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Add notebooks from MUSE2 repo, using helpers
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‎.pre-commit-config.yaml‎

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- id: end-of-file-fixer
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- id: pretty-format-json
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args: [--autofix, --indent, '4', --no-sort]
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exclude: \.ipynb$
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- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
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rev: v2.14.0
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hooks:

‎notebooks/capacity.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Calculating agent capacity\n",
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"\n",
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"This notebook contains example data processing using the output of the [`muse1_default`] example\n",
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"model.\n",
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"\n",
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"Output files are mostly in CSV format. The format of output files is documented [here][output-format].\n",
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"\n",
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"We begin by loading the `model.toml` input file to get the list of milestone years.\n",
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"\n",
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"[`muse1_default`]: ../../examples/muse1_default/\n",
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"[output-format]: https://energysystemsmodellinglab.github.io/MUSE2/file_formats/output_files.html"
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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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"id": "1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import tomllib\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"sys.path.append(\"..\")\n",
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"\n",
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"from muse2_data_analysis.helpers import get_example_input_dir, get_example_output_dir\n",
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"\n",
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"input_dir = get_example_input_dir()\n",
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"output_dir = get_example_output_dir()\n",
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"\n",
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"with (input_dir / \"model.toml\").open(\"rb\") as f:\n",
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" model = tomllib.load(f)\n",
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"\n",
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"# We need to know the milestone years for processing the assets file\n",
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"years = model[\"milestone_years\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2",
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"metadata": {},
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"source": [
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"## Load and process output data\n",
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"\n",
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"We next load the output data. In this case, we want to calculate how much capacity was invested in\n",
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"different processes for different agents. This information can be found in the `assets.csv` output\n",
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"file.\n",
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"\n",
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"The `assets.csv` file contains information about different assets, including when they were\n",
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"commissioned and decommissioned as well as their capacity. To calculate the overall capacity for a\n",
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"given agent and process type, we have to process this data. Note that different assets owned by the\n",
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"same agent may have the same process ID if the agent has reinvested in the same process type in a\n",
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"different year."
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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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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The assets.csv file contains info about which assets were invested in and when\n",
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"assets = pd.read_csv(output_dir / \"assets.csv\")\n",
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"\n",
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"# Assets with no decommission_year are effectively decommissioned after time horizon\n",
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"assets[\"decommission_year\"] = assets[\"decommission_year\"].fillna(years[-1] + 1)\n",
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"\n",
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"# Calculate capacity for each type of process for each agent\n",
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"capacity = pd.DataFrame()\n",
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"for year in years:\n",
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" active = assets[\n",
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" (year >= assets[\"commission_year\"]) & (year < assets[\"decommission_year\"])\n",
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" ]\n",
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"\n",
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" # This only works because each agent is responsible for one and only one commodity\n",
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" cap_sum = active.groupby([\"agent_id\", \"process_id\"])[\"capacity\"].sum().reset_index()\n",
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"\n",
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" df = pd.DataFrame(cap_sum)\n",
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" df[\"year\"] = year\n",
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"\n",
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" capacity = pd.concat([capacity, df])\n",
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"\n",
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"capacity"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4",
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"metadata": {},
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"source": [
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"## Plot results\n",
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"\n",
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"Finally, we plot the results.\n",
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"\n",
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"Note that each of the agents has invested in only one process type; otherwise there would be\n",
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"multiple bars per plot here."
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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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"id": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"agents = capacity[\"agent_id\"].unique()\n",
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"_, axes = plt.subplots(1, len(agents))\n",
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"for ax, agent in zip(axes, agents):\n",
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" capacity[capacity[\"agent_id\"] == agent].pivot(\n",
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" index=\"year\", columns=\"process_id\", values=\"capacity\"\n",
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" ).plot(kind=\"bar\", ax=ax)\n",
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" ax.set_title(agent)\n",
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" ax.set_xlabel(\"Year\")\n",
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" ax.set_ylabel(\"Capacity\")\n",
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" ax.legend(title=\"Process\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "muse2-data-analysis",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎notebooks/prices.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Plotting commodity prices\n",
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"\n",
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"This notebook shows how to plot commodity prices, using the [`muse1_default`] example model.\n",
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"\n",
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"We first load the prices from the `commodity_prices.csv` output file.\n",
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"\n",
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"[`muse1_default`]: ../../examples/muse1_default/"
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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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"id": "1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"sys.path.append(\"..\")\n",
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"\n",
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"from muse2_data_analysis.helpers import get_example_output_dir\n",
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"\n",
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"output_dir = get_example_output_dir()\n",
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"prices = pd.read_csv(output_dir / \"commodity_prices.csv\")\n",
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"prices.head()"
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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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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"\n",
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"COMMODITY_ID = \"heat\"\n",
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"\n",
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"# This won't work if there are multiple regions\n",
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"assert prices[\"region_id\"].nunique() == 1\n",
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"\n",
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"_, ax = plt.subplots()\n",
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"\n",
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"prices[prices[\"commodity_id\"] == COMMODITY_ID].pivot(\n",
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" index=\"milestone_year\", columns=\"time_slice\", values=\"price\"\n",
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").plot(kind=\"bar\", ax=ax)\n",
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"\n",
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"ax.set_xlabel(\"Milestone year\")\n",
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"ax.set_ylabel(\"Price\")\n",
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"ax.legend(title=\"Time slice\");"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "muse2-data-analysis",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎pyproject.toml‎

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]
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requires-python = ">=3.14"
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dependencies = []
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dependencies = ["matplotlib>=3.10.8", "notebook>=7.5.2", "pandas>=3.0.0"]
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[dependency-groups]
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dev = [

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