diff --git a/README.md b/README.md
index fd4dd57..ef30c4e 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,8 @@ Life Cycle Assessment (LCA).
``pathways`` is a work in progress. It reads in
scenarios and corresponding premise-generated LCA databases,
-and calculates the environmental impacts over a defined period.
+solve double-accounting issues and calculates the environmental
+impacts over a defined period.
## Installation
diff --git a/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb b/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb
index ef30b61..810cded 100644
--- a/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb
+++ b/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"id": "b3b37d5c-8f65-4fb3-8f1b-7bbc05327081",
"metadata": {},
"outputs": [
@@ -20,18 +20,18 @@
"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/45\" 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/45/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/89\" 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/89/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n"
]
}
],
"source": [
- "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\")"
+ "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2/datapackage.json\")"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"id": "2f3bf87a-5922-41b6-92e5-0a5e55500e8e",
"metadata": {
"tags": []
@@ -42,204 +42,1779 @@
"output_type": "stream",
"text": [
"Calculating LCA results for image...\n",
- "--- Calculating LCA results for SSP2-RCP19...\n",
- "------ Calculating LCA results for 2005...\n",
- "------ Calculating LCA results for 2010...\n",
- "------ Calculating LCA results for 2015...\n",
- "LCA matrices not found for the given model, scenario, and year.\n",
- "------ Calculating LCA results for 2020...\n",
- "------ Calculating LCA results for 2025...\n",
- "LCA matrices not found for the given model, scenario, and year.\n",
- "------ Calculating LCA results for 2030...\n",
- "------ Calculating LCA results for 2035...\n",
- "LCA matrices not found for the given model, scenario, and year.\n",
- "------ Calculating LCA results for 2040...\n",
- "------ Calculating LCA results for 2045...\n",
- "LCA matrices not found for the given model, scenario, and year.\n",
- "------ Calculating LCA results for 2050...\n",
- "------ Calculating LCA results for 2060...\n",
- "------ Calculating LCA results for 2070...\n",
- "------ Calculating LCA results for 2080...\n",
- "------ Calculating LCA results for 2090...\n",
- "------ Calculating LCA results for 2100...\n"
+ "--- Calculating LCA results for SSP2-Base...\n",
+ "------ Calculating LCA results for 2005...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "0% [##########################] 100% | ETA: 00:00:00\n",
+ "Total time elapsed: 00:17:59\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "------ Calculating LCA results for 2010...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "0% [###################### ] 100% | ETA: 00:02:45\n",
+ "KeyboardInterrupt\n",
+ "\n"
]
}
],
"source": [
- "p.calculate(\n",
- " methods=[\n",
- " 'IPCC 2021 - climate change - GWP 100a, incl. H',\n",
- " 'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - renewable energy resources',\n",
- " 'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - non-renewable energy resources',\n",
- " 'EN15804 - inventory indicators ISO21930 - use of net fresh water',\n",
- " 'EF v3.1 EN15804 - particulate matter formation - impact on human health',\n",
- " ],\n",
- " regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
- " #regions=[\"World\",],\n",
- " scenarios=[\n",
- " #\"SSP2-Base\",\n",
- " \"SSP2-RCP19\",\n",
- " ],\n",
- " variables=[\n",
- " v for v in p.scenarios.coords[\"variables\"].values\n",
- " if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
- " ],\n",
- " #variables=[\"Transport_Freight_(train)_Liquid_fossil\"],\n",
- " #variables = [\"Electricity\", ],\n",
- " characterization=True\n",
- ")\n",
- "arr = p.display_results(cutoff=0.01)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "55c1ae5b-f165-4b45-84b4-f46c8b859fdc",
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.to_netcdf(\"results_image_SSP2-RCP19.nc\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "0078f7fc-a267-4eab-b28d-d943133cd2c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
- " \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
- "])"
+ "import numpy as np\n",
+ "p.calculate(\n",
+ " methods=[\n",
+ " 'EF v3.1 - acidification - accumulated exceedance (AE)',\n",
+ " 'EF v3.1 - climate change - global warming potential (GWP100)',\n",
+ " 'EF v3.1 - ecotoxicity: freshwater - comparative toxic unit for ecosystems (CTUe)',\n",
+ " 'EF v3.1 - energy resources: non-renewable - abiotic depletion potential (ADP): fossil fuels',\n",
+ " 'EF v3.1 - eutrophication: freshwater - fraction of nutrients reaching freshwater end compartment (P)',\n",
+ " #'EF v3.1 - eutrophication: marine - fraction of nutrients reaching marine end compartment (N)',\n",
+ " #'EF v3.1 - eutrophication: terrestrial - accumulated exceedance (AE)',\n",
+ " #'EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)',\n",
+ " #'EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)',\n",
+ " #'EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235',\n",
+ " #'EF v3.1 - land use - soil quality index',\n",
+ " 'EF v3.1 - material resources: metals/minerals - abiotic depletion potential (ADP): elements (ultimate reserves)',\n",
+ " 'EF v3.1 - ozone depletion - ozone depletion potential (ODP)',\n",
+ " 'EF v3.1 - particulate matter formation - impact on human health',\n",
+ " #'EF v3.1 - photochemical oxidant formation: human health - tropospheric ozone concentration increase',\n",
+ " 'EF v3.1 - water use - user deprivation potential (deprivation-weighted water consumption)',\n",
+ " 'RELICS - metals extraction - Aluminium',\n",
+ " 'RELICS - metals extraction - Cobalt',\n",
+ " 'RELICS - metals extraction - Copper',\n",
+ " 'RELICS - metals extraction - Graphite',\n",
+ " 'RELICS - metals extraction - Lithium',\n",
+ " 'RELICS - metals extraction - Molybdenum',\n",
+ " 'RELICS - metals extraction - Neodymium',\n",
+ " 'RELICS - metals extraction - Nickel',\n",
+ " 'RELICS - metals extraction - Platinum',\n",
