MAINT: Refactor graphviz figures to be lecture_specific figures.ipynb assets

lectures/_static/lecture_specific/lake_model/figures.ipynb

@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3c1ab515-e765-48f5-bfaf-35d4f95581fd",
+   "metadata": {},
+   "source": [
+    "# Figures for Lake Model Lecture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "45727b3e-f531-4a31-b838-840ccf9d3c02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: graphviz in /Users/mmcky/anaconda3/envs/quantecon/lib/python3.11/site-packages (0.20.3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install graphviz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "68e95ec8-877e-416a-a8ff-665715dfe222",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphviz import Digraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "92a57207-4d79-4d7b-98c2-aea1a1e35f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lake_model_worker.png'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create Digraph object\n",
+    "G = Digraph(format='png')\n",
+    "G.attr(rankdir='LR')\n",
+    "\n",
+    "# Add nodes\n",
+    "G.attr('node', shape='circle')\n",
+    "G.node('1', 'New entrants', color='blue')\n",
+    "G.node('2', 'Unemployed')\n",
+    "G.node('3', 'Employed')\n",
+    "\n",
+    "# Add edges\n",
+    "G.edge('1', '2', label='b')\n",
+    "G.edge('2', '3', label='λ(1-d)')\n",
+    "G.edge('3', '2', label='α(1-d)')\n",
+    "G.edge('2', '2', label='(1-λ)(1-d)')\n",
+    "G.edge('3', '3', label='(1-α)(1-d)')\n",
+    "\n",
+    "# Save Plot\n",
+    "G.render(filename='lake_model_worker')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6c2af58-4d38-44c3-adb0-d1a92a344e51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lectures/_static/lecture_specific/lake_model/lake_model_worker

@@ -0,0 +1,12 @@
+digraph {
+	rankdir=LR
+	node [shape=circle]
+	1 [label="New entrants" color=blue]
+	2 [label=Unemployed]
+	3 [label=Employed]
+	1 -> 2 [label=b]
+	2 -> 3 [label="λ(1-d)"]
+	3 -> 2 [label="α(1-d)"]
+	2 -> 2 [label="(1-λ)(1-d)"]
+	3 -> 3 [label="(1-α)(1-d)"]
+}

lectures/_static/lecture_specific/markov_chains_I/Hamilton

@@ -0,0 +1,13 @@
+digraph {
+	rankdir=LR
+	ng
+	mr
+	sr
+	ng -> ng [label=0.971]
+	ng -> mr [label=0.029]
+	mr -> ng [label=0.145]
+	mr -> mr [label=0.778]
+	mr -> sr [label=0.077]
+	sr -> mr [label=0.508]
+	sr -> sr [label=0.492]
+}

lectures/_static/lecture_specific/markov_chains_I/Temple

@@ -0,0 +1,15 @@
+digraph {
+	rankdir=LR
+	Growth
+	Stagnation
+	Collapse
+	Growth -> Growth [label=0.68]
+	Growth -> Stagnation [label=0.12]
+	Growth -> Collapse [label=0.20]
+	Stagnation -> Stagnation [label=0.24]
+	Stagnation -> Growth [label=0.50]
+	Stagnation -> Collapse [label=0.26]
+	Collapse -> Collapse [label=0.46]
+	Collapse -> Stagnation [label=0.18]
+	Collapse -> Growth [label=0.36]
+}

