input.id, exc.output.id),\n", - " 'input': exc.input,\n", - " 'original': exc['amount'],\n", - " 'converter': self.config.converter_filter_function(exc.input),\n", - " 'electric': self.config.electric_filter_function(exc.input)\n", - " } for exc in self.config.steel_market.technosphere()]\n", - " total_electric = sum(o['original'] for o in exchanges if o['electric'])\n", - " total_converter = sum(o['original'] for o in exchanges if o['converter'])\n", - " logger.info(\"Original fraction blast furnace: {}\".format(total_converter / (total_converter + total_electric)))\n", - " logger.info(f\"Changing to {fraction_blast_oven_steel}\")\n", - "\n", - " multiplier_electric = (\n", - " (1 - fraction_blast_oven_steel) \n", - " / (total_electric / (total_converter + total_electric))\n", - " )\n", - " multiplier_converter = (\n", - " fraction_blast_oven_steel / \n", - " (total_converter / (total_converter + total_electric))\n", - " )\n", - "\n", - " logger.info(f\"Electric share conversion multiplier: {multiplier_electric}\")\n", - " logger.info(f\"Converter share conversion multiplier: {multiplier_converter}\")\n", - " \n", - " data, indices = [], []\n", - " for exc in exchanges:\n", - " if exc['converter']:\n", - " data.append(exc['original'] * multiplier_converter)\n", - " logger.info(f\"Changing input of {exc['input']} from {exc['original']} to {exc['original'] * multiplier_converter}\")\n", - " indices.append(exc['indices'])\n", - " elif exc['electric']:\n", - " data.append(exc['original'] * multiplier_electric)\n", - " logger.info(f\"Changing input of {exc['input']} from {exc['original']} to {exc['original'] * multiplier_electric}\")\n", - " indices.append(exc['indices'])\n", - "\n", - " dp.add_persistent_vector(\n", - " matrix=\"technosphere_matrix\",\n", - " data_array=np.array(data),\n", - " name=\"steel production technology shares\",\n", - " indices_array=np.array(indices, dtype=bwp.INDICES_DTYPE),\n", - " flip_array=np.ones(len(data), dtype=bool)\n", - " ) \n", - " \n", - " def calculate_mass_fly_ash(\n", - " self,\n", - " fraction_blast_oven_steel: float, \n", - " ) -> float:\n", - " total_steel_market_pv = sum(\n", - " get_exchange_production_volume(exc) \n", - " for exc in self.config.steel_market.technosphere()\n", - " if (\n", - " self.config.converter_filter_function(exc.input)\n", - " or self.config.electric_filter_function(exc.input)\n", - " )\n", - " )\n", - " logger.info(f\"Calculated total steel PV: {total_steel_market_pv}\")\n", - " mass_fly_ash = (self.config.mass_fly_ash_per_kilogram_steel \n", - " * fraction_blast_oven_steel \n", - " * total_steel_market_pv)\n", - " \n", - " logger.info(f\"Calculated fly ash mass (kg): {mass_fly_ash}\")\n", - " return mass_fly_ash" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ff8d7d1b-b50c-429c-ad08-430577f0ba58", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:flexible-chains:Calculated total steel PV: 153086538181.8209\n", - "INFO:flexible-chains:Calculated fly ash mass (kg): 191358172.72727612\n", - "INFO:flexible-chains:Original fraction blast furnace: 0.7421076548969351\n", - "INFO:flexible-chains:Changing to 0.5\n", - "INFO:flexible-chains:Electric share conversion multiplier: 1.938793490749709\n", - "INFO:flexible-chains:Converter share conversion multiplier: 0.6737566937905265\n", - "INFO:flexible-chains:Changing input of 'steel production, converter, low-alloyed' (kilogram, IN, None) from 0.0269397499900407 to 0.018150836884833193\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, CA-QC, None) from 0.000562259367102862 to 0.0010901048010520799\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, IN, None) from 0.0325097701964945 to 0.06302973084273243\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, CH, None) from 0.0098250242381601 to 0.01904869303940292\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, AT, None) from 1.13898549520319e-05 to 2.2082576641582787e-05\n", - "INFO:flexible-chains:Changing input of 'steel production, converter, low-alloyed' (kilogram, RER, None) from 0.124966788041178 to 0.0841972099442456\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, RoW, None) from 0.141748113466333 to 0.2748203197145776\n", - "INFO:flexible-chains:Changing input of 'steel production, converter, low-alloyed' (kilogram, RoW, None) from 0.590201116865716 to 0.39765195317092095\n", - "INFO:flexible-chains:Changing input of 'steel production, electric, low-alloyed' (kilogram, Europe without Switzerland and Austria, None) from 0.0732357879800223 to 0.14198906902559302\n", - "INFO:flexible-chains:Fly ash mass coming into cement market: 95679086.36363806\n", - "INFO:flexible-chains:Total clinker fraction before modification: 0.788\n", - "INFO:flexible-chains:Total clinker PV (kg) before modification: 3100000000.0\n", - "INFO:flexible-chains:Total fly ash fraction before modification: 0\n", - "INFO:flexible-chains:New fly ash fraction: 0.024321006469208643\n", - "INFO:flexible-chains:Clinker multiplier: 0.9691357785923748\n", - "INFO:flexible-chains:Changing input of 'clinker production' (kilogram, CH, None) from 0.788 to 0.7636789935307914\n" - ] - } - ], - "source": [ - "bo = BlastOff(my_configuration)\n", - "dp = bo.generate_datapackage(0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "8bc117de-1049-4ad3-a6c0-3fb6d8bd84e7", - "metadata": {}, - "source": [ - "Look at composite beam" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "94a6d887-0c57-4b18-aa14-de15d8dc83e6", - "metadata": {}, - "outputs": [], - "source": [ - "logger.setLevel(level=logging.WARNING)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "830be10d-f67d-48c5-9a30-1707e36ab0ee", - "metadata": {}, - "outputs": [], - "source": [ - "ipcc = ('IPCC 2021', 'climate change: fossil', 'global warming potential (GWP100)')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1e987a32-58cc-469d-9cce-933fb85bb2f6", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {\n", - " my_configuration.steel_market: 20, \n", - " my_configuration.cement_market: 100\n", - "} " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2306402a-76f4-4339-854b-51658ebf3c42", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = bd.prepare_lca_inputs(demand=demand, method=ipcc)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1a060db2-4f9f-47b2-bf7a-0a8680e534ab", - "metadata": {}, - "outputs": [], - "source": [ - "fractions = np.linspace(0.05, 0.95, 10)\n", - "scores = []\n", - "\n", - "for fraction in fractions:\n", - " # Can't reuse object because matrix changes each time\n", - " lca = bc.LCA(fu, data_objs = data_objs + [bo.generate_datapackage(fraction)], remapping_dicts=remapping)\n", - " lca.lci()\n", - " lca.lcia() \n", - " scores.append(lca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fb0026bf-a483-4769-8caf-570693df255c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sb.set_theme()\n", - "sb.scatterplot(x=fractions, y=scores)\n", - "plt.xlabel(\"Fraction of primary (blast oven) steel in market\")\n", - "plt.ylabel(r\"GWP 100 $kg\\ CO_{2}-eq.$\")\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "id": "d6313d21-de6e-4273-9831-346121be5bdb", - "metadata": {}, - "source": [ - "Make some changes to see differences just for cement with free fly ash" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "0103a183-0979-4ade-a5c9-866a919b4d2b", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {\n", - " my_configuration.cement_market: 100\n", - "} " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "67b36ce3-0383-4e7e-8caa-ba27bc55a847", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = bd.prepare_lca_inputs(demand=demand, method=ipcc)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c4be7317-045b-4796-bbdb-1dbed6532da8", - "metadata": {}, - "outputs": [], - "source": [ - "fractions = np.linspace(0.05, 0.95, 10)\n", - "scores = []\n", - "\n", - "for fraction in fractions:\n", - " # Can't reuse object because matrix changes each time\n", - " lca = bc.LCA(fu, data_objs = data_objs + [bo.generate_datapackage(fraction)], remapping_dicts=remapping)\n", - " lca.lci()\n", - " lca.lcia() \n", - " scores.append(lca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2e2f5d75-da4e-4d99-bd42-26d88ea2077d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sb.set_theme()\n", - "sb.scatterplot(x=fractions, y=scores)\n", - "plt.xlabel(\"Fraction of primary steel in market\")\n", - "plt.ylabel(r\"GWP 100 $kg\\ CO_{2}-eq.$\")\n", - "pass" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:autumn_school]", - "language": "python", - "name": "conda-env-autumn_school-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/Mini-Wind-Example/utilities.py b/archive/Mini-Wind-Example/utilities.py deleted file mode 100644 index 5745048..0000000 --- a/archive/Mini-Wind-Example/utilities.py +++ /dev/null @@ -1,229 +0,0 @@ -from datetime import datetime, timedelta -from typing import Union, Tuple, Optional -import bw2data as bd -import bw2calc as bc -import bw_processing as bwp -import uuid -import logging -import numpy as np -import warnings - - -def find_closest_date(target, dates): - """ - Find the closest date to the target in the dates list. - - :param target: Target datetime.datetime object. - :param dates: List of datetime.datetime objects. - :return: Closest datetime.datetime object from the list. - - --------------------- - # Example usage - target = datetime.strptime("2023-01-15", "%Y-%m-%d") - dates_list = [ - datetime.strptime("2020", "%Y"), - datetime.strptime("2022", "%Y"), - datetime.strptime("2025", "%Y"), - ] - - print(closest_date(target, dates_list)) - """ - - # If the list is empty, return None - if not dates: - return None - - # Sort the dates - dates = sorted(dates) - - # Use min function with a key based on the absolute difference between the target and each date - closest = min(dates, key=lambda date: abs(target - date)) - - return closest - -def safety_razor( - consumer: Union[bd.Node, Tuple[str, str], int], - previous_producer: Union[bd.Node, Tuple[str, str], int], - new_producer: Union[bd.Node, Tuple[str, str], int], - datapackage: Optional[bwp.Datapackage] = None, - amount: Optional[float] = None, - name: Optional[str] = None, - ) -> bwp.Datapackage: - """Replace an existing edge with another edge. Zeroes out the existing edge. - - Inputs: - consumer: Union[bd.Node, Tuple[str, str], int] - The consuming node - previous_producer: Union[bd.Node, Tuple[str, str], int] - The producing node which should be replaced - new_producer: Union[bd.Node, Tuple[str, str], int] - The new producing node - datapackage: Optional[bwp.Datapackage] - Append to this datapackage, if available. Otherwise create a new datapackage. - amount: Optional[float] - Amount of the new edge. Will be the *sum of all (previous_producer, consumer) edge amounts if not provided. - name: Optional[str] - Name of this datapackage resource. - - Returns a `bw_processing.Datapackage` with the modified data.""" - - def resolve_node(node: Union[bd.Node, Tuple[str, str], int]) -> bd.Node: - """Return a Brightway node from many different input possibilities. - - This isn't super-efficient - you could look up the `id` values ahead of time. - In production you don't need fancy logging messages.""" - if isinstance(node, tuple): - assert len(node) == 2 - return bd.get_node(database=node[0], code=node[1]) - elif isinstance(node, int): - return bd.get_node(id=int) - elif isinstance(node, bd.Node): - return node - else: - raise ValueError(f"Can't understand {node}") - - consumer = resolve_node(consumer) - previous_producer = resolve_node(previous_producer) - new_producer = resolve_node(new_producer) - - assert new_producer.get("type", "process") == "process", "Wrong type of edge source" - # Remove if creating new edge instead of moving or replacing existing an edge - assert any(exc.input == previous_producer for exc in consumer.technosphere()) - - if not name: - name = uuid.uuid4().hex - # logger.info(f"Using random name {name}") - - if not amount: - amount = sum( - exc['amount'] - for exc in consumer.technosphere() - if exc.input == previous_producer - ) - # logger.info(f"Using database net amount {amount}") - - # logger.info(f"Zeroing exchange from {previous_producer} to {consumer}") - # logger.info(f"Adding exchange of {amount} {new_producer} to {consumer}") - - if datapackage is None: - datapackage = bwp.create_datapackage() - - datapackage.add_persistent_vector( - # This function would need to be adapted for biosphere edges - matrix="technosphere_matrix", - name=name, - data_array=np.array([0, amount], dtype=float), - indices_array=np.array([ - (previous_producer.id, consumer.id), - (new_producer.id, consumer.id) - ], dtype=bwp.INDICES_DTYPE), - flip_array=np.array([False, True], dtype=bool) - ) - return datapackage - -def add_column_nearest_year_on_timeline(tl_df, dates_list): - """ - Add a column to a timeline with the year of the database, within the list of year of available databases, - that is the nearest to the date in the timeline. - - :param tl_df: Timeline as a dataframe. - :param dates_list: List of years of the available databases. - - :return: Timeline as a dataframe with a column 'nearest_year' (int64) added. - ------------------- - Example: - >>> dates_list = [ - datetime.strptime("2020", "%Y"), - datetime.strptime("2022", "%Y"), - datetime.strptime("2025", "%Y"), - ] - >>> add_column_nearest_year_on_timeline(tl_df, dates_list) - """ - if "date" not in list(tl_df.columns): - raise ValueError("The timeline does not contain dates.") - tl_df['nearest_year'] = tl_df['date'].apply(lambda x: find_closest_date(x, dates_list).year) - return tl_df - -def get_weights_for_interpolation_between_nearest_years(date, dates_list, interpolation_type="linear"): - """ - Find the nearest dates (before and after) a given date in a list of dates and calculate the interpolation weights. - - :param date: Target date. - :param dates_list: List of years of the available databases. - :param interpolation_type: Type of interpolation between the nearest lower and higher dates. For now, - only "linear" is available. - - :return: Dictionary with years of the available databases to use as keys and the weights for interpolation as values. - ------------------- - Example: - >>> dates_list = [ - datetime.strptime("2020", "%Y"), - datetime.strptime("2022", "%Y"), - datetime.strptime("2025", "%Y"), - ] - >>> date_test = datetime(2021,10,11) - >>> add_column_interpolation_weights_on_timeline(date_test, dates_list, interpolation_type="linear") - """ - dates_list = sorted (dates_list) - - diff_dates_list = [date - x for x in dates_list] - if timedelta(0) in diff_dates_list: - exact_match = dates_list[diff_dates_list.index(timedelta(0))] - return {exact_match.year: 1} - - closest_lower = min( - dates_list, - key=lambda x: abs(date - x) if (date - x) > timedelta(0) else timedelta.max - ) - closest_higher = min( - dates_list, - key=lambda x: abs(date - x) if (date - x) < timedelta(0) else timedelta.max - ) - - if closest_lower == closest_higher: - warnings.warn("Date outside the range of dates covered by the databases.", category=Warning) - return {closest_lower.year: 1} - - if interpolation_type == "linear": - weight = int((date - closest_lower).total_seconds())/int((closest_higher - closest_lower).total_seconds()) - else: - raise ValueError(f"Sorry, but {interpolation_type} interpolation is not available yet.") - return {closest_lower.year: weight, closest_higher.year: 1-weight} - -def add_column_interpolation_weights_on_timeline(tl_df, dates_list, interpolation_type="linear"): - """ - Add a column to a timeline with the weights for an interpolation between the two nearest dates, - from the list of dates from the available databases. - - :param tl_df: Timeline as a dataframe. - :param dates_list: List of years of the available databases. - :param interpolation_type: Type of interpolation between the nearest lower and higher dates. For now, - only "linear" is available. - - :return: Timeline as a dataframe with a column 'interpolation_weights' (object:dictionnary) added. - ------------------- - Example: - >>> dates_list = [ - datetime.strptime("2020", "%Y"), - datetime.strptime("2022", "%Y"), - datetime.strptime("2025", "%Y"), - ] - >>> add_column_interpolation_weights_on_timeline(tl_df, dates_list, interpolation_type="linear") - """ - if "date" not in list(tl_df.columns): - raise ValueError("The timeline does not contain dates.") - tl_df['interpolation_weights'] = tl_df['date'].apply(lambda x: get_weights_for_interpolation_between_nearest_years(x, dates_list, interpolation_type)) - return tl_df - -def extract_edge_from_row(row, database_dates_dict): - new_edges = [] - consumer = bd.get_node(id=row['consumer']) - previous_producer = bd.get_node(id=row['producer']) - - for date, share in row['interpolation_weights'].items(): - database = database_dates_dict[date] - new_amount = row['amount'] * share - new_producer = bd.get_activity((database, previous_producer['code'])) - new_edges.append((consumer, previous_producer, new_producer, new_amount)) - - return new_edges \ No newline at end of file diff --git a/archive/Mini-Wind-Example/wind-example-clean.ipynb b/archive/Mini-Wind-Example/wind-example-clean.ipynb deleted file mode 100644 index abe14a5..0000000 --- a/archive/Mini-Wind-Example/wind-example-clean.ipynb +++ /dev/null @@ -1,1338 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalisLCA\n", - "import bw_processing as bwp\n", - "from utilities import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set project and databases" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current('tictac')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 7 object(s):\n", - "\tbio\n", - "\twind-example\n", - "\twind-example-2020\n", - "\twind-example-2030\n", - "\twind-example-2035\n", - "\twind-example-2040\n", - "\twind-example-2050" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unitinterpolation_weights
02018-10-12 13:00:360.000167-1258Wind turbine constructionunitNaNNaN{2020: 1}
12018-10-12 13:00:360.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 1}
22018-10-12 13:00:360.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 1}
32019-10-12 18:49:480.000167-1258Wind turbine constructionunitNaNNaN{2020: 1}
42019-10-12 18:49:480.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 1}
52019-10-12 18:49:480.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 1}
62020-10-12 00:39:000.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 0.07802548133953828, 2030: 0.9219745186...
72020-10-12 00:39:000.000167-1258Wind turbine constructionunitNaNNaN{2020: 0.07802548133953828, 2030: 0.9219745186...
82020-10-12 00:39:000.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 0.07802548133953828, 2030: 0.9219745186...
92021-10-12 06:28:120.000167-1258Wind turbine constructionunitNaNNaN{2020: 0.17800974085226753, 2030: 0.8219902591...
102021-10-12 06:28:120.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 0.17800974085226753, 2030: 0.8219902591...
112021-10-12 06:28:120.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 0.17800974085226753, 2030: 0.8219902591...
122022-10-12 12:17:240.000167-1258Wind turbine constructionunitNaNNaN{2020: 0.27799400036499683, 2030: 0.7220059996...
132022-10-12 12:17:240.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 0.27799400036499683, 2030: 0.7220059996...
142022-10-12 12:17:240.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 0.27799400036499683, 2030: 0.7220059996...
152023-10-12 18:06:361.000000-1257Electricity production, windkilowatt hourNaNNaN{2020: 0.3779782598777261, 2030: 0.62202174012...
162023-10-12 18:06:360.000167-1258Wind turbine constructionunitNaNNaN{2020: 0.3779782598777261, 2030: 0.62202174012...
172023-10-12 18:06:360.000167-1255Electricity mixkilowatt hourNaNNaN{2020: 0.3779782598777261, 2030: 0.62202174012...
182023-10-12 18:06:360.000117-1256Electricity production, coalkilowatt hourNaNNaN{2020: 0.3779782598777261, 2030: 0.62202174012...
\n", - "" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 2018-10-12 13:00:36 0.000167 -1 258 \n", - "1 2018-10-12 13:00:36 0.000167 -1 255 \n", - "2 2018-10-12 13:00:36 0.000117 -1 256 \n", - "3 2019-10-12 18:49:48 0.000167 -1 258 \n", - "4 2019-10-12 18:49:48 0.000167 -1 255 \n", - "5 2019-10-12 18:49:48 0.000117 -1 256 \n", - "6 2020-10-12 00:39:00 0.000167 -1 255 \n", - "7 2020-10-12 00:39:00 0.000167 -1 258 \n", - "8 2020-10-12 00:39:00 0.000117 -1 256 \n", - "9 2021-10-12 06:28:12 0.000167 -1 258 \n", - "10 2021-10-12 06:28:12 0.000117 -1 256 \n", - "11 2021-10-12 06:28:12 0.000167 -1 255 \n", - "12 2022-10-12 12:17:24 0.000167 -1 258 \n", - "13 2022-10-12 12:17:24 0.000117 -1 256 \n", - "14 2022-10-12 12:17:24 0.000167 -1 255 \n", - "15 2023-10-12 18:06:36 1.000000 -1 257 \n", - "16 2023-10-12 18:06:36 0.000167 -1 258 \n", - "17 2023-10-12 18:06:36 0.000167 -1 255 \n", - "18 2023-10-12 18:06:36 0.000117 -1 256 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \\\n", - "0 Wind turbine construction unit NaN NaN \n", - "1 Electricity mix kilowatt hour NaN NaN \n", - "2 Electricity production, coal kilowatt hour NaN NaN \n", - "3 Wind turbine construction unit NaN NaN \n", - "4 Electricity mix kilowatt hour NaN NaN \n", - "5 Electricity production, coal kilowatt hour NaN NaN \n", - "6 Electricity mix kilowatt hour NaN NaN \n", - "7 Wind turbine construction unit NaN NaN \n", - "8 Electricity production, coal kilowatt hour NaN NaN \n", - "9 Wind turbine construction unit NaN NaN \n", - "10 Electricity production, coal kilowatt hour NaN NaN \n", - "11 Electricity mix kilowatt hour NaN NaN \n", - "12 Wind turbine construction unit NaN NaN \n", - "13 Electricity production, coal kilowatt hour NaN NaN \n", - "14 Electricity mix kilowatt hour NaN NaN \n", - "15 Electricity production, wind kilowatt hour NaN NaN \n", - "16 Wind turbine construction unit NaN NaN \n", - "17 Electricity mix kilowatt hour NaN NaN \n", - "18 Electricity production, coal kilowatt hour NaN NaN \n", - "\n", - " interpolation_weights \n", - "0 {2020: 1} \n", - "1 {2020: 1} \n", - "2 {2020: 1} \n", - "3 {2020: 1} \n", - "4 {2020: 1} \n", - "5 {2020: 1} \n", - "6 {2020: 0.07802548133953828, 2030: 0.9219745186... \n", - "7 {2020: 0.07802548133953828, 2030: 0.9219745186... \n", - "8 {2020: 0.07802548133953828, 2030: 0.9219745186... \n", - "9 {2020: 0.17800974085226753, 2030: 0.8219902591... \n", - "10 {2020: 0.17800974085226753, 2030: 0.8219902591... \n", - "11 {2020: 0.17800974085226753, 2030: 0.8219902591... \n", - "12 {2020: 0.27799400036499683, 2030: 0.7220059996... \n", - "13 {2020: 0.27799400036499683, 2030: 0.7220059996... \n", - "14 {2020: 0.27799400036499683, 2030: 0.7220059996... \n", - "15 {2020: 0.3779782598777261, 2030: 0.62202174012... \n", - "16 {2020: 0.3779782598777261, 2030: 0.62202174012... \n", - "17 {2020: 0.3779782598777261, 2030: 0.62202174012... \n", - "18 {2020: 0.3779782598777261, 2030: 0.62202174012... " - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tl_df = add_column_interpolation_weights_on_timeline(tl_df, dates_list)\n", - "tl_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing automated patching" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Creating a mock timeline\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# Number of rows in the DataFrame\n", - "n = 10\n", - "\n", - "# Generate random dates\n", - "start_date = pd.to_datetime('2020-01-01')\n", - "end_date = pd.to_datetime('2050-01-01')\n", - "date_range = (end_date - start_date).days\n", - "dates = [start_date + pd.to_timedelta(np.random.randint(0, date_range), unit='D') for _ in range(n)]\n", - "\n", - "# Generate random amounts, let's say between 10 and 1000\n", - "amounts = np.random.randint(10, 1000, size=n)\n", - "\n", - "# Generate random producer and consumer integers, let's say between 1 and 10\n", - "producers = np.random.randint(213, 230, size=n)\n", - "consumers = np.random.randint(213, 230, size=n)\n", - "\n", - "# Create DataFrame\n", - "df = pd.DataFrame({\n", - " 'date': dates,\n", - " 'amount': amounts,\n", - " 'producer': producers,\n", - " 'consumer': consumers\n", - "}).sort_values(by='date')" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = add_column_interpolation_weights_on_timeline(df, dates_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountproducerconsumerinterpolation_weights
82023-09-17774228222{2020: 0.37092800437996165, 2030: 0.6290719956...
72024-02-09144224219{2020: 0.4106214070626882, 2030: 0.58937859293...
62024-03-01237225216{2020: 0.41637010676156583, 2030: 0.5836298932...
42027-04-28898215215{2020: 0.7320010949904189, 2030: 0.26799890500...
12034-04-23660220214{2030: 0.4307228915662651, 2040: 0.56927710843...
52034-11-07171216223{2030: 0.48493975903614456, 2040: 0.5150602409...
92036-03-02229228215{2030: 0.6166484118291348, 2040: 0.38335158817...
02037-12-29681219224{2030: 0.7992880613362541, 2040: 0.20071193866...
22041-09-0870217217{2040: 0.16862852450041063, 2050: 0.8313714754...
32045-12-04208226227{2040: 0.5923898165891048, 2050: 0.40761018341...
\n", - "
" - ], - "text/plain": [ - " date amount producer consumer \\\n", - "8 2023-09-17 774 228 222 \n", - "7 2024-02-09 144 224 219 \n", - "6 2024-03-01 237 225 216 \n", - "4 2027-04-28 898 215 215 \n", - "1 2034-04-23 660 220 214 \n", - "5 2034-11-07 171 216 223 \n", - "9 2036-03-02 229 228 215 \n", - "0 2037-12-29 681 219 224 \n", - "2 2041-09-08 70 217 217 \n", - "3 2045-12-04 208 226 227 \n", - "\n", - " interpolation_weights \n", - "8 {2020: 0.37092800437996165, 2030: 0.6290719956... \n", - "7 {2020: 0.4106214070626882, 2030: 0.58937859293... \n", - "6 {2020: 0.41637010676156583, 2030: 0.5836298932... \n", - "4 {2020: 0.7320010949904189, 2030: 0.26799890500... \n", - "1 {2030: 0.4307228915662651, 2040: 0.56927710843... \n", - "5 {2030: 0.48493975903614456, 2040: 0.5150602409... \n", - "9 {2030: 0.6166484118291348, 2040: 0.38335158817... \n", - "0 {2030: 0.7992880613362541, 2040: 0.20071193866... \n", - "2 {2040: 0.16862852450041063, 2050: 0.8313714754... \n", - "3 {2040: 0.5923898165891048, 2050: 0.40761018341... " - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "255" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_activity(('wind-example-2020', 'electricity-mix')).id" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[64], line 13\u001b[0m\n\u001b[0;32m 9\u001b[0m new_edges\u001b[38;5;241m.\u001b[39mappend((consumer, previous_producer, new_producer, new_amount))\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m consumer\n\u001b[1;32m---> 13\u001b[0m \u001b[43mextract_edge_from_row\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[64], line 3\u001b[0m, in \u001b[0;36mextract_edge_from_row\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mextract_edge_from_row\u001b[39m(row):\n\u001b[0;32m 2\u001b[0m new_edges \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m----> 3\u001b[0m consumer \u001b[38;5;241m=\u001b[39m \u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_node\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mid\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconsumer\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m previous_producer \u001b[38;5;241m=\u001b[39m bd\u001b[38;5;241m.\u001b[39mget_node(\u001b[38;5;28mid\u001b[39m\u001b[38;5;241m=\u001b[39mrow[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mproducer\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m date, share \u001b[38;5;129;01min\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minterpolation_weights\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "def extract_edge_from_row(row):\n", - " new_edges = []\n", - " consumer = bd.get_node(id=row['consumer'])\n", - " previous_producer = bd.get_node(id=row['producer'])\n", - " for date, share in row['interpolation_weights'].items():\n", - " database = database_date_dict[date]\n", - " new_amount = row['amount'] * share\n", - " new_producer = bd.get_activity(key=(database, previous_producer['code']))\n", - " new_edges.append((consumer, previous_producer, new_producer, new_amount))\n", - " \n", - " return consumer\n", - "\n", - "extract_edge_from_row(df.iloc[2])\n", - "\n", - "# dp = safety_razor(\n", - "# consumer=exchange_output,\n", - "# previous_producer=old_exchange_input, \n", - "# new_producer=new_exchange_input, \n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{2020: 0.37092800437996165, 2030: 0.6290719956200383}" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[0]['interpolation_weights']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/Mini-Wind-Example/wind-example.ipynb b/archive/Mini-Wind-Example/wind-example.ipynb deleted file mode 100644 index a268549..0000000 --- a/archive/Mini-Wind-Example/wind-example.ipynb +++ /dev/null @@ -1,1290 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalisLCA\n", - "import bw_processing as bwp\n", - "from utilities import add_column_interpolation_weights_on_timeline" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('tictac')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 3 object(s):\n", - "\twind-example-2020\n", - "\twind-example-2035\n", - "\twind-example-2050" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00 1\u001b[0m tl \u001b[39m=\u001b[39m tlca\u001b[39m.\u001b[39;49mbuild_timeline(node_timeline\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw_temporalis\\lca.py:182\u001b[0m, in \u001b[0;36mTemporalisLCA.build_timeline\u001b[1;34m(self, node_timeline)\u001b[0m\n\u001b[0;32m 180\u001b[0m row_id \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnodes[edge\u001b[39m.\u001b[39mproducer_unique_id]\u001b[39m.\u001b[39mactivity_datapackage_id\n\u001b[0;32m 181\u001b[0m col_id \u001b[39m=\u001b[39m node\u001b[39m.\u001b[39mactivity_datapackage_id\n\u001b[1;32m--> 182\u001b[0m exchange \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_technosphere_exchange(\n\u001b[0;32m 183\u001b[0m input_id\u001b[39m=\u001b[39;49mrow_id,\n\u001b[0;32m 184\u001b[0m output_id\u001b[39m=\u001b[39;49mcol_id,\n\u001b[0;32m 185\u001b[0m )\n\u001b[0;32m 186\u001b[0m value \u001b[39m=\u001b[39m (\n\u001b[0;32m 187\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exchange_value(\n\u001b[0;32m 188\u001b[0m exchange\u001b[39m=\u001b[39mexchange,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 193\u001b[0m \u001b[39m/\u001b[39m node\u001b[39m.\u001b[39mreference_product_production_amount\n\u001b[0;32m 194\u001b[0m )\n\u001b[0;32m 195\u001b[0m producer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnodes[edge\u001b[39m.\u001b[39mproducer_unique_id]\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw_temporalis\\lca.py:296\u001b[0m, in \u001b[0;36mTemporalisLCA.get_technosphere_exchange\u001b[1;34m(self, input_id, output_id)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(exchanges) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m 289\u001b[0m \u001b[39mraise\u001b[39;00m MultipleTechnosphereExchanges(\n\u001b[0;32m 290\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m exchanges for link between \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[0;32m 291\u001b[0m \u001b[39mlen\u001b[39m(exchanges),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 294\u001b[0m )\n\u001b[0;32m 295\u001b[0m )\n\u001b[1;32m--> 296\u001b[0m \u001b[39mreturn\u001b[39;00m exchanges[\u001b[39m0\u001b[39;49m]\n", - "\u001b[1;31mIndexError\u001b[0m: list index out of range" - ] - } - ], - "source": [ - "tl = tlca.build_timeline(node_timeline=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tl' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 16\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m tl\u001b[39m.\u001b[39mbuild_dataframe()\n", - "\u001b[1;31mNameError\u001b[0m: name 'tl' is not defined" - ] - } - ], - "source": [ - "tl.build_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tl' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 17\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m tl\u001b[39m.\u001b[39madd_metadata_to_dataframe([\u001b[39m'\u001b[39m\u001b[39mwind-example-2020\u001b[39m\u001b[39m'\u001b[39m])\n", - "\u001b[1;31mNameError\u001b[0m: name 'tl' is not defined" - ] - } - ], - "source": [ - "tl.add_metadata_to_dataframe(['wind-example-2020'])" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tl' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 18\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m tl_df \u001b[39m=\u001b[39m tl\u001b[39m.\u001b[39mbuild_dataframe()\n", - "\u001b[1;31mNameError\u001b[0m: name 'tl' is not defined" - ] - } - ], - "source": [ - "tl_df = tl.build_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tl_df' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 19\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m tl_df\n", - "\u001b[1;31mNameError\u001b[0m: name 'tl_df' is not defined" - ] - } - ], - "source": [ - "tl_df" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 20\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m bd\u001b[39m.\u001b[39;49mget_node(key\u001b[39m=\u001b[39;49m(\u001b[39m'\u001b[39;49m\u001b[39mwind-example-2050\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mwind-turbine-construction\u001b[39;49m\u001b[39m'\u001b[39;49m))\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[39mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m results for the given search\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[39mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[39mreturn\u001b[39;00m candidates[\u001b[39m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "bd.get_node(key=('wind-example-2050', 'wind-turbine-construction'))" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tl' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 21\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m tl\u001b[39m.\u001b[39mdf\u001b[39m.\u001b[39mplot(x\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m\"\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mamount\u001b[39m\u001b[39m\"\u001b[39m, kind\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mscatter\u001b[39m\u001b[39m\"\u001b[39m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'tl' is not defined" - ] - } - ], - "source": [ - "tl.df.plot(x=\"date\", y=\"amount\", kind=\"scatter\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tl_dt = tl.add_metadata_to_dataframe(database_labels=[\"wind-example-2020\"], fields=['name', 'unit', 'location', 'categories'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tl_dt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tl_dt.date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tl = tlca.build_timeline(node_timeline=True)\n", - "tl.build_dataframe()\n", - "tl.add_metadata_to_dataframe(database_labels=[\"wind-example-2020\"], fields=['name', 'unit'])" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountproducerconsumer
62021-04-09474218216
52025-04-15288213224
02025-12-17191221223
72032-06-05864214215
92035-05-2882224228
22038-10-12112216224
82040-03-11558219215
32040-08-10985225219
12048-04-1440224224
42049-05-0762223224
\n", - "
" - ], - "text/plain": [ - " date amount producer consumer\n", - "6 2021-04-09 474 218 216\n", - "5 2025-04-15 288 213 224\n", - "0 2025-12-17 191 221 223\n", - "7 2032-06-05 864 214 215\n", - "9 2035-05-28 82 224 228\n", - "2 2038-10-12 112 216 224\n", - "8 2040-03-11 558 219 215\n", - "3 2040-08-10 985 225 219\n", - "1 2048-04-14 40 224 224\n", - "4 2049-05-07 62 223 224" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Creating a mock timeline\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# Number of rows in the DataFrame\n", - "n = 10\n", - "\n", - "# Generate random dates\n", - "start_date = pd.to_datetime('2020-01-01')\n", - "end_date = pd.to_datetime('2050-01-01')\n", - "date_range = (end_date - start_date).days\n", - "dates = [start_date + pd.to_timedelta(np.random.randint(0, date_range), unit='D') for _ in range(n)]\n", - "\n", - "# Generate random amounts, let's say between 10 and 1000\n", - "amounts = np.random.randint(10, 1000, size=n)\n", - "\n", - "# Generate random producer and consumer integers, let's say between 1 and 10\n", - "producers = np.random.randint(213, 230, size=n)\n", - "consumers = np.random.randint(213, 230, size=n)\n", - "\n", - "# Create DataFrame\n", - "df = pd.DataFrame({\n", - " 'date': dates,\n", - " 'amount': amounts,\n", - " 'producer': producers,\n", - " 'consumer': consumers\n", - "}).sort_values(by='date')\n", - "df" - "tl_df" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "from utilities import *\n", - "dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2030\", \"%Y\"),\n", - " datetime.strptime(\"2040\", \"%Y\"),\n", - " datetime.strptime(\"2050\", \"%Y\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountproducerconsumerinterpolation_weights
62021-04-09474215216{2020: 0.12701888858472488, 2030: 0.8729811114...
52025-04-15288213224{2020: 0.5286066246920339, 2030: 0.47139337530...
02025-12-17191221223{2020: 0.5959485354503148, 2030: 0.40405146454...
72032-06-05864216218{2030: 0.24260679079956188, 2040: 0.7573932092...
92035-05-2882224228{2030: 0.5402519167579408, 2040: 0.45974808324...
22038-10-12112216224{2030: 0.8778751369112815, 2040: 0.12212486308...
82040-03-11558219215{2040: 0.019162332329592115, 2050: 0.980837667...
32040-08-10985225219{2040: 0.060771968245277856, 2050: 0.939228031...
12048-04-1440224224{2040: 0.8283602518477964, 2050: 0.17163974815...
42049-05-0762223224{2040: 0.9345743224746783, 2050: 0.06542567752...
\n", - "
" - ], - "text/plain": [ - " date amount producer consumer \\\n", - "6 2021-04-09 474 215 216 \n", - "5 2025-04-15 288 213 224 \n", - "0 2025-12-17 191 221 223 \n", - "7 2032-06-05 864 216 218 \n", - "9 2035-05-28 82 224 228 \n", - "2 2038-10-12 112 216 224 \n", - "8 2040-03-11 558 219 215 \n", - "3 2040-08-10 985 225 219 \n", - "1 2048-04-14 40 224 224 \n", - "4 2049-05-07 62 223 224 \n", - "\n", - " interpolation_weights \n", - "6 {2020: 0.12701888858472488, 2030: 0.8729811114... \n", - "5 {2020: 0.5286066246920339, 2030: 0.47139337530... \n", - "0 {2020: 0.5959485354503148, 2030: 0.40405146454... \n", - "7 {2030: 0.24260679079956188, 2040: 0.7573932092... \n", - "9 {2030: 0.5402519167579408, 2040: 0.45974808324... \n", - "2 {2030: 0.8778751369112815, 2040: 0.12212486308... \n", - "8 {2040: 0.019162332329592115, 2050: 0.980837667... \n", - "3 {2040: 0.060771968245277856, 2050: 0.939228031... \n", - "1 {2040: 0.8283602518477964, 2050: 0.17163974815... \n", - "4 {2040: 0.9345743224746783, 2050: 0.06542567752... " - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.at[6, 'consumer'] = 216\n", - "df.at[6, 'producer'] = 215\n", - "df = add_column_interpolation_weights_on_timeline(df, dates_list)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "215" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_activity(('wind-example-2020', 'electricity-production-wind')).id" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "database_date_dict = {\n", - " 2020: 'wind-example-2020',\n", - " 2030: 'wind-example-2030',\n", - " 2040: 'wind-example-2040',\n", - " 2050: 'wind-example-2050',\n", - "} " - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 32\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m extract_edge_from_row(df\u001b[39m.\u001b[39;49miloc[\u001b[39m0\u001b[39;49m])\n", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\Mini-Wind-Example\\wind-example.ipynb Cell 32\u001b[0m line \u001b[0;36m8\n\u001b[0;32m 6\u001b[0m database \u001b[39m=\u001b[39m database_date_dict[date]\n\u001b[0;32m 7\u001b[0m new_amount \u001b[39m=\u001b[39m row[\u001b[39m'\u001b[39m\u001b[39mamount\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m*\u001b[39m share\n\u001b[1;32m----> 8\u001b[0m new_producer \u001b[39m=\u001b[39m bd\u001b[39m.\u001b[39;49mget_activity((database, previous_producer[\u001b[39m'\u001b[39;49m\u001b[39mcode\u001b[39;49m\u001b[39m'\u001b[39;49m]))\n\u001b[0;32m 9\u001b[0m new_edges\u001b[39m.\u001b[39mappend((consumer, previous_producer, new_producer, new_amount))\n\u001b[0;32m 11\u001b[0m \u001b[39mreturn\u001b[39;00m consumer\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:440\u001b[0m, in \u001b[0;36mget_activity\u001b[1;34m(key, **kwargs)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, numbers\u001b[39m.\u001b[39mIntegral):\n\u001b[0;32m 439\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mid\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m key\n\u001b[1;32m--> 440\u001b[0m \u001b[39mreturn\u001b[39;00m get_node(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[39mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m results for the given search\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[39mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[39mreturn\u001b[39;00m candidates[\u001b[39m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "def extract_edge_from_row(row):\n", - " new_edges = []\n", - " consumer = bd.get_node(id=row['consumer'])\n", - " previous_producer = bd.get_node(id=row['producer'])\n", - " for date, share in row['interpolation_weights'].items():\n", - " database = database_date_dict[date]\n", - " new_amount = row['amount'] * share\n", - " new_producer = bd.get_activity((database, previous_producer['code']))\n", - " new_edges.append((consumer, previous_producer, new_producer, new_amount))\n", - " \n", - " return consumer\n", - "\n", - "extract_edge_from_row(df.iloc[0])\n", - "\n", - "# dp = safety_razor(\n", - "# consumer=exchange_output,\n", - "# previous_producer=old_exchange_input, \n", - "# new_producer=new_exchange_input, \n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2020: 0.46208595674787845, 2030: 0.5379140432521216}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[0]['interpolation_weights']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/example_biosphere3_positive_and_negative_CO2.ipynb b/archive/example_biosphere3_positive_and_negative_CO2.ipynb deleted file mode 100644 index 9f97004..0000000 --- a/archive/example_biosphere3_positive_and_negative_CO2.ipynb +++ /dev/null @@ -1,1066 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `timex` example: Cradle-to-grave LCA of an electric vehicle" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2data as bd\n", - "\n", - "bd.projects.set_current(\"bw25_premise\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prospective databases\n", - "\n", - "Created using `premise` with updated electricity sectors" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case study setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases[\"foreground\"]\n", - "foreground = bd.Database(\"foreground\")\n", - "foreground.write({})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Getting some input processes with positive and negative carbon dioxide flows\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exchange: 1.8148820400238037 square meter 'Transformation, from arable land, unspecified use' (square meter, None, ('natural resource', 'land')) to 'land already in use, annual cropland to perennial crop' (kilogram, ES, None)>\n", - "Exchange: 1.8148820400238037 square meter 'Transformation, to permanent crop' (square meter, None, ('natural resource', 'land')) to 'land already in use, annual cropland to perennial crop' (kilogram, ES, None)>\n", - "Exchange: 28.747732162475586 kilogram 'Carbon dioxide, to soil or biomass stock' (kilogram, None, ('soil',)) to 'land already in use, annual cropland to perennial crop' (kilogram, ES, None)>\n" - ] - } - ], - "source": [ - "# a process with carbon dioxide to soil flow (uptake by soil)\n", - "land_use_change = [act for act in db_2020 if \"land already in use, annual cropland to perennial crop\" in act[\"name\"] and act[\"location\"] == \"ES\"][0]\n", - "for exc in land_use_change.biosphere():\n", - " print(exc)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "fg = bd.Database(\"foreground\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "fg.new_node(\"dummy system\", name=\"dummy system\", unit=\"unit\").save()\n", - "dummy_node = fg.get(\"dummy system\")\n", - "dummy_node.new_edge(input=dummy_node, amount=1, type=\"production\").save()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from bw_temporalis import TemporalDistribution, easy_timedelta_distribution\n", - "import numpy as np\n", - "\n", - "#add some co2 uptake\n", - "land_use_node = dummy_node.new_edge(input=land_use_change, amount=10, type=\"technosphere\")\n", - "land_use_node[\"temporal_distribution\"] = TemporalDistribution(\n", - " date=np.array([-10, -5, 0, 5], dtype=\"timedelta64[Y]\"), \n", - " amount=np.array([0.25, 0.25, 0.25, 0.25]) \n", - ")\n", - "land_use_node.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#adding some co2 emissions\n", - "co2_flow=bd.Database(\"biosphere3\").search(\"Carbon dioxide, fossil\")[0]\n", - "\n", - "co2_node = dummy_node.new_edge(input=co2_flow, amount=50, type=\"biosphere\")\n", - "co2_node[\"temporal_distribution\"]=TemporalDistribution(\n", - " date=np.array([-8, 8], dtype=\"timedelta64[Y]\"), \n", - " amount=np.array([0.5, 0.5]) \n", - ")\n", - "co2_node.save()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## LCA using `timex`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "method = (\"EF v3.1\", \"climate change\", \"global warming potential (GWP100)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timex_lca.py:86: UserWarning: No edge filter function provided. Skipping all edges within background databases.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 1\n" - ] - } - ], - "source": [ - "from timex_lca import TimexLCA\n", - "\n", - "tlca = TimexLCA({dummy_node.key: 1}, method, database_date_dict, cutoff=1e-9)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timeline_builder.py:317: Warning: Reference date 2014-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timeline_builder.py:317: Warning: Reference date 2019-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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date_producerproducer_namedate_consumerconsumer_nameamountinterpolation_weights
02014-01-01land already in use, annual cropland to perenn...2024-01-01dummy system2.5{'db_2020': 1}
12019-01-01land already in use, annual cropland to perenn...2024-01-01dummy system2.5{'db_2020': 1}
22024-01-01land already in use, annual cropland to perenn...2024-01-01dummy system2.5{'db_2020': 0.6000547495209416, 'db_2030': 0.3...
32024-01-01dummy system2024-01-01-11.0{'db_2020': 0.6000547495209416, 'db_2030': 0.3...
42029-01-01land already in use, annual cropland to perenn...2024-01-01dummy system2.5{'db_2020': 0.09991787571858746, 'db_2030': 0....
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" - ], - "text/plain": [ - " date_producer producer_name \\\n", - "0 2014-01-01 land already in use, annual cropland to perenn... \n", - "1 2019-01-01 land already in use, annual cropland to perenn... \n", - "2 2024-01-01 land already in use, annual cropland to perenn... \n", - "3 2024-01-01 dummy system \n", - "4 2029-01-01 land already in use, annual cropland to perenn... \n", - "\n", - " date_consumer consumer_name amount \\\n", - "0 2024-01-01 dummy system 2.5 \n", - "1 2024-01-01 dummy system 2.5 \n", - "2 2024-01-01 dummy system 2.5 \n", - "3 2024-01-01 -1 1.0 \n", - "4 2024-01-01 dummy system 2.5 \n", - "\n", - " interpolation_weights \n", - "0 {'db_2020': 1} \n", - "1 {'db_2020': 1} \n", - "2 {'db_2020': 0.6000547495209416, 'db_2030': 0.3... \n", - "3 {'db_2020': 0.6000547495209416, 'db_2030': 0.3... \n", - "4 {'db_2020': 0.09991787571858746, 'db_2030': 0.... " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "tlca.lci()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-237.47732162475586" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca.static_lcia()\n", - "tlca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\dynamic_characterization.py:58: UserWarning: No custom dynamic characterization functions provided. \n", - "Using default dynamic characterization functions for CO2, CH4, N2O, CO with decay functions from IPCC AR6. \n", - "The selected biosphere flows for these GHGs are based on the selection of the currently chosen impact category: ('EF v3.1', 'climate change', 'global warming potential (GWP100)') and their matrix ids can be looked up with .characterization_functions.keys()\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateamountflowflow_nameactivityactivity_nameamount_sum
02014-01-01 00:00:000.000000e+003636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)0.000000e+00
12015-01-01 05:49:12-1.183833e-133636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-1.183833e-13
22016-01-01 01:26:240.000000e+001170Carbon dioxide, fossil98280(foreground, dummy system)-1.183833e-13
32016-01-01 11:38:24-1.111291e-133636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-2.295124e-13
42016-12-31 07:15:364.118006e-141170Carbon dioxide, fossil98280(foreground, dummy system)-1.883323e-13
........................