+ " 'RELICS - metals extraction - Vanadium',\n",
+ " 'RELICS - metals extraction - Zinc',\n",
+ " ],\n",
+ " regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
+ " scenarios=[\n",
+ " \"SSP2-Base\",\n",
+ " #\"SSP2-RCP19\",\n",
+ " ],\n",
+ " variables=[\n",
+ " v for v in p.scenarios.coords[\"variables\"].values\n",
+ " if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
+ " ],\n",
+ " years=[2005, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
+ " characterization=True,\n",
+ " data_type=np.float32,\n",
+ " multiprocessing=False,\n",
+ " demand_cutoff=0.1,\n",
+ ")\n",
+ "arr = p.display_results()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83d7622b-18db-429b-9ec5-453244db74bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p.scenarios"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a63727ad-455f-4d7d-80fb-086678cbf901",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "
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+ " impact_category: 0, variable: 0, act_category: 0)>\n",
+ "array([], shape=(0, 0, 0, 0, 0, 0, 0), dtype=float64)\n",
+ "Coordinates:\n",
+ " * model (model) object \n",
+ " * scenario (scenario) object \n",
+ " * year (year) int64 \n",
+ " * region (region) object \n",
+ " * impact_category (impact_category) object \n",
+ " * variable (variable) float64 \n",
+ " * act_category (act_category) object
PandasIndex
PandasIndex(Index([], dtype='object', name='model'))
PandasIndex
PandasIndex(Index([], dtype='object', name='scenario'))
PandasIndex
PandasIndex(Index([], dtype='int64', name='year'))
PandasIndex
PandasIndex(Index([], dtype='object', name='region'))
PandasIndex
PandasIndex(Index([], dtype='object', name='impact_category'))
PandasIndex
PandasIndex(Index([], dtype='float64', name='variable'))
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"
+ ],
+ "text/plain": [
+ "\n",
+ "array([], shape=(0, 0, 0, 0, 0, 0, 0), dtype=float64)\n",
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+ " * region (region) object \n",
+ " * impact_category (impact_category) object \n",
+ " * variable (variable) float64 \n",
+ " * act_category (act_category) object "
+ ]
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+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "55c1ae5b-f165-4b45-84b4-f46c8b859fdc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.to_netcdf(\"results_image_SSP2.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0078f7fc-a267-4eab-b28d-d943133cd2c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'x'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\u001b[38;5;241m.\u001b[39mmelt(id_vars\u001b[38;5;241m=\u001b[39m[\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscenario\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregion\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimpact_category\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3\u001b[0m ])\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:3895\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[1;32m 3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 3895\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_multilevel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3896\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m 3897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:3953\u001b[0m, in \u001b[0;36mDataFrame._getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3951\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_multilevel\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m 3952\u001b[0m \u001b[38;5;66;03m# self.columns is a MultiIndex\u001b[39;00m\n\u001b[0;32m-> 3953\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3954\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, (\u001b[38;5;28mslice\u001b[39m, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m 3955\u001b[0m new_columns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns[loc]\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2925\u001b[0m, in \u001b[0;36mMultiIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2922\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m mask\n\u001b[1;32m 2924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 2925\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_level_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2926\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_to_slice(loc)\n\u001b[1;32m 2928\u001b[0m keylen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(key)\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/indexes/multi.py:3303\u001b[0m, in \u001b[0;36mMultiIndex._get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m 3299\u001b[0m end \u001b[38;5;241m=\u001b[39m algos\u001b[38;5;241m.\u001b[39msearchsorted(level_codes, idx, side\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mright\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;241m==\u001b[39m end:\n\u001b[1;32m 3302\u001b[0m \u001b[38;5;66;03m# The label is present in self.levels[level] but unused:\u001b[39;00m\n\u001b[0;32m-> 3303\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m 3304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mslice\u001b[39m(start, end)\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'x'"
+ ]
+ }
+ ],
+ "source": [
+ "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
+ " \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "42f0f2c3-2faf-42a6-adcf-b13d4942d2cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMain sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSub sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEnergy carrier\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvariable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:136\u001b[0m, in \u001b[0;36mforbid_nonstring_types.._forbid_nonstring_types..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 132\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use .str.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with values of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minferred dtype \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inferred_dtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 134\u001b[0m )\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:916\u001b[0m, in \u001b[0;36mStringMethods.split\u001b[0;34m(self, pat, n, expand, regex)\u001b[0m\n\u001b[1;32m 914\u001b[0m regex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39marray\u001b[38;5;241m.