diff --git a/notebooks/example_electric_vehicle_premise.ipynb b/notebooks/example_electric_vehicle_premise.ipynb index 69bb50f..7d647f7 100644 --- a/notebooks/example_electric_vehicle_premise.ipynb +++ b/notebooks/example_electric_vehicle_premise.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -381,22 +381,22 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -411,22 +411,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -448,22 +448,22 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -485,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -549,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -574,43 +574,40 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[0;31mInit signature:\u001b[0m \u001b[0mTimexLCA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatabase_date_dict\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m \n", + "\u001b[1;31mInit signature:\u001b[0m \u001b[0mTimexLCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdemand\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatabase_date_dict\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\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\u001b[0m\u001b[0m\n", + "\u001b[1;31mDocstring:\u001b[0m \n", "Class to perform time-explicit LCA calculations.\n", "\n", - "A TimexLCA retrieves the LCI of processes occuring at explicit points in time and relinks their technosphere\n", - "exchanges to match the technology landscape at that point in time, while keeping track of the timing of the\n", - "resulting emissions. As such, it combines prospective and dynamic LCA approaches.\n", + "A TimexLCA contains the LCI of processes occuring at explicit points in time. It tracks the timing of processes,\n", + "relinks their technosphere and biosphere exchanges to match the technology landscape at that point in time,\n", + "and also keeps track of the timing of the resulting emissions. As such, it combines prospective and dynamic LCA\n", + "approaches.\n", "\n", - "TimexLCA first calculates a static LCA, which informs a priority-first graph traversal. From the graph traversal,\n", - "temporal relationships between exchanges and processes are derived. Based on the timing of the processes, bw_timex\n", - "matches the processes at the intersection between foreground and background to the best available background\n", - "databases. This temporal relinking is achieved by using datapackages to add new time-specific processes. The new\n", - "processes and their exchanges to other technosphere processes or biosphere flows extent the technopshere and\n", + "TimexLCA first calculates a static LCA, which informs a priority-first graph traversal. From the\n", + "graph traversal, temporal relationships between exchanges and processes are derived. Based on\n", + "the timing of the processes, bw_timex matches the processes at the intersection between\n", + "foreground and background to the best available background databases. This temporal relinking is\n", + "achieved by using datapackages to add new time-specific processes. The new processes and their\n", + "exchanges to other technosphere processes or biosphere flows extent the technopshere and\n", "biosphere matrices.\n", "\n", - "Temporal information of both processes and biosphere flows are retained, allowing for dynamic LCIA.\n", - "\n", - "Currently absolute Temporal Distributions for biosphere exchanges are dealt with as a look up function:\n", - "If an activity happens at timestamp X then and the biosphere exchange has an absolute temporal\n", - "distribution (ATD), it looks up the amount from from the ATD correspnding to timestamp X.\n", - "E.g.: X = 2024, TD=(data=[2020,2021,2022,2023,2024,.....,2120 ], amount=[3,4,4,5,6,......,3]),\n", - "it will look up the value 6 corresponding 2024. If timestamp X does not exist it find the nearest\n", - "timestamp available (if two timestamps are equally close, it will take the first in order of\n", - "apearance (see numpy.argmin() for this behabiour).\n", - "\n", + "Temporal information of both processes and biosphere flows is retained, allowing for dynamic\n", + "LCIA.\n", "\n", "TimexLCA calculates:\n", - " 1) a static LCA score (`TimexLCA.base_lca.score`, same as `bw2calc.lca.score`),\n", - " 2) a static time-explicit LCA score (`TimexLCA.static_score`), which links LCIs to the respective background databases but without additional temporal dynamics of the biosphere flows,\n", - " 3) a dynamic time-explicit LCA score (`TimexLCA.dynamic_score`), with dynamic inventory and dynamic charaterization factors. These are provided for radiative forcing and GWP but can also be user-defined.\n", + " 1) a static \"base\" LCA score (`TimexLCA.base_score`, same as `bw2calc.lca.score`),\n", + " 2) a static time-explicit LCA score (`TimexLCA.static_score`), which links LCIs to the\n", + " respective background databases, but without dynamic characterization of the time-explicit inventory\n", + " 3) a dynamic time-explicit LCA score (`TimexLCA.dynamic_score`), with dynamic inventory and\n", + " dynamic charaterization. These are provided for radiative forcing and GWP but can also be\n", + " user-defined.\n", "\n", "Example\n", "-------\n", @@ -619,17 +616,20 @@ ">>> database_date_dict = {\n", " 'my_background_database_one': datetime.strptime(\"2020\", \"%Y\"),\n", " 'my_background_database_two': datetime.strptime(\"2030\", \"%Y\"),\n", + " 'my_background_database_three': datetime.strptime(\"2040\", \"%Y\"),\n", " 'my_foreground_database':'dynamic'\n", " }\n", - ">>> bw_timex = TimexLCA(demand, method, database_date_dict)\n", - ">>> bw_timex.build_timeline() # you can pass many optional arguments here, also for the graph traversal\n", - ">>> bw_timex.lci()\n", - ">>> bw_timex.static_lcia()\n", - ">>> print(bw_timex.static_score)\n", - ">>> bw_timex.dynamic_lcia(metric=\"radiative_forcing\") # different metrics can be used, e.g. \"GWP\", \"radiative_forcing\"\n", - ">>> print(bw_timex.dynamic_score)\n", - "\u001b[0;31mInit docstring:\u001b[0m\n", - "Instantiating a `TimexLCA` object calculates a static LCA, initializes time mapping dicts for activities and biosphere flows, and stores useful subsets of ids in the node_id_collection_dict.\n", + ">>> tlca = TimexLCA(demand, method, database_date_dict)\n", + ">>> tlca.build_timeline() # has many optional arguments\n", + ">>> tlca.lci()\n", + ">>> tlca.static_lcia()\n", + ">>> print(tlca.static_score)\n", + ">>> tlca.dynamic_lcia(metric=\"radiative_forcing\") # also available: \"GWP\"\n", + ">>> print(tlca.dynamic_score)\n", + "\u001b[1;31mInit docstring:\u001b[0m\n", + "Instantiating a `TimexLCA` object calculates a static LCA, initializes time mapping dicts\n", + "for activities and biosphere flows, and stores useful subsets of ids in the\n", + "node_id_collection_dict.\n", "\n", "Parameters\n", "----------\n", @@ -637,12 +637,13 @@ " The demand for which the LCA will be calculated. The keys can be Brightway `Node`\n", " instances, `(database, code)` tuples, or integer ids.\n", "method : tuple\n", - " Tuple defining the LCIA method, such as `('foo', 'bar')` or default methods, such as `(\"EF v3.1\", \"climate change\", \"global warming potential (GWP100)\")`\n", + " Tuple defining the LCIA method, such as `('foo', 'bar')` or default methods, such as\n", + " `(\"EF v3.1\", \"climate change\", \"global warming potential (GWP100)\")`\n", "database_date_dict : dict, optional\n", " Dictionary mapping database names to dates.\n", - "\u001b[0;31mFile:\u001b[0m ~/Documents/Coding/bw_timex/bw_timex/timex_lca.py\n", - "\u001b[0;31mType:\u001b[0m type\n", - "\u001b[0;31mSubclasses:\u001b[0m " + "\u001b[1;31mFile:\u001b[0m c:\\users\\mullera\\onedrive - vito\\documents\\04_coding\\tictac_lca\\bw_timex\\timex_lca.py\n", + "\u001b[1;31mType:\u001b[0m type\n", + "\u001b[1;31mSubclasses:\u001b[0m " ] } ], @@ -660,18 +661,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/timex/lib/python3.10/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.21e+13)\n", - " warnings.warn(msg, UmfpackWarning)\n" - ] - } - ], + "outputs": [], "source": [ "tlca = TimexLCA({driving: 1}, method, database_date_dict)" ] @@ -694,14 +686,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/timodiepers/Documents/Coding/bw_timex/bw_timex/timex_lca.py:194: UserWarning: No edge filter function provided. Skipping all edges within background databases.\n", + "C:\\Users\\MULLERA\\OneDrive - VITO\\Documents\\04_Coding\\tictac_lca\\bw_timex\\timex_lca.py:206: UserWarning: No edge filter function provided. Skipping all edges in background databases.