{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "tYzchKpM7DlA",
        "i4UTa6PqGjn4",
        "7jf1Ar-fKD64"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MATE Floats! Coding Notebook** - Day 4\n",
        "\n",
        "Created by Ethan C. Campbell for NCAT/MATE/GO-BGC Marine Technology Summer Program\n",
        "\n",
        "Thursday, August 24, 2023"
      ],
      "metadata": {
        "id": "OxvLAQ1SWpeR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np                         # NumPy is an array and math library\n",
        "import matplotlib.pyplot as plt            # Matplotlib is a visualization (plotting) library\n",
        "import pandas as pd                        # Pandas lets us work with spreadsheet (.csv) data\n",
        "from datetime import datetime, timedelta   # Datetime helps us work with dates and times"
      ],
      "metadata": {
        "id": "oMjc5MqnJrgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 1: Loading and plotting R/V Rachel Carson CTD casts"
      ],
      "metadata": {
        "id": "tYzchKpM7DlA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's download three `.csv` data files from Google Drive here: https://drive.google.com/drive/folders/1Am6XdlB-APQ3ccOvLeGK8DFPQ2OnPeJD?usp=sharing. Two of the files are CTD casts that were collected from the R/V Rachel Carson off of Carkeek Park near Seattle. ***Save these two files to your computer.***\n",
        "\n",
        "Let's first take a look at the raw files, including their headers.\n",
        "\n",
        "Next, we can upload the files to this Google Colab notebook. ***Click the sidebar folder icon on the left, then use the page-with-arrow icon at the top to select the files and upload them.*** NOTE: uploaded files will be deleted from Google Colab when you refresh this notebook!\n",
        "\n",
        "We will specify each **filepath** using string variables:"
      ],
      "metadata": {
        "id": "czGyp7MTAc5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filepath_0 = '/content/2023051001001_Carkeek.csv'\n",
        "filepath_1 = '/content/2023051101001_Carkeek.csv'"
      ],
      "metadata": {
        "id": "gnrD640dB5ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can load the files using `pandas`:\n",
        "\n",
        "> **`pd.read_csv(FILEPATH, ARGUMENTS...)`**\n",
        "\n",
        "This function is very customizable using the many optional `ARGUMENTS`, which allow it to handle almost any file. You can find documentation about the arguments [at this link](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html).\n",
        "\n",
        "***Let's first take a look at the data file using a simple text editor. Notice the long header. What argument can we use to exclude the header from being loaded?***\n",
        "\n",
        "Below, we'll load each data file using ``pd.read_csv()`` and store each file into a new variable.\n",
        "\n",
        "We can look at the data using **`display()`** (which is a fancy version of `print()` for DataFrames):"
      ],
      "metadata": {
        "id": "XaUCH7ikB6Sy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_0 = pd.read_csv(filepath_0,comment='#')\n",
        "data_1 = pd.read_csv(filepath_1,comment='#')\n",
        "\n",
        "display(data_0)"
      ],
      "metadata": {
        "id": "4boQwvSg7R5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "a59792ca-8e90-487c-91e3-181925361bdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Unnamed: 0   altM  CStarTr0    c0mS/cm  density00    depSM  latitude  \\\n",
              "0              0  98.53   71.0825  31.662958  1021.7317    2.101  47.71418   \n",
              "1              1  98.53   71.0825  31.662061  1021.7317    2.005  47.71418   \n",
              "2              2  98.53   71.0825  31.661464  1021.7323    2.045  47.71418   \n",
              "3              3  98.53   71.0825  31.660448  1021.7323    2.005  47.71418   \n",
              "4              4  98.53   71.0825  31.658416  1021.7325    1.981  47.71418   \n",
              "...          ...    ...       ...        ...        ...      ...       ...   \n",