\u001b[39m_str_split(pat, n, expand, regex)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturns_string\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:400\u001b[0m, in \u001b[0;36mStringMethods._wrap_result\u001b[0;34m(self, result, name, expand, fill_value, returns_string, returns_bool)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expand:\n\u001b[1;32m 399\u001b[0m cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor_expanddim\n\u001b[0;32m--> 400\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mcons\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;66;03m# Must be a Series\u001b[39;00m\n\u001b[1;32m 403\u001b[0m cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:809\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 808\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[0;32m--> 809\u001b[0m arrays, columns, index \u001b[38;5;241m=\u001b[39m \u001b[43mnested_data_to_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# error: Argument 3 to \"nested_data_to_arrays\" has incompatible\u001b[39;49;00m\n\u001b[1;32m 811\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type \"Optional[Collection[Any]]\"; expected \"Optional[Index]\"\u001b[39;49;00m\n\u001b[1;32m 812\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 813\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 814\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 815\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 816\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 817\u001b[0m mgr \u001b[38;5;241m=\u001b[39m arrays_to_mgr(\n\u001b[1;32m 818\u001b[0m arrays,\n\u001b[1;32m 819\u001b[0m columns,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 822\u001b[0m typ\u001b[38;5;241m=\u001b[39mmanager,\n\u001b[1;32m 823\u001b[0m )\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:520\u001b[0m, in \u001b[0;36mnested_data_to_arrays\u001b[0;34m(data, columns, index, dtype)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named_tuple(data[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;129;01mand\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 518\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m_fields)\n\u001b[0;32m--> 520\u001b[0m arrays, columns \u001b[38;5;241m=\u001b[39m \u001b[43mto_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:835\u001b[0m, in \u001b[0;36mto_arrays\u001b[0;34m(data, columns, dtype)\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arrays, columns\n\u001b[1;32m 834\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 835\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_list_to_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], abc\u001b[38;5;241m.\u001b[39mMapping):\n\u001b[1;32m 837\u001b[0m arr, columns \u001b[38;5;241m=\u001b[39m _list_of_dict_to_arrays(data, columns)\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:856\u001b[0m, in \u001b[0;36m_list_to_arrays\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 853\u001b[0m content \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mto_object_array_tuples(data)\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# list of lists\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m content \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_object_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m content\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "df[['Main sector', 'Sub sector', 'Energy carrier']] = df['variable'].str.split('_', expand=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "67b9d6b0-ed89-44b3-9bf1-b11aa20e1b84",
+ "metadata": {},
+ "outputs": [
+ {
+ "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/89\" 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/89/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pathways import Pathways\n",
+ "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2/datapackage.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "db6729e1-78a9-467f-a8cb-7fa272bb2734",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculating LCA results for image...\n",
+ "--- Calculating LCA results for SSP2-Base...\n",
+ "------ Calculating LCA results for 2020...\n",
+ "------ Calculating LCA results for 2030...\n",
+ "------ Calculating LCA results for 2040...\n",
+ "------ Calculating LCA results for 2050...\n",
+ "------ Calculating LCA results for 2060...\n",
+ "------ Calculating LCA results for 2070...\n",
+ "------ Calculating LCA results for 2080...\n",
+ "------ Calculating LCA results for 2090...\n",
+ "------ Calculating LCA results for 2100...\n",
+ "--- Calculating LCA results for SSP2-RCP19...\n",
+ "------ Calculating LCA results for 2020...\n",
+ "------ Calculating LCA results for 2030...\n",
+ "------ Calculating LCA results for 2040...\n",
+ "------ Calculating LCA results for 2050...\n",
+ "------ Calculating LCA results for 2060...\n",
+ "------ Calculating LCA results for 2070...\n",
+ "------ Calculating LCA results for 2080...\n",
+ "------ Calculating LCA results for 2090...\n",
+ "------ Calculating LCA results for 2100...\n"
+ ]
+ }
+ ],
+ "source": [
+ "metals = [\n",
+ " #\"Aluminium\",\n",
+ " #\"Cadmium\",\n",
+ " \"Cobalt\",\n",
+ " \"Graphite\",\n",
+ " #\"Iridium\",\n",
+ " #\"Iron\",\n",
+ " #\"Lanthanum\",\n",
+ " #\"Lead\",\n",
+ " \"Lithium\",\n",
+ " #\"Magnesium\",\n",
+ " #\"Manganese\",\n",
+ " \"Molybdenum\",\n",
+ " \"Neodymium\",\n",
+ " \"Nickel\",\n",
+ " #\"Palladium\",\n",
+ " \"Platinum\",\n",
+ " #\"Tin\",\n",
+ " #\"Silver\",\n",
+ " \"Titanium\",\n",
+ " #\"Uranium\",\n",
+ " \"Vanadium\",\n",
+ " \"Zinc\",\n",
+ "]\n",
+ "\n",
+ "p.calculate(\n",
+ " regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
+ " #regions=[\"World\",],\n",
+ " scenarios=[\n",
+ " \"SSP2-Base\",\n",
+ " \"SSP2-RCP19\",\n",
+ " ],\n",
+ " #regions=[\"WEU\",],\n",
+ " years=[2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
+ " variables=[\n",
+ " v for v in p.scenarios.coords[\"variables\"].values\n",
+ " if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
+ " ],\n",
+ " characterization=False,\n",
+ " flows=[(metal, \"natural resource\", \"in ground\", \"kilogram\") for metal in metals]\n",
