From 12002b9ab43206b0beb53bd6f3c9702774c3f78a Mon Sep 17 00:00:00 2001 From: romainsacchi Date: Fri, 12 Apr 2024 08:56:51 +0200 Subject: [PATCH] EODC --- README.md | 17 +- .../datapackage creation-checkpoint.ipynb | 137 ++ dev/datapackage creation.ipynb | 2 +- .../example-checkpoint.ipynb | 937 +++++------ example/datapackage_sample/datapackage.json | 880 +++++++++++ .../some model/Scenario B/2020/A_matrix.csv | 25 +- .../some model/Scenario B/2020/B_matrix.csv | 10 +- .../some model/Scenario B/2030/A_matrix.csv | 25 +- .../some model/Scenario B/2030/B_matrix.csv | 10 +- .../some model/Scenario B/2040/A_matrix.csv | 25 +- .../some model/Scenario B/2040/B_matrix.csv | 10 +- .../some model/Scenario B/2050/A_matrix.csv | 25 +- .../some model/Scenario B/2050/B_matrix.csv | 10 +- example/example.html | 1395 +++++++++++------ example/example.ipynb | 903 +++++------ example/fig1.png | Bin 0 -> 183194 bytes example/fig2.png | Bin 0 -> 207030 bytes example/fig3.png | Bin 0 -> 188338 bytes example/fig4.png | Bin 0 -> 189670 bytes example/fig5.png | Bin 0 -> 163249 bytes pathways/lca.py | 2 - pathways/pathways.py | 16 +- 22 files changed, 2807 insertions(+), 1622 deletions(-) create mode 100644 dev/.ipynb_checkpoints/datapackage creation-checkpoint.ipynb create mode 100644 example/fig1.png create mode 100644 example/fig2.png create mode 100644 example/fig3.png create mode 100644 example/fig4.png create mode 100644 example/fig5.png diff --git a/README.md b/README.md index 1849805..89c8699 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ or alternatively, you can clone the repository and install it from the source: ```bash - git clone https://github.com/yourrepository/pathways.git + git clone https://github.com/polca/pathways.git cd pathways pip install -r requirements.txt @@ -50,7 +50,8 @@ or alternatively, you can clone the repository and install it from the source: ## Usage ``pathways`` is a Python package, and can be used in Python scripts -or in a Python interpreter. +or in a Python interpreter. +See the [example notebook](https://github.com/polca/pathways/blob/main/example/example.ipynb). ### Python @@ -131,10 +132,16 @@ You can display the LCA results with an optional cutoff parameter to filter insi It can be further formatted to a pandas' DataFrame or export to a CSV/Excel file using the built-in methods of ``xarray``. + + ```python + + df = results.to_dataframe() + df.to_csv("results.csv") + + ``` - -which can then be visualized using your favorite plotting library. -![Screenshot](assets/screenshot.png) +Results can be visualized using your favorite plotting library. +![Screenshot](example/fig5.png) ## Contributing diff --git a/dev/.ipynb_checkpoints/datapackage creation-checkpoint.ipynb b/dev/.ipynb_checkpoints/datapackage creation-checkpoint.ipynb new file mode 100644 index 0000000..5a0489d --- /dev/null +++ b/dev/.ipynb_checkpoints/datapackage creation-checkpoint.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93725a69-da37-4dfb-913f-880652e04494", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "premise v.(2, 0, 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", + "- Extracting source database\n", + "- Extracting inventories\n", + "- Fetching IAM data\n", + "Done!\n" + ] + } + ], + "source": [ + "from premise import *\n", + "import bw2data\n", + "from datapackage import Package\n", + "bw2data.projects.set_current(\"ei39\")\n", + "ndb = PathwaysDataPackage(\n", + " scenarios=[\n", + " {\"model\": \"image\", \"pathway\": \"SSP2-RCP19\"},\n", + " ],\n", + " #years=[2005, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n", + " #years=[2020, 2050],\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=\",\n", + " #external_scenarios=[sps,]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6936b92-9ac1-49cb-95fb-a348a6cce296", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "`update()` will skip the following sectors: 'buses', 'cars', 'two_wheelers'.\n", + "If you want to update these sectors, please run them separately afterwards.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Updating: biomass: 0%| | 0/11 [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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelpathwayvariablesregionyear
some modelScenario Atechnology AEU20201000.0
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Scenario Btechnology AEU20201000.0
20301000.0
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\n", + "" + ], + "text/plain": [ + " \n", + "model pathway variables region year \n", + "some model Scenario A technology A EU 2020 1000.0\n", + " 2030 1000.0\n", + " 2040 1000.0\n", + " 2050 1000.0\n", + " Scenario B technology A EU 2020 1000.0\n", + " 2030 1000.0\n", + " 2040 1000.0\n", + " 2050 1000.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.scenarios.to_dataframe(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e5260d0b-8042-44d3-be14-e78aa4a767b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'units': {'technology A': 'kilogram'}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.scenarios.attrs" + ] + }, + { + "cell_type": "markdown", + "id": "49a3eba4-ccce-46ce-ace6-119a5fd11a1c", + "metadata": {}, + "source": [ + "We can also see the mapping used to map the scenario variables to the LCA datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "adfb8a71-efc0-4f32-8e91-6c6495b03699", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'technology A': {'dataset': [{'name': 'activity A',\n", + " 'reference product': 'product A',\n", + " 'unit': 'kilogram'}],\n", + " 'scenario variable': 'technology A'}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.mapping" + ] + }, + { + "cell_type": "markdown", + "id": "e03f9770-a644-4dcf-9f41-5eda5d6c28b3", + "metadata": {}, + "source": [ + "We can also list the LCIA methods available." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b13df1c4-9e99-4f6d-a02a-5c908db8bfbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CML v4.8 2016 no LT - acidification no LT - acidification (incl. fate, average Europe total, A&B) no LT',\n", + " 'CML v4.8 2016 no LT - climate change no LT - global warming potential [GWP100) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: freshwater no LT - freshwater aquatic ecotoxicity [FAETP inf) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: marine no LT - marine aquatic ecotoxicity (MAETP inf) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: terrestrial no LT - terrestrial ecotoxicity (TETP inf) no LT']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.lcia_methods[:5]" + ] + }, + { + "cell_type": "markdown", + "id": "6a29f446-72f4-460f-9f23-1879d4057936", + "metadata": {}, + "source": [ + "And most importantly, once the `datapackage.Package` is loaded, we can use the method `Pathways.calculate()` to calculate the LCA impacts.\n", + "\n", + "Arguments:\n", + "\n", + " * `methods`: list[str]. LCIA methods to use. To get a complete list of available LCIA methods, call `.lcia_methods`\n", + " * `scenarios`: list[str]. List of scenarios you want to calculate the impacts for.\n", + " * `variables`: list[str]. List of variables you want to calculate the impacts for (if the demand for them is non-null)\n", + " * `regions`: list[str]. Regions for which you want to calculate the impacts, provided the specified variables have a non-null demand in these regions.\n", + " * `years`: list[int]. Years for which you want to calculate the impacts.\n", + " * `multiprocessing`: bool. Multiprocessing may accelerate the process, as it processes each year in parallel.\n", + " * `demand_cutoff`: float. Between 0 and 1. Defines a ratio below which demand values are ignored. The default is 0.001.\n", + " * `use_distributions`: bool. Number of iterations to use for Monte Carlo analyses. The default is 0 (i.e., regular analysis)." + ] + }, + { + "cell_type": "markdown", + "id": "9703ede6-2a13-41f3-95a0-8d823ca3ac91", + "metadata": {}, + "source": [ + "In the example below, we do so using a stochastic approach (i.e., `use_distributions=500`), leveraging on the uncertainty distributions we defined for each exchange in the datapackage. `bw2calc` and the underlying library `stats_array` generates 500 pseudo-random exchange values and update the technosphere and biosphere matrices between each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "87aa6b87-b677-4acb-ad03-226591c4d7b0", "metadata": {}, "outputs": [ @@ -32,180 +279,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.5, nan, nan, 0.4 , 0.6 , False)\n", - " (5, 0.8, nan, nan, 0.7 , 0.9 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 0.6, nan, nan, 0.5 , 0.7 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 10. , nan, nan, 8. , 12. , False)\n", - " (5, 0.2, nan, nan, 0.15, 0.25, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 1.5, nan, nan, 1., 1.7, False) (5, 2. , nan, nan, 1., 7. , False)\n", - " (5, 2.3, nan, nan, 1., 2.7, False) (5, 1.2, nan, nan, 1., 1.7, False)\n", - " (5, 1.5, nan, nan, 1., 2. , False)]\n", "Calculating LCA results for some model...\n", "--- Calculating LCA results for Scenario A...\n", "------ Calculating LCA results for 2020...\n", - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.5, nan, nan, 0.4 , 0.6 , False)\n", - " (5, 0.8, nan, nan, 0.7 , 0.9 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 0.6, nan, nan, 0.5 , 0.7 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 10. , nan, nan, 8. , 12. , False)\n", - " (5, 0.2, nan, nan, 0.15, 0.25, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 1.5, nan, nan, 1., 1.7, False) (5, 2. , nan, nan, 1., 7. , False)\n", - " (5, 2.3, nan, nan, 1., 2.7, False) (5, 1.2, nan, nan, 1., 1.7, False)\n", - " (5, 1.5, nan, nan, 1., 2. , False)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0% [#] 100% | ETA: 00:00:01\n", - "Total time elapsed: 00:00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Warning: No valid output stream.\n", "------ Calculating LCA results for 2030...\n", - "[(0, nan, nan, nan, nan, nan, False)\n", - " (5, nan, nan, nan, 0.4 , 0.6, False)\n", - " (5, nan, nan, nan, 0.4 , 1.2, False)\n", - " (0, nan, nan, nan, nan, nan, False)\n", - " (0, nan, nan, nan, nan, nan, False)\n", - " (5, nan, nan, nan, 1.4 , 1.8, False)\n", - " (5, nan, nan, nan, 0.4 , 1.2, False)\n", - " (0, nan, nan, nan, nan, nan, False)\n", - " (5, nan, nan, nan, 0.04, 0.2, False)\n", - " (5, nan, nan, nan, 4. , 12. , False)\n", - " (5, nan, nan, nan, 0.1 , 0.3, False)\n", - " (0, nan, nan, nan, nan, nan, False)\n", - " (0, nan, nan, nan, nan, nan, False)]\n", - "[(5, nan, nan, nan, 1. , 1.7, False) (5, nan, nan, nan, 0.5, 1.7, False)\n", - " (5, nan, nan, nan, 1. , 2.7, False) (5, nan, nan, nan, 1. , 1.7, False)\n", - " (5, nan, nan, nan, 1. , 1.5, False)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:578: UmfpackWarning: Singular matrix\n", - " warnings.warn('Singular matrix', UmfpackWarning)\n", - "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:727: UmfpackWarning: Zeroing nan and inf entries...\n", - " warnings.warn('Zeroing nan and inf entries...', UmfpackWarning)\n", - "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:732: RuntimeWarning: divide by zero encountered in scalar divide\n", - " econd = 1.0 / self.info[UMFPACK_RCOND]\n", - "/Users/romain/anaconda3/envs/pathways/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:736: UmfpackWarning: (almost) singular matrix! 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<xarray.DataArray ()> Size: 8B\n",