\n", " warnings.warn(\n" ] }, @@ -717,9 +709,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/timodiepers/Documents/Coding/bw_timex/bw_timex/timeline_builder.py:527: Warning: Reference date 2040-08-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", + "C:\\Users\\MULLERA\\OneDrive - VITO\\Documents\\04_Coding\\tictac_lca\\bw_timex\\timeline_builder.py:523: Warning: Reference date 2040-10-01 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/bw_timex/bw_timex/timeline_builder.py:527: Warning: Reference date 2040-11-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", + "C:\\Users\\MULLERA\\OneDrive - VITO\\Documents\\04_Coding\\tictac_lca\\bw_timex\\timeline_builder.py:523: Warning: Reference date 2041-01-01 00:00:00 is higher than all provided dates. Data will be taken from the closest lower year.\n", " warnings.warn(\n" ] }, @@ -755,297 +747,297 @@ " \n", " \n", " 0\n", - " 2022-05-01\n", + " 2022-07-01\n", " glider production, passenger car, without EOL\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", " 588.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 1\n", - " 2022-06-01\n", + " 2022-08-01\n", " glider production, passenger car, without EOL\n", - " 2024-06-01\n", + " 2024-08-01\n", " production of an electric vehicle\n", " 588.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 2\n", - " 2023-05-01\n", + " 2023-07-01\n", " glider production, passenger car, without EOL\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", " 84.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 3\n", - " 2023-05-01\n", + " 2023-07-01\n", " powertrain production, for electric passenger ...\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", " 80.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 4\n", - " 2023-05-01\n", + " 2023-07-01\n", " battery production, Li-ion, LiMn2O4, rechargea...\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", " 280.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 5\n", - " 2023-06-01\n", + " 2023-08-01\n", " glider production, passenger car, without EOL\n", - " 2024-06-01\n", + " 2024-08-01\n", " production of an electric vehicle\n", " 84.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 6\n", - " 2023-06-01\n", + " 2023-08-01\n", " powertrain production, for electric passenger ...\n", - " 2024-06-01\n", + " 2024-08-01\n", " production of an electric vehicle\n", " 80.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 7\n", - " 2023-06-01\n", + " 2023-08-01\n", " battery production, Li-ion, LiMn2O4, rechargea...\n", - " 2024-06-01\n", + " 2024-08-01\n", " production of an electric vehicle\n", " 280.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 8\n", - " 2024-05-01\n", + " 2024-07-01\n", " glider production, passenger car, without EOL\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", " 168.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 9\n", - " 2024-05-01\n", + " 2024-07-01\n", " production of an electric vehicle\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 0.2\n", " None\n", " \n", " \n", " 10\n", - " 2024-06-01\n", + " 2024-08-01\n", " glider production, passenger car, without EOL\n", - " 2024-06-01\n", + " 2024-08-01\n", " production of an electric vehicle\n", " 168.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 11\n", - " 2024-06-01\n", - " production of an electric vehicle\n", " 2024-08-01\n", + " production of an electric vehicle\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 0.8\n", " None\n", " \n", " \n", " 12\n", - " 2024-08-01\n", + " 2024-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 13\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", - " 2024-08-01\n", + " 2024-10-01\n", " -1\n", " 1.0\n", " None\n", " \n", " \n", " 14\n", - " 2025-08-01\n", + " 2025-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 15\n", - " 2026-08-01\n", + " 2026-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 16\n", - " 2027-08-01\n", + " 2027-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 17\n", - " 2028-08-01\n", + " 2028-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 18\n", - " 2029-08-01\n", + " 2029-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0....\n", " \n", " \n", " 19\n", - " 2030-08-01\n", + " 2030-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 20\n", - " 2031-08-01\n", + " 2031-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 21\n", - " 2032-08-01\n", + " 2032-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 22\n", - " 2033-08-01\n", + " 2033-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 23\n", - " 2034-08-01\n", + " 2034-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 24\n", - " 2035-08-01\n", + " 2035-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 25\n", - " 2036-08-01\n", + " 2036-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 26\n", - " 2037-08-01\n", + " 2037-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 27\n", - " 2038-08-01\n", + " 2038-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 28\n", - " 2039-08-01\n", + " 2039-10-01\n", " market group for electricity, low voltage\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " 1875.0\n", " {'ei310_IMAGE_SSP2_RCP19_2030_electricity': 0....