feat(exercises): added exercise 1

Author: TomMonks

content/02_basic_simpy.ipynb

@@ -190,10 +190,9 @@
    "id": "c3aa041b-6b8f-4b15-becc-bd966d0eb794",
    "metadata": {},
    "source": [
-    "```{admonition} Exercise\n",
-    ":class: seealso\n",
-    "Before going any further have a go at the generators exercise. In the exercise you will need to modify the `prescription_arrival_generator` so that it has random arrivals. This exercise test that you have understood the basics of `simpy` and random sampling in `numpy`\n",
-    "```"
+    "## Exercise\n",
+    "\n",
+    "Before we learn anything more about `simpy` have a go at the generators exercise. In the exercise you will need to modify the `prescription_arrival_generator` so that it has random arrivals. This exercise test that you have understood the basics of `simpy` and random sampling in `numpy`\n"
    ]
   }
  ],

content/03a_exercise1.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2147e34-ce39-4d8c-a7db-e8acec2b63e0",
+   "metadata": {},
+   "source": [
+    "# Generator exercise\n",
+    "\n",
+    "To see the solutions please see the [generator exercise solutions notebook](./03b_exercise1_solutions.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c034caf3-6766-4c69-af8f-949f45283b37",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85fd4a85-bb77-498c-96ee-c14de89994a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import simpy\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfab7f97-9cad-419a-8d75-a2ba190edee8",
+   "metadata": {},
+   "source": [
+    "## Example code\n",
+    "\n",
+    "The code below is taken from the simple pharmacy example.  In this code arrivals occur with an IAT of exactly 5 minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee2439f2-0d35-41fd-a5e2-4b95954dd5c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prescription_arrival_generator(env):\n",
+    "    '''\n",
+    "    Prescriptions arrive with a fixed duration of\n",
+    "    5 minutes.\n",
+    "\n",
+    "    Parameters:\n",
+    "    ------\n",
+    "    env: simpy.Environment\n",
+    "    '''\n",
+    "    \n",
+    "    # don't worry about the infinite while loop, simpy will\n",
+    "    # exit at the correct time.\n",
+    "    while True:\n",
+    "        \n",
+    "        # sample an inter-arrival time.\n",
+    "        inter_arrival_time = 5.0\n",
+    "        \n",
+    "        # we use the yield keyword instead of return\n",
+    "        yield env.timeout(inter_arrival_time)\n",
+    "        \n",
+    "        # print out the time of the arrival\n",
+    "        print(f'Prescription arrives at: {env.now}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40c495d5-6f55-4c93-99e3-5bfa6cdff36d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model parameters\n",
+    "RUN_LENGTH = 25\n",
+    "\n",
+    "# create the simpy environment object\n",
+    "env = simpy.Environment()\n",
+    "\n",
+    "# tell simpy that the `prescription_arrival_generator` is a process\n",
+    "env.process(prescription_arrival_generator(env))\n",
+    "\n",
+    "# run the simulation model\n",
+    "env.run(until=RUN_LENGTH)\n",
+    "print(f'end of run. simulation clock time = {env.now}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b1bd501-1614-4891-a876-d07bd40c0496",
+   "metadata": {},
+   "source": [
+    "### Exercise: modelling a poisson arrival process for prescriptions\n",
+    "\n",
+    "**Task:**\n",
+    "\n",
+    "* Update `prescription_arrival_generator()` so that inter-arrival times follow an exponential distribution with a mean of 5.0 minutes between arrivals.\n",
+    "* Use a run length of 25 minutes.\n",
+    "\n",
+    "> **Bonus**: try this initially **without** setting a random seed.  Then update the method choosing an approach to control random sampling.\n",
+    "\n",
+    "**Hints:**\n",
+    "\n",
+    "We learnt how to sample using a `numpy` random number generator in the [sampling notebook](./01_sampling.ipynb). The basic form to draw a single sample followed this pattern (note this excludes a random seed).\n",
+    "\n",
+    "```python\n",
+    "rng = np.random.default_rng()\n",
+    "sample = rng.exponential(scale=12.0)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65823eff-8d0f-4eaf-a531-173c8ab6290c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}