-       "array(12977.17768715)
" - ], - "text/plain": [ - " Size: 8B\n", - "array(12977.17768715)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "p.lca_results.sum()" + "We can now access the attribute `.lca_results`, which is an `xarray.DataArray` where the results are stored. We can format it a little to present it in an `pandas.DataFrame`, for example." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "ca80ad98-860e-4645-a533-8c1e7eba477c", "metadata": {}, "outputs": [], "source": [ + "# interpolate in-between years\n", "arr = p.lca_results.interp(\n", - " year=range(p.lca_results.coords[\"year\"].values.min(), p.lca_results.coords[\"year\"].values.max() + 1)\n", + " year=range(\n", + " p.lca_results.coords[\"year\"].values.min(),\n", + " p.lca_results.coords[\"year\"].values.max() + 1\n", + " )\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "268f8502-e499-4ab5-8fe7-4882c7a181f3", "metadata": {}, "outputs": [], @@ -634,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "94ed4c32-bed3-46bf-84eb-580b4f336dcd", "metadata": {}, "outputs": [ @@ -642,7 +375,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "151\n" + "930\n" ] } ], @@ -655,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "00e0a5b9-1285-4272-898b-5c72df1b44cb", "metadata": {}, "outputs": [ @@ -674,24 +407,124 @@ " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# pivottablejs is very convenient way to visualize pivot tables\n", "from pivottablejs import pivot_ui\n", "from IPython.display import HTML\n", "pivot_ui(df, outfile_path='example.html')" ] }, + { + "cell_type": "markdown", + "id": "fa8931f2-f020-4ff6-89f9-6e9172bf00ea", + "metadata": {}, + "source": [ + "Impacts with process contributions\n", + "\n", + "![impacts with process contribution](fig2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "84941789-7247-48b1-abc1-92aa65aab730", + "metadata": {}, + "source": [ + "Impacts with breakdown by geographical location of impacts\n", + "\n", + "![impacts with impacts origins](fig3.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ba25fc8a-871c-4c3c-91af-e067c7ba628b", + "metadata": {}, + "source": [ + "Sum of impacts for the 5th, 50th and 95th quantiles\n", + "\n", + "![impacts with uncertainty](fig1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "643665bb-065d-4c6b-a3cf-bca3e791769e", + "metadata": {}, + "source": [ + "Comparison between two scenarios\n", + "\n", + "![scenarios comparison](fig4.png)" + ] + }, + { + "cell_type": "markdown", + "id": "b5c8c7e6-097f-49ba-9c9a-ec7a4d0acd21", + "metadata": {}, + "source": [ + "### Providing actual datapackages\n", + "While it is possible to manually build data packages such as the one used in this example, it is not very convenient when dealing with real LCA databases, which have more than half a million exchanges.\n", + "`premise` can output such datapackage as a zip file that can be directly given to `pathways.Pathways`.\n", + "\n", + "The following will produce a datapackage for all years contained in the IMAGE SSP2-RCP19 scenario." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "681f5561-0828-468f-a74d-3057c9f6eb97", + "metadata": {}, + "outputs": [], + "source": [ + "from premise import *\n", + "import bw2data\n", + "from datapackage import Package\n", + "bw2data.projects.set_current(\"ei39\")\n", + "ndb = PathwaysDataPackage(\n", + " scenarios=[\n", + " {\"model\": \"image\", \"pathway\": \"SSP2-RCP19\"},\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=\",\n", + ")\n", + "\n", + "ndb.create_datapackage(\n", + " name=\"image-SSP2\",\n", + " contributors=[\n", + " {\"name\": \"Romain\",\n", + " \"email\": \"r_s at me.com\",}\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "252f1d91-9a50-4baa-b98c-aa6b6558c707", + "metadata": {}, + "source": [ + "This allows for more complex analyses, such as, for example, the projected demand in cobalt for the global electricity system as projected in that same IMAGE scenario:" + ] + }, + { + "cell_type": "markdown", + "id": "cf142507-8453-4a77-855d-11266de5a9e3", + "metadata": {}, + "source": [ + "Life cycle-based annual demand for cobalt, global electricity supply\n", + "\n", + "![cobalt projection](fig5.png)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "45c4b8e7-5944-4a10-9da0-527511b594f3", + "id": "b926a237-22e3-487c-9203-d83b6fafa41c", "metadata": {}, "outputs": [], "source": [] diff --git a/example/datapackage_sample/datapackage.json b/example/datapackage_sample/datapackage.json index bb392ad..ec7b5dd 100644 --- a/example/datapackage_sample/datapackage.json +++ b/example/datapackage_sample/datapackage.json @@ -931,6 +931,886 @@ ] } }, + { + "path": "inventories/some model/Scenario B/2020/A_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "a_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "activity", + "type": "string", + "format": "default" + }, + { + "name": "product", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "location", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2020/B_matrix.csv", + "profile": "tabular-data-resource", + "name": "b_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of biosphere flow", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2020/A_matrix.csv", + "profile": "tabular-data-resource", + "name": "a_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of product", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2020/B_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "b_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "name", + "type": "string", + "format": "default" + }, + { + "name": "category", + "type": "string", + "format": "default" + }, + { + "name": "sub category", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2030/A_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "a_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "activity", + "type": "string", + "format": "default" + }, + { + "name": "product", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "location", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2030/B_matrix.csv", + "profile": "tabular-data-resource", + "name": "b_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of biosphere flow", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2030/A_matrix.csv", + "profile": "tabular-data-resource", + "name": "a_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of product", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2030/B_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "b_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "name", + "type": "string", + "format": "default" + }, + { + "name": "category", + "type": "string", + "format": "default" + }, + { + "name": "sub category", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2040/A_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "a_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "activity", + "type": "string", + "format": "default" + }, + { + "name": "product", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "location", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2040/B_matrix.csv", + "profile": "tabular-data-resource", + "name": "b_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of biosphere flow", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2040/A_matrix.csv", + "profile": "tabular-data-resource", + "name": "a_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of product", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2040/B_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "b_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "name", + "type": "string", + "format": "default" + }, + { + "name": "category", + "type": "string", + "format": "default" + }, + { + "name": "sub category", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2050/A_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "a_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "activity", + "type": "string", + "format": "default" + }, + { + "name": "product", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "location", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2050/B_matrix.csv", + "profile": "tabular-data-resource", + "name": "b_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of biosphere flow", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2050/A_matrix.csv", + "profile": "tabular-data-resource", + "name": "a_matrix", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "index of activity", + "type": "integer", + "format": "default" + }, + { + "name": "index of product", + "type": "date", + "format": "%d%m%y" + }, + { + "name": "value", + "type": "number", + "format": "default" + }, + { + "name": "uncertainty type", + "type": "integer", + "format": "default" + }, + { + "name": "loc", + "type": "number", + "format": "default" + }, + { + "name": "scale", + "type": "number", + "format": "default" + }, + { + "name": "shape", + "type": "number", + "format": "default" + }, + { + "name": "minimum", + "type": "number", + "format": "default" + }, + { + "name": "maximum", + "type": "number", + "format": "default" + }, + { + "name": "negative", + "type": "integer", + "format": "default" + }, + { + "name": "flip", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, + { + "path": "inventories/some model/Scenario B/2050/B_matrix_index.csv", + "profile": "tabular-data-resource", + "name": "b_matrix_index", + "format": "csv", + "mediatype": "text/csv", + "encoding": "utf-8", + "schema": { + "fields": [ + { + "name": "name", + "type": "string", + "format": "default" + }, + { + "name": "category", + "type": "string", + "format": "default" + }, + { + "name": "sub category", + "type": "string", + "format": "default" + }, + { + "name": "unit", + "type": "string", + "format": "default" + }, + { + "name": "value", + "type": "integer", + "format": "default" + } + ], + "missingValues": [ + "" + ] + } + }, { "path": "mapping/mapping.yaml", "profile": "data-resource", diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2020/A_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2020/A_matrix.csv index 0a85401..9bbdf5d 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2020/A_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2020/A_matrix.csv @@ -1,13 +1,14 @@ index of activity;index of product;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1;0;;;;;;0;0 -0;2;0.5;5;;;;0.4;0.6;0;1 -1;0;0.8;5;;;;0.4;1.2;0;1 -1;1;1;0;;;;;;;0;0 -2;2;1;0;;;;;;;0;0 -2;3;0.1;5;;;;1.4;1.8;0;1 -2;5;0.6;5;;;;0.4;1.2;0;1 -3;3;1;0;;;;;;;0;0 -3;0;0.1;5;;;;0.04;0.2;0;1 -4;1;0.2;5;;;;0.1;0.3;0;1 -4;4;1;0;;;;;;;0;0 -5;5;1;0;;;;;;;0;0 \ No newline at end of file +0;0;1;0;1;;;;;0;0 +0;2;0.5;5;0.5;;;0.4;0.6;0;1 +1;0;0.8;5;0.8;;;0.7;0.9;0;1 +1;1;1;0;1;;;;;0;0 +2;2;1;0;1;;;;;0;0 +2;3;0.1;5;0.1;;;0.08;0.2;0;1 +2;5;0.6;5;0.6;;;0.5;0.7;0;1 +3;3;1;0;1;;;;;0;0 +3;0;0.1;5;0.1;;;0.08;0.2;0;1 +3;4;10;5;10;;;8;12;0;1 +4;1;0.2;5;0.2;;;0.15;0.25;0;1 +4;4;1;0;1;;;;;0;0 +5;5;1;0;1;;;;;0;0 \ No newline at end of file diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2020/B_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2020/B_matrix.csv index 6466110..d152bc0 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2020/B_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2020/B_matrix.csv @@ -1,6 +1,6 @@ index of activity;index of biosphere flow;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1.5;5;;;;1.0;1.7;0;1 -1;0;0.2;5;;;;0.1;0.7;0;1 -2;0;2.3;5;;;;1.0;2.7;0;1 -3;0;1.2;5;;;;1.0;1.7;0;1 -4;0;0.5;5;;;;0.3;0.8;0;1 +0;0;1.5;5;1.5;;;1.0;1.7;0;1 +1;0;2;5;2;;;1;7;0;1 +2;0;2.3;5;2.3;;;1.0;2.7;0;1 +3;0;1.2;5;1.2;;;1.0;1.7;0;1 +4;0;1.5;5;1.5;;;1.0;2.0;0;1 diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2030/A_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2030/A_matrix.csv index 0a85401..0777f16 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2030/A_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2030/A_matrix.csv @@ -1,13 +1,14 @@ index of activity;index of product;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1;0;;;;;;0;0 -0;2;0.5;5;;;;0.4;0.6;0;1 -1;0;0.8;5;;;;0.4;1.2;0;1 -1;1;1;0;;;;;;;0;0 -2;2;1;0;;;;;;;0;0 -2;3;0.1;5;;;;1.4;1.8;0;1 -2;5;0.6;5;;;;0.4;1.2;0;1 -3;3;1;0;;;;;;;0;0 -3;0;0.1;5;;;;0.04;0.2;0;1 -4;1;0.2;5;;;;0.1;0.3;0;1 -4;4;1;0;;;;;;;0;0 -5;5;1;0;;;;;;;0;0 \ No newline at end of file +0;0;1;0;1;;;;;0;0 +0;2;0.5;5;0.5;;;0.4;0.6;0;1 +1;0;0.9;5;0.9;;;0.4;1.2;0;1 +1;1;1;0;1;;;;;0;0 +2;2;1;0;1;;;;;0;0 +2;3;0.1;5;0.1;;;0.05;0.5;0;1 +2;5;0.5;5;0.5;;;0.4;1.2;0;1 +3;3;1;0;1;;;;;0;0 +3;0;0.1;5;0.1;;;0.04;0.2;0;1 +3;4;8;5;8;;;4;12;0;1 +4;1;0.2;5;0.2;;;0.1;0.3;0;1 +4;4;1;0;1;;;;;0;0 +5;5;1;0;1;;;;;0;0 \ No newline at end of file diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2030/B_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2030/B_matrix.csv index 6466110..dfea8c6 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2030/B_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2030/B_matrix.csv @@ -1,6 +1,6 @@ index of activity;index of biosphere flow;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1.5;5;;;;1.0;1.7;0;1 -1;0;0.2;5;;;;0.1;0.7;0;1 -2;0;2.3;5;;;;1.0;2.7;0;1 -3;0;1.2;5;;;;1.0;1.7;0;1 -4;0;0.5;5;;;;0.3;0.8;0;1 +0;0;1.1;5;1.1;;;1.0;1.7;0;1 +1;0;1;5;1;;;0.5;1.7;0;1 +2;0;2.2;5;2.2;;;1.0;2.7;0;1 +3;0;1.0;5;1.1;;;1.0;1.7;0;1 +4;0;1.3;5;1.3;;;1.0;1.5;0;1 diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2040/A_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2040/A_matrix.csv index 0a85401..201db4d 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2040/A_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2040/A_matrix.csv @@ -1,13 +1,14 @@ index of activity;index of product;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1;0;;;;;;0;0 -0;2;0.5;5;;;;0.4;0.6;0;1 -1;0;0.8;5;;;;0.4;1.2;0;1 -1;1;1;0;;;;;;;0;0 -2;2;1;0;;;;;;;0;0 -2;3;0.1;5;;;;1.4;1.8;0;1 -2;5;0.6;5;;;;0.4;1.2;0;1 -3;3;1;0;;;;;;;0;0 -3;0;0.1;5;;;;0.04;0.2;0;1 -4;1;0.2;5;;;;0.1;0.3;0;1 -4;4;1;0;;;;;;;0;0 -5;5;1;0;;;;;;;0;0 \ No newline at end of file +0;0;1;0;1;;;;;0;0 +0;2;0.4;5;0.4;;;0.3;0.6;0;1 +1;0;1.1;5;1.1;;;0.8;1.2;0;1 +1;1;1;0;1;;;;;0;0 +2;2;1;0;1;;;;;0;0 +2;3;0.1;5;0.1;;;0.04;0.3;0;1 +2;5;0.4;5;0.4;;;0.3;1.2;0;1 +3;3;1;0;1;;;;;0;0 +3;0;0.1;5;0.1;;;0.04;0.2;0;1 +3;4;7;5;7;;;4;10;0;1 +4;1;0.2;5;0.2;;;0.1;0.3;0;1 +4;4;1;0;1;;;;;0;0 +5;5;1;0;1;;;;;0;0 \ No newline at end of file diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2040/B_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2040/B_matrix.csv index 6466110..2af3325 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2040/B_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2040/B_matrix.csv @@ -1,6 +1,6 @@ index of activity;index of biosphere flow;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1.5;5;;;;1.0;1.7;0;1 -1;0;0.2;5;;;;0.1;0.7;0;1 -2;0;2.3;5;;;;1.0;2.7;0;1 -3;0;1.2;5;;;;1.0;1.7;0;1 -4;0;0.5;5;;;;0.3;0.8;0;1 +0;0;0.8;5;0.9;;;0.5;1.7;0;1 +1;0;0.9;5;0.9;;;0.5;1.2;0;1 +2;0;1.8;5;1.8;;;1.0;2.7;0;1 +3;0;1.0;5;1.0;;;1.0;1.7;0;1 +4;0;1.1;5;1.1;;;1.0;1.5;0;1 diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2050/A_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2050/A_matrix.csv index 0a85401..7c10709 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2050/A_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2050/A_matrix.csv @@ -1,13 +1,14 @@ index of activity;index of product;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1;0;;;;;;0;0 -0;2;0.5;5;;;;0.4;0.6;0;1 -1;0;0.8;5;;;;0.4;1.2;0;1 -1;1;1;0;;;;;;;0;0 -2;2;1;0;;;;;;;0;0 -2;3;0.1;5;;;;1.4;1.8;0;1 -2;5;0.6;5;;;;0.4;1.2;0;1 -3;3;1;0;;;;;;;0;0 -3;0;0.1;5;;;;0.04;0.2;0;1 -4;1;0.2;5;;;;0.1;0.3;0;1 -4;4;1;0;;;;;;;0;0 -5;5;1;0;;;;;;;0;0 \ No newline at end of file +0;0;1;0;1;;;;;0;0 +0;2;0.4;5;0.4;;;0.3;0.6;0;1 +1;0;1.2;5;1.2;;;1.0;1.4;0;1 +1;1;1;0;1;;;;;0;0 +2;2;1;0;1;;;;;0;0 +2;3;0.1;5;0.1;;;0.01;0.3;0;1 +2;5;0.3;5;0.3;;;0.2;0.7;0;1 +3;3;1;0;1;;;;;0;0 +3;0;0.1;5;0.1;;;0.04;0.2;0;1 +3;4;5;5;5;;;4;8;0;1 +4;1;0.2;5;0.2;;;0.1;0.3;0;1 +4;4;1;0;1;;;;;0;0 +5;5;1;0;1;;;;;0;0 \ No newline at end of file diff --git a/example/datapackage_sample/inventories/some model/Scenario B/2050/B_matrix.csv b/example/datapackage_sample/inventories/some model/Scenario B/2050/B_matrix.csv index 6466110..26ae2d3 100644 --- a/example/datapackage_sample/inventories/some model/Scenario B/2050/B_matrix.csv +++ b/example/datapackage_sample/inventories/some model/Scenario B/2050/B_matrix.csv @@ -1,6 +1,6 @@ index of activity;index of biosphere flow;value;uncertainty type;loc;scale;shape;minimum;maximum;negative;flip -0;0;1.5;5;;;;1.0;1.7;0;1 -1;0;0.2;5;;;;0.1;0.7;0;1 -2;0;2.3;5;;;;1.0;2.7;0;1 -3;0;1.2;5;;;;1.0;1.7;0;1 -4;0;0.5;5;;;;0.3;0.8;0;1 +0;0;0.5;5;0.5;;;0.5;1.7;0;1 +1;0;0.5;5;0.5;;;0.5;0.9;0;1 +2;0;1.4;5;1.4;;;1.0;2.7;0;1 +3;0;1.0;5;1.0;;;1.0;1.7;0;1 +4;0;0.9;5;0.9;;;0.8;1.3;0;1 diff --git a/example/example.html b/example/example.html index d7c3864..7b45010 100644 --- a/example/example.html +++ b/example/example.html @@ -61,471 +61,936 @@ }); diff --git a/example/example.ipynb b/example/example.ipynb index 51b5091..e3e399c 100644 --- a/example/example.ipynb +++ b/example/example.ipynb @@ -1,5 +1,18 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "4d530e2b-8524-48b2-8259-38c8ce984a5b", + "metadata": {}, + "source": [ + "### Example\n", + "Author: [Romain Sacchi](romain.sacchi@psi.ch)\n", + "\n", + "``pathways``allows calculating LCA impacts of a product or system along a time axis, combining time series of demand with scenario-based LCA databases.