              "8200        8200  11.99   83.1087  31.920640  1024.1134  173.726  47.71316   \n",
              "8201        8201  11.99   83.1087  31.920640  1024.1135  173.726  47.71316   \n",
              "8202        8202  11.99   83.1087  31.920820  1024.1141  173.846  47.71316   \n",
              "8203        8203  11.99   83.1087  31.920579  1024.1129  173.613  47.71316   \n",
              "8204        8204  11.99   83.1087  31.920340  1024.1135  173.846  47.71316   \n",
              "\n",
              "      longitude  flECO-AFL  modError  sbeox0Mg/L  sbeox0ML/L     ph  \\\n",
              "0    -122.40854     2.8127         0     10.6450      7.4488  9.271   \n",
              "1    -122.40854     2.8127         0     10.6446      7.4484  9.271   \n",
              "2    -122.40854     2.8127         0     10.6443      7.4483  9.271   \n",
              "3    -122.40854     2.8713         0     10.6441      7.4481  9.271   \n",
              "4    -122.40854     3.1057         0     10.6443      7.4483  9.271   \n",
              "...         ...        ...       ...         ...         ...    ...   \n",
              "8200 -122.40812     0.1753         0      7.0198      4.9120  8.788   \n",
              "8201 -122.40812     0.1753         0      7.0201      4.9123  8.788   \n",
              "8202 -122.40812     0.1753         0      7.0204      4.9125  8.788   \n",
              "8203 -122.40812     0.1753         0      7.0205      4.9125  8.783   \n",
              "8204 -122.40812     0.1753         0      7.0209      4.9128  8.788   \n",
              "\n",
              "      potemp090C     prDM    sal00    t090C   scan  nbf  flag  \n",
              "0        10.2155    2.119  28.3385  10.2157   3408    0   0.0  \n",
              "1        10.2140    2.022  28.3388  10.2143   3409    0   0.0  \n",
              "2        10.2129    2.062  28.3391  10.2131   3410    0   0.0  \n",
              "3        10.2117    2.022  28.3390  10.2119   3411    0   0.0  \n",
              "4        10.2093    1.998  28.3389  10.2095   3412    0   0.0  \n",
              "...          ...      ...      ...      ...    ...  ...   ...  \n",
              "8200      8.3719  175.266  30.0190   8.3887  11608    0   0.0  \n",
              "8201      8.3717  175.266  30.0191   8.3886  11609    0   0.0  \n",
              "8202      8.3718  175.387  30.0191   8.3887  11610    0   0.0  \n",
              "8203      8.3719  175.152  30.0190   8.3887  11611    0   0.0  \n",
              "8204      8.3720  175.387  30.0184   8.3889  11612    0   0.0  \n",
              "\n",
              "[8205 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdc9c4fc-a8ee-4af4-853f-2935f079ba9d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>altM</th>\n",
              "      <th>CStarTr0</th>\n",
              "      <th>c0mS/cm</th>\n",
              "      <th>density00</th>\n",
              "      <th>depSM</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>flECO-AFL</th>\n",
              "      <th>modError</th>\n",
              "      <th>sbeox0Mg/L</th>\n",
              "      <th>sbeox0ML/L</th>\n",
              "      <th>ph</th>\n",
              "      <th>potemp090C</th>\n",
              "      <th>prDM</th>\n",
              "      <th>sal00</th>\n",
              "      <th>t090C</th>\n",
              "      <th>scan</th>\n",
              "      <th>nbf</th>\n",
              "      <th>flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>98.53</td>\n",
              "      <td>71.0825</td>\n",
              "      <td>31.662958</td>\n",
              "      <td>1021.7317</td>\n",
              "      <td>2.101</td>\n",
              "      <td>47.71418</td>\n",
              "      <td>-122.40854</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6450</td>\n",
              "      <td>7.4488</td>\n",
              "      <td>9.271</td>\n",
              "      <td>10.2155</td>\n",
              "      <td>2.119</td>\n",
              "      <td>28.3385</td>\n",
              "      <td>10.2157</td>\n",
              "      <td>3408</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>98.53</td>\n",
              "      <td>71.0825</td>\n",
              "      <td>31.662061</td>\n",
              "      <td>1021.7317</td>\n",
              "      <td>2.005</td>\n",
              "      <td>47.71418</td>\n",
              "      <td>-122.40854</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6446</td>\n",
              "      <td>7.4484</td>\n",
              "      <td>9.271</td>\n",
              "      <td>10.2140</td>\n",
              "      <td>2.022</td>\n",
              "      <td>28.3388</td>\n",
              "      <td>10.2143</td>\n",
              "      <td>3409</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>98.53</td>\n",
              "      <td>71.0825</td>\n",
              "      <td>31.661464</td>\n",
              "      <td>1021.7323</td>\n",
              "      <td>2.045</td>\n",
              "      <td>47.71418</td>\n",
              "      <td>-122.40854</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6443</td>\n",
              "      <td>7.4483</td>\n",
              "      <td>9.271</td>\n",
              "      <td>10.2129</td>\n",
              "      <td>2.062</td>\n",
              "      <td>28.3391</td>\n",
              "      <td>10.2131</td>\n",
              "      <td>3410</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>98.53</td>\n",
              "      <td>71.0825</td>\n",
              "      <td>31.660448</td>\n",
              "      <td>1021.7323</td>\n",
              "      <td>2.005</td>\n",
              "      <td>47.71418</td>\n",
              "      <td>-122.40854</td>\n",
              "      <td>2.8713</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6441</td>\n",
              "      <td>7.4481</td>\n",
              "      <td>9.271</td>\n",
              "      <td>10.2117</td>\n",
              "      <td>2.022</td>\n",
              "      <td>28.3390</td>\n",
              "      <td>10.2119</td>\n",
              "      <td>3411</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>98.53</td>\n",
              "      <td>71.0825</td>\n",
              "      <td>31.658416</td>\n",
              "      <td>1021.7325</td>\n",
              "      <td>1.981</td>\n",
              "      <td>47.71418</td>\n",
              "      <td>-122.40854</td>\n",
              "      <td>3.1057</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6443</td>\n",
              "      <td>7.4483</td>\n",
              "      <td>9.271</td>\n",
              "      <td>10.2093</td>\n",
              "      <td>1.998</td>\n",
              "      <td>28.3389</td>\n",
              "      <td>10.2095</td>\n",
              "      <td>3412</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8200</th>\n",
              "      <td>8200</td>\n",
              "      <td>11.99</td>\n",
              "      <td>83.1087</td>\n",
              "      <td>31.920640</td>\n",
              "      <td>1024.1134</td>\n",
              "      <td>173.726</td>\n",
              "      <td>47.71316</td>\n",
              "      <td>-122.40812</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0198</td>\n",
              "      <td>4.9120</td>\n",
              "      <td>8.788</td>\n",
              "      <td>8.3719</td>\n",
              "      <td>175.266</td>\n",
              "      <td>30.0190</td>\n",
              "      <td>8.3887</td>\n",
              "      <td>11608</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8201</th>\n",
              "      <td>8201</td>\n",
              "      <td>11.99</td>\n",
              "      <td>83.1087</td>\n",
              "      <td>31.920640</td>\n",
              "      <td>1024.1135</td>\n",
              "      <td>173.726</td>\n",
              "      <td>47.71316</td>\n",
              "      <td>-122.40812</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0201</td>\n",
              "      <td>4.9123</td>\n",
              "      <td>8.788</td>\n",
              "      <td>8.3717</td>\n",
              "      <td>175.266</td>\n",
              "      <td>30.0191</td>\n",
              "      <td>8.3886</td>\n",
              "      <td>11609</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8202</th>\n",
              "      <td>8202</td>\n",
              "      <td>11.99</td>\n",
              "      <td>83.1087</td>\n",
              "      <td>31.920820</td>\n",
              "      <td>1024.1141</td>\n",
              "      <td>173.846</td>\n",
              "      <td>47.71316</td>\n",
              "      <td>-122.40812</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0204</td>\n",
              "      <td>4.9125</td>\n",
              "      <td>8.788</td>\n",
              "      <td>8.3718</td>\n",
              "      <td>175.387</td>\n",
              "      <td>30.0191</td>\n",
              "      <td>8.3887</td>\n",
              "      <td>11610</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8203</th>\n",
              "      <td>8203</td>\n",
              "      <td>11.99</td>\n",
              "      <td>83.1087</td>\n",
              "      <td>31.920579</td>\n",
              "      <td>1024.1129</td>\n",
              "      <td>173.613</td>\n",
              "      <td>47.71316</td>\n",
              "      <td>-122.40812</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0205</td>\n",
              "      <td>4.9125</td>\n",
              "      <td>8.783</td>\n",
              "      <td>8.3719</td>\n",
              "      <td>175.152</td>\n",
              "      <td>30.0190</td>\n",
              "      <td>8.3887</td>\n",
              "      <td>11611</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8204</th>\n",
              "      <td>8204</td>\n",
              "      <td>11.99</td>\n",
              "      <td>83.1087</td>\n",
              "      <td>31.920340</td>\n",
              "      <td>1024.1135</td>\n",
              "      <td>173.846</td>\n",
              "      <td>47.71316</td>\n",
              "      <td>-122.40812</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0209</td>\n",
              "      <td>4.9128</td>\n",
              "      <td>8.788</td>\n",
              "      <td>8.3720</td>\n",
              "      <td>175.387</td>\n",
              "      <td>30.0184</td>\n",
              "      <td>8.3889</td>\n",
              "      <td>11612</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8205 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdc9c4fc-a8ee-4af4-853f-2935f079ba9d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fdc9c4fc-a8ee-4af4-853f-2935f079ba9d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fdc9c4fc-a8ee-4af4-853f-2935f079ba9d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-52be68e1-8fed-4538-a314-935596db02d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52be68e1-8fed-4538-a314-935596db02d9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-52be68e1-8fed-4538-a314-935596db02d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data in a `pandas` DataFrame is similar to a NumPy 2-D array, except we use **column labels** to refer to columns and **index** values to refer to rows.\n",
        "\n",
        "To retrieve a specific column, we use bracket notation: **`data_frame[COLUMN_LABEL]`**."
      ],
      "metadata": {
        "id": "HYem5ZznDUfk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For example:\n",
        "data_0['t090C']"
      ],
      "metadata": {
        "id": "-k030Au_Dyd_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91de3e6a-6f31-45d5-83ec-343b78685f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       10.2157\n",
              "1       10.2143\n",
              "2       10.2131\n",
              "3       10.2119\n",
              "4       10.2095\n",
              "         ...   \n",
              "8200     8.3887\n",
              "8201     8.3886\n",
              "8202     8.3887\n",
              "8203     8.3887\n",
              "8204     8.3889\n",
              "Name: t090C, Length: 8205, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With these tools, we can make line plots of temperature vs. depth that include both CTD casts.\n",
        "\n",
        "***Can you try plotting another parameter vs. depth? Note: the file contains salinity, oxygen, fluorescence, and pH data.***\n",
        "\n",
        "The following line of code flips the y-axis so the surface is at the top: **`plt.gca().invert_yaxis()`**."
      ],
      "metadata": {
        "id": "TDLpAjCnELuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Temperature vs. depth profile\n",
        "plt.plot(data_0['t090C'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')\n",
        "plt.plot(data_1['t090C'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')\n",
        "plt.legend()\n",
        "plt.gca().invert_yaxis()   # This reverses the y-axis\n",
        "plt.xlabel('Temperature (°C)')\n",
        "plt.ylabel('Depth (m)');\n",
        "\n",
        "# Write your code here:\n",
        "plt.figure()\n",
        "plt.plot(data_0['flECO-AFL'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')\n",
        "plt.plot(data_1['flECO-AFL'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')\n",
        "plt.legend()\n",
        "plt.gca().invert_yaxis()   # This reverses the y-axis\n",
        "plt.ylabel('Depth (m)');"
      ],
      "metadata": {
        "id": "dh6QZ2Np9gXs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "outputId": "4664c548-6050-46b4-e5e3-3c3c1934a68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***How do the casts look similar and how do they look different? What could be some causes of the differences?***\n",
        "\n",
        "***What do you observe in the other parameter(s) that you plotted?***"
      ],
      "metadata": {
        "id": "w0ZA-UHrGB1c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 2: Loading and plotting seaglider data"
      ],
      "metadata": {
        "id": "i4UTa6PqGjn4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this link is a file with a **seaglider** (or just **\"glider\"**) profile from the same cruise, one day after the second R/V Carson cast.\n",
        "\n",
        "If you haven't already, ***download*** the glider `.csv` data file from Google Drive here: https://drive.google.com/drive/folders/1Am6XdlB-APQ3ccOvLeGK8DFPQ2OnPeJD?usp=sharing. ***Save the file to your computer, then load it into Google Colab.***\n",
        "\n",
        "Let's load and display the data using `pandas`:"
      ],
      "metadata": {
        "id": "VkvUEcToGo4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filepath_2 = '/content/20230512_glider.csv'\n",
        "\n",
        "data_2 = pd.read_csv(filepath_2,parse_dates=['time'])\n",
        "\n",
        "display(data_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "-F0ldgmxttkL",
        "outputId": "e664930e-8e60-4ead-f0c4-7325c32197a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     sg_data_point                          time  pressure     depth  \\\n",
              "0                0 2023-05-12 19:14:17.464999936  1.076991  1.067980   \n",
              "1                1 2023-05-12 19:14:22.731000064  1.117253  1.107904   \n",
              "2                2 2023-05-12 19:14:29.736000000  1.157514  1.147829   \n",
              "3                3 2023-05-12 19:14:35.012999936  1.157514  1.147829   \n",
              "4                4 2023-05-12 19:14:40.288000000  1.268233  1.257621   \n",
              "..             ...                           ...       ...       ...   \n",
              "335            335 2023-05-12 20:06:59.275000064  0.513332  0.509038   \n",
              "336            336 2023-05-12 20:07:04.279000064  0.402614  0.399245   \n",
              "337            337 2023-05-12 20:07:08.697999872  0.422744  0.419208   \n",
              "338            338 2023-05-12 20:07:14.273999872  0.372418  0.369302   \n",
              "339            339 2023-05-12 20:07:19.279000064  0.342222  0.339359   \n",
              "\n",
              "     temperature  conductivity  salinity  sigma_theta  density   latitude  \\\n",
              "0      11.409176           NaN       NaN          NaN      NaN  47.715300   \n",
              "1      11.409607           NaN       NaN          NaN      NaN  47.715302   \n",
              "2      11.411506           NaN       NaN          NaN      NaN  47.715306   \n",
              "3      11.414287           NaN       NaN          NaN      NaN  47.715310   \n",
              "4      11.412624           NaN       NaN          NaN      NaN  47.715314   \n",
              "..           ...           ...       ...          ...      ...        ...   \n",
              "335    11.268633           NaN       NaN          NaN      NaN  47.724696   \n",
              "336    11.278361           NaN       NaN          NaN      NaN  47.724699   \n",
              "337    11.290254           NaN       NaN          NaN      NaN  47.724703   \n",
              "338    11.305961           NaN       NaN          NaN      NaN  47.724706   \n",
              "339    11.332550           NaN       NaN          NaN      NaN  47.724710   \n",
              "\n",
              "      longitude  buoyancy  speed  glide_angle  horz_speed  vert_speed  \\\n",
              "0   -122.398800       NaN    NaN          NaN         NaN         NaN   \n",
              "1   -122.398799       NaN    NaN          NaN         NaN         NaN   \n",
              "2   -122.398797       NaN    NaN          NaN         NaN         NaN   \n",
              "3   -122.398795       NaN    NaN          NaN         NaN         NaN   \n",
              "4   -122.398794       NaN    NaN          NaN         NaN         NaN   \n",
              "..          ...       ...    ...          ...         ...         ...   \n",
              "335 -122.397495       NaN    NaN          NaN         NaN         NaN   \n",
              "336 -122.397493       NaN    NaN          NaN         NaN         NaN   \n",
              "337 -122.397491       NaN    NaN          NaN         NaN         NaN   \n",
              "338 -122.397490       NaN    NaN          NaN         NaN         NaN   \n",
              "339 -122.397488       NaN    NaN          NaN         NaN         NaN   \n",
              "\n",
              "     polar_heading  \n",
              "0         1.495747  \n",
              "1         1.628392  \n",
              "2         1.516691  \n",
              "3         1.621411  \n",
              "4         1.574287  \n",
              "..             ...  \n",
              "335       2.405064  \n",
              "336       2.466150  \n",
              "337       2.069960  \n",
              "338       2.134538  \n",
              "339       1.870993  \n",
              "\n",
              "[340 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7485dd1-e23d-4817-aeee-2f3ad3462b98\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sg_data_point</th>\n",
              "      <th>time</th>\n",
              "      <th>pressure</th>\n",
              "      <th>depth</th>\n",
              "      <th>temperature</th>\n",
              "      <th>conductivity</th>\n",
              "      <th>salinity</th>\n",
              "      <th>sigma_theta</th>\n",
              "      <th>density</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>buoyancy</th>\n",
              "      <th>speed</th>\n",
              "      <th>glide_angle</th>\n",
              "      <th>horz_speed</th>\n",
              "      <th>vert_speed</th>\n",
              "      <th>polar_heading</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2023-05-12 19:14:17.464999936</td>\n",
              "      <td>1.076991</td>\n",
              "      <td>1.067980</td>\n",
              "      <td>11.409176</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.715300</td>\n",
              "      <td>-122.398800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.495747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2023-05-12 19:14:22.731000064</td>\n",
              "      <td>1.117253</td>\n",
              "      <td>1.107904</td>\n",
              "      <td>11.409607</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.715302</td>\n",
              "      <td>-122.398799</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.628392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2023-05-12 19:14:29.736000000</td>\n",
              "      <td>1.157514</td>\n",
              "      <td>1.147829</td>\n",
              "      <td>11.411506</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.715306</td>\n",
              "      <td>-122.398797</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.516691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2023-05-12 19:14:35.012999936</td>\n",
              "      <td>1.157514</td>\n",
              "      <td>1.147829</td>\n",
              "      <td>11.414287</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.715310</td>\n",
              "      <td>-122.398795</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.621411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2023-05-12 19:14:40.288000000</td>\n",
              "      <td>1.268233</td>\n",
              "      <td>1.257621</td>\n",
              "      <td>11.412624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.715314</td>\n",
              "      <td>-122.398794</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.574287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>335</td>\n",
              "      <td>2023-05-12 20:06:59.275000064</td>\n",
              "      <td>0.513332</td>\n",
              "      <td>0.509038</td>\n",
              "      <td>11.268633</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.724696</td>\n",
              "      <td>-122.397495</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.405064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>336</td>\n",
              "      <td>2023-05-12 20:07:04.279000064</td>\n",
              "      <td>0.402614</td>\n",
              "      <td>0.399245</td>\n",
              "      <td>11.278361</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.724699</td>\n",
              "      <td>-122.397493</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.466150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>337</td>\n",
              "      <td>2023-05-12 20:07:08.697999872</td>\n",
              "      <td>0.422744</td>\n",
              "      <td>0.419208</td>\n",
              "      <td>11.290254</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.724703</td>\n",
              "      <td>-122.397491</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.069960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>338</td>\n",
              "      <td>2023-05-12 20:07:14.273999872</td>\n",
              "      <td>0.372418</td>\n",
              "      <td>0.369302</td>\n",
              "      <td>11.305961</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.724706</td>\n",