6962128-01-02 00:10:48-1.800239e-311170Carbon dioxide, fossil39919(db_2020, a72f0604c64853d294b3010497d0d38d)-2.108455e-11
6952128-01-02 00:10:48-5.034345e-143636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-2.108455e-11
6972128-12-31 11:06:001.759567e-141170Carbon dioxide, fossil98280(foreground, dummy system)-2.106696e-11
6982129-12-31 16:55:121.755357e-141170Carbon dioxide, fossil98280(foreground, dummy system)-2.104940e-11
6992130-12-31 22:44:241.751215e-141170Carbon dioxide, fossil98280(foreground, dummy system)-2.103189e-11
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700 rows × 7 columns

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" - ], - "text/plain": [ - " date amount flow \\\n", - "0 2014-01-01 00:00:00 0.000000e+00 3636 \n", - "1 2015-01-01 05:49:12 -1.183833e-13 3636 \n", - "2 2016-01-01 01:26:24 0.000000e+00 1170 \n", - "3 2016-01-01 11:38:24 -1.111291e-13 3636 \n", - "4 2016-12-31 07:15:36 4.118006e-14 1170 \n", - ".. ... ... ... \n", - "696 2128-01-02 00:10:48 -1.800239e-31 1170 \n", - "695 2128-01-02 00:10:48 -5.034345e-14 3636 \n", - "697 2128-12-31 11:06:00 1.759567e-14 1170 \n", - "698 2129-12-31 16:55:12 1.755357e-14 1170 \n", - "699 2130-12-31 22:44:24 1.751215e-14 1170 \n", - "\n", - " flow_name activity \\\n", - "0 Carbon dioxide, to soil or biomass stock 39919 \n", - "1 Carbon dioxide, to soil or biomass stock 39919 \n", - "2 Carbon dioxide, fossil 98280 \n", - "3 Carbon dioxide, to soil or biomass stock 39919 \n", - "4 Carbon dioxide, fossil 98280 \n", - ".. ... ... \n", - "696 Carbon dioxide, fossil 39919 \n", - "695 Carbon dioxide, to soil or biomass stock 39919 \n", - "697 Carbon dioxide, fossil 98280 \n", - "698 Carbon dioxide, fossil 98280 \n", - "699 Carbon dioxide, fossil 98280 \n", - "\n", - " activity_name amount_sum \n", - "0 (db_2020, a72f0604c64853d294b3010497d0d38d) 0.000000e+00 \n", - "1 (db_2020, a72f0604c64853d294b3010497d0d38d) -1.183833e-13 \n", - "2 (foreground, dummy system) -1.183833e-13 \n", - "3 (db_2020, a72f0604c64853d294b3010497d0d38d) -2.295124e-13 \n", - "4 (foreground, dummy system) -1.883323e-13 \n", - ".. ... ... \n", - "696 (db_2020, a72f0604c64853d294b3010497d0d38d) -2.108455e-11 \n", - "695 (db_2020, a72f0604c64853d294b3010497d0d38d) -2.108455e-11 \n", - "697 (foreground, dummy system) -2.106696e-11 \n", - "698 (foreground, dummy system) -2.104940e-11 \n", - "699 (foreground, dummy system) -2.103189e-11 \n", - "\n", - "[700 rows x 7 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca.dynamic_lcia(metric=\"radiative_forcing\", fixed_TH=False) # default characterization function dictionaries mapping to biosphere3 flows from static LCIA method now calculated inside the method" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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dateamountflowflow_nameactivityactivity_nameamount_sum
02014-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-71.869330
12016-01-01 01:26:242.500000e+011170Carbon dioxide, fossil98280(foreground, dummy system)-46.869330
22019-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-118.738661
32024-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-190.607991
42029-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-262.477322
52029-01-01 00:00:00-2.569987e-161170Carbon dioxide, fossil39919(db_2020, a72f0604c64853d294b3010497d0d38d)-262.477322
62031-12-31 22:33:362.500000e+011170Carbon dioxide, fossil98280(foreground, dummy system)-237.477322
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dateamountflowflow_nameactivityactivity_nameamount_sum
02014-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-71.869330
12016-01-01 01:26:242.500000e+011170Carbon dioxide, fossil98280(foreground, dummy system)-46.869330
22019-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-118.738661
32024-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-190.607991
42029-01-01 00:00:00-7.186933e+013636Carbon dioxide, to soil or biomass stock39919(db_2020, a72f0604c64853d294b3010497d0d38d)-262.477322
52029-01-01 00:00:00-2.569987e-161170Carbon dioxide, fossil39919(db_2020, a72f0604c64853d294b3010497d0d38d)-262.477322
62031-12-31 22:33:362.500000e+011170Carbon dioxide, fossil98280(foreground, dummy system)-237.477322
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" - ], - "text/plain": [ - " date amount flow \\\n", - "0 2014-01-01 00:00:00 -7.186933e+01 3636 \n", - "1 2016-01-01 01:26:24 2.500000e+01 1170 \n", - "2 2019-01-01 00:00:00 -7.186933e+01 3636 \n", - "3 2024-01-01 00:00:00 -7.186933e+01 3636 \n", - "4 2029-01-01 00:00:00 -7.186933e+01 3636 \n", - "5 2029-01-01 00:00:00 -2.569987e-16 1170 \n", - "6 2031-12-31 22:33:36 2.500000e+01 1170 \n", - "\n", - " flow_name activity \\\n", - "0 Carbon dioxide, to soil or biomass stock 39919 \n", - "1 Carbon dioxide, fossil 98280 \n", - "2 Carbon dioxide, to soil or biomass stock 39919 \n", - "3 Carbon dioxide, to soil or biomass stock 39919 \n", - "4 Carbon dioxide, to soil or biomass stock 39919 \n", - "5 Carbon dioxide, fossil 39919 \n", - "6 Carbon dioxide, fossil 98280 \n", - "\n", - " activity_name amount_sum \n", - "0 (db_2020, a72f0604c64853d294b3010497d0d38d) -71.869330 \n", - "1 (foreground, dummy system) -46.869330 \n", - "2 (db_2020, a72f0604c64853d294b3010497d0d38d) -118.738661 \n", - 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y1atXT66urvLz89PQoUPVvHlzFS1aVE899ZQcHBy0Y8cO7dy5U++8806OvScAkNNsjrsjIiK0bt26DOf/8ccfioiIyJaiAAAAgHzP3U8KKC2F1rjx3xwKilKNHTtW9evX1xNPPKGGDRvqoYceUvXq1a3GjBs3TkWKFFFkZKQ6dOig119/XR4eHjla1+20b99eTk5O6tChg9WpXbdydHTU+fPn9dxzz6l06dJq27atHnvsMY0YMcJqXLdu3ZSQkJDmKCUvLy+9//77qlGjhmrWrKnDhw9r4cKFZqA2fvx4LV26VEWLFlW1atUkSY0bN9b8+fO1dOlS1axZUw8++KA++OCDTE+VAwB7YDFsPIF5zJgxGjNmjFasWGF1gTZJ2r59ux599FENGDBAAwYMyJFCc0tMTIx8fX11+fJl+fhk/8UFc0NiYqIWLlyopk2b5vm/BMF29M3+0DP7RN/sDz2zT/m1b3FxcTp06JBKlCiRaXBhr1JSUhQTEyMfH5975jSqY8eOqXjx4tq0aZMeeOCBu17e119/rZdfflknT55M9+Y99uZe7Bluj77ZH3vqWWZ/q2zNPGw+De3VV1/Vb7/9purVq5u3o7RYLNq7d6+WLVumevXq6dVXX73ztQEAAACA/y8xMVGnTp3SG2+8oQcffPCug6LY2FgdOnRI7777rnr06JEvgiIAyCk2x2HOzs5asmSJRo0apVOnTmnq1Kn67LPPdOrUKY0aNUpLlizJV/9qBAAAACDvrF27VmFhYdqyZYs+++yzu17emDFjVLVqVQUGBmrQoEHZUCEA5F82H1kk3QiM8sOpZgAAAADubZGRkbLxihk2GT58uIYPH55tywOA/OzePtEOAAAAAAAAuYqwCAAAAAAAACbCIgAAAAAAAJgIiwAAAAAAAGAiLAIAAAAAAIApS3dDk6R+/fqlO91iscjNzU3h4eFq0aKF/P3977o4AAAAAAAA5K4sH1m0detW/e9//9PUqVO1evVqrVq1StOmTdP//vc/LV++XP369VN4eLj27NmTE/UCAAAAuAORkZF65ZVXcvx1hg8frqpVq97VMjp37qwnn3wyW+rJqsOHD8tisWjbtm13vIy8rP9+ZrFY9PPPP+d1GWmsWrVKFotFly5dyvHXulffg3vRnXxW+fn53Tfvb5bDohYtWqhhw4Y6efKktmzZor/++ksnTpxQdHS02rdvrxMnTqh+/fp69dVXc6JeAAAAIF+4HJugg2euauvRizp49qouxybkdUnIJh9++KFmzJiR12UAaeRWaGwPXn/9dS1fvjyvy7hnZfk0tLFjx2rp0qXy8fExp/n4+Gj48OFq1KiRXn75ZQ0dOlSNGjXK1kIBAACA/OLkpesa+OMOrfnnnDmtfkSA3mtdWSEF3POwMvuQkJAgFxeXvC4jQ76+vnldArJBYmKinJ2d87qM+0puvudeXl7y8vLKldfKyL28jWX5yKLLly/rzJkzaaafPXtWMTExkqQCBQooIYF/GQEAAABudTk2IU1QJEm//3NOb/y4I9eOMPrqq69Uo0YNeXt7KygoSB06dLDaz089dWb58uWqUaOGPDw8VLduXe3fv99qOe+9954CAwPl7e2tbt26KS4uLtPXTU5OVrdu3VSiRAm5u7urTJky+vDDDzN9TmRkpPr06aN+/fopICBA0dHRkqQ9e/aoadOm8vLyUmBgoDp27Khz5/7vfV20aJEeeughFShQQAULFlTz5s118OBBq2X/+eefqlatmtzc3FSjRg1t3brVnGcYhsLDwzVu3Dir5+zatUsODg5plpXq1tPQIiMj1bdvXw0YMED+/v4KCgrS8OHDb7vOtx4B8uSTT6pz587m408//VRlypRRUFCQgoOD1aZNG6vax4wZo5IlS8rd3V1VqlTRDz/8kOlr3urgwYNq0aKFAgMD5eXlpZo1a2rZsmVWY4oXL67Ro0era9eu8vb2VrFixTR16lRzfuppfT/99JOioqLk4eGhKlWqaP369eaY9E4HmjhxoooXL24+3rRpk6KjoxUQECBfX181aNBAf/31V5bWJyUlRe+//77Cw8Pl6uqqYsWKadSoUVZ1fvfdd4qMjJSbm5u++uorpaSk6O2331ZoaKhcXV1VtWpVLVq0KM36ffPNN6pbt67c3NxUoUIFrVq1KsM6zp8/rw4dOqhChQry8vJSpUqVNGfOHKsxtmwz//zzj+rXry83NzeVL19eS5cuzXT9O3furNWrV+vDDz+UxWKRxWLR4cOHJUmrV69WrVq15OrqquDgYL3xxhtKSkrKcFkzZsxQgQIF9PPPP6t06dJyc3NTdHS0jh07ZjVu3rx5ql69utzc3FSyZEmNGDHCarkWi0WfffaZWrRoIU9PT73zzjs2P+/zzz9Xy5Yt5eHhoYiICP3666/mfFs+v27d7vLTNpYd7ug0tK5du2ru3Lk6fvy4Tpw4oblz56pbt27mB+Kff/6p0qVLZ3etAAAAgN07dzUhTVCU6vd/zunc1dwJixISEjRy5Eht375dP//8sw4dOqQuXbqkGTd48GCNHz9emzdvlpOTk7p27WrO++677zRs2DCNGjVKmzdvVnBwsD799NNMXzclJUWhoaH67rvvtGfPHg0dOlRvvvmmvvvuu0yfN3PmTDk5OWnt2rWaMmWKTp06pQYNGqhq1aravHmzFi1apP/++09t27Y1n3Pt2jX169dPmzZt0vLly+Xg4KCWLVsqJSXFnN+8eXOVKVNGW7Zs0fDhw/X666+bz7dYLOrataumT59uVcsXX3yhhx9+WKVKlcq05lvr9/T01MaNGzVmzBi9/fbbt/1yn5nNmzerb9++Gj58uP78808tXLhQ9evXN+e/9dZbmj59uiZPnqzdu3fr1Vdf1bPPPqvVq1fb/BpXr15V06ZNtWzZMm3dulWNGzfW448/rqNHj1qNGz9+vBm09erVSy+++KL27dtnNWbw4MF6/fXXtW3bNpUuXVrt27fPNIy41ZUrV9SpUyetWbNGGzZsUEREhJo2baorV67YvIxBgwbp/fff15AhQ7Rnzx7Nnj1bgYGBVmMGDhyovn37au/evWrcuLE+/PBDjR8/XuPGjdOOHTvUuHFjPfHEE/rnn3+snte/f3+99tpr2rp1q+rWrasnnnhC58+fT7eOuLg4Va9eXd9884127NihF154QR07dtTGjRutxmW2zaSkpKhVq1ZydHTUhg0b9Nlnn2ngwIGZrv+HH36oOnXq6Pnnn9epU6d06tQpFS1aVCdOnFDTpk1Vs2ZNbd++XZMnT9b//vc/M7jJSGxsrEaNGqWZM2dq7dq1iomJ0dNPP23OX7x4sZ599ln17dtXe/bs0ZQpUzRjxgwzPEk1bNgwtWjRQjt37lTXrl1tft6IESPUtm1b7dixQ02bNtUzzzyjCxcuWI3J7PPrVvlpG8sWRhZduXLF6N69u+Hi4mI4ODgYDg4OhouLi/H8888bV65cMQzDMLZu3Wps3bo1q4u+J1y+fNmQZFy+fDmvS7ljCQkJxs8//2wkJCTkdSnIAvpmf+iZfaJv9oee2af82rfr168be/bsMa5fv37Hy/jryAUjbOD8DH+2HrmQjRX/nwYNGhgvv/xyhvP//PNPQ5Jx7NgxIzk52Vi5cqUhyVi2bJk5ZsGCBYYkc/3r1Klj9OzZ02o5tWvXNqpUqZKl2nr16mW0bt3afNypUyejRYsWVrVXrVrV6jlDhgwxGjVqZDXt2LFjhiRj//796b7OmTNnDEnGzp07DcMwjClTphj+/v7GtWvXzDGTJ082JJnfZ06ePGk4OjoaGzduNAzjxrZdqFAhY8aMGRmuT3r1P/TQQ1ZjatasaQwcODDDZaTXrxYtWhidOnUyDMMwfvzxR8PHx8e4dOmScfHiRSM5Odkcd/XqVcPNzc1Yt26d1fO7detmtG/fPsPXtEX58uWNSZMmmY/DwsKMZ5991nyckpJiFC5c2Jg8ebJhGIZx6NAhQ5Lx+eefm2N2795tSDL27t1rGIZhDBs2LM02M2HCBCMsLCzDOpKSkgxvb29j3rx55jRJxty5c9MdHxMTY7i6uhrTpk1Ld35qnRMnTrSaHhISYowaNcpqWs2aNY1evXpZPe+9994z5ycmJhqhoaHG+++/bxiGYf4uXbx40RyTnJxs1bemTZsar732mjn/dtvM4sWLDUdHR+PYsWPm/N9++y3T9yB1ubduV2+++aZRpkwZIyUlxZz2ySefGF5eXlbb1c2mT59uSDI2bNhgTtu7d68hyfxdefjhh43Ro0dbPW/WrFlGcHCw+ViS8corr1iNsfV5b731lvn46tWrhsViMX777TfDMAybPr/S2+5udus2lpycbEgyfvzxx3TH5+U2dqvM/lbZmnlk+cgiLy8vTZs2TefPn9fWrVv1119/6fz585o6dap5vl/VqlXv+g4IAAAAQH7k45b59Sm8bzM/u2zdulUtWrRQWFiYvL29FRkZKUk6fvy41bjKlSub/x8cHCxJ5ulqe/fuVZ06dazG3/o4PZ999plq1KihQoUKmd8vbj1a5VY1atSwerxlyxatXLnSvO6Il5eXypYtK0nm6WEHDx5Uhw4dVLJkSfn4+KhEiRKSZL7W3r17VaVKFXl4eGRYf3BwsJo1a6YvvvhCkjR//nzFxcXpqaeeuu163uzm9zF1ueld3sNW0dHRCgsLU3h4uHr06KGvv/5asbGxkm6cnhcXF6fo6Gir9+fLL7/M8NS59Fy7dk0DBgxQ+fLlVaBAAXl5eWnfvn1penXzulksFgUFBaVZt8y2I1ucOXNGPXv2VOnSpeXr6ytfX19dvXr1tttNqr179yo+Pl6PPvpopuNu3s5iYmJ08uRJ1atXz2pMvXr1tHfvXqtpN283Tk5OqlGjRpoxqZKTkzV69GjVq1fP/B1YsmRJpu+rZL3N7N27V8WKFVNoaGi6NWRF6u+xxWKxWserV6+m+Ty4Wep6pipbtqwKFChgrveWLVv09ttvW22DqUc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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlca.plot_dynamic_characterized_inventory(\n", - " sum_emissions_within_activity=True, cumsum=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[28], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m tlca\u001b[38;5;241m.\u001b[39mplot_dynamic_characterized_inventory(\n\u001b[0;32m 2\u001b[0m sum_emissions_within_activity\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, cumsum\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 3\u001b[0m )\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timex_lca.py:796\u001b[0m, in \u001b[0;36mTimexLCA.plot_dynamic_characterized_inventory\u001b[1;34m(self, cumsum, sum_emissions_within_activity, sum_activities)\u001b[0m\n\u001b[0;32m 793\u001b[0m plot_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll activities\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 796\u001b[0m plot_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 797\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: bd\u001b[38;5;241m.\u001b[39mget_activity(row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_name\u001b[39m\u001b[38;5;124m\"\u001b[39m])[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 798\u001b[0m )\n\u001b[0;32m 800\u001b[0m \u001b[38;5;66;03m# Determine the plotting labels and titles based on the characterization method\u001b[39;00m\n\u001b[0;32m 801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_of_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mradiative_forcing\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\pandas\\core\\frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m 10373\u001b[0m )\n\u001b[1;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\pandas\\core\\apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[1;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\pandas\\core\\apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[0;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\pandas\\core\\apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timex_lca.py:797\u001b[0m, in \u001b[0;36mTimexLCA.plot_dynamic_characterized_inventory..\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m 793\u001b[0m plot_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll activities\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 796\u001b[0m plot_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m--> 797\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: bd\u001b[38;5;241m.\u001b[39mget_activity(row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactivity_name\u001b[39m\u001b[38;5;124m\"\u001b[39m])[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 798\u001b[0m )\n\u001b[0;32m 800\u001b[0m \u001b[38;5;66;03m# Determine the plotting labels and titles based on the characterization method\u001b[39;00m\n\u001b[0;32m 801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtype_of_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mradiative_forcing\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\bw2data\\utils.py:440\u001b[0m, in \u001b[0;36mget_activity\u001b[1;34m(key, **kwargs)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n\u001b[0;32m 439\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m key\n\u001b[1;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_node(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\timex_3\\Lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "tlca.plot_dynamic_characterized_inventory(\n", - " sum_emissions_within_activity=True, cumsum=False\n", - ")\n", - "\n", - "#TODO understand why it doesn't work in this cell" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "medusa", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/example_databases.py b/archive/example_databases.py deleted file mode 100644 index f51b09f..0000000 --- a/archive/example_databases.py +++ /dev/null @@ -1,3708 +0,0 @@ -import bw2data as bd -import numpy as np - -from bw_temporalis import ( - easy_timedelta_distribution, - TemporalDistribution, - easy_datetime_distribution, -) - - -def db_electrolysis(): - if "__test_electrolysis__" in bd.projects: - bd.projects.delete_project("__test_electrolysis__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_electrolysis__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "electricity_mix"): { - "name": "Electricity mix", - "location": "somewhere", - "reference product": "electricity mix", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_mix"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2020").write( - { - ("background_2020", "electricity_mix"): { - "name": "Electricity mix", - "location": "somewhere", - "reference product": "electricity mix", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2020", "electricity_mix"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2020", "electricity_wind"), - }, - ], - }, - ("background_2020", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2020", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "electrolysis"): { - "name": "Hydrogen production, electrolysis", - "location": "somewhere", - "reference product": "hydrogen", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "electrolysis"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("background_2024", "electricity_mix"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-1, 0, 1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.2, 0.6, 0.2]), - ), - }, - ], - }, - ("foreground", "electrolysis2"): { - "name": "Hydrogen production, electrolysis2", - "location": "somewhere", - "reference product": "hydrogen", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "electrolysis2"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("foreground", "electrolysis"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-3], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "heat_from_hydrogen"): { - "name": "Heat production, hydrogen", - "location": "somewhere", - "reference product": "heat", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "heat_from_hydrogen"), - }, - { - "amount": 0.7, - "type": "technosphere", - "input": ("foreground", "electrolysis2"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, 0], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_simple(): - if "__test_abc_simple__" in bd.projects: - bd.projects.delete_project("__test_abc_simple__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_simple__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 0.9, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_C_to_E(): - if "__test_abc_C_to_E__" in bd.projects: - bd.projects.delete_project("__test_abc_C_to_E__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_C_to_E__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 0.9, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([10, 5], dtype="timedelta64[Y]"), - amount=np.array([0.3, 0.7]), - ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_C_and_B_to_E(): - if "__test_abc_C_and_B_to_E__" in bd.projects: - bd.projects.delete_project("__test_abc_C_and_B_to_E__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_C_and_B_to_E__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 0.9, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([10, 5], dtype="timedelta64[Y]"), - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, -2], dtype="timedelta64[Y]"), - amount=np.array([0.4, 0.6]), - ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_B_to_E(): - if "__test_abc_B_to_E__" in bd.projects: - bd.projects.delete_project("__test_abc_B_to_E__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_B_to_E__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 0.9, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, -2], dtype="timedelta64[Y]"), - amount=np.array([0.4, 0.6]), - ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_B_to_E_simplified(): - if "__test_abc_B_to_E_simplified__" in bd.projects: - bd.projects.delete_project("__test_abc_B_to_E_simplified__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_B_to_E_simplified__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, -2], dtype="timedelta64[Y]"), - amount=np.array([0.4, 0.6]), - ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_B_to_E_simplified_and_E_with_TD(): - if "__test_abc_B_to_E_simplified_and_E_with_TD__" in bd.projects: - bd.projects.delete_project("__test_abc_B_to_E_simplified_and_E_with_TD__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_B_to_E_simplified_and_E_with_TD__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 0.9, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, -2], dtype="timedelta64[Y]"), - amount=np.array([0.4, 0.6]), - ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_loopA(): - if "__test_abc_loopA__" in bd.projects: - bd.projects.delete_project("__test_abc_loopA__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_loopA__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [10, 5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-4, -2], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.4, 0.6]), - ), - }, - { - "amount": 0.1, - "type": "technosphere", - "input": ("foreground", "A"), - # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime - # TemporalDistribution( - # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months - # amount=np.array([0.2,0.8]) - # ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_loopA_with_biosphere(): - if "__test_abc_loopA_with_biosphere__" in bd.projects: - bd.projects.delete_project("__test_abc_loopA_with_biosphere__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_loopA_with_biosphere__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [10, 5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-4, -2], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.4, 0.6]), - ), - }, - { - "amount": 0.1, - "type": "technosphere", - "input": ("foreground", "A"), - # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime - # TemporalDistribution( - # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months - # amount=np.array([0.2,0.8]) - # ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 17, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_loopA_with_biosphere_tds_CO2_and_CH4(): - project_name = "db_abc_loopA_with_biosphere_tds_CO2_and_CH4" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - # bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [10, 5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-4, -2], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.4, 0.6]), - ), - }, - { - "amount": 0.1, - "type": "technosphere", - "input": ("foreground", "A"), - # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime - # TemporalDistribution( - # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months - # amount=np.array([0.2,0.8]) - # ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 6, - "type": "biosphere", - "input": ("temporalis-bio", "CH4"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, 4, 6], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.2, 0.7, 0.1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 17, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [0, 3], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - (("temporalis-bio", "CH4"), 25), - ] - ) - - -def db_abcd_CO2_foreground_and_in_deep_background(): - project_name = "db_abcd_CO2_foreground_and_in_deep_backgasdfround" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - # no biosphere in intersecting node between background and foreground - ], - }, - # ('background_2024', 'D'): { - # 'name': 'D', - # 'location': 'somewhere', - # 'reference product': 'd', - # 'exchanges': [ - # { - # 'amount': 1, - # 'type': 'production', - # 'input': ('background_2024', 'D'), - # }, - # { - # 'amount': 3, - # 'type': 'technosphere', - # 'input': ('background_2024', 'electricity_wind'), - # }, - # ] - # }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - # no biosphere in intersecting node between background and foreground - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - # ('background_2008', 'D'): { - # 'name': 'D', - # 'location': 'somewhere', - # 'reference product': 'd', - # 'exchanges': [ - # { - # 'amount': 1, - # 'type': 'production', - # 'input': ('background_2008', 'D'), - # }, - # { - # 'amount': 3, - # 'type': 'technosphere', - # 'input': ('background_2008', 'electricity_wind'), - # }, - # ] - # }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 3, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([-1], dtype="timedelta64[Y]"), - amount=np.array([1]), - ), - }, - { - "amount": 6, - "type": "biosphere", - "input": ("temporalis-bio", "CH4"), - 'temporal_distribution': - TemporalDistribution( - date=np.array([-2, 4, 6], dtype='timedelta64[Y]'), - amount=np.array([0.2, 0.7, 0.1]) - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - # { - # "amount": 3, - # "type": "biosphere", - # "input": ("temporalis-bio", "CO2"), - # 'temporal_distribution': - # TemporalDistribution( - # date=np.array([0, 1], dtype='timedelta64[Y]'), - # amount=np.array([0.3, 0.7]) - # ), - # }, - { - "amount": 0.3, - "type": "technosphere", - "input": ("foreground", "B"), - # 'temporal_distribution': - # TemporalDistribution( - # date=np.array([-20, 15], dtype='timedelta64[Y]'), - # amount=np.array([0.9, 0.1]) - # ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - (("temporalis-bio", "CH4"), 25), - ] - ) - - -def db_abcd_CO2_only_in_deep_background(): - project_name = "db_abcd_CO2_only_in_deep_background" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - # no biosphere in intersecting node between background and foreground - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - # no biosphere in intersecting node between background and foreground - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 3, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([-1], dtype="timedelta64[Y]"), - amount=np.array([1]), - ), - }, - # no biosphere in foreground - # { - # 'amount': 6, - # 'type': 'biosphere', - # 'input': ('temporalis-bio', 'CH4'), - # # 'temporal_distribution': - # # TemporalDistribution( - # # date=np.array([-2, 4, 6], dtype='timedelta64[Y]'), - # # amount=np.array([0.2, 0.7, 0.1]) - # # ), - # }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - # no biosphere in foreground - # { - # 'amount': 3, - # 'type': 'biosphere', - # 'input': ('temporalis-bio', 'CO2'), - # # 'temporal_distribution': - # # TemporalDistribution( - # # date=np.array([0, 1], dtype='timedelta64[Y]'), - # # amount=np.array([0.3, 0.7]) - # # ), - # }, - { - "amount": 0.3, - "type": "technosphere", - "input": ("foreground", "B"), - # 'temporal_distribution': - # TemporalDistribution( - # date=np.array([-20, 15], dtype='timedelta64[Y]'), - # amount=np.array([0.9, 0.1]) - # ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - (("temporalis-bio", "CH4"), 25), - ] - ) - - -def db_abcd_CO2_foreground_deep_background_and_two_markets(): - project_name = "db_abcd_CO2_foreground_deep_background_and_two_markets" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - # biosphere in intersecting process (market) - { - "amount": 5, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - ("background_2024", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "D"), - }, - { - "amount": 3, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1.5, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - # biosphere in intersecting process (market) - { - "amount": 5, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - ("background_2008", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "D"), - }, - { - "amount": 3, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1.5, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([-1], dtype="timedelta64[Y]"), - amount=np.array([1]), - ), - }, - { - "amount": 6, - "type": "technosphere", - "input": ("background_2024", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-6], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 0, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - # 'temporal_distribution': - # TemporalDistribution( - # date=np.array([0, 1], dtype='timedelta64[Y]'), - # amount=np.array([0.3, 0.7]) - # ), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("foreground", "B"), - # 'temporal_distribution': - # TemporalDistribution( - # date=np.array([-20, 15], dtype='timedelta64[Y]'), - # amount=np.array([0.9, 0.1]) - # ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - (("temporalis-bio", "CH4"), 25), - ] - ) - - -def db_abc_loopA_with_biosphere_advanced(): - - project_name = "___abc_loopA_with_biosphere_advanced__" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - # bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-2, 1, 2], dtype="timedelta64[Y]"), - amount=np.array([0.2, 0.7, 0.1]), - ), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-2, 1, 2], dtype="timedelta64[Y]"), - amount=np.array([0.2, 0.7, 0.1]), - ), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [10, 5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.3, 0.7]), - ), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-4, -2], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.4, 0.6]), - ), - }, - { - "amount": 0.1, - "type": "technosphere", - "input": ("foreground", "A"), - # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime - # TemporalDistribution( - # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months - # amount=np.array([0.2,0.8]) - # ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [ - -1, - ], - dtype="timedelta64[Y]", - ), # `M` is months - amount=np.array([1]), - ), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-5], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 17, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-20, 15], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.9, 0.1]), - ), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [-2, -1], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.7, 0.3]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_abc_loopA_with_biosphere_advanced_simple(): - - project_name = "___abc_loopA_with_biosphere_advanced_simple__" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - # bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-2, 1, 2], dtype="timedelta64[Y]"), - amount=np.array([0.2, 0.7, 0.1]), - ), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2008", "electricity_wind"), - }, - ], - }, - ("background_2008", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "electricity_wind"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-2, 1, 2], dtype="timedelta64[Y]"), - amount=np.array([0.2, 0.7, 0.1]), - ), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "E"): { - "name": "E", - "location": "somewhere", - "reference product": "e", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "E"), - }, - { - "amount": 11, - "type": "technosphere", - "input": ("background_2024", "C"), - }, - { - "amount": 8, - "type": "technosphere", - "input": ("foreground", "B"), - }, - { - "amount": 0.1, - "type": "technosphere", - "input": ("foreground", "A"), - # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime - # TemporalDistribution( - # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months - # amount=np.array([0.2,0.8]) - # ), - }, - ], - }, - ("foreground", "D"): { - "name": "D", - "location": "somewhere", - "reference product": "d", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "D"), - }, - { - "amount": 5, - "type": "technosphere", - "input": ("foreground", "B"), - }, - { - "amount": 2, - "type": "technosphere", - "input": ("foreground", "E"), - }, - ], - }, - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 13, - "type": "technosphere", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CH4"), - "temporal_distribution": TemporalDistribution( - date=np.array( - [ - -2, - ], - dtype="timedelta64[Y]", - ), - amount=np.array([1]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 17, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - { - "amount": 4, - "type": "technosphere", - "input": ("foreground", "B"), - }, - { - "amount": 0.5, - "type": "technosphere", - "input": ("foreground", "D"), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - - -def db_dynamic_cf_test(): - - project_name = "___db_dynamic_cf_test_ch4_onenodetest__" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - ("temporalis-bio", "N2O"): { - "type": "emission", - "name": "nitrous oxide", - "temporalis code": "n2o", - }, - } - ) - - bd.Database("test").write( - { - ("test", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("test", "A"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CH4"), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - (("temporalis-bio", "CH4"), 28.41), # from Levasseur integrals - (("temporalis-bio", "N2O"), 263.97), # from Levasseur integrals - ] - ) - - -def db_test_dynamic_biosphere(): - if "__test_abc_simplasdfasdfe__" in bd.projects: - bd.projects.delete_project("__test_abc_simplasdfasdfe__") - bd.projects.purge_deleted_directories() - - bd.projects.set_current("__test_abc_simplasdfasdfe__") - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "electricity_wind"), - }, - ], - }, - ("background_2024", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "electricity_wind"), - }, - { - "amount": 900, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2035").write( - { - ("background_2035", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2035", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2035", "electricity_wind"), - }, - ], - }, - ("background_2035", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2035", "electricity_wind"), - }, - { - "amount": 300, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("background_2050").write( - { - ("background_2050", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2050", "C"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2050", "electricity_wind"), - }, - ], - }, - ("background_2050", "electricity_wind"): { - "name": "Electricity production, wind", - "location": "somewhere", - "reference product": "electricity, wind", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2050", "electricity_wind"), - }, - { - "amount": 100, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [2, 15, 25], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([0.34, 0.33, 0.33]), - ), - }, - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( # e.g. because some hydrogen was stored in the meantime - date=np.array( - [0], dtype="timedelta64[Y]" - ), # `M` is months - amount=np.array([1]), - ), - }, - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) - -def db_abc_simple(): - - project_name = "___abc_simple__" - if project_name in bd.projects: - bd.projects.delete_project(project_name) - # bd.projects.purge_deleted_directories() - - bd.projects.set_current(project_name) - - bd.Database("temporalis-bio").write( - { - ("temporalis-bio", "CO2"): { - "type": "emission", - "name": "carbon dioxide", - "temporalis code": "co2", - }, - ("temporalis-bio", "CH4"): { - "type": "emission", - "name": "methane", - "temporalis code": "ch4", - }, - } - ) - - bd.Database("background_2024").write( - { - ("background_2024", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2024", "C"), - }, - { - "amount": 1, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, 10, 20], dtype="timedelta64[Y]"), - amount=np.array([1/3, 1/3, 1/3]), - ), - }, - ], - }, - } - ) - - bd.Database("background_2008").write( - { - ("background_2008", "C"): { - "name": "C", - "location": "somewhere", - "reference product": "c", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("background_2008", "C"), - }, - { - "amount": 2, - "type": "biosphere", - "input": ("temporalis-bio", "CO2"), - "temporal_distribution": TemporalDistribution( - date=np.array([-4, 10, 20], dtype="timedelta64[Y]"), - amount=np.array([1/3, 1/3, 1/3]), - ), - }, - ], - }, - } - ) - - bd.Database("foreground").write( - { - - - ("foreground", "B"): { - "name": "B", - "location": "somewhere", - "reference product": "b", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "B"), - }, - { - "amount": 1, - "type": "technosphere", - "input": ("background_2024", "C"), - "temporal_distribution": TemporalDistribution( - date=np.array([-6], dtype="timedelta64[Y]"), - amount=np.array([1]),) - }, - - ], - }, - ("foreground", "A"): { - "name": "A", - "location": "somewhere", - "reference product": "a", - "exchanges": [ - { - "amount": 1, - "type": "production", - "input": ("foreground", "A"), - }, -# - { - "amount": 1, - "type": "technosphere", - "input": ("foreground", "B"), - "temporal_distribution": TemporalDistribution( - date=np.array([-2], dtype="timedelta64[Y]"), - amount=np.array([1]),) - }, - - ], - }, - } - ) - - bd.Method(("GWP", "example")).write( - [ - (("temporalis-bio", "CO2"), 1), - ] - ) diff --git a/archive/notebooks/bioflows_advanced.ipynb b/archive/notebooks/bioflows_advanced.ipynb deleted file mode 100644 index 0c0893c..0000000 --- a/archive/notebooks/bioflows_advanced.ipynb +++ /dev/null @@ -1,623 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution, easy_datetime_distribution\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.realpath('../'))\n", - "from medusa.edge_extractor import *\n", - "from medusa.matrix_modifier import MatrixModifier\n", - "from medusa.medusa_lca import *\n", - "from medusa.timeline_builder import TimelineBuilder\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 11699.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 9799.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 9383.23it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 15621.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - } - ], - "source": [ - "from tests.databases import *\n", - "db_abc_loopA_with_biosphere_advanced()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "31ced634", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cca6b8f2-12a3-43f9-8be2-c6a898268adf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 2657.2222266217445\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "b461bbb5", - "metadata": {}, - "source": [ - "# `MEDUSA` LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('background_2020')] + [\n", - " node.id for node in bd.Database('background_2024')\n", - "]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7b5649e3", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "database_date_dict = {\n", - " datetime.strptime(\"2008\", \"%Y\"): 'background_2008',\n", - " datetime.strptime(\"2024\", \"%Y\"): 'background_2024',\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "71bba776", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 34\n" - ] - } - ], - "source": [ - "mlca = MedusaLCA(slca, filter_function, database_date_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c40754e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist as brightway project databases\n" - ] - }, - { - "data": { - "text/html": [ - "
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hash_producerdate_producerproducerproducer_namehash_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
019971997-01-013C20022002-01-019B13.0{'background_2008': 1}
119981998-01-013C20032003-01-019B13.0{'background_2008': 1}
219991999-01-013C20042004-01-019B13.0{'background_2008': 1}
320002000-01-019B20202020-01-0110A3.6{'background_2008': 1}
420012001-01-019B20212021-01-0110A3.6{'background_2008': 1}
.................................
6920352035-01-019B20202020-01-0110A0.4{'background_2024': 1}
7020362036-01-019B20212021-01-0110A0.4{'background_2024': 1}
7120372037-01-019B20222022-01-0110A0.4{'background_2024': 1}
7220382038-01-019B20232023-01-0110A0.4{'background_2024': 1}
7320392039-01-019B20242024-01-0110A0.4{'background_2024': 1}
\n", - "

74 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " hash_producer date_producer producer producer_name hash_consumer \\\n", - "0 1997 1997-01-01 3 C 2002 \n", - "1 1998 1998-01-01 3 C 2003 \n", - "2 1999 1999-01-01 3 C 2004 \n", - "3 2000 2000-01-01 9 B 2020 \n", - "4 2001 2001-01-01 9 B 2021 \n", - ".. ... ... ... ... ... \n", - "69 2035 2035-01-01 9 B 2020 \n", - "70 2036 2036-01-01 9 B 2021 \n", - "71 2037 2037-01-01 9 B 2022 \n", - "72 2038 2038-01-01 9 B 2023 \n", - "73 2039 2039-01-01 9 B 2024 \n", - "\n", - " date_consumer consumer consumer_name amount interpolation_weights \n", - "0 2002-01-01 9 B 13.0 {'background_2008': 1} \n", - "1 2003-01-01 9 B 13.0 {'background_2008': 1} \n", - "2 2004-01-01 9 B 13.0 {'background_2008': 1} \n", - "3 2020-01-01 10 A 3.6 {'background_2008': 1} \n", - "4 2021-01-01 10 A 3.6 {'background_2008': 1} \n", - ".. ... ... ... ... ... \n", - "69 2020-01-01 10 A 0.4 {'background_2024': 1} \n", - "70 2021-01-01 10 A 0.4 {'background_2024': 1} \n", - "71 2022-01-01 10 A 0.4 {'background_2024': 1} \n", - "72 2023-01-01 10 A 0.4 {'background_2024': 1} \n", - "73 2024-01-01 10 A 0.4 {'background_2024': 1} \n", - "\n", - "[74 rows x 10 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1c833eff", - "metadata": {}, - "outputs": [], - "source": [ - "mlca.build_datapackage()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d0f38d8e", - "metadata": {}, - "outputs": [], - "source": [ - "mlca.lci()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b4ed53cf", - "metadata": {}, - "outputs": [], - "source": [ - "mlca.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 2653.3894588780245\n", - "Old static LCA Score: 2657.2222266217445\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', mlca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0d954b57", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2000 'methane' (None, None, None)\n", - "6 [array(['2000-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2001 'methane' (None, None, None)\n", - "6 [array(['2001-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2002 'methane' (None, None, None)\n", - "6 [array(['2002-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2003 'methane' (None, None, None)\n", - "6 [array(['2003-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2004 'methane' (None, None, None)\n", - "6 [array(['2004-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2014 'methane' (None, None, None)\n", - "6 [array(['2014-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2015 'methane' (None, None, None)\n", - "6 [array(['2015-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2016 'methane' (None, None, None)\n", - "6 [array(['2016-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2017 'methane' (None, None, None)\n", - "6 [array(['2017-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2018 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2018-01-01T00:00:00' '2020-12-31T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2018 'methane' (None, None, None)\n", - "6 [array(['2018-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2019 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2019-01-01T00:00:00' '2021-12-31T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2019 'methane' (None, None, None)\n", - "6 [array(['2019-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2020 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2020-01-01T00:00:00' '2022-12-31T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2020 'methane' (None, None, None)\n", - "6 [array(['2020-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2021 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2021-01-01T00:00:00' '2024-01-01T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2021 'methane' (None, None, None)\n", - "6 [array(['2021-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2022 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2022-01-01T00:00:00' '2024-12-31T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2022 'methane' (None, None, None)\n", - "6 [array(['2022-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2023 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2023-01-01T00:00:00' '2025-12-31T17:27:36']\n", - "{'amount': 17, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "A 2024 'carbon dioxide' (None, None, None) TemporalDistribution instance with 2 values and total: 1\n", - "[ 5.1 11.9] ['2024-01-01T00:00:00' '2026-12-31T17:27:36']\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2035 'methane' (None, None, None)\n", - "6 [array(['2035-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2036 'methane' (None, None, None)\n", - "6 [array(['2036-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2037 'methane' (None, None, None)\n", - "6 [array(['2037-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2038 'methane' (None, None, None)\n", - "6 [array(['2038-01-01T00:00:00'], dtype='datetime64[s]')]\n", - "{'amount': 6, 'type': 'biosphere', 'input': ('temporalis-bio', 'CH4'), 'output': ('foreground', 'B')}\n", - "B 2039 'methane' (None, None, None)\n", - "6 [array(['2039-01-01T00:00:00'], dtype='datetime64[s]')]\n" - ] - } - ], - "source": [ - "mlca.build_dynamic_biosphere()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19a3f3b3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/example_29032024.ipynb b/archive/notebooks/example_29032024.ipynb deleted file mode 100644 index 4a9ec44..0000000 --- a/archive/notebooks/example_29032024.ipynb +++ /dev/null @@ -1,2902 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `timex`LCA example" - ] - }, - { - "cell_type": "markdown", - "id": "d7f9b34c", - "metadata": {}, - "source": [ - "### Setup and definitions\n", - "\n", - "First, we set up a database with temporally distributed exchanges." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 15857.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 24456.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 57456.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 74235.47it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from example_databases import *\n", - "db_abc_loopA_with_biosphere_tds_CO2_and_CH4()" - ] - }, - { - "cell_type": "markdown", - "id": "25f0499e", - "metadata": {}, - "source": [ - "The point of `timex` is to choose processes from different background databases depending on the timing of a process. To do so, we need to know which database represents what time:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7b5649e3", - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"background_2008\": datetime.strptime(\"2008\", \"%Y\"),\n", - " \"background_2024\": datetime.strptime(\"2024\", \"%Y\"),\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "e45bc799", - "metadata": {}, - "source": [ - "Lastly, we of course need to define our demand (functional unit) and impact assessment method:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "method = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "68e84319", - "metadata": {}, - "source": [ - "### MedusaLCA calculation\n", - "\n", - "Now we can create our `MedusaLCA`. On creation, it automatically calculates a static LCA of the system, starts the graph traversal and stores the temporal information internally." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71bba776", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculation count: 27\n" - ] - } - ], - "source": [ - "from timex_lca import MedusaLCA\n", - "mlca = MedusaLCA(demand, method, None, database_date_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "5fc1079a", - "metadata": {}, - "source": [ - "Now, we can build the timeline of exchanges between processes. Note how in addition to the orignal database ids, we now have \"time mapped\" ids for all consumers and producers, repesenting the time-specific version of the processes." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "35e721a9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 1997-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 1998-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 1999-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 2000-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 2001-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 2002-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 2003-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:320: Warning: Reference date 2004-01-01 00:00:00 is lower than all provided dates. Data will be taken from the closest higher year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2027-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2028-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2032-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2033-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2034-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2035-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2036-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2037-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2038-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/timeline_builder.py:327: Warning: Reference date 2039-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
019975411997-01-01533C20025462002-01-01539B13.0{'background_2008': 1}
119985421998-01-01533C20035472003-01-01539B13.0{'background_2008': 1}
219995431999-01-01533C20045482004-01-01539B13.0{'background_2008': 1}
320005442000-01-01539B20205732020-01-01540A3.6{'background_2008': 1}
420015452001-01-01539B20215772021-01-01540A3.6{'background_2008': 1}
.......................................
6920355932035-01-01539B20205732020-01-01540A0.4{'background_2024': 1}
7020365942036-01-01539B20215772021-01-01540A0.4{'background_2024': 1}
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74 rows × 12 columns

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" - ], - "text/plain": [ - " hash_producer time_mapped_producer date_producer producer producer_name \\\n", - "0 1997 541 1997-01-01 533 C \n", - "1 1998 542 1998-01-01 533 C \n", - "2 1999 543 1999-01-01 533 C \n", - "3 2000 544 2000-01-01 539 B \n", - "4 2001 545 2001-01-01 539 B \n", - ".. ... ... ... ... ... \n", - "69 2035 593 2035-01-01 539 B \n", - "70 2036 594 2036-01-01 539 B \n", - "71 2037 595 2037-01-01 539 B \n", - "72 2038 596 2038-01-01 539 B \n", - "73 2039 597 2039-01-01 539 B \n", - "\n", - " hash_consumer time_mapped_consumer date_consumer consumer consumer_name \\\n", - "0 2002 546 2002-01-01 539 B \n", - "1 2003 547 2003-01-01 539 B \n", - "2 2004 548 2004-01-01 539 B \n", - "3 2020 573 2020-01-01 540 A \n", - "4 2021 577 2021-01-01 540 A \n", - ".. ... ... ... ... ... \n", - "69 2020 573 2020-01-01 540 A \n", - "70 2021 577 2021-01-01 540 A \n", - "71 2022 581 2022-01-01 540 A \n", - "72 2023 585 2023-01-01 540 A \n", - "73 2024 587 2024-01-01 540 A \n", - "\n", - " amount interpolation_weights \n", - "0 13.0 {'background_2008': 1} \n", - "1 13.0 {'background_2008': 1} \n", - "2 13.0 {'background_2008': 1} \n", - "3 3.6 {'background_2008': 1} \n", - "4 3.6 {'background_2008': 1} \n", - ".. ... ... \n", - "69 0.4 {'background_2024': 1} \n", - "70 0.4 {'background_2024': 1} \n", - "71 0.4 {'background_2024': 1} \n", - "72 0.4 {'background_2024': 1} \n", - "73 0.4 {'background_2024': 1} \n", - "\n", - "[74 rows x 12 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.build_timeline()" - ] - }, - { - "cell_type": "markdown", - "id": "1c45f1e6", - "metadata": {}, - "source": [ - "Next, we tell the MedusaLCA to build the datapackage including the matrix modifications, adding the time-specific version of processes, including the new technosphere and biosphere exchanges." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1c833eff", - "metadata": {}, - "outputs": [], - "source": [ - "mlca.build_datapackage()" - ] - }, - { - "cell_type": "markdown", - "id": "d857ac49", - "metadata": {}, - "source": [ - "The `MedusaLCA.datapackage` now stores a list of two `bw_processing.Datapackage` objects, essentially containing sparse matrix representations of the changes to the technosphere and biosphere. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b5bd1fb5", - "metadata": {}, - "outputs": [], - "source": [ - "# mlca.datapackage[0].data # technosphere changes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# mlca.datapackage[1].data # biosphere changes" - ] - }, - { - "cell_type": "markdown", - "id": "d4d420c4", - "metadata": {}, - "source": [ - "Just like a normal LCA, we can now calculate the inventory and do the impact assessment:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d0f38d8e", - "metadata": {}, - "outputs": [], - "source": [ - "mlca.lci()\n", - "mlca.lcia()" - ] - }, - { - "cell_type": "markdown", - "id": "48c557e8", - "metadata": {}, - "source": [ - "Let's compare the results of the static LCA and the MedusaLCA:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Old static LCA Score: 2657.222226621744\n", - "New MEDUSA LCA Score: 2653.3894588780236\n" - ] - } - ], - "source": [ - "print('Old static LCA Score:', mlca.static_lca.score)\n", - "print('New MEDUSA LCA Score:', mlca.score)" - ] - }, - { - "cell_type": "markdown", - "id": "9e0b421d", - "metadata": {}, - "source": [ - "The overall score in this example should be the same (or at least somewhat close), because we didn't change any exchange values." - ] - }, - { - "cell_type": "markdown", - "id": "f3fa17bd", - "metadata": {}, - "source": [ - "### Dynamic biosphere exchanges\n", - "\n", - "In addition to the overall score, representing temporal changes in technosphere exchanges, we can also look at temporal changes in the biosphere exchanges." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0fa00433", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df[\"flow\"] = df[\"flow\"].replace(flow_mapping) # replace code (uuid) with id\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/dynamic_characterization.py:121: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df[\"flow\"] = df[\"flow\"].replace(flow_mapping) # replace code (uuid) with id\n" - ] - }, - { - "data": { - "text/html": [ - "
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01997-01-01 00:00:003.318120531carbon dioxide541(background_2024, C)3.318120
11997-12-31 12:21:360.587207532methane544(foreground, B)3.905327
21998-01-01 00:00:001.404000531carbon dioxide542(background_2024, C)5.309327
31999-01-01 00:00:0046.800000531carbon dioxide543(background_2024, C)52.109327
41999-01-01 12:21:360.487665532methane545(foreground, B)52.596992
........................