\n", + "\n", + "We start by instantiating the `Pathways` class, and give it a file path pointing to a datapackage.json of a datapackage or directly the datapackage itself (.zip file)." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -10,21 +23,255 @@ "name": "stdout", "output_type": "stream", "text": [ - "Invalid datapackage: Descriptor validation error: {'path': 'mapping/mapping.yaml', 'profile': 'data-resource', 'name': 'mapping', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/17\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n", - "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/17/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n", - "Invalid datapackage: Descriptor validation error: {'path': 'classifications/classifications.yaml', 'profile': 'data-resource', 'name': 'classifications', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/18\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n", - "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/18/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n" + "Invalid datapackage: Descriptor validation error: {'path': 'mapping/mapping.yaml', 'profile': 'data-resource', 'name': 'mapping', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/33\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n", + "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/33/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n", + "Invalid datapackage: Descriptor validation error: {'path': 'classifications/classifications.yaml', 'profile': 'data-resource', 'name': 'classifications', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/34\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n", + "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/34/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n" ] } ], "source": [ "from pathways import Pathways\n", - "p = Pathways(datapackage=\"datapackage_sample/datapackage.json\", debug=True)" + "import numpy as np\n", + "p = Pathways(\n", + " datapackage=\"datapackage_sample/datapackage.json\",\n", + " debug=True # when `debug` is True, a local pathways.log file is created and allows tracking the workflow\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6628f5ae-0faa-4f2f-b670-b65d0436ad5a", + "metadata": {}, + "source": [ + "At this point, you can access all the resources of the `datapackage.Package`, such as the scenario data, for example.\n", + "We see that the demand for `technology A`, represented by `product A` (see `.mapping`), is 1'000 kilograms (see `.scenarios.attrs`) each year." ] }, { "cell_type": "code", "execution_count": 2, + "id": "c59ebddb-561f-4b70-8b82-7c1d867bbe0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelpathwayvariablesregionyear
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" + ], + "text/plain": [ + " \n", + "model pathway variables region year \n", + "some model Scenario A technology A EU 2020 1000.0\n", + " 2030 1000.0\n", + " 2040 1000.0\n", + " 2050 1000.0\n", + " Scenario B technology A EU 2020 1000.0\n", + " 2030 1000.0\n", + " 2040 1000.0\n", + " 2050 1000.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.scenarios.to_dataframe(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e5260d0b-8042-44d3-be14-e78aa4a767b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'units': {'technology A': 'kilogram'}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.scenarios.attrs" + ] + }, + { + "cell_type": "markdown", + "id": "49a3eba4-ccce-46ce-ace6-119a5fd11a1c", + "metadata": {}, + "source": [ + "We can also see the mapping used to map the scenario variables to the LCA datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "adfb8a71-efc0-4f32-8e91-6c6495b03699", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'technology A': {'dataset': [{'name': 'activity A',\n", + " 'reference product': 'product A',\n", + " 'unit': 'kilogram'}],\n", + " 'scenario variable': 'technology A'}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.mapping" + ] + }, + { + "cell_type": "markdown", + "id": "e03f9770-a644-4dcf-9f41-5eda5d6c28b3", + "metadata": {}, + "source": [ + "We can also list the LCIA methods available." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b13df1c4-9e99-4f6d-a02a-5c908db8bfbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CML v4.8 2016 no LT - acidification no LT - acidification (incl. fate, average Europe total, A&B) no LT',\n", + " 'CML v4.8 2016 no LT - climate change no LT - global warming potential [GWP100) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: freshwater no LT - freshwater aquatic ecotoxicity [FAETP inf) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: marine no LT - marine aquatic ecotoxicity (MAETP inf) no LT',\n", + " 'CML v4.8 2016 no LT - ecotoxicity: terrestrial no LT - terrestrial ecotoxicity (TETP inf) no LT']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.lcia_methods[:5]" + ] + }, + { + "cell_type": "markdown", + "id": "6a29f446-72f4-460f-9f23-1879d4057936", + "metadata": {}, + "source": [ + "And most importantly, once the `datapackage.Package` is loaded, we can use the method `Pathways.calculate()` to calculate the LCA impacts.