Added changes from Dr. D to explain how the tutorial will be taught in lecture.

1 files changed, 116 insertions, 5 deletions

diff --git a/tutorials/module_1/arrays.ipynb b/tutorials/module_1/arrays.ipynb
index 0a2afc2..efb14bb 100644
--- a/tutorials/module_1/arrays.ipynb
+++ b/tutorials/module_1/arrays.ipynb
@@ -5,7 +5,7 @@
    "id": "22b60740-09ee-4205-ab11-206bbef80709",
    "metadata": {},
    "source": [
-    "# Arrays\n",
+    "# matrixArrays\n",
     "\n",
     "In computer programming, an array is a structure for storing and retrieving data. We often talk about an array as if it were a grid in space, with each cell storing one element of the data. For instance, if each element of the data were a number, we might visualize a “one-dimensional” array like a list:\n",
     "\n",
@@ -58,7 +58,59 @@
     "```\n",
     "\n",
     "*Note: for every array we nest, we get a new dimension in our data structure.*\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dec9d37-266d-42a5-9ad4-9f8a17548be7",
+   "metadata": {},
+   "source": [
+    "# Display arrays\n",
     "\n",
+    "Using command print(\"\") \n",
+    "Accessing particular elements of an array ....."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfe1e32c-9fcb-4dc2-a948-b070e36938ab",
+   "metadata": {},
+   "source": [
+    "# Practice Problem\n",
+    "Problem statement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7f2bcfe9-6e3d-424d-a08f-520682b3bdb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 7 10 12]\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "x = np.array([7, 10 ,12])\n",
+    "\n",
+    "print(x)\n",
+    "\n",
+    "print(x[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "715d8fcd-8c8c-4f72-a8bd-7e369eb916a8",
+   "metadata": {},
+   "source": [
     "## Numpy array creation functions\n",
     "Numpy comes with some built-in function that we can use to create arrays quickly. Here are a couple of functions that are commonly used in python.\n",
     "### np.arange\n",
@@ -92,7 +144,42 @@
     "- `np.zeros()`\n",
     "- `np.ones()`\n",
     "- `np.eye()` \n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7c7fe0b-1dac-48bb-83e5-5a71cc11570b",
+   "metadata": {},
+   "source": [
+    "## Practice problem\n",
+    "Problem statement below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0c5de840-6256-4de2-9845-1c80a12c062d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10.  12.5 15.  17.5 20. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y=np.linspace(10,20,5)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae9aae08-b05b-4385-aa13-20dc1f8b88f2",
+   "metadata": {},
+   "source": [
     "## Working with Arrays\n",
     "Now that we have been introduced to some ways to create arrays using the Numpy functions let's start using them.\n",
     "### Indexing\n",
@@ -123,7 +210,31 @@
     "- Concatenation: `np.concatenate((arr1, arr2), axis=0)`\n",
     "- Stacking: `np.vstack()`, `np.hstack()`\n",
     "- Splitting: `np.split()`\n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1194250-2861-4001-899c-ced19f0f34ee",
+   "metadata": {},
+   "source": [
+    "## Practice problem\n",
+    "Problem statement here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "777186e0-f734-4bc4-93f0-6746b428f821",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29f020bf-b714-49a7-96bf-4e20138c7722",
+   "metadata": {},
+   "source": [
     "# Exercise\n",
     "Let's solve a statics problem given the following problem\n",
     "\n",
@@ -179,7 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7f2bcfe9-6e3d-424d-a08f-520682b3bdb5",
+   "id": "5c20b2fd-d9ce-49fa-b2d7-259e5b121bc7",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -201,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,