Figure size check

Lecture1.ipynb

@@ -529,7 +529,7 @@
     "rates = arrhenius(activation_energy, T)\n",
     "\n",
     "# Plotting\n",
-    "pl.figure()\n",
+    "plt.figure(figsize=(5, 3)) \n",
     "plt.plot(T, rates, label=f'Activation Energy = {activation_energy} eV')\n",
     "plt.xlabel('Temperature (K)')\n",
     "plt.ylabel('$D_{ion}$ (cm$^2$/s)')  # Adding units to y-axis\n",
@@ -575,7 +575,7 @@
     "T = np.linspace(100, 1000, 0)\n",
     "\n",
     "# Calculate rates and plot curves\n",
-    "plt.figure()\n",
+    "plt.figure(figsize=(5, 3)) \n",
     "for activation_energy in activation_energies:\n",
     "    rates = arrhenius(activation_energy, T)\n",
     "    plt.plot(T, rates, label=f'{activation_energy:.1f} eV')\n",
@@ -624,7 +624,7 @@
    "outputs": [],
    "source": [
     "# Plotting ln(R) vs 1000/T\n",
-    "plt.figure()\n",
+    "plt.figure(figsize=(5, 3)) \n",
     "for activation_energy in activation_energies:\n",
     "    rates = arrhenius(activation_energy, T)\n",
     "    plt.plot(1000/T, np.log(rates), label=f'{activation_energy:.1f} eV')\n",
@@ -716,6 +716,7 @@
     "error_bars = np.abs(residuals)\n",
     "\n",
     "# Plot the linear regression line\n",
+    "plt.figure(figsize=(5, 3)) \n",
     "plt.errorbar(df['X'], df['Y'], yerr=error_bars, fmt='o', color='blue', label='Data with Errors')\n",
     "plt.scatter(df['X'], df['Y'])\n",
     "plt.plot(df['X'], y_pred, color='red')\n",
@@ -820,22 +821,9 @@
    "source": [
     "### Tasks\n",
     "\n",
-    "Due to their importance in the electronics industry, diffusion of atoms in semiconductors has been well studied for decades. Below is a set of data for impurity diffusion in crystalline Si [Source: [Casey and Pearson (1975)](https://link.springer.com/chapter/10.1007/978-1-4684-0904-8_2)]. It has been arranged into a DataFrame for your convenience."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dt5CS9zOb5zt",
-    "outputId": "49d17988-e4ef-415f-a2e6-ef18d37e089e",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "Due to their importance in the electronics industry, diffusion of atoms in semiconductors has been well studied for decades. Below is a set of data for impurity diffusion in crystalline Si [Source: [Casey and Pearson (1975)](https://link.springer.com/chapter/10.1007/978-1-4684-0904-8_2)]. It has been arranged into a DataFrame for your convenience.\n",
+    "\n",
+    "```python\n",
     "import pandas as pd\n",
     "\n",
     "data = {\n",
@@ -845,24 +833,18 @@
     "}\n",
     "\n",
     "df = pd.DataFrame(data)\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mm-bgIL_b5zt"
-   },
-   "source": [
-    "You have two tasks to complete:\n",
+    "print(df)\n",
+    "```\n",
+    "\n",
+    "You have two tasks to complete for this exercise:\n",
     "\n",
     "1. Produce a single plot using Matplotlib that contains diffusion coefficient vs reciprocal temperature for each of the impurities.\n",
     "\n",
     "2. Assess if the activation energy is correlated to the atomic mass of the elements using a plot. Is there a simple linear relationship? Provide a brief written statement to explain your analysis.\n",
     "\n",
     "<details>\n",
     "<summary> Task hint </summary>\n",
-    "To speed up part 2: df['AtomicMass'] = [10.81, 26.98, 69.72, 114.82, 30.97, 74.92, 121.76, 208.98]\n",
+    "To speed up task 2: df['AtomicMass'] = [10.81, 26.98, 69.72, 114.82, 30.97, 74.92, 121.76, 208.98]\n",
     "</details>\n",
     "\n",
     "```{admonition} Submission\n",

_config.yml

@@ -4,7 +4,7 @@
 title: Machine Learning for Materials
 author: Aron Walsh
 logo: logo.png
-copyright: "2023"
+copyright: "2024"
 
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html