\n", + "\n", + "Arguments:\n", + "\n", + " * `methods`: list[str]. LCIA methods to use. To get a complete list of available LCIA methods, call `.lcia_methods`\n", + " * `scenarios`: list[str]. List of scenarios you want to calculate the impacts for.\n", + " * `variables`: list[str]. List of variables you want to calculate the impacts for (if the demand for them is non-null)\n", + " * `regions`: list[str]. Regions for which you want to calculate the impacts, provided the specified variables have a non-null demand in these regions.\n", + " * `years`: list[int]. Years for which you want to calculate the impacts.\n", + " * `multiprocessing`: bool. Multiprocessing may accelerate the process, as it processes each year in parallel.\n", + " * `demand_cutoff`: float. Between 0 and 1. Defines a ratio below which demand values are ignored. The default is 0.001.\n", + " * `use_distributions`: bool. Number of iterations to use for Monte Carlo analyses. The default is 0 (i.e., regular analysis)." + ] + }, + { + "cell_type": "markdown", + "id": "9703ede6-2a13-41f3-95a0-8d823ca3ac91", + "metadata": {}, + "source": [ + "In the example below, we do so using a stochastic approach (i.e., `use_distributions=500`), leveraging on the uncertainty distributions we defined for each exchange in the datapackage. `bw2calc` and the underlying library `stats_array` generates 500 pseudo-random exchange values and update the technosphere and biosphere matrices between each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "87aa6b87-b677-4acb-ad03-226591c4d7b0", "metadata": {}, "outputs": [ @@ -32,148 +279,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.5, nan, nan, 0.4 , 0.6 , False)\n", - " (5, 0.8, nan, nan, 0.7 , 0.9 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 0.6, nan, nan, 0.5 , 0.7 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 10. , nan, nan, 8. , 12. , False)\n", - " (5, 0.2, nan, nan, 0.15, 0.25, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 1.5, nan, nan, 1., 1.7, False) (5, 2. , nan, nan, 1., 7. , False)\n", - " (5, 2.3, nan, nan, 1., 2.7, False) (5, 1.2, nan, nan, 1., 1.7, False)\n", - " (5, 1.5, nan, nan, 1., 2. , False)]\n", "Calculating LCA results for some model...\n", "--- Calculating LCA results for Scenario A...\n", "------ Calculating LCA results for 2020...\n", - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.5, nan, nan, 0.4 , 0.6 , False)\n", - " (5, 0.8, nan, nan, 0.7 , 0.9 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 0.6, nan, nan, 0.5 , 0.7 , False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.08, 0.2 , False)\n", - " (5, 10. , nan, nan, 8. , 12. , False)\n", - " (5, 0.2, nan, nan, 0.15, 0.25, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 1.5, nan, nan, 1., 1.7, False) (5, 2. , nan, nan, 1., 7. , False)\n", - " (5, 2.3, nan, nan, 1., 2.7, False) (5, 1.2, nan, nan, 1., 1.7, False)\n", - " (5, 1.5, nan, nan, 1., 2. , False)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0% [#] 100% | ETA: 00:00:01\n", - "Total time elapsed: 00:00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Warning: No valid output stream.\n", "------ Calculating LCA results for 2030...\n", - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.5, nan, nan, 0.4 , 0.6, False)\n", - " (5, 0.9, nan, nan, 0.4 , 1.2, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.05, 0.5, False)\n", - " (5, 0.5, nan, nan, 0.4 , 1.2, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.04, 0.2, False)\n", - " (5, 8. , nan, nan, 4. , 12. , False)\n", - " (5, 0.2, nan, nan, 0.1 , 0.3, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 1.4, nan, nan, 1. , 1.7, False) (5, 1. , nan, nan, 0.5, 1.7, False)\n", - " (5, 2.3, nan, nan, 1. , 2.7, False) (5, 1.1, nan, nan, 1. , 1.7, False)\n", - " (5, 1.4, nan, nan, 1. , 1.5, False)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0% [#] 100% | ETA: 00:00:01\n", - "Total time elapsed: 00:00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Warning: No valid output stream.\n", + "------ Calculating LCA results for 2050...\n", + "Warning: No valid output stream.\n", "------ Calculating LCA results for 2040...\n", - "[(0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.4, nan, nan, 0.3 , 0.6, False)\n", - " (5, 1.1, nan, nan, 0.8 , 1.2, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.04, 0.3, False)\n", - " (5, 0.4, nan, nan, 0.3 , 1.2, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.04, 0.2, False)\n", - " (5, 7. , nan, nan, 4. , 10. , False)\n", - " (5, 0.2, nan, nan, 0.1 , 0.3, False)\n", - " (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 0.9, nan, nan, 0.5, 1.7, False) (5, 1. , nan, nan, 0.5, 1.2, False)\n", - " (5, 2. , nan, nan, 1. , 2.7, False) (5, 1.1, nan, nan, 1. , 1.7, False)\n", - " (5, 1.3, nan, nan, 1. , 1.5, False)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0% [#] 100% | ETA: 00:00:01\n", - "Total time elapsed: 00:00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Warning: No valid output stream.\n", + "--- Calculating LCA results for Scenario B...\n", + "------ Calculating LCA results for 2020...\n", + "Warning: No valid output stream.\n", + "------ Calculating LCA results for 2030...\n", + "Warning: No valid output stream.\n", "------ Calculating LCA results for 2050...\n", - "[(0, 1. , nan, nan, nan, nan, False) (5, 0.4, nan, nan, 0.3 , 0.6, False)\n", - " (5, 1.2, nan, nan, 1. , 1.4, False) (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False) (5, 0.1, nan, nan, 0.01, 0.3, False)\n", - " (5, 0.3, nan, nan, 0.2 , 0.7, False) (0, 1. , nan, nan, nan, nan, False)\n", - " (5, 0.1, nan, nan, 0.04, 0.2, False) (5, 5. , nan, nan, 4. , 8. , False)\n", - " (5, 0.2, nan, nan, 0.1 , 0.3, False) (0, 1. , nan, nan, nan, nan, False)\n", - " (0, 1. , nan, nan, nan, nan, False)]\n", - "[(5, 0.6, nan, nan, 0.5, 1.7, False) (5, 0.8, nan, nan, 0.5, 0.9, False)\n", - " (5, 1.6, nan, nan, 1. , 2.7, False) (5, 1.1, nan, nan, 1. , 1.7, False)\n", - " (5, 1.1, nan, nan, 0.8, 1.3, False)]\n" + "Warning: No valid output stream.