{ "cells": [ { "cell_type": "markdown", "id": "264ee9eb", "metadata": {}, "source": [ "# Calculate spatial emissions\n", "\n", "This tutorial is a full example how to calculate spatial emissions for BGT-soilmap combinations based on modelled emission data. We will use the data from the previous [coverage](./coverage.ipynb) and [flux](./flux.ipynb) examples in the same area.\n", "\n", "Normally, running `lusos` also includes loading the BGT, soilmap and emissions data from files, which\n", "will also be part of this tutorial. Each function to load lusos sample data also has an option to \n", "return filepaths instead of the actual data objects. We will set each of them to `True` to show the\n", "way data is typically loaded to run lusos. Mainly for the soilmap data must be loaded in a specific way. This is normally distributed in a Geopackage format and loading the data requires to combine\n", "information from several layers. Lusos has a dedicated `io` function to do this. The other data\n", "sources can be loaded with normal Geopandas `read` functions. First, we will get the filepaths, load the data, and define the grid." ] }, { "cell_type": "code", "execution_count": null, "id": "938bc6d2", "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import xarray as xr\n", "\n", "import lusos\n", "\n", "bgt_path = lusos.data.sample_bgt(as_path=True)\n", "soilmap_path = lusos.data.sample_soilmap(as_path=True)\n", "emissions_path = lusos.data.sample_emissions(as_path=True)\n", "\n", "# Load data\n", "soilmap = lusos.io.read_soilmap_geopackage(soilmap_path)\n", "bgt = gpd.read_parquet(bgt_path)\n", "emissions = gpd.read_parquet(emissions_path)\n", "\n", "# Grid definition\n", "xresolution = yresolution = 25\n", "xmin, ymin, xmax, ymax = 111_000, 455_000, 116_000, 460_000\n", "grid = lusos.LassoGrid(xmin, ymin, xmax, ymax, xresolution, yresolution)" ] }, { "cell_type": "markdown", "id": "cf822709", "metadata": {}, "source": [ "First, we do the necessary preprocessing of the emissions data again." ] }, { "cell_type": "code", "execution_count": 2, "id": "61b7cbe1", "metadata": {}, "outputs": [], "source": [ "emissions.columns = emissions.columns.str.lower()\n", "emissions.rename(columns={\"emission_t\": \"median\"}, inplace=True)" ] }, { "cell_type": "markdown", "id": "c055516e", "metadata": {}, "source": [ "Now we have the data loaded and the grid defined, we can calculate the spatial coverages of BGT-soilmap combinations and GHG fluxes" ] }, { "cell_type": "code", "execution_count": 3, "id": "4e660de5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\xugrid\\regrid\\structured.py:606: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n", " ds = xr.merge([ds_x, ds_y])\n", "c:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\xugrid\\regrid\\structured.py:606: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n", " ds = xr.merge([ds_x, ds_y])\n", "c:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\xugrid\\regrid\\structured.py:606: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n", " ds = xr.merge([ds_x, ds_y])\n" ] } ], "source": [ "coverage = lusos.bgt_soilmap_coverage(bgt, soilmap, grid)\n", "flux = lusos.calculate_somers_emissions(emissions, grid) # flux per m2" ] }, { "cell_type": "markdown", "id": "ee62a2bc", "metadata": {}, "source": [ "The idea is to assign an emission value based for every cell in the example area. However, when we plot the calculated flux, we see that not every cell has data. This is because in this example, we use modelled CO2 emission (i.e. an out flux of CO2) from [SOMERS](https://www.nobveenweiden.nl/wp-content/uploads/2024/11/rapportage-SOMERS-2.0-technische-beschrijving.pdf), which only has data for the BGT type \"percelen\". Therefore, we can only assign these modelled values to certain cells that have combination \"percelen-{some soilmap type}\"." ] }, { "cell_type": "code", "execution_count": 4, "id": "a896ab8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flux.plot.imshow()" ] }, { "cell_type": "markdown", "id": "bd14d905", "metadata": {}, "source": [ "We need to fill the missing values in another way. For this, lusos has emission factors available for BGT-soilmap combinations that we can use for the missing values. Our example area is situated in the \"low-Netherlands\". We can load emission factors for this." ] }, { "cell_type": "code", "execution_count": 5, "id": "50396f0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(36, 4)\n" ] }, { "data": { "text/html": [ "
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co2_uitch4_uitn2o_uitco2_in
layer
panden_peat1.0250.000
overig_groen_peat1.0250.000
percelen_peat1.0250.000
openbare_ruimte_peat1.0250.000
grote_wateren_peat0.0000.000
\n", "
" ], "text/plain": [ " co2_uit ch4_uit n2o_uit co2_in\n", "layer \n", "panden_peat 1.025 0.0 0 0\n", "overig_groen_peat 1.025 0.0 0 0\n", "percelen_peat 1.025 0.0 0 0\n", "openbare_ruimte_peat 1.025 0.0 0 0\n", "grote_wateren_peat 0.000 0.0 0 0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ef_factors = lusos.data.ef_low_netherlands()\n", "print(ef_factors.shape)\n", "ef_factors.head()" ] }, { "cell_type": "markdown", "id": "7a7c7b25", "metadata": {}, "source": [ "We can multiply these emission factors with the coverage percentages and the cell area to get the flux per BGT-soilmap combination in each cell based on the emission factors. The result has a flux value for every x,y-location." ] }, { "cell_type": "code", "execution_count": 6, "id": "02b799a4", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "\"['percelen_buried_deep', 'overig_groen_buried_deep', 'stedelijk_groen_buried_deep', 'openbare_ruimte_buried_deep', 'panden_buried_deep', 'erven_buried_deep', 'sloten_buried_deep', 'grote_wateren_buried_deep', 'overig_buried_deep'] not in index\"", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m flux_ef_factors = coverage * \u001b[43mef_factors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcoverage\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlayer\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mco2_uit\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m.values[\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, :]\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1185\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1183\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_scalar_access(key):\n\u001b[32m 