Merge branch 'osier_chapter' of github.com:arfc/2025-dotson-dissertat…

…ion into osier_chapter

analysis/Snakefile

@@ -27,6 +27,7 @@ rule collect_figures:
         elasticity_plot = f"{docs_path}figures/elasticity.pgf",
         example_pareto_plot = f"{docs_path}figures/truss2d_pareto.pgf",
         near_optimal_plot = f"{docs_path}figures/near-optimal-pareto.pgf",
+        interior_points_plot = f"{docs_path}figures/nd-mga-paretofront.pgf",
         pareto_3d = f"{docs_path}figures/3d-mga-paretofront.pgf"
     output:
         log = figure_log
@@ -82,6 +83,7 @@ rule plot_example_fronts:
     input: "scripts/pareto_front.py"
     output:
         example_pareto_plot = f"{docs_path}figures/truss2d_pareto.pgf",
+        interior_points_plot = f"{docs_path}figures/nd-mga-paretofront.pgf",
         near_optimal_plot = f"{docs_path}figures/near-optimal-pareto.pgf"
     script: f"{input}"
 

analysis/notebooks/energy-justice.ipynb

@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import us\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv(\"../../.env\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eia_key = os.environ['EIA_API_KEY']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Access power plant data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params =  {\n",
+    "    \"frequency\": \"monthly\",\n",
+    "    \"data\": [\n",
+    "        \"latitude\",\n",
+    "        \"longitude\",\n",
+    "        \"nameplate-capacity-mw\",\n",
+    "        \"operating-year-month\"\n",
+    "    ],\n",
+    "    \"facets\": {\n",
+    "        \"energy_source_code\": [\n",
+    "            \"BIT\",\n",
+    "            \"SUB\"\n",
+    "        ]\n",
+    "    },\n",
+    "    \"start\": \"2024-09\",\n",
+    "    \"end\": None,\n",
+    "    \"sort\": [\n",
+    "        {\n",
+    "            \"column\": \"period\",\n",
+    "            \"direction\": \"desc\"\n",
+    "        }\n",
+    "    ],\n",
+    "    \"offset\": 0,\n",
+    "    \"length\": 5000\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_URL = \"https://api.eia.gov/v2/\"\n",
+    "\n",
+    "dataset = \"electricity/operating-generator-capacity\"\n",
+    "route = dataset + \"/data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "headers = {\n",
+    "            \"X-Api-Key\": eia_key,\n",
+    "            \"X-Params\": json.dumps(params),\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(BASE_URL+route, headers=headers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total records: 428\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = r.json()['response']\n",
+    "\n",
+    "print(f\"Total records: {response['total']}\")\n",
+    "\n",
+    "data = response['data']\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df.drop(columns=['stateName','sector','sectorName','entityid',\n",
+    "                    'balancing-authority-name','statusDescription', \n",
+    "                    'entityName','nameplate-capacity-mw-units',\n",
+    "                    'energy_source_code','generatorid','status',\n",
+    "                    'unit', 'period'\n",
+    "                    ],\n",
+    "        inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['nameplate-capacity-mw'] = df['nameplate-capacity-mw'].astype('float')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.pivot_table(index=['stateid','plantid','balancing_authority_code','latitude','longitude'],\n",
+    "               columns=['technology'],\n",
+    "               values=['nameplate-capacity-mw']).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis",
+   "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": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

analysis/scripts/farthest_first_example.py

@@ -0,0 +1,82 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import pandas as pd
+import itertools as it
+from matplotlib import patches
+from mycolorpy import colorlist as mcp
+
+from scipy.optimize import nnls
+from scipy.optimize import curve_fit
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
+
+# pymoo imports
+from pymoo.problems import get_problem
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.optimize import minimize
+from pymoo.util.plotting import plot
+from pymoo.visualization.scatter import Scatter
+
+from osier import n_mga
+from osier.utils import *
+from osier import distance_matrix 
+from osier import farthest_first 
+from osier import check_if_interior
+
+mpl.use("pgf")
+plt.rcParams['pgf.texsystem'] = 'pdflatex'
+plt.rcParams['text.usetex'] = True
+plt.rcParams['pgf.rcfonts'] = False
+plt.rcParams['figure.edgecolor'] = 'k'
+plt.rcParams['figure.facecolor'] = 'w'
+plt.rcParams['savefig.dpi'] = 600
+plt.rcParams['savefig.bbox'] = 'tight'
+plt.rcParams['font.family'] = "serif"
+
+
+
+if __name__ == "__main__":
+    problem = get_problem("bnh")
+
+    pop_size = 100
+    n_gen = 200
+    algorithm = NSGA2(pop_size=pop_size)
+
+    res = minimize(problem,
+                algorithm,
+                ('n_gen', n_gen),
+                seed=1,
+                verbose=False,
+                save_history=True
+                )
+
+    F = problem.pareto_front()
+    a = min(F[:,0])
+    b = max(F[:,0])
+    f1 = F[:,0]
+    f2 = F[:,1]
+    shift = 0.75
+    slack = 0.2
+    alpha = 0.5
+    F1 = f1 * (1+slack)
+    F2 = f2 * (1+slack)
+    X_hist = np.array([history.pop.get("X") 
+                       for history in res.history]).reshape(n_gen*pop_size,2)
+    F_hist = np.array([history.pop.get("F") 
+                       for history in res.history]).reshape(n_gen*pop_size,2)
+
+    slack_front = np.c_[F1,F2]
+    int_pts = check_if_interior(points=F_hist, 
+                                par_front=F, 
+                                slack_front=slack_front)
+    X_int = X_hist[int_pts]
+    F_int = F_hist[int_pts]
+
+    D = distance_matrix(X=X_int)
+
+    n_pts = 10
+    idxs = farthest_first(X=X_int, D=D, n_points=n_pts, seed=45)
+
+    F_select = F_int[idxs]
+    X_select = X_int[idxs]

analysis/scripts/pareto_front.py

@@ -67,4 +67,37 @@
     ax.set_xlim(min(F1),max(F1))
     ax.set_ylim(min(F2),max(F2))
     ax.legend(fontsize=14)
-    plt.savefig("../docs/figures/near-optimal-pareto.pgf")
+    plt.savefig("../docs/figures/near-optimal-pareto.pgf")
+
+
+    """
+    Visualize interior points
+    """
+    F3 = F*(1+slack)
+
+    rng = np.random.default_rng(seed=1234)
+    R = rng.uniform(1000,2)
+    R[:,1] = R[:,1]*15e4
+    R[:,0] = R[:,0]*8e-2
+    R_sub = R[(R[:,1] < 12e4) & (R[:,0] < 0.06)]
+
+    interior_pts = []
+    for p in R_sub:
+        cond_1 = np.any((p < F3).sum(axis=1) == 2)
+        cond_2 = np.any((p > F).sum(axis=1) == 2)
+        if cond_1 and cond_2:
+            interior_pts.append(p)
+
+    fig, ax = plt.subplots(figsize=(8,6))
+    ax.plot(*zip(*F), color='black', lw=3, label='Pareto Front')
+    ax.plot(*zip(*F3), color='black', lw=1, alpha=0.2)
+    ax.scatter(*zip(*R_sub), c='tab:blue', s=3, label='Tested points')
+    ax.scatter(*zip(*np.array(interior_pts)), c='tab:red', s=20, label="Alternative solutions")
+    ax.fill(np.append(F[:,0], F3[:,0][::-1]), np.append(F[:,1],F3[:,1][::-1]), alpha=0.2, color='gray')
+    ax.fill_between(f1*0.98, f2*0.98, alpha=1, color='w')
+    ax.set_xlim(min(F1),max(F1))
+    ax.set_ylim(min(F2),max(F2))
+    ax.set_xlabel('f1', fontsize=14)
+    ax.set_ylabel('f2', fontsize=14)
+    ax.legend(fontsize=14, shadow=True, loc='upper right')
+    plt.savefig("../docs/figures/nd-mga-paretofront.pgf")