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EODC
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romainsacchi committed Mar 11, 2024
1 parent 5a29192 commit 32a85ff
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977 changes: 977 additions & 0 deletions dev/generate datapackages.ipynb

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2 changes: 1 addition & 1 deletion dev/metals.ipynb
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
"version": "3.11.8"
}
},
"nbformat": 4,
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9,799 changes: 68 additions & 9,731 deletions dev/pivot.ipynb

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29 changes: 5 additions & 24 deletions dev/timing.py
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from pathways import Pathways

p = Pathways(datapackage="/Users/romain/GitHub/premise/dev/image-SSP2/datapackage.json")
p = Pathways(datapackage="./image-SSP2-RCP19/datapackage.json")

for scenario in [
# "SSP2-Base",
"SSP2-RCP19"
]:
p.calculate(
methods=[
"EF v3.1 - acidification - accumulated exceedance (AE)",
"EF v3.1 - climate change - global warming potential (GWP100)",
"EF v3.1 - ecotoxicity: freshwater - comparative toxic unit for ecosystems (CTUe)",
"EF v3.1 - energy resources: non-renewable - abiotic depletion potential (ADP): fossil fuels",
"EF v3.1 - eutrophication: freshwater - fraction of nutrients reaching freshwater end compartment (P)",
"EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)",
"EF v3.1 - material resources: metals/minerals - abiotic depletion potential (ADP): elements (ultimate reserves)",
"EF v3.1 - particulate matter formation - impact on human health",
"EF v3.1 - water use - user deprivation potential (deprivation-weighted water consumption)",
"RELICS - metals extraction - Aluminium",
"RELICS - metals extraction - Cobalt",
"RELICS - metals extraction - Copper",
"RELICS - metals extraction - Graphite",
"RELICS - metals extraction - Lithium",
"RELICS - metals extraction - Molybdenum",
"RELICS - metals extraction - Neodymium",
"RELICS - metals extraction - Nickel",
"RELICS - metals extraction - Platinum",
"RELICS - metals extraction - Vanadium",
"RELICS - metals extraction - Zinc",
"RELICS - metals extraction - Molybdenum"
],
regions=[r for r in p.scenarios.coords["region"].values if r != "World"],
regions=["WEU",],
scenarios=[scenario],
years=[2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],
years=[2020, ],
variables=[
v
for v in p.scenarios.coords["variables"].values
if any(i in v for i in ["Industry", "Transport", "Heating"])
],
demand_cutoff=0.01,
)
arr = p.display_results(cutoff=0.0001)

arr = p.display_results()
arr.to_netcdf(f"results_image_{scenario}.nc")
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