\n", " \n", " \n", " 29\n", - " 2040-08-01\n", + " 2040-10-01\n", " used electric vehicle\n", - " 2024-08-01\n", + " 2024-10-01\n", " driving an electric vehicle\n", " -1.0\n", " None\n", " \n", " \n", " 30\n", - " 2040-11-01\n", + " 2041-01-01\n", " market for used Li-ion battery\n", - " 2040-08-01\n", + " 2040-10-01\n", " used electric vehicle\n", " -280.0\n", " {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}\n", " \n", " \n", " 31\n", - " 2040-11-01\n", + " 2041-01-01\n", " treatment of used powertrain for electric pass...\n", - " 2040-08-01\n", + " 2040-10-01\n", " used electric vehicle\n", " -80.0\n", " {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}\n", " \n", " \n", " 32\n", - " 2040-11-01\n", + " 2041-01-01\n", " treatment of used glider, passenger car, shred...\n", - " 2040-08-01\n", + " 2040-10-01\n", " used electric vehicle\n", " -840.0\n", " {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1}\n", @@ -1056,74 +1048,74 @@ ], "text/plain": [ " date_producer producer_name \\\n", - "0 2022-05-01 glider production, passenger car, without EOL \n", - "1 2022-06-01 glider production, passenger car, without EOL \n", - "2 2023-05-01 glider production, passenger car, without EOL \n", - "3 2023-05-01 powertrain production, for electric passenger ... \n", - "4 2023-05-01 battery production, Li-ion, LiMn2O4, rechargea... \n", - "5 2023-06-01 glider production, passenger car, without EOL \n", - "6 2023-06-01 powertrain production, for electric passenger ... \n", - "7 2023-06-01 battery production, Li-ion, LiMn2O4, rechargea... \n", - "8 2024-05-01 glider production, passenger car, without EOL \n", - "9 2024-05-01 production of an electric vehicle \n", - "10 2024-06-01 glider production, passenger car, without EOL \n", - "11 2024-06-01 production of an electric vehicle \n", - "12 2024-08-01 market group for electricity, low voltage \n", - "13 2024-08-01 driving an electric vehicle \n", - "14 2025-08-01 market group for electricity, low voltage \n", - "15 2026-08-01 market group for electricity, low voltage \n", - "16 2027-08-01 market group for electricity, low voltage \n", - "17 2028-08-01 market group for electricity, low voltage \n", - "18 2029-08-01 market group for electricity, low voltage \n", - "19 2030-08-01 market group for electricity, low voltage \n", - "20 2031-08-01 market group for electricity, low voltage \n", - "21 2032-08-01 market group for electricity, low voltage \n", - "22 2033-08-01 market group for electricity, low voltage \n", - "23 2034-08-01 market group for electricity, low voltage \n", - "24 2035-08-01 market group for electricity, low voltage \n", - "25 2036-08-01 market group for electricity, low voltage \n", - "26 2037-08-01 market group for electricity, low voltage \n", - "27 2038-08-01 market group for electricity, low voltage \n", - "28 2039-08-01 market group for electricity, low voltage \n", - "29 2040-08-01 used electric vehicle \n", - "30 2040-11-01 market for used Li-ion battery \n", - "31 2040-11-01 treatment of used powertrain for electric pass... \n", - "32 2040-11-01 treatment of used glider, passenger car, shred... \n", + "0 2022-07-01 glider production, passenger car, without EOL \n", + "1 2022-08-01 glider production, passenger car, without EOL \n", + "2 2023-07-01 glider production, passenger car, without EOL \n", + "3 2023-07-01 powertrain production, for electric passenger ... \n", + "4 2023-07-01 battery production, Li-ion, LiMn2O4, rechargea... \n", + "5 2023-08-01 glider production, passenger car, without EOL \n", + "6 2023-08-01 powertrain production, for electric passenger ... \n", + "7 2023-08-01 battery production, Li-ion, LiMn2O4, rechargea... \n", + "8 2024-07-01 glider production, passenger car, without EOL \n", + "9 2024-07-01 production of an electric vehicle \n", + "10 2024-08-01 glider production, passenger car, without EOL \n", + "11 2024-08-01 production of an electric vehicle \n", + "12 2024-10-01 market group for electricity, low voltage \n", + "13 2024-10-01 driving an electric vehicle \n", + "14 2025-10-01 market group for electricity, low voltage \n", + "15 2026-10-01 market group for electricity, low voltage \n", + "16 2027-10-01 market group for electricity, low voltage \n", + "17 2028-10-01 market group for electricity, low voltage \n", + "18 2029-10-01 market group for electricity, low voltage \n", + "19 2030-10-01 market group for electricity, low voltage \n", + "20 2031-10-01 market group for electricity, low voltage \n", + "21 2032-10-01 market group for electricity, low voltage \n", + "22 2033-10-01 market group for electricity, low voltage \n", + "23 2034-10-01 market group for electricity, low voltage \n", + "24 2035-10-01 market group for electricity, low voltage \n", + "25 2036-10-01 market group for electricity, low voltage \n", + "26 2037-10-01 market group for electricity, low voltage \n", + "27 2038-10-01 market group for electricity, low voltage \n", + "28 2039-10-01 market group for electricity, low voltage \n", + "29 2040-10-01 used electric vehicle \n", + "30 2041-01-01 market for used Li-ion battery \n", + "31 2041-01-01 treatment of used powertrain for electric pass... \n", + "32 2041-01-01 treatment of used glider, passenger car, shred... \n", "\n", " date_consumer consumer_name amount \\\n", - "0 2024-05-01 production of an electric vehicle 588.0 \n", - "1 2024-06-01 production of an electric vehicle 588.0 \n", - "2 2024-05-01 production of an electric vehicle 84.0 \n", - "3 2024-05-01 production of an electric vehicle 80.0 \n", - "4 2024-05-01 production of an electric vehicle 280.0 \n", - "5 2024-06-01 production of an electric vehicle 84.0 \n", - "6 2024-06-01 production of an electric vehicle 80.0 \n", - "7 2024-06-01 production of an electric vehicle 280.0 \n", - "8 2024-05-01 production of an electric vehicle 168.0 \n", - "9 2024-08-01 driving an electric vehicle 0.2 \n", - "10 2024-06-01 production of an electric vehicle 168.0 \n", - "11 2024-08-01 driving an electric vehicle 0.8 \n", - "12 2024-08-01 driving an electric vehicle 1875.0 \n", - "13 2024-08-01 -1 1.0 \n", - "14 2024-08-01 driving an electric vehicle 1875.0 \n", - "15 2024-08-01 driving an electric vehicle 1875.0 \n", - "16 2024-08-01 driving an electric vehicle 1875.0 \n", - "17 2024-08-01 driving an electric vehicle 1875.0 \n", - "18 2024-08-01 driving an electric vehicle 1875.0 \n", - "19 2024-08-01 driving an electric vehicle 1875.0 \n", - "20 2024-08-01 driving an electric vehicle 1875.0 \n", - "21 2024-08-01 driving an electric vehicle 1875.0 \n", - "22 2024-08-01 driving an electric vehicle 1875.0 \n", - "23 2024-08-01 driving an electric vehicle 1875.0 \n", - "24 2024-08-01 driving an electric vehicle 1875.0 \n", - "25 2024-08-01 driving an electric vehicle 1875.0 \n", - "26 2024-08-01 driving an electric vehicle 1875.0 \n", - "27 2024-08-01 driving an electric vehicle 1875.0 \n", - "28 2024-08-01 driving an electric vehicle 1875.0 \n", - "29 2024-08-01 driving an electric vehicle -1.0 \n", - "30 2040-08-01 used electric vehicle -280.0 \n", - "31 2040-08-01 used electric vehicle -80.0 \n", - "32 2040-08-01 used electric vehicle -840.0 \n", + "0 2024-07-01 production of an electric vehicle 588.0 \n", + "1 2024-08-01 production of an electric vehicle 588.0 \n", + "2 2024-07-01 production of an electric vehicle 84.0 \n", + "3 2024-07-01 production of an electric vehicle 80.0 \n", + "4 2024-07-01 production of an electric vehicle 280.0 \n", + "5 2024-08-01 production of an electric vehicle 84.0 \n", + "6 2024-08-01 production of an electric vehicle 80.0 \n", + "7 2024-08-01 production of an electric vehicle 280.0 \n", + "8 2024-07-01 production of an electric vehicle 168.0 \n", + "9 2024-10-01 driving an electric vehicle 0.2 \n", + "10 2024-08-01 production of an electric vehicle 168.0 \n", + "11 2024-10-01 driving an electric vehicle 0.8 \n", + "12 2024-10-01 driving an electric vehicle 1875.0 \n", + "13 2024-10-01 -1 1.0 \n", + "14 2024-10-01 driving an electric vehicle 1875.0 \n", + "15 2024-10-01 driving an electric vehicle 1875.0 \n", + "16 2024-10-01 driving an electric vehicle 1875.0 \n", + "17 2024-10-01 driving an electric vehicle 1875.0 \n", + "18 2024-10-01 driving an electric vehicle 1875.0 \n", + "19 2024-10-01 driving an electric vehicle 1875.0 \n", + "20 2024-10-01 driving an electric vehicle 1875.0 \n", + "21 2024-10-01 driving an electric vehicle 1875.0 \n", + "22 2024-10-01 driving an electric vehicle 1875.0 \n", + "23 2024-10-01 driving an electric vehicle 1875.0 \n", + "24 2024-10-01 driving an electric vehicle 1875.0 \n", + "25 2024-10-01 driving an electric vehicle 1875.0 \n", + "26 2024-10-01 driving an electric vehicle 1875.0 \n", + "27 2024-10-01 driving an electric vehicle 1875.0 \n", + "28 2024-10-01 driving an electric vehicle 1875.0 \n", + "29 2024-10-01 driving an electric vehicle -1.0 \n", + "30 2040-10-01 used electric vehicle -280.0 \n", + "31 2040-10-01 used electric vehicle -80.0 \n", + "32 2040-10-01 used electric vehicle -840.0 \n", "\n", " interpolation_weights \n", "0 {'ei310_IMAGE_SSP2_RCP19_2020_electricity': 0.... \n", @@ -1161,7 +1153,7 @@ "32 {'ei310_IMAGE_SSP2_RCP19_2040_electricity': 1} " ] }, - "execution_count": 36, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1187,22 +1179,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/timodiepers/anaconda3/envs/timex/lib/python3.10/site-packages/bw2calc/lca_base.py:127: SparseEfficiencyWarning: splu converted its input to CSC format\n", - " self.solver = factorized(self.technosphere_matrix)\n", - "/Users/timodiepers/anaconda3/envs/timex/lib/python3.10/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 5.78e+12)\n", - " warnings.warn(msg, UmfpackWarning)\n", - "/Users/timodiepers/anaconda3/envs/timex/lib/python3.10/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 5.78e+12)\n", - " warnings.warn(msg, UmfpackWarning)\n" - ] - } - ], + "outputs": [], "source": [ "tlca.lci()" ] @@ -1216,17 +1195,17 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "<61709x80718 sparse matrix of type ''\n", + "\twith 65916 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 40, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1239,27 +1218,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The standard, non-dynamic inventory has far less rows because the temporal resolution is missing. Looking at the timeline again, we see that we have processes at 23 different points in time (only counting the ones that actually directly procude emissions), which exactly matches the ratio of the dimensions of our two inventories:" + "The standard, non-dynamic inventory has far less rows because the temporal resolution is missing. Looking at the timeline again, we see that we have processes at 23 different points in time (only counting the ones that actually directly produce emissions), which exactly matches the ratio of the dimensions of our two inventories:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2683, 80718)" + "(2683, 80685)" ] }, - "execution_count": 41, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tlca.inventory.shape # (#rows, #cols)" + "tlca.base_lca.inventory.shape # (#rows, #cols)" ] }, { @@ -1279,7 +1258,7 @@ } ], "source": [ - "tlca.dynamic_inventory.shape[0]/tlca.inventory.shape[0]" + "tlca.dynamic_inventory.shape[0]/tlca.base_lca.inventory.shape[0]" ] }, { diff --git a/notebooks/example_electric_vehicle_standalone.ipynb b/notebooks/example_electric_vehicle_standalone.ipynb index 6b1e786..f03bc24 100644 --- a/notebooks/example_electric_vehicle_standalone.ipynb +++ b/notebooks/example_electric_vehicle_standalone.ipynb @@ -1281,7 +1281,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The standard, non-dynamic inventory has far less rows because the temporal resolution is missing. Looking at the timeline again, we see that we have processes at 23 different points in time (only counting the ones that actually directly procude emissions), which exactly matches the ratio of the dimensions of our two inventories:" + "The standard, non-dynamic inventory has far less rows because the temporal resolution is missing. Looking at the timeline again, we see that we have processes at 23 different points in time (only counting the ones that actually directly produce emissions), which exactly matches the ratio of the dimensions of our two inventories:" ] }, { @@ -1301,7 +1301,7 @@ } ], "source": [ - "tlca.inventory.shape # (#rows, #cols)" + "tlca.base_lca.inventory.shape # (#rows, #cols)" ] }, { @@ -1321,7 +1321,7 @@ } ], "source": [ - "tlca.dynamic_inventory.shape[0]/tlca.inventory.shape[0]" + "tlca.dynamic_inventory.shape[0]/tlca.base_lca.inventory.shape[0]" ] }, { @@ -2359,7 +2359,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.3" } }, "nbformat": 4,