\n", + "------ Calculating LCA results for 2040...\n", + "Warning: No valid output stream.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "0% [#] 100% | ETA: 00:00:01\n", - "Total time elapsed: 00:00:00\n", - "0% [####] 100% | ETA: 00:00:00\n", + "0% [########] 100% | ETA: 00:00:00\n", "Total time elapsed: 00:00:00\n" ] } ], "source": [ - "import numpy as np\n", "p.calculate(\n", " methods=['EF v3.1 EN15804 - climate change - global warming potential (GWP100)',],\n", " regions=[\"EU\",],\n", " scenarios=[\n", " \"Scenario A\",\n", + " \"Scenario B\",\n", " ],\n", " variables=[\n", " \"technology A\",\n", @@ -185,414 +327,37 @@ " 2050\n", " ],\n", " use_distributions=500,\n", - " multiprocessing=False\n", + " multiprocessing=True\n", ")" ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "ac95479d-585e-4544-bcc8-95cccbca694f", + "cell_type": "markdown", + "id": "a5812056-53a0-4314-a45c-84c544c7ee46", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray ()> Size: 8B\n",
-       "array(44658.2766215)
" - ], - "text/plain": [ - " Size: 8B\n", - "array(44658.2766215)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "p.lca_results.sum()" + "We can now access the attribute `.lca_results`, which is an `xarray.DataArray` where the results are stored. We can format it a little to present it in an `pandas.DataFrame`, for example." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "ca80ad98-860e-4645-a533-8c1e7eba477c", "metadata": {}, "outputs": [], "source": [ + "# interpolate in-between years\n", "arr = p.lca_results.interp(\n", - " year=range(p.lca_results.coords[\"year\"].values.min(), p.lca_results.coords[\"year\"].values.max() + 1)\n", + " year=range(\n", + " p.lca_results.coords[\"year\"].values.min(),\n", + " p.lca_results.coords[\"year\"].values.max() + 1\n", + " )\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "268f8502-e499-4ab5-8fe7-4882c7a181f3", "metadata": {}, "outputs": [], @@ -602,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "94ed4c32-bed3-46bf-84eb-580b4f336dcd", "metadata": {}, "outputs": [ @@ -610,7 +375,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "465\n" + "930\n" ] } ], @@ -623,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "00e0a5b9-1285-4272-898b-5c72df1b44cb", "metadata": {}, "outputs": [ @@ -642,24 +407,124 @@ " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# pivottablejs is very convenient way to visualize pivot tables\n", "from pivottablejs import pivot_ui\n", "from IPython.display import HTML\n", "pivot_ui(df, outfile_path='example.html')" ] }, + { + "cell_type": "markdown", + "id": "fa8931f2-f020-4ff6-89f9-6e9172bf00ea", + "metadata": {}, + "source": [ + "Impacts with process contributions\n", + "\n", + "![impacts with process contribution](fig2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "84941789-7247-48b1-abc1-92aa65aab730", + "metadata": {}, + "source": [ + "Impacts with breakdown by geographical location of impacts\n", + "\n", + "![impacts with impacts origins](fig3.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ba25fc8a-871c-4c3c-91af-e067c7ba628b", + "metadata": {}, + "source": [ + "Sum of impacts for the 5th, 50th and 95th quantiles\n", + "\n", + "![impacts with uncertainty](fig1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "643665bb-065d-4c6b-a3cf-bca3e791769e", + "metadata": {}, + "source": [ + "Comparison between two scenarios\n", + "\n", + "![scenarios comparison](fig4.png)" + ] + }, + { + "cell_type": "markdown", + "id": "b5c8c7e6-097f-49ba-9c9a-ec7a4d0acd21", + "metadata": {}, + "source": [ + "### Providing actual datapackages\n", + "While it is possible to manually build data packages such as the one used in this example, it is not very convenient when dealing with real LCA databases, which have more than half a million exchanges.\n", + "`premise` can output such datapackage as a zip file that can be directly given to `pathways.Pathways`.\n", + "\n", + "The following will produce a datapackage for all years contained in the IMAGE SSP2-RCP19 scenario." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "681f5561-0828-468f-a74d-3057c9f6eb97", + "metadata": {}, + "outputs": [], + "source": [ + "from premise import *\n", + "import bw2data\n", + "from datapackage import Package\n", + "bw2data.projects.set_current(\"ei39\")\n", + "ndb = PathwaysDataPackage(\n", + " scenarios=[\n", + " {\"model\": \"image\", \"pathway\": \"SSP2-RCP19\"},\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=\",\n", + ")\n", + "\n", + "ndb.create_datapackage(\n", + " name=\"image-SSP2\",\n", + " contributors=[\n", + " {\"name\": \"Romain\",\n", + " \"email\": \"r_s at me.com\",}\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "252f1d91-9a50-4baa-b98c-aa6b6558c707", + "metadata": {}, + "source": [ + "This allows for more complex analyses, such as, for example, the projected demand in cobalt for the global electricity system as projected in that same IMAGE scenario:" + ] + }, + { + "cell_type": "markdown", + "id": "cf142507-8453-4a77-855d-11266de5a9e3", + "metadata": {}, + "source": [ + "Life cycle-based annual demand for cobalt, global electricity supply\n", + "\n", + "![cobalt projection](fig5.png)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "45c4b8e7-5944-4a10-9da0-527511b594f3", + "id": "b926a237-22e3-487c-9203-d83b6fafa41c", "metadata": {}, "outputs": [], "source": [] 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