595 lines
21 KiB
Plaintext
595 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pypsa\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.style.use(\"ggplot\")\n",
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"import pycountry\n",
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"import json\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"available_models = {\n",
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" \"model_1\": \"elec_s_37_ec_lv1.0_.nc\",\n",
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" \"model_2\": \"elec_s_37_ec_lv1.0_3H_withUC.nc\",\n",
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" \"model_3\": \"elec_s_37_ec_lv1.0_Co2L-noUC-noCo2price.nc\",\n",
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" \"model_4\": \"elec_s_37_ec_lv1.0_Ep.nc\",\n",
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" \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\",\n",
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"}\n",
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"\n",
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"data_path = Path.cwd() / \"..\" / \"..\"\n",
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"model_path = data_path / available_models[\"model_5\"]\n",
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"\n",
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"with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n",
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" pypsa_generation_mapper = json.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"n = pypsa.Network(str(model_path))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def intersection(alist, blist):\n",
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" total_list = list()\n",
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" for val in alist:\n",
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" if val in blist:\n",
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" total_list.append(val)\n",
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" return total_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pypsa_generation_mapper"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries = set([col[:2] for col in n.generators_t.p.columns])\n",
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"gen = set([col[6:] for col in n.generators_t.p.columns])\n",
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"\n",
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"for i, country in enumerate(countries):\n",
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" df = pd.DataFrame(index=n.generators_t.p.index)\n",
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" # country_generation = [col for col in n.generators_t.p.columns if col.startswith(country)]\n",
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" country_generation = n.generators.loc[n.generators.bus.str.contains(country)]\n",
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"\n",
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" for key, gens in pypsa_generation_mapper.items():\n",
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" # curr_gen = country_generation.loc[\n",
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" # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n",
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" curr_gen = country_generation.loc[\n",
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" country_generation.carrier.apply(lambda carr: carr in gens)\n",
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" ].index\n",
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"\n",
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" if len(curr_gen):\n",
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" df[key] = n.generators_t.p[curr_gen].mean(axis=1)\n",
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" else:\n",
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" df[key] = np.zeros(len(df))\n",
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"\n",
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" df.to_csv(data_path / \"pypsa_data\" / (country + \".csv\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"\n",
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"\n",
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"for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n",
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" # country = \"GR.csv\"\n",
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" cc = country[:2]\n",
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"\n",
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" country_buses = np.unique(\n",
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" n.generators.loc[n.generators.bus.str.contains(cc)].bus.values\n",
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" )\n",
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" print(f\"Buses for country {country[:-4]}: \", country_buses)\n",
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"\n",
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" if not len(country_buses) == 1:\n",
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" print(\"Current implementation is for one bus per country\")\n",
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" print(f\"Skipping!\")\n",
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" continue\n",
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"\n",
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" bus = country_buses[0]\n",
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"\n",
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" \"\"\" \n",
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" pypsa_df = pd.read_csv(data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0)\n",
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" \"\"\"\n",
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" try:\n",
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" entsoe_df = pd.read_csv(\n",
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" data_path / \"harmonised_generation_data\" / (\"prepared_\" + country),\n",
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" parse_dates=True,\n",
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" index_col=0,\n",
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" )\n",
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"\n",
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" entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n",
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" entsoe_df = entsoe_df.iloc[1:]\n",
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" entsoe_df = entsoe_df.multiply(1e-3)\n",
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" except FileNotFoundError:\n",