1184\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._get_value(*key, takeable=\u001b[38;5;28mself\u001b[39m._takeable)\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1186\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1187\u001b[39m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[32m 1188\u001b[39m axis = \u001b[38;5;28mself\u001b[39m.axis \u001b[38;5;129;01mor\u001b[39;00m \u001b[32m0\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1369\u001b[39m, in \u001b[36m_LocIndexer._getitem_tuple\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1367\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[32m 1368\u001b[39m tup = \u001b[38;5;28mself\u001b[39m._expand_ellipsis(tup)\n\u001b[32m-> \u001b[39m\u001b[32m1369\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_lowerdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1371\u001b[39m \u001b[38;5;66;03m# no multi-index, so validate all of the indexers\u001b[39;00m\n\u001b[32m 1372\u001b[39m tup = \u001b[38;5;28mself\u001b[39m._validate_tuple_indexer(tup)\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1090\u001b[39m, in \u001b[36m_LocationIndexer._getitem_lowerdim\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1088\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m section\n\u001b[32m 1089\u001b[39m \u001b[38;5;66;03m# This is an elided recursive call to iloc/loc\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1090\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\u001b[33m\"\u001b[39m\u001b[33mnot applicable\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1192\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1190\u001b[39m maybe_callable = com.apply_if_callable(key, \u001b[38;5;28mself\u001b[39m.obj)\n\u001b[32m 1191\u001b[39m maybe_callable = \u001b[38;5;28mself\u001b[39m._check_deprecated_callable_usage(key, maybe_callable)\n\u001b[32m-> \u001b[39m\u001b[32m1192\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1421\u001b[39m, in \u001b[36m_LocIndexer._getitem_axis\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1418\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[33m\"\u001b[39m\u001b[33mndim\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key.ndim > \u001b[32m1\u001b[39m:\n\u001b[32m 1419\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mCannot index with multidimensional key\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1421\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1423\u001b[39m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[32m 1424\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1361\u001b[39m, in \u001b[36m_LocIndexer._getitem_iterable\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1358\u001b[39m \u001b[38;5;28mself\u001b[39m._validate_key(key, axis)\n\u001b[32m 1360\u001b[39m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1361\u001b[39m keyarr, indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1362\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._reindex_with_indexers(\n\u001b[32m 1363\u001b[39m {axis: [keyarr, indexer]}, copy=\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups=\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 1364\u001b[39m )\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexing.py:1559\u001b[39m, in \u001b[36m_LocIndexer._get_listlike_indexer\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1556\u001b[39m ax = \u001b[38;5;28mself\u001b[39m.obj._get_axis(axis)\n\u001b[32m 1557\u001b[39m axis_name = \u001b[38;5;28mself\u001b[39m.obj._get_axis_name(axis)\n\u001b[32m-> \u001b[39m\u001b[32m1559\u001b[39m keyarr, indexer = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1561\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6212\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m 6209\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6210\u001b[39m keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6212\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6214\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m 6215\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m 6216\u001b[39m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\src\\lulucf-somers\\.pixi\\envs\\default\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6264\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m 6261\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6263\u001b[39m not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m-> \u001b[39m\u001b[32m6264\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[31mKeyError\u001b[39m: \"['percelen_buried_deep', 'overig_groen_buried_deep', 'stedelijk_groen_buried_deep', 'openbare_ruimte_buried_deep', 'panden_buried_deep', 'erven_buried_deep', 'sloten_buried_deep', 'grote_wateren_buried_deep', 'overig_buried_deep'] not in index\"" ] } ], "source": [ "flux_ef_factors = coverage * ef_factors.loc[coverage[\"layer\"], \"co2_uit\"].values[None, None, :]" ] }, { "cell_type": "markdown", "id": "b363d689", "metadata": {}, "source": [ "We need to combine these fluxes with the previously calculated SOMERS fluxes. To do this, we must make sure that we do not double count the BGT-soilmap combinations that contain \"percelen\". However, we still need to include the cells that contain any of those combinations but do not have a modelling result from SOMERS. We can do this in the steps shown below:\n", "\n", "1. Sum the fluxes of BGT-soilmap combinations that contain \"percelen\".\n", "2. Combine with the summed fluxes with the SOMERS fluxes -> take the SOMERS fluxes when available, otherwise take the summed flux.\n", "3. Sum the fluxes of the remaining BGT-soilmap combinations and add these to the result of step 2." ] }, { "cell_type": "code", "execution_count": null, "id": "9cf77a76", "metadata": {}, "outputs": [], "source": [ "# Step 1\n", "flux_parcels = flux_ef_factors.sel(layer=coverage[\"layer\"].str.contains(\"percelen\")).sum(dim=\"layer\")\n", "\n", "# Step 2\n", "flux = xr.where(flux > 0, flux, flux_parcels)\n", "\n", "# Step 3\n", "flux_others = flux_ef_factors.sel(layer=~coverage[\"layer\"].str.contains(\"percelen\")).sum(dim=\"layer\")\n", "flux_total = flux + flux_others\n", "\n", "flux_total.plot.imshow()" ] }, { "cell_type": "markdown", "id": "38ed0025", "metadata": {}, "source": [ "The resulting grid is now a weighted average greenhouse gas flux based on the contributions of BGT-soilmap combinations and SOMERS modelling results." ] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }