Example run of forecast in a notebook with a plot (#39)

* example run of forecast in a notebook with a plot

* add a static plot to example notebook to render in Github

* small format change

* small punctuation change

* remove cell outputs

examples/example_notebook.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example Notebook to show how to predict solar power generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure to have installed the quart_solar_forecast package: \n",
+    "```\n",
+    "pip install quartz_solar_forecast\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import forecast script and PVSite class.\n",
+    "from quartz_solar_forecast.forecast import run_forecast\n",
+    "from quartz_solar_forecast.pydantic_models import PVSite\n",
+    "\n",
+    "# Import matplotlib and plotly for plotting.\n",
+    "import plotly.express as px\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a PVSite object with the site's latitude, longitude and capacity.\n",
+    "site = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=1.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the forecast for a specific initial timestamp.\n",
+    "# This generates a forecast at 15 minute intervals for the following 48 hours.\n",
+    "predictions_df = run_forecast(site=site, ts='2023-11-01')\n",
+    "print(predictions_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an interactive plot of the forecast using plotly.\n",
+    "fig = px.line(predictions_df)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a static plot of the forecast using matplotlib.\n",
+    "predictions_df.plot()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "quartz_open",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}