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" continue\n",
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"\n",
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" fig, axs = plt.subplots(4, 3, figsize=(20, 20))\n",
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"\n",
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" axs[0, 0].set_title(pycountry.countries.get(alpha_2=country[:2]).name)\n",
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"\n",
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" start = pd.Timestamp(\"2019-01-01\") # for small time frame\n",
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" end = pd.Timestamp(\"2019-01-14\")\n",
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" coarse_freq = \"3d\"\n",
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"\n",
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" num_techs_shown = 6\n",
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"\n",
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" energy_inflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
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" energy_outflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
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"\n",
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" # add generation\n",
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" country_gen = n.generators.loc[n.generators.bus == bus]\n",
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"\n",
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" for tech, pypsa_carrier in pypsa_generation_mapper.items():\n",
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" gens = country_gen.loc[\n",
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" country_gen.carrier.apply(lambda c: c in pypsa_carrier)\n",
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" ].index\n",
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" energy_inflow[tech] = n.generators_t.p[gens].sum(axis=1)\n",
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"\n",
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" # add inflows from lines\n",
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" lines0 = n.lines.loc[n.lines.bus0 == bus].index\n",
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" lines1 = n.lines.loc[n.lines.bus1 == bus].index\n",
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"\n",
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" lines_flow = np.zeros(energy_inflow.shape[0])\n",
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" if not lines0.empty:\n",
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" lines_flow = -n.lines_t.p0[lines0].sum(axis=1)\n",
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"\n",
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" if not lines1.empty:\n",
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" lines_flow -= n.lines_t.p1[lines1].sum(axis=1)\n",
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"\n",
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" energy_inflow[\"Inflow Lines\"] = np.maximum(np.zeros_like(lines_flow), lines_flow)\n",
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" energy_outflow[\"Outflow Lines\"] = np.minimum(np.zeros_like(lines_flow), lines_flow)\n",
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"\n",
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" # add inflows from links\n",
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" links0 = n.links.loc[n.links.bus0 == bus].index\n",
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" links1 = n.links.loc[n.links.bus1 == bus].index\n",
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"\n",
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" links_flow = np.zeros(energy_inflow.shape[0])\n",
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" if not links0.empty:\n",
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" links_flow = (\n",
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" -n.links_t.p0[links0]\n",
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" .multiply(n.links.loc[links0, \"efficiency\"])\n",
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" .sum(axis=1)\n",
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" )\n",
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"\n",
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" if not links1.empty:\n",
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" links_flow -= (\n",
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" n.links_t.p1[links1].multiply(n.links.loc[links1, \"efficiency\"]).sum(axis=1)\n",
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" )\n",
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"\n",
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" energy_inflow[\"Inflow Links\"] = np.maximum(np.zeros_like(links_flow), links_flow)\n",
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" energy_outflow[\"Outflow Links\"] = np.minimum(np.zeros_like(links_flow), links_flow)\n",
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"\n",
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" storage = n.storage_units.loc[n.storage_units.bus == bus].index\n",
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" if not storage.empty:\n",
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" storage_p = n.storage_units_t.p[storage].sum(axis=1).values\n",
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" # energy_inflow[\"Storage Discharge\"] = np.maximum(np.zeros_like(links_flow), storage_p)\n",
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" energy_inflow[\"Hydro\"] = energy_inflow[\"Hydro\"].values + np.maximum(\n",
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" np.zeros_like(links_flow), storage_p\n",
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" )\n",
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" energy_outflow[\"Storage Charge\"] = np.minimum(\n",
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" np.zeros_like(links_flow), storage_p\n",
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" )\n",
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"\n",
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" energy_inflow = energy_inflow.iloc[:-1].multiply(1e-3)\n",
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" energy_outflow = energy_outflow.iloc[:-1].multiply(1e-3)\n",
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" load = n.loads_t.p_set[bus].iloc[:-1].multiply(1e-3)\n",
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"\n",
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" show_techs = (\n",
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" energy_inflow.sum()\n",
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" .sort_values(ascending=False)\n",