812041-12-31 23:16:481.345973532methane596(foreground, B)2766.244164
822042-12-31 23:16:4844.865758532methane597(foreground, B)2811.109922
832043-01-01 10:55:120.454426532methane595(foreground, B)2811.564348
842044-01-01 10:55:120.192282532methane596(foreground, B)2811.756630
852044-12-31 10:55:126.409394532methane597(foreground, B)2818.166024
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86 rows × 7 columns

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" - ], - "text/plain": [ - " date amount flow flow_name activity \\\n", - "0 1997-01-01 00:00:00 3.318120 531 carbon dioxide 541 \n", - "1 1997-12-31 12:21:36 0.587207 532 methane 544 \n", - "2 1998-01-01 00:00:00 1.404000 531 carbon dioxide 542 \n", - "3 1999-01-01 00:00:00 46.800000 531 carbon dioxide 543 \n", - "4 1999-01-01 12:21:36 0.487665 532 methane 545 \n", - ".. ... ... ... ... ... \n", - "81 2041-12-31 23:16:48 1.345973 532 methane 596 \n", - "82 2042-12-31 23:16:48 44.865758 532 methane 597 \n", - "83 2043-01-01 10:55:12 0.454426 532 methane 595 \n", - "84 2044-01-01 10:55:12 0.192282 532 methane 596 \n", - "85 2044-12-31 10:55:12 6.409394 532 methane 597 \n", - "\n", - " activity_name amount_sum \n", - "0 (background_2024, C) 3.318120 \n", - "1 (foreground, B) 3.905327 \n", - "2 (background_2024, C) 5.309327 \n", - "3 (background_2024, C) 52.109327 \n", - "4 (foreground, B) 52.596992 \n", - ".. ... ... \n", - "81 (foreground, B) 2766.244164 \n", - "82 (foreground, B) 2811.109922 \n", - "83 (foreground, B) 2811.564348 \n", - "84 (foreground, B) 2811.756630 \n", - "85 (foreground, B) 2818.166024 \n", - "\n", - "[86 rows x 7 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.characterize_dynamic_lci(type=\"GWP\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5d2ed4a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "238.45593712643526" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.dynamic_inventory['CO2']['amount'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "af95b203", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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"(CH4, 2041-12-31T23:16:48) 0.0 \n", - "(CH4, 2044-01-01T10:55:12) 0.0 \n", - "(CH4, 2036-12-31T12:21:36) 1.2 \n", - "(CH4, 2042-12-31T23:16:48) 4.2 \n", - "(CH4, 2044-12-31T10:55:12) 0.6 \n", - "\n", - "[86 rows x 65 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.create_labelled_dynamic_biosphere_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4edf8fef", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/example_EV_setac.ipynb b/archive/notebooks/example_EV_setac.ipynb deleted file mode 100644 index 9869c04..0000000 --- a/archive/notebooks/example_EV_setac.ipynb +++ /dev/null @@ -1,1075 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## EV example SETAC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "copying processes via bw25 didn't work so well as it gave different scores for the process copies despite copying everything (at least I think so) -> see script ...debuggung\n", - "\n", - "instead, I've copied the processes in AB in a bw2 project and now convert it to bw25" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "from bw_temporalis import TemporalDistribution\n", - "sys.path.append(os.path.realpath('../'))\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#bi.backup.restore_project_directory(fp = r'C:\\Users\\MULLERA\\brightway2-project-bw25_premise_background_v2-backup.26-March-2024-01-40PM.tar.gz', project_name= \"bw25_EV_setac\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using existing project: bw25_EV_setac\n" - ] - }, - { - "data": { - "text/plain": [ - "Databases dictionary with 5 object(s):\n", - "\tbiosphere3\n", - "\tcutoff39\n", - "\tdb_2020\n", - "\tdb_2030\n", - "\tdb_2040" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bw2data as bd\n", - "PROJECT_NAME = \"bw25_EV_setac\"\n", - "RESET= False\n", - "\n", - "if PROJECT_NAME in bd.projects and not RESET: # use existing project\n", - " print(\"Using existing project: {}\".format(PROJECT_NAME))\n", - " bd.projects.set_current(PROJECT_NAME) \n", - " \n", - "else: # create project from scratch\n", - " print(\"Creating new project: {}\".format(PROJECT_NAME))\n", - " if PROJECT_NAME in bd.projects:\n", - " bd.projects.delete_project(PROJECT_NAME)\n", - " bi.backup.restore_project_directory(r'filepath/to/backup/directory') # tar file shared on slack\n", - " bd.projects.set_current(PROJECT_NAME)\n", - " \n", - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#del bd.databases[\"foreground\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.twofive" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# prospective databases were generated with premise, updating only electricity\n", - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")\n", - "foreground = bd.Database(\"foreground\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "write foreground db:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'transport, passenger car, electric' (kilometer, GLO, None)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#EV transport process 2020\n", - "EV_driving_bg = [x for x in db_2020 if (x['name'] == 'transport, passenger car, electric')][0]\n", - "EV_driving_bg\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "foreground = bd.Database('foreground')\n", - "foreground.register()\n", - "\n", - "EV= foreground.new_node(\n", - " code = \"EV\",\n", - " name = \"foreground transport, electric vehicle, 1km\",\n", - " location = \"GLO\",\n", - ")\n", - "EV.save()\n", - "\n", - "EV.new_edge(input=(\"foreground\", \"EV\"), amount=1, unit = \"kilometer\", type=\"production\").save() #add production amount\n", - "\n", - "#add technosphere exchanges\n", - "for exc in EV_driving_bg.exchanges(): #looping over the exchanges of the EV process in the background db and adding them to our EV process in the foreground\n", - " if not exc.input == exc.output and \"market for passenger car, electric, without battery\" not in exc.input[\"name\"]: #skip production exchange of process and car production, which we\"ll move to the foreground separately\n", - " #add other exchanges of EV process\n", - " EV.new_edge(input=exc.input.key, amount=exc['amount'], type=exc[\"type\"]).save() # " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# #add car production separately (we skip the ecoinvent GLO market for car production, as it only has 1 input of GLO car production, and no losses)\n", - "car_prod_background = [x for x in db_2020 if (x['name'] == 'passenger car production, electric, without battery')][0]\n", - "\n", - "car_prod = foreground.new_node(\n", - " code = \"car_prod\",\n", - " name = \"foreground car production, electric, without battery\",\n", - " location = \"GLO\",\n", - ")\n", - "car_prod.save()\n", - "\n", - "car_prod.new_edge(input=(\"foreground\", \"car_prod\"), amount=1, unit= \"kilogram\", type=\"production\").save() #add production amount\n", - "#TODO undertsand why impact of production is different betweeen foreground system and background system\n", - "#add technosphere exchanges\n", - "for exc in car_prod_background.exchanges(): #looping over the exchanges of the EV process in the background db and adding them to our EV process in the foreground\n", - " if not exc.input == exc.output: #skip production exchange\n", - " #add other exchanges of car production\n", - " car_prod.new_edge(input=exc.input.key, amount=exc['amount'], type=exc[\"type\"]).save() #\n", - "\n", - " \n", - "for exc in EV_driving_bg.exchanges():\n", - " if \"market for passenger car, electric, without battery\" in exc.input[\"name\"]:\n", - " amount_car_prod_background= exc[\"amount\"] #find amount of car production per 1 km EV driving in background db\n", - "\n", - "EV.new_edge( #add new car prod to EV process\n", - " input=(\"foreground\", \"car_prod\"),\n", - " amount=amount_car_prod_background,\n", - " type=\"technosphere\"\n", - ").save()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select EV processes in dbs" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'foreground transport, electric vehicle, 1km' (None, GLO, None)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#EV transport process foreground\n", - "EV_driving_fg = [x for x in foreground if (x['name'] == 'foreground transport, electric vehicle, 1km')][0]\n", - "EV_driving_fg" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'foreground car production, electric, without battery' (None, GLO, None)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#EV production foreground\n", - "car_prod_fg = [x for x in foreground if (x['name'] == 'foreground car production, electric, without battery')][0]\n", - "car_prod_fg" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# defining Temporal Distributions of system\n", - "td_production = TemporalDistribution(\n", - " date=np.array([-1], dtype='timedelta64[Y]'), #2023\n", - " amount=np.array([1]))\n", - "\n", - "td_use_phase = TemporalDistribution( #to be further refined based on use pattern\n", - " date=np.array([1, 6, 11, 16], dtype='timedelta64[Y]'), #2025, 2030, 2035, 2040\n", - " amount=np.array([0.25, 0.25, 0.25, 0.25]))\n", - "\n", - "td_eol = TemporalDistribution(\n", - " date=np.array([21], dtype='timedelta64[Y]'), #2045\n", - " amount=np.array([1]))\n", - "\n", - "td_eol_from_production_time = TemporalDistribution(\n", - " date=np.array([22], dtype='timedelta64[Y]'), #2045, counting from 2023\n", - " amount=np.array([1]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for brake wear emissions, passenger car'}\n", - "added TD to {'market for road wear emissions, passenger car'}\n", - "added TD to {'market for used Li-ion battery'}\n", - "added TD to {'market for tyre wear emissions, passenger car'}\n", - "added TD to {'market for road'}\n", - "added TD to {'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic'}\n", - "added TD to {'market for maintenance, passenger car, electric, without battery'}\n", - "added TD to {'market group for electricity, low voltage'}\n", - "added TD to {'foreground car production, electric, without battery'}\n" - ] - } - ], - "source": [ - "#add TD to electricity and car production\n", - "for exc in EV_driving_fg.exchanges():\n", - " if \"market for battery, Li-ion\"in exc.input[\"name\"]: #battery production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"foreground car production, electric, without battery\"in exc.input[\"name\"]: # foreground car production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market group for electricity, low voltage\" in exc.input[\"name\"]: #electricity while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market for used Li-ion battery\" in exc.input[\"name\"]: #battery recycling\n", - " exc[\"temporal_distribution\"]= td_eol\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"wear emissions, passenger car\" in exc.input[\"name\"]: #brake, road and tyre wear while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif \"market for maintenance, passenger car\" in exc.input[\"name\"]: #car maintenance while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == \"market for road\": # road usage while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == exc.output[\"name\"]: #skip production exchange\n", - " continue\n", - "\n", - " else:\n", - " print(\"no TD added to \", {exc.input[\"name\"]})\n", - "\n", - " # car recycling is modelled at the level of car production, and TD is added there" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market group for waste glass'}\n", - "added TD to {'market for waste mineral oil'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste mineral oil'}\n", - "added TD to {'market for waste mineral oil'}\n", - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "no TD added to {'market for glider, passenger car'}\n", - "no TD added to {'market for powertrain, for electric passenger car'}\n", - "added TD to {'market for manual dismantling of used electric passenger car'}\n" - ] - } - ], - "source": [ - "for exc in car_prod_fg.exchanges():\n", - " if \"waste\" in exc.input[\"name\"]: # all waste are EoL processes\n", - " exc[\"temporal_distribution\"]= td_eol_from_production_time \n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"manual dismantling\" in exc.input[\"name\"]: # all waste are EoL processes\n", - " exc[\"temporal_distribution\"]= td_eol_from_production_time\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == exc.output[\"name\"]: #skip production exchange\n", - " continue\n", - "\n", - " else:\n", - " print(\"no TD added to \", {exc.input[\"name\"]})\n", - " # production processes (glider and powertrain) are temporalized at the level of the transport process\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'foreground transport, electric vehicle, 1km' (None, GLO, None)\n", - "market for brake wear emissions, passenger car\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "market for road wear emissions, passenger car\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "market for used Li-ion battery\n", - "[662695992]\n", - "[1.]\n", - "market for tyre wear emissions, passenger car\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "market for road\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "market for battery, Li-ion, LiMn2O4, rechargeable, prismatic\n", - "[-31556952]\n", - "[1.]\n", - "market for maintenance, passenger car, electric, without battery\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "market group for electricity, low voltage\n", - "[ 31556952 189341712 347126472 504911232]\n", - "[0.25 0.25 0.25 0.25]\n", - "foreground car production, electric, without battery\n", - "[-31556952]\n", - "[1.]\n", - "'foreground car production, electric, without battery' (None, GLO, None)\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste rubber, unspecified\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market group for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste mineral oil\n", - "[694252944]\n", - "[1.]\n", - "market for waste rubber, unspecified\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste mineral oil\n", - "[694252944]\n", - "[1.]\n", - "market for waste mineral oil\n", - "[694252944]\n", - "[1.]\n", - "market for waste rubber, unspecified\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for waste glass\n", - "[694252944]\n", - "[1.]\n", - "market for manual dismantling of used electric passenger car\n", - "[694252944]\n", - "[1.]\n" - ] - } - ], - "source": [ - "# checking foreground links\n", - "for act in bd.Database('foreground'):\n", - " print(act)\n", - " for exc in act.exchanges():\n", - " if \"temporal_distribution\" in exc.keys():\n", - " print(exc.input[\"name\"])\n", - " print(exc[\"temporal_distribution\"].date)\n", - " print(exc[\"temporal_distribution\"].amount)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "select method:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('IPCC 2021 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC_method = [m for m in bd.methods if 'IPCC 2021' in str(m) and 'climate change no LT' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - "CC_method" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.22196471360166709" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "demand = {EV_driving_fg.key: 1}\n", - "method = CC_method\n", - "\n", - "lca = bc.LCA(demand, method)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.23022282296697355" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "demand = {EV_driving_bg.key: 1}\n", - "method = CC_method\n", - "\n", - "lca = bc.LCA(demand, method)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### see notebook ..._debugging for exploration why the scores are different\n", - "\n", - "for now, we simply acceot that the score is different." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now medusa LCA from here on:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " \"cutoff39\": datetime.strptime(\"2020\", \"%Y\"), # all databases need to have a corresponding time\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "72315" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('db_2020')] + [\n", - " node.id for node in bd.Database('db_2030')] + [\n", - " node.id for node in bd.Database('db_2040')\n", - " ] \n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE\n", - "\n", - "len(SKIPPABLE)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'EV'): 1}\n", - "method = CC_method" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 3\n" - ] - } - ], - "source": [ - "from timex_lca import MedusaLCA\n", - "mlca = MedusaLCA(demand, method, None, database_date_dict, max_calc=10) #aborted after 100 min for max_calc = 5000\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
02023982822023-01-0127559market for glider, passenger car2023982832023-01-0198281foreground car production, electric, without b...0.912635{'cutoff39': 0.6999726252395291, 'db_2030': 0....
12023982832023-01-0198281foreground car production, electric, without b...2024982842024-01-0198280foreground transport, electric vehicle, 1km0.006121{'cutoff39': 0.6999726252395291, 'db_2030': 0....
22024982842024-01-0198280foreground transport, electric vehicle, 1km2024-12024-01-01-1-11.0{'cutoff39': 0.6000547495209416, 'db_2030': 0....
32025982852025-01-0150069market group for electricity, low voltage2024982842024-01-0198280foreground transport, electric vehicle, 1km0.04975{'cutoff39': 0.4998631261976457, 'db_2030': 0....
42030982862030-01-0150069market group for electricity, low voltage2024982842024-01-0198280foreground transport, electric vehicle, 1km0.04975{'db_2030': 1}
52035982872035-01-0150069market group for electricity, low voltage2024982842024-01-0198280foreground transport, electric vehicle, 1km0.04975{'db_2030': 0.5, 'db_2040': 0.5}
62040982882040-01-0150069market group for electricity, low voltage2024982842024-01-0198280foreground transport, electric vehicle, 1km0.04975{'db_2040': 1}
\n", - "
" - ], - "text/plain": [ - " hash_producer time_mapped_producer date_producer producer \\\n", - "0 2023 98282 2023-01-01 27559 \n", - "1 2023 98283 2023-01-01 98281 \n", - "2 2024 98284 2024-01-01 98280 \n", - "3 2025 98285 2025-01-01 50069 \n", - "4 2030 98286 2030-01-01 50069 \n", - "5 2035 98287 2035-01-01 50069 \n", - "6 2040 98288 2040-01-01 50069 \n", - "\n", - " producer_name hash_consumer \\\n", - "0 market for glider, passenger car 2023 \n", - "1 foreground car production, electric, without b... 2024 \n", - "2 foreground transport, electric vehicle, 1km 2024 \n", - "3 market group for electricity, low voltage 2024 \n", - "4 market group for electricity, low voltage 2024 \n", - "5 market group for electricity, low voltage 2024 \n", - "6 market group for electricity, low voltage 2024 \n", - "\n", - " time_mapped_consumer date_consumer consumer \\\n", - "0 98283 2023-01-01 98281 \n", - "1 98284 2024-01-01 98280 \n", - "2 -1 2024-01-01 -1 \n", - "3 98284 2024-01-01 98280 \n", - "4 98284 2024-01-01 98280 \n", - "5 98284 2024-01-01 98280 \n", - "6 98284 2024-01-01 98280 \n", - "\n", - " consumer_name amount \\\n", - "0 foreground car production, electric, without b... 0.912635 \n", - "1 foreground transport, electric vehicle, 1km 0.006121 \n", - "2 -1 1.0 \n", - "3 foreground transport, electric vehicle, 1km 0.04975 \n", - "4 foreground transport, electric vehicle, 1km 0.04975 \n", - "5 foreground transport, electric vehicle, 1km 0.04975 \n", - "6 foreground transport, electric vehicle, 1km 0.04975 \n", - "\n", - " interpolation_weights \n", - "0 {'cutoff39': 0.6999726252395291, 'db_2030': 0.... \n", - "1 {'cutoff39': 0.6999726252395291, 'db_2030': 0.... \n", - "2 {'cutoff39': 0.6000547495209416, 'db_2030': 0.... \n", - "3 {'cutoff39': 0.4998631261976457, 'db_2030': 0.... \n", - "4 {'db_2030': 1} \n", - "5 {'db_2030': 0.5, 'db_2040': 0.5} \n", - "6 {'db_2040': 1} " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "mlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmlca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m mlca\u001b[38;5;241m.\u001b[39mlci()\n\u001b[0;32m 3\u001b[0m mlca\u001b[38;5;241m.\u001b[39mlcia()\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\medusa_lca.py:237\u001b[0m, in \u001b[0;36mMedusaLCA.build_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 233\u001b[0m \u001b[38;5;66;03m# Create matrix modifier that creates the new datapackages with the exploded processes and new links to background databases.\u001b[39;00m\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatrix_modifier \u001b[38;5;241m=\u001b[39m MatrixModifier(\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeline, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatabase_date_dict_static_only, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdemand_timing_dict\n\u001b[0;32m 236\u001b[0m )\n\u001b[1;32m--> 237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatapackage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatrix_modifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:211\u001b[0m, in \u001b[0;36mMatrixModifier.create_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_datapackage\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;124;03m Creates a list of datapackages for the technosphere and biosphere matrices.\u001b[39;00m\n\u001b[0;32m 210\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 211\u001b[0m technosphere_datapackage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_technosphere_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 212\u001b[0m biosphere_datapackge \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_biosphere_datapackage()\n\u001b[0;32m 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [technosphere_datapackage, biosphere_datapackge]\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:137\u001b[0m, in \u001b[0;36mMatrixModifier.create_technosphere_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 134\u001b[0m new_nodes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeline\u001b[38;5;241m.\u001b[39miloc[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mitertuples():\n\u001b[1;32m--> 137\u001b[0m \u001b[43madd_row_to_datapackage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 138\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatapackage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabase_date_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdemand_timing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_nodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 145\u001b[0m \u001b[38;5;66;03m# Adding ones on diagonal for new nodes\u001b[39;00m\n\u001b[0;32m 146\u001b[0m datapackage\u001b[38;5;241m.\u001b[39madd_persistent_vector(\n\u001b[0;32m 147\u001b[0m matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtechnosphere_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 148\u001b[0m name\u001b[38;5;241m=\u001b[39muuid\u001b[38;5;241m.\u001b[39muuid4()\u001b[38;5;241m.\u001b[39mhex,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 152\u001b[0m ),\n\u001b[0;32m 153\u001b[0m )\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:110\u001b[0m, in \u001b[0;36mMatrixModifier.create_technosphere_datapackage..add_row_to_datapackage\u001b[1;34m(row, datapackage, database_date_dict, demand_timing, new_nodes)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatabase_date_dict\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Create new edges based on interpolation_weights from the row\u001b[39;00m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m database, db_share \u001b[38;5;129;01min\u001b[39;00m row\u001b[38;5;241m.\u001b[39minterpolation_weights\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 109\u001b[0m \u001b[38;5;66;03m# Get the producer activity in the corresponding background database\u001b[39;00m\n\u001b[1;32m--> 110\u001b[0m producer_id_in_background_db \u001b[38;5;241m=\u001b[39m bd\u001b[38;5;241m.\u001b[39mget_node(\n\u001b[0;32m 111\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[0;32m 112\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m: database,\n\u001b[0;32m 113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 114\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproduct\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreference product\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 115\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 116\u001b[0m }\n\u001b[0;32m 117\u001b[0m )\u001b[38;5;241m.\u001b[39mid\n\u001b[0;32m 118\u001b[0m \u001b[38;5;66;03m# Add entry between exploded producer and producer in background database (\"Temporal Market\")\u001b[39;00m\n\u001b[0;32m 119\u001b[0m datapackage\u001b[38;5;241m.\u001b[39madd_persistent_vector(\n\u001b[0;32m 120\u001b[0m matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtechnosphere_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 121\u001b[0m name\u001b[38;5;241m=\u001b[39muuid\u001b[38;5;241m.\u001b[39muuid4()\u001b[38;5;241m.\u001b[39mhex,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 127\u001b[0m flip_array\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28;01mTrue\u001b[39;00m], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m),\n\u001b[0;32m 128\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "mlca.build_datapackage()\n", - "mlca.lci()\n", - "mlca.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "transport, passenger car, electric db_2020 transport, passenger car, electric\n", - "market for brake wear emissions, passenger car db_2020 brake wear emissions, passenger car\n", - "market for road wear emissions, passenger car db_2020 road wear emissions, passenger car\n", - "market for used Li-ion battery db_2020 used Li-ion battery\n", - "market for tyre wear emissions, passenger car db_2020 tyre wear emissions, passenger car\n", - "market for road db_2020 road\n", - "market for battery, Li-ion, LiMn2O4, rechargeable, prismatic db_2020 battery, Li-ion, LiMn2O4, rechargeable, prismatic\n", - "market for maintenance, passenger car, electric, without battery db_2020 maintenance, passenger car, electric, without battery\n", - "market for passenger car, electric, without battery db_2020 passenger car, electric, without battery\n", - "market group for electricity, low voltage db_2020 electricity, low voltage\n" - ] - }, - { - "ename": "KeyError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:222\u001b[0m, in \u001b[0;36mActivity.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 222\u001b[0m rp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrp_exchange\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 223\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:412\u001b[0m, in \u001b[0;36mActivity.rp_exchange\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 411\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 412\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 413\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find a single reference product exchange (found \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m candidates)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 414\u001b[0m \u001b[38;5;28mlen\u001b[39m(candidates)\n\u001b[0;32m 415\u001b[0m )\n\u001b[0;32m 416\u001b[0m )\n", - "\u001b[1;31mValueError\u001b[0m: Can't find a single reference product exchange (found 5 candidates)", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[35], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreference product\u001b[39m\u001b[38;5;124m\"\u001b[39m], )\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m exc \u001b[38;5;129;01min\u001b[39;00m EV_driving_fg\u001b[38;5;241m.\u001b[39mexchanges():\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[43mexc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreference product\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, )\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:224\u001b[0m, in \u001b[0;36mActivity.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 222\u001b[0m rp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrp_exchange()\n\u001b[0;32m 223\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m--> 224\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m\n\u001b[0;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m rp\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassifications\u001b[39m\u001b[38;5;124m\"\u001b[39m, []):\n\u001b[0;32m 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rp[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassifications\u001b[39m\u001b[38;5;124m\"\u001b[39m][key]\n", - "\u001b[1;31mKeyError\u001b[0m: " - ] - } - ], - "source": [ - "# maybe the problem lies in that the background databases have unique reference products on the exchanges, \n", - "# while the foreground processes have ValueError: Can't find a single reference product exchange (found 5 candidates)\n", - "# i don't know how to add them\n", - "\n", - "for exc in EV_driving_bg.exchanges():\n", - " print(exc.input[\"name\"], exc.input[\"database\"], exc.input[\"reference product\"], )\n", - "\n", - "for exc in EV_driving_fg.exchanges():\n", - " print(exc.input[\"name\"], exc.input[\"database\"], exc.input[\"reference product\"], )" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "market for waste glass db_2020 waste glass\n", - "market for waste rubber, unspecified db_2020 waste rubber, unspecified\n", - "market for waste glass db_2020 waste glass\n", - "market group for waste glass db_2020 waste glass\n", - "market for waste mineral oil db_2020 waste mineral oil\n", - "passenger car production, electric, without battery db_2020 passenger car, electric, without battery\n", - "market for waste rubber, unspecified db_2020 waste rubber, unspecified\n", - "market for waste glass db_2020 waste glass\n", - "market for waste mineral oil db_2020 waste mineral oil\n", - "market for waste mineral oil db_2020 waste mineral oil\n", - "market for waste rubber, unspecified db_2020 waste rubber, unspecified\n", - "market for waste glass db_2020 waste glass\n", - "market for waste glass db_2020 waste glass\n", - "market for waste glass db_2020 waste glass\n", - "market for waste glass db_2020 waste glass\n", - "market for glider, passenger car db_2020 glider, passenger car\n", - "market for powertrain, for electric passenger car db_2020 powertrain, for electric passenger car\n", - "market for manual dismantling of used electric passenger car db_2020 manual dismantling of used electric passenger car\n" - ] - }, - { - "ename": "KeyError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:222\u001b[0m, in \u001b[0;36mActivity.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 222\u001b[0m rp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrp_exchange\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 223\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:412\u001b[0m, in \u001b[0;36mActivity.rp_exchange\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 411\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 412\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 413\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find a single reference product exchange (found \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m candidates)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 414\u001b[0m \u001b[38;5;28mlen\u001b[39m(candidates)\n\u001b[0;32m 415\u001b[0m )\n\u001b[0;32m 416\u001b[0m )\n", - "\u001b[1;31mValueError\u001b[0m: Can't find a single reference product exchange (found 15 candidates)", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[36], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreference product\u001b[39m\u001b[38;5;124m\"\u001b[39m], )\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m exc \u001b[38;5;129;01min\u001b[39;00m car_prod_fg\u001b[38;5;241m.\u001b[39mexchanges():\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m], exc\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[43mexc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreference product\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, )\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\backends\\proxies.py:224\u001b[0m, in \u001b[0;36mActivity.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 222\u001b[0m rp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrp_exchange()\n\u001b[0;32m 223\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m--> 224\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m\n\u001b[0;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m rp\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassifications\u001b[39m\u001b[38;5;124m\"\u001b[39m, []):\n\u001b[0;32m 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rp[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassifications\u001b[39m\u001b[38;5;124m\"\u001b[39m][key]\n", - "\u001b[1;31mKeyError\u001b[0m: " - ] - } - ], - "source": [ - "for exc in car_prod_background.exchanges():\n", - " print(exc.input[\"name\"], exc.input[\"database\"], exc.input[\"reference product\"], )\n", - "\n", - "for exc in car_prod_fg.exchanges():\n", - " print(exc.input[\"name\"], exc.input[\"database\"], exc.input[\"reference product\"], )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Old static LCA Score:', mlca.static_lca.score)\n", - "print('New MEDUSA LCA Score:', mlca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlca.dynamic_inventory" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tictac2", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/example_EV_timo.ipynb b/archive/notebooks/example_EV_timo.ipynb deleted file mode 100644 index 5856431..0000000 --- a/archive/notebooks/example_EV_timo.ipynb +++ /dev/null @@ -1,2259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import bw2io as bi" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# bi.restore_project_directory('/Users/timodiepers/Documents/Coding/brightway2-project-bw25_premise_background_v2-backup.26-March-2024-01-40PM.tar.gz', overwrite_existing=True, project_name='bw25_premise')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2data as bd" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('bw25_premise')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['foreground']" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'transport, passenger car, electric' (kilometer, GLO, None)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ev_background = [x for x in db_2020 if (x['name'] == 'transport, passenger car, electric')][0]\n", - "ev_background" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in ev_background.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "foreground = bd.Database(\"foreground\")\n", - "foreground.write({})" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ev = ev_background.copy(name=\"copy of transport, passenger car, electric\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully switch activity dataset to database `foreground`\n" - ] - } - ], - "source": [ - "ev['database'] = 'foreground'" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "ev.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Exchange: 1.0 kilometer 'copy of transport, passenger car, electric' (kilometer, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.050395781021507e-06 kilogram 'market for brake wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.155435347754974e-05 kilogram 'market for road wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -0.0026199999265372753 kilogram 'market for used Li-ion battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -6.756759830750525e-05 kilogram 'market for tyre wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.00048747999244369566 meter-year 'market for road' (meter-year, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.0026199999265372753 kilogram 'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 6.666666649834951e-06 unit 'market for maintenance, passenger car, electric, without battery' (unit, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.006121466867625713 kilogram 'market for passenger car, electric, without battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.19900000095367432 kilowatt hour 'market group for electricity, low voltage' (kilowatt hour, World, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in ev.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from bw_temporalis import TemporalDistribution\n", - "import numpy as np\n", - "\n", - "# defining Temporal Distributions of system\n", - "td_production = TemporalDistribution(\n", - " date=np.array([-1], dtype='timedelta64[Y]'), #2023\n", - " amount=np.array([1]))\n", - "\n", - "td_use_phase = TemporalDistribution( #to be further refined based on use pattern\n", - " date=np.array([1, 6, 11, 16], dtype='timedelta64[Y]'), #2025, 2030, 2035, 2040\n", - " amount=np.array([0.25, 0.25, 0.25, 0.25]))\n", - "\n", - "td_eol = TemporalDistribution(\n", - " date=np.array([21], dtype='timedelta64[Y]'), #2045\n", - " amount=np.array([1]))\n", - "\n", - "td_eol_from_production_time = TemporalDistribution(\n", - " date=np.array([22], dtype='timedelta64[Y]'), #2045, counting from 2023\n", - " amount=np.array([1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for brake wear emissions, passenger car'}\n", - "added TD to {'market for road wear emissions, passenger car'}\n", - "added TD to {'market for used Li-ion battery'}\n", - "added TD to {'market for tyre wear emissions, passenger car'}\n", - "added TD to {'market for road'}\n", - "added TD to {'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic'}\n", - "added TD to {'market for maintenance, passenger car, electric, without battery'}\n", - "added TD to {'market for passenger car, electric, without battery'}\n", - "added TD to {'market group for electricity, low voltage'}\n" - ] - } - ], - "source": [ - "#add TD to electricity and car production\n", - "for exc in ev.exchanges():\n", - " if \"market for battery, Li-ion\"in exc.input[\"name\"]: #battery production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market for passenger car, electric, without battery\"in exc.input[\"name\"]: # foreground car production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market group for electricity, low voltage\" in exc.input[\"name\"]: #electricity while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market for used Li-ion battery\" in exc.input[\"name\"]: #battery recycling\n", - " exc[\"temporal_distribution\"]= td_eol\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"wear emissions, passenger car\" in exc.input[\"name\"]: #brake, road and tyre wear while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif \"market for maintenance, passenger car\" in exc.input[\"name\"]: #car maintenance while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == \"market for road\": # road usage while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == exc.output[\"name\"]: #skip production exchange\n", - " continue\n", - "\n", - " else:\n", - " print(\"no TD added to \", {exc.input[\"name\"]})\n", - "\n", - " # car recycling is modelled at the level of car production, and TD is added there" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "method = ('IPCC 2021 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " # \"cutoff39\": datetime.strptime(\"2020\", \"%Y\"), # all databases need to have a corresponding time\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculation count: 5\n" - ] - } - ], - "source": [ - "from timex_lca import TimexLCA\n", - "mlca = TimexLCA({ev.key: 1}, method, None, database_date_dict) #aborted after 100 min for max_calc = 5000\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
02023982852023-01-0135045market for battery, Li-ion, LiMn2O4, rechargea...2024982872024-01-0198284copy of transport, passenger car, electric0.00262{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
12023982862023-01-0143696market for passenger car, electric, without ba...2024982872024-01-0198284copy of transport, passenger car, electric0.006121{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
22024982872024-01-0198284copy of transport, passenger car, electric2024-12024-01-01-1-11.0{'db_2020': 0.6000547495209416, 'db_2030': 0.3...
32025982882025-01-0131847market for road2024982872024-01-0198284copy of transport, passenger car, electric0.000122{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
42025982892025-01-0140481market for maintenance, passenger car, electri...2024982872024-01-0198284copy of transport, passenger car, electric0.000002{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
52025982902025-01-0150069market group for electricity, low voltage2024982872024-01-0198284copy of transport, passenger car, electric0.04975{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
62030982912030-01-0131847market for road2024982872024-01-0198284copy of transport, passenger car, electric0.000122{'db_2030': 1}
72030982922030-01-0140481market for maintenance, passenger car, electri...2024982872024-01-0198284copy of transport, passenger car, electric0.000002{'db_2030': 1}
82030982932030-01-0150069market group for electricity, low voltage2024982872024-01-0198284copy of transport, passenger car, electric0.04975{'db_2030': 1}
92035982942035-01-0131847market for road2024982872024-01-0198284copy of transport, passenger car, electric0.000122{'db_2030': 0.5, 'db_2040': 0.5}
102035982952035-01-0140481market for maintenance, passenger car, electri...2024982872024-01-0198284copy of transport, passenger car, electric0.000002{'db_2030': 0.5, 'db_2040': 0.5}
112035982962035-01-0150069market group for electricity, low voltage2024982872024-01-0198284copy of transport, passenger car, electric0.04975{'db_2030': 0.5, 'db_2040': 0.5}
122040982972040-01-0131847market for road2024982872024-01-0198284copy of transport, passenger car, electric0.000122{'db_2040': 1}
132040982982040-01-0140481market for maintenance, passenger car, electri...2024982872024-01-0198284copy of transport, passenger car, electric0.000002{'db_2040': 1}
142040982992040-01-0150069market group for electricity, low voltage2024982872024-01-0198284copy of transport, passenger car, electric0.04975{'db_2040': 1}
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\\\n", - "(38a622c6-f086-4763-a952-7c6b3b1c42ba, 2023-01-... 0.000000e+00 \n", - "(541a823c-0aad-4dc4-9123-d4af4647d942, 2023-01-... 0.000000e+00 \n", - "(8cbaa905-41b0-4327-8403-bf1c8eb25429, 2023-01-... 0.000000e+00 \n", - "(f681eb3c-854a-4f78-bcfe-76dfbcf9df3c, 2023-01-... 0.000000e+00 \n", - "(a0fec60d-3f74-48bf-a2d2-58c30fc13e53, 2023-01-... 0.000000e+00 \n", - "... ... \n", - "(ddd99a3a-be86-423d-b36a-a9dc8af1b1f8, 2040-01-... 1.515790e-10 \n", - "(21e46cb8-6233-4c99-bac3-c41d2ab99498, 2040-01-... 1.587941e-02 \n", - "(295c9740-6fdb-4676-9eb8-15e3786f713d, 2040-01-... 6.659191e-02 \n", - "(5716c728-bd33-414d-8691-16e5534f5d37, 2040-01-... 1.540927e-01 \n", - "(b967e1bf-f09b-4c89-8740-ace21db47bba, 2040-01-... 1.220363e-02 \n", - "\n", - " ((db_2020, fec93a95a9a84d7fa0ede9c3082bb79f), 2040) \n", - "(38a622c6-f086-4763-a952-7c6b3b1c42ba, 2023-01-... 0.000000e+00 \n", - "(541a823c-0aad-4dc4-9123-d4af4647d942, 2023-01-... 0.000000e+00 \n", - "(8cbaa905-41b0-4327-8403-bf1c8eb25429, 2023-01-... 0.000000e+00 \n", - "(f681eb3c-854a-4f78-bcfe-76dfbcf9df3c, 2023-01-... 0.000000e+00 \n", - "(a0fec60d-3f74-48bf-a2d2-58c30fc13e53, 2023-01-... 0.000000e+00 \n", - "... ... \n", - "(ddd99a3a-be86-423d-b36a-a9dc8af1b1f8, 2040-01-... 1.345962e-13 \n", - "(21e46cb8-6233-4c99-bac3-c41d2ab99498, 2040-01-... 4.218233e-06 \n", - "(295c9740-6fdb-4676-9eb8-15e3786f713d, 2040-01-... 2.138521e-05 \n", - "(5716c728-bd33-414d-8691-16e5534f5d37, 2040-01-... 2.407989e-06 \n", - "(b967e1bf-f09b-4c89-8740-ace21db47bba, 2040-01-... 4.342115e-06 \n", - "\n", - "[10969 rows x 72331 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "mlca.create_labelled_dynamic_biosphere_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "co2_flows = [flow for flow in bd.Database('biosphere3') if 'Carbon dioxide' in flow['name'] and \"air\" in flow['categories']]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "co2_flow_codes = [flow['code'] for flow in co2_flows]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mlca.plot_dynamic_inventory(bio_flows=co2_flow_codes)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "from timex_lca.utils import add_flows_to_characterization_function_dict\n", - "from timex_lca.dynamic_characterization import characterize_co2" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "co2_flow_ids = [flow.id for flow in co2_flows]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "cf_dict = add_flows_to_characterization_function_dict(co2_flow_ids, characterize_co2)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/Documents/Coding/timex/timex_lca/dynamic_characterization.py:99: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " inventory_as_dataframe = pd.concat(dfs, ignore_index=True)\n", - "/Users/timodiepers/Documents/Coding/timex/timex_lca/dynamic_characterization.py:157: UserWarning: If there is no dynamic characterization function for a flow, the flow will be ingored.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateamountflowflow_nameactivityactivity_nameamount_sum
02023-01-01 00:00:000.000000e+00112Carbon dioxide, fossil98285.0(db_2020, 6bf26a8d0923502e6e5a2700be96a866)0.000000e+00
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182023-01-01 00:00:000.000000e+001168Carbon dioxide, fossil98286.0(db_2020, d555fddc42de6c636364ae4eeb4b391f)0.000000e+00
172023-01-01 00:00:000.000000e+00112Carbon dioxide, fossil98286.0(db_2020, d555fddc42de6c636364ae4eeb4b391f)0.000000e+00
162023-01-01 00:00:000.000000e+001164Carbon dioxide, non-fossil98286.0(db_2020, d555fddc42de6c636364ae4eeb4b391f)0.000000e+00
........................
139712139-01-01 00:10:483.071216e-19112Carbon dioxide, fossil98298.0(db_2020, adffc79f1159744571b425ae33d060bf)1.015424e-14
139702139-01-01 00:10:481.502607e-213642Carbon dioxide, from soil or biomass stock98298.0(db_2020, adffc79f1159744571b425ae33d060bf)1.015393e-14
139982139-01-01 00:10:485.994099e-261169Carbon dioxide, fossil98298.0(db_2020, adffc79f1159744571b425ae33d060bf)1.015797e-14
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139992139-01-01 00:10:486.010087e-203642Carbon dioxide, from soil or biomass stock98299.0(db_2020, fec93a95a9a84d7fa0ede9c3082bb79f)1.015803e-14
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" - ], - "text/plain": [ - " date amount flow \\\n", - "0 2023-01-01 00:00:00 0.000000e+00 112 \n", - "19 2023-01-01 00:00:00 0.000000e+00 3642 \n", - "18 2023-01-01 00:00:00 0.000000e+00 1168 \n", - "17 2023-01-01 00:00:00 0.000000e+00 112 \n", - "16 2023-01-01 00:00:00 0.000000e+00 1164 \n", - "... ... ... ... \n", - "13971 2139-01-01 00:10:48 3.071216e-19 112 \n", - "13970 2139-01-01 00:10:48 1.502607e-21 3642 \n", - "13998 2139-01-01 00:10:48 5.994099e-26 1169 \n", - "13983 2139-01-01 00:10:48 1.266081e-23 3632 \n", - "13999 2139-01-01 00:10:48 6.010087e-20 3642 \n", - "\n", - " flow_name activity \\\n", - "0 Carbon dioxide, fossil 98285.0 \n", - "19 Carbon dioxide, from soil or biomass stock 98285.0 \n", - "18 Carbon dioxide, fossil 98286.0 \n", - "17 Carbon dioxide, fossil 98286.0 \n", - "16 Carbon dioxide, non-fossil 98286.0 \n", - "... ... ... \n", - "13971 Carbon dioxide, fossil 98298.0 \n", - "13970 Carbon dioxide, from soil or biomass stock 98298.0 \n", - "13998 Carbon dioxide, fossil 98298.0 \n", - "13983 Carbon dioxide, from soil or biomass stock 98297.0 \n", - "13999 Carbon dioxide, from soil or biomass stock 98299.0 \n", - "\n", - " activity_name amount_sum \n", - "0 (db_2020, 6bf26a8d0923502e6e5a2700be96a866) 0.000000e+00 \n", - "19 (db_2020, 6bf26a8d0923502e6e5a2700be96a866) 0.000000e+00 \n", - "18 (db_2020, d555fddc42de6c636364ae4eeb4b391f) 0.000000e+00 \n", - "17 (db_2020, d555fddc42de6c636364ae4eeb4b391f) 0.000000e+00 \n", - "16 (db_2020, d555fddc42de6c636364ae4eeb4b391f) 0.000000e+00 \n", - "... ... ... \n", - "13971 (db_2020, adffc79f1159744571b425ae33d060bf) 1.015424e-14 \n", - "13970 (db_2020, adffc79f1159744571b425ae33d060bf) 1.015393e-14 \n", - "13998 (db_2020, adffc79f1159744571b425ae33d060bf) 1.015797e-14 \n", - "13983 (db_2020, 464f67b3c32dc2735beab79c8117b91f) 1.015621e-14 \n", - "13999 (db_2020, fec93a95a9a84d7fa0ede9c3082bb79f) 1.015803e-14 \n", - "\n", - "[14000 rows x 7 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlca.characterize_dynamic_lci(type=\"radiative_forcing\", fixed_TH = False, characterization_functions=cf_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mlca.plot_dynamic_characterized_inventory()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "medusa", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/example_EV_timo_incl_car.ipynb b/archive/notebooks/example_EV_timo_incl_car.ipynb deleted file mode 100644 index 9ba87cb..0000000 --- a/archive/notebooks/example_EV_timo_incl_car.ipynb +++ /dev/null @@ -1,1074 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import bw2io as bi" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# bi.restore_project_directory('/Users/timodiepers/Documents/Coding/brightway2-project-bw25_premise_background_v2-backup.26-March-2024-01-40PM.tar.gz', overwrite_existing=True, project_name='bw25_premise')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2data as bd" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('bw25_premise')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 6 object(s):\n", - "\tbiosphere3\n", - "\tcutoff39\n", - "\tdb_2020\n", - "\tdb_2030\n", - "\tdb_2040\n", - "\tforeground" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "del bd.databases['foreground']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'transport, passenger car, electric' (kilometer, GLO, None)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ev_background = [x for x in db_2020 if (x['name'] == 'transport, passenger car, electric')][0]\n", - "ev_background" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in ev_background.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "car_prod_background = [x for x in db_2020 if (x['name'] == 'passenger car production, electric, without battery')][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in car_prod_background.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "foreground = bd.Database(\"foreground\")\n", - "foreground.write({})" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully switch activity dataset to database `foreground`\n" - ] - } - ], - "source": [ - "#copy transport dataset\n", - "ev = ev_background.copy(name=\"copy of transport, passenger car, electric\")\n", - "ev['database'] = 'foreground'\n", - "ev.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully switch activity dataset to database `foreground`\n" - ] - } - ], - "source": [ - "# copy production dataset\n", - "car_prod = car_prod_background.copy(name=\"copy of passenger car production, electric, without battery\")\n", - "car_prod['database'] = 'foreground'\n", - "car_prod.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Exchange: 1.0 kilometer 'copy of transport, passenger car, electric' (kilometer, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.050395781021507e-06 kilogram 'market for brake wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.155435347754974e-05 kilogram 'market for road wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -0.0026199999265372753 kilogram 'market for used Li-ion battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -6.756759830750525e-05 kilogram 'market for tyre wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.00048747999244369566 meter-year 'market for road' (meter-year, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.0026199999265372753 kilogram 'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 6.666666649834951e-06 unit 'market for maintenance, passenger car, electric, without battery' (unit, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.006121466867625713 kilogram 'market for passenger car, electric, without battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.19900000095367432 kilowatt hour 'market group for electricity, low voltage' (kilowatt hour, World, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in ev.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# relink production dataset in transport dataset\n", - "\n", - "old_car_prod = [x for x in ev.exchanges() if x.input['name'] == 'market for passenger car, electric, without battery'][0]\n", - "prod_amount = old_car_prod['amount']\n", - "\n", - "old_car_prod.delete()\n", - "\n", - "ev.new_edge( \n", - " input=car_prod.key,\n", - " amount=prod_amount,\n", - " type=\"technosphere\"\n", - ").save()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Exchange: 1.0 kilometer 'copy of transport, passenger car, electric' (kilometer, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.050395781021507e-06 kilogram 'market for brake wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -1.155435347754974e-05 kilogram 'market for road wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -0.0026199999265372753 kilogram 'market for used Li-ion battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: -6.756759830750525e-05 kilogram 'market for tyre wear emissions, passenger car' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.00048747999244369566 meter-year 'market for road' (meter-year, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.0026199999265372753 kilogram 'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 6.666666649834951e-06 unit 'market for maintenance, passenger car, electric, without battery' (unit, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.19900000095367432 kilowatt hour 'market group for electricity, low voltage' (kilowatt hour, World, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>,\n", - " Exchange: 0.006121466867625713 kilogram 'copy of passenger car production, electric, without battery' (kilogram, GLO, None) to 'copy of transport, passenger car, electric' (kilometer, GLO, None)>]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[e for e in ev.exchanges()]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from bw_temporalis import TemporalDistribution\n", - "import numpy as np\n", - "\n", - "# defining Temporal Distributions of system\n", - "td_production = TemporalDistribution(\n", - " date=np.array([-1], dtype='timedelta64[Y]'), #2023\n", - " amount=np.array([1]))\n", - "\n", - "td_use_phase = TemporalDistribution( #to be further refined based on use pattern\n", - " date=np.array([1, 6, 11, 16], dtype='timedelta64[Y]'), #2025, 2030, 2035, 2040\n", - " amount=np.array([0.25, 0.25, 0.25, 0.25]))\n", - "\n", - "td_eol = TemporalDistribution(\n", - " date=np.array([21], dtype='timedelta64[Y]'), #2045\n", - " amount=np.array([1]))\n", - "\n", - "td_eol_from_production_time = TemporalDistribution(\n", - " date=np.array([22], dtype='timedelta64[Y]'), #2045, counting from 2023\n", - " amount=np.array([1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for brake wear emissions, passenger car'}\n", - "added TD to {'market for road wear emissions, passenger car'}\n", - "added TD to {'market for used Li-ion battery'}\n", - "added TD to {'market for tyre wear emissions, passenger car'}\n", - "added TD to {'market for road'}\n", - "added TD to {'market for battery, Li-ion, LiMn2O4, rechargeable, prismatic'}\n", - "added TD to {'market for maintenance, passenger car, electric, without battery'}\n", - "added TD to {'market group for electricity, low voltage'}\n", - "added TD to {'copy of passenger car production, electric, without battery'}\n" - ] - } - ], - "source": [ - "#add TD to ev process\n", - "for exc in ev.exchanges():\n", - " if \"market for battery, Li-ion\"in exc.input[\"name\"]: #battery production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"copy of passenger car production, electric, without battery\"in exc.input[\"name\"]: # foreground car production\n", - " exc[\"temporal_distribution\"]= td_production\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market group for electricity, low voltage\" in exc.input[\"name\"]: #electricity while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"market for used Li-ion battery\" in exc.input[\"name\"]: #battery recycling\n", - " exc[\"temporal_distribution\"]= td_eol\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"wear emissions, passenger car\" in exc.input[\"name\"]: #brake, road and tyre wear while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif \"market for maintenance, passenger car\" in exc.input[\"name\"]: #car maintenance while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == \"market for road\": # road usage while driving\n", - " exc[\"temporal_distribution\"]= td_use_phase\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == exc.output[\"name\"]: #skip production exchange\n", - " continue\n", - "\n", - " else:\n", - " print(\"no TD added to \", {exc.input[\"name\"]})\n", - "\n", - " # car recycling is modelled at the level of car production, and TD is added there" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for waste glass'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market group for waste glass'}\n", - "added TD to {'market for waste mineral oil'}\n", - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste mineral oil'}\n", - "added TD to {'market for waste mineral oil'}\n", - "added TD to {'market for waste rubber, unspecified'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "added TD to {'market for waste glass'}\n", - "no TD added to {'market for glider, passenger car'}\n", - "no TD added to {'market for powertrain, for electric passenger car'}\n", - "added TD to {'market for manual dismantling of used electric passenger car'}\n" - ] - } - ], - "source": [ - "for exc in car_prod.exchanges():\n", - " if \"waste\" in exc.input[\"name\"]: # all waste are EoL processes\n", - " exc[\"temporal_distribution\"]= td_eol_from_production_time \n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - "\n", - " elif \"manual dismantling\" in exc.input[\"name\"]: # all waste are EoL processes\n", - " exc[\"temporal_distribution\"]= td_eol_from_production_time\n", - " exc.save()\n", - " print(\"added TD to \", {exc.input[\"name\"]})\n", - " \n", - " elif exc.input[\"name\"] == exc.output[\"name\"]: #skip production exchange\n", - " continue\n", - "\n", - " else:\n", - " print(\"no TD added to \", {exc.input[\"name\"]})\n", - " # production processes (glider and powertrain) are already temporalized at the level of the transport process" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "method = ('IPCC 2021 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " # \"cutoff39\": datetime.strptime(\"2020\", \"%Y\"), # all databases need to have a corresponding time\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 8\n" - ] - } - ], - "source": [ - "from timex_lca import TimexLCA\n", - "tlca = TimexLCA({ev.key: 1}, method, None, database_date_dict, cutoff= 0.0000001) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\timeline_builder.py:327: Warning: Reference date 2045-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
02023982822023-01-0127559market for glider, passenger car2023982852023-01-0198281copy of passenger car production, electric, wi...0.912635{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
12023982832023-01-0146481market for powertrain, for electric passenger car2023982852023-01-0198281copy of passenger car production, electric, wi...0.087365{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
22023982842023-01-0135045market for battery, Li-ion, LiMn2O4, rechargea...2024982862024-01-0198280copy of transport, passenger car, electric0.00262{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
32023982852023-01-0198281copy of passenger car production, electric, wi...2024982862024-01-0198280copy of transport, passenger car, electric0.006121{'db_2020': 0.6999726252395291, 'db_2030': 0.3...
42024982862024-01-0198280copy of transport, passenger car, electric2024-12024-01-01-1-11.0{'db_2020': 0.6000547495209416, 'db_2030': 0.3...
