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Merge pull request #18 from arfc/osier_chapter
Osier chapter
2 parents cb5905d + fe4215c commit a65ad8a

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‎.github/workflows/CI.yml‎

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steps:
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# Checkout the repository
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- name: Checkout repository
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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- name: Create environment with Mamba
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uses: conda-incubator/setup-miniconda@v2
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# Upload the generated PDF as an artifact
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- name: Upload PDF artifact
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uses: actions/upload-artifact@v3
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uses: actions/upload-artifact@v4
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with:
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name: 2025-dotson-thesis
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path: docs/2025-dotson-thesis.pdf

‎.gitignore‎

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docs/_minted-thesis
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pres/_minted-arfc-pres
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_minted-*
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figure-log.txt

‎README.md‎

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snakemake --cores=1
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```
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> [!NOTE]
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> _Why use `snakemake` to build the dissertation when there is already a `Make` file in the `docs` directory?_
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>
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> Primarily because Windows _can't execute `make`_. There are additional
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> advantages as well. `snakemake` is arguably easier to understand and produces
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> a nice DAG model of the workflow itself.
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>
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# Compiling the dissertation
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If you only want to reproduce the document itself without running any analysis,

‎analysis/Snakefile‎

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@@ -8,6 +8,7 @@ latexopt = """
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"""
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docs_path = "../docs/"
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figure_log = "figures.log"
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with open(f"{docs_path}Makefile", "r") as file:
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lines = file.readlines()
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rule targets:
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input:
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thesis = f"{docs_path}{output_name}.pdf",
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dag = "dag.png"
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dag = "dag.png",
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rule collect_figures:
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input:
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social_max_plot = f"{docs_path}figures/social_max.pgf",
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elasticity_plot = f"{docs_path}figures/elasticity.pgf",
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example_pareto_plot = f"{docs_path}figures/truss2d_pareto.pgf",
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near_optimal_plot = f"{docs_path}figures/near-optimal-pareto.pgf",
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interior_points_plot = f"{docs_path}figures/nd-mga-paretofront.pgf",
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pareto_3d = f"{docs_path}figures/3d-mga-paretofront.pgf",
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example_mga_plot = f"{docs_path}figures/mga-fft-example.pgf",
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output:
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log = figure_log
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shell:
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"""
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for file in {input}; do
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echo $file >> {output.log}
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done
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"""
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if sys.platform == 'Windows':
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rule build_thesis:
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input:
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main_tex = f"{docs_path}{output_name}.tex"
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main_tex = f"{docs_path}{output_name}.tex",
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log = figure_log
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output: f"{docs_path}{output_name}.pdf"
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shell:
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f"""
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cd ../docs && \
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cd {docs_path} && \
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pdflatex {latexopt} {output_name}.tex && \
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biber {output_name} && \
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pdflatex {latexopt} {output_name}.tex && \
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elif sys.platform in ['darwin', 'linux']:
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rule build_thesis:
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input:
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main_tex = f"{docs_path}{output_name}.tex"
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main_tex = f"{docs_path}{output_name}.tex",
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log = figure_log
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output: f"{docs_path}{output_name}.pdf"
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shell:
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f"""
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output:
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"dag.png"
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shell:
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"snakemake --dag | dot -Tpng > {output}"
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"snakemake --dag | dot -Tpng > {output}"
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rule plot_social_welfare_max:
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input: "scripts/social_welfare_maximization.py"
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output:
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social_max_plot = f"{docs_path}figures/social_max.pgf",
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elasticity_plot = f"{docs_path}figures/elasticity.pgf"
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script: f"{input}"
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rule plot_example_fronts:
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input: "scripts/pareto_front.py"
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output:
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example_pareto_plot = f"{docs_path}figures/truss2d_pareto.pgf",
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interior_points_plot = f"{docs_path}figures/nd-mga-paretofront.pgf",
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near_optimal_plot = f"{docs_path}figures/near-optimal-pareto.pgf"
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script: f"{input}"
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rule plot_mga_example:
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input: "scripts/mga_fft_example.py"
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output:
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example_mga_plot = f"{docs_path}figures/mga-fft-example.pgf",
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script: f"{input}"
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rule plot_3d_fronts:
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input: "scripts/pareto_3D.py"
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output:
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pareto_3d = f"{docs_path}figures/3d-mga-paretofront.pgf"
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script: f"{input}"

‎analysis/dag.png‎

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‎analysis/methods_figures/pareto-front.ipynb‎

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‎analysis/methods_figures/social_welfare_maximization.ipynb‎

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‎analysis/notebooks/energy-justice.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import requests\n",
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"import matplotlib.pyplot as plt\n",
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"import json\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import geopandas as gpd\n",
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"import us\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv(\"../../.env\")"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"eia_key = os.environ['EIA_API_KEY']"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Access power plant data"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"params = {\n",
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" \"frequency\": \"monthly\",\n",
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" \"data\": [\n",
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" \"latitude\",\n",
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" \"longitude\",\n",
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" \"nameplate-capacity-mw\",\n",
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" \"operating-year-month\"\n",
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" ],\n",
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" \"facets\": {\n",
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" \"energy_source_code\": [\n",
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" \"BIT\",\n",
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" \"SUB\"\n",
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" ]\n",
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" },\n",
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" \"start\": \"2024-09\",\n",
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" \"end\": None,\n",
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" \"sort\": [\n",
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" {\n",
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" \"column\": \"period\",\n",
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" \"direction\": \"desc\"\n",
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" }\n",
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" ],\n",
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" \"offset\": 0,\n",
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" \"length\": 5000\n",
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"}"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"BASE_URL = \"https://api.eia.gov/v2/\"\n",
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"\n",
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"dataset = \"electricity/operating-generator-capacity\"\n",
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"route = dataset + \"/data\""
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"headers = {\n",
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" \"X-Api-Key\": eia_key,\n",
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" \"X-Params\": json.dumps(params),\n",
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" }"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"r = requests.get(BASE_URL+route, headers=headers)"
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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": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total records: 428\n"
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]
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}
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],
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"source": [
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"response = r.json()['response']\n",
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"\n",
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"print(f\"Total records: {response['total']}\")\n",
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"\n",
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"data = response['data']\n",
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"\n",
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"df = pd.DataFrame(data)\n",
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"df.drop(columns=['stateName','sector','sectorName','entityid',\n",
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" 'balancing-authority-name','statusDescription', \n",
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" 'entityName','nameplate-capacity-mw-units',\n",
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" 'energy_source_code','generatorid','status',\n",
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" 'unit', 'period'\n",
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" ],\n",
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" inplace=True)"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"df['nameplate-capacity-mw'] = df['nameplate-capacity-mw'].astype('float')"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df.pivot_table(index=['stateid','plantid','balancing_authority_code','latitude','longitude'],\n",
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" columns=['technology'],\n",
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" values=['nameplate-capacity-mw']).reset_index(drop=False)"
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "thesis",
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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": "3.11.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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