+ " #flows=[(\"Carbon dioxide, fossil\", \"air\", \"unspecified\", \"kilogram\"),]\n",
+ ")\n",
+ "arr = p.display_results(cutoff=0.01)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ee0e2805-adf5-43c4-92af-8e7c28f82bf5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "\n",
+ "arr = xr.open_dataarray(\"results_image_SSP2_metals2.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4b1f9ec1-4bba-4d79-90d0-2273b4ff5697",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.to_netcdf(\"results_image_SSP2_metals2.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a14f36a5-eab7-4510-a01b-a4bd048fe777",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " model | \n",
+ " scenario | \n",
+ " region | \n",
+ " impact_category | \n",
+ " variable | \n",
+ " year | \n",
+ " act_category | \n",
+ " value | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " image | \n",
+ " SSP2-Base | \n",
+ " BRA | \n",
+ " Cobalt - natural resource - in ground - kilogram | \n",
+ " Heating_Commercial (space heating)_Electricity | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " image | \n",
+ " SSP2-Base | \n",
+ " BRA | \n",
+ " Cobalt - natural resource - in ground - kilogram | \n",
+ " Heating_Commercial (space heating)_Liquid fossil | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " image | \n",
+ " SSP2-Base | \n",
+ " BRA | \n",
+ " Cobalt - natural resource - in ground - kilogram | \n",
+ " Heating_Commercial (space heating)_Natural gas | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " image | \n",
+ " SSP2-Base | \n",
+ " BRA | \n",
+ " Cobalt - natural resource - in ground - kilogram | \n",
+ " Heating_Commercial (space heating)_Solid biomass | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " image | \n",
+ " SSP2-Base | \n",
+ " BRA | \n",
+ " Cobalt - natural resource - in ground - kilogram | \n",
+ " Heating_Commercial (space heating)_Solid coal | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " model scenario region impact_category \\\n",
+ "0 image SSP2-Base BRA Cobalt - natural resource - in ground - kilogram \n",
+ "1 image SSP2-Base BRA Cobalt - natural resource - in ground - kilogram \n",
+ "2 image SSP2-Base BRA Cobalt - natural resource - in ground - kilogram \n",
+ "3 image SSP2-Base BRA Cobalt - natural resource - in ground - kilogram \n",
+ "4 image SSP2-Base BRA Cobalt - natural resource - in ground - kilogram \n",
+ "\n",
+ " variable year \\\n",
+ "0 Heating_Commercial (space heating)_Electricity 2020 \n",
+ "1 Heating_Commercial (space heating)_Liquid fossil 2020 \n",
+ "2 Heating_Commercial (space heating)_Natural gas 2020 \n",
+ "3 Heating_Commercial (space heating)_Solid biomass 2020 \n",
+ "4 Heating_Commercial (space heating)_Solid coal 2020 \n",
+ "\n",
+ " act_category value \n",
+ "0 Manufacture of basic chemicals NaN \n",
+ "1 Manufacture of basic chemicals NaN \n",
+ "2 Manufacture of basic chemicals NaN \n",
+ "3 Manufacture of basic chemicals NaN \n",
+ "4 Manufacture of basic chemicals NaN "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
+ " \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
+ "])\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9f451db5-7bcb-4b02-b8f6-76c3e260a3ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1906632"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "years = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100]\n",
+ "df_short = df.loc[df[\"year\"].isin(years)]\n",
+ "len(df_short)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d91c5cba-2b06-4237-96d5-c09a8752df8a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n",
+ "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n",
+ "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8a317d76-b749-462d-946a-af43d4dd3728",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/3107786552.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_short[\"impact_category\"] = df_short[\"impact_category\"].str.replace(\" - natural resource - in ground - kilogram\", \"\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_short[\"impact_category\"] = df_short[\"impact_category\"].str.replace(\" - natural resource - in ground - kilogram\", \"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "e02b9327-29db-4280-9401-121fb6eb8f54",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1133324581.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_short[\"value\"] /= 1000\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_short[\"value\"] /= 1000"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a078dea5-fd2e-48aa-83cf-5dbc42ae802b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Cobalt', 'Lithium', 'Nickel', 'Platinum', 'Titanium', 'Vanadium',\n",
+ " 'Zinc'], dtype=object)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_short[\"impact_category\"].unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e24ef9b7-094b-4b63-a725-ac570045e1fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "371076"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(df_short.loc[~df_short[\"value\"].isnull()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0b4be8e2-bf2b-4e75-978e-9933acedeada",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pivottablejs import pivot_ui\n",
+ "from IPython.display import HTML"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "8b0b8d60-9048-45be-be52-3ea3b32305d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pivot_ui(df_short.loc[~df_short[\"value\"].isnull()], outfile_path='pivottable_all_metals.html')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "75baf250-be1a-4123-995b-2189d7198a47",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "DataPackageException",
+ "evalue": "Unable to load JSON at \"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\"",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mMissingSchema\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/helpers.py:55\u001b[0m, in \u001b[0;36mretrieve_descriptor\u001b[0;34m(descriptor)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m req \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_descriptor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m req\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/sessions.py:575\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 563\u001b[0m req \u001b[38;5;241m=\u001b[39m Request(\n\u001b[1;32m 564\u001b[0m method\u001b[38;5;241m=\u001b[39mmethod\u001b[38;5;241m.\u001b[39mupper(),\n\u001b[1;32m 565\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 573\u001b[0m hooks\u001b[38;5;241m=\u001b[39mhooks,\n\u001b[1;32m 574\u001b[0m )\n\u001b[0;32m--> 575\u001b[0m prep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 577\u001b[0m proxies \u001b[38;5;241m=\u001b[39m proxies \u001b[38;5;129;01mor\u001b[39;00m {}\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/sessions.py:486\u001b[0m, in \u001b[0;36mSession.prepare_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 485\u001b[0m p \u001b[38;5;241m=\u001b[39m PreparedRequest()\n\u001b[0;32m--> 486\u001b[0m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 487\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 488\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCaseInsensitiveDict\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerged_cookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 498\u001b[0m \u001b[43m \u001b[49m\u001b[43mhooks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_hooks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhooks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhooks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m p\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/models.py:368\u001b[0m, in \u001b[0;36mPreparedRequest.prepare\u001b[0;34m(self, method, url, headers, files, data, params, auth, cookies, hooks, json)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_method(method)\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_headers(headers)\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/models.py:439\u001b[0m, in \u001b[0;36mPreparedRequest.prepare_url\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m scheme:\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MissingSchema(\n\u001b[1;32m 440\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid URL \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: No scheme supplied. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPerhaps you meant https://\u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 442\u001b[0m )\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m host:\n",
+ "\u001b[0;31mMissingSchema\u001b[0m: Invalid URL '/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json': No scheme supplied. Perhaps you meant https:///Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json?",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mDataPackageException\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43mPathways\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatapackage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:327\u001b[0m, in \u001b[0;36mPathways.__init__\u001b[0;34m(self, datapackage)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, datapackage):\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatapackage \u001b[38;5;241m=\u001b[39m datapackage\n\u001b[0;32m--> 327\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m validate_datapackage(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmapping \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_mapping()\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmapping\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_final_energy_mapping())\n",
+ "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:351\u001b[0m, in \u001b[0;36mPathways.read_datapackage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_datapackage\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataPackage:\n\u001b[1;32m 344\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Read the datapackage.json file.\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \n\u001b[1;32m 346\u001b[0m \u001b[38;5;124;03m Returns\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;124;03m The datapackage as a dictionary.\u001b[39;00m\n\u001b[1;32m 350\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataPackage\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatapackage\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/package.py:102\u001b[0m, in \u001b[0;36mPackage.__init__\u001b[0;34m(self, descriptor, base_path, strict, unsafe, storage, schema, default_base_path, **options)\u001b[0m\n\u001b[1;32m 99\u001b[0m descriptor[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresources\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m: bucket})\n\u001b[1;32m 101\u001b[0m \u001b[38;5;66;03m# Process descriptor\u001b[39;00m\n\u001b[0;32m--> 102\u001b[0m descriptor \u001b[38;5;241m=\u001b[39m \u001b[43mhelpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve_descriptor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdescriptor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 103\u001b[0m descriptor \u001b[38;5;241m=\u001b[39m helpers\u001b[38;5;241m.\u001b[39mdereference_package_descriptor(descriptor, base_path)\n\u001b[1;32m 105\u001b[0m \u001b[38;5;66;03m# Handle deprecated resource.path/url\u001b[39;00m\n",
+ "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/helpers.py:62\u001b[0m, in \u001b[0;36mretrieve_descriptor\u001b[0;34m(descriptor)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mIOError\u001b[39;00m, requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException) \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m 61\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnable to load JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m descriptor\n\u001b[0;32m---> 62\u001b[0m \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexceptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataPackageException\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Python2 doesn't have json.JSONDecodeError (use ValueErorr)\u001b[39;00m\n\u001b[1;32m 65\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnable to parse JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (descriptor, error)\n",
+ "File \u001b[0;32m:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
+ "\u001b[0;31mDataPackageException\u001b[0m: Unable to load JSON at \"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\""
+ ]
+ }
+ ],
+ "source": [