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" .iloc[:num_techs_shown]\n",
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" .index.tolist()\n",
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" )\n",
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" others = (\n",
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" energy_inflow.sum()\n",
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" .sort_values(ascending=False)\n",
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" .iloc[num_techs_shown:]\n",
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" .index.tolist()\n",
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" )\n",
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" # show_techs = entsoe_df.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
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"\n",
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" show_techs = intersection(show_techs, entsoe_df.columns.tolist())\n",
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" entsoe_df[\"Others\"] = entsoe_df.drop(columns=show_techs).sum(axis=1)\n",
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"\n",
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" # entsoe_df[show_techs + [\"Others\"]].loc[start:end].plot.area(ax=axs[0,0])\n",
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" index = load.loc[start:end].index\n",
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"\n",
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" entsoe_df.index = load.index\n",
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"\n",
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" energy_inflow[\"Others\"] = energy_inflow.drop(columns=show_techs).sum(axis=1)\n",
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"\n",
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" # plot timeframe\n",
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" axs[0, 0].plot(\n",
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" index,\n",
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" load.loc[index].values,\n",
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" linestyle=\"--\",\n",
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" color=\"k\",\n",
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" linewidth=2,\n",
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" label=\"PyPSA Load\",\n",
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" )\n",
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" axs[0, 1].plot(\n",
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" index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2\n",
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" )\n",
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"\n",
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" axs[0, 1].stackplot(\n",
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" index,\n",
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" *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
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" )\n",
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" axs[0, 1].stackplot(\n",
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" index,\n",
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" *[energy_outflow[col].loc[index].values for col in energy_outflow.columns],\n",
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" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
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" labels=energy_outflow.columns,\n",
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" )\n",
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"\n",
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" axs[0, 0].stackplot(\n",
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" index,\n",
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" *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
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" labels=show_techs + [\"Others\"],\n",
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" )\n",
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"\n",
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" axs[0, 1].plot(\n",
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" index,\n",
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" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values\n",
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" + energy_outflow.loc[index].sum(axis=1).values,\n",
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" color=\"brown\",\n",
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" linestyle=\":\",\n",
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" linewidth=2,\n",
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" label=\"Accum Gen\",\n",
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" )\n",
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"\n",
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" axs[0, 0].legend()\n",
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" axs[0, 1].legend()\n",
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"\n",
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" # plot whole year\n",
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"\n",
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" index = load.resample(coarse_freq).mean().index\n",
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"\n",
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" axs[1, 0].plot(\n",
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" index,\n",
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" load.resample(coarse_freq).mean().values,\n",
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" linestyle=\"--\",\n",
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" color=\"k\",\n",
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" linewidth=2,\n",
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" label=\"PyPSA Load\",\n",
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" )\n",
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" axs[1, 1].plot(\n",
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" index,\n",
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" load.resample(coarse_freq).mean().values,\n",
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" linestyle=\"--\",\n",
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" color=\"k\",\n",
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" linewidth=2,\n",
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" )\n",
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"\n",
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" axs[1, 1].stackplot(\n",
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" index,\n",
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" *[\n",
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" energy_inflow[col].resample(coarse_freq).mean().values\n",
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" for col in show_techs + [\"Others\"]\n",
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" ],\n",
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" )\n",
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" axs[1, 1].stackplot(\n",
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" index,\n",
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" *[\n",
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" energy_outflow[col].resample(coarse_freq).mean().values\n",
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" for col in energy_outflow.columns\n",
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" ],\n",
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" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
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" labels=energy_outflow.columns,\n",
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" )\n",
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"\n",
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" axs[1, 0].stackplot(\n",
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" index,\n",
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" *[\n",
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" entsoe_df[col].resample(coarse_freq).mean().values\n",
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" for col in show_techs + [\"Others\"]\n",
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" ],\n",
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" labels=show_techs + [\"Others\"],\n",
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" )\n",
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"\n",
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" axs[1, 1].plot(\n",
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" index,\n",
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" energy_inflow.resample(coarse_freq)\n",
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" .mean()[show_techs + [\"Others\"]]\n",
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" .sum(axis=1)\n",
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" .values\n",
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" + energy_outflow.resample(coarse_freq).mean().sum(axis=1).values,\n",
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" color=\"brown\",\n",
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" linestyle=\":\",\n",
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" linewidth=2,\n",
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" label=\"Accum Gen\",\n",
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" )\n",
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"\n",
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" axs[1, 0].legend()\n",
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" axs[1, 1].legend()\n",
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"\n",
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" y_min = pd.concat([energy_outflow.sum(axis=1)]).min()\n",
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" y_max = pd.concat(\n",
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" [energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True\n",
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" ).max()\n",
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"\n",
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" for ax in axs[:2, :2].flatten():\n",
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" ax.set_ylim(y_min, y_max)\n",
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" ax.set_ylim(y_min, y_max)\n",
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"\n",
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" axs[0, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[0, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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" axs[1, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[1, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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" axs[2, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[2, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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"\n",
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" # -------------------------- electricity prices comparison ----------------------------------\n",
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" prices_col = [\n",
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" col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])\n",
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" ]\n",
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" pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n",
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"\n",
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" full_index = pypsa_prices.index\n",
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"\n",
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" coarse_pypsa_prices = pypsa_prices.resample(coarse_freq).mean()\n",
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" pypsa_prices = pypsa_prices.loc[start:end]\n",
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"\n",
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" axs[0, 2].plot(\n",
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" pypsa_prices.index, pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\"\n",
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" )\n",
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" axs[1, 2].plot(\n",
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" coarse_pypsa_prices.index,\n",
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" coarse_pypsa_prices.values,\n",
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" label=\"PyPSA prices\",\n",
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" color=\"royalblue\",\n",
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" )\n",
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"\n",
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" try:\n",
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" entsoe_prices = pd.read_csv(\n",
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" data_path / \"price_data\" / country,\n",
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" index_col=0,\n",
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" parse_dates=True,\n",
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" ).iloc[:-1]\n",
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"\n",