              "      <td>-122.397490</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.134538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>339</td>\n",
              "      <td>2023-05-12 20:07:19.279000064</td>\n",
              "      <td>0.342222</td>\n",
              "      <td>0.339359</td>\n",
              "      <td>11.332550</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.724710</td>\n",
              "      <td>-122.397488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.870993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>340 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7485dd1-e23d-4817-aeee-2f3ad3462b98')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c7485dd1-e23d-4817-aeee-2f3ad3462b98 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c7485dd1-e23d-4817-aeee-2f3ad3462b98');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-06ac98cd-adf7-4ac8-afbc-4c8dfe25fffe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-06ac98cd-adf7-4ac8-afbc-4c8dfe25fffe')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-06ac98cd-adf7-4ac8-afbc-4c8dfe25fffe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What data parameters do you see, and what do you expect each parameter represents?***\n",
        "\n",
        "As a first step, let's see whether the glider was sampling the same region as the R/V Rachel Carson. We can plot each of their tracks using latitude and longitude:"
      ],
      "metadata": {
        "id": "brNjCdRzHjk-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(data_0['longitude'],data_0['latitude'],label='R/V Rachel Carson cast #1')\n",
        "plt.plot(data_1['longitude'],data_1['latitude'],label='R/V Rachel Carson cast #2')\n",
        "plt.plot(data_2['longitude'],data_2['latitude'],label='Glider')\n",
        "plt.legend()\n",
        "plt.xlabel('Longitude (°E)')\n",
        "plt.ylabel('Latitude (°N)')\n",
        "plt.title('Ship and glider tracks');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "wQFYa8SYHZYH",
        "outputId": "e54527b2-a604-4dd0-a3cc-e1901b4ec313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One way to visualize glider data is in **time-depth space**. In other words, time is on the x-axis and depth is on the y-axis. If we use a scatter plot (`plt.scatter()`), we can color the points by another quantity, like temperature or buoyancy.\n",
        "\n",
        "***Try changing the `c` (color) argument below to plot different quantities in time-depth space. What do you notice?***"
      ],
      "metadata": {
        "id": "4XlsqUInIb-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(data_2['time'],data_2['depth'],c=data_2['salinity'])\n",
        "plt.colorbar(label='Buoyancy (g)')   # This adds the color bar and color label on the right\n",
        "plt.gca().invert_yaxis()             # This reverses the y-axis\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Depth (m)');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "a9nA2EAtH_Ye",
        "outputId": "1b192c42-1def-4f4c-925a-0564707ad1cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Of course, we can also plot parameter vs. **depth profiles**.\n",
        "\n",
        "***Copy the code from above, where you plotted temperature profiles from the R/V Carson ship CTD casts. Then add the glider temperature data.***"
      ],
      "metadata": {
        "id": "mI9_9HJoJFxK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n",
        "plt.plot(data_0['t090C'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')\n",
        "plt.plot(data_1['t090C'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')\n",
        "plt.plot(data_2['temperature'],data_2['depth'],label='Glider (5/12/23)')\n",
        "plt.legend()\n",
        "plt.gca().invert_yaxis()   # This reverses the y-axis\n",
        "plt.xlabel('Temperature (°C)')\n",
        "plt.ylabel('Depth (m)');\n",
        "plt.ylim([25,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "A_jUPcj3Ikyu",
        "outputId": "86dc4b08-3c6a-46aa-d056-2c32ee2198e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25.0, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can zoom in to certain depths by changing the y-axis scale using:\n",
        "\n",
        "> **`plt.ylim([LOWER,UPPER])`**\n",
        "\n",
        "***By `plt.ylim()` to the plot above, what can we observe about the differences between the casts near the surface and near the bottom? Why do these differences exist?***\n",
        "\n",
        "***If we wanted to calibrate a glider sensor to the R/V Carson's sensor, what depths would we want to use?***"
      ],
      "metadata": {
        "id": "adHqnEBNJjI0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3: Exploring Argo float data"
      ],
      "metadata": {
        "id": "7jf1Ar-fKD64"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Links:**\n",
        "* Earth Nullschool map: http://data.scripps.earth/argoviz/\n",
        "* Argovis – get your API key here: https://argovis-keygen.colorado.edu/\n",
        "* Argovis: https://argovis.colorado.edu/\n",
        "* Google Slides for your plots: https://docs.google.com/presentation/d/1MqYfkOHBv9KWO41ZgK0MMnfQ8E9zah3AntKlZkrWrgU/edit?usp=sharing"
      ],
      "metadata": {
        "id": "sqrpf-KeKLq-"
      }
    }
  ]
}