52025982872025-01-0131847market for road2024982862024-01-0198280copy of transport, passenger car, electric0.000122{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
62025982882025-01-0140481market for maintenance, passenger car, electri...2024982862024-01-0198280copy of transport, passenger car, electric0.000002{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
72025982892025-01-0150069market group for electricity, low voltage2024982862024-01-0198280copy of transport, passenger car, electric0.04975{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
82030982902030-01-0131847market for road2024982862024-01-0198280copy of transport, passenger car, electric0.000122{'db_2030': 1}
92030982912030-01-0140481market for maintenance, passenger car, electri...2024982862024-01-0198280copy of transport, passenger car, electric0.000002{'db_2030': 1}
102030982922030-01-0150069market group for electricity, low voltage2024982862024-01-0198280copy of transport, passenger car, electric0.04975{'db_2030': 1}
112035982932035-01-0131847market for road2024982862024-01-0198280copy of transport, passenger car, electric0.000122{'db_2030': 0.5, 'db_2040': 0.5}
122035982942035-01-0140481market for maintenance, passenger car, electri...2024982862024-01-0198280copy of transport, passenger car, electric0.000002{'db_2030': 0.5, 'db_2040': 0.5}
132035982952035-01-0150069market group for electricity, low voltage2024982862024-01-0198280copy of transport, passenger car, electric0.04975{'db_2030': 0.5, 'db_2040': 0.5}
142040982962040-01-0131847market for road2024982862024-01-0198280copy of transport, passenger car, electric0.000122{'db_2040': 1}
152040982972040-01-0140481market for maintenance, passenger car, electri...2024982862024-01-0198280copy of transport, passenger car, electric0.000002{'db_2040': 1}
162040982982040-01-0150069market group for electricity, low voltage2024982862024-01-0198280copy of transport, passenger car, electric0.04975{'db_2040': 1}
172045982992045-01-0146589market for manual dismantling of used electric...2023982852023-01-0198281copy of passenger car production, electric, wi...1.0{'db_2040': 1}
\n", - "
" - ], - "text/plain": [ - " hash_producer time_mapped_producer date_producer producer \\\n", - "0 2023 98282 2023-01-01 27559 \n", - "1 2023 98283 2023-01-01 46481 \n", - "2 2023 98284 2023-01-01 35045 \n", - "3 2023 98285 2023-01-01 98281 \n", - "4 2024 98286 2024-01-01 98280 \n", - "5 2025 98287 2025-01-01 31847 \n", - "6 2025 98288 2025-01-01 40481 \n", - "7 2025 98289 2025-01-01 50069 \n", - "8 2030 98290 2030-01-01 31847 \n", - "9 2030 98291 2030-01-01 40481 \n", - "10 2030 98292 2030-01-01 50069 \n", - "11 2035 98293 2035-01-01 31847 \n", - "12 2035 98294 2035-01-01 40481 \n", - "13 2035 98295 2035-01-01 50069 \n", - "14 2040 98296 2040-01-01 31847 \n", - "15 2040 98297 2040-01-01 40481 \n", - "16 2040 98298 2040-01-01 50069 \n", - "17 2045 98299 2045-01-01 46589 \n", - "\n", - " producer_name hash_consumer \\\n", - "0 market for glider, passenger car 2023 \n", - "1 market for powertrain, for electric passenger car 2023 \n", - "2 market for battery, Li-ion, LiMn2O4, rechargea... 2024 \n", - "3 copy of passenger car production, electric, wi... 2024 \n", - "4 copy of transport, passenger car, electric 2024 \n", - "5 market for road 2024 \n", - "6 market for maintenance, passenger car, electri... 2024 \n", - "7 market group for electricity, low voltage 2024 \n", - "8 market for road 2024 \n", - "9 market for maintenance, passenger car, electri... 2024 \n", - "10 market group for electricity, low voltage 2024 \n", - "11 market for road 2024 \n", - "12 market for maintenance, passenger car, electri... 2024 \n", - "13 market group for electricity, low voltage 2024 \n", - "14 market for road 2024 \n", - "15 market for maintenance, passenger car, electri... 2024 \n", - "16 market group for electricity, low voltage 2024 \n", - "17 market for manual dismantling of used electric... 2023 \n", - "\n", - " time_mapped_consumer date_consumer consumer \\\n", - "0 98285 2023-01-01 98281 \n", - "1 98285 2023-01-01 98281 \n", - "2 98286 2024-01-01 98280 \n", - "3 98286 2024-01-01 98280 \n", - "4 -1 2024-01-01 -1 \n", - "5 98286 2024-01-01 98280 \n", - "6 98286 2024-01-01 98280 \n", - "7 98286 2024-01-01 98280 \n", - "8 98286 2024-01-01 98280 \n", - "9 98286 2024-01-01 98280 \n", - "10 98286 2024-01-01 98280 \n", - "11 98286 2024-01-01 98280 \n", - "12 98286 2024-01-01 98280 \n", - "13 98286 2024-01-01 98280 \n", - "14 98286 2024-01-01 98280 \n", - "15 98286 2024-01-01 98280 \n", - "16 98286 2024-01-01 98280 \n", - "17 98285 2023-01-01 98281 \n", - "\n", - " consumer_name amount \\\n", - "0 copy of passenger car production, electric, wi... 0.912635 \n", - "1 copy of passenger car production, electric, wi... 0.087365 \n", - "2 copy of transport, passenger car, electric 0.00262 \n", - "3 copy of transport, passenger car, electric 0.006121 \n", - "4 -1 1.0 \n", - "5 copy of transport, passenger car, electric 0.000122 \n", - "6 copy of transport, passenger car, electric 0.000002 \n", - "7 copy of transport, passenger car, electric 0.04975 \n", - "8 copy of transport, passenger car, electric 0.000122 \n", - "9 copy of transport, passenger car, electric 0.000002 \n", - "10 copy of transport, passenger car, electric 0.04975 \n", - "11 copy of transport, passenger car, electric 0.000122 \n", - "12 copy of transport, passenger car, electric 0.000002 \n", - "13 copy of transport, passenger car, electric 0.04975 \n", - "14 copy of transport, passenger car, electric 0.000122 \n", - "15 copy of transport, passenger car, electric 0.000002 \n", - "16 copy of transport, passenger car, electric 0.04975 \n", - "17 copy of passenger car production, electric, wi... 1.0 \n", - "\n", - " interpolation_weights \n", - "0 {'db_2020': 0.6999726252395291, 'db_2030': 0.3... \n", - "1 {'db_2020': 0.6999726252395291, 'db_2030': 0.3... \n", - "2 {'db_2020': 0.6999726252395291, 'db_2030': 0.3... \n", - "3 {'db_2020': 0.6999726252395291, 'db_2030': 0.3... \n", - "4 {'db_2020': 0.6000547495209416, 'db_2030': 0.3... \n", - "5 {'db_2020': 0.4998631261976457, 'db_2030': 0.5... \n", - "6 {'db_2020': 0.4998631261976457, 'db_2030': 0.5... \n", - "7 {'db_2020': 0.4998631261976457, 'db_2030': 0.5... \n", - "8 {'db_2030': 1} \n", - "9 {'db_2030': 1} \n", - "10 {'db_2030': 1} \n", - "11 {'db_2030': 0.5, 'db_2040': 0.5} \n", - "12 {'db_2030': 0.5, 'db_2040': 0.5} \n", - "13 {'db_2030': 0.5, 'db_2040': 0.5} \n", - "14 {'db_2040': 1} \n", - "15 {'db_2040': 1} \n", - "16 {'db_2040': 1} \n", - "17 {'db_2040': 1} " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "tlca.build_datapackage()\n", - "tlca.lci()\n", - "tlca.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Carbon dioxide, non-fossil' (kilogram, None, ('air', 'non-urban air or from high stacks')),\n", - " 'Carbon dioxide, from soil or biomass stock' (kilogram, None, ('air', 'non-urban air or from high stacks')),\n", - " 'Carbon dioxide, fossil' (kilogram, None, ('air',)),\n", - " 'Carbon dioxide, non-fossil' (kilogram, None, ('air', 'lower stratosphere + upper troposphere')),\n", - " 'Carbon dioxide, fossil' (kilogram, None, ('air', 'non-urban air or from high stacks')),\n", - " 'Carbon dioxide, from soil or biomass stock' (kilogram, None, ('air', 'lower stratosphere + upper troposphere')),\n", - " 'Carbon dioxide, non-fossil' (kilogram, None, ('air', 'urban air close to ground')),\n", - " 'Carbon dioxide, non-fossil' (kilogram, None, ('air', 'low population density, long-term')),\n", - " 'Carbon dioxide, fossil' (kilogram, None, ('air', 'lower stratosphere + upper troposphere')),\n", - " 'Carbon dioxide, fossil' (kilogram, None, ('air', 'low population density, long-term')),\n", - " 'Carbon dioxide, non-fossil' (kilogram, None, ('air',)),\n", - " 'Carbon dioxide, from soil or biomass stock' (kilogram, None, ('air', 'low population density, long-term')),\n", - " 'Carbon dioxide, fossil' (kilogram, None, ('air', 'urban air close to ground')),\n", - " 'Carbon dioxide, from soil or biomass stock' (kilogram, None, ('air', 'urban air close to ground')),\n", - " 'Carbon dioxide, from soil or biomass stock' (kilogram, None, ('air',))]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co2_flows = [flow for flow in bd.Database('biosphere3') if 'Carbon dioxide' in flow['name'] and \"air\" in flow['categories']]\n", - "co2_flows" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "co2_flows_codes = [flow.key[1] for flow in co2_flows]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from collections import defaultdict\n", - "\n", - "# Initialize a defaultdict to store summed amounts for each unique time point\n", - "time_amounts = defaultdict(float)\n", - "\n", - "# Iterate over each specified flow and aggregate amounts by time\n", - "for flow_id in co2_flows_codes:\n", - " if flow_id in tlca.dynamic_inventory:\n", - " flow_data = tlca.dynamic_inventory[flow_id]\n", - " for time, amount in zip(flow_data['time'], flow_data['amount']):\n", - " time_amounts[str(time)] += amount # Convert time to string for uniqueness\n", - "\n", - "# Sort the times and amounts for plotting\n", - "sorted_times = np.array(sorted(time_amounts.keys()))\n", - "sorted_amounts = np.array([time_amounts[time] for time in sorted_times])\n", - "\n", - "# Convert sorted times from strings back to datetime for plotting\n", - "sorted_times = np.array(sorted_times, dtype='datetime64')\n", - "\n", - "# Plotting\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(sorted_times, sorted_amounts, marker='o', linestyle='-')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Sum of Selected CO2 Flows')\n", - "plt.title('Sum of CO2 Flows Over Time')\n", - "plt.grid(True)\n", - "plt.xticks(rotation=45) # Rotate dates for better readability\n", - "plt.tight_layout() # Adjust layout to make room for the rotated date labels\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "medusa", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/example_premise_background.ipynb b/archive/notebooks/example_premise_background.ipynb deleted file mode 100644 index e1ca96a..0000000 --- a/archive/notebooks/example_premise_background.ipynb +++ /dev/null @@ -1,1272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Time explicit LCA with a dummy foreground and premise background\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "from bw_temporalis import TemporalDistribution\n", - "from timex_lca import TimexLCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using existing project: bw25_premise_background_v2\n" - ] - } - ], - "source": [ - "PROJECT_NAME = \"bw25_premise_background_v2\"\n", - "RESET = False\n", - "\n", - "if PROJECT_NAME in bd.projects and not RESET: # use existing project\n", - " print(\"Using existing project: {}\".format(PROJECT_NAME))\n", - " bd.projects.set_current(PROJECT_NAME)\n", - "\n", - "else: # create project from scratch\n", - " print(\"Creating new project: {}\".format(PROJECT_NAME))\n", - " if PROJECT_NAME in bd.projects:\n", - " bd.projects.delete_project(PROJECT_NAME)\n", - " bi.backup.restore_project_directory(\n", - " r\"filepath/to/backup/directory\"\n", - " ) # tar file shared on slack\n", - " bd.projects.set_current(PROJECT_NAME)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# prospective databases were generated with premise, updating only electricity\n", - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")" - ] - }, - { - "attachments": { - "image.png": { - 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Flow chart of system:

![image.png](attachment:image.png)\n", - "

\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select electricity process in dbs\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'market group for electricity, high voltage' (kilowatt hour, WEU, None)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# selecting Western Europe's electricity market mix as\n", - "electr_WEU_2020 = [\n", - " x\n", - " for x in db_2020\n", - " if (\n", - " x[\"name\"] == \"market group for electricity, high voltage\"\n", - " and \"period\" not in x[\"name\"]\n", - " and x[\"location\"] == \"WEU\"\n", - " )\n", - "][0]\n", - "electr_WEU_2020" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "electr_WEU_2030 = [\n", - " x\n", - " for x in db_2030\n", - " if (\n", - " x[\"name\"] == \"market group for electricity, high voltage\"\n", - " and \"period\" not in x[\"name\"]\n", - " and x[\"location\"] == \"WEU\"\n", - " )\n", - "][0]\n", - "\n", - "electr_WEU_2040 = [\n", - " x\n", - " for x in db_2040\n", - " if (\n", - " x[\"name\"] == \"market group for electricity, high voltage\"\n", - " and \"period\" not in x[\"name\"]\n", - " and x[\"location\"] == \"WEU\"\n", - " )\n", - "][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "write foreground db:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 8577.31it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.Database(\"foreground\").write(\n", - " {\n", - " (\"foreground\", \"A\"): {\n", - " \"name\": \"process A\",\n", - " \"exchanges\": [\n", - " {\n", - " \"amount\": 1,\n", - " \"type\": \"production\",\n", - " \"input\": (\"foreground\", \"A\"),\n", - " },\n", - " {\n", - " \"amount\": 1,\n", - " \"type\": \"technosphere\",\n", - " \"input\": (\"foreground\", \"B\"),\n", - " \"temporal_distribution\": TemporalDistribution(\n", - " np.array([1, 11], dtype=\"timedelta64[Y]\"), np.array([0.5, 0.5])\n", - " ),\n", - " },\n", - " ],\n", - " },\n", - " (\"foreground\", \"B\"): {\n", - " \"name\": \"process B\",\n", - " \"exchanges\": [\n", - " {\n", - " \"amount\": 1,\n", - " \"type\": \"production\",\n", - " \"input\": (\"foreground\", \"B\"),\n", - " },\n", - " {\n", - " \"amount\": 1,\n", - " \"type\": \"technosphere\",\n", - " \"input\": electr_WEU_2020.key, # market group for electricity, high voltage' (kilowatt hour, WEU, None)\n", - " }\n", - " ],\n", - " },\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "select method:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "method = (\n", - " \"IPCC 2021 no LT\",\n", - " \"climate change no LT\",\n", - " \"global warming potential (GWP100) no LT\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Standard Timex LCA from here on:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "demand = {(\"foreground\", \"A\"): 1}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculation count: 2\n" - ] - } - ], - "source": [ - "tlca = TimexLCA(\n", - " demand, method, None, database_date_dict\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
02024982872024-01-0198285process A2024-12024-01-01-1-11.0{'db_2020': 0.6000547495209416, 'db_2030': 0.3...
12025982882025-01-0198286process B2024982872024-01-0198285process A0.5{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
22025982892025-01-0149780market group for electricity, high voltage2025982882025-01-0198286process B1.0{'db_2020': 0.4998631261976457, 'db_2030': 0.5...
32035982902035-01-0198286process B2024982872024-01-0198285process A0.5{'db_2030': 0.5, 'db_2040': 0.5}
42035982912035-01-0149780market group for electricity, high voltage2035982902035-01-0198286process B1.0{'db_2030': 0.5, 'db_2040': 0.5}
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"(5716c728-bd33-414d-8691-16e5534f5d37, 2040-01-... 0.0 \n", - "(b967e1bf-f09b-4c89-8740-ace21db47bba, 2040-01-... 0.0 \n", - "\n", - "[6584 rows x 72322 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tlca.create_labelled_dynamic_biosphere_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "co2 = bd.get_node(id=1168)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'aa7cac3a-3625-41d4-bc54-33e2cf11ec46'" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "co2['code']" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlca.plot_dynamic_inventory(co2['code'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "compare with prospective-dynamic score with expected results\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Difference between medusa score and expected score 1.7552186933167402e-05\n" - ] - } - ], - "source": [ - "# compare with expected results: tiny deviation is fine!\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2020\n", - "lca_electr_WEU_2020 = bc.LCA(\n", - " {(electr_WEU_2020[\"database\"], electr_WEU_2020[\"code\"]): 1}, method\n", - ")\n", - "lca_electr_WEU_2020.lci()\n", - "lca_electr_WEU_2020.lcia()\n", - "score_2020 = lca_electr_WEU_2020.score\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2030\n", - "lca_electr_WEU_2030 = bc.LCA(\n", - " {(electr_WEU_2030[\"database\"], electr_WEU_2030[\"code\"]): 1}, method\n", - ")\n", - "lca_electr_WEU_2030.lci()\n", - "lca_electr_WEU_2030.lcia()\n", - "score_2030 = lca_electr_WEU_2030.score\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2040\n", - "lca_electr_WEU_2040 = bc.LCA(\n", - " {(electr_WEU_2040[\"database\"], electr_WEU_2040[\"code\"]): 1}, method\n", - ")\n", - "lca_electr_WEU_2040.lci()\n", - "lca_electr_WEU_2040.lcia()\n", - "score_2040 = lca_electr_WEU_2040.score\n", - "\n", - "# expected score according to temporal distributions\n", - "expected_score = 0.5 * (0.5 * score_2020 + 0.5 * score_2030) + 0.5 * (\n", - " 0.5 * score_2030 + 0.5 * score_2040\n", - ")\n", - "delta = expected_score - tlca.score\n", - "print(f\"Difference between Timex score and expected score {delta}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tictac2", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/example_premise_dummy_background.ipynb b/archive/notebooks/example_premise_dummy_background.ipynb deleted file mode 100644 index a611187..0000000 --- a/archive/notebooks/example_premise_dummy_background.ipynb +++ /dev/null @@ -1,817 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## premise dummy background" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating a simple case study, in which background databases contain only the electricity, high voltagey process from WEU, with the supply-chain-wide biosphere flow aggregated at this dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "from bw_temporalis import TemporalDistribution\n", - "sys.path.append(os.path.realpath('../'))\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using existing project: bw25_premise_background_v2\n" - ] - }, - { - "data": { - "text/plain": [ - "Databases dictionary with 5 object(s):\n", - "\tbiosphere3\n", - "\tcutoff39\n", - "\tdb_2020\n", - "\tdb_2030\n", - "\tdb_2040" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PROJECT_NAME = \"bw25_premise_background_v2\"\n", - "RESET= False\n", - "\n", - "\n", - "if PROJECT_NAME in bd.projects and not RESET: # use existing project\n", - " print(\"Using existing project: {}\".format(PROJECT_NAME))\n", - " bd.projects.set_current(PROJECT_NAME)\n", - " \n", - "else: # create project from scratch\n", - " print(\"Creating new project: {}\".format(PROJECT_NAME))\n", - " if PROJECT_NAME in bd.projects:\n", - " bd.projects.delete_project(PROJECT_NAME)\n", - " bi.backup.restore_project_directory(r'filepath/to/backup/directory') # tar file shared on slack\n", - " bd.projects.set_current(PROJECT_NAME)\n", - " \n", - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# prospective databases were generated with premise, updating only electricity\n", - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2030 = bd.Database(\"db_2030\")\n", - "db_2040 = bd.Database(\"db_2040\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select electricity process in dbs" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['market group for electricity, high voltage' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 20-year period' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 40-year period' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 60-year period' (kilowatt hour, WEU, None)]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db_2030.search(\"market group for electricity, high voltage WEU\")" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "#selecting Western Europe's electricity market mix as\n", - "electr_WEU_2020 = [x for x in db_2020 if (x['name'] == 'market group for electricity, high voltage' and \"period\"not in x[\"name\"] and x[\"location\"] == \"WEU\" )][0]\n", - "electr_WEU_2030 = [x for x in db_2030 if (x['name'] == 'market group for electricity, high voltage' and \"period\"not in x[\"name\"] and x[\"location\"] == \"WEU\")][0]\n", - "electr_WEU_2040 = [x for x in db_2040 if (x['name'] == 'market group for electricity, high voltage' and \"period\"not in x[\"name\"] and x[\"location\"] == \"WEU\")][0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "def copy_process_from_background_with_supply_chain_emissions(process_key, db_name, new_process_name, new_process_code):\n", - " \n", - " \"\"\"\n", - " Copy a process from a background database to a new database, adding supply chain emissions from the background database.\n", - "\n", - " \"\"\"\n", - " # BW needs a method but doesn't matter which one\n", - " CC_method = [m for m in bd.methods if 'IPCC 2021' in str(m) and 'climate change no LT' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - " lca = bc.LCA({process_key: 1}, method=CC_method)\n", - " lca.lci()\n", - " inventory = lca.inventory.sum(axis=1) #uncharacterized inventory matrix, summed over columns\n", - " inventory = [float(x) for x in inventory] #convert to list of floats\n", - "\n", - " bg_db = bd.Database(db_name)\n", - " bg_db.register()\n", - "\n", - " process = bg_db.new_node(\n", - " code = new_process_code,\n", - " name = new_process_name,\n", - " location = bd.get_activity(process_key)[\"location\"],)\n", - " process.save()\n", - "\n", - " process[\"reference product\"] = bd.get_activity(process_key)[\"reference product\"]\n", - " process.save()\n", - "\n", - " #add production amount\n", - " process.new_edge(input=(db_name, new_process_code), amount=1, unit = bd.get_activity(process_key)[\"unit\"], type=\"production\").save() \n", - "\n", - " #add biosphere exchanges\n", - " for idx, amount in zip(lca.dicts.biosphere.reversed.values(), inventory):\n", - " bio_node=bd.get_node(id=idx)\n", - " process.new_edge(input=bio_node, amount=amount, type=\"biosphere\").save()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "processes = [electr_WEU_2020.key, electr_WEU_2030.key, electr_WEU_2040.key]\n", - "db_names = [\"bg_2020\", \"bg_2030\", \"bg_2040\"\t] \n", - "new_process_names = [\"electricity, high voltage\", \"electricity, high voltage\", \"electricity, high voltage\"] #same as reference product\n", - "new_process_codes = [\"electr\", \"electr\", \"electr\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "#write processes with their supply chain emissions to foreground\n", - "\n", - "for proc, db, new_proc_name, new_proc_code in zip(processes, db_names, new_process_names, new_process_codes):\n", - " copy_process_from_background_with_supply_chain_emissions(proc, db, new_proc_name, new_proc_code)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'electricity, high voltage' (None, WEU, None) bg_2020\n", - "('bg_2020', 'electr')\n", - "electricity, high voltage\n", - "'electricity, high voltage' (None, WEU, None) bg_2030\n", - "('bg_2030', 'electr')\n", - "electricity, high voltage\n", - "'electricity, high voltage' (None, WEU, None) bg_2040\n", - "('bg_2040', 'electr')\n", - "electricity, high voltage\n" - ] - } - ], - "source": [ - "#check if they are there\n", - "for db in db_names:\n", - " database=bd.Database(db)\n", - " for act in database:\n", - " print(act, act[\"database\"])\n", - " print(act.key)\n", - " print(act[\"reference product\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " 2020\n", - "process copy: 0.3457984706911323\n", - "original process: 0.34579846666257064\n", - "\n", - " 2030\n", - "process copy: 0.08932478391536282\n", - "original process: 0.08932478248315465\n", - "\n", - " 2040\n", - "process copy: 0.04298181559558537\n", - "original process: 0.04298181694250643\n" - ] - } - ], - "source": [ - "#check if scores are the same\n", - "CC_method = [m for m in bd.methods if 'IPCC 2021' in str(m) and 'climate change no LT' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - "for year, a, b in zip([2020, 2030, 2040],[('bg_2020', 'electr'), ('bg_2030', 'electr'), ('bg_2040', 'electr')],[electr_WEU_2020.key, electr_WEU_2030.key, electr_WEU_2040.key]):\n", - " print(\"\\n\", year)\n", - " lca = bc.LCA({a: 1}, method=CC_method)\n", - " lca.lci()\n", - " lca.lcia()\n", - " print(\"process copy: \", lca.score)\n", - "\n", - " lca = bc.LCA({b: 1}, method=CC_method)\n", - " lca.lci()\n", - " lca.lcia()\n", - " print(\"original process: \", lca.score)" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Flow chart of system:

![image.png](attachment:image.png)\n", - "

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "write foreground db:" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 1978.45it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - } - ], - "source": [ - "bd.Database('foreground').write({\n", - " ('foreground', 'A'): {\n", - " 'name': 'process A',\n", - " \"reference product\": \"a\",\n", - " \"location\": \"GLO\",\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([1, 11], dtype='timedelta64[Y]'),\n", - " np.array([0.5, 0.5]\n", - ")), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'B'):\n", - " {\n", - " \"name\": \"process B\",\n", - " \"reference product\": \"b\",\n", - " \"location\": \"GLO\",\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('bg_2020', 'electr'), # market group for electricity, high voltage' (kilowatt hour, WEU, None)\n", - " }\n", - " ]\n", - "\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'process A' (None, GLO, None) a\n", - "Exchange: 1 None 'process B' (None, GLO, None) to 'process A' (None, GLO, None)> foreground\n", - "'process B' (None, GLO, None) b\n", - "Exchange: 1 None 'electricity, high voltage' (None, WEU, None) to 'process B' (None, GLO, None)> bg_2020\n" - ] - } - ], - "source": [ - "# checking foreground links\n", - "for act in bd.Database('foreground'):\n", - " print(act, act[\"reference product\"])\n", - " for exc in act.technosphere():\n", - " print(exc, exc.input[\"database\"])\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "select method:" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('IPCC 2021 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC_method = [m for m in bd.methods if 'IPCC 2021' in str(m) and 'climate change no LT' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - "CC_method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Standard medusa LCA from here on:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "\n", - "database_date_dict = {\n", - " \"bg_2020\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \"bg_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"bg_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - "\n", - " \"db_2020\": datetime.strptime(\"2020\", \"%Y\"), # all databases need to have a corresponding time\n", - " \"db_2030\": datetime.strptime(\"2030\", \"%Y\"),\n", - " \"db_2040\": datetime.strptime(\"2040\", \"%Y\"),\n", - " \"cutoff39\": datetime.strptime(\"2020\", \"%Y\"),\n", - " \n", - " \"foreground\": \"dynamic\", # flag databases that should be temporally distributed with \"dynamic\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "93570" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('db_2020')] + [\n", - " node.id for node in bd.Database('db_2030')] + [\n", - " node.id for node in bd.Database('db_2040')] + [\n", - " node.id for node in bd.Database('cutoff39')\n", - " ] \n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE\n", - "\n", - "len(SKIPPABLE)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 0.3457984706911323\n" - ] - } - ], - "source": [ - "demand = {(\"foreground\",'A'): 1}\n", - "slca=bc.LCA(demand,CC_method)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 2\n" - ] - } - ], - "source": [ - "from timex_lca import MedusaLCA\n", - "demand = {('foreground', 'A'): 1}\n", - "method = CC_method\n", - "mlca = MedusaLCA(demand, method, None, database_date_dict, max_calc=2) #aborted after 100 min for max_calc = 5000\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hash_producertime_mapped_producerdate_producerproducerproducer_namehash_consumertime_mapped_consumerdate_consumerconsumerconsumer_nameamountinterpolation_weights
02024982852024-01-0198283process A2024-12024-01-01-1-11.0{'cutoff39': 0.6000547495209416, 'db_2030': 0....
12025982862025-01-0198284process B2024982852024-01-0198283process A0.5{'cutoff39': 0.4998631261976457, 'db_2030': 0....
22025982872025-01-0198280electricity, high voltage2025982862025-01-0198284process B1.0{'cutoff39': 0.4998631261976457, 'db_2030': 0....
32035982882035-01-0198284process B2024982852024-01-0198283process A0.5{'db_2030': 0.5, 'db_2040': 0.5}
42035982892035-01-0198280electricity, high voltage2035982882035-01-0198284process B1.0{'db_2030': 0.5, 'db_2040': 0.5}
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" - ], - "text/plain": [ - " hash_producer time_mapped_producer date_producer producer \\\n", - "0 2024 98285 2024-01-01 98283 \n", - "1 2025 98286 2025-01-01 98284 \n", - "2 2025 98287 2025-01-01 98280 \n", - "3 2035 98288 2035-01-01 98284 \n", - "4 2035 98289 2035-01-01 98280 \n", - "\n", - " producer_name hash_consumer time_mapped_consumer \\\n", - "0 process A 2024 -1 \n", - "1 process B 2024 98285 \n", - "2 electricity, high voltage 2025 98286 \n", - "3 process B 2024 98285 \n", - "4 electricity, high voltage 2035 98288 \n", - "\n", - " date_consumer consumer consumer_name amount \\\n", - "0 2024-01-01 -1 -1 1.0 \n", - "1 2024-01-01 98283 process A 0.5 \n", - "2 2025-01-01 98284 process B 1.0 \n", - "3 2024-01-01 98283 process A 0.5 \n", - "4 2035-01-01 98284 process B 1.0 \n", - "\n", - " interpolation_weights \n", - "0 {'cutoff39': 0.6000547495209416, 'db_2030': 0.... \n", - "1 {'cutoff39': 0.4998631261976457, 'db_2030': 0.... \n", - "2 {'cutoff39': 0.4998631261976457, 'db_2030': 0.... \n", - "3 {'db_2030': 0.5, 'db_2040': 0.5} \n", - "4 {'db_2030': 0.5, 'db_2040': 0.5} " - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "mlca.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[67], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmlca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m mlca\u001b[38;5;241m.\u001b[39mlci()\n\u001b[0;32m 3\u001b[0m mlca\u001b[38;5;241m.\u001b[39mlcia()\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\medusa_lca.py:237\u001b[0m, in \u001b[0;36mMedusaLCA.build_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 233\u001b[0m \u001b[38;5;66;03m# Create matrix modifier that creates the new datapackages with the exploded processes and new links to background databases.\u001b[39;00m\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatrix_modifier \u001b[38;5;241m=\u001b[39m MatrixModifier(\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeline, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatabase_date_dict_static_only, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdemand_timing_dict\n\u001b[0;32m 236\u001b[0m )\n\u001b[1;32m--> 237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatapackage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatrix_modifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:211\u001b[0m, in \u001b[0;36mMatrixModifier.create_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_datapackage\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;124;03m Creates a list of datapackages for the technosphere and biosphere matrices.\u001b[39;00m\n\u001b[0;32m 210\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 211\u001b[0m technosphere_datapackage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_technosphere_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 212\u001b[0m biosphere_datapackge \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_biosphere_datapackage()\n\u001b[0;32m 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [technosphere_datapackage, biosphere_datapackge]\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:137\u001b[0m, in \u001b[0;36mMatrixModifier.create_technosphere_datapackage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 134\u001b[0m new_nodes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeline\u001b[38;5;241m.\u001b[39miloc[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mitertuples():\n\u001b[1;32m--> 137\u001b[0m \u001b[43madd_row_to_datapackage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 138\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatapackage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabase_date_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdemand_timing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_nodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 145\u001b[0m \u001b[38;5;66;03m# Adding ones on diagonal for new nodes\u001b[39;00m\n\u001b[0;32m 146\u001b[0m datapackage\u001b[38;5;241m.\u001b[39madd_persistent_vector(\n\u001b[0;32m 147\u001b[0m matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtechnosphere_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 148\u001b[0m name\u001b[38;5;241m=\u001b[39muuid\u001b[38;5;241m.\u001b[39muuid4()\u001b[38;5;241m.\u001b[39mhex,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 152\u001b[0m ),\n\u001b[0;32m 153\u001b[0m )\n", - "File \u001b[1;32mc:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\timex_lca\\matrix_modifier.py:110\u001b[0m, in \u001b[0;36mMatrixModifier.create_technosphere_datapackage..add_row_to_datapackage\u001b[1;34m(row, datapackage, database_date_dict, demand_timing, new_nodes)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatabase_date_dict\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Create new edges based on interpolation_weights from the row\u001b[39;00m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m database, db_share \u001b[38;5;129;01min\u001b[39;00m row\u001b[38;5;241m.\u001b[39minterpolation_weights\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 109\u001b[0m \u001b[38;5;66;03m# Get the producer activity in the corresponding background database\u001b[39;00m\n\u001b[1;32m--> 110\u001b[0m producer_id_in_background_db \u001b[38;5;241m=\u001b[39m bd\u001b[38;5;241m.\u001b[39mget_node(\n\u001b[0;32m 111\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[0;32m 112\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m: database,\n\u001b[0;32m 113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 114\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproduct\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreference product\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 115\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m\"\u001b[39m: previous_producer_node[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 116\u001b[0m }\n\u001b[0;32m 117\u001b[0m )\u001b[38;5;241m.\u001b[39mid\n\u001b[0;32m 118\u001b[0m \u001b[38;5;66;03m# Add entry between exploded producer and producer in background database (\"Temporal Market\")\u001b[39;00m\n\u001b[0;32m 119\u001b[0m datapackage\u001b[38;5;241m.\u001b[39madd_persistent_vector(\n\u001b[0;32m 120\u001b[0m matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtechnosphere_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 121\u001b[0m name\u001b[38;5;241m=\u001b[39muuid\u001b[38;5;241m.\u001b[39muuid4()\u001b[38;5;241m.\u001b[39mhex,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 127\u001b[0m flip_array\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28;01mTrue\u001b[39;00m], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m),\n\u001b[0;32m 128\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "mlca.build_datapackage()\n", - "mlca.lci()\n", - "mlca.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "electricity, from premise bg_2020 electricity, high voltage WEU\n", - "electricity, from premise bg_2030 electricity, high voltage WEU\n", - "electricity, from premise bg_2040 electricity, high voltage WEU\n", - "process B foreground b GLO\n", - "process A foreground a GLO\n" - ] - } - ], - "source": [ - "for name in [\"bg_2020\", \"bg_2030\", \"bg_2040\", \"foreground\"]:\t\n", - " db = bd.Database(name)\n", - " for act in db:\n", - " print(act[\"name\"], act[\"database\"], act[\"reference product\"], act[\"location\"])\n", - "# 112 \"database\": database,\n", - "# 113 \"name\": previous_producer_node[\"name\"],\n", - "# 114 \"product\": previous_producer_node[\"reference product\"],\n", - "# 115 \"location\": previous_producer_node[\"location\"]," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Old static LCA Score:', mlca.static_lca.score)\n", - "print('New MEDUSA LCA Score:', mlca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlca.dynamic_inventory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "compare with prospective-dynamic score with expected results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Difference between medusa score and expected score 2.1680213180275132e-11\n" - ] - } - ], - "source": [ - "# compare with expected results: tiny deviation is fine!\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2020\n", - "lca_electr_WEU_2020=bc.LCA({(electr_WEU_2020[\"database\"],electr_WEU_2020[\"code\"]): 1},CC_method)\n", - "lca_electr_WEU_2020.lci()\n", - "lca_electr_WEU_2020.lcia()\n", - "score_2020 = lca_electr_WEU_2020.score\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2030\n", - "lca_electr_WEU_2030=bc.LCA({(electr_WEU_2030[\"database\"],electr_WEU_2030[\"code\"]): 1},CC_method)\n", - "lca_electr_WEU_2030.lci()\n", - "lca_electr_WEU_2030.lcia()\n", - "score_2030 = lca_electr_WEU_2030.score\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2040\n", - "lca_electr_WEU_2040=bc.LCA({(electr_WEU_2040[\"database\"],electr_WEU_2040[\"code\"]): 1},CC_method)\n", - "lca_electr_WEU_2040.lci()\n", - "lca_electr_WEU_2040.lcia()\n", - "score_2040 = lca_electr_WEU_2040.score\n", - "\n", - "#expected score according to temporal distributions\n", - "expected_score=0.5*(0.5*score_2020+0.5*score_2030) + 0.5*(0.5*score_2030+0.5*score_2040)\n", - "delta=expected_score-mlca.score\n", - "print(f\"Difference between medusa score and expected score {delta}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tictac2", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/medusa_abc_example.ipynb b/archive/notebooks/medusa_abc_example.ipynb deleted file mode 100644 index ddc24de..0000000 --- a/archive/notebooks/medusa_abc_example.ipynb +++ /dev/null @@ -1,796 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)\n", - "\n", - "example with abc" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution\n", - "from edge_extractor import EdgeExtracter\n", - "from medusa_tools import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a51fc994", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.delete_project(\"medusa_abc_example\")\n", - "bd.projects.purge_deleted_directories()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "52469cd3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 33554.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 24385.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 34379.54it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 46603.38it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.projects.set_current(\"medusa_abc_example\")\n", - "\n", - "bd.Database('medusa-bio').write({\n", - " ('medusa-bio', \"CO2\"): {\n", - " \"type\": \"biosphere\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"temporalis code\": \"co2\",\n", - " },\n", - " },\n", - ")\n", - "\n", - "bd.Database(\"background_2024\").write(\n", - " {\n", - " (\"background_2024\", \"C\"): {\n", - " 'name': 'C',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'C',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': (\"background_2024\", 'C'),\n", - " },\n", - " \n", - " {\n", - " \"amount\": 5,\n", - " \"input\": (\"medusa-bio\", \"CO2\"),\n", - " \"type\": \"biosphere\",\n", - " },\n", - " ], \n", - " },\n", - " },\n", - " \n", - ")\n", - "\n", - "bd.Database(\"background_2022\").write(\n", - " {\n", - " (\"background_2022\", \"C\"): {\n", - " 'name': 'C',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'C',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': (\"background_2022\", 'C'),\n", - " }, \n", - " {\n", - " \"amount\": 15,\n", - " \"input\": (\"medusa-bio\", \"CO2\"),\n", - " \"type\": \"biosphere\",\n", - " },\n", - " ], \n", - " },\n", - " },\n", - " \n", - ")\n", - "\n", - "\n", - "\n", - "bd.Database(\"foreground\").write(\n", - " {\n", - " (\"foreground\", \"A\"): {\n", - " 'name': 'A',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'A',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " }, \n", - " {\n", - " \"amount\": 1,\n", - " \"input\": (\"foreground\", \"B\"),\n", - " 'temporal_distribution': easy_timedelta_distribution(\n", - " start=-2,\n", - " end=0, # Range includes both start and end\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=2,\n", - " ),\n", - " \"type\": \"technosphere\",\n", - " },\n", - " ],\n", - " },\n", - " \n", - " (\"foreground\", \"B\"): {\n", - " 'name': 'B',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'B',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'B'),\n", - " },\n", - " {\n", - " \"amount\": 1,\n", - " \"input\": (\"background_2024\", \"C\"),\n", - " \"type\": \"technosphere\",\n", - " },\n", - " ],\n", - " },\n", - " }\n", - ")\n", - "\n", - "bd.Method((\"GWP\", \"example\")).write([\n", - "((\"medusa-bio\", \"CO2\"), 1),\n", - "]) " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "31ced634", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cca6b8f2-12a3-43f9-8be2-c6a898268adf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 5.0\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "b461bbb5", - "metadata": {}, - "source": [ - "# `MEDUSA` LCA" - ] - }, - { - "cell_type": "markdown", - "id": "aa7f0158", - "metadata": {}, - "source": [ - "A MEDUSA LCA builds upon a static LCA, but adds a temporal dimensions, linking to prospective LCA databases. Similarly to a `Temporalis LCA`, the supply chain graph is traversed, taking into account temporal distributions of the edges. \n", - "\n", - "For now, only the foreground system is assumed to have temporal distributions. Therefore, we define a filter function, that tells to EdgeExtracter (which is doing the actual graph traversal and saves the edges with respective timestamps), when a database that is known to have no temporal distributions (i.e., the prospective background databases) is reached, so that the traversal can be stopped." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('background_2020')] + [\n", - " node.id for node in bd.Database('background_2023')\n", - "]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 2\n" - ] - } - ], - "source": [ - "eelca = EdgeExtracter(slca, edge_filter_function=filter_function)\n", - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "markdown", - "id": "ae2733bf", - "metadata": {}, - "source": [ - "Next, we define a dictionary containing the dates of our prospective background databases. Using this, we can create a timeline dataframe. \n", - "\n", - "The dates of the edges are mapped to the prospective background databases; interpolation is used for dates in between the dates of the background databases. The default is linear interpolation, another currently included option is \"nearest\", choosing the next best fitting database." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7b5649e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yearproducerconsumeramountdateinterpolation_weightsproducer_nameconsumer_nametimestamp
02022250.3333332022-01-01{'background_2022': 0.4996577686516085, 'backg...CB2022
12022540.3333332022-01-01{'background_2022': 0.4996577686516085, 'backg...BA2022
22023250.3333332023-01-01{'background_2022': 0.2498288843258042, 'backg...CB2023
32023540.3333332023-01-01{'background_2022': 0.2498288843258042, 'backg...BA2023
42024250.3333332024-01-01{'background_2024': 1}CB2024
520244-11.02024-01-01{'background_2024': 1}A-12024
62024540.3333332024-01-01{'background_2024': 1}BA2024
\n", - "
" - ], - "text/plain": [ - " year producer consumer amount date \\\n", - "0 2022 2 5 0.333333 2022-01-01 \n", - "1 2022 5 4 0.333333 2022-01-01 \n", - "2 2023 2 5 0.333333 2023-01-01 \n", - "3 2023 5 4 0.333333 2023-01-01 \n", - "4 2024 2 5 0.333333 2024-01-01 \n", - "5 2024 4 -1 1.0 2024-01-01 \n", - "6 2024 5 4 0.333333 2024-01-01 \n", - "\n", - " interpolation_weights producer_name \\\n", - "0 {'background_2022': 0.4996577686516085, 'backg... C \n", - "1 {'background_2022': 0.4996577686516085, 'backg... B \n", - "2 {'background_2022': 0.2498288843258042, 'backg... C \n", - "3 {'background_2022': 0.2498288843258042, 'backg... B \n", - "4 {'background_2024': 1} C \n", - "5 {'background_2024': 1} A \n", - "6 {'background_2024': 1} B \n", - "\n", - " consumer_name timestamp \n", - "0 B 2022 \n", - "1 A 2022 \n", - "2 B 2023 \n", - "3 A 2023 \n", - "4 B 2024 \n", - "5 -1 2024 \n", - "6 A 2024 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2020\", \"%Y\"): 'background_2022',\n", - " datetime.strptime(\"2024\", \"%Y\"): 'background_2024',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1f2eb640", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist as brightway project databases\n" - ] - } - ], - "source": [ - "# check that the strings of the databases (values of database_date_dict) exist in the databases of the brightway2 project:\n", - "for db in database_date_dict.values():\n", - " assert db in bd.databases, f\"{db} not in your brightway2 project databases. Please check spelling of this database in database_date_dict and whether you are in the correct project.\"\n", - "else:\n", - " print(\"All databases in database_date_dict exist as brightway project databases\")" - ] - }, - { - "cell_type": "markdown", - "id": "fb32fc50", - "metadata": {}, - "source": [ - "Now, we want to create a datapackage that takes care of relinking processes to our prospective databases. To do so, we need to provide the timeline dataframe, the dict of prospective databases and corresponding years, and a new dictionary that defines at which point in time our functional unit is assessed *(We can probably include this information in the database_date_dict in the future, but for now, this works)*." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d7d48585", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{5: 2024}\n", - "4\n" - ] - }, - { - "ename": "KeyError", - "evalue": "4", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m demand_timing_dict \u001b[38;5;241m=\u001b[39m create_demand_timing_dict(timeline_df, demand)\n\u001b[0;32m----> 3\u001b[0m dp \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_datapackage_from_edge_timeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeline_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatabase_date_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdemand_timing_dict\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Coding/tictac_lca/medusa_tools.py:361\u001b[0m, in \u001b[0;36mcreate_datapackage_from_edge_timeline\u001b[0;34m(timeline, database_date_dict, demand_timing, datapackage, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m consumer_timestamps[\n\u001b[1;32m 356\u001b[0m row\u001b[38;5;241m.\u001b[39mproducer\n\u001b[1;32m 357\u001b[0m ] \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 358\u001b[0m row\u001b[38;5;241m.\u001b[39mtimestamp\n\u001b[1;32m 359\u001b[0m ) \u001b[38;5;66;03m# the year of the producer will be the consumer year for this procuess until a it becomesa producer again\u001b[39;00m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;66;03m# print(row.timestamp, row.producer, row.consumer, consumer_timestamps[row.consumer])\u001b[39;00m\n\u001b[0;32m--> 361\u001b[0m \u001b[43madd_row_to_datapackage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 362\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 363\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatapackage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 364\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatabase_date_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 365\u001b[0m \u001b[43m \u001b[49m\u001b[43mdemand_timing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 366\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_nodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 367\u001b[0m \u001b[43m \u001b[49m\u001b[43mconsumer_timestamps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 368\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;66;03m# Adding ones on diagonal for new nodes\u001b[39;00m\n\u001b[1;32m 371\u001b[0m datapackage\u001b[38;5;241m.\u001b[39madd_persistent_vector(\n\u001b[1;32m 372\u001b[0m matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtechnosphere_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 373\u001b[0m name\u001b[38;5;241m=\u001b[39muuid\u001b[38;5;241m.\u001b[39muuid4()\u001b[38;5;241m.\u001b[39mhex,\n\u001b[1;32m 374\u001b[0m data_array\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mones(\u001b[38;5;28mlen\u001b[39m(new_nodes)),\n\u001b[1;32m 375\u001b[0m indices_array\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([(i, i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m new_nodes], dtype\u001b[38;5;241m=\u001b[39mbwp\u001b[38;5;241m.\u001b[39mINDICES_DTYPE),\n\u001b[1;32m 376\u001b[0m )\n", - "File \u001b[0;32m~/Documents/Coding/tictac_lca/medusa_tools.py:265\u001b[0m, in \u001b[0;36mcreate_datapackage_from_edge_timeline..add_row_to_datapackage\u001b[0;34m(row, datapackage, database_date_dict, demand_timing, new_nodes, consumer_timestamps)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28mprint\u001b[39m(consumer_timestamps)\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28mprint\u001b[39m(row\u001b[38;5;241m.\u001b[39mconsumer)\n\u001b[0;32m--> 265\u001b[0m new_consumer_id \u001b[38;5;241m=\u001b[39m row\u001b[38;5;241m.\u001b[39mconsumer \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000000\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[43mconsumer_timestamps\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconsumer\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;66;03m# print(f'New consumer id = {new_consumer_id}')\u001b[39;00m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# print(f'New added year= {extract_date_as_integer(row.date)}')\u001b[39;00m\n\u001b[1;32m 268\u001b[0m new_producer_id \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 269\u001b[0m row\u001b[38;5;241m.\u001b[39mproducer \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000000\u001b[39m \u001b[38;5;241m+\u001b[39m row\u001b[38;5;241m.\u001b[39mtimestamp\n\u001b[1;32m 270\u001b[0m ) \u001b[38;5;66;03m# In case the producer comes from a background database, we overwrite this. It currently still gets added to new_nodes, but this is not necessary.\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: 4" - ] - } - ], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "7db5ff18", - "metadata": {}, - "source": [ - "Finally, we just have to reformat our input data for the LCA, add our datapackage containing the patches, and run the lca." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71bba776", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'prepare_medusa_lca_inputs' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fu, data_objs, remapping \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_medusa_lca_inputs\u001b[49m(demand\u001b[38;5;241m=\u001b[39mdemand, demand_timing_dict\u001b[38;5;241m=\u001b[39mdemand_timing_dict, method\u001b[38;5;241m=\u001b[39mgwp) \n\u001b[1;32m 2\u001b[0m lca \u001b[38;5;241m=\u001b[39m bc\u001b[38;5;241m.\u001b[39mLCA(fu, data_objs \u001b[38;5;241m=\u001b[39m data_objs \u001b[38;5;241m+\u001b[39m [dp], remapping_dicts\u001b[38;5;241m=\u001b[39mremapping)\n\u001b[1;32m 3\u001b[0m lca\u001b[38;5;241m.\u001b[39mlci()\n", - "\u001b[0;31mNameError\u001b[0m: name 'prepare_medusa_lca_inputs' is not defined" - ] - } - ], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()" - ] - }, - { - "cell_type": "markdown", - "id": "b8f8795d", - "metadata": {}, - "source": [ - "Let's take a look at the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 78053.71897810219\n", - "Old static LCA Score: 1.0\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', lca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5616a354", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<11x11 sparse matrix of type ''\n", - "\twith 20 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca.technosphere_matrix\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33803a4b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<1x11 sparse matrix of type ''\n", - "\twith 2 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca.biosphere_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b925ff99", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'lca' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mlca\u001b[49m\u001b[38;5;241m.\u001b[39mtechnosphere_matrix\u001b[38;5;241m.\u001b[39mtoarray()) \u001b[38;5;66;03m#for excel visualization\u001b[39;00m\n\u001b[1;32m 2\u001b[0m df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 4\u001b[0m second_items_list \u001b[38;5;241m=\u001b[39m [item[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m lca\u001b[38;5;241m.\u001b[39mdicts\u001b[38;5;241m.\u001b[39mactivity\u001b[38;5;241m.\u001b[39mreversed\u001b[38;5;241m.\u001b[39mitems()]\n", - "\u001b[0;31mNameError\u001b[0m: name 'lca' is not defined" - ] - } - ], - "source": [ - "df= pd.DataFrame(lca.technosphere_matrix.toarray()) #for excel visualization\n", - "df.to_csv(\"test.csv\", index=False)\n", - "\n", - "second_items_list = [item[1] for item in lca.dicts.activity.reversed.items()]\n", - "second_items_list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bc6c611", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Electricity mix background_2023 ;\n", - "Electricity production, wind background_2023 ;\n", - "Electricity mix background_2020 ;\n", - "Electricity production, wind background_2020 ;\n", - "Heat production, hydrogen foreground ;\n", - "Hydrogen production, electrolysis foreground ;\n" - ] - }, - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[31], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m lca\u001b[38;5;241m.\u001b[39mactivity_dict:\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_activity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m], bd\u001b[38;5;241m.\u001b[39mget_activity(key)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\muell\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:440\u001b[0m, in \u001b[0;36mget_activity\u001b[1;34m(key, **kwargs)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n\u001b[0;32m 439\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m key\n\u001b[1;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_node(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\muell\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "\n", - "for key in lca.activity_dict:\n", - " print(bd.get_activity(key)['name'], bd.get_activity(key)[\"database\"], ';') #BW does not find the \"exploded nodes\", because they exist only in the datapackages?\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5582a2d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/medusa_hydrogen_example.ipynb b/archive/notebooks/medusa_hydrogen_example.ipynb deleted file mode 100644 index cde84dc..0000000 --- a/archive/notebooks/medusa_hydrogen_example.ipynb +++ /dev/null @@ -1,624 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution\n", - "from edge_extractor import EdgeExtracter\n", - "from medusa_tools import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d00df98a-fcae-4160-a30f-54aed29c1f19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current(\"medusa_hydrogen_example\")" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "26987fcb", - "metadata": {}, - "source": [ - "### Setup of our Example\n", - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00 bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 3\n" - ] - } - ], - "source": [ - "eelca = EdgeExtracter(slca, edge_filter_function=filter_function)\n", - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "markdown", - "id": "ae2733bf", - "metadata": {}, - "source": [ - "Next, we define a dictionary containing the dates of our prospective background databases. Using this, we can create a timeline dataframe. \n", - "\n", - "The dates of the edges are mapped to the prospective background databases; interpolation is used for dates in between the dates of the background databases. The default is linear interpolation, another currently included option is \"nearest\", choosing the next best fitting database." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7b5649e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Reference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateyearproducerproducer_nameconsumerconsumer_nameamountinterpolation_weights
02022-01-012022837Electricity mix842Hydrogen production, electrolysis0.5{2020: 0.333029197080292, 2023: 0.666970802919...
12022-01-012022842Hydrogen production, electrolysis841Heat production, hydrogen0.5{2020: 0.333029197080292, 2023: 0.666970802919...