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" def make_tz_time(time):\n",
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" return pd.Timestamp(time).tz_convert(\"utc\")\n",
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"\n",
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" # entsoe_prices.index = pd.Series(entsoe_prices.index).apply(lambda time: make_tz_time(time))\n",
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" entsoe_prices.index = full_index\n",
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" mean_abs_error = mean_absolute_error(\n",
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" entsoe_prices.values,\n",
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" n.buses_t.marginal_price[prices_col].mean(axis=1).values,\n",
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" )\n",
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"\n",
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" coarse_prices = entsoe_prices.resample(coarse_freq).mean()\n",
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" entsoe_prices = entsoe_prices.loc[start:end]\n",
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"\n",
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" axs[0, 2].plot(\n",
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" entsoe_prices.index,\n",
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" entsoe_prices.values,\n",
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" label=\"ENTSOE prices\",\n",
|
|
" color=\"darkred\",\n",
|
|
" )\n",
|
|
" axs[1, 2].plot(\n",
|
|
" coarse_prices.index,\n",
|
|
" coarse_prices.values,\n",
|
|
" label=\"ENTSOE prices\",\n",
|
|
" color=\"darkred\",\n",
|
|
" )\n",
|
|
"\n",
|
|
" except FileNotFoundError:\n",
|
|
" mean_abs_error = None\n",
|
|
" pass\n",
|
|
"\n",
|
|
" upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max()\n",
|
|
" for ax in axs[:2, 2]:\n",
|
|
" ax.set_ylim(0, upper_lim)\n",
|
|
" ax.set_ylabel(\"Electricty Prices [Euro/MWh]\")\n",
|
|
" ax.legend()\n",
|
|
"\n",
|
|
" if not mean_abs_error is None:\n",
|
|
" axs[1, -1].set_title(f\"Mean Abs Error: {np.around(mean_abs_error, decimals=2)}\")\n",
|
|
"\n",
|
|
" # remaining_cols = energy_inflow.drop(columns=show_techs+[\"Others\"]).columns.tolist()\n",
|
|
" # axs[1,0].set_title(f\"Others: {remaining_cols}\")\n",
|
|
"\n",
|
|
" # ------------------------------- duration curves ------------------------------\n",
|
|
"\n",
|
|
" entsoe_ddf = entsoe_df[show_techs + [\"Others\"]].reset_index(drop=True)\n",
|
|
"\n",
|
|
" entsoe_ddf = pd.concat(\n",
|
|
" [\n",
|
|
" entsoe_ddf[col].sort_values(ascending=False).reset_index(drop=True)\n",
|
|
" for col in entsoe_ddf.columns\n",
|
|
" ],\n",
|
|
" axis=1,\n",
|
|
" )\n",
|
|
"\n",
|
|
" axs[2, 0].stackplot(\n",
|
|
" range(len(entsoe_ddf)),\n",
|
|
" *[entsoe_ddf[col].values for col in entsoe_ddf.columns],\n",
|
|
" labels=entsoe_ddf.columns,\n",
|
|
" )\n",
|
|
"\n",
|
|
" pypsa_ddf = energy_inflow[show_techs + [\"Others\"]].reset_index(drop=True)\n",
|
|
" pypsa_ddf = pd.concat(\n",
|
|
" [\n",
|
|
" pypsa_ddf[col].sort_values(ascending=False).reset_index(drop=True)\n",
|
|
" for col in pypsa_ddf.columns\n",
|
|
" ],\n",
|
|
" axis=1,\n",
|
|
" )\n",
|
|
"\n",
|
|
" axs[2, 1].stackplot(\n",
|
|
" range(len(pypsa_ddf)),\n",
|
|
" *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
|
|
" labels=pypsa_ddf.columns,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ylim_max = max([pypsa_ddf.max(axis=0).sum(), entsoe_ddf.max(axis=0).sum()])\n",
|
|
"\n",
|
|
" pypsa_ddf = energy_outflow.reset_index(drop=True)\n",
|
|
" pypsa_ddf = pd.concat(\n",
|
|
" [\n",
|
|
" pypsa_ddf[col].sort_values(ascending=True).reset_index(drop=True)\n",
|
|
" for col in pypsa_ddf.columns\n",
|
|
" ],\n",
|
|
" axis=1,\n",
|
|
" )\n",
|
|
"\n",
|
|
" axs[2, 1].stackplot(\n",
|
|
" range(len(pypsa_ddf)),\n",
|
|
" *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
|
|
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
|
|
" labels=energy_outflow.columns,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ylim_min = energy_outflow.min(axis=0).sum()\n",
|
|
"\n",
|
|
" for ax in axs[2, :2]:\n",
|
|
" ax.legend()\n",
|
|
" ax.set_ylim(ylim_min, ylim_max)\n",
|
|
"\n",
|
|
" pypsa_totals = pd.concat(\n",
|
|
" [energy_inflow[show_techs + [\"Others\"]], energy_outflow], axis=1\n",
|
|
" ).sum()\n",
|
|
"\n",
|
|
" entsoe_totals = entsoe_df.sum()\n",
|
|
" totals = pd.DataFrame(index=pypsa_totals.index)\n",
|
|
"\n",
|
|
" for tech in pypsa_totals.index:\n",
|
|
" if tech not in entsoe_totals.index:\n",
|
|
" entsoe_totals.loc[tech] = 0.0\n",
|
|
"\n",
|
|
" totals[\"Pypsa\"] = pypsa_totals\n",
|
|
" totals[\"Entsoe\"] = entsoe_totals\n",
|
|
" totals[\"Technology\"] = totals.index\n",
|
|
"\n",
|
|
" totals = pd.concat(\n",
|
|
" [\n",
|
|
" pd.DataFrame(\n",
|
|
" {\n",
|
|
" \"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
|
|
" \"Technology\": pypsa_totals.index,\n",
|
|
" \"Total Generation\": pypsa_totals.values,\n",
|
|
" }\n",
|
|
" ),\n",
|
|
" pd.DataFrame(\n",
|
|
" {\n",
|
|
" \"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
|
|
" \"Technology\": entsoe_totals.index,\n",
|
|
" \"Total Generation\": entsoe_totals.values,\n",
|
|
" }\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" axis=0,\n",
|
|
" )\n",
|
|
"\n",
|
|
" sns.barplot(\n",
|
|
" data=totals,\n",
|
|
" x=\"Technology\",\n",
|
|
" y=\"Total Generation\",\n",
|
|
" hue=\"Source\",\n",
|
|
" ax=axs[2, 2],\n",
|
|
" palette=\"dark\",\n",
|
|
" alpha=0.6,\n",
|
|
" edgecolor=\"k\",\n",
|
|
" )\n",
|
|
"\n",
|
|
" axs[2, 0].set_xlabel(\"Hours\")\n",
|
|
" axs[2, 1].set_xlabel(\"Hours\")\n",
|
|
" axs[2, 2].set_ylabel(\"Total Generation [GWh]\")\n",
|
|
" axs[2, 2].set_xticks(\n",
|
|
" axs[2, 2].get_xticks(), axs[2, 2].get_xticklabels(), rotation=45, ha=\"right\"\n",
|
|
" )\n",
|
|
"\n",
|
|
" corrs = (\n",
|
|
" energy_inflow.corrwith(entsoe_df)\n",
|
|
" .drop(index=\"Others\")\n",
|
|
" .dropna()\n",
|
|
" .sort_values(ascending=False)\n",
|
|
" )\n",
|
|
"\n",
|
|
" for col, ax in zip(corrs.index[:2].tolist() + [corrs.index[-1]], axs[3]):\n",
|
|
" ax.scatter(\n",
|
|
" entsoe_df[col].values,\n",
|
|
" energy_inflow[col].values,\n",
|
|
" color=\"darkred\",\n",
|
|
" alpha=0.5,\n",
|
|
" s=20,\n",
|
|
" edgecolor=\"k\",\n",
|
|
" )\n",
|
|
" ax.set_title(f\"{col}; Pearson Corr {np.around(corrs.loc[col], decimals=4)}\")\n",
|
|
" ax.set_xlabel(\"ENTSO-E Generation [GW]\")\n",
|
|
" ax.set_ylabel(\"PyPSA-Eur Generation [GW]\")\n",
|
|
"\n",
|
|
" for ax in axs[:2].flatten():\n",
|
|
" ax.set_xlabel(\"Datetime\")\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "",
|
|
"language": "python",
|
|
"name": ""
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.10"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|