deploy: cff04f6

_sources/content/01-demand/01-demand.ipynb

@@ -3,23 +3,6 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(plt.style.available)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
    "metadata": {
     "cell_id": "211eaf07-febe-480e-9d9d-f7152cca2a4f",
     "tags": [
@@ -123,8 +106,8 @@
     "ardy = 0\n",
     "\n",
     "fig = plt.figure(figsize=[7,7])\n",
-    "plt.plot(xs, ys, label=\"Original Demand Curve\", linestyle='-', linewidth=2)\n",
-    "plt.plot(xs, ys2, label=\"Shifted Demand Curve\", linestyle='--', linewidth=2)\n",
+    "plt.plot(xs, ys, label=\"Original Demand Curve\", linewidth=2)\n",
+    "plt.plot(xs, ys2, label=\"Shifted Demand Curve\", linestyle=\"dashed\", linewidth=2, color=\"#049348\")\n",
     "plt.arrow(arx, ary, ardx, ardy, head_width=0.8, head_length=0.5, length_includes_head=True)\n",
     "plt.xticks([])\n",
     "plt.yticks([])\n",
@@ -173,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "remove_input"
@@ -188,17 +171,17 @@
     "q_2, p_2 = 8, -16\n",
     "\n",
     "fig = plt.figure(figsize=[7,7])\n",
-    "plt.plot(xs, ys)\n",
+    "plt.plot(xs, ys, color=\"#1F578E\")\n",
     "plt.scatter([q_1, q_2], [p_1, p_2], s=200, color=\"g\", zorder=15)\n",
-    "plt.arrow(q_1, p_1, q_2 - q_1, p_2 - p_1, color=\"tab:orange\", width=.1, head_length=1.5, head_width=0.5, length_includes_head=True, zorder=-1)\n",
+    "plt.arrow(q_1, p_1, q_2 - q_1, p_2 - p_1, color=\"#CB7432\", width=.1, head_length=1.5, head_width=0.5, length_includes_head=True, zorder=-1)\n",
     "\n",
     "# (q_1, p_1)\n",
-    "plt.vlines(q_1, -1000, p_1, linestyle=\"dashed\")\n",
-    "plt.hlines(p_1, -1000, q_1, linestyle=\"dashed\")\n",
+    "plt.vlines(q_1, -1000, p_1, linestyle=\"dashed\", color=\"#1F578E\")\n",
+    "plt.hlines(p_1, -1000, q_1, linestyle=\"dashed\", color=\"#1F578E\")\n",
     "\n",
     "# (q_2, p_2)\n",
-    "plt.vlines(q_2, -1000, p_2, linestyle=\"dashed\")\n",
-    "plt.hlines(p_2, -1000, q_2, linestyle=\"dashed\")\n",
+    "plt.vlines(q_2, -1000, p_2, linestyle=\"dashed\", color=\"#1F578E\")\n",
+    "plt.hlines(p_2, -1000, q_2, linestyle=\"dashed\", color=\"#1F578E\")\n",
     "\n",
     "plt.xticks([q_1, q_2], [r\"$q_1$\", r\"$q_2$\"], size=14)\n",
     "plt.yticks([p_1, p_2], [r\"$p_1$\", r\"$p_2$\"], size=14)\n",

_sources/content/02-supply/04-market-equilibria.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "tags": [
      "remove_cell"
@@ -16,7 +16,7 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
     "import matplotlib.patches as patches\n",
-    "plt.style.use('seaborn-muted')\n",
+    "# plt.style.use('seaborn-muted')\n",
     "mpl.rcParams['figure.dpi'] = 200\n",
     "%matplotlib inline\n",
     "\n",
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "cell_id": "5a6c6746-bad6-466e-8c18-bc16f5fad344"
    },
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {
     "cell_id": "b75d49b7-7c34-4c8a-a844-e16f26940df7"
    },
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {
     "cell_id": "254b8839-cf4f-460b-a597-c267ad6ebb84"
    },
@@ -330,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {
     "cell_id": "b7a2e982-d79e-4094-a295-d8edea5e3c12"
    },
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
     "cell_id": "42bf023c-0b5a-4f12-8158-dcf58f137185"
    },
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {
     "cell_id": "d06b28f9-96eb-480c-8a10-3ba4981be472"
    },
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {
     "cell_id": "f9654557-6972-4ba9-948b-f7ff7a48a61f"
    },
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -548,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {
     "cell_id": "6e28af89-0a83-465e-bb24-d7e66c9b978e"
    },
@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {
     "cell_id": "f5d296fd-a13d-4b0b-a22d-a22806cb498c",
     "tags": [
@@ -736,7 +736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

content/01-demand/01-demand.html

@@ -488,19 +488,7 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article">
 
-  <div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">available</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;Solarize_Light2&#39;, &#39;_classic_test_patch&#39;, &#39;_mpl-gallery&#39;, &#39;_mpl-gallery-nogrid&#39;, &#39;bmh&#39;, &#39;classic&#39;, &#39;dark_background&#39;, &#39;fast&#39;, &#39;fivethirtyeight&#39;, &#39;ggplot&#39;, &#39;grayscale&#39;, &#39;seaborn-v0_8&#39;, &#39;seaborn-v0_8-bright&#39;, &#39;seaborn-v0_8-colorblind&#39;, &#39;seaborn-v0_8-dark&#39;, &#39;seaborn-v0_8-dark-palette&#39;, &#39;seaborn-v0_8-darkgrid&#39;, &#39;seaborn-v0_8-deep&#39;, &#39;seaborn-v0_8-muted&#39;, &#39;seaborn-v0_8-notebook&#39;, &#39;seaborn-v0_8-paper&#39;, &#39;seaborn-v0_8-pastel&#39;, &#39;seaborn-v0_8-poster&#39;, &#39;seaborn-v0_8-talk&#39;, &#39;seaborn-v0_8-ticks&#39;, &#39;seaborn-v0_8-white&#39;, &#39;seaborn-v0_8-whitegrid&#39;, &#39;tableau-colorblind10&#39;]
-</pre></div>
-</div>
-</div>
-</div>
-<section class="tex2jax_ignore mathjax_ignore" id="demand-curves">
+  <section class="tex2jax_ignore mathjax_ignore" id="demand-curves">
 <h1>Demand Curves<a class="headerlink" href="#demand-curves" title="Link to this heading">#</a></h1>
 <p>In this chapter, we will explore one of the most foundational yet important concepts in economics: demand curves. The demand curve shows the graphical relationship between the price of a good or service and the quantity demanded for it over a given period of time.
 In other words, it shows the quantity of goods or services consumers are willing to buy at each market price.