lectures/_static/lecture_specific/markov_chains_I/figures.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "43b11347-069e-4c50-b093-4635361408fc",
+   "metadata": {},
+   "source": [
+    "# Figures for Markov Chains I"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "38b0e8a2-acab-4b27-95b3-96174c7d6863",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: graphviz in /Users/mmcky/anaconda3/envs/quantecon/lib/python3.11/site-packages (0.20.3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install graphviz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "606da6ac-e8b7-49c6-9d88-142e18e02bba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphviz import Digraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87430938-745b-45d3-9895-937bc357432d",
+   "metadata": {},
+   "source": [
+    "## Example 1\n",
+    "\n",
+    "Hamilton on US unemployment data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eff7a6b5-98d4-4f26-b72f-7100a911644e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hamilton.png'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dot = Digraph(format='png')\n",
+    "dot.attr(rankdir='LR')\n",
+    "dot.node(\"ng\")\n",
+    "dot.node(\"mr\")\n",
+    "dot.node(\"sr\")\n",
+    "\n",
+    "dot.edge(\"ng\", \"ng\", label=\"0.971\")\n",
+    "dot.edge(\"ng\", \"mr\", label=\"0.029\")\n",
+    "dot.edge(\"mr\", \"ng\", label=\"0.145\")\n",
+    "\n",
+    "dot.edge(\"mr\", \"mr\", label=\"0.778\")\n",
+    "dot.edge(\"mr\", \"sr\", label=\"0.077\")\n",
+    "dot.edge(\"sr\", \"mr\", label=\"0.508\")\n",
+    "\n",
+    "dot.edge(\"sr\", \"sr\", label=\"0.492\")\n",
+    "dot\n",
+    "\n",
+    "dot.render(filename='Hamilton')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7effdaa6-ee97-4634-811c-1b90d7e95543",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "Solution 2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bb57212a-c0f2-4922-a549-0edea60ec590",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Temple.png'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dot = Digraph(format='png')\n",
+    "dot.attr(rankdir='LR')\n",
+    "dot.node(\"Growth\")\n",
+    "dot.node(\"Stagnation\")\n",
+    "dot.node(\"Collapse\")\n",
+    "\n",
+    "dot.edge(\"Growth\", \"Growth\", label=\"0.68\")\n",
+    "dot.edge(\"Growth\", \"Stagnation\", label=\"0.12\")\n",
+    "dot.edge(\"Growth\", \"Collapse\", label=\"0.20\")\n",
+    "\n",
+    "dot.edge(\"Stagnation\", \"Stagnation\", label=\"0.24\")\n",
+    "dot.edge(\"Stagnation\", \"Growth\", label=\"0.50\")\n",
+    "dot.edge(\"Stagnation\", \"Collapse\", label=\"0.26\")\n",
+    "\n",
+    "dot.edge(\"Collapse\", \"Collapse\", label=\"0.46\")\n",
+    "dot.edge(\"Collapse\", \"Stagnation\", label=\"0.18\")\n",
+    "dot.edge(\"Collapse\", \"Growth\", label=\"0.36\")\n",
+    "\n",
+    "dot\n",
+    "\n",
+    "dot.render(filename='Temple')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aca758c-e819-4d01-a2aa-c5fd36f0e2ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lectures/_static/lecture_specific/markov_chains_II/Irre_1

@@ -0,0 +1,14 @@
+digraph {
+	rankdir=LR
+	poor
+	"middle class"
+	rich
+	poor -> poor [label=0.9]
+	poor -> "middle class" [label=0.1]
+	"middle class" -> poor [label=0.4]
+	"middle class" -> "middle class" [label=0.4]
+	"middle class" -> rich [label=0.2]
+	rich -> poor [label=0.1]
+	rich -> "middle class" [label=0.1]
+	rich -> rich [label=0.8]
+}

lectures/_static/lecture_specific/markov_chains_II/Irre_2

@@ -0,0 +1,12 @@
+digraph {
+	rankdir=LR
+	poor
+	"middle class"
+	rich
+	poor -> poor [label=1.0]
+	"middle class" -> poor [label=0.1]
+	"middle class" -> "middle class" [label=0.8]
+	"middle class" -> rich [label=0.1]
+	rich -> "middle class" [label=0.2]
+	rich -> rich [label=0.8]
+}

lectures/_static/lecture_specific/markov_chains_II/example4

@@ -0,0 +1,7 @@
+digraph {
+	rankdir=LR
+	0
+	1
+	0 -> 1 [label=1.0]
+	1 -> 0 [label=1.0]
+}