+ "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dcd996a6-3ba8-480a-9ea7-24aced9c2fc2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p.calculate(\n",
+ " methods=[\n",
+ " 'IPCC 2021 - climate change - GWP 100a, incl. H',\n",
+ " 'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - renewable energy resources',\n",
+ " 'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - non-renewable energy resources',\n",
+ " 'EN15804 - inventory indicators ISO21930 - use of net fresh water',\n",
+ " 'EF v3.1 EN15804 - particulate matter formation - impact on human health',\n",
+ " ],\n",
+ " regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
+ " #regions=[\"World\",],\n",
+ " scenarios=[\n",
+ " #\"SSP2-Base\",\n",
+ " \"SSP2-RCP19\",\n",
+ " ],\n",
+ " years=[2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
+ " variables=[\n",
+ " v for v in p.scenarios.coords[\"variables\"].values\n",
+ " if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
+ " ],\n",
+ " #variables=[\"Transport_Freight_(train)_Liquid_fossil\"],\n",
+ " #variables = [\"Electricity\", ],\n",
+ " characterization=True\n",
+ ")\n",
+ "arr = p.display_results(cutoff=0.01)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2685e409-e694-436c-81f4-55eb86889685",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.to_netcdf(\"results_image_SSP2.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fd21f031-f036-47e9-b378-c4593d356334",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Coordinates:\n",
+ " * model (model) object 'image'\n",
+ " * scenario (scenario) object 'SSP2-RCP19'\n",
+ " * region (region) object 'BRA' 'CAN' 'CEU' ... 'USA' 'WAF' 'WEU'\n",
+ " * impact_category (impact_category) object 'Cobalt - natural resource - in...\n",
+ " * variable (variable) object 'Heating_Commercial (space heating)_El...\n",
+ " * act_category (act_category) object 'Manufacture of basic chemicals' ....\n",
+ " * year (year) int64 2020 2021 2022 2023 ... 2097 2098 2099 2100"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr.coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "3e6b3af7-0d61-4719-9401-0ee62bbbc872",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.values[arr.values < 0] = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "43312db1-d957-402d-a936-99846e1f2768",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(\"results_metals_rcp19.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "975fb2b2-3111-4ccd-ae65-0481fac975b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2918916"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "9ca4a0e7-8ea6-48da-bd5d-054dec8ebaee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " model | \n",
+ " scenario | \n",
+ " region | \n",
+ " impact_category | \n",
+ " variable | \n",
+ " year | \n",
+ " act_category | \n",
+ " value | \n",
+ " Main sector | \n",
+ " Sub sector | \n",
+ " Energy carrier | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " image | \n",
+ " SSP2-RCP19 | \n",
+ " BRA | \n",
+ " Cobalt | \n",
+ " Heating_Commercial (space heating)_Electricity | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ " Heating | \n",
+ " Commercial (space heating) | \n",
+ " Electricity | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " image | \n",
+ " SSP2-RCP19 | \n",
+ " BRA | \n",
+ " Cobalt | \n",
+ " Heating_Commercial (space heating)_Liquid fossil | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ " Heating | \n",
+ " Commercial (space heating) | \n",
+ " Liquid fossil | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " image | \n",
+ " SSP2-RCP19 | \n",
+ " BRA | \n",
+ " Cobalt | \n",
+ " Heating_Commercial (space heating)_Natural gas | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ " Heating | \n",
+ " Commercial (space heating) | \n",
+ " Natural gas | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " image | \n",
+ " SSP2-RCP19 | \n",
+ " BRA | \n",
+ " Cobalt | \n",
+ " Heating_Commercial (space heating)_Solid biomass | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ " Heating | \n",
+ " Commercial (space heating) | \n",
+ " Solid biomass | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " image | \n",
+ " SSP2-RCP19 | \n",
+ " BRA | \n",
+ " Cobalt | \n",
+ " Heating_Commercial (space heating)_Solid coal | \n",
+ " 2020 | \n",
+ " Manufacture of basic chemicals | \n",
+ " NaN | \n",
+ " Heating | \n",
+ " Commercial (space heating) | \n",
+ " Solid coal | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " model scenario region impact_category \\\n",
+ "0 image SSP2-RCP19 BRA Cobalt \n",
+ "1 image SSP2-RCP19 BRA Cobalt \n",
+ "2 image SSP2-RCP19 BRA Cobalt \n",
+ "3 image SSP2-RCP19 BRA Cobalt \n",
+ "4 image SSP2-RCP19 BRA Cobalt \n",
+ "\n",
+ " variable year \\\n",
+ "0 Heating_Commercial (space heating)_Electricity 2020 \n",
+ "1 Heating_Commercial (space heating)_Liquid fossil 2020 \n",
+ "2 Heating_Commercial (space heating)_Natural gas 2020 \n",
+ "3 Heating_Commercial (space heating)_Solid biomass 2020 \n",
+ "4 Heating_Commercial (space heating)_Solid coal 2020 \n",
+ "\n",
+ " act_category value Main sector \\\n",
+ "0 Manufacture of basic chemicals NaN Heating \n",
+ "1 Manufacture of basic chemicals NaN Heating \n",
+ "2 Manufacture of basic chemicals NaN Heating \n",
+ "3 Manufacture of basic chemicals NaN Heating \n",
+ "4 Manufacture of basic chemicals NaN Heating \n",
+ "\n",
+ " Sub sector Energy carrier \n",
+ "0 Commercial (space heating) Electricity \n",
+ "1 Commercial (space heating) Liquid fossil \n",
+ "2 Commercial (space heating) Natural gas \n",
+ "3 Commercial (space heating) Solid biomass \n",
+ "4 Commercial (space heating) Solid coal "
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "8b959999-68c6-436a-9653-066f3a371f68",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
<xarray.DataArray 'value' (model: 1, pathway: 1, variables: 6)>\n",
+ "array([[[0.5404288940429688, 0.31976019287109375, 0.0, 0.03776106,\n",
+ " 0.5397548160000001, 0.0]]], dtype=object)\n",
+ "Coordinates:\n",
+ " * pathway (pathway) object 'SSP2-RCP19'\n",
+ " region <U3 'CHN'\n",
+ " year int64 2050\n",
+ " * model (model) <U5 'image'\n",
+ " * variables (variables) <U49 'Heating_Commercial (space heating)_Electrici...\n",
+ "Attributes:\n",
+ " units: {'dac_solvent': 'Mt CO2/yr', 'Heating_Commercial (space heating...