22024-01-012024837Electricity mix842Hydrogen production, electrolysis0.5{2023: 1}
32024-01-012024841Heat production, hydrogen-1-11.0{2023: 1}
42024-01-012024842Hydrogen production, electrolysis841Heat production, hydrogen0.5{2023: 1}
\n", - "
" - ], - "text/plain": [ - " date year producer producer_name consumer \\\n", - "0 2022-01-01 2022 837 Electricity mix 842 \n", - "1 2022-01-01 2022 842 Hydrogen production, electrolysis 841 \n", - "2 2024-01-01 2024 837 Electricity mix 842 \n", - "3 2024-01-01 2024 841 Heat production, hydrogen -1 \n", - "4 2024-01-01 2024 842 Hydrogen production, electrolysis 841 \n", - "\n", - " consumer_name amount \\\n", - "0 Hydrogen production, electrolysis 0.5 \n", - "1 Heat production, hydrogen 0.5 \n", - "2 Hydrogen production, electrolysis 0.5 \n", - "3 -1 1.0 \n", - "4 Heat production, hydrogen 0.5 \n", - "\n", - " interpolation_weights \n", - "0 {2020: 0.333029197080292, 2023: 0.666970802919... \n", - "1 {2020: 0.333029197080292, 2023: 0.666970802919... \n", - "2 {2023: 1} \n", - "3 {2023: 1} \n", - "4 {2023: 1} " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2020\", \"%Y\"): 'background_2020',\n", - " datetime.strptime(\"2023\", \"%Y\"): 'background_2023',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "markdown", - "id": "fb32fc50", - "metadata": {}, - "source": [ - "Now, we want to create a datapackage that takes care of relinking processes to our prospective databases. To do so, we need to provide the timeline dataframe, the dict of prospective databases and corresponding years, and a new dictionary that defines at which point in time our functional unit is assessed *(We can probably include this information in the database_date_dict in the future, but for now, this works)*." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d7d48585", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "7db5ff18", - "metadata": {}, - "source": [ - "Finally, we just have to reformat our input data for the LCA, add our datapackage containing the patches, and run the lca." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "71bba776", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()" - ] - }, - { - "cell_type": "markdown", - "id": "b8f8795d", - "metadata": {}, - "source": [ - "Let's take a look at the results:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 78053.71897810219\n", - "Old static LCA Score: 1.0\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', lca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/multibranch_timeline_fix.ipynb b/archive/notebooks/multibranch_timeline_fix.ipynb deleted file mode 100644 index 347c430..0000000 --- a/archive/notebooks/multibranch_timeline_fix.ipynb +++ /dev/null @@ -1,2656 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution, easy_datetime_distribution\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.realpath('../'))\n", - "from medusa.edge_extractor import *\n", - "from medusa.matrix_modifier import *\n", - "from medusa.medusa_lca import *\n", - "from medusa.timeline_builder import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "71f67330", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.delete_project(\"multibranch_timeline_fix\")\n", - "bd.projects.purge_deleted_directories()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d00df98a-fcae-4160-a30f-54aed29c1f19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current(\"multibranch_timeline_fix\")" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "26987fcb", - "metadata": {}, - "source": [ - "### Setup of our Example\n", - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 6168.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 40920.04it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 35098.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 69327.34it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.Database('temporalis-bio').write({\n", - " ('temporalis-bio', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"temporalis code\": \"co2\",\n", - " },\n", - "})\n", - "\n", - "bd.Database('background_2024').write({\n", - " ('background_2024', 'C'): {\n", - " 'name': 'C',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'c',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2024', 'C'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2024', 'electricity_wind'),\n", - " },\n", - " ]\n", - " },\n", - " ('background_2024', 'electricity_wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'electricity, wind',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2024', 'electricity_wind'),\n", - " },\n", - " {\n", - " 'amount': 0.9,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " },\n", - " ]\n", - " }\n", - "})\n", - "\n", - "bd.Database('background_2008').write({\n", - " ('background_2008', 'C'): {\n", - " 'name': 'C',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'c',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2008', 'C'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2008', 'electricity_wind'),\n", - " },\n", - " ]\n", - " },\n", - " ('background_2008', 'electricity_wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'electricity, wind',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2008', 'electricity_wind'),\n", - " },\n", - " {\n", - " 'amount': 1.2,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " },\n", - " ]\n", - " }\n", - "})\n", - "\n", - "bd.Database('foreground').write({\n", - " \n", - " ('foreground', 'E'): {\n", - " 'name': 'E',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'e',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'E'),\n", - " },\n", - " {\n", - " 'amount': 11,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2024', 'C'),\n", - " 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " TemporalDistribution(\n", - " date=np.array([10, 5], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([0.3,0.7])\n", - " ), \n", - " },\n", - " {\n", - " 'amount': 8,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " TemporalDistribution(\n", - " date=np.array([-4,-2], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([0.4,0.6])\n", - " ),\n", - " \n", - " },\n", - " {\n", - " 'amount': 4,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'A'),\n", - " # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " # TemporalDistribution(\n", - " # date=np.array([-3,-1], dtype='timedelta64[Y]'), # `M` is months\n", - " # amount=np.array([0.2,0.8])\n", - " # ),\n", - " \n", - " },\n", - " {\n", - " 'amount': 10,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " },\n", - " ]\n", - " },\n", - "\n", - "\n", - "\n", - " ('foreground', 'D'): {\n", - " 'name': 'D',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'd',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'D'),\n", - " },\n", - " {\n", - " 'amount': 5,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " TemporalDistribution(\n", - " date=np.array([-1,], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([1])\n", - " ),\n", - " \n", - " },\n", - " {\n", - " 'amount': 2,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'E'),\n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'B'): {\n", - " 'name': 'B',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'b',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'B'),\n", - " },\n", - " {\n", - " 'amount': 13,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2024', 'C'),\n", - " 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " TemporalDistribution(\n", - " date=np.array([-5], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([1])\n", - " ), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'A'): {\n", - " 'name': 'A',\n", - " 'location': 'somewhere',\n", - " 'reference product': 'a',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " },\n", - " # {\n", - " # 'amount': 4,\n", - " # 'type': 'technosphere',\n", - " # 'input': ('foreground', 'B'),\n", - " # 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " # TemporalDistribution(\n", - " # date=np.array([-20, 15], dtype='timedelta64[Y]'), # `M` is months\n", - " # amount=np.array([0.9, 0.1])\n", - " # ),\n", - " # },\n", - " {\n", - " 'amount': 0.5,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'D'),\n", - " 'temporal_distribution': # e.g. because some hydrogen was stored in the meantime\n", - " TemporalDistribution(\n", - " date=np.array([-2, -1], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([0.7, 0.3])\n", - " ),\n", - " },\n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "935351b5", - "metadata": {}, - "outputs": [], - "source": [ - "td = TemporalDistribution(\n", - " date=np.array([-2, 0], dtype='timedelta64[Y]'), # `M` is months\n", - " amount=np.array([0.4, 0.6])\n", - " )\n", - "c = bd.get_activity(('background_2008', 'C'))\n", - "c.new_exchange(input=bd.get_activity(('foreground', 'E')), amount=1.2, type='technosphere', temporal_distribution=td).save()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8e02b150-0249-4884-90ad-f129c2c13eb2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"example\")).write([\n", - " (('temporalis-bio', \"CO2\"), 1),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "31ced634", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cca6b8f2-12a3-43f9-8be2-c6a898268adf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: -63.18333174784977\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "b461bbb5", - "metadata": {}, - "source": [ - "# `MEDUSA` LCA" - ] - }, - { - "cell_type": "markdown", - "id": "aa7f0158", - "metadata": {}, - "source": [ - "A MEDUSA LCA builds upon a static LCA, but adds a temporal dimensions, linking to prospective LCA databases. Similarly to a `Temporalis LCA`, the supply chain graph is traversed, taking into account temporal distributions of the edges. \n", - "\n", - "For now, only the foreground system is assumed to have temporal distributions. Therefore, we define a filter function, that tells to EdgeExtracter (which is doing the actual graph traversal and saves the edges with respective timestamps), when a database that is known to have no temporal distributions (i.e., the prospective background databases) is reached, so that the traversal can be stopped." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "# SKIPPABLE = [node.id for node in bd.Database('background_2008')] + [\n", - "# node.id for node in bd.Database('background_2024')\n", - "# ]\n", - "SKIPPABLE = []\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 18\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/bw_graph_tools/graph_traversal.py:403: UserWarning: Stopping traversal due to calculation count.\n", - " warnings.warn(\"Stopping traversal due to calculation count.\")\n" - ] - } - ], - "source": [ - "eelca = EdgeExtractor(slca, edge_filter_function=filter_function, max_calc=16)\n", - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "markdown", - "id": "ae2733bf", - "metadata": {}, - "source": [ - "Next, we define a dictionary containing the dates of our prospective background databases. Using this, we can create a timeline dataframe. \n", - "\n", - "The dates of the edges are mapped to the prospective background databases; interpolation is used for dates in between the dates of the background databases. The default is linear interpolation, another currently included option is \"nearest\", choosing the next best fitting database." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fa5b9804", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=-1, producer=9, td_producer=1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 1 values and total: 1, abs_td_consumer=None),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_consumer=TemporalDistribution instance with 1 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 4, leaf=False, consumer=9, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 4, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 2 values and total: 4, abs_td_consumer=TemporalDistribution instance with 1 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 2, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 2.5, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 10, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 5.5, leaf=False, consumer=6, producer=2, td_producer=TemporalDistribution instance with 2 values and total: 11, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 22, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 4, leaf=False, consumer=6, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 8, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 16, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=6, producer=9, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 2 values and total: 2, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 1, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 2, leaf=False, consumer=9, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 4, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 8, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 4 values and total: 4, abs_td_consumer=TemporalDistribution instance with 4 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 1.25, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 4 values and total: 20, abs_td_consumer=TemporalDistribution instance with 4 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 6 values and total: 2.75, leaf=False, consumer=6, producer=2, td_producer=TemporalDistribution instance with 2 values and total: 11, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 44, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 5 values and total: 2, leaf=False, consumer=6, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 8, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 32, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=6, producer=9, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 4, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.125, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 2, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 6 values and total: 1, leaf=False, consumer=9, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 4, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 16, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.125, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 8 values and total: 8, abs_td_consumer=TemporalDistribution instance with 8 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.625, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 8 values and total: 40, abs_td_consumer=TemporalDistribution instance with 8 values and total: 2)]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "824b7602", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 2, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7b5649e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist as brightway project databases\n", - "['2024-01-30T15:59:26' '2024-01-30T15:59:26']\n", - "['2024-01-30T15:59:26' '2024-01-30T15:59:26']\n", - "['2022-01-30T04:21:02' '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2022-01-30T04:21:02' '2023-01-30T10:10:14'\n", - " '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2022-01-30T04:21:02' '2023-01-30T10:10:14'\n", - " '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2022-01-30T04:21:02' '2023-01-30T10:10:14'\n", - " '2023-01-30T10:10:14']\n", - "['2022-01-30T04:21:02' '2022-01-30T04:21:02' '2023-01-30T10:10:14'\n", - " '2023-01-30T10:10:14']\n", - "['2020-01-30T16:42:38' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2020-01-30T16:42:38' '2021-01-29T22:31:50'\n", - " '2021-01-29T22:31:50' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02' '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2020-01-30T16:42:38' '2021-01-29T22:31:50'\n", - " '2021-01-29T22:31:50' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02' '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2020-01-30T16:42:38' '2021-01-29T22:31:50'\n", - " '2021-01-29T22:31:50' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02' '2022-01-30T04:21:02']\n", - "['2020-01-30T16:42:38' '2020-01-30T16:42:38' '2021-01-29T22:31:50'\n", - " '2021-01-29T22:31:50' '2021-01-29T22:31:50' '2021-01-29T22:31:50'\n", - " '2022-01-30T04:21:02' '2022-01-30T04:21:02']\n", - "['2018-01-30T05:04:14' '2019-01-30T10:53:26' '2019-01-30T10:53:26'\n", - " '2020-01-30T16:42:38' '2019-01-30T10:53:26' '2020-01-30T16:42:38'\n", - " '2020-01-30T16:42:38' '2021-01-29T22:31:50']\n", - "['2018-01-30T05:04:14' '2019-01-30T10:53:26' '2019-01-30T10:53:26'\n", - " '2020-01-30T16:42:38' '2019-01-30T10:53:26' '2020-01-30T16:42:38'\n", - " '2020-01-30T16:42:38' '2021-01-29T22:31:50']\n", - " producer consumer leaf consumer_date producer_date amount\n", - "0 9 -1 False 2024-01-30 15:59:26 2024-01-30 15:59:26 1.0\n", - "1 7 9 False 2024-01-30 15:59:26 2022-01-30 04:21:02 0.35\n", - "1 7 9 False 2024-01-30 15:59:26 2023-01-30 10:10:14 0.15\n", - "2 8 9 False 2024-01-30 15:59:26 2004-01-30 19:35:26 3.6\n", - "2 8 9 False 2024-01-30 15:59:26 2039-01-30 07:17:26 0.4\n", - ".. ... ... ... ... ... ...\n", - "18 8 7 False 2020-01-30 16:42:38 2019-01-30 10:53:26 5.0\n", - "18 8 7 False 2019-01-30 10:53:26 2018-01-30 05:04:14 5.0\n", - "18 8 7 False 2020-01-30 16:42:38 2019-01-30 10:53:26 5.0\n", - "18 8 7 False 2020-01-30 16:42:38 2019-01-30 10:53:26 5.0\n", - "18 8 7 False 2021-01-29 22:31:50 2020-01-30 16:42:38 5.0\n", - "\n", - "[87 rows x 6 columns]\n", - "Warning: Reference date 2000-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher year.\n", - "Warning: Reference date 2001-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher year.\n", - "Warning: Reference date 2002-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher year.\n", - "Warning: Reference date 2003-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher year.\n", - "Warning: Reference date 2004-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher year.\n", - "Warning: Reference date 2025-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2026-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2027-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2028-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2030-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2031-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2032-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2033-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2035-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2036-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2037-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2038-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2039-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n" - ] - }, - { - "data": { - "text/html": [ - "
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020008920203.62000-01-01{'background_2008': 1}BA2000
120018920217.22001-01-01{'background_2008': 1}BA2001
220028920227.22002-01-01{'background_2008': 1}BA2002
320038920233.62003-01-01{'background_2008': 1}BA2003
420048920243.62004-01-01{'background_2008': 1}BA2004
520168620203.22016-01-01{'background_2008': 0.5, 'background_2024': 0.5}BE2016
620178620216.42017-01-01{'background_2008': 0.43737166324435317, 'back...BE2017
720178720185.02017-01-01{'background_2008': 0.43737166324435317, 'back...BD2017
820186720181.02018-01-01{'background_2008': 0.37491444216290215, 'back...ED2018
920187920200.352018-01-01{'background_2008': 0.37491444216290215, 'back...DA2018
1020188620204.82018-01-01{'background_2008': 0.37491444216290215, 'back...BE2018
1120188620226.42018-01-01{'background_2008': 0.37491444216290215, 'back...BE2018
12201887201915.02018-01-01{'background_2008': 0.37491444216290215, 'back...BD2018
1320196720193.02019-01-01{'background_2008': 0.312457221081451, 'backgr...ED2019
1420197920200.152019-01-01{'background_2008': 0.312457221081451, 'backgr...DA2019
1520197920210.72019-01-01{'background_2008': 0.312457221081451, 'backgr...DA2019
1620198620219.62019-01-01{'background_2008': 0.312457221081451, 'backgr...BE2019
1720198620233.22019-01-01{'background_2008': 0.312457221081451, 'backgr...BE2019
18201987202020.02019-01-01{'background_2008': 0.312457221081451, 'backgr...BD2019
1920206720204.02020-01-01{'background_2008': 0.25, 'background_2024': 0...ED2020
2020207920210.32020-01-01{'background_2008': 0.25, 'background_2024': 0...DA2020
2120207920220.72020-01-01{'background_2008': 0.25, 'background_2024': 0...DA2020
2220208620229.62020-01-01{'background_2008': 0.25, 'background_2024': 0...BE2020
23202087202115.02020-01-01{'background_2008': 0.25, 'background_2024': 0...BD2020
2420209620201.02020-01-01{'background_2008': 0.25, 'background_2024': 0...AE2020
2520216720213.02021-01-01{'background_2008': 0.18737166324435317, 'back...ED2021
2620217920220.32021-01-01{'background_2008': 0.18737166324435317, 'back...DA2021
2720217920230.352021-01-01{'background_2008': 0.18737166324435317, 'back...DA2021
2820218620234.82021-01-01{'background_2008': 0.18737166324435317, 'back...BE2021
29202187202210.02021-01-01{'background_2008': 0.18737166324435317, 'back...BD2021
3020219620212.02021-01-01{'background_2008': 0.18737166324435317, 'back...AE2021
3120226720222.02022-01-01{'background_2008': 0.12491444216290215, 'back...ED2022
3220227920230.152022-01-01{'background_2008': 0.12491444216290215, 'back...DA2022
3320227920240.352022-01-01{'background_2008': 0.12491444216290215, 'back...DA2022
3420228720235.02022-01-01{'background_2008': 0.12491444216290215, 'back...BD2022
3520229620222.02022-01-01{'background_2008': 0.12491444216290215, 'back...AE2022
3620236720231.02023-01-01{'background_2008': 0.06245722108145102, 'back...ED2023
3720237920240.152023-01-01{'background_2008': 0.06245722108145102, 'back...DA2023
3820239620231.02023-01-01{'background_2008': 0.06245722108145102, 'back...AE2023
3920249-120241.02024-01-01{'background_2024': 1}A-12024
4020252620207.72025-01-01{'background_2024': 1}CE2025
41202626202115.42026-01-01{'background_2024': 1}CE2026
42202726202215.42027-01-01{'background_2024': 1}CE2027
4320282620237.72028-01-01{'background_2024': 1}CE2028
4420302620203.32030-01-01{'background_2024': 1}CE2030
4520312620216.62031-01-01{'background_2024': 1}CE2031
4620322620226.62032-01-01{'background_2024': 1}CE2032
4720332620233.32033-01-01{'background_2024': 1}CE2033
4820358920200.42035-01-01{'background_2024': 1}BA2035
4920368920210.82036-01-01{'background_2024': 1}BA2036
5020378920220.82037-01-01{'background_2024': 1}BA2037
5120388920230.42038-01-01{'background_2024': 1}BA2038
5220398920240.42039-01-01{'background_2024': 1}BA2039
\n", - "
" - ], - "text/plain": [ - " year producer consumer consumer_datestamp amount date \\\n", - "0 2000 8 9 2020 3.6 2000-01-01 \n", - "1 2001 8 9 2021 7.2 2001-01-01 \n", - "2 2002 8 9 2022 7.2 2002-01-01 \n", - "3 2003 8 9 2023 3.6 2003-01-01 \n", - "4 2004 8 9 2024 3.6 2004-01-01 \n", - "5 2016 8 6 2020 3.2 2016-01-01 \n", - "6 2017 8 6 2021 6.4 2017-01-01 \n", - "7 2017 8 7 2018 5.0 2017-01-01 \n", - "8 2018 6 7 2018 1.0 2018-01-01 \n", - "9 2018 7 9 2020 0.35 2018-01-01 \n", - "10 2018 8 6 2020 4.8 2018-01-01 \n", - "11 2018 8 6 2022 6.4 2018-01-01 \n", - "12 2018 8 7 2019 15.0 2018-01-01 \n", - "13 2019 6 7 2019 3.0 2019-01-01 \n", - "14 2019 7 9 2020 0.15 2019-01-01 \n", - "15 2019 7 9 2021 0.7 2019-01-01 \n", - "16 2019 8 6 2021 9.6 2019-01-01 \n", - "17 2019 8 6 2023 3.2 2019-01-01 \n", - "18 2019 8 7 2020 20.0 2019-01-01 \n", - "19 2020 6 7 2020 4.0 2020-01-01 \n", - "20 2020 7 9 2021 0.3 2020-01-01 \n", - "21 2020 7 9 2022 0.7 2020-01-01 \n", - "22 2020 8 6 2022 9.6 2020-01-01 \n", - "23 2020 8 7 2021 15.0 2020-01-01 \n", - "24 2020 9 6 2020 1.0 2020-01-01 \n", - "25 2021 6 7 2021 3.0 2021-01-01 \n", - "26 2021 7 9 2022 0.3 2021-01-01 \n", - "27 2021 7 9 2023 0.35 2021-01-01 \n", - "28 2021 8 6 2023 4.8 2021-01-01 \n", - "29 2021 8 7 2022 10.0 2021-01-01 \n", - "30 2021 9 6 2021 2.0 2021-01-01 \n", - "31 2022 6 7 2022 2.0 2022-01-01 \n", - "32 2022 7 9 2023 0.15 2022-01-01 \n", - "33 2022 7 9 2024 0.35 2022-01-01 \n", - "34 2022 8 7 2023 5.0 2022-01-01 \n", - "35 2022 9 6 2022 2.0 2022-01-01 \n", - "36 2023 6 7 2023 1.0 2023-01-01 \n", - "37 2023 7 9 2024 0.15 2023-01-01 \n", - "38 2023 9 6 2023 1.0 2023-01-01 \n", - "39 2024 9 -1 2024 1.0 2024-01-01 \n", - "40 2025 2 6 2020 7.7 2025-01-01 \n", - "41 2026 2 6 2021 15.4 2026-01-01 \n", - "42 2027 2 6 2022 15.4 2027-01-01 \n", - "43 2028 2 6 2023 7.7 2028-01-01 \n", - "44 2030 2 6 2020 3.3 2030-01-01 \n", - "45 2031 2 6 2021 6.6 2031-01-01 \n", - "46 2032 2 6 2022 6.6 2032-01-01 \n", - "47 2033 2 6 2023 3.3 2033-01-01 \n", - "48 2035 8 9 2020 0.4 2035-01-01 \n", - "49 2036 8 9 2021 0.8 2036-01-01 \n", - "50 2037 8 9 2022 0.8 2037-01-01 \n", - "51 2038 8 9 2023 0.4 2038-01-01 \n", - "52 2039 8 9 2024 0.4 2039-01-01 \n", - "\n", - " interpolation_weights producer_name \\\n", - "0 {'background_2008': 1} B \n", - "1 {'background_2008': 1} B \n", - "2 {'background_2008': 1} B \n", - "3 {'background_2008': 1} B \n", - "4 {'background_2008': 1} B \n", - "5 {'background_2008': 0.5, 'background_2024': 0.5} B \n", - "6 {'background_2008': 0.43737166324435317, 'back... 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A \n", - "39 {'background_2024': 1} A \n", - "40 {'background_2024': 1} C \n", - "41 {'background_2024': 1} C \n", - "42 {'background_2024': 1} C \n", - "43 {'background_2024': 1} C \n", - "44 {'background_2024': 1} C \n", - "45 {'background_2024': 1} C \n", - "46 {'background_2024': 1} C \n", - "47 {'background_2024': 1} C \n", - "48 {'background_2024': 1} B \n", - "49 {'background_2024': 1} B \n", - "50 {'background_2024': 1} B \n", - "51 {'background_2024': 1} B \n", - "52 {'background_2024': 1} B \n", - "\n", - " consumer_name timestamp \n", - "0 A 2000 \n", - "1 A 2001 \n", - "2 A 2002 \n", - "3 A 2003 \n", - "4 A 2004 \n", - "5 E 2016 \n", - "6 E 2017 \n", - "7 D 2017 \n", - "8 D 2018 \n", - "9 A 2018 \n", - "10 E 2018 \n", - "11 E 2018 \n", - "12 D 2018 \n", - "13 D 2019 \n", - "14 A 2019 \n", - "15 A 2019 \n", - "16 E 2019 \n", - "17 E 2019 \n", - "18 D 2019 \n", - "19 D 2020 \n", - "20 A 2020 \n", - "21 A 2020 \n", - "22 E 2020 \n", - "23 D 2020 \n", - "24 E 2020 \n", - "25 D 2021 \n", - "26 A 2021 \n", - "27 A 2021 \n", - "28 E 2021 \n", - "29 D 2021 \n", - "30 E 2021 \n", - "31 D 2022 \n", - "32 A 2022 \n", - "33 A 2022 \n", - "34 D 2022 \n", - "35 E 2022 \n", - "36 D 2023 \n", - "37 A 2023 \n", - "38 E 2023 \n", - "39 -1 2024 \n", - "40 E 2025 \n", - "41 E 2026 \n", - "42 E 2027 \n", - "43 E 2028 \n", - "44 E 2030 \n", - "45 E 2031 \n", - "46 E 2032 \n", - "47 E 2033 \n", - "48 A 2035 \n", - "49 A 2036 \n", - "50 A 2037 \n", - "51 A 2038 \n", - "52 A 2039 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2008\", \"%Y\"): 'background_2008',\n", - " datetime.strptime(\"2024\", \"%Y\"): 'background_2024',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "b1185ed1", - "metadata": {}, - "outputs": [], - "source": [ - "timeline_df.to_excel(\"timeline_df.xlsx\")" - ] - }, - { - "cell_type": "markdown", - "id": "fb32fc50", - "metadata": {}, - "source": [ - "Now, we want to create a datapackage that takes care of relinking processes to our prospective databases. To do so, we need to provide the timeline dataframe, the dict of prospective databases and corresponding years, and a new dictionary that defines at which point in time our functional unit is assessed *(We can probably include this information in the database_date_dict in the future, but for now, this works)*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7d48585", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "7db5ff18", - "metadata": {}, - "source": [ - "Finally, we just have to reformat our input data for the LCA, add our datapackage containing the patches, and run the lca." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71bba776", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()" - ] - }, - { - "cell_type": "markdown", - "id": "b8f8795d", - "metadata": {}, - "source": [ - "Let's take a look at the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 260.69869977555595\n", - "Old static LCA Score: 189.54999524354938\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', lca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - }, - { - "cell_type": "markdown", - "id": "ce158e2f", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "228f2954", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"\n", - "[29 rows x 29 columns]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(lca.technosphere_matrix.toarray())\n", - "df.rename(lca.dicts.activity.reversed, inplace=True, axis=0)\n", - "df.rename(lca.dicts.activity.reversed, inplace=True, axis=1)\n", - "df\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6e47e80", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'new_edges_df' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/ajakobs/Documents/SCENE/prospective_dynamic_lca/tictac_lca/notebooks/multibranch_timeline_fix.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m new_edges_df\u001b[39m.\u001b[39mexplode([\u001b[39m'\u001b[39m\u001b[39mconsumer_date\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mproducer_date\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mamount\u001b[39m\u001b[39m'\u001b[39m])\n", - "\u001b[0;31mNameError\u001b[0m: name 'new_edges_df' is not defined" - ] - } - ], - "source": [ - "new_edges_df.explode(['consumer_date', 'producer_date', 'amount'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3990158", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: ('temporalis-bio', 'CO2'),\n", - " 2: ('background_2024', 'electricity_mix'),\n", - " 3: ('background_2024', 'electricity_wind'),\n", - " 4: ('background_2020', 'electricity_mix'),\n", - " 5: ('background_2020', 'electricity_wind'),\n", - " 6: ('foreground', 'someotherprocess'),\n", - " 7: ('foreground', 'electrolysis'),\n", - " 8: ('foreground', 'heat_from_hydrogen')}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rem = lca.remapping_dicts['activity']\n", - "rem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3f9a12c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2: 0,\n", - " 3: 1,\n", - " 4: 2,\n", - " 5: 3,\n", - " 6: 4,\n", - " 7: 5,\n", - " 8: 6,\n", - " 2002021: 7,\n", - " 2002022: 8,\n", - " 2002023: 9,\n", - " 2002024: 10,\n", - " 6002022: 11,\n", - " 6002024: 12,\n", - " 7002022: 13,\n", - " 7002024: 14,\n", - " 8002024: 15}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(lca.dicts.activity)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a328e2e4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/premise-example.ipynb b/archive/notebooks/premise-example.ipynb deleted file mode 100644 index 58bc10a..0000000 --- a/archive/notebooks/premise-example.ipynb +++ /dev/null @@ -1,1169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "import bw_processing as bwp\n", - "from utilities import *" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Brightway2 projects manager with 10 objects:\n", - "\tLCA_VL_masterdb\n", - "\tTest\n", - "\tas23_project\n", - "\tas23_project_bw2\n", - "\tdefault\n", - "\tei39\n", - "\tei39_premise_scenarios\n", - "\tpremise_ei39\n", - "\ttictac\n", - "\t🪒\n", - "Use `projects.report()` to get a report on all projects." - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current(\"ei39\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 3 object(s):\n", - "\tbiosphere3\n", - "\tecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2020\n", - "\tei39" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - } - ], - "source": [ - "del bd.databases['ecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2020']" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "from fs.zipfs import ZipFS" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "premise_dp = bwp.load_datapackage(ZipFS('8420384.zip'))" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'profile': 'tabular-data-package',\n", - " 'resources': [{'path': 'data/inventories.csv',\n", - " 'profile': 'tabular-data-resource',\n", - " 'name': 'inventories',\n", - " 'format': 'csv',\n", - " 'mediatype': 'text/csv',\n", - " 'encoding': 'utf-8',\n", - " 'schema': {'fields': [{'name': 'Database',\n", - " 'type': 'string',\n", - " 'format': 'default'},\n", - " {'name': 'remind SSP2-PkBudg1150',\n", - " 'type': 'string',\n", - " 'format': 'default'}],\n", - " 'missingValues': ['']}},\n", - " {'path': 'data/scenario_data.csv',\n", - " 'profile': 'tabular-data-resource',\n", - " 'name': 'scenario_data',\n", - " 'format': 'csv',\n", - " 'mediatype': 'text/csv',\n", - " 'encoding': 'utf-8',\n", - " 'schema': {'fields': [{'name': 'from activity name',\n", - " 'type': 'string',\n", - " 'format': 'default'},\n", - " {'name': 'from reference product', 'type': 'string', 'format': 'default'},\n", - " {'name': 'from location', 'type': 'string', 'format': 'default'},\n", - " {'name': 'from categories', 'type': 'string', 'format': 'default'},\n", - " {'name': 'from database', 'type': 'string', 'format': 'default'},\n", - " {'name': 'from key', 'type': 'string', 'format': 'default'},\n", - " {'name': 'from unit', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to activity name', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to reference product', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to location', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to categories', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to unit', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to database', 'type': 'string', 'format': 'default'},\n", - " {'name': 'to key', 'type': 'string', 'format': 'default'},\n", - " {'name': 'flow type', 'type': 'string', 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2020',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2025',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2030',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2035',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2040',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2045',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2050',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2060',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2070',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2080',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2090',\n", - " 'type': 'number',\n", - " 'format': 'default'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2100',\n", - " 'type': 'number',\n", - " 'format': 'default'}],\n", - " 'missingValues': ['']}}],\n", - " 'name': 'remind SSP2-PkBudg1150',\n", - " 'title': 'Remind ssp2-pkbudg1150',\n", - " 'description': 'Data package generated by premise (1, 7, 5).',\n", - " 'premise version': '(1, 7, 5)',\n", - " 'dependencies': [{'name': 'ecoinvent',\n", - " 'system model': 'cut-off',\n", - " 'version': '3.9',\n", - " 'type': 'source'},\n", - " {'name': 'biosphere3'}],\n", - " 'scenarios': [{'name': 'remind - SSP2-PkBudg1150 - 2020',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2020, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2025',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2025, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2030',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2030, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2035',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2035, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2040',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2040, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2045',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2045, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2050',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2050, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2060',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2060, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2070',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2070, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2080',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2080, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2090',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2090, and external scenario .'},\n", - " {'name': 'remind - SSP2-PkBudg1150 - 2100',\n", - " 'description': 'Prospective db, based on REMIND, pathway SSP2-PKBUDG1150, for the year 2100, and external scenario .'}],\n", - " 'keywords': ['ecoinvent', 'scenario', 'data package', 'premise'],\n", - " 'licenses': [{'id': 'CC0-1.0',\n", - " 'title': 'CC0 1.0',\n", - " 'url': 'https://creativecommons.org/publicdomain/zero/1.0/'}]}" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "premise_dp.metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;31mInit signature:\u001b[0m\n", - "\u001b[0mbc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnormalization\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpathlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw_processing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatapackage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mremapping_dicts\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlog_config\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mSource:\u001b[0m \n", - "\u001b[1;32mclass\u001b[0m \u001b[0mLCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"An LCI or LCIA calculation.\n", - "\n", - " Compatible with Brightway2 and 2.5 semantics. Can be static, stochastic, or iterative (scenario-based), depending on the ``data_objs`` input data..\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"technosphere_mm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"biosphere_mm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"characterization_mm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"normalization_mm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"weighting_mm\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#############\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Setup ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#############\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# Brightway 2 calling convention\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnormalization\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# Brightway 2.5 calling convention\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mPath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mremapping_dicts\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlog_config\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Create a new LCA calculation.\n", - "\n", - " Args:\n", - " * *demand* (dict): The demand or functional unit. Needs to be a dictionary to indicate amounts, e.g. ``{7: 2.5}``.\n", - " * *method* (tuple, optional): LCIA Method tuple, e.g. ``(\"My\", \"great\", \"LCIA\", \"method\")``. Can be omitted if only interested in calculating the life cycle inventory.\n", - "\n", - " Returns:\n", - " A new LCA object\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMapping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Demand must be a dictionary\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata_objs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_bw2data_available\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mremapping_dicts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprepare_lca_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweighting\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnormalization\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnormalization\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormalization\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mget_datapackage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDictionaryManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremapping_dicts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mremapping_dicts\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\"Initialized LCA object. Demand: {demand}, data_objs: {data_objs}\"\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"demand\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mwrap_functional_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"data_objs\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"bw2calc\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"pypardiso\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mPYPARDISO\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"numpy\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"matrix_utils\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"bw_processing\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"utc\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutcnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mkeep_first_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Set a flag to use the current values as first element when iterating.\n", - "\n", - " When creating the class instance, we already use the first index. This method allows us to use the values for the first index.\n", - "\n", - " Note that the methods ``.lci_calculation()`` and ``.lcia_calculation()`` will be called on the current values, even if these calculations have already been done.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeep_first_iteration_flag\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mskip_first_iteration\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"keep_first_iteration_flag\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeep_first_iteration_flag\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmatrix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix_labels\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_first_iteration\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\"Iterating {matrix}. Indexers: {indexer_state}\"\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mindexer_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"matrix\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"indexers\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"matrix_sum\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"utc\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutcnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_first_iteration\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"after_matrix_iteration\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mafter_matrix_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mskip_first_iteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdelattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"keep_first_iteration_flag\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlci_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"characterized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlcia_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mensure_bw2data_available\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Raises ``ImportError`` is bw2data not available or version < 4.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mprepare_lca_inputs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bw2data version >= 4 not found\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbuild_demand_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Turn the demand dictionary into a *NumPy* array of correct size.\n", - "\n", - " Args:\n", - " * *demand* (dict, optional): Demand dictionary. Optional, defaults to ``self.demand``.\n", - "\n", - " Returns:\n", - " A 1-dimensional NumPy array\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemand\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand_array\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34mf\"LCA can only be performed on products, not activities ({key} is the wrong dimension)\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mOutsideTechnosphere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34mf\"Can't find key {key} in product dictionary\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m######################\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Data retrieval ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m######################\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_lci_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnonsquare_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load inventory data and create technosphere and biosphere matrices.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMappedMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpackages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"technosphere_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_mapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_mapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnonsquare_ok\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mNonsquareTechnosphere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"Technosphere matrix is not square: {} activities (columns) and {} products (rows). \"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"Use LeastSquaresLCA to solve this system, or fix the input \"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"data\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_mapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMappedMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpackages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"biosphere_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcol_mapper\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_mapper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mempty_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"No valid biosphere flows found. No inventory results can \"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"be calculated, `lcia` will raise an error\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mremap_inventory_dicts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Remap ``self.dicts.activity|product|biosphere`` and ``self.demand`` from database integer IDs to keys (``(database name, code)``).\n", - "\n", - " Uses remapping dictionaries in ``self.remapping_dicts``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m\"product\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremapping_dicts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremapping_dicts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"product\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"activity\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"product\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"biosphere\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremapping_dicts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremapping_dicts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_lcia_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load data and create characterization matrix.\n", - "\n", - " This method will filter out regionalized characterization factors.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mglobal_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconsistent_global_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_objs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfltr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"col\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mglobal_index\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mglobal_index\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterization_mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMappedMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpackages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_objs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"characterization_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_mapper\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdiagonal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcustom_filter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfltr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterization_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterization_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_normalization_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load normalization data.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization_mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMappedMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpackages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_objs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"normalization_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_mapper\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdiagonal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_weighting_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load normalization data.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting_mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleValueDiagonalMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpackages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_objs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"weighting_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdimension\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_mapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_arrays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_arrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0muse_distributions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_distributions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseed_override\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed_override\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting_mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Calculations ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdecompose_technosphere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " Factorize the technosphere matrix into lower and upper triangular matrices, :math:`A=LU`. Does not solve the linear system :math:`Ax=B`.\n", - "\n", - " Doesn't return anything, but creates ``self.solver``.\n", - "\n", - " .. warning:: Incorrect results could occur if a technosphere matrix was factorized, and then a new technosphere matrix was constructed, as ``self.solver`` would still be the factorized older technosphere matrix. You are responsible for deleting ``self.solver`` when doing these types of advanced calculations.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mPYPARDISO\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PARDISO installed; this is a no-op\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfactorized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtocsc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msolve_linear_system\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " Master solution function for linear system :math:`Ax=B`.\n", - "\n", - " To most numerical analysts, matrix inversion is a sin.\n", - "\n", - " -- Nicolas Higham, Accuracy and Stability of Numerical Algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2002, p. 260.\n", - "\n", - " We use `UMFpack `_, which is a very fast solver for sparse matrices.\n", - "\n", - " If the technosphere matrix has already been factorized, then the decomposed technosphere (``self.solver``) is reused. Otherwise the calculation is redone completely.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"solver\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mspsolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " Calculate a life cycle inventory.\n", - "\n", - " #. Load LCI data, and construct the technosphere and biosphere matrices.\n", - " #. Build the demand array\n", - " #. Solve the linear system to get the supply array and life cycle inventory.\n", - "\n", - " Args:\n", - " * *factorize* (bool, optional): Factorize the technosphere matrix. Makes additional calculations with the same technosphere matrix much faster. Default is ``False``; not useful is only doing one LCI calculation.\n", - " * *builder* (``MatrixBuilder`` object, optional): Default is ``bw2calc.matrices.MatrixBuilder``, which is fine for most cases. Custom matrix builders can be used to manipulate data in creative ways before building the matrices.\n", - "\n", - " Doesn't return anything, but creates ``self.supply_array`` and ``self.inventory``.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"technosphere_matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_lci_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdemand\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__check_demand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_demand_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_demand_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfactorize\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mPYPARDISO\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecompose_technosphere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlci_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlci_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"The actual LCI calculation.\n", - "\n", - " Separated from ``lci`` to be reusable in cases where the matrices are already built, e.g. ``redo_lci`` and Monte Carlo classes.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupply_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve_linear_system\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# Turn 1-d array into diagonal matrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minventory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_matrix\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0msparse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspdiags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupply_array\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlcia\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " Calculate the life cycle impact assessment.\n", - "\n", - " #. Load and construct the characterization matrix\n", - " #. Multiply the characterization matrix by the life cycle inventory\n", - "\n", - " Doesn't return anything, but creates ``self.characterized_inventory``.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Must do lci first\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mEmptyBiosphere\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"characterization_matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_lcia_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdemand\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__check_demand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlcia_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlcia_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"The actual LCIA calculation.\n", - "\n", - " Separated from ``lcia`` to be reusable in cases where the matrices are already built, e.g. ``redo_lcia`` and Monte Carlo classes.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterized_inventory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterization_matrix\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minventory\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Multiply characterized inventory by flow-specific normalization factors.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"characterized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Must do lcia first\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"normalization_matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_normalization_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mnormalization_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"The actual normalization calculation.\n", - "\n", - " Creates ``self.normalized_inventory``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalized_inventory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalization_matrix\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterized_inventory\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Backwards compatibility. Switching to verb form consistent with ``.normalize``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please switch to `.weight`'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Multiply characterized inventory by weighting value.\n", - "\n", - " Can be done with or without normalization.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"characterized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Must do lcia first\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"weighting_value\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weighting_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mweighting_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"The actual weighting calculation.\n", - "\n", - " Multiples weighting value by normalized inventory, if available, otherwise by characterized inventory.\n", - "\n", - " Creates ``self.weighted_inventory``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"normalized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalized_inventory\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterized_inventory\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighted_inventory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighting_matrix\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " The LCIA score as a ``float``.\n", - "\n", - " Note that this is a `property `_, so it is ``foo.lca``, not ``foo.score()``\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"characterized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Must do LCIA first\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"weighted_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighted_inventory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"normalized_inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalized_inventory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharacterized_inventory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#########################\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Redo calculations ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#########################\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_switch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mCallable\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Switch a method, weighting, or normalization\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_bw2data_available\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprepare_lca_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdata_objs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mpkg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"matrix\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpkg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdata_objs\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_objs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_objs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34mf\"\"\"Switched LCIA {label}. data_objs: {data_objs}\"\"\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"data_objs\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_objs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"utc\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutcnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mswitch_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load a new method and replace ``.characterization_mm`` and ``.characterization_matrix``.\n", - "\n", - " Does not do any new calculations or change ``.characterized_inventory``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_switch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"method\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"characterization_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_lcia_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mswitch_normalization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormalization\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load a new normalization and replace ``.normalization_mm`` and ``.normalization_matrix``.\n", - "\n", - " Does not do any new calculations or change ``.normalized_inventory``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_switch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnormalization\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"normalization\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"normalization_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_normalization_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mswitch_weighting\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweighting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mFS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbwp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatapackageBase\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Load a new weighting and replace ``.weighting_mm`` and ``.weighting_matrix``.\n", - "\n", - " Does not do any new calculations or change ``.weighted_inventory``.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_switch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweighting\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"weighting\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"weighting_matrix\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weighting_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minvert_technosphere_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Use pardiso to efficiently calculate the inverse of the technosphere matrix.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"inventory\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Must do lci first\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mPYPARDISO\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"pardiso solver needed for efficient matrix inversion\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mMESSAGE\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\"Technosphere matrix inversion is often not the most efficient approach.\n", - " See https://github.com/brightway-lca/brightway2-calc/issues/35\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMESSAGE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverted_technosphere_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mspsolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverted_technosphere_matrix\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__check_demand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdemand\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Key '{key}' not in product dictionary; make sure to pass the integer id, not a key like `('foo', 'bar')` or an `Actiivity` or `Node` object.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mredo_lci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Redo LCI with same databases but different demand.\n", - "\n", - " Args:\n", - " * *demand* (dict): A demand dictionary.\n", - "\n", - " Doesn't return anything, but overwrites ``self.demand_array``, ``self.supply_array``, and ``self.inventory``.\n", - "\n", - " .. warning:: If you want to redo the LCIA as well, use ``redo_lcia(demand)`` directly.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please use .lci(demand=demand) instead of `redo_lci`.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mredo_lcia\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Redo LCIA, optionally with new demand.\n", - "\n", - " Args:\n", - " * *demand* (dict, optional): New demand dictionary. Optional, defaults to ``self.demand``.\n", - "\n", - " Doesn't return anything, but overwrites ``self.characterized_inventory``. If ``demand`` is given, also overwrites ``self.demand_array``, ``self.supply_array``, and ``self.inventory``.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Please use .lcia(demand=demand) instead of `redo_lci`.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlcia\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mto_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatrix_label\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"characterized_inventory\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow_dict\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_dict\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mannotate\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mNumber\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcutoff_mode\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"number\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Return all nonzero elements of the given matrix as a Pandas dataframe.\n", - "\n", - " The LCA class instance must have the matrix ``matrix_label`` already; common labels are:\n", - "\n", - " * characterized_inventory\n", - " * inventory\n", - " * technosphere_matrix\n", - " * biosphere_matrix\n", - " * characterization_matrix\n", - "\n", - " For these common matrices, we already have ``row_dict`` and ``col_dict`` which link row and column indices to database ids. For other matrices, or if you have a custom mapping dictionary, override ``row_dict`` and/or ``col_dict``. They have the form ``{matrix index: identifier}``.\n", - "\n", - " If ``bw2data`` is installed, this function will try to look up metadata on the row and column objects. To turn this off, set ``annotate`` to ``False``.\n", - "\n", - " Instead of returning all possible values, you can apply a cutoff. This cutoff can be specified in two ways, controlled by ``cutoff_mode``, which should be either ``fraction`` or ``number``.\n", - "\n", - " If ``cutoff_mode`` is ``number`` (the default), then ``cutoff`` is the number of rows in the DataFrame. Data values are first sorted by their absolute value, and then the largest ``cutoff`` are taken.\n", - "\n", - " If ``cutoff_mode`` is ``fraction``, then only values whose absolute value is greater than ``cutoff * total_score`` are taken. ``cutoff`` must be between 0 and 1.\n", - "\n", - " The returned DataFrame will have the following columns:\n", - "\n", - " * amount\n", - " * col_index\n", - " * row_index\n", - "\n", - " If row or columns dictionaries are available, the following columns are added:\n", - "\n", - " * col_id\n", - " * row_id\n", - "\n", - " If ``bw2data`` is available, then the following columns are added:\n", - "\n", - " * col_code\n", - " * col_database\n", - " * col_location\n", - " * col_name\n", - " * col_reference_product\n", - " * col_type\n", - " * col_unit\n", - " * row_categories\n", - " * row_code\n", - " * row_database\n", - " * row_location\n", - " * row_name\n", - " * row_type\n", - " * row_unit\n", - " * source_product\n", - "\n", - " Returns a pandas ``DataFrame``.\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatrix_label\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtocoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdict_mapping\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m'characterized_inventory'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m'inventory'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m'technosphere_matrix'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m'biosphere_matrix'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m'characterization_matrix'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mrow_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict_mapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmatrix_label\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcol_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict_mapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmatrix_label\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcol_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msorter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msorter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msorter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msorter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcutoff\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcutoff_mode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'fraction'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mcutoff\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"fraction `cutoff` value must be between 0 and 1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtotal\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mcutoff_mode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'number'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - 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"\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdct\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"row_product\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"reference product\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdct\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdict_for_obj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mget_node\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mannotate\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrow_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_metadata_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmetadata_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mget_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'row_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"row_\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow_metadata_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"row_id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcol_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcol_metadata_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmetadata_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mget_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'col_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"col_\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_metadata_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"col_id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Contribution ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# def top_emissions(self, **kwargs):\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# \"\"\"Call ``bw2analyzer.ContributionAnalyses.annotated_top_emissions``\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# try:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# from bw2analyzer import ContributionAnalysis\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# except ImportError:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# raise ImportError(\"`bw2analyzer` is not installed\")\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# return ContributionAnalysis().annotated_top_emissions(self, **kwargs)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# def top_activities(self, **kwargs):\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# \"\"\"Call ``bw2analyzer.ContributionAnalyses.annotated_top_processes``\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# try:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# from bw2analyzer import ContributionAnalysis\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# except ImportError:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# raise ImportError(\"`bw2analyzer` is not installed\")\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# return ContributionAnalysis().annotated_top_processes(self, **kwargs)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"Shortcut to find out if matrix data for type ``{label}_matrix`` is present in the given data objects.\n", - "\n", - " Returns a boolean. Will return ``True`` even if data for a zero-dimensional matrix is given.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpackage\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mresource\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresources\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresource\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"matrix\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mf\"{label}_matrix\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m### Compatibility ###\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m#####################\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mactivity_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This method is deprecated, please use `.dicts.activity` instead\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mproduct_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This method is deprecated, please use `.dicts.product` instead\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbiosphere_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This method is deprecated, please use `.dicts.biosphere` instead\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreverse_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This method is deprecated, please use `.dicts.X.reversed` directly\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreversed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFile:\u001b[0m c:\\users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py\n", - "\u001b[1;31mType:\u001b[0m ABCMeta\n", - "\u001b[1;31mSubclasses:\u001b[0m DenseLCA, LeastSquaresLCA" - ] - } - ], - "source": [ - "bc.LCA??" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updating all LCIA methods\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "762it [03:19, 3.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updating all LCI databases\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2it [00:37, 18.94s/it]\n" - ] - } - ], - "source": [ - "bd.projects.migrate_project_25()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "pear_market = bd.get_node(name=\"market for pear\", database=\"ei39\")\n", - "pear_china = bd.get_node(name=\"pear production\", location=\"CN\", database=\"ei39\")\n", - "apple = bd.get_node(name=\"apple production\", location=\"CL\", database=\"ei39\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "ipcc = ('IPCC 2021', 'climate change: fossil', 'global warming potential (GWP100)')\n", - "demand = {pear_market: 1}" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pear: 0.446075994204733\n" - ] - } - ], - "source": [ - "# Get the list of `data_objs` - our new datapackage will be appended to this list.\n", - "fu, data_objs, remapping = bd.prepare_lca_inputs(demand=demand, method=ipcc)\n", - "lca = bc.LCA(fu, data_objs=data_objs, remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "print(\"Pear:\", lca.score)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'combinatorial'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\timod\\OneDrive\\Dokumente\\Python\\tictac_lca\\premise-example.ipynb Cell 12\u001b[0m line \u001b[0;36m7\n\u001b[0;32m 1\u001b[0m dp \u001b[39m=\u001b[39m safety_razor(\n\u001b[0;32m 2\u001b[0m consumer\u001b[39m=\u001b[39mpear_market,\n\u001b[0;32m 3\u001b[0m previous_producer\u001b[39m=\u001b[39mpear_china, \n\u001b[0;32m 4\u001b[0m new_producer\u001b[39m=\u001b[39mapple, \n\u001b[0;32m 5\u001b[0m )\n\u001b[0;32m 6\u001b[0m lca \u001b[39m=\u001b[39m bc\u001b[39m.\u001b[39mLCA(fu, data_objs\u001b[39m=\u001b[39mdata_objs \u001b[39m+\u001b[39m [premise_dp, dp], remapping_dicts\u001b[39m=\u001b[39mremapping)\n\u001b[1;32m----> 7\u001b[0m lca\u001b[39m.\u001b[39;49mlci()\n\u001b[0;32m 8\u001b[0m lca\u001b[39m.\u001b[39mlcia()\n\u001b[0;32m 9\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mApples are not pears:\u001b[39m\u001b[39m\"\u001b[39m, lca\u001b[39m.\u001b[39mscore)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py:354\u001b[0m, in \u001b[0;36mLCA.lci\u001b[1;34m(self, demand, factorize)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 340\u001b[0m \u001b[39mCalculate a life cycle inventory.\u001b[39;00m\n\u001b[0;32m 341\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 351\u001b[0m \n\u001b[0;32m 352\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 353\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtechnosphere_matrix\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 354\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mload_lci_data()\n\u001b[0;32m 355\u001b[0m \u001b[39mif\u001b[39;00m demand \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 356\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__check_demand(demand)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py:191\u001b[0m, in \u001b[0;36mLCA.load_lci_data\u001b[1;34m(self, nonsquare_ok)\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mload_lci_data\u001b[39m(\u001b[39mself\u001b[39m, nonsquare_ok\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 190\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Load inventory data and create technosphere and biosphere matrices.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 191\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtechnosphere_mm \u001b[39m=\u001b[39m mu\u001b[39m.\u001b[39;49mMappedMatrix(\n\u001b[0;32m 192\u001b[0m packages\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpackages,\n\u001b[0;32m 193\u001b[0m matrix\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mtechnosphere_matrix\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m 194\u001b[0m use_arrays\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49muse_arrays,\n\u001b[0;32m 195\u001b[0m use_distributions\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49muse_distributions,\n\u001b[0;32m 196\u001b[0m seed_override\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mseed_override,\n\u001b[0;32m 197\u001b[0m )\n\u001b[0;32m 198\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtechnosphere_matrix \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtechnosphere_mm\u001b[39m.\u001b[39mmatrix\n\u001b[0;32m 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdicts\u001b[39m.\u001b[39mproduct \u001b[39m=\u001b[39m partial(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtechnosphere_mm\u001b[39m.\u001b[39mrow_mapper\u001b[39m.\u001b[39mto_dict)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\matrix_utils\\mapped_matrix.py:88\u001b[0m, in \u001b[0;36mMappedMatrix.__init__\u001b[1;34m(self, packages, matrix, use_vectors, use_arrays, use_distributions, row_mapper, col_mapper, seed_override, indexer_override, diagonal, transpose, custom_filter, empty_ok)\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[39mraise\u001b[39;00m EmptyInterface(\n\u001b[0;32m 82\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mDehydrated interfaces \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m in package \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m need to be rehydrated to be used in matrix calculations\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[0;32m 83\u001b[0m package\u001b[39m.\u001b[39mdehydrated_interfaces(), package\n\u001b[0;32m 84\u001b[0m )\n\u001b[0;32m 85\u001b[0m )\n\u001b[0;32m 87\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m([obj \u001b[39mfor\u001b[39;00m lst \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpackages\u001b[39m.\u001b[39mvalues() \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m lst])\n\u001b[1;32m---> 88\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_indexers(indexer_override, seed_override)\n\u001b[0;32m 90\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrow_mapper \u001b[39m=\u001b[39m row_mapper \u001b[39mor\u001b[39;00m ArrayMapper(\n\u001b[0;32m 91\u001b[0m array\u001b[39m=\u001b[39msafe_concatenate_indices(\n\u001b[0;32m 92\u001b[0m [obj\u001b[39m.\u001b[39munique_row_indices_for_mapping() \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups], empty_ok\n\u001b[0;32m 93\u001b[0m ),\n\u001b[0;32m 94\u001b[0m empty_ok\u001b[39m=\u001b[39mempty_ok,\n\u001b[0;32m 95\u001b[0m )\n\u001b[0;32m 96\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m diagonal:\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\matrix_utils\\mapped_matrix.py:185\u001b[0m, in \u001b[0;36mMappedMatrix.add_indexers\u001b[1;34m(self, indexer_override, seed_override)\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m resources:\n\u001b[0;32m 184\u001b[0m obj\u001b[39m.\u001b[39madd_indexer(indexer\u001b[39m=\u001b[39mpackage\u001b[39m.\u001b[39mindexer)\n\u001b[1;32m--> 185\u001b[0m \u001b[39melif\u001b[39;00m package\u001b[39m.\u001b[39;49mmetadata[\u001b[39m\"\u001b[39;49m\u001b[39mcombinatorial\u001b[39;49m\u001b[39m\"\u001b[39;49m]:\n\u001b[0;32m 186\u001b[0m package\u001b[39m.\u001b[39mindexer \u001b[39m=\u001b[39m CombinatorialIndexer(\n\u001b[0;32m 187\u001b[0m [obj\u001b[39m.\u001b[39mncols \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m resources \u001b[39mif\u001b[39;00m obj\u001b[39m.\u001b[39mncols]\n\u001b[0;32m 188\u001b[0m )\n\u001b[0;32m 189\u001b[0m \u001b[39mfor\u001b[39;00m i, obj \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(resources):\n", - "\u001b[1;31mKeyError\u001b[0m: 'combinatorial'" - ] - } - ], - "source": [ - "dp = safety_razor(\n", - " consumer=pear_market,\n", - " previous_producer=pear_china, \n", - " new_producer=apple, \n", - ")\n", - "lca = bc.LCA(fu, data_objs=data_objs + [premise_dp, dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "print(\"Apples are not pears:\", lca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tictac", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/project-adeline.ipynb b/archive/notebooks/project-adeline.ipynb deleted file mode 100644 index 4a6f4a7..0000000 --- a/archive/notebooks/project-adeline.ipynb +++ /dev/null @@ -1,2791 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Library import" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import bw2io as bi\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "#from bw_temporalis import easy_timedelta_distribution, TemporalisLCA\n", - "import bw_temporalis as bwt\n", - "import bw_processing as bwp\n", - "from utils import *\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set project and databases" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# try:\n", - "# bd.projects.delete_project(\"tictacthree\", True)\n", - "# except ValueError:\n", - "# pass" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#project_backup_directory = '/Users/jeromea/Downloads/brightway2-project-tictacthree-backup.12-October-2023-06-16PM.tar.gz'\n", - "#bi.backup.restore_project_directory(project_backup_directory)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "bd.projects.set_current('tictacthree')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 8 object(s):\n", - "\tbiosphere3\n", - "\tecoinvent-3.9-cutoff\n", - "\tecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2020\n", - "\tecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2030\n", - "\tecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2040\n", - "\tecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2050\n", - "\twind-example\n", - "\twind_db" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.twofive" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "ei = bd.Database('ecoinvent-3.9-cutoff')\n", - "premise_2020 = bd.Database('ecoinvent_cutoff_3.9_remind_SSP2-PkBudg1150_2020')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Foreground database" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Temporal distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Temporal distribution for wind electricity (onshore) in `Europe`, corresponding to `remind SSP2 - 1150` IAM scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([0.79, 1.57, 3.6, 6.03, 8.73, 10.66, 11.27, 11.31])\n", - "a = a/np.sum(a) # normalizing the trend in Exajoules to get an actual TD" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Absolute temproal distribution:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "d = np.array([str(2010+k*10)+\"-01-01\" for k in range(8)])\n", - "d = np.array(d,dtype=np.datetime64)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "inc_wind_turbine_energy_absolute = bwt.TemporalDistribution(\n", - " date=d,\n", - " amount=a\n", - ")\n", - "#inc_wind_turbine_energy_absolute.graph()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Relative temporal distribution:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "delta = np.array([np.timedelta64(10*(k+1), 'Y') for k in range(8)])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "inc_wind_turbine_energy_relative = bwt.TemporalDistribution(\n", - " date=delta,\n", - " amount=a\n", - ")\n", - "#inc_wind_turbine_energy_relative.graph()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "a_simplified = np.array([a[0], a[3], a[-1]])\n", - "delta_simplified = np.array([delta[0], delta[3], delta[-1]])\n", - "td_wt_energy_relative_simplified = bwt.TemporalDistribution(\n", - " date=delta_simplified,\n", - " amount=a_simplified\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Temporal distribution of WT construction" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "td_wt_construction = bwt.easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "td_wt_construction_simplified = bwt.easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=3,\n", - " kind = 'triangular',\n", - " param = -1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Temporal distribution of WT EoL treatment" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "td_wt_eol = bwt.easy_timedelta_distribution(\n", - " start=10,\n", - " end=20,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 15\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "td_wt_eol_simplified = bwt.easy_timedelta_distribution(\n", - " start=10,\n", - " end=20,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = 15\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Selection of useful activities" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "wind_construction = ei.get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\")\n", - "market_electricity = ei.get(name=\"market group for electricity, medium voltage\", location=\"RER\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "wt_construction_2020 = premise_2020.get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\")\n", - "market_electricity_2020 = premise_2020.get(name=\"market group for electricity, medium voltage\", location=\"RER\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In a foreground model with a loop (electricity produced by the wind turbine is used to produce the wind turbine), the electricity for the wind turbine production needs to be removed from the ecoinvent process:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "if \"wind_db\" in bd.databases:\n", - " del bd.databases[\"wind_db\"]\n", - "#bd.Database(\"wind_db\").register()\n", - "#wind_construction.copy(database=\"wind_db\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remove the electricity input during construction as it will be added in the foreground database:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# wt_construction = bd.Database(\"wind_db\").get(name=\"wind turbine construction, 2MW, onshore\", location=\"GLO\")\n", - "# ex = [e for e in wt_construction.exchanges() if \"electricity\" in e.input[\"name\"]][0]\n", - "\n", - "# # change amount\n", - "# ex[\"amount\"] = 0\n", - "# ex.save()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Foreground system without loop" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def wind_example_writting(\n", - " wt_construction, #Background process for the wind turbine construction\n", - " market_electricity, #Background process for energy production\n", - " td_wt_construction, #Temporal distribution for the construction process\n", - " td_wt_eol, #Temporal distribution for the EoL process of the wind turbine\n", - " td_wt_energy=inc_wind_turbine_energy_relative, #Temporal distribution of the wind turbine stock increase\n", - " wt_lifetime=20, #Wind turbine lifetime in years\n", - " wt_output=2000, #Wind turbine power output\n", - " wt_capacity=0.2 #Wind turbine capacity\n", - "):\n", - " \n", - " if \"wind-example\" in bd.databases:\n", - " del bd.databases['wind-example']\n", - "\n", - " generated_electricity_over_lifetime = wt_capacity*(wt_output*365*24)*wt_lifetime # Amount of electricity generated by a wind turbine over its lifetime in kWh, 20% of capacity factor\n", - "\n", - " bd.Database('wind-example').write({\n", - " ('wind-example', 'wind-electricity-production'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example', 'operation-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': td_wt_energy, \n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'operation-wind-turbine'): {\n", - " 'name': \"Wind turbine, operated\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': wind_construction,\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': td_wt_construction,\n", - " },\n", - " {\n", - " 'input': ('wind-example', 'eol-wind-turbine'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': td_wt_eol\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example', 'eol-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': market_electricity,\n", - " 'amount': 0.25*1.443e5, #Quantity retrieved on ecoinvent\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': wt_construction,\n", - " 'amount': 0.25, #Dummy process and quantity to add some impact\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " })" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2994.51it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "wind_example_writting(\n", - " wt_construction_2020, \n", - " market_electricity_2020, \n", - " td_wt_construction_simplified, \n", - " td_wt_eol_simplified, \n", - " td_wt_energy_relative_simplified\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Foreground system with a loop\n", - "\n", - "This is not ready as there is no temporal distribution for the share of wind electricity in the mix." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def wind_example_loop_writting(wt_lifetime=20, wt_output=2000, wt_capacity=0.2, share_of_wind_in_electricity_mix=0.7):\n", - " \n", - " if \"wind-example-loop\" in bd.databases:\n", - " del bd.databases['wind-example-loop']\n", - "\n", - " generated_electricity_over_lifetime = wt_capacity*(wt_output*365*24)*wt_lifetime # Amount of electricity generated by a wind turbine over its lifetime in kWh, 20% of capacity factor\n", - "\n", - " bd.Database('wind-example-loop').write({\n", - " ('wind-example-loop', 'electricity-mix+wind'): {\n", - " 'name': 'Electricity mix',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example-loop', 'electricity-production-wind'),\n", - " 'amount': share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " #'temporal_distribution' : TD_constant_increase_wind_share,\n", - " },\n", - " {\n", - " 'input': (market_electricity),\n", - " 'amount': 1-share_of_wind_in_electricity_mix,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example-loop', 'electricity-production-wind'): {\n", - " 'name': 'Electricity production, wind',\n", - " 'unit': 'kilowatt hour',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example-loop', 'electricity-production-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example-loop', 'operational-wind-turbine'),\n", - " 'amount': 1/generated_electricity_over_lifetime,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution' : inc_wind_turbine_energy_relative, #we would prefer to use the absolute TD, but for some reason the graph reversal isn't working with it...\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example-loop', 'operational-wind-turbine'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example-loop', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=-4,\n", - " end=0,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=5,\n", - " kind = 'triangular',\n", - " param = -1\n", - " ),\n", - " },\n", - " {\n", - " 'input': ('wind-example-loop', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': bwt.easy_timedelta_distribution(\n", - " start=10,\n", - " end=20,\n", - " resolution=\"Y\", # M for months, Y for years, etc.\n", - " steps=11,\n", - " kind = 'triangular',\n", - " param = 15\n", - " )\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example-loop', 'wind-turbine-construction'): {\n", - " 'name': 'Wind turbine construction',\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example-loop', 'wind-turbine-construction'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example-loop', 'electricity-mix+wind'),\n", - " 'amount': 0.75*1.443e5,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': wt_construction,\n", - " 'amount': 0.75,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " ('wind-example-loop', 'eol-wind'): {\n", - " 'name': \"End-of-life, wind turbine\",\n", - " 'unit': 'unit',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('wind-example-loop', 'eol-wind'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('wind-example-loop', 'electricity-mix+wind'),\n", - " 'amount': 0.25*1.443e5,\n", - " 'type': 'technosphere',\n", - " },\n", - " {\n", - " 'input': wt_construction,\n", - " 'amount': 0.25,\n", - " 'type': 'technosphere',\n", - " },\n", - " ]\n", - " },\n", - " })" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LCA method" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('IPCC 2013 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cc_method = [m for m in bd.methods if 'IPCC 2013' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - "cc_method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "fu_now = {('wind-example', 'wind-electricity-production'): 1}" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "lca_now_static = bc.LCA(fu_now, cc_method)\n", - "lca_now_static.lci()\n", - "lca_now_static.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.022686662179280174" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca_now_static.score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Dynamic LCA (temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n" - ] - } - ], - "source": [ - "tlca_now = bwt.TemporalisLCA(lca_now_static, starting_datetime=np.datetime64(40, 'Y'), max_calc=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[1;31mInit signature:\u001b[0m\n", - "\u001b[0mbwt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTemporalisLCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlca_object\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbw2calc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLCA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstarting_datetime\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'now'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.0005\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mbiosphere_cutoff\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-06\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmax_calc\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2000.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstatic_activity_indices\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mskip_coproducts\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunctional_unit_unique_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mgraph_traversal\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbw_graph_tools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_traversal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNewNodeEachVisitGraphTraversal\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m<\u001b[0m\u001b[1;32mclass\u001b[0m \u001b[1;34m'bw_graph_tools.graph_traversal.NewNodeEachVisitGraphTraversal'\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mSource:\u001b[0m \n", - "\u001b[1;32mclass\u001b[0m \u001b[0mTemporalisLCA\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"\n", - " Calculate an LCA using graph traversal, with edges using temporal distributions.\n", - "\n", - " Edges with temporal distributions should store this information using `\"temporal_distributions\"`:\n", - "\n", - " ```python\n", - " exchange[\"temporal_distribution\"] = bw_temporalis.TemporalDistribution(\n", - " times=numpy.array([-2, -1, 0, 1, 2], dtype=\"timedelta64[s]\"),\n", - " values=numpy.ones(5)\n", - " )\n", - " ```\n", - "\n", - " Temporal distribution times must always have the data type `timedelta64[s]`. Not all edges need to have temporal distributions.\n", - "\n", - " Temporal distributions are **not density functions** - their values should sum to the exchange amount.\n", - "\n", - " As graph traversal is much slower than matrix calculations, we can limit which nodes get traversed in several ways:\n", - "\n", - " * All activities in a database marked as `static`\n", - " * Any activity ids passed in `static_activity_indices`\n", - " * Any activities whose cumulative impact is below the cutoff score\n", - "\n", - " The output of a Temporalis LCA calculation is a `bw_temporalis.Timeline`, which can be characterized.\n", - "\n", - " Parameters\n", - " ----------\n", - " lca_object : bw2calc.LCA\n", - " The already instantiated and calculated LCA class (i.e. `.lci()` and `.lcia()` have already been done)\n", - " starting_datetime : datetime.datetime | str\n", - " When the functional unit happens. Must be a point in time. Normally something like `\"now\"` or `\"2023-01-01\"`.\n", - " cutoff : float\n", - " The fraction of the total score below which graph traversal should stop. In range `(0, 1)`.\n", - " biosphere_cutoff : float\n", - " The fraction of the total score below which we don't include separate biosphere nodes to be characterized in the `Timeline`. In range `(0, 1)`.\n", - " max_calc : int\n", - " Total number of LCA inventory calculations to perform during graph traversal\n", - " static_activity_indices : set[int]\n", - " Activity `id` values where graph traversal will stop\n", - " skip_coproducts : bool\n", - " Should we also traverse edges for the other products in multioutput activities?\n", - " functional_unit_unique_id : int\n", - " The unique id of the functional unit. Strongly recommended to leave as default.\n", - " graph_traversal : bw_graph_tools.NewNodeEachVisitGraphTraversal\n", - " Optional subclass of `NewNodeEachVisitGraphTraversal` for advanced usage\n", - "\n", - " \"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlca_object\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mLCA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstarting_datetime\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"now\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5e-4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mbiosphere_cutoff\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmax_calc\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstatic_activity_indices\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mskip_coproducts\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunctional_unit_unique_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mgraph_traversal\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mNewNodeEachVisitGraphTraversal\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNewNodeEachVisitGraphTraversal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlca_object\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunctional_unit_unique_id\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTemporalDistribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstarting_datetime\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"static\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstatic_activity_indices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabase\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtuples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Starting graph traversal\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mgt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_traversal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlca_object\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mstatic_activity_indices\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstatic_activity_indices\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmax_calc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_calc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mbiosphere_cutoff\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbiosphere_cutoff\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mseparate_biosphere_flows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mskip_coproducts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskip_coproducts\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mfunctional_unit_unique_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunctional_unit_unique_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Calculation count:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"calculation_count\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"nodes\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medges\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"edges\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medge_mapping\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0medge\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medges\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medge_mapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0medge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconsumer_unique_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0medge\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"flows\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflow_mapping\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mflow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflows\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflow_mapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity_unique_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mflow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbuild_timeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_timeline\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTimeline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mheap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtimeline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTimeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnode_timeline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"\"\"This functionality is experimental, and will change.\n", - "You have been warned.\"\"\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0medge\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medge_mapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0medge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproducer_unique_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mheappush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mheap\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumulative_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt0\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0medge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mamount\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mheap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheappop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - 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"\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mexchange\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExchangeDataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNoExchange\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mrow_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mcol_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mmatrix_label\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTemporalDistribution\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mloader_registry\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexchange\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNoExchange\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"temporal_distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"__loader__\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtd\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloader_registry\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"__loader__\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"Can't find correct loader {} in `loader_registry`\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"__loader__\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTemporalDistribution\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTDAware\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34mf\"Can't understand value for `temporal_distribution` in exchange {exchange}\"\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"type\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"generic consumption\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"technosphere\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmatrix_label\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"technosphere_matrix\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtechnosphere_matrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexchange\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNoExchange\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# Assume technosphere input so negative sign, unless we have a\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# positive value and the number is on the diagonal, or the\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# IDs are the same (shared product/process, not on the diagonal\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# for whatever reason).\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrow_id\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcol_id\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0msign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mamount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msign\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmatrix_label\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"biosphere_matrix\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mamount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mexchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"fraction\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexchange\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNoExchange\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere_matrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbiosphere\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlca_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdicts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivity\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Unknown matrix type {matrix_label}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mamount\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mtd\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mamount\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_exchange_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0minp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minput_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0moutp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_code\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_database\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabase\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_code\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0moutp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_database\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0moutp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabase\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_biosphere_exchanges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflow_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivity_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mexchanges\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exchange_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mflow_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivity_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"amount\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mexc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mexchanges\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mexc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mexchanges\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"fraction\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"amount\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mtotal\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mexchanges\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mNoExchange\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_technosphere_exchange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mED\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"{}|{}|{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatabase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mexchanges\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exchange_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mMultipleTechnosphereExchanges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;34m\"Found {} exchanges for link between {} and {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexchanges\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mexchanges\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;31m# Edge injected via datapackage, no exchange in dataset\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mNoExchange\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mexchanges\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFile:\u001b[0m c:\\users\\jeromea\\appdata\\local\\continuum\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw_temporalis\\lca.py\n", - "\u001b[1;31mType:\u001b[0m type\n", - "\u001b[1;31mSubclasses:\u001b[0m " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlca_now = bwt.TemporalisLCA??" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "tl_now = tlca_now.build_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "tl_df_now = tl_now.build_dataframe()\n", - "tl_df_now = tl_now.add_metadata_to_dataframe(database_labels=[\"wind-example\"], fields=['name', 'unit'])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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- "tl_df_now" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unit
02018-10-12 03:14:080.000167113118Wind turbine constructionunitcarbon dioxidekilogram
12018-10-12 03:14:080.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
22018-10-12 03:14:080.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
32019-10-12 09:03:200.000167113118Wind turbine constructionunitcarbon dioxidekilogram
42019-10-12 09:03:200.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
52019-10-12 09:03:200.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
62020-10-11 14:52:320.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
72020-10-11 14:52:320.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
82020-10-11 14:52:320.000167113118Wind turbine constructionunitcarbon dioxidekilogram
92021-10-11 20:41:440.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
102021-10-11 20:41:440.000167113118Wind turbine constructionunitcarbon dioxidekilogram
112021-10-11 20:41:440.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
122022-10-12 02:30:560.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
132022-10-12 02:30:560.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
142022-10-12 02:30:560.000167113118Wind turbine constructionunitcarbon dioxidekilogram
152023-10-12 08:20:080.000167113119End-of-life, wind turbineunitcarbon dioxidekilogram
162023-10-12 08:20:080.000167113118Wind turbine constructionunitcarbon dioxidekilogram
172023-10-12 08:20:080.000117113116Electricity production, coalkilowatt hourcarbon dioxidekilogram
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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 2018-10-12 03:14:08 0.000167 113 118 \n", - "1 2018-10-12 03:14:08 0.000117 113 116 \n", - "2 2018-10-12 03:14:08 0.000167 113 119 \n", - "3 2019-10-12 09:03:20 0.000167 113 118 \n", - "4 2019-10-12 09:03:20 0.000117 113 116 \n", - "5 2019-10-12 09:03:20 0.000167 113 119 \n", - "6 2020-10-11 14:52:32 0.000117 113 116 \n", - "7 2020-10-11 14:52:32 0.000167 113 119 \n", - "8 2020-10-11 14:52:32 0.000167 113 118 \n", - "9 2021-10-11 20:41:44 0.000167 113 119 \n", - "10 2021-10-11 20:41:44 0.000167 113 118 \n", - "11 2021-10-11 20:41:44 0.000117 113 116 \n", - "12 2022-10-12 02:30:56 0.000117 113 116 \n", - "13 2022-10-12 02:30:56 0.000167 113 119 \n", - "14 2022-10-12 02:30:56 0.000167 113 118 \n", - "15 2023-10-12 08:20:08 0.000167 113 119 \n", - "16 2023-10-12 08:20:08 0.000167 113 118 \n", - "17 2023-10-12 08:20:08 0.000117 113 116 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \n", - "0 Wind turbine construction unit carbon dioxide kilogram \n", - "1 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "2 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "3 Wind turbine construction unit carbon dioxide kilogram \n", - "4 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "5 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "6 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "7 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "8 Wind turbine construction unit carbon dioxide kilogram \n", - "9 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "10 Wind turbine construction unit carbon dioxide kilogram \n", - "11 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "12 Electricity production, coal kilowatt hour carbon dioxide kilogram \n", - "13 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "14 Wind turbine construction unit carbon dioxide kilogram \n", - "15 End-of-life, wind turbine unit carbon dioxide kilogram \n", - "16 Wind turbine construction unit carbon dioxide kilogram \n", - "17 Electricity production, coal kilowatt hour carbon dioxide kilogram " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tl_dt" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jeromea\\AppData\\Local\\Continuum\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw_temporalis\\lca.py:129: UserWarning: This functionality is experimental, and will change.\n", - "You have been warned.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "tl = tlca.build_timeline(node_timeline=True)\n", - "tl.build_dataframe()\n", - "tl_df = tl.add_metadata_to_dataframe(database_labels=[\"wind-example-2020\"], fields=['name', 'unit'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Interpolation\n", - "\n", - "## First: nearest year" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def add_column_nearest_year_on_timeline(tl_df, dates_list):\n", - " \"\"\"\n", - " Add a column to a timeline with the year of the database, within the list of year of available databases, \n", - " that is the nearest to the date in the timeline.\n", - "\n", - " :param tl_df: Timeline as a dataframe.\n", - " :param dates_list: List of years of the available databases.\n", - " \n", - " :return: Timeline as a dataframe with a column 'nearest_year' (int64) added.\n", - " -------------------\n", - " Example:\n", - " >>> dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2022\", \"%Y\"),\n", - " datetime.strptime(\"2025\", \"%Y\"),\n", - " ]\n", - " >>> add_column_nearest_year_on_timeline(tl_df, dates_list)\n", - " \"\"\"\n", - " if \"date\" not in list(tl_df.columns):\n", - " raise ValueError(\"The timeline does not contain dates.\")\n", - " tl_df['nearest_year'] = tl_df['date'].apply(lambda x: find_closest_date(x, dates_list).year)\n", - " return tl_df" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2035\", \"%Y\"),\n", - " datetime.strptime(\"2050\", \"%Y\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unitnearest_year
02018-10-12 03:14:080.000167-1118Wind turbine constructionunitNaNNaN2020
12018-10-12 03:14:080.000167-1115Electricity mixkilowatt hourNaNNaN2020
22018-10-12 03:14:080.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
32018-10-12 03:14:080.000167-1119End-of-life, wind turbineunitNaNNaN2020
42019-10-12 09:03:200.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
52019-10-12 09:03:200.000167-1118Wind turbine constructionunitNaNNaN2020
62019-10-12 09:03:200.000167-1115Electricity mixkilowatt hourNaNNaN2020
72019-10-12 09:03:200.000167-1119End-of-life, wind turbineunitNaNNaN2020
82020-10-11 14:52:320.000167-1118Wind turbine constructionunitNaNNaN2020
92020-10-11 14:52:320.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
102020-10-11 14:52:320.000167-1119End-of-life, wind turbineunitNaNNaN2020
112020-10-11 14:52:320.000167-1115Electricity mixkilowatt hourNaNNaN2020
122021-10-11 20:41:440.000167-1119End-of-life, wind turbineunitNaNNaN2020
132021-10-11 20:41:440.000167-1115Electricity mixkilowatt hourNaNNaN2020
142021-10-11 20:41:440.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
152021-10-11 20:41:440.000167-1118Wind turbine constructionunitNaNNaN2020
162022-10-12 02:30:560.000167-1115Electricity mixkilowatt hourNaNNaN2020
172022-10-12 02:30:560.000167-1118Wind turbine constructionunitNaNNaN2020
182022-10-12 02:30:560.000167-1119End-of-life, wind turbineunitNaNNaN2020
192022-10-12 02:30:560.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
202023-10-12 08:20:081.000000-1117Electricity production, windkilowatt hourNaNNaN2020
212023-10-12 08:20:080.000167-1115Electricity mixkilowatt hourNaNNaN2020
222023-10-12 08:20:080.000167-1118Wind turbine constructionunitNaNNaN2020
232023-10-12 08:20:080.000167-1119End-of-life, wind turbineunitNaNNaN2020
242023-10-12 08:20:080.000117-1116Electricity production, coalkilowatt hourNaNNaN2020
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" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 2018-10-12 03:14:08 0.000167 -1 118 \n", - "1 2018-10-12 03:14:08 0.000167 -1 115 \n", - "2 2018-10-12 03:14:08 0.000117 -1 116 \n", - "3 2018-10-12 03:14:08 0.000167 -1 119 \n", - "4 2019-10-12 09:03:20 0.000117 -1 116 \n", - "5 2019-10-12 09:03:20 0.000167 -1 118 \n", - "6 2019-10-12 09:03:20 0.000167 -1 115 \n", - "7 2019-10-12 09:03:20 0.000167 -1 119 \n", - "8 2020-10-11 14:52:32 0.000167 -1 118 \n", - "9 2020-10-11 14:52:32 0.000117 -1 116 \n", - "10 2020-10-11 14:52:32 0.000167 -1 119 \n", - "11 2020-10-11 14:52:32 0.000167 -1 115 \n", - "12 2021-10-11 20:41:44 0.000167 -1 119 \n", - "13 2021-10-11 20:41:44 0.000167 -1 115 \n", - "14 2021-10-11 20:41:44 0.000117 -1 116 \n", - "15 2021-10-11 20:41:44 0.000167 -1 118 \n", - "16 2022-10-12 02:30:56 0.000167 -1 115 \n", - "17 2022-10-12 02:30:56 0.000167 -1 118 \n", - "18 2022-10-12 02:30:56 0.000167 -1 119 \n", - "19 2022-10-12 02:30:56 0.000117 -1 116 \n", - "20 2023-10-12 08:20:08 1.000000 -1 117 \n", - "21 2023-10-12 08:20:08 0.000167 -1 115 \n", - "22 2023-10-12 08:20:08 0.000167 -1 118 \n", - "23 2023-10-12 08:20:08 0.000167 -1 119 \n", - "24 2023-10-12 08:20:08 0.000117 -1 116 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \\\n", - "0 Wind turbine construction unit NaN NaN \n", - "1 Electricity mix kilowatt hour NaN NaN \n", - "2 Electricity production, coal kilowatt hour NaN NaN \n", - "3 End-of-life, wind turbine unit NaN NaN \n", - "4 Electricity production, coal kilowatt hour NaN NaN \n", - "5 Wind turbine construction unit NaN NaN \n", - "6 Electricity mix kilowatt hour NaN NaN \n", - "7 End-of-life, wind turbine unit NaN NaN \n", - "8 Wind turbine construction unit NaN NaN \n", - "9 Electricity production, coal kilowatt hour NaN NaN \n", - "10 End-of-life, wind turbine unit NaN NaN \n", - "11 Electricity mix kilowatt hour NaN NaN \n", - "12 End-of-life, wind turbine unit NaN NaN \n", - "13 Electricity mix kilowatt hour NaN NaN \n", - "14 Electricity production, coal kilowatt hour NaN NaN \n", - "15 Wind turbine construction unit NaN NaN \n", - "16 Electricity mix kilowatt hour NaN NaN \n", - "17 Wind turbine construction unit NaN NaN \n", - "18 End-of-life, wind turbine unit NaN NaN \n", - "19 Electricity production, coal kilowatt hour NaN NaN \n", - "20 Electricity production, wind kilowatt hour NaN NaN \n", - "21 Electricity mix kilowatt hour NaN NaN \n", - "22 Wind turbine construction unit NaN NaN \n", - "23 End-of-life, wind turbine unit NaN NaN \n", - "24 Electricity production, coal kilowatt hour NaN NaN \n", - "\n", - " nearest_year \n", - "0 2020 \n", - "1 2020 \n", - "2 2020 \n", - "3 2020 \n", - "4 2020 \n", - "5 2020 \n", - "6 2020 \n", - "7 2020 \n", - "8 2020 \n", - "9 2020 \n", - "10 2020 \n", - "11 2020 \n", - "12 2020 \n", - "13 2020 \n", - "14 2020 \n", - "15 2020 \n", - "16 2020 \n", - "17 2020 \n", - "18 2020 \n", - "19 2020 \n", - "20 2020 \n", - "21 2020 \n", - "22 2020 \n", - "23 2020 \n", - "24 2020 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_column_nearest_year_on_timeline(tl_df, dates_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Second: interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2035\", \"%Y\"),\n", - " datetime.strptime(\"2050\", \"%Y\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 1, 1, 0, 0)" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "date_test = datetime(2019,10,11)\n", - "diff_test = [date_test - x for x in dates_list]\n", - "\n", - "if timedelta(0) in diff_test:\n", - " print(\"Exact match\")\n", - " exact_match = dates_list[diff_test_bis.index(timedelta(0))]\n", - "else:\n", - " closest_lower = min(\n", - " dates_list, \n", - " key=lambda date: abs(date_test - date) if (date_test - date) > timedelta(0) else timedelta.max\n", - " )\n", - " closest_higher = min(\n", - " dates_list, \n", - " key=lambda date: abs(date_test - date) if (date_test - date) < timedelta(0) else timedelta.max\n", - " )\n", - "closest_higher" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[89], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdate_test\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mclosest_lower\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_seconds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosest_higher\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mclosest_lower\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_seconds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" - ] - } - ], - "source": [ - "int((date_test - closest_lower).total_seconds())/int((closest_higher - closest_lower).total_seconds())" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "def get_weights_for_interpolation_between_nearest_years(date, dates_list, interpolation_type=\"linear\"):\n", - " \"\"\"\n", - " Find the nearest dates (before and after) a given date in a list of dates and calculate the interpolation weights.\n", - "\n", - " :param date: Target date.\n", - " :param dates_list: List of years of the available databases.\n", - " :param interpolation_type: Type of interpolation between the nearest lower and higher dates. For now, \n", - " only \"linear\" is available.\n", - " \n", - " :return: Dictionary with years of the available databases to use as keys and the weights for interpolation as values.\n", - " -------------------\n", - " Example:\n", - " >>> dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2022\", \"%Y\"),\n", - " datetime.strptime(\"2025\", \"%Y\"),\n", - " ]\n", - " >>> date_test = datetime(2021,10,11)\n", - " >>> add_column_interpolation_weights_on_timeline(date_test, dates_list, interpolation_type=\"linear\")\n", - " \"\"\"\n", - " dates_list = sorted (dates_list)\n", - " \n", - " diff_dates_list = [date - x for x in dates_list]\n", - " if timedelta(0) in diff_dates_list:\n", - " exact_match = dates_list[diff_dates_list.index(timedelta(0))]\n", - " return {exact_match.year: 1}\n", - "\n", - " closest_lower = min(\n", - " dates_list, \n", - " key=lambda x: abs(date - x) if (date - x) > timedelta(0) else timedelta.max\n", - " )\n", - " closest_higher = min(\n", - " dates_list, \n", - " key=lambda x: abs(date - x) if (date - x) < timedelta(0) else timedelta.max\n", - " )\n", - "\n", - " if closest_lower == closest_higher:\n", - " warnings.warn(\"Date outside the range of dates covered by the databases.\", category=Warning)\n", - " return {closest_lower.year: 1}\n", - " \n", - " if interpolation_type == \"linear\":\n", - " weight = int((date - closest_lower).total_seconds())/int((closest_higher - closest_lower).total_seconds())\n", - " else:\n", - " raise ValueError(f\"Sorry, but {interpolation_type} interpolation is not available yet.\")\n", - " return {closest_lower.year: weight, closest_higher.year: 1-weight}" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jeromea\\AppData\\Local\\Temp\\ipykernel_12048\\2354799543.py:20: Warning: Date outside the range of dates covered by the databases.\n", - " warnings.warn(\"Date outside the range of dates covered by the databases.\", category=Warning)\n" - ] - }, - { - "data": { - "text/plain": [ - "{2020: 1}" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_weights_for_interpolation_between_nearest_years(date_test, dates_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jeromea\\AppData\\Local\\Temp\\ipykernel_12048\\2354799543.py:20: Warning: Date outside the range of dates covered by the databases.\n", - " warnings.warn(\"Date outside the range of dates covered by the databases.\", category=Warning)\n" - ] - } - ], - "source": [ - "tl_df['interpolation'] = tl_df['date'].apply(lambda x: get_weights_for_interpolation_between_nearest_years(x, dates_list))" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "def add_column_interpolation_weights_on_timeline(tl_df, dates_list, interpolation_type=\"linear\"):\n", - " \"\"\"\n", - " Add a column to a timeline with the weights for an interpolation between the two nearest dates,\n", - " from the list of dates from the available databases.\n", - "\n", - " :param tl_df: Timeline as a dataframe.\n", - " :param dates_list: List of years of the available databases.\n", - " :param interpolation_type: Type of interpolation between the nearest lower and higher dates. For now, \n", - " only \"linear\" is available.\n", - " \n", - " :return: Timeline as a dataframe with a column 'interpolation_weights' (object:dictionnary) added.\n", - " -------------------\n", - " Example:\n", - " >>> dates_list = [\n", - " datetime.strptime(\"2020\", \"%Y\"),\n", - " datetime.strptime(\"2022\", \"%Y\"),\n", - " datetime.strptime(\"2025\", \"%Y\"),\n", - " ]\n", - " >>> add_column_interpolation_weights_on_timeline(tl_df, dates_list, interpolation_type=\"linear\")\n", - " \"\"\"\n", - " if \"date\" not in list(tl_df.columns):\n", - " raise ValueError(\"The timeline does not contain dates.\")\n", - " tl_df['interpolation_weights'] = tl_df['date'].apply(lambda x: get_weights_for_interpolation_between_nearest_years(x, dates_list, interpolation_type))\n", - " return tl_df" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jeromea\\AppData\\Local\\Temp\\ipykernel_12048\\2354799543.py:20: Warning: Date outside the range of dates covered by the databases.\n", - " warnings.warn(\"Date outside the range of dates covered by the databases.\", category=Warning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateamountflowactivityactivity_nameactivity_unitflow_nameflow_unitinterpolation_weights
02018-10-12 03:14:080.000167-1118Wind turbine constructionunitNaNNaN{2020: 1}
12018-10-12 03:14:080.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 1}
22018-10-12 03:14:080.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 1}
32018-10-12 03:14:080.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 1}
42019-10-12 09:03:200.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 1}
52019-10-12 09:03:200.000167-1118Wind turbine constructionunitNaNNaN{2020: 1}
62019-10-12 09:03:200.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 1}
72019-10-12 09:03:200.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 1}
82020-10-11 14:52:320.000167-1118Wind turbine constructionunitNaNNaN{2020: 0.05194740186435751, 2035: 0.9480525981...
92020-10-11 14:52:320.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 0.05194740186435751, 2035: 0.9480525981...
102020-10-11 14:52:320.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 0.05194740186435751, 2035: 0.9480525981...
112020-10-11 14:52:320.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 0.05194740186435751, 2035: 0.9480525981...
122021-10-11 20:41:440.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 0.11860965775046811, 2035: 0.8813903422...
132021-10-11 20:41:440.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 0.11860965775046811, 2035: 0.8813903422...
142021-10-11 20:41:440.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 0.11860965775046811, 2035: 0.8813903422...
152021-10-11 20:41:440.000167-1118Wind turbine constructionunitNaNNaN{2020: 0.11860965775046811, 2035: 0.8813903422...
162022-10-12 02:30:560.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 0.18527191363657872, 2035: 0.8147280863...
172022-10-12 02:30:560.000167-1118Wind turbine constructionunitNaNNaN{2020: 0.18527191363657872, 2035: 0.8147280863...
182022-10-12 02:30:560.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 0.18527191363657872, 2035: 0.8147280863...
192022-10-12 02:30:560.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 0.18527191363657872, 2035: 0.8147280863...
202023-10-12 08:20:081.000000-1117Electricity production, windkilowatt hourNaNNaN{2020: 0.2519341695226893, 2035: 0.74806583047...
212023-10-12 08:20:080.000167-1115Electricity mixkilowatt hourNaNNaN{2020: 0.2519341695226893, 2035: 0.74806583047...
222023-10-12 08:20:080.000167-1118Wind turbine constructionunitNaNNaN{2020: 0.2519341695226893, 2035: 0.74806583047...
232023-10-12 08:20:080.000167-1119End-of-life, wind turbineunitNaNNaN{2020: 0.2519341695226893, 2035: 0.74806583047...
242023-10-12 08:20:080.000117-1116Electricity production, coalkilowatt hourNaNNaN{2020: 0.2519341695226893, 2035: 0.74806583047...
\n", - "
" - ], - "text/plain": [ - " date amount flow activity \\\n", - "0 2018-10-12 03:14:08 0.000167 -1 118 \n", - "1 2018-10-12 03:14:08 0.000167 -1 115 \n", - "2 2018-10-12 03:14:08 0.000117 -1 116 \n", - "3 2018-10-12 03:14:08 0.000167 -1 119 \n", - "4 2019-10-12 09:03:20 0.000117 -1 116 \n", - "5 2019-10-12 09:03:20 0.000167 -1 118 \n", - "6 2019-10-12 09:03:20 0.000167 -1 115 \n", - "7 2019-10-12 09:03:20 0.000167 -1 119 \n", - "8 2020-10-11 14:52:32 0.000167 -1 118 \n", - "9 2020-10-11 14:52:32 0.000117 -1 116 \n", - "10 2020-10-11 14:52:32 0.000167 -1 119 \n", - "11 2020-10-11 14:52:32 0.000167 -1 115 \n", - "12 2021-10-11 20:41:44 0.000167 -1 119 \n", - "13 2021-10-11 20:41:44 0.000167 -1 115 \n", - "14 2021-10-11 20:41:44 0.000117 -1 116 \n", - "15 2021-10-11 20:41:44 0.000167 -1 118 \n", - "16 2022-10-12 02:30:56 0.000167 -1 115 \n", - "17 2022-10-12 02:30:56 0.000167 -1 118 \n", - "18 2022-10-12 02:30:56 0.000167 -1 119 \n", - "19 2022-10-12 02:30:56 0.000117 -1 116 \n", - "20 2023-10-12 08:20:08 1.000000 -1 117 \n", - "21 2023-10-12 08:20:08 0.000167 -1 115 \n", - "22 2023-10-12 08:20:08 0.000167 -1 118 \n", - "23 2023-10-12 08:20:08 0.000167 -1 119 \n", - "24 2023-10-12 08:20:08 0.000117 -1 116 \n", - "\n", - " activity_name activity_unit flow_name flow_unit \\\n", - "0 Wind turbine construction unit NaN NaN \n", - "1 Electricity mix kilowatt hour NaN NaN \n", - "2 Electricity production, coal kilowatt hour NaN NaN \n", - "3 End-of-life, wind turbine unit NaN NaN \n", - "4 Electricity production, coal kilowatt hour NaN NaN \n", - "5 Wind turbine construction unit NaN NaN \n", - "6 Electricity mix kilowatt hour NaN NaN \n", - "7 End-of-life, wind turbine unit NaN NaN \n", - "8 Wind turbine construction unit NaN NaN \n", - "9 Electricity production, coal kilowatt hour NaN NaN \n", - "10 End-of-life, wind turbine unit NaN NaN \n", - "11 Electricity mix kilowatt hour NaN NaN \n", - "12 End-of-life, wind turbine unit NaN NaN \n", - "13 Electricity mix kilowatt hour NaN NaN \n", - "14 Electricity production, coal kilowatt hour NaN NaN \n", - "15 Wind turbine construction unit NaN NaN \n", - "16 Electricity mix kilowatt hour NaN NaN \n", - "17 Wind turbine construction unit NaN NaN \n", - "18 End-of-life, wind turbine unit NaN NaN \n", - "19 Electricity production, coal kilowatt hour NaN NaN \n", - "20 Electricity production, wind kilowatt hour NaN NaN \n", - "21 Electricity mix kilowatt hour NaN NaN \n", - "22 Wind turbine construction unit NaN NaN \n", - "23 End-of-life, wind turbine unit NaN NaN \n", - "24 Electricity production, coal kilowatt hour NaN NaN \n", - "\n", - " interpolation_weights \n", - "0 {2020: 1} \n", - "1 {2020: 1} \n", - "2 {2020: 1} \n", - "3 {2020: 1} \n", - "4 {2020: 1} \n", - "5 {2020: 1} \n", - "6 {2020: 1} \n", - "7 {2020: 1} \n", - "8 {2020: 0.05194740186435751, 2035: 0.9480525981... \n", - "9 {2020: 0.05194740186435751, 2035: 0.9480525981... \n", - "10 {2020: 0.05194740186435751, 2035: 0.9480525981... \n", - "11 {2020: 0.05194740186435751, 2035: 0.9480525981... \n", - "12 {2020: 0.11860965775046811, 2035: 0.8813903422... \n", - "13 {2020: 0.11860965775046811, 2035: 0.8813903422... \n", - "14 {2020: 0.11860965775046811, 2035: 0.8813903422... \n", - "15 {2020: 0.11860965775046811, 2035: 0.8813903422... \n", - "16 {2020: 0.18527191363657872, 2035: 0.8147280863... \n", - "17 {2020: 0.18527191363657872, 2035: 0.8147280863... \n", - "18 {2020: 0.18527191363657872, 2035: 0.8147280863... \n", - "19 {2020: 0.18527191363657872, 2035: 0.8147280863... \n", - "20 {2020: 0.2519341695226893, 2035: 0.74806583047... \n", - "21 {2020: 0.2519341695226893, 2035: 0.74806583047... \n", - "22 {2020: 0.2519341695226893, 2035: 0.74806583047... \n", - "23 {2020: 0.2519341695226893, 2035: 0.74806583047... \n", - "24 {2020: 0.2519341695226893, 2035: 0.74806583047... " - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_column_interpolation_weights_on_timeline(tl_df, dates_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/notebooks/simple_loop.ipynb b/archive/notebooks/simple_loop.ipynb deleted file mode 100644 index 086d86c..0000000 --- a/archive/notebooks/simple_loop.ipynb +++ /dev/null @@ -1,2479 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution, easy_datetime_distribution\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.realpath('../'))\n", - "from medusa.edge_extractor import *\n", - "from medusa.matrix_modifier import *\n", - "from medusa.medusa_lca import *\n", - "from medusa.timeline_builder import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "71f67330", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "loop is not a project", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprojects\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_project\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mloop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m bd\u001b[38;5;241m.\u001b[39mprojects\u001b[38;5;241m.\u001b[39mpurge_deleted_directories()\n", - "File \u001b[0;32m~/anaconda3/envs/medusa/lib/python3.11/site-packages/bw2data/project.py:317\u001b[0m, in \u001b[0;36mProjectManager.delete_project\u001b[0;34m(self, name, delete_dir)\u001b[0m\n\u001b[1;32m 315\u001b[0m victim \u001b[38;5;241m=\u001b[39m name \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m victim \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m is not a project\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(victim))\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt delete only remaining project\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: loop is not a project" - ] - } - ], - "source": [ - "bd.projects.delete_project(\"loop\")\n", - "bd.projects.purge_deleted_directories()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d00df98a-fcae-4160-a30f-54aed29c1f19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current(\"loop\")" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "26987fcb", - "metadata": {}, - "source": [ - "### Setup of our Example\n", - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 22671.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3/3 [00:00<00:00, 38956.38it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.Database('temporalis-bio').write({\n", - " ('temporalis-bio', 'CO2'): {\n", - " 'type': 'emission',\n", - " 'name': 'carbon dioxide',\n", - " 'temporalis code': 'co2',\n", - " },\n", - "})\n", - "\n", - "bd.Database('foreground').write({\n", - " ('foreground', 'A'): {\n", - " 'name': 'A',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('foreground', 'A'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('foreground', 'B'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': \n", - " TemporalDistribution(\n", - " date=np.array([-1], dtype='timedelta64[Y]'),\n", - " amount=np.array([1])\n", - " ), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'B'): {\n", - " 'name': 'B',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('foreground', 'B'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('foreground', 'C'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': \n", - " TemporalDistribution(\n", - " date=np.array([-2], dtype='timedelta64[Y]'),\n", - " amount=np.array([1])\n", - " ), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'C'): {\n", - " 'name': 'C',\n", - " 'exchanges': [\n", - " {\n", - " 'input': ('foreground', 'C'),\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " },\n", - " {\n", - " 'input': ('foreground', 'A'),\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'temporal_distribution': \n", - " TemporalDistribution(\n", - " date=np.array([-5], dtype='timedelta64[Y]'),\n", - " amount=np.array([1])\n", - " ), \n", - " },\n", - " {\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " },\n", - " \n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8e02b150-0249-4884-90ad-f129c2c13eb2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"example\")).write([\n", - " (('temporalis-bio', \"CO2\"), 1),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "31ced634", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cca6b8f2-12a3-43f9-8be2-c6a898268adf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/bw2calc/lca.py:259: UserWarning: No valid biosphere flows found. No inventory results can be calculated, `lcia` will raise an error\n", - " warnings.warn(\n", - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:293: MatrixRankWarning: Matrix is exactly singular\n", - " warn(\"Matrix is exactly singular\", MatrixRankWarning)\n" - ] - }, - { - "ename": "EmptyBiosphere", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mEmptyBiosphere\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m slca \u001b[38;5;241m=\u001b[39m bc\u001b[38;5;241m.\u001b[39mLCA(demand, gwp)\n\u001b[1;32m 2\u001b[0m slca\u001b[38;5;241m.\u001b[39mlci()\n\u001b[0;32m----> 3\u001b[0m \u001b[43mslca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlcia\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStatic LCA score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mslca\u001b[38;5;241m.\u001b[39mscore\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/medusa/lib/python3.11/site-packages/bw2calc/lca.py:436\u001b[0m, in \u001b[0;36mLCA.lcia\u001b[0;34m(self, demand)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minventory\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust do lci first\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdicts\u001b[38;5;241m.\u001b[39mbiosphere:\n\u001b[0;32m--> 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m EmptyBiosphere\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcharacterization_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_lcia_data()\n", - "\u001b[0;31mEmptyBiosphere\u001b[0m: " - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "b461bbb5", - "metadata": {}, - "source": [ - "# `MEDUSA` LCA" - ] - }, - { - "cell_type": "markdown", - "id": "aa7f0158", - "metadata": {}, - "source": [ - "A MEDUSA LCA builds upon a static LCA, but adds a temporal dimensions, linking to prospective LCA databases. Similarly to a `Temporalis LCA`, the supply chain graph is traversed, taking into account temporal distributions of the edges. \n", - "\n", - "For now, only the foreground system is assumed to have temporal distributions. Therefore, we define a filter function, that tells to EdgeExtracter (which is doing the actual graph traversal and saves the edges with respective timestamps), when a database that is known to have no temporal distributions (i.e., the prospective background databases) is reached, so that the traversal can be stopped." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "# SKIPPABLE = [node.id for node in bd.Database('background_2008')] + [\n", - "# node.id for node in bd.Database('background_2024')\n", - "# ]\n", - "SKIPPABLE = []\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 18\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/medusa/lib/python3.11/site-packages/bw_graph_tools/graph_traversal.py:403: UserWarning: Stopping traversal due to calculation count.\n", - " warnings.warn(\"Stopping traversal due to calculation count.\")\n" - ] - } - ], - "source": [ - "eelca = EdgeExtractor(slca, edge_filter_function=filter_function, max_calc=16)\n", - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "markdown", - "id": "ae2733bf", - "metadata": {}, - "source": [ - "Next, we define a dictionary containing the dates of our prospective background databases. Using this, we can create a timeline dataframe. \n", - "\n", - "The dates of the edges are mapped to the prospective background databases; interpolation is used for dates in between the dates of the background databases. The default is linear interpolation, another currently included option is \"nearest\", choosing the next best fitting database." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fa5b9804", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=-1, producer=9, td_producer=1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 1 values and total: 1, abs_td_consumer=None),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_consumer=TemporalDistribution instance with 1 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 2, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 2.5, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 10, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 5.5, leaf=False, consumer=6, producer=2, td_producer=TemporalDistribution instance with 2 values and total: 11, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 22, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 4, leaf=False, consumer=6, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 8, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 16, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.5, leaf=False, consumer=6, producer=9, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 2 values and total: 2, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 1, abs_td_consumer=TemporalDistribution instance with 2 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 4 values and total: 4, abs_td_consumer=TemporalDistribution instance with 4 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 1.25, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 4 values and total: 20, abs_td_consumer=TemporalDistribution instance with 4 values and total: 1),\n", - " Edge(distribution=TemporalDistribution instance with 6 values and total: 2.75, leaf=False, consumer=6, producer=2, td_producer=TemporalDistribution instance with 2 values and total: 11, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 44, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 5 values and total: 2, leaf=False, consumer=6, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 8, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 32, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 3 values and total: 0.25, leaf=False, consumer=6, producer=9, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 4 values and total: 4, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.125, leaf=False, consumer=9, producer=7, td_producer=TemporalDistribution instance with 2 values and total: 0.5, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 2, abs_td_consumer=TemporalDistribution instance with 4 values and total: 4),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.125, leaf=False, consumer=7, producer=6, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 8 values and total: 8, abs_td_consumer=TemporalDistribution instance with 8 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.625, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 8 values and total: 40, abs_td_consumer=TemporalDistribution instance with 8 values and total: 2),\n", - " Edge(distribution=TemporalDistribution instance with 8 values and total: 1.375, leaf=False, consumer=6, producer=2, td_producer=TemporalDistribution instance with 2 values and total: 11, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 16 values and total: 88, abs_td_consumer=TemporalDistribution instance with 8 values and total: 8),\n", - " Edge(distribution=TemporalDistribution instance with 6 values and total: 1, leaf=False, consumer=6, producer=8, td_producer=TemporalDistribution instance with 2 values and total: 8, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 16 values and total: 64, abs_td_consumer=TemporalDistribution instance with 8 values and total: 8),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 0.125, leaf=False, consumer=6, producer=9, td_producer=TemporalDistribution instance with 1 values and total: 1, td_consumer=TemporalDistribution instance with 1 values and total: 1, abs_td_producer=TemporalDistribution instance with 8 values and total: 8, abs_td_consumer=TemporalDistribution instance with 8 values and total: 8)]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "824b7602", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Edge(distribution=TemporalDistribution instance with 2 values and total: 2.5, leaf=False, consumer=7, producer=8, td_producer=TemporalDistribution instance with 1 values and total: 5, td_consumer=TemporalDistribution instance with 2 values and total: 0.5, abs_td_producer=TemporalDistribution instance with 2 values and total: 10, abs_td_consumer=TemporalDistribution instance with 2 values and total: 0.5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7b5649e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist as brightway project databases\n", - "['2024-01-30T16:00:11' '2024-01-30T16:00:11']\n", - "['2022-01-30T04:21:47' '2023-01-30T10:10:59']\n", - "['2022-01-30T04:21:47' '2023-01-30T10:10:59']\n", - "['2022-01-30T04:21:47' '2022-01-30T04:21:47' '2023-01-30T10:10:59'\n", - " '2023-01-30T10:10:59']\n", - "['2022-01-30T04:21:47' '2022-01-30T04:21:47' '2023-01-30T10:10:59'\n", - " '2023-01-30T10:10:59']\n", - "['2022-01-30T04:21:47' '2023-01-30T10:10:59']\n", - "['2022-01-30T04:21:47' '2022-01-30T04:21:47' '2023-01-30T10:10:59'\n", - " '2023-01-30T10:10:59']\n", - "['2020-01-30T16:43:23' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47']\n", - "['2020-01-30T16:43:23' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47']\n", - "['2020-01-30T16:43:23' '2020-01-30T16:43:23' '2021-01-29T22:32:35'\n", - " '2021-01-29T22:32:35' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47' '2022-01-30T04:21:47']\n", - "['2020-01-30T16:43:23' '2020-01-30T16:43:23' '2021-01-29T22:32:35'\n", - " '2021-01-29T22:32:35' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47' '2022-01-30T04:21:47']\n", - "['2020-01-30T16:43:23' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47']\n", - "['2020-01-30T16:43:23' '2020-01-30T16:43:23' '2021-01-29T22:32:35'\n", - " '2021-01-29T22:32:35' '2021-01-29T22:32:35' '2021-01-29T22:32:35'\n", - " '2022-01-30T04:21:47' '2022-01-30T04:21:47']\n", - "['2018-01-30T05:04:59' '2019-01-30T10:54:11' '2019-01-30T10:54:11'\n", - " '2020-01-30T16:43:23' '2019-01-30T10:54:11' '2020-01-30T16:43:23'\n", - " '2020-01-30T16:43:23' '2021-01-29T22:32:35']\n", - "['2018-01-30T05:04:59' '2019-01-30T10:54:11' '2019-01-30T10:54:11'\n", - " '2020-01-30T16:43:23' '2019-01-30T10:54:11' '2020-01-30T16:43:23'\n", - " '2020-01-30T16:43:23' '2021-01-29T22:32:35']\n", - "['2018-01-30T05:04:59' '2018-01-30T05:04:59' '2019-01-30T10:54:11'\n", - " '2019-01-30T10:54:11' '2019-01-30T10:54:11' '2019-01-30T10:54:11'\n", - " '2020-01-30T16:43:23' '2020-01-30T16:43:23' '2019-01-30T10:54:11'\n", - " '2019-01-30T10:54:11' '2020-01-30T16:43:23' '2020-01-30T16:43:23'\n", - " '2020-01-30T16:43:23' '2020-01-30T16:43:23' '2021-01-29T22:32:35'\n", - " '2021-01-29T22:32:35']\n", - "['2018-01-30T05:04:59' '2018-01-30T05:04:59' '2019-01-30T10:54:11'\n", - " '2019-01-30T10:54:11' '2019-01-30T10:54:11' '2019-01-30T10:54:11'\n", - " '2020-01-30T16:43:23' '2020-01-30T16:43:23' '2019-01-30T10:54:11'\n", - " '2019-01-30T10:54:11' '2020-01-30T16:43:23' '2020-01-30T16:43:23'\n", - " '2020-01-30T16:43:23' '2020-01-30T16:43:23' '2021-01-29T22:32:35'\n", - " '2021-01-29T22:32:35']\n", - "['2018-01-30T05:04:59' '2019-01-30T10:54:11' '2019-01-30T10:54:11'\n", - " '2020-01-30T16:43:23' '2019-01-30T10:54:11' '2020-01-30T16:43:23'\n", - " '2020-01-30T16:43:23' '2021-01-29T22:32:35']\n", - " producer consumer leaf consumer_date producer_date amount\n", - "0 9 -1 False 2024-01-30 16:00:11 2024-01-30 16:00:11 1.0\n", - "1 7 9 False 2024-01-30 16:00:11 2022-01-30 04:21:47 0.35\n", - "1 7 9 False 2024-01-30 16:00:11 2023-01-30 10:10:59 0.15\n", - "2 6 7 False 2022-01-30 04:21:47 2022-01-30 04:21:47 1.0\n", - "2 6 7 False 2023-01-30 10:10:59 2023-01-30 10:10:59 1.0\n", - ".. ... ... ... ... ... ...\n", - "18 9 6 False 2020-01-30 16:43:23 2020-01-30 16:43:23 1.0\n", - "18 9 6 False 2019-01-30 10:54:11 2019-01-30 10:54:11 1.0\n", - "18 9 6 False 2020-01-30 16:43:23 2020-01-30 16:43:23 1.0\n", - "18 9 6 False 2020-01-30 16:43:23 2020-01-30 16:43:23 1.0\n", - "18 9 6 False 2021-01-29 22:32:35 2021-01-29 22:32:35 1.0\n", - "\n", - "[113 rows x 6 columns]\n", - "Warning: Reference date 2025-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2026-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2027-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2028-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2028-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2029-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2030-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2031-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2032-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", - "Warning: Reference date 2033-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n" - ] - }, - { - "data": { - "text/html": [ - "
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120158620199.62015-01-01{'background_2008': 0.5624572210814511, 'backg...BE2015
220168620184.82016-01-01{'background_2008': 0.5, 'background_2024': 0.5}BE2016
3201686202012.82016-01-01{'background_2008': 0.5, 'background_2024': 0.5}BE2016
4201786201914.42017-01-01{'background_2008': 0.43737166324435317, 'back...BE2017
520178620219.62017-01-01{'background_2008': 0.43737166324435317, 'back...BE2017
620178720185.02017-01-01{'background_2008': 0.43737166324435317, 'back...BD2017
720186720181.02018-01-01{'background_2008': 0.37491444216290215, 'back...ED2018
820187920200.352018-01-01{'background_2008': 0.37491444216290215, 'back...DA2018
9201886202019.22018-01-01{'background_2008': 0.37491444216290215, 'back...BE2018
1020188620226.42018-01-01{'background_2008': 0.37491444216290215, 'back...BE2018
11201887201915.02018-01-01{'background_2008': 0.37491444216290215, 'back...BD2018
1220189620181.02018-01-01{'background_2008': 0.37491444216290215, 'back...AE2018
1320196720193.02019-01-01{'background_2008': 0.312457221081451, 'backgr...ED2019
1420197920200.152019-01-01{'background_2008': 0.312457221081451, 'backgr...DA2019
1520197920210.72019-01-01{'background_2008': 0.312457221081451, 'backgr...DA2019
16201986202114.42019-01-01{'background_2008': 0.312457221081451, 'backgr...BE2019
1720198620233.22019-01-01{'background_2008': 0.312457221081451, 'backgr...BE2019
18201987202020.02019-01-01{'background_2008': 0.312457221081451, 'backgr...BD2019
1920199620193.02019-01-01{'background_2008': 0.312457221081451, 'backgr...AE2019
2020206720204.02020-01-01{'background_2008': 0.25, 'background_2024': 0...ED2020
2120207920210.32020-01-01{'background_2008': 0.25, 'background_2024': 0...DA2020
2220207920220.72020-01-01{'background_2008': 0.25, 'background_2024': 0...DA2020
2320208620229.62020-01-01{'background_2008': 0.25, 'background_2024': 0...BE2020
24202087202115.02020-01-01{'background_2008': 0.25, 'background_2024': 0...BD2020
2520209620204.02020-01-01{'background_2008': 0.25, 'background_2024': 0...AE2020
2620216720213.02021-01-01{'background_2008': 0.18737166324435317, 'back...ED2021
2720217920220.32021-01-01{'background_2008': 0.18737166324435317, 'back...DA2021
2820217920230.352021-01-01{'background_2008': 0.18737166324435317, 'back...DA2021
2920218620234.82021-01-01{'background_2008': 0.18737166324435317, 'back...BE2021
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3220226720222.02022-01-01{'background_2008': 0.12491444216290215, 'back...ED2022
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3620229620222.02022-01-01{'background_2008': 0.12491444216290215, 'back...AE2022
3720232620187.72023-01-01{'background_2008': 0.06245722108145102, 'back...CE2023
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3920237920240.152023-01-01{'background_2008': 0.06245722108145102, 'back...DA2023
4020239620231.02023-01-01{'background_2008': 0.06245722108145102, 'back...AE2023
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5020312620219.92031-01-01{'background_2024': 1}CE2031
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" - ], - "text/plain": [ - " year producer consumer consumer_datestamp amount date \\\n", - "0 2014 8 6 2018 3.2 2014-01-01 \n", - "1 2015 8 6 2019 9.6 2015-01-01 \n", - "2 2016 8 6 2018 4.8 2016-01-01 \n", - "3 2016 8 6 2020 12.8 2016-01-01 \n", - "4 2017 8 6 2019 14.4 2017-01-01 \n", - "5 2017 8 6 2021 9.6 2017-01-01 \n", - "6 2017 8 7 2018 5.0 2017-01-01 \n", - "7 2018 6 7 2018 1.0 2018-01-01 \n", - "8 2018 7 9 2020 0.35 2018-01-01 \n", - "9 2018 8 6 2020 19.2 2018-01-01 \n", - "10 2018 8 6 2022 6.4 2018-01-01 \n", - "11 2018 8 7 2019 15.0 2018-01-01 \n", - "12 2018 9 6 2018 1.0 2018-01-01 \n", - "13 2019 6 7 2019 3.0 2019-01-01 \n", - "14 2019 7 9 2020 0.15 2019-01-01 \n", - "15 2019 7 9 2021 0.7 2019-01-01 \n", - "16 2019 8 6 2021 14.4 2019-01-01 \n", - "17 2019 8 6 2023 3.2 2019-01-01 \n", - "18 2019 8 7 2020 20.0 2019-01-01 \n", - "19 2019 9 6 2019 3.0 2019-01-01 \n", - "20 2020 6 7 2020 4.0 2020-01-01 \n", - "21 2020 7 9 2021 0.3 2020-01-01 \n", - "22 2020 7 9 2022 0.7 2020-01-01 \n", - "23 2020 8 6 2022 9.6 2020-01-01 \n", - "24 2020 8 7 2021 15.0 2020-01-01 \n", - "25 2020 9 6 2020 4.0 2020-01-01 \n", - "26 2021 6 7 2021 3.0 2021-01-01 \n", - "27 2021 7 9 2022 0.3 2021-01-01 \n", - "28 2021 7 9 2023 0.35 2021-01-01 \n", - "29 2021 8 6 2023 4.8 2021-01-01 \n", - "30 2021 8 7 2022 10.0 2021-01-01 \n", - "31 2021 9 6 2021 3.0 2021-01-01 \n", - "32 2022 6 7 2022 2.0 2022-01-01 \n", - "33 2022 7 9 2023 0.15 2022-01-01 \n", - "34 2022 7 9 2024 0.35 2022-01-01 \n", - "35 2022 8 7 2023 5.0 2022-01-01 \n", - "36 2022 9 6 2022 2.0 2022-01-01 \n", - "37 2023 2 6 2018 7.7 2023-01-01 \n", - "38 2023 6 7 2023 1.0 2023-01-01 \n", - "39 2023 7 9 2024 0.15 2023-01-01 \n", - "40 2023 9 6 2023 1.0 2023-01-01 \n", - "41 2024 2 6 2019 23.1 2024-01-01 \n", - "42 2024 9 -1 2024 1.0 2024-01-01 \n", - "43 2025 2 6 2020 30.8 2025-01-01 \n", - "44 2026 2 6 2021 23.1 2026-01-01 \n", - "45 2027 2 6 2022 15.4 2027-01-01 \n", - "46 2028 2 6 2018 3.3 2028-01-01 \n", - "47 2028 2 6 2023 7.7 2028-01-01 \n", - "48 2029 2 6 2019 9.9 2029-01-01 \n", - "49 2030 2 6 2020 13.2 2030-01-01 \n", - "50 2031 2 6 2021 9.9 2031-01-01 \n", - "51 2032 2 6 2022 6.6 2032-01-01 \n", - "52 2033 2 6 2023 3.3 2033-01-01 \n", - "\n", - " interpolation_weights producer_name \\\n", - "0 {'background_2008': 0.624914442162902, 'backgr... B \n", - "1 {'background_2008': 0.5624572210814511, 'backg... B \n", - "2 {'background_2008': 0.5, 'background_2024': 0.5} B \n", - "3 {'background_2008': 0.5, 'background_2024': 0.5} B \n", - "4 {'background_2008': 0.43737166324435317, 'back... B \n", - "5 {'background_2008': 0.43737166324435317, 'back... B \n", - "6 {'background_2008': 0.43737166324435317, 'back... B \n", - "7 {'background_2008': 0.37491444216290215, 'back... E \n", - "8 {'background_2008': 0.37491444216290215, 'back... D \n", - "9 {'background_2008': 0.37491444216290215, 'back... B \n", - "10 {'background_2008': 0.37491444216290215, 'back... B \n", - "11 {'background_2008': 0.37491444216290215, 'back... B \n", - "12 {'background_2008': 0.37491444216290215, 'back... A \n", - "13 {'background_2008': 0.312457221081451, 'backgr... E \n", - "14 {'background_2008': 0.312457221081451, 'backgr... D \n", - "15 {'background_2008': 0.312457221081451, 'backgr... D \n", - "16 {'background_2008': 0.312457221081451, 'backgr... B \n", - "17 {'background_2008': 0.312457221081451, 'backgr... B \n", - "18 {'background_2008': 0.312457221081451, 'backgr... B \n", - "19 {'background_2008': 0.312457221081451, 'backgr... A \n", - "20 {'background_2008': 0.25, 'background_2024': 0... E \n", - "21 {'background_2008': 0.25, 'background_2024': 0... D \n", - "22 {'background_2008': 0.25, 'background_2024': 0... D \n", - "23 {'background_2008': 0.25, 'background_2024': 0... B \n", - "24 {'background_2008': 0.25, 'background_2024': 0... B \n", - "25 {'background_2008': 0.25, 'background_2024': 0... A \n", - "26 {'background_2008': 0.18737166324435317, 'back... E \n", - "27 {'background_2008': 0.18737166324435317, 'back... D \n", - "28 {'background_2008': 0.18737166324435317, 'back... D \n", - "29 {'background_2008': 0.18737166324435317, 'back... B \n", - "30 {'background_2008': 0.18737166324435317, 'back... B \n", - "31 {'background_2008': 0.18737166324435317, 'back... A \n", - "32 {'background_2008': 0.12491444216290215, 'back... E \n", - "33 {'background_2008': 0.12491444216290215, 'back... D \n", - "34 {'background_2008': 0.12491444216290215, 'back... D \n", - "35 {'background_2008': 0.12491444216290215, 'back... B \n", - "36 {'background_2008': 0.12491444216290215, 'back... A \n", - "37 {'background_2008': 0.06245722108145102, 'back... C \n", - "38 {'background_2008': 0.06245722108145102, 'back... E \n", - "39 {'background_2008': 0.06245722108145102, 'back... D \n", - "40 {'background_2008': 0.06245722108145102, 'back... A \n", - "41 {'background_2024': 1} C \n", - "42 {'background_2024': 1} A \n", - "43 {'background_2024': 1} C \n", - "44 {'background_2024': 1} C \n", - "45 {'background_2024': 1} C \n", - "46 {'background_2024': 1} C \n", - "47 {'background_2024': 1} C \n", - "48 {'background_2024': 1} C \n", - "49 {'background_2024': 1} C \n", - "50 {'background_2024': 1} C \n", - "51 {'background_2024': 1} C \n", - "52 {'background_2024': 1} C \n", - "\n", - " consumer_name timestamp \n", - "0 E 2014 \n", - "1 E 2015 \n", - "2 E 2016 \n", - "3 E 2016 \n", - "4 E 2017 \n", - "5 E 2017 \n", - "6 D 2017 \n", - "7 D 2018 \n", - "8 A 2018 \n", - "9 E 2018 \n", - "10 E 2018 \n", - "11 D 2018 \n", - "12 E 2018 \n", - "13 D 2019 \n", - "14 A 2019 \n", - "15 A 2019 \n", - "16 E 2019 \n", - "17 E 2019 \n", - "18 D 2019 \n", - "19 E 2019 \n", - "20 D 2020 \n", - "21 A 2020 \n", - "22 A 2020 \n", - "23 E 2020 \n", - "24 D 2020 \n", - "25 E 2020 \n", - "26 D 2021 \n", - "27 A 2021 \n", - "28 A 2021 \n", - "29 E 2021 \n", - "30 D 2021 \n", - "31 E 2021 \n", - "32 D 2022 \n", - "33 A 2022 \n", - "34 A 2022 \n", - "35 D 2022 \n", - "36 E 2022 \n", - "37 E 2023 \n", - "38 D 2023 \n", - "39 A 2023 \n", - "40 E 2023 \n", - "41 E 2024 \n", - "42 -1 2024 \n", - "43 E 2025 \n", - "44 E 2026 \n", - "45 E 2027 \n", - "46 E 2028 \n", - "47 E 2028 \n", - "48 E 2029 \n", - "49 E 2030 \n", - "50 E 2031 \n", - "51 E 2032 \n", - "52 E 2033 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2008\", \"%Y\"): 'background_2008',\n", - " datetime.strptime(\"2024\", \"%Y\"): 'background_2024',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "b1185ed1", - "metadata": {}, - "outputs": [], - "source": [ - "timeline_df.to_excel(\"timeline_df.xlsx\")" - ] - }, - { - "cell_type": "markdown", - "id": "fb32fc50", - "metadata": {}, - "source": [ - "Now, we want to create a datapackage that takes care of relinking processes to our prospective databases. To do so, we need to provide the timeline dataframe, the dict of prospective databases and corresponding years, and a new dictionary that defines at which point in time our functional unit is assessed *(We can probably include this information in the database_date_dict in the future, but for now, this works)*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7d48585", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "7db5ff18", - "metadata": {}, - "source": [ - "Finally, we just have to reformat our input data for the LCA, add our datapackage containing the patches, and run the lca." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71bba776", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()" - ] - }, - { - "cell_type": "markdown", - "id": "b8f8795d", - "metadata": {}, - "source": [ - "Let's take a look at the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 260.69869977555595\n", - "Old static LCA Score: 189.54999524354938\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', lca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - }, - { - "cell_type": "markdown", - "id": "ce158e2f", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "228f2954", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m new_edges_df\u001b[39m.\u001b[39mexplode([\u001b[39m'\u001b[39m\u001b[39mconsumer_date\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mproducer_date\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mamount\u001b[39m\u001b[39m'\u001b[39m])\n", - "\u001b[0;31mNameError\u001b[0m: name 'new_edges_df' is not defined" - ] - } - ], - "source": [ - "new_edges_df.explode(['consumer_date', 'producer_date', 'amount'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3990158", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: ('temporalis-bio', 'CO2'),\n", - " 2: ('background_2024', 'electricity_mix'),\n", - " 3: ('background_2024', 'electricity_wind'),\n", - " 4: ('background_2020', 'electricity_mix'),\n", - " 5: ('background_2020', 'electricity_wind'),\n", - " 6: ('foreground', 'someotherprocess'),\n", - " 7: ('foreground', 'electrolysis'),\n", - " 8: ('foreground', 'heat_from_hydrogen')}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rem = lca.remapping_dicts['activity']\n", - "rem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3f9a12c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2: 0,\n", - " 3: 1,\n", - " 4: 2,\n", - " 5: 3,\n", - " 6: 4,\n", - " 7: 5,\n", - " 8: 6,\n", - " 2002021: 7,\n", - " 2002022: 8,\n", - " 2002023: 9,\n", - " 2002024: 10,\n", - " 6002022: 11,\n", - " 6002024: 12,\n", - " 7002022: 13,\n", - " 7002024: 14,\n", - " 8002024: 15}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(lca.dicts.activity)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a328e2e4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/test_TD_at_two_consecutive_processes.ipynb b/archive/notebooks/test_TD_at_two_consecutive_processes.ipynb deleted file mode 100644 index fd56cec..0000000 --- a/archive/notebooks/test_TD_at_two_consecutive_processes.ipynb +++ /dev/null @@ -1,1527 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#import libraries\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution\n", - "from edge_extractor import EdgeExtracter\n", - "from medusa_tools import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "02fa7aea", - "metadata": {}, - "source": [ - "## Test with 2 connected TDs at two processes \n", - "\n", - "(see issue #6 https://github.com/TimoDiepers/tictac_lca/issues/6)" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "0ef91d5b", - "metadata": {}, - "source": [ - "\n", - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "88c6f135", - "metadata": {}, - "outputs": [], - "source": [ - "#setup example\n", - "bd.projects.set_current(\"test_abc\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fc35276f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 3462.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 9341.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 3521.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 6083.11it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - } - ], - "source": [ - "bd.Database('temporalis-bio').write({\n", - " ('temporalis-bio', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"temporalis code\": \"co2\",\n", - " },\n", - " ('temporalis-bio', \"CH4\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"methane\",\n", - " \"temporalis code\": \"ch4\",\n", - " },\n", - "})\n", - "\n", - "bd.Database('background_2022').write({\n", - " ('background_2022', 'C'): {\n", - " 'name': 'process C',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'C',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2022', 'C'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " }, ]},\n", - "},\n", - "\n", - " )\n", - "\n", - "bd.Database('background_2020').write({\n", - " ('background_2020', 'C'): {\n", - " 'name': 'process C',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'C',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2020', 'C'),\n", - " },\n", - " {\n", - " 'amount': 2,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " }, ]},\n", - "},\n", - "\n", - " )\n", - "\n", - "bd.Database('foreground').write({\n", - " ('foreground', 'A'): {\n", - " 'name': 'process A',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'A',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-4, -2], dtype='timedelta64[Y]'),\n", - " np.array([0.5, 0.5])), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'B'):\n", - " {\n", - " \"name\": \"process B\",\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'B',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2022', 'C'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-1], dtype='timedelta64[Y]'),\n", - " np.array([1])), \n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'B'), \n", - " }\n", - " ]\n", - "\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3bb58c55", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Foreground\n", - "'process A' (None, somewhere, None)\n", - "{'amount': 1, 'type': 'production', 'input': ('foreground', 'A'), 'output': ('foreground', 'A')}\n", - "{'amount': 1, 'type': 'technosphere', 'input': ('foreground', 'B'), 'temporal_distribution': TemporalDistribution instance with 2 values and total: 1, 'output': ('foreground', 'A')}\n", - "[0.5 0.5]\n", - "[-126227808 -63113904]\n", - "'process B' (None, somewhere, None)\n", - "{'amount': 1, 'type': 'technosphere', 'input': ('background_2022', 'C'), 'temporal_distribution': TemporalDistribution instance with 1 values and total: 1, 'output': ('foreground', 'B')}\n", - "[1.]\n", - "[-31556952]\n", - "{'amount': 1, 'type': 'production', 'input': ('foreground', 'B'), 'output': ('foreground', 'B')}\n", - "\n", - " Background\n", - "'process C' (None, somewhere, None)\n", - "{'amount': 1, 'type': 'production', 'input': ('background_2022', 'C'), 'output': ('background_2022', 'C')}\n", - "{'amount': 1, 'type': 'biosphere', 'input': ('temporalis-bio', 'CO2'), 'output': ('background_2022', 'C')}\n" - ] - } - ], - "source": [ - "print('Foreground')\n", - "for act in bd.Database(\"foreground\"):\n", - " print(act)\n", - " for ex in act.exchanges():\n", - " print(ex.as_dict())\n", - " if \"temporal_distribution\" in ex.as_dict().keys():\n", - " print(ex[\"temporal_distribution\"].amount)\n", - " print(ex[\"temporal_distribution\"].date)\n", - " # if \"temporal_distribution\" in act:\n", - " # print(act[\"temporal_distribution\"].amount)\n", - " # print(act[\"temporal_distribution\"].date)\n", - " \n", - "print('\\n Background')\n", - "for act in bd.Database(\"background_2022\"):\n", - " print(act)\n", - " for ex in act.exchanges():\n", - " print(ex.as_dict())\n", - " if \"temporal_distribution\" in ex.as_dict().keys():\n", - " print(ex[\"temporal_distribution\"].amount)\n", - " print(ex[\"temporal_distribution\"].date)" - ] - }, - { - "cell_type": "markdown", - "id": "4e8b479b", - "metadata": {}, - "source": [ - "The TD on the exchange between B and A is not stored at the activity (see print above) but at the level of exchanges (see print below)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9f507a13", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'process B' (None, somewhere, None)\n", - "There is a temporal information for Exchange: 1 None 'process C' (None, somewhere, None) to 'process B' (None, somewhere, None)>\n", - "[1.]\n", - "[-31556952]\n", - "'process A' (None, somewhere, None)\n", - "There is a temporal information for Exchange: 1 None 'process B' (None, somewhere, None) to 'process A' (None, somewhere, None)>\n", - "[0.5 0.5]\n", - "[-126227808 -63113904]\n" - ] - } - ], - "source": [ - "for act in bd.Database(\"foreground\"):\n", - " for exc in act.exchanges():\n", - " if \"temporal_distribution\" in exc:\n", - " print(act)\n", - " print(f\"There is a temporal information for {exc}\")\n", - " print(exc[\"temporal_distribution\"].amount)\n", - " print(exc[\"temporal_distribution\"].date)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "00b4f8bf", - "metadata": {}, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"example\")).write([\n", - " (('temporalis-bio', \"CO2\"), 1),\n", - " (('temporalis-bio', \"CH4\"), 25),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9c4913b7", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0ecb9c8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 1.0\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "20ea6d12", - "metadata": {}, - "source": [ - "Medusa LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e5a7f7bb", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [] #node.id for node in bd.Database('background_2020')] #+ [\n", - " #node.id for node in bd.Database('background_2022')\n", - "#]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b6bdd1dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 2\n" - ] - }, - { - "data": { - "text/plain": [ - "[Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=-1, producer=17),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 1, leaf=False, consumer=17, producer=18),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 1, leaf=False, consumer=18, producer=15)]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eelca = EdgeExtracter(slca, edge_filter_function=filter_function)\n", - "timeline = eelca.build_edge_timeline()\n", - "timeline\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ecbf47b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys([datetime.datetime(2022, 1, 1, 0, 0), datetime.datetime(2020, 1, 1, 0, 0)])\n", - "Warning: Reference date 2019-01-01 00:00:00 is lower than all provided dates. Data will be taken from closest higher .\n", - "Warning: Reference date 2024-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n" - ] - }, - { - "data": { - "text/html": [ - "
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dateyearproducerproducer_nameconsumerconsumer_nameamountinterpolation_weights
02019-01-01201915process C18process B0.5{2020: 1}
12020-01-01202018process B17process A0.5{2020: 1}
22021-01-01202115process C18process B0.5{2020: 0.4993160054719562, 2022: 0.50068399452...
32022-01-01202218process B17process A0.5{2022: 1}
42024-01-01202417process A-1-11.0{2022: 1}
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" - ], - "text/plain": [ - " date year producer producer_name consumer consumer_name amount \\\n", - "0 2019-01-01 2019 15 process C 18 process B 0.5 \n", - "1 2020-01-01 2020 18 process B 17 process A 0.5 \n", - "2 2021-01-01 2021 15 process C 18 process B 0.5 \n", - "3 2022-01-01 2022 18 process B 17 process A 0.5 \n", - "4 2024-01-01 2024 17 process A -1 -1 1.0 \n", - "\n", - " interpolation_weights \n", - "0 {2020: 1} \n", - "1 {2020: 1} \n", - "2 {2020: 0.4993160054719562, 2022: 0.50068399452... \n", - "3 {2022: 1} \n", - "4 {2022: 1} " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2022\", \"%Y\"): 'background_2022',\n", - " datetime.strptime(\"2020\", \"%Y\"): 'background_2020',\n", - " }\n", - "print(database_date_dict.keys())\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict.keys(), interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "de066a44", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{17: 2024}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "demand_timing_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6aa3f0a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.749658002735978" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "90ed8c11", - "metadata": {}, - "source": [ - "### Investigation of matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c5aa6844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0. , 0. , -1. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , -0.50068399, 0. ],\n", - " [ 0. , 1. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , -1. ,\n", - " 0. , -0.49931601, 0. ],\n", - " [ 0. , 0. , 1. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , -1. , 1. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 1. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. 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- } - }, - "cell_type": "markdown", - "id": "2a501c0c", - "metadata": {}, - "source": [ - "The problem is that the new inputs of C to the new exploded process copies of B are done for the time of C, in this case 2019 and 2021, instead of at the time of B, in this case 2020 and 2022 (column N and P). This leads to empty inputs from C for new process copies of B in 2020 and 2022, which is are the rows consumed by A, which results in a wrong LCIA score.\n", - "\n", - "![image-2.png](attachment:image-2.png)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6417b2aa", - "metadata": {}, - "outputs": [], - "source": [ - "df= pd.DataFrame(lca.technosphere_matrix.toarray()) #for excel visualization\n", - "df.to_csv(\"test.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4f0c6914", - "metadata": {}, - "outputs": [], - "source": [ - "lca.load_lci_data(nonsquare_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c35d196e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 165),\n", - " (1, 166),\n", - " (2, 167),\n", - " (3, 168),\n", - " (4, 1652019),\n", - " (5, 1652021),\n", - " (6, 1672020),\n", - " (7, 1672022),\n", - " (8, 1672024),\n", - " (9, 1682019),\n", - " (10, 1682020),\n", - " (11, 1682021),\n", - " (12, 1682022)}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(lca.dicts.activity.reversed.items())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "5aac9e48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[165,\n", - " 166,\n", - " 167,\n", - " 168,\n", - " 1652019,\n", - " 1652021,\n", - " 1672020,\n", - " 1672022,\n", - " 1672024,\n", - " 1682019,\n", - " 1682020,\n", - " 1682021,\n", - " 1682022]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "second_items_list = [item[1] for item in lca.dicts.activity.reversed.items()]\n", - "second_items_list" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e4460812", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "product_dict (mapping between Ids and csr_matrix column index):\n", - " {165: 0, 166: 1, 167: 2, 168: 3, 1652019: 4, 1652021: 5, 1672020: 6, 1672022: 7, 1672024: 8, 1682019: 9, 1682020: 10, 1682021: 11, 1682022: 12}\n", - "\n", - "activity_dict (mapping between IDS and csr_matrix row index):\n", - " {165: 0, 166: 1, 167: 2, 168: 3, 1652019: 4, 1652021: 5, 1672020: 6, 1672022: 7, 1672024: 8, 1682019: 9, 1682020: 10, 1682021: 11, 1682022: 12}\n" - ] - } - ], - "source": [ - "print(\"product_dict (mapping between Ids and csr_matrix column index):\\n\", lca.product_dict)\n", - "print(\"\\nactivity_dict (mapping between IDS and csr_matrix row index):\\n\", lca.activity_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "293e81be", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "165 -> 'process C' (None, somewhere, None) background_2022\n", - "166 -> 'process C' (None, somewhere, None) background_2020\n", - "167 -> 'process A' (None, somewhere, None) foreground\n", - "168 -> 'process B' (None, somewhere, None) foreground\n" - ] - }, - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[20], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m lca\u001b[38;5;241m.\u001b[39mactivity_dict:\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m->\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_activity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m, bd\u001b[38;5;241m.\u001b[39mget_activity(key)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac2\\lib\\site-packages\\bw2data\\utils.py:440\u001b[0m, in \u001b[0;36mget_activity\u001b[1;34m(key, **kwargs)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n\u001b[0;32m 439\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m key\n\u001b[1;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_node(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac2\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "for key in lca.activity_dict:\n", - " print(key, \"->\",bd.get_activity(key), bd.get_activity(key)[\"database\"]) #BW does not find the \"exploded nodes\", because they exist only in the datapackages?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c9bf06a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f=lca.demand_array\n", - "f" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15146b22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0. , 0. , -1. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , -0.50068399, 0. ],\n", - " [ 0. , 1. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , -1. ,\n", - " 0. , -0.49931601, 0. ],\n", - " [ 0. , 0. , 1. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , -1. , 1. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 1. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 0. ,\n", - 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42024-01-01202411process A-1-11.0{2022: 1}
32022-01-01202212process B11process A0.5{2022: 1}
22021-01-0120219process C12process B0.5{2020: 0.4993160054719562, 2022: 0.50068399452...
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" - ], - "text/plain": [ - " date year producer producer_name consumer consumer_name amount \\\n", - "4 2024-01-01 2024 11 process A -1 -1 1.0 \n", - "3 2022-01-01 2022 12 process B 11 process A 0.5 \n", - "2 2021-01-01 2021 9 process C 12 process B 0.5 \n", - "1 2020-01-01 2020 12 process B 11 process A 0.5 \n", - "0 2019-01-01 2019 9 process C 12 process B 0.5 \n", - "\n", - " interpolation_weights \n", - "4 {2022: 1} \n", - "3 {2022: 1} \n", - "2 {2020: 0.4993160054719562, 2022: 0.50068399452... \n", - "1 {2020: 1} \n", - "0 {2020: 1} " - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline_df.iloc[::-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024 11 -1 2024\n", - "2022 12 11 2024\n", - "2021 9 12 2022\n", - "2020 12 11 2024\n", - "2019 9 12 2020\n" - ] - } - ], - "source": [ - "consumer_years = {}\n", - "for row in timeline_df.iloc[::-1].itertuples():\n", - " if row.consumer == -1:\n", - " consumer_years[row.consumer] = row.year\n", - " consumer_years[row.producer] = row.year # the year of the producer will be the consumer year for this procuess until a it becomesa producer again\n", - " print(row.year, row.producer, row.consumer, consumer_years[row.consumer])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/test_amounts.ipynb b/archive/notebooks/test_amounts.ipynb deleted file mode 100644 index ea3f704..0000000 --- a/archive/notebooks/test_amounts.ipynb +++ /dev/null @@ -1,1516 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution, easy_datetime_distribution\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.realpath('../'))\n", - "from medusa.edge_extractor import *\n", - "from medusa.matrix_modifier import *\n", - "from medusa.medusa_lca import *\n", - "from medusa.timeline_builder import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d3622ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 6626.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 54120.05it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 42799.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 81442.80it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from tests.databases import *\n", - "# db_abc_simple() # works\n", - "# db_abc_C_to_E() # works\n", - "# db_abc_C_and_B_to_E() # BREAKS THINGS\n", - "# db_abc_B_to_E() # BREAKS THINGS\n", - "db_abc_B_to_E_simplified() # BREAKS THINGS\n", - "# db_abc_B_to_E_simplified_and_E_with_TD()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8d9405d9", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "markdown", - "id": "31ced634", - "metadata": {}, - "source": [ - "# Static LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cca6b8f2-12a3-43f9-8be2-c6a898268adf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 122.8499967455864\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "b461bbb5", - "metadata": {}, - "source": [ - "# `MEDUSA` LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('background_2020')] + [\n", - " node.id for node in bd.Database('background_2024')\n", - "]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "markdown", - "id": "dead855e", - "metadata": {}, - "source": [ - "Now we can do the graph traversal and create a timeline of edges:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 8\n", - "7 6\n" - ] - } - ], - "source": [ - "eelca = EdgeExtractor(slca, edge_filter_function=filter_function)\n", - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "55ca7596", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Edge(distribution=TemporalDistribution instance with 2 values and total: 32.5, leaf=True, consumer=8, producer=2, td_producer=TemporalDistribution instance with 1 values and total: 13, td_consumer=TemporalDistribution instance with 1 values and total: 5, abs_td_producer=TemporalDistribution instance with 2 values and total: 26, abs_td_consumer=TemporalDistribution instance with 2 values and total: 10)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline[-2]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7b5649e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist as brightway project databases\n" - ] - }, - { - "data": { - "text/html": [ - "
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220152015-01-012C20202020-01-018B13.0{'background_2008': 0.5624572210814511, 'backg...
320162016-01-012C20212021-01-018B26.0{'background_2008': 0.5, 'background_2024': 0.5}
420172017-01-012C20222022-01-018B13.0{'background_2008': 0.43737166324435317, 'back...
520182018-01-018B20222022-01-016E3.2{'background_2008': 0.37491444216290215, 'back...
620192019-01-018B20232023-01-016E3.2{'background_2008': 0.312457221081451, 'backgr...
720202020-01-018B20222022-01-016E4.8{'background_2008': 0.25, 'background_2024': 0...
820212021-01-018B20222022-01-017D5.0{'background_2008': 0.18737166324435317, 'back...
920212021-01-018B20232023-01-016E4.8{'background_2008': 0.18737166324435317, 'back...
1020222022-01-016E20222022-01-017D1.0{'background_2008': 0.12491444216290215, 'back...
1120222022-01-018B20232023-01-017D5.0{'background_2008': 0.12491444216290215, 'back...
1220222022-01-017D20242024-01-019A0.35{'background_2008': 0.12491444216290215, 'back...
1320232023-01-016E20232023-01-017D1.0{'background_2008': 0.06245722108145102, 'back...
1420232023-01-017D20242024-01-019A0.15{'background_2008': 0.06245722108145102, 'back...
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" - ], - "text/plain": [ - " hash_producer date_producer producer producer_name hash_consumer \\\n", - "0 2013 2013-01-01 2 C 2018 \n", - "1 2014 2014-01-01 2 C 2019 \n", - "2 2015 2015-01-01 2 C 2020 \n", - "3 2016 2016-01-01 2 C 2021 \n", - "4 2017 2017-01-01 2 C 2022 \n", - "5 2018 2018-01-01 8 B 2022 \n", - "6 2019 2019-01-01 8 B 2023 \n", - "7 2020 2020-01-01 8 B 2022 \n", - "8 2021 2021-01-01 8 B 2022 \n", - "9 2021 2021-01-01 8 B 2023 \n", - "10 2022 2022-01-01 6 E 2022 \n", - "11 2022 2022-01-01 8 B 2023 \n", - "12 2022 2022-01-01 7 D 2024 \n", - "13 2023 2023-01-01 6 E 2023 \n", - "14 2023 2023-01-01 7 D 2024 \n", - "15 2024 2024-01-01 9 A 2024 \n", - "\n", - " date_consumer consumer consumer_name amount \\\n", - "0 2018-01-01 8 B 13.0 \n", - "1 2019-01-01 8 B 13.0 \n", - "2 2020-01-01 8 B 13.0 \n", - "3 2021-01-01 8 B 26.0 \n", - "4 2022-01-01 8 B 13.0 \n", - "5 2022-01-01 6 E 3.2 \n", - "6 2023-01-01 6 E 3.2 \n", - "7 2022-01-01 6 E 4.8 \n", - "8 2022-01-01 7 D 5.0 \n", - "9 2023-01-01 6 E 4.8 \n", - "10 2022-01-01 7 D 1.0 \n", - "11 2023-01-01 7 D 5.0 \n", - "12 2024-01-01 9 A 0.35 \n", - "13 2023-01-01 7 D 1.0 \n", - "14 2024-01-01 9 A 0.15 \n", - "15 2024-01-01 -1 -1 1.0 \n", - "\n", - " interpolation_weights \n", - "0 {'background_2008': 0.6873716632443532, 'backg... \n", - "1 {'background_2008': 0.624914442162902, 'backgr... \n", - "2 {'background_2008': 0.5624572210814511, 'backg... \n", - "3 {'background_2008': 0.5, 'background_2024': 0.5} \n", - "4 {'background_2008': 0.43737166324435317, 'back... \n", - "5 {'background_2008': 0.37491444216290215, 'back... \n", - "6 {'background_2008': 0.312457221081451, 'backgr... \n", - "7 {'background_2008': 0.25, 'background_2024': 0... \n", - "8 {'background_2008': 0.18737166324435317, 'back... \n", - "9 {'background_2008': 0.18737166324435317, 'back... \n", - "10 {'background_2008': 0.12491444216290215, 'back... \n", - "11 {'background_2008': 0.12491444216290215, 'back... \n", - "12 {'background_2008': 0.12491444216290215, 'back... \n", - "13 {'background_2008': 0.06245722108145102, 'back... \n", - "14 {'background_2008': 0.06245722108145102, 'back... \n", - "15 {'background_2024': 1} " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2008\", \"%Y\"): 'background_2008',\n", - " datetime.strptime(\"2024\", \"%Y\"): 'background_2024',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d7d48585", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "71bba776", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4a51cd8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New MEDUSA LCA Score: 123.71881278316832\n", - "Old static LCA Score: 122.8499967455864\n" - ] - } - ], - "source": [ - "print('New MEDUSA LCA Score:', lca.score)\n", - "print('Old static LCA Score:', slca.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "228f2954", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" 6: ('foreground', 'E'),\n", - " 7: ('foreground', 'D'),\n", - " 8: ('foreground', 'B'),\n", - " 9: ('foreground', 'A')}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rem = lca.remapping_dicts['activity']\n", - "rem" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e3f9a12c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2: 0,\n", - " 3: 1,\n", - " 4: 2,\n", - " 5: 3,\n", - " 6: 4,\n", - " 7: 5,\n", - " 8: 6,\n", - " 9: 7,\n", - " 2001999: 8,\n", - " 2002016: 9,\n", - " 2002017: 10,\n", - " 2002027: 11,\n", - " 2002028: 12,\n", - " 2002032: 13,\n", - " 2002033: 14,\n", - " 2002034: 15,\n", - " 6002022: 16,\n", - " 6002023: 17,\n", - " 7002022: 18,\n", - " 7002023: 19,\n", - " 8002004: 20,\n", - " 8002021: 21,\n", - " 8002022: 22,\n", - " 8002039: 23,\n", - " 9002024: 24}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(lca.dicts.activity)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5ce28844", - "metadata": {}, - "outputs": [], - "source": [ - "abs = timeline[1].abs_td_consumer" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c48b6743", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['2024-01-30T18:45:57'], dtype='datetime64[s]')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abs.date" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ab642b21", - "metadata": {}, - "outputs": [], - "source": [ - "td = TemporalDistribution(\n", - " date=np.array(\n", - " [\n", - " 0,\n", - " ],\n", - " dtype=\"timedelta64[Y]\",\n", - " ),\n", - " amount=np.array([1]),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "68c97d1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0], dtype='timedelta64[s]')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "td.date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab253647", - "metadata": {}, - "outputs": [], - "source": [ - "def join_datetime_and_timedelta_distributions(\n", - " td_producer: TemporalDistribution, td_consumer: TemporalDistribution\n", - " ) -> TemporalDistribution:\n", - " \"\"\"TODO: Needs description\"\"\"\n", - " # if an edge does not have a TD, then return the consumer_td so that the timeline continues\n", - " if isinstance(td_consumer, TemporalDistribution) and isinstance(\n", - " td_producer, Number\n", - " ):\n", - " return td_consumer\n", - " # Else, if both consumer and producer have a td (absolute and relative, respectively) join to TDs\n", - " if isinstance(td_producer, TemporalDistribution) and isinstance(\n", - " td_consumer, TemporalDistribution\n", - " ):\n", - " if not (td_consumer.date.dtype == datetime_type):\n", - " raise ValueError(\n", - " f\"`td_consumer.date` must have dtype `datetime64[s]`, but got `{td_consumer.date.dtype}`\"\n", - " )\n", - " if not (td_producer.date.dtype == timedelta_type):\n", - " raise ValueError(\n", - " f\"`td_producer.date` must have dtype `timedelta64[s]`, but got `{td_producer.date.dtype}`\"\n", - " )\n", - " date = (\n", - " td_consumer.date.reshape((-1, 1)) + td_producer.date.reshape((1, -1))\n", - " ).ravel()\n", - " amount = np.array(len(td_consumer) * [td_producer.amount]).ravel()\n", - " return TemporalDistribution(date, amount)\n", - " else:\n", - " raise ValueError(\n", - " \"Can't join TemporalDistribution and something else: Trying with {} and {}\".format(\n", - " type(td1), type(td2)\n", - " )\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/test_background_dbs_1.ipynb b/archive/notebooks/test_background_dbs_1.ipynb deleted file mode 100644 index 04184c1..0000000 --- a/archive/notebooks/test_background_dbs_1.ipynb +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9ca8cef0-e595-4671-8641-381584d5319d", - "metadata": {}, - "source": [ - "## Medusa test with premise temporal background databases\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "08b6f470", - "metadata": {}, - "source": [ - "Step 1: create premise dbs in BW25 -> done in other script, imported here from tar.gz file" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "59cf6d4a-9974-44be-943a-4a09a7b4aade", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\MULLERA\\AppData\\Local\\anaconda3\\envs\\tictac_premise\n" - ] - } - ], - "source": [ - "#using a venv with bw2 and premise installed\n", - "\n", - "import bw2data as bd\n", - "import bw2io as bi\n", - "from premise import *\n", - "\n", - "import sys\n", - "print(sys.prefix) #shows you in which environment you are" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6a809b6f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.set_current(\"bw25_premise_db_medusa\")\n", - "bd.database" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f22daa5c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "premise v.(1, 8, 1)\n", - "+------------------------------------------------------------------+\n", - "| Warning |\n", - "+------------------------------------------------------------------+\n", - "| Because some of the scenarios can yield LCI databases |\n", - "| containing net negative emission technologies (NET), |\n", - "| it is advised to account for biogenic CO2 flows when calculating |\n", - "| Global Warming potential indicators. |\n", - "| `premise_gwp` provides characterization factors for such flows. |\n", - "| It also provides factors for hydrogen emissions to air. |\n", - "| |\n", - "| Within your bw2 project: |\n", - "| from premise_gwp import add_premise_gwp |\n", - "| add_premise_gwp() |\n", - "+------------------------------------------------------------------+\n", - "+--------------------------------+----------------------------------+\n", - "| Utils functions | Description |\n", - "+--------------------------------+----------------------------------+\n", - "| clear_cache() | Clears the cache folder. Useful |\n", - "| | when updating `premise`or |\n", - "| | encountering issues with |\n", - "| | inventories. |\n", - "+--------------------------------+----------------------------------+\n", - "| get_regions_definition(model) | Retrieves the list of countries |\n", - "| | for each region of the model. |\n", - "+--------------------------------+----------------------------------+\n", - "| ndb.NewDatabase(...) | Generates a summary of the most |\n", - "| ndb.generate_scenario_report() | important scenarios' variables. |\n", - "+--------------------------------+----------------------------------+\n", - "Keep uncertainty data?\n", - "NewDatabase(..., keep_uncertainty_data=True)\n", - "\n", - "Disable multiprocessing?\n", - "NewDatabase(..., use_multiprocessing=False)\n", - "\n", - "Hide these messages?\n", - "NewDatabase(..., quiet=True)\n", - "\n", - "//////////////////// EXTRACTING SOURCE DATABASE ////////////////////\n", - "Cannot find cached database. Will create one now for next time...\n", - "Getting activity data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████| 21255/21255 [00:00<00:00, 98740.17it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding exchange data to activities\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████| 676292/676292 [01:19<00:00, 8459.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling out exchange data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████| 21255/21255 [00:05<00:00, 3604.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set missing location of datasets to global scope.\n", - "Set missing location of production exchanges to scope of dataset.\n", - "Correct missing location of technosphere exchanges.\n", - "Correct missing flow categories for biosphere exchanges\n", - "Remove empty exchanges.\n", - "Remove uncertainty data.\n", - "Done!\n", - "\n", - "////////////////// IMPORTING DEFAULT INVENTORIES ///////////////////\n", - "Cannot find cached inventories. Will create them now for next time...\n", - "Importing default inventories...\n", - "\n", - "Extracted 1 worksheets in 0.36 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.05 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.05 seconds\n", - "Remove uncertainty data.\n", - "Extracted 7 worksheets in 0.09 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.07 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.04 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 1.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "| fluorspar production, 97% purity | fluorspar, 97% purity | GLO | lci-PV.xlsx |\n", - "| metallization paste production, back side | metallization paste, back side | RER | lci-PV.xlsx |\n", - "| metallization paste production, back side, alumini | metallization paste, back side | RER | lci-PV.xlsx |\n", - "| metallization paste production, front side | metallization paste, front sid | RER | lci-PV.xlsx |\n", - "| photovoltaic module production, building-integrate | photovoltaic module, building- | RER | lci-PV.xlsx |\n", - "| photovoltaic module production, building-integrate | photovoltaic module, building- | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for facad | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for flat- | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic mounting system production, for slant | photovoltaic mounting system, | RER | lci-PV.xlsx |\n", - "| photovoltaic panel factory construction | photovoltaic panel factory | GLO | lci-PV.xlsx |\n", - "| polyvinylfluoride production | polyvinylfluoride | US | lci-PV.xlsx |\n", - "| polyvinylfluoride production, dispersion | polyvinylfluoride, dispersion | US | lci-PV.xlsx |\n", - "| polyvinylfluoride, film production | polyvinylfluoride, film | US | lci-PV.xlsx |\n", - "| silicon production, metallurgical grade | silicon, metallurgical grade | NO | lci-PV.xlsx |\n", - "| vinyl fluoride production | vinyl fluoride | US | lci-PV.xlsx |\n", - "| wafer factory construction | wafer factory | DE | lci-PV.xlsx |\n", - "+----------------------------------------------------+--------------------------------+----------+-------------+\n", - "Extracted 1 worksheets in 0.08 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "| carbon dioxide, captured at cement production plan | carbon dioxide, captured and r | RER | lci-synfuels-from-methanol-from-cement-plant.xlsx |\n", - "+----------------------------------------------------+--------------------------------+----------+---------------------------------------------------+\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "The following datasets to import already exist in the source database. They will not be imported\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "| Name | Reference product | Location | File |\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "| methanol distillation, hydrogen from coal gasifica | methanol, purified | RER | lci-synfuels-from-methanol-from-coal.xlsx |\n", - "| methanol synthesis, hydrogen from coal gasificatio | methanol, unpurified | RER | lci-synfuels-from-methanol-from-coal.xlsx |\n", - "+----------------------------------------------------+----------------------+----------+-------------------------------------------+\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 5 worksheets in 0.21 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.04 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.12 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 2 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.01 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.14 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.07 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.03 seconds\n", - "Remove uncertainty data.\n", - "Extracted 1 worksheets in 0.02 seconds\n", - "Migrating to 3.8 first\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Applying strategy: migrate_datasets\n", - "Applying strategy: migrate_exchanges\n", - "Remove uncertainty data.\n", - "Data cached. It is advised to restart your workflow at this point.\n", - "This allows premise to use the cached data instead, which results in\n", - "a faster workflow.\n", - "Done!\n", - "\n", - "/////////////////////// EXTRACTING IAM DATA ////////////////////////\n", - "Done!\n" - ] - } - ], - "source": [ - "ndb = NewDatabase(\n", - " scenarios=[\n", - " {\"model\":\"image\", \"pathway\":\"SSP2-RCP19\", \"year\":2020},\n", - " {\"model\":\"image\", \"pathway\":\"SSP2-RCP19\", \"year\":2025},\n", - " {\"model\":\"image\", \"pathway\":\"SSP2-RCP19\", \"year\":2030},\n", - " ],\n", - " source_db=\"cutoff39\", # <-- name of the database in the BW2 project. Must be a string.\n", - " source_version=\"3.9\", # <-- version of ecoinvent. Can be \"3.5\", \"3.6\", \"3.7\" or \"3.8\". Must be a string.\n", - " key='tUePmX_S5B8ieZkkM7WUU2CnO8SmShwmAeWK9x2rTFo='# <-- decryption key\n", - " # to be requested from the library maintainers if you want ot use default scenarios included in `premise`\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "8fa64f14", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "/////////////////////////// ELECTRICITY ////////////////////////////\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "ndb.update_electricity()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "36882125", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Write new database(s) to Brightway.\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████| 24328/24328 [00:01<00:00, 20281.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: db_2020\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████| 24328/24328 [00:01<00:00, 16349.25it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: db_2025\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████| 24328/24328 [00:02<00:00, 8887.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Created database: db_2030\n", - "Starting IO table write\n", - "Adding technosphere matrix\n", - "Adding biosphere matrix\n", - "Finalizing serialization\n", - "Generate scenario report.\n", - "Report saved under C:\\Users\\MULLERA\\OneDrive - VITO\\Documents\\04_Coding\\tictac_lca\\export\\scenario_report.\n", - "Generate change report.\n", - "Report saved under C:\\Users\\MULLERA\\OneDrive - VITO\\Documents\\04_Coding\\tictac_lca.\n" - ] - } - ], - "source": [ - "ndb.write_db_to_brightway(name = [\"db_2020\", \"db_2025\", \"db_2030\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "44fabebf", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Databases dictionary with 5 object(s):\n", - "\tbiosphere3\n", - "\tcutoff39\n", - "\tdb_2020\n", - "\tdb_2025\n", - "\tdb_2030" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f2009fe4-20f6-4888-a67f-ffb9fa331bf9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "24328\n", - "24328\n", - "24328\n" - ] - } - ], - "source": [ - "db_2020=bd.Database(\"db_2020\")\n", - "print(len(db_2020))\n", - "\n", - "db_2025=bd.Database(\"db_2025\")\n", - "print(len(db_2025))\n", - "\n", - "db_2025=bd.Database(\"db_2030\")\n", - "print(len(db_2025))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9b15881d-1c18-4e54-96ba-dea76ba3453a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.projects.twofive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56f89435-7edf-4722-b30a-6fc2f10b8d7c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#create a backup of bw25 dbs\n", - "bi.backup.backup_project_directory('bw25_premise')\n", - "#TODO: can't export bw25 to tar.gz file?" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a1a191f7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using existing project bw25_premise_medusa\n" - ] - } - ], - "source": [ - "# PROJECT_NAME = \"bw25_premise_medusa\"\n", - "# RESET = False\n", - "\n", - "# if PROJECT_NAME in bd.projects and not RESET:\n", - "# print(f\"Using existing project {PROJECT_NAME}\")\n", - "# else:\n", - "# print(f\"Importing existing project {PROJECT_NAME} from tar file.\") \n", - "# #bi.backup.restore_project_directory('/Users/Warlock/surfdrive/Marko.tar.gz')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1510d42f-3734-41bf-99ab-1db85048cbbc", - "metadata": {}, - "outputs": [], - "source": [ - "my_activity = ei.random()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5646e3d8-8805-4cef-ac2e-aefd0a29a3a2", - "metadata": {}, - "outputs": [], - "source": [ - "my_edge = next(iter(my_activity.technosphere()))\n", - "my_edge" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8febbe3-541c-46b1-b188-9d32b616d5d2", - "metadata": {}, - "outputs": [], - "source": [ - "my_edge['temporal distribution'] = bwt.easy_timedelta_distribution(\n", - " start=0,\n", - " end=10,\n", - " resolution=\"Y\",\n", - " steps=11,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5c46945f-60b9-44ab-9d87-ad817ebd0995", - "metadata": {}, - "source": [ - "Be sure to save!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3529cd0b-70d3-4ca1-bdb4-0bc7706a96ce", - "metadata": {}, - "outputs": [], - "source": [ - "my_edge.save()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bda61611-bc97-4f6c-9be6-0f9568a88a65", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/notebooks/test_background_dbs_2.ipynb b/archive/notebooks/test_background_dbs_2.ipynb deleted file mode 100644 index 79f60eb..0000000 --- a/archive/notebooks/test_background_dbs_2.ipynb +++ /dev/null @@ -1,2776 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Medusa test with dummy foreground and premise background" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution\n", - "from edge_extractor import EdgeExtracter\n", - "from medusa_tools import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw2io as bi\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using existing project: bw25_premise_db_medusa\n" - ] - }, - { - "data": { - "text/plain": [ - "Databases dictionary with 6 object(s):\n", - "\tbiosphere3\n", - "\tcutoff39\n", - "\tdb_2020\n", - "\tdb_2025\n", - "\tdb_2030\n", - "\tforeground" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PROJECT_NAME = \"bw25_premise_db_medusa\"\n", - "RESET= False\n", - "\n", - "if PROJECT_NAME in bd.projects and not RESET: # use existing project\n", - " print(\"Using existing project: {}\".format(PROJECT_NAME))\n", - " bd.projects.set_current(PROJECT_NAME)\n", - " \n", - "else: # create project from scratch\n", - " print(\"Creating new project: {}\".format(PROJECT_NAME))\n", - " if PROJECT_NAME in bd.projects:\n", - " bd.projects.delete_project(PROJECT_NAME)\n", - " bi.backup.restore_project_directory(r'filepath/to/backup/directory')\n", - " bd.projects.set_current(PROJECT_NAME)\n", - " \n", - "bd.databases" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# prospective databases were generated with premise, updating only electricity\n", - "db_2020 = bd.Database(\"db_2020\")\n", - "db_2025 = bd.Database(\"db_2025\")\n", - "db_2030 = bd.Database(\"db_2030\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['market group for electricity, high voltage' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 60-year period' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 40-year period' (kilowatt hour, WEU, None),\n", - " 'market group for electricity, high voltage, 20-year period' (kilowatt hour, WEU, None)]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db_2025.search(\"market group for electricity, high voltage WEU\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'db_2020'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#selecting Western Europe's electricity market mix as\n", - "electr_WEU_2020 = [x for x in db_2020 if (x['name'] == 'market group for electricity, high voltage' and x[\"location\"] == \"WEU\")][0]\n", - "electr_WEU_2020[\"database\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'location': 'WEU',\n", - " 'name': 'market group for electricity, high voltage',\n", - " 'reference product': 'electricity, high voltage',\n", - " 'unit': 'kilowatt hour',\n", - " 'database': 'db_2025',\n", - " 'code': '30314cfb4d8743e69fc4b4673f844443',\n", - " 'comment': 'Dataset created by `premise` from the IAM model IMAGE using the pathway SSP2-RCP19 for the year 2025.',\n", - " 'log parameters': {'distribution loss': 0.0,\n", - " 'transformation loss': 0.02537298221117431,\n", - " 'renewable share': 0.4126781669284817},\n", - " 'id': 74397}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "electr_WEU_2025 = [x for x in db_2025 if (x['name'] == 'market group for electricity, high voltage' and x[\"location\"] == \"WEU\")][0]\n", - "electr_WEU_2025.as_dict()\n", - "#it's a different id between the two databases but since we match on name, location and reference product instead of id, it's fine." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'market group for electricity, high voltage' (kilowatt hour, WEU, None)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_activity((electr_WEU_2020[\"database\"],electr_WEU_2020[\"code\"]))" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Flow chart of system:

![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 1999.67it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.Database('foreground').write({\n", - " ('foreground', 'A'): {\n", - " 'name': 'process A',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-2, 0], dtype='timedelta64[Y]'),\n", - " np.array([0.5, 0.5])), \n", - " },\n", - " ]\n", - " },\n", - " ('foreground', 'B'):\n", - " {\n", - " \"name\": \"process B\",\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ((electr_WEU_2020[\"database\"],electr_WEU_2020[\"code\"])), # market group for electricity, high voltage' (kilowatt hour, WEU, None)\n", - " }\n", - " ]\n", - "\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'process A' (None, None, None)\n", - "Exchange: 1 None 'process B' (None, None, None) to 'process A' (None, None, None)>\n", - "'process B' (None, None, None)\n", - "Exchange: 1 kilowatt hour 'market group for electricity, high voltage' (kilowatt hour, WEU, None) to 'process B' (None, None, None)>\n" - ] - } - ], - "source": [ - "# checking foreground links\n", - "for act in bd.Database('foreground'):\n", - " print(act)\n", - " for exc in act.technosphere():\n", - " print(exc)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('IPCC 2013 no LT',\n", - " 'climate change no LT',\n", - " 'global warming potential (GWP100) no LT')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC_method = [m for m in bd.methods if 'IPCC 2013' in str(m) and 'climate change' in str(m) and 'GWP100' in str(m) and 'no LT' in str(m)][0]\n", - "CC_method" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 0.39192919898930817\n" - ] - } - ], - "source": [ - "demand = {(\"foreground\",'A'): 1}\n", - "slca=bc.LCA(demand,CC_method)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [node.id for node in bd.Database('db_2020')] + [\n", - " node.id for node in bd.Database('db_2025')] + [\n", - " node.id for node in bd.Database('db_2030')\n", - " ]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "72984" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SKIPPABLE)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 4\n" - ] - } - ], - "source": [ - "eelca = EdgeExtracter(slca, edge_filter_function=filter_function, max_calc=300, cutoff=0.1, starting_datetime='2024-01-01', static_activity_indices = SKIPPABLE)\n", - "timeline = eelca.build_edge_timeline()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I'm not sure why the cell above runs for 15 minutes. I tried to limit the runs by adding max_calc = 300 and cutoff = 0.1, and static_activity_indices, but this doesn't seem to reduce runtime. TODO: check why runtime here so long." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dateyearproducerproducer_nameconsumerconsumer_nameamountinterpolation_weights
02021-01-01202150069market group for electricity, high voltage98950process B0.5{2020: 0.7996715927750411, 2025: 0.20032840722...
12021-01-01202198950process B98949process A0.5{2020: 0.7996715927750411, 2025: 0.20032840722...
22024-01-01202450069market group for electricity, high voltage98950process B0.5{2020: 0.20032840722495893, 2025: 0.7996715927...
32024-01-01202498949process A-1-11.0{2020: 0.20032840722495893, 2025: 0.7996715927...
42024-01-01202498950process B98949process A0.5{2020: 0.20032840722495893, 2025: 0.7996715927...
\n", - "
" - ], - "text/plain": [ - " date year producer producer_name \\\n", - "0 2021-01-01 2021 50069 market group for electricity, high voltage \n", - "1 2021-01-01 2021 98950 process B \n", - "2 2024-01-01 2024 50069 market group for electricity, high voltage \n", - "3 2024-01-01 2024 98949 process A \n", - "4 2024-01-01 2024 98950 process B \n", - "\n", - " consumer consumer_name amount \\\n", - "0 98950 process B 0.5 \n", - "1 98949 process A 0.5 \n", - "2 98950 process B 0.5 \n", - "3 -1 -1 1.0 \n", - "4 98949 process A 0.5 \n", - "\n", - " interpolation_weights \n", - "0 {2020: 0.7996715927750411, 2025: 0.20032840722... \n", - "1 {2020: 0.7996715927750411, 2025: 0.20032840722... \n", - "2 {2020: 0.20032840722495893, 2025: 0.7996715927... \n", - "3 {2020: 0.20032840722495893, 2025: 0.7996715927... \n", - "4 {2020: 0.20032840722495893, 2025: 0.7996715927... " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2020\", \"%Y\"): 'db_2020',\n", - " datetime.strptime(\"2025\", \"%Y\"): 'db_2025',\n", - " datetime.strptime(\"2030\", \"%Y\"): 'db_2030',\n", - " }\n", - "\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict.keys(), interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict) #the new integer-ids had to be made shorter, as they were too large to be converter to C long \n", - "# (OverflowError: Python int too large to convert to C long), see changes in medusa_tools.py" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dynamic LCA score: 0.34418291965101344\n", - "Static LCA score: 0.39192919898930817\n", - "Change: -0.04774627933829473\n" - ] - } - ], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=CC_method) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "print(f'Dynamic LCA score: {lca.score}')\n", - "print(f'Static LCA score: {slca.score}')\n", - "print(f'Change: {lca.score - slca.score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Difference between medusa score and expected score 2.1680213180275132e-11\n" - ] - } - ], - "source": [ - "# compare with expected results: tiny deviation is fine!\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2020\n", - "lca_electr_WEU_2020=bc.LCA({(electr_WEU_2020[\"database\"],electr_WEU_2020[\"code\"]): 1},CC_method)\n", - "lca_electr_WEU_2020.lci()\n", - "lca_electr_WEU_2020.lcia()\n", - "score_2020 = lca_electr_WEU_2020.score\n", - "\n", - "\n", - "# expected lca results from 1 kWh electricty WEU 2025\n", - "lca_electr_WEU_2025=bc.LCA({(electr_WEU_2025[\"database\"],electr_WEU_2025[\"code\"]): 1},CC_method)\n", - "lca_electr_WEU_2025.lci()\n", - "lca_electr_WEU_2025.lcia()\n", - "score_2025 = lca_electr_WEU_2025.score\n", - "\n", - "#expected score according to temporal distributions\n", - "expected_score=0.5*(0.2*score_2020+0.8*score_2025) + 0.5*(0.8*score_2020+0.2*score_2025)\n", - "delta=expected_score-lca.score\n", - "print(f\"Difference between medusa score and expected score {delta}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### some more matrix explorations:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length of technosphere matrix: 94247\n" - ] - } - ], - "source": [ - "columns= lca.technosphere_matrix.shape[1]\n", - "print(f\"length of technosphere matrix: {columns}\")\t" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3.78812024e-06, 1.54677455e-05, 3.78812024e-06, 1.62712411e-06,\n", - " 7.73400814e-07, 2.24487706e-10, 8.34675218e-10, 5.00893726e-08,\n", - " 4.48975413e-07, 1.17062306e+00, 1.85466241e-04, 1.80775501e-07,\n", - " 4.65752148e-09, 5.54788244e-07, 7.16002774e-07, 1.52865147e-13,\n", - " 7.73400825e-06, 1.16010824e-05, 1.54677455e-05, 7.73400814e-07,\n", - " 1.54677451e-07, 6.74959722e-07, 4.50472015e-07, 5.21276775e-07,\n", - " 1.74362185e-07, 2.24487717e-09, 7.73400825e-05, 7.73400825e-06,\n", - " 1.20468655e-07, 4.27906707e-06, 2.65389577e-08, 9.50542526e-05,\n", - " 7.73400814e-07, 7.92556065e-08, 4.48975425e-06, 6.46482874e-03,\n", - " 1.54857025e-05, 1.04139335e-01, 3.80426457e-07, 9.24754702e-03,\n", - " 9.40753040e-08, 2.59667395e-05, 4.29655866e-09, 3.61256883e-04,\n", - " 4.50437376e-08, 3.53141183e-09, 2.84011203e-05, 3.73352549e-08,\n", - " 1.46598722e-06, 3.00175134e-06, 5.48461855e-07, 5.17290027e-06,\n", - " 5.30319267e-06, 1.85680278e-06, 7.37473798e-08, 5.29704529e-08,\n", - " 1.85403148e-09, 2.62389108e-07, 1.20714605e-04, 8.37634730e-07,\n", - " 2.73684805e-07, 2.95311748e-03, 7.94564130e-07, 2.66618754e-05,\n", - " 3.25734472e-05, 5.47369545e-08, 1.76563264e-09, 1.51846771e-05,\n", - " 5.37884944e-07, 9.33635320e-08, 1.26077095e-03, 1.03787497e-01,\n", - " 1.04760932e-06, 1.04617314e-07, 1.76563264e-09, 6.03327171e-05,\n", - " 3.91745614e-03, 1.31994850e-04, 8.99728111e-05])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca.technosphere_matrix[:,1].data\n", - "lca.biosphere_matrix[:,1].data" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{94241: 500692021,\n", - " 94242: 500692024,\n", - " 94243: 989492021,\n", - " 94244: 989492024,\n", - " 94245: 989502021,\n", - " 94246: 989502024}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#new keys added by medusa\n", - "all_keys = lca.dicts.activity.reversed\n", - "\n", - "new_keys = {key: value for key, value in all_keys.items() if value > 100000}\n", - "new_keys" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{3775: ('biosphere3', '00012c0a-9bff-4787-a7eb-56c3d2f43692'),\n", - " 4077: ('biosphere3', '00143719-33a7-5738-aa1b-131f97b4fef3'),\n", - " 2765: ('biosphere3', '0015315d-a2b8-4b72-8e60-0d34a72e6de8'),\n", - " 286: ('biosphere3', '0015ec22-72cb-4af1-8c7b-0ba0d041553c'),\n", - " 3084: ('biosphere3', '0017271e-7df5-40bc-833a-36110c1fe5d5'),\n", - " 2124: ('biosphere3', '001790f3-fd86-4a0d-a2a1-06c7099d90c8'),\n", - " 3688: ('biosphere3', '0017ce28-9f7a-404b-ad55-d3f43ad13cae'),\n", - " 4475: ('biosphere3', '004dbd05-6759-507a-a7f6-9f288706a1cd'),\n", - " 1401: ('biosphere3', '0061b12a-9084-499b-9fc0-bf66025198eb'),\n", - " 1988: ('biosphere3', '006aa3f7-59ba-450f-aa45-a2b2d1752647'),\n", - " 2423: ('biosphere3', '007de693-1d9e-40a5-a8b4-3d11bce8be9a'),\n", - " 817: ('biosphere3', '00907a61-b501-4f47-b688-1dc2b51d48c1'),\n", - " 4680: ('biosphere3', '0097bf60-3729-40b6-94d0-06bd6146cfc2'),\n", - " 2622: ('biosphere3', '0098b967-2131-43d2-b684-254583fbadc7'),\n", - " 2229: ('biosphere3', '009a49e8-09a7-4432-9cb9-f2dd18e8f922'),\n", - " 202: ('biosphere3', '009f3374-e604-4e24-88f5-d785cd93aac7'),\n", - " 423: ('biosphere3', '00acb9f0-1640-4f8c-89ef-d857e1428be0'),\n", - " 2806: ('biosphere3', 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- "lca.remapping_dicts['product']" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "98950" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(lca.remapping_dicts['biosphere'])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tictac2", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/test_issue6.ipynb b/archive/notebooks/test_issue6.ipynb deleted file mode 100644 index e29a545..0000000 --- a/archive/notebooks/test_issue6.ipynb +++ /dev/null @@ -1,1312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#import libraries\n", - "from bw_temporalis import easy_timedelta_distribution, TemporalDistribution\n", - "from edge_extractor import EdgeExtracter\n", - "from medusa_tools import *\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "02fa7aea", - "metadata": {}, - "source": [ - "## Test with 2 connected TDs at two processes \n", - "\n", - "(see issue #6 https://github.com/TimoDiepers/tictac_lca/issues/6)" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "0ef91d5b", - "metadata": {}, - "source": [ - "\n", - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "88c6f135", - "metadata": {}, - "outputs": [], - "source": [ - "#setup example\n", - "bd.projects.set_current(\"test_heat\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fc35276f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 3310.42it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 11096.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 5622.39it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n", - "Not able to determine geocollections for all datasets. This database is not ready for regionalization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3/3 [00:00<00:00, 14479.76it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vacuuming database \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "bd.Database('temporalis-bio').write({\n", - " ('temporalis-bio', \"CO2\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"carbon dioxide\",\n", - " \"temporalis code\": \"co2\",\n", - " },\n", - " ('temporalis-bio', \"CH4\"): {\n", - " \"type\": \"emission\",\n", - " \"name\": \"methane\",\n", - " \"temporalis code\": \"ch4\",\n", - " },\n", - "})\n", - "\n", - "bd.Database('background_2022').write({\n", - " ('background_2022', 'C'): {\n", - " 'name': 'process C',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'C',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2022', 'C'),\n", - " },\n", - " {\n", - " 'amount': 10,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " }, ]},\n", - "},\n", - "\n", - " )\n", - "\n", - "bd.Database('background_2020').write({\n", - " ('background_2020', 'C'): {\n", - " 'name': 'process C',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'C',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('background_2020', 'C'),\n", - " },\n", - " {\n", - " 'amount': 30,\n", - " 'type': 'biosphere',\n", - " 'input': ('temporalis-bio', 'CO2'),\n", - " }, ]},\n", - "},\n", - "\n", - " )\n", - "\n", - "bd.Database('foreground').write({\n", - " ('foreground', 'A'): {\n", - " 'name': 'process A',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'A',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'A'),\n", - " },\n", - " {\n", - " 'amount': 0.7,\n", - " 'type': 'technosphere',\n", - " 'input': ('foreground', 'B'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-3, -1], dtype='timedelta64[Y]'),\n", - " np.array([0.5, 0.5])), \n", - " },\n", - " \n", - " # {\n", - " # 'amount': 1,\n", - " # 'type': 'technosphere',\n", - " # 'input': ('foreground', 'D'),\n", - " # 'temporal_distribution': TemporalDistribution(\n", - " # np.array([-3, -2, -1, 0], dtype='timedelta64[Y]'),\n", - " # np.array([0.25, 0.25, 0.25, 0.25])),\n", - " # },\n", - " ]\n", - " },\n", - " ('foreground', 'B'):\n", - " {\n", - " \"name\": \"process B\",\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'B',\n", - " \"exchanges\": [\n", - " {\n", - " 'amount': 5,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2022', 'C'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-1, 0], dtype='timedelta64[Y]'),\n", - " np.array([0.5, 0.5])), \n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'B'), \n", - " }\n", - " ]\n", - "\n", - " },\n", - " \n", - " ('foreground', 'D'): {\n", - " 'name': 'process D',\n", - " \"location\": \"somewhere\",\n", - " 'reference product': 'D',\n", - " 'exchanges': [\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'production',\n", - " 'input': ('foreground', 'D'),\n", - " },\n", - " {\n", - " 'amount': 1,\n", - " 'type': 'technosphere',\n", - " 'input': ('background_2022', 'C'),\n", - " 'temporal_distribution': TemporalDistribution(\n", - " np.array([-1], dtype='timedelta64[Y]'),\n", - " np.array([1])), \n", - " },\n", - " \n", - " ]\n", - " },\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "00b4f8bf", - "metadata": {}, - "outputs": [], - "source": [ - "bd.Method((\"GWP\", \"example\")).write([\n", - " (('temporalis-bio', \"CO2\"), 1),\n", - " (('temporalis-bio', \"CH4\"), 25),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9c4913b7", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('foreground', 'A'): 1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0ecb9c8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Static LCA score: 34.99999940395356\n" - ] - } - ], - "source": [ - "slca = bc.LCA(demand, gwp)\n", - "slca.lci()\n", - "slca.lcia()\n", - "print(f'Static LCA score: {slca.score}')" - ] - }, - { - "cell_type": "markdown", - "id": "20ea6d12", - "metadata": {}, - "source": [ - "Medusa LCA" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5a7f7bb", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [] #node.id for node in bd.Database('background_2020')] #+ [\n", - " #node.id for node in bd.Database('background_2022')\n", - "#]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b6bdd1dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 2\n" - ] - }, - { - "data": { - "text/plain": [ - "[Edge(distribution=TemporalDistribution instance with 1 values and total: 1, leaf=False, consumer=-1, producer=33, value=1),\n", - " Edge(distribution=TemporalDistribution instance with 2 values and total: 0.7, leaf=False, consumer=33, producer=34, value=TemporalDistribution instance with 2 values and total: 0.7),\n", - " Edge(distribution=TemporalDistribution instance with 4 values and total: 3.5, leaf=False, consumer=34, producer=31, value=TemporalDistribution instance with 2 values and total: 5)]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eelca = EdgeExtracter(slca, edge_filter_function=filter_function)\n", - "timeline = eelca.build_edge_timeline()\n", - "timeline\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dist total 1.0\n", - "1\n", - "dist total 0.699999988079071\n", - "amount value [0.34999999 0.34999999]\n", - "dist total 3.4999999403953552\n", - "amount value [2.5 2.5]\n" - ] - } - ], - "source": [ - "for item in timeline:\n", - " print('dist total', item.distribution.total)\n", - " try: \n", - " print('amount value', item.value.amount)\n", - " except AttributeError:\n", - " print(item.value)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ecbf47b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys([datetime.datetime(2022, 1, 1, 0, 0), datetime.datetime(2020, 1, 1, 0, 0)])\n", - "All databases in database_date_dict exist as brightway project databases\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'int' object has no attribute 'amount'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/ajakobs/Documents/SCENE/prospective_dynamic_lca/tictac_lca/test_issue6.ipynb Cell 13\u001b[0m line \u001b[0;36m6\n\u001b[1;32m 1\u001b[0m database_date_dict \u001b[39m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m datetime\u001b[39m.\u001b[39mstrptime(\u001b[39m\"\u001b[39m\u001b[39m2022\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m%\u001b[39m\u001b[39mY\u001b[39m\u001b[39m\"\u001b[39m): \u001b[39m'\u001b[39m\u001b[39mbackground_2022\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m 3\u001b[0m datetime\u001b[39m.\u001b[39mstrptime(\u001b[39m\"\u001b[39m\u001b[39m2020\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m%\u001b[39m\u001b[39mY\u001b[39m\u001b[39m\"\u001b[39m): \u001b[39m'\u001b[39m\u001b[39mbackground_2020\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m }\n\u001b[1;32m 5\u001b[0m \u001b[39mprint\u001b[39m(database_date_dict\u001b[39m.\u001b[39mkeys())\n\u001b[0;32m----> 6\u001b[0m timeline_df \u001b[39m=\u001b[39m create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mlinear\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 7\u001b[0m timeline_df\n", - "File \u001b[0;32m~/Documents/SCENE/prospective_dynamic_lca/tictac_lca/medusa_tools.py:110\u001b[0m, in \u001b[0;36mcreate_grouped_edge_dataframe\u001b[0;34m(tl, database_date_dict, temporal_grouping, interpolation_type)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mSorry, but temporal grouping is not yet available for \u001b[39m\u001b[39m'\u001b[39m\u001b[39mday\u001b[39m\u001b[39m'\u001b[39m\u001b[39m and \u001b[39m\u001b[39m'\u001b[39m\u001b[39mhour\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 109\u001b[0m \u001b[39m# Extract edge data into a list of dictionaries\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m edges_data \u001b[39m=\u001b[39m [extract_edge_data(edge) \u001b[39mfor\u001b[39;49;00m edge \u001b[39min\u001b[39;49;00m tl]\n\u001b[1;32m 112\u001b[0m \u001b[39m# Convert list of dictionaries to dataframe\u001b[39;00m\n\u001b[1;32m 113\u001b[0m edges_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(edges_data)\n", - "File \u001b[0;32m~/Documents/SCENE/prospective_dynamic_lca/tictac_lca/medusa_tools.py:110\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mSorry, but temporal grouping is not yet available for \u001b[39m\u001b[39m'\u001b[39m\u001b[39mday\u001b[39m\u001b[39m'\u001b[39m\u001b[39m and \u001b[39m\u001b[39m'\u001b[39m\u001b[39mhour\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 109\u001b[0m \u001b[39m# Extract edge data into a list of dictionaries\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m edges_data \u001b[39m=\u001b[39m [extract_edge_data(edge) \u001b[39mfor\u001b[39;00m edge \u001b[39min\u001b[39;00m tl]\n\u001b[1;32m 112\u001b[0m \u001b[39m# Convert list of dictionaries to dataframe\u001b[39;00m\n\u001b[1;32m 113\u001b[0m edges_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(edges_data)\n", - "File \u001b[0;32m~/Documents/SCENE/prospective_dynamic_lca/tictac_lca/medusa_tools.py:80\u001b[0m, in \u001b[0;36mcreate_grouped_edge_dataframe..extract_edge_data\u001b[0;34m(edge)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mextract_edge_data\u001b[39m(edge: Edge) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mdict\u001b[39m:\n\u001b[1;32m 65\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[39m Stores the attributes of an Edge instance in a dictionary.\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \n\u001b[1;32m 68\u001b[0m \u001b[39m :param edge: Edge instance\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[39m :return: Dictionary with attributes of the edge \u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[39mreturn\u001b[39;00m {\n\u001b[1;32m 73\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdatetime\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mdistribution\u001b[39m.\u001b[39mdate,\n\u001b[1;32m 74\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mamount\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mdistribution\u001b[39m.\u001b[39mamount, \u001b[39m# Do we even need this? \u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mproducer\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mproducer,\n\u001b[1;32m 76\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mconsumer\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mconsumer,\n\u001b[1;32m 77\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mleaf\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mleaf,\n\u001b[1;32m 78\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtotal\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mvalue\u001b[39m.\u001b[39mtotal \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(edge\u001b[39m.\u001b[39mvalue, TemporalDistribution) \u001b[39melse\u001b[39;00m edge\u001b[39m.\u001b[39mvalue,\n\u001b[1;32m 79\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mshare\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39mvalue\u001b[39m.\u001b[39mamount\u001b[39m/\u001b[39medge\u001b[39m.\u001b[39mvalue\u001b[39m.\u001b[39mtotal \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(edge\u001b[39m.\u001b[39mvalue, TemporalDistribution) \u001b[39melse\u001b[39;00m edge\u001b[39m.\u001b[39mvalue,\n\u001b[0;32m---> 80\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mshare\u001b[39m\u001b[39m\"\u001b[39m: edge\u001b[39m.\u001b[39;49mvalue\u001b[39m.\u001b[39;49mamount \u001b[39m/\u001b[39m edge\u001b[39m.\u001b[39mvalue\u001b[39m.\u001b[39mtotal,\n\u001b[1;32m 81\u001b[0m }\n", - "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'amount'" - ] - } - ], - "source": [ - "database_date_dict = {\n", - " datetime.strptime(\"2022\", \"%Y\"): 'background_2022',\n", - " datetime.strptime(\"2020\", \"%Y\"): 'background_2020',\n", - " }\n", - "print(database_date_dict.keys())\n", - "timeline_df = create_grouped_edge_dataframe(timeline, database_date_dict, interpolation_type=\"linear\")\n", - "timeline_df" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a104bc8c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All databases in database_date_dict exist in brightway2 project databases\n" - ] - } - ], - "source": [ - "# check that the strings of the databases (values of database_date_dict) exist in the databases of the brightway2 project:\n", - "for db in database_date_dict.values():\n", - " assert db in bd.databases, f\"{db} not in your brightway2 project databases. Please check spelling of this database in database_date_dict and whether you are in the correct project.\"\n", - "else:\n", - " print(\"All databases in database_date_dict exist in brightway2 project databases\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'background_2020': 0.4993160054719562, 'background_2022': 0.5006839945280438}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timeline_df['interpolation_weights'].iloc[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "de066a44", - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict = create_demand_timing_dict(timeline_df, demand)\n", - "\n", - "dp = create_datapackage_from_edge_timeline(timeline_df, database_date_dict, demand_timing_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{5: 2024}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "demand_timing_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6aa3f0a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "39.36302942541083" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fu, data_objs, remapping = prepare_medusa_lca_inputs(demand=demand, demand_timing_dict=demand_timing_dict, method=gwp) \n", - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "90ed8c11", - "metadata": {}, - "source": [ - "### Investigation of matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c5aa6844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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- } - }, - "cell_type": "markdown", - "id": "2a501c0c", - "metadata": {}, - "source": [ - "The problem is that the new inputs of C to the new exploded process copies of B are done for the time of C, in this case 2019 and 2021, instead of at the time of B, in this case 2020 and 2022 (column N and P). This leads to empty inputs from C for new process copies of B in 2020 and 2022, which is are the rows consumed by A, which results in a wrong LCIA score.\n", - "\n", - "![image-2.png](attachment:image-2.png)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6417b2aa", - "metadata": {}, - "outputs": [], - "source": [ - "df= pd.DataFrame(lca.technosphere_matrix.toarray()) #for excel visualization\n", - "df.to_csv(\"test.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f0c6914", - "metadata": {}, - "outputs": [], - "source": [ - "lca.load_lci_data(nonsquare_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c35d196e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 85),\n", - " (1, 86),\n", - " (2, 87),\n", - " (3, 88),\n", - " (4, 89),\n", - " (5, 85002019),\n", - " (6, 85002021),\n", - " (7, 87002024),\n", - " (8, 88002020),\n", - " (9, 88002022)}" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(lca.dicts.activity.reversed.items())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5aac9e48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[85, 86, 87, 88, 89, 85002019, 85002021, 87002024, 88002020, 88002022]" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "second_items_list = [item[1] for item in lca.dicts.activity.reversed.items()]\n", - "second_items_list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4460812", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "product_dict (mapping between Ids and csr_matrix column index):\n", - " {85: 0, 86: 1, 87: 2, 88: 3, 89: 4, 85002019: 5, 85002021: 6, 87002024: 7, 88002020: 8, 88002022: 9}\n", - "\n", - "activity_dict (mapping between IDS and csr_matrix row index):\n", - " {85: 0, 86: 1, 87: 2, 88: 3, 89: 4, 85002019: 5, 85002021: 6, 87002024: 7, 88002020: 8, 88002022: 9}\n" - ] - } - ], - "source": [ - "print(\"product_dict (mapping between Ids and csr_matrix column index):\\n\", lca.product_dict)\n", - "print(\"\\nactivity_dict (mapping between IDS and csr_matrix row index):\\n\", lca.activity_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "293e81be", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "85 -> 'process C' (None, somewhere, None) background_2022\n", - "86 -> 'process C' (None, somewhere, None) background_2020\n", - "87 -> 'process A' (None, somewhere, None) foreground\n", - "88 -> 'process B' (None, somewhere, None) foreground\n", - "89 -> 'process D' (None, somewhere, None) foreground\n" - ] - }, - { - "ename": "UnknownObject", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnknownObject\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[114], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m lca\u001b[38;5;241m.\u001b[39mactivity_dict:\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m->\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mbd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_activity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m, bd\u001b[38;5;241m.\u001b[39mget_activity(key)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabase\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\muell\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:440\u001b[0m, in \u001b[0;36mget_activity\u001b[1;34m(key, **kwargs)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n\u001b[0;32m 439\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m key\n\u001b[1;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_node(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\muell\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2data\\utils.py:422\u001b[0m, in \u001b[0;36mget_node\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MultipleResults(\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m results for the given search\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mlen\u001b[39m(candidates))\n\u001b[0;32m 420\u001b[0m )\n\u001b[0;32m 421\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m candidates:\n\u001b[1;32m--> 422\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnknownObject\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m candidates[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[1;31mUnknownObject\u001b[0m: " - ] - } - ], - "source": [ - "for key in lca.activity_dict:\n", - " print(key, \"->\",bd.get_activity(key), bd.get_activity(key)[\"database\"]) #BW does not find the \"exploded nodes\", because they exist only in the datapackages?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c9bf06a", - "metadata": {}, - "outputs": [], - "source": [ - "f=lca.demand_array\n", - "f" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15146b22", - "metadata": {}, - "outputs": [], - "source": [ - "lca.technosphere_matrix.toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00c13408", - "metadata": {}, - "outputs": [], - "source": [ - "A_inv= np.linalg.inv(lca.technosphere_matrix.toarray())\n", - "A_inv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1bf6c67", - "metadata": {}, - "outputs": [], - "source": [ - "s= np.matmul(A_inv, f)\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b314e5d", - "metadata": {}, - "outputs": [], - "source": [ - "lca.biosphere_matrix.toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0a14674", - "metadata": {}, - "outputs": [], - "source": [ - "b= np.matmul(lca.biosphere_matrix.toarray(), s)\n", - "b" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "demand_timing_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timeline_df.iloc[::-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timeline[1].distribution.date[0] < timeline[1].distribution.date[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "edges_dict_list = [{\"datetime\": edge.distribution.date, 'amount': edge.distribution.amount, 'producer': edge.producer, 'consumer': edge.consumer, \"leaf\": edge.leaf} for edge in timeline]\n", - "edges_dataframe = pd.DataFrame(edges_dict_list)\n", - "edges_dataframe.explode(['datetime', \"amount\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yearproducerconsumeramountdateinterpolation_weightsproducer_nameconsumer_nametimestamp
620241091071.02024-01-01{'background_2022': 1}process Dprocess A2024
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420231051091.02023-01-01{'background_2022': 1}process Cprocess D2023
320221081070.52022-01-01{'background_2022': 1}process Bprocess A2022
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" - ], - "text/plain": [ - " year producer consumer amount date \\\n", - "6 2024 109 107 1.0 2024-01-01 \n", - "5 2024 107 -1 1.0 2024-01-01 \n", - "4 2023 105 109 1.0 2023-01-01 \n", - "3 2022 108 107 0.5 2022-01-01 \n", - "2 2021 105 108 0.5 2021-01-01 \n", - "1 2020 108 107 0.5 2020-01-01 \n", - "0 2019 105 108 0.5 2019-01-01 \n", - "\n", - " interpolation_weights producer_name \\\n", - "6 {'background_2022': 1} process D \n", - "5 {'background_2022': 1} process A \n", - "4 {'background_2022': 1} process C \n", - "3 {'background_2022': 1} process B \n", - "2 {'background_2020': 0.4993160054719562, 'backg... process C \n", - "1 {'background_2020': 1} process B \n", - "0 {'background_2020': 1} process C \n", - "\n", - " consumer_name timestamp \n", - "6 process A 2024 \n", - "5 -1 2024 \n", - "4 process D 2023 \n", - "3 process A 2022 \n", - "2 process B 2021 \n", - "1 process A 2020 \n", - "0 process B 2019 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "timeline_df.iloc[::-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024 109 107 2024\n", - "2024 107 -1 2024\n", - "2023 105 109 2024\n", - "2022 108 107 2024\n", - "2021 105 108 2022\n", - "2020 108 107 2024\n", - "2019 105 108 2020\n" - ] - } - ], - "source": [ - "consumer_years = {}\n", - "for row in timeline_df.iloc[::-1].itertuples():\n", - " # if row.consumer == -1:\n", - " # consumer_years[row.consumer] = row.year\n", - " if row.consumer not in consumer_years.keys():\n", - " consumer_years[row.consumer] = row.year\n", - " consumer_years[row.producer] = row.year # the year of the producer will be the consumer year for this procuess until a it becomesa producer again\n", - " print(row.year, row.producer, row.consumer, consumer_years[row.consumer])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/archive/test.ipynb b/archive/test.ipynb deleted file mode 100644 index faa5344..0000000 --- a/archive/test.ipynb +++ /dev/null @@ -1,1096 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bee706e3", - "metadata": {}, - "source": [ - "# `MEDUSA`\n", - "aka. Dynamic-Prospective LCA aka. Union(premise, temporalis)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "d8cd7a3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bw_temporalis import easy_timedelta_distribution, easy_datetime_distribution, TemporalisLCA, Timeline, TemporalDistribution\n", - "from bw_temporalis.lcia import characterize_methane, characterize_co2\n", - "import bw2data as bd\n", - "import bw2calc as bc\n", - "import bw_graph_tools as graph\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "d00df98a-fcae-4160-a30f-54aed29c1f19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "bd.projects.set_current(\"medusa_test\")" - ] - }, - { - "cell_type": "markdown", - "id": "26987fcb", - "metadata": {}, - "source": [ - "### Create some example databases\n", - "This is based on the forest example, but creating two versions\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "79a523bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:001\u001b[0m lca \u001b[39m=\u001b[39m bc\u001b[39m.\u001b[39mLCA({(\u001b[39m'\u001b[39m\u001b[39mtemporalis-example\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mEOL\u001b[39m\u001b[39m'\u001b[39m): \u001b[39m1\u001b[39m}, (\u001b[39m\"\u001b[39m\u001b[39mGWP\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mexample\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m lca\u001b[39m.\u001b[39;49mlci()\n\u001b[0;32m 3\u001b[0m lca\u001b[39m.\u001b[39mlcia()\n\u001b[0;32m 4\u001b[0m lca\u001b[39m.\u001b[39mscore\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py:363\u001b[0m, in \u001b[0;36mLCA.lci\u001b[1;34m(self, demand, factorize)\u001b[0m\n\u001b[0;32m 361\u001b[0m \u001b[39mif\u001b[39;00m factorize \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m PYPARDISO:\n\u001b[0;32m 362\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdecompose_technosphere()\n\u001b[1;32m--> 363\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlci_calculation()\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py:371\u001b[0m, in \u001b[0;36mLCA.lci_calculation\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 365\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mlci_calculation\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 366\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"The actual LCI calculation.\u001b[39;00m\n\u001b[0;32m 367\u001b[0m \n\u001b[0;32m 368\u001b[0m \u001b[39m Separated from ``lci`` to be reusable in cases where the matrices are already built, e.g. ``redo_lci`` and Monte Carlo classes.\u001b[39;00m\n\u001b[0;32m 369\u001b[0m \n\u001b[0;32m 370\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msupply_array \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msolve_linear_system()\n\u001b[0;32m 372\u001b[0m \u001b[39m# Turn 1-d array into diagonal matrix\u001b[39;00m\n\u001b[0;32m 373\u001b[0m count \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdicts\u001b[39m.\u001b[39mactivity)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\bw2calc\\lca.py:336\u001b[0m, in \u001b[0;36mLCA.solve_linear_system\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolver(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdemand_array)\n\u001b[0;32m 335\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 336\u001b[0m \u001b[39mreturn\u001b[39;00m spsolve(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtechnosphere_matrix, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdemand_array)\n", - "File \u001b[1;32mc:\\Users\\timod\\anaconda3\\envs\\tictac\\lib\\site-packages\\scipy\\sparse\\linalg\\_dsolve\\linsolve.py:290\u001b[0m, in \u001b[0;36mspsolve\u001b[1;34m(A, b, permc_spec, use_umfpack)\u001b[0m\n\u001b[0;32m 288\u001b[0m indptr \u001b[39m=\u001b[39m A\u001b[39m.\u001b[39mindptr\u001b[39m.\u001b[39mastype(np\u001b[39m.\u001b[39mintc, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m 289\u001b[0m options \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(ColPerm\u001b[39m=\u001b[39mpermc_spec)\n\u001b[1;32m--> 290\u001b[0m x, info \u001b[39m=\u001b[39m _superlu\u001b[39m.\u001b[39;49mgssv(N, A\u001b[39m.\u001b[39;49mnnz, A\u001b[39m.\u001b[39;49mdata, indices, indptr,\n\u001b[0;32m 291\u001b[0m b, flag, options\u001b[39m=\u001b[39;49moptions)\n\u001b[0;32m 292\u001b[0m \u001b[39mif\u001b[39;00m info \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 293\u001b[0m warn(\u001b[39m\"\u001b[39m\u001b[39mMatrix is exactly singular\u001b[39m\u001b[39m\"\u001b[39m, MatrixRankWarning)\n", - "\u001b[1;31mRuntimeError\u001b[0m: failed to factorize matrix at line 413 in file ../scipy/sparse/linalg/_dsolve/SuperLU/SRC/dpanel_bmod.c\n" - ] - } - ], - "source": [ - "lca = bc.LCA({('temporalis-example', 'EOL'): 1}, (\"GWP\", \"example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "markdown", - "id": "fef29cd7-aad5-4b5b-9a38-4bd6f796299b", - "metadata": {}, - "source": [ - "# Custom `EdgeExtractor` class" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d77883c7-1e2d-46f5-b12c-25c7f6be7d6e", - "metadata": {}, - "outputs": [], - "source": [ - "from dataclasses import dataclass\n", - "from heapq import heappop, heappush\n", - "from typing import Callable, List\n", - "\n", - "from bw_temporalis import TemporalisLCA, TemporalDistribution\n", - "\n", - "\n", - "@dataclass\n", - "class Edge:\n", - " \"\"\"\n", - " Class for storing a temporal edge with source and target.\n", - "\n", - " Leaf edges link to a source process which is a leaf in\n", - " our graph traversal (either through cutoff or a filter\n", - " function).\n", - "\n", - " Attributes\n", - " ----------\n", - " distribution : TemporalDistribution\n", - " leaf : bool\n", - " consumer : int\n", - " producer : int\n", - "\n", - " \"\"\"\n", - "\n", - " distribution: TemporalDistribution\n", - " leaf: bool\n", - " consumer: int\n", - " producer: int\n", - "\n", - "\n", - "class EdgeExtracter(TemporalisLCA):\n", - " def __init__(self, *args, edge_filter_function: Callable = None, **kwargs):\n", - " super().__init__(*args, **kwargs)\n", - " if edge_filter_function:\n", - " self.edge_ff = edge_filter_function\n", - " else:\n", - " self.edge_ff = lambda x: False\n", - "\n", - " def build_edge_timeline(self, node_timeline: bool | None = False) -> List:\n", - " heap = []\n", - " timeline = []\n", - "\n", - " for edge in self.edge_mapping[self.unique_id]:\n", - " node = self.nodes[edge.producer_unique_id]\n", - " heappush(\n", - " heap,\n", - " (\n", - " 1 / node.cumulative_score,\n", - " self.t0 * edge.amount,\n", - " node,\n", - " ),\n", - " )\n", - " timeline.append(\n", - " Edge(\n", - " distribution=self.t0 * edge.amount,\n", - " leaf=False,\n", - " consumer=self.unique_id,\n", - " producer=node.activity_datapackage_id,\n", - " )\n", - " )\n", - "\n", - " while heap:\n", - " _, td, node = heappop(heap)\n", - "\n", - " for edge in self.edge_mapping[node.unique_id]:\n", - " row_id = self.nodes[edge.producer_unique_id].activity_datapackage_id\n", - " col_id = node.activity_datapackage_id\n", - " exchange = self.get_technosphere_exchange(\n", - " input_id=row_id,\n", - " output_id=col_id,\n", - " )\n", - " value = (\n", - " self._exchange_value(\n", - " exchange=exchange,\n", - " row_id=row_id,\n", - " col_id=col_id,\n", - " matrix_label=\"technosphere_matrix\",\n", - " )\n", - " / node.reference_product_production_amount\n", - " )\n", - " producer = self.nodes[edge.producer_unique_id]\n", - " leaf = self.edge_ff(row_id)\n", - "\n", - " distribution = (td * value).simplify()\n", - " timeline.append(\n", - " Edge(\n", - " distribution=distribution,\n", - " leaf=leaf,\n", - " consumer=node.activity_datapackage_id,\n", - " producer=producer.activity_datapackage_id,\n", - " )\n", - " )\n", - " if not leaf:\n", - " heappush(\n", - " heap,\n", - " (\n", - " 1 / node.cumulative_score,\n", - " distribution,\n", - " producer,\n", - " ),\n", - " )\n", - " return timeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31af46a8-ceee-4ccd-8964-fbc9698f43b3", - "metadata": {}, - "outputs": [], - "source": [ - "SKIPPABLE = [\n", - " node.id for node in bd.Database('dummy-database')\n", - "]\n", - "\n", - "def filter_function(database_id: int) -> bool:\n", - " return database_id in SKIPPABLE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3d56515-377a-4086-921f-c8fd7efca39f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting graph traversal\n", - "Calculation count: 12\n" - ] - } - ], - "source": [ - "eelca = EdgeExtracter(lca, edge_filter_function=filter_function)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71a4206c-130e-4e91-b189-6b3c9cd11eeb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "timeline = eelca.build_edge_timeline()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a40392ca", - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "dates_list = [\n", - " datetime.strptime(\"2019\", \"%Y\"),\n", - " datetime.strptime(\"2023\", \"%Y\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b5649e3", - "metadata": {}, - "outputs": [], - "source": [ - "from utils import create_grouped_edge_dataframe, get_datapackage_from_edge_timeline\n", - "timeline_df = create_grouped_edge_dataframe(timeline, dates_list, \"linear\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6855aa93", - "metadata": {}, - "outputs": [], - "source": [ - "database_date_dict = {\n", - " 2019: 'dummy-database',\n", - " 2023: 'dummy-database-2',\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7d48585", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023 dummy-database-2 Drive an electric car\n", - "129 138 131 138002023\n" - ] - } - ], - "source": [ - "dp = get_datapackage_from_edge_timeline(timeline_df, database_date_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cdbdc9cb", - "metadata": {}, - "outputs": [], - "source": [ - "demand = {('temporalis-example', 'EOL'):1}\n", - "gwp = ('GWP', 'example')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80378c0a", - "metadata": {}, - "outputs": [], - "source": [ - "fu, data_objs, remapping = bd.prepare_lca_inputs(demand=demand, method=gwp)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "672597c0", - "metadata": {}, - "outputs": [], - "source": [ - "lca = bc.LCA(fu, data_objs = data_objs + [dp], remapping_dicts=remapping)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "63d05bf2", - "metadata": {}, - "outputs": [], - "source": [ - "lca.load_lci_data(nonsquare_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "bd8c462f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 129),\n", - " (1, 130),\n", - " (2, 133),\n", - " (3, 134),\n", - " (4, 135),\n", - " (5, 136),\n", - " (6, 137),\n", - " (7, 138),\n", - " (8, 139),\n", - " (9, 140),\n", - " (10, 141),\n", - " (11, 142),\n", - " (12, 143),\n", - " (13, 144),\n", - " (14, 1002023),\n", - " (15, 129002023),\n", - " (16, 134002023),\n", - " (17, 135002023),\n", - " (18, 136002023),\n", - " (19, 137002023),\n", - " (20, 138002022),\n", - " (21, 138002023),\n", - " (22, 139002019),\n", - " (23, 139002022),\n", - " (24, 139002023),\n", - " (25, 140002019),\n", - " (26, 140002022),\n", - " (27, 140002023),\n", - " (28, 141002023),\n", - " (29, 142002023),\n", - " (30, 143002023),\n", - " (31, 144002019),\n", - " (32, 144002022),\n", - " (33, 144002023)}" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(lca.dicts.activity.reversed.items())" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "c5e5e00a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'dummy-database-2'" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bd.get_node(id=131)['database']" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "24c2d490", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 129),\n", - " (1, 130),\n", - " (2, 131),\n", - " (3, 133),\n", - " (4, 134),\n", - " (5, 135),\n", - " (6, 136),\n", - " (7, 137),\n", - 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" 0. , 0. , 0. , -1.5 , 1. ,\n", - " 0. , 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , -0.2 , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 1. , 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , -0.80000001, 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 1. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 1. , 1. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 1. ,\n", - " 0. , 0. , 0. , 1. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 1. ]])" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lca.technosphere_matrix.toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "256cf6e5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51eae1e5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "3b2f3ce3", - "metadata": {}, - "source": [ - "# Static Case" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "eb76d405", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lca = bc.LCA({('temporalis-example', 'EOL'): 1}, (\"GWP\", \"example\"))\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "78c73f89", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['carbon dioxide' (None, None, None), 'methane' (None, None, None), 'Drive an electric car' (None, None, None), 'Drive an electric car 3' (None, None, None), 'Drive an electric car 3' (None, None, None), 'Drive an electric car' (None, None, None), 'Avoided impact - waste' (None, None, None), 'EOL' (None, None, None), 'Thinning' (None, None, None), 'Sawmill' (None, None, None), 'Production' (None, None, None), 'Waste' (None, None, None), 'Forest' (None, None, None), 'Avoided impact - thinnings' (None, None, None), 'Landfill' (None, None, None), 'Functional Unit' (None, None, None), 'Use' (None, None, None), 'Production' (None, None, None)]\n", - "18\n" - ] - } - ], - "source": [ - "all_acts = [act for db in bd.databases for act in bd.Database(db)]\n", - "print(all_acts)\n", - "print(len(all_acts))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18666827", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}