Adding new notebook about gliderpy GSoC 2024

jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2024-08-27-plotting-interface-gliderpy.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Plotting interface to acess data GLIDERS from servers ERDAPP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Created: 2024-08-29\n",
+    "\n",
+    "Updated: 2024-08-29\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook walk you through donwloading glider data and demonstrating all the functionalities of the plotting interface gliderpy. Information about gliderpy can be found in the GiThub repository [here](https://github.com/ioos/gliderpy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gliderpy has a plotting interface for quick simple diagnostic figures they are: `plot_ track`, `plot_ctd`, and `plot_transect` for plotting the glider track, a vertical transect for a specific variable, or a single cast (glider dive). Let's take a look on how to use them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    " First we will load a glider dataset as a pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from gliderpy.fetchers import GliderDataFetcher\n",
+    "\n",
+    "glider_grab = GliderDataFetcher()\n",
+    "\n",
+    "glider_grab.fetcher.dataset_id = \"whoi_406-20160902T1700\"\n",
+    "df = glider_grab.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see how the plotting functions work in gliderpy\n",
+    "\n",
+    "The `plot_track` method will returns a map with the glider's track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = df.plot_track()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "The `plot_ctd` groups all the casts by their position (latitude and longitude) giving the user access to each individual cast using the index (`profile_number`) of the grouped DataFrame. In the cell below, you can see an example of the first usage of plot_ctd."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = df.plot_cast(profile_number=0, var=\"temperature\", color=\"blue\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the methods accept and `ax` argument and more complex figures can be create. For example, let's add a second variable to the cast above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax0 = df.plot_cast(profile_number=0, var=\"temperature\", color=\"blue\")\n",
+    "\n",
+    "ax1 = ax0.twiny()\n",
+    "df.plot_cast(profile_number=0, var=\"salinity\", color=\"red\", ax=ax1)\n",
+    "\n",
+    "ax0.legend()\n",
+    "ax1.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check a whole transec with the `plot_transect` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = df.plot_transect(var=\"temperature\", cmap=\"viridis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a given matplotlib we can create a fancier version with two variables in a subplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(\n",
+    "    figsize=(15, 9),\n",
+    "    nrows=2,\n",
+    "    sharex=True,\n",
+    "    sharey=True,\n",
+    ")\n",
+    "\n",
+    "df.plot_transect(var=\"temperature\", ax=ax0, cmap=\"viridis\")\n",
+    "df.plot_transect(var=\"salinity\", ax=ax1, cmap=\"cividis\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "GLIDERPY",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}