PandasIndex
PandasIndex(Index(['SSP2-RCP19'], dtype='object', name='pathway'))
PandasIndex
PandasIndex(Index(['image'], dtype='object', name='model'))
PandasIndex
PandasIndex(Index(['Heating_Commercial (space heating)_Electricity',\n",
+ " 'Heating_Residential (space heating)_Electricity',\n",
+ " 'Industry_Chemical (ammonia)_Electricity',\n",
+ " 'Industry_Chemical (high value)_Electricity',\n",
+ " 'Heating_Residential (water heating)_Electricity',\n",
+ " 'Transport_Freight (Intl. Shipping)_Electricity'],\n",
+ " dtype='object', name='variables'))
- units :
- {'dac_solvent': 'Mt CO2/yr', 'Heating_Commercial (space heating)_Solid coal': 'EJ/yr', 'Heating_Commercial (space heating)_Electricity': 'EJ/yr', 'Heating_Commercial (space heating)_Natural gas': 'EJ/yr', 'Heating_Commercial (space heating)_Liquid fossil': 'EJ/yr', 'Heating_Commercial (space heating)_Solid biomass': 'EJ/yr', 'Heating_Residential (space heating)_Solid coal': 'EJ/yr', 'Heating_Residential (space heating)_Electricity': 'EJ/yr', 'Heating_Residential (space heating)_Natural gas': 'EJ/yr', 'Heating_Residential (space heating)_Liquid fossil': 'EJ/yr', 'Heating_Residential (space heating)_Solid biomass': 'EJ/yr', 'Industry_Chemical (ammonia)_Electricity': 'EJ/yr', 'Industry_Chemical (ammonia)_Natural gas': 'EJ/yr', 'Industry_Chemical (ammonia)_Liquid biomass': 'EJ/yr', 'Industry_Chemical (ammonia)_Liquid fossil': 'EJ/yr', 'Industry_Chemical (ammonia)_Other': 'EJ/yr', 'Industry_Chemical (ammonia)_Solid biomass': 'EJ/yr', 'Industry_Chemical (ammonia)_Solid coal': 'EJ/yr', 'Industry_Chemical (high value)_Electricity': 'EJ/yr', 'Industry_Chemical (high value)_Natural gas': 'EJ/yr', 'Industry_Chemical (high value)_Liquid biomass': 'EJ/yr', 'Industry_Chemical (high value)_Liquid fossil': 'EJ/yr', 'Industry_Chemical (high value)_Solid biomass': 'EJ/yr', 'Industry_Chemical (high value)_Solid coal': 'EJ/yr', 'Heating_Residential (water heating)_Solid coal': 'EJ/yr', 'Heating_Residential (water heating)_Electricity': 'EJ/yr', 'Heating_Residential (water heating)_Natural gas': 'EJ/yr', 'Heating_Residential (water heating)_Liquid fossil': 'EJ/yr', 'Heating_Residential (water heating)_Solid biomass': 'EJ/yr', 'Transport_Freight (Intl. Shipping)_Liquid biomass': 'EJ/yr', 'Transport_Freight (Intl. Shipping)_Electricity': 'EJ/yr', 'Transport_Freight (Intl. Shipping)_Natural gas': 'EJ/yr', 'Transport_Freight (Intl. Shipping)_Hydrogen': 'EJ/yr', 'Transport_Freight (Intl. Shipping)_Liquid fossil': 'EJ/yr', 'biomass crops - purpose grown': 'EJ/yr', 'biomass - residual': 'EJ/yr', 'steel - secondary': 'Mtonne/yr', 'steel - primary': 'Mtonne/yr', 'biodiesel, oil, with CCS': 'EJ/yr', 'biodiesel, oil': 'EJ/yr', 'bioethanol, grass, with CCS': 'EJ/yr', 'bioethanol, grass': 'EJ/yr', 'bioethanol, grain, with CCS': 'EJ/yr', 'bioethanol, grain': 'EJ/yr', 'bioethanol, sugar, with CCS': 'EJ/yr', 'bioethanol, sugar': 'EJ/yr', 'bioethanol, wood, with CCS': 'EJ/yr', 'bioethanol, wood': 'EJ/yr', 'diesel, synthetic, from grass, with CCS': 'EJ/yr', 'diesel, synthetic, from grass': 'EJ/yr', 'diesel, synthetic, from wood, with CCS': 'EJ/yr', 'diesel, synthetic, from wood': 'EJ/yr', 'methanol, grass, with CCS': 'EJ/yr', 'methanol, grass': 'EJ/yr', 'methanol, wood, with CCS': 'EJ/yr', 'methanol, wood': 'EJ/yr', 'diesel': 'EJ/yr', 'Biomass IGCC CCS': 'EJ/yr', 'Biomass CHP CCS': 'EJ/yr', 'Biomass ST': 'EJ/yr', 'Biomass IGCC': 'EJ/yr', 'Biomass CHP': 'EJ/yr', 'Coal IGCC CCS': 'EJ/yr', 'Coal PC CCS': 'EJ/yr', 'Coal PC': 'EJ/yr', 'Coal IGCC': 'EJ/yr', 'Coal CHP': 'EJ/yr', 'Gas CC CCS': 'EJ/yr', 'Gas CHP CCS': 'EJ/yr', 'Gas ST': 'EJ/yr', 'Gas CC': 'EJ/yr', 'Gas CHP': 'EJ/yr', 'Hydro': 'EJ/yr', 'Nuclear': 'EJ/yr', 'Oil CC CCS': 'EJ/yr', 'Oil CHP CCS': 'EJ/yr', 'Oil ST': 'EJ/yr', 'Oil CC': 'EJ/yr', 'Oil CHP': 'EJ/yr', 'Geothermal': 'EJ/yr', 'Solar CSP': 'EJ/yr', 'Solar PV Centralized': 'EJ/yr', 'Solar PV Residential': 'EJ/yr', 'Storage, Flow Battery': 'EJ/yr', 'Wind Onshore': 'EJ/yr', 'Wind Offshore': 'EJ/yr', 'hydrogen, biomass, with CCS': 'EJ/yr', 'hydrogen, biomass': 'EJ/yr', 'hydrogen, coal, with CCS': 'EJ/yr', 'hydrogen, coal': 'EJ/yr', 'hydrogen, electrolysis': 'EJ/yr', 'hydrogen, nat. gas, with CCS': 'EJ/yr', 'hydrogen, nat. gas': 'EJ/yr', 'hydrogen, solar': 'EJ/yr'}
"
+ ],
+ "text/plain": [
+ "\n",
+ "array([[[0.5404288940429688, 0.31976019287109375, 0.0, 0.03776106,\n",
+ " 0.5397548160000001, 0.0]]], dtype=object)\n",
+ "Coordinates:\n",
+ " * pathway (pathway) object 'SSP2-RCP19'\n",
+ " region 1\u001b[0m df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMain sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSub sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEnergy carrier\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvariable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:136\u001b[0m, in \u001b[0;36mforbid_nonstring_types.._forbid_nonstring_types..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 132\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use .str.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with values of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minferred dtype \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inferred_dtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 134\u001b[0m )\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:916\u001b[0m, in \u001b[0;36mStringMethods.split\u001b[0;34m(self, pat, n, expand, regex)\u001b[0m\n\u001b[1;32m 914\u001b[0m regex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39marray\u001b[38;5;241m.\u001b[39m_str_split(pat, n, expand, regex)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturns_string\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:400\u001b[0m, in \u001b[0;36mStringMethods._wrap_result\u001b[0;34m(self, result, name, expand, fill_value, returns_string, returns_bool)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expand:\n\u001b[1;32m 399\u001b[0m cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor_expanddim\n\u001b[0;32m--> 400\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mcons\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;66;03m# Must be a Series\u001b[39;00m\n\u001b[1;32m 403\u001b[0m cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:809\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 808\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[0;32m--> 809\u001b[0m arrays, columns, index \u001b[38;5;241m=\u001b[39m \u001b[43mnested_data_to_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# error: Argument 3 to \"nested_data_to_arrays\" has incompatible\u001b[39;49;00m\n\u001b[1;32m 811\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type \"Optional[Collection[Any]]\"; expected \"Optional[Index]\"\u001b[39;49;00m\n\u001b[1;32m 812\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 813\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 814\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 815\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 816\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 817\u001b[0m mgr \u001b[38;5;241m=\u001b[39m arrays_to_mgr(\n\u001b[1;32m 818\u001b[0m arrays,\n\u001b[1;32m 819\u001b[0m columns,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 822\u001b[0m typ\u001b[38;5;241m=\u001b[39mmanager,\n\u001b[1;32m 823\u001b[0m )\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:520\u001b[0m, in \u001b[0;36mnested_data_to_arrays\u001b[0;34m(data, columns, index, dtype)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named_tuple(data[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;129;01mand\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 518\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m_fields)\n\u001b[0;32m--> 520\u001b[0m arrays, columns \u001b[38;5;241m=\u001b[39m \u001b[43mto_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:835\u001b[0m, in \u001b[0;36mto_arrays\u001b[0;34m(data, columns, dtype)\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arrays, columns\n\u001b[1;32m 834\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 835\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_list_to_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], abc\u001b[38;5;241m.\u001b[39mMapping):\n\u001b[1;32m 837\u001b[0m arr, columns \u001b[38;5;241m=\u001b[39m _list_of_dict_to_arrays(data, columns)\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:856\u001b[0m, in \u001b[0;36m_list_to_arrays\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 853\u001b[0m content \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mto_object_array_tuples(data)\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# list of lists\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m content \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_object_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m content\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
- "source": [
- "df[['Main sector', 'Sub sector', 'Energy carrier']] = df['variable'].str.split('_', expand=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "67b9d6b0-ed89-44b3-9bf1-b11aa20e1b84",
- "metadata": {},
- "outputs": [],
- "source": [
- "from pathways import Pathways\n",
- "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "db6729e1-78a9-467f-a8cb-7fa272bb2734",
- "metadata": {},
- "outputs": [
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Calculating LCA results for image...\n",
- "--- Calculating LCA results for SSP2-RCP19...\n",
- "------ Calculating LCA results for 2020...\n",
- "------ Calculating LCA results for 2030...\n",
- "------ Calculating LCA results for 2040...\n"
- ]
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "